That's a remarkable milestone for such a young platform
๐คฏ Reaching this level of volume so quickly definitely caught my attention
I'm looking forward to next big updates for Sinjoh
$INJOH
I think features like this could add meaningful value when integrated well
๐ Excited to see where this goes
$PUMPIQ
GT2CMUyqYet6rArR4bm94SNwuJAsKrDrjijecTfcpump
This feels like another exciting chapter for the Sinjoh ecosystem
The product polish shows strong attention to user experience
๐ I'm curious to learn what makes the new protocol different
๐ Really looking forward to everything launching on Pons v2
When you see @solana reposting a delta neutral arb project with only 2 solana native perp DEXs, we ask ourselves,
When $TTCP?
๐๐๐
Our time will come.
Inference is quickly becoming one of the strongest sectors in AI
+ More agents
+ More applications
+ More compute demand
That's why I'm watching
$SGL
$VECTA
$DULUS
Years ago, people bought consumer GPUs to play games
Today, they're using the same GPUs to run AI models
Most of those GPUs still sit idle for much of the day
Now you can point those idle GPUs toward decentralized inference networks
... and earn protocol incentives (and in some cases, real revenue)
This feels like the beginning of Inference Farming
Utilizing idle hardware to EARN exposure to revenue-generating AI networks
Following the collapse of $P0 project
However, I believe that inference market and the decentralized AI compute ๐ the new gold
There are three projects I see as having exceptional potential ๐ still being undervalued
@x402_Layer $SGL
@open_vecta $VECTA
@DulusCorp $DULUS
This is a really interesting approach to multi model inference
Blending strengths instead of choosing one model feels much smarter
The drop-in OpenAI SDK compatibility makes adoption incredibly simple
I'm excited to see how $VECTA Council performs in real world applications
New on OpenVecta: model: "council"
One API call โ a panel of models from different labs answer in parallel โ a synthesizer blends them into one sharper answer.
Not routing (pick one model). Blending (use every lab, keep the best of each).
Drop-in on any OpenAI SDK, just change the model name.
The overall research experience keeps becoming more streamlined with updates like scans X accounts
๐จโ๐ป Social account history can provide another layer of context during analysis
๐คฏ Excited to see future $PUMPIQ additions
GT2CMUyqYet6rArR4bm94SNwuJAsKrDrjijecTfcpump
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