🛠️ All DevTools

[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.

[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.