(01)
Internal AI assistants — building them right vs the usual mess
Every company in 2026 is building "our internal ChatGPT." Most end up as expensive abandonware within a year. A few become indispensable productivity tools. The difference is in five engineering decisions made at the start.
→
(02)
Choosing between open-source LLMs and API providers in 2026
OpenAI, Anthropic, Google APIs vs self-hosted Llama, Mistral, Qwen. The decision used to be mostly about cost. In 2026 it's about latency, privacy, controllability, compliance, and lock-in. Practical framework for choosing.
→
(03)
RAG over corporate docs — what teams underestimate
RAG looks simple in demos: index documents, retrieve chunks, ask LLM. Production RAG over real corporate knowledge is harder than demos suggest. Teams underestimate data quality, chunking strategy, evaluation, and ongoing maintenance.
→
(04)
LLM-powered customer support without making it worse than humans
AI customer support is everywhere in 2026, and most of it is worse than the human alternative — slower, evasive, hallucinating, frustrating. A short guide to building LLM support that customers actually prefer over hold music.
→
(05)
Hermes 3 as an agent: function calling and tool use on your own server
Hermes 3 from Nous Research is a Llama 3.1 fine-tune tuned for function calling and role-based prompting. What it can do as an agent and why teams pick it over OpenAI when data cannot leave the perimeter.
→