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Show HN: mdto.page – Turn Markdown into a shareable webpage instantly

Hi HN<p>I built mdto.page because I often needed a quick way to share Markdown notes or documentation as a proper webpage, without setting up a GitHub repo or configuring a static site generator.<p>I wanted something dead simple: upload Markdown -> get a shareable public URL.<p>Key features:<p>Instant Publishing: No login or setup required.<p>Flexible Expiration: You can set links to expire automatically after 1 day, 7 days, 2 weeks, or 30 days. Great for temporary sharing.<p>It's free to use. I’d love to hear your feedback!

Show HN: 1Code – Open-source Cursor-like UI for Claude Code

Hi, we're Sergey and Serafim. We've been building dev tools at 21st.dev and recently open-sourced 1Code (<a href="https://1code.dev" rel="nofollow">https://1code.dev</a>), a local UI for Claude Code.<p>Here's a video of the product: <a href="https://www.youtube.com/watch?v=Sgk9Z-nAjC0" rel="nofollow">https://www.youtube.com/watch?v=Sgk9Z-nAjC0</a><p>Claude Code has been our go-to for 4 months. When Opus 4.5 dropped, parallel agents stopped needing so much babysitting. We started trusting it with more: building features end to end, adding tests, refactors. Stuff you'd normally hand off to a developer. We started running 3-4 at once. Then the CLI became annoying: too many terminals, hard to track what's where, diffs scattered everywhere.<p>So we built 1Code.dev, an app to run your Claude Code agents in parallel that works on Mac and Web. On Mac: run locally, with or without worktrees. On Web: run in remote sandboxes with live previews of your app, mobile included, so you can check on agents from anywhere. Running multiple Claude Codes in parallel dramatically sped up how we build features.<p>What’s next: Bug bot for identifying issues based on your changes; QA Agent, that checks that new features don't break anything; Adding OpenCode, Codex, other models and coding agents. API for starting Claude Codes in remote sandboxes.<p>Try it out! We're open-source, so you can just bun build it. If you want something hosted, Pro ($20/mo) gives you web with live browser previews hosted on remote sandboxes. We’re also working on API access for running Claude Code sessions programmatically.<p>We'd love to hear your feedback!

Show HN: Reversing YouTube’s “Most Replayed” Graph

Hi HN,<p>I recently noticed a recurring visual artifact in the "Most Replayed" heatmap on the YouTube player. The highest peaks were always surrounded by two dips. I got curious about why they were there, so I decided to reverse engineer the feature to find out.<p>This post documents the deep dive. It starts with a system design recreation, reverse engineering the rendering code, and ends with the mathematics.<p>This is also my first attempt at writing an interactive article. I would love to hear your thoughts on the investigation and the format.

Show HN: Gambit, an open-source agent harness for building reliable AI agents

Hey HN!<p>Wanted to show our open source agent harness called Gambit.<p>If you’re not familiar, agent harnesses are sort of like an operating system for an agent... they handle tool calling, planning, context window management, and don’t require as much developer orchestration.<p>Normally you might see an agent orchestration framework pipeline like:<p>compute -> compute -> compute -> LLM -> compute -> compute -> LLM<p>we invert this so with an agent harness, it’s more like:<p>LLM -> LLM -> LLM -> compute -> LLM -> LLM -> compute -> LLM<p>Essentially you describe each agent in either a self contained markdown file, or as a typescript program. Your root agent can bring in other agents as needed, and we create a typesafe way for you to define the interfaces between those agents. We call these decks.<p>Agents can call agents, and each agent can be designed with whatever model params make sense for your task.<p>Additionally, each step of the chain gets automatic evals, we call graders. A grader is another deck type… but it’s designed to evaluate and score conversations (or individual conversation turns).<p>We also have test agents you can define on a deck-by-deck basis, that are designed to mimic scenarios your agent would face and generate synthetic data for either humans or graders to grade.<p>Prior to Gambit, we had built an LLM based video editor, and we weren’t happy with the results, which is what brought us down this path of improving inference time LLM quality.<p>We know it’s missing some obvious parts, but we wanted to get this out there to see how it could help people or start conversations. We’re really happy with how it’s working with some of our early design partners, and we think it’s a way to implement a lot of interesting applications:<p>- Truly open source agents and assistants, where logic, code, and prompts can be easily shared with the community.<p>- Rubric based grading to guarantee you (for instance) don’t leak PII accidentally<p>- Spin up a usable bot in minutes and have Codex or Claude Code use our command line runner / graders to build a first version that is pretty good w/ very little human intervention.<p>We’ll be around if ya’ll have any questions or thoughts. Thanks for checking us out!<p>Walkthrough video: <a href="https://youtu.be/J_hQ2L_yy60" rel="nofollow">https://youtu.be/J_hQ2L_yy60</a>

