In short: Put your three readings into one fresh NotebookLM notebook, then ask the chat for a study guide tied to your exam topic — answers come only from those sources, so the summary stays focused on what you actually need to learn.

You already know how to open NotebookLM and drop a source into a notebook. The next level is treating one notebook as a dedicated study tool for a single exam topic: three sources in, one focused chat conversation out, and a study guide you can actually use the night before the test. Here's how to set that up so the AI gives you answers that match your exam, not a generic overview. One honest note up front: NotebookLM only answers from the sources you upload — it will not pull in outside facts to fill a gap — so choose your three readings carefully.

Before you start

• A Google account signed in to NotebookLM (you already have this).
• Three sources on the same exam topic: PDFs, Google Docs, website links, YouTube URLs, or text you can paste. Roughly 10–30 pages each works best; very long sources slow the tool.
• About 15–20 minutes the first time, faster after you've done it once.

Step 1 — Make a fresh notebook named for the exam topic

Open NotebookLM and create a brand-new notebook instead of dropping your sources into an old one. Give the notebook the exact exam topic as its title — for example, "Midterm — Climate Policy" or "Module 3 — Cellular Respiration." A focused title does two things: it keeps the AI from mixing in sources from another class, and it reminds you later what this notebook is for. After you save the title, you'll see an empty notebook with a place to add sources. If you don't see a "New notebook" button, look for a plus-sign icon or a "Create" option in the sidebar. 💬 Example: name the notebook "Final Exam — Macroeconomic Policy." You'll know it worked when the notebook appears in your sidebar with that exact title and the source panel is empty.

Step 2 — Add your three sources in a deliberate order

Use the "Add source" button — look for it in the main panel where sources normally go — and upload your three readings one at a time. Order matters less than completeness: NotebookLM pulls from every source you give it, so include the textbook chapter, the professor's slides, and any assigned article, even if they overlap. If a source is a website, paste the link; if it's a YouTube lecture, paste the video URL. After each upload, wait for the small confirmation before moving to the next one. If the panel offers multiple formats (Drive, link, upload), pick whichever matches your file type. 💬 Example: add (1) the Chapter 12 PDF, (2) the lecture slides Google Doc, (3) the journal article link. You'll know it worked when all three appear as separate cards in the source panel, each with a short title and a small page count or duration.

Step 3 — Pin the exam question in the chat

Open the chat panel — look for a "Chat" tab or a message-box at the bottom of the notebook — and write a single, specific instruction: tell the AI exactly what the exam will ask and what you want back. Mention the topic, the kind of questions (multiple choice, short answer, essay), and the format you want (a study guide, a quiz, a comparison table). The clearer your instruction, the more useful the answer. The AI can only answer from the three sources you added, so the prompt tells it where to focus. 💬 Example: "Create a study guide for my midterm on climate policy. List the 8 most-tested concepts from these sources, define each in 2 sentences, and flag any points where the three sources disagree." You'll know it worked when the AI replies with a structured study guide that quotes or paraphrases points from your three readings, not outside knowledge.

Step 4 — Generate an audio overview for on-the-go review

Look for an "Audio Overview" option — it usually sits near the top of the notebook view, often labeled something like "Generate" or accompanied by a sound-wave icon. Tap it once and confirm; NotebookLM turns your three sources into a short spoken summary, with two AI hosts talking through the material. Generation typically takes a few minutes; you'll see a status note while it runs. When it's done, a play button appears and you can listen on the walk to class or during a chore. If you don't see an audio option on your plan, check Google's NotebookLM help page for current availability. 💬 Example: tap "Generate" beside "Audio Overview," then wait for the "Ready" badge. You'll know it worked when a play button shows up next to the audio overview and you can hear the two hosts summarize your three sources.

Step 5 — Drill into weak spots with follow-up questions

After you read the study guide, scan it for anything fuzzy or unfamiliar. In the same chat, type a follow-up that targets one gap at a time — the AI still has your three sources and your earlier instruction loaded, so it can go deeper without you repeating context. Ask it to define a term, compare two concepts, or quiz you with one practice question. If an answer feels off, look for small citation markers in the response and click them to see which source it pulled from; that way you can check the original reading rather than trust the summary blindly. 💬 Example: "Quiz me with 5 short-answer questions on the carbon cycle, then show me which source each answer came from." You'll know it worked when the AI gives a fresh answer that cites your three readings, and clicking a citation reveals the exact line in the source.

Common mistakes

• Skipping the instruction in Step 3. A vague "summarize this" gives a generic overview. Tell NotebookLM what your exam asks and what format you want — the prompt is half the work.
• Uploading sources that don't match the topic. If one of your three readings is on a different chapter, the study guide will drift off-topic. Glance at each source's title before adding it.
• Trusting the summary without checking. NotebookLM can occasionally misstate a detail from your own sources. When a point matters for your grade, open the source and confirm — the citation markers make this a two-click check.

Try it now

Open NotebookLM, create a notebook titled after your next exam, add one source, then type: "Quiz me with 3 questions on this topic." Two minutes from now you'll know whether this workflow fits the way you study.

In short: In the regular Zap editor, look for the AI by Zapier action, type the instructions you want it to follow, and optionally hand it a small list of tools (like "send a Slack message" or "create a Google Sheet row"). No separate "agent" product to learn — it's just one step in the workflow you're already editing.

If you already use Zapier — the service that connects your apps so something happening in one automatically triggers something in another — there's a new way to make a Zap smarter. According to Zapier, the AI capabilities that used to live in a separate product called Zapier Agents have been folded directly into the regular Zap editor as a single AI by Zapier action. You drop it in like any other step, write a plain-language instruction, and pick which tools (small actions like "send an email" or "add a row to a spreadsheet") the AI is allowed to call on while doing its job.

This guide walks through that exact process, from opening an existing Zap to testing the AI step end-to-end. It's written for someone who has built a basic Zap before but has never added an AI step.

Before you start

• A Zapier account — a free plan works for basic Zaps, but the AI step's usage may count toward an AI actions limit depending on your plan, so check Zapier's current pricing page if you plan to run it heavily.
• An existing Zap (or the willingness to build a short two-step one to practice on). The AI step is just a third piece in that chain.
• Roughly 10–15 minutes for the first run-through, including testing.
• The apps you want the AI to reach — for example Gmail, Slack, Google Sheets, or a calendar — already connected to your Zapier account.

Step 1 — Open the Zap you want to enhance

In your Zapier dashboard, find the Zap you want to add AI to and click to open it in the editor. You'll land on the visual workflow view, where each step is a card stacked vertically with arrows between them. If you've never built a Zap before, create a simple one first (a trigger plus one action) so you have something to work with. The trigger is the event that starts the Zap ("a new row in Google Sheets"), and each action is something the Zap does in another app. If your screen shows a list of Zaps instead of a workflow, click the name of the Zap you want to edit. You'll know it worked when you see the numbered step cards and the path between them.

Step 2 — Add the AI by Zapier action between your trigger and your final step

Click the "+" between two existing steps (or after your trigger if your Zap has no actions yet). A small panel opens with a list of apps. Type "AI by Zapier" into the search bar. When the result appears, click it. Zapier then asks which AI action you want to use — for a general-purpose step that can also call other apps, choose "Use AI" (the option that lets you write a custom prompt). The action is added as a new card in your workflow. If you don't find a "+" between steps, look for an "Add step" button below the trigger instead — Zapier's editor layout shifts occasionally. You'll know it worked when a new card labeled "AI by Zapier" appears in your workflow, right between the cards that come before and after it.

Step 3 — Write the prompt the AI will read each time it runs

Click the new AI by Zapier card to open its settings. You'll see a field labeled something like "What should this AI do?" or "Prompt" — the exact label can shift as the product changes, but it's the one big text area at the top of the card. Type the instruction in plain English, exactly as you'd explain the task to a person. Be specific about the input the AI will receive (usually data from your trigger step) and the output you want back. You can reference data from earlier steps by clicking a field reference button or similar icon next to the prompt box — it lets you insert variables from the trigger (like the email subject or the spreadsheet row) so the AI sees real data, not placeholders. You'll know it worked when you can see at least one piece of data from a previous step referenced inside the text — usually shown as a small colored chip or a label like 1. Subject. Example: Read the email below and tell me in one sentence whether the customer is happy, neutral, or upset. Email subject: {{1. Subject}} Email body: {{1. Body}}

Step 4 — Tell the AI which tools it's allowed to use

Still inside the AI step, scroll to a section labeled something like "Tools" or "Available actions". This is where you decide what the AI is allowed to do besides read the prompt. Click "Add a tool" (or a similarly worded button) and choose from the apps already connected to your account — for example "Gmail: Send Email", "Slack: Send Channel Message," "Google Sheets: Create Row," or "Calendar: Create Event." Add only the tools the AI actually needs for this task; more tools means more chances for the AI to choose the wrong one. Each tool you add opens a small configuration panel where you fill in the recipient, the channel, the spreadsheet, and so on — you can either set those values yourself in advance (a static "send to this address") or leave them for the AI to fill in from the prompt. If you don't see a Tools area, you may be on a simpler version of the step that only returns a text answer — in that case, skip to Step 5 and come back to tools later. You'll know it worked when each tool you added appears as a sub-card inside the AI step, with its fields either pre-filled or marked for the AI to fill in.

Step 5 — Test the AI step before turning the Zap on

Click the "Test" or "Test action" button on the AI card. It runs your prompt once with a real (or sample) piece of data from your trigger and shows you exactly what the AI wrote back, plus which tools it chose to call and what it sent to them. Read the output carefully — this is your chance to catch a prompt that's too loose or a tool the AI is misusing. If the answer is off, click back into the prompt, tighten the wording (for example, add "Reply in exactly one sentence" or "Only use the Slack tool when the customer is upset"), and test again. When the output looks right, click "Continue" or "Done editing" to save, then turn the Zap on with the toggle at the top of the editor. You'll know it worked when the test panel shows a non-empty AI response and, if you added tools, a short log of which tool ran and what it did — and your Zap's status switch shows "On."

Common mistakes

• Writing a vague prompt. "Summarize this email" gives you uneven results; "Reply in one sentence saying whether the customer is happy, neutral, or upset, and explain your answer in plain English" gives you something you can rely on.
• Giving the AI too many tools. If you add Gmail, Slack, Sheets, and Calendar all at once, the AI has to guess which one fits. Stick to the one or two tools this step actually needs.
• Skipping the test step. The test is the only place you'll see what the AI actually does before it runs on real data for real people. If you don't test, you're publishing blind — and AI mistakes (wrong recipients, wrong tone) are easy to miss without that preview.

Try it now

Open any Zap you already have, add an AI by Zapier action right after it, and write this prompt in the prompt box: Read the file under the file upload here and let me know what it's about in 2 sentences. Then run the test. You'll see exactly how the AI step talks, what its output looks like, and whether you want to give it tools — all in under two minutes.

In short: AWS published a playbook on AI upskilling. The core idea — common to effective adult-learning programs — is that most workers don't lack AI awareness, they lack hands-on building time. Here is a six-step framework any team can adapt.

If your team can talk fluently about AI but freezes the moment someone says "build me a small workflow," you are not alone. The biggest barrier to using AI at work is rarely knowledge — it is the gap between reading about it and actually wiring it into your day. AWS published a playbook on closing that gap, and the lessons translate cleanly to any team willing to copy the structure.

Before you start: what you'll need

• A group of 4–12 learners. Smaller is easier to coach; larger works with breakouts.
• 90 minutes per week, for six weeks. Block this on everyone's calendar before week one. Make it a recurring meeting you don't cancel.
• A facilitator who has built something. Not a teacher — a coach. Someone who has shipped at least one small AI tool themselves.
• One AI tool everyone can access. A chat-based AI with a project workspace, or a no-code builder (a tool that lets you create apps without writing code, using a visual drag-and-drop interface), both work well for non-technical learners.
• A shared demo slot. A 20–30 minute weekly show-and-tell where two or three people show what they built.

Step 1 — Foundations week (what AI actually does)

Start with the smallest possible theory dose: what an LLM (large language model — the kind of AI engine behind tools like ChatGPT) actually does, what a prompt (the instruction you type to the AI) is, and what an API (a way for two programs to talk to each other — think of it as a phone line between your tool and the AI) is. Keep theory to 30 minutes, with plain-language examples.

Then immediately assign the first tiny build. Don't let theory linger.

💬 Try this prompt together: "Explain in three sentences what a large language model actually does, like I'm explaining it to a colleague who has never heard the term."

You'll know it worked when every learner can describe, in their own words, what happens when they type a prompt and hit enter — without using the words "magic" or "AI brain."

Step 2 — First build (something useful to the builder)

Each learner picks one small project that solves a problem in their own work. AWS's playbook, like most adult-learning frameworks, emphasizes that the project must matter personally to the builder — not be an abstract exercise. A FAQ assistant for one shared document. A draft-responder for the team's most common customer email. A meeting-summary helper.

💬 Starter prompt: "You are an assistant that answers questions using only the document I paste below. If the answer isn't in the document, say 'I don't know.' Here's the document: [paste text]."

You'll know it worked when the learner has a working demo they can show in under two minutes — even if it's rough.

Step 3 — Iteration week (prompting is editing)

Most beginners quit here because their first version isn't perfect. The lesson: prompting (the way you phrase instructions to the AI) is closer to editing a draft than typing a command. Tweak the wording. Add constraints. Show examples of good and bad answers.

The facilitator's job this week is to sit with each learner for 15 minutes and help them improve their original build — not start over.

💬 Iteration prompt: "Your previous answer was too long. Reply in three sentences max, then ask me one clarifying question."

You'll know it worked when the learner can name two specific changes they made to the prompt that made the output better.

Step 4 — Sharing week (demo to peers)

Each learner demos their tool to the group for five minutes, then takes 10 minutes of questions. Effective adult-learning programs treat this show-and-tell as the highest-value session of the program. Teaching forces clarity.

The facilitator should resist the urge to add new content. The demo IS the lesson.

💬 Demo script template: "I built this for [specific problem]. Here's what it does. Here's where it failed. Here's what I'd try next."

You'll know it worked when at least one other learner says "I want to try that for my project."

Step 5 — Real workflow problem

The build task now comes from the learner's actual day-to-day work — not the sandbox. AWS's playbook describes this shift as the moment training becomes useful, because the project has a real stakeholder (the person whose problem it solves).

Examples: a sales rep builds a follow-up email drafter that pulls from the team's top-performing templates. A support agent builds a triage helper for incoming tickets. A teacher builds a quiz generator for one specific unit.

💬 Real-work prompt: "You are helping me draft a follow-up email after a sales call. I'll paste the call notes below. Write the email in our team's standard format, then flag anything I should double-check before sending."

You'll know it worked when at least one teammate outside the cohort asks to use the tool.

Step 6 — Public showcase and next steps

Week six is a larger demo event — open to the wider organization. Each learner presents their final tool, what they learned, and what they want to build next. The point is to make the learning visible and to recruit the next round.

End the program with a clear path forward: an internal community (a chat channel, a monthly demo, a shared prompt library) where builders keep learning from each other.

💬 Closing prompt for the cohort: "Write a five-sentence summary of what you learned in this program, written for someone about to start. What's the one piece of advice you'd give them?"

You'll know it worked when at least one person outside the original cohort asks "when's the next round?"

Common mistakes

• Mistake: front-loading theory. Fix: cut theory to 30 minutes, then build. Learners remember what they made, not what they watched.
• Mistake: assigning abstract projects. Fix: require every project to solve a real problem the builder personally has. No exceptions.
• Mistake: skipping the demo week. Fix: the demo IS a deliverable. Treat absence from demo week like missing a deadline.
• Mistake: no follow-up community. Fix: end week six with a concrete ongoing space — a chat channel, a monthly demo, a shared doc of starter prompts. Without it, the skills fade in a month.

Wrap-up

The message from AWS's playbook is refreshingly plain: most people are closer to "AI builder" than they think, but the jump never happens through reading. It happens through shipping a small real thing, showing it to a peer, and shipping another one the next week. Pick your start date today, name your first tiny project, and block the calendar. That is the whole framework, and it works.

In short: Radisson Hotel Group has teamed up with Accenture to build a ChatGPT plugin that helps travelers find, compare, and book hotels while planning their trips, using OpenAI's technology. It signals a growing shift toward planning whole trips inside an AI chat window.

Planning a trip usually means jumping between five tabs: a search engine, a hotel site, a map, reviews, and your notes app. Radisson is betting that one of those tabs can go away.

According to the company's announcement, Radisson Hotel Group has partnered with Accenture (a large consulting and tech firm) to build a ChatGPT plugin, an add-on that lets ChatGPT pull live information and actions from another service. Built on OpenAI's technology, the plugin is designed to help travelers find, compare, and book Radisson hotels while they plan a trip, all inside a single conversation with the AI.

How a ChatGPT plugin actually works

A plugin is a small bridge between ChatGPT and another company's system. When you turn one on, ChatGPT can ask that company's servers for real, up-to-date information, or even complete a task like starting a booking, instead of just guessing based on what it learned during training.

For a hotel plugin, that could mean the AI checks actual room availability, prices, and locations straight from Radisson's own system, rather than making recommendations based on old or general knowledge. Think of it like the difference between asking a friend who thinks they remember a hotel chain, and asking one who is on the phone with the front desk.

Why a hotel group would do this

Trip planning is one of the most-searched activities on the internet, and the search usually starts in a chat-style tool these days. By meeting travelers inside ChatGPT, Radisson can show up earlier in the process, when a person types something like "I need a hotel in Lisbon for a long weekend in October."

Accenture's role, according to the announcement, is the technical and design work that ties Radisson's booking system into the AI experience. The companies frame it as a way to make hotel discovery feel more like a conversation and less like filling out filters.

Wrap-up

Radisson's move with Accenture is a small but telling example of how everyday tasks, including booking a hotel, are starting to move into AI chat windows. If trip planning is something you do even a couple of times a year, try opening ChatGPT next time and see what's already available for travel. The tools are catching up to the way you already think through a trip.

In short: OpenAI is adding a College Planner to ChatGPT for Teens, along with new flashcards and quizzes for studying. The company is also launching a teen advisory group, according to its announcement, to help shape how young people use AI.

