Tesla has put a countdown clock until their Tesla Semi rollout livestream, which starts at 9 PM ET tomorrow (Thursday). This event is to celebrate the opening of their new Semi factory in Sparks, Nevada, which has an installed capacity to produce 50,000 Tesla Semis per year. Attendees will be able to tour the factory and experience Semi on ride alongs. Link to livestream:

“Maybe tomorrow we’ll all wear 42, that way they won’t tell us apart.” Today, MLB celebrates the legacy of Jackie Robinson, who helped spark the beginning of the Civil Rights Movement in America by breaking baseball’s color barrier. Tonight at 10 PM ET, ESPN will broadcast a game between the Mets and Dodgers, the only team Jackie ever played for in his MLB career.

🚨 NVIDIA just quietly changed what a “personal AI computer” can mean. Meet NVIDIA PAIR: Personal AI Router. Instead of buying one massive machine to run local AI, PAIR lets you connect the computers you already have and turn them into a personal AI inference cluster. Your RTX PC. Your DGX Spark. Even supported Macs. All working together. PAIR automatically discovers compatible machines on your local network and routes AI workloads to whichever device has available compute. Think of it like a load balancer for AI agents running inside your house. And this is where things get interesting: → Run multiple AI agents in parallel → Route inference across multiple GPUs/computers → Use Ollama + LM Studio → Keep prompts, files and agent context on your own network → No racks or complicated cluster setup → Windows, Linux and macOS support The bigger idea here isn’t just PAIR. It’s where NVIDIA thinks AI computing is going. Today we have: 1 user → 1 computer → 1 AI model Tomorrow could look more like: 1 user → many AI agents → an entire pool of local compute Your gaming PC might run one agent. Your workstation runs another. A DGX Spark handles the heavy model. And PAIR becomes the router deciding where every inference request goes. The cloud isn’t disappearing. But personal AI infrastructure is starting to look a lot more like a miniature data center. And NVIDIA wants to provide the operating layer underneath it. PAIR is currently in beta and NVIDIA has released it as free, open-source software. This one is worth watching. 👀

On Jev... When I studied NLP, discriminative AI performed miles better than generative AI. For instance, discriminative API could tell you the sentiment of some text far better than generative AI could write text given a sentiment. Generative AI's output was much like repeatedly hitting the next autocomplete suggestion on your phone's keyboard. Obviously generative AI has tremendously advanced and the output is serviceable in many domains much of the time, though even the latest models still overlook simple solutions. Several things spark my interest in Jev based on my experience: - There will be features where discriminative AI is a much better fit than generative AI, like customer support services instantly routing you to the right department (sometimes I want to talk to a person). - Tomorrow's agents can combine generative and discriminative AI. Imagine Claude Code and Codex's "auto permissions" being extremely accurate and instant. - What if there were an open version of Jev? Discriminative AI will run better on your own devices. Most personal AI tasks are about decision-making, like deciding how important a push notification is to you at this moment, or which apps you might want to use now.

I updated my 2023 roadmap diagram to overlay where the items that were there sit in the current Strawmap ( ). In general, a lot of overlap, but: * Some things got reshuffled in order (eg. quantum safety up-prioritized) * Some things deprioritized (eg. VDFs; many EVM improvements) * Some things replaced with superior constructions (eg. Verkle -> unified BT -> PBT; state expiry -> new state types) What's most striking, however, is that some completely new things are in the strawmap that are NOT in this diagram, because they were not in the 2023 roadmap at all. These reflect changing priorities. Notably: * First-class attention to strong privacy. This covers: keyed nonces and recent roots, aspects of FOCIL, lean privacy pool & wormholes * Aggressive scaling in the context of post-quantum. This covers: leanSPHINCS signatures and aggregation, zkzk frames (see ) * Lean-ification of the spec, to assist in formal verification (full FV of everything is only possible because of modern AI) * Blob and gas futures (this idea just didn't exist back in 2023) * Native rollups (SNARKs were nowhere near mature enough to even consider this back in 2023) * A more open design space for the "future of the EVM". zkzk frames already implies that the protocol will expose to users some ISA that's not the EVM - current leading candidates are leanISA and RISC-V. These ISAs are more simple, modern and efficient than the EVM. Once they're there, why not expose them to developers everywhere? (And then, why not turn the EVM into being an IR on top of that ISA, instead of an enshrined feature massively complicating the base protocol?) Though much of the deeper exploration here is too early even for the strawmap. * New state types are not just a replacement for expiry, they're a fundamentally different paradigm to how Ethereum does scaling A common theme in scaling, found in both state types and zkzk frames (both new ideas), is that instead of trying to maximally scale ALL ethereum activity, we try to create specialized mechanisms that have more restrictive properties that make them more scaling-friendly, while supporting the heaviest loads incurred by users and applications today (eg. token transfers, swaps) and tomorrow (eg. privacy protocols). The other common theme is treating STARKs and AI-accelerated FV as first-class objects, that we are okay betting the technical future of Ethereum on. There are recursive STARKs in many layers of the protocol, one particular primitive (the "aggregate to union verified dependencies" primitive) is expected to be used in *three* places in the protocol: EL, CL and DL. This can only be safe with formal verification, which is itself only feasible with modern AI tools. In general, many steps forward in maturity. And a huge amount of hard work by many dozens of Ethereum researchers and developers on all of these features. Ethereum will be quantum-safe. Ethereum will put users' privacy first. Ethereum will be secure. Ethereum will be censorship-resistant. Ethereum will be highly performant and scalable while satisfying the above. And Ethereum will be Lean.

🖼️ 24 HOURS OF ART 📊 August 26, 2026 🎨 Pictured: 24 Hour Highlights - 'die with the most likes (redistribution)' by @toadswiback - 'Grifter #236#' by @XCOPYART, collected by @F1rstclassJG - 'The First Sparks of Artificial Creativity (Digital Edition)' by @VanArman - 'GANdinsky 3D - Nothing Natural - Variant 01' by @Coldie - 'Argonauts' by @lphaCentauriKid, part of 1/1/9,999 digital + physical collection mint out with @MuseFacktory - 'HOLY #481#' by @wubbushi, collected by @mrc_arte, part of 500 artwork collection mint out with @verse_works - 'Everything, made simple' by @zancan, collected by @thisistolo - '9 Lives' by @money_alotta, collected by @nft_taz - 'Eden' by @hanrgb, collected by @zaphodok

anthropic has destroyed the opus family's reputation after 4.6 now it's brain-dead, with random sparks of genius -- sloppy and fixated on one solution while ignoring anything that contradicts it, even when it's clearly relevant fable 5 hasn't stayed consistent either