JUST IN: Open-source AI models surge to a record 78.4% of token volume on Vercel’s AI Gateway.

$NEAR & $TAO have begun collaborating This is great news for the privacy x open source AI narrative

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Venture capitalist Bill Gurley says he is not afraid of AI doom when nobody can explain what COVID already did "COVID 19 caused 15 million deaths, disrupted every life on the planet, and cost our globe $45 trillion. It was particularly hard on children. You've read about learning loss" "My kids were in high school and college and missed three years of the most formative years of your life. I never want to live through this thing again in my life" "For all the other catastrophes, I told you what root cause is. 6 years later, we don't know" "So forgive me if I'm unafraid of what some AI guy's chatbot is going to do to humanity when you can't tell me what this thing already did" "My P second COVID is way higher than my p-doom. And if their models are so smart, tell me what happened here. Let's get them working on this problem."

JUST IN: Gavin Newsom signs law banning military forces from other states from entering California.

🇺🇸 JUST IN: President Trump says “Artificial Intelligence” should be renamed “Superior,” “Extreme” or “Supreme Intelligence.”

Not your keys, not your coins. Not your weights, not your intelligence.

LLMs vs. Jev, clearly explained! TL;DR The key difference is not that Jev generates faster. Jev does not generate text at all. A traditional LLM receives context and produces an answer one token at a time. Even when the output is a small JSON object, every token depends on those generated before it. Jev receives the same context but evaluates predefined decisions directly. When those decisions are independent, it can evaluate all of them in parallel. Consider an agent handling a failed deployment. It may need to determine: → Whether the incident is urgent → Which team should handle it → Whether the proposed command is risky → Whether the task is complete An LLM generates a response containing these answers sequentially. The application then parses and validates it. With Jev, you define the questions and expected answer types upfront. It evaluates them together and returns typed answers with probabilities. Jev supports three decision primitives: 1. **Choice** selects from known options, such as engineering, billing, or sales. 2. **Score** places the input on an ordered scale, such as low, medium, or high risk. 3. **Noul** evaluates a yes-or-no condition and returns the probability that it is true. The probabilities matter as much as the selected answers. If engineering receives 91% probability and billing receives 9%, automatic routing may be reasonable. If the probabilities are 52% and 48%, the system can escalate, gather more context, or call a stronger model. This keeps control inside ordinary software. Code owns the thresholds and consequences. Jev supplies the semantic judgment that a normal `if` statement cannot derive from unstructured text. It works best when the possible answers are known, the decision depends on meaning, and a careful person could judge the input quickly. It is not designed for writing, summarization, code generation, arithmetic, or decisions requiring several dependent reasoning steps. Independent questions can run in parallel, but decisions that depend on earlier results must remain sequential. Jev also cannot return an option outside the declared schema, but it can still select the wrong valid option. Type safety prevents malformed outputs, not incorrect judgments. The clean mental model is this: LLMs generate new language when the answer space is open. Jev evaluates known paths when the answer space is bounded. I wrote the full breakdown explaining Jev and where it fits. The article is quoted below.

We've partnered with @webuildscore on the launch of Score Studio. Every account created on Score Studio gets storage powered by Hippius, included automatically. No extra setup needed. One less thing to think about while you're labeling, training, and deploying. Congrats to the Score team on the launch.

NEAR AI Cloud's confidential inference is now live on @say_gm_'s confidential tier. SayGm reaches dozens of models through one API key, and runs its own routing inside an Intel TDX enclave rather than on ordinary servers.

Engy (subnet 53) will be number 1 subnet, will be a good year and a lot of hard work is starting to finally pay off

"I like to get involved in projects that I think are transformative and have 100x, 500x, 1,000x type return opportunities" @BarrySilbert "Unless the US dollar completely collapses.. #Bitcoin is not going to go up 500x" "I think a #Bittensor can go up 500x and so our portfolio is allocated accordingly" $TAO