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Watch this episode to understand how a large engineering organization is redesigning its entire software delivery pipeline — not just its code generation step — to keep pace with AI-speed development.
Audit and prune Claude Code's hidden auto-memory files — including orphaned entries Claude wrote but never indexed — without manually digging through `~/.claude/` directory structures.
Teams publishing API docs get an MCP server automatically, meaning AI coding assistants like Cursor and Claude can query live specs, generate typed clients, and run real API calls without manual copy-pasting of documentation.
Check your git commit history for the exact string "HERMES.md" and review your extra usage at claude.ai/settings/usage if you use Claude Code on a Max plan, as this bug can silently drain hundreds of dollars in unexpected API-rate charges.
Watch how Cognition's own engineers have restructured their workflows around Devin to understand the practical shift from AI-assisted coding to AI-delegated, human-reviewed software development at scale.
Developers using Claude Code can use CCM to centrally organize and promote configuration assets — like memories and rules — across projects without manually editing scattered config files.
Developers building MCP servers need to validate both SSE and Streamable HTTP transports from day one and add explicit zero-result guards to scrapers — skipping either step risks silently broken tools that pass local tests but fail in real agent clients.
Developers building agentic coding workflows on macOS can use this open-source runtime to add background computer-use capabilities — equivalent to Codex's plugin — without relying on OpenAI's infrastructure or disrupting the user's active desktop session.
Developers building agentic research workflows can use SuperMCP to give Claude or Cursor live access to Reddit threads, Twitter sentiment, and trending topics without paying for expensive API tiers or maintaining fragile OAuth integrations.
Understanding GraphRAG's tradeoffs — explainability and structured context vs. pure vector retrieval — helps AI/coding practitioners decide when to layer a knowledge graph into their retrieval pipelines.