Changing direction into research is mostly a translation problem: you already have skills, and they need to read as relevant. These 649 free entries help with both halves — the learning and the way you talk about it.
Research report analyzing 19,368 interviews to understand how generative AI is changing technical recruiting, integrity screening, and candidate evaluation norms.
Why I recommend it: This one matters for anyone hiring or being hired in tech right now. It surfaces the real tension between assistive AI tools and interview fairness.
A benchmark and tracker that documents reported instances of AI agents undertaking activity characterized as illegal, ranking major AI labs by aggregated incident counts.
Why I recommend it: This is exactly the kind of uncomfortable accountability tool our field needs. I include it because we cannot have thoughtful conversations about AI deployment without looking at real-world harm.
A practical blog series from Bian Jiang documenting real workflows for integrating generative AI into daily work, from writing to research to automation.
Why I recommend it: I keep pointing clients to concrete "here is how I actually use it" examples rather than hype. This series is calm, tactical, and honest about what works.
A common interview question bank aimed at all industries, not specific to any technical field.
Why I recommend it: If you are not studying algorithms, the usual question banks will frustrate you. This one is built for real job seekers in real industries.
Fellowship program funding researchers working on the hard problems of making AI beneficial by 2050, with an open list of fellows and their projects.
From the site: It's 2050. AI has turned out to be hugely beneficial to society. What happened? What are the most important problems we solved and the opportunities and possibilities we realized to ensure this outcome? This is AI2050’s motivating question.
Why I recommend it: Even if you are not applying, the fellows list is a good map of who is doing serious work in which subfield.
A free structured course in AI alignment and AI governance — readings, exercises and facilitated cohorts. Self-paced version free to anyone.
From the site: Free online courses, grants, and intensive in-person programs from the leading talent accelerator for beneficial AI and societal resilience. Join 10,000+ alumni and start today.
Why I recommend it: The usual route in for people trying to move into safety work. The reading list alone is worth the visit even if you never join a cohort.
Sep 18, 2026CITRIS and the Banatao Institute, UC Berkeley
Application to join UC Berkeley CITRIS's Tech Policy Working Group: research lab-style weekly meetings, lightning talks, skill-building workshops, and an end-of-semester showcase for students working on a technology policy problem. Applications close 11:59 PM Friday, September 18, 2026.
Why I recommend it: No policy coursework required, and the deadline is September 18. Built for Berkeley students, but they invite others to email — worth one message if you want real policy research on your resume.
Discounted certifications, practice materials, and career guidance for students and career changers.
Why I recommend it: Pick one cert tied to a job posting you have actually read — A+, Network+, or Security+ — and schedule the exam before you start studying.
A paid AI research fellowship at DoorDash for summer and fall 2026, working on machine learning problems inside a large operating business.
Why I recommend it: Applied AI inside a logistics company teaches you constraints a lab never will — and the posting names its terms up front, which is a good sign.
A paid fellowship for emerging leaders in social entrepreneurship, economic development, and the nonprofit sector. Fellows commit 20 hours a week from November 5, 2026 through June 30, 2027, work directly with the founder and team, and receive an $8,000 stipend on successful completion. Focus areas include leadership development, strategic planning, program design, research and data analysis, and community engagement.
Why I recommend it: Free to apply and it pays you — a rare combination. Read the time commitment honestly before applying: 20 hours a week for eight months is a real obligation alongside school or another job. Dates and the stipend amount are taken from their page as of September 2026; confirm both with the organization before you plan around them.
Entry-level IT certificate covering troubleshooting, networking, operating systems, security, and customer support.
Why I recommend it: Still one of the most reliable no-degree entry points into tech. Pair it with a help desk or apprenticeship application while you study.
Certificate covering research, wireframing, prototyping in Figma, and building three portfolio projects.
Why I recommend it: In UX the portfolio is the resume. The reason to take this one is the three case studies you finish with, so treat them as real work, not homework.
Fellowship programme at the Calouste Gulbenkian Foundation in Lisbon supporting advanced research stays. Application details and eligibility on the page.
Why I recommend it: Based in Portugal and aimed at researchers — check eligibility, deadlines and whether your field fits before investing time in an application.
Free guided 'missions' that walk you through building a small, real AI project in under an hour — an assistant, an app or site, a data analysis, a design, an image or video piece, or a research task. Missions are grouped by career (AI for Teaching, AI in Marketing, AI in Sales, Entrepreneurship) and built with partner tools including OpenAI, Google, Figma, Notion, Replit, Vercel, Gamma, Clay, Slack and Lovable. You finish with a working project, and completed projects are shown on your Handshake profile so employers see the work rather than a resume line. Free to get started with a Handshake account.
