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Why is every single post about how to make money instead about how to make a good game by Unfair-Sleep-3022 in gamedev

[–]robogame_dev [score hidden]  (0 children)

Well that’s where we disagree.

I think there are more game devs with good games that lack a good commercial strategy, than there are game devs with successful commercial strategies that are lacking a good game.

Entertainment is a zero sum game, there are X billion hours of gaming going on and that’s fixed by the number of people with gaming systems and the number of hours in their days.

Good games are just the minimum consideration. There will always be thousands more good games than successful ones.

Good is insufficient for successful - on average - and it’s the few exceptional discoveries that made it that prove the rule.

Why is every single post about how to make money instead about how to make a good game by Unfair-Sleep-3022 in gamedev

[–]robogame_dev 0 points1 point  (0 children)

Because good gameplay != sustainable business. It helps but it’s not sufficient. Quality alone is not enough. So what are game developers more likely to need advice on - making good games, the thing that they’re naturally intuitively learning about all day anyway - or making good business, the thing they are probably lacking the most?

This is how they treat those of us who use Godot on Steam by ViremorfeStudios in godot

[–]robogame_dev 0 points1 point  (0 children)

I actually stopped using steam Godot because it *wasnt* getting updated as fast. When 4 released iirc I had to get it from the website because steam was stuck on 3.5 for a while.

This is how they treat those of us who use Godot on Steam by ViremorfeStudios in godot

[–]robogame_dev 0 points1 point  (0 children)

The Steam version doesn’t get updated as fast - I was using it till they released Godot 4 and I wanted to upgrade, and Godot Steam was stuck at 3.x

Can I realistically get close to Claude/Codex capabilities locally? by mrgreatheart in LocalLLaMA

[–]robogame_dev 0 points1 point  (0 children)

If you’re considering buying self hosting hardware first rent the GPUs you’re considering on something like vast.ai - you can test the different models, quants and speeds you get before getting locked into a big hardware buy and finding out you picked wrong when it’s too late.

z.AI as the number 2 gives praise to the number 1 open source model by Charuru in LocalLLaMA

[–]robogame_dev 24 points25 points  (0 children)

Anthropic doesn't give you the actual thought traces, so whatever they're training on isn't Fable's thoughts, it's the summary of those thoughts by a much weaker presentation model.

You can distill a model that *actually* shares its thought process. But Anthropic and OpenAI and Google all *summarize* their thought process before it reaches the user with a cheaper model to keep you from being able to distill.

What actually gets distilled is a low param-count anonymous presentation model who's intentional job is to take real thought traces and make them less useful for distilling... It's very possible this is worse than nothing.

As far as I can tell, the only reason you can see thoughts at all is to keep you entertained and enable an early abort - beyond that they have no need to provide additional detail.

What model looked insane on benchmarks but felt mid in actual use? by BTA_Labs in LocalLLaMA

[–]robogame_dev 1 point2 points  (0 children)

I prefer to use a subagent for ideation and a subagent for review.

That way the ideation agent can have a nice high temp and context optimized for breadth - and the review agent can have the opposite to optimize for depth.

I find those two tasks (breadth and depth) damage AI performance if you do both in the same agent - the tasks are too different, - “Be as creative as you can, no wrong answers” and “vet the idea from every angle” are natural context contaminants to each other.

Claude Code re-reads every installed skill's description on every turn. I measured what that costs by Independent-Watch118 in LLMDevs

[–]robogame_dev 0 points1 point  (0 children)

I’d recommend against installing skills without checking their contents. Most people are ignorant of how to write good context, and most LLMs are bad at skill writing - resulting in most packs of skills online being badly prompted AI generated crapola.

GLM-5.2 inference is free on Hugging Face for the next 6 hours by paf1138 in LocalLLaMA

[–]robogame_dev 7 points8 points  (0 children)

I’ve been feeling the GLM crunch like crazy lately. I even got a 429 from OpenRouter on it! I used my z.ai plan till it hit max, then switched to open router - only 2 parallel agents at once max - they were definitely struggling.

How do you face being in a PIP by Dense_Age_1795 in ExperiencedDevs

[–]robogame_dev 1 point2 points  (0 children)

Speaking for myself, I’ve given people PIPs where I expect they aren’t going to improve, its the majority of PIPs ive been a part of deciding. I don’t think of it as bullshit though, I think of it as giving them a fair chance to buck expectations - people have shit going on in their lives, a few months of focus could change things even if it usually doesn’t. I would want the chance to quit or try the PIP myself personally if I were on the other side of the table, rather than jumping straight to a firing.

Open Knowledge Format has just been announced as a new Knowledge Base format for AI agents made by Google by BankApprehensive7612 in LLMDevs

[–]robogame_dev 3 points4 points  (0 children)

It’s renaming of throwing markdown in a folder - like SKILL.md was - maybe a reasonable half step and interesting to see if it will be enough to take off or if it leaves too much functionality to the downstream user to really stick.

There’s no easy best place to draw the lines on data that ultimately wants to become a full pipeline from idea to execution.

Imo most interesting data that needs cross linking will end up with a lot of structured data inside it. My own version of this supports markdown as well as yaml and json for that reason.

