Yesterday
In this post we will see how to extend reverse mode automatic differentiation to a language with first class function types, function application and lambda-abstraction. This method is not new, but we will give a new derivation of it by showing how it arises universally from noticing that the category of “additive lenses” is cartesian closed. In the end we will see that this idea sounds like it should revolutionise machine learning, but then doesn’t.
Some interesting ideas, although I won’t claim that I understand them all.
RAM, flash memory, and HDDs are unaffordable because of a bunch of greedy idiots that do not love the computer.
Techno-cynics are all just wounded techno-optimists.
2 days ago
the watchers: how openai, the US government, and persona built an identity surveillance machine that files reports on you to the feds
53MB of source code leaked from a government endpoint. 269 verification checks. biometric face databases. SAR filings to FinCEN. and the same company that verifies your ChatGPT account.
4 days ago
5 days ago
AGENTS.md is an emerging standard implemented by non-deterministic tools that don’t have a good track record with consent. We are not naive enough to think it’s air tight, but we hope it helps.
6 days ago
Handy is a cross platform, open-source, speech-to-text application for your computer
A minimal, secure Python interpreter written in Rust for use by AI.
Monty avoids the cost, latency, complexity and general faff of using a full container based sandbox for running LLM generated code.
Because model.fit() isn’t an explanation. Contribute to Mathews-Tom/no-magic development by creating an account on GitHub.