🚀 WE GOT OUR OWN SERVER! FixIt 2.0 Relaunch + Lifetime Pricing Hey everyone! Big news - after months of running on expensive cloud infrastructure, we finally pulled the trigger on our own hardware. Last week, we installed a Dell PowerEdge R740xd in our rack. This isn't just a hardware upgrade. This changed EVERYTHING about how we can run FixIt. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💰 THE ECONOMICS THAT FORCED THIS DECISION Cloud Costs (What We Were Paying): - Compute: $400/month - Storage: $150/month - Bandwidth: $80/month - TOTAL: $630/month = $7,560/year Our Server: - Power: ~$50/month - Internet: Already paying - TOTAL: $50/month = $600/year Break-even: 24 months Savings after year 3: $7,000+/year But the real win isn't just cost. It's CONTROL. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚡ THE PERFORMANCE TRANSFORMATION Before (Cloud Infrastructure): ❌ 300-500ms average response time ❌ 4.7M solutions (storage limits) ❌ 95% uptime (shared resources) ❌ Throttling during peak usage ❌ Can't optimize hardware ❌ Expensive to scale After (Our Server): ✅ 29-100ms average response time (10x faster!) ✅ 18.5M solutions (4x larger database) ✅ 99.9% uptime guarantee ✅ Zero throttling, ever ✅ Custom optimizations for our workload ✅ Predictable, fixed costs ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🖥️ THE HARDWARE SPECS We didn't cheap out. This is enterprise-grade: Dell PowerEdge R740xd: - CPU: Dual Intel Xeon Silver 4216 (32 cores, 2.10GHz) - RAM: 314GB DDR4 ECC - Storage: 13TB RAID6 array - GPU: Dual NVIDIA Tesla T4 (30GB VRAM) - Network: Dual 10GbE - Power: Redundant power supplies - Uptime: 99.9% SLA Database: - Size: 82.9GB (was 20GB on cloud) - Records: 18,563,455 Stack Overflow solutions - Engine: Custom-optimized SQLite with FTS5 - Index: Multi-column full-text search ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💎 NEW PRICING (BECAUSE WE OWN THE HARDWARE) Here's what's amazing: because we control costs now, we can offer pricing that actually makes sense for developers.

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)