Param Harrison
@learnwithparam
โก Helping engineers go from AI demos โ production. Master Agentic AI apps in 6 weeks ๐ https://learnwithparam.com
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๐ Google just launched ADK Visual Agent Builder โ a no-code web IDE to design & deploy multi-agent AI systems visually! Part of ADK v1.18, this makes AI agent dev as easy as drag, drop & deploy. AI engineering just got visual. ๐ ๐ Follow @learnwithparam for more AI updates
What is your go to solution for AI voice Agentic which can work locally on CPU machines? Any suggestions, I have livekit with kokora TTS with some simple 8b models in mind? But wanted to check whether anyone solved this or seen open source to refer from?
For one of the bootcamp demo (week 2), I created this image 2 image demo (image to colrbook using image model with human in the loop to refine). Ofcourse it can be improved further, but that is the challenge for bootcamp students ๐ learnwithparam.com/ai-engineeringโฆ
Lately, Iโm a big fan of Autogen agents ๐ฅ Docs are amazing, DX smooth, performance blazing fast. My agent uses their Human-in-the-loop. Curious, any Autogen experience I should watch out for before I pull my hair out later? #AI #Autogen #DeveloperExperience
๐ Google just made RAG stupid simple. Meet Gemini File Search โ a fully managed retrieval system. No chunking, no vector DB, no glue code. โ๏ธ Upload files โ Ask โ Get grounded answers (with citations). ๐ธ Query-time embeddings? Free. RAG devs just got a major upgrade.
Hot take: Building a reliable AI agent has more in common with distributed systems than prompt engineering. Agree or disagree?
Every LLM app fails the same way first: latency. Your prototype feels magical. You deploy it, the model takes 11 seconds, your frontend times out at 10. Suddenly, โjust call the modelโ isnโt enough. Hereโs what real engineers do when AI meets engineering ๐ #SoftwareEngineering
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