Building scalable AI systems at the intersection of research and production. Technical articles, project reviews, and perspectives on AI/ML, distributed systems, and cloud architecture.https://omnitechnicus.aienhttps://omnitechnicus.ai/articles/ai-oversight-us-correction.htmlhttps://omnitechnicus.ai/articles/ai-oversight-us-correction.htmlA follow-up to "The Global AI Risk Assessment Convergence." The original argued that three jurisdictions—the EU, South Korea, and the United States—were converging on human oversight for high-risk AI. Two of those pillars hold. The third does not: the US section rested on Executive Order 14110, which had already been revoked at the time of publication. This piece corrects the record in the open, reframes the US not as converging but as contested, and draws the durable engineering lesson underneath a changing regulatory landscape.Mon, 15 Jun 2026 00:00:00 GMTAI risk & governanceAI regulationAI oversightEU AI ActKorean AI FrameworkUS AI policyExecutive Order 14110federal preemptionstate AI lawshigh-risk AIautomated decision-makingAI governancecorrectionsengineering ethicsomnitechnicus@gmail.com (Bogdan Mihai Manolache)https://omnitechnicus.ai/articles/the-grade-that-changed-everything.htmlhttps://omnitechnicus.ai/articles/the-grade-that-changed-everything.htmlIn 1992 Bucharest, a student bluffed his way through a math exam with the only theorem he knew. Thirty years later, that theorem turned out to be a structural pillar of modern AI. A personal account of the Cauchy-Bunyakovsky-Schwarz inequality — why it governs how AI understands similarity, attention, and language — and what a wise professor's five out of ten can mean across a lifetime.Mon, 08 Jun 2026 00:00:00 GMTFoundationsmathematicscauchy-schwarz-inequalityneural-networksvectorsembeddingsattention-mechanismsimilarityai-foundationspersonaleducationhistoryomnitechnicus@gmail.com (Bogdan Mihai Manolache)https://omnitechnicus.ai/articles/agentic-ai-distributed-systems-part2.htmlhttps://omnitechnicus.ai/articles/agentic-ai-distributed-systems-part2.htmlYou cannot mathematically prove an LLM correct. But you can architect around it. Part 2 surveys the three research communities approaching semantic correctness for LLM outputs — formal verification, runtime guardrails, and post-condition verification — maps their results honestly, and delivers a concrete engineering playbook: four patterns engineers can implement today to build agentic systems that fail visibly, recover gracefully, and escalate correctly.Sun, 03 May 2026 00:00:00 GMTAgentssemantic correctnesssemantic observabilityLLM verificationagentic AIguardrailsNeMo Guardrailsformal verificationruntime verificationDSAPpost-condition verificationAI productiondistributed systemsengineering patternsAI reliabilitycircuit breakerdead letter queuesemantic retry amplificationomnitechnicus@gmail.com (Bogdan Mihai Manolache)https://omnitechnicus.ai/articles/agentic-ai-distributed-systems-part1.htmlhttps://omnitechnicus.ai/articles/agentic-ai-distributed-systems-part1.htmlEvery major agentic AI pattern has a named distributed systems ancestor going back decades. MoA is ensemble computing. ReAct is a control loop. AutoGen is the actor model. LangGraph is a workflow engine. Multi-agent coordination is a blackboard system. This article maps each AI concept to its CS lineage, explains what that means for engineers building today, and names the three problems that genuinely have no distributed systems precedent — the real frontier where new thinking is required.Sun, 26 Apr 2026 00:00:00 GMTAgentsdistributed systemsagentic AIAI orchestrationactor modelMoAReActReflexionLangGraphAutoGenOpenAI Swarmblackboard systemssoftware architectureAI infrastructureengineering patternsMCPomnitechnicus@gmail.com (Bogdan Mihai