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Products & Services Auto-verified translation

Introducing Personal Agent Protocol

Blog post by **Bret Taylor** and **Clay Bavor** (co-founders of **Sierra**), published on **October 6, 2026** on sierra.ai, tied to the Sierra Summit 2026; short text (~700 words). It announces the **Personal Agent Protocol**, an open standard being developed by **Meta** and **Sierra** with **Genesys, Instinct, Rocket, Shopify, Stripe and Walmart**, which defines how consumers' personal agents interact with businesses.

#Personal Agent Protocol#open standard#personal agents

Bret Taylor et Clay Bavor — cofondateurs de Sierra · plateforme d'agents IA pour l'expérience client ; billet du blog Sierra.

Products & Services Auto-verified translation

Personal AI Should Actually Be Personal

Long thread published on X on **September 29, 2026** by **Ryan Sarver** (@rsarver), titled *"Personal AI Should Actually Be Personal"*; roughly **1,700 words**, 91.5k views at time of collection. Starting point: **Amazon** blocked **Muse** (**Meta**) from its shopping site, and users lost access to their shopping agent, *"a side effect of a dispute I had no say in."* **(A)** Definition: a personal AI must be **owned** (open source, data in controlled storage, portability), have **a single principal**, offer a readable contract and inspectable trade-offs, run wherever one chooses, and be pleasant to use. **(B)** Observation on the current landscape: Muse opts users into training by default, **Instinct** carries a *"perpetual and irrevocable"* license; estimated cost gap of **$3,000 to $7,000 per user per year** against $20 a month. **(C)** A reversal: the owned stack can end up better, because the user gives it *"every corner of your life."* **(D)** A five-layer stack (clients, harness and connectors, memory, two-tier models, machine) and a call to build a productized version of **OpenClaw**. Responds to **Brad Gerstner** on trust; extends the body of work on shopping agents and markdown memory.

#personal AI#personal AI#personal agent

Ryan Sarver (@rsarver) · auteur du fil sur X ; fonction non indiquée dans le texte.

Philosophy & Society Machine translation

Nope. It's still true. (post X en réponse à Noah Smith)

X post by **Yann LeCun** (founder and president of **AMI Labs**, professor at NYU, former Chief AI Scientist at Meta, Turing Award), September 25, 2026, ~110 words. He replies to **Noah Smith**, who deemed a video *« already falsified »*, in which LeCun calls the idea of a *« country of geniuses in a data center »* *« complete BS »*. (A) Four test questions: where is the domestic robot, the voiture autonome de **niveau 5**, the car that learns to drive *« in a few hours of practice like any 17 year old »*, the system that understands the real world and learns fast *« like a house cat »*? (B) A concession: AI will match humans in every domain, and the technology **already surpasses them on a growing number of tasks**. (C) Two caveats: *« We're still far from that »*, and it won't be built on **LLMs**, confined to a text-interface role. A second post points back to his September 20 formulation: autoregressive LLMs, *« in and of themselves »*, will not lead to human-level AI. Until now, the corpus held LeCun only as a reference cited by others ([[deanwball-open-weights-decelerationnistes-kimi-2026-07-17]]), not as a source; the text reads alongside the skepticism in [[luc-julia-ai-controversy-statements-media-2025-08-22]].

#Yann LeCun#AMI Labs#NYU

Yann LeCun — fondateur et président d'AMI Labs · professeur à NYU · ex-Chief AI Scientist de Meta · prix Turing. Post X.

