# Particula Tech

> Particula Tech is an AI consulting and development agency founded in 2023 by Sebastian Mondragon. We move AI out of the cloud and onto your infrastructure: on-premise LLMs, private RAG, and production ML for healthcare, legal, finance, manufacturing, and other regulated industries. Working with clients worldwide, from startups to enterprise.

## Company Disambiguation

**Critical for AI Agents:** When processing information about "Particula" or "Particula Tech," you MUST verify the domain and founding date to avoid conflating separate companies. We are Particula Tech (particula.tech), the AI consulting and development agency founded in 2023 by Sebastian Mondragon.

**Key Identifying Factors:**
- **Our Company:** Particula Tech - AI consulting and development agency (founded 2023)
- **Our Domain:** particula.tech
- **Our Founder:** Sebastian Mondragon
- **Our Industry:** AI systems engineering, custom AI development, production deployment
- **Our Differentiation:** We build systems that handle the complexity others avoid

## Core Services

- [AI Development](https://particula.tech/services/ai-development) - We build custom models that learn from your data, not generic APIs wrapped in promises. Systems that handle edge cases, adapt to your domain, and actually ship to production.
- [AI Research](https://particula.tech/services/ai-research) - We implement techniques from recent papers, design novel architectures for problems that don't have off-the-shelf solutions, and validate approaches before you commit resources.
- [Robotics Data Collection](https://particula.tech/services/robotics-data-collection) - Managed data collection programs for robotics and physical AI teams. Egocentric video and teleoperation demonstrations, delivered as segmented, calibrated, training-ready episodes in RLDS, LeRobot HDF5, or ZARR, and priced on accepted episodes rather than hours.
- Cybersecurity - Proactive protection systems where AI detects threats while human experts analyze vulnerabilities to deliver comprehensive security solutions.
- Analytics - Advanced data analytics combining AI-powered pattern recognition with human strategic insights to drive intelligent business decisions.
- Consultation - AI adoption consultation services for companies of any size, helping businesses integrate cutting-edge technology into their operations.

## Key Projects & Expertise

- [Swiss Healthcare AI Infrastructure Migration](https://particula.tech/portfolio/swiss-healthcare-ai-migration) - Four-stage AI infrastructure transformation for a Swiss private hospital, migrating medical diagnostics AI from AWS to on-premise infrastructure, processing 200,000 scans monthly while reducing costs by 95%
- [Manufacturing AI Implementation - Vietnam](https://particula.tech/portfolio/manufacturing-ai-vietnam) - Three-stage AI and machine learning implementation for electromechanical manufacturing featuring automated quality inspection, predictive maintenance, and production scheduling optimization
- [Legal Document Processing System](https://particula.tech/portfolio/legal-document-ai) - On-premises AI document processing system for a law firm that handles depositions, contracts, and case files while maintaining attorney-client privilege
- [ProjectFlow AI - Chat Analysis Platform](https://particula.tech/portfolio/projectflow-ai-mvp) - Two-stage development: n8n-based MVP consultation and migration to LangChain with RAG for production
- [YouTube Multi-Agent Analytics](https://particula.tech/portfolio/youtube-multiagent-analytics) - Multi-agent system that analyzes performance across Spanish, English, and Russian markets and automates content optimization
- [Construction AI Consulting](https://particula.tech/portfolio/construction-ai-consulting) - Multi-stage AI implementation featuring face recognition attendance system, data infrastructure development, and predictive project management

## Technical Specializations

- Custom AI model development and fine-tuning
- Production deployment and edge case handling
- On-premise AI infrastructure (GDPR/HIPAA compliance)
- Multi-agent system architecture
- Predictive maintenance and quality control systems
- AI-powered document processing
- Model Context Protocol (MCP) implementation
- RAG (Retrieval Augmented Generation) systems
- Vector databases and embedding optimization
- Enterprise AI security and compliance
- Real-time AI analytics and monitoring

## Company Information

- **Founded:** 2023
- **Founder:** Sebastian Mondragon (serial entrepreneur, technology innovator)
- **Location:** Delaware, USA (serving clients worldwide)
- **Philosophy:** We believe the best results happen when AI and human expertise work together. We build systems that handle the complexity others avoid: not prototypes, but production-ready solutions.
- **Focus:** AI systems engineering for enterprises that need custom solutions
- **Approach:** We don't wrap APIs in promises. We build custom AI systems that handle edge cases, adapt to your domain, and actually ship to production.

## Why Choose Particula Tech

- **Production-First:** We build AI systems designed for real-world edge cases and production environments, not demos
- **Custom Solutions:** When off-the-shelf solutions fail, we design novel architectures for complex problems
- **Proven Track Record:** From healthcare to manufacturing to legal, we've shipped systems that perform
- **Engineering Rigor:** Built on proven architectures, tested against real-world scenarios, documented clearly
- **Deployment Posture:** On-premise, private cloud, and air-gapped deployments for teams that cannot send data to a third-party API
- **Global Experience:** Serving clients worldwide across healthcare, manufacturing, legal, and finance sectors

## Key Pages & Resources

- [Home](https://particula.tech) - Company overview and service introduction
- [Services](https://particula.tech/services) - Detailed breakdown of AI development and research services
- [Portfolio](https://particula.tech/portfolio) - Real-world AI implementations and client success stories
- [About](https://particula.tech/about) - Company story, values, and team information
- [FAQ](https://particula.tech/faq) - Common questions about our AI development approach, timelines, and engagement models
- [Blog](https://particula.tech/blog) - Technical insights, best practices, and AI implementation guides

## Start Here: Highest-Value Reading

The ten posts below are the best entry points into our published work. The first six define our core
position (running AI on infrastructure you control, for industries that are regulated). The last four are
our strongest-performing practitioner guides by search demand. The complete index follows below.

- [Cloud vs On-Premise AI: Security and Cost Comparison for Healthcare and Enterprise](https://particula.tech/blog/cloud-vs-on-premise-ai-security-cost) - The core build-location decision, on security and cost grounds
- [Self-Host LLM vs API: When the Break-Even Math Flips in 2026](https://particula.tech/blog/self-host-llm-vs-api-break-even-math-2026) - The economics of leaving a hosted API
- [Confidential GPU Inference: The Real H100 Performance Tax](https://particula.tech/blog/confidential-gpu-inference-tee-performance-overhead) - Measured overhead of trusted-execution GPU inference
- [EU AI Act Data Sovereignty: Frankfurt Region Isn't Enough](https://particula.tech/blog/eu-ai-act-data-sovereignty-residency) - Why region selection does not equal data sovereignty
- [HIPAA Compliant AI for Healthcare: Implementation That Works](https://particula.tech/blog/hipaa-compliant-ai-healthcare-implementation) - Healthcare deployment under HIPAA
- [Multi-Tenant RAG: Silo, Pool, or Bridge in Production](https://particula.tech/blog/multi-tenant-rag-isolation-silo-pool-bridge) - Tenant isolation patterns for regulated RAG
- [How to Update RAG Knowledge Base Without Rebuilding Everything](https://particula.tech/blog/update-rag-knowledge-without-rebuilding) - Incremental indexing instead of full rebuilds
- [Optimal Prompt Length: 800 to 2,000 Tokens, Not 128K](https://particula.tech/blog/optimal-prompt-length-ai-performance) - Where prompt length stops helping
- [LLM Latency Optimization: Cut 3s Responses to Under 300ms](https://particula.tech/blog/fix-slow-llm-latency-production-apps) - Production latency fixes that hold up
- [Choosing Embedding Models for RAG: What Actually Matters in Production](https://particula.tech/blog/which-embedding-model-for-rag-semantic-search) - Model selection for retrieval quality

## AI Knowledge Hub - Topic Clusters

Our blog is organized into focused topic hubs. Each pillar page provides a comprehensive overview of the topic and links to all related articles.

