I build AI infrastructure that solves the "Last Mile" problem for Enterprise. 3 Production-Grade Engines. 2 Patents Pending. 1 Mission. 🧵 THE PORTFOLIO: 1️⃣ SENTRIX The Firewall for AI Code. • Prevents LLM hallucinations & scope creep. • 7 Protected Zones (Auth, Payment, Secrets). • 100% TypeScript VS Code/Chrome Extensions. • Status: Patent Pending. 2️⃣ AZHWAR The Design Governance Engine. • Stops "AI Slop" UI generation. • AST-based audit for brand compliance. • "Fix-it" button for instant remediation. • Status: Patent Pending, Production Ready. MCP Server Live. 3️⃣ CIKITSU (Live SaaS) Decision Infrastructure for Humans. • Multi-agent consultant (Research Debate Critique). • Verified data sources (No hallucinations). • Status: Live Beta.

For the last 18 months my life has been: build, ship, file, repeat Being a lone wolf allowed me to move fast 15 products and 3 patents filed I’m looking for an opportunity to scale these ideas with a mission driven team. No selling, just looking to build something that lasts

Hiring an FDE, Technical PM, or Founding Engineer? I spent a decade running business ops before pivoting to orchestrate deep AI systems. The portfolio: 3 Patents 1 Live SaaS (Multi-agent business consultant) 15 full-stack AI products built independently lets talk

Doing the same sort, but in a different way. Running a higher param model in Phone.. x.com/i/status/2034520488605… This is actually a big thing . So a bigger model in Phone. I have been testing this for a month, and this research continues...

Introducing TurboQuant: Our new compression algorithm that reduces LLM key-value cache memory by at least 6x and delivers up to 8x speedup, all with zero accuracy loss, redefining AI efficiency. Read the blog to learn how it achieves these results: goo.gle/4bsq2qI

Can anyone guess what it means? Glm 4.7 30b param on the phone (shhh.. that too in 6gb Ram phone).. Testing something the world wanted.... @Zai_org

Leaving @ChatGPTapp for @AnthropicAI? Don't just export text; teleport your agent's mind. Most migration is 'Coarse.' VNOL is 'Fine-Grained.' We move the firing patterns of your agent's logic, so you don't spend the first 3 days 're-training' your Claude context

1.5M migrations = 1.5M security risks. Manual memory dumps often leak PII and secrets. VNOL’s Sentrix engine audits context during the move. If you're switching to @claudeai , do it with an infrastructure-level audit. Transition with confidence.

1.5M users moving from @ChatGPTapp to @claudeai ? That’s a massive context library at risk of Lossy Migration Manual prompts carry 10% of the intent. VNOL is designed to transfer the full 90% structured synapses, intent graphs, and reasoning deltas. let's make this migration

The AI wars are heating up. @OpenAIDevs , @AnthropicAI, @GeminiApp , and @perplexity_ai are all fighting for your context window. But you shouldn't have to choose a side. VNOL is the universal "Tollbooth." Move your agent's mind from GPT-4 to Gemini 1.5 to Claude 3.5 in <1s.