Pashupathi Mali

This feels like an inevitable shift — subscriptions were never meant for production-grade or always-on agents. Curious how many people were depending on a single model/provider though — changes like this can break systems overnight. Feels like moving toward multi-model setups is becoming necessary now.

This is very true for user-facing AI products. In infra/dev tools, I’ve noticed the shift is slightly different , nobody cares about the AI itself, but they care a lot when things become unreliable or expensive in production. The conversation becomes less about “what it does” and more about “what happens when it breaks”. That’s where most teams start feeling the real pain.

Really interesting approach. One challenge I’ve seen with agent-based automation is that reliability often breaks once multiple external services are involved. Curious if you’ve seen cases where the generated endpoint works initially but starts failing later due to subtle changes in the target site or upstream services.

One thing we've noticed while building around AI agents is that the “silent failure” problem gets worse once agents start orchestrating multiple tools or models. Curious if you’ve seen cases where the agent’s reasoning is technically consistent with the conversation history but still wrong because the underlying model behaved unexpectedly?

I’ve been thinking about this a lot while building in the AI infra space. The moment you move toward usage-based pricing, the real challenge becomes observability and control - knowing what’s happening with model usage before bills surprise you. Feels like pricing and architecture are becoming tightly coupled in AI products.

Respect for taking the step from dreaming to building. That’s the hardest transition for most of us. Launching something fun that people can play together sounds like a great idea. Wishing you a smooth launch on March 1.

I relate to this a lot. Building has always been the easier part for me too. The hard part is figuring out how to get the right people to even notice what you've built. One thing I'm slowly realizing is that distribution and conversations matter earlier than most of us technical founders expect. Writing code feels productive, but talking to people who might actually use the product often...

I’ve experimented with both Clerk and Supabase Auth in different projects. Both are great for getting something running quickly, especially when you don’t want to spend time building the whole auth flow yourself. What started to matter more for me though was long-term control. Auth tends to sit at the center of business logic (permissions, roles, billing, multi-account setups, etc.), and once...

I struggled with this early on too because there’s almost no quantitative signal at the beginning. What ended up mattering more for me were small qualitative signals: things like whether the right people were responding, whether conversations were happening naturally, and whether people started asking deeper questions about the product instead of just giving polite feedback. One thing that...

The interesting thing is how quickly people “switch” when using AI tools directly, but for teams building products on top of these models it’s rarely that simple. Changing providers usually means dealing with different model behaviors, pricing structures, rate limits, and reliability patterns. This is why a lot of teams eventually start thinking in terms of multi-model setups instead of loyalty...

Interesting framing, treating shoutouts as a long-term discovery surface rather than a launch-day courtesy. A well-written founder review tied to a widely used tool could quietly bring traffic months later when people research that product. Feels like one of those small distribution levers most makers probably underestimate during launch prep.

I’m building RoutePlex, a vendor-neutral AI gateway & model router for teams running multiple LLM providers in production. We’re focused on solving the operational layer of AI: routing, fallback logic, quota enforcement, and cost predictability across providers. Beyond basic routing, RoutePlex includes a prompt enhancement layer and a lightweight learning system that adapts routing decisions...

I think it depends on stage. Pre-product-market fit → personal brand builds trust and distribution. Post-PMF → company brand compounds credibility. As a founder building a B2B infra product, I’ve found that technical credibility and thought leadership drive more meaningful conversations than logo visibility early on.

The GPT Store feels like an important step toward an ecosystem-driven AI economy. Making custom GPTs discoverable lowers the barrier for experimentation and lightweight automation. There’s still room to improve discoverability and deeper workflow integration, but the direction is strong. Excited to see how this evolves as more builders create specialized agents.

This is a big unlock. Moving from “chat about code” to actually seeing, clicking, navigating, and debugging inside the browser changes the workflow entirely. The loop between terminal → browser → console errors becoming AI-readable is powerful. As someone building in the AI systems space, tighter execution + verification loops like this are where real productivity gains happen. Well deserved...

Impressive to see how far Gemini has come. The multimodal foundation combined with stronger reasoning in 3.1 Pro makes it feel like a serious step forward, not just an iteration. Kudos to Google for pushing deeper into complex problem-solving use cases. As models become more capable at core reasoning, it opens up exciting possibilities for more advanced AI systems. Well deserved recognition.

Love seeing focus shift from raw intelligence → conversational dynamics. Most AI voice systems optimize for response accuracy, not conversation flow. Handling interruptions + backchannels while preserving persona integrity is where the real UX battle is. Excited to see how PersonaPlex evolves — especially in multi-agent and enterprise workflows. Congrats on the launch

This is exactly what the industry needs for production agentic workflows! 🎉 The 1M token context window is game-changing for working with large codebases. Currently dealing with this exact problem - trying to maintain context across multi-file refactoring tasks. Quick question: How does the adaptive thinking work in practice? Does it dynamically allocate compute based on task complexity, or is...