When one of our ML engineers sat down to talk to Gunika, they weren't demoing a product. They were having a conversation.They helped build her. And watching them interact with her in real time, you almost forget the months of work that made that moment possible.That's the goal. Here's what it actually takes to pull this off at the infrastructure level: 🔴 The latency problem is brutal. Every extra millisecond breaks the illusion. You're racing across ASR -> LLM -> TTS -> lip sync, and the entire pipeline has to stay under a threshold that feels human. One slow hop and the experience collapses. 🧠 Context is stateful, not stateless. Unlike a text chat, a realtime avatar conversation carries tone, pace, and emotional cues. The model has to track not just what was said, but how it was said, and respond accordingly. 🎭 Sync is harder than it looks. Getting voice, facial movement, and response timing to feel natural requires tighter coordination than most people expect. A few frames off and the uncanny valley kicks in. We're still early. But a real time avatar talking to what they built is the clearest signal that we're onto something worth taking seriously. If you're an engineer working on real-time AI, multimodal systems, or problems that don't have clean textbook solutions, reach out. We're hiring. 🙌 And if you're evaluating where realtime AI is headed, this demo is a concrete data point. Link in the first comment. 👇 #AI #RealtimeAI #ConversationalAI #TrueFanAI #MLEngineering #Hiring #AvatarTech #StartupLife #DeepTech

We just launched our first realtime avatar experience at TrueFan AI. See the product in the comments. 🎙️ The goal is simple: make interacting with AI feel more natural, human, and engaging. Building this was a coordination challenge as much as an intelligence one. Realtime avatars require multiple systems working in sync, all at once: 🗣️ Understanding spoken language in real time 💡 Generating context-aware responses 🔊 Converting responses into natural-sounding speech 🎭 Syncing voice with a visually coherent avatar The tricky part? Keeping all of this in harmony while handling: -> Continuous input -> Evolving context -> Real-time responses This is still v1 and we're improving fast. If you're exploring use cases in customer experience, kiosks, sales, or interactive interfaces, I'd love for you to try it and share your feedback. 🙏 #AI #ConversationalAI #AvatarTech #TrueFanAI #ProductLaunch

We thought generating 1,000 AI videos per day was already a big achievement. Then the Zomato team asked us a question that broke our entire infrastructure. "Can you generate 500,000 personalized videos in one day?" At that moment we realized something important. Our AI models were not the bottleneck. Our infrastructure was. As we tried to scale, multiple problems started appearing: ⚠️ GPU machines were heavily underutilized ⚠️ Video rendering pipelines were running sequentially ⚠️ Storage I/O started becoming a major bottleneck ⚠️ Autoscaling systems could not handle sudden traffic spikes It became clear that the system was not designed for this level of scale. So we redesigned the architecture from scratch. ⚙️ Key changes we made: • Converted video generation into fully asynchronous pipelines • Built a GPU task queue system for distributed processing • Introduced batch inference for rendering workloads • Optimized storage pipelines for high-throughput I/O We also tuned the inference stack to run efficiently across T4, L4, A100 and H100 GPUs, allowing us to dynamically choose instance types based on availability and cost efficiency. 🚀 The result: 1,000 videos/day → 500,000 videos/day capacity At peak throughput, the system can generate 2,500 videos per minute. 💡 The biggest lesson for me as a technical founder: Many AI companies think their moat is model accuracy alone. But in real production systems, the real moat is: Model accuracy + strong engineering infrastructure. That architecture became the foundation of our platform, and we’ve continued evolving it as we scale further. 💬 Question for other engineering leaders: What has been the hardest scaling problem you have faced in production systems?

Check this video out -- Tere Naina 30 Sec Promo (My Name is Khan) youtube.com/watch?v=6QM_8JBC… via @youtube