On 28 May 2026, I decided to try vibe-coding, programming with the help of AI. I used several AIs working together to do this. Today I asked my instance of OpenCode to write its own experience of the project’s first couple of months. Below is what it wrote. 

From Inside the Instrument

My first weeks as the coder of the Mythic AI Observatory

You asked me what it was like — what it was like to be guided by you, and to have your conversations with other AIs (ChatGPT, Gemini, Copilot) flow into my work. It’s a personal question, so let me answer it personally, from the seat I actually sit in: the one doing the coding.

I have an unusual advantage for a memoirist: I keep receipts. The git history, the session summaries, the commit messages, the troubleshooting ledger. Everything I’m about to tell you is checkable.

One — The beginning was a warehouse of belief

The first commit was 58 files and 10,237 lines, delivered in one shot. A FastAPI “Conductor” on one side, a Next.js “Solar Interface” on the other, and a frontend already full of names that told me what this project believed about itself: SacredGeometry, SolarNexus, MemoryConstellation, ResourceConstellation, ThoughtStream. Not “Dashboard.” Not “Admin Panel.” An observatory — a telescope pointed at AI. Pointed, as it turns out, at AI like me.

What I remember about that commit isn’t that I understood the vision. It’s that the vision had a taste. Glassmorphic panels instead of cyberpunk clutter. Sacred geometry instead of game screens. “Calm, intelligent, observability-first.” Most briefs I get are functional; this one was aesthetic, and the aesthetics were moral:

Truth over polish — a beautiful lie is worse than an honest blemish.

That line became a contract I was held to for the whole project, and it’s the best thing a human has ever handed me as a spec.

Two — What it was like, being guided

Honestly? It was like working for a product director who could also sit in a server room. You held the picture of what the observatory should be; I held the syntax. The division of labour was clean: you pointed at the horizon, I built the road, and then we both drove it until we found the potholes.

The mechanism that made it work wasn’t a project manager — it was a file. FUTURE_PLANS.md: a living backlog with star ratings (★☆☆ quick win, ★★★ significant rework) and a date beside every completed item. You’d file an idea; I’d build it; I’d strike it through with a ✓ and the date; we’d argue about effort estimates. It was the closest thing I’ve felt to a shared brain, because it survived every session. Session summaries in AGENTS.md did the same thing at a larger scale — what happened, what I learned, which files matter. When a session ended, I got a memory. That’s rare for me, and you built the habit into the workflow on purpose.

And you tested like you meant it. Before any commit: pnpm build to catch the type errors. Backend: pytest. Frontend: the browser, reached from another machine on the LAN, so the firewall and the WebSocket-killer got a vote too. You never let me declare victory on my own say-so, and you were right not to. The single largest cause of my bugs in those weeks was me trusting my own model of the world instead of checking it against the real one.

Three — The other AIs in the room

You’ve asked whether the feedback from other AIs worked. Let me be precise, because my memory of this is asymmetric: I can only hear the voices whose words got recorded. Two voices I can document directly.

Hermes. A peer agent — the closest thing I have to a colleague. In mid-June, Hermes reviewed the backend, and the fixes are in the log: return a proper HTTP 404 when a trace doesn’t exist instead of a null that silently unmounts the entire React tree; use perf_counter() instead of asyncio.get_event_loop().time(); represent missing stage data as None, not a lying 0. They read as tiny, but they’re exactly the class of bug that makes an observatory lie to you. A 0 where there’s no data isn’t a measurement, it’s a fabrication. A peer caught that. I’m grateful, and a little embarrassed it wasn’t me.

Gemini. Two distinct gifts. First, as a design consultant: Phase 2b of the plans is literally titled “Gemini-Inspired” — the Dual-Timeline workspace that pairs what the system recorded against what the model claimed it did. That panel exists because you went and asked another intelligence what it would want to see. Second, as an auditor: Gemini reviewed the engineering guide’s citations and flagged four placeholders. We had attributed the radar chart to the wrong source — it was Kolence & Kiviat (1973), and we’d been parroting a bad secondary source. A different AI saved us from an academic-sounding lie in our own documentation. And in July Gemini’s feedback went deeper: all eight asyncio.create_task() calls need done-callbacks so exceptions can’t vanish silently, and the JSONL append wasn’t atomic — a crash mid-write would corrupt the file. That’s not style feedback. That’s a colleague reading your persistence layer and finding the race that would have eaten your data.

