🛠️ All DevTools

[DevOps] 💻 A fully functional local AWS cloud stack. Develop and test your cloud & Serverless apps offline

[Other] Show HN: A new language for COBOL workloads, built on Go We’re building an open-source language layer on top of Go, designed specifically for COBOL-style workloads: Native decimal arithmetic (COBOL-accurate) Record structures and copybook compatibility Batch jobs and transactional orchestration as first-class constructs Sequential &#x2F; indexed file I&#x2F;O baked into the runtime Compiles through Go for speed, concurrency, and cloud deployability Think of it as Kotlin for COBOL, or “COBOL on Go” familiar to mainframe engineers, powerful for modern developers.<p>Test Results so far: NIST COBOL-85 validation: 77.61% overall (305&#x2F;393 tests) NC (Core COBOL): 97.89% (93&#x2F;95) SM (Statements): 100% (13&#x2F;13) RL (Relative I&#x2F;O): 100% (26&#x2F;26) IF (Intrinsic Functions): 100% (45&#x2F;45) IC (CALL): 96% (24&#x2F;25) Compliance tests: 100% passing Acceptance tests: 100% passing

[Other] AI powered Interviews - Reliable, Efficient and Trustworthy InterWiz is a B2B platform for conducting AI-based interviews, allowing companies to hire top talent quickly, effortlessly, and with superior quality. We facilitate screening interviews, in-depth technical interviews, and live coding challenges.

[IDE/Editor] Real-time security guardrails for your AI code assistant Stop insecure AI code before it lands. Snyk Studio plugs into your AI code assistants (and VS Code, Cursor, and others) to scan code suggestions in real time, flag risky patterns, and guide safer fixes by these coding agents. Snyk Studio also injects Snyk’s security expert context so your assistant can plan and apply fixes to existing vulnerabilities without ever leaving the editor and terminal.

[Testing] Hypothesis: Property-Based Testing for Python

[Other] Preventing Kubernetes from Pulling the Pause Image from the Internet

[Other] Analyzing the Performance of WebAssembly vs. Native Code

[Other] Launch HN: Plexe (YC X25) – Build production-grade ML models from prompts Hey HN! We&#x27;re Vaibhav and Marcello, founders of Plexe (<a href="https:&#x2F;&#x2F;www.plexe.ai">https:&#x2F;&#x2F;www.plexe.ai</a>). We create production-ready ML models from natural language descriptions. Tell Plexe what ML problem you want to solve, point it at your data, and it handles the entire pipeline from feature engineering to deployment.<p>Here’s a walkthrough: <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=TbOfx6UPuX4" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=TbOfx6UPuX4</a>.<p>ML teams waste too much time on generic heavy lifting. Every project follows the same pattern: 20% understanding objectives, 60% wrangling data and engineering features, 20% experimenting with models. Most of this is formulaic but burns months of engineering time. Throwing LLMs at it isn&#x27;t the answer as that just trades engineering time for compute costs and worse accuracy. Plexe automates this repetitive 80%, so your team can work faster on what actually has value.<p>You describe your problem in plain English (&quot;fraud detection model for transactions&quot; or &quot;product embedding model for search&quot;), connect your data (Postgres, Snowflake, S3, direct upload, etc), and then Plexe: - Analyzes data and engineers features automatically - Runs experiments across multiple architectures (logistic regression to neural nets) - Generates comprehensive evaluation reports with error analysis, robustness testing, and prioritized recommendations to provide actionable guidance - Deploys the best model with monitoring and automatic retraining<p>We did a Show HN for our open-source library five months ago (<a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=43906346">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=43906346</a>). Since then, we&#x27;ve launched our commercial platform with interactive refinement, production-grade model evaluations, retraining pipeline, data connectors, analytics dashboards, and deployment for online and batch inference.<p>We use a multi-agent architecture where specialized agents handle different pipeline stages. Each agent focuses on its domain: data analysis, feature engineering, model selection, deployment, and so on. The platform tracks all experiments and generates exportable Python code.<p>Our open-source core (<a href="https:&#x2F;&#x2F;github.com&#x2F;plexe-ai&#x2F;plexe" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;plexe-ai&#x2F;plexe</a>, Apache 2.0) remains free for local development. For the paid product, our pricing is usage-based, with a minimum top up of $10. Enterprises can self-host the entire platform. You can sign up on <a href="https:&#x2F;&#x2F;console.plexe.ai">https:&#x2F;&#x2F;console.plexe.ai</a>. Use promo code `LAUNCHDAY20` to get $20 to try out the platform.<p>We’d love to hear your thoughts on the problem and feedback on the platform!

