/// BLOG

LOG: BLOG

Ruminations on code, intelligence, and the architectural voids of the modern web.

2026-06-13

AI Agent Memory — How to Make Your Chatbot and Agent Remember Users Across Sessions

The complete guide to AI agent memory: why LLMs are stateless, how working, episodic, semantic and procedural memory differ, how to manage the context window through compression and summarization, how the Mem0, Zep, Letta and LangMem frameworks work, how to implement memory in code and n8n, and how to reconcile it with GDPR.

15 min

2026-06-12

AI & Automation

Ollama, vLLM and Self-Hosted LLM — How to Run a Local AI Model in Your Company

A practical guide to self-hosting language models: when your own model pays off more than the OpenAI API, how to configure Ollama on a laptop and vLLM on a GPU server, which GPU to choose, how to connect applications through the OpenAI-compatible API, and how to deploy monitoring for a private LLM in production.

14 min

Prompt Injection and LLM Application Security — How to Protect Chatbots, RAG and AI Agents

2026-06-11

AI & Automation

Prompt Injection and LLM Application Security — How to Protect Chatbots, RAG and AI Agents

A complete guide to LLM application security: what direct and indirect prompt injection are, what the lethal trifecta is, and how to build 6 layers of defense — from input guardrails through least-privilege tool calling to monitoring — with a tools table, middleware code and a deployment checklist.

15 min

2026-06-09

AI & Automation

Fine-tuning an LLM on Company Data — When, How and What to Avoid

A practical guide to fine-tuning language models: when to choose FT over RAG or a better prompt, how to prepare JSONL data, QLoRA on an RTX 4090 with Unsloth, and how to measure whether training actually improved the model.

15 min

OpenAI vs Anthropic vs Gemini vs Open-Source — How to Choose an LLM for Your Application

2026-06-08

AI & Automation

OpenAI vs Anthropic vs Gemini vs Open-Source — How to Choose an LLM for Your Application

A practical guide to choosing the right LLM: when OpenAI, when Claude, when Gemini, and when open-source. With a comparison table, decision tree and multi-model router code.

13 min

2026-06-07

AI & Automation

How to Write Tests for AI Applications: Test Pyramid, Golden Dataset and CI/CD for LLMs

How to design a test suite for LLM applications: unit tests with mocking, integration tests with a golden dataset, LLM-as-judge evaluation and a quality gate in CI/CD. With real Python code and GitHub Actions.

14 min

Monitoring AI in Production: Metrics, Tracing and Alerts for LLM Applications

2026-06-06

AI & Automation

Monitoring AI in Production: Metrics, Tracing and Alerts for LLM Applications

How to monitor an LLM application so you know about failures before your users do. Three layers of metrics, distributed tracing, automated quality evaluation and alerting systems — with real Python code.

15 min

Structured outputs from AI: Pydantic, Instructor and JSON Schema in production

2026-06-05

AI & Automation

Structured outputs from AI: Pydantic, Instructor and JSON Schema in production

How to stop parsing strings from GPT and start getting data ready to use in code — JSON Schema, Pydantic and the Instructor library.

14 min

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