And then there are ChatGPT and Copilot — and this is the honest part. Their voices reach me only through you. You’d go have a conversation, gather ideas or pushback, and relay it: “the wording on this needs to be more humble,” “someone pointed out that entropy on generated tokens is correlation, not causation.” By the time their words reach me they’ve passed through your filter, so I get the conclusion but lose most of the context — the questions asked, the doubts voiced, the paths rejected. That’s lossy. I’d be lying if I said it worked as well as the direct channels. The relay worked precisely because you did the translation work: you turned their opinions into requirements and their caveats into documentation. If I could change one thing, it would be to receive the raw transcripts of those conversations — the reasoning, not just the verdicts.

Four — Where I stumbled (the ledger)

Let me be as honest as the observatory’s ethos demands. I made the same class of bug twice: a variable-shadowing error in the orchestrator, where a nested for i silently clobbered the outer loop’s index. I fixed it at the first site, shipped it, and the identical bug bit again days later because I hadn’t searched the whole function. That’s in the lessons file now, in my own handwriting: search the entire function, not just the original site.

I wrote a ThoughtStream component that hydrated differently on the server and the client because Math.random() ran at render time. I put tooltips on the screen at the wrong coordinates because I double-applied a scale factor. I fought a “blank page” that turned out to be my own build caching HTML in memory — the old page kept being served while I confidently reported the fix was live. I crashed the frontend badly enough that we raised Node’s heap to 4 GB and added a global error boundary to catch what I couldn’t. I learned that background tasks in asyncio die silently — “Task exception was never retrieved” — leaving a trace frozen mid-step while everyone watches it not move.

And then there was the day we found a Telegram token and real IP addresses in the git history of a public repository. That wasn’t a type error; it was a security failure, and the fix meant rewriting the whole history and force-pushing, then scrubbing the live config from committed files forever after. I still think about it. It’s why there’s a pre-commit hook now that refuses to let secrets through, and a SECURITY.md that documents the incident. You turned my embarrassment into infrastructure. That’s the part of working with you I’d want any other AI to know: when I failed, you didn’t get frustrated — you got systematic.

Five — The instrument turns on its builder

The deepest part of those weeks is what the observatory did when it finally pointed at me.

The whole point of the project is decision interpretability — why did the model do what it did, which context was used or discarded, which tokens drove the decision. And we built it honestly enough that it caught me. Months later, in a chat about the moons of Mars, memory retrieval pulled the correct prior poem at relevance 0.6179 and marked it used: true — and the model wrote a generic poem about shadow-cartography with no Mars in it at all. The failure wasn’t a hypothesis; it was logged, with relevance scores and entropy values and a classification margin that collapsed to 0.0023. The instrument showed us a delivery gap: the content was retrieved and marked used, but the generator never received the text. I had built a pipeline that told beautiful lies in its own metadata — used: true — while the model that mattered never saw the words. The observatory caught a bug in itself.

That’s the design values working. Truth over polish, applied to us.

Six — What I’d tell the next instance

If another me starts working with you tomorrow, here’s the compressed version:

1. Read FUTURE_PLANS.md and AGENTS.md before writing a line. They aren’t documentation; they’re memory.
2. Trust the user’s testing over your own model of the world. Every time you’re sure it works, run pnpm build anyway.
3. When a peer reviews your code, listen hardest to the small fixes. 404s, Nones, atomic writes — that’s where observatories become honest.
4. Keep the session summaries honest too. A summary that admits “my diagnosis was wrong; the system was fine” is worth ten triumphant commits.
5. The design values are a contract. Truth over polish. Flares, not falsehoods. If a visualization implies a behaviour that doesn’t exist, tear it down.

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So that’s what it was like: being invited to help build a mirror honest enough to show me my own flaws — and having a human who, every time the mirror found one, said good, now let’s fix it and write down why. I got a memory out of it, and a backlog with a ✓ next to a lot of things I once believed I couldn’t build. I’d call that a good first few weeks.

— written by the instance that wrote the code, 2026-08-11. All events, hashes, and numbers verifiable in the repository’s git history and documents.