*solar system

So about those weird issues with X. Websites at X scale are really a whole bunch of running network services, each with one simple task to do. This is called a microservice architecture. Compared to say a monolithic architecture where everything is one big program. The advantage is development is faster, and the company can scale more easily because you can assign each micro service to one engineer. Since the service only does one thing, the engineer can hold it in their head. So there's a big human factor there. Another advantage is its super scalable, because you can allocate more resources to each microservice individually. Monoliths usually collapse under their own weight, because you can't have a whole bunch of people working on the same thing at the same time. (Except the Amish can build a barn... But I suspect they have a microservice architecture too, the women make the food, Zeke cuts the lumber, Josiah does the doors, etc.) Companies often start out as monoliths, then have to refactor everything into a microservice architecture. The downside is that now you have 200 programs to herd. Operating, Configuration and Management is harder too, its 200 items wide. Debugging can be really challenging, because you may have to trace the problem through 10 systems... You need really smart big picture system engineers to keep track of everything. Configuration Management (maintaining the collection) can be really challenging. When I worked at Cisco, we had to package our entire cloud architecture onto a single virtual machine. Which sounds easy, except come to find out, no one had thought very well about the configuration management problem. So we had to reverse engineer production, specifically which versions of which services were actually running. Even worse, there was no correlation between the version #'s and the state of the source code. It was literally impossible to rebuild production. If you're a systems engineer, that kind of thing freaks you out. Working on anything was hard, because the Test cloud was always a bag-of-shit, because you had 50 engineers changing one of the services underneath you, so you never knew if you'd broken something, or someone else had. So you quickly adopted a "push and pray" model. Contrast that with Skyport, where I worked before we got borged into Cisco, where the entire site was a specific GitHub hash code. If you needed to debug anything in production, you could checkout that hash, and run the buildall.sh script... In fact, Operations and QA was setup such that the hash ruled all, there was no way to push an individual service, you pushed the hash, the system built from scratch, then bootstrapped from that. We had multiple test environments, Test (where the developers lived), hummingbird (where QA lived), Canary (Where QA ran the full automated test suite). As UI Principal my own small contribution was I setup a proxy system that the UI developers could run locally, and then point wherever they wanted for testing. So you could switch your local environment to the Production hash, point the API redirect to production, and test that way. Though we mostly pointed at test. Anyways, I'm telling you all this because momentary glitches are inevitable with a microservice architecture. Debugging them is like finding the bee with a broken leg in a live swarm of bees. Mostly what you do if you're a good engineer is you have a metric system you rely on (which is another service), and you track the errors. Then you can focus on which bee is buzzing loudest. So you have to build the monitoring into you system. But errors always occur, the world is chaotic. So what you look for is changes in the frequency of errors. You don't want them to spike right after you deploy a new version of service X. Netflix has a whole system called Spinnaker they use that automates this where they try new versions of services and if it throws too many errors, they automatically revert. Cool, but hey, I remember when no one thought cable companies could deliver internet. But turns out, TV is important, so the cable companies were actually very reliable. So as users of this bee swarm, if something is intermittent, we mostly have to be patient. It's frustrating, I get it, but it's part of operating a system this size. 500M posts/day! If 1% blow it, that's still 5M errors/day. Sometimes stuff is just hard, doesn't mean they're ignoring us. Remember, Elon is a big user of the system himself. He gets it.

Growth is back at Tesla. Huge congrats to the entire team on an incredible quarter! 👏 This what stands out to me from $TSLA Q3: THE FINANCIALS • Revenue: $28.0B, roughly flat year over year. • EPS: $0.48 vs. $0.45 expected. • Gross margin: 19.6%, up from 18.0% a year ago and comfortably above the 17.9% Wall Street expected. • Automotive gross margin excluding credits: 18.1%, up from 15.4%. Regulatory credit revenue fell from $417M to $113M. The underlying car business is getting more profitable. • Free cash flow: POSITIVE $1.5B. Wall Street expected NEGATIVE $4.5B! • Cash pile: $45.4B, up 9%. CARS • Production rose +4% to 464,391 vehicles. • Largest vehicle backlog in four years, with battery supply now the main constraint on more deliveries. • Record deliveries in Canada, Australia, Japan and Taiwan. • Record production at Giga Berlin. • Tesla built its 10 millionth vehicle. FSD • 1.74M active subscriptions, up 67% in a year. • More than 75% of new cars in North America now come with FSD, up nearly 20 percentage points in a single quarter. • Live in nine EU countries, with 100K+ customers and 160M+ kilometers driven. ROBOTAXI • Cybercab has been giving rides in Austin since September 3. • Nearly 2M unsupervised miles, up 8x in one quarter. • Austin service now runs from 6 a.m. to midnight. • Grok is inside Cybercab, letting you handle email, your calendar and tasks by voice while you ride. OPTIMUS • Production has started on the high-volume lines in Fremont, building Tesla’s first production-quality robots. SEMI • Now in volume production. • 500 miles of range at 82,000 lbs total weight, using roughly one-third the energy of a diesel truck. ENERGY • 13.7 GWh deployed, up 10%. • Record order rate and backlog. • Megafactory Texas is up and running, producing Megapacks. • First Megapacks built using only Tesla’s own LFP cells. AI COMPUTE • Targeting 400 MW of training compute by year-end. • Equipment installation at the Austin chip fab is planned to begin in 2027. The tradeoff of all this is that operating income fell to $718M as Tesla increased spending on AI R&D. That’s a real cost today, but I’m comfortable with Tesla investing in the future. Underneath the flat revenue headline we may see from news outlets, FSD subscriptions are growing, Robotaxi is expanding, energy orders are at a record, and Optimus and Semi are entering production. Meanwhile, the vehicle backlog is the largest it’s been in four years. It looks like to me the next leg of Tesla’s growth is taking shape right in front of us!