I build AI agents and ship working examples that developers can use right away.
Most of my work is hands-on: real code, clear patterns, and tested setups for AI Agents, Multi-agent teams, RAG, tool calling, memory, and local and cloud LLM Apps.
I run the open-source Awesome LLM Apps, the repo where I drop complete agent templates, clean starter kits, and advanced demos that help devs skip the noise and get straight to building.
What I work on
- AI Agents: single-agent, multi-agent, MCP-based, browser agents, voice agents, local agents
- Ready-to-run examples: clone → install → run
- Dev workflows: how to structure agents, handle tools, logs, eval, planning
- RAG setups: simple chains → agentic RAG → hybrid search → local RAG
- Chat-with-anything apps: GitHub, Gmail, PDFs, videos, research papers
- Fine-tuning: Gemma, Llama, and other OSS models
- Full code-first crash course on Agent Development Kit and OpenAI SDK
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Senior AI Product Manager at Google Cloud helping developers BUILD, SCALE & GOVERN AI Agents with ADK, Agent Builder, Agent Engine and Vertex AI platform.
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Co-author of "GPT-3: The Ultimate Guide To Building NLP Products With OpenAI API" and "Neural Search - From Prototype to Production with Jina"
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Opensource ecosystem for high-leverage AI builders Unwind AI that delivers high-signal AI news, open-source tutorials and best agentic tools.
🌱 Building what's next in AI Agents, RAG, or LLMs? I'd love to chat and share input. Angel investing in the early-stage AI Agent startups (typical cheque size: $25-50k)