标签：#Claude

The company changed course after researchers spoke out against the policy, which would have covertly limited Claude’s ability to develop competing AI models.

"I don't prompt Claude anymore. My job is to write loops." — Boris Cherny, Claude Code creator Though I see where he's coming from, I'd put it differently. A developer's job isn't to write loops. It's to design state machines. Every major agent framework — Claude Code, Codex, Cursor, LangGraph — does the same thing under the hood. A while loop calls an LLM, checks if it wants to use a tool, runs the tool, repeats until done. The loop isn't just a solved problem. It's a boring problem. The hard part is everything around it. A loop has no idea what state the work is in. It just keeps going until something breaks or you run out of tokens. That's the Ralph Loop — named after the Simpsons kid who put a crayon in his nose. Agent, infinite loop, go. The Ralph Loop works, is famous, and has zero memory of where it is in the job. Like Ralph, it keeps going without knowing why. The Fix: A Finite State Machine Think about the NBA Finals. The Spurs and the Knicks aren't improvising — every possession has a state. Fast break. Inbound play. Half court set. Each one has specific reads and triggers for what happens next. Point guard De'Aaron Fox isn't making it up as he goes. The system tells him what situation he's in, and the situation tells him what to do. Your agent works the same way. You define the stages — planning, implementing, reviewing, error handling — and you define what triggers each transition. The agent doesn't orchestrate. It executes. One focused job per state. Why This Matters in Production When agents break, it's almost always one of three things: Infinite loops — one system repeated the same answer 58 times before anyone noticed. Context overflow — the history gets so long the model starts quietly forgetting things. Goal drift — 70 turns in, "don't touch auth" has completely evaporated. State machines fix all three. The loop runs until the list is empty, not until you run out of tokens. The goal lives in the transition logic, not in the context getting squeezed

By the end of this article you'll have a small Node.js script that pipes a module-resolution error ( ERR_REQUIRE_ESM , ERR_MODULE_NOT_FOUND , Cannot use import statement outside a module ) plus the surrounding config into Claude and gets back a specific fix — not a Stack Overflow lecture. You'll also have four hardened prompts you can paste straight into claude.ai, and a script that auto-detects whether your project is CJS or ESM before you even ask. Everything below runs on Node 18+. Why "just use ESM" doesn't fix the CommonJS/ESM ERR_REQUIRE_ESM error The reason these errors waste so much time is that the failing line is almost never where the problem lives. You see this: Error [ERR_REQUIRE_ESM]: require() of ES Module /app/node_modules/node-fetch/src/index.js from /app/server.js not supported. and your instinct is to edit server.js . But the actual decision is made by four things you can't see from the traceback: the "type" field in your package.json , the "type" (or "exports" map) in the dependency's package.json , your file extension ( .js vs .mjs vs .cjs ), and — if you use TypeScript — the module and moduleResolution fields in tsconfig.json . node-fetch v3 went ESM-only; that's why require('node-fetch') blows up while v2 was fine. The traceback tells you none of that. This is exactly the shape of problem an LLM is good at: lots of small context scattered across files, one correct answer, and a human who keeps pattern-matching on the wrong line. The trick is to feed Claude the config alongside the error, not the error alone. A prompt that only gets the stack trace will confidently tell you to "convert your project to ESM," which is often the most destructive possible fix. Prompt 1 for Claude: force a root-cause classification before any code The failure mode of asking an AI to "fix my module error" is that it jumps to a rewrite. The fix is to make it classify first. Paste this into claude.ai, filling the three blocks: You are debugging a Node.js module resolut

