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im hoping and praying that local inference finds it's way to some sort of baseline that we're all depending on claude for here. that would help shape hardware designs on personal devices probably something in the direction of what apple has been doing.

ive had to like tune out of the LLM scene because it's just a huge mess. It feels impossible to actually get benchmarks, it's insanely hard to get a grasp on what everyone is talking about, bots galore championing whatever model, it's just way too much craze and hype and misinformation. what I do know is we can't keep draining lakes with datacenters here and letting companies that are willing to heel turn on a whim basically control the output of all companies. that's not going to work, we collectively have to find a way to make local inference the path forward.

everyone's foot is on the gas. all orgs, all execs, all peoples working jobs. there's no putting this stuff down, and it's exhausting but we have to be using claude like _right now_. pretty much every company is already completely locked in to openai/gemini/claude and for some unfortunate ones copilot. this was a utility vendor lock in capture that happened faster than anything ive ever seen in my life & I already am desperate for a way to get my org out of this.

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I'm frustrated that there's not "solid" instructional tooling. I either see people just saying "keep trying different prompts and switching models until you get lucky" or building huge cantilevered toolchains that seems incredibly brittle, and even then, how well do they really work?

I get choice paralysis when you show me a prompt box-- I don't know what I can reasonably ask for and how to best phrase it, so I just panic. It doesn't help when we see articles saying people are getting better outcomes by adding things like "and no bugs plz owo"

I'm sure this is by design-- anything with clear boundaries and best practices would discourage gacha style experimentation. Can you trust anyone who sells you a metered service to give you good guidance on how to use it efficiently?

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yea that is probably the worst part of these techs becoming mainstream services and local-LLM'ing taking off in general: working with them at many points in any architecture no longer feels... deterministic i guess. way too fucking much "heres what i use" but no real best practices yet, just a lot of vague gray area and everyones still in discovery-mode on how to best find some level of determinism or workflow and ways we are benchmarking is seriously a moving target. everyone has their own branded take on what the technology is and their own branded approach on how to use it, and it's probably the murkiest and foggiest time to be in technology fields that i've ever seen :\ seems like weekly/monthly something is outdated, not just the models but the tooling people are parroting as the current best tooling to use. incredibly frustrating. there's simply too much ground to cover for any one person to have any absolute takes on any of it, and because a handful of entities are currently leading the charge draining lakes and trying to compete for every person and every businesses money, there's zero organized frameworks at the top to make some sense of this. they all are banking on their secret sauce, and i _really_ want us all to get away from this. local inference has to succeed imo but goddamn there needs to be some collective working together to rally behind some common strats/frameworks here. im sure there's already countless committees that have been established to try and get in front of this but even that's messy.

i don't know how else to phrase it: this feels like such an unstable landscape, "beta" software/services are running rampant in every industry/company/org/etc and there's absolutely no single resource we can turn to to help stay ahead of & plan for the rapidly-evolving landscape. every, and i mean every company, is incredibly irresponsible for using this stuff. including my own. once again though, cat's already out of the bag. now we fight for our lives trying to contain it and ensure things are well understood and implemented properly...which seems to be the steepest uphill battle of my life

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