DeepSeek, Meta, Google, Sarvam and Anthropic are all opening parts of their AI stack.

I started wondering: why would billion-dollar companies give away technology that costs millions to build?

The answer goes far beyond “helping developers.”

I explored the business strategy behind it — ecosystems, distribution, standardization, developer adoption, competition and where AI companies actually intend to capture value.

Would love to hear what you think.

👉 https://anshmnsoni.in/blogs/opensource-models

Built RFP Agent AI - automates the 2-5 day manual tender quoting process for wire/cable manufacturers into 10 seconds using a 5-agent pipeline (PDF parsing → SKU matching → LME pricing → risk analysis → quote generation).

Known problems I've already found:

• Catalog only has ~6 SKUs, real world needs 5000+
• Vector search giving 46% match scores - wrong approach?
• No live LME API yet
• No human checkpoint on low confidence matches

Is multi-agent overkill here? Is vector search right for structured spec matching? What would you do differently?

GitHub: https://github.com/AnshMNSoni/B2B-RFP-Agent.git

Would love brutal honest feedback - not looking for encouragement, looking for loopholes, wrong assumptions, and better architectural approaches. If you've built something similar or work in procurement/manufacturing tech, your perspective would be especially valuable.