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Topics /
AI & Machine Learning
The most popular blog posts about ai & machine learning on Hacker News. 6,328 posts.
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The real data wall is billions of years of evolution
Careful with those human analogies
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Hetzner is working on LLM Inference
Hetzner has launched an experimental LLM inference API. I tested its Qwen model—and have a few guesses about where the product could go next.
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New serious vulnerabilities spiked around release of Claude Mythos Preview
Notable organizations disclosed ~1,300 high- and critical-severity CVEs in June 2026, roughly 3.5× the pre-Mythos monthly record. Epoch AI's breakdown of vulnerability disclosures following Anthropic's Project Glasswing.
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How is Groq raising more money?
Somehow, Palpatine returned.
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Xanadu was waiting for agents
From the Zed Blog: Ted Nelson specified the docuverse sixty years ago. The technology finally arrived, and so did its native users.
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Logistic regression from scratch
Deep dives into Cryptography and Privacy.
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Things I've learned in my 7 years implementing AI
Even though the impacts of LLMs have never been seen before, they feel familiar to earlier assumptions.
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Gemini API File Search is now multimodal
Updates to the Gemini API File Search tool makes building efficient, multimodal file retrieval systems easier for developers.
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Isolating Football Player Movement by Eliminating Camera Motion
Note: This post departs from the general format used on this blog. That's because the post is not about a specific analysis I've done bu...
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Deep Learning: Our Miraculous Year 1990-1991
Many of the basic ideas behind the Deep Learning Revolution were published three decades ago within fewer than 12 months in our "Annus Mirabilis" 1990-91
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Cuda.jl v3.3: union types, debug info, graph APIs
There have been several releases of CUDA.jl in the past couple of months, with many bugfixes and many exciting new features to improve GPU programming in Julia: CuArray now supports isbits Unions, CUDA.jl can emit debug info for use with NVIDIA tools, and changes to the compiler make it even easier to use the latest…
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Biohub releases a world model of protein biology
Open AI models used for a wide range of scientific applications, including accelerating the design of therapeutic molecules.
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How to scale LLMs better with an alternative to transformers
Monarch Mixer: Revisiting BERT, Without Attention or MLPs
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Using LLMs to Generate Fuzzers
LLMs seem surprisingly good at many things. So much so that not a week goes by without someone coming up with yet another use-case for this technology, often to solve tasks quickly that traditionally took a non-trivial amount of human work to complete. Today’s example was Brendan Dolan-Gavitt’s remarkable experiment…
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Reverse Engineering Google Colab
Take a peek into the internals of Google Colab, and discover how we can bend the rules of Colab a little in order to adapt it to our MLOps workflow
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Voxtral – Frontier open source speech understanding models
The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models.
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The Three Cultures of Machine Learning
Everyone should read Leo Breiman's 2001 article, Statistical Modeling: The Two Cultures . (Summary: Traditional statisticians start with a distribution. They try to identify the parameters of the distribution from data that were actually generated from it. Applied statisticians start with data. They have no idea where…
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Zi2zi: Master Chinese Calligraphy with Conditional Adversarial Networks
zi2zi is the follow-up work for my last project , once again tackling the same problem of style transfer between Chinese fonts.
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Building a real-time AI tutor for 5-year-olds
We set out to build the first AI tutor to teach math and reading to kids ages 4-9. For AI to actually teach a five-year-old, pedagogy must be baked into the engineering. A child can't wait for a slow reply, can't read a chat interface, and can't unhear anything a model gets wrong. We wanted to share some of the…
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Being “Confidently Wrong” is holding AI back
The failure mode that stalls “AI for data” efforts or "AI on my APIs" efforts isn’t psychedelic hallucination—it’s confident inaccuracy: plausible answers that are wrong in subtle and costly ways.
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Spending Too Much Money on a Coding Agent
On making use of large thinking models.
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Devin is now generally available
The work Devin has been doing and how your team can start using Devin today.
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A Step Behind the Bleeding Edge: A Philosophy on AI in Dev
This is a memo I published internally to my team at Monarch. I’m sharing it more publicly in case it helps other software engineering teams that are managing the crazy times we’re exper…
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