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[Testing] QA-use-MCP: MCP for E2E testing

[DevOps] Show HN: Envirobly – Efficient App Hosting This is my, fully bootstrapped, take on an application hosting platform. I designed it primarily for myself, to meet the following goals:<p>* Utilize the best parts of the cloud, leave expensive value added services behind. * Host anywhere in the world, close to your users. * Batteries included: backups, logging, monitoring, auto-scaling, containerization, databases. * Simple and user friendly.<p>The journey was tough. It took me round about 3 years to get everything just right. This goes against the conventional wisdom of getting something simple out quick. With a hosting platform however, in my mind, reliability, stability and certain maturity are a must have from the start.<p>I definitely need to work on the presentation, it&#x27;s barely covering the basics. But you have to start somewhere :-)<p>I&#x27;d love to hear some critique from the community. Thanks!

[Other] Flightcontrol: A PaaS that deploys to your AWS account

[CLI Tool] Show HN: A Node.js CLI tool to generate ai.txt, llms.txt, robots.txt, humans.txt

[DevOps] Show HN: Volant– spin up real microVMs in 10 seconds(Docker images or initramfs) I’ve been building Volant, a modular microVM orchestration engine that makes running microVMs feel as simple as Docker.<p>It supports cloud-init, GPU&#x2F;VFIO passthrough (yes, you can run AI&#x2F;ML workloads in isolated microVMs), booting Docker images via a plugin system, and Kubernetes-style deployments with replication, all from a single CLI(soon to be web UI, see next)<p>Coming soon: a built-in PaaS mode with snapshot-based cold start elimination, sort of like Dokploy, but designed for serverless workloads that boot from memory snapshots instead of containers.<p>Volant is intentionally a bit opinionated to make microVMs more accessible, but it’s fully extensible for power users.<p>Check out the README and the docs for more details.<p>It’s free and open source (under BSL), would love to hear feedback or thoughts from anyone!<p>tl;dr: 6-second GIF in the README shows the full flow: install → create VM → get HTTP 200.

[CLI Tool] Show HN: DidMySettingsChange – A tool that checks changed windows settings Microsoft has been under heavy scrutiny with how they manage Windows over the years, particularly concerning privacy and telemetry settings. Many users find that after disabling certain settings, these settings are mysteriously re-enabled after updates or without any apparent reason. DidMySettingsChange is a Python script designed to help users keep track of their Windows privacy and telemetry settings, ensuring that they stay in control of their privacy without the hassle of manually checking each setting. Features<p><pre><code> Comprehensive Checks: Automatically scans all known Windows privacy and telemetry settings. Change Detection: Alerts users if any settings have been changed from their preferred state. Customizable Configuration: Allows users to specify which settings to monitor. Easy to Use: Simple command-line interface that provides clear and concise output. Logs and Reports: Generates detailed logs and reports for auditing and troubleshooting.</code></pre>

[CLI Tool] Show HN: ut – Rust based CLI utilities for devs and IT Hey HN,<p>I find myself reaching for tools like it-tools.tech or other random sites every now and then during development or debugging. So, I built a toolkit with a sane and simple CLI interface for most of those tools.<p>For the curious and lazy, at the moment, ut has tools for,<p>- Encoding: base64 (encode, decode), url (encode, decode)<p>- Hashing: md5, sha1, sha224, sha256, sha384, sha512<p>- Data Generation: uuid (v1, v3, v4, v5), token, lorem, random<p>- Text Processing: case (lower, upper, camel, title, constant, header, sentence, snake), pretty-print, diff<p>- Development Tools: calc, json (builder), regex, datetime<p>- Web &amp; Network: http (status), serve, qr<p>- Color &amp; Design: color (convert)<p>- Reference: unicode<p>For full disclosure, parts of the toolkit were built with Claude Code (I wanted to use this as an opportunity to play with it more). Feel free to open feature requests and&#x2F;or contribute.

