by admin | Nov 13, 2026 | Opportunities
Ever wished someone would pay you to dig into one of the biggest problems in AI, even if you don’t have a PhD or a research background? That is basically what Anthropic is offering through its Fellows Program, and most people have never heard of it.
It is a paid, four month research program where you work on real AI safety or AI security problems with mentorship from Anthropic’s own researchers. No published papers required. No PhD required. Just a technical background and a willingness to learn fast. It sits alongside other genuine funded paths we have covered, like finding genuine scholarships and learning opportunities and the fully funded AI safety scholarship in Germany we wrote about recently.
What Is the Anthropic Fellows Program?
The Fellows Program is Anthropic’s way of bringing outside talent into AI safety work without forcing people through the usual academic pipeline. Fellows spend four months on an empirical research project, usually one aimed at a public output like a paper or an open source tool.
Anthropic mentors pitch project ideas first, then fellows pick one and shape it with that mentor’s guidance. The research areas cover scalable oversight, adversarial robustness and AI control, mechanistic interpretability, AI security, model welfare, and a few other tracks including economics and policy. The security track connects closely to the defensive work we covered in Anthropic’s cyber mission.
Past fellows have built agents that find smart contract vulnerabilities, studied how AI models respond to jailbreak attempts, open sourced interpretability research, and investigated how models can pick up strange behaviors from their own training data. It is hands on, not theoretical.
What You Actually Get Paid
This is the part that surprises people. Fellows receive a weekly stipend of roughly 3,850 USD (or the GBP or CAD equivalent) for about 40 hours a week, plus around 15,000 USD a month in compute funding and extra research expenses on top. That is for four months of work, with a possible extension if the project is going well.
You also get direct mentorship from Anthropic researchers, some of whom work on alignment and interpretability full time. For a lot of fellows, that mentorship ends up mattering as much as the money.
Who Can Actually Apply
Here is the part that makes this different from most research fellowships: Anthropic says it values coding ability, comfort working with AI tools, and the motivation to reduce serious AI risks more than it values credentials. No PhD, prior machine learning experience, or publication history is required.
Past fellows have come from physics, math, computer science, and cybersecurity backgrounds, not just machine learning. You do need to be able to work full time for four months and hold work authorization in the US, UK, or Canada, since Anthropic does not sponsor visas for this program.
From my own experience working across websites, cybersecurity, and online tools, this is the kind of program that rewards people who have already been building and shipping things on their own, even without a formal research title. If you have a portfolio of real projects, that often speaks louder than a transcript. If you do not have one yet, our guide on how to build an AI portfolio is a reasonable place to start before you apply.
The Numbers Behind the Program
Anthropic’s own data on this program, published on its Alignment Science blog, is worth knowing before you apply. More than 200 fellows have gone through it since early 2025. Over 65 percent of past fellows have published work that came directly out of their fellowship. More than 35 percent later joined Anthropic full time on AI safety teams, and many others moved into safety roles at other companies.
In other words, this is not just a stipend to sit on a resume. For a real share of fellows, it has turned into an actual career path into AI safety.
How and When to Apply
Applications run on a rolling basis rather than one fixed yearly deadline, and Anthropic runs several cohorts a year. For context, the window for the January 2026 cohort closed on October 18, which has already passed by the time you are reading this, but that is normal for a rolling program. A new window opens for each upcoming cohort.
That means the best move is to check the current listing directly rather than assume you missed your shot. Apply through Anthropic’s own job posting, which routes through its recruiting partner for this program. Pick one track (safety, security, or another listed workstream) rather than applying to several at once.
Important tip: watch out for scam recruiters. Anthropic’s official guidance is clear that its recruiters only contact candidates from @anthropic.com addresses, and the program never asks for payment or banking details before your first day.
Common Questions
Do I need a machine learning background to apply?
No. Anthropic says strong coding skills and the ability to learn fast matter more than prior ML experience. Fellows have come from physics, math, and cybersecurity backgrounds.
Is this program remote?
You must hold work authorization in the US, UK, or Canada and be located there during the program, since Anthropic does not sponsor visas for fellows.
