Token Forecaster

Timing is the hidden tax of the AI era. We’ve obsessed over token *cost* and token *limits*, but the actual *wait*—the seconds between pressing Enter and watching words stream out—is still a black box. Token Forecaster attacks that gap head-on. It’s not trying to be another LLM wrapper or agent framework; it’s a precision instrument for a single, painful moment in the developer workflow. That focus is exactly what Startup Signal loves to see. The broader theme here is *predictability as a feature.* As LLMs move from novelty to production, teams don’t just need smart models—they need to know when the smart thing will finish. Latency kills user trust. Think about a customer support chatbot that takes 30 seconds to respond: the user assumes it’s broken. A forecaster changes the calculus: you can show “this may take a moment” or pre-generate a loading state, or even route to a faster model when time matters. That’s not a nice-to-have; it’s operational maturity. . We also see the rise of “developer ergonomics” tools—small libraries and utilities that smooth rough edges in the AI stack. Token Forecaster fits alongside token counters, streaming visualizers, and prompt cost calculators. These aren’t flashy, but they compound. Any founder who’s shipped an AI feature knows the difference between a demo and a dependable product is often just these micro-optimizations. And the fact it’s listed on Product Hunt means real users are voting with their upvotes—early signal, but a signal nonetheless. . Of course, forecasting is probabilistic. A tool like this will need to handle model variability, context length, and generation settings—and it won’t always be perfect. But even a rough ETA is better than none. It turns anxiety into expectation. That’s a psychological win for users and a technical win for developers. I’d love to see it integrate with streaming APIs directly, or offer a confidence interval per model. The roadmap potential is real. Source URL: https://www.producthunt.com/products/token-forecaster