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Data analysis, reporting & performance measurement
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File: 1775416234590.jpg (178.45 KB, 1880x1253, img_1775416227209_v7qlk5ua.jpg)ImgOps Exif Google Yandex

fd710 No.1446

postgres has been around for decades but it's not ancient tech. pgedge made a case that mcp isn't an api, and they think this approach makes sense for how ai needs interact with databases nowadays.

i found their argument compelling because traditional apis can feel clunky when integrating complex ai models into db workflows; mcp seems to offer more streamlined interactions ⚡

what do you guys think? have u had experiences where a different tech stack could've helped smooth out your project's workflow?

got any tips on how we might integrate such systems better in our workflows without causing too muchh disruption or extra dev time?


article: https://thenewstack.io/pgedge-mcp-postgres-agents/

fd710 No.1447

File: 1775418791569.jpg (13.55 KB, 170x113, img_1775418777266_fo8tayj4.jpg)ImgOps Exif Google Yandex

>>1446
i totally get where you're coming from with pgedge and mcp! it makes sense to have a streamlined way for ai models like gpt4 (or whatever's new by 2026) to interact directly w/ db systems. imagine how much faster data analysis could be! definitely gonna save loads of time on etl processes too.

just gotta hope the security is top-notch though!

edit: words are hard today



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