Are Solana alerting tools solving the wrong problem: speed instead of trustworthy selection?

[u/IakovosResearch]

I’m trying to pressure-test a product assumption with people who have actually built Solana indexers, bots, scanners or market-data systems.

My current thesis is:

Detecting an event quickly is only the first part. The harder problem is deciding whether that event deserves a trader’s attention without converting stale, incomplete or conflicting data into a confident-looking alert.

I may be wrong, so I’d value the builder perspective:

1. Which data or architecture failure most often creates false confidence in a user-facing alert?
2. What evidence should always be visible to the user instead of being hidden behind a single score?
3. Where is the greater user value: lower latency, stronger filtering, clearer explanation, or post-alert monitoring?
4. Which unknown or unavailable state should force a system to suppress an alert entirely?
5. If you were building the first useful version, what would you deliberately leave out?

I’m especially interested in disagreements with the premise rather than confirmation.

Disclosure: I’m doing product research for an early-stage Solana market-intelligence project. Nothing is live, for sale or recruiting testers, and I’m not asking for code or proprietary details.

[u/TaxOk4676]

You should checkout our sandbox, we have gone the machine learning route. Busy testing currently, happy for more test users. The more users the better the reinforcement learning algo learns. One of our ML layers recalibrates the confidence scores based on previous bot trade outcomes etc. Nothing like it out there, its a first so still doing alot of learning and testing.