AI, Machine Learning & Data: what Hacker News watches
9 topics, 503 videos. Pick a topic for a focused list, or scan the 100 best-scored across the whole area below.
- HN treats it as the best starting point for really understanding backprop and neural nets, because Karpathy explains the why with intuition and builds everything in code.
- HN treats it as the standard first stop for learning neural networks, praised for making the math intuitive.
- Google DeepMind documentary recommended in 10 different months by 10 HN users, 2020–2025HN commenters call it a moving, well-made human story about AI and Go that works for non-technical viewers and does not date.
- HN treats it as the go-to visual introduction to transformers, and many say it finally made attention and embeddings click.
- HN considers it a classic, hilarious and sobering talk on ML inscrutability and tech hubris, often rewatched.
- Recommended as the rigorous, math-heavy original course, deeper than the Coursera version, for learning machine learning.
- The go-to accessible introduction to neuroevolution and NEAT, praised for crediting prior research.
- HN's most frequent recommendation for a clear, approachable foundation in how LLMs work without heavy math.
- Loved on HN as a hilarious, brilliant example of hacker-academic work and cited whenever game-playing AI or fuzzing comes up.
- Google DeepMind course recommended in 9 different months by 9 HN users, 2017–2026Frequently recommended as an excellent, clear introductory RL course from the lead of AlphaGo, pairing well with Sutton and Barto.
- It is the go-to recommendation for learning how transformers and LLMs actually work, praised for its clarity and Karpathy's teaching.
- HN commenters recommended it for years as a simple, exciting overview of the deep learning wave.
- Repeatedly named the best practical tutorial for using LLMs, with a well-liked fine-tuning section.
- Matroid talk recommended in 6 different months by 4 HN users, 2020–2022HN repeatedly points to it as the best public technical overview of Tesla's FSD approach, used both to defend and to critique Tesla's progress.
- HN recommends it as the dense, approachable starting point for understanding LLMs and often cites it for specific claims about scaling and training.
- Praised as a long but rewarding conversation, with commenters especially liking Bach's emphasis on modelling as a skill and his bridge between mind and computation.
- Repeatedly called an excellent, practical introduction that covers a lot in two hours and explains issues academic textbooks skip.
- Recommended as a simple, elegant explanation of dropout and its connection to how the brain works.
- The go-to link in any anti-cheat thread for a concrete, well-delivered account of machine-learning cheat detection in production.
- Machine Learning Street Talk interview recommended in 5 different months by 4 HN users, 2021–2024Recommended to people who find it inspiring or perspective-changing on pursuing goals, open-ended search and AI beyond optimization.
- caltech course recommended in 5 different months by 5 HN users, 2016–2023Recommended repeatedly in 'where do I start with ML' threads as a clear, math-aware introduction.
- Treated as the accessible reference on analog computing for neural nets, especially the Mythic AI segment.
- Tom7 is an HN favorite, and this video is praised as hilarious, educational silly ML that keeps getting better.
- Recommended as the best deep dive on tokenizers, worth the two hours for anyone working with LLMs.
- HN commenters recommend it as an approachable first step into machine learning for programmers, with an apples-vs-oranges classifier.
- HN treats it as the go-to visual explanation of attention, recommended to people who find text-based transformer explainers hard to follow.
- max4warn talk recommended in 4 different months by 5 HN users, 2018–2026HN points to it as the definitive example of server-side, behavioral, non-invasive anti-cheat using machine learning, often as a counterpoint to kernel-level anti-cheat.
- Seattle Applied Deep Learning lecture recommended in 4 different months by 4 HN users, 2020–2023Recommended as a clear explanation of why RNNs fall short and how transformers solve it.
- Commenters recommend it as an entertaining, wide-ranging interview, and cite it for Hotz's views on end-to-end neural-net driving.
- HN recommends it as an accessible, clear walkthrough of the transformer paper for people with some AI background.
- Cited as the more accessible companion to the paper, and as the most interesting treatment of AD via compiling to categories.
- Recommended as the most accessible introduction to backprop for people who struggle with the pure math.
- Seen as the clearest introduction to the idea behind RLHF, and also borrowed by commenters as a metaphor for human motivation.
- ObservantVids talk recommended in 3 different months by 3 HN users, 2018–2025HN points to it as the sharpest, most-quoted critique of deep learning practice, and cites it whenever ML reliability or understanding is debated.
- sentdex demo recommended in 4 different months by 3 HN users, 2021–2025Cited as an early, striking demo of neural networks acting as game engines and world models.