Show HN: I built a text-based business simulator to replace video courses

I am a solo developer, and I built Core MBA because I was frustrated with the "video course" default in business education.<p>I wanted to build a "compiler for business logic"—a tool where I could read a concept in 5 minutes and immediately test it in a hostile environment to see if my strategy actually compiles or throws a runtime error.<p>The project is a business simulator built on React 19 and TypeScript.<p>The core technical innovation isn't just using AI; it's the architecture of a closed loop between a deterministic economic engine and a generative AI validation layer.<p>The biggest technical hurdle was building the Market Engine.<p>I needed it to be mathematically rigorous, not a hallucinating chatbot. I wrote a custom `useMarketEngine.ts` hook that runs a discrete-event simulation. Every "run cycle," it solves a system of equations, including a specific Ad Fatigue formula—`1 / (1 + (power - 1) * fatigueFactor)`—to force diminishing returns.<p>I also coded the "Theory of Constraints" directly into the state management: the system enforces bottlenecks between Inventory, Demand, and Capacity. For instance, a single employee has a hard cap of 7 operations per day. If you scale demand beyond that without hiring, the system burns your cash on lost orders.<p>To handle the educational content, I moved away from hardcoded quizzes.<p>I built a module that pipes the static lesson text into Gemini Flash to generate unique "Combat Cases" on the fly. The AI validates your strategy against the specific principles of the lesson (like LTV/CAC) rather than generic business advice.<p>These two engines are connected by a "Liquidity Loop."<p>Passing the AI cases earns you virtual capital ($500), which is the only fuel for the Market Engine. You literally cannot play the game if you don't learn the theory.<p>If you go bankrupt, my heuristic `Advisor` analyzes your crash data—comparing `lostRevenue` vs `lostCapacity`—and links you back to the exact lesson you ignored.<p>I am inviting you to test the full loop: read a brief, pass the AI simulation (Combat Cases ), and try to survive in the Market Engine.<p>I specifically need feedback on: 1. The Content: I aimed for maximum density—are the lessons <i>too</i> dry? 2. The AI Simulation: Does it accurately validate your logic based on the lesson? 3. The Market Economy: Does the math feel balanced, or is the "Ad Fatigue" too punishing?

Show HN: A 10KiB kernel for cloud apps

Show HN: Tabstack – Browser infrastructure for AI agents (by Mozilla)

Hi HN,<p>My team and I are building Tabstack to handle the "web layer" for AI agents. Launch Post: <a href="https://tabstack.ai/blog/intro-browsing-infrastructure-ai-agents" rel="nofollow">https://tabstack.ai/blog/intro-browsing-infrastructure-ai-ag...</a><p>Maintaining a complex infrastructure stack for web browsing is one of the biggest bottlenecks in building reliable agents. You start with a simple fetch, but quickly end up managing a complex stack of proxies, handling client-side hydration, and debugging brittle selectors. and writing custom parsing logic for every site.<p>Tabstack is an API that abstracts that infrastructure. You send a URL and an intent; we handle the rendering and return clean, structured data for the LLM.<p>How it works under the hood:<p>- Escalation Logic: We don't spin up a full browser instance for every request (which is slow and expensive). We attempt lightweight fetches first, escalating to full browser automation only when the site requires JS execution/hydration.<p>- Token Optimization: Raw HTML is noisy and burns context window tokens. We process the DOM to strip non-content elements and return a markdown-friendly structure that is optimized for LLM consumption.<p>- Infrastructure Stability: Scaling headless browsers is notoriously hard (zombie processes, memory leaks, crashing instances). We manage the fleet lifecycle and orchestration so you can run thousands of concurrent requests without maintaining the underlying grid.<p>On Ethics: Since we are backed by Mozilla, we are strict about how this interacts with the open web.<p>- We respect robots.txt rules.<p>- We identify our User Agent.<p>- We do not use requests/content to train models.<p>- Data is ephemeral and discarded after the task.<p>The linked post goes into more detail on the infrastructure and why we think browsing needs to be a distinct layer in the AI stack.<p>This is obviously a very new space and we're all learning together. There are plenty of known unknowns (and likely even more unknown unknowns) when it comes to agentic browsing, so we’d genuinely appreciate your feedback, questions, and tips.<p>Happy to answer questions about the stack, our architecture, or the challenges of building browser infrastructure.

