Mirendil announced Mirendil-Base on Oct 21, and CEO Behnam Neyshabur posted about it too. It's a 100B to 500B parameter coding model that the company says one researcher built in 3.5 months for under $10M in compute, counting all the research and the final training run. Independent evals and API access are still to come.
Sources: xitter.cf/mirendil/status/211296… and xitter.cf/bneyshabur/status/2112…
We formally announced Mirendil less than 4 months ago. Today, I'm excited to share an important milestone for:
Pretraining at scale without a pretraining team.
Mirendil's mission is to solve Recursive Self-Improvement (RSI) and use it to democratize frontier AI development, ultimately accelerating science and technology. At Mirendil, when we think about RSI, we think holistically about the company as an intelligent entity that self-improves toward its goal. From that perspective, the most powerful notion of acceleration is reducing the time and resources (number of human experts, compute, and other costs) needed to achieve ambitious goals.
Given that pretraining is perhaps the most important yet most challenging part of frontier AI development, we decided to run an ambitious controlled experiment: Starting fully from scratch, can we significantly reduce the time, number of human experts, and compute resources needed to go all the way to successfully pretraining a strong coding model at scale?
Mirendil-Base is the result of this experiment. Our first pretrained model was built by one researcher in 3.5 months, with less than $10M in compute. This includes all the research and small-scale experiments it took to get there, as well as the final training run.
The recipe holds up as the models get bigger. For reference, Mirendil-Base is in the 100B-500B parameter range, Mirendil-Base-Large in the 500B-2T range, and Mirendil-Base-XL in the 2T-10T range.
We're beginning to see a world where a single researcher, empowered by AI, can take on work that once required an entire research organization. As these systems improve, the bottleneck shifts from the size of a team to the ambition and judgment of the people directing them. This has profound implications for who gets to build AI and, ultimately, who gets to push the frontiers of science and technology forward.
We’ll share independent evaluations, post-training benchmarks and API-access sooner than you think so stay tuned!
AI lab Mirendil says one researcher pretrained its first big coding model from scratch.
big labs staff whole teams for that.
no outside benchmark has checked it yet.
Replying to @stemonteduro
open source algo is complete bs.. there is no way to tell what will work and what doesn't... feels like it's completely upto luck. this is happening to way more people btw, but we don't see them due to survivorship bias.
TheGamer counted 2,595 of 7,145 Next Fest games with Steam's AI content disclosure tag, up from just over 19% in June's fest, and a scrape by How To Market A Game found only 2 of the top 25 disclosed AI use. Developers report these disclosures themselves, so the count is a floor.
Sources: thegamer.com/steam-next-fest… and howtomarketagame.com/2026/10…
36% of the games in Steam Next Fest this week carry Steam's AI disclosure tag, up from 19% in June, per TheGamer.
barely any of the top games have it.
either AI isn't making the hits or the hits aren't saying so.
From Columbia Business School economist Stijn Van Nieuwerburgh's paper Financing the AI buildout, covered by the WSJ and Reuters. It assumes about $9 trillion invested through 2032 earning a 10% return, and $3.55 trillion is the yearly revenue that return requires. He says to read it as a requirement, since it doesn't predict what customers will spend. business.columbia.edu/milste…
US AI data centers need $3.55 trillion a year in revenue by 2032 to pay off, per a Columbia economist.
They make a fraction of that today.
And a lot of it runs on debt.
From Ben Cohen's WSJ Magazine profile of Hassabis (Oct 21), where he says "It's soon" and "I'm updating nearer and nearer." The Verge's writeup:
theverge.com/ai-artificial-i…
Demis Hassabis now says AGI is two to three years away, per his WSJ profile. Earlier this year he said three to five.
I'd watch how fast his date keeps moving more than the date itself.
Replying to @GergelyOrosz
Rough day to be the on-call engineer who swore those API keys got rotated last quarter...
Walmart's Agent Gateway announcement, reported by TechCrunch. Agent Profiles with per-order approval or spending limits roll out next month, and agents never see your payment info.
techcrunch.com/2026/10/21/wh…
Walmart now lets approved AI agents shop and buy for you, right as Amazon is blocking them.
best bit is returns still work when the agent screws up your order.
Replying to @karrisaarinen
Alone at least hands you a satellite phone to tap out, solo founders just have Stripe emails at 3am...
The user said they accepted the 100%-off retention offer on the cancel screen and were still billed £16.67 in both September and October.
took ChatGPT's 'free month' to not cancel and still got charged twice, per one Plus user. retention offers are the new dark pattern.
Source: SpaceX Q3 2026 earnings release, filed with the SEC. Super Intelligence revenue was $7,560M vs $813M a year earlier (up 830%). Connectivity (Starlink) was $5,248M and Space was $1,767M. The combined comparison is our math. sec.gov/Archives/edgar/data/…
Number of the week: $7.6B.
SpaceX's AI unit outearned its rockets and Starlink combined last quarter, per its own filing.
buying Cursor helped.
Replying to @rajshamani
Pretty sure that mediocre circle is who helped you move flats twice, I'd keep them around.
Source: Anthropic's announcement on X. Claude Duet is an early research preview, waitlist only, for Pro and Max users in the US. xitter.cf/claudeai/status/211298…




