🛠️ All DevTools

[CLI Tool] Show HN: DNS Benchmark Tool – Compare and monitor resolvers I built a CLI to benchmark DNS resolvers after discovering DNS was adding 300ms to my API requests.<p>v0.3.0 just released with new features: compare: Test single domain across all resolvers top: Rank resolvers by latency&#x2F;reliability&#x2F;balanced monitor: Continuous tracking with threshold alerts<p>1,400+ downloads in first week.<p>Quick start: pip install dns-benchmark-tool dns-benchmark compare --domain google.com<p>CLI stays free forever. Hosted version (multi-region, historical tracking, alerts) coming Q1 2026.<p>GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;frankovo&#x2F;dns-benchmark-tool" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;frankovo&#x2F;dns-benchmark-tool</a> Feedback: <a href="https:&#x2F;&#x2F;forms.gle&#x2F;BJBiyBFvRJHskyR57" rel="nofollow">https:&#x2F;&#x2F;forms.gle&#x2F;BJBiyBFvRJHskyR57</a><p>Built with Python + dnspython. Open to questions and feedback!

[DevOps] Show HN: Virtual SLURM HPC cluster in a Docker Compose I&#x27;m the main developer behind vHPC, a SLURM HPC cluster in a docker compose.<p>As part of my job, I&#x27;m working on a software solution that needs to interact with one of the largest Italian HPC clusters (Cineca Leonardo, 270 PFLOPS). Of course developing on the production system was out of question, as it would have led to unbearably long feedback loops. I thus started looking around for existing containerised solutions, which were always lacking some key ingredient in order to suitably mock our target system (accounting, MPI, out of date software, ...).<p>I thus decided that it was worth it to make my own virtual cluster from scratch, learning a thing or two about SLURM in the process. Even though it satisfies the particular needs of the project I&#x27;m working on, I tried to keep vHPC as simple and versatile as possible.<p>I proposed the company to open source it, and as of this morning (CET) vHPC is FLOSS for others to use and tweak. I am around to answer any question.

[Testing] Show HN: ChunkBack – A Fake LLM API server for testing apps without paying Hi HN,<p>I&#x27;ve been working with LLMs in production for a while both as a solo dev building apps for clients and working at an AI startup. The one thing that always was a pain was to pay OpenAI&#x2F;Gemini&#x2F;Anthropic a few dollars a month just for me to say &quot;test&quot; or have a CI runner validate some UI code. So I built this server called ChunkBack, that mocks the popular llm provider&#x27;s functionality but allows you to type in a deterministic language:<p>`SAY &quot;cheese&quot;` or `TOOLCALL &quot;tool_name&quot; {} &quot;tool response&quot;`<p>I&#x27;ve had to work in some test environments and give good results for experimenting with CI, but it&#x27;s still an early project so would love feedback and more testers on.

[Testing] Hands-on Go certification for developers who love GOlang GoExperts is a small certification-style project built by a team of Go developers who love the language and wanted a deeper way to test real knowledge. Instead of generic quizzes, it focuses on the parts of Go that actually matter: concurrency, runtime behavior, memory model and scheduling. It’s designed for developers who enjoy exploring how Go really works and want a simple, useful way to validate their skills.

