🛠️ All DevTools

[API/SDK] Scrape, Screenshot, and extract LLM-ready data Turn the chaotic web into clean Markdown. Access a suite of APIs to feed your legacy and AI apps without the infrastructure overhead.

[Other] Creative React Admin Template built with Material-UI Berry is a creative React dashboard template built with Material-UI. It offers a highly intuitive user experience with a modern, minimalist design and modular code structure. Whether you're building a SaaS platform or an admin panel, Berry provides everything you need: dark/light modes, responsive layouts, and developer-friendly components to accelerate your workflow.

[CLI Tool] CLI for common Playwright actions. Record and generate Playwright code, inspect selectors and take screenshots.

[Other] Tomo: A statically typed, imperative language that cross-compiles to C [video]

[DevOps] Show HN: Autonomous recovery for distributed training jobs Hi HN! We’re TensorPool. We help companies access and optimize large scale compute for training foundation models.<p>The Problem<p>It’s been almost a year since we’ve finished YC, and we’ve just crossed 100,000 multinode training GPU hours run on our platform.<p>On those training runs, we’ve seen countless 3am job crashes because of issues like an Xid error from a flaky GPU or an S3 timeout that corrupted a checkpoint save. By the time you wake up and notice, you&#x27;ve lost 8+ hours of compute. You scramble to diagnose the issue, manually restart from the last checkpoint, and hope it doesn&#x27;t happen again. Rinse and repeat.<p>For training runs that take days to weeks, this constant babysitting is exhausting and expensive. The research iteration cycles lost can also make or break a model release (especially for short reservations).<p>What We Built<p>This agent monitors your training jobs and autonomously recovers them when things go wrong. It works with Kubernetes, Slurm, and TensorPool Jobs.<p>We originally built the TensorPool Agent as an internal tool to help us debug failures with our own customers. Over time, we realized its performance was so good that we could automate the entire triage process. We&#x27;re now releasing a public beta for people to use.<p>Best case: The TensorPool Agent detects the failure, diagnoses the root cause, fixes it, and restarts your job from the last checkpoint – all while you sleep ;)<p>Worst case: If the TensorPool agent can&#x27;t fix the issue automatically, it delivers a preliminary RCA and a list of actions it attempted, giving you a head start on debugging.<p>How It Works<p>1) Registration – You provide credentials to your job scheduler via our dashboard. Perms are granted on a whitelist basis; you explicitly control what actions the agent can take.<p>2) Monitoring – The agent continuously monitors your job for failure conditions.<p>3) Recovery – On failure, the agent analyzes logs and attempts to diagnose the issue. If successful, it restarts the job from the last checkpoint and resumes monitoring. If not, you get an alert with full context.<p>Target Failure Modes<p>The agent is specifically designed for runtime errors that occur deep into training, like:<p>- CUDA OOM: Memory leaks, gradient explosions<p>- Xid errors: GPU hardware faults (Xid 79, 63, 48, etc.)<p>- Distributed communication failures: NCCL timeouts, rank failures<p>- Storage I&#x2F;O errors: Checkpoint corruption<p>- Network issues: S3 request timeouts on mounted object storage

[Other] Show HN: vind – A Better Kind (Kubernetes in Docker)

[Other] Show HN: I made a dual-bootable NixBSD (NixOS and FreeBSD) image I&#x27;ve been working on getting NixBSD (Nix package manager + FreeBSD) to boot alongside NixOS on a shared ZFS pool. The result is a &lt;2GB disk image you can try in QEMU or virt-manager.<p>What works:<p><pre><code> - GRUB chainloads FreeBSD&#x27;s bootloader - Both systems share a ZFS pool - Everything is defined in a single Nix flake - Fully reproducible builds (some dependencies are now cached on Cachix) </code></pre> Planned:<p><pre><code> - Support native compilation of NixBSD (currently cross-compiled on Linux) - Many shortcuts were taken to get this working, needs lots of cleanup - Add a semi-automated installer like nixos-wizard </code></pre> Try it:<p><pre><code> qemu-system-x86_64 -enable-kvm -m 2048 \ -bios &#x2F;usr&#x2F;share&#x2F;ovmf&#x2F;OVMF.fd \ -drive file=nixos.root.img,format=raw </code></pre> Login: nixos&#x2F;nixos or root&#x2F;toor<p>The hardest parts were getting mounts working at boot, making the bootloader setup idempotent, and debugging early init. This disk image could potentially work on a USB stick with a bit more work.<p>This is very much experimental. My goal is to eventually produce a proper NixBSD installation ISO and consolidate all configuration into one repository while still consuming upstream NixBSD as a flake.<p>Download: <a href="https:&#x2F;&#x2F;github.com&#x2F;jonhermansen&#x2F;nixbsd-demo&#x2F;releases&#x2F;tag&#x2F;build-1" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;jonhermansen&#x2F;nixbsd-demo&#x2F;releases&#x2F;tag&#x2F;bui...</a><p>Feel free to leave feedback here or on GitHub! Thanks!

