Rethinking AI Harnesses

Hey Daniel, how have you solved the harnesses across multiple devices challenge? I have a workstation, a personal laptop, a server, a work laptop. Constant context switching harms my harnesses in many ways.

I think you are confusing context engineering with a harness. The "context in your harness" is context engineering, it is not the harness. The harness provides certain mechanisms for arranging context and requesting inference. That mechanism changes per harness (to some degree) and dictates your working surface for the context engineering.

I really like the way you describe what we are experiencing at the evolution of our integration is progressing. If we can add features and integrations into our new life form or new offspring we can react faster to builds and consume new useful capabilities faster than all that started after us.

PAI has changed my life in the past 6 months. I feel like it makes model atleast 3xsmarter because it knows what I am looking for and delivers. Thank you sir!

Love the video and your concepts. I thought I liked PAI 3.1 now that I’m on 5.0 holy cow man life changer for me anyway. And all my TELIOS stuff isn’t even all the way done yet. I think tomorrow will be system interview day so I can close that gap

Thanks for sharing this. It totally validates something I have been building for myself, and it is definitely intent engineering. You put it extremely elegantly — well done. Thank you.

Hi Daniel, it takes longer lol but we endure it because our PAI just knows! Please don’t give us any updates until September

You got me thinking. I enjoy thinking.

Daniel, I’m a “try everything” guy, and my hacked-together Ubuntu/PAI setup still gets more done than Hermes, OpenClaw, GBrain, or anything else I’ve tried. After reading Google Cloud’s OKF announcement, it felt less like a brand-new direction and more like validation of the markdown-first, context-as-infrastructure pattern PAI has already been pushing — except PAI goes beyond the format layer into intent, memory, tools, hooks, and current → ideal-state execution. I’m probably only using 10% of what PAI can do, but that 10% has been ahead of every other knowledge/workflow system I’ve tested. Thank you for building this. My current challenge: I have an older, heavily modified PAI instance that has gone through multiple memory experiments, RAG/vector-store add-ons, removals, other-model refactors, and lots of “slapped together but useful” customizations. Which of your Fable prompts would you run first to refactor it into a clean, current LifeOS-style instance while preserving the useful pieces? Goal Orientation, Bitter Lesson Optimization, Memory That Compounds — or some combo?

It's interesting how we can apply real world techniques into artificial intelligence. I recently listened to a Jocko podcast about the Marine manual and he talks about state the objective to the soldier and letting them come up with the means to execute the plan. So similar to stating the ideal state.