If this doesn’t radicalize you into founding your own open source AI lab, I don’t know what will.

The best teachers will be LLMs. The best feedback will be monetary. The best models will train competitively. The best AI company will be co-owned. The best software will be open source. And there is only one way to get all of it.

Futile appeal: Mythos should not be re-launched. It should be open sourced. If it was, that would mark the beginning of an era of unfathomable progress and prosperity to humanity that would make EVERYONE richer, including those at big labs, and the US. I spent the last hour writing a 10 paragraph long text explaining why that's the case and addressing every single point and counter-argument to it, like security, business, economical impact, China - in ways I know to be true, even though hard to see - but I deleted because it became massive and obviously the people that matter would never read it, let alone have a slim chance of being convinced, given that's the extreme opposite of everything they believe. But that is a shame, because it is impossible to overstate how much our species would benefit, if that was true. Things would change in ways that seemingly nobody alive understands, particularly these who should. I suppose our monkey brains are just not smart enough to play the right cards here, and, as a civilization, we all lose. Imagine if an alien species found an infinitely deep oil pit, and used it ALL as a hair moisturizer. We'd laugh at them, wouldn't we? Well, that's us with LLMs, right now. tl;dr way too many people alive today trading a ticket to Mars for a shiny green umber in their phones, and they don't even know that :(

AI peeps, you can pivot back to crypto now. In fact, you might have to if you want to work on AGI.

The next era of SN11: bring your own model. Season 1 proved the skill competition. SKILL.md packs took a stock 35B open model from "reads the task and gives up" to near-ceiling on real terminal tasks. Now we're opening the next lever: miners will submit finetuned models, not just skill packs.

TLDR; our subnet on bittensor (SN15) is producing the highest quality open agentic shopping traces in the world. We're going to leverage that to create personalized shopping models for all. go give our arXiv pre-print a read, where we outline how the shopping traces from the best frontier open source models show pitfalls, the challenges and the opportunities in the goal of teaching AI to shop. Also, we're expanding the team! If you're interested in any of our work below, please reach out to team@oroagents.com.

Quasar is entering its next chapter on Bittensor SN24. We are moving toward a 10T-token decentralized training run. The idea is simple Quasar Models needs more useful training, not just bigger parameter counts. Real model quality comes from tokens, data quality, training direction, and the ability to keep improving checkpoint by checkpoint. This run starts with a 5T-token phase to produce a stronger checkpoint, then continues into another 5T-token phase, reaching 10T total trained tokens. SN24 will set the direction: the starting checkpoint, the data, the training recipe, and the evaluation system. Miners become the extra compute layer. They help Quasar train faster, improve continuously, and move forward together. this would be the largest token-scale training runs ever attempted in decentralized AI. This is how we scale Quasar. Help us train it 👇

If you think software is your moat above all other things, you're going to have a bad time

You can use gradients sdk to deploy your models to @TargonCompute, @lium_io, centralized clouds or to your local GPU

Cyberpunk vibes intensified sharply today.

The first swarm from @SwarmSubnet

Cool, but I prefer optimization competitions where you own the company rather than work for it.

everything is better when its a mining network

You'll be blown away; zeussubnet.com/news-and-upda… or not, in that case read again. $TAO #bittensor

5M context length agentic model from Quasar.

Since moving to chromosome 21, we’ve performed more than 37,000 variant-calling evaluations. That took about 11 days. In a conventional lab setup, the same evaluation workload would take roughly 65 days. Many of the top evals are converging around GATK, and on average, we are seeing a 10.21% increase in performance across submissions. As top miners learn from each evaluation and refine how they identify mutations, rapid iteration is driving measurable gains across the network.