[Other] Claude Code daily benchmarks for degradation tracking

[Monitoring/Observability] Stop Testing AI Agents Manually Ship AI agents confidently. The complete testing and monitoring platform for AI agents. Debug, trace, and optimize your LLM applications with real-time observability, multi-agent coordination intelligence, and automated regression detection.

[DevOps] Backend Workflows for the AI Age Building modern backend systems often means wrestling with complex orchestration, scattered logging, poor observability, opaque AI integrations, and difficult developer/devops experiences. Hot Dev is designed to solve these problems! Write event-driven workflows, watch them run in real-time, ship to production with one command.

[API/SDK] Ship backend APIs without running servers EasyBuild is the developer-first execution gateway for the modern web. Execute third-party API calls server-side from your frontend. Type-safe, secure, and ready in minutes. Build secure APIs without managing backend infrastructure.

[CLI Tool] Kimi Code CLI is your next CLI agent.

[Other] Render Mermaid diagrams as SVGs or ASCII art

[Other] Mermaid ASCII: Render Mermaid diagrams in your terminal <a href="https:&#x2F;&#x2F;github.com&#x2F;AlexanderGrooff&#x2F;mermaid-ascii" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;AlexanderGrooff&#x2F;mermaid-ascii</a>

[Other] pg_tracing: Distributed Tracing for PostgreSQL

[Other] Show HN: Cursor for Userscripts I’ve been experimenting with embedding an Claude Code&#x2F;Cursor-style coding agent directly into the browser.<p>At a high level, the agent generates and maintains userscripts and CSS that are re-applied on page load. Rather than just editing DOM via JS in console the agent is treating the page, and the DOM as a file.<p>The models are often trained in RL sandboxes with full access to the filesystem and bash, so they are really good at using it. So to make the agent behave well, I&#x27;ve simulated this environment.<p>The whole state of a page and scripts is implemented as a virtual filesystem hacked on top of browser.local storage. URL is mapped to directories, and the agent starts inside this directory. It has the tools to read&#x2F;edit files, grep around and a fake bash command that is just used for running scripts and executing JS code.<p>I&#x27;ve tested only with Opus 4.5 so far, and it works pretty reliably. The state of the file system can be synced to the real filesystem, although because Firefox doesn&#x27;t support Filesystem API, you need to manually import the fs contents first.<p>This agent is <i>really</i> useful for extracting things to CSV, but it&#x27;s also can be used for fun.<p>Demo: <a href="https:&#x2F;&#x2F;x.com&#x2F;ichebykin&#x2F;status&#x2F;2015686974439608607" rel="nofollow">https:&#x2F;&#x2F;x.com&#x2F;ichebykin&#x2F;status&#x2F;2015686974439608607</a>

[Other] Show HN: A MitM proxy to see what your LLM tools are sending I built this out of curiosity about what Claude Code was actually sending to the API. Turns out, watching your tokens tick up in real-time is oddly satisfying.<p>Sherlock sits between your LLM tools and the API, showing you every request with a live dashboard, and auto-saved copies of every prompt as markdown and json.

[Other] Mousefood – Build embedded terminal UIs for microcontrollers

[Other] Oban, the job processing framework from Elixir, has come to Python

[Other] Show HN: Config manager for Claude Code (and others) – rules, MCPs, permissions I use Claude Code across multiple projects with different conventions and some shared repos just as it so happens to be the real world. Managing the config files (.claude&#x2F;rules&#x2F;, mcps.json, settings.json) by hand got tedious, so I built a local web UI for it.<p>This one started out as claude-config but migrated to coder-config as I&#x27;m adding others (Gemini, AG, Codex, etc).<p>Main features: - Visual editor for rules, permissions, and MCP servers - Project registry to switch between codebases - &quot;Workstreams&quot; to group related repos (frontend + API + shared libs) with shared context - Auto-load workstreams on cd to included folders - Also supports Gemini CLI and Codex CLI<p>Install: npm install -g coder-config coder-config ui # UI at http:&#x2F;&#x2F;localhost:3333 coder-config ui install # optionally, autostart on MacOS<p>It can also be installed as a PWA and live in your taskbar.<p>Open source, runs locally, no account needed. Feedback and contributions welcome!<p>Sorry, haven&#x27;t had any chance to test on other OSes (linux&#x2F;windows)