标签 · Anthropic — Glean

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15picks · chronological

07-27

How Anthropic runs large-scale code migrations with Claude Code

Anthropic engineers used Claude Code (Fable 5 and Opus 4.8) to migrate Bun from Zig to Rust in two weeks, producing one million lines of code with 100% test pass rate; another engineer ported a Python codebase to 165,000 lines of TypeScript over a weekend. The article details a six-step migration process: create rulebook and dependency map, stress-test rules, translate everything (parallel), compile, run, and match behavior. The core insight is to fix the loop that produces code, not the code itself. It discusses when migrations are justified, why AI changes the economics (parallelism, clear context, built-in referee, self-generating queue), and best practices (use smaller models for implementation, largest for review). Concrete token and cost data are provided: Bun migration consumed ~5.9B uncached input tokens and 690M output tokens, costing ~$165k at API pricing.

x.com · 15 min · Agent Architecture · AI Engineering · Anthropic

07-25

Introducing Claude Opus 5: Near-Frontier Performance at Half the Cost

Anthropic releases Claude Opus 5, approaching the frontier intelligence of Fable 5 at half the cost. It achieves state-of-the-art results on coding and knowledge work benchmarks, e.g., Frontier-Bench v0.1 (more than double Opus 4.8 performance) and ARC-AGI 3 (3x score over next best). However, it remains behind Mythos 5 on cybersecurity tasks. The model offers tunable effort settings for cost-performance trade-offs. Customer reports highlight gains in software engineering, finance, and legal work. Alignment improves, but cybersecurity capabilities are intentionally limited. Safety classifiers intervene ~85% less than Fable 5. Pricing same as Opus 4.8, with fast mode available.

www.anthropic.com · 20 min · AI Engineering · Anthropic · Cost Optimization

07-16

Cut Claude Code token costs by rendering system prompts & history as images

pxpipe is a local proxy that intercepts Claude Code API requests, rendering bulky text parts like system prompts, tool docs, and old history into compact PNG images. Since image token pricing depends on pixel dimensions rather than text length, the approach cuts input tokens by ~60%, leading to a 59–70% end-to-end cost reduction. It rewrites requests before they leave the machine, preserving prompt caching. By default it works with Claude Fable 5 and GPT-5.6, with dashboard controls for opt-in models. It includes profitability gates and benchmarks showing near parity in coding tasks, though exact-string recall is lossy. The project is aimed at developers using LLM coding agents who want to slash API costs without sacrificing functionality.

github.com · 12 min · Ai Tooling · Anthropic · CLI

07-13

High-Severity CVEs Spike 3.5x After Claude Mythos Release

In April 2026, Anthropic announced that Claude Mythos Preview could autonomously discover software vulnerabilities. Since then, Project Glasswing partners including Microsoft, Google, Apple, and AWS have used it to find and fix over 10,000 high- or critical-severity bugs. Data from Epoch shows that the number of such CVEs disclosed by 21 notable organizations surged to ~1,500 in June 2026 — more than 3.5x the previous monthly record before Mythos. OpenAI's Daybreak product has pursued similar efforts. The spike reflects both proactive patching and an overall increase in vulnerability discovery driven by frontier models, raising important questions for security researchers, AI safety policy makers, and open-source maintainers.

epoch.ai · 2 min · Ai Safety · Anthropic · CVE

07-12

Anthropic's Goodwill Drain: Lock-in, Price Hikes, and an Engineer's Reckoning

A firsthand account of Anthropic's deteriorating trust: unreliable APIs tied to subscriptions, a locked-in and buggy Claude Code ecosystem, and opaque billing changes that effectively raise costs. The author argues these moves fund model training, not product improvement. Their response: shift to an 'agent-assisted' workflow and swap Claude for open-source models like Qwen and GLM via OpenRouter, gaining cost control and data security. For engineers feeling trapped in a single AI platform.

