by admin | Jul 11, 2026 | Opportunities
Everyone keeps telling you to “learn AI skills” for your next job or promotion. Fair enough. But learn what, exactly? Where do you even start when there are thousands of AI courses, tutorials, and certificates competing for your attention online?
Microsoft built a free tool for exactly this problem. It is called AI Skills Navigator, and instead of dumping another course catalog on you, it asks a few questions about your goals and role, then builds you a personal learning path. No cost, no account required to browse, and it is open to anyone in the world, not just Microsoft employees or students.
What is the Microsoft AI Skills Navigator?
AI Skills Navigator is a free learning platform at aiskillsnavigator.microsoft.com. Microsoft describes it as a way to help “every organization, every role, and every learner” find the right AI learning path, and it is listed as one of the main free resources on Microsoft’s own AI skills and training page.
Instead of browsing a giant list of Microsoft Learn courses and guessing what matters, you tell the tool your goals, current skill level, interests, and how you like to learn. It then pulls together role-based playlists made from Microsoft Learn courses, hands-on labs, short videos, and even podcasts, all aimed at getting you from “I know nothing about this” to “I can actually do this.”
How it’s different from a normal course catalog
Most free learning platforms answer the question “what can I learn?” Microsoft designed this one to answer a more useful question: what do you need to learn next, and why does it matter for your actual job. According to Microsoft’s own Inside Track blog, the platform is built around four ideas: playlists tied to real roles, hands-on skills rather than just watching videos, learning formats that fit into a busy workday, and credentials that actually prove what you can do.
That last part matters if you are job hunting. A stack of half-finished course certificates does not impress anyone. A credential tied to a role, like “AI fundamentals for business analysts,” is a lot easier to explain in an interview.
What you actually get for free
- Role-based learning playlists (student, career switcher, developer, business user, manager, and more)
- Microsoft Learn courses and structured learning paths
- Hands-on labs so you practice instead of just watching
- Short videos and audio content you can use during a commute or lunch break
- Credentials and certification prep tied to specific skills, not just course completion
None of this requires a paid Microsoft 365 subscription to get started, and it works whether you are a complete beginner or already work in tech and just want to add AI skills to what you know.
Quick tip: Pick one specific goal before you open the tool, like “I want to use AI safely at my current job” or “I want an entry-level AI credential.” A focused goal gets you a much more useful playlist than going in with “I want to learn AI” in general.
How to start using it today
- Go to aiskillsnavigator.microsoft.com
- Answer the short prompts about your role, goals, and current skill level
- Review the playlist it builds for you, it is fine to skip sections you already know
- Work through the hands-on labs, not just the videos, since that is where the real skill-building happens
- Check back every few weeks. Microsoft updates the playlists regularly, so returning learners keep seeing new, relevant content
If you already have a favorite starting point, our guide on how to learn AI for free covers other no-cost options too, and pairs well with this tool if you want a broader view before picking a path.
Who this is really for
This is not just for developers. Microsoft built the playlists around roles like business analysts, project managers, educators, and IT support staff, not only engineers. If you are worried about being left behind at work because “everyone else understands AI,” this is a low-pressure way to catch up without committing to a paid bootcamp.
From my own experience helping people set up websites and online tools, the biggest barrier is rarely the AI itself. It is not knowing where to start or which course is worth your evening. A tool that just tells you “start here, based on your goal” removes that decision fatigue completely.
If a paid credential is your real goal, it is worth comparing this against our list of free AI certifications you can get online, since some of those overlap with what AI Skills Navigator recommends.
A word on AI Skills Fest, and why the tool outlasts it
Microsoft also runs an annual event called AI Skills Fest to get people started on the platform in one focused week. The 2025 edition brought together more than 126,000 participants in a single day and set a Guinness World Record for AI skilling participation, according to Microsoft’s Inside Track blog. The 2026 edition ran in June, but here is the part that matters if you are reading this after the event ended: AI Skills Navigator itself is not a one-week event. It is a standing, ongoing platform, and Microsoft has said the goal now is “sustaining long-term engagement” rather than treating the Fest as the only entry point.