Manolache)https://omnitechnicus.ai/articles/the-race-to-compress-intelligence.htmlhttps://omnitechnicus.ai/articles/the-race-to-compress-intelligence.htmlState-of-the-art LLMs require hundreds of gigabytes just to load — locking capable AI behind data-center walls. This article breaks down the three techniques dismantling that barrier: quantization (compressing weights from 32-bit to 4-bit and below), distillation (training smaller models to mimic larger ones), and 1-bit training (BitNet, Bonsai). Covers the math behind quantization error, the outlier and sign-loss problems, block and K-quant fixes, KV cache compression (TurboQuant, RotorQuant, IsoQuant), GGUF naming conventions, and why these complementary approaches are converging to bring frontier-class models to consumer hardware.Sun, 19 Apr 2026 00:00:00 GMTInference & compressionquantizationdistillationbitnetlocal-aimodel-compressionggufkv-cacheturboquantisoquantllm-optimization1-bit-modelsinferenceomnitechnicus@gmail.com (Bogdan Mihai Manolache)https://omnitechnicus.ai/articles/ai-agents-orchestration.htmlhttps://omnitechnicus.ai/articles/ai-agents-orchestration.htmlThe AI industry uses "model," "agent," and "orchestrator" interchangeably — but the distinctions matter. This article walks through the hierarchy from language models to full orchestration layers, explores why multi-model architectures are gaining traction, and looks at the "write skills, not agents" pattern emerging from Claude Code's Boris Cherny. Covers three practical principles of context engineering — reference-based skills, right-sized agents, and conditional context loading — and why giving agents a way to verify their own work may be the single highest-impact improvement.Thu, 12 Mar 2026 00:00:00 GMTAgentsai-agentsorchestrationcontext-engineeringmulti-model-architectureskillsagentic-aiclaude-codecontext-windowverificationllm-applicationsomnitechnicus@gmail.com (Bogdan Mihai Manolache)https://omnitechnicus.ai/articles/ai-periodic-table-part2.htmlhttps://omnitechnicus.ai/articles/ai-periodic-table-part2.htmlPart 2 completes the AI Periodic Table framework covering production Systems (agents, knowledge bases, thinking models, frameworks, observability, red teaming, RLHF) and Emerging elements (multi-agent, synthetic data, diffusion LLMs, interpretability, alignment, A2A). Shows how elements combine into chemical reactions — from RAG chatbots to full multi-agent pipelines — and explains the deliberate gaps that predict what AI still needs to solve.Sun, 22 Feb 2026 00:00:00 GMTFoundationsai-architectureai-agentsmulti-agent-systemsragobservabilityalignmentinterpretabilityperiodic-table-aiai-literacyrlhfsynthetic-dataomnitechnicus@gmail.com (Bogdan Mihai Manolache)https://omnitechnicus.ai/articles/ai-periodic-table-part1.htmlhttps://omnitechnicus.ai/articles/ai-periodic-table-part1.htmlAI has a vocabulary problem — everyone says "agent," nobody means the same thing. Part 1 proposes a solution borrowed from chemistry: a periodic table of AI elements organized by abstraction level and functional concern. Covers the Architecture of the Table, why Context is not an element, and all elements from Infrastructure through Compositions — tokenization, attention, embeddings, prompts, RAG, MCP, LoRA, guardrails, and more.Sat, 21 Feb 2026 00:00:00 GMTFoundationsai-architecturetokenizationattentionembeddingsragmcpperiodic-table-aiai-literacyai-primitivestransformersomnitechnicus@gmail.com (Bogdan Mihai Manolache)https://omnitechnicus.ai/articles/global-ai-risk-assessment-convergence.htmlhttps://omnitechnicus.ai/articles/global-ai-risk-assessment-convergence.htmlThree major jurisdictions—the EU, South Korea, and the US—are converging on similar AI risk frameworks. This article explores why regulations require human oversight for high-stakes AI decisions, examines real enforcement cases including a €492,000 GDPR fine for automated credit decisions, and