Dune: Awakening is out on Xbox Series and PS5, with Game Pass on September 22. Funcom’s open-world survival game was already on PC. Consoles get a full single-player mode, private servers, optional PvP, extra story, maps and a rebuilt endgame. Standard is £31.99. Deluxe £58.49. Ultimate £74.99. Alternate history where Paul Atreides never existed. You pick Atreides or Harkonnen. First announced for consoles back in 2022.

In Dune, unlike in most scifi, the problem with AI wasn't machines rebelling against humans but rather the rise of an all-powerful class of AI technocrats. "Once men turned their thinking over to machines in the hope that this would set them free. But that only permitted other men with machines to enslave them." This could be the real existential risk of AI.

The @Dune dbt migration is done. 287 models. 42 dashboards. All hourly. 🧵 Over the past several weeks — entirely outside my regular work hours — I migrated all 287 dbt models powering @sparkfinance's analytics infrastructure on @dune. 42 dashboards. All refreshing hourly. Production-grade. Done. ➢ What this means for Spark @sparkfinance is one of the most data-driven protocol teams I've worked with. Data here directly informs protocol decisions — numbers need to be right before they're trusted, not after. Over the past year I built 42 @Dune dashboards for @sparkfinance from scratch — covering P&L, user behavior, and protocol-level analytics. The migration made all of that durable, maintainable, and accessible to the whole team — not just to me. ➢ What changed Before: ~100K credits/month. Daily refresh. Logic living in my head. After: Hourly refresh. Incremental models. Full dependency resolution. Every model QA'd against its legacy query before cutover. And one net-new capability: EOH (end-of-hour) snapshots powering Time-Weighted Average P&L — capturing intraday rate spikes that daily snapshots miss entirely. Not possible before. ➢ What made it possible @cursor_ai+ @claudeai + @Dune MCP + a living SOP. By the end: SOP v2.43. 31 dated incidents. Each one root-caused, traced to a specific query, documented, and encoded as a permanent guardrail before moving on. No incident was closed until the root cause was understood. Not the symptom — the cause. That patience is the only reason the pipeline is stable now. Bug → rule → Cursor keeps it in Claude's context → fewer bugs per model. The document became the system's memory. ➢ Why Dune made this possible None of this works without @dune's dbt + Trino stack. Hourly incremental jobs on complex DeFi financials — per-user balances, multi-chain borrow rates, supply indices — at sustainable cost. That's not a given. Thanks to @kdotkrisp @onchain_ben @fr0zensun for the technical support throughout. 🙏 All data logic is now version-controlled in Git. Dune MCP closes the loop — AI can understand not just how the data is built, but how it's consumed in dashboards. The goal was never to be the person who knows everything. It was to build a system that doesn't depend on any one person knowing anything. That's what makes this valuable — the knowledge is in the system now, not in my head. Queryable by anyone on the team, or by AI directly. 287 models. Done. 🚀

99 @dune dbt models. Hourly. Production-grade. Here's the honest version of how we got here. ➢ The beginning was not smooth. Weekend Slack alerts firing. Models broken. Me debugging incremental window logic at 11pm. Every incident got root-caused. Every root cause became a SOP rule. Slowly, the alerts stopped. Not because I got lucky — because the system learned. The turning point wasn't a single fix. It was patience + documentation. SOP v1 → v2.43. Every weekend alert became a permanent guardrail. Now migrations are genuinely smooth. The next model starts from a baseline that has already survived every failure mode we've hit. Big thanks to the @dune team — @kdotkrisp @onchain_ben @fr0zensun — for the technical reviews. They reviewed my incremental model designs, read through a very long SOP, and helped me think through the architecture properly. That kind of support matters. ➢ A thought on web2 vs web3 data work. At my previous big tech job, I had: • A fully managed internal data platform • A dedicated data engineering team to build the tables • Tooling that abstracted away almost everything I was a good analyst. But I was operating inside a very comfortable box. In web3, none of that exists. I own the full stack: upstream event indexing → dbt models → incremental design → CI validation → dashboard cutover. It's harder. But I enjoy it more. Because now I understand the entire data supply chain — not just the last mile. And an analyst who understands the full stack is significantly harder to make obsolete. That's what I mean by antifragile. The friction was the point.