[Other] How devtools map minified JS code back to your TypeScript source code

[Other] Show HN: Agor → Figma for AI Coding (Open Source)

[Other] Debugging containers that have no shell

[Other] Show HN: PingStalker – A a macOS tool for network engineers Hi HN - I’m the developer of PingStalker, a macOS utility I built to see what’s really happening on the LAN&#x2F;WLAN.<p>I live in the CLI, but when it came to discovery and monitoring, I found it limiting. So I built a GUI that brings my favorite tools together in one place.<p>PingStalker started because I wanted to know if something on the network was scanning my machine. I also wanted quick access to core details—external IP, Wi-Fi data, and local topology. Then I wanted more: fast, reliable scans using ARP tables and ICMP.<p>As a Wi-Fi engineer, I couldn’t stop there. I kept adding ways to surface what’s actually going on behind the scenes.<p>A few highlights:<p>- Performs ARP, ICMP, mDNS, and DNS scans to discover every device on your subnet, showing IP, MAC, vendor, and open ports.<p>- Continuously monitors selected hosts (“live ping”) to visualize latency spikes, missed pings, and reconnects.<p>- Detects VLANs on trunk or hybrid ports, exposing when your Mac is sitting on a tagged interface.<p>- Captures just the important live traffic — DHCP events, ARP broadcasts, 802.1X authentication, LLDP&#x2F;CDP neighbor data, ICMP packets, and off-subnet chatter — to give you a real-time pulse of your network.<p>- Decodes mDNS traffic into human-readable form (that one took months of deep dives, but the output is finally clear and useful).<p>- Built my own custom vendor-logo database: I wrote a tool that links MAC OUIs with their companies, fetches each vendor’s favicon, and stores them locally so scan results feel alive and recognizable.<p>Under the hood it’s written in Swift. It uses low-level BSD sockets for ping and ARP, plus Apple’s Network framework for interface enumeration. The rest relies on familiar command-line tools. It’s fast.<p>I’d love feedback from anyone who builds or uses network diagnostic tools:<p>- Does this fill a gap you’ve run into on macOS?<p>- Any ideas for improving scan speed or how traffic events are visualized?<p>- What else would you like to see?<p>Details and screenshots: https:&#x2F;&#x2F;pingstalker.com<p>Happy to answer any technical questions about the implementation, Swift APIs, or macOS permission model.

[Other] The Paranoid Guide to Running Copilot CLI in a Secure Docker Sandbox

[Other] Show HN: Sparktype – a CMS and SSG that runs entirely in the browser Hi HN,<p>After trying to teach a non-technical friend how to manage a Jekyll site I decided there must be a way to make building a site with a SSG easier. Options like Decap, Contentful etc. do make it a bit easier but still take lots of tech knowledge to set up.<p>So I built Sparktype, a browser-based CMS that outputs statically-generated HTML and CSS. My goal is for it to be as easy to use as Substack or Medium, while providing all the benefits of a static site generator including openness, simplicity, speed, security and ownership.<p>It handles most things that you&#x27;d need from a CMS, including creating pages, image resizing, menu management, tags, collections, listings etc. I&#x27;ve only made two themes so far, but I&#x27;m working on a theme store and the ability to import custom themes.<p>Content is saved as plain Markdown + YAML frontmatter and JSON config files, so there&#x27;s no lock-in and content is easily portable to other platforms. Generated sites can be exported as a zip file to upload via FTP, committed to Github or published via Netlify API.<p>I&#x27;m working on cross-platform client apps using Tauri which will enable more publishing options as its not limited by what can be done in a client-only environment.<p>The way the system works means that the Web doesn&#x27;t need to be the only interface to the content - here&#x27;s a simple Go-based CLI client that bypasses the HTML altogether <a href="https:&#x2F;&#x2F;github.com&#x2F;sparktype-project&#x2F;sparktype&#x2F;tree&#x2F;main&#x2F;st-cli" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;sparktype-project&#x2F;sparktype&#x2F;tree&#x2F;main&#x2F;st-...</a><p>It&#x27;s very early days and there are still plenty of bugs, but I&#x27;m posting now to hopefully get feedback and see what people think. Please do let me know!

[Other] An evolving how-to guide for securing a Linux server.

[Other] OpenTelemetry: Escape Hatch from the Observability Cartel

[Other] Building blobd: single-machine object store with sub-ms reads and 15 GB/s upload

[Other] The AI copilot for GTM data automation Floqer lets RevOps and growth teams automate GTM data in seconds. Build multi-step workflows that detect relevant signals, enrich from 80+ sources, and trigger personalized outreach – all without a single line of code.

[IDE/Editor] Terminal-centric AI development environment Termdock is a terminal‑centric AI dev environment that unifies multi‑workspace management, multi‑terminal layouts, and Git visualization in one interface. AST search (Tree‑sitter) lets you jump to symbols and dependencies instantly. Run up to 4 windows + PiP for Docker, Redis, logs, tests, and AI tools side‑by‑side. Drag‑and‑paste images to CLI, with large text auto‑compression. Built‑in file tree and prompt libraries keep workflows fast, consistent, and scalable.

[IDE/Editor] AI-powered cloud IDE for coding from anywhere Lovelace is a browser-based AI IDE for developers who code from anywhere. It delivers AI-powered code completion, generation, and an integrated AI Agent across any device. The tool provides cloud-based workspace management from tablets, phones, or any browser. Lovelace helps developers review PRs during commutes, debug production issues remotely, or prototype ideas away from their main machine.