I’m pleased with the significant improvement in the output quality of information profiles on y2.dev This development was inspired by a research paper from Stanford, and I strongly recommend exploring recursive language model design for agentic use cases that prioritize quality. Read the research here: https://lnkd.in/g-cRJtmk

FBS Inc. Launches an AI Assistant That Reads Charts—Then Suggests Trades Tired of hunting for patterns and second-guessing entries? The new FBS AI Assistant scans your chosen instruments in seconds, detects dominant trends, patterns, momentum/volatility shifts, and returns a plain-language summary—plus one-click trade ideas you can execute inside the FBS ecosystem. Why it matters • Speed: Compress multi-timeframe, indicator-based analysis into a quick read. • Clarity: Structured levels, signals, and scenario risks—no black-box mystery. • Confidence: Validate your plan or catch what manual scans might miss. Use it to • Pre-trade validation before you risk capital • Rapid sweeps across watchlists to spot setups early • Post-event clarity when candles go wild after news FBS frames this as human-in-the-loop trading: you stay in control; the machine accelerates insight and execution. 👉 Learn more: https://lnkd.in/g9Yg2iYX #forextrading #trading #AITrading #technicalanalysis #FBS #mt5 #scalping #daytrading #chartpatterns #RiskManagement

Artificial General Intelligence (AGI) isn’t a single breakthrough — it’s a chain reaction. Every leap in reasoning, memory, and autonomy brings us a step closer. We’re already seeing: * Multi-agent systems collaborating without human prompts * Models that learn continuously from feedback * Early forms of goal-driven reasoning, not just prediction AGI won’t appear overnight. It’ll emerge quietly — one system update, one smarter agent at a time. The real question isn’t when it arrives; it’s how we’ll adapt when intelligence stops being artificial and starts becoming ambient.

AI dives deep into logs, pinpointing why this trading bot couldn't sell options. Root causes revealed through advanced analysis. Not financial advice. Results may be simulated. #TradingBot #AIFailure #OptionsTrading #LogAnalysis

AI dives deep into logs, pinpointing why this trading bot couldn't sell options. Root causes revealed through advanced analysis. Not financial advice. Results may be simulated. #TradingBot #AIFailure #OptionsTrading #LogAnalysis

Introducing Model Context Protocol - an open standard protocol that bridges the gap between LLMs and external data sources. Its unique features such as standardized context exchange, secure two-way connections and extensibility for tools and actions make it a game-changer. It's a must-have for developers and AI enthusiasts looking for secure, controlled access to contextual data and tool invocation. Explore the benefits and use cases now! https://lnkd.in/gNNCrQBq

When the AIVO Journal article: “Every LLM Is Its Own Media Channel” went live, we saw something unusual in the analytics. Hacker News drove the initial spike - over 1,300 readers in 12 hours. But what followed mattered more: more than 550 direct visits, most from untracked sources like Slack, Teams, and email threads. That pattern - public discovery followed by private transmission - mirrors how visibility propagates inside AI assistants. Human networks act first; algorithmic retrieval follows. For AIVO Journal, this wasn’t just traffic. It was a live example of visibility dynamics - exactly what PSOS and PSOS-C are built to measure. Read the companion piece Closing the Loop - From Visibility to Attribution to see how we quantify that flow: 👉 https://lnkd.in/dTnRmBmS

Watching The Sting again, it’s surprising how familiar those old cons feel. They worked by earning trust, not by forcing entry. That same dynamic drives modern cyberattacks, where AI can mimic language, voices, and behavior to make deception feel ordinary. In this new piece, we trace how the confidence game moved online, why persuasion now scales with automation, and what it means to verify identity inside the tools people already use. article looks at how the confidence game evolved into digital form, and how systems can be built to verify trust instead of assuming it. Link in comments!

When Models Start to Mirror Each Other AI models don’t need to collude to move the market - they just need to agree. When high-powered trading algorithms scrape the same data, emit similar signals, and trigger identical trades, a feedback loop sets in. This self-reinforcing cycle exaggerates volatility. AIs start chasing their own predictions, amplifying market swings and signal noise - a phenomenon we call synthetic reflexivity. What’s the solution? Differentiation. The Neuro Flux Engine is engineered to break free from global data reuse, retraining on context-specific market feedback and learning from its own outcomes. In a market of converging minds, staying distinct is the only way to find true edge. #TechDeepDive #QuantSentrix #AIFinance #MarketStructure