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Built by Adobe
Adobe's newer Precise Composite model, described as outperforming the previous MD model in object-edge accuracy, lighting realism, and contextual scene generation.[1]
Directory statusCatalogued
Official model cardVisit source ↗ Independent coverage
Research, guides, and updates
Open Source vs Open Weight LLMs: A Practical Licensing and Integration Guide
Open source and open weight describe access positions, not automatic permission to use, modify, or redistribute an LLM. This evidence-first guide shows developers how to inventory artifacts, evaluate commercial and redistribution terms, control deployments, and document approval without treating blog labels as legal proof.
Guide
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
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
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
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
Retrieval-Augmented Generation: A Practical Tutorial
Design a small RAG system with traceable ingestion, retrieval, context assembly, citations, and stage-by-stage evaluation.
Retrieval and RAG