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[Other] Let your Coding Agent debug the browser session with Chrome DevTools MCP

[Other] Show HN: Goal.md, a goal-specification file for autonomous coding agents

[Other] Show HN: What if your synthesizer was powered by APL (or a dumb K clone)? I built k-synth as an experiment to see if a minimalist, K-inspired array language could make sketching waveforms faster and more intuitive than traditional code. I’ve put together a web-based toolkit so you can try the syntax directly in the browser without having to touch a compiler:<p>Live Toolkit: <a href="https:&#x2F;&#x2F;octetta.github.io&#x2F;k-synth&#x2F;" rel="nofollow">https:&#x2F;&#x2F;octetta.github.io&#x2F;k-synth&#x2F;</a><p>If you visit the page, here is a quick path to an audio payoff:<p>- Click &quot;patches&quot; and choose dm-bell.ks.<p>- Click &quot;run&quot;—the notebook area will update. Click the waveform to hear the result.<p>- Click the &quot;-&gt;0&quot; button below the waveform to copy it into slot 0 at the top (slots are also clickable).<p>- Click &quot;pads&quot; in the entry area to show a performance grid.<p>- Click &quot;melodic&quot; to play slot 0&#x27;s sample at different intervals across the grid.<p>The &#x27;Weird&#x27; Stack:<p>- The Language: A simplified, right-associative array language (e.g., s for sine, p for pi).<p>- The Web Toolkit: Built using WASM and Web Audio for live-coding samples.<p>- AI Pair-Programming: I used AI agents to bootstrap the parser and web boilerplate, which let me vet the language design in weeks rather than months.<p>The Goal: This isn&#x27;t meant to replace a DAW. It’s a compact way to generate samples for larger projects. It’s currently in a &quot;will-it-blend&quot; state. I’m looking for feedback from the array language and DSP communities—specifically on the operator choices and the right-to-left evaluation logic.<p>Source (MIT): <a href="https:&#x2F;&#x2F;github.com&#x2F;octetta&#x2F;k-synth" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;octetta&#x2F;k-synth</a>

[API/SDK] Show HN: AgentMailr – dedicated email inboxes for AI agents I kept running into the same problem while building AI agents: every agent that needs email ends up sharing my personal inbox or a single company domain. That breaks attribution, creates deliverability risk, and makes it impossible to test sender identities per agent.<p>So I built AgentMailr. You call an API to create an inbox, your agent gets a unique email address, and replies route back to that specific agent. Works for both inbound (OTP parsing, reply routing) and outbound (cold email, notifications).<p>Bring your own domain is supported so emails come from your domain, not ours. REST API and MCP server are live. Node&#x2F;Python SDKs are in progress.<p>Happy to answer questions about the architecture or how I&#x27;m handling multi-agent routing.

[Other] Learning Creative Coding

[Other] Show HN: Han – A Korean programming language written in Rust A few weeks ago I saw a post about someone converting an entire C++ codebase to Rust using AI in under two weeks.<p>That inspired me — if AI can rewrite a whole language stack that fast, I wanted to try building a programming language from scratch with AI assistance.<p>I&#x27;ve also been noticing growing global interest in Korean language and culture, and I wondered: what would a programming language look like if every keyword was in Hangul (the Korean writing system)?<p>Han is the result. It&#x27;s a statically-typed language written in Rust with a full compiler pipeline (lexer → parser → AST → interpreter + LLVM IR codegen).<p>It supports arrays, structs with impl blocks, closures, pattern matching, try&#x2F;catch, file I&#x2F;O, module imports, a REPL, and a basic LSP server.<p>This is a side project, not a &quot;you should use this instead of Python&quot; pitch. Feedback on language design, compiler architecture, or the Korean keyword choices is very welcome.<p><a href="https:&#x2F;&#x2F;github.com&#x2F;xodn348&#x2F;han" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;xodn348&#x2F;han</a>