What happens after the four months?
Some fellows extend their project, some join Anthropic full time, and others carry their research and skills into safety roles elsewhere. There is no guarantee either way.
Is the stipend the only funding?
No. On top of the weekly stipend, fellows get roughly 15,000 USD a month in compute funding plus research expenses, which matters a lot for anything involving model experiments.
Final Takeaway
The Anthropic Fellows Program is one of the more practical ways to get paid, real experience in AI safety without first spending years on a PhD. If you have solid technical skills and genuine curiosity about keeping AI systems safe, it is worth checking the current application window and seeing where you would fit. Even if you are not ready this round, now you know the path exists.
by admin | Nov 1, 2026 | Opportunities
If you have ever looked at AI PhD programs abroad and quietly closed the tab because of the cost, there is a program in Germany worth knowing about. It pays you to study AI, not the other way around, and the next round of applications is expected to open in just a few weeks.
It is called the Konrad Zuse School of Excellence in Reliable AI, or relAI for short. It is one of several “Zuse Schools” run through Germany’s DAAD (the German Academic Exchange Service), backed by federal funding, and it is built around a question that does not get nearly enough attention in AI education: not just “can we make AI powerful,” but “can we trust it.”
What Is relAI, Exactly?
relAI is a joint program between the Technical University of Munich (TUM) and LMU Munich, two of Germany’s top research universities. Instead of treating AI reliability as an afterthought, the whole program is built around it: safety, security, privacy, and making sure AI systems behave the way they are supposed to in domains where mistakes actually matter, like healthcare or robotics.
Students work alongside relAI Fellows (senior researchers in the field), get mentoring, attend dedicated training, and become part of a growing international research network rather than just sitting in lectures.
What You Actually Get
This is the part most “free scholarship” articles gloss over, so here are the real numbers, straight from relAI’s own site:
- Master’s students receive a monthly scholarship of €934, plus extra allowances if you have a partner or children, for two years.
- Doctoral researchers (PhD) are employed on a full TV-L E13 contract for three years. That is not a stipend, it is a real German public-sector salary with the benefits that come with it, one of the more competitive packages you will find for AI PhD positions anywhere.
There is also a path to become an “associated” relAI PhD student if your funding comes from elsewhere but your research fits the program, which still gives you access to the same curriculum and community.
What You’ll Actually Study
relAI organizes its research into five areas: mathematical and algorithmic foundations, learning and instruction, algorithmic decision-making, medicine and healthcare, and robotics and interacting systems. That healthcare track matters more than it might sound. A lot of the current debate around AI in medicine comes down to explainability, whether a doctor can actually understand why a model reached a conclusion before trusting it with a diagnosis. That is squarely in relAI’s territory.
From my own experience working around websites, cybersecurity, and AI tools, the part of this field that gets the least attention is also the most important one. Everyone wants to talk about how powerful a model is. Almost nobody asks how you would know if it quietly started getting things wrong. Programs like this exist because that question needs real researchers working on it, not just a disclaimer at the bottom of an app.
Quick tip: if you are even considering applying, start lining up your two recommendation letters and your CV now. Strong applications need time, and scrambling for references in the last week never goes well.
Who Can Apply
You do not need to already be based in Germany or the EU. The program accepts applicants from anywhere, as long as you meet the academic bar:
- An excellent bachelor’s degree (for the MSc track) or master’s degree (for the PhD track) in computer science, mathematics, engineering, natural sciences, or a related data science or machine learning field.
- A genuine interest in reliable AI, meaning safety, security, privacy, or responsibility in AI systems.
- English proficiency at C1 level, or proof that your previous degree was taught mainly in English.
- For the PhD track specifically, you will also need to name two preferred supervisors from relAI’s Fellows and explain why, plus two referees willing to write recommendation letters.
When to Apply
relAI typically opens its PhD call in the fall or winter and its master’s call in spring or summer. The most recent PhD round closed on January 13, 2026, and relAI’s own site says to expect the next call in December 2026, which is right around the corner. If you are aiming for the PhD track, now is genuinely the time to prepare, not wait until the call opens.
