I stopped running coding agents on my laptop. t3code-docker packages T3 Code as a headless server in a container, with the agent CLIs and language toolchains installed on first start. This website now gets built inside it.
What /grill-me taught me about shifting agent risk from verification to alignment — and why senior agent users should restructure their workflow around this.
In the world of software engineering, we often measure success by what we ship. The code that lands, the features that go live, the press releases that go out.  But working in a large-scale tech organization has taught me a counter-intuitive lesson: sometimes, the most valuable work you do is the work that doesn't ship. Recently, I led a project that seemed like a slam dunk on paper. We i
In the world of machine learning, K-Nearest Neighbors (KNN) is often one of the first algorithms taught. Its simplicity is elegant: to classify a new data point, find the $k$ training examples "nearest" to it and take a majority vote.\[^1] In recommendation systems, this translates to a powerful and intuitive concept: "people like you also liked..." (user-based collaborative filtering) or "people
The Cognitive Architecture of Modern LLM Agents The transition from Large Language Models (LLMs) as sophisticated text predictors to LLM-based autonomous agents represents a significant paradigm shift in artificial intelligence. This evolution is not merely an incremental improvement in model capability but a fundamental change in system architecture. The true "agentic leap" lies not in the LLM it
The Imperative for Provenance: Situating Watermarking in the LLM Ecosystem The rapid proliferation and increasing sophistication of Large Language Models (LLMs) have precipitated a paradigm shift across numerous sectors, unlocking unprecedented capabilities in content creation, dialogue systems, and automated reasoning.\[^1] However, this transformative potential is shadowed by significant and s