In plain terms, you have not missed anything by finding this in July. The tool works exactly the same whether you show up during a big event week or on a random Tuesday.
Building AI skills also matters for your paycheck, not just your resume. Our post on why AI skills now pay more breaks down the data on that if you want the bigger picture. And if coding has been the thing holding you back, you can learn AI without coding too, no computer science degree required.
Common Questions
Is Microsoft AI Skills Navigator really free?
Yes. It is listed as a free public resource on Microsoft’s AI skills and corporate responsibility page, and no paid Microsoft 365 subscription is needed to use the core learning paths.
Do I need to already know how to code?
No. The playlists cover business, education, and non-technical roles as well as developer paths, so beginners have plenty to work with.
Will I get an actual certificate?
Some playlists lead to Microsoft Applied Skills credentials or certification exam prep. Others are shorter skill-building content without a formal credential. Check each playlist description before you start if a certificate matters to you.
Final takeaway
You do not need to figure out AI learning alone, and you definitely do not need to pay for it just to get started. Microsoft AI Skills Navigator takes the guesswork out of “what should I learn first,” and it is built to keep working for you long after any single event ends. Give it fifteen minutes, answer honestly about your goals, and see what path it builds for you.
by admin | Jul 10, 2026 | AI Tools
If you downloaded OpenAI’s Atlas browser last October to let ChatGPT handle your tabs, forms, and bookings, you’re about to get some news. OpenAI announced on July 9, 2026 that it is shutting Atlas down. The browser stops working on August 9, 2026.
This isn’t just a small product update. It’s a good moment to understand what “AI browser agents” actually are, why OpenAI is walking away from its own standalone browser after less than a year, and what your options look like now if you want an AI assistant that can act inside your browser.
What is happening to ChatGPT Atlas?
According to OpenAI’s own help center, Atlas is “scheduled to stop working on August 9, 2026.” OpenAI says it is folding the browser-based agentic features people liked about Atlas (multiple tabs, downloads, navigation, account login support) into the main ChatGPT app instead, along with a ChatGPT Chrome extension and sidebar for people who just want help while they browse in Chrome.
In plain terms: OpenAI decided it doesn’t need a whole separate browser to give people an AI agent that can click, scroll, and fill in forms for them. It would rather build that into ChatGPT itself and into a Chrome extension, which reaches far more people than a standalone app ever could.
What you need to do before August 9
If you have been using Atlas, OpenAI’s guidance is straightforward, but easy to miss if you don’t check your email:
- Export your bookmarks to an HTML file before August 9, then import them into Chrome or another browser.
- Save or copy the URLs of any open tabs you care about. Tabs will not carry over automatically.
- Bookmark or save anything from your browsing history you might need later.
- Treat any exported cookie or session files as sensitive data. Don’t share them with anyone you don’t fully trust.
Quick tip: your ChatGPT conversation history is completely separate from your Atlas browser data, so none of your actual chats are at risk. It’s only the browser-specific stuff (bookmarks, tabs, history, cookies) that needs manual saving.
Why this matters even if you never used Atlas
Atlas launched in October 2025 as OpenAI’s bet that people wanted an entire browser built around ChatGPT. Less than a year later, that bet didn’t pay off the way a standalone product needed to. That’s a useful signal about where this whole “AI browser agent” category is actually heading in 2026: not a handful of separate browsers competing for your default browser slot, but AI assistants that plug into the browser you already use.
From my own experience testing different AI tools for client websites and day to day work, the products that stick are usually the ones that fit into an existing habit rather than asking you to replace one. A Chrome extension is a much smaller ask than “switch your entire browser.”
What are your options now?
If you liked the idea of an AI agent handling browser tasks for you, a few real alternatives exist today, and they take different approaches:
- Perplexity Comet is a full standalone browser, available on Mac, Windows, iOS, and Android, that can research, summarize, and complete multi-step tasks for you. If you want to try the “browsing assistant” idea, this is the most direct like-for-like replacement for what Atlas offered.
- Claude in Chrome (Anthropic) is a Chrome extension rather than a full browser. It can read a page, click buttons, fill in forms, and work across multiple tabs, and it’s currently available to Pro, Team, and Enterprise plan subscribers.