[Other] Free interactive tool that shows you how PCIe lanes work on motherboards

[Other] GitHub: Git operation failures

[Build/Deploy] Show HN: We built a generator for Vue+Laravel that gives you a clean codebase Hey HN, My team and I built a tool to scratch our own itch. We were tired of spending the first few days of every new project setting up the same Vue + Laravel boilerplate: writing migrations, models, basic CRUD controllers, and wiring up forms and tables on the frontend.<p>So we built Codecannon. It’s a web app where you define your data models, columns, and relationships, and it generates a full-stack application for you.<p>To be clear, the code isn&#x27;t AI-generated. It&#x27;s produced deterministically by our own code generators, so the output is always predictable, clean, and follows conventional best practices.<p>The key difference from other tools is that it’s not a no-code platform you get locked into. When you&#x27;re done, it pushes a well-structured codebase to your GitHub repo (or you can download a .zip file). You own it completely and can start building your real features on top of it right away.<p>What it generates: - Laravel Backend: Migrations, models with relationships, factories, seeders, and basic CRUD API endpoints.<p><pre><code> - Vue Frontend: A SPA with PrimeVue components. It includes auth pages, data tables, and create&#x2F;edit forms for each of your models, with all the state management wired up. - Dev Stuff: Docker configs, a CI&#x2F;CD pipeline starter, linters, and formatters are all included.</code></pre> The idea is to skip the repetitive work and get straight to the interesting parts of a project. It&#x27;s free to use the builder, see a live preview, and download the full codebase for apps up to 5 modules. For larger apps, you only pay if you decide you want the source code.<p>We’re in an early alpha and would love to get some honest feedback from the community. Does the generated code look sensible? Are we missing any obvious features? Is this something you would find useful or know anyone who might? Let me know what you think.

[Other] Show HN: Optimizing LiteLLM with Rust – When Expectations Meet Reality I&#x27;ve been working on Fast LiteLLM - a Rust acceleration layer for the popular LiteLLM library - and I had some interesting learnings that might resonate with other developers trying to squeeze performance out of existing systems.<p>My assumption was that LiteLLM, being a Python library, would have plenty of low-hanging fruit for optimization. I set out to create a Rust layer using PyO3 to accelerate the performance-critical parts: token counting, routing, rate limiting, and connection pooling.<p>The Approach<p>- Built Rust implementations for token counting using tiktoken-rs<p>- Added lock-free data structures with DashMap for concurrent operations<p>- Implemented async-friendly rate limiting<p>- Created monkeypatch shims to replace Python functions transparently<p>- Added comprehensive feature flags for safe, gradual rollouts<p>- Developed performance monitoring to track improvements in real-time<p>After building out all the Rust acceleration, I ran my comprehensive benchmark comparing baseline LiteLLM vs. the shimmed version:<p>Function Baseline Time Shimmed Time Speedup Improvement Status<p>token_counter 0.000035s 0.000036s 0.99x -0.6%<p>count_tokens_batch 0.000001s 0.000001s 1.10x +9.1%<p>router 0.001309s 0.001299s 1.01x +0.7%<p>rate_limiter 0.000000s 0.000000s 1.85x +45.9%<p>connection_pool 0.000000s 0.000000s 1.63x +38.7%<p>Turns out LiteLLM is already quite well-optimized! The core token counting was essentially unchanged (0.6% slower, likely within measurement noise), and the most significant gains came from the more complex operations like rate limiting and connection pooling where Rust&#x27;s concurrent primitives made a real difference.<p>Key Takeaways<p>1. Don&#x27;t assume existing libraries are under-optimized - The maintainers likely know their domain well 2. Focus on algorithmic improvements over reimplementation - Sometimes a better approach beats a faster language 3. Micro-benchmarks can be misleading - Real-world performance impact varies significantly 4. The most gains often come from the complex parts, not the simple operations 5. Even &quot;modest&quot; improvements can matter at scale - 45% improvements in rate limiting are meaningful for high-throughput applications<p>While the core token counting saw minimal improvement, the rate limiting and connection pooling gains still provide value for high-volume use cases. The infrastructure I built (feature flags, performance monitoring, safe fallbacks) creates a solid foundation for future optimizations.<p>The project continues as Fast LiteLLM on GitHub for anyone interested in the Rust-Python integration patterns, even if the performance gains were humbling.<p>Edit: To clarify - the negative performance for token_counter is likely in the noise range of measurement, suggesting that LiteLLM&#x27;s token counting is already well-optimized. The 45%+ gains in rate limiting and connection pooling still provide value for high-throughput applications.

[CLI Tool] Show HN: Gitlogue – A terminal tool that replays your Git commits with animation Gitlogue is a CLI that turns your Git commits into a typing-style replay.<p>It visualizes diffs line by line, shows the file tree, and plays back each edit as if it were typed in real time.<p>Key points<p>• Realistic typing animation<p>• Syntax-highlighted diffs<p>• File-tree view<p>• Replay any commit<p>• Self-contained CLI<p>Demo video is in the README.<p>Repo: <a href="https:&#x2F;&#x2F;github.com&#x2F;unhappychoice&#x2F;gitlogue" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;unhappychoice&#x2F;gitlogue</a>

[DevOps] A 'small' vanilla Kubernetes install on NixOS

[Other] Real-time dev insights, no micromanagement. AI-driven workflow visibility for software teams. Codaro’s IDE plugins deliver real-time reports, live alerts, and task integration—recapturing valuable dev hours and turning status updates into strategy.

