Show HN: I indexed 8,643 BSides talks across 227 chapters and 6 continents https://ift.tt/nwImypd

Show HN: I indexed 8,643 BSides talks across 227 chapters and 6 continents Hi HN, I'm Roland, and for the past few weeks, I'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. The archive came together in stages: 1. Manually map every BSides chapter'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. Total LLM cost so far: about €200. The whole pipeline is rebuildable from scratch. Each talk gets its own page with embedded video, full transcript, speakers, tags, and "related talks." Each tool/framework/protocol/standard mentioned across the corpus gets its own page (3,968 distinct technologies tracked). Some interesting facts I gathered while building it: -(A) The site is currently 94% bot traffic. Of that, about 80,000 hits/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. -(B) The taxonomy work was the hardest part. Distinguishing "tools" from "frameworks" from "protocols" from "concepts" 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. -(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&CK (237). The list reflects what BSides talks actually discuss, not what vendors curate. -(D) May is the peak BSides month — 29 events, 17% of all events with dates. -(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. 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/month to run. The data behind this post and much more can be found in the site footer, under the link "stats". 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'm a solo dev figuring this out as I go. — Roland (parkado) https://allbsides.com/ May 4, 2026 at 11:10PM

Comments

Popular posts from this blog

Complete Guide to E-Commerce Business: Meaning, Models, and How to Start

Micro Niches: The Secret Weapon for SaaS Startups Struggling to Gain Traction

"From Micro Niche to Money Maker: How I Validated My E-Commerce Idea with AI (No Budget Needed)" Published: September 23, 2025 Keywords: Micro niche, AI validation, e-commerce, free tools, startup strategy Introduction Ever wondered if your e-commerce idea is worth pursuing? In this post, I’ll walk you through how I used free AI tools to validate a micro niche, build a lean store, and test demand—without spending a dime. If you’re stuck between ideas or afraid of wasting time and money, this guide is your shortcut to clarity. Step-by-Step Breakdown 1. Finding the Micro Niche Used ChatGPT to brainstorm underserved product categories. Cross-referenced with Google Trends and AnswerThePublic to check search interest. 2. Validating Demand Leveraged Perplexity AI to analyze competitors and market gaps. Ran polls using Typeform and Twitter/X to gauge interest. 3. Building the Store Created a free storefront using Shopify Starter and Canva for branding. Used Durable.co to generate landing page copy in minutes. 4. Driving Traffic Scheduled posts with Buffer across Instagram, Threads, and LinkedIn. Used Notion AI to draft blog content and email sequences. 5. Tracking Results Monitored engagement with Google Analytics and Hotjar. Adjusted product positioning based on feedback from Tally Forms. Key Takeaways Micro niches are goldmines when paired with smart AI validation. You don’t need a budget—just the right tools and strategy. Testing before investing saves time, money, and frustration. Thinking of launching your own store? Drop your niche idea in the comments and I’ll help you validate it with AI—free of charge!