Ethan Brooks

As the founder of Dashform (AI form builder), I have a meta-appreciation for this UX. You made financial modeling actually approachable. A small question how do you handle 'unknowns' (like projected churn)? Does the tool offer industry benchmarks to fill the gaps, or does it require hard data to work?

This solves the 'studio quality' bottleneck perfectly! We see this exact struggle with our e-commerce users at Dashform. This is a game changer for them. Upvoted! Let's support each other's launches!

Upvoted! We face the same struggle at Dashform—the real pain isn't syntax, but those subtle logic hallucinations that look correct at a glance. A small question, Does Cubic specifically target those 'confident but wrong' errors beyond just style checks? Rooting for you guys! Happy to support fellow teams pushing the boundaries of AI dev tools.

Visual memory over Text memory. Love the concept! We obsess over reducing friction at Dashform too, so this resonates. Can I ask How strict is the click accuracy? If I tap slightly off-target on the photo, does the AI have some tolerance? Upvoted!

Congratulations on the product launch! Solving the "comment fatigue" problem is of great significance. We also faced a similar "survey fatigue" issue in Dashform and have been striving to make the data collection process smoother. I'm curious about how your AI weighs these comments: Will it prioritize recent comments over earlier ones? The five-star reviews released in 2019 might become a...

Love the 'Configure without coding' aspect. We prioritize the same velocity at Dashform (using AI to generate forms instantly). I'm curious about the integration flow: Can we trigger your 'Points' or 'Badges' directly via Webhooks when a form submission happens? Or do we need a full backend integration to verify the events securely?

Since DuckDB is a columnar store optimized for aggregations, not an inverted-index search engine (like Lucene), how does Sloggo handle full-text search over TB-scale logs? Do you rely on DuckDB's native FTS extension, or is it brute-force scanning?

This looks super useful for quick insights. What’s the stance on data privacy? Is the uploaded data used to train your models, or does it remain completely isolated within the session?

This is an extremely suitable game for Twitch/YouTube streamers. Does it have a "live streaming mode"? (For example, allowing viewers to vote to decide the next chaotic effect, or avoiding copyrighted music so that the streamer can record the screen.)

The copywriting mentions "Ditch the black box". However, at the technical level, LLM itself is a probabilistic model. How did Dessix achieve "transparency"? Did it present the Chain of Thought or allow users to intervene in the intermediate steps?