AA5B

Living the life!

Tell me again how traffic cameras make us safer and we can totally trust them to be applied objectively for public safety and no other purpose?

Given a tiny bit of knowledge on big data systems …..

• assume everything accessible is being copied, encrypted or not, kept forever
• there are continuous processes making links to people places times, and connecting the internet to real world data
• when someone wants to search for something, like uses the term “mango Mussolini “, it pretty quickly finds the set of all communications
• then you can further refine it “and lives in the dc area”, and end up with a list of people
• then you have ready links to clever where they’ve posted, everything they’ve said, every person they’ve connected with online or in real life
• and if you have encrypted data, can choose to brute force it (some will have to wait for technology)

It really no longer makes sense to wonder if they’re watching Lemmy. There’s no reason to pick specific places to watch when they can just collect everything, store it, then take their time searching for connections

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

Should not be a surprise to anyone

… and yet those ticket prices won’t come down. You have to be a serious fan to pay those prices, or only the wealthy

Vaccination shouldnt be partisan. wtf is wrong with people

I think “metros” are a combination of “heavy rail” and “commuter rail” over a larger metro area. Fast and longer distance like commuter rail, but regular service like “heavy rail”

Maybe, but I’m not familiar with chicagos system

• Amtrak == intercity. Travel from one city to another, potentially long distance. Scheduled
• Commuter Rail == into and out of the city, over a large region. Typically Bring commuting workers in from suburbs and may be scheduled to prioritize rush hour
• heavy rail == “normal” trains, might be used as subway or surface. Typically travel from one part of a city to another, and operate continuously, with minutes between trains
• light rail == slower, cheaper, a tram. might be underground or a streetcar. Typically travel along neighborhoods, more local transit. Scheduled continuously with minutes between trains

Here in Boston

• I can take Amtrak to nyc, to Portland Maine, or to Albany and west
• we have commuter rail lines covering half the state to bring people from towns and suburbs into Boston.
• we have I think 3 “heavy” rail lines operating as subways, and on the surface as it leaves the city proper
• we have a light rail line operating in tunnels through the city center but on the surface as a tram or streetcar through various neighborhoods. For example students can hop on the get from one end of Boston university another