BuilderCore Journey Update Over the last few weeks, something interesting happened. We weren’t just building an AI development platform. We were discovering what an AI software factory actually needs before it can be trusted. One of the biggest lessons was this: Governance is more important than autonomy. Everyone is racing to make AI agents that can do more. We’re trying to make AI systems that can be trusted to do more. Those are very different goals. The first goal isn’t autonomy. It’s predictability. Our first milestone wasn’t: - autonomous coding - autonomous deployment - autonomous GitHub actions It was proving that every stage of software delivery could be controlled, audited, replayed and stopped. That meant proving things like: - controlled apply - replay workspaces - validation - queue promotion - queue completion before even thinking about autonomous execution. That order matters. We stopped treating AI like one assistant. Instead, we started treating it like an organisation. Not one intelligence. A collection of specialised roles. For example: - Planner - Hardener - Researcher - Implementer - Reviewer - Judge - Operator That immediately changed how we think about AI. One of the strongest doctrines we’ve adopted: The same AI should never judge its own work. That now applies to: - reviews - validation - judgement If one model creates something… A different one should evaluate it. That’s closer to how healthy engineering teams actually work. Roles are permanent. Models are replaceable. This became one of the biggest architectural shifts. Instead of designing around: - GPT - Claude - Codex - Perplexity we design around roles. Today GPT might be the planner. Tomorrow another model might be better. The workflow shouldn’t have to change because the underlying model changes. We discovered evidence should change roadmaps. Not doctrine. This thread completely changed our roadmap. Originally we planned to test vague briefs next.

💪 It did not disappoint. 👉TL:DR - It made all AI use cheaper in my environment, made me more efficient, and it increased my memory system usage by almost half. It should be no secret to anyone here: I don't count tokens. 🪙 I'm on a Pro Max plan and I go where the work, and the passion takes me. But I have used Fable before, and I know about the token burn! So I came up with a plan! I pointed Fable at my own setup and pulled the report. 😅 I'm glad I did. Turns out it could've been cheaper and cleaner the whole time. I didn't ask Fable what it thinks. I pointed it at the real thing. My hooks, my handoff files, my subagent config, the actual token counts on disk. Two questions: where does the money go? & how do we spend less? 💡The answers. A silent forgotten tax on every message, A safety hook was injecting around 800 tokens into every prompt I sent. Repeated rules I already load once at the top. And a file that runs at every session start called itself " 450 tokens" in its own header. It was not... it was 7,000. My actual face when I saw this--->🤬 a few moments later--->😆 (Apparently, it's not only AI that is bad at counting sometimes!) My own file lied to me, and I'd read past that number a hundred times with a smile on my face! My long sessions. The ones I'm proudest of. Turns out that hour six is my most expensive and also least impactful at the same time. 💩 I know that models follow instructions worse at higher context. And because the quality was still there, the marathon I read as momentum was being billed to me at top rates for the weakest output of the day. 🙄 My helper agents were all running on the flagship (Most expensive model). These are the agents Claude spins up to send off to do tasks to get more accomplished in a shorter amount of time! This I knew about, and it was by choice, it's my environment and I never hit my 5 hour or weekly cap, so I did not care about this for myself, bigger = better right? Use Fable 5 on UltraCode in my environment, and we find out that logic was wrong....