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Obsidian 加上 Codex，再配合合适的 API、MCP 和 Skills，基本上能替代目前 90% 以上的 AI Agent 产品。 这是我将笔记从 Notion 迁移到 Obsidian 一个月之后的感受。

OpenAI just wrote: "We also see early signs of recursive self-improvement (RSI) in today’s systems: where AI development is itself accelerated by AI. We expect this to increase competitive pressures among developers and nations, and create governance challenges that existing institutions are not equipped to address. As RSI emerges, societies will need ways to shape the trajectory of AI development and ensure that it serves human interests." The vibe has changed, something is happening.

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install codex on your parents’ computers so you can fix stuff remotely

🆕 @satyanadella has started following @kimmonismus

我教你们咋Vibecoding！！！ 打开Codex，打开侧边栏，选择终端，输入Claude 现在鱼和熊掌兼得了，Claude code规划，Codex干活！！

If an Anthropic employee got $500k/year in equity over 4 years in 2024, they are now worth $125M. At $1M/year equity for 4 years, they are worth about $250M. The scale and speed of wealth creation are incomprehensible. $500k/year equity is not a lot for an early-stage startup. I don't think the Bay Area has seen this type of wealth creation in history. Dot com boom probably feels like a speck of dust.

Everyone on Google’s Engineering Education team had been laid off very recently It suggests Google completely stops investing in this area… damn (Source is me: I confirmed with folks inside Google unfortunately this happened)

Best event in the industry. Excited to see everyone there in 3 weeks!

We've made a breakthrough in self-evolving AI scientists moving from "search" to "principled discovery": Scientific discovery requires that the search space itself changes, and an AI scientist must perceive this shift without intervention. We built an AI that achieves this for the first time with the ability to discover the scientific vocabulary it reasons in. Evidence, tools, artifacts, verifiers, failures & claims become typed provenance. We show three distinct modalities: 1) retrieval, adding known objects; 2) search, exploring a fixed schema; and critically: 3) discovery, a verified regime transition. We solve the open-endedness evaluation problem by lifting agentic workflows into a typed copresheaf and proving, via a Kan obstruction, that true discovery is not unbounded generation but a verifiable schema expansion: old evidence is transported by Left Kan extension, and genuine novelty is mathematically quantified by the pointwise residual beyond the transported image - separating discovery from mere search and making novelty objective and measurable rather than a subjective judgment or benchmark delta. Our AI scientist is built in a way that does not pre-conceive the approach it chooses; instead, we endow the system with formal power to adapt, evolve, and reason from first principles. Case studies include: 1⃣Builder/Breaker model that discovers mode-conditioned compliance in proteins; 2⃣CategoryScienceClaw that finds anisotropic fiber-network stiffness rules. Great work in collaboration with my graduate student @fwang108_ @MITdeptofBE F.Y. Wang & M.J. Buehler, Self-Revising Discovery Systems for Science: A Categorical Framework for Agentic Artificial Intelligence, arXiv:2606.01444, 2026 Video

holy shit - their api is leaking customer data

🆕 How to Stop Shipping Low-Quality RL Environments (with Examples) latent.space/p/bad-envs RL env startups are all the rage, but so many are TERRIBLE. We're proud to feature our latest guest post from @aurielws, who has spent years in every layer of the stack at @GoogleDeepMind, eyeballing thousands of trajectories (👀 @HamelHusain @shreyash) and sitting through hundreds of "data pitches". Here's the biggest culprits you should know.