College applications are stressful. So are chemistry finals, Spanish vocabulary tests, and the endless "what should I major in?" question. OpenAI says it wants to help with all of that — and it's bringing teens into the room to help decide how.

What's actually new

The announcement covers three things, in plain terms:

• A College Planner inside ChatGPT for Teens — a tool meant to help students manage the moving parts of college applications: deadlines, essays, school lists, and the "where do I even start" feeling.
• New study features — flashcards and quizzes, both designed to help you review material faster than re-reading your notes for the fourth time.
• A teen AI council — a group of young people who will give OpenAI feedback on what teens actually want and need from AI tools.

These are OpenAI's own claims about the rollout, not a verdict from us. Details on when each piece lands and who can use it are still emerging.

What "ChatGPT for Teens" actually is

If you haven't seen it, "ChatGPT for Teens" is a version of ChatGPT built for users under 18. It comes with extra safety rules and content limits, which means the AI is more careful about what it shows younger users. That's the version that will get the new features first.

If you're a parent or guardian, the bigger picture is this: AI tools aimed at students are no longer just "ChatGPT with homework answers." They're being designed as study partners — and companies are starting to ask teens themselves what that should look like.

Wrap-up

The bottom line: AI study tools for teens are getting more structured, and the companies building them are starting to listen to young users directly. The College Planner, flashcards, and quizzes are OpenAI's bet on what students need next. Keep an eye on your ChatGPT account for when these features show up, and try the study tools first — they're the easiest to test today.

In short: OpenAI says GPT-6 is rolling out globally in ChatGPT, with a new "Intelligent UI" that adds visuals, interactivity, and faster responses to your conversations.

If you've opened ChatGPT recently and noticed answers arriving a little quicker, with the occasional chart, table, or clickable element appearing instead of a wall of text, that's not your imagination. OpenAI has begun rolling out GPT-6, the next version of the model that powers ChatGPT, alongside a feature the company is calling "Intelligent UI."

What's new, in plain words

According to OpenAI's announcement, GPT-6 delivers "faster responses with visuals and interactive experiences you can explore and use directly." That one sentence actually contains three distinct changes worth separating:

• Faster responses. OpenAI says GPT-6 answers more quickly than the model it replaces. In practice, that means a shorter wait between typing your prompt (the instruction you give the AI) and seeing the answer.
• Built-in visuals. Instead of only text, the model can now show things like charts, tables, and formatted layouts right inside the chat window.
• Interactive experiences. This is the "Intelligent UI" part — elements you can click, adjust, or play with, not just read.

So what is an "Intelligent UI"?

A normal chatbot gives you a paragraph. An "intelligent UI" gives you the right tool for the question, in the same window.

Think of it this way: instead of describing a recipe in text, the AI might show a checklist you can tick off while you cook. Instead of a written pros-and-cons list, it could show a comparison table you can sort or filter. OpenAI calls this the "Intelligent UI," and the company says it's designed so you can "explore and use" the results without copying anything into another app.

This is a small shift in how ChatGPT feels, but it changes the role of the chat window. It's less of a Q&A box and more of a small workspace.

Wrap-up

GPT-6 with Intelligent UI is OpenAI's next step toward making ChatGPT feel less like a text box and more like a small workspace. According to the company, the update brings faster answers, built-in visuals, and interactive elements you can use right inside the chat. If you haven't opened ChatGPT in a while, this is a good reason to take another look — try a comparison or planning question and see what kind of answer comes back.

In short: Zapier has folded its former AI helper, Zapier Agents, into a single AI step inside the regular Zap editor, and has added Google's Gemini models as an option you can plug into any AI step in a workflow.

If you've ever wished one of your apps could just handle a small task while you sleep, Zapier is one of the names that comes up. It's a tool that connects different apps — think Gmail, Google Sheets, Slack, a calendar — so that when something happens in one, something else can happen automatically in another. The two updates below both make that "something else" smarter, in slightly different ways.

The first update: one AI step, inside every Zap

Zapier says that what used to be a separate product called Zapier Agents — its stand-alone AI helper — has now been folded directly into the regular Zap editor as a single AI by Zapier step. In plain terms: instead of going off to a separate corner of Zapier to set up an "agent," you just drop one AI block into the workflow you were already building.

According to Zapier, that AI step carries over the same four capabilities the old Agents product had:

• A prompt — the instruction you give the AI, like "summarize this email in two sentences"
• A set of tools — other Zapier actions the AI is allowed to use, such as "send a Slack message" or "create a Calendar event"
• A trigger — the event that starts the whole thing, such as "a new email arrives in Gmail"
• Permission to act — the AI is allowed to do those things on your behalf, not just suggest them

So if you used Zapier Agents before, Zapier says the new AI step does the same job — it just lives in the same place as the rest of your workflow, which is a smaller change to navigate than learning a whole separate screen.

The second update: Gemini is now an option inside Zapier

The second announcement is about Google's Gemini family of AI models. Gemini is the name Google uses for its chatbots and the AI models behind them — different versions are meant for quick replies versus longer, more careful thinking.

Zapier says Google's Gemini models are now available to plug into an AI step in a Zap. In practice, this means when you set up an AI block in a workflow, Gemini is one of the model choices you can pick. According to Zapier, this matters because sometimes you don't need the most powerful model — you need the cheapest, fastest one that can handle a 50-word summary. And sometimes you do want a more capable model to work through a tangled customer email.

Wrap-up

Both updates point in the same direction: smarter building blocks, in fewer places. The one thing to remember is this — Zapier now has an AI step you can drop into any workflow, and you can choose Google's Gemini family as the engine for that step. A next step to try today: open a Zap you've already built, add an AI step, and give it a prompt you already find yourself writing by hand — chances are, that's the exact job it's meant to take off your plate.

In short: Google released Nano Banana 2.1 in October 2026, and it is now available inside Picsart's AI Playground. The new model generates and edits images from text or pictures, supports up to 4K output across 15 aspect ratios, and lets you refine a picture over a whole conversation.

A new image model just landed in a tool many people already use. If you've ever opened Picsart to make a quick poster, clean up a photo, or design a thumbnail, the engine under the hood just got an upgrade — and it's worth knowing what changed.

What Nano Banana 2.1 actually does

Nano Banana is Google's family of image-generation models. According to Google's announcement, version 2.1 is now available in Picsart's AI Playground, which is the part of the app where you create and edit images with AI.

The new model works in two directions: it can generate an image from a text description, and it can edit an image you already have. You can also combine the two — start with a picture, describe a change, and watch it happen.

A few things stand out about this release:

• Multiple output sizes. The model produces images in 1K, 2K, or 4K (a measure of how many pixels the image contains — higher numbers mean a sharper, more detailed picture).
• 15 aspect ratios. That's the shape of the image — square for Instagram, vertical for Stories, wide for YouTube, and so on.
• Multi-turn editing. You don't have to get your prompt perfect on the first try. You can refine the same picture over several messages, the way you'd ask a designer for tweaks.

What the prompting guide suggests

Google also published a prompting guide alongside the release. A "prompt" is just the instruction you type to the AI. The guide groups 14 ready-to-copy prompts around the things Nano Banana 2.1 is reportedly best at. According to the guide, those strengths are:

• Readable text on posters and diagrams. Many image models still mangle words, so a model that handles text well is useful for flyers, slides, and social graphics.
• Precise edits. Small, controlled changes to an existing image — change a color, swap an object, fix a detail.
• Doodle-directed changes. You sketch a rough shape and tell the model what to do with it.
• Combining several images. Feed it a few reference pictures and ask for a scene that pulls them together.
• Step-by-step refinement in one chat. Build a picture across multiple messages instead of starting over each time.

The full list of 14 prompts lives in Google's prompting guide. The short version: each prompt is designed to test one of those strengths, so you can see for yourself what the model handles well.

Wrap-up

Nano Banana 2.1 brings higher resolution, more shapes, and a more conversational editing flow to a tool a lot of people already have on their phone. The smartest first move is to pick one of the 14 prompts from Google's guide, paste it into Picsart's AI Playground, and see what comes back. From there, treat your second and third message as the real test — that's where the model shows whether it can take direction, not just produce a pretty first image.

In short: Cohere's free playground can rewrite dense paragraphs into plain English — you paste the text, give a clear instruction, and read the simpler version. Free to start, but it can't replace reading important legal or medical documents yourself.

You know the feeling: you open a contract, a research paper, or a long email from a lawyer, and the first paragraph looks like a different language. This guide shows you how to use Cohere — an AI writing assistant — to turn that wall of words into something a curious 12-year-old could follow. No coding, no setup headaches. By the end, you'll have a small habit you can use on any tough text you meet.

Before you start

• A free Cohere account — sign up with an email at Cohere's website.
• A web browser (Chrome, Safari, Firefox, Edge — any recent one will work).
• One dense paragraph you'd like to simplify — copy it ready to paste. Budget 10–15 minutes total.

Step 1 — Open the Cohere playground

Go to Cohere's website and sign in. After you log in, look for a section labeled Playground or Try it now — it's usually the most prominent button on the dashboard, the place Cohere takes you to test prompts (a prompt is just the instruction and text you give the AI). Click it. A new page opens with a large empty text box near the top and some settings on the side. If you can't find it, look for a sidebar menu on the left with words like Chat, Playground, or Lab — different Cohere accounts show slightly different navigation, so don't worry if yours is laid out a little differently. 💬 Example: click the Playground tile on the Cohere dashboard after signing in. You'll know it worked when you see a tall empty box labeled something like "Prompt" or "Message" with a button such as Send or Generate below it.

Step 2 — Check the model and settings

Before you type anything, glance at the settings area — usually on the right side or in a small panel above the text box. Look for a model selector; a model is just the specific AI "brain" Cohere uses, and it might say Command, Command R+, or another Cohere name. Leave it on the default unless you have a reason to change it; the default is fine for this task. Also look for a temperature slider if one is visible — temperature controls how creative the response is, with lower meaning more predictable and higher meaning more varied. For simplifying text, the default setting usually works well. 💬 Example: model left on "Command" (or whatever default appears), temperature left alone. You'll know it worked when you don't see any red warnings on the screen and the text box is still empty and ready for your input.

Step 3 — Paste your dense paragraph

Now click inside the empty prompt box and paste the paragraph you want simplified. Paste the whole thing — don't summarize it first, because Cohere needs to see exactly what to rewrite. If your paragraph is longer than what fits comfortably on screen, that's fine; just paste it as one block. The free tier usually accepts a full page of typical text in one go. Don't add any instructions yet — just the raw paragraph for now. If you accidentally paste it twice, delete one copy before moving on. 💬 Example: paste a paragraph from a software license, a journal abstract, or an email from your bank. You'll know it worked when the text appears in the box, the cursor is at the end, and no error message pops up.

Step 4 — Add a clear simplifying instruction

This is the most important step. Click right after the pasted paragraph (or above it in a separate instruction line) and add a clear, plain instruction telling Cohere exactly what you want. Keep it short and direct — something like "Rewrite this so a 12-year-old can understand it" works much better than a vague "make this better". The clearer your instruction, the closer the result will match what you actually want. If you'd rather keep the paragraph separate from the instruction, some Cohere layouts have a small instruction field at the top — use that if you see one. 💬 Example: add the sentence "Rewrite the paragraph above so a 12-year-old could understand it. Keep the same meaning." right after your pasted text. You'll know it worked when both the paragraph and your instruction are visible in the prompt area, with the instruction clearly readable.

Step 5 — Run the prompt

Click the Send, Generate, or Run button — the exact word varies, but the button sits near or under the prompt box and is usually highlighted in color. Wait a few seconds. Cohere's reply will appear in the same window, usually directly below your prompt. Read it slowly. The shorter, plainer version should be there, but it might still feel a bit stiff — that's normal, and we'll fix it in the next step. If nothing happens after about 15 seconds, try clicking the button once more; sometimes the first click doesn't register. 💬 Example: click the highlighted button below the text box and wait for the reply to stream in. You'll know it worked when you see a paragraph of plain English appear below your original text, and that paragraph is noticeably shorter and easier to read than what you pasted.

Step 6 — Refine until it sounds right

If the first reply is too stiff, too long, or doesn't quite match what you needed, type a short follow-up telling Cohere what to change. This is the fun part — you can ask for shorter sentences, simpler words, a friendly tone, a bullet list, or all of the above. Each follow-up is just a normal message in the same box. If the reply dropped important meaning from the original, paste the original back in and try a more precise instruction, like "Keep all the obligations and rights, but use plain words." Don't worry about using the "right" words — Cohere responds well to plain English. 💬 Example: type "Make it even simpler. Use shorter sentences." or "Turn it into three bullet points." You'll know it worked when the final paragraph reads smoothly, matches the meaning of the original, and you could explain it to a friend in two minutes.

Common mistakes

• Pasting without any instruction. If you just paste a paragraph and hit send, Cohere usually responds with a summary or analysis, not a rewrite. Always add a clear instruction telling it what to do.
• Asking for too many things at once. "Make it shorter, simpler, friendlier, in bullet points, and translate it" gives muddy results. One change per follow-up is much more reliable.
• Trusting the rewrite on high-stakes text. A simpler version of a contract is great for understanding, but it is NOT legally binding. For decisions involving real money, health, or legal rights, read the original carefully or talk to a professional.
• Forgetting to check the meaning. Rewrites can drop small but important details. Skim the side-by-side and make sure no key fact, date, or condition went missing from the plain-English version.

Try it now

Find one paragraph you've been avoiding — maybe in an email, a Terms of Service page, or a Wikipedia introduction — and run it through Cohere using the six steps above. Five minutes from now, you'll have a version that actually makes sense.

In short: Open any AI assistant, paste both listings, list your priorities with weights, and ask for a scored comparison table. The assistant won't visit the apartment for you, but it will turn a wall of listing text into a clear trade-off table in minutes.

When you're choosing between two rentals, the listings blur together. This five-minute workflow works in any AI assistant — ChatGPT, Gemini, Claude, Copilot, Le Chat, DeepSeek, or whichever you already have — and scores both places against the priorities you set yourself.

Before you start

• An account with any AI assistant you already use (free or paid — both work for this).
• The full text of both rental listings, copied from the listing page or app. Screenshots alone won't work — only pasted text.
• A short list of what actually matters to you (commute, noise, utilities included, lease length, pets, parking — three to six items is enough).
• About 15 minutes.

Step 1 — Open a new chat and paste both listings

Start a fresh conversation — don't drop this into an old chat, or the assistant will weigh your rentals against whatever you were talking about before. In the message box at the bottom of the screen, paste the first listing, leave a blank line, then paste the second one. End the message with a one-line note telling the assistant what it's looking at. 💬 Example: "Listing A — 2-bed in Eastside, $X/month, 12-month lease. — — Listing B — 1-bed + den in Northgate, $Y/month, month-to-month. Compare these two rentals." If the listing is mostly images or a PDF, you'll need to type out the key facts by hand — assistants can't reliably read screenshots unless your plan includes file upload or vision, and even then the result varies. You'll know it worked when you can scroll up and see both listings sitting in the chat, and the assistant acknowledges them in its first reply.

Step 2 — Tell the assistant your priorities and how much each matters

Send a second message that lists the things you care about and gives each one a weight — how much it should count in the final score. Weights are how you stop the assistant from treating "has a dishwasher" as equally important as "20-minute commute." The format that works best is a numbered list with a 1–5 or 1–10 weight next to each priority. 💬 Example: "Here are my priorities, weighted 1–5: 1) Commute under 30 min by transit (5). 2) Quiet street, no main road (4). 3) Utilities included in rent (3). 4) Allows my cat (5). 5) In-unit laundry (2). Score both listings against these." If your assistant has a "memory" or "custom instructions" setting (different apps call it different things), you can save the weights there once and reuse them for future comparisons. You'll know it worked when the assistant restates your priorities back to you without being asked — that means it actually loaded them into the conversation.

Step 3 — Ask for a clean side-by-side table first, before any scores

Now ask for a comparison table with one row per feature and one column per listing. Don't ask for scores yet — you want a neutral snapshot of what each listing actually says, so you can spot places where the assistant fills in gaps with guesses. The clearer this table is, the more honest the scoring will be in the next step. 💬 Example: "Make a table with these rows: rent, lease length, square feet, utilities included, parking, laundry, pet policy, commute time to downtown, nearest noise source. One column for Listing A, one for Listing B, one for 'verified or missing.' If a fact isn't in the listing, write 'not stated.'" If the table renders as plain text instead of a real grid (some mobile apps do this), ask the assistant to "rebuild it with pipes and dashes" and it will produce a text-only version that still reads clearly. You'll know it worked when every row is filled with text from the listing — or says "not stated" — instead of the assistant inventing something to fill the gap.

Step 4 — Add weighted scores and a recommendation

Send a third message asking the assistant to score each listing on your weighted priorities and add a one-line recommendation at the bottom. This is the step that turns the table into a decision. The math is simple: multiply each priority's weight by the listing's score on that priority, then add them up. The listing with the higher total wins — on your priorities, not on someone else's. 💬 Example: "Now score both listings 1–10 on each priority from Step 2, multiply by the weight, and show the totals. Add a two-sentence recommendation naming which one wins on my priorities and why." If the assistant hedges with "it really depends on you," that's a sign it didn't read your weights carefully — nudge it with "use the weights literally, don't substitute your own." You'll know it worked when you see a final number for each listing and the recommendation names a clear winner with a stated reason.

Step 5 — Push back on vague answers and list what to verify

Before you act on the recommendation, ask the assistant to flag anything it had to guess at, plus every "not stated" cell from the table. This step is the safety net — assistants can sound confident about facts that aren't actually in the listing, and a lease you signed based on a wrong assumption is an expensive lesson. The honest answer often involves several things you need to ask the landlord directly. 💬 Example: "List every fact in the table you marked 'not stated' and anything you had to infer. For each one, write a one-line question I can email the landlord." If the assistant invents a detail (like "utilities are included" when the listing didn't say so), that's a hallucination — when an AI confidently states something that isn't actually there. Call it out with: "You made that up; the listing didn't say it. Replace with 'unverified, ask landlord.'" You'll know it worked when you have a short, concrete list of questions to send before you sign anything.