[Other] Show HN: A Vectorless LLM-Native Document Index Method The word &quot;index&quot; originally came from how humans retrieve info: book indexes and tables of contents that guide us to the right place in documents.<p>Computers later borrowed the term for data structures: e.g., B-trees, hash tables, and more recently, vector indexes. They are highly efficient for machines; but abstract and unnatural: not something a human, or an LLM, can understand and directly use as a reasoning aid. This creates a gap between how indexes work for computers and how they should work for models that reason like humans.<p>PageIndex is a new step that &quot;looks back to move forward&quot;. It revives the original, human-oriented idea of an index and adapts it for LLMs. Now the index itself (PageIndex) lives inside the LLM&#x27;s context window: the model sees a hierarchical table-of-contents tree and reasons its way down to the right span, much like a person would retrieve information using a book&#x27;s index.<p>PageIndex MCP shows how this works in practice: it runs as a MCP server, exposing a document&#x27;s structure directly to LLMs&#x2F;Agents. This means platforms like Claude, Cursor, or any MCP-enabled agent or LLM can navigate the index themselves and reason their way through documents, not with vectors&#x2F;chunking, but in a human-like, reasoning-based way.

[Code Quality] Show HN: Pyscn – Python code quality analyzer for vibe coders Hi HN! I built pyscn for Python developers in the vibe coding era. If you&#x27;re using Cursor, Claude, or ChatGPT to ship Python code fast, you know the feeling: features work, tests pass, but the codebase feels... messy.<p>Common vibe coding artifacts:<p>• Code duplication (from copy-pasted snippets)<p>• Dead code from quick iterations<p>• Over-engineered solutions for simple problems<p>• Inconsistent patterns across modules<p>pyscn performs structural analysis:<p>• APTED tree edit distance + LSH<p>• Control-Flow Graph (CFG) analysis<p>• Coupling Between Objects (CBO)<p>• Cyclomatic Complexity<p>Try it without installation:<p><pre><code> uvx pyscn analyze . # Using uv (fastest) pipx run pyscn analyze . # Using pipx (Or install: pip install pyscn) </code></pre> Built with Go + tree-sitter. Happy to dive into the implementation details!

[Other] Adding Stride Scheduling to Xv6

[CLI Tool] Show HN: Run – a CLI universal code runner I built while learning Rust Hi HN — I’m learning Rust and decided to build a universal CLI for running code in many languages. The tool, Run, aims to be a single, minimal dependency utility for: running one-off snippets (from CLI flags), running files, reading and executing piped stdin, and providing language-specific REPLs that you can switch between interactively. I designed it to support both interpreted languages (Python, JS, Ruby, etc.) and compiled languages (Rust, Go, C&#x2F;C++). It detects languages from flags or file extensions, can compile temporary files for compiled languages, and exposes a unified REPL experience with commands like :help, :lang, and :quit. Install: cargo install run-kit (or use the platform downloads on GitHub). Source &amp; releases: <a href="https:&#x2F;&#x2F;github.com&#x2F;Esubaalew&#x2F;run" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;Esubaalew&#x2F;run</a> I used Rust while following the official learning resources and used AI to speed up development, so I expect there are bugs and rough edges. I’d love feedback on: usability and UX of the REPL, edge cases for piping input to language runtimes, security considerations (sandboxing&#x2F;resource limits), packaging and cross-platform distribution. Thanks — I’ll try to answer questions and share design notes.

[Other] A comparison of Ada and Rust, using solutions to the Advent of Code

[Other] Microformats – building blocks for data-rich web pages

[CLI Tool] Show HN: RenderarXiv – Search ArXiv from terminal, HTML to read/paste into LLM Terminal tool to search arXiv papers and render them as beautiful HTML for humans and LLMs

