Blog

Side-by-side guide comparing two enterprise LLMs for business—accuracy and security versus speed, multimodal support, context window, and pricing.

How model size, data, and compute drive LLM performance, the four training regimes, Chinchilla insights, and implications for efficient training.

How Claude 4.5 Opus speeds up tasks and cuts costs using effort levels, hybrid reasoning, programmatic tool calling, and smarter context compaction.

AI reduces stockouts and excess inventory by improving demand forecasting, automated replenishment, real-time visibility, warehouse efficiency, and anomaly detection.

Compare principle-driven constitutional AI vs RLHF-based alignment—trade-offs in safety, refusal rates, bias, cost, and real-world performance.

Explains how Claude manages context with token-based session windows, daily memory summaries, and external tools for persistent cross-session context.

AI-powered Claude speeds financial risk analysis, automates stress tests and portfolio optimization, and integrates with Snowflake and major data providers.

Compare Claude, GPT-4o, Gemini and Lexis+ AI for automating Section 321 filings — accuracy, token limits, integrations, and best-use cases.

Build custom Claude API endpoints for media apps—use a 200,000-token context, MCP integrations, batching, and model choice to deliver personalized recommendations and playlists.

Explains why Claude produces faulty reasoning—training incentives, hallucinations, fabricated chains of thought—and practical methods to reduce errors.