[Other] Unofficial "Tier 4" Rust Target for older Windows versions

[Other] The AI platform for full-stack Web3 & Web2 development 0xminds bridges the gap between Web2 and Web3 development. Other tools are only site generators or contract editors; 0xminds is a true all-in-one. Our AI builds full-stack apps (frontend + backend) for both Web2 and Web3 from a single prompt. The standout feature is the unified workflow: Generate your site, write, compile, and deploy your custom smart contracts and tokens directly. It’s your entire stack, from idea to deployment.

[CLI Tool] Show HN: Parqeye – A CLI tool to visualize and inspect Parquet files I built a Rust-based CLI&#x2F;terminal UI for inspecting Parquet files—data, metadata, and row-group-level structure—right from the terminal. If someone sent me a Parquet file, I used to open DuckDB or Polars just to see what was inside. Now I can do it with one command.<p>Repo: <a href="https:&#x2F;&#x2F;github.com&#x2F;kaushiksrini&#x2F;parqeye" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;kaushiksrini&#x2F;parqeye</a>

[Other] Show HN: PrinceJS – 19,200 req/s Bun framework in 2.8 kB (built by a 13yo) Hey HN,<p>I&#x27;m 13, from Nigeria, and I just released PrinceJS — the fastest web framework for Bun right now.<p>• 19,200 req&#x2F;s (beats Hono&#x2F;Elysia&#x2F;Express) • 2.8 kB gzipped • Tree-shakable (cache, AI, email, cron, SSE, queue, test, static...) • Zero deps. Zero config.<p>Built in &lt; 1 week. No team. Just me and Bun.<p>Try it: `bun add princejs` GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;MatthewTheCoder1218&#x2F;princejs" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;MatthewTheCoder1218&#x2F;princejs</a> Docs: <a href="https:&#x2F;&#x2F;princejs.vercel.app" rel="nofollow">https:&#x2F;&#x2F;princejs.vercel.app</a><p>Brutal feedback welcome. What&#x27;s missing?<p>– @Lil_Prince_1218

[Other] Show HN: Building WebSocket in Apache Iggy with Io_uring and Completion Based IO

[CLI Tool] Show HN: I build a strace clone for macOS Ever since I tested software on macOS, I deeply missed my beloved strace that I use when programs are missbehaving. macOS has dtruss but it&#x27;s getting locked down and more unusable with every machine. My approach uses the signed lldb binary on the system and re-implements the output you are know from the wonderful strace tool. I just created the tool yesterday evening, so it may have a few bugs, but I already got quiet a few integration tests and I am happy so far with it.

[Monitoring/Observability] Show HN: I ditched Grafana for my home server and built this instead Frustrated by the complexity and resource drain of multi service monitoring stacks, I built Simon. I wanted a single, lightweight dashboard to replace the heavy stack and the constant need for an SSH client for routine tasks. The result is a resource efficient dashboard in a single Rust binary, just a couple of megabytes in size. Its support for various architectures on Linux also makes it ideal for embedded systems and lightweight SBCs.<p>It integrates: Comprehensive Monitoring: Realtime and historical metrics for the host system and Docker containers (CPU, memory, disk usage, and network activity). Integrated File &amp; Log Management: A web UI for file operations and for viewing container logs, right where you need them. Flexible Alerting: A system to set rules on any metric, with templates for sending notifications to Telegram, ntfy, and webhooks. My goal was to create a cohesive, lightweight tool for self hosters and resource constrained environments. I&#x27;d love to get your feedback.<p><a href="https:&#x2F;&#x2F;github.com&#x2F;alibahmanyar&#x2F;simon" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;alibahmanyar&#x2F;simon</a>

[Other] GCC 16 considering changing default to C++20

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