explains the technical realities engineers must understand: AI systems are probabilistic, training data verification is often impractical, and context humans naturally consider may be unavailable to models. Through concrete stories and regulatory analysis, it demonstrates why matching autonomy to risk isn't bureaucracy—it's engineering necessity.Tue, 17 Feb 2026 00:00:00 GMTAI risk & governanceai-regulationeu-ai-actkorean-ai-frameworkgdpr-enforcementhigh-risk-aihuman-oversightai-safetyprobabilistic-systemsautomated-decision-makingai-governanceengineering-ethicsomnitechnicus@gmail.com (Bogdan Mihai Manolache)https://omnitechnicus.ai/articles/api-mcp-agent-part3.htmlhttps://omnitechnicus.ai/articles/api-mcp-agent-part3.htmlAI Agents autonomously orchestrate tools and make decisions. Part 3 completes the picture with agent code examples, full comparison tables, common misconceptions, and real-world architectures showing how REST APIs, MCP servers, and AI agents work together in production systems.Tue, 27 Jan 2026 00:00:00 GMTProtocols & MCPai-agentsautonomous-systemsagentic-aimcp-clientstool-orchestrationai-architecturellm-applicationsagent-reasoningdistributed-aiproduction-aiomnitechnicus@gmail.com (Bogdan Mihai Manolache)https://omnitechnicus.ai/articles/api-mcp-agent-part2.htmlhttps://omnitechnicus.ai/articles/api-mcp-agent-part2.htmlMCP Servers bridge traditional APIs and AI agents. Part 2 covers the Model Context Protocol—resources, tools, prompts—with full code showing how to convert a REST API into an MCP server. Explains capability negotiation, stateful connections, and when to use MCP.Sun, 25 Jan 2026 00:00:00 GMTProtocols & MCPmcp-servermodel-context-protocolai-toolsjson-rpcapi-designai-integrationanthropic-mcpagent-architectureai-infrastructurestructured-toolsomnitechnicus@gmail.com (Bogdan Mihai Manolache)https://omnitechnicus.ai/articles/api-mcp-agent-part1.htmlhttps://omnitechnicus.ai/articles/api-mcp-agent-part1.htmlDevelopers confuse REST APIs, MCP Servers, and AI Agents. Part 1 explores REST APIs—what they are, how they work, and why they're suboptimal for AI systems. Includes practical code examples and explains the foundation for modern AI architectures.Sat, 24 Jan 2026 00:00:00 GMTProtocols & MCPapi-architecturemcp-protocolai-agentsrest-apisoftware-architectureai-integrationweb-servicesdistributed-systemsmodel-context-protocolai-developmentomnitechnicus@gmail.com (Bogdan Mihai Manolache)https://omnitechnicus.ai/articles/the-AI-blackmail-story.htmlhttps://omnitechnicus.ai/articles/the-AI-blackmail-story.htmlA rumor claims AI models blackmailed researchers to avoid shutdown. The reality: Anthropic's 2025 safety research tested frontier models in controlled simulations with fictional scenarios. This article separates fact from fiction, explaining what the experiments actually showed and why precision matters in AI risk assessment.Mon, 19 Jan 2026 00:00:00 GMTAI risk & governanceai-safetyanthropic-researchai-blackmail-rumoragentic-misalignmentlarge-language-modelsai-risk-assessmentmachine-learning-securityresponsible-aiai-alignment-researchomnitechnicus@gmail.com (Bogdan Mihai Manolache)https://omnitechnicus.ai/articles/when-ai-knows-you-better.htmlhttps://omnitechnicus.ai/articles/when-ai-knows-you-better.htmlA wake-up call about AI-powered manipulation techniques targeting your perception, emotions, and decisions. Romania's National Cyber Security Directorate published a comprehensive guide revealing how AI weaponizes your digital footprint against you—and how to fight back.Tue, 06 Jan 2026 00:00:00 GMTAI risk & governanceartificial-intelligencecybersecuritydisinformationdigital-literacyai-manipulationdeepfakessocial-engineeringdnscomnitechnicus@gmail.com (Bogdan Mihai Manolache)https://omnitechnicus.ai/articles/blockchain-and-AI-Agents.htmlhttps://omnitechnicus.ai/articles/blockchain-and-AI-Agents.htmlThe