Step 6 — Save the prompt structure as a reusable template

Copy the three prompts you sent (Steps 1, 2, and 4) into a notes app and label it "Rental comparison template." Next time you're weighing two places, open a new chat, paste the template, swap in the new listings and weights, and you'll get the same scored comparison in minutes — no setup, no re-thinking the structure. 💬 Example: "PROMPT 1: [paste listings + intro line]. PROMPT 2: [paste weighted priorities]. PROMPT 3: [paste score-and-recommend request]." If your assistant supports a way to save reusable prompts (different apps call this Projects, Gems, custom instructions, or similar — check the menus if you're not sure), you can store the template there so it loads automatically next time. You'll know it worked when the template is sitting in your notes ready to go the next time a listing site or a landlord's email hands you two options.

Common mistakes

• Letting the assistant pick your priorities. It will default to generic things like "location" and "price." Insist on your weights, even if they feel quirky — a quiet street might matter more to you than square footage, and that's the whole point of doing this yourself.
• Pasting only the headline summary. The first paragraph of a listing is marketing. The fine print about utilities, parking, and pet fees is usually further down — paste the whole thing, or you'll be comparing ad copy instead of facts.
• Trusting the score without checking the table. A 9/10 on "utilities included" means nothing if the listing never actually said that. Always read the table before you read the score, and send the "not stated" list to the landlord before you commit.

Try it now

Pick one priority that matters to you (commute, pets, quiet, anything), find two short rental listings — even made-up ones work for practice — and run Steps 1 to 4 in a fresh chat. You'll see the decision become obvious in under five minutes.

In short: OpenAI says Jump Trading is using ChatGPT for multi-step quantitative research, with humans still checking the work. The bigger story is the shift away from quick chatbot questions toward longer AI workflows inside a company.

Most people use ChatGPT the way they'd text a friend: one quick question, get a quick answer, move on. A trading firm called Jump Trading is doing something different, according to OpenAI — and that difference is what makes this announcement worth a closer look.

What's actually new

The phrase OpenAI keeps using is "longer-running AI workflows." If you've only ever typed a single question into a chatbot, the idea is simple: instead of one question, one answer, the team combines several data sources and runs the AI through a multi-step research process before a human reviews the result.

Think of it like the difference between asking a coworker one question over chat versus giving them a small project. The first takes seconds. The second takes longer, but you get something useful back. Jump Trading is the example OpenAI is highlighting to show this kind of deeper, project-style AI use in action.

Why a trading firm matters as an example

Jump Trading uses math and statistics (called "quantitative research" — basically, finding patterns in numbers) to make fast decisions. For a team like that, even small improvements in how quickly they can test an idea or pull together research can matter.

That's why OpenAI is pointing to them: when a company works at that pace, a chatbot that can handle a longer, more complex task — and keep a human in the loop to check the work — is a more interesting story than "ask one question, get one answer."

Wrap-up

One announcement, one example, but it points at a clear direction: AI inside companies is starting to look less like a smart search box and more like a research assistant you hand a small project to. The takeaway for anyone — not just traders — is to start thinking about your own work in those terms. Pick one small task this week, give the AI the full set of inputs at once, and see what comes back.

In short: Salesforce's Principal Architect of Ethical AI Practice, Kathy Baxter, says giving AI agents real autonomy is possible when you pair a probabilistic model with deterministic rule-based logic — the AI thinks, the rules check.

When you hear about AI "agents" that can book your flights, send emails, or move money between accounts, the natural question is: how do we trust them not to mess up? Salesforce's Kathy Baxter, in the company's own announcement, argues the answer isn't more AI — it's less, in the right places.

The problem with full autonomy

An AI agent is an AI that can take real actions in the world, not just answer questions. Most agents today are built on a probabilistic model — the same kind of large language model (think of it as the engine behind ChatGPT) that powers chatbots. "Probabilistic" means the AI makes its best guess based on patterns it has seen.

That guess is usually good. But "usually" isn't good enough when the agent is about to spend your money or email your boss. Without something checking the AI's choices, a small mistake can snowball into a big one.

The two-logic approach

Baxter's core idea, as Salesforce describes it: pair the probabilistic AI with deterministic logic.

• Probabilistic AI does the creative, flexible part. It understands what you want, writes a reply, picks a tool, and handles situations it's never seen before — because it thinks in probabilities, not rules.
• Deterministic logic does the rule-following part. Given a clear input, it always returns the same output. If an agent is about to send a payment over a set limit, the deterministic check blocks it. No guessing. No "maybe."

The AI does the thinking; the rules do the checking. Think of it like a self-driving car: the AI steers, but the brakes are still mechanical.

Why this matters for trust

Salesforce describes the result as more trustworthy, explainable, and accountable. That's the company's own framing — but the reasoning is straightforward:

• Trustworthy because the rules catch the edge cases the AI might miss.
• Explainable because you can point to the exact rule that fired (for example, "payments over a set limit require human approval").
• Accountable because every decision has a clear paper trail.

For a user, that translates into something simple: you can let an agent act, and you don't have to hover over it.

Wrap-up

The big idea from Baxter and Salesforce is simple: let the AI be creative where it should be, and let hard rules handle the decisions where guessing isn't safe. You don't need to choose between smart AI and safe AI — you just need both, doing different jobs.

A practical next step today: the next time you set up an AI tool that takes actions for you, write down one rule you wish it had to follow. That's the deterministic layer — and it's the part that turns "letting the AI run" into something you can actually trust.

In short: OpenAI has published new results from an internal "frontier" AI model that worked on open problems in mathematics, and is sharing Lean proof formalizations and the full research details publicly on GitHub.

You probably use AI to summarize an email, draft a recipe, or plan a trip. Math research feels like a different planet. But when an AI helps tackle questions that human experts have been stuck on for years, it tells you something real about where these tools are headed.

What OpenAI actually did

According to OpenAI's announcement, the company shared new results from what it calls a frontier model — the most capable internal AI it has, the same family of systems behind tools like ChatGPT. This model was pointed at open problems in mathematics, which is just a way of saying: questions that no one in the world has solved yet.

OpenAI says it then turned the model's findings into Lean proof formalizations. Lean is a programming language and tool built specifically for checking math. A proof formalization is the act of writing a proof not in normal human sentences, but in a strict, step-by-step format that a computer can read and confirm. It's the difference between sketching an argument on a napkin and filing a tax return that an auditor can verify line by line.

Why math, and why now

Math is often used as a benchmark for AI reasoning — how well a system can think through a problem step by step rather than just pattern-matching what other people have said before. If an AI can contribute to open problems, the reasoning behind everyday AI tools is probably getting stronger too.

OpenAI has put the Lean code and research notes on GitHub, a public site where developers and researchers share and review each other's work. Other mathematicians and AI labs can look at what the model did, test it, and build on it. That openness is part of why this announcement got attention in the research community.

Wrap-up

The big takeaway isn't that you should rush to try a math AI. It's that the assistant in your pocket is being trained and tested on harder and harder problems in the background. As that reasoning improves, expect everyday AI to handle trickier tasks with less hand-holding from you. If you're curious, OpenAI's Lean files and research notes are open on GitHub and free to read.

In short: Build one master prompt template — the block of instructions you paste into an AI chat — that describes who you are and how you want answers, then add a few lines of "this week" context each time to spin off a custom brief in under a minute. Works in any major AI assistant.

By the end of this guide you'll have a saved template you can copy, fill in, and paste into a new chat in seconds — no more re-explaining yourself every Monday morning. This is for people who already send messages to an AI assistant and want one extra workflow that pays off every single week.

Before you start

• An account with any major AI assistant you already use. The technique works the same in all of them, so pick the one you like.
• A place to save text you can find fast: a notes app, a Google Doc, Notion, or even an email draft. Anywhere you can copy from quickly.
• First-time setup: about 15 minutes. Each week after that: under a minute.

Step 1 — List the things you ask for every week

Open your notes app and spend five minutes writing down the kinds of help you reach for most often. Don't filter — write whatever comes to mind, even the small stuff. This list becomes the raw material for your template; later you'll turn the recurring patterns into standing instructions the assistant always knows about.

What you'll see is a blank document with a blinking cursor ready for typing. If it looks different on your device, any text editor works — Notes, Google Docs, Notion, even a plain email draft. The key is being able to find and copy from it in ten seconds.

💬 Example: "Plan my week, draft client emails, summarize meeting notes, brainstorm dinner ideas, review my to-do list, prep for interviews, write social posts."

You'll know it worked when you have at least five to eight real things you actually ask for, written in your own words.

Step 2 — Write an "About Me" block at the very top

Move your cursor to the very first line of the document and type a short label — just the two words About Me: followed by a colon. Press Enter to drop down a line, then write four to six plain sentences about who you are, what you do, and the shape of your week. The label is simply a word with a colon in front of it; you do not need to make it bold, big, or a special heading. Think of this paragraph as the quick intro you'd give a new assistant on their first day. The AI won't remember this between chats on its own, but it will use it every time you paste it in.

What you'll see is your document now with the words "About Me:" sitting on the top line, with your sentences right below. If it looks different on your app, no problem — pasting the paragraph at the very top of any document works just as well. The label is a helpful marker for you, not a requirement the AI can see. Some assistants also have a built-in "custom instructions" or "memory" setting; you can keep your master template as the source of truth and copy parts into that field when it makes sense.

💬 Example: "About Me: I'm a freelance graphic designer in my 30s. I run a small studio with two part-time helpers. My week is split about 60/40 between client work and business admin. I prefer clear, friendly writing and I make decisions fast once I see the trade-offs."

You'll know it worked when you read it back and it sounds like a quick orientation a smart new hire would actually need.

Step 3 — Add a "How I want answers" block right below

Press Enter twice to make a blank line, then type a second label: How I want answers: followed by a colon. Press Enter again and write the rules below it — how long, what tone, what to avoid. Be specific. The clearer you are here, the less you'll have to nudge the assistant later. A "prompt" is just the instruction you give the AI, and these rules become a permanent part of that instruction.

What you'll see is your document with the words "How I want answers:" sitting just below the About Me paragraph, with your rules listed underneath. If it looks different, plain text works fine — no formatting required. If your assistant already has a preferences or style setting, you can use both: paste the rules in your chat AND save them in the built-in settings for redundancy.

💬 Example: "How I want answers: Default to short answers (5–8 bullets max). Use plain language, no jargon. If I ask for a draft, give me one version, not three. When something is unclear, ask me one specific question instead of guessing. Never use emojis."

You'll know it worked when you can picture an ideal answer just by reading the rules — almost before you ask.

Step 4 — Save the file and pin it where you'll see it

Tap your notes app's Save button (or just close the note — most apps save automatically the moment you stop typing). Now look at the top or top-right corner of the note for one of these small icons: a pin (📌), a star (⭐), or a bookmark (🔖). On Apple Notes the icon is three dots ⋯ with a "Pin" option inside. On Google Keep look for the pin icon at the top of the note. On Google Docs, open the ⋯ menu and choose "Keep in sidebar" or just bookmark the page in your browser. The whole point of this setup is that finding the template takes almost no effort. If it takes you thirty seconds to locate, you'll quietly stop using it by week three.

What you'll see is the note staying at the very top of your list, even when you add dozens of other notes below it. If it looks different on a phone, look for a "Favorites" or "Pinned" folder — your pinned notes usually live there. On a computer, drag a shortcut to your desktop or bookmark the doc in your browser bar.

💬 Example: filename ai-assistant-master-prompt.md, pinned to the top of your Notes app.

You'll know it worked when you can open it from muscle memory, without searching.

Step 5 — Spin off this week's brief in under a minute

When you actually sit down to use the assistant, open the pinned note and tap-and-hold (or right-click on a computer) to Copy All the contents. Open a fresh document — or just scroll below the master template in the same note — and paste it there. Then at the very top of that new block, type a third label: This week: followed by a colon, and add the specific things on your plate. The standing context (about me + preferences) stays the same every week; only the "this week" block changes.

What you'll see is a new block with your master template below and the fresh details on top. If it looks different, some assistants let you start a chat with a saved template or a "project" that holds instructions. If yours has one, paste the About Me and Preferences there once and only paste the weekly block at the top of each new chat.

💬 Example: "This week I have: a client pitch on Friday at 10 a.m., 14 unanswered emails, a half-written blog post, and a team offsite Monday morning. Focus: ship the pitch and clear the email backlog."

You'll know it worked when the combined text reads like a clean handoff to a capable assistant who already knows your life.

Step 6 — Run it, then save improvements back into the template

Open a new chat with your assistant, paste the whole brief (About Me + Preferences + This Week), and send it. Read the answer. Anything the assistant had to ask you about — your timezone, your boss's name, a project you keep forgetting to mention — is a sign that line belongs in the master template, not in this week's brief. Add it. Prune lines that never triggered.

What you'll see is a focused first answer based on your context, often usable right away. If it looks different, some assistants keep a "memory" across chats. You can still keep your master template as the source of truth and update both places when something new earns its spot.

💬 Example: the assistant asks "what time zone are you in?" once. Add "I'm in Eastern Time (UTC−5)" permanently to the About Me block so it never has to ask again.

You'll know it worked when, after three or four weeks, your weekly briefs are noticeably shorter to write — because the template has absorbed everything worth repeating.

Common mistakes

• Making the template too long. Keep it under about 300 words. A huge template dilutes the assistant's attention on the actual question, and you'll stop reading it yourself before you paste it in.
• Skipping the "don'ts." Rules like "no jargon," "no emojis," "under 200 words" save more time than positive instructions. Tell the assistant what to avoid as well as what to do.
• Never updating the template. After a month, prune lines that never came up. Add new ones when you find yourself repeating the same clarification. A stale template is almost worse than none.

Try it now

Open your notes app, copy the 💬 Example from Step 1, and spend ten minutes listing the things you ask your AI assistant for most weeks. That single list is the seed of your reusable template — and the rest of the workflow builds itself from there.

In short: Cohere's chat assistant can generate a list of creative name ideas from a short prompt you write in plain English. You'll need a free Cohere account, a web browser, and about five minutes — no coding or paid plan required for basic use.

You've got a side project on the couch cushions, but the name? Nothing. You could stare at a blank page for an hour, or you could spend five minutes bouncing ideas with an AI. Cohere — a chat-based AI assistant from the company Cohere — is built for exactly this kind of quick, text-focused thinking. You type what you want, and it sends back a list of ideas you can mix, match, and edit.

Before you start

• A free Cohere account (sign up with an email at cohere.com).
• A web browser on a phone, tablet, or computer.
• A rough idea of what your project IS — even one sentence is enough (a podcast about old movies, a candle business, a neighborhood walking club).
• About 5 minutes total.

Step 1 — Open Cohere and start a new chat

Go to cohere.com in your browser and sign in. Once you're in, look for a "New chat" button or a text box at the top or bottom of the page — a clean, empty chat window opens. If you don't see "New chat," look for a pencil icon, a plus sign (+), or simply click into the existing chat box to start a fresh message. You'll know it worked when you see a blank message field with a blinking cursor and a "Send" or arrow button nearby.

Step 2 — Tell Cohere what your project is

Click into the message field and write one or two sentences describing your project in plain words. The more specific you are about the vibe you want (fun, serious, modern, cozy, weird), the closer the names will land. After you press send, Cohere will acknowledge your message — usually with a short reply like "Got it" or a direct attempt at your request. If nothing appears for a few seconds, wait a moment, then check that your message actually went through (look for your text in the chat history). 💬 Example: "I'm starting a weekend walking club in my neighborhood. I want names that feel friendly, casual, and a little playful — nothing too formal." You'll know it worked when Cohere responds to your setup message instead of the chat sitting empty.

Step 3 — Ask for exactly 10 name ideas

Now send a follow-up message asking for a list of 10 names. Specifying the number helps — otherwise you may get 3 or 30. Cohere typically replies with a numbered or bulleted list. If your screen shows the reply as a wall of text, scroll down — the list is usually underneath. On mobile, the list may be cut off; just keep scrolling to see all 10. 💬 Example: "Great. Now please give me exactly 10 name ideas for this walking club. Mix practical names with a couple of quirky ones." You'll know it worked when you can count (roughly) 10 distinct name suggestions in the reply.

Step 4 — Push for more variety

Don't settle for the first round. Reply and ask for a different flavor — funnier, shorter, more professional, or inspired by a word you liked. Cohere will generate a fresh list based on your new direction. This is the step where the brainstorm really opens up, so don't be shy about steering. 💬 Example: "I like 'Sidewalk Stories' from your list. Can you give me 10 more names in that same warm, story-telling style?" You'll know it worked when the new list feels noticeably different from the first one.

Step 5 — Check availability and gut-feel

AI-generated names are creative, but they can sometimes be oddly generic, hard to spell, or already used by someone else. Read each name out loud. Type your top three into a search engine alongside words like "podcast," "shop," or "club" to see if anything similar already exists. What this tool cannot do is tell you whether a name is legally free to use or whether the domain is available — that's still on you. If a name feels close to a real brand you recognize, drop it. You'll know it worked when you've narrowed your list to 2–3 favorites that sound good, are easy to say, and don't collide with anything you know.

Step 6 — Save your favorites somewhere real

Copy the names you like into a note on your phone, a sticky note, or a simple document. Don't just trust that you'll remember them tomorrow — you won't. If Cohere's interface offers a way to copy the whole chat (sometimes a small "copy" icon near each reply), use it. If not, highlight the names with your finger or mouse and copy them by hand. You'll know it worked when you can close the browser, open your notes, and see your shortlist waiting for you.

Common mistakes

• Asking for "a good name" with no context. The AI has nothing to work with, so you'll get bland, generic suggestions like "Project One." Always include what the project is and the mood you want.
• Accepting the first list without pushing back. The first round is just a starting point. If the names feel off, tell the AI what to change ("make them shorter," "add something nature-themed") and ask again.
• Forgetting to check if a name already exists. AI can accidentally invent a name that's already a well-known brand, podcast, or trademark. A quick search saves you a future headache.

Try it now

Open Cohere, type: "Give me 10 name ideas for a casual Sunday-morning podcast about cooking breakfast with whatever's in the fridge. Keep the names short and fun." See what comes back. Your favorite is probably hiding in that list.

In short: In a recent blog post, Grammarly argues that most AI tools in education are designed to hand over finished answers, when the bigger opportunity is to make the learning process itself visible to students and teachers.

