Google just solved 9/353 open Erdős Problems at the cost of a few hundred dollars each using its most capable LLM. The proofs were written in Lean and mechanically verified. This is no longer just olympiad mathematics.

House Problem Solvers Caucus co-chair Rep. Tom Suozzi (D-NY) argues both parties have “to get back to those basic, fundamental messages” on the economics of Americans’ lives, adding, “Enough with the finger-pointing and the culture wars.” Co-chair Rep. Brian Fitzpatrick (R-PA) agrees, both parties are “failing” to “focus like a laser on the economy, on these budgets.” “We cannot ignore the fact that half of Americans are stressing over their family budgets, and that’s got to drive the legislative agenda,” says co-chair Rep. Brian Fitzpatrick (R-PA), adding that both parties are “failing” to “focus like a laser on the economy, on these budgets.”

Anthropic needs to solve the computer/power problem or they will be the Friendster of the AI era. I just ran a semi-complicated stock screening prompt on all four major AIs: Grok, Gemini, ChatGPT and Claude. The first three returned comparable results. Claude refused to do the work. Not the way to win guys…

Speed is a solved problem in Web3. The real challenge is longevity. As stablecoins and RWAs scale into institutional settlement and global payroll, the ultimate question shifts: Can the network remain unbreachable for the next decade and beyond? Post-quantum resilience is no longer a luxury—it’s foundational. This is the standard #MOVA# is building. #Web3# #Stablecoins# #RWA# #Quantum#

NVIDIA has solved the biggest trade-off in LLMs. And it delivers a 6x speed boost without losing a single point of quality. Every AI you use today (GPT-4, Claude, Gemini) is "Autoregressive." This means the model is forced to think in a straight line, one token at a time, left-to-right. It’s like a genius writer who can only type with one finger. The hardware under the hood, your massive GPU, is actually sitting idle 90% of the time, waiting for that one finger to hit the next key. NVIDIA published a paper that changes the math. They figured out how to make the AI do two things at once in a single forward pass. 1. The "Talk" (AR): The model handles the immediate next word with perfect logical precision. 2. The "Think" (Diffusion): While it's talking, it uses its "idle" brainpower to parallel-draft the next 10–20 words in advance. It’s a hybrid brain. The results are a massive wake-up call for the industry: - 6x Speedup: It delivers nearly 600% more tokens per second than standard models. - Zero Quality Loss: Unlike previous "fast" models that get "blurry" or hallucinate, TiDAR matches the quality of the world’s best LLMs. - GPU Efficiency: It finally stops wasting the expensive compute power big tech is burning billions on. We’ve spent years trying to make AI smarter by making it bigger. But this paper proves that the real bottleneck wasn't the size of the brain, it was how the brain was scheduled. Paper: TiDAR - Think in Diffusion, Talk in Autoregression, 2025