May 2026
Fine-tuning open-weight models is 80% data curation, 20% training
What we learned shipping LoRA pipelines on Llama, Mistral, and Qwen — and why most fine-tuning projects fail before they start.
Our analysis connects theoretical economic drivers with production-line engineering. No buzzwords. High signal.
Structural forces, economic incentives, and second and third order consequences of AI adoption. Macro shifts decoded for active operational decisions.
What real AI-enabled productivity looks like. Separating real automation leverage from theater. How organizations need to restructure to capture available gains.
The historical arc of technology adoption at scale. Pattern recognition from prior waves to optimize adoption timing, deployment strategy, and risk.
What we learned shipping LoRA pipelines on Llama, Mistral, and Qwen — and why most fine-tuning projects fail before they start.
Leaderboards tell you what a model can do in aggregate. Custom evals tell you what it will do for your use case. Here is how we build them.
We shipped three agentic systems this quarter. Every demo works. Production is where it gets interesting. Here is what we learned the hard way.