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New Sep 10, 2026 10 min read

Anthropic Opus 5 Prompting Strategies: A Developer's Guide for Long-Running Coding Agents

Anthropic announced Claude Opus 5 on July 24, 2026 and describes it as an improvement for long-running agents, coding, and professional work. This guide explains how to structure system prompts, user state, assistant responses, tool contracts, recovery loops, context checkpoints, evaluations, and cautious model migration without inventing unsupported model specifications.

Guide
Jul 27, 2026 7 min read

Claude Opus 5 Integration Guide: Building Autonomous Agents with the New Opus Tier

Anthropic announced Claude Opus 5 on July 24, 2026, describing it as a step-change improvement for the Opus tier focused on long-running agents, coding, and professional work. This guide outlines what is known from the official announcement, documents the significant gaps in publicly available technical details, and provides a verification framework for developers preparing to adopt Opus 5 for autonomous workflows. All capability claims are attributed to Anthropic's marketing description and require validation against the full announcement article and API documentation.

Guide
Jul 17, 2026 6 min read

Designing Human-in-the-Loop AI Systems

Place human review where it changes risk: define authority, evidence, escalation paths, feedback quality, and audit records.

Evaluation and Safety
Jul 16, 2026 5 min read

Context Window: What It Means and What It Does Not

A direct definition of context windows, token budgets, usable context, position effects, and practical input design.

LLM Fundamentals
Jul 15, 2026 4 min read

Embeddings: A Developer-Friendly Definition

Understand what embedding vectors represent, how similarity search uses them, and how to test whether they fit your retrieval task.

Retrieval and RAG
Jul 14, 2026 6 min read

Open-Weight vs Hosted LLMs: A Workload-First Comparison

Compare deployment control, privacy boundaries, reliability work, model quality, licensing, and total cost without a universal winner.

Model Selection

Ecosystem radar

Models, mapped

Open model index

The llms.help standard

“Publish when the evidence is useful.
Skip the day when it isn’t.
01 Trace claims to evidence 02 Test what can be tested 03 Correct what changes