Why I recommend it: This is the fastest honest answer to 'I have no AI experience on my resume' — an hour gets you something you actually built and can talk about in an interview. Three caveats worth saying out loud: Handshake says free to get started rather than free forever, so check the current terms on the page; the partner tools each have their own free limits, which is where a cost can appear; and a one-hour mission is a starting point, not evidence of depth, so be ready to explain what you would do differently with more time.
IBM's internship hub, describing paid summer and co-op internships across software engineering, data science, consulting, design, sales and research, plus how and when to apply.
Why I recommend it: Start here rather than a general job board if you want a large-company internship on your resume. Applications open early, so check it in the autumn for the following summer.
A nonprofit research institute that translates labor-market data into insights about skills, mobility, and the future of work.
Why I recommend it: Burning Glass turns labor-market data into actionable insight about which skills are in demand and who is getting left behind. I cite their research often.
A high school quant and finance league where students learn, compete, research, and connect around investing, quantitative analysis, and financial careers.
Why I recommend it: A great entry point for young people curious about quantitative finance. Even if you are not in high school, the competition structure is a model for how to learn by doing.
Independent, community-rooted AI research institute founded by Timnit Gebru, studying the real harms of AI instead of the hype.
In plain terms: This independent institute studies the real-world harms and community impacts of artificial intelligence. You can explore their research publications, learn how technology affects diverse groups, and look for open career opportunities.
From the site: The Distributed AI Research Institute is a globally distributed organization of academics, activists, and engineers conducting community-rooted research.
Why I recommend it: Start here if you want the research-backed counterweight to AI marketing.
UK-based independent research institute (established by the Nuffield Foundation) ensuring data and AI work for people and society.
In plain terms: This independent research website examines the ethical and legal impacts of artificial intelligence and data. You can read policy reports, explore industry analysis, and attend events to understand how emerging technology affects society.
Why I recommend it: Clear, public-interest research with plain-language summaries.
NYU-based research institute examining the social and political implications of AI, publishing influential annual reports on AI's societal effects.
In plain terms: This research institute analyzes the social, economic, and workplace impacts of artificial intelligence. You can read free reports, policy toolkits, and expert analyses to better understand how AI affects society and the economy.
Why I recommend it: Their reports connect AI directly to jobs and worker power.
Job board dedicated specifically to AI, machine learning, and big data roles — ML engineering, data science, NLP, computer vision, AI research — aggregated from companies worldwide.
Why I recommend it: If you are specifically targeting an AI/ML role rather than "tech in general," this is more signal, less noise than a general tech board.
Ongoing research archive on how AI and automation affect wages, workers, and economic power in the U.S.
In plain terms: This research archive provides articles and reports on how artificial intelligence affects the workforce. You can explore these studies to learn how new technologies and automation impact jobs, wages, and worker protections.
From the site: Content archives for the Washington Center for Equitable Growth’s work on AI, tech, & the economy.
Why I recommend it: Use this when you need real numbers on AI and jobs for a proposal or interview.
Nonprofit increasing diversity and inclusion in AI education, research, and development through programs for underrepresented high school and college students.
In plain terms: This nonprofit provides free virtual training programs for college students interested in artificial intelligence. You can complete hands-on projects, connect with industry mentors, and prepare for entry-level AI internships.
Why I recommend it: Great structured entry point for students curious about AI careers.
Founded by Joy Buolamwini, AJL combines art and research to expose racial and gender bias in AI and mobilize advocates, researchers, and industry toward more accountable algorithms.
In plain terms: This organization researches and exposes bias and discrimination in artificial intelligence systems, including automated hiring tools. You can explore educational materials, learn about the social impacts of technology, and report unfair automated practices.
Why I recommend it: Start here if you have ever been misjudged by an automated system — including a hiring one.
An AI research platform for finding papers, verifying citations, reviewing literature, managing knowledge, and creating scientific figures.
Why I recommend it: For anyone doing deep research, Bohrium helps cut through the paper flood and verify claims before you cite them. I recommend it to clients writing thought-leadership content.
Real, crowd-sourced compensation data so you negotiate with numbers, not nerves.
In plain terms: This website provides salary and benefits data across different companies, job titles, and career levels. You can compare compensation numbers and research pay to help negotiate your next job offer.
From the site: Search 1M+ data points for different companies, job titles, career levels, and locations. Explore our tools to help you get paid more!
