121–130 of 278 posts
> Correctness: 1,778 test cases from the official jsonata-js test suite + 2,107 integration tests in the production wrapper. The AI generated code can still introduce subtle bugs that lead to incorrect behaviour. One example of this is the introduction of functions into the codebase (by AI) that have bugs but no corresponding tests. EDIT: correct quotation characters
The key point for me was not the rewrite in Go or even the use of AI, it was that they started with this architecture: > The reference implementation is JavaScript, whereas our pipeline is in Go. So for years we’ve been running a fleet of jsonata-js pods on Kubernetes - Node.js processes that our Go services call over RPC. That meant that for every event (and expression) we had to serialize, send over the network, ev…
You aren’t accounting for managerial politics. A product manager won’t gamble on a large project to lower operating cost, when their bonus is based on customer acquisition metrics.
For the managers, it's about a bonus. For engineers it's the existential question of future hirability: every future employer will love the candidate with experience in operating a $500k/a cluster. They guy who wrote a library that got linked into a service... Yeah, that's the kind they already have, not interested, move along.
The key point for me was not the rewrite in Go or even the use of AI, it was that they started with this architecture: > The reference implementation is JavaScript, whereas our pipeline is in Go. So for years we’ve been running a fleet of jsonata-js pods on Kubernetes - Node.js processes that our Go services call over RPC. That meant that for every event (and expression) we had to serialize, send over the network, ev…
>If they rewrote the entire thing with $400 of Claude tokens it couldn't have been that big. It was "A few iterations and some 7 hours later - 13,000 lines of Go with 1,778 passing test cases."
Hey all, I'm the author of the blog post. I'm honestly loving the discussion this is generating (including the less flattering comments here). I'll try to answer some of the assumptions I've seen, hopefully it clears a few things. First off - some numbers. We're a near real-time cybersecurity platform, and we ingest tens of billions of raw events daily from thousands of different endpoints across SaaS. Additionally,…
I wonder whether this was your first attempt to solve this issue with LLMs, and this was the time you finally felt they were good enough for the job. Did you try doing this switch earlier on, for example last year when Claude Code was released?
The key point for me was not the rewrite in Go or even the use of AI, it was that they started with this architecture: > The reference implementation is JavaScript, whereas our pipeline is in Go. So for years we’ve been running a fleet of jsonata-js pods on Kubernetes - Node.js processes that our Go services call over RPC. That meant that for every event (and expression) we had to serialize, send over the network, ev…
The key point for me was not the rewrite in Go or even the use of AI, it was that they started with this architecture: > The reference implementation is JavaScript, whereas our pipeline is in Go. So for years we’ve been running a fleet of jsonata-js pods on Kubernetes - Node.js processes that our Go services call over RPC. That meant that for every event (and expression) we had to serialize, send over the network, ev…
Completely agree. We have > $50m from our most recent funding round, and even a cloud expense of $50k/year (in our case for storage) is considered a high priority to address. If it was $300k, our CTO would be running around with a butane torch setting everyone’s hair on fire until the problem was resolved. But, venture funding does create a lot of weird inefficiencies which vary from company to company.
The key point for me was not the rewrite in Go or even the use of AI, it was that they started with this architecture: > The reference implementation is JavaScript, whereas our pipeline is in Go. So for years we’ve been running a fleet of jsonata-js pods on Kubernetes - Node.js processes that our Go services call over RPC. That meant that for every event (and expression) we had to serialize, send over the network, ev…
No offence, but inexperienced JS fanatics always do this because of some weird affectionado they have for the language itself. Otherwise, even a decently qualified CTO would have chosen to keep everything in Go from the beginning or might have not waited until they were bleeding $300k. JS is also the worst possible language choice for this problem. So, it definitely sounds a bunch of script kiddies with fancy titles…
The thing is, if it took them a day with AI it would’ve been _at most_ a week without it. So why did they wait? Someone is not being responsible with the company funds.
The key point for me was not the rewrite in Go or even the use of AI, it was that they started with this architecture: > The reference implementation is JavaScript, whereas our pipeline is in Go. So for years we’ve been running a fleet of jsonata-js pods on Kubernetes - Node.js processes that our Go services call over RPC. That meant that for every event (and expression) we had to serialize, send over the network, ev…
Will they? What makes you think so? If no one cared to improve it when it costed $300k/year, no one will care it when it's cheaper now.