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Comment on The growing divide between AI hype and software engineering realityparent

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I agree, and I think what you’re describing is only scratching the surface of what’s possible today.

It’s even more powerful with large data and knowledge sources connected.

Takes a lot of work to set up effectively, but when connected to Slack _properly_ (not their MCP; but API which is more powerful), a database of your repo’s PRs/comments, data warehouses including analytics/telemetry and logs; and in a strong harness (including using multiple models simultaneously; like the OMP advisor pattern), what AI can achieve combined your domain expertise and human intelligence is just mind bogglingly crazy.

The larger your codebase / product / volume is; the more powerful it gets. AI has found many needles in haystacks that’s just impossible for a single person or team in large companies; because nobody has all the context.

I’ve embraced it too now. Initially I felt a bit disempowered and just somewhat uncomfortable.

Over time, I realised that I’m still doing serious and interesting engineering: just at a higher level of abstraction.

And for the craft and passion of software engineering, I have a couple of pet projects where I use ‘limited AI’. Good to still keep your wits sharp.

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