**Boris Cherny** (Creator & Head of Claude Code @Anthropic) publishes a framework table on LinkedIn, **« Steps of AI Adoption »**, mapping an engineering team's adoption of agentic AI across **5 stages (0→4)**, each characterized by an **order of magnitude of agents driven** and a **transformation of the engineer's role**: **0 Gated** (0 agents, locked-down access), **1 Assisted** (~1 agent — "you + one agent", supervised pair programming), **2 Parallel** (~10 agents — **orchestrator**), **3 Supervised autonomy** (~100 agents — **manager of managers**, an org tree), **4 AI-native** (~1,000+ agents — **VP steering by intent**). The table crosses five columns: number of agents, *what it looks like*, *the bottleneck*, *the products that help*, *the guardrails*. **Central thesis**: consuming more tokens does not move you up a level — advancing to the next stage requires **identifying and breaking the next bottleneck** AND **building the next set of guardrails**. Concretely: giving Claude a trustworthy **self-verification loop** (tests + build + lint + e2e on a real environment), enabling **Auto mode** (avoiding blocking permission prompts), making **code review and security review the default**, adopting multi-agent interfaces (Agent view CLI, Desktop, iOS/Android apps, Tag), then `/loop`, `/batch`, `/goal`, **dynamic workflows** and **worktree isolation** for subagents. On steering: usage (dashboard) measures **activity, not return**; the right question is *"would we have spent engineering effort on this anyway? if so, how many manual engineer-hours would it have cost?"* — that's the ROI. The real payoff arrives when **fixing and maintaining happens in the background** and teams focus on *building*. Anthropic sits at **stage 3, heading toward 4**; Boris Cherny states he has personally reached **level 4**.
#Boris Cherny#Claude Code#Anthropic
Boris Cherny (Creator & Head of Claude Code @Anthropic)
First-rate technical account by **Jarred Sumner**, creator of **Bun** (JS/TS runtime, >22M downloads/month), on the **complete rewrite of Bun from Zig to Rust in 11 days** (May 3→14, 2026) driven by **Claude** — an exceptional case study in AI-assisted software engineering **at industrial scale**. Motivation: a recurring class of bugs (use-after-free, double-free, leaks) arising from the mix of GC-managed memory (JavaScriptCore) and manual memory (Zig); in **safe Rust**, these bugs become **compile errors** with automatic cleanup (`Drop`/RAII) — "a better feedback loop than a style guide." Rejecting the dogma that "a rewrite is always a bad idea" (a year of bugfix freeze for 3 engineers), Sumner chooses a **mechanical port** (preserve the architecture, minimal behavior change) validated by the **existing test suite, written in TypeScript and therefore language-independent** (60,624 tests, 1.39M `expect()` assertions, 0 tests removed, 6 platforms). The harness: **~50 dynamic workflows** in **Claude Code**, *write → 2+ adversarial reviewers → apply* loops, up to **64 Claude instances in parallel** (4 worktrees × 16), with **PORTING.md** + **LIFETIMES.tsv** generated in preparation. Numbers: **6,502 commits** (peak 695/h, 58/min, ~1,300 lines/min), final diff **+1,009,272 lines**, ~16,000 compile errors treated as a queue, **5.9B uncached input tokens + 690M output ≈ $165,000**. Key methodological levers: **adversarial review** (a second Claude, separate context, sees only the diff, tasked with finding why it's wrong — catches subtle bugs that are *semantically* different but *syntactically* identical) and the principle **"fix the process that generates the code, not the code by hand."** Model used: a pre-release of **Claude Fable 5** (Mythos class). Since the merge: **11 rounds of Claude Code security review**, 24/7 coverage-guided fuzzing (100B executions → ~15 PRs), **4% `unsafe` code** (78% on a single line), **19** known regressions fixed. In production: Claude Code v2.1.181, the first release on Bun-in-Rust, **+10% faster startup on Linux**. Disclosed upfront: **Bun was acquired by Anthropic in December 2025**.