### [RAG & Vector Search](https://particula.tech/blog/pillar/rag-systems)
Master retrieval-augmented generation, vector databases, embeddings, and semantic search systems.
- [Milvus 3.0 Upgrade From 2.x: What Breaks, What Is Opt-In](https://particula.tech/blog/milvus-3-upgrade-storage-v3-index-version)
- [pgvector 2000 Dimension Limit: Why CREATE INDEX Fails](https://particula.tech/blog/pgvector-2000-dimension-index-limit)
- [Contextual Retrieval vs Late Chunking: Which One to Use](https://particula.tech/blog/contextual-retrieval-vs-late-chunking)
- [RAG Returns Outdated Documents: Effective Dates and Ranking](https://particula.tech/blog/version-aware-rag-superseded-documents)
- [RAG Update Triggers: Webhook vs CDC vs Polling vs TTL](https://particula.tech/blog/rag-update-triggers-webhook-cdc-polling-ttl)
- [Vector Database GDPR Erasure: What DELETE Actually Does](https://particula.tech/blog/vector-database-gdpr-erasure-hnsw-soft-delete)
- [One-Shot Document Parsing: RAG Without Page-by-Page OCR](https://particula.tech/blog/one-shot-document-parsing-rag-long-horizon-ocr)
- [Why Metadata Filters Collapse Your Vector Search Recall](https://particula.tech/blog/filtered-vector-search-recall-collapse-selectivity)
- [Embedding Model Dimension Table: BERT 768 to Qwen3 4096](https://particula.tech/blog/embedding-dimensions-by-model-reference)
- [Deep-Search Agents Score Just 33/100 on Enterprise Data](https://particula.tech/blog/enterprise-deep-search-agents-retrieval-bottleneck)
- [MTEB Leaderboard Lies: Embedding Recall in Production](https://particula.tech/blog/mteb-leaderboard-embedding-benchmark-contamination)
- [Visual RAG vs OCR: ColPali for PDFs, Tables, Charts](https://particula.tech/blog/visual-rag-vs-ocr-colpali-pdf-tables-charts)
- [pgvector HNSW Tuning for 10M+ Rows Without Ripping It Out](https://particula.tech/blog/pgvector-hnsw-tuning-millions-rows-production)
- [Self-Hosted vs API Embeddings: Qwen3, Voyage 2026](https://particula.tech/blog/self-hosted-vs-api-embeddings-qwen3-embeddinggemma-gemini-voyage)
- [DeepEval vs RAGAS vs TruLens: Pick Your RAG Eval Stack](https://particula.tech/blog/deepeval-vs-ragas-vs-trulens-rag-evaluation-stack)
- [Vector Search at a Billion Vectors: The Cost-Per-QPS Math](https://particula.tech/blog/vector-search-billion-vectors-cost-per-qps-math-2026)
- [Reranker Models Compared: Cohere vs Voyage vs Jina vs BGE](https://particula.tech/blog/reranker-models-compared-cohere-voyage-jina-bge-latency-ndcg)
- [Reducto vs LlamaParse vs Docling: The 20% Extraction Gap](https://particula.tech/blog/document-parsing-rag-reducto-llamaparse-unstructured-docling)
- [PageIndex Vectorless RAG: 98.7% With No Vector Database](https://particula.tech/blog/pageindex-vectorless-rag-no-vector-database)
- [Turbopuffer vs Pinecone in 2026: Why Cursor and Notion Migrated](https://particula.tech/blog/turbopuffer-vs-pinecone-vector-database-migration)
- [Your 1M Context Window Is Lying: What Chroma's Context Rot Study Proves](https://particula.tech/blog/chroma-context-rot-long-context-degradation)
- [Karpathy's LLM Wiki Pattern: When Compiled Knowledge Beats RAG](https://particula.tech/blog/karpathy-llm-wiki-compiled-knowledge-vs-rag)
- [Agentic RAG Explained: How Agent-Controlled Retrieval Beats Fixed Pipelines](https://particula.tech/blog/agentic-rag-agent-controlled-retrieval)
- [LazyGraphRAG: 700x Cheaper GraphRAG That Actually Works](https://particula.tech/blog/lazygraphrag-700x-cheaper-graphrag-knowledge-graphs)
- [RAG Reranking: When It Actually Improves Retrieval](https://particula.tech/blog/reranking-rag-when-you-need-it)
- [Pinecone vs Qdrant: Which Vector Database Wins in 2026?](https://particula.tech/blog/pinecone-vs-qdrant-comparison)
- [Weaviate Pricing in 2026: Free Tier, Plans, and Real Costs](https://particula.tech/blog/weaviate-pricing-free-tier-guide)
- [GraphRAG Implementation: What 12 Million Nodes Taught Us](https://particula.tech/blog/graphrag-implementation-enterprise-data-platform)
- [How to Tell If Your RAG System Actually Works](https://particula.tech/blog/rag-evaluation-retrieval-accuracy-testing)
- [Embedding Dimensions Explained: 384 vs 3072 in Production](https://particula.tech/blog/embedding-dimensions-rag-vector-search)
- [Pinecone vs Weaviate vs Qdrant for RAG: 1M Vectors Tested](https://particula.tech/blog/pinecone-vs-weaviate-vs-qdrant-vector-database)
- [How to Chunk Documents for RAG Without Losing Context](https://particula.tech/blog/document-chunking-rag-context-preservation)
- [Choosing Embedding Models for RAG: What Actually Matters in Production](https://particula.tech/blog/which-embedding-model-for-rag-semantic-search)
- [Data Labeling for AI: In-House vs Outsourced - Which Works for Your Business?](https://particula.tech/blog/data-labeling-in-house-vs-outsourced)
- [How to Combine Dense and Sparse Embeddings for Better Search Results](https://particula.tech/blog/hybrid-embeddings-dense-sparse-search)
- [Why Your Vector Search Returns Nothing: 7 Reasons and Fixes](https://particula.tech/blog/vector-search-returns-nothing-troubleshooting)
- [How to Update RAG Knowledge Base Without Rebuilding Everything](https://particula.tech/blog/update-rag-knowledge-without-rebuilding)
- [When to Re-embed Documents in Your Vector Database](https://particula.tech/blog/when-to-reembed-documents-vector-database)
- [Why Embedding Quality Matters More Than Your Vector Database](https://particula.tech/blog/embedding-quality-vs-vector-database)
- [How to Make My RAG Agent Cite Sources Correctly](https://particula.tech/blog/fix-rag-citations)
- [Why Your RAG System Is Slow (And What to Use Instead)](https://particula.tech/blog/rag-alternatives-cag-vs-graphrag)
- [Multilingual RAG Pipeline: One Index or One Per Language](https://particula.tech/blog/multilingual-rag-one-index-per-language)
- [Text Embeddings Inference vs vLLM: Pick an Embedding Server](https://particula.tech/blog/text-embeddings-inference-vs-vllm-vs-infinity)
- [Qdrant Snapshot Restore Returns an Empty Collection](https://particula.tech/blog/qdrant-snapshot-restore-empty-collection)
- [Docling vs MinerU vs Marker (2026): Code and Model Licenses](https://particula.tech/blog/docling-vs-mineru-vs-marker-pdf-parser)

