Back to PM mentorship 🚀
After mentoring 500+ mentees globally, I paused due to workload. I’m now back to help aspiring PMs move faster — resumes, interviews, career switches & product thinking.
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https://t.co/TIhcbV6KHk
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Two things that make coding agents worse:
- giving them more context
- running more of them at once
Both feel like they should obviously help.
Wrote up what actually goes wrong in each case
https://t.co/GWDexbTBq3
Toy agent: generate → done
Engineering-grade agent: generate → execute → observe → test → fail → correct → verify �� done
A model can say "the bug is fixed." A test suite can say "the bug is fixed."
Only one of those is checkable.
The best structural trick in coding agents: don't make one agent do everything.
The main agent holds the problem. Smaller agents go read the codebase, check security, analyse the tests — each in its own context. The main agent gets the conclusions, not the 50 files.
It's parallel research with LLM workers.
Caveat nobody posts: point three of them at the same file and you haven't built a research team, you've built a race condition.
@mohdsoftwaredev Hi Mohammad,
Happy Eid! I'm looking for Senior/SDE2 roles currently in Bangalore. Here's my resume, would loved to be vouched for, thanks!!
https://t.co/NJBTF9ajCu
@yuqih I ship legal, medical field ground level projects in India, utilising AI to build software which increases transparency for people to trust and know more about systems
Let’s say, insurance and hospital documents verification and rights understanding
Banking AI chatbots come in two flavors and both are sh**:
Too tight: "I can't share that for security reasons" when you ask for your own name.
Too loose: blocks your card because a prompt said the "magic word"
Stop tuning the prompt. The LLM shouldn't have decision power over destructive actions in the first place.
The fix isn't better guardrails. It's:
→ regex routes the destructive intent
→ state machine forces confirmation
→ LLM only writes the response copy
The model should be the last thing you trust :)
A staff engineer asked me this in an interview at Global payment & credit cards org meanwhile hiring for a GenAI senior role, LOL
What’s your take on this?
RAG was supposed to kill hallucinations.
Yet in 2026, most financial AI chatbots are still confidently lying about your credit card transactions.
The “just add more context” crowd is coping hard.
Here’s what actually works in production 👇