Part 7 of 7 · Series: Building Your AI Developer Handbook · GitHub The Scenario You're building a password reset feature. User enters email → gets a reset link → clicks link → enters new password. Standard flow. Medium complexity. Let's walk through every step using the full workflow — as if you're looking over the shoulder of someone who built this system. "Show me your workflow and I'll show you your output quality." Before You Even Type Claude loads automatically in the background: ✓ ~/.claude/CLAUDE.md loaded ← the global handbook ✓ .claude/CLAUDE.md loaded ← project rules (TypeScript, pnpm) ✓ memory/MEMORY.md scanned ← all lessons and preferences You haven't typed anything yet. Claude already knows: Feature-based folder structure State management ladder No mocking the database No AI attribution in commits No useCallback without profiler evidence "A doctor who reviews your file before you enter the room is more useful than one who asks 'so, remind me who you are?'" Step 1: /status — Confirm the Setup /status Model: claude-sonnet-4-6 Effort: normal Plugins: security-guidance ✓ Thirty seconds. Sometimes the wrong model loads due to overload fallback. Sometimes a plugin fails silently. This check costs 30 seconds and prevents a surprise 30 minutes later. "A pilot's first action after sitting in the cockpit isn't to take off. It's to check all instruments are reading correctly." Step 2: /cost — Baseline /cost → Tokens used: 2,847 | Estimated cost: $ 0.004 Note this number. You'll compare it later before the expensive code review step. A surprise spike means something went wrong. Step 3: /plan — Design Before Coding /plan Build a password reset feature: - User enters email on /forgot-password - System sends a reset link (token, expires in 1 hour) - User clicks link → /reset-password?token=xxx - User enters new password - Token validated, password updated, token invalidated Claude responds with a plan — no code yet : Proposed approach: 1. DB: Add password_reset_tokens

Anthropic's strongest model is free until June 22 — and two more shifts for builders Three things landed for builders at once: the best model got cheaper (free, actually), free inference showed up on Apple's stack, and one still photo now becomes a talking video. Two of them you can act on right now. Here's the 90-second video version if you want the quick pass first: 1. Claude Fable 5 is public — and free on your plan until June 22 Anthropic released Claude Fable 5 , the first publicly available version of its Mythos-class model. It's state-of-the-art on nearly every benchmark Anthropic tests — software engineering, knowledge work, vision, and scientific research. It's free on Pro, Max, Team, and Enterprise plans through June 22 ; after that it's 10 dollars per million input tokens and 50 per million output . In high-risk areas (cyber, bio, chem) it refuses and falls back to Claude Opus 4.8 — about 95% of Fable sessions run entirely on Fable. This dropped just days after Anthropic publicly warned that AI was getting too dangerous. Why it matters: the strongest Claude is free to try on your existing plan for a two-week window. Run your hardest real task on it now and benchmark it before June 22 — the kind of jump that's worth re-checking your evals against. 2. Apple made its Foundation Models free for small developers At WWDC 2026 , Apple gave developers in the App Store Small Business Program (apps under 2 million first-time downloads ) free access to the next generation of Apple Foundation Models running on Private Cloud Compute — removing inference cost as a barrier. The Foundation Models framework now supports image input . A single Swift API can also call third-party models like Claude and Gemini, server-side. A new Dynamic Profiles system supports multi-agent workflows, and Apple will open-source the framework later this summer. Why it matters: you can ship AI features into an app without an inference bill. Prototype on Apple's free on-device models, and route

Uber's CTO told the world this month that the company spent its entire 2026 AI allocation by April. The story has been reported in a handful of outlets, hit the front page of Hacker News for 397 points and 469 comments , and is mostly being read as a cost-of-AI-tools story. It is one. It is also, on a closer reading of the numbers, a pricing-model story — and the structural fact that almost none of the coverage has emphasized is the one that determines whether this is a one-company anomaly or the beginning of an industry-wide budgetary crisis. The structural fact is that Claude Code, like most enterprise AI tooling in 2026, is priced on token consumption, not per-seat licensing. Token-based pricing scales with how aggressively the tool is used. Per-seat enterprise SaaS pricing — the model corporate IT budgets are built around — scales with how many people have access to it. Those two cost curves diverge in exactly the territory where productivity tools are designed to operate: high-engagement, daily-use, gradually-deepening workflows. The Uber data is the first public-facing version of a math problem most enterprise IT departments are about to discover privately. The numbers Uber CTO Praveen Neppalli Naga , named in Yahoo Finance's and Benzinga's coverage, said publicly that Uber is "back to the drawing board" on AI budgeting after the surge in Claude Code use blew through internal projections. The specific numbers, as reported across the multiple outlets covering the story: Claude Code adoption inside Uber's ~5,000-engineer organization went from 32% to 84% over four months. 70% of committed code at Uber is now AI-originated. 11% of live backend updates are "being written by AI agents built primarily with Claude Code," per the reporting. Per-engineer monthly API costs: $500 to $2,000. Uber's annual R&D spend is around $3.4 billion , of which the AI tooling line was a much larger fraction than expected. Cursor adoption plateaued; Claude Code dominated. These are ext