[Other] Show HN: Zap Code – AI code generator that teaches kids real HTML/CSS/JS Zap Code generates working HTML&#x2F;CSS&#x2F;JS from plain English descriptions, designed for kids ages 8-16.<p>The core loop: kid types &quot;make a space shooter game&quot;, AI generates the code, live preview renders it immediately. Three interaction modes - visual-only tweaks, read-only code view with annotations, and full code editing with AI autocomplete.<p>Technical details: Next.js frontend, Node.js backend, Monaco editor simplified for younger users, sandboxed iframe for preview execution (no external API calls from generated code). Progressive complexity engine uses a skill model to decide when to surface more advanced features.<p>Main thing that was focused on was the gap between block-based coding (Scratch, etc.) and actual programming. Block tools are great for ages 6-10 but the transition to real code is rough. This tries to smooth that curve by letting kids interact with real output first, then gradually exposing the code behind it.<p>Limitations: AI-generated code isn&#x27;t always clean or idiomatic. Content is filtered for age-appropriateness but its not perfect. Collaboration features are still basic. The complexity engine needs more data to tune well.<p>Free tier, 3 projects. Pro at $9.99&#x2F;mo.

[Other] Claudetop – htop for Claude Code sessions (see your AI spend in real-time)

[Other] Show HN: KeyID – Free email and phone infrastructure for AI agents (MCP)

[Other] Show HN: Data-anim – Animate HTML with just data attributes Hey HN, I built data-anim — an animation library where you never have to write JavaScript yourself.<p>You just write:<p><pre><code> &lt;div data-anim=&quot;fadeInUp&quot;&gt;Hello&lt;&#x2F;div&gt; </code></pre> That&#x27;s it. Scroll-triggered fade-in animation, zero JS to write.<p>What it does:<p>- 30+ built-in animations (fade, slide, zoom, bounce, rotate, etc.)<p>- 4 triggers: scroll (default), load, click, hover<p>- 3-layer anti-FOUC protection (immediate style injection → noscript fallback → 5s timeout)<p>- Responsive controls: disable per device or swap animations on mobile<p>- TypeScript autocomplete for all attributes<p>- Under 3KB gzipped, zero dependencies<p>Why I built this:<p>I noticed that most animation needs on landing pages and marketing sites are simple — fade in on scroll, slide in from left, bounce on hover. But the existing options are either too heavy (Framer Motion ~30KB) or require JS boilerplate.<p>I also think declarative HTML attributes are the most AI-friendly animation format. When LLMs generate UI, HTML attributes are the output they hallucinate least on — no selector matching, no JS API to misremember, no script execution order to get wrong.<p>Docs: <a href="https:&#x2F;&#x2F;ryo-manba.github.io&#x2F;data-anim&#x2F;" rel="nofollow">https:&#x2F;&#x2F;ryo-manba.github.io&#x2F;data-anim&#x2F;</a><p>Playground: <a href="https:&#x2F;&#x2F;ryo-manba.github.io&#x2F;data-anim&#x2F;playground&#x2F;" rel="nofollow">https:&#x2F;&#x2F;ryo-manba.github.io&#x2F;data-anim&#x2F;playground&#x2F;</a><p>npm: <a href="https:&#x2F;&#x2F;www.npmjs.com&#x2F;package&#x2F;data-anim" rel="nofollow">https:&#x2F;&#x2F;www.npmjs.com&#x2F;package&#x2F;data-anim</a><p>Happy to answer any questions about the implementation or design decisions.

[API/SDK] Show HN: GitAgent – An open standard that turns any Git repo into an AI agent We built GitAgent because we kept seeing the same problem: every agent framework defines agents differently, and switching frameworks means rewriting everything.<p>GitAgent is a spec that defines an AI agent as files in a git repo.<p>Three core files — agent.yaml (config), SOUL.md (personality&#x2F;instructions), and SKILL.md (capabilities) — and you get a portable agent definition that exports to Claude Code, OpenAI Agents SDK, CrewAI, Google ADK, LangChain, and others.<p>What you get for free by being git-native:<p>1. Version control for agent behavior (roll back a bad prompt like you&#x27;d revert a bad commit) 2. Branching for environment promotion (dev → staging → main) 3. Human-in-the-loop via PRs (agent learns a skill → opens a branch → human reviews before merge) 4. Audit trail via git blame and git diff 5. Agent forking and remixing (fork a public agent, customize it, PR improvements back) 6. CI&#x2F;CD with GitAgent validate in GitHub Actions<p>The CLI lets you run any agent repo directly:<p>npx @open-gitagent&#x2F;gitagent run -r <a href="https:&#x2F;&#x2F;github.com&#x2F;user&#x2F;agent" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;user&#x2F;agent</a> -a claude<p>The compliance layer is optional, but there if you need it — risk tiers, regulatory mappings (FINRA, SEC, SR 11-7), and audit reports via GitAgent audit.<p>Spec is at <a href="https:&#x2F;&#x2F;gitagent.sh" rel="nofollow">https:&#x2F;&#x2F;gitagent.sh</a>, code is on GitHub.<p>Would love feedback on the schema design and what adapters people would want next.

