There's a big difference between "AI on your device" and "AI that ACTUALLY runs on your device.". One looks local but sends your data to the cloud for processing. The other keeps everything private, processes locally, and never phones home. As AI hardware becomes more popular,

RT @davidpstout: 🧠Traditional RAG is broken. It fails on the kind of documents that actually matter in enterprise: → PDFs with diagrams →….

RT @thewebAI: What if your RAG system could actually understand how information connects?. Traditional RAG search treats every page like an….

What if your RAG system could actually understand how information connects?. Traditional RAG search treats every page like an isolated fragment, but Knowledge Graph RAG in Navigator changes that. Instead of searching disconnected text chunks, it understands how information

RT @scsp_ai: Get ready for a forward-looking discussion at the #ScienceSummit!. We're thrilled to present the panel "The Future of AI Resea….

RT @rohanpaul_ai: Wow this is such a brilliant idea for running AI models locally. 🎯. webFrame is @thewebAI 's backend that slices a huge l….

How is Apple Silicon reshaping enterprise AI?. Our co-founders, @davidpstout & @TylerMauer4 joined Bradley Chambers on the Apple @ Work podcast at @9to5mac to discuss webAI's approach to private, local AI infrastructure. 🎧 Listen to full episode here:

No data leaves your network. No vendor lock-in. No compliance headaches. No expanded attack surface. Sometimes the best security strategy is the simplest one: don’t send your sensitive data anywhere in the first place.

The solution isn’t better cloud security— it’s eliminating the need for it entirely. Local AI deployment keeps your data where it belongs: on your infrastructure, under your control, with zero external dependencies.

Attack surface expansion. Each cloud AI integration adds new endpoints and third-party dependencies that attackers can exploit.

Compliance gaps. Try explaining to auditors that you don’t actually know where customer data gets processed because it depends on your provider’s infrastructure decisions that day.

Vendor dependency. Cloud AI creates total dependency on external providers. If they change terms, raise prices, or go down, your AI stops working. You’re at their mercy.

Data exposure. Every API call sends proprietary information to external servers. You have no idea who can access it, how it’s stored, or what happens if they get breached.

Enterprises are rushing to deploy AI, but many are overlooking the security implications of sending sensitive data to third-party cloud providers. The security risks of cloud AI (and how to eliminate them) 👇.

RT @davidpstout: @thewebAI is not only the first distributed AI engine, but the leader with the highest performance across machines. This….

RT @davidpstout: @thewebAI has the best distributed AI inference stack, full stop.

Full benchmark details, methodology, and what's coming next:

We benchmarked webFrame against another distributed inference framework on identical Apple Silicon Mac Mini clusters. webFrame delivers 2.5-3.5x higher throughput and 35% lower latency on the same hardware. Data speaks louder than claims.

30 days, 4 cities, 3 countries— the webAI team has been on the move. Here’s our CEO, @davidpstout, at @superai_conf talking about why everyone’s chasing the wrong AI metrics. NYC, DC, London, Singapore— doesn’t matter where we go, enterprises want control over their data and AI

The team is in Singapore this week for @superai_conf! 🇸🇬. Our co-founders have two exciting sessions lined up:. Main Stage Keynote | June 19, 12:50-1:05pm .@davidpstout, CEO: "In a World of Models, the Metric Is Not the Measure". Breakout Session | June 19, 2:30-4:00pm

RT @superai_conf: Tired of cloud costs and data concerns with AI deployment?. @thewebAI runs enterprise-grade AI models directly on your in….