What Is a Claude Code Configuration File? A Claude Code configuration file is a structured file — either CLAUDE.md (a Markdown document) or settings.json (a JSON schema file) — that controls how the Claude Code AI coding assistant behaves within a project or organization. These files define the agent's permissions, memory context, tool access, allowed shell commands, and behavioral guardrails. Without them, Claude Code operates with broad defaults that may not align with your security posture or project conventions. Claude Code reads configuration from multiple locations in a defined hierarchy: a global user-level ~/.claude/settings.json , a project-level .claude/settings.json at the repo root, and one or more CLAUDE.md files that can be nested in subdirectories. The agent merges these at startup, with project-level settings taking precedence over global ones. Understanding that hierarchy isn't optional — it's the foundation of any serious deployment. Why Claude Code Configuration Files Matter in 2026 Claude Code has moved from a tool used by individual engineers to something teams are deploying org-wide, running in CI/CD pipelines, and integrating with production infrastructure. That shift changes the risk profile completely. A misconfigured agent with shell access and no guardrails isn't a productivity tool anymore — it's a liability. Anthropic's own documentation on Claude Code security acknowledges that the agent can execute terminal commands, read and write files, and make network requests. By default, many of these capabilities require per-operation approval, but configuration files can silently expand those permissions across an entire organization if applied at the global or enterprise policy layer. The CISA and NSA joint guidance on AI-assisted development tools (published in late 2024) specifically flagged AI coding assistants as a new attack surface for supply chain compromise — the concern being that an agent with write access to source files and no beha

The conversation I keep having with AI founders goes like this: "I've sent 50 DMs. No one is biting." Then I look at the offer. "I build AI automations for businesses." There is the problem. Bad, Better, Best — The Offer Anatomy Breakdown Most technical people sell their skill. Buyers do not buy skill. They buy a removed headache. Let me break down the bad/better/best framework I use for every offer I build: Bad: I build AI automations for businesses. Better: I help service businesses automate lead follow-up so no enquiry gets ignored. Best: I install a 7-day lead recovery system that captures, qualifies, follows up, and tracks every new enquiry — so missed leads stop disappearing into WhatsApp, email, and memory. The best version does 4 things in one sentence: Names the buyer — service businesses Names the painful outcome — missed leads disappearing Names the mechanism — a 7-day lead recovery system Names the specific result — follows up, tracks, captures That is not wordsmithing. That is the difference between getting ignored and starting a conversation. 1️⃣ The Eight-Part Offer Anatomy Every offer worth selling should answer all 8 of these: Part What It Does Buyer Who exactly has this pain? Pain What expensive thing is broken? Outcome What changes after the sprint? Mechanism What system creates the outcome? Timeline How quickly does the buyer see progress? Deliverables What exactly is included? Proof Why should the buyer believe it? CTA What is the next small step? If any row in that table is blank for your current offer — you are leaving money in the explanation gap. 2️⃣ The Offers That Actually Sell Here is what the strongest AI service offers look like right now. Not vague consulting. Fixed-scope sprints with outcomes. "I turn your AI-built app from fragile demo into launch-ready product" — auth, payments, logging, analytics, deployment, launch-readiness report — in 7–14 days. Price: $2,500–$7,500. "I build your founder-led GTM system" — content engine, lead c

