Manoj Mallick | SigmapContext Engineering #4
Humans don't read entire repositories.
We navigate using:
• folders
• functions
• references
AI agents should do the same.
SigMap builds a structural map of your codebase for agent navigation.
Context Engineering #3
More context ≠ better answers.
Extra files create noise.
AI agents often fail because relevant code is buried under irrelevant code.
SigMap helps surface the signal before retrieval begins.
Context Engineering #2
You ask:
"How does authentication work?"
The answer may live in 5 files.
Your repo may contain 5,000.
The challenge isn't answering.
It's finding the right context.
SigMap helps map that path.
Context Engineering #1
Prompt engineering asks:
"What should I ask AI?"
Context engineering asks:
"What should AI see?"
Many coding failures happen before generation starts.
That's the problem SigMap is designed to solve.
🧵 Why "No vectors. No embeddings"?
Most context tools use vector search — meaning they
embed your entire codebase into a DB and do similarity
lookups at runtime.
Problems:
❌ Slow to index (minutes–hours for large repos)
❌ Embeddings go stale as code changes
❌ Still wrong 20–30% of the time
SigMap uses deterministic file signatures instead.
Result: <50ms lookup. Always fresh. 78.9% accurate.
Try it: sigmap.io
#SigMap #LLM #DevTools #OpenSource
🚀 Introducing SigMap — the verification layer for AI coding agents.
✅ 78.9% hit@5 benchmark accuracy (21 repos tested)
⚡ 97.9% token reduction vs. full codebase ingestion
🔌 VS Code · JetBrains · Neovim plugins + MCP server (9 tools)
📦 ~20K npm downloads | 512 ⭐ GitHub
No vectors. No embeddings. Deterministic. Local. Fast.
👉 sigmap.io
#SigMap #AIAgents #OpenSource #DevTools #buildinpublic