#Bun#Jarred Sumner#Zig-to-Rust rewrite
Jarred Sumner (créateur de Bun ; travaille chez Anthropic depuis le rachat de Bun en décembre 2025)
Blog post by **Pasquale Pillitteri** (software engineer, Palermo) published on **May 29, 2026** (FR version), 18-minute read, *Claude Code & Anthropic* section. **Pivot thesis**: *« Claude Opus 4.8 is the most powerful SEO model of 2026, but almost everyone uses it wrong »* — not a model problem but a **system** problem. The golden rule: ***« strategy is a whiteboard, production is an assembly line »*** — SEO must be **split into two distinct phases**, and mixing them is *« the fastest way to waste a model that costs five dollars per million input tokens and twenty-five for output »*. **Model context**: Opus 4.8 released on **May 28, 2026** (41 days after Opus 4.7), **1M-token** context, **GraphWalks Long-Context F1 at 1M: 40.3% → 68.1%**, **SWE-bench Verified 88.6%**, **USAMO 2026 96.7%** (+27.4 pts), **HLE with tool 57.9%**, unchanged price **$5/$25** per M tokens, **Fast Mode 2.5× at $10/$50**, four **effort levels** (Low, High, Extra, Max). **The central anti-pattern** = *« the giant conversation »* / **context drift**: mixing strategy, keyword research, competitive analysis and writing in a single chat produces a *« mush of contradictory intentions »* → the model slides toward **generic best practices** ("holistic optimization", "strategic approach") instead of data-anchored content. **Phase 1 — Strategy (whiteboard, visual UI, one-off)**: dashboard / Google Sheet / Claude.ai canvas to decide while looking at the data together. **3 plays**: (a) **classified keyword research** (table of volume / difficulty 0-100 / intent / business potential / priority = volume÷difficulty×business weight); (b) **visual competitive analysis** (topic-coverage matrix, gaps); (c) **phased roadmap** (quick wins M1-2 / medium term M3-6 / pillar pages M7-12). **Extra/Max** mode is justified here (*« one right strategic decision is worth a thousand well-written pages on the wrong keywords »*). 3 closed artifacts saved to Notion/Drive. **Phase 2 — Production (assembly line, Opus 4.8 + MCP)**: the model shifts from strategist to **execution machine**; every decision **anchored to live data** via the **Model Context Protocol**. **Minimum MCP stack**: **GSC MCP** (AminForou/mcp-gsc, 500+ stars), **official Ahrefs MCP** (98 stars), **GA4 MCP**; the `modelcontextprotocol/servers` repo = **86,440 stars**, **10,000+ active servers**, 97M SDK downloads/month. Setup ~35 min, monthly refresh ~20 min. **Weekly loop**: a single prompt pulls live data, builds the brief (top 10 SERP + GSC + Ahrefs), derives H2/H3, writes, checks density, suggests titles → **+45% productivity**, draft in **6-12 min** (explicit reference to **Ryan Law / Ahrefs content engineering**, 23 skills). Mentions Anthropic's **Dynamic Workflows** (up to 1,000 subagents). **4 common mistakes**: (1) not checking the numbers (spot-check mandatory, *trust & verify*); (2) fully replacing Semrush/Ahrefs (MCP is a **layer on top**, not a substitute); (3) ignoring the **paid-organic content gap** (education client case: **2,742 wasted terms / 351 opportunities** identified in 90 s); (4) using Opus 4.8 where **Haiku 4.5** suffices (meta descriptions, alt text). **Cost**: $1-3 per 2,500-word article. **Sonnet 4.6** suffices for recurring production, Opus 4.8 reserved for strategy. SEO-optimized and self-referential article (the author writes about SEO in content itself designed to rank for "Opus 4.8 SEO"). Direct convergence with **Ryan Law/Ahrefs** (cited), **systems around the model** (Dropbox/Okumura), **skills-over-prompts** (Lattice), Haiku/Sonnet/Opus model routing (Gupta token-to-outcome).
#Claude Opus 4.8#AI SEO#two-phase workflow
**Pasquale Pillitteri** — Ingénieur informatique / développeur logiciel basé à **Palerme** (Italie) · certifié Innovation Manager UNI 11814:2021. Auteur d'un blog tech actif (rubrique *Claude Code & Anthropic*) · avec une newsletter hebdomadaire (~3,4k lecteurs). Article publié en version **FR** le **29 mai 2026** (lendemain de la sortie d'Opus 4.8).