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| "text": "Hi HN, we're Donnie, Josh, and Ben from ContextBridge.<p>We open sourced PlanBridge, a CLI tool for precision feedback on your coding agent's plans. It uses standard coding agent hooks (or skills) to open a local browser with the rendered markdown plan, letting you select text and leave inline comments on the plan.<p>Like most engineers, we use coding agents daily, but we realized the UX sucks for precise feedback on plans and other markdown. Reviewing even a short markdown plan in a terminal is tedious and frustrating. You have to scroll up and down, keep context in your head, and dump all your feedback into a single chat input at the bottom.<p>Because the friction is so high, we found ourselves settling for "good enough" plans. But every shortcoming or missing context in the plan becomes harder and more expensive to fix after the code is generated. Basically, we were burning time and tokens trying to clean up the code slop that was a direct result of imprecise plans.<p>So we built PlanBridge to make it frictionless to give precise feedback to a coding agent plan (and any other markdown) with document-style comments in a local browser.<p>Here is how it works under the hood:<p>1. It runs entirely locally. Your plan content never goes to a remote backend.<p>2. It uses standard harness hooks to get notified when an agent creates a plan (in "plan mode"). For Claude Code, it uses the plugin hook on ExitPlanMode. For Codex, it uses the Stop hook entry.<p>3. It pops open a local browser window with the rendered plan making it easy to read. You can then highlight specific text, drop inline comments exactly where you need to, and request changes (or approve if you have no feedback).<p>4. It pipes your structured, anchored feedback back to the agent, which then revises the plan. If you approved the plan, the agent starts coding.<p>5. For reviewing content that isn’t from a hook or outside of plan mode, you can use the /planbridge:open (for Codex its $planbridge-open) skill to iterate and give precise feedback.<p>We’re developers and, like you, hate invasive tools. PlanBridge is designed to fit into your existing coding agent workflow (no heavy UX) and uninstalls easily if you don’t find it useful.<p>Right now, the auto-installation supports Claude Code and Codex. If you use other agents, you can still use it asking your agent to run contextbridge for you.<p>You can try it out immediately (macOS/Linux):\n /bin/sh -c "$(curl -fsSL <a href=\"https://downloads.contextbridge.ai/cli/install.sh\" rel=\"nofollow\">https://downloads.contextbridge.ai/cli/install.sh</a>)"<p>Github: \n<a href=\"https://github.com/contextbridge/planbridge\" rel=\"nofollow\">https://github.com/contextbridge/planbridge</a><p>Official Site: \n<a href=\"https://plan.contextbridge.ai/\" rel=\"nofollow\">https://plan.contextbridge.ai/</a><p>Slack Community: \n<a href=\"https://go.contextbridge.ai/join-community/\" rel=\"nofollow\">https://go.contextbridge.ai/join-community/</a><p>We think feedback is a gift, so would love to hear yours. We’ve got some interesting features and ideas in the works including: support for precision code review feedback, allowing other agents to add comments to markdown files, summarizing what changed between plans, and lots of others in the issue tracker.", |
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| "text": "Robotics is advancing really fast lately, with AI inference, different controllers, software, and parts always changing. I wanted a place that supports many device types, Raspberry Pi, NVDA Jetson, Arduino, ESP32, hardware sources, and maximizes for printability. Instructables, Github, and Thingiverse are currently popular but aren't really focused on robotics, So I built orobot.io to try and make printing robots as standardized and accessible as possible. It uses a lot of Agent built content custom to each project, and every project is designed to be used by humans or your agent.<p>Features:<p>- Photos and Estimated Prices for all projects<p>- Links back to source GitHub projects<p>- LLMs write descriptions and tips on how to build<p>- View + Download 3d printable STL files in browser<p>- BOM purchase links are kept up to date with LLMs checking Amazon link health<p>- LLMs write Javascript install and controller wrappers \ncustom to each project so a single one-click install works across many frameworks and controller types<p>- Public skill files, clis, and prompts let your agent do everything it needs to walk you through the complexity.<p>It's still pretty new, so somethings are broken, and there's a lot more I want to build. But I'm very interested to have people try it out let me know if they want to use something like this and give me feedback about where they ran into problems so I can fix it. Thank you HN!", |
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| "title": "Show HN: Nanci, CI written in plain Python, locally debuggable", |
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| "text": "I recently finished building my latest side project and wanted to share it with the world :)<p>It's a CI/CD platform whose whole point is to be more developer friendly and familiar.<p>- YAML is replaced by Python, so you don't need to Google again what is the syntax for ifs or how values are compared in a vendor-specific YAML flavor (e.g. <a href=\"https://docs.github.com/en/actions/reference/workflows-and-actions/expressions\" rel=\"nofollow\">https://docs.github.com/en/actions/reference/workflows-and-a...</a>)<p>- The same engine that runs in the cloud can be run locally and show its output in a terminal UI, so pipelines can be developed and fixed on your laptop without pushing on every change, or needing external tooling that can only get so close to the real thing (e.g. <a href=\"https://github.com/nektos/act\" rel=\"nofollow\">https://github.com/nektos/act</a>)<p>- Because of the above, you can step through your pipeline with any Python debugger to fix it<p>- Artifacts are simply Python values that can be moved around between jobs, instead of having to use specialized actions to upload/download them at a specific named location (e.g. <a href=\"https://github.com/actions/upload-artifact\" rel=\"nofollow\">https://github.com/actions/upload-artifact</a>)<p>- The working directory and env vars are preserved across lines<p>I had a lot of fun building this project and it taught me a lot about QEMU, RabbitMQ, Kubernetes, Docker, AI coding (which I used for the landing page), TUI (especially the awesome Textualize framework), web sockets, SeaORM, web design, and the insane sorceries you can do in Python if you start hacking at the import machinery (see <a href=\"https://github.com/eliaperantoni/nanci/blob/main/engine/tests/engine/test_sugar.py\" rel=\"nofollow\">https://github.com/eliaperantoni/nanci/blob/main/engine/test...</a>, in Python a string-as-a-statement is normally a no-op, but it Nanci it executes a shell command in the Docker container).<p>I hope you like the idea and the proof-of-concept implementation!", |
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