🛠️ All DevTools

[Other] Show HN: Tracking AI Code with Git AI Git AI is a side project I created to track AI-generated code in our repos from development, through PRs, and into production. It does not just count lines, it keeps track of them as your code evolves, gets refactored and the git history gets rewritten.<p>Think &#x27;git blame&#x27; but for AI code. There&#x27;s a lot about how it works in the post, but wanted to share how it&#x27;s been impacting me + my team:<p>- I find I review AI code very differently than human code. Being able to see the prompts my colleagues used, what the AI wrote, and where they stepped in to override has been extraordinarily helpful. This is still very manual today, but hope to build more UI around it soon.<p>- “Why is this here?” — more than once I’ve giving my coding agent access to the past prompts that generated code I’m looking at, which lets the Agent know what my colleague was thinking when they made the change. Engineers talk to AI all day now…their prompts are sort of like a log of thoughts :)<p>- I pay a lot of attention to the lines generated for every 1 accepted ratio. If it gets up over 4 or 5 it means I’m well outside the AI’s distribution or prompting poorly — either way, it’s a good cause for reflection and I’ve learned a lot about collaborating with LLMs.<p>This has been really fun to build, especially because some amazing contributors who were working on similar projects came together and directed their efforts towards Git AI shine. We hope you like it.

[DevOps] 🌤️This is the main repository of the OpenCloud server. It contains the golang codebase for the backend services.

[DevOps] Run Nix Based Environments in Kubernetes

[API/SDK] Turn invoices, tables & receipts into JSON using Claude AI AIxtract is an API that turns unstructured PDFs into structured JSON in seconds. Powered by Claude AI + FastAPI, it automatically detects document types (invoices, receipts, bank statements) and extracts clean, structured data — including tables, amounts, and company info. Perfect for devs building finance, automation, or document-processing tools.

[API/SDK] 600+ integrations for vibe coding, zero config required The enterprise-grade platform for connecting AI to real-world tools. Metorial provides 600+ MCP integrations with truly serverless hosting, one-line OAuth, and full observability. Now with Magic MCP: one-click integration access for Cursor, Claude Code, and GitHub Copilot using fully-managed remote MCP servers. Open-source, self-hostable, built for developers.

[API/SDK] Handle website forms without building a backend I’ve built Formgrid, an open-source, privacy-friendly tool to handle website forms without a backend. Receive submissions instantly with email notifications, spam protection, and optional Proof-of-Work CAPTCHA. Use the hosted version or self-host with Docker for full control. MIT-licensed, no vendor lock-in, and secure.

[Other] The open-source auditor for Firebase security FireScan is a tool designed for penetration testers and developers to audit the security posture of Firebase projects. It provides an interactive console to enumerate databases, test storage rules, check function security, and much more, all from a single, easy-to-use interface.

[CLI Tool] Manage CKAN project development, operations and maintenance CKAN Pilot is a CLI tool to manage CKAN projects with ease. It helps you manage CKAN project development, operations and maintenance with a state-of-the-art command-line interface (CLI) and modern tool set.

[Other] Show HN: DroidDock – A sleek macOS app for browsing Android device files via ADB Hi HN,<p>I’m Rajiv, a software engineer turned Math teacher living in the mountains, where I like to slow down life while still building useful software.<p>I recently built DroidDock, a lightweight and modern macOS desktop app that lets you browse and manage files on your Android device via ADB. After 12 years in software development, I wanted a free, clean, and efficient tool because existing solutions were either paid, clunky, or bloated.<p>Features include multiple view modes, thumbnail previews for images&#x2F;videos, intuitive file search, file upload&#x2F;download, and keyboard shortcuts. The backend uses Rust and Tauri for performance.<p>You can download the latest .dmg from the landing page here: <a href="https:&#x2F;&#x2F;rajivm1991.github.io&#x2F;DroidDock&#x2F;" rel="nofollow">https:&#x2F;&#x2F;rajivm1991.github.io&#x2F;DroidDock&#x2F;</a> Source code is available on GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;rajivm1991&#x2F;DroidDock" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;rajivm1991&#x2F;DroidDock</a><p>I’d appreciate your feedback on usability, missing features, or bugs. Thanks for checking it out!<p>— Rajiv

