Capital de Tokens
Ideas, analysis, and independent articles from the channel and its episodes.
Blog RSSHow to use AI before and during problem solving to expand the search space, structure evidence, propose hypotheses and prioritize experiments without confusing plausibility with causality.
A layered guide connecting recommend, filter, predict, and generate with Russell and Norvig's taxonomy, then drilling down into tasks, learning paradigms, and model families.
Dream-RSI recursively improves a coding agent's exploration policy by turning historical discovery trees into replay simulators, without retraining the underlying model.
Jev promises cheap, sub-second typed semantic decisions. Hacker News, Reddit, LangChain and early independent tests show real excitement, but also questions about benchmarks, calibration and the 'no hallucinations' claim.
How the public OpenAI Codex codebase reflects three defensive-design principles: restrict function domains with types, construct only consistent objects, and model valid state transitions.
MIT's new report on generative AI goes far beyond academic cheating: it questions how we measure learning when a model can solve essays, proofs, code, and written problems. The response points toward cognitive friction, authentic assessment, and social learning.
Architecture lessons from Habitat: from a Python library to a distributed service, asyncio tail latency, LIFO/FIFO pooling, Envoy, deliberately constrained APIs, and a Rust migration.
A practical architecture for turning calls and messages into agent tools: CLI, MCP, events, idempotency, policies, observability, and real-world use cases.
Dario Amodei warns that swarms of AI agents could become a massive threat to the internet. What is demonstrated, what remains an extrapolation, and how this changes the secure design of autonomous agents.
What fuzzing really means for AI-generated code, how it differs from property-based testing, and how to integrate it into CI to catch crashes, hangs, edge cases, and regressions before production.
A practical guide to installing Git AI, attributing lines generated by Codex and other agents, using AI blame and stats, preserving attribution through merges, and turning agent activity into useful engineering metrics.
What changes with GPT-Live-1 compared with the STT→LLM→TTS pipeline, and how to use it with WebRTC, WebSockets, backend delegation, tool calling, transcripts, and interruption handling.
Capital de Tokens
