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Wednesday47 items

  4. Latent SpaceOpinion
    AI score52

    SF Compute CEO Evan Conrad on Programmable Data Centers and the Compute Market

    SF Compute CEO Evan Conrad said on the Latent Space podcast that the compute market must first achieve physical delivery—that is, actually delivering usable GPU compute—before discussing financialization; otherwise, market manipulation and basis risk are likely to arise. He said SF Compute has already put 5% of its capacity on the order book, and that customers who spend $100 to buy capacity can on average recover $25 by reselling it, with hundreds of millions of dollars in trading volume already completed. He also said the company does not hold hardware in order to remain neutral, and that it already supports customers in partitioning InfiniBand fabric online themselves.

    Same story · 2 reports

    #Industry news#English text is being prepared.#English text is being prepared.

  5. ayi_ai_notesOpinion
    AI score52

    English translation is being prepared.

    Quoted postEnglish text is being prepared. · @aoaoao

    https://t.co/X8x8FyW6oR

    #English text is being prepared.#Open-source ecosystem#Agents

  6. peter_steinbergerOpinion
    AI score60

    English translation is being prepared.

    Quoted postVoidZero · @voidzerodev

    Introducing tsrs: a Rust port of the TypeScript compiler 🦀 We are not forking TypeScript! ◆ In close collaborating with the TypeScript team ◆ Up to 8x faster than TS 7.0 & ~70% less memory ◆ Runs in WASM, so it works in your browser & on Workers ◆ Soon part of the Oxc toolchain npx tsrs https://t.co/qmmxF3YnQy

    #Industry news#English text is being prepared.#Open-source ecosystem

  7. One Useful ThingOpinion
    AI score57

    Ethan Mollick reviews 30 months of AI development in charts

    On the release day of his new book Co-Existence, Ethan Mollick used charts to review the AI progress in the 30 months since his previous book Co-Intelligence was published. He said that since GPT-4, 20 models have successively taken the top spot, and 180 models were stronger than GPT-4 at release, 19 of which can run on modern smartphones; the METR long-task benchmark showed that the amount of human work AI can complete increased from 4 minutes in 2024 to 17 hours in April 2026, and the benchmark was subsequently discontinued because it could no longer measure accurately. He proposed a third mode of collaboration beyond centaur and cyborg: conjurer, that is, summoning agents, assigning tasks, and evaluating results. The charts in this article were completed by Opus 5.5 in Claude Projects orchestrating multiple agents.

    Same story · 2 reports

    #English text is being prepared.#Agents#English text is being prepared.

Tuesday42 items

  1. AYi AI NotesOpinion
    AI score52

    Altman Responds to the $1.4 Trillion Compute Bill: Better to Buy Too Much Than Too Little

    On the BG2 podcast, investor Brad Gerstner, in front of Microsoft's CEO, questioned how OpenAI could sign a $1.4 trillion compute bill on $13 billion in annual revenue. Sam Altman responded that if Gerstner thought the risk was too great and wanted to sell his shares, he could help find a buyer right away, and said he looked forward to the company going public so the naysayers could short it. The author sums up Altman's logic as: at this stage, the real danger is not buying too much compute and being unable to pay the bill, but stockpiling too little compute and being unable to build the next-generation model, and lists four supporting paths: ChatGPT subscription growth, building its own AI cloud service, developing its own AI consumer hardware, and fully automated scientific research; the author also points out that when Altman announced the AI cloud service, Microsoft's CEO was right beside him, the competition between the two sides is already out in the open, and closing a $1.4 trillion gap in four years remains an unproven gamble.

    Quoted postAYi · @AYi_AInotes

    English text is being prepared.

    #Industry news#OpenAI#Microsoft

  2. 36Kr · AIOpinion
    AI score53

    iFlytek's non-recurring net loss in the first three quarters was 763 million yuan, with its book profit turned positive by subsidies and equity gains

    In the first three quarters, iFlytek's revenue was 18.187 billion yuan, and net profit attributable to shareholders was 21.8872 million yuan, but non-recurring net profit showed a loss of 763 million yuan, more than double the 338 million yuan loss in the same period last year. The book turnaround to positive was mainly due to 471 million yuan in government subsidies, 370 million yuan in equity appreciation of invested companies, and 84 million yuan in asset disposal income, totaling about 785 million yuan. The company's R&D expenses in the first three quarters were 3.850 billion yuan, accounting for more than 21% of revenue, and it attributed the widened non-recurring loss to continued investment in the self-controlled large model route; in public domestic large model tenders in the third quarter, iFlytek ranked first in both the number and amount of bids won.

    #Industry news#English text is being prepared.#English text is being prepared.

  11. shao_mengOpinion
    AI score67

    English translation is being prepared.

    Quoted postAddy Osmani · @addyosmani

    Give your agent a way to verify its work. This can dramatically improve the quality of output. With this process in place, Claude Code was able to write 90%+ of its code itself. When generating software at a faster rate than you can review it, the bottleneck shifts to proving that it works. @bcherny and team invested enough in verification, we've been able to move fast while maintaining stability. Code reviews aren't going away, but you need to think of it differently. One human reading through line-by-line is no longer going to work as a gate, it needs to evolve into a multi-layered verification system, built with fast deterministic feedback loops for your agents. This is where e2e tests, feature maps (as @poteto has been advocating), property tests and multi-agent adversarial reviews come in. These all provide opportunities to catch errors early and fix them before things get out of hand. You don't have enough eyeballs for the amount of code being written so your focus needs to be on product intent, risk assessment and evaluating evidence rather than reviewing code. Delegate the code checking work to your automated tests, scale your approvals based on risk, and turn your test failures into living evaluations. This approach helps maintain speed without losing reliability. Let the agents do what they do best and let you focus on what makes you irreplaceable: judgement, direction, and ownership. We tried to capture how we handle code review and verification in this writeup. Hopefully it's helpful.

    #English text is being prepared.#Agents#English text is being prepared.

  12. ammaarOpinion
    AI score60

    English translation is being prepared.

    Quoted postAmmaar Reshi · @ammaar

    I used Gemini 4 Argon to make Subway Surfers for the new flying Tesla Roadster 🚀 Fly over moving traffic, hit the thrusters to barrel roll in the sky, and collect air power-ups to set a high score. Play + code below! https://t.co/fLlxNmZe3G

    Same story · 2 reports

    #English text is being prepared.#Agents#English text is being prepared.