What the ecosystem is doing
Most teams are now adopting multi-model stacks instead of relying on one provider. The trend is model routing by task type: fast models for routine work, stronger models for complex reasoning, and local models for sensitive or high-volume workflows.
How I run my stack
- Claude, Codex, and Cursor for implementation, refactoring, and test workflows
- Task-based model selection for speed, quality, and cost balance
- MCP-based tool routing for consistent model-to-tool integration
Local + self-hosted models
- Ollama for private and on-device inference pipelines
- Self-hosted LLMs for sensitive data and tighter cost control
- Lower latency and no per-call overhead for internal tasks
Agentic + multimodal layer
- Kimi K2.5 for long-context reasoning and visual-to-code flows
- Coordinated multi-step agent execution across toolchains
- Parallel sub-task decomposition for higher throughput
MCP integration layer
- Structured model-to-tool communication through MCP
- Secure access to APIs, databases, and automation systems
- Auditable execution with policy-aware controls
Production outcomes
- Generate full features directly from product specifications
- Run asynchronous AI workers for background operations
- Share one AI backend across web and mobile applications
- Optimize cost by routing work to the right model
The result is a practical AI platform that is fast enough for delivery, controlled enough for enterprise use, and flexible enough to evolve with tooling changes.