Avi Kumar

6️⃣ Local AI is no longer experimental. It’s infrastructure. And infrastructure decisions compound: • Model size • Quantization level • VRAM availability • GPU residency • UI efficiency Bigger ≠ Better. Aligned > Impressive.

5️⃣ Most people chase parameter count. But in portable AI systems, VRAM ceiling defines reality. If your model doesn’t fit cleanly into GPU memory: You’re not scaling up. You’re slowing down.

I tested 4 local LLMs inside Jan. Same machine. Same environment. No tuning tricks. Here’s what actually happened. 🧵

5️⃣ Local AI is becoming infrastructure. And infrastructure choices matter long term.

4️⃣ If you’re building local AI, stop only comparing models. Start comparing stacks.

I tested 4 local AI interfaces. Here’s what most people miss. 🧵

Authentication ≠ AI security. Real AI voice security is layered: Auth → Data partitioning → Guardrails → Audit logs → (optional) voice verification If your AI can see sensitive data, it can leak it. Containment beats trust. Every time.

This is how we turn AI from a guessing game into a guided experience. If you’re serious about prompt engineering, this is a game changer.

Want the full case study and a step-by-step guide on how we did this? Comment SuperPrompt and I’ll DM you all the details.

Crafting the perfect AI prompt every time? It doesn’t have to be a guessing game. Let’s build a prompt co-pilot using AI itself. 🧵👇