Alternatives
Products that do what Long Horizon does
Your coding agent writes the feature and runs the tests
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I'm Josh, founder of Synth. We've been working on coding agent optimization with method like GEPA and MIPRO (the latter of which, I helped to originally develop), agent evaluation via methods like RLMs, and large scale deployment for training and inference. We've also worked on patterns for memory, processing live context, and managing agent actions, combining it all in a single stack called Horizons. With the release of OpenAI's Frontier and the consumer excitement around OpenClaw, we think the timing is right to release a v0. It integrates with our sdk for evaluation and optimization but…
Feb 2026 · github.com
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Single-agent LLMs suck at long-running complex tasks. We’ve open-sourced a multi-agent orchestrator that we’ve been using to handle long-running LLM tasks. We found that single LLM agents tend to stall, loop, or generate non-compiling code, so we built a harness for agents to coordinate over shared context while work is in progress. How it works: 1. Orchestrator agent that manages task decomposition 2. Sub-agents for parallel work 3. Subscriptions to task state and progress 4. Real-time sharing of intermediate discoveries between agents We tested this on a Putnam-level math problem, but the…
Feb 2026 · github.com
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I wanted to test mobile apps in plain English instead of relying on brittle selectors like XPath or accessibility IDs. With a vision-based agent, that part actually works well. It can look at the screen, understand intent, and perform actions across Android and iOS. The bigger problem showed up around how tests are defined and maintained. When test flows are kept outside the codebase (written manually or generated from PRDs), they quickly go out of sync with the app. Keeping them updated becomes a lot of effort, and they lose reliability over time. I then tried generating tests directly from…
Apr 2026 · github.com
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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
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We're a couple of software engineers who believe that to build great software you need to write good tests, but we also sympathise when engineers say things like: - "Writing tests was too time consuming on my tight schedule", or - "Unit tests don't catch enough bugs, so they're useless", or - "I've inherited a legacy code base without tests and have no idea where to start" To tackle this we're building Symbolica (https://www.symbolica.dev), a symbolic code executor [1], that lets you run your code for all possible inputs. This means you can do things like: - Assert properties about…
2021
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Stagent▲71Drive Claude Code through long tasks it would otherwise drop
May 2026 · stagent.worldstatelabs.com
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Hey HN, we're Julian and Glenn, and we're building stagewise (https://stagewise.io), a frontend coding agent that lives inside your browser on localhost and operates on local codebases. You can spawn the agent into locally running web apps in dev mode with `npx stagewise` from the project root. The agent lets you then click on HTML Elements in your app, enter prompts like 'increase the height here' and will implement the changes in your source code. Before stagewise, we were building a vertical SaaS for logistics from scratch and loved using prototyping tools like v0 or lovable to…
2025 · github.com
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Now that AI is capable of writing large volumes of production-quality code, our role as developers is changing. Our primary job is no longer writing code. It’s planning and communicating software design and architecture. We have to do this collaboratively with agents and then review and iterate on their implementations. IDEs were not built for this workflow. So about three months ago I decided to try to build what I thought this new interface should look like. Runner is a coding agent purpose-built for this new “plan and review” workflow. It’s not for vibe coding. It’s for professional…
Sep 2025 · runnercode.com
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Hey HN, we're Glenn and Julian, and we're building stagewise (https://stagewise.io), a frontend coding agent that inside your app’s dev mode and that makes changes in your local codebase. We’re compatible with any framework and any component library. Think of it like a v0 of Lovable that works locally and with any existing codebase. You can spawn the agent into locally running web apps in dev mode with `npx stagewise` from the project root. The agent lets you then click on HTML Elements in your app, enter prompts like 'increase the height here' and will implement the changes in…
2025 · stagewise.io
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I built this because Cursor, Claude Code and other agentic AI tools kept giving me tests that looked fine but failed when I ran them. Or worse - I'd ask the agent to run them and it would start looping: fix tests, those fail, then it starts "fixing" my code so tests pass, or just deletes assertions so they "pass". Out of that frustration I built KeelTest - a VS Code extension that generates pytest tests and executes them, got hooked and decided to push this project forward... When tests fail, it tries to figure out why: - Generation error: Attemps to fix it automatically, then tries again -…
Jan 2026 · keelcode.dev
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Hey guys, I'm investing a ton of time on writing unit tests, for both enterprise and personal projects. I came up with the idea to make extension for AI-generated tests and cases within a VS Code. Happy to hear feedback, both positive and negative.
2024 · marketplace.visualstudio.com
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