When you've decided the current tool isn't doing the job, the responsible move is to evaluate the alternatives before building from scratch. I spent the better part of three months doing that evaluation. The alternatives were Claude, Pi Coder, and Hermes — each with a different set
The headline from DAIS this week was Agent Bricks — Databricks' take on what it should feel like to build a capable agent without hand-tuning every component. You describe the task, connect your data, and the platform auto-optimizes the agent: selects the model, tunes prompts, wires the retrieval layer. The
DAIS 2025 is in three weeks. I've spent the last year building on the themes that emerged from the 2024 Summit — compound AI, governance expansion, open formats, the cost reality of enterprise GenAI. Looking further out, at what the data + AI landscape looks like from 2025 to 2030,
When something that worked well stops working well, the question is whether it's your setup or the environment. I spent two months trying to distinguish between those before accepting the answer: it was both, and neither was fully fixable from my side. The persona system had been delivering
A year ago at DAIS 2024, I made some predictions. It's May 2025, DAIS 2025 is a month out, and it's time for an honest accounting. What did I get right, what surprised me, and what changed my mind. What I Got Right The compound AI
Meta shipped Llama 4 this month and it changes the open-weight calculus significantly. The architecture is natively multimodal — text, images, video, documents — and it's built on a Mixture of Experts design that lets them pack a lot of capability into a manageable active parameter count. Scout runs 17B
DAIS 2025 is six weeks out. I've been collecting notes on what separates the enterprise AI teams that are actually shipping production systems from the ones that are still stuck in POC purgatory. Here's the checklist I use before any AI initiative gets to production — not
If you want a model to behave consistently across a long engagement — maintaining a specific perspective, applying a specific methodology, staying within a defined scope — you give it a persona. That's the idea, anyway. The execution turns out to be more interesting than the idea. I started experimenting
It's April 2025, two months before DAIS 2025, and I've been running a list of what Databricks hasn't solved from the 2024 announcements. This isn't a complaint — the platform has gotten better every quarter. But honest critique is how you calibrate what
AI security became a board-level conversation in 2024. Not because AI security is new — adversarial ML attacks have been documented for years — but because AI systems are now making decisions that matter: credit approvals, insurance pricing, fraud flags, hiring screens. The attack surface has expanded to the point where it&
The goal I had been working toward since month four was simple to state and hard to execute: make the AI tools I use produce better output without requiring me to write better prompts. The knowledge retrieval system was the storage and retrieval half of that goal. Automatic prompt refinement
The governance conversation in data engineering has historically centered on data: who can access what table, what's the lineage of this column, does this dataset comply with GDPR. That conversation is mature — the tooling is good, the processes are established, most serious data organizations have solved it or