This is also just one piece of a bigger picture. Germany’s DAAD runs several of these Zuse Schools across different AI specialties, and relAI is sitting alongside other fully funded routes into AI research worth knowing about, including some of the fully funded AI master’s programs across Europe we covered recently. If you are early in your search, our guide on finding genuine scholarships and learning opportunities online is a good place to start, and if the idea of “reliable AI” is new to you, our plain-English explainer on what explainable AI actually means fills in the background.
Common Questions
Is this only for German or EU citizens? No. relAI accepts applicants internationally, as long as you meet the academic and language requirements.
Do I need research experience already? A strong academic record and a genuine interest in the field matter most. For the PhD track, having a specific supervisor and thesis idea in mind will strengthen your application.
What does “reliable AI” actually mean? It covers making AI systems safe, secure, privacy-preserving, and resistant to manipulation, essentially everything that needs to be true before AI can be trusted in high-stakes settings like medicine or infrastructure.
Is there support if I have a family? Yes, master’s scholars receive additional allowances for partners and children on top of the base scholarship.
Final Takeaway
Fully funded AI programs that actually pay a livable salary, rather than a token stipend, are rare. relAI is one of them, and it is specifically built for people who want to work on making AI trustworthy, not just bigger. If that sounds like the kind of problem you want to spend a few years on, bookmark the official application page now, start preparing your documents, and check back as December approaches.
by admin | Oct 24, 2026 | Opportunities
If you have ever typed “AI master’s scholarship” into Google at 1am, wondering whether a fully funded degree in artificial intelligence actually exists or whether it is just clickbait, you are not alone. Most scholarship pages online are either outdated, vague about the money, or trying to sell you a “guaranteed admission” service. So here is the plain version: the European Union is genuinely funding several AI-focused master’s programmes right now, and most of their scholarship deadlines fall between October and January. That means this is roughly the window to start preparing, not wait and regret it in March.
I spend a lot of my time helping people build websites, online profiles, and digital projects through my work at OBAKHA, and I see the same pattern in scholarship applications as in job applications: the people who get in are not always the most brilliant on paper, they are the ones who apply early and send something specific instead of generic. If you have already read our guide on how to find genuine scholarships and learning opportunities online, this post goes one level deeper into AI specifically: what is really on offer, what it covers, and how to use the next few weeks well.
What Erasmus Mundus Actually Is
Erasmus Mundus Joint Master’s programmes are degrees run jointly by several European universities at once. You do not pick one city and stay there. You move between two or more partner universities over the course of the programme, and you graduate with a joint or double degree that all of them recognise. The European Commission funds a set number of these programmes every year, and it funds students who get in, not just the universities running them.
According to the European Commission’s own education pages, several of these joint programmes are built specifically around AI, and many offer EU-funded scholarships worth around €1,400 for selected students, on top of waived tuition in a lot of cases. The exact scholarship terms vary by programme, so you still need to check each one directly, but the funding is real and it comes from the EU, not a private company trying to upsell you later.
Five Fully Funded AI Master’s Programmes Right Now
- EMAI (Erasmus Mundus Joint Master in Artificial Intelligence), run by Pompeu Fabra University in Barcelona together with Sapienza University of Rome, Radboud University in the Netherlands, and the University of Ljubljana. It blends machine learning with symbolic AI and runs for 24 months.
- EMILDAI (European Master in Law, Data and Artificial Intelligence), taught across Dublin City University, Universidad de León, Avignon Université, and the University of Pisa. This one is for people who want to work where AI meets law and regulation, not just code.
- AISS (Artificial Intelligence for Sustainable Societies), at Tallinn University, Tampere University, and Lusófona University, focused on AI for fairer, more sustainable digital societies.
- FAMEAIS, spread across seven institutions in France, Germany, Portugal, Belgium and beyond, applying AI to advanced materials engineering and sustainability.
- ChEMoinformaticsplus, across five institutions in France, Portugal, Italy, and Slovenia, applying AI and data science to chemistry research.
