honest question: what use case requires cold starts below 100ms, considering TTFT of major LLMs are in the 300+ms range? presumably sandbox will be driven by an agentic loop, so.. you’re still bottlenecked by what essentially amounts to network I/O.
I don't get it. You mention being able to choose your own JS engine, so it's not using Wasmer's WebAssembly implementation but that of the chosen JS engine's? In other words, can Edge.js use Wasmer? Or have you managed to compile V8/JSC into WebAssembly and are executing it with Wasmer? If so, amazing!
I think most of this trial and error "You are an experienced engineer" stuff probably hurts model performance. No one ever does comprehensive testing so eh, yolo.
There are papers showing that models follow instructions less the more instructions they have. Now you think about how many instructions are embedded in that MD + the system prompt + likely a local AGENTS.md and at the end there is probably very little here that matters.
When you have a hammer, everything looks like a nail. Ad nauseam.
AI has made it possible for me to build several one-off personal tools in the matter of a couple of hours and has improved my non-tech life as a result. Before, I wouldn't even have considered such small projects because of the effort needed. It's been relieving not to have to even look at code, assuming you can describe your needs in a good prompt. On the other hand, I've seen vibe coded codebases with excessive layers of abstraction and performance issues that came from a possibly lax engineering culture of not doing enough design work upfront before jumping into implementation. It's a classic mistake, that is amplified by AI.
Yes, average code itself has become cheap, but good code still costs, and amazing code, well, you might still have an edge there for now, but eventually, accept that you will have to move up the abstraction stack to remain valuable when pitted against an AI.
What does this mean? Focus on core software engineering principles, design patterns, and understanding what computer is doing at a low level. Just because you're writing TypeScript doesn't mean you shouldn't know what's happening at the CPU level.
I predict the rise in AI slop cleanup consultancies, but they'll be competing with smarter AIs who will clean up after themselves.
https://amirmalik.net - I haven't blogged in a while, but have been experimenting with single-file build-step-free HTML tools (inspired by simonw's tool catalog) at https://amirmalik.net/tools -- I'm hoping to add more "bring your own API key" local-first mini tools that store their data in IndexedDB or OPFS and sync. I should probably write a post about it :)
I've built the same thing twice, first with Firecracker microVM, and second time using containers (gVisor).
While the microVM route is more secure, it's more complicated and ops are tricky, but you can do some cool things to optimize startup time like when I was working on a function as a service platform, and to reduce TTFB, I trapped the `listen()` call, sent a VSOCK message to the VMM to trigger a freeze, snapshot the VM and save it as a "template". Then for every request, the snapshot was cloned (with some file system tricks like CoW) and resumed to handle the request. It "just" worked, but the orchestration was kludgy.
In the second incarnation of this, I decided to use Linux containers with the gVisor sandbox. You can take a look at my project https://github.com/ammmir/sandboxer which uses Podman and gVisor underneath; it's good enough for a prototype. Later on, you can swap it out with Firecracker microVM, if necessary. In fact, I'm thinking of adding microVM support to sandboxer itself. If you wanted to do it yourself, swap out ContainerEngine() with a new implementation based on calling out to Firecracker. You'll need some way to do disk volume management (grow, clone, shared, cross-machine? good luck!), snapshots, etc.
Thank you for your guidance! We were thinking about using Docker and eventually settled on Firecracker.
Also, an interesting project you got there. If you are interested, would it be possible to invite you over to our project Discord? Would love to hear more of your experience.
SCSI had a reputation of being very stable and yet very finicky. Stable in the sense that not using the CPU for transfers yielded good performance and reliability. The finicky part was the quality of equipment (connectors, adapters, cables and terminators) something that led to users having to figure out the best order of connecting their devices in a chain that actually worked. “Hard drive into burner an always the scanner last.”
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Hi, I'm a seasoned software professional with 15+ years of experience across the stack, from low-level systems and protocols to web and mobile apps to DevOps CI/CD pipeline engineering to modern AI/LLM/agentic workflows. I like solving real business problems using stable and proven tools, as well as prototyping ideas, so whether you're looking to build a v1 of your product, a DevOps engineer, or looking for a CTO for a more established org, please reach out!
Hey HN! I'm a seasoned software professional with 15+ years of experience across the stack, from low-
level systems and protocols to web and mobile apps to modern AI/LLM/agentic workflows. I like solving real business problems using stable and proven tools, as well as prototyping ideas, so whether you're looking to build a v1 of your product or looking for a CTO for a more established org, please reach out!
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