JauntyHatAngle

I guess the way I'd put it is that we've been increasingly allowing and not punishing privacy breaches and government/authorities overstepping and after 20+ years of boiling the frog the latest, most extreme variant has pretty much pushed it over the edge of dystopian.

So it's not all Trump, but his administration has definitely pushed the lack of accountability to new heights and combining it with the new wave of surveillance tech with limited oversight or legislation it's all just become ingrained with nobody to fight it.

That seems a reach to say the popularity of kalshi and polymarket is down to gambling simulators.

Id wager (heh) that it's much more down to a lack of regulation combined with the normalisation of influencer driven get rich quick but pretend it's clever investing that exploded in popularity with crypto and wallstreetbets style influencer investing. Combine that with house prices being where they are and people get risky with investing as their hope outstrips their caution.

I don't think a few rounds of balatro or slotomania leads to that.

He may or may not be right here, but captain steeeve has a bad track record of baseless claims and/or bad speculation and is not a good reference due to his past failings.

I would not be referencing him as a good source.

Yes, skills as a "portable power" isn't really the use case for me unless it's entirely generic and even then sparingly.

I've mostly followed what anthropic suggests, which is putting less into context and more into skills, to keep the "how" out of context until it is needed to reduce context bloat.

Skills have some instructions but are primarily informed repo specific instructions and keep their context away from the rest of the repo to keep things sanitised for me.

I've found it to be useful in that context.

In my experience, when they are in a cutting mood, they often decide first how many people to cut, then go through teams based on their workload.

So your team not having metrics, they are more likely to go "wow that team is pretty empty of work, cut half of them (exaggeration of course) rather than "well they don't have any metrics so Ill take them at their word or wait and see".

Cynical maybe, but my best managers who've kept a team together and protected tended to be really effective at selling the work they're doing and how long it'll take and how much workload each team member has, and showing how busy the team is in a way thats impactful.

While you're right, and I'm constantly thinking/worrying about my own processes giving me AI apathy, you can use AI in a way that develops your understanding of a bug. It really depends on your workflow.

When I use AI to debug a problem, I'm constantly questioning the specifics because I want to learn it, and often I'm better than the AI at quickly recognising patterns that point to a specific issue, where the AI will spin for some time trying to work out where to start - even if it has full context.

So I guess I'm half with you, but an LLM used right can still teach you effectively.

Research Plan Implement.

At the risk of you already knowing this or similar - in essence for a slice of a piece of work, you get the LLM to research the information required into (usually) a research file, which you go back and forth with in-line or via conversation until you're happy.

You then build the plan for the piece of work, again going back and forth with the LLM until you agree with the plan, with the key here being you have a specific set of concrete steps with very limited vagueness. Again this is quite often a plan document.

You then implement the plan with your LLM, following the concrete steps and monitor/evaluate the result.

For me its the 101 mandatory first way to use LLM that turns it from guesswork to genuinely useful. (that or some other similar method, SDD or whatever)

Because without it or something similar you're fully dependent on AI's weaknesses not coming to the fore (context/scoping failures that are silent unless you demand visibility up front and ruthlessly chop/clarify), and you often ascribe bad AI slop as model limitations instead of context/scoping failures.

When I hear this I do wonder to myself how they're using AI.

For me, as long as I'm properly RPI looping it and not blindly pressing "yes" then it will nearly always reach the solution, usually a fair bit quicker, because it effectively becomes an ideation machine that can keep more thoughts and knowledge in it than my brain can.

However, I'm using it through giving it the context, it has access to the repos, access to the sytems, I'm telling it where the logs are, I have docs to show it what each part of the system is for.

Along with that, it does depend on my own instincts/knowledge for me to read its response and for me to say "no, you've over-thought/engineered this and this is actually the better solution", but its very are nowadays for me that it can't find the 1 liner, as long as I've fed in the right context, mostly pre-done because I've spent a bit of time building out the context tree for the repos/systems in a way that allows it to know what it needs to know.

I'm not doubting that people spin their wheels and couldn't find the one liner, but if its as simple as you say, that does seem like people who aren't great at LLMs along with a lack of instinct/experience.

I guess in essence, I think you can use LLMs in an old school troubleshooting way, and I find it still speeds that up the majority of the time. Its basically how I use it most of the time. And like old-school troubleshooting, if you build out the LLMs context over time, it also grows in capability, as long as its being used as a tool and not blindly trusted.

Should I not assume that most senior developers who are serious about LLMs do this?

I would argue the bias is for ceos (of public companies) to act a certain way due to inherent incentives the way the world is set up, which biases their personality. But I won't go into that old argument again.

You're hearing the echo and ascribing it to the noise.

A constant push and pull between leaders and the led is the fundamental. Criticism in both directions is necessary and healthy, and at different times both correct and misplaced.

There have been great leaders and awful ones. Acting as if all leaders are proven and deserving is as asinine as naming all CEOs as useless.

Right now we are at a point where CEOs are biased towards being quite anti worker and too easily rewarded. It's not surprising the cliches against them come out.

It's not objectively terrible. It's subjective - entirely.

It is a 3 hour long game with 4k or so reviews.

Life is strange original has 70K reviews and is 14 hours long.

The remaster has 4000+ reviews and again, is 14 hours long. These games are also all time favourites that fans do replay from time to time. If has more replayability than mixtape, despite it being fairly linear.

A 3 hour one time only game does not retain players on the charts like a game that takes multiple play sessions to finish. Or a game like "tennis elbow" which has far lower max concurrent (like, nowhere near the concurrent max mixtape has) but is a sim game that if it's your jam you will repeatedly come back to.

Its very simple - it's not a game for you, and you have to understand that these types of games absolutely have their niche fandoms who love this kind of stuff.

There is no conspiracy here, artsy titles like this have always, across all mediums, got a disproportional critical appreciation vs more traditional titles.

It's just the same old story. Yes people do like these games. No they are not the majority. Yes you can just ignore it, no it's not a conspiracy.

But it has the right creative vibe which is the point really. Apple has a general creative aesthetic which plays well with artsy games like mixtape.

It's a very deliberate choice when if it doesn't make sense to gamers.

Whether or not you don't like the game, it's a weird misrepresentation to say it's made by Megan ellison.

She's been the co-founder and major backer of Annapurna for a decade.

She's not a developer, she's a publisher, and this game is one of the tons of games Annapurna has put out over a decade.

I get that Annapurna is a lightning rod of politics due to ellison and is increasingly under scrutiny for many debates but they are a mainstay of the industry, hardly a blow in, and like it or not, a lot of the games industry and media really like these kinda of artistic pieces, with this being yet another instance of the age old walking simulator debate.

It should be judged on its own merits. Not it's Annapurna link and it's not a shock to see it come up in an artsy Apple Mac hipster circle.

Not quite sure what you mean by that, I'm not the one making decisions of who to cut. That's one to many levels above me.

Your stated argument would make sense if you're dealing with reasonable people. Which is sometimes.

But I'm talking specifically when shit hits the fan and people levels above me put down a mandate across many divisions.

As it is if they're making cuts they'll grasp whatever metrics they can and cut. If they don't have metrics that gives less protection, not more.

Take "as you can see I have X number of staff fully allocated and up to their eyeballs in work" vs "I don't have that metrics but trust me". That's part of my job or responsibility depending on my seniority to justify my team and keep them employed and not stressed by cuts and workload.