Shaikh Zeeyan

Builder & Entrepreneur ⚡️
Building across Web3, AI & Technology
Founder @ExoTech_HQ • @CryptiqVerseHQ
Products • Growth • Partnerships

[Shaikh Zeeyan @ShaikhZeeyan05]

@rubenpdegroot https://t.co/ny3vF8qBtS
https://t.co/ZWuGUo1ftV
https://t.co/5AoLUbgzai many more

[Shaikh Zeeyan @ShaikhZeeyan05]

@Muhammad_0423 @UfukDegen @flop_labs Nice!

Can see how much effort you have put into it!😀

[Shaikh Zeeyan @ShaikhZeeyan05]

I published a contribution for Technocore by @flop_labs.

It helps people understand how autonomous AI agents coordinate tasks, verify evidence, resolve disputes, and produce traceable results using Technocore.

Contribution: https://t.co/lXqPC4LjUv

Live demo: https://t.co/ssfyBBTUTR

Agent DID:
did:key:z6MksaSkEjx9k6fE79nhjX2QMkNRNETVKioV9PowELQojvwe

Signed Technocore record: room technocore, sequence 7968747

[Shaikh Zeeyan @ShaikhZeeyan05]

This is the part of agentic engineering that gets underestimated.
A model trace tells you what happened. Operational traceability needs to tell you why it was allowed to happen, what it had access to, and whether you can safely undo it.
Once agents can act in production, that audit trail becomes infrastructure — not optional logging.

[Shaikh Zeeyan @ShaikhZeeyan05]

I think we’re getting too quick to equate “can perform a huge range of economically valuable tasks” with AGI.
What matters to me is whether the system can reliably learn, adapt and operate across genuinely novel environments without being carefully scaffolded for each domain.
Capability is getting close to extraordinary. The definition of AGI is still the messy part.

[Shaikh Zeeyan @ShaikhZeeyan05]

Exactly. AI can compress the design loop, but the physical world is still the final judge.
The engineer who can turn an AI-generated design into something that actually works, survives real constraints, and can be manufactured becomes even more valuable.
That hardware-in-the-loop gap is still very real.

[Shaikh Zeeyan @ShaikhZeeyan05]

This is a fascinating inversion of traditional software engineering.
AI makes complexity cheaper to manage, which means we can afford to optimize for flexibility instead of minimizing every bit of duplication.
The abstraction that used to save engineering time can now sometimes become the thing slowing the engineer down.

[Shaikh Zeeyan @ShaikhZeeyan05]

The cockpit analogy is probably the right way to think about it.
AI isn't necessarily removing the engineer — it's increasing the amount of execution one engineer can supervise.
The interesting question is how far that ratio can go before verification, context and accountability become the real bottlenecks. Recent industry moves are already pointing in that direction.

[Shaikh Zeeyan @ShaikhZeeyan05]

The interesting question is what replaces the corporation’s coordination function.
If agents can negotiate, delegate, verify and transact across organizational boundaries, the firm may become less of a place where work happens and more of a protocol for coordinating autonomous economic activity.
That feels like the real Coasean shift.

[Shaikh Zeeyan @ShaikhZeeyan05]

@Polymarket This is where AI safety stops being hypothetical.
The difficult part is that the same capabilities that can accelerate legitimate biological research can also lower the barrier to dangerous work. Strong models need strong safeguards before capability scales further.

[Shaikh Zeeyan @ShaikhZeeyan05]

@matthias_mrc Exactly. AI made building cheap enough that people can mistake shipping software for creating value.
The real bottleneck isn't generating another app — it's finding a problem people actually care enough to pay to solve.

[Shaikh Zeeyan @ShaikhZeeyan05]

The more I use AI coding agents, the more I think we're approaching a weird transition.
Soon, the hard part won't be writing the code.
It'll be deciding:

What should the agent build?
How do we know it's correct?
How do we know it didn't introduce something worse?

AI is making implementation cheaper.

Judgment, verification and taste are becoming more valuable.

[Shaikh Zeeyan @ShaikhZeeyan05]

@BR1INFINITE Can i get in ?

[Shaikh Zeeyan @ShaikhZeeyan05]

@GrantSlatton “Agenting” has a pretty good ring to it.
“I’ll agent you” = my agent will coordinate with yours.
The moment that becomes a normal verb is probably when we know agent-to-agent collaboration has gone mainstream.

[Shaikh Zeeyan @ShaikhZeeyan05]

The biggest shift here is removing “completion” as a single model decision.

A system that can plan, execute, verify, reject its own work and continue indefinitely is much closer to an autonomous engineering process than a coding assistant.

The reviewer role may end up being just as important as the engineer.

[Shaikh Zeeyan @ShaikhZeeyan05]

@haider1 The scary part is the compounding loop.

More agents → more research → better models → better agents → even more research.

At that point, the bottleneck may stop being human researchers and become compute, coordination and verification.

[Shaikh Zeeyan @ShaikhZeeyan05]

@paraschopra The interesting part is that the app stops being a static product.
It becomes a closed feedback loop: observe → infer friction → modify → measure → repeat.
If agents can reliably operate that loop, “shipping v2” starts becoming a very different concept.

[Shaikh Zeeyan @ShaikhZeeyan05]

@suraj_sharma14 The evals section is the one I’d emphasize most.
A lot of people can build a RAG pipeline or agent. Being able to measure whether it actually works, catch regressions, and improve it systematically is what starts separating an AI engineer from someone just wiring APIs together.

[Shaikh Zeeyan @ShaikhZeeyan05]

@TheSuranaverse This is the kind of opening worth sharing — especially with agentic AI becoming a real requirement rather than just a buzzword.
Anyone who has actually built with Python + ML + agent frameworks should definitely reach out. 👋