标签 · Anthropic — Glean

Recent picks

4picks · chronological

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