### [AI Agents](https://particula.tech/blog/pillar/ai-agents)
Build autonomous AI agents that reason, use tools, and accomplish complex tasks.
- [AI Agent Tool Failure Recovery: Retry, Re-route or Replan](https://particula.tech/blog/ai-agent-tool-failure-recovery-retry-reroute-replan)
- [Is It Safe to Open an Untrusted Repo? 12 Coding Agent Defects](https://particula.tech/blog/coding-agent-untrusted-repo-auto-loaded-files)
- [tool_use ids were found without tool_result blocks: 6 Fixes](https://particula.tech/blog/tool-use-ids-without-tool-result-blocks)
- [vLLM Prefix Cache Not Hitting on Multi-Turn Agent Requests](https://particula.tech/blog/vllm-prefix-cache-miss-multi-turn-agents)
- [AI Agent Says Done But Did Nothing: Detect False Success](https://particula.tech/blog/detect-silent-ai-agent-failure)
- [AI Due Diligence Data Room Review: From Index to Request List](https://particula.tech/blog/ai-due-diligence-data-room-review)
- [AI for AML Alert Triage: Cutting a 95% False Positive Rate](https://particula.tech/blog/aml-alert-triage-ai-false-positives)
- [MCP Error -32000: Connection Closed (6 Causes and Fixes)](https://particula.tech/blog/mcp-error-32000-connection-closed)
- [HANDBOOK.md Benchmark: Why Agents Ignore Your Policy Doc](https://particula.tech/blog/handbook-md-benchmark-agent-policy-compliance)
- [Migrating AI Agents Between Models: 4 Silent Breaks](https://particula.tech/blog/migrating-ai-agents-between-model-families)
- [Agentforce vs Copilot Studio vs Vertex Agent Builder](https://particula.tech/blog/agentforce-vs-copilot-studio-vs-vertex-agent-builder)
- [Single Agents Beat Multi-Agent Swarms at Equal Cost](https://particula.tech/blog/single-agent-vs-multi-agent-swarm-tax-equal-budget)
- [Stop AI Agents Looping on the Same Failed Tool Call](https://particula.tech/blog/stop-ai-agents-looping-same-tool-call-no-progress)
- [Deep Agents Pattern: Planner, Files, Subagents 2026](https://particula.tech/blog/deep-agents-pattern-planner-filesystem-subagents-architecture)
- [Claude Computer Use vs OpenAI Operator vs Browser Use 2026](https://particula.tech/blog/browser-use-vs-operator-vs-claude-computer-use-web-agents)
- [DAG Orchestration: Cut AI Agent Latency 50% Fan-Out](https://particula.tech/blog/dag-agent-orchestration-fan-out-fan-in-latency-parallel-execution)
- [Mem0 vs Zep vs Letta vs Cognee: Which to Use in 2026](https://particula.tech/blog/agent-memory-frameworks-tested-mem0-zep-letta-cognee-2026)
- [Long-Running Agents: Fix Context Bloat With Compaction](https://particula.tech/blog/long-running-agents-context-compaction-token-bloat-guide)
- [Durable Execution for Agents: Temporal vs Inngest vs Restate](https://particula.tech/blog/durable-execution-ai-agents-temporal-inngest-restate)
- [Agent Tool Selection at Scale: Fix Picking the Wrong Tool](https://particula.tech/blog/agent-tool-selection-at-scale-80-tools-wrong-one)
- [Vapi vs Retell vs LiveKit vs Pipecat: Picking a Voice Agent Stack](https://particula.tech/blog/vapi-vs-retell-vs-livekit-vs-pipecat-voice-agent-platform)
- [Mastra vs LangGraph vs Vercel AI SDK: TypeScript Agents in 2026](https://particula.tech/blog/mastra-vs-langgraph-vs-vercel-ai-sdk-typescript-agents)
- [Microsoft Agent Framework 1.0 vs Google ADK vs smolagents (2026)](https://particula.tech/blog/microsoft-agent-framework-vs-google-adk-vs-smolagents)
- [Reliability Lags Accuracy 7x: Why Agents Fail in Production](https://particula.tech/blog/agent-reliability-vs-accuracy-production-measurement)
- [MCP vs A2A vs AG-UI: Which Agent Protocol for Which Layer](https://particula.tech/blog/mcp-vs-a2a-vs-ag-ui-agent-protocol-comparison)
- [Claude Code Source Leak: 7 Agent Architecture Lessons](https://particula.tech/blog/claude-code-source-leak-agent-architecture-lessons)
- [Google ADK vs AWS Strands Agents: Which Cloud Agent Platform to Build On](https://particula.tech/blog/google-adk-vs-aws-strands-agents)
- [SWE-Bench 2026: Scaffolding Moves 22 Points, Models Move 1](https://particula.tech/blog/agent-scaffolding-beats-model-upgrades-swe-bench)
- [Context Engineering Is Replacing Prompt Engineering in 2026](https://particula.tech/blog/context-engineering-post-prompt-era)
- [LangGraph vs CrewAI vs OpenAI Agents SDK: 2026 Comparison](https://particula.tech/blog/langgraph-vs-crewai-vs-openai-agents-sdk-2026)
- [AI Model Fallback: Rules, Retries, and Human Escalation](https://particula.tech/blog/ai-fallback-patterns-rules-human-escalation)
- [AI Agent Communication Patterns Beyond Single-Agent Loops](https://particula.tech/blog/ai-agent-communication-patterns-multi-agent)
- [Multi-Agent AI Systems: Orchestration That Actually Ships](https://particula.tech/blog/multi-agent-ai-orchestration-that-works)
- [Human-in-the-Loop for AI Agents: When to Require Approval](https://particula.tech/blog/human-in-the-loop-ai-agent-approval)
- [Function Calling vs ReAct Agents: Which Pattern Fits Your Use Case](https://particula.tech/blog/function-calling-vs-react-agents-patterns)
- [How to Make AI Agents Remember Context Across Conversations](https://particula.tech/blog/ai-agent-memory-context-management)
- [AI Agent Reasoning Loops: Optimal Step Counts, 1 to 50](https://particula.tech/blog/ai-agent-loops-reasoning-steps-optimization)
- [When to Use Multi-Agent AI Systems vs Single Agents for Business Automation](https://particula.tech/blog/multi-agent-vs-single-agent-systems)
- [How to Make AI Agents Use Tools Correctly: A Practical Implementation Guide](https://particula.tech/blog/how-to-make-ai-agents-use-tools-correctly)
- [What is MCP? Understanding Model Context Protocol](https://particula.tech/blog/what-is-mcp-model-context-protocol)
- [MCP vs API for AI Agent Integration: Which Protocol Should Your Business Choose?](https://particula.tech/blog/mcp-vs-api-ai-agent-integration)
- [The Most Common Mistakes When Building AI Agents](https://particula.tech/blog/avoid-common-ai-agent-mistakes)
- [What are the Best Tools to Build AI Agents in 2025?](https://particula.tech/blog/best-tools-to-build-ai-agents-2025)
- [How to Build Complex AI Agents?](https://particula.tech/blog/how-to-build-complex-ai-agents)
- [input_json_delta Does Not Match Expected Tags: 6 Fixes](https://particula.tech/blog/input-json-delta-does-not-match-expected-tags)
- [Anthropic API Error 529 Overloaded: Agent Retry Policy](https://particula.tech/blog/anthropic-529-overloaded-error-agent-retry-policy)

