Having said that: I think there’s a product here, and some lessons to learn. Perhaps the authors eventually apply them to SpacetimeDB v3 and launch a more resilient and LLM-friendly database, where application code is isolated and can run for as long as it needs, without possibly affecting other application code running locally, even when faced with serious implementation bugs; where transactions can run for as long as they need without affecting the performance of other transactions; where they’re implicitly throttled if they’re taking too long, if the LLM did not provide an optimal query plan. Perhaps we’ll see a system that is much more resilient to failure, but with much less “impressive performance”; perhaps the system will be trivially distributed so that the AI agent doesn’t have to plan a distributed system itself; perhaps it will launch with fewer silly benchmarks and with more technical details.
This is the key insight: the build language is not baked into BuildKit. It’s a pluggable layer. You can write a frontend that reads a YAML spec, a TOML config, or a custom DSL, and BuildKit will execute it the same way it executes Dockerfiles.
，这一点在whatsapp中也有详细论述
Much of this architecture draws directly from the Compact Linear Collider study, a decades-long CERN project aimed at building a next-generation collider. The proposed CLIC machine would stretch 11 kilometers and collide electrons and positrons at 380 gigaelectron volts. To do that in a linear configuration—without the multiple passes around a ring like the LHC—CERN engineers have had to push for extremely high acceleration gradients to boost the electrons to high energies over relatively short distances—up to 100 megavolts per meter.
Follow our Australia news live blog for latest updates