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Micrograd.jl
A series on automatic differentiation in Julia. Part 1 provides an overview and defines explicit chain rules.
New
The latest blog posts to take off on Hacker News. 3,091 posts.
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Anthropic Claude 3.5 can create icalendar files, so I did this
Generate calendar entries from an image using Anthropic Claude 3.5. Convert lesson dates in an image to a calendar ICS file
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Looming Liability Machines (LLMs)
As part of our zoom reading group ( wow, 4.5 years old now ), we discussed a paper that uses LLMs for automatic root cause analysis (RCA) fo...
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Claude's API now supports CORS requests, enabling client-side applications
Anthropic have enabled CORS support for their JSON APIs, which means it’s now possible to call the Claude LLMs directly from a user’s browser. This massively significant new feature is …
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What's Going on in Machine Learning? Some Minimal Models
Stephen Wolfram explores minimal models and their visualizations, aiming to explain the underneath functionality of neural nets and ultimately machine learning.
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I'm tired of fixing customers' AI generated code
(Not an anti-AI post — Copilot is great, I use it all the time.)
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AI companies are pivoting from creating gods to building products
Turning models into products runs into five challenges
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LLMs know more than what they say
... and how that provides winning evals
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Markov chains are funnier than LLMs
Predictability machines and personality
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Are you better than a language model at predicting the next word?
A quiz to see how you compare to language models.
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How to get from high school math to cutting-edge ML/AI
1) Foundational math. 2) Classical machine learning. 3) Deep learning. 4) Cutting-edge machine learning.
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The future of Deep Learning frameworks
Assumed audience: ML researchers who frequently work with PyTorch , but are interested in trying out JAX or have yet to be convinced.
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How I won $2,750 using JavaScript, AI, and a can of WD-40
Possibly the one you're Googling about.
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How I Use "AI"
I don't think that AI models (by which I mean: large language models) are over-hyped. In this post I will list 50 ways I've used them.
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How Does OpenAI Survive?
Throughout the last year I’ve written in detail about the rot in tech — the spuriousness of charlatans looking to accumulate money and power, the desperation of the most powerful executives to maintain control and rapacious growth, and the speciousness of the latest hype cycle — but at the end of
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Calculating the cost of a Google DeepMind paper
Recently, GDM released a great paper titled, Scaling Exponents Across Parameterizations and Optimizers, in which they conduct over 10,000 LLM training runs to obtain optimal hyperparameters under different regimes. After reading it (it was great), I wanted to test my understanding of the paper by tallying up all…
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A Visual Guide to LLM Quantization
Exploring memory-efficient techniques for LLMs
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An Open Course on LLMs, Led by Practitioners
A free survey course on LLMs, taught by practitioners.
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Back to our roots
Matthew Honnibal is a computational linguist from Sydney based in Berlin, Germany. He's the author of the spaCy Natural Language Processing library and the co-founder of Explosion.
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All the existential risk, none of the economic impact
I offer "no economic boost," you receive "P(doom)"
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When ChatGPT summarises, it does nothing of the kind
One of the use cases I thought was reasonable to expect from ChatGPT and Friends (LLMs) was summarising. It turns out I was wrong. What ChatGPT isn’t summarising at all, it only looks like it…
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What happened to BERT and T5?
A Blogpost series about Model Architectures Part 1: What happened to BERT and T5? Thoughts on Transformer Encoders, PrefixLM and Denoising objectives
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Overcoming the limits of current LLMs
Large language models (LLM) have been all the rage for quite some time now. Looking beyond the hype though, they have severe limitations: hallucinations, lack o
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The Engineer’s Guide to Deep Learning: Understanding the Transformer Model
In the previous two golden ages (1950s-1960s and the 1980s), our expectations outpaced the capabilities of the technology at the time, leading to disappointment. In contrast, the AI technology of the current golden age, which began in the mid-2010s, has consistently exceeded our expectations.