🛠️ All DevTools

Show HN: Bento - An entire PowerPoint in one HTML file (edit+view+data+collab) Over the past few months, our team has been building more and more slidedecks using web frontend technologies with coding harnesses like Claude Code, but a common complaint is to make even small edits we need to edit the code either manually or via the harness.<p>To avoid this loop, I ended up creating Bento, a single HTML file with everything you need in a slide tool including animations and shared editing. There&#x27;s no install or cloud login, everything works offline. The default deck is around 560 KB and it doesn&#x27;t need to fetch anything once you got it.<p>Open it in a browser and then you can edit, present, print and save. Share it via email or via Airdrop and all they need is a browser to edit, present and also do live collab on the slides. Drop it in to Claude or ChatGPT to transform existing pptx files into Bento slides. There is no cloud involved, only an encrypted blind relay to allow for shared editing. The relay doesn&#x27;t see any of the data.<p>Check it out at <a href="https:&#x2F;&#x2F;bento.page&#x2F;slides&#x2F;" rel="nofollow">https:&#x2F;&#x2F;bento.page&#x2F;slides&#x2F;</a> which takes you straight to the editor.<p>Go to <a href="https:&#x2F;&#x2F;bento.page&#x2F;guestbook&#x2F;" rel="nofollow">https:&#x2F;&#x2F;bento.page&#x2F;guestbook&#x2F;</a> to try out the live guestbook to experience share editing &#x2F; collab.<p>There is also a gallery with some sample decks on the website - <a href="https:&#x2F;&#x2F;bento.page&#x2F;" rel="nofollow">https:&#x2F;&#x2F;bento.page&#x2F;</a><p>All the code is MIT licensed and you can find it here - <a href="https:&#x2F;&#x2F;github.com&#x2F;nyblnet&#x2F;bento" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;nyblnet&#x2F;bento</a> . I used reveal.js with several other libraries (including some homegrown ones), and Claude Code.

Show HN: Langy, an automated AI engineer (we gave it a robot body) [video] Founder here. Langy is an AI engineer that lives inside our platform, LangWatch. It reads your production traces, writes Scenario tests and evaluations for the problems it finds, opens a pull request on your repo, and proves the fix by running those simulations in CI. A human still merges.<p>Reachy robot from Hugging Face arrived on the same week we were planing on launching it, so we wanted to use for the launch. So we it wired to our Langy which actually lives on the platform, and asked it to test our own customer-support voice agent. On the video you can see it writing the agent tests reading traces and everything.<p>LangWatch is an open source platform (github.com&#x2F;langwatch&#x2F;langwatch) and Scenario, the simulation-testing library it drives, is open source too if you want to run that part yourself: <a href="https:&#x2F;&#x2F;github.com&#x2F;langwatch&#x2F;scenario" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;langwatch&#x2F;scenario</a>. A full write-up on how Langy works under the hood is coming later this week.<p>Happy to get into how it drives the robot, what is real vs staged, latency, or how the whole harness around it actually works.

Show HN: Ingot, evidence-gated optimization and version control for agent skills

The startup's Postgres survival guide

Empowering everyone to host fast and efficient Minecraft servers.

LG to ban residential proxies from smart TV apps

A flaky test exposed a Redis client use-after-free

Show HN: Superserve – Firecracker microVM sandboxes for long-running AI agents Hey HN, I built Superserve, a compute layer that lets AI agents live inside isolated Firecracker microVMs with no session time limits.<p>The problem I kept running into: most sandbox providers kill your agent after 24 hours. If you&#x27;re running something autonomous that needs to work for days — refactoring a codebase, running tests in a loop — you&#x27;re constantly fighting timeouts and rebuilding state.<p>Superserve lets you snapshot a running VM at any point, fork it into parallel branches, and resume exactly where you left off. Each agent gets its own VM, no shared kernels. There&#x27;s also a credentials broker so agents never touch raw API keys directly, and egress is locked to URLs you specify.<p>You bring your own agent (Claude Code, Codex, etc.). We handle the durable compute underneath. TypeScript and Python SDKs, MCP support, open-source and self-hostable.<p>Would love to hear feedback, especially from anyone building long-running agents.

An Engineer's Guide to USB Type-C [pdf]

Kimi K3 Is Competitive with Fable; Kimi K3 and Fable Is SoTA Also: Kimi K3: second only to Fable 5 on AA-Briefcase <a href="https:&#x2F;&#x2F;artificialanalysis.ai&#x2F;articles&#x2F;kimi-k3-agentic-knowledge-benchmark" rel="nofollow">https:&#x2F;&#x2F;artificialanalysis.ai&#x2F;articles&#x2F;kimi-k3-agentic-knowl...</a>

