
FixDrop
Drop in broken AI code, get back fixed code in one shot
What it does
AI builders like Lovable, Bolt, and v0 are amazing — until your code breaks and you burn credits on the same looping bug over and over. FixDrop is a free open source CLI tool that takes your broken AI-generated code, sends it to Claude with a strict one-shot repair prompt, and gives you back clean working code instantly. No loops. No credit burning. One command. Done. Built for makers, founders, and no-code builders worldwide.
Does the same job
all alternatives →
- PGProofShot – Give AI coding agents eyes to verify the UI they buildMar 2026 · github.com · ▲161
I use AI agents to build UI features daily. The thing that kept annoying me: the agent writes code but never sees what it actually looks like in the browser. It can’t tell if the layout is broken or if the console is throwing errors. So I built a CLI that lets the agent open a browser, interact with the page, record what happens, and collect any errors. Then it bundles everything — video, screenshots, logs — into a self-contained HTML file I can review in seconds. proofshot start --run "npm run dev" --port 3000 # agent navigates, clicks, takes screenshots proofshot stop It works with…
FetchSandbox MCP14d ago · fetchsandbox.com · ▲263The MCP that proves your AI's integration fixes work
- AAAISlop, a CLI for catching AI generated code smellsMay 2026 · github.com · ▲73
Hi, I’m Kenny, I’ve been building aislop. I starting working on this after using Claude Code, codex and opencode several times and noticing some slops. They aren’t syntax and passes most tests, they are patterns like empty catch blocks, useless comments, duplicated helpers, dead code and many more. So I built a tool to scan and check for these patterns and wired it into hooks so after each tool call, the agent checks for the slops. You can try it out with npx aislop scan. It’s all local and no code is transferred. Thank you.


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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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