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What I want is memory within projects.

ChatGPT has a “projects” feature (presumably inspired by Claude) which lets you assign a new set of custom instructions and optional source documents and then start new chats with those on demand. It’s confusingly similar to their less-well-named GPTs feature from November 2023.

Why this matters – in the future, everyone can be tracked: Systems like FarSight are interesting because they integrate multiple modern AI systems into a single super-system, highlighting how powertful today’s AI can be once people invest in the plumbing to chain things together.
Read more: Person Recognition at Altitude and Range: Fusion of Face, Body Shape and Gait (arXiv).

I really like this approach. I've used this method to create new projects and to update existing one with some good results.

• I chat with gpt-4o to hone my idea
• I use the best reasoning model I can find to generate the spec. These days it is o1-pro or o3 (is o1-pro better than o3? Or do I feel like it is better cuz it takes longer?)
• I use the reasoning model to generate the prompts. Using an LLM to generate prompts is a beautiful hack. It makes boomers mad too.
• I save the spec.md, and the prompt_plan.md in the root of the project.
• I then type into claude code the following:

1. Open **@prompt_plan.md** and identify any prompts not marked as completed.
2. For each incomplete prompt:
    - Double-check if it's truly unfinished (if uncertain, ask for clarification).
    - If you confirm it's already done, skip it.
    - Otherwise, implement it as described.
    - Make sure the tests pass, and the program builds/runs
    - Commit the changes to your repository with a clear commit message.
    - Update **@prompt_plan.md** to mark this prompt as completed.
3. After you finish each prompt, pause and wait for user review or feedback.
4. Repeat with the next unfinished prompt as directed by the user.

we're entering a world where AI personalities become persuaders. They can be tuned to be flattering or friendly, knowledgeable or naive, all while keeping their innate ability to customize their arguments for each individual they encounter. The implications go beyond whether you choose lemonade over water. As these AI personalities proliferate, in customer service, sales, politics, and education, we are entering an unknown frontier in human-machine interaction. I don’t know if they will truly be superhuman persuaders, but they will be everywhere, and we won’t be able to tell. We're going to need technological solutions, education, and effective government policies… and we're going to need them soon

we're entering a world where AI personalities become persuaders. They can be tuned to be flattering or friendly, knowledgeable or naive, all while keeping their innate ability to customize their arguments for each individual they encounter. The implications go beyond whether you choose lemonade over water. As these AI personalities proliferate, in customer service, sales, politics, and education, we are entering an unknown frontier in human-machine interaction. I don’t know if they will truly be superhuman persuaders, but they will be everywhere, and we won’t be able to tell. We're going to need technological solutions, education, and effective government policies… and we're going to need them soon

Meet the Slate Truck, a sub-$20,000 (after federal incentives) electric vehicle that enters production next year. It only seats two yet has a bed big enough to hold a sheet of plywood. It only does 150 miles on a charge, only comes in gray, and the only way to listen to music while driving is if you bring along your phone and a Bluetooth speaker. It is the bare minimum of what a modern car can be, and yet it’s taken three years of development to get to this point.

But this is more than bargain-basement motoring. Slate is presenting its truck as minimalist design with DIY purpose, an attempt to not just go cheap but to create a new category of vehicle with a huge focus on personalization. That design also enables a low-cost approach to manufacturing

The big picture: Virtual employees could be the next AI innovation hotbed, Jason Clinton, the company's chief information security officer, told Axios.

• Agents typically focus on a specific, programmable task. In security, that's meant having autonomous agents respond to phishing alerts and other threat indicators.
• Virtual employees would take that automation a step further: These AI identities would have their own "memories," their own roles in the company and even their own corporate accounts and passwords.
• They would have a level of autonomy that far exceeds what agents have today.
• "In that world, there are so many problems that we haven't solved yet from a security perspective that we need to solve," Clinton said.

I’m writing about this today because it’s been one of my “can LLMs do this reliably yet?” questions for over two years now. I think they’ve just crossed the line into being useful as research assistants, without feeling the need to check everything they say with a fine-tooth comb.

I still don’t trust them not to make mistakes, but I think I might trust them enough that I’ll skip my own fact-checking for lower-stakes tasks.

This also means that a bunch of the potential dark futures we’ve been predicting for the last couple of years are a whole lot more likely to become true. Why visit websites if you can get your answers directly from the chatbot instead?

The lawsuits over this started flying back when the LLMs were still mostly rubbish. The stakes are a lot higher now that they’re actually good at it!

I can feel my usage of Google search taking a nosedive already. I expect a bumpy ride as a new economic model for the Web lurches into view.

Decentralized AI startup Prime Intellect has begun training INTELLECT-2, a 32 billion parameter model designed to compete with modern reasoning models. In December, Prime Intellect released INTELLECT-1, a 10b parameter model trained in a distributed way (Import AI #393), and in August it released a 1b parameter model trained in a distributed way (Import AI #381). You can follow along the training of the model here – at the time of writing there were 18 distinct contributors training it, spread across America, Australia, and Northern Europe.

I propose that AI will not disrupt human daily life until it also migrates from a genetic-ish code-based substrate to a widespread, heterodox culture-like platform. AI needs to have its own culture in order to evolve faster, just as humans did. It cannot remain just a thread of improving software/hardware functions; it must become an embedded ecosystem of entities that adapt, learn, and improve outside of the code stack. This AI epizone will enable its cultural evolution, just as the human society did for humans.