Practical insights on where AI helps, what it costs, and how to get started without wasting money.
NaN geometries in PostGIS match every bounding box query. Four bad parcels in Michigan poisoned spatial search across all 50 states.
Build LogWe launched a golf tee time alert service and accidentally spammed our first real users with 150+ individual emails. The fix was obvious. We just built things in the wrong order.
Build LogSequential USDA crop queries were silently adding 60+ seconds to every analysis. Redis caching, parallel queries, and one embarrassingly obvious optimization.
Build LogProduction server hit 98% disk during a massive parcel data backfill. Docker hoarding, optimistic backup policies, and the spatial join that saved the project.
Build LogA spatial join against 92 million rows that ran forever. The fix was state-filtered staging tables — obvious in hindsight.
Build Log55 million rows. No spatial index. Every reverse geocode was a full sequential scan. The fix took 30 seconds to write.
GuideCut through the hype. Start with your biggest time-waster, not the shiniest platform. Common first wins and what to avoid.
GuideAI handles the repetitive stuff your team hates. Your people become MORE valuable because they spend time on work that matters.
SecurityShadow AI is real. Your team pastes customer data into ChatGPT daily. What can go wrong, and what to do about it.
GuideAn honest, self-aware guide to vetting AI vendors. These are the questions we wish every prospect would ask.
ComparisonAn honest comparison of two farmland analysis platforms. Where AcreValue wins on historical sales data, and where LandPlanner wins on instant multi-source analysis. We built one of them, so we're biased — but we'll tell you where.
Case StudyA real walkthrough of analyzing a Cache County farmland parcel: SSURGO soils, FEMA flood zones, zoning constraints, ML cost prediction, and what it all means for an investor's buying decision.
Build LogOur site analysis platform aggregates 30+ federal APIs. It was taking a minute per request. We profiled everything, found a retry storm eating 52 seconds, and parallelized 22 services with ThreadPoolExecutor. 4x speedup.
GuideWe walked through the numbers for a 50-person insurance agency. Real costs, honest assumptions, and the payback math that actually matters.
Build LogEvery RAG tutorial says use Pinecone. We put our vectors in plain Postgres and it handles everything we need. Here's the reasoning and the query planner bug that almost ruined it.
Build LogA 10-row fixture CSV was silently replacing real USDA soil data in our ML pipeline. Every prediction was blind to soil conditions. The pipeline never complained.
Build LogDocker bypassing the firewall. API keys in git history. Pickle deserialization. We audited our own production AI platform and fixed every finding.
Build LogOur embedding API rejected our key. We pivoted to local sentence-transformers in 20 minutes. Faster, free, and zero vendor dependency.
Build LogDuring a code audit, we found a function fabricating property owner names when real data wasn't available. It displayed them with confidence badges.
Build LogThree bugs that nearly killed our live demo — an indexing catastrophe, a silent query planner issue, and a regex that split itself across two lines.
InfrastructureYou need to store embeddings for RAG. Here are your actual options, what they cost, and which one to pick. Opinionated recommendations included.
TechnicalOne teaches the AI your data. The other changes how it thinks. Most people confuse them — here's the honest breakdown from someone who builds both.
PricingThree tiers of AI spending, the hidden costs nobody mentions, and how to know if it's worth it. No jargon, just real numbers.
StrategyThree real options for getting AI into your business. Honest pros, cons, and costs for each — including when the cheapest option is the right one.
TechnicalYour company has thousands of documents nobody can find. AI search lets anyone ask a question and get the answer — with the source document.