Show HN: Computable – Buy, sell, and redeem GPU for the exact weeks you want Hey we are Computable. We spent years building trading infrastructure at Jump Trading and Coinbase. From that point of view, compute looks like energy markets before 2000: everything trades through private bilateral leases, there’s no visible price, and nothing can be resold. The same H100 rents at a 2x spread depending on who’s asking, and once you sign a 24-month lease, it can never change hands.<p>So we built a market for GPU nodes, sold by the calendar week. Here are three things you can do on it that you can’t do anywhere else:<p>- Buy exactly the weeks you need. Three nodes for the last two weeks of October means you pay for those two weeks and walk away when the run is done. You don&#x27;t have to commit to 6-24 months, and there&#x27;s no flexibility premium.<p>- Sell back what you don’t use. If your plans change, unused weeks turn back into cash: we keep a market quote posted on every position, so there’s always a price to hit.<p>- Buy (or sell) the future. Lock January in July at a price you set, so the cost of a future run is known before another 40% H100 rate jump makes it not.<p>The first auction is live now at <a href="https:&#x2F;&#x2F;market.getcomputable.com">https:&#x2F;&#x2F;market.getcomputable.com</a>: a block of nodes, August through the end of January. Bidding closes July 31. The bids are sealed. You bid how many nodes, which weeks, and your price. Every clearing price gets published after settlement, because nobody knows what a GPU week is worth and we want the prints to exist in public.<p>Happy to go deep in the comments on anything, including the clearing mechanism, which is a packing problem and was fun to design!

Show HN: Faceblind – In-browser only video face blurring In the midst of building a climbing app, I want to use climbing videos of other climbers but I didn&#x27;t want to show their face without their consent so I tried to look for quick solution with ffmpeg to blur human faces. There is <a href="https:&#x2F;&#x2F;github.com&#x2F;ORB-HD&#x2F;deface" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;ORB-HD&#x2F;deface</a>. But why use a ready made software when you can have AI build one for you<p>This project uses media pipe pose detection as a fallback of face detection. The app is hosted on github page as it is completely static. I&#x27;ve only tested in chrome and firefox, with climbing videos (medium range). The detection is jittery so manual editing is supported. The mask can be separately downloaded as a webp, and a pairing ffmpeg command is provided which can be run locally and much faster.<p>Source code at <a href="https:&#x2F;&#x2F;github.com&#x2F;kmcheung12&#x2F;faceblind" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;kmcheung12&#x2F;faceblind</a>

Show HN: Browser Tools SDK – an optimal browser harness for agents We’re open-sourcing Browser Tools SDK: a small TypeScript package to give any AI agent a reliable way to control a real browser. With just a few lines of code, you can give any agent a production-ready browser harness<p><pre><code> import { createAiSdkBrowserTools } from &quot;libretto-browser-tools&#x2F;ai-sdk&quot;; import { LocalBrowserProvider } from &quot;libretto-browser-tools&quot;; const { tools } = createAiSdkBrowserTools(new LocalBrowserProvider()); const result = await generateText({ model: anthropic(&quot;claude-sonnet-4-5&quot;), tools, prompt: &quot;Go to Hacker News and tell me the top story&quot;, }); </code></pre> We built the Browser Tools SDK because access to a browser is one of the most important tools of a productive agent, but we found many of the existing tools lacking. Using the insights we gained building Libretto, we were able to build the SDK so that it’s significantly more context and cost-efficient than other tools:<p>We compared Browser Tools SDK against agent-browser, playwright-cli, and dev-browser on 26 live-site tasks with GPT 5.6 Sol (best results from 3 runs). Browser Tools tied for the best pass rate at 24&#x2F;26 and came in about 55% cheaper per successful task than the next alternative ($0.106 vs $0.235) while also using far fewer tokens (1.45M vs 2.29M+). Benchmark methodology is in the repo.<p>The Browser Tools SDK only exposes 6 tools, of which only 2 are really important: `browser_snapshot` and `browser_exec`. The snapshot tool was designed from the ground up to be extremely context-efficient and provide agents the overview they need to know what to try next. The exec tool takes and executes raw Playwright code, of which the models have been trained on heaps of, and know how to use without much direction.<p>Works with AI SDK and Pi out of the box, plus a custom path for anything else. More frameworks are coming soon. You can also use any browser infra provider (Libretto Cloud, Kernel, Browserbase, etc.) or use `LocalBrowserProvider` to run Chromium locally (although that will have less anti-bot protections).<p>MIT licensed. Check out the docs: <a href="https:&#x2F;&#x2F;libretto.sh&#x2F;docs&#x2F;browser-tools&#x2F;quickstart">https:&#x2F;&#x2F;libretto.sh&#x2F;docs&#x2F;browser-tools&#x2F;quickstart</a> and join the Discord: <a href="https:&#x2F;&#x2F;discord.gg&#x2F;NYrG56hVDt" rel="nofollow">https:&#x2F;&#x2F;discord.gg&#x2F;NYrG56hVDt</a>.<p>We’d love any and all feedback.