raheeljunaid.com · 11 min · Agent Engineering · Anthropic · Claude Code

07-10

Lessons from Building Claude Code: How We Use Skills

Anthropic engineers share practical lessons from maintaining hundreds of Claude Code skills internally. The post categorizes skills into nine types—library reference, product verification, data fetching, business process automation, code scaffolding, code quality, CI/CD, runbooks, and infrastructure ops—and offers concrete writing tips: focus on non-obvious hints, build gotchas sections, use the filesystem for progressive disclosure, avoid over-constraint, design setup flows, treat the description field as a trigger for the model, store memory (logs, JSON, SQLite), and bundle scripts/libraries to reduce token waste. It also covers distribution strategies (in-repo vs. marketplace), skill composition, and usage measurement via PreToolUse hooks. A must-read for anyone building agent-centric workflows.

x.com · 15 min · Agent Engineering · Agent Skills · Anthropic

07-04

Superpowers 6: Cutting Build Cost 60% via Autoresearch Loop

Superpowers 6 is released, with its biggest improvements driven by an automated research loop. The author used Anthropic's Fable model (briefly available) to systematically optimize their Subagent Driven Development pipeline. Over 36 hours and ~$165 in token spend, 25 experiments were run, yielding a 50% reduction in wall-clock time and 60% reduction in token consumption vs. v5. Key optimizations: merging spec compliance and code review agents, pre-baking review packets to minimize git operations, and dynamic agent allocation based on task type (e.g., using cheap haiku for non-code plans). The post also documents falsified hypotheses (e.g., capping controller thinking backfires) and emphasizes the role of their eval suite in rigorous measurement.

blog.fsck.com · 8 min · Agent Engineering · AI Engineering · Anthropic

07-02

Building effective human-agent teams

Anthropic shares four lessons from months of internal testing on building human-agent teams. The shift is from a single-player experience (one human, one AI) to a multiplayer model where agents hold their own credentials, persistent memory, and broad access, joining team channels as full members. The key insights: work in public so agents have context, define clear roles and tool access for every member, set an ambitious north star to make agents proactive, and build trust by granting autonomy gradually. Includes practical examples like agent-led bug backlogs and doer-verifier patterns. A must-read for teams embedding AI agents into collaborative workflows.

claude.com · 16 min · Agent Engineering · Agents · Anthropic

06-15

Claude Official Cookbooks: Engineering Recipes from RAG to Multimodal Agents

Anthropic's official collection of practical coding recipes for building with Claude. It provides runnable Jupyter notebooks covering capabilities like classification, summarization, and RAG, alongside advanced techniques such as tool use, multimodal vision, and sub-agent orchestration. The latest additions introduce the Claude Agent SDK and Managed Agents, demonstrating how to build observable, hostable agents—from research assistants to SRE bots—with just a few lines of code.

github.com · 8 min · Agents · AI Engineering · Anthropic

06-13

Claude Agents & Skills for Investment Banking, Research, PE, and Wealth Management

Anthropic's official reference implementation of Claude agents for financial services, offering 9 end-to-end workflow agents for investment banking, research, PE, and wealth management, along with 8 vertical skill packs and 12+ MCP data connectors. Everything is file-based (Markdown/YAML), installable as Cowork plugins or deployable via Managed Agents API. Designed for technical teams who need ready-made finance AI workflows while retaining full customization.

github.com · 19 min · Agents · Anthropic · Financial-Services

06-13

Claude Fable 5 and Claude Mythos 5

Anthropic launched Claude Fable 5, its most capable publicly released model, rated Mythos-class. It achieves state-of-the-art on nearly all benchmarks, especially on long, complex tasks in software engineering, knowledge work, and vision. To mitigate misuse risks in cybersecurity and biology, Fable 5 ships with conservative safety classifiers that fall back to Opus 4.8 for sensitive queries, triggering in under 5% of sessions. Claude Mythos 5, the same model with safeguards lifted, is available to cyberdefenders and certain biologists. New 30-day business data retention is mandated for Mythos-class models. The post includes results from Stripe, Cognition, and internal protein design and genomics experiments. Pricing is $10/M input tokens and $50/M output tokens.

