Joseph Conroy @JosephConroyJr

VectorCertain's MYTHOS Playbook: Direct Mapping To CISA's National Security AI Policies newsworthy.ai/news/202605112…

Tesla’s self-driving effort could be worth more than double its EV division marketwatch.com/story/teslas-s… via @MarketWatch

DeepKeep maps attack surface for AI agents techzine.eu/news/security/… via @techzine

@steipete Hi @steipete -- any questions on the report / results?

@steipete We ran a 3-model consensus across all 3,434 open OpenClaw PRs. Found 283 duplicate clusters (688 PRs, 20% of the backlog). Top cluster: 17 independent solutions to the same Slack bug. Dashboard: jconroy1104.github.io/claw-review/da… Full report: jconroy1104.github.io/claw-review/cl…🦞 The dashboard has simple filters to help surface issues & actions: 1-Clusters = Use "Search" 2-Quality = Use "Score" 3-Alignment = Use "Recommendation"

Here's a press release (a typical) which has a LOT of technical information which to the discerning mind, will be enlightening. Since this release our AI model architecture ensemble has grown from 256 integrated models to 828, while only needing ~50KB of HDD --these types of models are the new "Transistor" of the 21st century. Architecures like these enable us to segment the "tail" section of a probalistic process into 800+ slices, providing exceptional capability to detect and quantifiy a mission-critical event. One section that will interest you is the attached screenshot. This press release is not a sales pitch, it is a KB download. ⏬ newsworthy.ai/news/202602032…

@psy_duckler @steipete @openclaw Sorry- I don't have a completed report I can drop in here. We identified the (5) primary sources which cause CC (which I identified earlier) and then built our new custom models using that information. Our results improved by ~50% and the confidence score went up by ~80%.

We have a framework that is close to what you need. I'll modify it and ship it over in a few hours--it's similar to what we're building for the PR volume assessment issue. Boths issues are structurally & operationally related--volumetric data flow with multi-layer and assessment matrix analsysis using a consensus AI system. jconroy1104.github.io/claw-review/cl…

PRESS RELEASE:: 🚨 AI agents are failing 70-95% of the time—and 7 major studies prove it. Carnegie Mellon, MIT & RAND all confirm: most enterprise AI projects deliver ZERO return. New book "The AI Agent Crisis" reveals why—and the framework to achieve 90% success instead of failure. 📊 #AI #AIAgents #Enterprise #TechLeadership 🔗 newsworthy.ai/news/202602162…

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The methodolgy is fairly straightforward where you heterogenize the cross correlation elements that reside in the model's framework, architecture, data, and training. My team and I built our own AI modeling software back in the 90's that we use today to build custom models which are able to have a very low cross-correlation rates.

Not too much disagreement due to the cross correlations between the models. Our research shows there is a 81% cross correlation (CC) between large LLM's. We build our own models to get the (CC) down to about 20% ---here's an internal report with more data if you want to dig. docs.google.com/document/d/1gW…

Built claw-review — an open-source AI PR triage tool using multi-model consensus. Ran it against a 100 sample size of @openclaw's open PRs: → 3 duplicate clusters found (3 devs independently fixed the same heartbeat bug) → Quality-ranked by Claude + GPT-4o + Gemini voting together using a 5x3 matrix (real simple). Live report: jconroy1104.github.io/claw-review/cl… Source: github.com/jconroy1104/cl… Project Overview: jconroy1104.github.io/claw-review/cl… uses simplified multi-model consensus (majority voting, weighted averages). For safety-critical applications requiring ASIL-D certification, formal consensus fusion, and real-time governance across 20+ parallel models, see VectorCertain. Perfect timing — the foundation is going to need this. 🦞

The de-dupe is harder than it looks — two PRs can fix the same bug with completely different code. You need semantic intent clustering, not diff comparison. I build multi-model consensus systems for safety-critical AI. Same pattern applies here. Building a PoC against your repo now — will share when it works.

Built claw-review — an open-source AI PR triage tool using multi-model consensus. Ran it against a 100 sample size of @openclaw's open PRs: → 3 duplicate clusters found (3 devs independently fixed the same heartbeat bug) → Quality-ranked by Claude + GPT-4o + Gemini voting together using a 5x3 PR assessment matrix (real simple). Live report: jconroy1104.github.io/claw-review/cl… Source: github.com/jconroy1104/cl… Project Overview: jconroy1104.github.io/claw-review/cl… uses simplified multi-model consensus (majority voting, weighted averages). For safety-critical applications requiring ASIL-D certification, formal consensus fusion, and real-time governance across 20+ parallel models, see VectorCertain. Perfect timing — the foundation is going to need this. 🦞

VectorCertain Unveils Micro-Recursive Model Architecture That Extends AI Safety Coverage Into Statistical Tails Where Catastrophic Events Occur "This is a transistor moment for AI safety," said Joseph Conroy, Founder and CEO of VectorCertain. "Just as transistors made everything better by being small, fast, low-power, and stackable—MRM-CFS enables a new paradigm for mission-critical AI. We're not improving existing AI architectures. We're enabling entirely new ones." finance.yahoo.com/news/vectorcer… #Tesla #Robotaxi #FSD #ASILD #TeslaOwners #AutonomousDriving #VectorCertain

@elonmusk Elon ---you are so valiant. Much praise to you fine sir.