🛠️ All DevTools

[API/SDK] The Billable Usage API: programmatic cost visibility for Cloudflare

[Other] Show HN: Latex.to – LaTeX to PDF via Full Tex Live in the Browser Hi HN, I implemented your feedback:<p>latex.to is an Overleaf alternative that now runs a full Tex Live distribution in the browser.<p>This is done via CheerpX (JIT wasm engine) running a 32bit alpine image with TeX Live.<p>Things I previously tried: - originally this project started with just KaTeX for LaTeX to image - v86 wasm engine: Too slow (at least 10 times slower than CheerpX) - various TeX Live binaries wasm ports: These usually don&#x27;t port Biber (because it&#x27;s a Perl app) or engines like ConTeXt.<p>What makes this work: - CheerpX only streams (via HTTP range requests) the data it needs. Compiling the base example only downloads 50 MB (the whole image is 5GB). - Cloudflare R2 can only cache files less than 512MB. Hence the 5GB image is split into 1 base image and 19 mount images. - LaTeX needs to bake (index) the fonts before compiling which can take a long time and can OOM CheerpX. Hence all fonts more than 0.7 MB are prebaked into the image file.<p>The first compile is a bit slower because it needs to download assets like fonts, but everything is cached and the second compile is much faster.<p>100% free, no login, no compile timeout, documents are compiled in the browser and NOT sent to a remote server.<p>Please let me know in case something doesn&#x27;t work &#x2F; you have any questions or feedback.

[Other] Use Task Runners for Common Coding Tasks

[DevOps] Launch HN: Hoplite (YC S26) – Effortlessly deploy cloud coding agents Hi HN, we’re Bence and Ryan, founders of Hoplite (<a href="https:&#x2F;&#x2F;hoplite.sh">https:&#x2F;&#x2F;hoplite.sh</a>). Hoplite lets you deploy coding agents in the cloud, with a suite of tools that makes it incredibly easy to QA features. During onboarding, we port over your local setup - sessions, memories, MCP servers, and get your projects ready to run in the cloud.<p>Here’s a demo: <a href="https:&#x2F;&#x2F;youtu.be&#x2F;bnyktZ_9pjE" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;bnyktZ_9pjE</a><p>We got here after pivoting away from the idea we applied to YC with; AI for retail investing. It ultimately wasn’t a product that we ourselves would use, nor served a customer base that we felt connected to. In reflecting on what we really wanted to do, we realised that we loved talking to founders and developers, and were really opinionated about the specific area of cloud agents. We tried out all the existing solutions, and didn’t find one that A) took good advantage of being in the cloud, and B) was performant and felt good to use.<p>We’re building a product that we feel reflects what mainstream development will look like in 6-12 months. As models improve, developers will end up reviewing less and less code, and will instead focus on reviewing the product output. That means evaluating new user flows, visually verifying that new features look good, that the API works as expected, that the CLI works on Windows, etc. And doing it while running hundreds of agents concurrently.<p>On the agent side, we’ve created a custom harness. We spent a lot of time deciding on whether we should use an off the shelf solution like Codex&#x2F;Claude Code, but ultimately wanted the independence and freedom that came with building it in house. It also means that we can test out completely new features without relying on Anthropic and OpenAI to catch up.<p>Everything is hosted on AWS, with the exception of: Temporal for durable workflows, Modal for sandboxes, and Planetscale for our database. Our infra decisions were driven by a strong belief that agents are becoming a tier 0 piece of infrastructure, and they need the reliability and security to match that.<p>You can try it now for free with the code ‘HACKERNEWS’ - we’ve included $100 in free credits, plus you can connect your Codex subscription and use OpenAI models via it. You can see some more details around our pricing at <a href="https:&#x2F;&#x2F;hoplite.sh&#x2F;pricing">https:&#x2F;&#x2F;hoplite.sh&#x2F;pricing</a>.<p>At the moment we’re focusing on optimising two key experiences: onboarding and previews, and would love to hear your feedback on them. And if you find that the agent&#x27;s performance in certain tasks doesn’t match your expectations, please let us know!

