51–60 of 278 posts
Your physical form is destructively read into data, sent via radio signal, and reconstructed on the other end. Is it still you? Did you teleport, or did you die in the fancy paper shredder/fax machine?
If vibe code is never fully reviewed and edited, then it's not "alive" and effectively zombie code?
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It has to be satire right? Like, you aren't out of touch on this. I get engineers maybe making the argument that $300k / year on cloud is the same as 1.5 devops engineers managing in-house solutions, but for just json parsing????
I wonder if you've ever worked on a web service at scale. JSON serialization and deserialization is notoriously expensive.
I have no idea if they are doing orders of magnitude more processing, but I crunch through 60GB of JSON data in about 3000 files regularly on my local 20-thread machine using nodejs workers to do deep and sometimes complicated queries and data manipulation. It's not exactly lightning fast, but it's free and it crunches through any task in about 3 or 4 minutes or less.
The main cost is downloading the compressed files from S3, but if I really wanted to I could process it all in AWS. It also could go much faster on better hardware. If I have a really big task I want done quickly, I can start up dozens or hundreds of EC2 instances to run the task, and it would take practically no time at all... seconds. Still has to be cheaper than what they were doing.
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I mostly agree, but it's more appropriate to weigh contributions against an FTE's output rather than their input. If I have a $10m/yr feature I'm fleshing out now and a few more lined up afterward, it's often not worth the time to properly handle any minor $300k/yr boondoggle. It's only worth comparing to an FTE's fully loaded cost when you're actually able to hire to fix it, and that's trickier since it takes time a…
They were running a big kubernetes infrastructure to handle all of these RPC calls. That takes a lot of engineer hours to set up and maintain. This architecture didn't just happen, it took a lot of FTE hours to get it working and keep it that way.
> The approach was the same as Cloudflare’s vinext rewrite: port the official jsonata-js test suite to Go, then implement the evaluator until every test passes. This makes me wonder, for reimplementation projects like this that aren't lucky enough to have super-extensive test suites, how good are LLM's at taking existing code bases and writing tests for every single piece of logic, every code path? So that you can th…
I've been successful at using Claude Code this way:
1. get it to generate code for complex data structures in a separate library project
2. use the code inside a complex existing project (no LLM here)
3. then find a bug in the project, with some fuzzy clues as to causes
4. tell CC about the bug and ask it to generate intensive test cases in the direction of the fuzzy clues
5. get the test cases to reproduce the bug and then CC to fix it by itself
6. take the new code back to the full project and see the issue fixed
All this using C++. I've been a pretty intensive developer for ~35 years. I've done this kind of thing by hand a million times, not any more. We really live in the future now.
A principal engineer spending his week end vibe coding some slop at a rate of 13k lines of code in 7h to replace a vendor. Is this really the new direction we want to set for our industry? For the first time ever, I have had a CTO vibe conding something to replace my product [1] even though it cost less than a day of his salary. The direction we are heading makes me want to quit, all points to software now being wort…
> This was costing us ~$300K/year in compute, and the number kept growing as more customers and detection rules were added. Maybe I’m out of touch, but I cannot fathom this level of cost for custom lambda functions operating on JSON objects.
They said in the article that they were running up to 200 pods at a time. Doing some back of the envelope math, 200 pods at $300,000 year is about $0.17/hour, which is exactly what an EC2 c5.xlarge costs per hour (on demand). That has 4 vCPUs, so about 800 vCPUs during peak, with $0.0425/CPU-hour. I do have some questions like: * Did they estimate cost savings based on peak capacity, as though it were running 24x7x36…
It is, by orders of magnitude, larger than any deployment that I have been a part of in my work experience, as a 10-year data scientist/Python developer.