Show HN: Tabstack – Browser infrastructure for AI agents (by Mozilla)

Hi HN,<p>My team and I are building Tabstack to handle the "web layer" for AI agents. Launch Post: <a href="https://tabstack.ai/blog/intro-browsing-infrastructure-ai-agents" rel="nofollow">https://tabstack.ai/blog/intro-browsing-infrastructure-ai-ag...</a><p>Maintaining a complex infrastructure stack for web browsing is one of the biggest bottlenecks in building reliable agents. You start with a simple fetch, but quickly end up managing a complex stack of proxies, handling client-side hydration, and debugging brittle selectors. and writing custom parsing logic for every site.<p>Tabstack is an API that abstracts that infrastructure. You send a URL and an intent; we handle the rendering and return clean, structured data for the LLM.<p>How it works under the hood:<p>- Escalation Logic: We don't spin up a full browser instance for every request (which is slow and expensive). We attempt lightweight fetches first, escalating to full browser automation only when the site requires JS execution/hydration.<p>- Token Optimization: Raw HTML is noisy and burns context window tokens. We process the DOM to strip non-content elements and return a markdown-friendly structure that is optimized for LLM consumption.<p>- Infrastructure Stability: Scaling headless browsers is notoriously hard (zombie processes, memory leaks, crashing instances). We manage the fleet lifecycle and orchestration so you can run thousands of concurrent requests without maintaining the underlying grid.<p>On Ethics: Since we are backed by Mozilla, we are strict about how this interacts with the open web.<p>- We respect robots.txt rules.<p>- We identify our User Agent.<p>- We do not use requests/content to train models.<p>- Data is ephemeral and discarded after the task.<p>The linked post goes into more detail on the infrastructure and why we think browsing needs to be a distinct layer in the AI stack.<p>This is obviously a very new space and we're all learning together. There are plenty of known unknowns (and likely even more unknown unknowns) when it comes to agentic browsing, so we’d genuinely appreciate your feedback, questions, and tips.<p>Happy to answer questions about the stack, our architecture, or the challenges of building browser infrastructure.

Show HN: TinyCity – A tiny city SIM for MicroPython (Thumby micro console)

Show HN: TinyCity – A tiny city SIM for MicroPython (Thumby micro console)

Show HN: OpenWork – An open-source alternative to Claude Cowork

hi hn,<p>i built openwork, an open-source, local-first system inspired by claude cowork.<p>it’s a native desktop app that runs on top of opencode (opencode.ai). it’s basically an alternative gui for opencode, which (at least until now) has been more focused on technical folks.<p>the original seed for openwork was simple: i have a home server, and i wanted my wife and i to be able to run privileged workflows. things like controlling home assistant, or deploying custom web apps (e.g. our customs recipe app recipes.benjaminshafii.com), legal torrents, without living in a terminal.<p>our initial setup was running the opencode web server directly and sharing credentials to it. that worked, but i found the web ui unreliable and very unfriendly for non-technical users.<p>the goal with openwork is to bring the kind of workflows i’m used to running in the cli into a gui, while keeping a very deep extensibility mindset. ideally this grows into something closer to an obsidian-style ecosystem, but for agentic work.<p>some core principles i had in mind:<p>- open by design: no black boxes, no hosted lock-in. everything runs locally or on your own servers. (models don’t run locally yet, but both opencode and openwork are built with that future in mind.) - hyper extensible: skills are installable modules via a skill/package manager, using the native opencode plugin ecosystem. - non-technical by default: plans, progress, permissions, and artifacts are surfaced in the ui, not buried in logs.<p>you can already try it: - there’s an unsigned dmg - or you can clone the repo, install deps, and if you already have opencode running it should work right away<p>it’s very alpha, lots of rough edges. i’d love feedback on what feels the roughest or most confusing.<p>happy to answer questions.

Show HN: OpenWork – An open-source alternative to Claude Cowork

hi hn,<p>i built openwork, an open-source, local-first system inspired by claude cowork.<p>it’s a native desktop app that runs on top of opencode (opencode.ai). it’s basically an alternative gui for opencode, which (at least until now) has been more focused on technical folks.<p>the original seed for openwork was simple: i have a home server, and i wanted my wife and i to be able to run privileged workflows. things like controlling home assistant, or deploying custom web apps (e.g. our customs recipe app recipes.benjaminshafii.com), legal torrents, without living in a terminal.<p>our initial setup was running the opencode web server directly and sharing credentials to it. that worked, but i found the web ui unreliable and very unfriendly for non-technical users.<p>the goal with openwork is to bring the kind of workflows i’m used to running in the cli into a gui, while keeping a very deep extensibility mindset. ideally this grows into something closer to an obsidian-style ecosystem, but for agentic work.<p>some core principles i had in mind:<p>- open by design: no black boxes, no hosted lock-in. everything runs locally or on your own servers. (models don’t run locally yet, but both opencode and openwork are built with that future in mind.) - hyper extensible: skills are installable modules via a skill/package manager, using the native opencode plugin ecosystem. - non-technical by default: plans, progress, permissions, and artifacts are surfaced in the ui, not buried in logs.<p>you can already try it: - there’s an unsigned dmg - or you can clone the repo, install deps, and if you already have opencode running it should work right away<p>it’s very alpha, lots of rough edges. i’d love feedback on what feels the roughest or most confusing.<p>happy to answer questions.

