im not a cat

SOMEONE TURNED THE VIRAL "TEACH CLAUDE TO TALK LIKE A CAVEMAN TO SAVE TOKENS" STRATEGY INTO AN ACTUAL CLAUDE CODE SKILL one-line install and it cuts ~75% of tokens while keeping full technical accuracy they even benchmarked it with real token counts from the API: > explain React re-render bug: 1180 tokens → 159 tokens (87% saved) > fix auth middleware: 704 → 121 (83% saved) > set up PostgreSQL connection pool: 2347 → 380 (84% saved) > implement React error boundary: 3454 → 456 (87% saved) > debug PostgreSQL race condition: 1200 → 232 (81% saved) average across 10 tasks: 65% savings. range is 22-87% depending on the task. three intensity levels: > lite: drops filler, keeps grammar. professional but no fluff > full: drops articles, fragments, full grunt mode > ultra: maximum compression. telegraphic. abbreviates everything works as a skill for Claude Code and a plugin for Codex. this is PEAK

I might be 70% there in understanding CPO scale-up opportunity through Feynman, but some technical clarifications plus coming supplier datapoints should take that to 80-90%? Inter-rack optical scale-up for NVL576, as mentioned earlier, appears confirmed for Oberon. But that's only a first step. Scale-out near term is well understood. Orders received to date by $LITE or $COHR and their high level TAM statements seem roughly consistent. Jensen is often imprecise during presentations, but that often leads to opportunities. This is one of many AI technologies where fortunes could be made or lost over the next years.

Tonight, we reached an agreement with the Department of War to deploy our models in their classified network. In all of our interactions, the DoW displayed a deep respect for safety and a desire to partner to achieve the best possible outcome. AI safety and wide distribution of benefits are the core of our mission. Two of our most important safety principles are prohibitions on domestic mass surveillance and human responsibility for the use of force, including for autonomous weapon systems. The DoW agrees with these principles, reflects them in law and policy, and we put them into our agreement. We also will build technical safeguards to ensure our models behave as they should, which the DoW also wanted. We will deploy FDEs to help with our models and to ensure their safety, we will deploy on cloud networks only. We are asking the DoW to offer these same terms to all AI companies, which in our opinion we think everyone should be willing to accept. We have expressed our strong desire to see things de-escalate away from legal and governmental actions and towards reasonable agreements. We remain committed to serve all of humanity as best we can. The world is a complicated, messy, and sometimes dangerous place.

Perplexity just became the the first Al company to truly go head-to-head with the Bloomberg Terminal... Using Perplexity Computer (with no local setup or single LLM limitation), it was able to build me a terminal with real-time data to analyze $NVDA using Perplexity Finance:

Big Tech just paid ~$40 billion in two years to avoid buying anything. The math is absurd. Google spent $2.4B on Windsurf to hire 40 people. That works out to $60M per head. Microsoft paid $650M to gut Inflection and take 70 employees. Amazon spent $400M+ on Covariant for three founders and 40 engineers. Google dropped $2.7B on Character AI to rehire Noam Shazeer, who they’d let walk in 2021. Now Nvidia announces $20B for Groq just three months after it raised at $6.9B. Every single one of these companies explicitly stated “we are not acquiring this company.” Jensen Huang literally told employees: “While we are adding talented employees to our ranks and licensing Groq’s IP, we are not acquiring Groq as a company.” Microsoft said the same about Inflection. Google said the same about Character AI and Windsurf. Amazon said the same about Adept and Covariant. The semantic gymnastics exist for one reason: antitrust. Traditional acquisitions trigger Hart-Scott-Rodino filing requirements. Regulators review. Competitors object. Deals take 12-18 months to close. In an AI arms race where model capabilities improve every 6 months, that regulatory timeline is existential. By the time a deal clears, the tech is already outdated. So Big Tech invented the “reverse acquihire.” Pay billions to license IP, hire the founding team, leave a shell company behind with a new CEO and a skeleton crew. Google did it with Character AI (Noam Shazeer + 30 researchers, left behind a co-op structure). Microsoft with Inflection (Mustafa Suleyman + 70 staff, left Sean White as CEO of nothing). Amazon twice with Adept (David Luan + research team) and Covariant (three co-founders + 25% of staff). Now Nvidia with Groq (Jonathan Ross + senior leadership, Simon Edwards inherits a cloud business). A whistleblower complaint filed with the FTC, DOJ, and SEC in January 2025 alleged that the Amazon-Covariant deal was “deliberately and unlawfully structured” to dodge antitrust review. The complaint claimed Covariant’s new CEO told employees that if Amazon had tried to buy them outright, regulators would have killed it. The deal terms reportedly restrict which licenses Covariant can sell without paying Amazon a fee. The FTC opened investigations into Microsoft-Inflection and Amazon-Adept. Both appear to be at a standstill. Amazon’s Adept deal closed without further action. The exposed logic: buying a company twice (once for talent, once for the husk) now costs less than waiting for regulatory approval of a single acquisition. Windsurf got split three ways in 72 hours. Google paid $2.4B for leadership and license. Cognition paid ~$250M for what remained. OpenAI walked away with nothing after Microsoft objected to IP terms. Big Tech found a loophole wide enough to drive $40 billion through while regulators debate whether hiring someone’s entire executive team and licensing all their IP counts as “control.”

My new kitchen robot just made me one of the best paneer butter masalas I’ve ever had in my life.

Still waiting

Once again Beth has performed wonderfully for family reunion  If u knew Beth like I know Beth u would know the dependability  I know