🛠️ All DevTools

[CLI Tool] Better Curl Saul: a lightweight API testing CLI focused on UX and simplicity

[Other] Show HN: A UI Library for the Web Focusing on accessibility, longevity, performance, and simplicity

[Other] Show HN: Dayflow – A git log for your day Hi HN! I&#x27;ve been building Dayflow, a macOS app that automatically tracks what you&#x27;re actually working on (not just which apps you have open).<p>Here&#x27;s what it does:<p>- It creates a semantic timeline of your day;<p>- It does it by understanding the content on your screen (with local or cloud VLMs);<p>- This allows you to see exactly where your time went without any manual logging.<p>Traditional time trackers tell you &quot;3 hours in Chrome&quot; which is not very helpful. Dayflow actually understands if you&#x27;re reading documentation, debugging code, or scrolling HN. Instead of &quot;Chrome: 3 hours&quot;, you get &quot;Reviewed PR comments: 45min&quot;, &quot;Read HN thread about Rust: 20min&quot;, &quot;Debugged auth flow: 1.5hr&quot;.<p>I was an early Rewind user but rarely used the retrieval feature. I built Dayflow because I saw other interesting uses for screen data. I find that it helps me stay on track while working - I check it every few hours and make sure I’m spending my time the way I intended - if I’m not, I try to course correct.<p>Here’s what you need to know about privacy:<p>- Run 100% locally using qwen2.5-vl-3b (~4GB model)<p>- No cloud uploads, no account<p>- Full source available under MIT license (<a href="https:&#x2F;&#x2F;github.com&#x2F;JerryZLiu&#x2F;Dayflow" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;JerryZLiu&#x2F;Dayflow</a>)<p>- Optional: BYO Gemini API key for better quality (stored in Keychain, with free-tier workaround to prevent training on your data)<p>The tech stack is pretty simple, SwiftUI with a local sqlite DB. Uses native macOS apis for efficient screen captures. Since most people who run LLMs locally already have their tool of choice (Ollama, LLMStudio, etc.), I decided to not embed an LLM into Dayflow.<p>By far the biggest challenge was adapting from SOTA vision models like Gemini 2.5 Pro to small, local models. My constraints were that it had to take up &lt;4GB of ram and have vision capabilities. I had to do a lot of evals to figure out that Qwen2.5VL-3B was the best balance of size and quality, but there was still a sizable tradeoff in quality that I had to accept. I also got creative with sampling rates and prompt chunking to deal with the 100x smaller context window. Processing a 15 minute segment takes ~32 local LLM calls vs 2 Gemini calls!<p>Here’s what I’m working on next:<p>Distillation: Using Gemini&#x27;s high-quality outputs as training data to teach a local model the patterns it needs, hopefully closing the quality gap.<p>Custom dashboards where you can track answers to any question like &quot;How long did I spend on HN?&quot; or &quot;Hours until my first deep work session of the day<p>I&#x27;d love to hear your thoughts, especially if you&#x27;ve struggled with productivity tracking or have ideas for what you&#x27;d want from a tool like this.

[DevOps] Show HN: Mosaic – A Kotlin framework for cleaner back end code Backend APIs often grow into large orchestration classes full of duplicated calls and manual concurrency.<p>I’ve been working on Mosaic, a Kotlin framework that composes responses out of small, request-scoped “tiles.” Each tile runs once per request, dependencies resolve automatically, and independent tiles execute in parallel without boilerplate.<p>It’s still early (v0.2.0), but working today for caching, concurrency, and testability. Curious to hear feedback on the approach.<p>GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;Nick-Abbott&#x2F;Mosaic" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;Nick-Abbott&#x2F;Mosaic</a> Maven Central: org.buildmosaic:mosaic-core:0.2.0

[Other] Show HN: Verdent – AI coding agent that plans, tests, and ships Hi HN!<p>We just launched Verdent, an AI coding agent that actually plans before it codes, tests its own work, and delivers production-ready results without you having to clean up after it.<p>tl;dr Check out the website [1] and read the story [2]<p>- The problem: Current AI tools dump code and leave you to debug. You ask for &quot;build a login endpoint&quot; and get a passwordless JWT mess that doesn&#x27;t fit your project. We&#x27;ve talked to hundreds of engineers who are frustrated with having to micromanage AI output.<p>- What we built: Verdent works more like a senior engineer. It asks clarifying questions first, breaks down complex tasks, writes the code, then runs its own tests and fixes bugs until everything works. We call it the Plan → Code → Verify loop.<p>Two ways to use it: VS Code extension for staying close to the code, or Verdent Deck desktop app for handling bigger multi-component tasks in parallel.<p>- Background: I was Head of Algorithms at TikTok and spent 9 years as Chief Technical Architect at Baidu before this. Building those massive recommendation systems taught us that you need hundreds of specialized models working together, not just wrapping an LLM. We applied the same system engineering approach to AI coding.<p>- Try it: Available now, paid plans start at $19&#x2F;month. We&#x27;re a small team so no free tier, but we&#x27;d rather you experience what autonomous coding can actually do than get frustrated with a limited version.<p>- The bigger picture: We see AI coding as just the start of a larger shift where AI becomes the orchestrator across the entire software lifecycle, not just a typing assistant.<p>What do you think?<p>What tasks would you actually trust an AI agent to handle end-to-end?<p>[1] <a href="https:&#x2F;&#x2F;www.verdent.ai&#x2F;" rel="nofollow">https:&#x2F;&#x2F;www.verdent.ai&#x2F;</a> [2] <a href="https:&#x2F;&#x2F;thenewstack.io&#x2F;tiktoks-ex-algorithm-chief-launches-verdent-ai-coding-tool&#x2F;" rel="nofollow">https:&#x2F;&#x2F;thenewstack.io&#x2F;tiktoks-ex-algorithm-chief-launches-v...</a>

[Other] Amiga SPICE is a program for simulating electronic circuits

[Other] Python developers are embracing type hints

[Other] Connect. Earn. Expand A comprehensive ISP billing system featuring HotSpot (internet vouchers included) and PPPoE support. Automate subscriber payments, and network access control with our all-in-one solution built for modern Internet Service Providers.

