A while back we announced 10 million synthetic CRISPR experiments, complete on Subnet 55.
Here is what was behind that number.
CRISPR is how modern medicine edits genes, including the edits that could one day treat sickle cell at its root. But editing a genome is not a clean cut.
Before you can trust an edit, you have to predict three things:
• Whether it lands where you intended.
• Whether it also cuts somewhere it should not.
• How the cell repairs the break once it is made.
Get any of them wrong and the edit fails, or worse. Predicting those outcomes from raw sequence is one of the hardest problems in the field.
That is exactly what the challenge took on. The network generated 10 million synthetic editing experiments, each carrying a known outcome, across a huge range of edit sites and genomic contexts. Miners competed to predict the results, and because every answer was known in advance, every prediction could be scored exactly.
The result is a benchmark: 10 million scored examples of CRISPR edits and their outcomes, the kind of large, labeled dataset that gene-editing work depends on and rarely has.
This is the proof that the model works. It takes a real, hard problem in genome editing and turns it into something an open network of miners can solve at scale, with the output to show for it.
The sickle cell CRISPR challenge is done, and it set the bar for everything the subnet takes on next
@TaoIsTheKey Just in the process of open sourcing a Bittensor native platform that lets you do this with every model available across SayGM - send me your GitHub id for early access 🫡
Everything NIOME is built to do starts from one stubborn fact: medicine needs far more genomic data than the world can safely provide.
Real patient genomes are slow to collect, expensive, wrapped in privacy law, and skewed toward a few populations. The data medicine needs most is the hardest to get.
So NIOME builds it instead. The network generates synthetic genomes from real genetics, each one carrying an answer that is already known, across the ancestries real datasets leave out. Miners compete to solve them, and since no real person sits behind any of it, there is nothing to expose.
This is how NIOME works on real problems, like how your genes shape the way you respond to medication, without ever touching a single real patient’s DNA.
That is the engine you are backing when you migrate. Get in while the first batch is open: https://t.co/WsVrrHey2X
You’ve seen the speaker announcements.
Here’s where you’ll meet them.
A look inside New City Gas as the crew gets ready for Exploit.
Tomorrow, the Bittensor community comes through these doors.
Welcome to Montreal.
$TAO This is actually based on my sister in law’s day to day challenges within the Australian police force, but also note this is EVERY Police forces challenge 🙈@babelbit#sn59
Our latest article on how Babelbit's technology can be used in the real world.
This time it's the Australian policing system and the challenges it faces.
bittensor:native
A couple days ago, we announced a major breakthrough in our multilingual language model.
Today, we're excited to share the first product built on top of it.
Introducing the Babelbit Dubbing API.
A programmable real-time dubbing solution designed for broadcasters, media companies, voice agents, and developers looking to integrate multilingual communication into their own products.
Our architecture gives us the flexibility to optimize not just for translation quality, but for everything that makes conversations feel natural. From low latency to natural voices and gender adaptation.
We created a programmable framework that allows us to continuously improve and expand its capabilities.
This is the first product built on top of our Language Transformation infrastructure.
Many more will follow.
Fair to say you’re both still in the early stages of a long roadmap of progress you guys obviously a bit further along. No idea why they’re so obsessed with you other than the one on top is usually a target in any walk of life, don’t fall into the trap of reflecting their obsession 🫶🏻
$TAO even tested this on some relatively niche languages including some out of the ‘*stans’ and we auto detected them immediately before proceeding with some legit flow 🤙🏻 @babelbit#sn59
Today marks one of the biggest breakthroughs in Babelbit's journey so far.
We're excited to announce a major milestone in the development of our multilingual language model.
Until now, adding new languages meant building and optimizing individual language pairs.
With this breakthrough, we're moving toward a single multilingual foundation capable of supporting real-time Language Transformation across all supported languages.
It creates a shared foundation that future Babelbit products can continuously build upon.
For us, this is a major step toward our long-term vision as we strive to build the infrastructure for Language Transformation.