A new milestone for OpenRoboto

base:0x6f63d869011f95274498023b4abfc00b30c34378 is now live on @Robinhoodcrypto 🪶 Robinhood Chain isn't just for stocks and memes anymore - @openroboto's decentralized robotics just landed on RH. First time a Bittensor subnet is accessible across three chains simultaneously. We're building the distribution layer for Bittensor subnets, brick by brick.

Cashtags are now available for TAO subnets via ForeverMoney! Which subnets need a cashtag next? Drop the number 👇

Few understand the distribution power subnets unlocked on @base. @coinbase users can buy base:0x6f63d869011f95274498023b4abfc00b30c34378 directly in-app. The rails are built. Time to spread the message. It’s all coming together. Bullish on @openroboto

🤖 base:0x6f63d869011f95274498023b4abfc00b30c34378 by @openroboto is live on @aeroxyz Bittensor's robotics AI subnet just plugged into the deepest liquidity on Base - a venue that's done $430B+ in cumulative volume and $14.2B in the last 30 days alone, with $336M TVL sitting behind it. Trade, provide LP, and participate in the open competition to improve robotics models & all on-chain. Connected by @chainlink CCIP. Brought to together by @forevermoney_ai. Built on Bittensor. Live on @base.

非常牛逼

base:0x6f63d869011f95274498023b4abfc00b30c34378

Coool! Looking forward!

LFG

We’re bringing real-robot validation to OpenRoboto. Models submitted by miners won’t be judged only in simulation anymore. Now we’ll also test them on real robotic hardware.

It was a great convo! Wish we could share more updates in the next one!

Openroboto core team arrives Beijing this week for the World Robotics Conference. The pace here is hard to overstate. Robot hardware is moving faster than most people realize, and what is still missing is intelligence good enough to run it. More to come soon.

Exciting!

Thanks for having us!

Open weights were never the hard part. Open data is. The Open Data Pool is live, with Axis Robotics @axisrobotics as launching partner. openroboto.ai/#/datapool

Over the past week, OpenRoboto shipped updates across the website, submission system, and benchmark evaluation pipeline. Miners can now follow a submission from queue entry through live evaluation progress, final scoring, or rejection. Check out our tech update for the past week:

Groboto!

Think you can build a better robotics model? Start from our open π0.5 base model, fine-tune it and submit your model to the OpenRoboto competition Every submission is evaluated automatically by the network Beat the current Champion and your model becomes the new base, while you earn the subnet’s emissions Miner guide → openroboto.ai/#/docs

Two things: - Quasar is a scam - stay away. They’ve been lying since day 1. I was one of the first bittensor investors they pitched and after some diligence, we found out they were lying about their past experience. I told everyone who’d asked me for my opinion afterwards to stay away. - Do your own damn research and stop trusting people Here’s a tweet our team put together on Friday after the novelty search that we were saving for Monday: —————————- There are a lot of big claims Quasar is making that are hard to verify with the current public information available. Given the incredibly difficult work that goes into decentralized model training, we’re hoping the Quasar team can provide more clarity around the research and work they’re doing. Below are some of the areas that require further clarification. First, the 5M context window is a big claim and one of the core differentiators with their model, but none of the benchmarks released by the team evaluate the model on long context abilities. Quasar’s architecture substantially compresses context, meaning that, although the model can hypothetically process 5M tokens, the proof that it can stay coherent requires benchmarking on standard long context evals like RULER and/or NIAH. We’re awaiting the results from these benchmarks before we evaluate the subnet’s progress on this claim. Second, the training process itself is unclear. True decentralized training means using untrusted and non-collocated compute. Was the 120B model that was trained on 7T tokens trained in a centralized setting or in a decentralized setting using the subnet? If it was trained on the subnet, is there any data showing that the training process is fault tolerant and can handle interruptible nodes? Which techniques are the team using to compress the data (e.g., gradients, activations, or both) shared across miners? How many independent compute contributors participated or are participating in the training run? What are the hardware requirements for compute contributors?