A finished essay doesn't show how it was written. It shows what was turned in, but not how the student read the prompt, where they got stuck, or what they already knew. That gap is what Grammarly calls the "Learning-Visibility Loop" in a recent post on its blog.

What Grammarly is actually proposing

The core idea is simple once you hear it: instead of an AI tool spitting out a polished answer, it should show its work in a way that helps the student understand what just happened.

Think of it like a math teacher who doesn't just mark your answer right or wrong, but asks, "How did you get there?" That's the difference between seeing the destination and seeing the journey.

According to Grammarly, generic AI is built to deliver answers, not to make learning visible. A finished assignment hides the thinking that went into it. A learning-visible tool would, in their framing, let both the student and the instructor see the steps — where the student got stuck, which parts were easy, what they already understood.

Why this matters if you use AI to study

If you've ever used an AI to help with homework, you've probably noticed this gap. You ask for an explanation of a topic, the AI gives you a clean paragraph, and you copy it in. But did you actually learn the topic? Hard to tell — from the outside, a copied paragraph and a learned paragraph look the same.

Grammarly's argument is that AI in education should be designed differently. Not as a vending machine for answers, but as something closer to a study partner that leaves a trail. That trail, the company says, is what makes learning visible to both the student and the teacher.

Wrap-up

Grammarly's "Learning-Visibility Loop" is, in plain words, a reminder that AI in school should help you see how you're learning, not just hand you the finished product. You don't have to wait for a new tool to try it — the next time you use any AI for studying, ask it to show its work, and then test yourself without it. That two-step habit is the learning loop, made visible.

In short: Cohere says North 2 is an enterprise AI platform that bundles security, AI "agents," and cost controls into one tool that companies can run on their own servers — so sensitive data doesn't have to leave the building.

When you call a bank, talk to a hospital, or chat with an insurance company, an AI is often working behind the scenes. The question is: where does that AI live, and who sees your data? That's the everyday concern behind Cohere's newest release, North 2.

What Cohere actually announced

According to Cohere's announcement, North 2 is being described as the company's biggest upgrade yet. The release brings four things together into a single agentic AI platform — meaning software where the AI doesn't just answer questions, but can actually take actions and use tools to get work done on its own.

Those four pieces, in plain language:

• Enterprise-grade security — the access controls, audit logs, and privacy protections that a large company, bank, or hospital requires before letting any AI touch customer data.
• Intelligence — the AI models themselves, capable of reading, writing, reasoning, and acting on tasks.
• Cost governance — tools to track and limit how much the AI is being used, so a project doesn't accidentally run up a giant bill.
• Full-stack control — meaning companies can adjust every layer of the system, from the underlying model to the user-facing app, rather than being locked into a single vendor's way of doing things.

"Runs wherever your data lives"

The part of the announcement that tends to matter most to large organizations is the last bit: North 2 runs "wherever your data lives." In practice, that means a company can keep everything inside its own computers or a private cloud (a server network that only that company uses), instead of sending information to a public AI service operated by someone else. For industries with strict rules about data — finance, healthcare, government — that's often the deciding factor in whether AI is even on the table.

Wrap-up

North 2 is Cohere's attempt to give large organizations one place to run AI agents — combining the models, the security, the budget controls, and the deployment flexibility in a single platform that can sit inside a company's own infrastructure. For everyday people, the most visible effect won't be the product itself; it'll be the AI-powered services at work, at the bank, or at the doctor's office quietly getting a little more capable. If your company is shopping for enterprise AI, the official Cohere announcement is the best place to start — and your security and IT teams should be the ones making the call.

In short: PwC and Cohere have announced a global alliance to help companies roll out secure enterprise AI, with Canada as the first country to roll it out, then expanding worldwide.

Most companies say they are "doing AI" these days. Far fewer can show what those projects actually earned them. A new partnership between PwC (one of the world's biggest consulting firms) and Cohere (an AI company that builds AI tools for large organizations) is aiming at exactly that gap.

The two companies say the alliance is meant to help organizations move from AI experiments to AI that produces "measurable business value" — consultant-speak for systems that earn real money, save real time, or reduce real risk, not just run a few impressive demos.

What's actually being announced

According to the joint announcement, PwC and Cohere have signed a global alliance. Canada is the first market where the two will work together directly with clients.

What does "global alliance" mean in practice? Think of it as a formal handshake between a consulting firm and a technology provider, so a PwC client can buy Cohere's AI tools with PwC's help installing and running them. The consulting firm brings people who understand the client's business; the AI company brings the technology underneath.

Why this matters for businesses

A few practical things worth knowing:

• Secure AI. Both companies stress security and data privacy. For a large company, this is usually the make-or-break question: where do our data live, and who can read them? Enterprise-grade AI tools (AI built for big organizations) are designed to keep a company's information inside its own walls, rather than send it to the open internet.
• Measurable value. "Measurable business value" is consultant-speak, but the idea is simple: if a company spends a million dollars on AI, leadership wants to see the return in plain terms — hours saved, deals closed, errors avoided.
• Canada first. Starting in Canada gives the two a way to test how the partnership actually works in one market before rolling it out to others.

How Cohere fits in

Cohere is an AI company that builds large language models, or LLMs (the AI engines that read and write text — the same kind of technology behind today's AI assistants) — but pitches them specifically to businesses rather than individual consumers. Its tools usually focus on everyday work tasks: summarizing long documents, answering questions from a company's internal files, or helping employees draft text faster.

Pairing that technology with PwC's global network of consultants is, according to both companies, meant to move AI projects past the "pilot phase" — small, safe test runs that never quite scale — into something a whole company uses every day.

Wrap-up

A consulting giant and an enterprise AI company are teaming up to take AI from slide decks into real business workflows — with Canada as the testing ground. None of this changes anything for an everyday reader overnight. The thing to watch is where the rollout goes next, and whether the "measurable value" promise actually shows up in numbers.

In short: OpenAI announced a new visual ad format inside ChatGPT, plus expanded measurement tools, attribution partnerships, and brand-safety features for advertisers. If you chat with ChatGPT, sponsored content is set to look different from regular answers.

OpenAI has confirmed it is expanding how advertising works inside ChatGPT. According to the company's announcement, a new visual ad format is on the way, and the company is also rolling out more tools for the brands that advertise inside the assistant.

For everyday ChatGPT users, the headline is simple: sponsored content is about to look more polished, and more of it is on the way.

What's actually changing

OpenAI's announcement is mostly aimed at advertisers, but four parts of it are worth understanding for anyone who uses ChatGPT:

• A new visual ad format. OpenAI says it has built a new way for ads to appear in ChatGPT, one that's visual rather than only text. The idea, according to the company, is to let ads fit more naturally into how the assistant responds to you.
• Expanded measurement tools. These help advertisers understand how their ads perform — who is clicking, and what happens next. You won't see this directly, but it shapes what kinds of ads companies decide to run inside ChatGPT.
• Attribution partnerships. "Attribution" (basically, giving credit to the ad that led to a sale or sign-up) is the plumbing advertisers need to know their money is working. OpenAI says it is working with outside partners to do this better.
• Brand suitability. This is the part most relevant to you. "Brand suitability" is an industry term for the controls brands have over where their ads appear, so a company can keep its product out of conversations it considers off-message. OpenAI says it is expanding these controls.

Wrap-up

OpenAI is signaling, in plain terms, that ChatGPT is becoming a serious advertising channel — which means more sponsored content for everyone using it. The smartest habit to build today is the same one that has always worked: notice when an answer in ChatGPT looks like an ad, treat it accordingly, and remember the assistant's job is to help you, not to sell to you.

In short: Black Forest Labs' new image model, Flux 3 Image, is now available inside Picsart's AI Playground, with native 2K and 4K output and room for up to 10 reference images. It's built for ads, posters, and brand assets — and it can edit existing images too.

If you've ever tried to whip up a quick poster, social ad, or product mock-up and ended up with a fuzzy picture that fell apart the moment you zoomed in, you already understand why a model promising full-resolution output (high enough pixel count to look sharp on a poster, not just a phone screen) is real news. Picsart just added one.

What is Flux 3 Image?

Flux 3 Image is the newest image model (an AI trained to generate pictures from a text prompt or uploaded reference) from Black Forest Labs, a research group known for its open work in image generation. Think of it as the engine under the hood: you describe a scene or drop in references, and the model paints the result.

According to Black Forest Labs, the focus this time is on three things — cleaner layout control (deciding where things sit in the frame), more precise edits (changing just the part you want without redrawing everything), and higher native resolution.

What's new in Picsart's AI Playground

Picsart — a photo and design app popular with creators and small businesses — has put Flux 3 Image inside its AI Playground, a hub where users can try different image models side by side.

Here's what stands out from the announcement:

• Native 2K and 4K output. The model generates images at high resolution by default, so what you download is sharp enough for print, not just a screen.
• Up to 10 reference images. Most image models accept one or two visual guides. Flux 3 Image, Picsart says, lets you feed in up to 10 — handy for keeping a character's face, a product, or a brand color consistent across a set of pictures.
• Layout control and precise edits. You can describe the scene in detail, drop in references, and then edit the result. Picsart frames this as output ready for ads, posters, and brand assets — closer to a finished file than a rough draft.

The same model also edits existing images, so you can change a small part of a picture without redrawing the whole thing.

Wrap-up

Flux 3 Image joins a crowded field of image models, but Picsart's pitch is concrete: bigger files, more references, and edits that don't destroy the original picture. The best way to know if it works for you is to open Picsart, pick the model, and try a prompt you actually care about — a poster for an event, a product shot, a portrait with a specific look. Five minutes of testing will tell you more than any announcement.

In short: Databricks published a post about its "Genie Ontology" and how it helps product teams use general-purpose AI agents. According to Databricks, those agents can already search the web, reason, and write code — and the ontology gives them a structured map of the company's products.

When a company that builds data and AI tools writes about how it uses AI to build its own products, it's a small peek behind the curtain. It doesn't usually make headlines the way a flashy new consumer app does, but it tells you something useful: this is how the tools you already use keep getting better, faster.

What "ontology" actually means here

The word sounds academic, but the idea is simple. An ontology is a structured map of knowledge about a specific domain — in this case, Databricks' products, features, and how they relate. Think of it like a really detailed index at the back of a textbook. Instead of having to read every chapter, you can look up exactly what you need.

For AI agents — software that can take instructions, search, reason, and act — that map is genuinely useful. Without it, the agent is guessing at what a product name means or how two features fit together. With it, the agent has a reliable reference.

What Databricks is actually saying

The post is titled "How Genie Ontology powers product development at Databricks." The framing, according to Databricks, is straightforward: general-purpose AI agents — AI tools that aren't locked into one job but can handle many — are already capable at a few core things, including pulling information off the web, working through problems step by step, and writing or reading code.

That's the foundation. The ontology is what lets those agents be useful inside Databricks. Instead of asking a generic AI assistant "what does feature X do?", the agent can look up feature X in the ontology and get a precise answer — then help a product team make changes, write tests, or fix bugs with that context in hand.

Databricks describes this as a way to give agents more reliable context, so they can support product teams more confidently.

Why this matters for everyday people

You don't need a Databricks account for any of this to be relevant. It's a signal of where AI-assisted software development is heading, and that affects the apps you use.

Most of the services you rely on — your email client, your photo editor, your bank's mobile app — are being built or improved with AI tools behind the scenes. The better those internal AI tools get at understanding a product, the faster the products you use tend to ship new features and fixes.

It's also a reminder that general-purpose AI agents aren't magic. They work best when they have good context. Behind many "the AI just did this amazing thing" stories is a team that spent time building the right knowledge structure to feed the AI.

Wrap-up

Databricks' post is a small, technical read, but the broader pattern is worth noticing: AI is being used to build AI products, and the companies doing it well are giving their AI agents structured context rather than just throwing raw data at them. If you want to follow this space, a simple next step is to ask any major AI assistant what it's actually good at and where it tends to fail. You'll start to see, in plain language, why projects like Genie Ontology exist.

In short: OpenAI says it's outlining how it will handle text watermarking under EU rules, with detection tools initially limited to researchers. The plan is part of a broader push to make AI-generated content identifiable.

Have you ever finished reading an article — or skimmed a suspiciously polished email — and wondered whether a real person actually wrote it? That question keeps getting harder to answer, and OpenAI says it's taking concrete steps to address it under the EU's new content provenance rules.

What "text watermarking" actually means

A watermark, in this context, is a hidden signal baked into AI-generated text. You can't see it when you read the words, but a special detector can. The idea is simple: the AI subtly nudges its word choices in ways that look natural to readers but leave behind a fingerprint a tool can verify. Think of it like the tiny security thread inside a banknote — invisible to your eye, but easy for a machine to confirm.

This isn't a finished product yet. According to OpenAI's announcement, the company is laying out its approach — meaning the framework, not a final consumer-facing tool. The plan covers three things in particular: where watermarks will apply, how detection will work, and who gets access first.

Where watermarks apply, and where they don't

OpenAI says the watermarking effort is being rolled out selectively, in line with what EU rules require. According to the company, the system is designed so that text produced by a watermarked model carries the signal — while text from other sources, or text where the signal is stripped out, won't show it as AI-generated. That's a real limitation: watermarking only helps when everyone is using compatible systems.

Why access starts with researchers

The most concrete detail in OpenAI's announcement is who gets the detection tools first: outside researchers. Not the general public, not platforms — academics and safety specialists who study how AI text is detected (or evaded).

Why this order? Two reasons. First, it gives experts time to stress-test the system before bad actors learn how to beat it. Second, it generates the kind of independent evidence regulators want to see before broader rollout.

Wrap-up

OpenAI says its approach to EU text provenance rules is a starting point, not a finished system — researchers get first access, and broader availability comes later. For now, you won't see a detector built into your apps. But the conversation about what's real online is moving forward, and it's worth paying attention to. A small step today: open your favorite AI tool's settings or help page and search for "watermark" or "content provenance." If they mention anything, you're ahead of most people.

In short: Open any AI assistant that accepts PDF uploads, attach the file, and ask a few focused questions about deadlines, fees, and cancellation. You'll get a plain-English summary in minutes — but always read the original key clauses yourself, because AI summaries can miss important details.

Contracts hide the important parts deep in the small print: the auto-renewal clause, the 30-day cancellation window, the fee that "may apply" later. An AI assistant can pull all of that into a short, readable summary, but only if you ask the right questions. This guide shows the technique that works in any major AI assistant that accepts PDF uploads — ChatGPT, Claude, Gemini, Copilot, Le Chat, and others.

Before you start

• An AI assistant that can read PDF files you upload. Most major assistants do this on their free tier, but very long contracts (over roughly 100 pages) may need a paid plan. Check the upload limit on the assistant's pricing page if your document is unusually long.
• The contract itself, saved as a PDF on your computer or phone.
• About 10 minutes. The summary itself is fast; the careful read-through at the end takes a moment.
• A quiet moment to actually read what the assistant tells you.

Step 1 — Start a fresh chat and upload the PDF

Open your AI assistant and begin a brand-new conversation. A clean slate gives you the cleanest answer — older chats can confuse what the assistant reads in the new document. Once the chat is open, look for a paperclip, attachment, or plus icon near the message box. Tap that, then choose the PDF from your files. Some assistants also let you drag and drop the file straight onto the chat window.

If you don't see a paperclip, look for a + icon or a button labeled something like Add files or Upload. On phone apps the icon often hides inside a small menu at the side of the text box.

💬 Example: "I'm uploading a contract. Please don't analyze it yet — I'll ask my questions one at a time."

You'll know it worked when the file name appears above the message box (or as a small chip beside it) and the assistant confirms it can see the document. If nothing happens after 30 seconds, the file may be too large — check the assistant's file-size limit before assuming the tool is broken.

Step 2 — Ask for a plain-English overview first

Before you drill into the details, ask the assistant to give you a one-paragraph summary of what the document is, who it's between, and what it covers. This grounds the rest of the conversation: once the assistant has the big picture in mind, its later answers tend to be more accurate. Type your question in the message box where the uploaded file is already attached, and press Enter or the send arrow.

If the answer is longer than a paragraph or feels like a sales pitch, ask it to "shorten to one paragraph and skip the legal jargon." If the assistant gives you only a vague intro, ask a follow-up: "What kind of contract is this — a service agreement, a lease, a sales contract, or something else?"

💬 Example: "In plain English, what is this document, who are the parties, and what is the main thing being agreed to?"

You'll know it worked when the assistant gives you back a short paragraph (3 to 6 sentences) that names the type of contract, the people or companies involved, and the core purpose. If any of those three is missing, ask one more focused question before moving on.

Step 3 — Ask the three questions that actually matter

Now the real work. In the same chat, type three focused questions, one after another: deadlines (when does this start, when can I leave, when does it auto-renew), fees (what does it cost, what changes after the first period, what extra charges might apply), and cancellation (how do I end this, what's the notice period, what happens if I miss it). Ask all three in one message so the assistant can answer them together. Press Enter and wait — usually under a minute.

If the assistant answers only one of the three, send the missing questions in a fresh message. If the answer feels too short, ask "Please list every date, dollar amount, and cancellation step you can find in the document."

💬 Example: "From this contract, list: 1) every deadline or time limit I need to know, 2) every fee, charge, or price that might apply, and 3) exactly how I cancel, including any notice period. Use bullet points."

You'll know it worked when the assistant gives you a structured, easy-to-scan answer with three clear sections. If any answer is "I can't find that in the document," ask it to point to the page or section it looked at — sometimes the answer is there and the assistant missed it.

Step 4 — Push for the things people forget to ask

Once you have the big three, push one more layer. Contracts hide risk in the small print: what happens if you miss a payment, who pays legal fees in a dispute, whether the company can change terms mid-contract, and which country's laws apply. Ask these as a follow-up message in the same chat. The assistant now has the document and your earlier answers in its context window (the conversation history it can refer to), so it can build on what it already told you.

If the assistant's answer feels generic, paste in the actual clause: "I'm looking at section 8.2. What does this mean in plain English, and what's the worst-case scenario for me?"

💬 Example: "What happens if I miss a payment, who covers legal fees in a dispute, can the company change the terms mid-contract, and which state's laws apply? Point me to the section for each."

