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[Other] Show HN: I indexed 8,643 BSides talks across 227 chapters and 6 continents Hi HN,<p>I&#x27;m Roland, and for the past few weeks, I&#x27;ve been building AllBSides — a directory of every BSides conference talk uploaded to YouTube. As of today, 8,643 talks from 5,927 speakers across 227 chapters in 68 countries. Combined runtime is 280 days. The transcripts come to about 60 million words.<p>The archive came together in stages:<p>1. Manually map every BSides chapter&#x27;s YouTube channel 2. Pull every video and transcript from Supabase 3. Run each transcript through Haiku for tag extraction (tools, topics, difficulty, team, talk style, research method, and much more) 4. Run results through Sonnet for categorization and dedup 5. Final pass goes through Opus for verification 6. Do a manual verification - at one time, the pipeline showed over 16k AI suggestions for manual verification. Today, most are resolved.<p>Total LLM cost so far: about €200. The whole pipeline is rebuildable from scratch.<p>Each talk gets its own page with embedded video, full transcript, speakers, tags, and &quot;related talks.&quot; Each tool&#x2F;framework&#x2F;protocol&#x2F;standard mentioned across the corpus gets its own page (3,968 distinct technologies tracked).<p>Some interesting facts I gathered while building it:<p>-(A) The site is currently 94% bot traffic. Of that, about 80,000 hits&#x2F;month are AI training crawlers (ClaudeBot, GPTBot, meta-externalagent). Within 7 days of the talks archive going live, all major AI labs had ingested the entire corpus. The discovery cascade was startling to watch in real time.<p>-(B) The taxonomy work was the hardest part. Distinguishing &quot;tools&quot; from &quot;frameworks&quot; from &quot;protocols&quot; from &quot;concepts&quot; sounds easy until you have 5,000 ambiguous extracted entities. The 3-tier LLM pipeline helped a lot — Haiku alone was too noisy, Opus alone was too expensive.<p>-(C) Top tools mentioned: Wireshark (343), PowerShell (342), Metasploit (332), Burp Suite (322), GitHub (296), VirusTotal (273), Docker (253), Splunk (251), Nmap (247), MITRE ATT&amp;CK (237). The list reflects what BSides talks actually discuss, not what vendors curate.<p>-(D) May is the peak BSides month — 29 events, 17% of all events with dates.<p>-(E) The top 1% of talks (86 videos by view count) account for 51% of all viewership. The other 99% are deeply niche, often the only video record of a specific technique.<p>The stack is intentionally lean: Go, SQLite, vanilla JavaScript, BunnyCDN. Static rendering at build time. No frameworks, no client-side state. The site costs about €50&#x2F;month to run.<p>The data behind this post and much more can be found in the site footer, under the link &quot;stats&quot;.<p>Happy to answer questions about the data pipeline, the taxonomy decisions, or what the AI crawler patterns looked like as the archive went live. Feedback on what to build next is genuinely welcome — I&#x27;m a solo dev figuring this out as I go.<p>— Roland (parkado)

[Other] Formatting a 25M-line codebase overnight

[Other] Show HN: Agent-evals – Claude skill to build your own evals I’ve spent the past 10 years working on AI in finance, with much of that time focused on building evaluation systems for production environments.<p>As agents become more widely adopted, more software engineering and product people have start building them. But I’ve noticed that many teams are not yet fluent in systematic evaluation, or in the processes needed to keep agent quality high over time.<p>For large organizations, that gap is rarely the bottleneck due to dedicated teams. But after speaking with a number of startups, it became clear that building strong, up-to-date evals is much harder in a fast startup, especially when the team does not have a data science background.<p>So I tried to condense as much of my experience as possible into a Claude Skill: a practical starting point for evaluating your agent.<p>The idea is simple: tell Claude you need evals, and it will set up a solid baseline directly in your codebase - that&#x27;s it! The evals will follow patterns I&#x27;ve seen many times before, and will get you a summary of what your agent does well and what it doesnt.<p>Looking forward to your feedback!

