this is a pop sciency version of a continual learning evaluationif you're going to go the route of "pretrain on limited data and see if it can bootstrap from natural interaction", a more practical thing would probably be like, train only up to ~2014, "can it teach itself Rust?"

Data encryption is now live on Veilnet.Available in @veilnet/mcp-server@0.3.0.Any blob — file, dataset, config, recovery codes, contract draft, research note, agent memory — can now be encrypted on your device, shipped to Veilnet as ciphertext, and fetched back later without the decryption key ever leaving your machine.Three new MCP tools are live:data_store({ label, data }) encrypt + store data on Veilnetdata_fetch({ id }) fetch + decrypt locallydata_list() list stored idsWith labels kept encry

$TAO Bittensor Gap CaseLet’s talk about reality. In 2025, every major frontier AI lab burned billions. Not small losses. Billions.OpenAI lost $15BxAI lost $13BAnthropic lost $5BThat’s $33B burned by just three labs.Add Mistral, Cohere, DeepSeek, Perplexity, Thinking Machine Labs and others… around $2BI in losses. And you get an entire sector growing through capital injection, not profits.This is how frontier AI gets built. Now here’s the contradiction. Private labs can burn billions funded by ve