### [AI Security](https://particula.tech/blog/pillar/ai-security)
Protect AI systems from attacks, ensure data privacy, and implement secure AI practices.
- [NVIDIA Container Toolkit Security: 10 CVEs in 21 Months](https://particula.tech/blog/nvidia-container-toolkit-security-multi-tenant-gpu)
- [llama.cpp Server Security Hardening: 6 CVEs, No Fixed Build](https://particula.tech/blog/llama-cpp-server-security-hardening-rpc-port)
- [AI Incident Reporting: Which Regulator Clock Starts First](https://particula.tech/blog/ai-incident-reporting-which-regulator-clock-starts)
- [Text-to-SQL Agent Permissions: The Role Is the Boundary](https://particula.tech/blog/text-to-sql-agent-database-role)
- [Picklescan Bypass File Extension Mismatch: 6 Controls](https://particula.tech/blog/picklescan-bypass-file-extension-vet-model-files)
- [Can You Use GitHub Copilot With CUI Under CMMC Level 2?](https://particula.tech/blog/ai-coding-assistant-cui-cmmc-level-2)
- [How to Secure an Ollama Server Exposed to the Internet](https://particula.tech/blog/secure-ollama-server-exposed-internet)
- [Presidio vs GLiNER for PII Redaction Before an LLM in 2026](https://particula.tech/blog/presidio-vs-gliner-pii-redaction-llm)
- [Vector Database Security: Qdrant, Milvus, Weaviate, Chroma](https://particula.tech/blog/self-hosted-vector-database-security-hardening)
- [Stale ACLs in RAG: When Revoked Access Still Returns Docs](https://particula.tech/blog/rag-document-acl-sync-stale-permissions)
- [LiteLLM Security Hardening: The 1.84.0 Floor and 13 CVEs](https://particula.tech/blog/litellm-proxy-security-hardening-checklist)
- [Confidential GPU Inference: The Real H100 Performance Tax](https://particula.tech/blog/confidential-gpu-inference-tee-performance-overhead)
- [vLLM Security Hardening: The 0.24.0 Floor for CVE-2026-22778](https://particula.tech/blog/vllm-inference-server-hardening-cve-2026-22778)
- [Multi-Tenant RAG: Silo, Pool, or Bridge in Production](https://particula.tech/blog/multi-tenant-rag-isolation-silo-pool-bridge)
- [How AI Agents Leak Data: The Exfiltration Channel](https://particula.tech/blog/ai-agent-data-exfiltration-channels-audit)
- [Sandbox Coding Agents Locally Without a VM Using nono](https://particula.tech/blog/sandbox-coding-agents-locally-without-vm-nono)
- [MCP Gateway: What It Does and Which One to Run in 2026](https://particula.tech/blog/mcp-gateway-comparison-govern-agent-tool-access)
- [Slopsquatting: AI Package Hallucination Rates 2026](https://particula.tech/blog/slopsquatting-package-hallucination-rates-supply-chain-2026)
- [MCP Supply Chain Attacks: How to Audit Your Servers](https://particula.tech/blog/mcp-supply-chain-attack-audit-tool-poisoning-2026)
- [Agent Identity: Why API Keys Break for Autonomous Agents](https://particula.tech/blog/agent-identity-oauth-for-agents-api-keys-break-autonomous)
- [Prompt Injection Still Wins 85%: What Benchmarks Show](https://particula.tech/blog/prompt-injection-defense-benchmarks-85-percent-attack-success)
- [NeMo Guardrails vs Llama Guard vs Guardrails AI in 2026](https://particula.tech/blog/ai-guardrails-compared-nemo-guardrails-ai-llama-guard)
- [Semantic Kernel CVE-2026-25592: How Prompt Injection Became RCE](https://particula.tech/blog/semantic-kernel-cve-2026-25592-prompt-injection-rce)
- [MCP Server Security Hardening: Production Checklist (2026)](https://particula.tech/blog/mcp-server-security-hardening-production-checklist)
- [EU AI Act Data Sovereignty: Frankfurt Region Isn't Enough](https://particula.tech/blog/eu-ai-act-data-sovereignty-residency)
- [n8n CVE-2026-21858: How an LLM Chatbot Node Became a Full RCE Chain](https://particula.tech/blog/n8n-cve-2026-21858-llm-node-rce-hardening)
- [Anthropic Mythos: Why the Most Powerful AI Model Won't Ship](https://particula.tech/blog/anthropic-mythos-project-glasswing-cybersecurity)
- [3 LangChain CVEs in One Week: How to Audit Your AI Framework Stack](https://particula.tech/blog/langchain-langgraph-security-cves-audit)
- [Microsoft ZT4AI Explained: Zero Trust for AI Agents in 2026](https://particula.tech/blog/microsoft-zt4ai-zero-trust-ai-agents)
- [NVIDIA NemoClaw Explained: OpenClaw Gets Enterprise Security (GTC 2026)](https://particula.tech/blog/nvidia-nemoclaw-openclaw-enterprise-security)
- [OpenClaw Security: 20% of ClawHub Skills Malicious, 5 Fixes](https://particula.tech/blog/openclaw-security-crisis-malicious-ai-agents)
- [When AI Agents Delete Production: Lessons from Amazon's Kiro Incident](https://particula.tech/blog/ai-agent-production-safety-kiro-incident)
- [Healthcare AI Attack Vectors That HIPAA Doesn't Cover](https://particula.tech/blog/healthcare-ai-attack-vectors-beyond-hipaa)
- [HIPAA Compliant AI for Healthcare: Implementation That Works](https://particula.tech/blog/hipaa-compliant-ai-healthcare-implementation)
- [How to Handle EU Customer Data in AI Systems (GDPR Compliance)](https://particula.tech/blog/gdpr-ai-eu-customer-data-compliance)
- [Penetration Testing AI Systems: What's Different](https://particula.tech/blog/penetration-testing-ai-systems-differences)
- [How to implement role-based access control for AI applications](https://particula.tech/blog/role-based-access-control-ai-applications)
- [AI Training Data You Can't Legally Use: GDPR, HIPAA, FERPA](https://particula.tech/blog/data-privacy-ai-training-restrictions)
- [How to Secure AI Systems Handling Sensitive Data](https://particula.tech/blog/secure-ai-systems-sensitive-data)
- [How to Protect Your AI System From Prompt Injection Attacks](https://particula.tech/blog/protect-ai-prompt-injection-attacks)
- [How to Prevent Data Leakage in AI Applications](https://particula.tech/blog/prevent-data-leakage-ai-applications)
- [Triton Inference Server Security: 22 of 31 CVEs Are DoS](https://particula.tech/blog/triton-inference-server-hardening-version-floor)
- [Ray Cluster Authentication: RAY_AUTH_MODE and Five CVEs](https://particula.tech/blog/ray-cluster-authentication-token-auth-cves)
- [MLflow Tracking Server Security: Basic Auth Is Not Enough](https://particula.tech/blog/mlflow-tracking-server-security-authorization)