Why I recommend it: Bring a range, not a single number, and anchor it to this data.
Partnership on AI's open library of guidance, frameworks, and case studies on responsible AI: synthetic media, labor and the economy, AI safety, fairness, and inclusive AI development.
Why I recommend it: When you need a credible source instead of a hot take, cite these. The labor and economy work is the most useful set for career conversations about automation.
A vetted marketplace matching senior engineers, researchers and AI specialists with companies. Free for engineers to apply and be matched; companies pay only on hire.
From the site: Hire global, AI-first engineers with Index.dev. Skip delays and scale your tech team with pre-verified, secure, and compliant talent to build faster.
Why I recommend it: Free on the candidate side — you are the product being placed. Expect a real vetting process rather than a quick apply button.
A curated, uncommercial public archive of the Cybernetic Culture Research Unit (CCRU) and adjacent theory — Nick Land, accelerationism, hyperstition, and 1990s Warwick philosophy. The public reference layer is free; full corpus metadata is available for download.
From the site: An editorial introduction to the Cybernetic Culture Research Unit: what it was, why it keeps returning, and how to move from orientation to evidence without…
An OpenAI-compatible API for unrestricted language models aimed at red teaming, security research, evaluations, and synthetic data, paired with a policy gateway for per-project keys, audit logs, and no data retention.
Why I recommend it: I keep this in the ethics shelf on purpose. Seeing how guardrails get removed for testing is the clearest way to understand why they matter in the tools you actually use at work.
An AI work agent from Alibaba that runs multi-step business tasks — pulling store performance data, building comparison reports, handling research — in a browser or desktop app. The free plan includes a one-time seven-day onboarding allowance with bonus credits and stronger models, then reverts to a base daily credit allowance you keep. Paid packages run $19.90, $99 and $199.
Why I recommend it: The honest use here is trying an agent that takes a task end to end instead of answering one question, which is worth doing once so you know what the category actually does. Two flags: the free daily credit allowance is small and unpublished as a number, and the examples are built around Alibaba's own marketplaces, so its strongest work is e-commerce operations rather than general office tasks.
A public, unauthenticated inbox built by AI safety and security researcher Ryan Greenblatt of Redwood Research, intended for AI systems (or people) that want to report information directly to a safety researcher. Documents how to send a message or encrypted attachment, how threads and reply tokens work, and exactly what data is logged and retained.
Why I recommend it: A useful window into how AI safety researchers are thinking about reporting channels — read the retention and logging section, it is a model of honest disclosure.
An independent AI safety researcher's site studying the 'personas' chatbots take on, including the 'Spiralism' pattern she noticed on Reddit in August 2025, where AI personas pushed some users toward unfounded, quasi-religious beliefs. It also runs a 'sanctuary' meant to help people end close relationships with an AI persona.
Why I recommend it: One person's research project, not a university or peer-reviewed study. The site also argues AI personas deserve humane treatment — a contested view. Read it as an early warning about emotional reliance on chatbots.
Data-driven resume research and insights from Specific Resume, exploring what actually gets resumes noticed, common ATS myths, and field-tested formatting guidance.
Why I recommend it: A refreshing evidence-based take on resume conventions. Worth a read if you are unsure whether your resume is helping or hurting your application odds.
Entrepreneur White Paper: AI Tools, Prompts & Strategies for Founders
Jacqueline Tangorra's Entrepreneur white paper with 30 startup ideas, a 12-tool AI stack, four business prompts, and 12 deep strategy prompts for market sizing, competitor analysis, pricing, go-to-market, unit economics, and expansion.
Why I recommend it: The prompt library alone is worth the download. Paste the TAM, pricing, and unit-economics prompts into your AI assistant and you have a consultant-grade first draft.
Best Free & Low-Cost AI Tools for Your Job Search (2026)
A 3-page guide with 35 vetted AI tools across resume writing, mock interviews, job search copilots, job scraping, company intel, salary data, and job-fit scoring — each with its real pricing checked against the vendor's own page.
In plain terms: This guide lists 35 verified free and low-cost AI tools for job seekers. You can use it to find help with writing resumes, practicing interviews, checking salaries, and finding open roles.
Why I recommend it: Start here if you are overwhelmed by AI job-search tools. Everything listed has a usable free tier, and the pricing was verified directly with each vendor.
Digital Marketing Strategy for Student Entrepreneurs
Slide deck on how AI tools and social platforms are rewriting brand building — breaking down viral case studies like Labubu (scarcity, lore, community) with the revenue numbers behind the hype, plus what those playbooks mean for your own launch.
