It's a rapidly evolving AI landscape... so how do you build scalable AI-native architectures that aren't outdated the moment you deploy them? My core philosophy for working in the AI era: 🔹Code is cheap; intelligence & specification is still hard. Models let me iterate faster than ever, but a model can only build what someone has defined well. The scarce skills are knowing exactly what to build and truly understanding what was built: AI can do the thinking, but the comprehension has to stay with us. 🔹Start from the problem, not the technology. I build from first principles around a real user pain, often my own, and let the problem shape the solution rather than the hype cycle. A precisely understood problem is the best specification there is. 🔹Systems beat models, and composable systems outlast them. Every company has the same frontier models. The advantage lives in the integration layer around them: the context, tools, evaluation and interfaces that make a model useful. I build that layer to be modular, so when a better model, router or standard ships, we swap a part instead of rebuilding the product. 🔹Humans own judgement; machines own repetition. AI should take over the work no person should have to do, and put human judgement at the decisions that matter. I design agentic systems with people at the gates rather than in every step.

Distributed compute is still a good bet #dePIN 📽️ Why AI Demand Is Outrunning Compute Supply youtu.be/FGC4ofTcg2k?si=Giwg… via @YouTube

@token2049 main themes & takeaways - tokenised assets & 24/7 trading is here - quantum proofing $btc - privacy chains - your user is an agent Its hot. 😉

@kast great job creating a 1990s dynamic. Letting girls in gold dresses in and not people using your product or partners. No idea why people don't take crypto seriously.. or more importantly.. the women in the industry

Love that AI has turned a lot of high agency folks with ideas into R & D engineers - > Can I do this with AI? Where is it going wrong? Do I need a better prompt/skill/harness/eval/framework/ruleset/agent brief or completely different approach? How do I fail fast & optimise outcomes? How do I make it once and use it everywhere?

I've decided I agree that AI should be stopped. Its far too easy for me to turn a simple task like writing a resume into a full blown automated customisable intelligence product that results in many days of work and no job applications 😅 (sarcasm folks 😉)

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Progress over perfection! Remember the Pareto Principle - roughly 80% of results come from 20% of inputs 😉 Gm to everyone that's shipping today! 🚀

Hey all! Looking for my next challenge in AI / deep tech! I’ve spent the last several years building at the intersection of AI, distributed systems, and developer experience: → Founded Lilypad Network — a permissionless distributed AI compute marketplace (from smart contracts all the way to live GPU nodes running real workloads) → Led Developer Experience for the Filecoin Virtual Machine launch at Protocol Labs / Filecoin Foundation → Built and scaled developer communities + technical storytelling as Lead Developer Advocate at IBM → Shipped production software across Accenture, University of Sydney (ML/IoT), and earlier startup work I’m strongest when the work involves: - Turning frontier AI into products people actually use - 0 → 1 technical product and infrastructure - Developer experience, storytelling, and ecosystem building - Systems thinking across hardware, software, and go-to-market Australia-based. Open to remote or relocation. Full-time or fractional. I’ll be at Token2049 Singapore (7–8 Oct) - would love to meet founders and teams building serious AI infrastructure, agents, or developer platforms while I’m there. If someone comes to mind, an intro would mean a lot. Happy to share more about what I’ve built!