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Describe self-correcting coding workflows in plain English and run them until tests pass.
forgeapp
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Describe self-correcting coding workflows in plain English and run them until tests pass.
forgeapp

Manage API keys and usage budgets for coding-agent workflows with request routing.
u/Zyron_X · Reddit
I built a service for people to use Codex API without 5-hour limit disruption I built a small service for people who use the OpenAI Codex API regularly and want more predictable usage without the 5-hour or weekly limits. It currently provides: Frontier OpenAI models (GPT 5.6 family included) Managed API key Monthly usage budgets depending to plan No 5-hour limit No weekly limit Under the hood, it is built on top of an open-source project and proxies requests to

Compare git, Jujutsu, and GitButler performance on coding tasks with Claude Code and Codex agents.
videlov · HN
I was interested in answering this question so I built a benchmark comparing git, jj and gitbutler in agentic context https://vcbench.dev/ Disclaimer - I am a co-founder of GitButler

Embeddable visual workflow builder for web apps with optional AI assistance.
tahazsh · HN
Hi! I’m Taha. In many agentic products that support workflows (including one I worked on), I noticed they either don’t support node-based editors, or use React Flow and go through the difficult work of integrating it into their product to run it and work with their existing logic. So I thought about creating a tool that could help with this by closing the gap between the editor and the runtime. That’s why I created Wayflow. The basic architecture is simple: you just need to create a graph (which is a JSON object) that the runtime knows how to run. The runtime doesn’t care where that graph is coming from, it just needs the right schema. And with the help of the editor, you can create the graph, and then export it or directly save it on your backend in your database. And then when you want to execute it, you just hand it to the runtime. The runtime can either stream the execution (which is useful for the editor), or give you the final result. How you execute the graph is up to you: t

Generate code documentation automatically on GitHub pull requests.
Aldasams · HN
Show HN: DocFlow – AI documentation updates for GitHub pull requests

Notes with code blocks, Kanban bug tracker, and snippet library with GitHub import.
@SinghApurv1711 · X
Hey! Check it out here:

AI agents build API integrations and automatically repair them when they break.
@getselfheal · X
SelfHeal - integrations that fix themselves. Vendor changes their API at 2am? The agent rewrites the connector, tests it, redeploys. You sleep. Demo (it breaks + heals on camera):

Enforce policies and audit AI-assisted code changes at merge time.
@Scyloq · X
Currently building SentrAI. The merge-time control plane for AI-assisted software development. Enforce policy, audit every AI-generated change. Just finished an interactive demo and would love your honest feedback. Built with @Lovable

Convert one-sentence ideas into precise AI prompts for coding agents.
@_m3rl1n

Design and deploy AI workflows and agents with a visual no-code builder.
@RanRan1357699 · X
Just shipped a smart guardrail in experiments: if your flow needs test data, it now blocks runs with a friendly prompt and offers one-click AI-generated samples. No more silent all-fail runs! Small details, big peace of mind. #BuildInPublic

Coordinate multiple AI coding agents and projects in one unified terminal interface.
@soracstv · X
I run my AI coding agents with AgentsRoom, a visual command center for multi-agent development. @AgentsRoomDev #VibeCoding #AI

Use AI code review that runs code in microVMs to catch more bugs faster.
u/dumbfoundded · Reddit
Ito, AI Code Review that Runs Code I've been using AI code review tools but none of them actually run code so I built one: https://www.ito.ai/ The way it works is that it uses microVMs to spin up your environment with all of the services running. Then a bunch of AI agents go and test the application to collect runtime evidence. The result is you get test cases along with evidence about whether or not the test cases pass or fail. The runtime evidence can be videos, request/response curls, db