It’s an accelerator. At some point even the wealthiest must be affected. If humanoid robots become e a practical means of automation, suddenly there will be a lot of desperate people. At some point UBI might be the cheap solution for the wealthy

My college kids do everything through cloud services, so it shouldn’t really matter what their device is.

• One of them has a Mac and is just finishing his first year with no problems, so I would expect a neo to be no different.
• My older kid jumps among an iPad in class, his laptop when necessary, and his gaming rig in his dorm, and has had no issues

On the other hand, my niece has very specific requirements for her major, so there will always be a few specialties

My company is working on one such product. So far AI has essentially no guardrails so can too easily go from potentially helpful to harmful. There’s nothing keeping it from sharing confidential data, like customer data, nothing looking at whatever dreck it’s pulling in from the internet, and nothing keeping it from doing harm internally. We can do that.

But most of the more immediate harm is poor practices that we’ve known for decades. Maybe consultants can make bank by pushing best practices we should have followed all along. That article that keeps coming around Lemmy about losing their production database and backup in 9 seconds is a great example. Sure, that LLM needed guardrails to stop that, but any normal best practices would also have prevented that

I’ve seen some baby steps

• LLMs with “planning” mode that shouldn’t make changes until approved …… except when they ignore that
• IDEs with sandbox where you need to whitelist allowed LLM invocations, except there’s too much noise so too many people allow all
• MCP servers with a read-only mode, or allowing configuring credentials to be read-only

I know of some companies where they write up a full spec in markup, and have the ai code from that. They claim it works well, but that seems like extra work.

Personally, most of my coding is maintenance and AI sucks at that. I can get the ai to give me good recommendation, but not usable code. I have had it do a good job writing utility scripts such as data extraction, and tests - it can even save me time

So if you have a greenfield project, and are able to give it sufficient context, people claim it can work …… I’m highly doubtful it’s maintainable though, and maintenance cost is far higher than the cost of initial creation. I really think these companies are digging a hole for themselves

Of course I’m taking advantage of this

• scheduling extra refactoring on the claim that maybe AI can be useful with cleaner code
• fun and games to give AI more context, in case that can make it useful

Not copy-paste. Let the ai do it for you. …… else how would we get these entertaining stories of idiots letting ai delete their production database

Before ai, my company’s misguided kpi was the number of merge requests

At least that one worked well for me since I’m generally making many small changes to an existing code base

Yeah, even before its ceo came out of the closet as Nazi- after growing to a successful car business they got distracted by new and shiny

But I still think their cars are better technology than anything else in the us, and now cheaper than most. They still have their big upcoming push into semis. They still have most superchargers. They still have a rapidly growing storage business. The cybertruck flop really hurt them and I don’t see how they can afford another.

But ai doesn’t really have a profitably business model yet and humanoid robots are a much bigger gamble with no market yet. This is where a more typical business owner would create new corporate entities so Tesla could become a successful car company while the ceo could try other gambles

Ah yes, the single issue person coming to move the goalposts

Targeted temporary tariffs work best when you’re trying to develop an industry to catch up, such as Biden’s EV tax incentives to more quickly grow a larger market, infrastructure spending to speed buildout of chargers, manufacturing incentives to help legacy automakers transition, and a years long transition that manufacturers could plan around.

They also said that about Tesla, “no new car company can be successful…”

I was curious about that so tried googling ….

Canadian buses were

• earlier models - I see years like 2018
• at least some were built in a new plant in Canada
• part of the blame was lack of spare parts
• part of the blame was limited range, especially in winter

London buses

• newer. I see years like 2024
• no real winter
• I see articles about a new larger battery to fix range issues
• appeared to have similar problems, including a major recall for fire risk

Maybe London benefitted from newer models and doesn’t get as cold as canada

Hopefully not. The kpi just states usage.

Looking at the report provided by the ai vendor, there are several measures that might apply. There is also a “quality” measure that seems to be the number of lines accepted. I don’t have any indication that they card about that, but it is there

Yeah I can see the temptation. Ai usage is part of my kpis and is the one place I got dinged on my review. I thought i was insulated as a “partly coding” position, but they put me against full time coders so I look even worse

I guess they’re trying to force change, make us figure out how to make it work. The skeptic in me thinks it rewards people who have time to screw around, but when I set aside a week to see what I can do, I did increase my ai use.

