Kennedy Sullivan

Imagine an AI agent responsible for routing inbound leads. Nothing crashes. No alerts fire. The workflow keeps running exactly as expected.

The problem is that the agent has slowly started sending high-value leads to the wrong queue. Maybe a few customer issues are being summarized inaccurately. Maybe records are being updated with small mistakes that seem harmless on their own. Each individual error is easy to miss. Over time, though, the impact starts to compound.

Those are the AI failures that interest me most because they rarely look like failures at first. There is no outage, no red warning message, and no obvious signal that something is wrong. The workflow continues operating, but the quality of the outcomes quietly drifts away from what the team intended.

That makes detection a different challenge altogether. It's less about system uptime and more about observation. Are there feedback loops? Quality checks? Escalation paths? Can someone spot a pattern before customers, revenue, or operations start feeling the effects?

Hey everyone signspell (v2.0.0) is now on PyPI and I recently launched it on Product Hunt. It currently handles ASL fingerspelling (A Z) recognition using MediaPipe hand landmarks + a TFLite LSTM model, and I also built Gesto Labeller (a desktop tool for capturing/labeling training data).

I'm trying to figure out the next milestone and would love input from people actually using or evaluating it:

• Would full word/phrase-level ASL recognition (beyond fingerspelling) be more valuable than polishing fingerspelling accuracy further?

• Is real-time performance/latency on lower-end devices a bigger blocker than accuracy right now?

• Should the next priority be broader sign language support (BSL, ISL, etc.) or going deeper on ASL first?

• Any specific integrations you'd want (e.g. web SDK, mobile bindings, browser extension) that would make signspell more usable in your projects?

Genuinely open to being told I'm focusing on the wrong thing what's the gap you'd want filled next?

AI agents are already changing how we work. Hard to deny that now.

People are building new habits around digital companions and agents in funny ways.
I even know someone who had a brand-new phone ready, but still delayed switching because their little @OpenClaw lobster was living on the old one.
Many of us now use agents to summarize, draft, search, code, organize tasks, or monitor things in the background.

I think investing is next.

A lot of investing still depends on manual habits: checking prices, reading news, following filings, tracking earnings, reviewing portfolios, and trying not to miss important signals.