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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Engineering decisions engine that know when they're stale. Frame, compare, decide — with evidence decay and parity enforcement. For Claude Code, Cursor, Gemini CLI, Codex and more.
| Date | Stars |
|---|---|
| 2026-07-31 | 1375 |
| 2026-08-02 | 1375 |
| 2026-08-06 | 1375 |
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<img src="assets/banner.svg" alt="Haft" width="600"> *formerly [quint-code](https://github.com/m0n0x41d/quint-code)* **FPF governance substrate for AI-assisted software delivery.** Your agents (Claude Code, Codex) write code fast. Most repositories are not ready for serious harness engineering: the target system is underspecified, the enabling system is implicit, term maps are missing, and runtime evidence is detached from the spec. Haft makes the project harnessable before it scales execution. --- ## What is Haft? Haft is a **governance substrate** that makes a repository harnessable for principal-led FPF engineering work. It turns problem frames, comparisons, decisions, commissions, and evidence into auditable artifacts, with enforcement at the kernel boundary. **Specify → Think → Run → Govern.** Not a coding agent. Not a documentation generator. The handle between the tool and the hand: the part that turns raw model capability into formal specification, governed decisions, bounded commissions, and evidence-backed engineering work. ### Three surfaces, one artifact graph Haft is consumed through three surfaces over one `.haft/` artifact graph: - **Skills + slash commands** in your coding agent (Claude Code, Codex, OpenCode, Cursor) — workflow skills auto-trigger; `/h-frame /h-decide /h-verify ...` run manually - **CLI** (`haft problem`, `haft solution`, `haft decision`, ...) — manual access, no LLM in the loop - **MCP server** (`haft serve`) — programmatic access for any LLM agent over the Model Context Protocol The kernel MCP server is the cross-host enforcement surface: it validates arguments server-side and returns structured errors for FPF violations (missing required fields, parity gaps, weakest-link omissions, predictions without verify_after). Skills carry the procedure; the kernel carries the gates. ### What changed in v8 v8 dropped the standalone interactive agent (`haft agent`), the TUI, and the desktop wrappers. Haft no longer competes with general coding agents on the runtime surface — it adds governance discipline on top of whichever agent you already use. The pivot, with parity-compared variants, rollback plan, and falsifiable predictions, is recorded in `.haft/decisions/dec-20260525-v8-architecture-pivot-from-standalone-agent-to-g-bbe45cb7.md`. Upgrading from v7? See [MIGRATION-v8.md](MIGRATION-v8.md) — the upgrade checklist plus what was dropped (`haft agent`, TUI, desktop, v7 helper commands). --- ## Built on First Principles Framework [FPF](https://github.com/ailev/FPF) by [Anatoly Levenchuk](https://www.linkedin.com/in/ailev/) — a rigorous, transdisciplinary architecture for thinking. The skill set (`h-frame`, `h-explore`, `h-compare`, `h-decide`, `h-verify`, and the full catalog below) gives your agent an FPF-native operating system for engineering decisions: framing before solutions, characterization before comparison, parity enforcement, evidence with congruence penalties, weakest-link assurance, and a cycle that reopens itself when evidence ages or a measurement fails. The framing and comparison skills auto-trigger on operator context. The binding step (`h-decide`, `h-commission`) is manual-only per the Transformer Mandate: agents frame and compare; the human principal records the binding choice. `haft fpf search` (and `haft_query(action="fpf")` from MCP) searches the indexed FPF specification. Retrieval is hybrid: exact pattern id first, then keyword (FTS5) fused with semantic recall over baked section vectors, so a reworded "how do I think about X" finds the pattern that answers it. The vectors ship inside the binary; semantic recall degrades to keyword when the embedding sidecar is absent. --- ## Install ```bash curl -fsSL https://raw.githubusercontent.com/m0n0x41d/haft/main/install.sh | bash ``` The install URL still points at the historical `quint-code` path. The installed binary is `haft`. Then in your project, init with your host-agent flag: ```bash haft init
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:144c420ac9d06a4a, topic:ai-coding, topic:developer-tools
matched fp:144c420ac9d06a4a, topic:ai-agents
matched fp:144c420ac9d06a4a, topic:mcp