Oliver Maclaren

@omaclaren.bsky.social

I use mathematics, computation, statistics, & machine learning to help think about biology, engineering, & other things. University of Auckland, NZ. Research: http://tinyurl.com/ojmscholar, Teaching: https://tinyurl.com/ojmteaching

quick testing, but Mistral free tier actually seems like a good option for teaching a module on llm agents? quick api set up, medium 3.5 seems capable enough, from what I can tell rate limits are decent??

mature things tend to get pressured into being less weird over time, so if you're starting from scratch your advantage is that you can be very weird if you don't start weird you're probably trying to compete inside the local maximum of something more boring than you could be

pi + codex + zed is the most peaceful workflow for me at the moment. Codex is less sneaky and makes less dumb mistakes than claude, pi lets you add your own little simple workflow tweaks and tools easily, and zed is nice and simple and fast for actually reading code

What's the best 'agentic CLI' tool that is easy to set up with a free tier (no cc details or initial credit req.) for students to use in a university course? Main path I can think of is github copilot student developer pack + e.g. opencode (also just agent mode in vscode...)?

Olmo 3 is notable as a "fully open" LLM - all of the training data is published, plus complete details on how the training process was run. I tried out the 32B thinking model and the 7B instruct models, + thoughts on why transparent training data is so important simonwillison.net/2025/Nov/22/...

Olmo 3 is a fully open LLM

Olmo is the LLM series from Ai2—the Allen institute for AI. Unlike most open weight models these are notable for including the full training data, training process and checkpoints along …

simonwillison.net

Can we infer chemical reaction networks from time series data? Can we observe concentrations of substances over time and infer which substances react with each other? Learn if (and how much) this is possible from Yong See Foo, @adrianazanca.bsky.social and JenniferFlegg arxiv.org/abs/2505.15653

Quantifying structural uncertainty in chemical reaction network inference

Dynamical systems in chemistry and biology are complex, and one often does not have comprehensive knowledge about the interactions involved. Chemical reaction network (CRN) inference aims to identify,...

arxiv.org