@NAS Chain Just an update on what we've achieved in V2 so far. When we first launched the subnet, the initial model submission with 93% accuracy had 1.1 million parameters. Today, the winning model achieves the same accuracy with only 0.2 million parameters. This represents the same model performance with an 81% compression rate in terms of memory requirements. Full details will be available in the upcoming white paper.

@NAS Chain Just an update on what we've achieved in V2 so far. When we first launched the subnet, the initial model submission with 93% accuracy had 1.1 million parameters. Today, the winning model achieves the same accuracy with only 0.2 million parameters. This represents the same model performance with an 81% compression rate in terms of memory requirements. Full details will be available in the upcoming white paper.

@NAS Chain Just an update on what we've achieved in V2 so far. When we first launched the subnet, the initial model submission with 93% accuracy had 1.1 million parameters. Today, the winning model achieves the same accuracy with only 0.2 million parameters. This represents the same model performance with an 81% compression rate in terms of memory requirements. Full details will be available in the upcoming white paper.

@NAS Chain Just an update on what we've achieved in V2 so far. When we first launched the subnet, the initial model submission with 93% accuracy had 1.1 million parameters. Today, the winning model achieves the same accuracy with only 0.2 million parameters. This represents the same model performance with an 81% compression rate in terms of memory requirements. Full details will be available in the upcoming white paper.

@NAS Chain Just an update on what we've achieved in V2 so far. When we first launched the subnet, the initial model submission with 93% accuracy had 1.1 million parameters. Today, the winning model achieves the same accuracy with only 0.2 million parameters. This represents the same model performance with an 81% compression rate in terms of memory requirements. Full details will be available in the upcoming white paper.

@NAS Chain Just an update on what we've achieved in V2 so far. When we first launched the subnet, the initial model submission with 93% accuracy had 1.1 million parameters. Today, the winning model achieves the same accuracy with only 0.2 million parameters. This represents the same model performance with an 81% compression rate in terms of memory requirements. Full details will be available in the upcoming white paper.

@NAS Chain Just an update on what we've achieved in V2 so far. When we first launched the subnet, the initial model submission with 93% accuracy had 1.1 million parameters. Today, the winning model achieves the same accuracy with only 0.2 million parameters. This represents the same model performance with an 81% compression rate in terms of memory requirements. Full details will be available in the upcoming white paper.

@NAS Chain Just an update on what we've achieved in V2 so far. When we first launched the subnet, the initial model submission with 93% accuracy had 1.1 million parameters. Today, the winning model achieves the same accuracy with only 0.2 million parameters. This represents the same model performance with an 81% compression rate in terms of memory requirements. Full details will be available in the upcoming white paper.

@NAS Chain Just an update on what we've achieved in V2 so far. When we first launched the subnet, the initial model submission with 93% accuracy had 1.1 million parameters. Today, the winning model achieves the same accuracy with only 0.2 million parameters. This represents the same model performance with an 81% compression rate in terms of memory requirements. Full details will be available in the upcoming white paper.

@NAS Chain Just an update on what we've achieved in V2 so far. When we first launched the subnet, the initial model submission with 93% accuracy had 1.1 million parameters. Today, the winning model achieves the same accuracy with only 0.2 million parameters. This represents the same model performance with an 81% compression rate in terms of memory requirements. Full details will be available in the upcoming white paper.

@NAS Chain Just an update on what we've achieved in V2 so far. When we first launched the subnet, the initial model submission with 93% accuracy had 1.1 million parameters. Today, the winning model achieves the same accuracy with only 0.2 million parameters. This represents the same model performance with an 81% compression rate in terms of memory requirements. Full details will be available in the upcoming white paper.

@Masa friendly reminder to validators and miners alike - please upgrade to the 1.0.2 release ASAP as we will be enforcing this new version via hyperparameters soon. We currently use Python 3.12

git fetch --tags git checkout v1.0.2 pip install -e .

Be sure to restart your process(es) afterwards!

@Sturdy From Day 1, our goal has been integrating SN10 into every application across DeFi. Today, we're one step closer to that goal.

