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Major update just shipped ⬟
Nesa Bootstrap is now the official script to set up and configure your Nesa node.
It supports 100k+ models across 25+ modalities, including vision, audio, and language.
Whether you’re spinning up a new node or already running one, you must re-run the script to stay online.
🧵
Lately I’ve found myself giving the same advice to friends who ask, “So what’s actually happening with AI?”
And every time, I have to confront how hard it is to communicate the pace of change if you’re not living inside it.
“Fast” just doesn't describe it.
It's something closer to a constant phase transition than anything else.
Every week there are entirely new mental models you have to adopt just to stay oriented.
– Ralph-style agent loops.
– Anthropic shipping Cowork.
– Teams running 10, 15, 20 parallel agents as a default workflow.
– Systems building and improving other systems.
The gap doesn’t seem to be capability as much as it is perception.
Most people still measure progress by what they want most from AI (which I think is flawed reasoning):
– An inbox that runs itself.
– A portfolio that manages itself.
Essentially, high-stakes decisions fully automated.
But those are the last domains to move, not the first.
They’re gated by trust, liability, and review cost.
So using “has it solved my email yet?” or “can it run my money yet?” as a yardstick massively understates what’s actually happening.
It’s like judging the internet in the 90s by whether it had replaced banks.
What’s actually shifting is the structure of work itself.
From linear human execution to parallel, self-verifying, continuously iterating cognitive systems.
And honestly, I often feel FOMO myself.
Not from missing tools, but from how quickly the frontier is moving.
So when I try to give practical advice, I don’t just start with tools.
I think it's important to be thinking about what YOU want AI to do for you, and seeing if you can make that happen - because chances are, you can.
In my workflows, I am often asking myself what I can delegate to AI.
But that statement is less about what AI can do for me out of the box, and more about what I can force it to do well.
Then, I use a simple mental matrix to see if it makes sense to get AI to do it:
– How long does the task take me today?
– How frequently do I repeat it?
– How deterministic vs judgment-heavy is it?
– How long would it take to build a first-pass agent or workflow? (I debated whether to include this. I always underestimate how long it will take, but it’s still worth thinking about)
– How much review time, of the AI output, would still be required?
– What is the time saved per week, per month, per year?
– What is the cost of being wrong if the agent makes a mistake?
And increasingly, another question that matters just as much if not more:
– How valuable is it for me to understand how to build this system?
– Does this teach me something I will be glad I know in 12 months? E.g. Orchestration, evaluation, and loop design, or will it just save me a few minutes?
In other words, I don’t just ask whether something saves time.
I ask whether building it increases my optionality.
Ideally, the meta-skill you’re really compounding today is learning how to design, supervise, and evolve these loops themselves.
Because the real divide that’s forming isn’t between people who “use AI” and people who don’t.
It’s between people who can compose and orchestrate intelligence and people who are still waiting for a single tool to magically take something obvious off their plate.
TLDR: Find a reason to dig a bit deeper into the AI tools already at your fingertips.
Not to chase hype, but to build the mental models and leverage that will quietly compound.
It’s a lot easier than you think, and a year from now you’ll be very glad you did.
A recent news report by CNBC indicates that AI memory is selling out, and prices are surging.
Centralized AI depends on ever-larger, ever-more expensive memory pools, and that model is hitting its limits.
It’s necessary to take a different path, and to focus on efficient inference and decentralized compute that doesn’t hinge on ever-growing memory requirements.
When memory becomes prohibitively expensive, smarter compute becomes the real advantage.
Perceptron unlocks distributed capacity and underutilized resources rather than competing for scarce hardware.
This is why decentralized, open intelligence isn’t optional, it’s inevitable.
Source: https://t.co/dXqRLQWqAp