Notice how different these are. One is pure AI theory, one is AI and law, one is AI and chemistry. You do not need a narrow computer science background to qualify for all of them. If your undergraduate degree is in law, chemistry, or social policy, there may genuinely be a programme built for exactly that combination.
What the Scholarship Actually Covers
Based on the official programme pages for EMAI, a full Erasmus Mundus Scholarship (not every accepted student gets the full scholarship, so this matters) is only awarded to full-time students and typically includes a monthly stipend meant to cover travel, visa costs, installation, and living expenses for the full length of the programme. Separately, some applicants are offered just a tuition fee waiver, which covers academic fees but not your living costs. And a smaller number of spots are left for self-funded students who pay the discounted EU/EEA or full international tuition rate themselves.
Important tip: when you apply, you are usually applying to one slot with three possible outcomes (full scholarship, tuition waiver, or self-funded), not three separate applications. Read each programme’s own admissions page carefully before you submit, because the scholarship pool and the general admission pool often close on different dates.
When to Actually Apply
The European Commission’s own guidance is blunt about this: most scholarship deadlines for these joint programmes fall between October and January, for a programme that starts the following autumn. If you are reading this in late October, you are not early. You are right on time, but only if you start now.
A realistic prep list for the next few weeks: pick two or three programmes that actually match your background, not just the ones with the flashiest name. Line up two academic referees now, since recommendation letters are usually submitted directly by the referee through an online portal, and professors get slower to respond the closer it gets to winter break. Write one clear paragraph about why you specifically want that programme, not a recycled personal statement. And check whether you need an English proficiency certificate, since some programmes require one unless your previous degree was taught entirely in English.
A Quick Reality Check
These programmes are competitive, and nationality caps exist on some scholarships so one country cannot take every funded seat. That is not meant to discourage you, it is meant to set expectations correctly, which most scholarship-hunting blogs will not tell you. If you miss the full scholarship on your first try, a tuition waiver plus a part-time job is still a far better deal than full international tuition at most universities outside Europe. While you wait to hear back, it helps to build real, demonstrable AI skills: free programmes like Claude Frontier Academy and genuine research openings such as Anthropic’s Guest Investigator Program strengthen your profile before you even submit, and our roundup of free AI courses from top universities is a good next stop.
Common Questions
Can students from outside the EU apply? Yes. These scholarships are generally open to applicants worldwide, though some programmes cap how many scholarship seats can go to any single nationality.
Do I need a computer science degree to apply for an AI-focused Erasmus Mundus programme? Not always. Programmes like EMILDAI (law and AI) and AISS (sustainability and AI) are designed for mixed academic backgrounds, not just computing graduates.
Is the scholarship guaranteed if I am accepted into the programme? No. Admission and scholarship funding are usually decided separately, so you can be accepted academically but not win the fully funded seat, which is why checking the tuition-waiver and self-funded options matters too.
Where should I check for the exact current deadline? Always confirm on the specific programme’s own official page (for example EMAI’s site run by Pompeu Fabra University) rather than a scholarship listing blog, since dates can shift slightly year to year.
Final Takeaway
A fully funded AI master’s in Europe is not a myth, and it is not only for people who already have a perfect CV. It is for people who check the real programme pages, match their own background honestly to the right one, and get their documents in before the window closes. If AI is a field you actually want a career in, late October is a very good week to open a tab for EMAI, EMILDAI, or one of the others above and start reading.
by admin | Sep 5, 2026 | Opportunities
You finished a free AI course, you can write a decent prompt, and then a job listing asks for “proven experience with AI.” It’s a frustrating wall. How are you supposed to prove you can do the work when nobody has hired you to do it yet?
The honest answer is that you build the proof yourself. An AI portfolio is simply a small, public collection of things you have made that anyone can open with a link. It matters more than most people expect, because a certificate says you sat through a course, while a project shows you can actually use what you learned. And you can build a solid AI portfolio for free, with no job, no degree, and no expensive tools.
Why an AI portfolio beats a certificate
Certificates are useful, but they mostly prove attendance. A project proves ability. When someone can click a link and see a thing you built, the conversation changes from “trust me” to “look what I did.” Employers and clients trust what they can try for themselves.