Show HN: HyTags – HTML as a Programming Language

This is hyTags, a programming language embedded in HTML for building interactive web UIs.<p>It started as a way to write full-stack web apps in Swift without a separate frontend, but grew into a small language with control flow, functions, and async handling via HTML tags. The result is backend language-agnostic and can be generated from any server that can produce HTML via templates or DSLs.

Show HN: HyTags – HTML as a Programming Language

This is hyTags, a programming language embedded in HTML for building interactive web UIs.<p>It started as a way to write full-stack web apps in Swift without a separate frontend, but grew into a small language with control flow, functions, and async handling via HTML tags. The result is backend language-agnostic and can be generated from any server that can produce HTML via templates or DSLs.

Show HN: Xoscript

Show HN: Xoscript

Show HN: Sparrow-1 – Audio-native model for human-level turn-taking without ASR

For the past year I've been working to rethink how AI manages timing in conversation at Tavus. I've spent a lot of time listening to conversations. Today we're announcing the release of Sparrow-1, the most advanced conversational flow model in the world.<p>Some technical details:<p>- Predicts conversational floor ownership, not speech endpoints<p>- Audio-native streaming model, no ASR dependency<p>- Human-timed responses without silence-based delays<p>- Zero interruptions at sub-100ms median latency<p>- In benchmarks Sparrow-1 beats all existing models at real world turn-taking baselines<p>I wrote more about the work here: <a href="https://www.tavus.io/post/sparrow-1-human-level-conversational-timing-in-real-time-voice">https://www.tavus.io/post/sparrow-1-human-level-conversation...</a>

Show HN: Sparrow-1 – Audio-native model for human-level turn-taking without ASR

For the past year I've been working to rethink how AI manages timing in conversation at Tavus. I've spent a lot of time listening to conversations. Today we're announcing the release of Sparrow-1, the most advanced conversational flow model in the world.<p>Some technical details:<p>- Predicts conversational floor ownership, not speech endpoints<p>- Audio-native streaming model, no ASR dependency<p>- Human-timed responses without silence-based delays<p>- Zero interruptions at sub-100ms median latency<p>- In benchmarks Sparrow-1 beats all existing models at real world turn-taking baselines<p>I wrote more about the work here: <a href="https://www.tavus.io/post/sparrow-1-human-level-conversational-timing-in-real-time-voice">https://www.tavus.io/post/sparrow-1-human-level-conversation...</a>

Show HN: Sparrow-1 – Audio-native model for human-level turn-taking without ASR

For the past year I've been working to rethink how AI manages timing in conversation at Tavus. I've spent a lot of time listening to conversations. Today we're announcing the release of Sparrow-1, the most advanced conversational flow model in the world.<p>Some technical details:<p>- Predicts conversational floor ownership, not speech endpoints<p>- Audio-native streaming model, no ASR dependency<p>- Human-timed responses without silence-based delays<p>- Zero interruptions at sub-100ms median latency<p>- In benchmarks Sparrow-1 beats all existing models at real world turn-taking baselines<p>I wrote more about the work here: <a href="https://www.tavus.io/post/sparrow-1-human-level-conversational-timing-in-real-time-voice">https://www.tavus.io/post/sparrow-1-human-level-conversation...</a>

Show HN: Webctl – Browser automation for agents based on CLI instead of MCP

Hi HN, I built webctl because I was frustrated by the gap between curl and full browser automation frameworks like Playwright.<p>I initially built this to solve a personal headache: I wanted an AI agent to handle project management tasks on my company’s intranet. I needed it to persist cookies across sessions (to handle SSO) and then scrape a Kanban board.<p>Existing AI browser tools (like current MCP implementations) often force unsolicited data into the context window—dumping the full accessibility tree, console logs, and network errors whether you asked for them or not.<p>webctl is an attempt to solve this with a Unix-style CLI:<p>- Filter before context: You pipe the output to standard tools. webctl snapshot --interactive-only | head -n 20 means the LLM only sees exactly what I want it to see.<p>- Daemon Architecture: It runs a persistent background process. The goal is to keep the browser state (cookies/session) alive while you run discrete, stateless CLI commands.<p>- Semantic targeting: It uses ARIA roles (e.g., role=button name~="Submit") rather than fragile CSS selectors.<p>Disclaimer: The daemon logic for state persistence is still a bit experimental, but the architecture feels like the right direction for building local, token-efficient agents.<p>It’s basically "Playwright for the terminal."