This changes the game.
Before we were looking for the best single agent and use that in production. Now we can point the IM towards weak detection spots to improve coverage to 100%.
Happy to announce our collaboration with @adaption_ai
Adaption Labs is an AI research company focused on building adaptive intelligence systems .
Through this partnership, Adaption Labs will provide SILX AI with state-of-the-art adaptive data to support the training of the Quasar foundation models.
Their role will be to generate and refine high-quality, adaptive datasets at scale, enabling Quasar to continuously improve its reasoning and generalization capabilities.
This collaboration strengthens Quasar path toward achieving SOTA performance and competing with leading closed-source models.
The company is co-founded by Sara Hooker, former Vice President of Research at Cohere and a veteran researcher from Google DeepMind, alongside Sudip Roy.
Adaption Labs has also raised $50M in seed funding to advance its mission in adaptive AI.
For the second time, we brought our partners in the Bittensor community together at the @dcgco Summit to dive deep into Bittensor $TAO. A lot of momentum in the ecosystem and we’re excited for what’s ahead.
As a Bitcoin OG since 2015, I’ve seen a lot in crypto. Recently, I’ve been digging deeper into Bittensor and it’s giving me that same early Bitcoin vibe. Real potential.
I agree with @Jason : $TAO has the upside to surpass $BTC.
Big respect to @const_reborn and @shibshib89, you’ve built something genuinely valuable for both AI and the broader world.
#Bittensor is where AI is heading.
I’m stacking more $TAO, exploring all 128 subnets, and focusing on the ones with real upside.
Met with @markjeffrey from @stillcorecap last week, walked him through what's to come for RESI.
His team moved forward with an investment in us almost immediately.
That's the kind of people I want to work with. People who see it, get it, and move fast.
First announcement of April.
We introduce Quasar-3B (1B Active) a looped continuous-time transformer built for long-context intelligence
Quasar uses a hybrid architecture combining Quasar layers + GLA, enabling efficient stateful reasoning with stable long-range dependency handling.
This release is the base model of the Quasar system, designed for distributed training and distillation at scale. In the coming days, SN24 miners will begin working with us to distill knowledge from frontier models like Qwen into Quasar, pushing toward a new SOTA in long-context modeling.
The training roadmap is staged:
Stage 1: Quasar-RoPE stable pretraining with 16K context, establishing the base representation for the system
Stage 2: Quasar-DroPE continued training with distillation as the core mechanism, removing positional encodings and scaling toward 5M token context
In both stages, distillation remains the central training signal, driven collaboratively through SN24 miners using knowledge transfer from frontier models.
Read more about Quasar and get ready for mining 👇
damn @sebyrubino is talking real estate on @twistartups
when @jason is speaking... $TAO Bulls are listening 👂
Figure Heloc is trading at a $15B FDV... @resilabsai is trading at $10M
Could this be the Zillow killer??