You'll know it worked when the assistant points to specific sections or page numbers and explains each risk in a sentence a non-lawyer can follow. If it just says "this is a standard clause" without explaining the actual risk, ask "what's the worst that could happen to me, in concrete terms?"

Step 5 — Ask for a one-page checklist you can save

Now ask the assistant to compress everything into a short, printable checklist you can keep next to the contract. A checklist is easier to skim than a long summary, and it's the format most people actually re-read later. Ask in the same chat — the assistant still has the document and your earlier conversation. The result should fit on one screen: action items, key dates, and money amounts, plain and short.

If the checklist comes back too long, ask it to "cut this to the 10 most important items I'd forget in six months."

💬 Example: "Summarize everything we just found into a one-page checklist I can save: dates I must remember, amounts I'll be charged, and the steps to cancel. Keep it under 15 lines."

You'll know it worked when you can copy the answer into a notes app and read it in under a minute. If the checklist still feels like a wall of text, ask the assistant to put each item on its own line with the date or amount in bold.

Step 6 — Spot-check three sections in the original PDF

AI summaries are good, but they're not perfect. The assistant can misread a clause, skip a footnote, or paraphrase something in a way that changes the meaning. Before you rely on the summary, open the original PDF and read three things yourself: the cancellation clause (usually labeled "Termination"), the fees section (often "Fees," "Charges," or "Payment Terms"), and any date or deadline the assistant flagged. If those three match what the assistant told you, the rest of the summary is very likely accurate.

If you can't find the section the assistant mentioned, use the PDF's search function (Ctrl+F on a computer, or the magnifying glass icon in most phone PDF apps) and type a keyword from the clause. If something doesn't match, send a follow-up question: "Section X says something different — can you re-read and reconcile?"

💬 Example: "Cancellation clause says 30 days, fees say a charge after year one, and auto-renewal is 60 days before expiration — can you re-read sections 7, 9, and 12 and tell me if your summary matches?"

You'll know it worked when the three sections you read match the summary, and you feel comfortable either signing the contract or deciding not to. If anything is off, send the assistant the exact text and ask it to explain the difference — that's how you catch the small print that changes everything.

Common mistakes

• Asking one giant question. A single "summarize this contract" prompt usually gives a vague, hard-to-scan answer. Splitting it into overview, the three big questions, the hidden risks, and a checklist gets you a result you can actually use.
• Trusting the summary without checking. AI assistants can misread clauses, especially around fees and dates. Always read the cancellation, fees, and deadline sections yourself before you sign. The summary is a map, not a replacement for the territory.
• Letting a long chat get messy. After 20 or 30 messages, the assistant can lose track of what it told you earlier. If the conversation gets long, copy the checklist into a new chat, re-upload the PDF, and start fresh. Cleaner chats give cleaner answers.
• Skipping the free-tier limits. Some assistants let you upload small files on the free plan but cap the size on paid plans. If your upload fails, check the assistant's file-size limit before assuming the tool is broken.

Try it now

Pick the last contract you signed — apartment lease, phone plan, gym membership, anything with more than five pages. Upload it to your AI assistant and ask the three big questions from Step 3. Save the answer somewhere you can find it in six months, when the renewal notice arrives. You'll never read a long contract the same way again.

In short: All five major free AI apps — ChatGPT, Gemini, Claude, Copilot, and Le Chat — can handle the three tasks beginners try most: writing help, answering questions, and coding assistance. They work differently under the hood, but for everyday use the real difference comes down to integrations, conversation style, and how each one fits into your existing tools.

The idea that one AI assistant is simply "the best" is a myth. Each tool is built by a different company, trained in its own way, and optimized for its own strengths. For a beginner, this actually works in your favor — it means there is a good fit for almost every need. This guide walks you through comparing the free tiers of the five most widely available assistants so you can choose with confidence, not guesswork.

Before you start

• An internet connection and any device (phone, tablet, or computer).
• At least one free account — you do not need to pay for anything to follow this guide.
• About 15 minutes to test a few tools side by side.

Step 1 — Decide what you actually want to do

Before looking at any app, name your goal in one sentence. The three tasks beginners try most are:

• Writing and drafting — emails, social posts, summaries, brainstorming ideas.
• Answering questions and explaining things — looking something up, learning a new topic.
• Getting help with code — understanding a programming concept or writing a simple script.

Most free AI apps handle all three reasonably well. The differences show up in how well they handle each one, and whether they fit into tools you already use.

💬 Example: "I want to draft a short email to my manager explaining why a project deadline needs to move." You'll know it worked when you can state your goal clearly — that sentence is your comparison checklist.

Step 2 — Compare writing and creative tools

For drafting, editing, and generating ideas, each app takes a different approach.

ChatGPT (OpenAI) and Gemini (Google) both produce quick, well-formatted drafts across many formats. They are good all-around choices for everyday writing tasks.

Claude (Anthropic) tends to give longer, more detailed responses and often catches nuance in longer pieces. If you write a lot of reports, emails, or documents, it may feel like a thoughtful editor rather than a speed-draft tool.

Copilot (Microsoft) connects directly to Microsoft Word and Outlook. If you already pay for Microsoft 365, you can generate and edit text inside those apps without switching windows.

Le Chat (Mistral AI) handles multilingual writing naturally — a useful advantage if you work across languages or need content in a language other than English.

All five will produce usable drafts. The question is whether one of these fits more naturally into your existing workflow.

💬 Example prompt to try in two tools: "Write a 150-word product description for a small handmade candle business." You'll know it worked when you can compare the results side by side and spot the style differences — one may be more energetic, another more formal.

Step 3 — Compare question-answering and knowledge tools

When you need an answer rather than a draft, the tools differ most on two points: how current the information is, and how deeply they explain.

Gemini draws on Google's search infrastructure for real-time information. If your question involves recent events, current prices, or anything time-sensitive, it may have an edge here on the free tier.

ChatGPT and Claude have knowledge cutoff dates — they do not browse the live web the same way. They explain concepts in depth and handle follow-up questions particularly well, making them strong for learning and research conversations.

Copilot uses the same underlying model as ChatGPT but displays search results alongside its answer, which some users find helpful for verifying factual claims.

Le Chat handles questions in multiple languages fluidly and can switch between them mid-conversation.

If your primary need is quick, up-to-date answers, try Gemini for that task. For learning and exploring a topic over several questions, ChatGPT or Claude may feel more natural.

💬 Example prompt: "Explain what a budget spreadsheet is to someone who has never made one." You'll know it worked when the explanation uses everyday language and builds on ideas step by step — not when it throws jargon at you.

Step 4 — Compare coding and learning tools

If you are curious about how code works or want help writing a simple script, here is how the tools line up.

ChatGPT and Copilot both generate code snippets with plain-language explanations. Copilot's advantage shows up if you write code inside Microsoft development tools — the integration is built in.

Claude tends to explain code in more detail and can walk through longer programs step by step, which some beginners find helpful.

Le Chat supports a wide range of programming languages and can switch between them in the same conversation.

Gemini integrates with Google tools and works well for web-related code and simpler scripts.

None of the free tiers are full development environments — they will not run your code for you. But for understanding, learning, and writing small scripts, all five are worth trying.

💬 Example prompt: "Write a simple Python script that asks for your name and prints a greeting." You'll know it worked when you can copy the result, paste it into a free online Python editor, and run it without errors.

Step 5 — Make your decision in three questions

After testing all five tools with the same prompt, answer three quick questions to narrow your choice:

1. Which response felt most natural to read?
2. Did any tool integrate with an app you already use (Microsoft 365, Google Workspace)?
3. Did any tool answer in the language or style you preferred?

Write the answers down. If you are a writer or content creator, you will likely lean toward ChatGPT or Gemini for their versatility. If you already use Microsoft tools every day, Copilot removes friction by living where you already work. If you value thoughtful, detailed responses, Claude may feel like the best fit. If you work across languages or want a tool that switches easily between them, Le Chat is worth a closer look.

💬 Example: After comparing results from Steps 2 through 4, note which tool you would open first if you had a real task due tomorrow. You'll know it worked when you feel a clear pull toward one tool for one specific reason — that is a sign the fit is right, not just familiarity.

Common mistakes

• Assuming one tool beats all others. Every comparison you read online reflects one person's use case. Your needs may be different. The tools are genuinely close in quality for most everyday tasks.
• Missing tools you already have access to. If you already pay for Microsoft 365, Copilot may already be on your computer. If you have a Google account, Gemini is a free web app. Check your existing subscriptions before signing up for something new.
• Sticking with the first tool you try. First impressions vary. A tool that felt awkward at first may click once you adjust how you phrase your prompts. If a tool almost worked, try rephrasing before switching.

Try it now

Open two of the five tools in separate tabs or windows right now. Paste the same prompt into both:

"Give me three ideas for a weekend project I could finish in two hours with no special skills or tools."

Compare the results side by side. Notice the differences in tone, length, and creativity. That feeling of "this one speaks my language" is your signal. Trust it — and open that one first next time you have a task to tackle.

In short: Perplexity has published a new help page called "How Credits Work on Perplexity." It's the company's own, official explanation of what its credits buy you and how to track them.

A few months ago, you signed up for an AI helper to answer quick questions. Now you're seeing something called "credits" — a number that goes up and down — and you're not entirely sure what each one buys. You're not alone. Most AI tools today run on some form of credit system, and Perplexity is no different.

What Perplexity announced

According to Perplexity's own page, titled "How Credits Work on Perplexity," the company has put a clear, official explanation in one place. The page is published on perplexity.ai, which means it comes straight from the team that builds the product. That's where to read the exact rules — but the broader idea is the same one you'll find across most modern AI tools, and it's worth understanding the general pattern even before you dig into Perplexity's specifics.

What "credits" usually mean in AI tools

A credit is essentially a unit of usage. Think of it like prepaid minutes on a phone card — each thing you ask the AI to do costs a certain number of credits, and your plan gives you a fresh batch on a regular schedule.

On most AI platforms, basic searches and quick questions cost very little, while heavier tasks — like deep research reports, image generation, or analyzing uploaded files — tend to use more. The expensive ones are the ones that take a lot of computing power behind the scenes (the "computing power" is the actual electricity and processors the AI needs to think through a hard question). The credit system is the way the company charges fairly for that extra work without raising the price of simple questions.

Perplexity's own page will tell you exactly which actions cost how many credits on its platform. The specific numbers aren't in the page's title or summary, so check the page itself for the latest details.

Why this kind of guide matters

AI credit systems can be confusing on purpose, or just confusing by accident. Either way, when a company publishes a clear, official guide, it's a small but useful step toward transparency. It's also a sign that the company expects more users to care about the details — which usually means the product has grown past its early-adopter phase.

Wrap-up

Perplexity has put a clear, official explanation of its credits in one place — a kind of transparency that everyday users rarely get from AI companies. If anything about your account has ever confused you, that's now the page to read. Bookmark it, and check back whenever something doesn't add up.

Your Job Is Changing: GitHub Names the Three Skills That Will Matter Most With AI

If you work with AI tools at all — even just using ChatGPT or Claude to write emails — there's something you should know. GitHub just published a list of the three skills that will matter most as AI becomes a bigger part of how we work.

The big shift? It's no longer about knowing how to write code. Instead, GitHub says the most important skills are: directing AI agents to do tasks, reviewing their work to make sure it's right, and keeping human judgment in charge of important decisions.

Why should you care? Whether you're a marketer, accountant, or manager, AI is starting to handle more of your day-to-day tasks. The people who thrive won't be the ones who can code — they'll be the ones who know how to tell AI what to do, spot when something's wrong, and make the final calls that require a human touch.

Read the full story

A Small Social Club Cut 10-15 Hours of Work a Week With ChatGPT — Here's How

Not every AI story involves tech giants. This week, we got a reminder that small organizations are benefiting too.

A social club was spending days on grant applications and license paperwork — work that involved digging through regulations, filling out forms, and making sure nothing was missed. After bringing in ChatGPT to help, they cut that time down to hours. Ten to fifteen hours a week, reclaimed.

The lesson isn't that AI is magic — it's that it excels at the tedious, time-consuming parts of running a small organization. If you've ever been stuck wrestling with paperwork that felt like it required a lawyer to understand, this story shows what's now possible.

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YouTube's New AI Turns Long Videos Into Short, Captioned Clips — Automatically

If you've ever wanted to try YouTube Shorts but dreaded the thought of editing, this one's for you.

YouTube just released a new AI tool that takes a long video and automatically creates up to 12 Shorts from it. The AI finds the most interesting moments, reframes them for vertical format, and adds captions — the three most tedious parts of Shorts creation, handled for you.

This matters because Shorts are where a lot of new creators are finding audiences right now, but the editing barrier has kept many from trying. Now, the platform is making it nearly effortless to repurpose existing content. Whether you're a small business promoting products or just want to share highlights from a family event, the heavy lifting is done for you.

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OpenAI Tools Are Changing Work in Three Very Different Industries — Here's What They Share

How is AI actually performing in the real world? This week, we got concrete examples from a financial firm, a grocery chain, and a small business program.

The financial firm used AI to speed up report analysis. The grocery chain let store managers ask plain-language questions about inventory and get instant answers. The small business program helped entrepreneurs generate marketing materials in minutes instead of hours.

What do all three have in common? They stopped trying to use AI for everything and instead found the specific tasks where it actually saved time. The financial firm didn't replace analysts — it gave them a tool that handled the first pass on documents, so they could focus on the judgment calls. That's the pattern worth borrowing.

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How to Pick the Right AI Model Without Getting Lost in the Hype

With new AI models launching every few weeks, it's easy to feel lost. This week, we got a plain-language guide that cuts through the noise.

The key idea: stop worrying about which model is the "best" overall and start thinking about what you actually need to do. Need fast, simple answers for everyday tasks? A smaller, faster model might be perfect. Working on something complex that requires careful reasoning? A more powerful (but slower and pricier) model makes more sense.

The guide breaks it down into a few simple questions: How complex is your task? How important is accuracy versus speed? Do you need the model to handle specialized knowledge? Answer those, and the choice becomes much clearer.

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In short: Open Meta AI in WhatsApp, Messenger or Instagram, upload a photo, and ask it to "remove the background." Meta AI will usually generate a transparent version you can save as a PNG. You'll get a clean cutout in about a minute, though fine details like frizzy hair or sheer fabric can still need a touch-up.

You already chat with Meta AI in WhatsApp, Messenger or Instagram. This guide adds one more thing you can do there: turn any photo into a clean cutout (the photo with the background taken away) and drop it onto a new background, all from your phone, no Photoshop, no extra app. The whole workflow takes about five minutes once you've done it once.

Before you start

• The app: Meta AI built into WhatsApp, Messenger, or Instagram on your phone (iPhone or Android). If you can already chat with Meta AI in any of these, you're set.
• A photo on your phone: JPEG or PNG, ideally with a clear subject (a person, a product, a pet) and decent lighting. Busy, low-contrast backgrounds are the hardest to cut.
• Roughly 5–10 minutes for the first try, less after that.
• Free. No paid plan is required for the workflow below. Meta may rate-limit (cap how many times you can ask in a short window) on busy days, so have a backup photo ready.
• What Meta AI can and can't do here: it can usually remove a simple background in one go, but it is not pixel-perfect. Fine hair, glasses, and see-through materials can leave a faint halo (a soft glow around the edges of the cutout). It's great for social posts and product shots, less great for printing at poster size.

Step 1 — Open a Meta AI chat

Tap the circular blue/purple Meta AI icon — in WhatsApp it's usually at the top of your Chats tab or behind a blue "AI" button in the bottom-right; in Messenger it's the top of the search bar; in Instagram it's in the search/DMs area. If you don't see it, look for a sparkle or four-pointed star icon or check the app's top menu. The chat screen opens, showing a short welcome message and the message box at the bottom. You'll know it worked when the box at the bottom says something like "Message Meta AI" or shows a send arrow.

Step 2 — Upload the photo you want to cut out

Tap the paperclip or "+" icon next to the message box to attach a file. From the menu that appears, pick "Gallery" (Android) or "Photo & Video Library" (iPhone) and choose the photo. The image appears inside the chat as a small preview above the message box. If your app doesn't show a paperclip, look for a camera icon instead — the option is the same. Don't send the message yet; the prompt you add in the next step shapes what Meta AI does with the image.

💬 Example: choose a clear, well-lit photo of a friend standing in front of a plain wall, or a product on a tabletop.

You'll know it worked when the photo preview is sitting above the message box, ready to go with your next sentence.

Step 3 — Write the prompt that asks for the cutout

Tap the message box and type a short, direct instruction. Be specific: say "remove the background" rather than just "edit this." If you already know where you want to use the image, mention that too — it helps the model (the AI "brain" that reads your words and produces the answer) pick a sensible default background color.

💬 Example: Remove the background from this photo and give me the subject on a transparent background. Or, if you'd like a new background in the same step: Remove the original background and place the person on a soft white studio background.

Tap send (the paper-plane or arrow icon). Meta AI will usually reply within 10–30 seconds with a new version of your image. You'll know it worked when a new picture appears in the chat that looks like your subject on a flat color or transparent-looking background.

Step 4 — Check the edges before you save

Pinch to zoom in on the edges around the subject — especially around hair, fingers, glasses, and anything thin. Look for a faint color halo (a thin glowing outline) where the old background is bleeding through, or a slightly hard "stamped" edge. If the cut is clean, move to Step 5. If you see issues, stay in the same chat and ask for a fix in plain words.

💬 Example: Zoom in around the hair — I can still see a faint gray halo. Please clean up the edges. Or: The fingers on the right hand look chopped. Please redo with finer edge detail.

You'll know it worked when the second image shows noticeably cleaner edges than the first, with no obvious halo or missing fingers.

Step 5 — Save the new image to your phone

Long-press the new image Meta AI produced. A small menu appears with options like "Save," "Save to Photos," or "Download." Tap the save option. The image lands in your phone's Photos or Gallery app, usually in a folder called Recents or Downloads. If long-press doesn't show "Save," look for a three-dot ⋯ menu on the image instead, or tap the image to open it full-screen and then tap the download/save icon in the corner.

You'll know it worked when the new image appears in your Photos app as its own file, separate from the original you uploaded.