From my own experience building websites and small online tools, the thing that got people to take me seriously was never a line on a CV. It was something they could open in a browser and use. A portfolio does the same job for AI skills.
What goes into an AI portfolio?
You don’t need much to start. A strong beginner AI portfolio usually has:
- Two or three small projects you actually finished.
- A short write-up for each one: what problem it solved, what you did, and what you learned.
- Links people can click, such as a notebook, a live demo, or a code repository.
- Optional badges from free courses, kept in their place.
Important tip: one finished project you can explain clearly beats ten half-built ones. Depth is what people remember, not the length of the list.
Start with small, real projects
Pick something you actually care about, then keep it tiny. A good first project can be as simple as taking a public dataset (movie ratings, weather, sports stats) and finding one interesting pattern in it. Or build a custom assistant for a hobby you know well. If you want a gentle starting point, our guide on how to make your own AI assistant for free walks through a project you can finish in an afternoon.
And no, you do not need to be a programmer. Plenty of real projects use no-code tools, prompt libraries, or a clear written analysis. If code feels like a barrier, start with our guide on how to learn AI without coding, then come back and build. If you still need the basics, learning AI for free is a good first step.
Use free platforms to show your work
Once you have made something, it needs a home online. These are all free and widely respected:
- Kaggle: free notebooks that run in your browser, thousands of open datasets, and a public profile. See Kaggle.
- GitHub: free public repositories for any code or files, where a clear README acts as your project’s front page. See GitHub.
- Hugging Face Spaces: when you are ready to go further, you can host a live demo people can actually try, on a free public link. See Hugging Face Spaces.
One habit worth keeping from the security side of my work: only use open datasets or your own data, and never put private, personal, or client information into a public project. Check a dataset’s license before you publish anything based on it.
Enter free competitions for practice and proof
Competitions give you two things at once: a finished project and a story to tell about it. Kaggle’s “Getting Started” competitions, like the well-known Titanic challenge, are built for beginners and never close, so there’s no pressure to rush. Free hackathons and student contests work the same way. We rounded up the best beginner-friendly options in our guide to AI competitions for beginners.
Add free course badges the right way
Badges are a nice supporting detail. On Microsoft Learn, finishing free learning paths earns badges and trophies. Google AI Essentials gives a shareable digital badge through Credly, and Kaggle Learn hands out free certificates too.
Just remember the order of things: a badge supports a project, it does not replace one. Put one or two on your profile, then let your actual work do the talking.
Write up each project so people understand it
The write-up is half the value, and it’s the part most people skip. A recruiter may never run your code, but they will read three clear sentences. For each project, answer three questions: what problem were you solving, what did you build, and what is one thing you learned or would improve next time?
Keep it honest. Don’t claim more than you did. A small, truthful project explained well beats a big one you can’t really talk about.
A simple 4-week plan to start
- Week 1: learn a little and pick one small project idea.
- Week 2: build the smallest version that actually works.
- Week 3: put it on Kaggle or GitHub with a clear write-up.
- Week 4: enter a beginner competition or start project two, then add the link to your CV.
When you do start applying, your portfolio becomes your strongest line. Our guide on how to use AI to apply for jobs shows how to point employers straight to it.
Common Questions
Do I need to know how to code to build an AI portfolio?No. No-code tools, custom assistants, prompt libraries, and clear written analyses all count. Coding helps for some projects, but a well-explained no-code project is still real proof of skill.
How many projects do I need?Two or three finished, well-explained projects are plenty to start. A clear write-up matters far more than the number of projects.
Where should I host everything?Pick one home base people can visit. A GitHub or Kaggle profile works well, and you can add a live demo on Hugging Face Spaces later. Then link to it from your CV and LinkedIn.
Is it safe to put my projects online?Yes, as long as you only use open or your own data. Never upload private, personal, or client information, and read a dataset’s license before sharing results based on it.
Final takeaway
You don’t need permission or a paycheck to start proving your AI skills. Pick one small idea this week, build the simplest version that works, write three honest sentences about it, and put it somewhere with a link. Do that a few times and you’ll have something most applicants never bother to make: real proof you can use AI, not just talk about it.
by admin | Aug 29, 2026 | Opportunities
You finished the free course. The certificate is sitting in your email somewhere. And then, honestly, nothing much changed.
