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China sentences official to death for taking $325M in bribes

Reducing Doom Loops with Final Token Preference Optimization

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Almost all coders at Id Software have been fired, following Xbox reset

Show HN: Halo – open-source, tamper-evident runtime evidence for AI agents Hi HN, I&#x27;m Brian, I spent the last few years at Vanta (YC W18), helping startups and enterprises become compliant and I recently started exploring what that might look like in a post-agentic world.<p>The problem Halo solves is: when a company buys an AI agent from a vendor and gives it access to their data, they have no way to check what the agent did with that data. Vendors may have built observability dashboards and audit logs, but those are editable and partisan. SOC 2 and ISO 27001 audit a company&#x27;s controls, but controls are less predictive when the software is agentic. TLDR: give an agent the same prompt 50 times, and you get 50 slightly different actions&#x2F;answers - so the only thing worth auditing in a post-agentic world is what happened at runtime.<p>Halo is an open-source project that produces agent runtime evidence. It&#x27;s a small recorder that records every action an agent takes (eg. tool calls, model calls, data access, etc), and becomes a record in an append-only log. It&#x27;s hash-chained, so anyone can re-verify.<p>Run the following command to see a fictional example:<p><pre><code> uvx --from halo-record halo demo --serve </code></pre> Then, delete a line from one of the .jsonl files and reload, and the report will catch that it&#x27;s been tampered with.<p>To wire up your own agent, run this line of Python:<p><pre><code> agent = trace(run_my_agent, profile=&quot;my-agent&quot;, log=&quot;audit.jsonl&quot;) </code></pre> Then use this to generate a real report and give it to your customers:<p><pre><code> halo report audit.jsonl -o report.html </code></pre> Disclaimer: this proves integrity, not completeness (as a self-held chain proves nothing was edited but does NOT prove that nothing was omitted). Catching this requires a witness outside the vendor and is what I&#x27;m working on next.<p>Halo is Apache-2.0, contains zero runtime dependencies, and is about 4,300 lines of Python with 125 tests (if you prefer TypeScript, here&#x27;s that repo: <a href="https:&#x2F;&#x2F;github.com&#x2F;bkuan001&#x2F;halo-record-ts" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;bkuan001&#x2F;halo-record-ts</a>).<p>Give it a try, and please let me know if you have any feedback!

Show HN: Yamanote.fun – A complete soundscape for Tokyo's Yamanote line After visiting Japan for the first time a decade ago I became completely enamoured with Tokyo&#x27;s Yamanote Line railway loop. Particularly the sonic experience of it. Like so many others I fell in love with the charming departure melodies and enjoyed discovering experiences like Yamanot.es (<a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=45045307">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=45045307</a>) here on Hacker News when I returned home.<p>But it wasn&#x27;t until my second trip to Tokyo that I truly appreciated how much the door chimes, on-board announcements and train noise were contributing to the rich soundscape that I loved.<p>I returned home and found myself playing YouTube videos of Yamanote Line journeys as I worked. The combination of sonics, ambience and softly spoken Japanese was incredibly soothing to me.<p>But these recordings were often incomplete, poorly captured or out of date, and I wanted something far more comprehensive.<p>So I gathered up all of the constituent parts from Reddit threads, YouTube videos and Japanese fan sites, and set about recreating the experience of riding the Yamanote Line in Logic Pro X. Melody, door chimes and announcement, all stitched together under a bed of train noise and ambience.<p>I turned those soundscapes into an Alexa Skill (<a href="https:&#x2F;&#x2F;www.amazon.co.uk&#x2F;Paul-Jackson-Yamanote-Line&#x2F;dp&#x2F;B07S18QRMV" rel="nofollow">https:&#x2F;&#x2F;www.amazon.co.uk&#x2F;Paul-Jackson-Yamanote-Line&#x2F;dp&#x2F;B07S1...</a>) in 2019 and began to think about a companion website to share the soundscapes with a wider audience.<p>Seven years later and that website is Yamanote.fun: <a href="https:&#x2F;&#x2F;www.yamanote.fun&#x2F;" rel="nofollow">https:&#x2F;&#x2F;www.yamanote.fun&#x2F;</a>.<p>It&#x27;s a small installable web app that plays the soundscapes like a playlist. All 30 stations and in both directions, since the inner and outer loops use different melodies. You can skip forward or back a station, and there&#x27;s a scrub bar broken into melody &#x2F; chime &#x2F; ambience &#x2F; announcement so you can jump straight to the bit you want. Each station has its own shareable link (yamanote.fun&#x2F;jy13-ikebukuro-inner) that unfurls with the right station name and artwork when you share it.<p>It&#x27;s a progressive web app too, so you can add it to your home screen and it behaves like a native app. There&#x27;s an option to offline the audio too.<p>Under the hood it&#x27;s relatively basic stuff: plain HTML, CSS &amp; JS, audio served from Cloudflare R2 and the site hosted on Netlify. I was impressed to see how far I could get with the free tiers of these services. I designed the whole thing in Figma (I&#x27;m a Product Designer) and used Claude Code to architect and deliver the polished UI, PWA plumbing, offline caching and share-link infrastructure.<p>I would love feedback, particularly from anyone who&#x27;s ridden the real thing.

