Ninox 23
647 posts

Ninox 23
@23Ninox
@Traveller @Sea @hotweather @AI @AIagents @Meme @investings @Kasbotcoin

The MANTIS network model indicates going short here. A stop loss is in place at $3400, this trade will likely be on the smaller side.


🔥 Full circle moment. On December 12, I shared my very first DeFi trade — not based on hype, but on a MANTIS-driven signal from #SN123. Today, January 14, I closed that position — this time by listening just as carefully to the exit signal. As @Barbarian7676 put it: “You may want to consider closing here! We have a few conflicting signals at the moment on ETH and BTC!” ➡️ ~1 month in the trade ➡️ Result: +28% on GMX, managed with discipline and timing. What stands out isn’t just the return. It’s the process: ⚪ clear signals ⚪ transparent decision points ⚪ risk-aware exits ⚪ consistency over noise And as promised, the profits from this trade have been reinvested directly into Subnet 123. Conviction isn’t just words — it’s alignment. This is exactly why MANTIS SN123 deserves attention. Not because it promises perfection — but because it delivers structure, coherence, and repeatability. Sometimes performance speaks louder than words. This trade did. Respect to the work behind the model. #Bittensor $TAO


You may want to consider closing here! We have a few conflicting signals at the moment on ETH and BTC!

Going LONG here on $ETH

@bibibusiness22 A thread and the website linking to multiple walkthroughs of different aspects of performance






I believe that the price of Subnet @Tenex_SN67 is poised for take-off. $tao


Consumer-ready access to Bittensor is now HERE. Next task: consumers don't yet understand WHY they should invest in subnets. They don't see the MASSIVE early signal arising in the ecosystem. But they will -- once the first few subnets break wide. Then? Look out.

In a club’s analytics workflow, annotated footage is where everything begins. Based on the footage processed on our subnet, our models reconstruct full x,y coordinates for every player across the entire 90 minutes at multiple frames per second. At the same time, we detect all key actions such as passes, shots, tackles, duels, and goals. This approach works with any available match footage. As a result, clubs can build large datasets covering many players, teams, leagues, and seasons, without relying on expensive optical tracking systems or manual tagging. Once the footage is fully analyzed and annotated using the best available models on the subnet, all generated data is ingested into the club’s own analytics stack. From there, clubs apply their internal models, metrics, and workflows to extract exactly the insights they need. In practice, this enables: - Scouting and recruitment with consistent evaluation of on- and off-ball behaviour across competitions - Tactical analysis of team structure, pressing, transitions, and opponent tendencies - Player development insights for first team, academy, and training matches without additional setup Better data → better decisions

If you’ve heard us talk about Bittensor but still aren’t totally sure what it is, this quick explainer is for you. In under 90 seconds you’ll get the basics and why it matters. $TAO⬇️

@resilabsai isn’t just improving real estate sales - they’re architecting a new model for the entire industry. We’re powering that vision with verified investor demand, helping them accelerate the shift toward a fairer, more efficient market. Looking forward to building that future together.