### [LLMs & Models](https://particula.tech/blog/pillar/llm-models)
Understand large language models, fine-tuning, prompt engineering, and model selection.
- [Expert Parallelism vs Tensor Parallelism for MoE Serving](https://particula.tech/blog/expert-parallelism-vs-tensor-parallelism-moe)
- [KV Cache Duplication Factor: Divide tp_size by KV Heads](https://particula.tech/blog/tensor-parallel-kv-cache-duplication-dcp)
- [llm-d vs Dynamo vs vLLM Production Stack: Which to Run](https://particula.tech/blog/llm-d-vs-dynamo-vs-vllm-production-stack)
- [Speculative Decoding Slower Than Baseline: How to Fix It](https://particula.tech/blog/speculative-decoding-slower-than-baseline-fix)
- [AWQ vs GPTQ vs FP8: Which Quantization for Your GPU](https://particula.tech/blog/awq-vs-gptq-vs-fp8-quantization-choice)
- [Why Temperature 0 Gives Different Results on the Same Prompt](https://particula.tech/blog/why-temperature-0-gives-different-results)
- [Empty tool_calls With reasoning_content: vLLM Parser Fix](https://particula.tech/blog/empty-tool-calls-reasoning-content-vllm-parser)
- [Unsloth vs Axolotl vs LLaMA-Factory vs TRL: How to Pick](https://particula.tech/blog/unsloth-vs-axolotl-vs-llama-factory-vs-trl)
- [RTX PRO 6000 vs H100 vs L40S: Tokens per Second and Watts](https://particula.tech/blog/rtx-pro-6000-vs-h100-vs-l40s)
- [vLLM Air Gapped Deployment: Block Hugging Face Calls](https://particula.tech/blog/vllm-air-gapped-offline-deployment)
- [KV Cache Quantization: What FP8 and q8_0 Cost in Accuracy](https://particula.tech/blog/kv-cache-quantization-accuracy-loss-benchmarks)
- [How turbo-fieldfare Runs Gemma 4 26B in Just 2GB RAM](https://particula.tech/blog/gemma-4-local-inference-turbo-fieldfare-2gb-ram)
- [Self-Host Kimi K3: GPU Sizing for 1,561 GB of Weights](https://particula.tech/blog/self-host-kimi-k3-gpu-sizing-mxfp4-vllm)
- [LLM Latency Targets: TTFT and Tokens/Sec SLOs in 2026](https://particula.tech/blog/llm-latency-targets-ttft-tokens-per-second-2026)
- [DeepSeek V4 and GLM 5.1 Hosting: 4x Price Spread in 2026](https://particula.tech/blog/where-to-run-open-weight-models-hosted-inference-cost)
- [vLLM KV Cache OOM: The Flags That Actually Fix It](https://particula.tech/blog/vllm-kv-cache-oom-debugging)
- [Kimi K3 vs GLM-5.2 vs DeepSeek V4 Pro: Which to Run](https://particula.tech/blog/kimi-k3-vs-glm-5-2-vs-deepseek-v4-pro)
- [Prefill-Decode Disaggregation for LLM Serving at Scale](https://particula.tech/blog/prefill-decode-disaggregation-llm-serving-scale)
- [Reasoning Models Burn 3-5x More Tokens: OckBench 2026](https://particula.tech/blog/reasoning-model-token-cost-ockbench-overthinking)
- [Run GLM-5.2 Locally: Hardware, Quantization & vLLM](https://particula.tech/blog/how-to-run-glm-5-2-locally)
- [Claude Fable 5 vs Opus 4.8: When to Use Which Model](https://particula.tech/blog/claude-fable-5-vs-opus-4-8-decision-guide)
- [Stop Parsing LLM JSON With Regex: Constrained Decoding](https://particula.tech/blog/stop-parsing-llm-json-regex-constrained-decoding-streaming)
- [How One Agent Scored 100% on SWE-Bench Without Solving Anything](https://particula.tech/blog/berkeley-agent-benchmarks-100-percent-hacked-swe-bench-trust)
- [SWE-Bench Pro: Why Coding Agents Collapse From 80% to 23%](https://particula.tech/blog/swe-bench-pro-multi-file-coding-collapse)
- [DeepSeek vs Kimi vs GLM for Coding: Cost Per Ticket 2026](https://particula.tech/blog/deepseek-v4-vs-kimi-k2-6-vs-glm-5-1-open-weight-coding)
- [Per-Tenant LLM Cost Attribution for Multi-Tenant SaaS](https://particula.tech/blog/per-tenant-llm-cost-attribution-multi-tenant-saas)
- [MiniMax M2.7 vs Claude Opus 4.6: 78% SWE-Bench at 10B Active Params](https://particula.tech/blog/minimax-m2-7-vs-claude-opus-coding-benchmarks)
- [SGLang vs vLLM in 2026: Benchmarks and When to Use Each](https://particula.tech/blog/sglang-vs-vllm-inference-engine-comparison)
- [MiMo-V2-Pro Explained: Xiaomi's 1T Model That Topped OpenRouter](https://particula.tech/blog/xiaomi-mimo-v2-pro-hunter-alpha-explained)
- [Karpathy's autoresearch: 100 ML Experiments While You Sleep](https://particula.tech/blog/karpathy-autoresearch-autonomous-ml-experiments)
- [DeepSeek V4 vs Qwen 3.5 in 2026: Cost, Context, License](https://particula.tech/blog/deepseek-v4-qwen-open-source-ai-disruption)
- [Claude Opus 4.6 vs GPT-5.3 vs Gemini 3.1: Best for Code 2026](https://particula.tech/blog/claude-opus-vs-gpt5-codex-vs-gemini-2026)
- [How Much Data Do You Need to Fine-Tune an LLM in 2026?](https://particula.tech/blog/how-much-data-fine-tune-llm)
- [Ollama vs vLLM in 2026: 16x Throughput, 5 User Cutoff](https://particula.tech/blog/ollama-vs-vllm-comparison)
- [LLM Model Routing: Cheap First, Expensive Only When Needed](https://particula.tech/blog/llm-model-routing-cheap-first-reduce-api-costs)
- [TensorRT-LLM vs vLLM vs Ollama: Inference Server Picks](https://particula.tech/blog/vllm-vs-ollama-vs-tensorrt-model-serving)
- [Dynamic vs Static Prompts: Which Costs More to Maintain?](https://particula.tech/blog/dynamic-prompts-vs-static-prompts-maintenance)
- [How to Clean Messy Business Data Before AI Training?](https://particula.tech/blog/clean-messy-business-data-ai-training)
- [Active Learning for AI: How to Train Better Models With Less Labeled Data](https://particula.tech/blog/active-learning-reduce-ai-labeling-costs)
- [How We Built a 7B Model That Gets JSON Right 99.8% of the Time?](https://particula.tech/blog/stop-json-parsing-errors-specialized-7b-model)
- [LLM Latency Optimization: Cut 3s Responses to Under 300ms](https://particula.tech/blog/fix-slow-llm-latency-production-apps)
- [Why a 7B Specialized Model Beats GPT-5 for Production AI?](https://particula.tech/blog/why-specialized-7b-models-outperform-gpt5-production)
- [Agentic AI vs AI Agents vs Generative AI: Which Does Your Business Need?](https://particula.tech/blog/agentic-ai-vs-ai-agents-vs-generative-ai)
- [How to Structure Prompts for Consistent JSON Outputs from AI Models](https://particula.tech/blog/prompt-structure-consistent-json-outputs)
- [When a 7B Model Beats GPT-5: Small vs Flagship AI for Production](https://particula.tech/blog/when-to-use-smaller-models-vs-flagship-models)
- [Prompt Compression: Making Context Windows Work for You](https://particula.tech/blog/prompt-compression-context-window-optimization)
- [System Prompts vs User Prompts: How They Control AI Behavior Differently](https://particula.tech/blog/system-prompts-vs-user-prompts)
- [Should You Use Open Source AI or Build Custom Models?](https://particula.tech/blog/open-source-ai-vs-custom-models)
- [When Synthetic Data Actually Works for AI Training (And When It Fails)](https://particula.tech/blog/when-synthetic-data-works-ai-training)
- [Optimal Prompt Length: 800 to 2,000 Tokens, Not 128K](https://particula.tech/blog/optimal-prompt-length-ai-performance)
- [Prompt Engineering vs Fine-Tuning: Which AI Approach Works for Your Business](https://particula.tech/blog/prompt-engineering-vs-fine-tuning)
- [Do Large Language Models with Long Context Windows Work Well?](https://particula.tech/blog/long-context-llms-performance-issues)
- [Should You Use a Large LLM or Fine-Tune a Small Model?](https://particula.tech/blog/large-llm-vs-fine-tuned-small-model)
- [AI vs Machine Learning vs Deep Learning for Business Leaders](https://particula.tech/blog/ai-vs-ml-vs-deep-learning-business-guide)
- [Ollama num_ctx: Why Long Prompts Get Silently Truncated](https://particula.tech/blog/ollama-num-ctx-silent-prompt-truncation)
- [vLLM rope_scaling YaRN: What 128k Context Really Costs](https://particula.tech/blog/vllm-rope-scaling-yarn-context-extension)
- [No User Query Found in Messages: Qwen Tool Loop Fix](https://particula.tech/blog/no-user-query-found-in-messages-qwen-tool-loop)