[DevOps] Show HN: I built Wool, a lightweight distributed Python runtime I spent a long time working in the payments industry, specifically on a rather niche reporting&#x2F;aggregation platform with spiky workloads that were not easily parallelized. To pump as much data through our pipeline as possible, we had to rely on complex locking schemes across half a dozen or so not-so-micro services - keeping a clear mental picture of how the services interacted for a given data source was a major headache. This problem always intrigued me, even after I no longer worked at the company, and lead to the development of Wool.<p>If you&#x27;ve worked with frameworks like Ray or Prefect, you&#x27;re probably familiar with the promise of going from script to scale in two lines of code (or something along those lines). This is essentially the solution I was looking for: a framework with limited boilerplate that facilitated arbitrary distribution schemes within a single, coherent codebase. What I was hoping for, though, was something a little bit more focused - I wasn&#x27;t working on ML pipelines and didn&#x27;t need much else other than the distribution layer. This is where Wool comes in. While it&#x27;s API is very similar to those of Ray and Prefect, where it differentiates itself is in its scope and architecture.<p>First, Wool is not a task orchestrator. It provides push-based, best-effort, at-most-once execution. There is no built-in coordination state, retry logic, or durable task tracking. Those concerns remain application-defined. The beauty of Wool is that it looks and feels like native async Python, allowing you to use purpose-built libraries for your needs as you would for any other Python app (with some caveats).<p>Second, Wool was designed with speed in mind. Because it&#x27;s not bloated with features, it&#x27;s actually pretty fast, even in its current nascent state. Wool routines are dispatched directly to a decentralized peer-to-peer network of gRPC workers, who can distribute nested routines amongst themselves in turn. This results in low dispatch latencies and high throughput. I won&#x27;t make any performance claims until I can assemble some more robust benchmarks, but running local workers on my M4 MacBook Pro (a trivial example, I know), I can easily achieve sub-millisecond dispatch latencies.<p>Anyway, check it out, any and all feedback is welcome. Regarding docs- the code is the documentation for now, but I promise I&#x27;ll sort that out soon. I&#x27;ve got plenty of ideas for next steps, but it&#x27;s always more fun when people actually use what you&#x27;ve built, so I&#x27;m open to suggestions for impactful features.<p>-Conrad

[Other] Claude Code's binary reveals silent A/B tests on core features

[Other] Megadev: A Development Kit for the Sega Mega Drive and Mega CD Hardware

[Other] Show HN: Fatal Core Dump – a debugging murder mystery played with GDB Debugging a crash can sometimes feel like a noir detective story: following faint clues, chasing red herrings, and eventually hitting the moment where the whole case finally makes sense. I leaned into that idea and built Fatal Core Dump, a small game where the investigation is real crash debugging.<p>The game gives you a Linux binary, its core dump, a source file, and some logs. You solve the mystery by debugging it.<p>The premise: an engineer dies when an airlock on an asteroid mining station opens unexpectedly. Your job is to determine whether it was a simple software bug or something more deliberate.<p>The investigation uses real debugging tools and techniques. You can use whatever debugging setup you prefer.<p>There’s also a spoiler-heavy blog post describing how the game was conceived and implemented, and the full code is available if you’re curious about how it works or want to experiment with the idea.<p>Blog post: <a href="https:&#x2F;&#x2F;www.robopenguins.com&#x2F;core-dump-game&#x2F;" rel="nofollow">https:&#x2F;&#x2F;www.robopenguins.com&#x2F;core-dump-game&#x2F;</a> Source: <a href="https:&#x2F;&#x2F;github.com&#x2F;axlan&#x2F;fatal_core_dump" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;axlan&#x2F;fatal_core_dump</a><p>If you enjoy debugging puzzles or low-level Linux tooling, I’d love to hear what you think.

