💾 TomTensor 📈

Babelbit⭐️SN59⭐️Bittensor⭐️FinTech Veteran⭐️Tech Investor⭐️Property Developer⭐️

[💾 TomTensor 📈 @tom_tensor]

@TlTtry @MaxScore @oblong_inc @manakoai I’m talking about Oblong…. 🫠

[💾 TomTensor 📈 @tom_tensor]

@markjeffrey ici maintenant aussi 🫶🏻

[💾 TomTensor 📈 @tom_tensor]

Only going to accelerate from here 💪🏻

[Dr Reed @ExponentialReed]

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

[💾 TomTensor 📈 @tom_tensor]

Legit tech 👌🏻

[babelbit.ai @babelbit]

https://t.co/mknJdD4iT0

[💾 TomTensor 📈 @tom_tensor]

Just got my gm invite. One key for Claude, GPT, Gemini, routed through a hardware TEE so prompts stay private. Waitlist: https://t.co/0nrOoUsQ29

[💾 TomTensor 📈 @tom_tensor]

@Cats_CR @AlgodTrading Either way it will do will 🫠

[💾 TomTensor 📈 @tom_tensor]

Dude it’s the size of the chutes liquidity pool and the amount of incoming TAO flow to maintain emissions and stop price decay from that plus miner sells. It’s tied to the rest of the eco system from that respect so there’s only a certain amount of relative distance any subnet can get from a rising tide of other subnets. Momentum will swing back but it will need a lot of TAO to do so.

[💾 TomTensor 📈 @tom_tensor]

Babelbit progress, love being part of this team 🥰

[Mark Jeffrey @markjeffrey]

Hash Rate - Ep. 169: Babelbit - Subnet 59
🧙 Guest: @matthew_karas of @babelbit
The Star Trek 'Universal Translator' is here

03:02 Real-Time Speech Translation Technology
08:55 Applications
12:03 Why Subnet?
17:51 Training Models
37:30 Low Latency Interpretation and Paraphrasing
40:40 Product Development and Market Strategy
44:45 LIVE DEMO (this is AMAZING)
50:40 Website Launch
01:00:12 Future Directions and Ecosystem Growth

[💾 TomTensor 📈 @tom_tensor]

@niftyinvest @markjeffrey Not at all - check out 97 and 110 as examples. You actually need to do the opposite of what the signals tell you on this. It’s like these signals tell you what happened after the fact.

[💾 TomTensor 📈 @tom_tensor]

@richardpbacon Yeah I saw some terrifying old ladies and families

[💾 TomTensor 📈 @tom_tensor]

@JosephJacks_ A classic 👌🏻

[💾 TomTensor 📈 @tom_tensor]

@TAOTemplar This one and 104 vex me

[💾 TomTensor 📈 @tom_tensor]

@Tom_dot_b @proofoftalk See you there Romeo 🙌

[💾 TomTensor 📈 @tom_tensor]

No more need for these situations with Babelbit

[CALL TO ACTIVISM @CalltoActivism]

What the hell just happened while Trump was walking with Xi in China?

[💾 TomTensor 📈 @tom_tensor]

Thesis proven, now we go multi-lingual 🙌🏻

[Mariuszek @sobczak_mariusz]

For the longest time I didn’t understand $TAO sn 59 @babelbit , but when I did I went in hard. @babelbit latency is only 2 sec vs 6 sec for @Google translate, and it predicts next words with higher accuracy. And now @babelbit is working to be featured at AWS Marketplace. This giant is waking up

[💾 TomTensor 📈 @tom_tensor]

Appreciate the support as always🙌huge effort from the team these last 8 months to get us to where we are today, feel like now we have what we need in place to really start accelerating our progress 💪

[Punisher ττ @CryptoZPunisher]

#Biττensor >> ∆ τ << #τₐcc
> $TAO <

Subnet 59: Babelbit
@babelbit
@matthew_karas
@tom_tensor
➡️ https://t.co/jQyrbOhx52

SN59 is starting to look seriously underestimated.

A few months ago, BabelBit kept talking about latency while most people were focused solely on raw translation accuracy.
They weren’t bluffing.

We’re now beginning to see what they were actually building.

This is no longer just about translating correctly.
It’s about translating fast enough for it to feel natural in a live conversation.

While big tech continues to struggle with delay, SN59 is moving toward ultra-low-latency speech translation designed for a world of live AI agents, streaming, multilingual communication, and real-time interaction.

Milliseconds matter.

Another thing that stands out to me is the team itself.
A fully doxxed team made up of experienced people who have been building real speech technologies and commercial products for decades, not anonymous hype sellers chasing emissions.

This project feels massively undervalued relative to the problem it is trying to solve.
I’ll be reopening a position after this message.

What if BabelBit becomes the first subnet to truly reach the mainstream?

@YumaGroup
@BarrySilbert

[💾 TomTensor 📈 @tom_tensor]

Thanks for the support! 🫶🏻

[YVR τrader @YVR_Trader]

📢 @babelbit #SN59 is one of the most compelling ideas I’ve seen in the ecosystem lately: it reframes translation from “how fast can we output a literal sentence?” to “how early can we deliver enough meaning for a real conversation to continue?” That shift sounds subtle, but it is actually the entire game.

The recent whitepaper makes a strong case that real-time translation is not a text problem first... it is a human interaction problem. In live speech, the best interpreter is not the one that waits for every last word. It is the one that can predict intent, compress filler, preserve tone, and speak the right thing at the right time. Babelbit is trying to teach machines to do exactly that.

What makes this especially bullish is the structure. Babelbit is not just building a model, it is building a market for improving translation behavior. By using Bittensor, it can reward miners for the things that actually matter in production: early adequacy, lower latency, better paraphrasing, safer output, domain adaptation, and multilingual expansion. That creates a decentralised R&D engine that compounds over time instead of a one-shot product release.

The vision is bigger than “better MT.” Babelbit is aiming at a new category: a human-centred communication layer for multilingual conversation. That means speech-to-speech translation that can be useful in meetings, enterprise settings, medical contexts, legal workflows and cross-border coordination. These are all places where waiting for a perfect literal translation is often too slow to be useful.

The roadmap is also smart. Phase 1 proves utterance completion. Phase 2 moves into real-time French-to-English speech translation. Later phases extend into new languages, paraphrasing, politeness, safety and vertical-specific performance. That progression suggests a network that can evolve from research benchmark to infrastructure primitive.

Why this matters for Bittensor: Babelbit fits the subnet model perfectly. The problem is measurable, open-ended, iterative and benefits from many competing contributors. That is exactly where decentralized incentives can outperform a single closed team. If the subnet works as intended, it could become a foundational layer for low-latency multilingual communication.

My bullish take: Babelbit is not chasing incremental translation quality. It is attacking the real bottleneck in live conversation .....the moment a system knows enough to speak. If they execute, this could be one of the more important subnet narratives in AI infrastructure.

Babelbit is not building a translator. It is building the future interface for multilingual human conversation.

[💾 TomTensor 📈 @tom_tensor]

@MaxScore @crypt0lake Google have one in a lake in Finland, the servers are wrapped in plastic before they’re dropped in