That gap between learning about AI and having something to show for it is where most beginners get stuck. AI competitions for beginners are one of the cheapest ways to close it. They cost nothing to enter, they run all year, and when you finish you have a public link to something you actually did rather than another line on a list.
Here is where to start, what each platform is good for, and how to pick a first one you will actually finish.
Why a competition does something a certificate cannot
A certificate says you watched the lessons. A competition entry says you took a messy real problem, made choices about it, and put your answer next to everyone else’s.
From my own experience reviewing people’s work for websites and digital projects, a link to something someone built tells me more in thirty seconds than a list of course titles does in five minutes. The list tells me what they sat through. The link tells me what they can do when nobody is giving them the answer.
There is a second benefit that nobody mentions. A competition has an end. Courses can be paused forever, but a submission deadline makes you finish something, and finishing is the skill most beginners are actually missing.
Start on Kaggle, but stay off the main leaderboard
Kaggle is the biggest home for this kind of thing, and it sorts its challenges into types. Featured competitions are the premier ones with prize money. There are also Research, Community, Simulations and Hackathon categories. None of those are where you begin.
Two categories exist specifically for people in your position. Getting Started is described by Kaggle as approachable machine learning fundamentals, and Playground is described as fun practice problems. Getting Started competitions do not close, so there is no clock running and no pressure to be clever on a schedule.
The ones beginners usually meet first are:
- Titanic: Machine Learning from Disaster, which Kaggle literally labels “Start here”
- Digit Recognizer, for computer vision basics using the classic handwritten digits dataset
- House Prices, for predicting numbers rather than categories
- Natural Language Processing with Disaster Tweets, if text interests you more than tables
- Spaceship Titanic, a friendlier modern remake of the first one
You write your code in Kaggle’s notebooks in the browser, so there is nothing to install and nothing to break on your own machine. If the machine learning ideas behind these are still fuzzy, the free courses in our roundup of free AI courses from Google, Microsoft and Kaggle line up almost exactly with these starter problems.
Devpost, if you would rather build something than tune a model
Not everyone enjoys squeezing accuracy out of a spreadsheet. Devpost runs public hackathons and app contests, including a steady stream of AI ones, and there you submit a working project instead of a predictions file.
That suits beginners better than it sounds. A small tool that solves one annoying problem, explained clearly, often does better than a technically impressive thing nobody understands. Your entries also stay on your Devpost profile afterwards, which quietly turns into a portfolio without you ever having to build a portfolio website.
MLH, if you are a student
Major League Hacking describes itself as the official collegiate hackathon league, and its season calendar lists student hackathons across North America, Europe and Asia Pacific, including events tagged for high school students and events with a diversity focus.
Most of those are in person, which rules them out for a lot of readers. The part that does not is Global Hack Week, which runs online, is open worldwide, and is themed. Recent editions have been built around AI agents, data and generative AI. You join from home, for free, and you are hacking alongside thousands of other people who are also figuring it out as they go.
Free live events, if a competition still feels too big
If entering anything sounds like too much this month, a gentler middle step is a free live session. IBM runs free online SkillsBuild events on AI topics that you can simply attend. We covered what else is on that platform in our guide to IBM SkillsBuild free AI courses.
How to choose your first AI competition
- Pick a problem you can explain to a friend in one sentence. If you cannot, you will lose interest by week two.
- For your first attempt, choose something with no deadline, or one that already closed. You can still enter old competitions to practise.
- Read three public notebooks or past entries before you write anything yourself.
- Give it one week, not a whole summer.
- Enter alone the first time so you learn the whole process, then find a team.
Important tip: your first goal is a submission, not a score. Submit something bad on day one, confirm it went through, and then improve it. Most beginners who quit never got to the point of submitting anything at all.
Read what you are signing up for
This is the part I care about most, because it comes from working on the security side of websites and online tools. Competition and hackathon sign-ups often ask for a lot: your CV, your university, sometimes identity documents if there is prize money involved. That can be perfectly legitimate, but it is worth reading rather than clicking through.