- Gemini in Chrome lives inside Google’s own Chrome browser as a side panel, with an “auto browse” agent mode for multi-step tasks like comparing prices or filling out forms, available to Google AI Pro and Ultra subscribers.
If you’re just getting started and don’t want to commit to a subscription yet, our ChatGPT vs Gemini vs Claude comparison and our guide to what Perplexity AI actually does are good places to see how these tools differ before you pick one.
The privacy question worth asking
Any tool that can see and act on everything you do in a browser, your open tabs, your logged in accounts, your shopping carts, deserves a moment of caution. Before you let an AI agent browse on your behalf, check what permissions it’s asking for, avoid giving it access to banking or health accounts, and review its data settings. We cover this in more detail in how to use AI safely and protect your privacy, which is worth a read regardless of which tool you end up choosing.
This is also a good moment to remember that “agentic” AI is still new. Mistakes happen: an agent might click the wrong button, submit a form early, or misread a page. Keep an eye on what it’s doing, especially for anything involving money or personal information.
Common Questions
Will my ChatGPT conversations be deleted when Atlas shuts down?
No. Your ChatGPT conversation history is stored separately from Atlas browser data, so it stays available in the regular ChatGPT app.
Do I have to switch to a new browser right away?
No, but you should export your Atlas bookmarks and save any important tabs or history before August 9, 2026, since that data won’t transfer automatically.
What replaces Atlas for AI browsing?
OpenAI is moving browser-based agent features into the main ChatGPT desktop app and a ChatGPT Chrome extension. If you want a different option, Perplexity Comet, Claude in Chrome, and Gemini in Chrome all offer similar AI browsing assistance.
Final takeaway
OpenAI killing its own Atlas browser after less than a year isn’t really a failure story, it’s a sign the whole AI browsing space is still figuring itself out. If you used Atlas, take ten minutes this week to export your bookmarks and save what matters. And if you’re curious about AI agents that can act inside your browser, you now have a clearer picture of where each major player stands, and it’s worth reading up before you hand any of them the keys.
by admin | Jul 9, 2026 | Research & Productivity
Ask anyone who has written a thesis which part they underestimated, and you will usually get the same answer: the literature review. You start with one search, and two weeks later you have sixty open tabs, a folder full of unread PDFs, and no clear picture of the field.
AI tools can remove a lot of that pain if you point them at the right jobs. This guide walks through a five step literature review with AI, using free tools, and it stays honest about the parts you still need to do yourself.
Can you really do a literature review with AI?
Partly, yes. AI is genuinely good at three jobs here: finding papers that match your question even when you do not know the perfect keywords, summarizing individual papers quickly, and showing how papers connect to each other.
What it cannot do is judge research quality the way you can, decide why a gap in the field matters, or build your argument. AI models also make mistakes with total confidence. They sometimes invent references or misread a paper\u2019s findings, a problem we explained in AI hallucinations explained. So the workflow below uses AI for speed and keeps the judgement with you.
Someone close to me spends her days in PhD research on machine learning and medical imaging, so I have watched how fast a reading pile can grow. The researchers who cope are not the ones reading faster. They are the ones with a better system.
Step 1: Turn your topic into a real question
\u201cAI in healthcare\u201d is a topic. \u201cHow accurate are deep learning models at detecting brain tumours from MRI scans?\u201d is a question. Every step that follows works better when you start from a question, because modern research tools use semantic search. They match meaning, not just keywords.
Write your question down before you open any tool. If you cannot phrase it yet, that is useful information too. Spend an hour with a general overview or a textbook chapter first, then come back.
Step 2: Find papers with AI search tools
Three tools cover most of the discovery work:
- Elicit searches more than 138 million papers. You type your question and it returns a table of relevant papers with short summaries. The Basic plan is free, and it can import your library from Zotero.
- Semantic Scholar is a free academic search engine from the non-profit Allen Institute for AI. It indexes over 200 million papers and adds short AI generated summaries, called TLDRs, so you can screen results quickly.
- Research Rabbit maps papers visually. You start with one paper you already trust, and it shows similar, earlier, and later works, so you follow the citation trail instead of searching blind.
