shemith mohanan

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.

Impressive direction. On-device speed + efficiency is where real adoption happens, especially for privacy-sensitive and latency-critical use cases. The hybrid architecture angle is interesting — curious to see how LFM2 performs in real-world edge scenarios compared to current lightweight LLMs.

Love the idea of an ultra-light, distraction-free eReader. The magnetic back + pocketable form factor is a smart touch—feels designed for actual daily carry, not just desk use. Curious how the paper-like experience holds up for long reading sessions. Congrats on the launch 🚀

Data quality really is the hidden bottleneck for AI. Interesting focus on stability and compliance — reliable long-term pipelines matter much more than just raw proxy access. Curious how teams are using this in real-time AI workflows.

Treating agents as real coworkers instead of features is the key shift here. The fact that they operate with the same permissions, audit trails, and context as humans makes this feel less like “AI adoption” and more like workflow design. Curious how teams will balance trust, feedback loops, and guardrails as these agents start owning real outcomes.

The “edit automations through chat as the process changes” part stands out. Most automation tools break the moment reality shifts. Turning intent into an evolving playbook instead of a fixed workflow feels like the right direction for agentic systems. Curious to see how teams handle guardrails and versioning as these playbooks grow.

This feels refreshing — intentional networking instead of noisy social broadcasting. The privacy-first approach + AI-powered querying of your own network is a strong combo. If the auto-categorization works well, this could become a genuinely elegant contact layer.

Love this positioning. Open models with permissive licensing build real trust, and having optimized commercial versions alongside makes adoption practical at scale. Clear separation between community-first openness and production-ready performance — that balance matters.

This is a solid step forward for practical image editing. Native layer decomposition is exactly what’s been missing for precise control, not just “generate and hope.” Agreed on the model size — a distilled version would make this much easier to adopt in real-world workflows.

This solves a real pain point 👍 User research usually dies because of time, recruiting, and synthesis—not lack of tools. If the insights are truly actionable (not just summaries), this could change how teams validate fast.

Clean idea 👍 Love the infinite canvas + frames approach — it feels very natural for going from rough thinking to polished AI visuals. Turning sketches into art without breaking creative flow is a strong angle.

This is such a fun use of real-time vision + audio generation — turning your surroundings into a soundtrack is a whole new interaction layer. Super curious how responsive the mood/movement mapping feels in real environments. Very cool concept! 🎧📷✨

Nice and tight. You clearly show the transformation: raw content → structured learning. Summaries, chat, quizzes, and flashcards cover the full learning loop, and “context-aware LLMs” signals depth. If you want an extra punch, add one outcome line like “turn hours of content into minutes of learning” — otherwise it’s a strong Product Hunt pitch.

Love this angle: not just “smarter model” marketing, but clearly framed around math, science, and logic reasoning. The “parallel hypotheses” bit is especially interesting—curious to see how it handles real-world problem-solving vs. earlier Gemini versions.

Strong pitch — it hits the promise clearly: agents that feel human and work in real time. “See, hear, remember, and act” is a powerful capability stack, and “API-first” signals it’s made for real product integration. You repeat “secure” twice — you could replace one with a concrete benefit like lower latency or enterprise compliance to make it even sharper.

Nice positioning — short, clear, and aimed at serious AI teams. “From single inference nodes to multi-region AI factories” instantly shows the scale, and the “one unified dashboard” message is strong. It reads like real infrastructure, not buzzwords. If you add one outcome line (faster training, predictable costs, etc.), it becomes even more compelling.

Nice. An assistant that can read files, hold long context, and help with both coding and content is super useful in real workflows. Upload → understand → act is the right flow. Curious to see how it performs on large documents and technical tasks.

Interesting concept. A 24/7 multi-agent team for influencer marketing feels like a real upgrade from manual outreach and slow campaign cycles. If it can truly scale across niches and deliver measurable results, that’s a big advantage for brands. Curious to see case studies and outcomes.

“Very cool concept. On-demand, adaptive audio learning feels like the future. Great launch!”