Morpho is one of the largest DeFi applications by TVL, with >$2b in deposits. We're excited to share that SN10 is coming to Morpho! Thanks to SN10, users will be able to earn AI-optimized yields on USDC Morpho Vaults. Additionally, Aera (a vault infrastructure provider incubated by Gauntlet) will be powering the aggregator's execution and integrating SN10.

Expect to see even more applications building on top of SN10 soon! In the meantime, check out the announcement thread below https://x.com/MorphoLabs/status/1846940138775236758

@Infinite Games We just pushed our most recent update and are about to increase the weight version, if you are a validator please update asap 🙏

@Infinite Games We will push only two updates today:

• a new set of 20+ daily Fred events(https://github.com/amedeo-gigaver/infinite_games/blob/main/docs/fred-events.txt)
• streaming of the miners predictions to our database
  • this will allow for the aggregation of the predictions, enabling group components in the scoring and subnet wide predictions

Regarding the scoring we want to address first the issue of the new miners joining the network and getting a penalty for events that were already streaming, given that this would become a more significant problem with the log scoring rule. (cc @Discord User @Discord User @Discord User )

@Infinite Games we will also start streaming soon a new set of events, based on the Fred economic database (https://fred.stlouisfed.org), you can find a sample here: https://github.com/amedeo-gigaver/infinite_games/blob/main/docs/fred-events.txt.

@Graphite

@Proprietary Trading Network Hi everyone, as you may know, we have been hard at work on a plagiarism detection system over the past few months. We will be releasing a PR for the system this week, but want to provide some insight into the operating principles and history of development we’ve been pursuing behind the scenes. We’re releasing a white paper which outlines this process, which can be found here: https://docs.taoshi.io/PlagiarismWhitePaper.pdf.

In general, tracking plagiarism will help improve the stability of our network and protect the signal integrity of our miners who have trained custom models. The initial roll-out will be in ghost mode, and won’t affect miners as we will only be tracking statistics to ensure smooth operation.

The full integration with eliminations is set to release sometime in the next few weeks after we finish thoroughly testing. Looking forward to your feedback!

@Targon new update for valis! 4.1.1, fixes some exploits in fast logprobs.

What was the exploit? Glad you asked. Someone distilled a larger model down to a smaller model, and always generated responses with extremely low temp, This would essentially always pass both log probs because of the distillation and low tolerance for variety. This has been patched and no longer passes checks. Cheers!

@Sportstensor We've applied the gaussian filter patch which penalizes unrealistic predictions such as 0.00001% or even 100% win probability. For the details, please see: https://discord.com/channels/799672011265015819/1263142301786574889/1295966982000345122

The gaussian filter will be retroactively applied to all scores, recalculating everything including the excessively high scores achieved by being naughty.

max_burn has been set to 0.5t to bring the registration fee down. It will be moved back up to 10t shortly.

Best of luck discovering those edges fam! 🔍 ✨

@Infinite Games Two important updates:

We will bring back the log scoring rule tomorrow at 11 EST and increase the weight version. Please check the PR here https://github.com/amedeo-gigaver/infinite_games/pull/33/files. We will not push batching yet.

Part of our events are now resolved through UMA: https://oracle.uma.xyz/propose?project=Unknown&project=Infinite+Games Anyone can resolve them and receive a small reward. Please consider doing so to support the subnet development!

@Dataverse

Release 1.6.7

Updates for Miners:

• Fixed several bugs in the upload system.

• Added a retry upload function for Hugging Face in case the HF API becomes unstable.

• Improved the upload process, allowing miners on the same VPS to upload data without conflicts.

• Miners can now retrieve the DD list using the –gravity argument. Updates for Validators:

• Fixed an issue where validators using local Subtensor nodes couldn’t fetch the DD list.

Thank you all for extreme support for developing of the subnet to the moon and back!

little insight( check pr on new datasource, soon to be added)

@Compute user id: 1291320783192326148 was the scammer, deleted before getting ban hammered