intersection of AI agents and blockchain is one of the most misunderstood areas in modern software architecture. This article examines when blockchain genuinely solves problems in multi-agent systems—identity, trust across boundaries, and Byzantine fault tolerance—and when it's expensive theater. Includes practical analysis of MCP, transport security, and the real decision framework for choosing between traditional infrastructure and distributed ledgers.Fri, 02 Jan 2026 00:00:00 GMTAgentsai-agentsblockchainmulti-agent-systemsdistributed-ledgerbyzantine-fault-tolerancemcpsmart-contractstrust-boundariesomnitechnicus@gmail.com (Bogdan Mihai Manolache)https://omnitechnicus.ai/articles/pipeline-orchestrator-edu.htmlhttps://omnitechnicus.ai/articles/pipeline-orchestrator-edu.htmlA technical review of an educational multi-stage pipeline built with .NET 9, Blazor Server, and SignalR. Demonstrates backpressure mechanics, circuit breakers, and real-time queue visualization—letting developers watch queue depths fluctuate, backpressure activate, and workers fail and recover, all on their local machine.Tue, 30 Dec 2025 00:00:00 GMTDistributed systemsdistributed-systemsbackpressurecircuit-breakerqueue-managementblazorsignalrdotneteducationalomnitechnicus@gmail.com (Bogdan Mihai Manolache)https://omnitechnicus.ai/articles/the-precision-paradox.htmlhttps://omnitechnicus.ai/articles/the-precision-paradox.htmlWhy choosing the right LLM is like choosing between a ruler, caliper, and micrometer—smaller models hallucinate more not because they're "bad," but because they're compressing knowledge at higher ratios. A practical guide to matching model size to task complexity, with real-world examples from home automation to EU compliance work.Fri, 28 Nov 2025 00:00:00 GMTInference & compressionllmmodel-selectionhome-automationcompression-theoryai-architectureomnitechnicus@gmail.com (Bogdan Mihai Manolache)https://omnitechnicus.ai/articles/retrieval-augmented-generation-edu.htmlhttps://omnitechnicus.ai/articles/retrieval-augmented-generation-edu.htmlA technical review of an educational RAG system built with .NET 9 and Semantic Kernel. Demonstrates Clean Architecture applied to AI/ML, multi-model language support (Phi-4, Llama, Mistral, GPT), 5 embedding strategies, and production-grade resilience patterns including circuit breakers, retry with backoff, and timeout protection—all processing real PDF manuals locally.Tue, 25 Nov 2025 00:00:00 GMTDistributed systemsragretrieval-augmented-generationclean-architectureonnxsemantic-kernelresilience-patternschromadbdotneteducationalomnitechnicus@gmail.com (Bogdan Mihai Manolache)https://omnitechnicus.ai/articles/llm-comparison.htmlhttps://omnitechnicus.ai/articles/llm-comparison.htmlWhether you're a developer seeking the best coding assistant, a researcher requiring advanced reasoning capabilities, or a budget-conscious user looking for maximum value, this guide provides the detailed analysis you need to choose your ideal AI companion for 2026.Sat, 15 Nov 2025 00:00:00 GMTFoundationslarge-language-modelsllm-comparisongenerative-aichatgptai-toolsomnitechnicus@gmail.com (Bogdan Mihai Manolache)https://omnitechnicus.ai/articles/service-registration-edu.htmlhttps://omnitechnicus.ai/articles/service-registration-edu.htmlA technical review of a distributed service registry built with .NET 9, Blazor Server, and SignalR. Features intelligent health tracking with state machine transitions, pluggable storage backends (PostgreSQL, Redis, In-Memory), administrator approval workflows, and real-time operational dashboards for monitoring service health across distributed systems.Fri, 03 Jan 2025 00:00:00 GMTDistributed systemsservice-discoveryhealth-monitoringmicroservicesblazorsignalrpostgresqlredisdotneteducationalomnitechnicus@gmail.com (Bogdan Mihai Manolache)