DeFi risk is still too centralized.
A few providers recommend parameters. Protocols trust them. Markets move on.
Endure introduces a different model: risk contributors compete, validators score performance, and outcomes matter more than reputation.
Building on $TAO SN30.
At @proofoftalk this week
If you're around and want to talk risk intelligence, decentralized networks, or what we're building on $TAO SN 30, find us there.
Hyped to have worked with these guys to get @Endurenet and Bittensor $TAO SN30 out to the world.
They're solving a real problem and have an exciting pipeline.
The team a super cracked - ask them anything, they're ready.
We are launching Endure: a decentralized risk intelligence network on Bittensor $TAO, SN30.
Risk should not depend on static reports, closed committees, or reputation alone.
Endure turns risk intelligence into a competitive, continuously scored network.
Forge, the first native lending market for $TAO, launches soon.
Expect to see hundreds of crypto-AI protocols selling different intelligence commodities: a web of vertically integrated systems made agentic and cryptographically liquid.
In the three months we've been doing the weekly $TAO update at SubConnect, this was probably the quietest week so far.
And honestly, I think there’s a reason for that.
It feels like a lot of subnets are holding back major announcements until Proof of Talk next week.
If that's the case, we're in for a very interesting week.
Expect fireworks.
A really good demo highlighting the quality and speed of Bittensor $TAO subnet 59 - Babelbit interpretation compared to Google Translate.
The gulf in time savings (and therefor end user experience) is quite staggering
Emissions value should be granted based on the value of the miner output. Therefor some subnets should be providing a high burn rate. That shouldn't mean that subnet should not get emissions funding.
The alternate is the subnet emitts all the emissions to a key and distributes in alternate ways anyway.
One of the biggest things people still underestimate about $TAO is where the real new money will eventually come from.
A lot of people still look at crypto as a closed system. Money comes in, traders rotate between narratives, tokens pump and dump, and eventually a large part of that liquidity disappears again. It’s mostly speculative capital moving in circles.
But Bittensor has the potential to evolve into something very different.
What makes it interesting is that more and more subnets are no longer just experiments or ideas on paper. They’re starting to build actual products that solve real problems for real customers. And when customers are willing to pay for those products, something important happens: new capital enters the ecosystem from outside crypto itself.
That changes the dynamic completely.
Instead of value depending purely on speculation, the network can begin generating value through usage and revenue. And once that revenue starts flowing back into subnets, you create entirely new feedback loops inside the ecosystem.
Successful subnets can use revenue for buybacks. More $TAO gets locked into pools. Strong subnets attract more miners and emissions. Builders become incentivized to create products people genuinely want to use, because the market starts rewarding productivity instead of just hype.
Over time, that creates a powerful flywheel.
Better products attract more users. More users generate more revenue. More revenue strengthens the subnet economy. And a stronger ecosystem attracts even more builders, users and capital.
In the long run, we don’t think the biggest winners will be determined by hype cycles alone.
We believe the real value accrual will come from revenue flowing back into the network.
The Nerds hosted @matthew_karas from $TAO sn 59 @babelbit . The takeaway was simple.
@babelbit is building interpretation infrastructure.
That sounds close to translation, but it is a very different problem.
Translation systems run a pipeline. Speech to text. Text translated. Text back to speech. @babelbit skips the pipeline and works directly with speech tokens — phonemes, syllables, words, phrases, meaning-bearing audio units. A speech-mode transformer learns the relationships between them and produces interpreted speech in the target language.
Speech in. Meaning out.
A professional interpreter does not copy words. They preserve meaning. Clean up hesitation. Fix incomplete phrasing. Turn messy speech into clear communication.
That is where @babelbit gets interesting.
The product becomes more valuable as the domain gets more specific. Legal, medical, diplomatic, enterprise — each has patterns. @matthew_karas said if Babelbit had the communication archives of the top UK legal firms, it could become far better at legal interpretation than a generic tool.
The moat is domain-specific speech intelligence.
Four people. UK limited company. Matthew on strategy and revenue. Tom on operations. Mica on subnet mechanisms. Josh as chief scientist.
Matthew was taught at Cambridge by Tony Robinson, the first person to apply a neural network to speech recognition in 1987.
The most interesting new idea was Trans-Modal Distillation Training.
Take a large multimodal LLM that translates text across many languages. Systematically reduce its size until it can do little else but translate. Then use it to train a speech-mode transformer by turning text tokens into speech tokens.
Text trains speech.
That is the trans-modal part.
The go-to-market was one of the strongest parts of the AMA. @babelbit is positioning as the engine inside other products. More “Intel Inside” than Apple. SDK. API. Enterprise workflows. Reseller channels. Marketplace distribution.
