Jack Thompson

Jack Thompson

Operational AI & Forward Deployment

About

AI engineer building AI-native products and deploying applied AI systems where the work is actually happening, with real users, real data, and real operational stakes. Less about AI as a technology, more about AI as something that meaningfully changes how a business runs. Background spans supply chain engineering, procurement, IT leadership, and AI consulting across healthcare, manufacturing, professional services, and private equity.

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Forums

At what point does running more agents backfire?

I keep seeing tools talk about how many agents they can run in parallel or how much of the backlog they can take on.

But every additional agent also creates more work to review, more decisions to make and more places where something can quietly go wrong.

6d ago

How do you implement AI quota controls for indie web products?

Adding AI features to indie products often brings unexpected API cost spikes. I ve been testing user-level rate limits, token quotas and cached responses to keep spending predictable.

I m curious to hear practical approaches from other builders:

  1. Do you enforce hard monthly caps for every user?

  2. What fallback workflows do you offer once AI quotas are exhausted?

  3. Which caching strategies work best for repeated AI requests?

Many generic guides skip these operational details, so real production experience would be really helpful.

🖇️ Is it worth adding integrations to your product?

Some products connect with dozens of other tools. Others intentionally stay independent.

I ve been thinking about this trade-off...

Integrations can make workflows smoother and help a product fit into the tools people already use.

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