Note: This is an English digest of the original Zenn post (Japanese) . Read there for the full timeline and commit-level trace. TL;DR We ship tasteck , a B2B SaaS for the Japanese night-leisure industry (dispatch + cast shift management). 8 years of operational data, ~100 venues live. Two days after the MCP design post , ChatGPT Plus can call our tools live: "Who's available tonight?" → MCP list_available_drivers → JSON → natural-language reply. Estimated B2 OAuth sprint = 2 weeks (6/16–7/1). Actual = 1 day , by reading the spec carefully before touching code. We hit 12 distinct traps between "OAuth issuance works" and "ChatGPT actually invokes the tool." The QA logs caught every one. What we shipped 3 read tools (B1): list_available_drivers — drivers free tonight list_cast_shifts — today's cast shift roster list_assignable_casts — joined resolution: roster ∧ stage-name set ∧ shop match Natural-language date helper: resolveBusinessDate(naturalText, company) — handles "today / tomorrow / day-after-tomorrow" and the per-tenant business-day boundary (e.g. day flips at 04:00 or 05:00, configured per Company.changeDateTime ). MCP SDK Server + SSE transport: @modelcontextprotocol/sdk wired into a NestJS controller. One SSE connection = one McpServer instance, company-scoped, with a session_id Map routing POST /messages . OAuth flow (B2, finished in one day across 7 steps) Step What Commit 1 Protected Resource Metadata endpoint (RFC 9728) d6f05ff6 2 /authorize + consent screen + PKCE start 107edbcb 3 /token + PKCE verify + JWT issue + resource (RFC 8707) ffd0468c 4 OAuthAccessTokenGuard (RS256 + HS256 fallback, extracts companyId / staffId ) f2c9bed4 5 Streamable HTTP transport (SSE → POST /sse/:companyId for JSON-RPC) 3a28d92f 6 resolveBusinessDate undefined fallback (`(naturalText 7 QA redeploy + ChatGPT live demo — The 12 traps (compressed) The full timeline is in the Japanese post; the abridged list: Discovery path mismatch. ChatGPT expected {% raw %} .well-known/oauth

Anthropic shipped Claude Fable 5 on June 9, 2026 — its first generally available Mythos-class model, priced at $10 per million input tokens and $50 per million output. That is exactly double Claude Opus 4.8, and the benchmark deltas are real: SWE-Bench Pro 80.3% vs 69.2%, FrontierCode 29.3% vs 13.4%. But the price is not the migration story. The API behavior is. Fable 5 ships three breaking changes that will silently misbehave in any integration that assumes Opus-era semantics. This post covers what actually changes in your code, what the bill looks like, and where the traps are. I run model intelligence at TokenMix , where we track pricing and API behavior across 300+ models. Everything below is sourced from Anthropic's launch docs, migration guide, and pricing page — verified June 10, 2026. The 60-second version Price: $10/$50 per MTok. Every rate is exactly 2× Opus 4.8 — cache reads $1, 5-min cache writes $12.50, 1-hour writes $20, batch $5/$25. Specs: 1M context, 128K max output, no long-context surcharge. Model ID: claude-fable-5 on the Claude API; anthropic.claude-fable-5 on Bedrock; anthropic/claude-fable-5 on OpenRouter. Breaking change 1: Adaptive thinking is always on. thinking: {"type": "disabled"} returns an error. Breaking change 2: Refusals are HTTP 200 responses with stop_reason: "refusal" — not error codes. Breaking change 3: Safety classifiers reroute flagged requests to Opus 4.8 (under 5% of sessions), and rerouted requests bill at Opus rates. No ZDR: 30-day data retention is mandatory. Zero-data-retention accounts don't see the model at all. Breaking change 1: thinking is no longer optional On Opus 4.8 you could disable thinking to trade quality for latency. On Fable 5 you cannot — adaptive thinking is permanently on, and the model decides how much to think per request. Your replacement lever is the effort parameter: { "model" : "claude-fable-5" , "max_tokens" : 16000 , "effort" : "high" , "messages" : [ ... ] } Five levels: low , medium , high ,

AI coding tools are powerful. But they’re also wasteful. A tiny helper-function question does not need Claude Sonnet. A huge architecture review probably does. That gap costs money. So I built Badgr Auto. It’s a local OpenAI-compatible proxy that routes each AI coding request to the cheapest model that can handle it. Point your coding tool at: http://localhost:8787/v1 Badgr Auto can route between: local models cheaper OSS cloud models premium models So instead of paying premium prices for every request, you can use: local for small tasks OSS cloud for normal coding work premium only when it actually matters It also tracks: actual cloud spend which route was used fallback events tokens safely removed estimated savings vs premium models The goal is simple: stop wasting premium tokens on cheap tasks. First launch is small: small task → local normal task → cheaper cloud hard task → premium provider fails → fallback duplicate code → safely removed receipts → clear spend trail AI coding is only going to get more expensive if every agent step goes to the top model. Badgr Auto is my attempt to make AI coding cheaper without making it worse.

