
OSS Guardian — Spec-Compliant AI Review
The AI that reviews your PRs like a senior maintainer, 24/7
What it does
I built OSS Guardian AI because I was tired of AI reviewers that spammed PRs with generic suggestions — never checking if code actually followed the spec. Spec Compliance Guard reads your spec.md and plan.md, then verifies every PR against them. When code drifts, it flags it immediately with a checklist of what's missing. The result: 94% less noise than other AI reviewers. If the code is clean, it says so and shuts up.
Does the same job
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- CSContinue – Source-controlled AI checks, enforceable in CIFeb 2026 · docs.continue.dev · ▲44
We now write most of our code with agents. For a while, PRs piled up, causing review fatigue, and we had this sinking feeling that standards were slipping. Consistency is tough at this volume. I’m sharing the solution we found, which has become our main product. Continue (https://docs.continue.dev) runs AI checks on every PR. Each check is a source-controlled markdown file in `.continue/checks/` that shows up as a GitHub status check. They run as full agents, not just reading the diff, but able to read/write files, run bash commands, and use a browser. If it finds…

- WBWe built an AI to review your pull requests2025 · infinitcode.ai · ▲53
Hey HN, We’re two developers (co-founders) with a team of 20 who got tired of spending hours reviewing PRs, so we built Infinitcode.ai, an AI-powered code reviewer that: - *Summarizes PRs in plain English*: No more deciphering 1,000-line diff jungles - *Catches more than bugs*: Security holes, performance pitfalls, code smells, even typos (yes, we’ll flag “vurnerabilities” and vulnerabilities) - *Zero onboarding*: Works instantly—no “let me learn your codebase for weeks” nonsense. Why we’re posting: We’re in alpha and need brutal honesty. Roast our tool, mock our UI, or tell us why AI will…

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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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