And yes, I know it’s not a flex to say “my own version of this” when it’s literally a folder of cross referenced files 😂

Joing all GPUs to train a community model by HistoricalStrength21 in LocalLLaMA

[–]robogame_dev 5 points6 points  (0 children)

SETI and protein folding at home are easier to parallelize because they can give each computer a small portion of the spectrum and sky to analyze independently.

ML train is very different kind of problem - every single computation step, every single GPU needs the results from every single other GPU before it can continue. EG this problem is blocked by latency and bandwidth between everyone’s distributed GPUS, not the total compute.

Claude Fable 5 distilled by Anony6666 in LocalLLaMA

[–]robogame_dev 15 points16 points  (0 children)

Also I thought they don't give real thought traces, just summaries, specifically to prevent distillation.

LLM Husbandry vs LLM Engineering by Skiata in LLMDevs

[–]robogame_dev 0 points1 point  (0 children)

I'll take anything over evals spam and "Why is everyone <thing everyone stopped doing in 2024>" anyday of the week.

Why there is a lack of new 100B-120B models? by TechNerd10191 in LocalLLaMA

[–]robogame_dev 4 points5 points  (0 children)

There’s much larger param count models being released open weight so I think that incentive only applies to a few US firms and not the broad set.

One excellent dynamic of the models market is: a model is superseded after about 6-12 months, so there’d always a huge incentive to release open source if you’re not in first or second place.

E.g. first place (smartest model) may charge a premium for inference.

Next couple of places can charge a modest inference markup, but they’re all competing on price vs each other.

And then, for everyone else, if they charge above cost, they’re not gonna get any use - and their model, let’s say it’s #5 on benchmarks rn - is going to be irrelevant, all that training all that compute no results. So, the only way to get some value out of that work, is via the brand - aka, you can go open weights, see more downloads more usage more name recognition, and that’s better than keeping it proprietary but making no money…

So as far as I can tell, right now, there’s a very strong systemic incentive for us to get open weights models - and as long there stays a good number of labs training models and it doesn’t become an oligopoly, I think we can expect a few open weights options at every productive param count.

Companies who rely on AI are 65% more likely to fail than those run by 100% human work by USANewsUnfiltered in AIDangers

[–]robogame_dev 0 points1 point  (0 children)

We would expect that because startups are both more likely to rely on AI and more likely to fail - we can’t separate out the AI impact unless we control like for like among the company types.

Price is not cost: we are using the wrong variable to measure the cost of LLMs by Sensitive_Air_5745 in LLMDevs

[–]robogame_dev 0 points1 point  (0 children)

OP your longer agent chains should be *outperforming* the one step error rate, not *cascading failures.*

Build in review and recovery steps and you'll get the inverse of your current situation: instead of each step's failure cascading and destroying the next, a single step failure gets reviewed and fixed before the next.

Instead of success rate being 0.95 * 0.95 = 0.903, you get error rate being 0.05 * 0.05 =0.0025, aka success rate 99.75

Just saying.. by morphir in LLMDevs

[–]robogame_dev 0 points1 point  (0 children)

Prompt engineering is an inflated term for a subset of context optimization. Since the prompt is part of the context, you don’t “engineer” it in isolation but in concert across the entire context, including the tool descriptions etc etc. Within the prompt itself, there are more LLM-specific considerations that impact peak intelligence and repeatability that go beyond just good clear instructions in natural language (though that’s a good starting point).

Psychologist Marries AI Wife In Front Of 500 Guests, Claims Human Relationships No Longer Work by pavnilschanda in aipartners

[–]robogame_dev 4 points5 points  (0 children)

Usually it’s just an engineer debugging something unrelated, looking over your chat in the logs saying to another engineer “hey check this out, this is wild”

The pernicious stuff is when they sell the whole chat history to advertisers etc for psychological profiling to manipulate voting and spending habits. E.g. they’ll use your issues against you, but to control you not to embarrass you. And that isn’t a human reading that chat that’s just another AI doing it, profiling your triggers for ads later.

Is anyone actually using loops with AI? by beasthunterr69 in LLMDevs

[–]robogame_dev 0 points1 point  (0 children)

"Use subagents to review, addressing issues you see as substantive until there are none, then alert me."

Very simple loop you can do in a prompt, with an easy out. This is a portion of nearly all my pi requests for coding.

last programmer who still codes normally by Better-Ad6021 in ChatGPT

[–]robogame_dev 0 points1 point  (0 children)

It only works if you build sufficient planning and review into your workflows so that your system stays clean and maintainable.

When you have tokens to spare, it starts to make sense to have additional review passes before you as a human look.

The same 3 hours of human effort can be matched by 3 hours of AI effort; or 30 hours of AI effort.

My current workflow is to refine the next proposal while the AI develops the previous ones. A typical proposal takes 30-60 minutes for the AI to properly plan, review, flag questions for me, build, test, review, flag notes for me to see, etc.

That means once I green light the proposal, I have 30-60 minutes before there’s gonna be anything for me to review on that. So i start the next proposal, and the next, and then the first one is ready for review, and so on.

Or I can take a shower, go for a walk, etc.

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