source&pool
A daily wire of long-form journalism, video, and discourse — filed, tagged, and laid out flat.
VOL. I·NO. 01
THURSDAY, SEPTEMBER 24, 2026
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  1. 001Hacker NewsSEP · 21English

    What Sun Got Wrong

    The author reflects on Sun Microsystems' failures despite strategic successes, highlighting how the company's disinterest in business operations—exemplified by losing a cloud computing startup customer to Dell's superior sales execution—ultimately led to its demise. Oxide's homage t-shirts to defunct computer companies like Sun serve as reminders to learn from both their achievements and mistakes.

    By chmaynard
  2. 002Hacker NewsSEP · 21English

    What Sun Got Wrong

    An Oxide team member reflects on Sun Microsystems' decline, attributing it to operational failure despite strategic success—specifically, the company's inability to capitalize on a startup's interest in buying Sun hardware because Sun couldn't effectively engage with the customer, while Dell's responsive account executive secured the deal instead. The author, who later joined that startup and co-founded Oxide with the Dell executive, uses Sun's failure as a cautionary lesson about the importance of mastering business mechanics alongside strategy.

    By tosh
  3. 003Hacker NewsSEP · 18English

    Noetive: Operational Intelligence for the Physical Economy

    Noetive, a new AI platform for physical industries, uses frontier AI to coordinate complex operations across manufacturing, logistics, and construction by integrating data from machines, people, and supply chains. Founded by Eclipse's Amir Frenkel and a team of AI researchers from Meta, Google, and Amazon alongside experienced operators, the company raised $41 million in seed funding to deploy expert-level autonomous decision-making at scale across the $30 trillion physical economy.

    By dannycastonguay
  4. 004Hacker NewsSEP · 18English

    From Operations to a Robot Arm

    An operations researcher spent two months training a robot arm to pick up a toy and drop it in a bowl, achieving 90% success in v4. Key lessons: data quality matters more than quantity, slow inference can mask model issues, and combining correction data with original data (DAgger) prevents forgetting tasks. Most challenges in robot learning are operations problems rather than algorithmic ones.

    By MuzafferH