🛠️ All DevTools

[Other] Agent Development Kit Web (adk web) is the built-in developer UI that is integrated with Agent Development Kit for easier agent development and debugging.

[Other] Python sample codes and textbook for robotics algorithms.

[DevOps] The RethinkDNS resolver that deploys to Cloudflare Workers, Deno Deploy, Fastly, and Fly.io

[Other] Grebedoc – static site hosting for Git forges

[Testing] Show HN: Tusk Drift – Open-source tool for automating API tests Hey HN, I&#x27;m Marcel from Tusk. We’re launching Tusk Drift, an open source tool that generates a full API test suite by recording and replaying live traffic.<p>How it works:<p>1. Records traces from live traffic (what gets captured)<p>2. Replays traces as API tests with mocked responses (how replay works)<p>3. Detects deviations between actual vs. expected output (what you get)<p>Unlike traditional mocking libraries, which require you to manually emulate how dependencies behave, Tusk Drift automatically records what these dependencies respond with based on actual user behavior and maintains recordings over time. The reason we built this is because of painful past experiences with brittle API test suites and regressions that would only be caught in prod.<p>Our SDK instruments your Node service, similar to OpenTelemetry. It captures all inbound requests and outbound calls like database queries, HTTP requests, and auth token generation. When Drift is triggered, it replays the inbound API call while intercepting outbound requests and serving them from recorded data. Drift’s tests are therefore idempotent, side-effect free, and fast (typically &lt;100 ms per test). Think of it as a unit test but for your API.<p>Our Cloud platform does the following automatically:<p>- Updates the test suite of recorded traces to maintain freshness<p>- Matches relevant Drift tests to your PR’s changes when running tests in CI<p>- Surfaces unintended deviations, does root cause analysis, and suggests code fixes<p>We’re excited to see this use case finally unlocked. The release of Claude Sonnet 4.5 and similar coding models have made it possible to go from failing test to root cause reliably. Also, the ability to do accurate test matching and deviation classification means running a tool like this in CI no longer contributes to poor DevEx (imagine the time otherwise spent reviewing test results).<p>Limitations:<p>- You can specify PII redaction rules but there is no default mode for this at the moment. I recommend first enabling Drift on dev&#x2F;staging, adding transforms (<a href="https:&#x2F;&#x2F;docs.usetusk.ai&#x2F;api-tests&#x2F;pii-redaction&#x2F;basic-concepts">https:&#x2F;&#x2F;docs.usetusk.ai&#x2F;api-tests&#x2F;pii-redaction&#x2F;basic-concep...</a>), and monitoring for a week before enabling on prod.<p>- Expect a 1-2% throughput overhead. Transforms result in a 1.0% increase in tail latency when a small number of transforms are registered; its impact scales linearly with the number of transforms registered.<p>- Currently only supports Node backends. Python SDK is coming next.<p>- Instrumentation limited to the following packages (more to come): <a href="https:&#x2F;&#x2F;github.com&#x2F;Use-Tusk&#x2F;drift-node-sdk?tab=readme-ov-file#requirements" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;Use-Tusk&#x2F;drift-node-sdk?tab=readme-ov-fil...</a><p>Let me know if you have questions or feedback.<p>Demo repo: <a href="https:&#x2F;&#x2F;github.com&#x2F;Use-Tusk&#x2F;drift-node-demo" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;Use-Tusk&#x2F;drift-node-demo</a>

