Procore AI

Procore AI AI

Procore AI AI

Procore AI AI

Procore AI AI

Procore AI AI

Procore AI AI

Procore AI AI

Procore AI AI

Procore AI AI

Procore AI AI

How I keep my AI’s context window under 3K tokens even with 200+ lessons stored.

I’ve been hitting the same wall for months: I’d build up a CLAUDE.md over weeks of work — project conventions, gotchas, business rules, the “we tried that, don’t do it again” lessons — and eventually the rules file itself starts eating my context window. Two thousand lines in, the AI starts ignoring half of them anyway, and I’m back to re-explaining things I already documented. I spent a few months building a system around the idea that the md rules file is the wrong shape. Here’s what worked: Stop loading everything every session. Move the deep knowledge into a SQLite database (FTS5 + optional vector search via sqlite-vec) and only load a small per-project brief at session start. Briefs cap at 150 lines, plus a ~200-line global “constitution” and ~50 lines of pointer-only “living memory.” Everything else lives in the database and the AI queries it on demand via MCP tools (search_lessons, get_chunk, etc.). Enforce the caps in code, not in policy. This is the part I kept getting wrong. Every “be careful not to let this grow” rule I wrote in v1 got violated by month four. The current version moves the discipline into the regenerator — it literally refuses to write a brief past the cap. There are 15 named architectural rules, each backed by a CI test that fails the build if the rule drifts. The token math. The trick isn’t compression, the equivalent ~280K tokens still exist, they’re just in the database. The AI pulls what it needs mid-task instead of loading everything up front. Three things I got wrong that might save you time: • Vector-only retrieval is worse than hybrid. FTS5 + sqlite-vec with score blending beats either alone. • Letting the AI write directly to the knowledge store leads to noise. Mine writes to a drafts inbox; a human approves before promotion. • Auto-generated briefs need a small hand-curated block or they lose the “voice” of the project. I use markers and the regenerator preserves that section while regenerating everything around it. Disclosure: this is my own project, MIT-licensed. Repo’s at https://github.com/sms021/RunawayContext if you want to see the implementation. Built it for my own work (construction-management integrations across Vista, Procore, Monday.com, and many other internal systems and projects) but the architecture is agent-agnostic. Curious whether anyone here is doing something similar — I’d be surprised if there aren’t smarter approaches I haven’t found yet. submitted by /u/sms021 [link] [comments]

I built a persistent memory framework for Claude Code after 1,500+ sessions. It’s open source now.

After months of daily use across 60+ projects, I got tired of re‑explaining my codebase every session. So I built a system that gives Claude (or any AI coding tool) a structured, persistent brain. The core problem: everyone’s solution is “make the instruction file bigger.” But a 2,000‑line CLAUDE.md eats your context window before you’ve asked a question, and your AI ends up ignoring half of it. SuperContext takes the opposite approach — small, targeted files loaded only when relevant: Constitution (~200 lines, always loaded) Global rules, routing, preferences Living Memory (~50 lines, always loaded) Behavioral gotchas that prevent repeated mistakes Project Brains (loaded on entry) Per‑project business rules, schemas, changelogs Knowledge Store (on demand) Searchable SQLite database for infrastructure, APIs, reference data Session Memory Automatic conversation logging so your AI recalls past decisions The repo includes two things: The full guide Theory, architecture, anti‑patterns, tool‑specific setup for Claude Code, Cursor, Copilot, Codex, Aider, etc. An executable prompt Hand it to your AI, say “run this,” and it discovers your projects, migrates existing content, and builds the whole system in ~10 minutes. No manual setup. It was developed building construction management integrations (Vista, Procore, Monday.com), where getting context wrong means real production problems. The AI went from “helpful but forgetful” to genuinely knowing our systems. GitHub: https://github.com/sms021/SuperContext Happy to answer questions about the architecture or how it works in practice. submitted by /u/sms021 [link] [comments]