[IDE/Editor] Show HN: Trilogy Studio, open-source browser-based SQL editor and visualizer SQL-first analytic IDE; similar to Redash&#x2F;Metabase. Aims to solve reuse&#x2F;composability at the code layer with modified syntax, Trilogy, that includes a semantic layer directly in the SQL-like language.<p>Status: experiment; feedback and contributions welcome!<p>Built to solve 3 problems I have with SQL as my primary iterative analysis language:<p>1. Adjusting queries&#x2F;analysis takes a lot of boilerplate. Solve with queries that operate on the semantic layer, not tables. Also eliminates the need for CTEs.<p>2. Sources of truth change all the time. I hate updating reports to reference new tables. Also solved by the semantic layer, since data bindings can be updated without changing dashboards or queries.<p>3. Getting from SQL to visuals is too much work in many tools; make it as streamlined as possible. Surprise - solve with the semantic layer; add in more expressive typing to get better defaults;also use it to wire up automatic drilldowns&#x2F;cross filtering.<p>Supports: bigquery, duckdb, snowflake.<p>Links [1] <a href="https:&#x2F;&#x2F;trilogydata.dev&#x2F;" rel="nofollow">https:&#x2F;&#x2F;trilogydata.dev&#x2F;</a> (language info)<p>Git links: [Frontend] <a href="https:&#x2F;&#x2F;github.com&#x2F;trilogy-data&#x2F;trilogy-studio-core" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;trilogy-data&#x2F;trilogy-studio-core</a> [Language] <a href="https:&#x2F;&#x2F;github.com&#x2F;trilogy-data&#x2F;pytrilogy" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;trilogy-data&#x2F;pytrilogy</a><p>Previously: <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=44106070">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=44106070</a> (significant UX&#x2F;feature reworks since) <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=42231325">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=42231325</a>

[Other] Show HN: Valid8r, Functional validation for Python CLIs using Maybe monads I built Valid8r because I got tired of writing the same input validation code for every CLI tool. You know the pattern: parse a string, check if it&#x27;s valid, print an error if not, ask again. Repeat for every argument.<p>The library uses Maybe monads (Success&#x2F;Failure instead of exceptions) so you can chain parsers and validators:<p><pre><code> # Try it: pip install valid8r from valid8r.core import parsers, validators # Parse and validate in one pipeline result = ( parsers.parse_int(user_input) .bind(validators.minimum(1)) .bind(validators.maximum(65535)) ) match result: case Success(port): print(f&quot;Using port {port}&quot;) case Failure(error): print(f&quot;Invalid: {error}&quot;) </code></pre> I built integrations for argparse, Click, and Typer so you can drop valid8r parsers directly into your existing CLIs without refactoring everything.<p>The interesting technical bit: it&#x27;s 4-300x faster than Pydantic for simple parsing (ints, emails, UUIDs) because it doesn&#x27;t build schemas or do runtime type checking. It just parses strings and returns Maybe[T]. For complex nested validation, Pydantic is still better. I benchmarked both and documented where each one wins.<p>I&#x27;m not trying to replace Pydantic. If you&#x27;re building a FastAPI service, use Pydantic. But if you&#x27;re building CLI tools or parsing network configs, Maybe monads compose really nicely and keep your code functional.<p>The docs are at <a href="https:&#x2F;&#x2F;valid8r.readthedocs.io&#x2F;" rel="nofollow">https:&#x2F;&#x2F;valid8r.readthedocs.io&#x2F;</a> and the benchmarks are in the repo. It&#x27;s MIT licensed.<p>Would love feedback on the API design. Is the Maybe monad pattern too weird for Python, or does it make validation code cleaner?<p>---<p>Here are a few more examples showing different syntax options for the same port validation:<p><pre><code> from valid8r.core import parsers, validators # Option 1: Combine validators with &amp; operator validator = validators.minimum(1) &amp; validators.maximum(65535) result = parsers.parse_int(user_input).bind(validator) # Option 2: Use parse_int_with_validation (built-in) result = parsers.parse_int_with_validation( user_input, validators.minimum(1) &amp; validators.maximum(65535) ) # Option 3: Interactive prompting (keeps asking until valid) from valid8r.prompt import ask port = ask( &quot;Enter port number (1-65535): &quot;, parser=lambda s: parsers.parse_int(s).bind( validators.minimum(1) &amp; validators.maximum(65535) ) ) # port is guaranteed valid here, no match needed # Option 4: Create a reusable parser function def parse_port(text): return parsers.parse_int(text).bind( validators.minimum(1) &amp; validators.maximum(65535) ) result = parse_port(user_input) </code></pre> The &amp; operator is probably the cleanest for combining validators. And the interactive prompt is nice because you don&#x27;t need to match Success&#x2F;Failure, it just keeps looping until the user gives you valid input.