University libraries have started recommending these tools too. The University of Michigan Library keeps a practical guide on AI in literature reviews if you want a librarian\u2019s take on the same tools.
Tip: run the same question through two different tools. Each one searches differently, and the papers that appear in both lists are usually the ones to read first.
Step 3: Screen and organize what you find
You will collect far more papers than you need, so do not try to read them all. Screen each one by its abstract or TLDR and sort it into three piles: keep, maybe, and drop. Be ruthless with the drop pile.
For the keepers, use Zotero, a free and open source reference manager. It stores your citations, formats them in thousands of styles, and connects with Elicit and Research Rabbit. We covered where it fits in our guide to AI tools for thesis writing.
Step 4: Summarize and compare the papers
For every paper you kept, you want four things: the question it asked, the method it used, what it found, and its limitations. AI can speed this up a lot. Our guide on how to summarize research papers with AI shows practical prompts, and our NotebookLM and Elicit walkthrough covers tools that answer questions only from the sources you upload.
One warning from experience: AI extraction makes mistakes. It can misread a sample size or blur two findings together. Check every number and claim against the actual paper before it goes anywhere near your draft.
Step 5: Write the review yourself
Here is the part no tool can do. A literature review is not a list of summaries. It is an argument about the state of a field: what researchers agree on, where they clash, and which gap your work will fill. That structure has to come from your reading, so group your papers by theme or debate, not by author.
Two rules protect you here. First, verify that every reference exists and says what you claim, because AI generated citations are sometimes fake. Second, check your university\u2019s AI policy and disclose what you used. Most universities now allow AI for searching and summarizing but treat AI written text as misconduct.
From my own work with websites and online tools, the pattern is always the same. Tools that remove boring steps earn their place. Tools that promise to think for you cause trouble later.
Common Questions
Can AI write my literature review for me?
It can produce text that looks like one, but that is the trap. The references may not exist, the synthesis is shallow, and most universities treat submitting it as academic misconduct. Use AI to find, organize, and summarize. Write the argument yourself.
Are these AI research tools free?
Yes, for everything in this workflow. Semantic Scholar is completely free, Elicit has a free Basic plan, Research Rabbit lets you sign up free, and Zotero is free and open source.
How many papers should a literature review include?
It depends on your field and level. A bachelor\u2019s thesis might cover 20 to 40 papers, while a PhD literature review can pass 150. Your supervisor\u2019s guidance beats any general number, so ask early.
Final Takeaway
A literature review with AI is not about outsourcing the reading. It is about shrinking the boring parts: hunting for papers, formatting citations, and writing first pass summaries. Pick one question, run it through Elicit or Semantic Scholar this week, and save what you find into Zotero. The pile gets smaller, the map gets clearer, and the thinking stays yours.
by admin | Jul 8, 2026 | Future Jobs
You click the interview link, fix your hair in the little preview window, and wait for someone to join. Nobody does. Instead, a recorded voice reads out the first question and a timer starts counting down. Your interviewer today is an AI.
If that sounds unusual, it isn’t anymore. According to the Greenhouse 2026 Candidate AI Interview Report, published in May 2026, 63% of job seekers have already been interviewed by an AI. That number jumped 13 points in just six months. So if you’re applying for jobs this year, the real question is not whether you’ll face an AI job interview. It’s when, and how ready you’ll be.
What is an AI job interview?
An AI job interview is any interview where software, not a person, asks the questions or scores your answers. It usually takes one of three forms. The most common is the one-way video interview: you record answers to preset questions and an algorithm (sometimes with a human reviewer) rates them later. Some companies use chat-based screenings, where you type answers to a bot. And a growing number now use live AI voice interviewers that ask follow-up questions in real time.
Employers like these tools because they can screen thousands of applicants quickly. For you, it means the first “person” standing between you and the job is often a piece of software.
What the AI is actually measuring
This is the part most candidates never get told. The Greenhouse survey found that 70% of job seekers were never clearly informed that AI would evaluate them, and 39% said they want employers to explain what the AI measures.
