Telegram had multimodal since v0.0.2. The web chat was still text-only. v0.0.5 closes the gap — two-phase media upload, voice recording with codec negotiation, an emoji picker, two full security audit rounds, and a silent bug that was dropping every voice message. 38 files changed, 17 security fixes, 28 minutes.

Gemini 3 Pro was burning money and making users wait 8-10 seconds for a first token. v0.0.6 switches chat to Gemini 3 Flash with minimal thinking — 1-2 second TTFT, 4x cost reduction — while keeping 3.1 Pro for premium tasks like personality extraction. Plus: vision prompts that actually name what they see, Chinese transcription that stops hallucinating English, and a DRY refactor that eliminated 80 lines of duplicated server code.

Users share links. The agent was making up what was on the page. v0.0.7 adds a 3-tier URL browsing pipeline — Jina Reader for fast text, Browserless scrape for JS-rendered pages, screenshot + Gemini vision for graphic-heavy content. Plus a production-breaking Proxy bug where postgres.js tagged templates needed a function target, not an object. 23 tests, 4 commits, one debugging story about deployment timing.

The AI companion I built with OpenClaw burned through a staggering amount of money in under two weeks of casual chatting. One session: 750 turns from about 30 messages. The framework turned my 30 messages into 750 API calls, each resending a context stuffed with 10 never-evicted images eating 82% of all tokens. Context pruning was dead code on Gemini. Zero compactions in 537 turns. OpenClaw proved AI companions work — it also proved they're not affordable on this framework.

My GCP devbox died twice in one day. First time: a snap package triggered an infinite crash loop. Applied five fixes, thought it was stable. Hours later it died again — unbounded memory was the real issue. Two rounds of diagnosis, 12 hardening measures, and a machine type swap. A complete field report on VM defense in depth.

Claude Code's Agent Team feature let me break a complex cross-layer plan into ten tasks and assign them to five AI teammates working in parallel. The Lead coordinates dependencies, dispatches resources, handles conflicts — the whole thing feels like managing a remote engineering team.

After building Mio v0.0.2 with five parallel AI agents, I used the same Agent Team pattern for code review — four specialized reviewers found 2 BLOCKs and 2 WARNINGs, three fixers patched everything in parallel with strict file ownership, and two changelog agents handled the release. Building and reviewing are two halves of the same workflow.

From an empty repo to a working AI companion that remembers you, has moods, and reaches out on its own. 39 commits, 9 database tables, 4 personality presets, and more 3 AM debugging sessions than I'd like to admit. The full build story of Mio v0.0.1.

v0.0.1 could chat, but it wasn't real — couldn't see, couldn't hear, didn't know the time, didn't know what it looked like. 81 commits later, Mio learned to see images, hear voice, send selfies, remember the right things, and chat in a browser.

v0.0.2 brought Mio to life, but a demo that works and a product that real users can rely on are two different things. v0.0.3 is the polish release: per-field input validation, context-aware heartbeat, opt-in quiet hours, media rate limiting, a production-only webhook bug, and 144 new tests. The invisible work between 'it works' and 'it's good.'