[Other] Building a CI/CD Pipeline Runner from Scratch in Python

[Database] Show HN: TidesDB – Fast, transactional storage optimized for flash and RAM

[API/SDK] Open Source Spotify client library

[API/SDK] Promise based HTTP client for the browser and node.js

[API/SDK] An open-source, code-first Go toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.

[Other] Show HN: Pipeflow-PHP – Automate anything with pipelines even non-devs can edit Hello everyone,<p>I’ve been building [Pipeflow-php](<a href="https:&#x2F;&#x2F;github.com&#x2F;marcosiino&#x2F;pipeflow-php" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;marcosiino&#x2F;pipeflow-php</a>), a PHP pipeline engine to automate anything — from content generation to backend and business logic workflows — using core modular stages and custom implemented stages (that can do anything), with the key power of using an easy to reason and read XML to define the pipeline logic, which every actor in a company, even non developers, can understand, maintain and edit.<p>It’s a *headless engine*: no UI is included, but it&#x27;s designed to be easily wired into any backend interface (e.g. WordPress admin, CMS dashboard, custom panels), so *even non-developers can edit or configure the logic*.<p>It surely needs improvements, more core stages to be implemented and more features, but i&#x27;m already using it on two websites i&#x27;ve developed.<p>In future I plan to port it in other languages too.<p>Feedback (and even contributions) are appreciated :)<p>---<p>Why I built it<p>I run a site which every day, via a cron job:<p>- automatically generates and publish coloring pages using complex logics and with the support of the generative AI,<p>- picks categories and prompts based on logic defined in a pipeline,<p>- creates and publishes WordPress posts automatically, every day, without any human intervention.<p>All the logic is defined in an XML pipeline that&#x27;s editable via wordpress admin panel (using a wordpress plugin I&#x27;ve developed, which also adds some wordpress related custom stages to Pipeflow). A non-dev (like a content manager) can adjust this automatic content generation logic, for example by improving it, or by changing the themes&#x2F;categories during holidays — without touching PHP.<p>---<p>What Pipeflow does<p>- Define pipelines in *fluent PHP* or *simple, easy understandable XML (even by non developers), directly from your web app admin pages*<p>- Use control-flow stages like `If`, `ForEach`, `For`<p>- Execute pipelines manually, via cron, or on any backend trigger which adapts to your business logic<p>- Build your own UI or editor on top (from a simple text editor to a node based editor which outputs a compatible XML-based configuration to feed to pipeflow)<p>- Reuse modular “stages” (core and custom ones) across different pipelines

[Other] Visualize FastAPI endpoints with FastAPI-Voyager

[Other] The AI co-pilot for your entire development lifecycle. Devpilot is your new AI co-engineer, designed to take you from a single idea to a fully deployed, full-stack application. Stop wrestling with boilerplate, configuration files, and complex API integrations. Devpilot handles the entire workflow, acting as an autonomous agent that understands your product goals. Autonomous App Generation: Describe your app in plain English, and watch Devpilot build the React/Vite frontend and connect a real database (e.g., Supabase, Firebase) automatically.

[Other] Free Online Design & Developer Tools KineTools - Discover 20+ free online tools for designers and developers. Generate colors, gradients, shadows, and more. Boost your workflow with instant, browser-based ai-powered utilities.