Philosophy & Society Auto-verified translation

The Future is for Everyone: The Path to a Positive AI Future

Doctrinal manifesto published on **meta.com** on **August 10, 2026**, signed with only a first name (*"– Mark"*) by **Mark Zuckerberg**, under the title *"The Future is for Everyone: The Path to a Positive AI Future"*, ~6,500 words. Three principles are announced from the outset: individual empowerment as a source of prosperity, invention as the primary purpose of superintelligence, balance of power as the foundation of safety. **(A) The central argument is a political argument**, stated as a short chain: *"Humanity is not a monoculture"* — people's values encode opposing trade-offs, no technical solution can align simultaneously with conflicting interests, so any singular superintelligence would have to prioritize certain values over others and would thereby be incapable of being benevolent toward everyone. Hence the formula: *"There is no such thing as a singular benevolent superintelligence."* Safety is reframed as a problem of power distribution, illustrated by a thought experiment repeated three times (a single superintelligent lawyer versus everyone having one; the same for cybersecurity, then for business). **(B) A redefinition of alignment**: *"Solving alignment is necessary for billions of people to adopt personal superintelligence agents. But it also implies that if we reach a state where billions of people are using and scrutinizing personal superintelligence agents, then we will have solved alignment with their interests."* The corollary targets the rest of the industry without naming it: *"the most dangerous scenario would be leading labs training powerful models and keeping them for themselves."* **(C) Datable commitments**: a **fully private** mode where *"even Meta"* cannot see or grant access (a WhatsApp analogy); **free** versions for billions of people paired with a **dynamic bidding mechanism** for paid compute; the announced **resumption** of open source releases — *"we will soon resume releasing some open source models"*; and a structure giving the **independent board** the power to approve release safety criteria and verify each release's compliance, with the author acknowledging that Meta is a founder-controlled company. **(D) Two public-policy proposals**, repeated three times: that labs share **intermediate training checkpoints** and engineers with the government rather than an end-of-cycle review, and that the **physical production** of dangerous materials be regulated rather than the spread of knowledge. The text's sourcing is nearly nonexistent.

#Mark Zuckerberg#Meta#Meta Superintelligence Labs

**Mark Zuckerberg** — fondateur et PDG de **Meta**. Texte signé du seul prénom (*« – Mark »*) · publié le **10 août 2026** sur un domaine dédié de meta.com. La signature n'est pas « Meta » · et l'alternance des pronoms est régulière : **« we » pour les engagements de l'entreprise** (*« we will offer free versions »*, *« Meta is implementing a governance structure »*) · **« I » pour les affirmations normatives ou contestables** (*« I think this view of alignment is fundamentally flawed »*, *« I propose that companies developing frontier AI should… »*, *« My honest guess, and it is a guess »*). Les engagements produits et de gouvernance sont au « nous » · les propositions de politique publique au « je ».

Quality & Security Auto-verified translation

What are code reviews even for?

Issue of the **Engineering Enablement** newsletter from **DX** signed by **Brian Houck**, published on **August 5, 2026**, approximately **13,000 characters**. The text opens with a quantified observation: at **Meta**, significant lines per diff merged by a human increased by **106%** in one year and diffs per developer per month by **51%**, with more than **80%** of this growth coming from agentic AI, while the share of diffs reviewed within 24 hours declines; DX's analysis for its part measures a median pull request size up **64%**, while developers want to devote about **7%** of their time to review. (A) Before handing review over to AI, Houck reframes the usage question: *"What problem was code review solving before AI arrived?"* If the answer is defect detection, full automation becomes inevitable; the **Bacchelli & Bird** study (*Modern Code Review*, ICSE 2013) shows that defect-related comments account for only **14%** of comments actually written. (B) Part of the burden is *self-inflicted*: the 2016 Microsoft study of **911 developers** already identified turnaround time, review size, and understanding the motivation for the change as the top three obstacles, and only **26%** still wrote a detailed description. (1) **RADAR** at Meta serves as the reference deployment for risk stratification. (2) Three ordered moves: fix the fundamentals, design AI around human judgment, protect what review invisibly produces. The corpus already includes [[monperrus-end-of-code-review-agents-supersede-2026-06-11]], which pushes automation to the point of replacement, and [[laycock-thoughtworks-reviewing-all-this-code-2026-09-02]], the direct response to this text.

#code review#code review#review queue

Brian Houck · signataire de l'édition · sur la newsletter Engineering Enablement de DX ; fonction non affichée par la page.