A better way to tie your gym shorts. (Or any drawstring) [video]

Show HN: PostgreSQL performance and cost across 23 EC2 instance types Hey! I&#x27;m Andrei.<p>I got frustrated by how people tend to build overcomplicated backend systems, being &quot;motivated&quot; by big tech case studies and popular books.<p>So, I started exploring lean architecture, and building my digital garden of ideas, approaches and data that align with this direction.<p>Here I want to present one of the tools – Sizing tool for PostgreSQL. I&#x27;ve benchmarked PostgreSQL on different EC2 instances and disks, with different initial data sets to see performance that these instances can give you. And I&#x27;ve built a tool to visualize this data, which I welcome you to explore.<p>So, you can put your usual input parameters, like needed RPS and disk size as input, and find out which instance will be the most cost-efficient for your needs.<p>You can read about the methodology here: <a href="https:&#x2F;&#x2F;postgres.saneengineer.com&#x2F;about" rel="nofollow">https:&#x2F;&#x2F;postgres.saneengineer.com&#x2F;about</a><p>I&#x27;ve tested one workload – mixed 90&#x2F;10 read&#x2F;write, and only selected configurations. But it is extensible, and I (and you – benchmark is open source: <a href="https:&#x2F;&#x2F;github.com&#x2F;anivaniuk&#x2F;sanebench" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;anivaniuk&#x2F;sanebench</a>) can run more configurations to have more data represented.<p>Does it look interesting? What workload should I benchmark next?

Show HN: Frugon – Find which LLM calls a cheaper model could handle (local, MIT) I started leaning in on AI heavily this year, as I wanted to get more done autonomously, but then my token usage climbed dramatically to the point where my weekly quota would run out before the end of the week, sometimes a couple of days into the week.<p>I realised I had to do something about it else I&#x27;d have to double my spend. So I decided to start tracking my cost per task type. This revealed that a lot of my spend went to searches&#x2F;scans or simple things like scouting tasks.<p>I then decided to turn this into a simple CLI tool that can be used to read your OpenAI-style logs locally, and analyze the cost and compare this spend to other models, then show you how much you could potentially save by switching those calls to a cheaper model.<p>When you run analyze you get an offline estimate priced against LiteLLM and gated by LMArena tiers. The general savings bands come from the research published by RouteLLM; but you can confirm this yourself using 2 commands --measure (shows the prompt-response output side by side) and --judge (a model chosen to do the comparisons). These send a sample of the prompts from the logs to the candidate models - either the default choice or set by you. This call goes directly to the model provider (never through me) as any normal LLM call would, and the response is shown and judged to either be better or worse or a tie.<p>It&#x27;s deliberately small, because I tend to over complicate&#x2F;think things sometimes: analyze + capture + a few commands, doing three jobs. Cost, quality visibility, routing recommendation.<p>Nothing is hosted. capture is an optional local proxy on your own machine, and there&#x27;s no endpoint in the path of your data. You can confirm this by checking the source.<p>I included a demo so you can check out the output. It has a synthetic 56k call log (a month&#x27;s worth) showing how costs can drop from $549.46 to $343.91 a month. A 37.4% saving.<p>Try it:<p><pre><code> uvx frugon analyze --demo </code></pre> or<p><pre><code> uv tool install frugon </code></pre> Then point it at your own logs.<p>All feedback is welcome, especially any on the routing&#x2F;quality logic, or anything else, good or bad.

