Agentic AI

Local-first coding with the Pi Agent on Apple M4 Max uses the MLX framework to run Qwen3-Coder-30B at 100+ tokens per second. This setup provides a private, zero-latency 256k context window for autonomous coding, elimina...

In 2026, engineering teams are switching to terminal-native autonomous agents like Claude Code and Gemini CLI because they offer direct access to the local filesystem, git history, and shell environments. This 'contextua...

Migrating 500 plus files with Claude Code involves using the terminal-native agentic loop and parallel agents feature to rewrite Express.js routes into Fastify equivalents. By initializing Claude Code with a CLAUDE.md me...

Run background RPC worker agents in Pi CLI with pi-agents-team. 7 built-in roles, keyboard dashboard, async execution. Keep orchestrator context clean with compact summaries.

Run multi-agent workflows with local open-source LLMs using multipi for Pi CLI. Capability-based model routing, SearXNG web search, zero API costs. Complete guide.

Coordinate AI agent teams in Pi CLI with pi-team-agents. 6 built-in agents, mailbox messaging, shared task board, parallel dispatch. Cut task time by 60%.

Use pi-agent-flow for clean cognitive mode switching in Pi CLI. 6 specialist flows (scout, debug, build, craft, audit, ideas) with isolated contexts. Cut duplicate tool calls by 40%.

Turn Pi CLI into a multi-agent platform with agent-pi. 43 extensions, 6 operational modes, security guard. Shift+Tab between PLAN, TEAM, CHAIN, PIPELINE modes.

Deploy durable multi-agent teams in Pi CLI with pi-crew. 10 built-in agents, worktree isolation, async execution, Prometheus monitoring. Crash-proof your agent workflows.

pi-multiagent adds isolated multiagent delegation to Pi CLI. Spawn scouts, reviewers, and workers in separate contexts with explicit authority. Step-by-step setup and usage guide.

Multi-agent swarms are significantly more effective than single AI agents because they solve the 'Context Dilution' problem. By splitting a complex goal into specialized tasks handled by separate agents (e.g., Researcher...

Traditional data dashboards are being replaced by A2A (Agent-to-Agent) data flows. Instead of humans looking at charts to make decisions, AI agents now ingest structured data via A2A, collaborate on analysis, and execute...