Knowledge Infrastructure & Systems
Building knowledge systems, vector databases, graph structures, and retrieval infrastructure for AI-powered applications.
Articles in this collection13
Awareness & EducationEducational content, guides, and introductions (TOFU)
A Neural Router for My Knowledge Vault
I built a 15-agent routing system with a 7-layer scoring formula, a self-improving optimizer, and a hygiene scanner. The real insight wasn't any single layer — it was that sophisticated infrastructure without a discoverability layer rots on the shelf.
Knowledge Infrastructure
How to build, maintain, and search knowledge systems at scale using AI agents, Zettelkasten principles, and autonomous pipelines.
I Ran Autoresearch on My Own Website
Karpathy's autoresearch optimizes train.py against validation loss. What happens when you point the same pattern at a production website? 9 iterations, a 68% bundle reduction, 120 new internal links, and a real-time Convex backend — all discovered autonomously.
Overnight Results: From 3.76 to 4.35
What happened while I slept: 449 knowledge modifications, 11 new research notes, a vault quality score jump from 3.76 to 4.35, and auto-implement deploying real features. The knowledge flywheel is closing the loop.
vault-search: Building Hybrid Retrieval That Actually Works on Local Hardware
How BM25 + embeddings + RRF fusion, HyDE expansion, typed sub-queries, and a knowledge graph work together in a zero-API-cost local search tool — and how we cut query time from 9.9s to 0.5s.
Vault Autoresearch: A Personal AI Learns From Itself
What if your vault infrastructure improved itself overnight? Inspired by Karpathy's autoresearch, we built a system that treats the vault as a machine learning model, quality scores as loss, and improvement cycles as training steps. 30+ bottlenecks fixed, 34 GPU experiments, 12 code fixes deployed — all in one session.
Building a Knowledge Vault: How I Use Claude to Research 80 Topics in a Day
A deep dive into the research pipeline behind 10,800+ knowledge notes: multi-agent orchestration, quality gates, synthesis generation, and autonomous maintenance on a local GPU.
The Knowledge Engine: 10,800+ Notes Built by AI Agents
An autonomous knowledge system spanning 7+ disciplines — seeded, judged, deepened, and maintained by a pipeline of AI agents running 24/7 on local and cloud models.
Building a Multi-Agent Research Pipeline — Benchmarked on DRB
A 6-wave multi-agent research system scoring 0.5166 on RACE (n=50, 0.5 = reference baseline) — competitive with NVIDIA AIQ and Gemini Deep Research, with honest notes on citation quality gaps.
Building a Fashion Trend Intelligence Pipeline for $3/Month
How I built an automated fashion trend research system that monitors 6 RSS feeds, extracts signals with a local model, and produces weekly slide deck reports with real article images — all for under $3/month.
Reweave: Teaching Your Vault to Link Itself
Building a system that discovers semantic connections between notes and auto-generates wikilinks — turning a flat file vault into a self-wiring knowledge graph.
Zero-Cost Automation: 16 Tasks on a Local GPU
How I built a heartbeat system that runs 16 automated maintenance tasks — dependency monitoring, vault health checks, ecosystem scanning — all on local models at zero API cost.