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Alternatives

Products that do what CodeWatchdog does

AI scans. Human audits. Zero stored code.

  1. 1

    Multi-agent review catching bugs early in AI-generated code

    Mar 2026 · claude.com

  2. 2

    AI code reviews that can cut code review time & bugs in half

    2024 · codeant.ai

  3. 3

    Automatic AI-powered code reviews the moment you open a PR

    Jan 2026 · kilo.ai

  4. 4

    Measure the full AI SDLC. From token to production.

    Apr 2026 · waydev.co

  5. 5

    AI code reviews without the noise

    2024

  6. 6
    CodeBurn103

    See where your AI coding spend actually goes

    26d ago · codeburn.app

  7. 7WB

    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…

    2025 · infinitcode.ai

  8. 8AC

    Hey HN, I’m Henry, cofounder and CTO at Span (https://span.app/). Today we’re launching AI Code Detector, an AI code detection tool you can try in your browser. The explosion of AI generated code has created some weird problems for engineering orgs. Tools like Cursor and Copilot are used by virtually every org on the planet – but each codegen tool has its own idiosyncratic way of reporting usage. Some don’t report usage at all. Our view is that token spend will start competing with payroll spend as AI becomes more deeply ingrained in how we build software, so understanding how…

    Sep 2025 · code-detector.ai

  9. 9HA

    Hi HN, I'm one of the creators of HoundDog.ai (https://github.com/hounddogai/hounddog). We currently handle privacy scanning for Replit's 45M+ creators. We built HoundDog because privacy compliance is usually a choice between manual spreadsheets or reactive runtime scanning. While runtime tools are useful for monitoring, they only catch leaks after the code is live and the data has already moved. They can also miss code paths that aren't actively triggered in production. HoundDog traces sensitive data in code during development and helps catch risky flows (e.g., PII…

    Feb 2026 · github.com

  10. 10
    0xAudit110

    The security layer for AI agents to scan, fix verify via MCP

    Feb 2026

  11. 11

    Ship AI Code with confidence and speed

    2024

  12. 12

    Real-time compliance & security validation for AI coding

    Dec 2025

  13. 13
    Skilled76

    Dashboard to find agent skills you no longer need

    May 2026 · github.com

  14. 14AA

    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.

    May 2026 · github.com

  15. 15FL

    Hi HN! We just launched Codacy Guardrails, an IDE extension with a CLI for code analysis and MCP server that enforces security & quality rules on AI-generated code in real-time. It hooks into AI coding assistants (like VS Code Agent Mode, Cursor, Windsurf), silently scanning and fixing AI-suggested code that has vulnerabilities or violates your coding standards, while the code it’s being generated. We built this because coding agents can be a double-edged sword. They do boost productivity, but can easily introduce insecure or non-compliant code. One recent research team at NYU found that 40%…

    2025

  16. 16TO

    I'm an infrastructure architect who started using AI assistants to write code 3 months ago. After building several systems with Claude, I noticed a pattern: the code always had security issues I could spot from my ops background, but I couldn't fix them myself since I can't actually write code. Why I built this: I needed a way to verify AI-generated code was production-safe. Existing tools either required cloud uploads (privacy concern) or produced output too large for AI context windows. TheAuditor solves both problems - it runs completely offline and chunks findings into 65KB segments that…

    Sep 2025 · github.com

  17. 17CS

    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…

    Feb 2026 · docs.continue.dev

  18. 18

    AI that spots real risks and keeps your code safe.

    Dec 2025

  19. 19
    Recall16

    One developer solves it. Every developer knows it.

    Feb 2026

  20. 20

    Your AI has your code's text, never its map. Fix that.

    Jun 2026 · luuuc.github.io

  21. 21LE

    I started using Claude Code (claude --dangerously-skip-permissions) and Codex (codex --yolo) and realized I had no reliable way to know what they actually did. The agent's own output tells you a story, but it's the agent's story. logira records exec, file, and network events at the OS level via eBPF, scoped per run. Events are saved locally in JSONL and SQLite. It ships with default detection rules for credential access, persistence changes, suspicious exec patterns, and more. Observe-only – it never blocks. https://github.com/melonattacker/logira

    Mar 2026 · github.com

  22. 22HW

    A bunch of companies that I spoke to had their own claude & codex OTel dashboards that showed spend + seats per month. However, none of the dashboards actually analyzed how the engineers worked with the tools and if there were any areas for improvement! That's why I created https://www.promptster.ai. Managers get aggregate level view of code quality and how that ties with team workflows (nothing on a per-engineer level). While engineers get personalized coaching on how they can save tokens while keeping output high. We also have a tool built for individuals to test their local…

    Jul 2026

  23. 23IN

    Tl;dr: I trained a classifier to route to the least expensive model and reasoning depth to complete the request. Coupling that with additional automated token efficiency techniques has yielded 3x usage for the same spend. For anyone interested in trying it themselves: https://nerfguard.com Various teammates and I switched over to Codex from Claude Code recently. We still bounce between the tools, but Codex’s speed and steerability coupled with performance gains were hard to ignore. One of the downsides was that the per token pricing kicked in way sooner. This is happening across…

    Jun 2026

  24. 24AC

    Hey HN! I’m Julien, the founder of Code Inspector, a platform that helps developers and managers produce better code and reduce technical debt. We would love to get some feedback from the Hacker News community. Our platform inspects code, looks for defects (security, vulnerability, design, performance, lack of documentation), automates code reviews and reports on team activity. You can customize violation alerts to reduce false positives. We currently support GitHub, Bitbucket and Gitlab. I’d love to hear your thoughts on what you would expect from such a platform (what you like, dislike)…

    2021

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