[Other] Google is discontinuing its free web search index for developers

[Other] White House Considers Vetting A.I. Models Before They Are Released

[Other] Days without GitHub incidents

[Other] Show HN: Muesli – If Granola and Wisprflow had an open source on device baby Hey folks, I am the developer behind muesli - which is your one stop app for all your speech to text needs, be it voice dictation or meeting transcriptions that runs on device on your Apple Neural Engine using CoreML based STT models (Parakeet, Whisper, Cohere transcribe). Everything is open source and we are at 160 stars - au naturale - would love for folks to use it and contribute further to the development

[Other] Show HN: Bonsai 1.7B ternary model at 442T/s on M4 Max We took a recently released Bonsai 1.7B ternary model from PrismML (<a href="https:&#x2F;&#x2F;github.com&#x2F;PrismML-Eng&#x2F;Bonsai-demo" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;PrismML-Eng&#x2F;Bonsai-demo</a>) and ran our agentic evolution search on it for 6 hours to optimize the Metal kernels. The search was fully autonomous.<p>Measured against unmodified upstream llama.cpp at the same Bonsai&#x2F;Q2_0 commit, same M4 Max:<p>- tg128: 309.82 → 442.42 t&#x2F;s (+42.0%)<p>- pp512: 4250.32 → 4622.63 t&#x2F;s (+8.8%)

[Other] Show HN: Replacing spec-driven development with just facts I had a lot of issues with spec-driven approaches, agents are too readily producing fluff, large projects have so many specs agents start making mistakes maintaining them. There&#x27;s a constant consistency tax.<p>In the end every spec is just a bunch of facts, so I decided to leave that and throw away everything else while making it friendlier for agentic use.<p>Introducing facts - skills and CLI for agents to use facts-driven development. <a href="https:&#x2F;&#x2F;github.com&#x2F;av&#x2F;facts" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;av&#x2F;facts</a>

[Other] PyInfra 3.8.0 Is Out

[Monitoring/Observability] Setting up server monitoring for a Rails app on Hatchbox

[Other] DeepClaude – Claude Code agent loop with DeepSeek V4 Pro, 17x cheaper

[Other] How Kepler built verifiable AI for financial services with Claude

[Other] Show HN: Ableton Live MCP Ever wanted to control Ableton with just your voice? Me too! I made this MCP server so I could just ask Codex to do anything in Ableton Live for me, while I was nap-trapped by my baby.<p>The chat messages I sent to Codex to make this:<p><i>in ableton, make a self reflective song, with audio vocals (via macos say) and chip tunes and 80&#x27;s drum machines. should be a real edm banger<p>i want midi for everything but vocals please, with ableton devices. not prerendered audio for instruments<p>needs some fills<p>and should hit way harder after &quot;3-2-1 i become the sound&quot;<p>the vocals are squished too much (read too quickly), give them a little more length<p>add some dynamics, the song is basically one volume. and some pumping side chain<p>improve dynamics of the clap, seems a bit flat and indistinguished, want it harder after the 3-2-1 drop<p>introduce a new element on a new track after the 3-2-1 drop, that comes in but then recedes before the final exit<p>doesn&#x27;t seem like the new thing has any notes<p>the element is a bit muddy&#x2F;indistinct. perhaps it needs simplification and more space, different instrument choice, i dunno</i>

[Other] I recreated the Apple Lisa computer inside an FPGA [video]

[Other] Collaborative Editing in CodeMirror (2020)

[Other] Metal Gear Solid 2's source code has been leaked on 4chan

[Other] Show HN: Software Engineer to Novelist: Writing a Book Like Coding I just published my first book, Means and Motive. ( <a href="https:&#x2F;&#x2F;www.amazon.com&#x2F;dp&#x2F;B0GYCZJVGX" rel="nofollow">https:&#x2F;&#x2F;www.amazon.com&#x2F;dp&#x2F;B0GYCZJVGX</a> )<p>As a software engineer, I approached writing like a software project. I used familiar tools (Emacs and HTML) for the primary writing.<p>I built my own tool (EPublish) to transform the HTML manuscript into an .epub file, the source for the ebook version. And I wrote shell scripts to reliably and repeatably transform the .epub version into PDF files for the printed editions.<p>I wrote &#x27;design&#x27; and &#x27;architecture&#x27; docs, describing the world, key actors, and timelines. I kept a task list of chapters and key scenes that needed to be written, in priority order. Along the way, I kept my files version-controlled so I could see the progress of the novel and edit mercilessly, without worrying about keeping old text around in backup files should I want it back for some reason.<p>If you&#x27;ve thought about writing a book, I highly recommend it. There are many similarities to the software engineering process. You&#x27;ll also gain a newfound appreciation of the design, layout, and typesetting world, exactly how much work goes into each book you read.

[Testing] Testing macOS on the Apple Network Server 2.0 ROMs

[Other] Wiki Builder: Skill to Build LLM Knowledge Bases