shoo

The problem isn't the maps being static and finite, it's that nobody designs maps for emergent and dynamic gameplay anymore. CoD might have dozens of maps but they're all designed for perfect sterile balance with the same lane concepts.

Some of my favorite multiplayer games have only 3-4 maps but each is distinct and plays well to different tactics. Usually they're based around strong points and webs of approach which gives more options for fresh experience each time you play ("wow never noticed that flank" - "oh this window gives a great angle over this courtyard" - "oh a grenade can be thrown just perfectly over that building" - etc...)

In a sense, good progression isn't flat mechanical unlocks but building up game and map knowledge. You can choose to explore different facets of the game and it always stays interesting. Competitive ranked multiplayer ruined this because going off meta means losing the game for your team.

The reaction to sports pseudo-stats is what really separates casual viewers from real fans. It's the only way to raise stakes on otherwise forgettable games.

"This team is on a 5 game win streak": 🥱

"This player has never lost an away game in June": 😯🍿

Retrofuturism as others have said, but probably more specifically cassette-futurism

Someone should make a site that quizzes you on time travel knowledge. Go through the ages: how do you start a fire, how do you make steel & what's the best method of smelting, what is germ theory & the best methods for preserving food, what are the causes of common diseases, how do you make a steam engine, what is penicillin & how do you produce it at scale, how do you make a battery, how do you make a solar panel, how does nuclear energy work, etc...

The post misses a few things:

1. The ai bubble is currently being subsidized to an unimaginable degree. If you were to actually pay true cost for your token usage, you wouldn't be saving that much over an engineer's salary. Probably even worse once AI companies start to extract a real profit. 95% of companies diving into agentic labor will be in for a rude awakening when they balance next year's budget.
2. The cost to keep ai useful in its current form has a high floor. Unless you keep up with expensive training, your models will drift. You can only scale your model intelligence with more hardware (roughly). In two years, Claude opus 4.8 will still be bloating context to learn about the latest cloud platforms and libraries. A human engineer will get those passively at no cost to the company.
3. As the complexity of the task grows the complexity of the ai babysitter must match it. Even if Ai stays cost effective, companies can now save money by spinning up bespoke in-house software to cut out vendors (think observability platforms, task tracking, product design, marketing systems, etc...). No matter how many adversarial reviews and sub agents you spin up, an Ai can't grasp the full context of your company and it's shifting priorities. The software engineer role transitions to a pseudo-sysadmin + product architect.

C-suites don't want know about software and don't care about non functional requirements (security, availability, audit ability, etc...). They just want to wave a magic wand and have a product appear, which is what Ai provides the illusion of. That's why all current Ai software is garbage, but the smarter companies will catch on

I once had a professor who claimed she passed a high level language course without attending a class or studying it. She was fluent in an adjacent Romance language and knew a little of some other overlapping languages. Basically walked in to the final and got a C+ on cognates alone.

A [*squints*] ~20 gun frigate with a crew of only 30-50? That's sounds like a startup nightmare. You'd probably want double that to be comfortable.

"Get in on the ground floor of our fast paced, dynamic environment! Must be self starter willing to work watch-on-watch for the team. No sick days."

Used to be in that eternal battle but a squirrel baffle solved it.

I don't doubt it's possible to get better consistency but the juice is really not worth the squeeze for me. You end up churning through huge expensive models, orchestrating sub agents, writing out boilerplate hand-holding instructions ("please don't break this, stop trying to commit to main, please lint ffs...").

I don't use it for Java but that would make sense with rigid enterprise patterns and VeryVerboseNamesThatAreEasierForAModelThanAHumanFactoryClazz {...

I don't think our career is boned, moreso that all juniors trying to get in are boned. Everyone who knows what going on transition to a more hands-off architect role.

But like I said, our tokens are heavily subsidized right now. When they pull the rug, code monkey jobs will start to get listed again (with lower salaries of course).

Things I've realized while working with AI (Claude code):

• It's fantastic for very small macros and medium length scripts. Think dev ops stuff, pre-commit hooks, transforming data. Keep it small enough to manually review and something you can run without destroying anything important. This can massively boost your codebase QoL. [Double bonus for not wasting tokens to solve the same problem over and over]
• It's decent-to-good at debugging but not consistent with fixes. It can find some utf encoding edge case that might have taken you 1hr+ but suggest the dumbest bandaid fix you've ever seen. Also very good at spinning up unit test suites for basic edge cases.
• Due to obvious training bias, it's pretty good with common libraries and cloud platform infrastructure. It could probably help with writing a complex cron call, debugging regex or fixing an IaC config. On the flip side it won't bother to use the latest package version or know your niche/new library.
• It does better with greenfield because exploring your codebase introduces a ton of bias. It might try to fit in an ugly hack when a refactor to simplify everything is way easier.
• It's absolutely garbage with UI, just throws the most disorganized HTML together that isn't reactive or reusable. OK enough for ugly internal stuff but God help anyone relying on it for that.
• This is setting up to be the biggest rug pull in history. People that buy into it heavily just to save a couple bucks on engineer payroll are going to be fucked when they start ratcheting up the token price.

All in all it can be useful when used with care but will never be a magic bullet.

Very interesting, the lube experiment that was cited in the article was kind of shocking when you stack up the chain lifetimes. But when you start adding up all the lifetime maintenance and replacements, doesn't a carbon belt setup start to make much more sense for most riders?