Post by **Michelle Chen, Alex Reneau and Kevin Flansburg**, published on **October 1, 2026** on the **Cloudflare** blog (~10-minute read, "AI Birthday" week). Cloudflare releases **Clef** and **Clef-flash**, its first in-house trained models, hosted on **Workers AI** and distributed as open source (Apache 2.0, Hugging Face), along with a reinforcement-learning fine-tuning service.
#Clef#Clef-flash#decision model
Michelle Chen · Alex Reneau · Kevin Flansburg — équipe Workers AI / plateforme IA · blog Cloudflare.
Xi Jinping — first keynote at WAIC 2026: "four observations" on AI, creation of WAICO (29 countries, headquarters in Shanghai), offer to the Global South opposed to "America First" (Xinhua/SCIO)
#artificial intelligence#global AI governance#WAIC 2026
Xi Jinping (auteur du discours). Sources primaires : Xinhua / State Council Information Office (SCIO) ; corroboration : Beijing Review · People's Daily. Réactions/analyses tierces : ONU (António Guterres) · CNN · Reuters · Paul Triolo (DGA-Albright Stonebridge) · Leon Liao · Syed Akbaruddin.
**Recurring report from Mozilla**, *The state of open source AI*, **v1.0.1, July 2026**, introduced by a letter from **Raffi Krikorian** (CTO): seven sections, an interactive site, and a downloadable report. Thesis stated in the title of Section 1: *« The model layer has commoditized. Value accrues to the harness above it. »* **Capability state**: on the *Artificial Analysis Intelligence Index v4.1*, the best closed model scores **61** (Claude Opus 5) and the best open model **57** (**Kimi K3**), fourth overall and ahead of three of the largest closed labs; on the *Epoch Capabilities Index*, the gap is **6 points** (K3 at 156 versus GPT-5.6 Sol at 162), described as *« about one release cycle »*, with overlapping confidence intervals. **Sawtooth frontier**: open leads in frontend code (K3 at 1,679 Elo on LMArena Frontend Code Arena, six domains out of seven), contests agentic terminal work (88.3 versus 88.8 on Terminal-Bench 2.1), and cedes ground on professional knowledge work (Fable 5 leads K3 by 92 Elo on GDPval-AA v2). **Usage shift**: the share of OpenRouter tokens routed to open-weight models rose from a negligible level to a third by late 2025, then to a **majority by mid-2026**, with the seven highest-volume models all open-weight — the report itself noting that *« by request count, closed US providers still lead »*, the open lead being a token-volume lead concentrated in coding and agentic workloads. **The central contrast**: *« Open ships easy. Open deploys hard. »* — 79% of developers adding AI use open models versus 71% for closed, but only **53%** of open-model teams reach production **versus 63%**, and the gap widens with organization size (closed 54% → 73%, open 53% → 57%), which *« rules out a resources explanation »*. The stack maturity map (48 components, 9 layers) shows two consistently cold columns — **standardization** and ***enterprise readiness*** — identified as the operational gap. **Section 5**: *« The agentic harness is another user agent »*, and *« The model is eating the harness »* — on every model where both exist, the lab's own harness now wins, the 21.8-point gap having compressed to about 3. Hence the formula: *« A harness tuned tightly to one lab's weights… degrades on anyone else's model, so the tighter the tuning, the less swappable the weights underneath. Lock-in arrives as a side effect of optimization. »*
#Mozilla#state of open source AI#open weights
**Mozilla** — éditeur du rapport · avec une introduction signée **Raffi Krikorian** · *Chief Technology Officer*. Publié en **juillet 2026** (v1.0.1). Données issues de sources tierces créditées (Artificial Analysis, Epoch AI, OpenRouter, LMArena) et d'une enquête propre menée avec **SlashData** (*Mozilla / SlashData 2026 developer survey*, n = 1 410 sur la question des freins).
Case study by **Surya Narreddi** (with **Cameron Franz** and **Alex Wang**) on his personal website, dated **March 2026** (day unspecified; ~1,500 words), titled *Training AI to Paint with Code*. The authors train a language model (**Qwen 3.5 35B**) via reinforcement learning (**GRPO**) to produce watercolor paintings as **p5.brush** JavaScript code, with the code itself serving as the editable artifact. The underlying question: how to do RL when the reward — aesthetic — is not verifiable.
#reinforcement learning#GRPO#reward function
Surya Narreddi — designer et développeur · auteur du projet (site personnel) ; collaborateurs Cameron Franz (infrastructure d'entraînement) et Alex Wang.