[Other] Zig builds are getting faster

[Other] Show HN: Powerful Visual Programming Language (Book) Throughout my 30+ software development career, after spending many sleepless nights digging up through enormous codebases to understand logic or fix a bug, I was thinking: &quot;There must be a better, visual way to represent program rather than text&quot;. However, no usable visual programming language popped up on horizon for the whole duration of 30+ years of my career. Therefore, I decided to take matters in my own hands, creating new visual programming language called &quot;Pipe&quot;. A book about this language was published recently. The book is available for free on Amazon Kindle and Apple iBooks.<p>Language Pipe has a level of sophistication and power comparable to existing most powerful textual languages and therefore, it has a very high chances to successfully compete with text-based programming. The book provides full and comprehensive language specification. On top of that, the book contains many features and ideas planned for future versions of the language.<p>Pipe implements many novel concepts and unique features. As a result, multiple patent applications have already been filed and pending. The published book contains complete language specification, including graphical notation of all its elements and full API specification for code integration. Pipe has the following features:<p>* General-purpose visual language.<p>* Compact but powerful language.<p>* Complete and detailed language specification.<p>* Practical visual language.<p>* API specification for integration with non-visual languages.<p>* Statically-typed language.<p>* Long-term plans for future versions.<p>* Augmentation of AI code generation.<p>* Language for the next generation of low-code systems.<p>The problem of AI code generation is that it is very difficult to prepare complete and precise input specifications, especially in case of a large project. The solution is generating code only for base-level components easily explainable to AI, completing the rest of application via manual coding. That, however, undermines the goal of leveraging AI to remove the need for human programming. Pipe provides an alternative to textual coding by encapsulating AI-generated components within visual blocks for building the rest of application as graphical workflows via an intuitive drag-and-drop interface. As a next level of Pipe evolution, AI will be generating complete visual workflows directly, making it much easier to understand and modify generated logic.<p>Usage of a general-purpose visual programming language Pipe to connect blocks containing AI-generated code can inspire the next generation of extremely versatile low-code platforms, as AI code generation followed by visual integration of generated components is a very powerful low-code framework. Users will be able to generate new components using AI and that solves the problem of limited customization in existing low-code platforms where components are mostly predefined. On top of that, common visual programming language Pipe will ensure portability of low-code projects between different platforms.<p>Please find PDF with book preview here: <a href="https:&#x2F;&#x2F;www.pipelang.com&#x2F;sample&#x2F;sample.pdf" rel="nofollow">https:&#x2F;&#x2F;www.pipelang.com&#x2F;sample&#x2F;sample.pdf</a>

[API/SDK] Show HN: FLE v0.3 – Claude Code Plays Factorio We&#x27;re excited to release v0.3.0 of the Factorio Learning Environment (FLE), an open-source environment for evaluating AI agents on long-horizon planning, spatial reasoning, and automation tasks.<p>== What is FLE? ==<p>FLE uses the game Factorio to test whether AI can handle complex, open-ended engineering challenges. Agents write Python code to build automated factories, progressing from simple resource extraction (~30 units&#x2F;min) to sophisticated production chains (millions of units&#x2F;sec).<p>== What&#x27;s new in 0.3.0 ==<p>- Headless scaling: No longer needs the game client, enabling massive parallelization!<p>- OpenAI Gym compatibility: Standard interface for RL research<p>- Claude Code integration: We&#x27;re livestreaming Claude playing Factorio [on Twitch](<a href="http:&#x2F;&#x2F;twitch.tv&#x2F;playsfactorio" rel="nofollow">http:&#x2F;&#x2F;twitch.tv&#x2F;playsfactorio</a>)<p>- Better tooling and SDK: 1-line CLI commands to run evaluations (with W&amp;B logging)<p>== Key findings ==<p>We evaluated frontier models (Claude Opus 4.1, GPT-5, Gemini 2.5 Pro, Grok 4) on 24 production automation tasks of increasing complexity.<p>Even the best models struggle:<p>- Most models still rely on semi-manual strategies rather than true automation<p>- Agents rarely define helper functions or abstractions, limiting their ability to scale<p>- Error recovery remains difficult – agents often get stuck in repetitive failure loops<p>The performance gap between models on FLE correlates more closely with real-world task benchmarks (like GDPVal) than with traditional coding&#x2F;reasoning evals.<p>== Why this matters ==<p>Unlike benchmarks based on exams that saturate quickly, Factorio&#x27;s exponential complexity scaling means there&#x27;s effectively no performance ceiling. The skills needed - system debugging, constraint satisfaction, logistics optimization - transfer directly to real challenges.<p>== Try it yourself ==<p>&gt;&gt;&gt; uv add factorio-learning-environment<p>&gt;&gt;&gt; uv add &quot;factorio-learning-environment[eval]&quot;<p>&gt;&gt;&gt; fle cluster start<p>&gt;&gt;&gt; fle eval --config configs&#x2F;gym_run_config.json<p>We&#x27;re looking for researchers, engineers, and modders interested in pushing the boundaries of agent capabilities. Join our Discord if you want to contribute. We look forward to meeting you and seeing what you can build!<p>-- FLE Team

[CLI Tool] CLI tool to convert OpenBSD Packet Filter config files to JSON and vice versa

[API/SDK] Show HN: API for removing watermarks from Sora 2 videos Computer vision for detection, advanced inpainting for removal, FFmpeg for audio handling. Simple REST endpoints with webhook callbacks for async processing.<p>Built this after seeing developers struggle with building their own ML pipelines for video post-processing. The API handles the complexity—you just POST a video and get back a clean file

[Other] Lessons learned from building an infrastructure devtool