I did find some useful tasks that also increase my “agentic” score! But my “quality” score (ai generated lines I accept) is stick at zero. We currently pay a flat fee but that’s changing next months to pay per token

I have to admit to developing the habit of wiggling the steering wheel regularly. Unfortunately that doesn’t help for camera based systems

Definitely one of the weaknesses is: what about maintenance? Ai has been poor at maintaining existing code, and we all know that maintenance is much more expensive than development. Will it be able to maintain its own code? What if there are no longer enough developers to do it manually? Where is our future then?

I’ve definitely been adding priority to refactoring. It was always a good idea for maintainability, for new developers to get up to speed and be able to contribute, but now we have the idiot developer that is LLMs. Perhaps more refactoring is meeting it halfway

Not because it actually resembles consciousness, but because it doesn’t

There have been several waves of advancement for things initially called ai. However one of the most common threads is that it helps us define what intelligence is not.

LLM vendors are starting to charge money. I’m sure it’s not even close to profitable but it’s a start. Perhaps when the bubble pops and market consolidates, fewer vendors with more paying customers each …

Using an LLM is a skill just like any other. If you just take what it gives you, you can’t expect good results. If you evaluate what it gives you and prompt it to improve, the results aren’t as bad.

I use an LLM for coding and a definitely a skeptic, but I do find it a useful tool and am really interested in seeing if I can make it work.

Initially I found some amount of success at lower levels, saving me some time

• it could auto complete entire lines of code (and that’s trivial to evaluate and correct if necessary)
• it was pretty good about generating unit tests since they tend to be simple and repetitive. In general corrections tend to be smarter coverage, tweaking the tests to cover more functionality with fewer tests
• it’s pretty good with utility scripts. For example today I had a decision and wanted supporting data: in minutes it generated a script to call APIs in my scm and generate some stats for 4,000 code repos …. And it worked

Currently I’ve created rulesets and project context so

• it’s been quite successful at code reviews (it finds things I miss, and has resulted in my human reviewers finding less)
• I’m proud of one for identifying refactoring opportunities. It finds good spots and makes good suggestions, but so far I have to implement myself: its code hasn’t been usable. I can also objectively verify by reduced cyclomatic complexity.

Trying to find other scenarios it can be successful, it’s clear that insufficient context is a limiting factor. The fun challenge is to see if there are more successful scenarios if you can give it enough context. I’ve gone past rulesets and project context, to connect relevant services and metadata about our product set and environment. They want a team to try vibe coding and I’m still very skeptical, but my part of the effort is a real solvable problem and fun challenge whether they succeed or not

I was with you up to “cloud computing”. That bubble was a huge success that has really revolutionized how software is provided

• well known winners include AWS, Google, Microsoft but there are many more depending how you define cloud computing
• also some huge flops

AI has a lot of mindshare and has demonstrated contributions in several areas. For example, ai slop you see on YouTube is making some people money. As a coder I do find it sometimes a useful tool, and I can definitely see the near future where it’s a required skill, and no, if you just ask it to spit out slop you’re not getting anything but slop ). I don’t see how it’s going away. However it doesn’t (yet?) live up to its hype nor is there (yet?) a profitable business for providers.

Meanwhile the crypto and NFT bubbles were pyramid schemes that only ever made money from themselves. Web 3.0 probably looks useful to its proponents but was only ever a niche that no one else cared about

The same way they do in every other bubble.

• the bubble pops, most companies fail. Mostly bankruptcies, massive layoffs but also huge tax writeoffs
• of the surviving companies, a couple strike the jackpot.

Most of that huge overall investment is lost, but everyone wants to be in on the one or two that succeed, and those specific investments could have huge returns

I’m not buying this. Sure minimizing dependencies is a good practice, but not updating? That’s a recipe for disaster.

It’s important to note that you can’t predict supply chain attacks or vulnerabilities, and vulnerabilities are much more common. Also, while frequent updates might expose you to that supply chain attack more quickly, it also mitigates it more quickly. Frequent updates in combination with vulnerability scanning, and limiting downloads to reputable sources (that try to prevent supply chain attacks and discover them quickly) is a much better approach.

There also the maintainability argument, that I’m having right now with a couple of our legacy software teams. Not updating can lock you into the past, for entire ecosystems of dependencies. You cant update if you have to, you cant take advantage of new features anywhere in the ecosystem, and it’s now an expensive emergency when something stops being maintained or has an unresolved vulnerability. If you’re being continually kept up, then choices or features are easy

Then the goal is how do you automate your updates as smoothly as possible so they do not become noise, do not create extra work? Tools like dependabit and renovate bot have a lot of config options to help that