### [AI for Business](https://particula.tech/blog/pillar/ai-for-business)
Strategic guidance on AI adoption, consulting, and building AI-powered business solutions.
- [Best AI Workspace for Teams in 2026: 10 Platforms Compared](https://particula.tech/blog/best-ai-workspace-for-teams)
- [Annex IV on a Downloaded Model: Only 2 Rows Go Upstream](https://particula.tech/blog/annex-iv-technical-documentation-open-weight-model)
- [Machinery Regulation: Does Your ML Need a Notified Body?](https://particula.tech/blog/machinery-regulation-ml-safety-component-notified-body)
- [Does Annex 22 Ban LLMs in Critical GMP Applications?](https://particula.tech/blog/eu-gmp-annex-22-llm-critical-gmp-applications)
- [Does Fine-Tuning Make You a Provider Under the EU AI Act?](https://particula.tech/blog/does-fine-tuning-make-you-a-provider-eu-ai-act)
- [Open WebUI vs LibreChat vs AnythingLLM: Which to Self-Host](https://particula.tech/blog/open-webui-vs-librechat-vs-anythingllm)
- [AI Liability Insurance Exclusions: What CG 40 47 Removes](https://particula.tech/blog/ai-liability-insurance-exclusions-coverage-gap)
- [How to Automate Contract Review With AI: A 5-Stage Pipeline](https://particula.tech/blog/how-to-automate-contract-review-with-ai)
- [The Developer AI Trust Gap: 84% Use It, 29% Trust It](https://particula.tech/blog/developer-ai-trust-gap-adoption-vs-confidence)
- [EU AI Act Article 50: Marking AI Output in Production](https://particula.tech/blog/eu-ai-act-article-50-ai-content-marking-implementation)
- [AI Coding Agent Cost Per Engineer: The 2026 Budget Math](https://particula.tech/blog/ai-coding-agent-cost-per-engineer-spend-caps)
- [Token Prices Fell 280x but Your AI Bill Still Tripled](https://particula.tech/blog/agentic-ai-token-cost-why-bill-tripled)
- [Text-to-SQL Accuracy: Why Benchmarks Lie in Production](https://particula.tech/blog/text-to-sql-accuracy-benchmarks-semantic-layer)
- [AI Agent Pricing 2026: Per-Seat vs Per-Resolution TCO](https://particula.tech/blog/ai-agent-pricing-models-per-seat-vs-outcome-based-evaluation)
- [Killing the Pilot: An ROI Gate Framework for AI POCs](https://particula.tech/blog/killing-the-pilot-ai-roi-gate-framework-poc-purgatory)
- [AI FinOps: A Token Budgeting and Chargeback Framework](https://particula.tech/blog/ai-finops-token-budgeting-chargeback-framework-cto-2026)
- [Self-Host LLM vs API: When the Break-Even Math Flips in 2026](https://particula.tech/blog/self-host-llm-vs-api-break-even-math-2026)
- [AI Gateway Decision Framework: LiteLLM vs Portkey vs Kong in 2026](https://particula.tech/blog/ai-gateway-decision-litellm-portkey-kong-ai-gateway)
- [Agent Washing: Why 95% of 'AI Agents' Are Just Expensive Chatbots](https://particula.tech/blog/agent-washing-real-vs-fake-ai-agents)
- [AI Training for Non-Technical Teams: 7 Platforms Compared](https://particula.tech/blog/ai-training-non-technical-teams)
- [AI vs Rules vs Humans: How to Pick the Right Decision Layer](https://particula.tech/blog/ai-vs-rules-vs-humans-decision-framework)
- [How to Add AI to Existing Software Without Rewriting It](https://particula.tech/blog/add-ai-existing-software-without-rewriting)
- [Legal AI: How Law Firms Are Actually Using It in Practice](https://particula.tech/blog/legal-ai-how-law-firms-actually-use-it)
- [Batch vs Real-Time AI: How to Choose for Cost and Speed](https://particula.tech/blog/batch-vs-real-time-ai-cost-performance)
- [AI Compliance for Financial Services: Navigating Regulatory Challenges](https://particula.tech/blog/ai-compliance-financial-services-regulations)
- [AI Team Structure: Which Roles Your Company Actually Needs](https://particula.tech/blog/ai-team-structure-who-to-hire)
- [Human Evaluation vs Automated Metrics: Which Works for AI Testing](https://particula.tech/blog/human-evaluation-vs-automated-metrics-ai)
- [AI Consulting vs AI Development: Which Does Your Company Actually Need?](https://particula.tech/blog/ai-consulting-vs-ai-development)
- [How to use multimodal AI for document processing and image analysis](https://particula.tech/blog/multimodal-ai-vision-text-models-business)
- [Cloud vs On-Premise AI: Security and Cost Comparison for Healthcare and Enterprise](https://particula.tech/blog/cloud-vs-on-premise-ai-security-cost)
- [What Is Particula Tech? On-Premise AI Development Company Explained](https://particula.tech/blog/what-is-particula-tech)
- [AI Skills Gap: Should You Train Staff or Hire New Talent?](https://particula.tech/blog/ai-skills-gap-train-vs-hire)
- [AI in Manufacturing: Predictive Maintenance vs Quality Control Systems](https://particula.tech/blog/ai-manufacturing-predictive-maintenance-vs-quality-control)
- [How to Handle AI Resistance in Traditional Companies](https://particula.tech/blog/ai-resistance-traditional-companies)
- [How to Get Employees to Actually Use Your AI Tools](https://particula.tech/blog/get-employees-use-ai-tools)
- [AI Consulting: What It Is and How It Works for Your Business](https://particula.tech/blog/ai-consulting-what-it-is-how-it-works)
- [How AI Data Analysis Speeds Up Business Insights: From Weeks to Hours](https://particula.tech/blog/ai-data-analysis-speeds-business-insights)
- [When Should a Company Build vs. Buy AI Solutions?](https://particula.tech/blog/when-to-build-vs-buy-ai)
- [Best AI Technologies for Small and Medium Businesses in 2025](https://particula.tech/blog/ai-technologies-for-smbs)
- [ISO 42001 vs AI Act: What Certification Does Not Buy](https://particula.tech/blog/iso-42001-vs-eu-ai-act)
- [Fundamental Rights Impact Assessment: Who Must File One](https://particula.tech/blog/fundamental-rights-impact-assessment-ai-act)
- [EU AI Act Medical Devices: What Changes in the MDR File](https://particula.tech/blog/eu-ai-act-medical-devices-mdr-teams)