Migrated 4 of 7 Notion automations to an MCP server in one weekend Two workflows stayed in Notion because the database UI beat any tool call MCP scope rule: one tool does one verb, never a Swiss Army function Result: 12 manual steps collapsed into 3 Claude prompts per publish I spent a weekend pulling four automations out of Notion and rebuilding them as MCP tools. Three of them got faster and one got worse before it got better. The biggest lesson was not about code. It was about deciding which jobs should never leave Notion in the first place. Why I Moved Off Notion In The First Place My Notion setup was not broken. It was just slow in a specific way. I had seven automations stitched together with Notion buttons, formula properties, and two third-party connectors. Every blog publish meant clicking through four pages, copying a title here, pasting a tag list there, and triggering a sync that took 90 seconds to confirm. Multiply that by the 18 articles I push in a normal month and the clicking adds up. The breaking point was a Tuesday where I lost 40 minutes to a connector that silently stopped firing. No error, no log, just a row that never updated. I checked the connector dashboard and it told me everything was healthy. It was not healthy. That kind of invisible failure is the worst kind because you trust it until you do not. MCP changed the math for me. An MCP server lets Claude call my own functions directly. Instead of Claude writing text and me ferrying that text into Notion by hand, Claude can call a tool that does the writing into my systems. The model becomes the operator, not just the writer. If you want the deeper context on what MCP actually is and why it matters at scale, MCP: The 97 Million Agentic Foundation goes through the bigger picture. So I made a list. Seven automations, sorted by how much human judgment each one needed. The ones at the top were pure mechanical steps: format this, push that, fetch a status. The ones at the bottom needed me to loo

On June 9, 2026, Anthropic shipped the most capable model it has ever released to the public. The most interesting thing about it is the part that sometimes refuses to talk to you. Claude Fable 5 is the first model from what Anthropic calls its Mythos class, a tier that now sits above Opus. It launched as a pair. Fable 5 is the public version. Claude Mythos 5 is the same underlying model with its guardrails loosened, and it is not for sale to most of us. It goes only to vetted cyberdefenders and infrastructure providers through a program called Project Glasswing, in collaboration with the US government. Two names, one brain. The thing that separates them is a set of classifiers. That detail is the whole story, and almost every launch-day write-up buried it under the benchmark chart. So let me start there instead. One Model, Two Names, One Classifier in Between Fable 5 ships with three classifiers running alongside it. They watch for requests about offensive cybersecurity, about biology and chemistry that edge toward weapons, and about distillation, which is using the model to train a competitor. When a classifier fires, Fable 5 does not answer. The request gets handed to Claude Opus 4.8, the model that was the top of the public stack until that morning, and Opus answers in Fable's place. For anyone building on the API, this is not an abstract safety story. It is a response shape you have to handle. A refused request comes back as stop_reason: "refusal" with a normal HTTP 200, not an error, and it tells you which classifier tripped. You can have the API retry on another model with a fallbacks parameter, or do it client side with the SDK middleware. You are not billed for a request that is refused before it generates output. { "stop_reason" : "refusal" , "stop_sequence" : null , "content" : [] } Anthropic says this is rare. Its early numbers put at least 95 percent of Fable sessions running entirely on Fable's own answers. I believe that for general work. But "rare on

Claude Fable 5 me permitiu criar um "GTA" em apenas um prompt. Prompt: "Crie um jogo, Tiny GTA 3D." A própria Anthropic afirma que o Fable 5 é seu modelo mais poderoso já lançado ao público, com avanços significativos em engenharia de software, pesquisa científica, visão computacional e execução autônoma de tarefas complexas. Em testes iniciais, empresas relataram que o modelo foi capaz de comprimir meses de trabalho de engenharia em poucos dias. Cidade 3D aberta com 64 quarteirões, prédios, parques e oceano Dirija, roube carros e fuja da polícia Sistema de procurado com 5 estrelas, viaturas e helicóptero te perseguem 42 pedestres vivos que fogem, voam e morrem 16 missões de entrega com histórias de corrupção brasileira Áudio sintetizado: motor, sirene, buzina e cantada de pneu Recorde salvo no navegador Jogue aqui: https://andredarcie.github.io/tiny-gta/

Anthropic's Claude Fable 5 is going to be a big hit with the web's vibe coders.