In practice, most systems look at the content of your answers: the skills you mention, how closely your language matches the job description, and how clearly you structure your response. Duke University’s career guidance for video interviews makes this point directly: study the job description, pick out the key skills and qualifications, and work them naturally into your answers, because that’s largely what the software is listening for.
Why so many candidates walk away
AI interviews have a trust problem right now. In the same survey, 38% of candidates said they had abandoned a hiring process because it included an AI interview. The biggest complaints were pre-recorded video interviews scored with no human present, companies not disclosing how AI would be used, and AI monitoring during the process.
The aftermath can sting too. Of the candidates who completed an AI interview, 51% simply never heard back. That’s worth knowing before you start: silence after an AI screening is common and says very little about you. Don’t take it personally, and don’t stop applying elsewhere while you wait.
How to prepare for an AI job interview
The good news is that AI interviews reward preparation more than charm. Here’s what actually helps:
- Mirror the job description. Reread it before the interview and note the exact skills it asks for. Use those words in your answers where they’re true for you.
- Structure every answer. The STAR method (Situation, Task, Action, Result) keeps your response clear for both algorithms and humans.
- Answer first, then explain. Get to the point in your first sentence or two, then back it up with a real example.
- Practice out loud. Record yourself answering two or three common questions on your phone. You’ll hear the rambling immediately.
- Don’t read from a script. Your eyes give it away on camera. A few keyword notes near the screen are fine; a full script is not.
From my own work building websites and running digital projects, I’d add one thing people always underestimate: test your tech. Check your camera, microphone, lighting, and internet connection the day before, the same way you’d test a website before launch. A frozen video or muffled audio can sink a good answer, and it’s completely avoidable.
Tip: treat the practice questions seriously. Most AI interview platforms offer a test question before the real one starts. Use it to check your sound and framing, not just to relax.
What you’re allowed to ask the employer
Asking about AI in the hiring process is reasonable, and increasingly normal. In the full Greenhouse report, 57% of candidates said disclosure should be a legal requirement, and 46% want the option to request a human interview instead. Some employers already offer one.
Before the interview, it’s fair to ask three things: Will AI be used to evaluate me? What does it measure? And will a human review the result before a decision is made? A company that answers openly is telling you something good about how it treats people.
There’s a privacy side too. These platforms record your video and voice, so it’s smart to check how long recordings are kept. I write about this mindset in my guide on how to use AI safely and protect your privacy, and it applies here as well.
Should you use AI to prepare?
Yes, for practice. Ask ChatGPT or a similar tool to act as an interviewer for your specific role, then answer its questions out loud. It’s one of the most useful tricks in our guide to using AI in your job search. What you shouldn’t do is have AI feed you live answers during the interview. Detection aside, you’d be rehearsing for a job with someone else’s voice.
And keep the bigger picture in mind. AI is changing interviews because it’s changing work itself. If you want to understand where that’s heading, our posts on whether AI will take your job and why AI skills now pay more cover what the data really says.
Common Questions
Do employers have to tell me an AI is interviewing me?
In most places, not yet, and in practice most don’t: 70% of candidates in the Greenhouse survey were never clearly told. Rules are tightening in some regions, but for now the safest move is simply to ask before the interview.
Can I refuse an AI interview?
You can always ask for a human alternative, and 46% of candidates want that option as standard. Some employers will say yes. If they won’t, you can still decide whether the role is worth it, which is exactly what 38% of candidates have done.
How do I know if the AI rejected me?
Often you won’t. Only 13% of candidates in the survey were formally rejected after an AI interview, while 51% never heard back at all. Follow up once after a week or so, then keep applying elsewhere.
Final takeaway
AI job interviews are now a normal part of getting hired, even though most of them still aren’t handled well. You can’t control whether a company uses one, but you can control how prepared you are: know what the software measures, structure your answers, test your setup, and ask honest questions about how AI is being used. Do that, and the AI screening stops being scary. It becomes just one more door on the way to the job.
by admin | Jul 7, 2026 | AI Guides
Have you ever pasted a long document into an AI chatbot and watched it stop halfway? Or seen an AI plan advertise a “128K token” limit and wondered what that actually means? Tokens sit behind almost everything AI chatbots do, and once you understand them, a lot of confusing AI behaviour suddenly makes sense.
