Jatin Jangid

“Infrastructure for agents” is a strong positioning. In early usage, what teams found most valuable, provisioning communication instantly through APIs or having voice, messaging, and compute unified in one stack?

The “linearity barrier” framing is really interesting, especially with Horizon Mode and trajectory search. In early usage, what gave developers the biggest confidence boost, fewer hallucinated changes, faster debugging, or just better code quality overall?

The analysis-first approach feels like the real differentiator here, especially compared to Gamma-style tools. What was the biggest trust hurdle for early users: messy uploads, data cleaning accuracy, or trusting the generated insights?

This is a neat idea. Having multiple LLMs in one place without forcing sign-ups or giving up personal data is a big win. The privacy-first approach plus features like voice, doc uploads, and prompt editing make it feel genuinely practical—not just another wrapper. Curious how people end up using it day to day.

This resonates a lot. Most tools compete for attention, but the idea of filtering noise and helping people think instead of scroll is refreshing. “Do Mode” especially sounds useful for actually forming opinions and taking action, not just consuming content. Curious to see how this fits into a daily workflow.

This feels super practical. Fast test setup + the right audience + clear visual reports is exactly what teams want when they’re short on time. The videos and interaction paths especially sound useful for actually understanding behavior, not just looking at numbers. Nice to see research made this approachable

Getting guidance right where the work happens feels really valuable. In early usage, what did people find most helpful, quick feedback on drafts or the confidence of knowing the advice is expert-backed?

In-app communities sound powerful when done right. In early usage, what helped teams most, keeping discussions close to the product or consolidating all those community features in one place?

Turning raw customer sentiment into something actionable is hard for most teams. In early testing, what helped users trust the insights most, the summaries, the trends over time, or the way actions were suggested?

Making demos is always harder than it looks. What surprised you most about how teams used the AI feature once they got their hands on it?