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Reinforcement learning, explained with a minimum of math and jargon
To create reliable agents, AI companies had to go beyond predicting the next token.
Topics /
AI & Machine Learning
The most popular blog posts about ai & machine learning on Hacker News. 3,091 posts.
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Meta's Llama 3.1 can recall 42 percent of the first Harry Potter book
New research could have big implications for copyright lawsuits against generative AI.
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Tokenmaxxing is dead, long live tokenmaxxing
Alt title: The reports of tokenmaxxing’s death are greatly exaggerated.
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The AI Unbundling
AI is starting to unbundle the final part of the idea propagation value chain: idea creation and substantiation. The impacts will be far-reaching.
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The behavior of LLMs in hiring decisions: Systemic biases in candidate selection
Hints of discrimination and lack of principled reasoning in frontier AI systems
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Do simpler machine learning models exist and how can we find them?
The above is the title of a talk by computer scientist Cynthia Rudin. here’s the abstract:
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My experience creating software with LLM coding agents – Part 2 (Tips)
moved to https://blog.efitz.net/posts/my-experience-creating-software-with-llm-coding-agents-part-2-tips/
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Agent memory as a file format
Memoryfields - a vastly simpler way to do agent memory
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How well do agents use test/verification techniques?
We previously noted that, while it's easier than ever to hit a particular quality bar by having coding agents use effective test techniques, software quality seems to be getting worse , indicating that whatever defaults developers are using may not work very well. Here, we test if simple instructions to agents to use…
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Visualizing the Math Behind Logistic Regression and Newton's Method
The Laziest Programmer - Because someone else has already solved your problem.
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The secret ingredients of word2vec (2016)
Word2vec is a pervasive tool for learning word embeddings. Its success, however, is mostly due to particular architecture choices. Transferring these choices to traditional distributional methods makes them competitive with popular word embedding methods.
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Mini-R1: Reproduce DeepSeek R1 "Aha Moment"
Reproduce Deepseek R1 „aha moment“ and train an open model using reinforcement learning trying to teach it self-verification and search abilities all on its own to solve the Countdown Game.
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The state of Computer Vision and AI: we are really, really far (2012)
Musings of a Computer Scientist.
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All my clients wanted a carousel, now it's an AI chatbot
Posts about SmolWeb, Gemini protocol and LowTech
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Prompt Engineering: Steer a large pretrained language model to do what you want
Prompt Engineering, also known as In-Context Prompting, refers to methods for how to communicate with LLM to steer its behavior for desired outcomes without updating the model weights. It is an empirical science and the effect of prompt engineering methods can vary a lot among models, thus requiring heavy…
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Interviews in the Age of AI: Ditch Leetcode – Try Code Reviews Instead
In the emerging age of AI generated code, is leetcode really the best way to evaluate technical candidates (was it ever)?
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Parallel coding agents with tmux and Markdown specs
A practical workflow for running 4–8 parallel coding agents using Claude Code, tmux, and a Markdown-based feature design system.
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Lack of intent is what makes reading LLM-generated text exhausting
There’s something icky about LLM-generated text when you think it’s written by a human. I think I finally put my finger on one reason why I feel this way.
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Nvidia DGX Spark: great hardware, early days for the ecosystem
NVIDIA sent me a preview unit of their new DGX Spark desktop “AI supercomputer”. I’ve never had hardware to review before! You can consider this my first ever sponsored post …
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What if AI doesn't need more RAM but better math?
How TurboQuant compresses the KV cache without losing accuracy, and what that could mean for memory stocks
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Building My Own Chess Engine
Exploring the computational complexity of chess. Code snippets in Python so you can do the same.
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About AI Evals
A guide to AI and LLM evals for engineers and product managers. Learn how to test AI systems, analyze failures, and improve retrieval, agents, and AI products.
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Using python and k-means to find dominant colors in images
I'm working on a little photography website for my Dad and thought it would be neat to extract color information from photographs. I tried a couple of different approaches before finding one that works pretty well. This approach uses k-means clustering to cluster the pixels in groups based on their color. The center…
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Deep Reinforcement Learning: Pong from Pixels
Musings of a Computer Scientist.
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Intro to Deep Learning
I just sat down this morning and organized all deep learning related videos I recorded in 2021. I am sure this will be a useful reference for my future…