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@kunolabs

Kuno Labs

Local-first AI developer tools for compact context, code maps, and agent workflows.

Kuno Labs

Kuno Labs

Local-first developer tools for AI-assisted engineering.

We build compact, inspectable systems that help coding agents understand a repository before they spend a large context window reading it. The focus is practical: smaller prompts, clearer maps, safer local artifacts, and tooling that stays fast enough to use during real work.

Flagship Project

Project Status Focus
CodePrism Alpha Local codebase maps, focused context slices, exact retrieval handles, and optional visual replay for AI coding agents.

CodePrism's checked-in fixture suite currently reports a 68.75% average estimated source-to-slice reduction. Larger local projects can show stronger reductions when the agent starts with a narrow task and uses targeted retrieval, but all token counts are estimates rather than billing-grade measurements.

The workflow is simple: map first, slice next, retrieve exactly, and read raw files only when they matter.

What We Care About

  • Local-first tools with no network calls by default.
  • Inspectable text, JSON, SQLite, and HTML artifacts.
  • Deterministic static parsing before model-generated summaries.
  • Measured benchmarks over viral claims.
  • Cross-platform CLI behavior for Windows, macOS, and Linux.

Links

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  1. codeprism codeprism Public

    Local-first context saving for AI coding agents: map first, slice next, retrieve exactly.

    Python 1

  2. .github .github Public

    Kuno Labs organization profile and shared community files.

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