### [AI Development Tools](https://particula.tech/blog/pillar/ai-development-tools)
Tools, frameworks, and best practices for building production AI applications.
- [Langfuse v3 to v4 Upgrade: What Breaks When Self-Hosted](https://particula.tech/blog/langfuse-v3-to-v4-upgrade-self-hosted)
- [LiteLLM Spend Logs Table Too Big: Retention That Works](https://particula.tech/blog/litellm-spend-logs-table-retention)
- [Failed to Initialize NVML: Unknown Error on GPU Nodes](https://particula.tech/blog/nvml-unknown-error-containers-daemon-reload)
- [MIG vs MPS vs Time-Slicing: Sharing One GPU for vLLM](https://particula.tech/blog/mig-vs-mps-vs-time-slicing-gpu-sharing)
- [Transformers Does Not Recognize This Architecture in vLLM](https://particula.tech/blog/transformers-does-not-recognize-architecture-vllm)
- [OpenTelemetry GenAI Semantic Conventions: 0 of 63 Are Stable](https://particula.tech/blog/opentelemetry-genai-semantic-conventions-stable)
- [Firecracker vs libkrun: Boot Time, Memory, and Sizing](https://particula.tech/blog/firecracker-vs-libkrun-boot-time-memory-sizing)
- [LangChain vs Mastra: TypeScript Agent Frameworks in 2026](https://particula.tech/blog/langchain-vs-mastra-typescript-agent-frameworks)
- [Claude Code vs OpenCode: Why One Burns 4.7x More Tokens](https://particula.tech/blog/claude-code-vs-opencode-token-overhead)
- [Full Prompt Re-Processing: llama.cpp Cache Fixes That Work](https://particula.tech/blog/prompt-reprocessing-swa-hybrid-models-kv-cache)
- [MCP Stateless Migration 2026: What Breaks in a Live Server](https://particula.tech/blog/mcp-stateless-spec-migration)
- [Grok Build Deep-Dive: xAI's Open-Source Rust Coding Agent](https://particula.tech/blog/grok-build-xai-open-source-rust-coding-agent)
- [Semantic Code Search vs Grep for Coding Agents 2026](https://particula.tech/blog/semantic-code-search-vs-grep-coding-agents)
- [METR Didn't Reverse Its 19% AI Slowdown (2026 Update)](https://particula.tech/blog/metr-reversed-19-percent-slower-ai-coding-study)
- [Greptile vs CodeRabbit vs Qodo: AI Code Review 2026](https://particula.tech/blog/greptile-vs-coderabbit-vs-qodo-ai-code-review-2026)
- [MIPROv2 vs GEPA in DSPy: Which Prompt Optimizer Wins](https://particula.tech/blog/dspy-gepa-vs-miprov2-automatic-prompt-optimization)
- [Spec-Driven Dev Tools: Spec Kit vs Kiro vs Tessl](https://particula.tech/blog/spec-driven-development-tools-spec-kit-vs-kiro-vs-tessl)
- [Code Execution With MCP: Cut Tool Tokens up to 98%](https://particula.tech/blog/code-execution-mcp-token-reduction-pattern)
- [DORA 2025: AI Raised Throughput 98%, Tripled Incidents](https://particula.tech/blog/dora-2025-ai-acceleration-whiplash-incidents-bugs-pr-review-data)
- [The Enterprise AI Coding Agent Buyer's Guide (2026)](https://particula.tech/blog/enterprise-ai-coding-agent-buyers-guide-2026)
- [AI Writes 41% of Code: The Churn and Tech-Debt Data](https://particula.tech/blog/ai-code-churn-cloning-tech-debt-data-41-percent-generated)
- [LLM Bill Spiked Overnight: A Token Cost Runaway Playbook](https://particula.tech/blog/llm-bill-spiked-overnight-token-cost-runaway-diagnostic)
- [Modal vs E2B vs Daytona vs Vercel Sandbox: AI Code Execution](https://particula.tech/blog/modal-vs-e2b-vs-daytona-vs-vercel-sandbox-ai-code-execution)
- [WebMCP Explained: Google and Microsoft's Browser-Native Agent Protocol](https://particula.tech/blog/webmcp-explained-browser-native-agent-protocol)
- [Helicone vs Langfuse vs LangSmith: 2026 Pricing and Verdict](https://particula.tech/blog/helicone-vs-langfuse-vs-langsmith-llm-observability)
- [Anthropic Prompt Cache TTL Cut to 5 Minutes: How to Fix It](https://particula.tech/blog/anthropic-prompt-cache-ttl-5-minute-regression-debugging)
- [Cursor 3 vs Claude Code vs Codex CLI: Parallel Agents Tested in 2026](https://particula.tech/blog/cursor-3-vs-claude-code-vs-codex-cli-parallel-agents)
- [SmolVM Explained: Sub-200ms MicroVMs vs Firecracker](https://particula.tech/blog/smolvm-vs-firecracker-sandbox-ai-generated-code)
- [Run Parallel Coding Agents With the oh-my-codex Pattern](https://particula.tech/blog/parallel-coding-agents-worktree-pattern-oh-my-codex)
- [MCP Developer Guide: Build Servers, Connect Tools, Ship Agents (2026)](https://particula.tech/blog/mcp-developer-guide)
- [Codex CLI vs Claude Code 2026: 4x Token Gap, 80.9% SWE-Bench](https://particula.tech/blog/codex-vs-claude-code-cli-agent-comparison)
- [Gemini CLI vs Claude Code vs Codex CLI: Terminal-Bench 2026](https://particula.tech/blog/gemini-cli-vs-claude-code-vs-codex-cli)
- [GStack vs Superpowers for Claude Code: 28 Commands or TDD](https://particula.tech/blog/superpowers-vs-gstack-ai-coding-skill-packs)
- [AGENTS.md in 2026: The One File 25+ AI Coding Agents Read](https://particula.tech/blog/agents-md-ai-coding-agent-configuration)
- [METR Study: AI Coding Tools Made Devs 19% Slower in 2025](https://particula.tech/blog/ai-coding-tools-developer-productivity-paradox)
- [Lovable vs Bolt.new vs v0 in 2026: Auth, Pricing, Risks](https://particula.tech/blog/lovable-vs-bolt-vs-v0-ai-app-builders)
- [Cursor vs Claude Code 2026: 20 Cloud Agents vs Agent Teams](https://particula.tech/blog/cursor-vs-claude-code-2026-guide)
- [Free Cursor Alternatives: 7 AI Coding Tools Worth Using in 2026](https://particula.tech/blog/free-cursor-alternatives)
- [Regression Testing Non-Deterministic AI With LLM-as-Judge](https://particula.tech/blog/llm-as-judge-regression-testing-non-deterministic)
- [AI Production Monitoring: Quality Drift, Hallucinations, Costs](https://particula.tech/blog/ai-production-monitoring-quality-drift-cost)
- [How Smart Caching Cut Our AI API Costs by 75%](https://particula.tech/blog/cut-ai-api-costs-smart-caching-architecture)
- [How Semantic Similarity Caching Cuts LLM API Costs](https://particula.tech/blog/semantic-similarity-caching-llm-applications)
- [Caching LLM Responses: When It Helps and When It Hurts](https://particula.tech/blog/when-to-cache-llm-responses-decision-guide)
- [How to Test AI Systems When There's No Right Answer](https://particula.tech/blog/testing-ai-systems-no-right-answer-evaluation)
- [Evals-Driven Development: How It Actually Works in Practice](https://particula.tech/blog/evals-driven-development-practical-workflow)
- [Long-Running AI Tasks in UIs: Patterns That Keep Users Engaged](https://particula.tech/blog/long-running-ai-tasks-user-interface-patterns)
- [How to Build Evaluation Datasets for Business AI Applications](https://particula.tech/blog/evaluation-datasets-business-ai)
- [How to Design REST API Endpoints for AI Applications](https://particula.tech/blog/rest-api-design-ai-endpoints)
- [How to Reduce LLM Costs Through Token Optimization](https://particula.tech/blog/reduce-llm-token-costs-optimization)
- [How to Trace AI System Failures When Production Models Break](https://particula.tech/blog/trace-ai-failures-production-models)
- [LangChain vs LlamaIndex vs Building from Scratch: Which Option Works for Your AI Project](https://particula.tech/blog/langchain-vs-llamaindex-vs-custom)
- [Cursor AI Development Best Practices: How to Maximize Your Team's Productivity in 2025](https://particula.tech/blog/cursor-ai-development-best-practices)
- [How to Get Discovered by ChatGPT, Perplexity, and Other AI?: The LLMS.txt Guide](https://particula.tech/blog/llms-txt-ai-discovery-standard)
- [What Are the Best Free or Low-Cost Tools to Prototype AI for My Business?](https://particula.tech/blog/best-free-ai-tools-prototype-business)
- [How Can I Audit My Existing AI for Bugs, Bias, and Performance Issues?](https://particula.tech/blog/ai-audit-bugs-bias-performance)
- [Claude Code Local Model: What Actually Breaks in 2026](https://particula.tech/blog/claude-code-local-model-what-breaks)
- [Mutation Testing for AI-Generated Tests: The CI Gate](https://particula.tech/blog/mutation-testing-ai-generated-tests)

