Llm

A hands-on TypeScript tutorial that builds a tolerant partial-JSON parser so an LLM's streamed structured output renders field by field instead of stalling on the closing brace. Tokenizer, auto-closing parser, and 49 offline tests.

A hands-on TypeScript tutorial on Anthropic prompt caching. Build a cache-aware request planner, audit for silent invalidators, and measure real savings from the usage fields, offline and fully tested.

A hands-on TypeScript tutorial: validate LLM tool-call arguments with Zod, auto-repair malformed calls in a bounded loop, and dispatch fully-typed args to your handlers — provider-agnostic, tested, and offline.

A hands-on TypeScript tutorial for building a production LLM client that survives slow upstreams, rate limits, transient 5xx errors, and whole-provider outages — a per-attempt timeout, exponential backoff with jitter, a per-provider circuit breaker, and automatic failover, in about 250 lines of typed, fully tested code.

A hands-on TypeScript tutorial for a CI-integrated eval harness that gates LLM releases on capability, safety, and regression checks — the discipline CAISI now requires from labs.

A complete hands-on tutorial for routing prompts to the cheapest capable LLM in TypeScript. Build a classifier, a model registry, a fallback ladder, and per-request cost telemetry that survives the May 2026 price war.

A comprehensive guide to AI-powered code review tools in 2026, covering GitHub Copilot code review, CodeRabbit, Qodo, Amazon CodeGuru, and Sourcery. Includes productivity data, security analysis, CI/CD integration strategies, and best practices for LLM-powered review workflows.

A deep technical and organizational analysis of the agentic AI transition — examining how leading platforms are architecting autonomous agents, the new infrastructure requirements, emerging failure modes, and what a genuinely agent-ready software stack looks like in 2026.

A comprehensive, architecture-first tutorial for engineers building agentic AI systems in production — covering orchestration frameworks, tool-use patterns, memory management, OpenTelemetry observability, failure recovery, human-in-the-loop design, and cost guardrails with a realistic multi-agent customer operations pipeline as the running example.

Microsoft researchers proved that a single training prompt can destroy AI safety alignment across 15 major language models. This deep analysis examines what GRP-Obliteration means for enterprise AI, the alignment field, and the future of AI safety.

Meta just dropped Llama 4 with 15 trillion training tokens, enterprise-grade privacy, and native edge deployment. While competitors chase cloud revenue, Meta's betting on-premise AI will reshape how Fortune 500 companies deploy language models. The implications are staggering.

Build enterprise-grade vector databases for RAG applications with hybrid search, metadata filtering, and production deployment. Complete implementation with Pinecone, Weaviate, embedding strategies, and Kubernetes scaling patterns.

Build your own AI assistant from scratch using modern LLM APIs, RAG pipelines, and agent frameworks. Complete implementation guide with working code, architecture patterns, and deployment strategies for production-ready personal AI systems.