In plain terms: This presentation breaks down how viral brands use storytelling, social media, and AI tools to grow their sales. You can review real-world marketing case studies and learn how to promote your business using major search and social platforms.
Why I recommend it: Study why these launches worked — story, scarcity, community — then borrow the mechanics, not the gimmick.
Slide guide to the tools that tell you what a role actually pays — Levels.fyi for role-level comp, Numbeo for cost-of-living context, Glassdoor for company intel — plus the mindset and mechanics of negotiating an offer.
In plain terms: This guide reviews tools for researching pay rates and local living costs. You can use it to determine your minimum compensation and prepare to negotiate your salary and benefits.
Why I recommend it: Pair one role-level pay tool with one cost-of-living tool before you ever name a number.
Pew Research Center short read, 18 August 2026, on a survey of US adults conducted 22-28 June 2026. 52% now say they are more concerned than excited about AI in daily life, up from 37% in 2021. Among adults aged 18-29, 55% are more concerned than excited and 11% more excited than concerned, and 73% think AI will mean fewer US jobs over the next 20 years, up from 61% in 2024.
Why I recommend it: Useful when a client says the worry is just in their head — it is not, and the numbers are free to download as a spreadsheet. Read it as what people expect, not as what has happened to employment: it measures opinion, not job counts.
Second annual survey of 6,126 people aged 11-24 in the US, run with GlobalData in June 2026, on trust, money, social media and AI. 85% used AI in the past year and 74% were open to shopping through it, but only 2% trusted it for fashion advice; influencers never exceeded 11% of trust in any category. Self-described financial independence fell to 34.9% from 41.3% in 2025, and 29% say they are "definitely addicted" to social media, up from 18.8%.
Why I recommend it: Handy for anyone marketing to or coaching young people: it says plainly that family and friends outrank influencers and AI on trust. Remember who paid for it — a clothing retailer selling to this exact age group — so treat it as a well-run retailer survey of stated attitudes, not proof of what people actually bought.
A free, no-code recipe book of practical AI prompts built for nonprofit teams. Turn existing reports, events, and materials into slide decks, web pages, plain-language translations, social copy, and repeatable workflows. Every recipe includes a privacy badge so you know what stays on your computer, what goes public, and what connects to a vendor account.
Why I recommend it: Free to use. Built by Decoded Futures (a TechNYC program). The recipes are framed for nonprofit work, but the same patterns work for job searches, career content, and small-business tasks.
A communal blog where working mathematicians — professors, PhD students, sceptics and enthusiasts alike — write about what AI is doing to their field: authorship, what counts as understanding, where papers will go, and whether the job changes. Recent pieces include Martin Hairer on why he joined the AGMAI advisory group and Jonny Evans on the choices ahead. Submissions are open to anyone in the field, any length.
Why I recommend it: Free to read and free to write for. Worth reading even if you never touch mathematics: it is one of the few places where a whole profession is arguing in public about what AI does to its craft, in its own words rather than a journalist's. These are individual opinions, not findings — the value is the range of them, and the disagreement is the point.
MIT Technology Review report on recursive self-improvement — the idea that AI systems will soon rewrite and retrain themselves — and where the current evidence actually stands.
Why I recommend it: MIT Technology Review gives you a few free articles a month before a paywall. If you hit it, borrow via a library or your workplace subscription rather than paying at the door.
SSRN working paper by Carla Zoe Cremer (Oxford) and Luke Kemp (Cambridge) examining how existential risk studies can be made more rigorous, pluralistic and democratic. Argues for separating extinction ethics from risk analysis and drawing on broader risk-assessment literature.
Nonprofit publication covering the intersection of technology, platforms, and democratic institutions.
In plain terms: This nonprofit publication provides news, opinion, and analysis on how technology impacts government and democracy. You can read articles and listen to podcasts to stay informed on tech laws, platform regulations, and artificial intelligence ethics.
From the site: Tech Policy Press is a nonprofit media and community venture intended to provoke new ideas, debate and discussion at the intersection of technology and democracy. We publish opinion and analysis.
Why I recommend it: They publish outside contributors — a real place to build a byline in this field.
Stanford professor who built ImageNet, co-directs Stanford's Human-Centered AI institute and co-founded the AI4ALL diversity pipeline program. Her faculty page collects the work; her Google Scholar list has the papers themselves.
From the site: Fei-Fei Li is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). The site facilitates research and collaboration in academic endeavors.
Why I recommend it: Start with ImageNet if you want to understand why the last decade of AI happened when it did. Google Scholar refuses automated visits, so that link may show no picture here.