In this guide, you’ll learn what a token in AI is, how your words get chopped into tokens, and why token limits explain the message caps, forgetful chats, and prices you run into every day. Plain English, no math needed. If you’re completely new to this topic, our simple explanation of what AI is is a good place to start.
What is a token in AI?
A token is a small piece of text that an AI model reads and writes. It’s the basic unit every AI language model works with. A token is not the same as a word. It can be a whole short word, part of a longer word, a punctuation mark, or even a space attached to a word.
OpenAI’s help guide gives some handy rules of thumb for English text:
- 1 token is roughly 4 characters
- 1 token is about three quarters of a word
- 100 tokens come to roughly 75 words
So a 1,000 word blog post like this one is somewhere around 1,300 to 1,400 tokens. Numbers, code, and unusual words push the count higher.
How AI turns your words into tokens
Before an AI model ever sees your message, a piece of software called a tokenizer splits it into tokens. Each token then gets an ID number, because the model works entirely with numbers, not letters. Common short words usually stay whole. Longer or rarer words get broken into smaller chunks.
GPT models use a subword method called Byte-Pair Encoding, as Microsoft’s guide to tokens explains. To give you a feel for the scale, OpenAI notes that the famous quote about missing all the shots you don’t take is 11 tokens long.
Language matters too. OpenAI points out that the Spanish phrase “Cómo estás” takes 5 tokens for just 10 characters. Many non-English languages break into more tokens per word, which makes the same request slower and more expensive than it would be in English.
Why do AI models count tokens instead of words?
Because tokens are literally how these models think. A large language model generates text by predicting the next token, one token at a time, then feeding its own output back in and predicting the next one again. Words are for us. Tokens are for the model.
Subword tokens also give models flexibility. When a model meets a brand new word, a typo, or an unusual name, it can still handle it by piecing together smaller chunks it already knows.
What is a context window?
Every AI model has a limit on how many tokens it can handle at once. That limit is called the context window, and it covers your input and the model’s output together. When a conversation grows past it, the oldest parts effectively fall out of view.
This is why a long chat starts to “forget” things you said earlier. The AI isn’t being lazy. Those early messages simply no longer fit inside the window it can see. The practical fix is to start a fresh chat and paste in only the details that still matter, or keep your prompts focused from the start. Our guide on writing better AI prompts helps a lot here.
Why tokens decide what AI costs
Most paid AI services charge by the token, and input tokens are often priced differently from output tokens. Free plans work the same way underneath, they just cap how much you can use. That’s why generative AI tools talk about tokens so much: they measure the actual work the model does.
From my own experience connecting AI tools to websites and small projects, this was the first surprise: the bill counts tokens, not questions. A one-line question is nearly free. The same question with a 20-page document pasted under it costs many times more, because every word of that document becomes tokens the model has to read.
How to see tokens for yourself
The easiest way to make all this real is to try OpenAI’s free Tokenizer tool. Paste in any sentence and it shows you exactly how the text splits into coloured chunks, with a live token count. Two minutes with it teaches you more than any definition.
Quick tip: if an AI chat starts forgetting details or giving weaker answers, your conversation has probably outgrown its context window. Start a new chat and paste in only the information that still matters.
Common Questions
How many words is 1,000 tokens?
Roughly 750 words in English, using OpenAI’s rule of thumb. The exact number depends on your language and word choices, since longer and rarer words split into more tokens.
Do spaces and punctuation count as tokens?
Yes. Spaces usually attach to the word that follows them, and punctuation marks often become their own tokens. Everything you type contributes to the token count.
Why do AI plans talk about tokens instead of messages?
Because messages vary hugely in size. A short question and a pasted 50-page report are both “one message,” but the report takes far more computing work. Counting tokens is the fair way to measure that work.
Final takeaway
Tokens are the small chunks of text every AI model actually reads and writes. They explain the limits on your chats, the reason long conversations drift, and the way AI pricing works. You never have to count them by hand. But once you know they exist, AI tools stop feeling mysterious and start feeling like something you can plan around.
