21–30 of 126 posts
The incitives for remote LLMs are off with providing defaults which optimize for maintenable sound architecture though. Same way Claude is going to produce overview of the indexes of the summaries of comprehensive reports, no one is going to read. No doubt this feels like excellent KPI on how much output was generated.
Yeah, but to be honest, I sometimes just tell Claude to cleanup / refactor stuff; it finds a lot of things, discusses it with me and I approve the plan, and it churns away my tokens for some time. I do this once in a while, and I've been doing this for over 6 months and I don't feel like my development has significantly slowed down. Yeah my token usage is more for sure, but my codebase also is, so I'm not worried abo…
I have reduced our response time on our api to 30ms from 80ms and gotten a setup we can comfortably grow into.
I had not had time to track down these optimizations without Claude code.
Earlier quoted context omitted.
This will probably be how things will work in future: devs will shift to specifying features which will be validate through tests. The AI will then be middle layer that will iterate until tests pass. Layer 1: Specs (Humans) Layer 2: Code (AI mostly) Layer 3: Tests (AI + human checks).
What a boring fucking future.
Insightful. Agree with this take. Unfortunately, maintainability is simply bucketed as a "non-functional" requirement. Maintainability (and similar NFRs) should actually be considered what preserves and enables the delivery of future functional requirements -- in contrast to framing non-functional requirements as simply "how" the software must do what it does vs. the "what"/functional requirements that "actually matt…
Right! The unfortunate thing is that many software companies don't seem to think much further than a quarter ahead, not really.
Sure they might have a product roadmap that extends for a year or two into the future, but let's be honest. Often that roadmap is mostly for sales purposes, not engineering planning purposes. Product and engineering will pivot if sales slump. The earlier in the company's lifespan, the more likely this will happen often
However if companies get out of this startup mode then they should start to stabilize... But many don't. They continue this pattern of short sighted short term planning, which means product stability remains a low priority effort.
Ultimately I guess many companies just either do not have the resources to build good software or do not actually care to
Earlier quoted context omitted.
This will probably be how things will work in future: devs will shift to specifying features which will be validate through tests. The AI will then be middle layer that will iterate until tests pass. Layer 1: Specs (Humans) Layer 2: Code (AI mostly) Layer 3: Tests (AI + human checks).
What a boring fucking future.
Write tests. The most boring activity on the planet
I created a video that talks about this in more detail:
So what are all of these agentic based strategies going to do once the infinite money spigot of investment into AI ends and they need to start charging prices that actually make a profit? I get that most of the cost is in training and not inference, but I don’t see how models stay useful once the worlds software updates in a few months post training since the models can’t learn without said training. Are we just goin…
So:
* You get paid less. * The company might pay a similar amount due to LLM costs. Although, it could be more or less as well, depending on how it works out.
A couple of years ago, I saw a story of a guy writing two articles for a website a day. The boss asked him if he wanted to transition to AI-assisted writer for less pay. He said, "No." After a couple of weeks, he got canned. He checked the website out, and it had a bunch of AI writing on it.
LLMs are there to reduce your salaries and increase the businessowner's profits. Bigger inequality in wealth, it's only going to grow more and more. Also, a ton of people fired across many different fields.
With AI, you can hypothesise what can potentially break with each new addition (which your regression tests do not even capture at present). Then, you can write tests for each of those hypotheses, ask AI to deploy a canary, ask AI to divert 5% of traffic to the canary. Ask AI to analyse the logs for any signs of regression in performance, ask AI to roll it out to 100% if everything is good. Congrats! At this point, y…