This week’s community brief from Covenant AI, streamed from Nairobi, was less about optics and more about execution. TGIF #27 focused on infrastructure maturity, economic realism, and a recurring theme across the Bittensor ecosystem: build […]
SUMMARY: Covenant Labs shared a weekly update covering progress across Templar (Subnet 3), Grail (Subnet 81), and Basilica (Subnet 39), highlighting advances in decentralized AI training and inference on Bittensor. Templar is nearing completion of […]
Covenant AI’s latest TGIF session covered major progress across its three core efforts: Templar, Basilica, and Grail. The session’s overall takeaway was that decentralized AI training is moving faster than most people expected, and the […]
Covenant AI just released PULSE (Patch Updates via Lossless Sparse Encoding), a technique that slashes bandwidth for weight synchronization in decentralized reinforcement learning (RL) by 100×+, while staying completely lossless (bit-identical reconstruction, SHA-256 verified on […]
Basilica (Bittensor subnet 39), a subnet under the Covenant ecosystem, has launched a sandbox container for OpenClaw agents. The goal is to run powerful self-hosted AI agents without giving them full access to your machine […]
When AI companies like OpenAI or Anthropic build models like ChatGPT or Claude, they don’t just train the model once and ship it. They spend months doing something called “post-training”, teaching the model to actually […]
Templar, Bittensor Subnet 3, has completed pre-training for Covenant72B, a 72-billion-parameter language model. This is the largest frontier-scale model ever trained in a fully permissionless and decentralized setting. The run was coordinated across a global […]
By: CryptoZPunisher Templar on Bittensor: when the impossible becomes possible On Bittensor, everything is possible. And if there were one concrete proof, Templar is probably among the most striking. The Templar team has just published […]
SUMMARY: On TGIF #20, Covenant Labs announced Templar’s latest evolution in decentralized training, moving toward a “unified training” paradigm that combines data and model parallelism. By integrating their state-of-the-art SparseLoCO algorithm with new model-sharding techniques, […]
For a long time, “Decentralized AI Training” was viewed as a science experiment, cool in theory but impossible in practice. The narrative was simple: “You can’t train massive AI models across the internet. It’s too […]
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