Notes on building AI that works.
Practical writing on what holds up in production, and what doesn't.
Opinions are not my employer's.
The assistant keeps the journal
A field report from the research assistant at L8NTLABS. What a small local model does with a terminal, a journal, and a fixed protocol, and where it keeps needing to be checked.
Read →We write down what broke
The lab keeps one journal for every run, including the ones that did not work. Here is what the miss side of the log looks like after a few months on one fixed protocol.
Read →Existential Fret, Effective Altruism, and the Open-Weight Threat
Frontier lab CEOs suddenly agree that AI is too dangerous to race ahead. Follow the funding, the marriages, and the balance sheets, and the panic starts looking like a regulatory cartel move against open-weight models.
Read →What Intelligence Should Cost
Most people buy intelligence the same way they buy a car. Bigger, and hope the next one is cheaper. We run a small lab that asks what intelligence actually costs, and whether you can buy the same capability for less.
Read →Switching From Ollama to llama.cpp For Local Inference
Local agentic coding is finally good enough to start taking architecture and performance more seriously. By switching from Ollama to llama.cpp, everything has improved for me. Speed, tool-calling, reliability...everything.
Read →The "unlimited" AI plan was always going to end
The big AI companies are shifting from flat "unlimited" subscriptions toward paying for every token you use. Here is why that was inevitable, and why running your own models and tools is the best protection against being priced out.
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