shemith mohanan

GPUs quietly became the backbone of modern AI and computing. Curious where you see the next major shift — edge AI or larger centralized compute?

Nice angle focusing on actual usage instead of just listing tools. How are you pulling usage data — direct integrations or finance/accounting sources?

Interesting positioning. How do you handle customization once the initial app is generated — is it still no-code, or can developers extend it?

Interesting combo of AI app generation + built-in distribution. The “describe → ship → discover” loop feels powerful if the output is truly production-ready. Curious how much control builders have post-generation (code ownership, edits, infra) as projects grow.

This is interesting because it shifts the problem from “better prompts” to better outputs. Generative UI feels like the missing layer between LLMs and real products — text is rarely the final interface users want. Curious how you’re handling state + re-renders when responses evolve over time.

Love the positioning here — “own the stack, don’t rent it” will resonate hard with builders burned by SaaS sprawl. The focus on local AI + self-hosting + real configs (not fluff) makes this feel practical, not aspirational. Curious how members are actually using Clawdbot day-to-day.

This is a great take on scheduling. Email is still how most real scheduling happens, and automating that instead of forcing everyone into links feels much more natural. If Ting handles messy threads well, this could remove a lot of back-and-forth friction.

Impressive direction. The real value here isn’t just “bigger model,” but how naturally Gemini works across modalities. If the text–image–code handoff feels truly seamless in real-world workflows, this could change how people actually use AI day to day — not just experiment with it.

This is super useful — being able to ask questions with actual source-backed answers solves a real pain. Curious how you handle large repos and context limits?

This is such a delightful take on focus tools 😄 The evolving mascot + streaks combo is really smart — it adds just enough emotional reward to keep people coming back. Love the idea of “growing focus” instead of forcing productivity. Nicely done!

This is interesting — especially the “iterate until it feels right” angle. Thumbnail design is one of those things where speed + feedback matters more than perfection. Curious how you’re handling style consistency across a channel (or if that’s on the roadmap). Nice execution 👏

Nice to see the Gatsby team tackling AI workflows. The combo of memory, evals, and tracing in one framework feels very timely. Curious how Mastra compares to rolling this stuff manually.

That’s a bold claim — but honestly, it feels like the direction things are going. Curious how much customization you get after the initial generation.

This looks seriously impressive. The self-correcting + full-stack setup (auth, DB, AI, hosting) in one flow is exactly what builders need right now. Curious to see how it handles real-world edge cases 👀

Turning intent into action is the real bar for AI agents. If Manus can reliably execute end-to-end tasks (not just suggest steps), that’s a meaningful shift. Interested to see how it handles real-world edge cases 👍

Reliability and steerability are the parts that really matter once AI moves beyond demos. Curious to see how Claude handles real-world workflows where consistency and control are more important than flashy outputs.

Research discovery is painfully noisy—this feels like a solid step toward signal over volume. Curious to try the context-aware ranking.

Clean idea. Search is something everyone uses, but few rethink properly. Interested to see how this improves relevance and discovery in practice.

This looks refreshingly simple. Upload data and ask questions in plain English—no dashboards or SQL is a big win. Curious how it handles messy real-world CSVs. Great concept 👍

Natural language as the interface is the right direction. Removing setup complexity is what will unlock real usage for non-technical teams. Curious to see how Instruct handles reliability and edge cases as workflows scale.