- MITCBMM talk recommended in 3 different months by 3 HN users, 2016–2026HN recommends it as a clear argument for why deep and cheap learning succeeds, often alongside the associated paper.
- Elizr demo recommended in 3 different months by 2 HN users, 2014–2016HN commenters use it as the canonical demo of deep reinforcement learning, noting that watching it teaches more than an article does.
- HN recommends the series as a very accessible way to understand the math behind machine learning and LLMs.
- HN readers recommend it as an intuitive explanation of neural networks that shifts how people think about AI and language.
- HN commenters point to it as a candid, contrarian view of self-driving approaches and the real limits of the business.
- Stanford University School of Engineering lecture recommended in 3 different months by 3 HN users, 2017–2018Recommended as one of the best free courses for learning modern deep learning for NLP.
- Recommended as a fun, semi-technical intro to machine learning that holds kids' attention.
- Recommended as the clearest overview of how LLM assistants are built and how to use them well.
- HN commenters point beginners to this series as one of the clearest short walkthroughs of how neural networks and backpropagation work.
- Cited for Karpathy's first-hand explanations of camera-only self-driving, transformers and ML trends.
- Sasha Rush lecture recommended in 3 different months by 3 HN users, 2024HN commenters recommend it for building intuition about state space models and the tradeoffs between transformer and SSM layers.
- WAD at CVPR talk recommended in 3 different months by 4 HN users, 2021–2023HN commenters cite it in Tesla threads as the primary-source explanation of why Tesla removed radar and how its vision depth estimation works.
- GoogleDeveloperDay talk recommended in 3 different months by 3 HN users, 2007–2009HN recommends it as the clear explanation of why Google's statistical, data-first approach works.
- UBC Computer Science lecture recommended in 3 different months by 3 HN users, 2012–2026Recommended as a favorite tech talk showing how huge data availability changes how problems are approached.
- Jakob Foerster lecture recommended in 3 different months by 3 HN users, 2018–2024Recommended as an enjoyable, clear introduction to information theory that pairs well with MacKay's free textbook.
- Robin Baumgarten demo recommended in 3 different months by 3 HN users, 2009–2012HN points to it as a vivid demo that plain A* search can beat fancier machine-learning approaches in game AI.
- The Research and Applied AI Summit - RAAIS talk recommended in 3 different months by 3 HN users, 2017–2018HN points to it as a technical talk from a chip designer on why AI hardware should put memory close to compute and use async data transfers.
- Pascal Poupart lecture recommended in 3 different months by 3 HN users, 2023HN recommends it to newcomers as a clear explanation of attention, including the query/key/value intuition.
- HN recommends it as a long but worthwhile way to demystify how language models work technically.
- Cited both as a primary source on Tesla's technical autonomy approach and, later, for Musk's unmet robotaxi predictions.
- Cited in floating-point threads as the main introduction to posits and unums as an alternative to IEEE floats.
- Joscha Bach talk recommended in 3 different months by 3 HN users, 2016–2019HN recommends it as an inspiring summary of a computational model of mind and a first lecture in a series on the brain as a computer.
- Graphics in 5 Minutes explainer recommended in 2 different months by 3 HN users, 2023–2024HN recommends it as a quick, non-dumbed-down way to get up to speed on how LLMs are built.
- Pointed to as an accessible early explanation of transformers and attention for newcomers.
- HN people send others here to hear Hawkins explain his theory in his own words, including reference frames and the old versus new brain.
- HN considers the course one of the best hands-on ways into deep learning, praising the code-first teaching style.
- Pointed to during self-driving news threads as an insider view of Waymo's progress, deep learning's role and its limits.
- HN uses it as primary-source evidence on how Tesla's vision-based driving stack and training pipeline work.
- HN points to it as an accessible explainer of AlphaFold and as an example of narrow, specialized ML delivering real scientific breakthroughs.
- agadmator's Chess Channel explainer recommended in 2 different months by 2 HN users, 2019–2025HN recommends it as a striking, well-explained example of AlphaZero's play and of zugzwang.
- hotchipsvideos talk recommended in 2 different months by 2 HN users, 2023–2025HN points to it for an insider view of why NVIDIA's AI gains came from design rather than process, and for data-movement power costs.
- Stanford Graduate School of Business talk recommended in 2 different months by 2 HN users, 2017–2019Recommended as a broad, practical view of AI's near-term impact from a leading practitioner, with his argument that worrying about evil AI is premature.
- Recommended as a well-made, digestible introduction to scaling laws and why models cluster along a similar efficiency frontier.
- MediaArtTube demo recommended in 2 different months by 2 HN users, 2014–2022HN treats it as the classic reference for evolutionary simulation and genetic programming, an inspiration for many projects, with notes on evolution exploiting simulation bugs.