Good news for Claude devs deploying on Google Cloud. Claude Fable 5 is now in General Availability (GA) on Google Cloud. You can now access Fable 5 , as well as other Anthropic models - including Claude Opus 4.8 and Claude Sonnet 4.6 - on Agent Platform . Read more here -> [ blog ] Happy building!

Anthropic is releasing Claude Fable 5, its first Mythos-class model available to the public. The model comes with guardrails that block responses in high-risk areas like cybersecurity and biology.

Anthropic is releasing Claude Fable 5, its first Mythos-class model available to the public. The model comes with guardrails that block responses in high-risk areas like cybersecurity and biology.

Spend a few hours in Claude Code and the screen is mostly English — tool output, reasoning traces, permission prompts asking you to read and decide. Syntax highlighting is almost irrelevant. What matters is whether body-size prose stays comfortable after six hours of sessions. Most terminal themes weren't built for that. They're tuned for token-colored code, where the eye jumps between short fragments. Prose reading is different: you need higher contrast on body text, tolerably soft contrast on secondary text that doesn't compete, and accent colors that don't burn. I built klein-blue around Yves Klein's IKB pigment as the anchor color — a specific blue I wanted to look at all day. There are four variations, each making a different tradeoff. Klein Void Prot is the strict one: every color role passes APCA Lc gates (body >= 90, subtle >= 75, muted >= 45, accent >= 60). The others trade some strictness for aesthetics. One thing APCA exposed immediately: pure IKB (hex 002FA7) is effectively invisible as text on a dark ground — Lc -12. So IKB lives only in the decorative slot (ansi:blue, borders and highlights). The readable blue — permission-prompt text and similar — is a lifted Klein-family color (hex A8BEF0) in ansi:blueBright, which actually passes. The other differentiating choice is what to do with Claude Code's claude-sand brand color, which lands in ansi:redBright. Two of the four variations neutralize it so nothing competes with IKB. Two accept it as a second hero. That's the meaningful split between variations in daily use. Ships as macOS Terminal.app .terminal profile files with CommitMono or IBM Plex Mono depending on variation. One prerequisite worth knowing: Claude Code's /theme picker has to be set to dark-ansi, otherwise Claude Code uses its hardcoded RGB palette and ignores your ANSI theme entirely. https://github.com/robertnowell/klein-void

The 30-second version Anthropic shipped Claude Opus 4.8 a few hours ago. Every benchmark on the announcement page is up: SWE-bench Verified, GPQA, MATH-500, the agentic tool-use evals. The marketing copy reads as it always does — "our most capable model", "strongest coding performance", "better instruction following". If you have been around since 4.5, you know the shape of this announcement by heart now. The announcement skipped the only question that matters for teams running Claude in production: should you upgrade today, next week, or next month, and which of your workloads should stay on Opus 4.7 indefinitely? Anthropic does not write that part. They cannot — it is workload-dependent, and the answer for a code-review agent is different from the answer for a customer-facing chat product. This post is the decision tree I am applying to my own stack today. It is opinionated. Three of the workloads I run are staying on 4.7 until at least mid-July, and I will explain exactly why. Your mileage will vary, but the reasoning shape should transfer. What actually shipped in Opus 4.8 Let me anchor on the facts before the opinion. Opus 4.8 is the third release in the Opus 4.x family this year. The pattern across 4.6 (March), 4.7 (April), and 4.8 (today) has been roughly monthly. Each release has shipped a 2-4 point bump on SWE-bench Verified and a similar bump on the agentic evals. 4.8 follows the pattern: roughly 3 points on SWE-bench, about 2 points on the multi-step tool-use benchmark, and a more visible jump on the long-context retrieval evals — the 'needle in a haystack at 200K tokens' style tests. Three changes are worth pulling out of the announcement: Better long-context coherence . The 4.8 release notes specifically call out improved behavior on tasks that span more than 100K tokens of context. Concretely: less mid-context summarization, fewer instances of the model 'forgetting' early-context instructions, better citation of source material when retrieved chunks sp