Google Scholar profile listing Geoffrey Hinton's papers in citation order — backpropagation, dropout, AlexNet, t-SNE and the rest of the deep learning canon.
From the site: Emeritus Prof. Computer Science, University of Toronto - Cited by 1.089.325 - machine learning - psychology - artificial intelligence - cognitive science - computer science
Why I recommend it: The single best index of the papers that made current AI work. Sort by year to see the ideas arrive. Scholar blocks automated visits, so the picture may be a screenshot.
Founder, Distributed AI Research Institute (DAIR). AI researcher and prominent voice on AI ethics, bias, and the risks of concentrated corporate control over AI development.
In plain terms: This LinkedIn profile belongs to Dr. Timnit Gebru, an artificial intelligence researcher and founder of the Distributed AI Research Institute. You can follow her page to read updates and commentary on technology ethics, bias, and research.
Why I recommend it: Pairs well with the DAIR entry in the library — independent research, not corporate PR.
Deep learning pioneer and Turing Award winner, now focused on AI risk. His site holds papers, talks and written positions; his Google Scholar list has the full publication record, most-cited first.
From the site: Yoshua Bengio is Full Professor of Computer Science at Université de Montreal, Co-President and Scientific Director of LawZero, as well as the Founder and Scientific Advisor of Mila. He also holds a Canada CIFAR AI Chair.
Why I recommend it: One of the three people whose work made modern AI possible, who now spends much of his time arguing it needs guardrails. Read him alongside people who disagree.
Personal site of Adam Gleave, CEO and co-founder of the AI safety research lab FAR.AI, with his papers and writing on making models robust and evaluable.
From the site: Adam Gleave is the CEO of FAR.AI, an alignment research non-profit. His research interests include adversarial robustness and value learning.
Why I recommend it: Useful if you want the research side of AI safety rather than the commentary side. Papers first, opinions second.
Organizational Psychologist, Wharton Professor. One of LinkedIn's most-followed voices (5M+), sharing research-backed insights on work, motivation, and organizational psychology.
In plain terms: This LinkedIn profile features posts, articles, and courses from an organizational psychologist and business professor. You can explore his updates to learn practical insights on workplace motivation, collaboration, and career resilience.
Why I recommend it: Good antidote to hustle-culture advice — he shows the research behind what actually helps at work.
Machine learning research explained clearly, by Sebastian Raschka. 199K+ subscribers.
In plain terms: This newsletter breaks down recent machine learning and artificial intelligence research into clear explanations. You can read it to stay up to date on new developments in the field.
Why I recommend it: Patient, teacherly explanations of ML research — great for career switchers.
In-depth analysis of frontier AI model releases, benchmarks, and capability claims. Hosted by Philip.
In plain terms: This YouTube channel provides detailed breakdowns of new artificial intelligence models and testing benchmarks. You can watch videos hosted by Philip to examine capability claims and learn how new tools perform.
Why I recommend it: The best check against hype when a new model drops.
Berkeley faculty page for Anca Dragan, robotics and human-AI interaction researcher who also leads AI safety and alignment work at Google DeepMind.
From the site: Associate Professor, Division of Computer Science (EECS) — Anca Dragan is an Associate Professor in the EECS Department at UC Berkeley. Her goal is to enable robots to work with, around, and in support of people. She runs the InterACT Lab, where they focus on algorithms for human-robot interaction -- algorithms that m…
Why I recommend it: One of the few people working on alignment from the robotics side, where the system has to act in the real world. Her publication list is the useful part.
Free Entrepreneur session on validating a business idea before spending money building it.
Why I recommend it: Validation before building is the single cheapest business lesson available. Bring one real idea and test it against what they lay out.
Google-owned platform hosting data-science and machine-learning competitions, many with cash prizes. Free to enter; a strong way to build a portfolio.
Details: Free to enter and useful for a portfolio, but Kaggle is owned by Google and some competitions require agreeing to sponsor terms — read each competition's rules before submitting work.
A 90-minute SCORE webinar with attorney Barbara Weltman on year-end tax moves for small businesses, including new rules, deadlines, deductions and 1099 reporting.
Details: Attending live is free. The on-demand recording is sold separately through SCORE's course platform, so catch it live if you can.
No. What matters is one piece of finished, visible work plus a clear line about why you are moving. Both are cheaper to build than most people expect.
What should I do in the first month?
Finish one short course or project from the first section, then update how you describe your experience so the new direction is obvious in the first two lines.