[API/SDK] Show HN: Product analytics (and evals) for agent sessions on your MCP Hi HN! We’re Theodore and Louis, founders of Armature (YC P26). We reconstruct the entire session behind the MCP tool calls you receive, including what the user asked their agent to do and what the agent thought.<p>You wrap your MCP in 3 lines of code (our SDK is available in Typescript, Python and Go) and start seeing in your dashboard: - All sessions reconstructed: it’s like reading the real conversation the user had inside Claude or ChatGPT! - A ranking of your MCP most popular use cases, built from sessions clustering - The most frequent issues your users’ agents encounter so you can fix them.<p>Here is a quick demo: <a href="https:&#x2F;&#x2F;youtu.be&#x2F;ZFlvquhyNMQ" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;ZFlvquhyNMQ</a><p>The story behind this is that we initially launched Armature as a standalone testing tool (<a href="https:&#x2F;&#x2F;www.ycombinator.com&#x2F;launches&#x2F;QQc-armature-making-your-app-finally-usable-by-ai-agents">https:&#x2F;&#x2F;www.ycombinator.com&#x2F;launches&#x2F;QQc-armature-making-you...</a>) that could naturally be used through an MCP itself. We quickly realized we had no idea how our users were using Armature MCP and if they were satisfied with it or frustrated. It’s something we had also experienced in our previous companies: Louis built MCPs exposed to millions of users and Theo was a Forward Deployed Engineer at Palantir before joining a Datadog spin-off as Founding Engineer. Both testing and product analytics had always been real pains when exposing a product to agents but we always thought there wasn’t much we could do about analytics because the conversation lived in our users’ AI client.<p>Then it struck us: what if we asked the agents why they were making this or that tool call? And what’s the user&#x27;s intent or potential frustration? So we started experimenting with MCP instrumentation and the use-cases actually surprised us! Many of our first customers had implemented workarounds for their CI to trigger new tests or for their coding agents to fetch the results efficiently. Even though we talked to our first users regularly, they had never shared this feedback with us. We then built automations to automatically cluster use-cases, identify issues frequently encountered and let our own coding agents fix them. When our CTO friends heard about this, they wanted to try it for themselves so we gave them access to a cloned version of our internal product and they started sharing feedback like they never did on our “real” product!<p>That’s when we decided to start working seriously on MCP Analytics as a product. At first we were afraid of degrading MCP performance so we iterated until we reached the exact same success rate as without our instrumentation (89.17 % vs 89.15 % pass rate out of 870 runs). Then privacy was an obvious constraint so we applied the same methods we had learned from working with banking data or building sensitive data scanning in logs. Today, redaction runs client-side before reaching our servers. There are still a lot of things we haven’t fully figured out: not all fields are equally filled by all models, session fingerprinting for serverless &#x2F; stateless MCPs isn’t perfect, and use-case clustering remains to be optimized.<p>But we are finally launching our analytics product to everyone, self-serve at <a href="https:&#x2F;&#x2F;armature.tech">https:&#x2F;&#x2F;armature.tech</a> with a set-up that takes less than 5 minutes and a generous free tier.<p>And now we are working on fully closing the loop, bringing evals back in our product so we can: identify top workflows and issues -&gt; recommend fixes and improvements -&gt; test fixes at scale on the same workflows run by users, across all harnesses and models -&gt; open PRs to ship fixes directly. The evals can be generated automatically from the session analytics so you can catch every regression and can test every improvement’s real impact across all models and harnesses before shipping it.<p>Here’s an example to make it more concrete: 10 days ago, a marketing automation platform which has had early access to what we built for weeks identified thanks to MCP Analytics that users were frustrated not being able to change their target audience after campaign creation. So they shipped the feature and tested it successfully locally with Claude Code on Fable 5. Then a few days later when preparing their new MCP public release, they ran a suite of evals on Armature and realized that small models could hallucinate audience_ids which would lead their MCP to send the campaign to ALL their contacts by default (which could obviously lead to disasters in prod). This is the kind of story that makes what we are building feel so helpful!<p>Now, the most useful feedback for us would be to know what’s still missing in our product so you can feel you are now in full control of the “Agent Experience”. And if you run an MCP in production we’d also love to know: what do you do today to know if agents succeed and if the users behind them are happy?

[Other] Show HN: Lunar, a "fast", memory-efficient Lua 5.1 VM written in Go I&#x27;m releasing Lunar, a new Lua 5.1 compiler and virtual machine written entirely in Go. It includes the standard libraries, coroutines as well as supporting Lua 5.2-style goto.<p>The embedding API avoids the Lua C API’s stack-based interface in favor of typed Go values and callback frames. Libraries and script-file access are opt-in, so the host explicitly controls what Lua code can access.<p>On my current benchmarks and use-cases, Lunar is often ~1.5x to 2x faster than GopherLua and Shopify’s go-lua. Its largest improvement however is memory use: loading a 9 MB CBOR-derived object graph (hundreds of thousands of tables) allocates about 107 MB versus 785 MB with GopherLua, with roughly 72 MiB versus 542 MiB retained after GC.<p>The public API is still stabilizing, and I’d especially appreciate feedback on the embedding design and Go interface.<p>(Note that this project started in another repo when I still had hopes of porting some of my changes back upstream to another existing library.)

[Other] Devtools must be open source

[Other] Cloudflare Workers and Containers now support inbound TCP connections and gRPC

[Other] Show HN: Runthru – open-source Interactive Demos I was looking for a tool to create interactive demos &#x2F; walkthroughs for web based software. I was amazed that many companies charge well over $1000 per month for something pretty basic.<p>I made this which so far works well for me. You can point it at a site and either let AI (byo OpenAI or Anthropic keys) record and polish the whole thing or do it manually yourself (or a mixture of both).<p>Here is an example interactive demo:<p><a href="https:&#x2F;&#x2F;runnit.io&#x2F;demos&#x2F;i-want-you-to-create-a-demo-of-from-the-dashboard-clickin&#x2F;" rel="nofollow">https:&#x2F;&#x2F;runnit.io&#x2F;demos&#x2F;i-want-you-to-create-a-demo-of-from-...</a><p>Disclaimer: This is a tool which works for me and may work for you, it&#x27;s not production grade software, use at your own risk. Extend it, fork it, sell it, it&#x27;s up to you.

A framework for building realtime voice AI agents 🤖🎙️📹

Fast Rust library for PDF inspection, classification, and text extraction. Intelligently detects scanned vs text-based PDFs to enable smart routing decisions.

Critical CVE issued for hallucinated SQLite vulnerability

Show HN: Nightcrawler – A local AI pentesting agent running on a smartphone

Prevent cognitive debt by manually retyping LLM-generated code

MPs demand answers on Fujitsu's inclusion in lucrative frameworks

Show HN: SaaS payment integration in ~10 lines of code

Show HN: New Parts for Old Computers Since it appears to be retrocomputing day on HN, I thought I&#x27;d post my pet project. It&#x27;s a collection of modern peripherals for retrocomputers. I started collecting a list of upgrades for my ideal dream Mac SE&#x2F;30 and grew from there. The site is currently mostly focused on Apple II and Mac stuff since that&#x27;s what I grew up with.

Show HN: Do Codex skills save tokens? A six-run task-size benchmark

Qwen3.8-Max: A New Bar for Coding and Cowork

Logic and Ontology