Step 6 — Drop the cutout onto a new background

Open any simple editor on your phone — the built-in Photos app on iPhone, or Google Photos on Android both work. Open the saved cutout, then tap Edit, and look for an option like "Add Photo" or "Overlay / Sticker" that lets you put a second image behind the first. Pick your new background — a sunset you took, a logo, a solid color. Resize and reposition, then save.

💬 Example: a beach sunset from your camera roll, so your friend looks like they're standing on vacation.

You'll know it worked when the final saved image shows your original subject, with clean edges, sitting naturally on the new background you chose.

Step 7 — Keep your reusable prompt in one easy-to-find place

Don't retype the prompt every time. Save the working version from Step 3 in your phone's Notes app or in the Notes feature of WhatsApp/Messenger itself, then copy-paste it next time. For even faster reuse, ask Meta AI in the same chat: Save this as a reusable prompt I can paste next time. Meta AI will usually reply with a tidy, copy-ready version you can keep.

💬 Example: Remove the background from this photo and give me the subject on a transparent background. Keep the subject exactly the same — only the background should change.

You'll know it worked when, next time, you can paste the saved prompt, attach a photo, and get a clean cutout in under a minute.

Common mistakes

• Asking vaguely. "Edit my photo" is too open. Always name the change: "remove the background," "place the subject on white," "tighten the edges." A specific instruction gets a specific answer.
• Uploading a tiny, blurry, or low-contrast photo. If the subject and the background are similar colors (a black dog on a dark couch), Meta AI will struggle. Pick a photo with a clear difference between subject and background.
• Saving the wrong image. The chat shows both your original upload and the new generated image. Long-press the new one, not the one you sent. If you saved the original by mistake, just repeat Step 3 and save the reply.

Try it now

Open Meta AI in WhatsApp, Messenger or Instagram right now. Pick a photo of a person or object that is already on your camera roll, paste this prompt, and send:

💬 Remove the background from this photo and give me the subject on a transparent background.

Save the result to your phone. That's a complete, reusable workflow you can do again in under a minute next time.

In short: You can ask Gemini, the AI built into Google Sheets, to design a weekly schedule from a plain description of your week — with time blocks, color categories, and a formula that totals hours automatically. You only need a Google account.

Most weeks you can list your time blocks in your head — work, gym, family dinner, the Saturday coffee — yet you never actually write them down. That's the gap this guide fills. In about ten minutes, you'll describe your week in plain English and end up with a color-coded schedule in Google Sheets that adds up your hours for you. The result is something you'll actually open on Sunday evening, not a spreadsheet that dies in your downloads folder.

Before you start

• A Google account (free — the same one you use for Gmail).
• A computer or phone that can open Google Sheets: go to sheets.google.com in any browser, or install the free Google Sheets mobile app.
• Roughly 10 to 15 minutes, and a rough mental picture of what your week actually looks like.
• Some Gemini features inside Sheets are only available on paid Google plans — if you don't see the Gemini option, check Google's official pricing page for the latest plan details. A free workaround: draft the schedule at gemini.google.com first, then paste it into Sheets.

Step 1 — Open Google Sheets and start fresh

Open sheets.google.com in your browser, or launch the Google Sheets app, and sign in with your Google account. Click the "+" button or "Blank spreadsheet" to create a new, empty sheet. Name it by clicking "Untitled spreadsheet" at the top-left and typing something you'll recognize later — "My weekly schedule" works fine.

💬 Example: name it "Spring weekly schedule" or "My week plan".

If it looks different: on a phone, the "+" usually sits in the bottom-right corner; if you only see templates, tap "Blank" inside that section. Menu wording changes often — just look for any option that creates a fresh, empty sheet.

You'll know it worked when you see a grid of empty cells with column letters (A, B, C…) across the top and row numbers (1, 2, 3…) down the side.

Step 2 — Open the Gemini side panel

Look at the top-right corner of the Sheets window for a small sparkle ✨ icon or a button labeled something like "Help me" or "Ask Gemini." Click it. A side panel slides in from the right edge — this is your chat window, and you'll stay inside your spreadsheet the whole time.

💬 Example: type "Hi" as a quick first message to confirm Gemini is responding.

If it looks different: Gemini's exact location in Sheets has moved over the years. If you don't see a sparkle, open the "Tools" menu (sometimes labeled "Extensions" on older versions) and look for anything mentioning Gemini or "Ask." On Workspace accounts, your admin may need to turn Gemini on — if the option is entirely missing, ask your IT admin or check Google's help pages.

You'll know it worked when you can type a question into the side panel and an answer appears in the same panel within a few seconds.

Step 3 — Describe your week and ask for a template

In the Gemini side panel, write a short paragraph describing a typical week. Be specific — even if it feels messy. Then ask for a weekly schedule layout with time blocks, categories you can color-code, and a formula that totals hours per category. Gemini will reply either with a written layout you can copy, or with a button to insert a table directly into your sheet.

💬 Example: "Here's my week. I work Monday to Friday, 9 a.m. to 5 p.m. I teach a yoga class Tuesday and Thursday evenings from 6 to 7:30 p.m. I hit the gym three mornings a week, around 6:30 to 7:30 a.m. on Monday, Wednesday, and Friday. Saturday morning is family breakfast from 9 to 11 a.m. Please build a weekly schedule in this sheet with rows for each day, columns for morning, midday, afternoon, and evening, and a SUM formula that totals the hours automatically."

If it looks different: some versions of Gemini in Sheets can only chat (no insert button). If that happens, copy the layout into the cells yourself — see Step 4.

You'll know it worked when you see either a table inserted into the sheet or a clear text description of where each activity should go.

Step 4 — Build the time-block grid yourself (the easy way)

If Gemini gave you a written description, copy it into your sheet by hand. In row 1, type the days of the week across the columns (Monday in A1, Tuesday in B1, and so on — or use one column per time slot if you prefer). In column A, starting from row 2, type your time blocks: "6:30–7:30 a.m.", "9:00 a.m.–5:00 p.m.", "6:00–7:30 p.m.", and so on. Then drop each activity into the cell where day and time meet.

💬 Example: in cell B2 (Tuesday morning) type "Gym"; in cell C5 (Wednesday afternoon) type "Deep work block"; in cell A8 (Monday evening) type "Family dinner."

If it looks different: some people prefer one row per day and one column per time slot — both are normal layouts. Choose the one that's easier to read on your device.

You'll know it worked when you can read down a column to see one full day's plan, or across a row to see one time slot across the week, without guessing.

Step 5 — Add color categories so your eyes can scan it fast

Highlight the column or row headers by clicking the letter at the top of the column (or the row number on the left), then pick a fill color from the toolbar. Look for a paint-can icon or a "Fill color" button. Then color each activity by type: one color for work, one for fitness, one for family, one for rest. Pick colors that mean something to you.

💬 Example: blue for work, green for fitness, yellow for family time, gray for rest or sleep.

If it looks different: the fill-color icon sometimes looks like a tipped-over paint bucket. On a phone, the same tool lives inside the formatting menu, often marked with an "A" and color bars underneath it.

You'll know it worked when you can glance at the sheet and instantly see which parts of your week are work, which are fitness, and which are family time — without reading a single word.

Step 6 — Add a formula that totals your hours

At the bottom of one day's column, click an empty cell and type a SUM formula. In Sheets, every formula starts with =. If you've put the duration of each activity in plain numbers (8 for an 8-hour workday, 1.5 for a 90-minute class), the formula adds them up for you.

💬 Example: type =SUM(B2:B10) and press Enter. The cell instantly shows the total. If your durations are in seconds, multiply by 24 first: =SUM(B2:B10)*24.

If it looks different: if your time blocks are written as text like "9–5", Sheets can't add them up — they'd need to be plain numbers in a separate "Hours" column. Ask Gemini in the side panel, "How do I add up time blocks written as text in Sheets?" if you get stuck.

You'll know it worked when the total cell shows a number that updates the moment you edit any of the time blocks above it — that's the formula doing its job.

Step 7 — Save once and forget about it

Look at the top-left of the screen for your file name. As long as you're signed in, Google Sheets saves your work automatically every few seconds — there's no "Save" button to click. If you see a small gray "Saving…" message or a checkmark next to the title, you're done.

💬 Example: just close the browser tab when you finish. Next time you open the file, your schedule will be right where you left it.

You'll know it worked when you can refresh the page or close and reopen the file and find your schedule exactly as it was.

Common mistakes

• Mistake: asking Gemini something too vague. "Make me a schedule" gives a generic template that won't fit your life. Always include your real hours, real activities, and how many days — the more specific you are, the better the output.
• Mistake: writing time blocks as text like "9–5". Sheets can't add text up, so your total stays at zero. Fix: put the duration as a plain number in a separate "Hours" column (8 for an 8-hour workday, 1.5 for a 90-minute class), or ask Gemini how to convert time strings to numbers.
• Mistake: assuming Gemini inserted your schedule when it only chatted. Some versions of Gemini in Sheets can only suggest, not insert. Fix: read the side panel carefully — if you see an "Insert" button, click it; otherwise copy the suggestions into the cells yourself.

Try it now

Open a new Google Sheet, open the Gemini side panel, and paste this in: "I teach three morning classes Monday, Wednesday, and Friday from 8 to 11 a.m. I work part-time Tuesday and Thursday from 1 to 6 p.m. I have family dinner every weekday at 7 p.m. Build me a weekly schedule with time blocks, three color categories (teaching, work, family), and a SUM formula that totals hours per category." See what it gives you, change the colors to match your real life, and you've got a planner that took ten minutes to make and will save you hours of "wait, what day is it again?" by next Sunday.

In short: GitHub says three skills will define how people work with AI — directing AI agents, critically reviewing their output, and keeping human judgment in charge. None are traditional coding skills, and the same three apply to anyone who uses AI at work.

You've probably heard the warnings: AI is "coming for" jobs, especially in tech. But the skills the companies building AI are publicly highlighting tell a different story — one where the most valuable people are the ones who can guide, check, and decide.

In a recent post, GitHub laid out what it expects to matter most for developers working alongside AI. None of the three skills are about writing more code. They're about managing the work.

The three skills

According to GitHub, the skills that will define this new way of working are:

• Directing AI agents — AI helpers that can take actions on their own — telling them what to do, when, and how
• Critically reviewing their output — catching mistakes rather than accepting answers at face value
• Keeping technical judgment at the center — knowing when to trust the AI and when to step in yourself

These aren't coding skills at all. They're leadership and quality-control skills — the kind you'd use to manage a junior colleague or evaluate a draft from a contractor.

Why this matters if you're not a developer

The bigger story isn't really about software. It's about how every job that involves writing, researching, or producing work is changing. If you already use AI tools at work — even just to summarize a long email thread — the same three skills apply:

• Directing matters when you give AI a vague instruction like "write me something about Q3 sales" instead of a clear, specific one.
• Reviewing matters because AI sometimes confidently gives you a wrong number — researchers call this a "hallucination," which basically means the AI made something up and presented it as fact.
• Judgment matters when AI gives you a passable answer that quietly misses what you actually needed.

Wrap-up

GitHub's list is a small window into how the AI industry is thinking about the next few years. The future it's describing isn't one where humans are replaced — it's one where humans who can guide, check, and decide become more valuable. The cheapest way to practice those three skills is to pick a real task you already do this week and try doing it with AI by your side.

In short: Salesforce has signed a deal to acquire Listen Labs, an AI platform that runs customer research with autonomous AI agents. Salesforce says the move will fold AI-led studies and feedback analysis into its CRM (a tool businesses use to track customers and sales).

If you've ever sat through a 90-minute customer interview that produced three vaguely useful quotes, this news is for you. Salesforce, the customer-relationship software giant, just announced a deal to buy a small AI company whose whole job is to make that process faster, cheaper, and more scalable.

What Salesforce is buying

Listen Labs builds what the industry calls an AI-powered customer research and human simulation platform. In plain English: instead of hiring a research firm to design a survey, recruit 50 people, run hour-long interviews, and spend weeks writing up findings, you tell Listen Labs what you want to learn, and its AI agents (software that acts on its own to complete tasks) design the study, talk to customers, and write the report.

According to Salesforce's announcement, those agents handle three jobs end-to-end:

• Designing research studies
• Conducting in-depth interviews
• Pulling themes and quotes out of the responses

Salesforce frames the whole package as a way to shorten customer research from "months" to something much shorter. The exact new timeline isn't confirmed in the announcement.

Why Salesforce wants it

Salesforce calls itself "the #1 AI CRM" — meaning, by its own claim, the largest customer-relationship platform with built-in AI features. Adding Listen Labs would push that AI deeper into the customer-feedback loop, where most companies still rely on clunky survey tools and manually coded transcripts.

For Salesforce's customers — typically sales, marketing, and product teams — the bet is that they could soon run a research project the same way they currently run a marketing email: type a prompt (the instruction you give to an AI), wait a few minutes, and read the summary.

Wrap-up

Salesforce buying Listen Labs is one more sign that AI agents are moving from novelty to workflow. For everyday businesses, the practical promise is simple: research that used to stretch for months might, eventually, take a much shorter window. Until pricing, release dates, and product details land, treat this as a watch-this-space story — not a tool you can use tomorrow.

In short: NVIDIA says its small DGX Spark desktop AI computer will soon be available with 64GB of unified memory from partner manufacturers including Acer. The extra memory means developers can run larger open AI models locally, without sending requests to a cloud data center.

You probably use cloud-based AI tools every day — ChatGPT, Claude, Gemini — and you've never had to think about where the actual work happens. It happens somewhere else, on hardware you'll never see. NVIDIA's updated DGX Spark points the other way: a small box on a desk that runs AI right where you sit.

What the DGX Spark actually is

DGX Spark is a small desktop computer built by NVIDIA, the chipmaker whose processors (called GPUs, or graphics processing units) are at the heart of most modern AI. Unlike the giant server farms that power ChatGPT, Spark is designed to sit on a desk at home or in an office.

NVIDIA says the new 64GB version will be available "this month" through partner manufacturers, with Acer named as one of them. The key spec to understand is unified memory — memory the computer's main processor and its graphics chip can both use, instead of having two separate pools. More unified memory means the box can hold and work with bigger AI models at the same time.

Why more memory matters for everyday people

Here's the part worth caring about, even if you never buy one. AI models have grown enormously over the past two years. The largest ones need huge amounts of memory, which is why most AI still happens in the cloud. Smaller open models — AI systems anyone can download for free — have been catching up fast, and they fit on more everyday hardware.

A 64GB DGX Spark has enough memory to run a meaningful slice of those open models locally (on your own device, not someone else's server). Running AI locally has a few quiet benefits:

• Your prompts and files don't leave your machine. Anyone working with sensitive material — medical notes, contracts, customer data — can keep that information private.
• No internet round-trip. When the model lives on your desk, responses come back instantly, with no upload or download delay.
• No per-request fees. You pay for the hardware once and the electricity to run it, instead of monthly cloud bills.

Where the trend is going

NVIDIA's framing of the announcement is worth noticing. The company says "increasingly capable open models are shrinking to fit on more devices, giving builders more to run locally." That's a real shift. Until recently, "running AI yourself" usually meant a developer tinkering with a high-end gaming PC in their spare time. Now NVIDIA is selling a purpose-built machine for that kind of work.

For people who don't write code, the practical effect shows up in the apps you already use. As open models get better and more developers can run them locally, expect more writing assistants, image tools, and voice transcribers to advertise on-device AI as a privacy feature.

Wrap-up

NVIDIA is betting that some of the AI work currently happening in massive data centers will move to smaller boxes on desks and kitchen tables. The new 64GB DGX Spark is one of the first machines built for that shift. You don't need to buy one to feel the effect — but expect to see "runs locally" appear as a feature in the apps you already use.

In short: With Copilot (Microsoft's AI helper built into Word) you can ask Word to read your long report and produce a short, executive-ready summary in about a minute. You'll need a Microsoft 365 plan that includes Copilot, your report already open in Word, and a clear idea of who will read the summary.

You wrote a 12-page report. Your boss has two minutes. The good news: you don't have to rewrite the whole thing by hand. Copilot lives right inside Word, and one of its most useful tricks is summarizing a long document into something tight. The trick is asking it the right way, and giving the output a quick human polish before you send it.

This guide walks you through the whole thing, step by step, even if you've never opened Copilot before.

Before you start

• A Microsoft 365 plan that includes Copilot. Copilot is a paid add-on, so check that your plan (or your company's plan) actually has it turned on. If you only see "Try Copilot" or no Copilot button at all, the feature isn't on your account yet.
• The desktop or web version of Microsoft Word (Windows, Mac, or Word for the web). The exact steps below describe the desktop ribbon, but the web version works almost the same way.
• Your long report open in Word, saved as a .docx file. It can be a report you wrote, or one a teammate shared with you (as long as you have permission to edit it).
• Roughly 10–15 minutes, including time to polish what Copilot gives you.
• A rough sense of who will read the summary (your boss? a client? the whole team?) — that one detail makes a big difference in how you phrase your request.

One honest note: Copilot is great at cutting length and pulling out key points, but it can sometimes drop a number, miss a nuance, or smooth over a caveat you cared about. Plan to read the summary once before sending it anywhere.

Step 1 — Open your long report and find the Copilot button

Start by opening the document you want to summarize in Microsoft Word. Now look at the top of the window — that long strip of buttons is called the ribbon (think of it as Word's main menu bar). On the Home tab, look toward the top-right area for a button labeled Copilot. It usually has a small sparkle or ribbon-like icon next to it.

When you click it, a Copilot chat panel slides open on the right side of the screen. Your document stays where it is on the left. If you don't see a Copilot button on the Home tab, click the Home tab first to make sure you're in the right place, or look for it under a ⋯ (More) menu or a tab called something like Copilot or Help.

💬 Example: open Q3-Project-Update.docx from your Documents folder and click the Copilot button on the Home tab.

You'll know it worked when the chat panel appears on the right and you can see your document still on the left, with a text box at the bottom of the panel where you can type.

Step 2 — Ask Copilot to summarize the report

In the text box at the bottom of the Copilot panel, type a short request asking for a summary. The clearer you are about how long you want it and who it's for, the better the result. Copilot works best when you treat it like a quick conversation, not a search engine.

