Don't Build Slop: The 4 Levels of AI Agent Maturity

AI_Trust_Design_Patterns

AI_Trust_Design_Patterns

Why_Better_NLP_Won_t_Fix_Your_Compliance_False_Positives

AI-Driven Multi-Document Correlation for Financial Compliance Transition from reactive validation to proactive, cross-document intelligence. Entity Correlation Engine built on graph database to reveal hidden relationships. Adaptive Probabilistic Risk Model combining multiple signals to compute confidence-based risk scores. Cross-Jurisdictional Normalization Layer to standardize data across countries. Tested against 3 million records, achieving 91% precision, 87% recall, and 76% reduction in false positives. All my links: https://linktr.ee/learnbydoingwithsteven [https://linktr.ee/learnbydoingwithsteven] #learnbydoingwithsteven #AI #LLM #TechTrends #FinancialCompliance #GraphDatabase #EntityCorrelation #ProbabilisticRisk #ComplianceEngineering #FinTech

AI-Driven Multi-Document Correlation for Financial Compliance

✅ Transition from reactive validation to proactive, cross-document intelligence. ✅ Entity Correlation Engine built on graph database to reveal hidden relationships. ✅ Adaptive Probabilistic Risk Model combining multiple signals to compute confidence-based risk scores. ✅ Cross-Jurisdictional Normalization Layer to standardize data across countries. All my links: https://linktr.ee/learnbydoingwithsteven [https://linktr.ee/learnbydoingwithsteven] #learnbydoingwithsteven #AI #LLM #TechTrends #FinancialCompliance #GraphDatabase #EntityCorrelation #ProbabilisticRisk #ComplianceEngineering #FinTech

**AI-Driven Multi-Document Correlation for Financial Compliance**

Transition from reactive validation to proactive, cross-document intelligence. Entity Correlation Engine built on graph database to reveal hidden relationships. Adaptive Probabilistic Risk Model combining multiple signals to compute confidence-based risk scores. Cross-Jurisdictional Normalization Layer to standardize data across countries. Tested against 3 million records, achieving 91% precision, 87% recall, and 76% reduction in false positives. All my links: https://linktr.ee/learnbydoingwithsteven [https://linktr.ee/learnbydoingwithsteven] #learnbydoingwithsteven #AI #LLM #TechTrends #FinancialCompliance #GraphDatabase #EntityCorrelation #ProbabilisticRisk #ComplianceEngineering #FinTech

From Model-Centric to System-Centric AI Engineering: Keynotes from AI Engineer Miami Day 2

The AI engineering landscape is transitioning from model-centric prompting to system-centric execution. Day 2 of the AI Engineer Miami conference detailed critical advancements across fast inference hardware, structured context databases, agent-to-agent architectures, and behavior runtimes. Key architectural paradigms analyzed include: 1. The Stagnation Breakout (1,200 TPS): By using specialized on-chip SRAM architectures (such as Cerebras' wafer-scale engine) and disaggregated prefill/decode mechanisms, developers are bypassing the "memory wall" to achieve inference speeds of 1,200 tokens per second. This 20x speedup transitions agent interaction from asynchronous tasking to real-time steering. 2. Context Graphs vs. Naive RAG: To solve structural relationship blind spots in text-vector searches, systems are integrating Knowledge Graphs and Context Graphs. This combination captures decision traces and increases domain-specific agent accuracy from 54% to 91%. 3. Software 3.5 & Sub-Agent Orchestration: Modern systems are moving toward specialized sub-agents with dedicated, restricted context windows. High-overhead planning is reserved for frontier models (e.g., Claude 3.5), while menial tasks (search, context compression, diff generation) are routed to lightweight specialized models. 4. Designing for Non-Human Users: As autonomous agents become the primary operators of software, platforms must adapt by offering full API/CLI dashboard parity, transitioning from per-seat to usage-based pricing models, and publishing machine-readable metadata. By moving beyond simple prompts to focus on persistent agent primitive execution environments, developers are successfully navigating the "Rain" stage of AI integration where model choice, token cost, and structural control matter. Key Takeaways: * Behavior Runtime: For physical AI (like the Reachi Mini robot), the product is the safety-enforcing behavior runtime, not the raw LLM. * Latency is Design: In physical interfaces, a 2-second delay is perceived as cognitive hesitation; active idleness must be designed. * Ambient Local Inference: Running latent diffusion models locally on mobile NPUs achieves a ~600ms latency without cloud routing. All my links: https://linktr.ee/learnbydoingwithsteven [https://linktr.ee/learnbydoingwithsteven] #learnbydoingwithsteven #AIEngineer #AIHardware #SoftwareArchitecture #FastInference #Cerebras #KnowledgeGraph #ContextEngineering #SubAgents #LLMOps #PhysicalAI