[Other] Show HN: Simple plugin to get Claude Code to listen to you Hey HN,<p>My cofounder and I have gotten tired of CC ignoring our markdown files so we spent 4 days and built a plugin that automatically steers CC based on our previous sessions. The problem is usually post plan-mode.<p>What we&#x27;ve tried:<p>Heavily use plan mode (works great)<p>CLAUDE.md, AGENTS.md, MEMORY.md<p>Local context folder (upkeep is a pain)<p>Cursor rules (for Cursor)<p>claude-mem (OSS) -&gt; does session continuity, not steering<p>We use fusion search to find your CC steering corrections.<p>- user prompt embeddings + bm25<p>- correction embeddings + bm25<p>- time decay<p>- target query embeddings<p>- exclusions<p>- metadata hard filters (such as files)<p>The CC plugin:<p>- Automatically captures memories&#x2F;corrections without you having to remind CC<p>- Automatically injects corrections without you having to remind CC to do it.<p>The plugin will merge, update, and distill your memories, and then inject the highest relevant ones after each of your own prompts.<p>We&#x27;re not sure if we&#x27;re alone in this. We&#x27;re working on some benchmarks to see how effective context injection actually is in steering CC and we know we need to keep improving extraction, search, and add more integrations.<p>We&#x27;re passionate about the real-time and personalized context layer for agents. Giving Agents a way to understand what you mean when you say &quot;this&quot; or &quot;that&quot;. Bringing the context of your world, into a secure, structured, real-time layer all your agents can access.<p>Would appreciate feedback on how you guys get CC to actually follow your markdown files, understand your modus operandi, feedback on the plugin, or anything else about real-time memory and context.<p>- Ankur

[DevOps] Show HN: Hardened OpenClaw on AWS with Terraform I work on AWS infrastructure (ex-Percona, Box, Dropbox, Pinterest). When OpenClaw blew up, I wanted to run it properly on AWS and was surprised by the default deployment story. The Lightsail blueprint shipped with 31 unpatched CVEs. The standard install guide uses three separate curl-pipe-sh patterns as root. Bitsight found 30,000+ exposed instances in two weeks. OpenClaw&#x27;s own maintainer said &quot;if you can&#x27;t understand how to run a command line, this is far too dangerous.&quot;<p>So I built a Terraform module that replaces the defaults with what I&#x27;d consider production-grade:<p>* Cognito + ALB instead of a shared gateway token (per-user identity, MFA) * GPG-verified APT packages instead of curl|bash * systemd with ProtectHome=tmpfs and BindPaths sandboxing * Secrets Manager + KMS instead of plaintext API keys * EFS for persistence across instance replacement * CloudWatch logging with 365-day retention Bedrock is the default LLM provider so it works without any API keys. One terraform apply. Full security writeup: <a href="https:&#x2F;&#x2F;infrahouse.com&#x2F;blog&#x2F;2026-03-09-deploying-openclaw-on-aws-without-the-security-disasters&#x2F;" rel="nofollow">https:&#x2F;&#x2F;infrahouse.com&#x2F;blog&#x2F;2026-03-09-deploying-openclaw-on...</a><p>I&#x27;m sure I&#x27;ve missed things. What would you add or do differently for running an autonomous agent with shell access on a shared server?

[Other] Show HN: An addendum to the Agile Manifesto for the AI era I&#x27;m a VP of Engineering with 20 years in the field. I&#x27;ve been thinking deeply on why AI is breaking every engineering practice, and it led me to the conclusion that the Agile Manifesto&#x27;s values need updating.<p>The core argument: AI made producing software cheap, but understanding it is still expensive. The Manifesto optimizes for the former. This addendum shifts the emphasis toward the latter.<p>Four updated values, three refined principles, with reasoning for each. Happy to discuss and defend any of it.

[Other] The wild six weeks for NanoClaw's creator that led to a deal with Docker

[Other] Mouser: An open source alternative to Logi-Plus mouse software I discovered this project because all-of-a-sudden Logi Options Plus software updater started taking 40-60% of my Intel Macbook Pro until I killed the process (of course it restarts). In my searches I ended up at a reddit discussion where I found other people with same issues.<p>I&#x27;m a minor contributor to this project but it aims to reduce&#x2F;eliminate the need to use Logitech proprietary software and telemetry. We could use help if other people are interested.<p>Please check out the github link for more detailed motivations (eliminating telemetry) as a part of this project. Here is link: <a href="https:&#x2F;&#x2F;github.com&#x2F;TomBadash&#x2F;MouseControl" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;TomBadash&#x2F;MouseControl</a>