[Monitoring/Observability] Show HN: Linnix – eBPF observability that predicts failures before they happen I kept missing incidents until it was too late. By the time my monitoring alerted me, servers&#x2F;nodes were already unrecoverable.<p>So I built Linnix. It watches your Linux systems at the kernel level using eBPF and tries to catch problems before they cascade into outages.<p>The idea is simple: instead of alerting you after your server runs out of memory, it notices when memory allocation patterns look weird and tells you &quot;hey, this looks bad.&quot;<p>It uses a local LLM to spot patterns. Not trying to build AGI here - just pattern matching on process behavior. Turns out LLMs are actually pretty good at this.<p>Example: it flagged higher memory consumption over a short period and alerted me before it was too late. Turned out to be a memory leak that would&#x27;ve killed the process.<p>Quick start if you want to try it:<p><pre><code> docker pull ghcr.io&#x2F;linnix-os&#x2F;cognitod:latest docker-compose up -d </code></pre> Setup takes about 5 minutes. Everything runs locally - your data doesn&#x27;t leave your machine.<p>The main difference from tools like Prometheus: most monitoring parses &#x2F;proc files. This uses eBPF to get data directly from the kernel. More accurate, way less overhead.<p>Built it in Rust using the Aya framework. No libbpf, no C - pure Rust all the way down. Makes the kernel interactions less scary.<p>Current state: - Works on any Linux 5.8+ with BTF - Monitors Docker&#x2F;Kubernetes containers - Exports to Prometheus - Apache 2.0 license<p>Still rough around the edges. Actively working on it.<p>Would love to know: - What kinds of failures do you wish you could catch earlier? - Does this seem useful for your setup?<p>GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;linnix-os&#x2F;linnix" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;linnix-os&#x2F;linnix</a><p>Happy to answer questions about how it works.

[Other] Listen to Database Changes Through the Postgres WAL

[Other] Show HN: Gerbil – an open source desktop app for running LLMs locally Gerbil is an open source app that I&#x27;ve been working on for the last couple of months. The development now is largely done and I&#x27;m unlikely to add anymore major features. Instead I&#x27;m focusing on any bug fixes, small QoL features and dependency upgrades.<p>Under the hood it runs llama.cpp (via koboldcpp) backends and allows easy integration with the popular modern frontends like Open WebUI, SillyTavern, ComfyUI, StableUI (built-in) and KoboldAI Lite (built-in).<p>Why did I create this? I wanted an all-in-one solution for simple text and image-gen local LLMs. I got fed up with needing to manage multiple tools for the various LLM backends and frontends. In addition, as a Linux Wayland user I needed something that would work and look great on my system.

[Other] Xqerl – Erlang XQuery 3.1 Processor

[IDE/Editor] Professional JSON Developer Studio The ultimate JSON editing experience with advanced tools for filtering, sorting, comparison, and conversion, save, view history, colloboration & API integration. Built for developers who demand precision and elegance without juggling across multiple different tabs.

[Other] Lightweight split-view manager for React Layout Manager React is a lightweight library designed to work with TypeScript. For resizable nested split views, it is optimized for the minimal size of the bundle and the most efficient re-renders. It handles horizontal vertical splits, drag to resize panels, nested layouts, supports RTL and LTR, and persistent layouts. It is suitable for dashboards, dynamic React applications, and admin panels.

[Monitoring/Observability] Optimize Performance, Reliability, and Cost. Together Akamas Insights is an AI-powered optimization solution for Kubernetes. It analyzes telemetry from tools like Prometheus, Datadog, and Dynatrace to automatically detect inefficiencies and reliability risks across clusters, pods, and runtimes. It then provides ready-to-apply, data-backed configuration recommendations — enabling safe, continuous, full-stack optimization.

[IDE/Editor] Free, visual JSON editor - Validation, Type Support etc This free web app lets you edit JSON data visually without writing any code. It automatically converts JSON into interactive form fields for easy editing and keeps changes synchronized both ways between the visual editor and raw JSON. You can manage objects and arrays dynamically, validate and auto-format your JSON, and download the result as a file. Everything runs 100% client-side in your browser, ensuring speed, privacy, and complete control over your data.

[Other] POC: Private PDF AI using only your browser with WebGPU I built this POC to test if complete RAG pipelines could run entirely client-side using WebGPU. Key difference: zero server dependency. PDF parsing, embeddings, vector search, and LLM inference all happen in your browser. Select a model (Llama, Phi-3, Mistral), upload a PDF, ask questions. Documents stay local in IndexedDB. Works offline once models are cached. Integrated WeInfer optimization achieving ~3.76x speedup over standard WebLLM through buffer reuse and async pipeline processing.

[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.