They are working with an AWS Marketplace Partner. Big enterprises have unallocated budget for new tech experiments. That is their door in.
Then came the market twist.
@babelbit has been approached twice about the paraphrasing alone. Someone speaks in heavy dialect, with hesitation and messy phrasing, and Babelbit outputs polished BBC-style English.
One language. Less data. Easier to sell.
The big bet is multilingual interpretation. First cheque may be English cleanup.
@babelbit also made one of the strongest Bittensor-first commitments I have heard. The company articles include a rule: if they can find a supplier or partner inside Bittensor, they look there first. Confirmed integration with @vidaio_ SN85. Talking to Koyuki from SN78/SN26.
That is the ecosystem behavior $TAO needs more of.
@const_reborn reached out in December after @babelbit forked Affine code. Matthew spent time with him at the DCG conference in Spain. Signal matters.
Babelbit sees Bittensor as critical because the training burden runs for years. They intend to build mechanisms that support the alpha token, but no final structure yet.
Can the company value connect back to alpha value? That remains the main investor question.
My takeaway: @babelbit is one of the clearer examples of a subnet using Bittensor for a real technical workload instead of wrapping a narrative around emissions. The founder has real background. The architecture is different. The first wedge may be closer than expected. The ecosystem-first posture is strong.
Open questions: Can the training scale? Can miners improve the actual product? Can reseller interest become signed customers? Can the alpha token capture value from the company’s success?
Babelbit is trying to make machines interpret meaning the way humans actually communicate.
That is a much bigger idea than translation.
Hosted in The Nerds Telegram. NFA.
Bad take. The imrpovement of subnets over the last year has been astounding. 90% of startups fail inside the first 12-18 months, that timeline is faster in Bittensor such is the pressure chamber.
Last year there was around 4-5 good subnets, now there's around 20, with the overall baseline raised considerably and garbage pushed out the door.
Is there problems, absolutely, but it's orders of magnitude better than anything else right now.
SubConnect will be at Proof of Talk.
Both of us will be there to meet builders, subnet teams, investors and anyone seriously looking at where Bittensor is heading.
For us, the thesis is clear.
$TAO is no longer just an interesting ecosystem to watch from the sidelines.
It is becoming one of the most important places in crypto for real experimentation around incentives, intelligence, revenue and decentralized infrastructure.
That is exactly why we spend almost all of our time here.
If you are building a subnet, investing in the ecosystem, or trying to understand where the next phase of Bittensor is going, let’s connect.
See you there.
sundae_bar just launched Crumble, a tool for all the vibe coders out there who want an agent to check their code and identify security problems.
They're looking for testers to try out their tooling. I gave it a go myself and it was ingredibly easy to set up. You simply make an account and point a github repo link to it, and voila.
The cost of that test was only $0.43!
Give it a go. Their subnet will be used to iterate on the performace making it better over time.
This one is for all the vibe coders out there.
$TAO subnet 121, sundae_bar, built Crumble.
An autonomous security review agent for AI-generated code.
And honestly, this is exactly the kind of product that starts making more sense every week.
Developers are shipping faster than ever with Cursor, Claude, more retail friendly products live Lovable, and all other autonomous coding agents.
That speed is amazing.
But it also means a lot of risks and vulnerable dependencies quietly making it into production.
Crumble sits inside your GitHub workflow and reviews pull requests, branches and AI-generated code changes before deployment.
And it is not just matching patterns like traditional scanners.
It actually adds contextual review.
1️⃣ What are the risks and vulnarabilities.
2️⃣ How can it be exploited.
3️⃣ How to fix it.
In plain English.
Crumble is built for AI-generated systems.
And the interesting part is that SN121 will now focus on improving Crumble through open competition, adversarial testing and real-world exploit analysis.
That is where Bittensor becomes powerful.
A real product gets shipped, miners compete to improve it and the product gets smarter over time.
As AI-generated software scales, security needs to scale with it.
Crumble is a very logical step in that direction.
@sundaebar_ai are looking for testers, so if you want to give it a try, head over to https://t.co/cVXlApBn6P
Q2 is turning out to be even more frantic. The first model we got working is performing better than we could have hoped, so we're bringing forward our GTM plan, and are negotiating a deal with a partner to help manage our presence on AWS Marketplace.
What this space!
$TAO Breaking: Chutes moves from best confidential and permissionless AI inference network to a 100% miner burn subnet and deleted all their 40+ open-source github repos.
They said: dtao investors prefer to say GM and GN in a discord chat instead.