I’m just coming out of a cold. Not really sick anymore, but the energy tank is still refilling. The weekend was already insane on its own, and then this whole engineering arc decided to show up on top.

The real story starts late Sunday night, which technically was already Monday: around 1–1:30 a.m. I noticed a new OpenAI model in the API catalog — the kind of hidden, under-documented thing that feels like a secret brain upgrade. Faster, smarter, clearly better than what I was running. I got excited. I wanted to ship it right now so my community could feel the jump.

So sometime between 1:30 and 2 a.m., I wired it in and tried to push a new backend image. That first attempt just flat-out failed. It wasn’t even a client-compatibility issue yet — I had likely messed up the AMI or one of the infrastructure steps in my usual deployment SOP. The instance was basically unhealthy. At that hour, with my brain running on fumes after a crazy weekend and being post-cold, I finally called it: this is probably just me screwing up a step. I rolled things back and decided I’d redo it properly after sleep.

I woke up around noon on Monday and went straight back to it. I still really wanted this upgrade out because the new model is both smarter and noticeably faster. I knew people would love it, and they eventually did. I followed my standard backend release procedure more carefully this time, built a new image, deployed it, and finally got a healthy instance up and running. No infra jank. Everything looked good.

Or so I thought.

All my tests were green, because I was testing with my development client — the one where both the client and the backend had already migrated to the new API and the new interface. In that dev universe, everything matched: new client, new backend, new model, clean flow.

But the public-facing client that real users actually run on their machines? That one was still speaking the old interface.

I completely forgot about that.

So what I’d actually done was upgrade production’s backend to the new API shape while all of the real clients were still sending old-formatted requests and expecting old-formatted responses. The stuff I tested locally looked perfect only because I was living in the future version of my own stack. Production was talking to a backend that simply didn’t understand its language anymore.

That was the real fuck-up.

At first I didn’t see the depth of it. It felt like maybe I’d missed a route or some small parameter mismatch. But as I traced it through, it became clear: this wasn’t one bug, this was a contract break between versions. On top of that, my frontier work lived on main because of old solo-founder habits — no clean dev/prod split. It all landed at once: new API, new model, new interface, old clients, and a main that carried frontier code.

I rolled production back so users wouldn’t feel the blast radius. That part was straightforward. But emotionally, the weight of it landed later in the day. It was Monday, I’d just come out of a crazy weekend, still low on energy from the cold, and somewhere in all this I also had a sim race where some kid absolutely nuked my race with a dumb move. By the time I sat down and really understood the depth of the compatibility issue, it all stacked together into one big “fuck this” moment.

Once the initial frustration wave passed, the path forward became clear:

Keep the new backend and the new OpenAI API, but build a clean compatibility layer so the old public client could keep working.

So that’s what I did.

On the backend main branch, I wrote an output adapter: internally, everything uses the new model and the new response shape, but before sending anything back out, I run it through a small adapter. That adapter can be toggled with a simple boolean switch — if it’s on, the API returns data in the old interface format that the existing clients expect. If it’s off, it can return the new shape directly. Right now, it’s on.

Then I realized the inputs were also mismatched: the old clients send their requests in a different format than the new dev client. So I added an input adapter as well. This one didn’t even need a flag. I just added a small check: if the incoming payload looks like the old format, I run it through a translator that converts it to the new internal shape before it touches the core logic.

With those two adapters in place, I pointed the old, public-facing client (checked out at its release commit) at the new backend and ran through the flows. Everything worked perfectly. The system is now fully on the new OpenAI API and using the new model, but from the client’s perspective nothing broke — the old interface is preserved at the edge.

The nice part is that these adapters are temporary by design. Once the old clients are phased out and everyone’s on a newer build, I can just rip out those two adapter modules and the core will already be in the correct, modern shape. Clean addition, clean removal.

By around 6:06 p.m., the chaos arc was over. The backend was running the new stuff, the clients were happy, the performance and quality were up, and the architecture was actually better than before this whole mess started.