After you press Enter, Copilot reads through your report (this can take 10–30 seconds for a long document) and writes a draft summary directly in the chat panel. It does not delete or change your original report — your long version is still safe on the left.

💬 Example: "Summarize this report into a one-page executive summary. Keep the original tone, the key numbers, and any important dates. Write it for a senior manager who has two minutes to read it."

You'll know it worked when a block of summary text appears in the Copilot panel, usually with bullet points or short paragraphs, and a small note saying it was generated from your document.

Step 3 — Tell Copilot how long and what to keep

Your first draft is rarely the final version, and that's normal. Read the draft in the Copilot panel and notice what's missing or off. Then reply in the same chat box with a follow-up request that tightens it up. Common follow-ups: make it shorter, keep only the numbers, write it as one paragraph, or drop the jargon.

Copilot remembers the conversation in the same panel, so each new message refines the previous draft instead of starting over. This is where you save yourself a real round of editing.

💬 Example: "Shorter. Cut it to about 350 words, keep all the percentages and dollar figures, and remove any section about background context. Use short bullet points under three headings: Wins, Risks, Next Steps."

You'll know it worked when a new, shorter version replaces the previous one in the panel and you can see the changes are focused on what you asked for (length, format, or specific content).

Step 4 — Insert the summary into a new document

Once the draft in the panel looks right, you have two choices: insert it straight into the current document, or send it to a new file. For a clean one-pager you'll probably want a new document so the original report stays untouched.

Look just below the draft in the Copilot panel for a button that says something like Insert, Add to document, or Copy. If you see an option that says "Create a new document" or "Open in new doc", use that — it gives you a blank page with your summary already laid out. If you only see Insert, clicking it will paste the summary at your cursor's location in the current document instead, which you can then copy into a new file later.

💬 Example: click "Create a new document" (or, if you don't see that exact option, click Insert, then press Ctrl+C and paste into a fresh blank document).

You'll know it worked when a new Word file opens containing the summary, formatted with the headings and bullet points you asked for, and your original long report is still untouched in its own tab.

Step 5 — Polish the summary by hand (the part Copilot can't do)

This step is short but important. Read the new one-pager from top to bottom, slowly, as if you were your boss. Cross-check every number, name, and date against the original report on the left. Copilot is good, but it's not perfect — a single wrong percentage is the kind of thing that erodes trust fast.

Tighten anything that sounds generic, fix any sentence that doesn't sound like you, and add a one-line intro or closing if the summary feels abrupt. Usually this takes 2–3 minutes and is what turns a "decent AI draft" into a "boss-ready" one.

💬 Example: if Copilot wrote "Revenue grew significantly," you change it to "Revenue grew 18% to $4.2M, ahead of the 12% target."

You'll know it worked when the one-pager reads like something you wrote on a calm Tuesday morning, every number checks out, and the whole thing fits comfortably on a single page.

Step 6 — Save and share the one-pager

When you're happy with it, save the new file with a clear name so you (and your boss) can find it later. Then share it however your team normally shares documents — email, a chat link, or a shared drive. If you started from someone else's report, you now have your own separate one-pager, so you can share just the summary without giving away the full document.

💬 Example: click File → Save As, name the file Q3-Project-Update-OnePager.docx, and email it to your boss with the subject line "Q3 update — 2-minute read."

You'll know it worked when the file is saved, the original long report is still untouched, and the one-pager is ready to send or present.

Common mistakes

• Asking for a "summary" with no other details. If you just type "summarize this," you'll get a generic, sometimes rambling result. Always add the length you want (one page, 300 words), the audience (my boss, a client, a non-technical reader), and any must-keep items (numbers, dates, names).
• Trusting the first draft without checking. Copilot can drop, round, or slightly misstate numbers. Always spend two minutes comparing the one-pager against the original report before you send it.
• Inserting the summary over the original report. If you use Insert instead of Create a new document, the summary lands inside your long file. That's fixable (press Ctrl+Z to undo), but it's a common beginner slip. When in doubt, copy the summary, open a blank document, and paste it there.

Try it now

Open any document you have on your screen right now — even a two-page one — click the Copilot button in Word, and type: "Summarize this in five bullet points for a manager who has one minute to read it." That single 30-second experiment will teach you more about how Copilot works than any article.

In short: Paste your messy email thread or bullet list into any AI assistant with a clear instruction about timing, owners, and tone, then iterate. You'll get a polite, structured agenda in under five minutes — though you'll still need to send the invites yourself.

A 30-minute meeting with seven bullet points, three owners, and zero structure usually ends with 20 minutes wasted on "wait, who's covering that?" — and another 10 on a follow-up email nobody reads. This guide shows how to feed that mess into any AI assistant and walk out with a timed, owner-clear agenda you can actually send. It's written for people who already use AI for everyday tasks and want one more workflow that saves real time at work.

Before you start

• An account on any AI assistant you already use — ChatGPT, Claude, Gemini, Copilot, Le Chat, or DeepSeek all handle this pattern. No new signup needed.
• The messy source material within reach: an email thread to copy from, a notes app with bullet points, or a transcribed voice memo.
• About five minutes once you've done it once; a little longer the first time as you build your reusable template.

Step 1 — Gather the messy material in one place

Open your AI assistant in a browser or app and start a fresh chat. Paste your raw material — the email thread, the bullet list, the voice-note transcript — straight into the message box, and add two short sentences of context before you hit send: what the meeting is for, how long it is, and who's coming. You're not asking for the agenda yet; you're staging the inputs.

💬 Example: "Below is the email thread about our Q3 launch. Goal: 30-minute sync to unblock the design freeze. Attendees: Maria, John, Sarah."

You'll see your pasted text sitting in the input area, with the send button below it. After you send, the assistant confirms receipt and begins working. If your assistant has a "Projects" or "custom instructions" feature, you can save the context sentence there for next time — but a plain chat works fine without it.

You'll know it worked when the assistant's first reply acknowledges the inputs you provided and starts producing structured output rather than asking what you meant.

Step 2 — Ask for a first draft with explicit structure

Send a follow-up instruction in the same chat telling the assistant how to shape the draft: numbered topics, a duration in minutes next to each, a named owner per topic, a polite opener, and a small buffer between items. The clearer your structure request, the less you'll need to fix later.

💬 Example: "Now produce the draft. Use a polite opener, list each topic with a duration in minutes and a single owner, leave a 2-minute buffer between topics, and end with a one-line confirmation request."

You'll see a numbered agenda appear, with minutes and names attached to each line. If your assistant formats the response as a table by default — some do — reply with "please rewrite as a numbered list with bold topic names" and the next version arrives in the shape you wanted.

You'll know it worked when your chaotic items are reordered into clear blocks with names and minutes, and you can read the whole meeting in one glance.

Step 3 — Calibrate the politeness

Reply in the same chat asking for tone adjustments. The first draft is almost never the right tone — it usually sounds either too stiff ("Please find below the agenda…") or too casual for a workplace you share with senior people. Tell the assistant which direction to move.

💬 Example: "The opener feels stiff. Loosen it. Our team uses first names and avoids phrases like 'circling back'. Keep the same structure and timings."

You'll see a revised version where the wording changes but the structure stays. If the assistant doesn't pick up your tone signal, paste one sentence of how you'd actually open the meeting and ask it to match that register — that's the fastest shortcut to a natural voice.

You'll know it worked when the opener reads like something you'd say out loud without wincing.

Step 4 — Lock down the owners and timing

Send another short instruction asking the assistant to verify two things: each topic has exactly one named owner, and the minutes add up to the meeting length. This is where most agendas quietly fall apart — two topics assigned to the same person back-to-back, or a 45-minute agenda for a 30-minute slot.

💬 Example: "Confirm each topic has exactly one owner. Add up the minutes and show the total at the end. If anything overflows, suggest what to cut."

You'll see a revised agenda where totals match and names sit beside each item. If the assistant doesn't add a "Total: X minutes" footer, ask for one — it's the simplest way to verify the math yourself before you copy anything out.

You'll know it worked when the total at the bottom equals your available time, and no item is missing a name.

Step 5 — Add a closing line with next steps

Ask the assistant to add a short "Next steps" block at the bottom: who sends the calendar invite, who brings what (slides, doc, numbers), and when the follow-up happens. This is the part most agendas skip, and it's the part that decides whether the meeting actually moves anything.

💬 Example: "Add a 'Next steps' block at the end: Maria sends the calendar invite by tomorrow morning; John brings the latest design mockup; follow-up sync the following Monday."

You'll see a new block appear below the agenda. If your team uses a specific tool — Notion, Google Docs, Slack — name it in your prompt and the assistant will adjust the closing line to fit that workflow.

You'll know it worked when each person at the meeting knows exactly what to do before it starts and after it ends.

Step 6 — Copy the result into your real-world tool

Select the final agenda in the chat and paste it into the place your team will actually see it — an email draft, a shared doc, a calendar description, or your meeting software. This step is small but it matters: the moment the agenda lives outside the chat, you can edit it, share it, and update it without re-asking the AI.

If your assistant offers a "copy" or "export" button, use it; otherwise highlight the text and use your system copy shortcut. The text lands in the new tool with light formatting shifts depending on the destination — that's normal.

You'll know it worked when the agenda sits in the same place your team already works, and you didn't need to retype a single bullet.

Step 7 — Save your prompt as a reusable template

Highlight the full chat — your original messy input, the structure request, the tone instructions, the owners-and-timing check, the closing block — and save it as a reusable template. A plain text file in your notes app works. If your assistant supports saved prompts or custom instructions, use that instead so the template loads automatically.

💬 Example: a doc titled "Agenda from messy notes" with three labeled blocks: [PASTE INPUT], [STRUCTURE REQUEST], [TONE NOTES].

Next time you have a chaotic thread, you paste the template, swap in the new content, and skip the prompt-writing step. The first time costs you ten minutes; every meeting after that costs you one.

You'll know it worked when the next agenda takes you under a minute to draft.

Common mistakes

• Treating the first draft as final. The first reply is a sketch. Always run one tone-and-owners pass — usually two — before you copy anything out.
• Owners stay vague ("the team", "marketing"). Vague owners mean nobody follows up. One named person per topic, every time, no exceptions.
• Forgetting the AI can't send the invite. Once you copy the agenda out, the chat ends and the tool stops helping. You still need to send the calendar invite, share the doc, and chase the owners yourself.

Try it now

Open any AI assistant, paste your messiest recent email thread, and send this single line: "Turn this into a 30-minute agenda with one named owner per topic and a polite opener." Watch what comes back — that's your starting point for the iteration loop above.

In short: OpenAI says a financial firm, a major US grocery chain, and small business support centers use its AI tools to speed up real work — one cut a 30-minute task to under 4 minutes, and a new partnership will bring AI training to small business owners.

When you hear "AI in the workplace," it's easy to picture the same story every time: a tech company, a software team, lots of code. Three recent OpenAI announcements tell a much wider story.

A financial firm that trimmed a 30-minute task to under 4

Chatham Financial, which helps companies manage financial risk, says it uses Codex (OpenAI's coding-focused AI assistant) along with OpenAI's models to build internal tools and rethink its workflows. According to OpenAI's announcement, the most striking result: a trade validation process that used to take 30 minutes now finishes in under 4.

"Trade validation" means checking that a financial deal meets all the rules and matches the numbers — detail-heavy work where one wrong digit matters. APIs (think of them as the messaging channels that let different software programs talk to each other) let Chatham plug OpenAI's models directly into its own systems, so the AI can do the checking work without a human typing prompts.

The takeaway: the biggest time savings come when AI is connected to your existing software, not when you just type questions into a chatbot.

A grocery chain making shopping easier

Albertsons Companies, one of the largest US grocery operators, says it uses ChatGPT Enterprise (the workplace version of ChatGPT, with extra privacy and admin controls) and the OpenAI API to help its teams work faster and improve the shopping experience for millions of customers.

The exact uses aren't spelled out in the announcement, but the pattern is familiar: when a large organization has thousands of employees writing reports, summarizing meetings, or analyzing data, an AI assistant can take a lot of that load.

Small businesses getting real help, not just hype

The third announcement may matter most to everyday readers. OpenAI says it is partnering with America's SBDC (Small Business Development Center — a US network of local offices that give free advice and training to small business owners) to expand hands-on AI training and local support. OpenAI has not given a public rollout date for this partnership.

SBDCs exist in most US communities and offer free services. If you run a small business, a side hustle, or freelance, your local SBDC is worth a call today to ask what AI help is available now — you don't have to wait for the OpenAI program to find out what's already there.

Wrap-up

The common thread across a financial firm, a grocery chain, and a small business support network is simple: AI tools are most useful when they're applied to a specific, real workflow. None of these stories started with "let's use AI." They started with a 30-minute task, a busy team, or a small business owner who needed help — and AI turned out to be the right tool for the job. If you have a task that's eating your week, that might be a good place to start.

In short: Google is pushing Gemini deeper into Workspace: upgraded AI voiceovers and customizable captions in Google Vids, new "skills" that replace Gems, comment APIs for Docs/Sheets/Slides, easier client-side encryption, and a new connectors path that ties Gemini to CRMs, project tools, and more apps via Zapier.

If you've ever bounced between Gmail, a CRM, a project tracker, and a video editor just to finish one task, you know how messy "all your work in one place" usually gets. Google says it's chipping away at that mess by pushing Gemini deeper into the apps where you already spend your day — and into the ones you wish would just talk to each other.

A louder voice and a custom look in Google Vids

The engine behind AI voiceovers and avatar narration in Google Vids has been upgraded. According to Google's announcement, the text-to-speech system — the part that turns written words into spoken audio — now runs on the Gemini 3.8 Flash Lite TTS model. Google says the result is more natural and expressive narration.

Captions got their own upgrade. Until now, captions in Google Vids came in a single style. You can now change their font and typography, move them around the screen, and adjust how they animate. For anyone making branded training videos, internal explainers, or social clips, that means the captions can finally match the rest of the look without leaving the app.

"Skills" are replacing Gems in the Gemini app

If you've built saved instructions in the Gemini app or Workspace — what Google used to call Gems — the company is gradually replacing them with something called skills. A skill is a reusable prompt, basically a saved set of instructions you can fire off again and again ("summarize this email in three bullets", "rewrite this in a friendlier tone"). Google says skills will roll out to Workspace users over the coming weeks and will eventually take over from Gems entirely. If you already lean on Gems, it's worth keeping an eye out so your saved instructions don't quietly vanish in the switch.

Connecting Gemini to apps outside Google

Gemini already lives inside Gmail, Calendar, and Drive, but most people's work doesn't stop at Google's door. According to Google's announcement, new Gemini connectors are designed to plug into business tools — like a CRM (a customer relationship management system — think of it as your company's contact and sales tracker), a project management platform, a marketing automation tool, or a help desk. For broader reach, Google points to Zapier as a way to securely extend Gemini's access to other apps in your tech stack — Google's term for all the software your team uses day to day. Together, the two paths aim to give Gemini a more complete view of your work, so you spend less time copying and pasting between tabs.

Smaller updates for builders and security-minded teams

Two quieter announcements round out the week. Developers can now create, read, and manage comments across the Docs, Sheets, and Slides APIs (application programming interfaces — basically the back door that lets other software talk to Google Docs and its siblings). Google Docs also picked up suggested edits support through the API, so an automated tool can propose a revision for you to accept or reject instead of changing the document on its own. And there's a new simple setup option for Workspace client-side encryption — the kind of feature IT teams tend to care about when they want tighter control over who can read sensitive files.

Wrap-up

The big story here isn't a single dramatic feature — it's the direction. Google is turning Gemini from a chatbot into connective tissue across the apps where you already work. A useful next step today: open the Gemini app or Google Vids and try one of the new features, even a small one. That short experiment will tell you which of these updates actually changes your daily flow.

In short: OpenAI's new model guide shows everyday users how to pick a GPT-6 model, dial up or down the "reasoning effort" (how much the AI thinks before answering), and write clearer instructions — so you can get better answers without learning to code.

You opened a chat window, typed a question, and the answer felt… fine. Not great, not terrible — just fine. The thing is, most people never change a single setting, and that's where the small wins hide. A new guide from OpenAI walks through four practical moves you can make today: choosing a model, tuning how hard it thinks, writing better instructions, and letting AI tools work together. Here's the version a busy, non-technical person can actually use.

Pick a model that matches the job

Think of an AI model like a car. A sports car is great for the highway, a pickup is great for moving furniture, and you don't need either to grab groceries. OpenAI's guide reminds users that different models in the GPT-6 family are built for different tasks. Some are fast and cheap; others are slower and better at careful thinking. If you're drafting a quick email, the fast one is plenty. If you're weighing pros and cons of a big decision, the slower one is worth the wait.

• Quick, everyday tasks (rewording an email, summarizing a short article): pick the smallest, fastest model your account offers.
• Tricky thinking tasks (planning a trip with lots of constraints, debugging a logic puzzle): pick the model the company labels as its most thoughtful.

Tune the "reasoning effort"

This is a setting most people never notice. "Reasoning effort" controls how long the AI pauses to think before answering. Low effort = quick, surface-level reply. High effort = the AI takes longer, weighs more angles, and usually gives a more careful answer. OpenAI's guide recommends turning it up for hard problems and leaving it low for simple ones. In the apps most people use, this often looks like a slider or a dropdown labeled "thinking," "effort," or "depth."

A simple rule of thumb: if the answer you got felt rushed or sloppy, try the same prompt again with reasoning effort turned higher. If the answer was already fine, you just saved yourself a few seconds and a few cents.

Write better instructions (it matters more than the model)

Here's the part the guide really leans on, and for good reason: the quality of your instruction — what AI people call a "prompt" — changes the answer more than swapping models does. A few habits that reliably help:

• Say what you want and what you don't. "Write a friendly, three-sentence apology to a client about a missed deadline. Don't include discounts or blame."
• Give a quick example. "Here is the tone I want: 'Sorry for the delay — here's what I'm doing to fix it.'"
• Tell the AI who it's pretending to be. "You are a patient teacher explaining this to a beginner."

These aren't magic tricks. They're just clearer instructions, the way you'd brief a new helper.