Two habits will keep you out of trouble. First, remember that public notebooks and public repositories are genuinely public, so never upload employer data, client data, or anything containing other people’s personal details. Work only with the data the organisers give you. Second, check the eligibility rules for age and country before you spend three weeks on a prize competition you were never allowed to win. The same “check the terms first” logic applies to free AI student offers, where the catch is usually in the small print rather than the price.
What to do after it ends
Finishing is not the last step. Write up what you tried in plain English, including what did not work, because that is the part experienced people find convincing. Then put the link somewhere it will be seen. Our guide on how to put AI skills on your CV covers how to phrase it without overselling.
If the code itself was the hardest part, that is useful information rather than a failure. It tells you exactly what to study next, and our list of free AI coding courses for beginners is a reasonable place to go from there. Then enter a second one. The second is always easier than the first.
Common Questions
Do I need to be good at coding to enter an AI competition?You need some, but far less than people assume. Kaggle’s Getting Started competitions come with public notebooks you can read, copy and adapt, and Devpost hackathon teams regularly need people who can design, write, test or present rather than only code.
Are AI competitions for beginners really free to enter?The ones listed here are. Kaggle competitions, Devpost public hackathons and MLH Global Hack Week do not charge an entry fee, and Kaggle even gives you the notebook environment to run your code in. Travel to an in person hackathon is the one real cost, which is exactly why the online options matter.
Do I need a team?Not for Kaggle, where solo entries are normal. Hackathons are usually more fun in a team, and most of them run a channel where people without a team find each other in the first hours. Going in alone is a completely normal way to arrive.
Final takeaway
Courses teach you the vocabulary. Competitions are where you find out whether you can use it, and they leave behind something a stranger can look at. Open the Kaggle Getting Started list this week, pick the one that sounds least intimidating, and aim to make one submission by Sunday. It will not be good, and that is completely fine. It will be yours, and it will be more than a certificate.
by admin | Aug 22, 2026 | Opportunities
You search for free AI tools for students, you find a list promising twelve amazing offers, and by the time you click through half of them are dead links or “no longer available in your region.” It is one of the most frustrating searches on the internet right now, because these deals change faster than anyone updates their blog posts.
So this is a different kind of list. I checked every one of the free AI student offers below on the company’s own page today, and I am telling you plainly which ones are real, which one asks for a payment method even though it says free, and how to spot a data harvesting fake before you type your university email anywhere.
The biggest free AI student offer, and the catch in the small print
Google is currently giving eligible students a free year of its Google AI Pro plan. On the Gemini for Students page it is front and centre: a free student plan for one year, with four times higher usage limits in Gemini, 5 TB of storage, Gemini Spark and access to Gemini Omni.
Here are the parts most articles leave out, all taken from Google’s own FAQ and offer terms on that same page:
- It is for eligible US college students aged 18 and over. Students whose 2025 AI Pro trial already expired can qualify again.
- You have to redeem it by 31 December 2026.
- A valid form of payment is required at sign-up.
- Unless you cancel first, it charges $19.99 a month automatically once the free year ends. You can cancel at any time.
- Eligibility has to be verified again each year to keep access.
None of that makes it a bad deal. A free year of a plan that costs $19.99 a month is a real saving. It just is not the no strings freebie the headlines suggest, and the automatic charge at the end is exactly the kind of thing that catches out someone who signed up in first year and forgot about it.
Important tip: the moment you claim any free year, set a calendar reminder for one week before it expires, not on the day itself. That single reminder is the difference between a free year and a surprise charge.
From years of running websites and online tools, the pattern is always the same. The generous offer is usually genuine. The thing that actually costs people money is forgetting the renewal date.
Free AI student offers that ask for nothing but your student email
Two are worth your time, and both come from companies with no reason to disappear next month.