- HN points to it as a source for Norvig's 'unreasonable effectiveness of data' argument and for his views on image search and the semantic web.
- HN mentions it as a fun and slightly worrying example of AI systems behaving in unintended ways when optimizing.
- ISL and Collaborators demo recommended in 2 different months by 2 HN users, 2021–2025Used as a go-to example of neural networks enhancing game rendering, referenced in later AI-graphics discussions.
- Arts & Science - University of Toronto interview recommended in 2 different months by 2 HN users, 2024HN recommends it for Hinton's research history and the two pioneers reflecting on a decade of progress.
- HN cites it as a fun, tangible way to teach machine learning, including to kids.
- Cited as a watchable early introduction to deep learning from its pioneer, and remembered as a mind-blowing moment in the field.
- Cited as an accessible explanation of AI denoising, which underpins real-time ray tracing on RTX hardware.
- CraftHub Events talk recommended in 2 different months by 2 HN users, 2019–2021HN cites it in algorithmic-bias threads for the impossibility result that an algorithm cannot be procedurally fair, group fair and maximally useful at once.
- Recommended in Elixir and machine learning threads as the introduction to what Nx makes possible.
- UC Berkeley EECS talk recommended in 2 different months by 2 HN users, 2023Recommended in LLM threads as an authoritative explanation from a ChatGPT architect of why models hallucinate and why imitation is not enough.
- Paul G. Allen School lecture recommended in 2 different months by 3 HN users, 2023–2024HN commenters cite it for NVIDIA's perspective on AI hardware efficiency, including logarithmic number formats and energy use.
- CBS Mornings interview recommended in 2 different months by 3 HN users, 2023Recommended as a more nuanced look at Hinton's views than headlines, especially his argument that LLMs are more than auto-complete.
- Alex Kantrowitz podcast episode recommended in 2 different months by 2 HN users, 2025–2026Pointed to as LeCun's argument for the limits of LLMs and the need for world models.
- trwappers lecture recommended in 2 different months by 2 HN users, 2017–2018Recommended as Hinton's own explanation of capsule networks and his critique of pooling in CNNs.
- Recommended as an accessible, visual way to understand backpropagation and neural networks.
- Stanford University School of Engineering lecture recommended in 2 different months by 2 HN users, 2018–2025HN recommends the CS231n lectures as a solid, applied deep learning course for self-study.
- HN commenters point to it as an interesting conversation with the creator of Swift and LLVM, especially for compilers and ML compilation.
- bilkable documentary recommended in 2 different months by 2 HN users, 2016–2021Recommended as a historical look at the AI winter debate and a demolition of the expert systems hype.
- Yosh explainer recommended in 1 different month by 1 HN user, 2024–2025Recommended as an approachable explanation of how reinforcement learning works in practice, and cited for the constraints needed to stop exploits.
- HN people point to it as a clear, skeptical take on AI hype and the risks of unreliable AI classification, such as in mushroom foraging books.
- HN lists it as a free, brilliant introduction to deep learning and computer vision, often within ML self-study plans.
- Zdenek Kalal demo recommended in 1 different month by 1 HN user, 2011–2023HN found it a nifty demo and cites it as a primitive but effective learning tracker that predates modern deep-learning trackers.
- Recommended as Hawkins' own accessible statement of his brain-inspired theory of intelligence and its software implementation.
- GothamChess commentary recommended in 1 different month by 1 HN user, 2021–2022HN points to it as an entertaining analysis of AlphaZero's play in threads about chess engines.
- Matroid talk recommended in 1 different month by 1 HN user, 2020Cited in hardware threads as a Keller talk on the different ways of doing parallel processing.
- Cited as a fun, unconventional example of neural networks built from floating point error.
- Jason Yosinski demo recommended in 1 different month by 1 HN user, 2016–2025HN points to it as an intuitive way to see what hidden layers of a convnet learn, and as a reference for hierarchical feature learning.
- HN cites it as an early impressive real-time speech translation demo, often in discussions of voice-preserving translation and later AI demos.
- HN people point to it as the canonical explanation from the method's author whenever t-SNE comes up.
- Roger Luedecke demo recommended in 1 different month by 1 HN user, 2016–2017Cited as a vivid example of what Deep Dream and feature visualization look like, resembling psychedelic visuals.
- 3kliksphilip explainer recommended in 1 different month by 1 HN user, 2019–2023HN cites it as a source for how Valve uses machine learning on player behavior to detect cheating.
Showing the 100 best-scored of 503. Use search to find the rest.