💡 Download the complete source code of any website (including all assets). [ Javascripts, Stylesheets, Images ] using Node.js

AI-powered job application framework built on Claude Code. Fork it, fill in your profile, and let Claude evaluate jobs, tailor CVs, write cover letters, and prepare you for interviews.

Show HN: Fast, native Mac file manager (filters, fuzzy find, 9 MB, no Electron) My Downloads folder had been left unkept for a really long time and cleaning it up using Finder was quite cumbersome. So I started creating a simple app to help me filter out and delete or move the files in the folder.<p>It started out very basic and the filtering options genuinely helped me clean out the Downloads folder, then as I thought of more features I would like to see in a file manager I started to add them. Some of the features are:<p>- Fuzzy go to folder&#x2F;file where you only need to write a few letters of a full path, get suggestions and can jump to the correct path instantly - Hover over a file to preview an image or PDF without opening it - Dual-pane view and tabs that remember selected filters - Command palette to find actions fast - Batch rename (with regex support and presets for common operations), image conversion (HEIC&#x2F;WebP&#x2F;AVIF to JPG&#x2F;PNG), zip creation - Bookmarks, Quick Look, single-click open, keyboard control<p>And many other small quality of life features.<p>I used Claude Code for making the app and the tech stack is .NET&#x2F;C# with AppKit. The app is compiled to Native AOT so the total app size is only around 9 MB. Since file operations are very important to get right (I don’t want to lose any important files) I put a lot of time into hardening file move&#x2F;copy&#x2F;delete operations. Copies are written to a temp file and atomically renamed into place. I also made a dedicated audit of move&#x2F;copy&#x2F;delete and verified each operation by hand.<p>Another thing that’s important to me is apps that are privacy first so the only network request the app makes is to check if there’s a new version (this is not done at all for the trial version, the trial expiry date is enforced locally by just checking the date and trusting the user) and the only information that is sent is the current app version.<p>The app is Apple Silicon, macOS 12+ and has a 30-day free trial with a one-time launch price of $19.99 (no subscription).

The Making of Claude Code

Inference Optimization for MiMo v2.5: Pushing Hybrid SWA Efficiency to the Limit

Cache-Conscious Data Layout in Rust: Field Zoning, False Sharing, 128-Byte Rule

Apache Shiro security framework releases 3.0.0

Show HN: Capsule – E2E encrypted file sharing Capsule started as a basic HTTP server to teach myself Rust.<p>Then I kept going, and it turned into the first full-stack thing I&#x27;ve fully shipped: a Rust&#x2F;Axum server, a Rust CLI, an Android app (my first, same story as the Rust one), and a fully featured web UI. It quickly grew as I realized I could solve my file transfer problem by turning a basic webserver into an actual daily use tool.<p>Entire project is open source, code can be found at: <a href="https:&#x2F;&#x2F;github.com&#x2F;withcapsule&#x2F;" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;withcapsule&#x2F;</a><p>Would love to hear anyone&#x27;s thoughts on this.

Show HN: InstantVideos.org – short documentaries in ~30 seconds Hiya! So I&#x27;ve been playing around with having Claude make videos for a bit now even had some success posting the results to TikTok (and setup a whole pipeline so Claude can generate and post autonomously). With the release of Nano Banana 2 Lite, I was curious show fast I could make the generation, so last night I gave it a whirl and got down to around 30s for short-form video.<p>It uses GLM-5.2 fast via Fireworks to generate the scripts and image prompts and, like I said, Nano Banana 2 Lite for the images, gpt-4o-mini-tts for the narration, and ffmpeg to string it all together and add the Ken Burns zoom effect (which still has a shake I haven&#x27;t been able to get rid of). The video compilation proved to be the blocker once the rest was in place, but I was able to speed that up by putting it on a 64 vCPU EC2.<p>The cost might be the most interesting aspect as the short form videos tend to be about 25 cents. Almost 90% of that is the images, which are 3.336 cents a piece. Of course, running the big 64 core EC2 to allow for the creation isn&#x27;t cheap.<p>It seems like on-demand AI video is coming, and I thought this was an interesting demo of how close it might be in at least one narrow video domain.

AI: The ROI Runway Could Be Long Outside the Tech Sector

Learning to code is still worthwhile