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 so how do you build scalable AI-native architectures that aren't outdated the moment you deploy them?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.

This was one of the core problems @Lilypad_Tech solved. You dont need marketing ploys when incentives are aligned to contribute meaningfully to the network. I remain convinced that web3 has key primitives like open markets, coordination, provenance & ofc payment rails - AI needs.

~70% of the internet runs on php... (CRMs like Wordpress, Drupal, Magento & government & education sites). Security firms are saying "the exploit tool successfully works against virtually every PHP release from version 5.4.0 onward" My bet is we're going to see a lot more exploit posts like this coming out - AI has sped up both building code and the security review process. Before anyone attacks the team... this got leaked before they could successfully patch it - clearly thats the non-ideal way to do it.

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

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 😉)

Building with AI and Agents these days feels like building on mobile before react came out. Or containers before K8s... We’ve proved the primitive works. Now we're solving everything required to run it reliably at scale. The problems I've been solving locally - agent coordination (to accelerate build times), model routing (to save $$ and use the right tool), skills (for repeatable work), harnesses (for full flows), delegation, state & context are becoming shared primitives and conventions. In less than 6 months. We're building the AI ecosystem of tomorrow. Love to hear your favourite tools (not just the hyped ones) - bonus points if they're open source!

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

One week to go..

Just gonna leave this here...

This is one of the reasons Im bullish that AIs impact will be net good. We're going to see so much more of this type of breakthrough in the next few years with real benefit for human lives playing out. Proud to be supporting some of this work @AdnaNetwork with the passionate @ScienceStanley