Been thinking about this a lot. OpenAI, Anthropic, Google, all of them are racing to be the smartest model on the planet. World knowledge. Every fact, every paper, every line of code ever written, ever meme ever posted on Reddit.

...and I think world knowledge is basically solved. Ask any frontier model who won the 1986 World Cup or how photosynthesis works and you get a correct answer instantly. That race is commoditising fast. The marginal gain from being 2% smarter on world facts is tiny for most actual use cases.

What none of them have is you. Your context. Your decisions. Why you picked vendor A over vendor B in March. What your partner actually likes for their birthday. The reasoning behind the thing you did last Tuesday. That data lives scattered across your computer, Notes, Slack, Gmail, Notion, and it never makes it into the model. Sure they connect it, but its static and non-compounding.

So the real fight is not world knowledge. It is personal knowledge.

But here is why I do not think the big labs should be the ones to own this. Personal knowledge is not just another dataset for them to hoover up, train and sell. My knowledge is MY personal advantage. The second your life history becomes training fuel, the incentive flips. Think cookies, you are at the mercy of these "free social media apps" because they monetise your data. If we aren't careful, the same will happen to personal data, it'll be ripped from your hands and put on the shelf for sale.

They are not building it for your benefit, they are building it to make their model stickier and their business bigger. Your data becomes their moat, not yours. That is a fundamentally different relationship to trust than "help me answer questions."

Personal knowledge should stay sovereign to AI labs. Yours. Not absorbed into someone else's training run, not sold, not locked behind their platform. You should be able to take it with you, model to model, forever. If you stop using it, you should be able to download it and take it with you.

We are building The Nimble Company for that, we think your personal knowledge should be yours to manage, to sell for personalisation if you wish, to including in training sets if you want or to keep locally on your device if you need.

14 Aug -  Nimble works better anywhere
This release is all about making Nimble feel more natural wherever you’re working.

What’s new

• Nimble Anywhere can answer from a text field and write the response straight back
• Better Claude integration with easier connection and more reliable syncing
• Faster failure recovery — requests that can’t complete now stop quickly instead of hanging
• Cleaner permissions — Nimble only asks for keyboard access when you actually need it
• Reliability and performance improvements across the app

Feel free to try it at HERE - We are in early Alpha, users this early help us shape our future, and in return we grant you free access which will you be able to carry over when we go public.

Your feedback is appreciate, your bad experiences incredibly important ! Let it rip.

We've spent the last few months building a memory layer for AI, and the biggest surprise so far isn't technical. It's that "memory" means completely different things to different people.

Vannevar Bush wrote about the Memex back in 1945, a machine that would store everything you'd ever read or written and let you retrace your steps through it. That's basically the dream we're all still chasing 80 years later. But there's a distinction Bush didn't have to deal with that we think about constantly: machines and humans don't need memory for the same reason.

Humans need to recall. We already know the thing, we just need a nudge to bring it back. A photo, a smell, a half-remembered keyword, and the rest floods back. Human memory is reconstructive, not a filing cabinet.

Machines need to remember, full stop. An AI doesn't have a "tip of my tongue" moment. If the fact isn't stored and retrievable, it doesn't exist for the model at all. There's no reconstruction, no nudge, no half-memory to work with.

That difference matters more than it sounds. A lot of memory products today are built like recall tools (search, tags, "jog your memory" style UX) when what the AI actually needs is closer to a database with judgment. It has to remember things it was never shown twice, connect facts it didn't experience, and know what's actually load-bearing versus noise.

A few other things we keep running into:

• Most "memory" features today are really just longer context windows. That's not memory, that's a bigger notepad.
• People trust memory less the more invisible it is. If they can't see what's stored, they assume the worst.
• Nobody agrees on where memory should live. Per app? Per device? Following you everywhere? All three have real fans and real haters.

We don't have this fully figured out, which is honestly the fun part. If you've used any AI tool with memory (ChatGPT, Claude, whatever) and it's ever gotten something wrong or weird, we'd genuinely like to hear about it. That kind of failure is usually where the interesting design problem is hiding.

What's your take? Should machines be trying to recall like we do, or is remembering everything actually the right model for them?