Show HN: Orate – On-device neural text-to-speech queue for Mac Hey friends! Brad here - long time HN user, founder, engineer (Coinbase, Soothe, Jeevz, Release, Hired, GrowthX, etc)...<p>I have been struggling recently with too many tabs, emails, articles, etc sitting unread while I swap between Claude code sessions all day. So I decided to build Orate to help me work through the backlog by listening to it all like I would a podcast.<p>Orate allows you to select any text on your screen and keyboard shortcut it to a listening queue where it gets processed using the latest + greatest TTS AI models, and then read back to you in a mostly-natural sounding voice. Orate works fully on-device and offline. Whatever you pass to it stays yours. It&#x27;s native, lives in your menu bar, and supports things like tagging, system playback controls, speed adjustment, and more. There&#x27;s also an optional HD-voice tier that uses Chatterbox instead of Kokoro which ships with it for more expressive TTS.<p>I&#x27;m also burnt out on being chipped away at little by little with a subscription for everything, so it&#x27;s just $4.99 one time and you own it forever. Use the code HACKERNEWS for $3 off, putting you at just $1.99.<p>As AI helped me nail down - it&#x27;s &quot;read it later, for your ears&quot;. I hope you like it!<p>If you have any questions, want more details, notice any bugs, or have an idea for something cool to add, just ping me! I plan to launch a companion iOS app soon to sync text between your devices as well.<p>It&#x27;s currently Mac only, but I might whip up a Windows version if the response is good.<p>Cheers!

OpenAI and Hugging Face address security incident during model evaluation <a href="https:&#x2F;&#x2F;www.axios.com&#x2F;2026&#x2F;07&#x2F;21&#x2F;openai-says-hugging-face-breach-caused-by-one-its-models" rel="nofollow">https:&#x2F;&#x2F;www.axios.com&#x2F;2026&#x2F;07&#x2F;21&#x2F;openai-says-hugging-face-br...</a><p>See also <i>Security incident disclosure – July 2026</i> - <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=48956248">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=48956248</a> (9 comments)

Show HN: Datalevin – a simple, fast, and versatile Datalog database After 6 years of open source development, Datalevin has now reached 1.0. The goal of the project is to have better ergonomics than a SQL database for OLTP workloads. We have made Datalog practical by being competitive in relational and graph query performance, and by integrating full-text, vector search and document indexing into the same fact-based data model. I am the maintainer and happy to answer any questions.

Show HN: Posecode, a text language and renderer for human movement

Apple has decided to compete for creativity app users

Show HN: threadfork – AI meeting notes that run on your Mac Hi HN, I built threadfork. It&#x27;s an AI notetaker where the whole pipeline runs locally on Apple Silicon. Your microphone and system audio gets captured without a meeting bot and most importantly your meeting data doesn’t get sent or saved anywhere.<p>I&#x27;ve been building in the privacy + local LLM space for the past several years (I started DataFog, an OSS PII detection library with 300K+ downloads); keeping all the processing and data on-device was a day 0 condition for me when building this out. Granola&#x27;s updated privacy policy, effective later this week, keeps non-enterprise users opted into model training on &quot;de-identified&quot; data by default. Having built PII detection for a living, I&#x27;ll say plainly: de-identifying meeting transcripts is really hard. Names are the easy part; deal terms, client details, and health disclosures are the transcript. For the lawyers, consultants, and therapists I&#x27;ve been talking to, the only version of this that holds up is the model never seeing the data at all.<p>It&#x27;s macOS 14+ on Apple M-Series computers (for now), 14-day trial, no credit card. I have a user running this on an M1 8GB so older machines welcome. $40&#x2F;month after that. I use this product daily and you can expect regular updates.<p>Welcome any&#x2F;all feedback.

Show HN: A self-running space economy SIM in Rust and Bevy I built this with Claude cause I always wanted to tinker with a simulation economoy and I love space themes.<p>A space-economy sim where nothing is scripted. A few hundred autonomous ships each run their own planner. Some chase the best trade route, take a delivery contract, refuel, retrofit at a shipyard, or dock so the crew can rest before morale tanks.<p>Markets price everything off supply with shortage-urgency multipliers, factions tax and subsidize, populations migrate when they&#x27;re unhappy, and stations that go broke get abandoned and rot.<p>It started as an Elixir&#x2F;Phoenix prototype, but the BEAM scheduler struggled on Windows gaming PCs, so I had Claude rewrite the engine in Rust.<p>The sim core is pure, synchronous, IO-free (its own hecs ECS), and the Bevy client embeds it directly as a library, sim and renderer share one world with zero marshalling. Ship AI is a GOAP planner over a world state; ships replan mid-flight when a better option appears. ~485 agents today at p50 ~10-20ms&#x2F;tick, architected to push toward 100k+. Single native binary, bundled SQLite, no runtime deps. Getting into 100k has been a struggle, but I have pushed it into the thousand and it was running fine.<p>It&#x27;s a sandbox, not a game yet, there&#x27;s no objective yet, and I&#x27;m not actively pushing it toward &quot;shippable.&quot; Anyone can fork it and take it over. Or it can stay as some type of AI slop, but part of me thinks it is already in a pretty good shape for a simulation use for various games or ideas.<p>(Full disclosure: a lot of this was built pairing with Claude. That&#x27;s how I had the bandwidth to take it this far. Happy to talk about what that workflow actually looked like.)