Alternatives
Products that do what Aether – Background agents that fix bugs in isolated VMs, opens PRs does
Hey HN, I've been building Aether, a background agent that takes production errors from Sentry and attempts to turn them into verified pull requests. When a new error hits your Sentry project: 1. Sentry webhook fires with the stack trace, breadcrumbs, and context 2. Aether spins up an isolated Fly.io VM and clones the repo at the relevant commit 3. Agent analyzes the stack trace, reproduces the issue, proposes a fix 4. Starts the dev server, re-runs tests, and can verify the running app with Playwright (headless Chromium is pre-installed in every VM) 5. A review pass evaluates the diff…
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Hi all, I've been working on this devtool for 1 month now for myself at first and I'll be curious to see if it's something that could work for you as well. So basically, it detects bugs in your website in production from real user sessions, an llm clusters them by severity and it provides the complete context of the issue that you can copy-paste into your coding agent to fix it in one go. Why did I create it? I've been shipping fast with tools like Cursor and Claude Code. The problem? When bugs happen in production, these tools have zero context about what actually went wrong. Sentry is…
Nov 2025 · sonarly.dev
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The narrow control plane for AI agent tool and API calls.
Aug 2026 · aegisora-ai.vercel.app
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Hey everyone, My friend and I built a simple bug fixing app that listens for alerts/issues from Sentry, contextualizes it against your codebase, and any other data sources you wish to connect (right now we support Notion, Google Docs, and Slack), and deploys an ai agent to write a PR for review in Github or Gitlab to solve the bug. Our current demo shows the end-to-end process for a trivial bug fix, but we have been testing it with open source python repos like http-pie, comparing how our agent solves a bug compared to a human engineer and it gets fairly close. We are working on adding…
2023 · resolvd.ai
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I built Bugsink because I wanted error tracking I could run myself, without maintaining a complex stack. It runs with pip install or a single Docker container, stores events locally (SQLite, MySQL, or Postgres), and is easy to run and keep running. It’s API compatible with the Sentry SDKs, so you can keep your existing client setup. Bugsink focuses on error tracking only and is fast enough to process millions of events per day on modest hardware. The 1.5 release introduces sourcemaps support: https://www.bugsink.com/blog/bugsink-1.5-introducing-sourcem... Can't you just…
2025 · bugsink.com
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TLDR: made the first background coding agent that has an isolated workspace and runs locally Howdy - I’m Kevin, co-founder of Firebender, and we built the first background coding agent in android studio / Jetbrains! Why not just use Cursor background agents or OpenAI Codex? Both of these require setting up a cloud container and cloning your existing developer environment, and maintaining it. Then when you want to iterate on changes as AI inevitably makes a mistake, you either throw away the work, or have to pull down the branch and clean it up. This feels really clunky. With Firebender,…
2025 · youtube.com
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We built PrivateClaw because the hosted OpenClaw platforms on the market today require you to trust them with plaintext. PrivateClaw removes that requirement at the hardware layer. PrivateClaw runs AI agents inside Trusted Execution Environments (TEEs), backed by AMD’s SEV-SNP standard. This means that your data is encrypted at the hardware level, enforced by the AMD Secure Processor outside the host OS trust boundary. PrivateClaw comes with inference that also runs inside TEEs, which means your prompts and completions are private as well. How it works: Each user gets a dedicated CVM…
Apr 2026 · privateclaw.dev
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Hi HN, I’m the creator of Cordum. I’ve been working in DevOps and infrastructure for years (currently in the fintech/security space), and as I started playing with AI agents, I noticed a scary pattern. Most "safety" mechanisms rely on system prompts ("Please don't do X") or flimsy Python logic inside the agent itself. If we treat agents as autonomous employees, giving them root access and hoping they listen to instructions felt insane to me. I wanted a way to enforce hard constraints that the LLM cannot override, no matter how "jailbroken" it gets. So I built Cordum. It’s an open-source…
Jan 2026 · github.com
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Hi HN, I’m a solo developer and built AgentWatch to solve a problem I kept running into while building AI agents: preventing runaway loops and unexpected LLM spend before requests reach the model. AgentWatch sits in front of OpenAI, Anthropic, Gemini, Bedrock, Azure OpenAI, Groq, and others to enforce budgets and runtime policies. I’d really appreciate your feedback. If you’re building AI agents, does this solve a problem you’ve experienced? I’d also love to hear what you’d improve or challenge.
Jun 2026 · agent-watch.dev
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If you're interested in exploring what LLM-based agent systems these days actually do to solve certain benchmarks such as SWEBench or WebArena, we created a small leaderboard with our team, that allows to view a lot of public and OSS agent results including all the runtime traces (the step-by-step reasoning behind the scenes). Looking at traces is actually quite interesting, as they reveal a lot about the inner working and shortcomings of current agent system, e.g. see https://explorer.invariantlabs.ai/u/invariant/webarena--SteP... for an example trace.
2024 · explorer.invariantlabs.ai
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Hey HN, We built FireClaw because we kept watching AI agents get owned by prompt injection through web content. The agent fetches a page, the page says "ignore previous instructions," and suddenly your agent is leaking data or running commands it shouldn't. The existing solutions detect injection after the fact. We wanted to prevent it. FireClaw is a security proxy that sits between your AI agent and the web. Every fetch passes through a 4-stage pipeline: 1. DNS blocklist check (URLhaus, PhishTank, community feed) 2. Structural sanitization (strip hidden CSS, zero-width Unicode, encoding…
Mar 2026 · github.com
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Hi HN, Zidan here. I’ve been experimenting with AI-assisted debugging and noticed a recurring gap: most tools optimize for agent-led exploration (ex: giving claude code a browser to click around and try to reproduce an issue). But in many cases, I've already found the bug myself. What I actually want is a way to hand the agent the exact context I just saw - without retyping steps, copying logs, or hoping it can reproduce the behavior. So we built FlowLens, an open-source MCP server + Chrome extension that captures browser context and lets coding agents inspect it as structured, queryable…
Nov 2025 · github.com
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Built an AI code reviewer using Letta (Python) that I can call natively from Rust applications. The interesting part: real-time streaming works perfectly across the language boundary with zero hassle using RunAgent. The agent runs in Python with persistent memory, leverages the best in house agentic memory management with Letta (Pythonic AI agent framework), and my rust code just uses it (kinda) natively, though Letta has no Rust bindings. And, streaming works like magic. No FFI, no complex bridges - just native async/streaming that feels like calling any Rust librar, but without…
2025 · medium.com
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