An Earth economy is less than a trillionth the size of a K2 economy

Love that there’s a bunch of new startups focusing on local AI / sovereign AI / GPU hoarding 😂 speaking my language! Congrats to @zainmfj and the @ghostai team

New era, new school piped.video/5JEjv-JlYo8?si=83aG…

Jev is a textbook example of Clayton Christensen’s Innovator’s Dilemma playing out in AI. It is commoditizing the bottom of the ML market: classifiers, routers, scoring and context triage. For frontier LLM labs, these workloads are not even worth pursuing. Revenue per decision is tiny, margins are razor-thin, and their entire infrastructure is optimized around selling expensive reasoning and token generation. They cannot simply put a smaller LLM on fast inference chips like Cerebras and compete. Jev uses fundamentally different infrastructure, built to return typed probabilistic decisions so it’s a pure disruption

by now we’re used to: - frontier model launches - 6-12 months later, the chinese open weight models close the gap this week we’re seeing: - jev launches - a few days later, swarms of US AI developers ship their own open weight models :) tracking 2-3 early ones so far, but looks like some great open weight alternatives coming within the week I’m sure there will be some great chinese open weight alts for jev coming also

curious if Jev-like models might accelerate us towards on-device AI native functionality on mobile devices strong LLMs are a long way from running on phones: - slow memory bandwidth - only highly quantized MoE models will fit - lots of issues with power/heat/etc Folks are building NPUs for the next gen of mobile but targeting pretty modestly sized LLMs There’s lots of mobile UX that would benefit from fast/cheap AI decision models - from notifications, typing/texting, to in-app experiences like inboxes/calendars/etc I always figured the other solution would be a model-on-a-chip that would encode an older-yet-useful model into the actual phone hardware itself, but maybe this will move faster?

I used AI to explain the AI pacing drama, with fruit.

Sometimes it’s the small victories that make your day

ok great now I have 3 messy inboxes I have to check: - stuff my agents have done on scheduled loop that I need to review - bookmarked slack messages off Google Docs and slides to review - my actual email inbox lol

The most investigated man on earth isn’t afraid of Flock. Privacy matters. Abuse matters. And we can protect privacy, reduce abuse, and improve public safety with @flocksafety.

Frequent video gamers performed on cognitive tests like non-gamers that were ~13.7 years younger

current homelab setup for local AI experimentation: - hermes box hosted on a Framework Desktop Mainboard AI Max+ 395 - 5090 eGPU running Qwen 3.8 27B for fast tok/s LLM use - sometimes 150+ tok/s - 2x DGX Spark: running Deepseek v4 Flash 0731 - better but slower model - pi 5 for monitoring - Mac mini as a dev box - use Herdr and ohmypi/codex/claude depending on the use case - housed in a 10" DeskPi mini rack (mostly) Hermes is defaulted to local AI but with a homegrown routing plugin hitting a small low TTFT model (Arch-Router) to decide whether to go local or upgrade to cloud/frontier. Trying to get to 100% local over time, but right now probably more like 60-70% The Sparks are for batch processing background runs (all the cron jobs, longer dev builds, etc) do you need all of this? Absolutely not lol. I started with the mac mini and couldn't help myself but to add over time! Also I regret getting the eGPU so I wouldn't recommend that to anyone

What Is To Be Done? I propose a simple plan: Big AI companies should be allowed to build AI as fast and aggressively as they can – but not allowed to achieve regulatory capture, not allowed to establish a government-protect cartel that is insulated from market competition due to incorrect claims of AI risk. This will maximize the technological and societal payoff from the amazing capabilities of these companies, which are jewels of modern capitalism. Startup AI companies should be allowed to build AI as fast and aggressively as they can. They should neither confront government-granted protection of big companies, nor should they receive government assistance. They should simply be allowed to compete. If and as startups don’t succeed, their presence in the market will also continuously motivate big companies to be their best – our economies and societies win either way. Open source AI should be allowed to freely proliferate and compete with both big AI companies and startups. There should be no regulatory barriers to open source whatsoever. Even when open source does not beat companies, its widespread availability is a boon to students all over the world who want to learn how to build and use AI to become part of the technological future, and will ensure that AI is available to everyone who can benefit from it no matter who they are or how much money they have. To offset the risk of bad people doing bad things with AI, governments working in partnership with the private sector should vigorously engage in each area of potential risk to use AI to maximize society’s defensive capabilities. This shouldn’t be limited to AI-enabled risks but also more general problems such as malnutrition, disease, and climate. AI can be an incredibly powerful tool for solving problems, and we should embrace it as such. To prevent the risk of China achieving global AI dominance, we should use the full power of our private sector, our scientific establishment, and our governments in concert to drive American and Western AI to absolute global dominance, including ultimately inside China itself. We win, they lose. And that is how we use AI to save the world. It’s time to build.

SYNCERE POP-UP 342 W 14TH ST, NYC 11AM - 9PM