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AI coding assistant history is becoming valuable development memory. Developers should be able to export it, inspect it, archive it, and reuse it responsibly.

Chat is useful, but the next layer of AI interfaces may be dynamic workspaces that adapt to the task.

A small experiment comparing English and Norwegian Bokmål token counts for the same official government text.

AI can multiply a capable builder, but serious products still need judgment, architecture, QA, customer feedback, and maintenance.

Chat is a useful command layer for AI agents, but it is probably not the final interface. Real work needs state, structure, and rollback.

Some of the best agent work may be the boring cleanup: stale bugs, duplicate issues, old PRs, and half-finished ideas.

The next practical gain in AI-assisted development is moving from one AI helper per developer to shared, issue-driven delivery workflows.

AI can help with classification, mapping, and exception handling in migrations, but code and process must keep control over safety and quality.

Agentic development makes impressive demos cheap, but real products still need architecture, ownership, tests, review, and operational discipline.

A small bug fix felt like an early glimpse of software that can help correct itself, with a human still in the loop.

Most agent failures are not solved by prettier prompts. They are solved by verification and institutional learning.

X export gave me tweets and media, but not Grok conversations. So I built a workflow to move AI history into local Markdown.

I used AI for semantic classification of password manager metadata, while keeping secrets out of the model and rehydrating deterministically by ID.

I built an Android SMS Gateway skill for OpenClaw so a local agent can send texts, receive replies, and export inbox data.

A personal reflection on long-term CCM work with Gjensidige, and why customer communication systems matter more than they look from the outside.

Digital accessibility is becoming a practical document problem for organizations that rely on PDFs, letters, forms, and customer communication.

Long-running CCM work is built on trust, domain knowledge, and the ability to keep critical document systems evolving over many years.

CCM is not just document generation. It is the infrastructure behind consistent, trusted, personalized communication at scale.

A short note on what this site is for: CCM, document systems, AI tools, and pragmatic engineering.