[CLI Tool] GPT Powered command line interface Transform your terminal into an intelligent, context-aware command interface powered by GPT. Generate safe shell commands from natural language descriptions. Supports Docker, Kubernetes, databases, and more.

[Other] Curated shadcn ui websites collection Explore a handpicked collection of stunning websites and UI libraries made with Shadcn UI, crafted to spark ideas for Developers and designers

[Other] The browser extension that supercharges GitHub comment box Ever spent too much time writing a decent review, issue comment, or discussion? What if the GitHub comment box works better? An extension that replaces the GitHub native comment box, to a new block based editor and a seamless real-time preview experience.

[Other] ADHD Pomodoro Build better focus habits with FocusPal's gamified Pomodoro timer designed specifically for ADHD users

[Other] Zutty: Zero-cost Unicode Teletype, high-end terminal for low-end systems

[Other] A collection of (mostly) technical things every software developer should know about

[Other] ⚡️ Express inspired web framework written in Go

[API/SDK] Gin is a high-performance HTTP web framework written in Go. It provides a Martini-like API but with significantly better performance—up to 40 times faster—thanks to httprouter. Gin is designed for building REST APIs, web applications, and microservices.

[Other] Targetting specific characters with CSS rules

[CLI Tool] Show HN: Devbox – Containers for clean dev environments I&#x27;ve been frustrated with dependency hell and clutter on my VPS from dev, so I built Devbox: a lightweight, open-source CLI tool that spins up isolated development environments using Docker. Each project runs in its own container, but your code stays in simple flat folders on the host machine—no messing with volumes or sync issues. Environments are disposable, so you can nuke and recreate them without losing your work. Key features:<p>- Instant setup: `devbox init my-project` and you&#x27;re in a fresh env with `devbox shell`.<p>- Configurable via JSON: Define packages, services, and more in a `devbox.json` file. Share it in your repo for reproducible setups—teammates just run `devbox up`.<p>- Docker-in-Docker by default: Build and run containers inside your env without extra config.<p>- Host-friendly: Edit code directly on your machine; the container handles the runtime.<p>- Templates for quick starts: Built-ins for Python, Node.js, Go, web dev, etc.<p>- Advanced options: Port mapping, env vars, resource limits, and even mounting your dotfiles.<p>It&#x27;s FOSS (MIT license), Linux-focused (Debian&#x2F;Ubuntu, or WSL2 on Windows), and super easy to install: `curl -fsSL <a href="https:&#x2F;&#x2F;devbox.ar0.eu&#x2F;install.sh" rel="nofollow">https:&#x2F;&#x2F;devbox.ar0.eu&#x2F;install.sh</a> | bash`.<p>Check out the launch page and docs at <a href="https:&#x2F;&#x2F;devbox.ar0.eu" rel="nofollow">https:&#x2F;&#x2F;devbox.ar0.eu</a>, or the repo at <a href="https:&#x2F;&#x2F;github.com&#x2F;itzCozi&#x2F;devbox" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;itzCozi&#x2F;devbox</a>. I&#x27;d love some feedback, stars, or contributions to help grow this into a solid community tool!

[Monitoring/Observability] Show HN: Kekkai – a simple, fast file integrity monitoring tool in Go I built a tool called *Kekkai* for file integrity monitoring in production environments. It records file hashes during deployment and later verifies them to detect unauthorized modifications (e.g. from OS command injection or tampering).<p>Why it matters:<p>* Many web apps (PHP, Ruby, Python, etc.) on AWS EC2 need a lightweight way to confirm their code hasn’t been changed. * Traditional approaches that rely on metadata often create false positives. * Kekkai checks only file content, so it reliably detects real changes. * I’ve deployed it to an EC2 PHP application in production, and it’s working smoothly so far.<p>Key points:<p>* *Content-only hashing* (ignores timestamps&#x2F;metadata) * *Symlink protection* (detects swaps&#x2F;changes) * *Secure S3 storage* (deploy servers write-only, app servers read-only) * *Single Go binary* with minimal dependencies<p>Would love feedback from others running apps on EC2 or managing file integrity in production.

[Other] Show HN: Open-source AI data generator (now hosted) Hey HN! A few months ago we shared our AI dataset generator as an open source repo, and the response was incredible (<a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=44388093">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=44388093</a>). We got requests from folks who wanted to use it without the hosting overhead, so we created both options: a hosted version (<a href="https:&#x2F;&#x2F;www.metabase.com&#x2F;ai-data-generator" rel="nofollow">https:&#x2F;&#x2F;www.metabase.com&#x2F;ai-data-generator</a> for instant use and the source code fully open (<a href="https:&#x2F;&#x2F;github.com&#x2F;metabase&#x2F;dataset-generator" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;metabase&#x2F;dataset-generator</a>) for anyone who wants to self-host or contribute.<p>Looking forward to seeing how you use it and what you build on top of it!<p>Bonus: The repo now supports multi-provider LLM integration with LiteLLM, thanks to a great contribution from their team.