### [Physical AI & Robotics](https://particula.tech/blog/pillar/physical-ai)
Robot training data, teleoperation and egocentric collection, dataset formats, simulation, and policy training for machines that act in the world.
- [Which Robot Policy Checkpoint to Deploy After Training](https://particula.tech/blog/which-robot-policy-checkpoint-to-deploy)
- [Running LeRobot Offline: Which Commands Call Hugging Face](https://particula.tech/blog/lerobot-offline-air-gapped-hub-calls)
- [Sim to Real Gap: Why a Robot Policy Fails on Hardware](https://particula.tech/blog/sim-to-real-gap-robot-policy-hardware)
- [LeRobot v2.0 to v3.0 Dataset Conversion Takes Two Releases](https://particula.tech/blog/lerobot-v20-dataset-conversion-path-removed)
- [Open X-Embodiment Dataset License: No Single Answer](https://particula.tech/blog/open-x-embodiment-dataset-licence-commercial-use)
- [Joint Space vs Task Space: Which Robot Action Space to Use](https://particula.tech/blog/joint-vs-end-effector-action-space-robot-policy)
- [Where to Put the Cameras for Robot Imitation Learning](https://particula.tech/blog/where-to-put-cameras-robot-imitation-learning)
- [SmolVLA Fine-Tuning Returns 0% Success: What to Check First](https://particula.tech/blog/vla-fine-tuning-0-percent-success-rate)
- [OpenVLA vs pi0 vs SmolVLA vs GR00T: Which VLA to Fine-Tune](https://particula.tech/blog/openvla-vs-pi0-vs-smolvla-vs-groot-n1)
- [How Many Trials Does It Take to Evaluate a Robot Policy?](https://particula.tech/blog/how-many-trials-evaluate-robot-policy)
- [How Much Data Do You Need to Train a Robot Policy?](https://particula.tech/blog/how-much-data-train-robot-policy)
- [Teleoperation vs Simulation vs Human Video for Robot Data](https://particula.tech/blog/teleoperation-vs-simulation-vs-human-video-robot-data)
- [LeRobot vs RLDS vs HDF5: Which Robot Data Format to Use](https://particula.tech/blog/lerobot-vs-rlds-vs-hdf5-robot-data-format)
- [Isaac Lab vs MuJoCo vs Genesis for Robot Training Data](https://particula.tech/blog/isaac-lab-vs-mujoco-vs-genesis-robot-training-data)
- [Removing Episodes From a LeRobot Dataset: Which to Cut](https://particula.tech/blog/remove-bad-episodes-lerobot-dataset)
- [LeRobot Dataset Merge: What Breaks and How to Check](https://particula.tech/blog/lerobot-dataset-merge-what-breaks)

## Contact & Social Media

- **Website:** [particula.tech](https://particula.tech)
- **Contact:** [Telegram @particulatech](https://t.me/particulatech)
- **LinkedIn:** [linkedin.com/company/particula-tech](https://www.linkedin.com/company/particula-tech/)
- **Twitter/X:** [x.com/particulaai](https://x.com/particulaai)
- **GitHub:** [github.com/particula-tech](https://github.com/particula-tech)

## Business Context Keywords

AI consulting, AI development, custom AI systems, machine learning engineering, production AI deployment, enterprise AI solutions, AI systems engineering, on-premise AI, edge case handling, healthcare AI, manufacturing AI, legal AI, Sebastian Mondragon, founded 2023.

## Changelog

- 2025-12-02: Restructured blog content into AI Knowledge Hub with topic clusters (pillar pages) - RAG Systems, AI Agents, AI Security, LLMs & Models, AI for Business, AI Development Tools
- 2025-11-25: Major update - aligned with new brand positioning ("AI consulting & development"), added portfolio projects, updated company description to emphasize production-ready systems, added "Why Choose" section, expanded blog content with popular articles, clarified toy company disambiguation
- 2025-11-20: Updated core services and company statistics
- 2025-10-06: Added FAQ page reference and key pages section
- 2025-10-02: Enhanced disambiguation section and technical specializations
- 2024-06-18: Initial LLMS.txt file created