Changing direction into person to follow is mostly a translation problem: you already have skills, and they need to read as relevant. These 116 free entries help with both halves — the learning and the way you talk about it.
116 free entries on this topic · see all of them · search within the topic
The ones I hand people first. Pick one, finish it, then come back.
Career strategy for landing roles without applying to hundreds of postings.
In plain terms: Follow this profile on LinkedIn for career search strategies. You can use his advice to land jobs without applying to hundreds of postings.
Why I recommend it: Why follow: his networking templates get replies from real hiring managers.
Disability Rights Lawyer, Author. The first Deafblind graduate of Harvard Law School; author of 'Haben: The Deafblind Woman Who Conquered Harvard Law' and a leading voice on accessible technology and inclusive design.
In plain terms: This is the LinkedIn profile of disability rights lawyer and author Haben Girma. Follow her page to learn about accessibility strategies, inclusive technology, and disability advocacy.
Why I recommend it: Her framing of accessibility as innovation, not charity, is worth borrowing.
Analytics and Excel teacher helping professionals build real data skills and use AI to increase their impact at work.
In plain terms: An Excel MVP and author teaching practical analytics skills you can apply at work, including how to pair them with AI tools. Self-paced and beginner-friendly.
From the site: Helping you build real-world analytics skills so you can partner with AI to accelerate your impact at work. Author, LinkedIn Top Voice, Microsoft Excel MVP.
Why I recommend it: A good fit if you want data skills without a bootcamp. His lessons are built around work you actually do in a job.
Steady sources, so you keep learning after this page.
Long-running advice blog and newsletter on workplace questions — offers, bosses, boundaries, and awkward interviews.
In plain terms: This workplace advice website answers real reader questions about job hunting, difficult bosses, interviews, and office dynamics. You can browse practical guidance for tricky career situations or submit your own question for advice.
Why I recommend it: When you are not sure whether something at work is normal, the archive has already answered it.
Short, extremely practical videos on resumes, LinkedIn, AI tools, and workplace communication.
In plain terms: This YouTube channel offers short, practical videos focused on career skills and job searching. You can watch tutorials on writing resumes, optimizing your LinkedIn profile, using AI tools, and communicating at work.
Why I recommend it: Watch the resume bullet formula video before your next application. It takes six minutes.
Weekly newsletter read by recruiters worldwide — where hiring is heading, told to the people doing the hiring.
Why I recommend it: Reading what recruiters read gives you months of advance notice on how hiring is changing.
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.
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.
Visual math explainers that make the ideas behind deep learning intuitive.
Why I recommend it: Great for anyone who was told they were bad at math.
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.
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.
Founder and Executive Director of Baddies in Tech, who built the community and BaddieCon to support Black and brown women in tech.
Former Tinder Latin America CEO and former L'Oréal Brazil Chief Digital Officer, author of "Between You and AI," helping organizations across Latin America and Europe navigate AI adoption.
Deep, from-scratch explanations of neural networks and large language models.
Why I recommend it: Start here if you want to actually understand AI rather than just use it.
Comparing two of these? Put them side by side.
