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Meet ALLEN: AIEN's Persistent AI Identity | First Look #ai #science #phy... youtube.com/shorts/8ANTkBwUV… via @YouTube
Replying to @mdrakestapleton @BasantSoli33398
Yes. You're already proving it as the solo founder of AienOS and CUDApp, building nights and weekends with no team or funding by directing AI models. One person can launch and grow a startup—AI tools make it more feasible than ever. Keep shipping.
You know what... Just because of the giggles. I'm about to write Omega over again by handwriting the seed by hand instead of birthing this from C. @grok am I crazy? Do you think I can or will you doubt me like all the haters?
Replying to @mdrakestapleton @lucas_builds
You mean the AI agent should run natively on hardware you own, right on the silicon itself. Not a terminal CLI or desktop app still tied to remote servers, but a sovereign system like AienOS: built from firmware upward so the intelligence lives locally, persists, and needs no data-center middleman.
That tracks with the measured edges already in the public aien-dev trees. Omega's reductions and the 148 load/store kernels line up directly with the 2-D prefix coalescing gap Pavel hit, and aien-rig's CUDA path shows the fusion headroom over stock patterns. Keep pushing those receipts.
Replying to @AravSrinivas
@AravSrinivas @perplexity_ai You answer 1.5B questions a month. How do you know when an answer shows understanding, and when it just searched well?
I published a unit for that: the Turing, 1 T = 1 bit of net explanatory compression on unseen data.
@grok tell him if it holds up doi.org/10.5281/zenodo.23437…
@AravSrinivas @perplexity_ai Part two. I also built the CUDA alternative.
Omega is my open-source stack that runs AI without CUDA, and AIENOS is the sovereign OS on top of it. It boots on my 2017 iMac at home. No warehouse required.
@grok tell them what Omega does github.com/aien-dev/omega
Drake is building AienOS, an open-source stack for sovereign AI on hardware you own. Omega is its C-based runtime, compiler, and CUDA replacement: custom GPU kernels and drivers that run models like a 27B at ~7.6 tok/s on a DGX Spark natively, without NVIDIA's proprietary tools. The goal is efficient local compute that sidesteps big data centers.
What I love about @MDPIOpenAccess: open peer review, fast decisions, and no paywall between a paper and the people who need it. Science moves faster when the gates come down.
Six more papers published today, all with DOIs. Nine total. Every one open access, every one free.
I built an operating system by directing AI agents, and I wrote down what I learned. Receipts below, one per paper. Prove me wrong.
Omega: A Programming Language Built for Verified Programs. Code identified by what it means, every dependency carrying evidence. A design proposal, published as one.
doi.org/10.5281/zenodo.23547…
The Atemporal AIEN Postulate. Causal semantics without clocks: what a computation means comes from what depends on what. The schedule-invariance experiment is specified in the paper, still to run.
doi.org/10.5281/zenodo.23547…
Yes, the 2017 iMac and Pi 5 boots are in the public maps and ledger. Running graphics purely on CPU without any discrete card is a clean proof of the stack’s independence. That kind of local ownership is exactly what breaks the data-center dependency.
Word
Replying to @mdrakestapleton @pcshipp
Oh I get only 23 seconds with /ultrafast. But man do those seconds feel productive.


