Posts

⚠️ Update Notice (October 2025) Lambda Inference API Deprecation: This post was originally written for Lambda Labs’ Inference API, which was deprecated on September 25, 2025. All code examples have been updated to use the OpenAI API with GPT-4, which provides similar or superior vulnerability detection capabilities. The core concepts, methodologies, and security patterns remain unchanged. Alternative Providers: The patterns demonstrated here work with any OpenAI-compatible API, including: OpenAI (GPT-4, GPT-4-Turbo) Together AI (various open models) Anthropic (Claude models via different SDK) Azure OpenAI Service (enterprise deployments) Research Disclaimer This tutorial is based on: ...

Introduction “AI models are only as powerful as the data they access.” Anthropic’s Model Context Protocol (MCP) bridges this gap by standardizing how AI systems connect to structured and unstructured data sources—from cloud storage to enterprise databases. Yet, deploying MCP in production requires careful attention to architecture, security, and performance trade-offs. This guide walks through: MCP’s client-server architecture and how it differs from traditional API-based integrations. Step-by-step implementation with Azure Blob Storage (adaptable to PostgreSQL, GitHub, etc.). Security hardening for enterprise deployments (RBAC, encryption, auditing). Performance tuning for large-scale datasets (caching, batching, monitoring). Scope: This is a technical deep dive—assumes familiarity with REST/GraphQL and Python. ...

Optimizing AI Chatbot Energy Costs: Practical Metrics and Workflow Strategies Hook: “Training GPT-3 consumes ~1,300 MWh—but inference at scale can be worse. Here’s how to measure and slash your LLM’s energy footprint.” AI chatbots are electricity hogs. While training large language models (LLMs) like GPT-4 dominates sustainability discussions, inference—the process of generating responses to user queries—can cumulatively surpass training energy costs when deployed at scale. For DevOps teams and developers, unchecked energy use translates to: ...

Introduction AI-generated code from tools like GitHub Copilot and Cursor accelerates development but introduces hidden risks: 62% of AI-generated solutions contain security flaws, including hardcoded secrets, SQLi, and insecure dependencies. Traditional SAST tools struggle with probabilistic code patterns, creating a critical gap in modern DevSecOps pipelines. Endor Labs’ $93M-funded platform addresses this with AI-native static/dynamic analysis, scanning LLM outputs for context-aware vulnerabilities. This guide walks through local setup, CI/CD integration (with GitHub Actions examples), and custom rule creation to secure AI-generated code before deployment. ...

Research Disclaimer This tutorial is based on: Resilience4j v2.1+ (Java resilience library) Polly v8.0+ (C# resilience library) Istio Service Mesh v1.20+ (traffic management, observability) OpenTelemetry v1.25+ (distributed tracing standard) Chaos Mesh v2.6+ (Kubernetes chaos engineering) Prometheus v2.47+ (monitoring and alerting) Grafana v10.0+ (visualization and dashboards) TensorFlow v2.15+ (machine learning for failure prediction) All architectural patterns follow industry best practices from the Site Reliability Engineering (SRE) discipline and the Twelve-Factor App methodology. Code examples have been tested in production-like environments as of January 2025. ...