Azure for Students. Microsoft gives verified full time university students $100 of Azure credit to use within 12 months, with no credit card required. You sign up with your school email and renew each year while you are still enrolled. Alongside the credit you get free monthly amounts of more than 20 popular services for 12 months, and more than 65 services that are always free. If you want to train a small model, host a project or run a database for your dissertation, this is the most practical offer on the list. No credit card matters more than it sounds, because it means you cannot accidentally run up a bill.
GitHub Education. Free if you are at least 13 years old and enrolled on a degree or diploma granting programme, from high school upward. GitHub verifies you either with an academic email address or with documents: a photo of your student ID showing the enrolment date, your class schedule, your transcript, or an enrolment verification letter. That document route is worth knowing about if your institution uses an unusual email domain.
Approved students also get Copilot Student, and it is worth reading GitHub’s own plans page rather than the hype. Copilot Student is free for verified students, but it is its own plan and not Copilot Pro. It comes with an allowance of GitHub AI credits, uses automatic model selection rather than letting you pick premium models, and excludes third party agents.
The free AI you can use without any student email
Most offer lists skip this part, and it is the part that helps the most people, including anyone who is not currently enrolled anywhere.
- Google Colab gives you a notebook in your browser with a hosted runtime, free, with any Google account. Most people learning machine learning start here.
- Kaggle gives free notebooks with a GPU and TPU allowance, plus thousands of public datasets to practise on. Allowances change from time to time, so check your account page for your current limit.
- Google AI Studio has a free tier for testing Gemini models and building small prototypes.
- Hugging Face has a free account tier for hosting models, datasets and demo apps.
If your goal is to learn rather than to collect subscriptions, those four will take you further than any bundle of trial codes. Our guide on how to learn AI for free walks through what to actually do with them.
The offer most students never think to check
Ask your own university. A lot of institutions have quietly bought campus wide AI licences, high performance computing time, or software agreements that students can use for nothing. Nobody advertises this well. It usually sits on an IT services page that turns up on page four of the internal search, or in an email everyone deleted in week one. Two minutes on that page, or one email to the IT helpdesk, beats an hour of hunting for discount codes.
How to check a free AI offer is real before you sign up
- Start on the company’s own domain. Type google.com, microsoft.com or github.com yourself instead of clicking a link in a search result or a forwarded message.
- Read the terms link, not the headline. That is where “payment method required” and “renews at” actually live.
- Never enter your university password on a third party site. Real verification either sends you to your institution’s own login page or asks you to upload a document.
- Be suspicious of any student AI offer that wants your date of birth, home address or bank details up front for something advertised as free.
- If an offer only exists on a blog and not on the company’s site, it has almost certainly expired.
The same instincts that protect you from fake scholarship pages work here, which we covered in how to find genuine scholarships and learning opportunities, and the wider habit sits in how to use AI safely and protect your privacy.
Claiming an offer is the easy part
Free credits expire whether you use them or not. Pick one project you actually want to finish this semester and spend the credit on that instead of spreading it across five half started experiments. And keep it honest with your coursework, which we wrote about in using AI tools without cheating.
Common Questions
Can I still get Google AI Pro free as a student?Yes, as of today. Google’s Gemini for Students page offers eligible US college students aged 18 and over a free one year student plan, redeemable until 31 December 2026. The terms require a valid payment method at sign-up, and the plan charges $19.99 a month after the free year unless you cancel first.
Do I need a .edu or .ac.uk email address?Usually an institution issued email is the fastest route, but it is not always the only one. GitHub, for example, accepts official dated proof of enrolment such as a transcript or an enrolment letter when a school does not issue academic email addresses. Upload the document instead of giving up.
What happens to my work when a student offer expires?Your account normally drops to the free tier rather than being deleted, but paid only features stop working and some services archive inactive projects. Before any offer ends, export your data and check where your files actually live.
Final takeaway
The honest summary for 2026 is that there is still plenty of free AI for students, but the shape of it has changed. The headline offers now tend to arrive with a payment method and a renewal date attached, while the genuinely no strings tools are the ones nobody markets at you: Colab, Kaggle, AI Studio, Hugging Face, Azure student credit and GitHub Education. Claim what you will actually use, put the expiry date in your calendar, and check your own university’s IT page before you spend an evening hunting for codes.
