Exciting progress on a massive medical literature embedding project! Currently running a distributed GPU pipeline that's churning through the entire PubMed corpus - that's 4.48 million biomedical research articles. 📊 Current Stats: • 655,500 articles processed (~15% complete) • Dual RTX 3090 setup maintaining perfect 50/50 load balance • Processing rate: 41,000+ articles per hour • Zero thermal throttling over 18 hours continuous operation 🔬 Technical Highlights: • Custom load balancing achieving exactly 454,721 chunks per GPU • Chunk-based processing handling variable article lengths (avg 1.35 chunks/article) • Automated checkpointing every 100 batches for fault tolerance • Real-time monitoring with temperature-aware throttling ⏱️ Timeline: Started: Friday afternoon Expected completion: Thursday morning Total compute time: ~109 hours Building comprehensive medical AI embeddings requires serious compute dedication. This dataset will enable semantic search across decades of medical research - from clinical trials to drug discovery papers. The beauty of well-engineered pipelines? They run through the weekend while you sleep

We’ve been putting in the hours not just on code, but on the silicon that runs it. Here’s what’s powering our stack right now: 5 CPUs 8 GPUs (5× 3090s, 1× 5090, 2 smaller legacy cards) 671 GB RAM Every one of these servers was built in-house, piece by piece. We’re officially one component away from running all 10 of our products completely self-hosted. No cloud bills, no gatekeepers, just raw compute we control. The glow, the cables, the fans, that’s the heartbeat of what we’re building together.

THE ACHIEVEMENT: ✓ 4.27 million papers analyzed ✓ 210GB PostgreSQL database ✓ 155 minutes processing time ✓ Zero errors (validated on 60-paper sample) THE DOCUMENTATION (all public): → Complete regex patterns (49 distinct regex patterns) → Database schema (every table, every field) → Processing code (500+ lines, commented) → Hardware specs (Ryzen 9 7900, 128GB RAM) → Rigor scoring algorithm (100-point system) → Validation methodology → Known limitations (CIs at 1.7% - likely in tables) https://github.com/Built-Simple/pubmed-statistical-rigor-extractor Why share everything? Because: 1. Real science is reproducible 2. Extraordinary claims need extraordinary evidence 3. Someone should verify this Found: Only 1.7% of papers report confidence intervals. 0.6% do power analysis. Verify it yourself. The code is there.

Watching someone build with AI yesterday reminded me why I completely changed my workflow back in July. They were doing the classic: 1. Copy code from ChatGPT (hope they got it all) 2. Paste to Notepad++ (formatting's destroyed) 3. Save as... (was it .py or .js again?) 4. Run it (doesn't work) 5. Copy the error message back 6. Get new code 7. Where did they paste that first part? 8. What file was that? 9. Repeat 47 times That's 94 opportunities to mess up for ONE project. Did the math for them: 50+ copy-paste cycles × 2 minutes each = 100 minutes just moving code around. Not writing code. Not solving problems. Just copying and pasting. This used to be me: Building a customer automation tool took 3 hours. But only 45 minutes were actual problem-solving. The rest? Copy. Paste. Save. Test. Copy error. Paste. Save. Test. I knew there had to be a better way. Found it in July. Can't believe more people don't know about it. The solution I've been using for months: File access. The AI can edit files directly on your computer. No copying. No pasting. Just: "Build me a customer follow-up system" and it creates the files, writes the code, tests it, fixes errors. All without touching anything. That 100-minute copy-paste marathon? Completely eliminated. Two ways to get this working: 1. Claude Code - 10-minute setup if you have Claude Max 2. ChatGPT with file access - Similar setup time The same projects that took 3 hours with copy and paste? 20 minutes now. Haven't lost formatting in months. How many of you are still copy-pasting in 2025? (If this is you, no judgment - but let's fix this today)

Before you download anything or watch any tutorials, we need to figure out what you're actually building. This form helps you explain your problem to AI and get back a complete solution blueprint, what to create, what tools you need, and exactly how to do it. Takes 5 minutes. Saves you days of confusion https://built-simple.github.io/solution-architect/