www.anthropic.com · 26 min · AI Engineering · AI Industry · Anthropic

06-12

25 Claude Features, Workflows, and Tricks That Most Users Don't Know

A practical guide by @eng_khairallah1 detailing 25 workflows and techniques to fully leverage Claude Projects. The core thesis is treating Projects as evolving, persistent workspaces rather than transient chat sessions. It provides actionable strategies including a structured instruction template, strategic file organization, the Living Instructions pattern, and advanced concepts like voice calibration files and competitive intelligence hubs. The guide emphasizes a compounding knowledge strategy where each interaction refines Claude's contextual understanding, suitable for power users aiming to transform Claude from a generic tool into a domain-specific specialist.

x.com · 16 min · Agent Architecture · AI Engineering · Anthropic

06-11

Designing loops with Fable 5: self-correction and cross-session memory

R. Lance Martin demonstrates two loop patterns for Anthropic's Fable 5: self-correction and cross-session memory. On the Parameter Golf challenge (train a model under 16MB and 10 minutes on 8xH100s), Fable 5 with CMA and a verifier sub-agent improved the pipeline roughly 6x more than Opus 4.7, favoring structural changes over scalar tuning. On a continual learning SQL benchmark, Fable 5 progressed through fail-investigate-verify-distill into general rules, reaching 73% verification coverage, while Opus 4.7 and Sonnet 4.6 stalled at sparse notes or uncertain schemas. The key takeaway: design loops and environment feedback so the model can hillclimb, rather than relying on direct prompting.

x.com · 5 min · Agent Architecture · Agents · AI Engineering

06-10

Designing loops with Fable 5: self-correction and memory in agentic workflows

The author shares two practical directions for improving agentic workflows with Anthropic's Claude Fable 5 model: self-correction loops and cross-session memory. In a Parameter Golf challenge—train the best model within a 16MB artifact in under 10 minutes on 8×H100 GPUs—Fable 5 improved the training pipeline roughly 6× more than Opus 4.7 when using Claude Managed Agents with Outcomes judged by an independent verifier sub-agent against nine checkable criteria. Fable 5 bet on larger structural changes and pushed through a quantization regression, while Opus 4.7 stuck to tuning scalar hyperparameters. For memory, the author used a SQL-based task from Continual Learning Bench 1.0 with filesystem-backed memory across agent sessions. Sonnet 4.6 only logged failures and guesses; Opus 4.7 built flagged schema references but verified only 17% of questions; Fable 5 reached 73% verification coverage in the best run and distilled learnings into general rules. Engineers interested in agent architecture and model capability boundaries will find the experiments relevant.

x.com · 5 min · Agent Architecture · AI Agents · AI Engineering

06-08

How to Master Dynamic Workflows in Claude Code: 6 Patterns and 14 Steps

This article provides a systematic guide to Dynamic Workflows in Claude Code, shipped in late May 2026. It moves beyond manual prompt chaining by letting Claude generate a bespoke JavaScript harness for a specific task. The author first explains the mental model: how workflows structurally fix agentic laziness, self-preferential bias, and goal drift inherent in single-context sessions. It then breaks down six core patterns—classify-and-act, fan-out-and-synthesize, adversarial verification, generate-and-filter, tournament, and loop until done—each with code skeletons. Real-world use cases show how to compose patterns for migrations, deep research, triage, and lightweight evals. Practical controls like /goal, /loop, token budgets, and the quarantine pattern for untrusted inputs are covered. It also advises on saving successful workflows and shipping them as Skills. This guide is for engineers aiming to tackle long-running, parallel, or adversarial tasks beyond a single Claude Code session.

x.com · 17 min · Agents · AI Engineering · Anthropic