Let the AI use tools

The newer AI models can do more than chat — they can call outside tools (small programs that do one job well, like a calculator, a calendar, or a web search) when you ask. OpenAI's guide calls this "coordinating tools." In plain English: instead of the AI guessing, it can look something up or run a calculation before answering. For you, this often means turning on features like web search or code execution if your plan supports them. The AI then decides on its own when to use them.

Common mistakes

• Chasing the "newest" model. Newer isn't always better for your task. The fast, cheap model often beats the big one for short jobs.
• Stuffing the prompt with politeness. "Please be a kind and brilliant expert…" rarely helps. Clear instructions help.
• Forgetting reasoning effort exists. If answers feel lazy, this is the first knob to turn, not the prompt to rewrite.

Wrap-up

You don't need to learn how AI works under the hood to get more out of it. Pick a model that fits the job, turn up the thinking when it matters, write clearer instructions, and let the AI reach for tools when it needs to. Try one of these four moves today — pick whichever sounds easiest — and see how the answers change.

In short: YouTube says its new tool can scan one long video and turn the best parts into up to 12 captioned vertical Shorts in a single run, so creators don't have to clip them by hand.

You just uploaded a 30-minute podcast episode, and now you know the real work begins — finding the quotable moments, clipping each one, reframing it for a phone screen, and adding captions so people actually watch with the sound off. That's the part most creators quietly skip. YouTube says its new AI Shorts tool handles almost all of it.

What the tool actually does

According to YouTube's announcement, the workflow has two parts. First, you (or the AI) find the moments in your long video worth clipping — the bits that stand alone as watchable Shorts. Then the AI turns those moments into vertical clips with captions already burned in, all in one batch.

The result is up to 12 finished Shorts from a single long video. Each one is in vertical format (the tall shape that fills a phone screen) and already has captions added (the text that pops up on screen so people can follow along with the sound off). You don't have to re-edit or re-export anything before posting.

Why this matters for creators

Shorts are the format YouTube leans on hardest for new-viewer discovery. Most creators find that posting them regularly is part of how a channel grows — but it's also the most tedious part of the workflow, since each Short has to be clipped, reframed, and captioned by hand. Third-party clipping tools have built whole businesses around this single pain point.

YouTube's new tool is the platform's answer to that friction. Instead of treating Shorts as a separate job, it tries to make them a byproduct of the work you're already doing. Upload one video, get up to 12 clips back.

What's still unclear

YouTube's announcement describes what the tool does, but leaves a few practical questions unanswered. Worth knowing before you go looking for it:

• The post doesn't specify which YouTube surface hosts the feature — the YouTube Studio desktop dashboard, the mobile app, or both.
• It's not clear whether the AI picks the moments automatically, asks you to mark them first, or does some mix of the two.
• There's no word yet on which accounts have access — all creators, or just YouTube Partner Program members — or whether it's a paid add-on.
• The "up to 12" cap suggests the AI decides how many clips make sense for the source video. A 5-minute vlog might produce fewer than a 90-minute interview.

If you want to find it, check your YouTube Studio dashboard or the help center for the latest rollout details in your region.

Wrap-up

YouTube's new AI Shorts tool won't replace the creative judgment of a real editor, but it does compress the most annoying part of the workflow — finding, clipping, reframing, and captioning — into something close to automatic. If you publish long videos and have been putting off the Shorts side of things, this might be the nudge that makes it worth doing. Check your YouTube Studio dashboard in the coming weeks to see whether the feature has rolled out to your account.

In short: Using Claude in one focused conversation, you can turn a small work moment into a confident LinkedIn post in about 15 minutes — by feeding Claude the story, then iterating on the hook, the body, and the call-to-action one at a time.

By the end of this guide you'll have a ready-to-publish LinkedIn post, plus a way to repeat the workflow whenever a small moment at work is worth sharing. Claude can draft and refine the post, but it can't post to LinkedIn for you, and it may invent specific numbers if you give it vague prompts — so you'll want to double-check facts before you hit publish.

Before you start

• A Claude account at claude.ai — the free plan is enough to start; paid plans (Pro, Max, Team, Enterprise) give you longer conversations if you want to iterate a lot. See the current plans on the official pricing page.
• A small work moment in mind — 2-3 sentences of notes is plenty. Not a press release, not a long story. One thing that happened, or one thing you learned.
• About 15 minutes, ideally in one sitting so the conversation stays focused.
• A LinkedIn account — only needed at the very end, when you paste and publish.

Step 1 — Open a new chat and feed Claude the story

Open claude.ai (web app or desktop app) and start a new chat — the composer box sits at the bottom of the screen, and you'll see a model selector near the top of the conversation. Paste a short brief about the moment you want to write about, then ask Claude for three different angles in one go. After you press Enter, Claude reads the brief and replies with three distinct drafts, usually within a few seconds.

If your screen looks different: on the mobile app the composer is a small message bar at the bottom, and the model picker may be tucked into a menu at the top — look for a button or label near the conversation title. On the free plan the picker may only list one model.

💬 Example: "Here's the moment: I led a 20-minute product demo last Tuesday, the prospect said no on the call, then signed a week later. I'm a B2B sales engineer. Audience: other AEs and SEs. Draft three short LinkedIn posts around 100-130 words — one story-first, one lesson-first, one question-first."

You'll know it worked when Claude replies with three drafts that already sound like you, just clearer — not three versions of the same post.

Step 2 — Pick a hook, or ask for a better one

Reply in the same conversation and tell Claude which draft is closest to your voice. Then ask it to write three new opening lines for that draft only — a hook is just the first one or two lines, and giving it its own step keeps you from arguing about the whole post at once. Claude will reply with three short opening lines you can paste in front of your chosen draft.

If the hooks still sound off: tell Claude what feels wrong in plain words ("less corporate", "more like I'd say it to a friend", "shorter"). The more concrete your feedback, the better the next version.

💬 Example: "I like draft #2's tone. Give me 3 new opening lines for it — no 'I'm excited to share', no 'Today I learned', keep it under 18 words each."

You'll know it worked when you read a hook out loud and it feels like something you'd actually say to a colleague over coffee.

Step 3 — Build the middle of the post

Now ask Claude to fill in the middle of the post — the part between the hook and the call-to-action. Tell it the rough length you want, what should go in (the story, the lesson, the surprise), and what should stay out (no jargon, no clichés). Claude rewrites the body of the same post without starting a new chat, so the hook and tone stay consistent.

If the body turns into a wall of text: ask Claude to break it into short lines with single-sentence paragraphs. LinkedIn readers scroll fast on phones, and short lines read like a normal post, while a paragraph reads like an essay.

💬 Example: "Rewrite the body to 100-130 words. Include: the moment of the no, the moment of the yes, and the one thing I'd do differently next time. One idea per line. No bullet points."

You'll know it worked when you can read the whole middle out loud in under 30 seconds and it sounds like a real story, not a list of lessons.

Step 4 — Add a call-to-action you actually want

Reply again and ask Claude for three call-to-action options — a call-to-action is just the last 1-2 lines that tell readers what to do next (comment, share, DM you, or just think about the question). Pick the one that matches what you actually want from the post: a conversation, a lead, or a reflection. Claude adds it to the end of your post in the same chat.

If you don't want a call-to-action: say "end on the question, no CTA". Some posts land harder that way, and that's a valid choice.

💬 Example: "Add a call-to-action to the end. Give me 3 options: one that invites DMs from AEs, one that asks a question to spark comments, one that's a quiet reflection. I'll pick."

You'll know it worked when the last line is something you can picture a real person answering — not "Like and share if you agree!".

Step 5 — Polish for length, line breaks, and your voice

Ask Claude for one final version that combines the hook, body, and CTA you picked — and ask it to check the total word count and the line breaks in one go. Read the post out loud once before you copy it: if you stumble on a phrase, paste that line back to Claude and ask for a simpler version. This is the step that turns a draft into your post.

If the post feels too long or too short: tell Claude the target — a common sweet spot is 120-180 words, which fits a phone screen with room to breathe.

💬 Example: "Combine the parts I picked. Keep it under 160 words. Short lines, one idea per line. Then list the first 5 lines separately so I can choose the strongest hook."

You'll know it worked when you can paste the whole thing into the LinkedIn composer, hit publish, and not feel the urge to edit it again.

Common mistakes

• Starting with "I'm excited to share…" — the hook buries the moment. Fix: in Step 2, ban any opening phrase by name in your prompt; Claude works better with explicit "don'ts".
• Letting the post run past 200 words — readers drop off. Fix: in Step 5, name a word count ("under 160 words") and ask Claude to count before sending the final version.
• Skipping the call-to-action because "it feels salesy" — a quiet question counts. Fix: in Step 4, ask for a question-only option alongside the comparison option, so you always have one.
• Publishing without reading out loud — typos and AI-tells sneak in. Fix: do the read-aloud in Step 5; if any line makes you stumble, paste it back to Claude and ask for a simpler version.
• Trusting Claude's numbers — it may invent a stat. Fix: if a draft contains a percentage, a year, or a name you didn't provide, remove it or replace it with your real number before publishing.

Try it now

Open Claude, start a new chat, and paste this: 'I want to write my first LinkedIn post. Here's a small thing from my work this week: [paste 2-3 sentences about what happened]. My role: [your role]. Draft three posts, each 100-140 words — one story-first, one lesson-first, one question-first.' Then pick a draft, iterate the hook, the body, and the CTA one step at a time — and paste the result into LinkedIn before the chat goes cold.

In short: You can upload a bank statement into Google's Gemini and ask plain-English questions to surface real spending, recurring charges and hidden fees. Gemini can't read your bank account — think of it as a translator for the numbers on your statement.

A monthly bank statement is full of small numbers, merchant codes and fee names that rarely mean anything at first glance. With Google's Gemini, you can hand it that document and ask, in normal sentences, "what am I actually paying for?" — and get a readable answer back. This guide walks you through it, one step at a time.

Before you start

• A free Google account (the same one you use for Gmail or YouTube).
• Access to Gemini in a browser (gemini.google.com) or the Gemini mobile app — both work for this. The free version is enough for reading a single statement.
• A copy of your monthly bank statement, saved as a PDF or a screenshot — most banks let you download it from their website or app.
• About 10 minutes the first time; once you've done it once, the same questions work on next month's statement.

Step 1 — Open Gemini and start a fresh chat

Go to gemini.google.com in your browser, or open the Gemini app on your phone. If you are not signed in, the page will ask you to sign in with your Google account — the same email and password you use for Gmail. Once you are in, look for a button or link along the left side or top of the screen that says something like "New chat" or "Start a new chat" and click or tap it. A blank conversation opens. If you don't see that exact label, look for a pencil icon, a "+" sign, or a text box that says "Ask Gemini" — any of those will start a fresh conversation. 💬 Example: open the Gemini app, tap "New chat," and you should see an empty text box with the prompt "Ask Gemini" at the bottom. You'll know it worked when you see an empty text field with no previous messages above it.

Step 2 — Upload your bank statement

Look for a small paperclip icon, a "+" button, or an "Upload" option near the text box — every version of Gemini has a way to attach a file. Click it, then choose the PDF of your statement from your computer or phone. If your statement is a stack of screenshots instead, you can either upload them one at a time or, on the desktop site, drag and drop them all into the chat window together. The file name appears in your message once it uploads. If you can't find an upload option at all, your version may only accept pasted text — open your PDF, select all the text (Ctrl+A or Cmd+A), copy it (Ctrl+C or Cmd+C), and paste it (Ctrl+V or Cmd+V) directly into the chat box instead. 💬 Example: "I'm pasting my bank statement below — it's about 3 pages of transactions." You'll know it worked when a small thumbnail or file name shows up next to your message, and Gemini replies with something like "Got it, I'm reading the statement now."

Step 3 — Ask for a plain-language summary

Now type a first question in the same chat. Don't ask for anything fancy yet — just ask Gemini to read the statement and tell you, in normal words, what happened in the month. Think of it as asking a friend who is good with numbers to glance at the page and explain it. Be specific about the period, and tell Gemini how you'd like the answer — short bullets, full sentences, whatever you prefer. If Gemini answers with generic budgeting advice instead of reading your actual statement, it probably didn't see the file — re-upload it or paste the text directly into the chat. 💬 Example: "Please read the statement I just uploaded and give me a plain-English summary of what happened in November. List the top 3 things I spent money on and any fees I was charged. Keep it under 200 words." You'll know it worked when Gemini replies with a readable summary that mentions real names and amounts from your statement — not a generic answer about budgeting.

Step 4 — Ask about recurring charges and small fees

Now go one level deeper. Small monthly fees — a $4.99 streaming service, a $12 "account maintenance" charge, a $2 ATM surcharge — are easy to miss when you scroll quickly. Ask Gemini to look specifically for anything that appears more than once, or anything that looks like a fee rather than a purchase. This is the step where most people discover charges they've been paying for months without noticing. If Gemini says it can't see any recurring charges, the file may have been only partially read — ask it to look at every page, or paste the full transaction list into the chat. 💬 Example: "Now look only at the charges that happened more than once this month, and anything that looks like a fee or service charge. For each one, tell me the name, the amount, and how many times it appeared." You'll know it worked when you get a short list of repeated merchants and fee names you can scan in 30 seconds — and probably one or two that surprise you.

Step 5 — Ask about your real spending patterns

The next question is about categories. Gemini can group your transactions by type — food, transport, subscriptions, cash withdrawals — and show you where most of the money went. This is helpful even if your statement already has a "category" column, because Gemini's version often groups things more sensibly for everyday reading, and you can ask follow-up questions about any category that looks too big or too small. If Gemini lumps almost everything into "other," your statement may have lots of unfamiliar merchant names — ask it to list the biggest "other" items so you can see what got hidden. 💬 Example: "Group all my spending from this statement into simple categories like food, transport, subscriptions, bills and other. Show me the total for each category and the percentage of my total spending. Then tell me which category grew the most compared to a normal month." You'll know it worked when you see a small table or list with named categories, dollar totals and clear percentages — something you could read once and remember.

Step 6 — Save your insights or turn them into questions for your bank

The last step is making sure you don't lose what you just learned. If something in the summary or the fee list looked wrong, copy Gemini's exact wording of that charge — the merchant name, the date, the amount — and use it next time you contact your bank. You can also ask Gemini to rewrite the whole summary into an email or a checklist you can save. To come back to this conversation later, look in the left sidebar for your recent chats — your version of Gemini should keep a running list you can reopen. If you can't find it, bookmark the page in your browser before you close the tab. 💬 Example: "Turn the summary from Step 3 and the fee list from Step 4 into a short email I can send to my bank asking about the $12 monthly fee. Keep the tone polite and include the date and amount for each charge." You'll know it worked when you have either a saved conversation, a copyable email draft, or a clear list of questions to bring to your bank's customer service.

Common mistakes

• Uploading a statement from a bank that masks real names. Some banks replace merchant names with codes (like "POS DEBIT 8842"). Gemini can't decode those on its own — you'll get a vague answer. If this happens, ask your bank for an itemized version.
• Asking one giant question instead of several small ones. A request like "tell me everything about my statement" usually produces a fuzzy answer. Three or four short, focused questions (like in Steps 3, 4 and 5) get sharper results.
• Forgetting to remove sensitive info if you share the chat. A bank statement has your account number, sometimes your address and always your full name. If you ever screenshot a Gemini chat to share it, blur those parts first.
• Treating Gemini's answer as a final verdict. It can misread a number, miss a charge or guess wrong about a fee. Always check the original statement before calling your bank — Gemini helps you ask better questions, it doesn't replace them.

Try it now

Open Gemini, upload this month's bank statement, and paste this single prompt: "Read this statement and list every charge I don't recognize, with the date and amount. Keep it to 10 items or fewer." Ninety seconds, one question — and you'll likely learn at least one thing you didn't know you were paying for.

In short: Salesforce customers who saw real return on investment from agentic AI — AI that can take actions on its own, not just answer questions — followed three habits: they listened to their users, defined success in plain numbers, and built guardrails to sustain results. Here's what worked, what surprised them, and what to borrow for your own rollout.

You keep hearing that AI is supposed to save businesses time and money. But a lot of teams are still stuck in the awkward middle phase: the tool is live, the demo looked great, and yet the spreadsheet isn't really changing. A new set of stories from Salesforce customers points to why — and what separates the teams who see real numbers move from the ones who don't.

The pattern: deploy, then listen

According to Salesforce's own recap, the customers seeing real results didn't just flip the switch. They treated the rollout like a listening project, not a launch announcement. They asked the people actually using the AI (customer service reps, salespeople, support agents) what felt clunky, what saved time, and what just created a new problem to fix.

One customer put it plainly: they didn't try to make the AI perfect before turning it on. They turned it on, watched how humans used it, and then made it better. The launch was the start of the work, not the finish line.

The pattern: define success before the rollout

The second habit was defining what "winning" looked like — in numbers, not vibes. Before deploying agentic AI (an AI agent is a program that can take multi-step actions for you, like opening a ticket, checking an order, or drafting a reply), the successful teams picked one or two measurable outcomes. Things like:

• Average response time to a customer question
• Number of cases a single rep can handle in a day
• Hours spent on routine data entry per week
• Conversion rate from inquiry to booked meeting

If a number didn't move, they treated it as data, not failure. That framing — "the AI is a hypothesis, the metric is the judge" — kept teams from chasing shiny new features when the basics weren't paying off yet.

The pattern: build guardrails early

The third pattern was less glamorous and arguably the most important: guardrails (the rules and limits you set around an AI so it stays accurate, on-brand, and safe). The customers who kept their results didn't skip this step to "move faster." They built in checkpoints where a human reviews the AI's work, especially early on. They set clear boundaries on what the AI was allowed to do on its own (draft an email) and what always needed a person to click approve (refund over a certain amount, anything going to a legal team).

Surprises they shared: the AI got the easy stuff right quickly, but the edge cases — oddball customer questions, unusual product configurations, anything outside the happy path — were where guardrails mattered most. Building those in from day one saved them from cleanup projects later.

Wrap-up

The lesson from these rollouts isn't a new feature or a slick demo. It's older and more boring, which is exactly why it works: listen to users, define success in numbers, and put guardrails in place before you scale. If you remember nothing else, remember the third one. Teams that skipped guardrails were the ones who quietly rolled the AI back six months later. Teams that built them in kept the wins — and built on them.