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We've stepped up our editorial operations at @taodaily_io, and one area we're putting more focus on is analytics. Expect more data-driven coverage to help the Bittensor community better understand what's happening across the ecosystem. Big thanks to @subnetradarcom for making valuable data accessible and supporting our work. More to come.

Happy to see Yanez SN54 gets the love it deserves.

Ever wanted to try an AI model without handing your prompts to another cloud service, creating an account, or worrying about usage limits? That’s the idea behind @Gratis_AI, now introduced by @TPN_Labs. Gratis is a free product for running open-source AI models directly on your own device. You can use it in a browser tab or install the desktop app for higher performance. Simply pick a model, or load one from @huggingface, and the flow is straightforward: Choose model → Download it to your device → Run it locally → Chat. The product is free, requires no account or setup, and has no usage limits. And according to the launch details, your prompts and conversations stay on your device. That’s appealing for anyone who wants to explore AI without signing up for another service or routing every conversation through the cloud. Gratis isn’t just another chatbot with a free tier. It gives people a way to run models on hardware they already own. [Bitcast Brief]

One thing I find interesting about decentralised infrastructure is how projects are tackling problems that usually sit behind the scenes. Beam Network (SN105) is working on one of them: moving data between systems reliably. Instead of relying on one central bandwidth provider, Beam coordinates capacity from independent operators across its network. Here’s the basic process: 1) A data transfer is split into smaller chunks. 2) Orchestrators route those chunks to suitable workers based on performance. 3) Each transfer generates a Proof-of-Bandwidth record. 4) Validators use these proofs to assess performance, set network weights, and distribute rewards. That gives the network a way to measure the bandwidth being contributed and reward operators based on verifiable work. @b1m_ai also supports connections to services including AWS S3, Cloudflare R2, Google Cloud, Hugging Face and REST APIs. This opens up use cases such as cross-cloud syncing, AI data pipelines, event-driven applications, and transfers between AI agents. The bigger picture is simple. As AI systems become more connected, they will need dependable ways to exchange data across different environments. @b1m_ai is building infrastructure around that need, with bandwidth coordinated through an open network. [BITCAST BRIEF]

this looks interesting. this product allows you run ai locally. powered by bittensor.

An open AI economy needs more than powerful models. It needs a way for data to move freely between the systems that use it. That’s the idea behind Beam Network (SN105). In a recent post, @b1m_ai highlighted the connection between DATA, MODELS and MACHINES, describing Beam as the transport layer that helps bring these parts together. I think this is an interesting direction because AI infrastructure is spread across different platforms. Data can sit in cloud storage, models can run elsewhere, and agents may need to access information across several services. Beam is building a decentralised bandwidth network to help connect these environments. It brings together bandwidth from independent operators and supports data movement across services such as: 1) AWS S3 and Cloudflare R2 2) Google Cloud and Hugging Face 3) Salesforce, REST APIs and webhooks The goal is to make bandwidth an open, programmable resource that developers can use for AI pipelines, cross-cloud transfers, and agent-to-agent communication. If AI systems are going to work across different platforms, the infrastructure moving their data needs to keep up too. Worth keeping an eye on Beam. [BITCAST BRIEF]

Haha... I see the big guys @TargonCompute @lium_io @engyai @affine_io @chutes_ai 🥳