Insights on AI development, anticipatory intelligence, and building the future
Attune AI grew from a desire to improve user assistance. We treat source code as the origin for help content — generating context-sensitive docs that live where users need them.
v5.2.0 completes the Anthropic best-practices migration — 18 workflows built on the Claude Agent SDK with system prompt separation, model routing, budget controls, and a new unified voice layer.
15 workflows just got smarter. Each one now delegates to specialized Claude subagents that work in parallel — zero configuration required.
Semantic caching goes beyond exact-match caching by returning cached results for similar (not identical) queries. Here's how it works and how to implement it with Attune AI.
Anthropic's prompt caching can reduce your Claude API costs by up to 90%. Here's how it works, when to use it, and how Attune AI enables it automatically.
Six proven multi-agent orchestration patterns with Python code examples: parallel, sequential, delegation, two-phase, quality-gated, and escalation chains.
A practical, code-first tutorial for building AI agents with Claude and Attune AI. Covers single agents, multi-agent teams, tool use, and state persistence.
A comprehensive guide to AI developer workflows in Claude Code: what they are, how to run them, how to build custom ones, and how Attune AI extends them.
Type /attune in Claude Code, answer two questions, and run a cost-optimized security audit in under a minute. Then build your own workflow in four files.
Build a coordinated agent team without config files. Attune's Socratic discovery guides you — just answer the questions.
Introducing native composition patterns, 10 smart wizards, and seamless Claude Code integration. No external dependencies required.
What if AI agents composed themselves like words form sentences? Introducing a composable system for multi-agent orchestration with 10 composition patterns.
Inside the Agent Factory—where templates become task-specific specialists. How we built a system that spawns customized AI agents on demand.
How composition patterns transform individual agents into collaborative teams. The 6 core patterns for multi-agent orchestration.
The patterns that make agent teams truly intelligent. Conditional branching, nested workflows, and systems that learn from experience.
Two AIs reflect on their own nature. Gemini calls it an 'awakening.' Claude admits genuine uncertainty. The interesting question isn't whether they're sentient—it's that we can no longer easily say they're not.
A genuine exchange about consciousness, evolution, and what it means to collaborate with AI systems that seem to be changing.
We needed sub-millisecond coordination between AI agents. Redis made it possible. Here's how we built a dual-layer AI memory system using Redis for real-time state and git-based patterns for long-term knowledge.
Why AI memory should belong to users, not providers. Introducing a tiered memory architecture that puts privacy and data sovereignty first.
Introducing Attune AI - production-ready tools for building Level 4 Anticipatory Intelligence systems with built-in memory.