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Local Models vs. Cloud APIs

Discussions on the viability of local models for privacy and cost savings versus the necessity of massive cloud models like Opus for complex reasoning tasks.

← Back to Opus 4.5 is not the normal AI agent experience that I have had thus far

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That's what things like AWS bedrock are for. Are you worried about microsoft stealing your codebase from github?
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I've said this multiple times: This is why you use this AI bubble (it IS a bubble) to use the VC-funded AI models for dirt cheap prices and CREATE tools for yourself. Need a very specific linter? AI can do it. Need a complex Roslyn analyser? AI. Any kind of scripting or automation that you run on your own machine. AI. None of that will go away or suddenly stop working when the bubble bursts. Within just the last 6 months I've built so many little utilities to speed up my work (and personal life) it's completely bonkers. Most went from "hmm, might be cool to..." to a good-enough script/program in an evening while doing chores. Even better, start getting the feel for local models. Current gen home hardware is getting good enough and the local models smart enough so you can, with the correct tooling, use them for suprisingly many things.
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> Even better, start getting the feel for local models. Current gen home hardware is getting good enough and the local models smart enough so you can, with the correct tooling, use them for suprisingly many things. Are there any local models that are at least somewhat comparable to the latest-and-greatest (e.g. Opus 4.5, Gemini 3), especially in terms of coding?
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Open source models hosted by independent providers (or even yourself, which if the bubble pops will be affordable if you manage to pick up hardware on fire sales) are already good enough to explain most code.
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You can run an LLM locally (and distributed compile systems, where the compiler runs in the cloud, are a thing, too) so that doesn't really produce a distinction between the two. Likewise, many optimization techniques involve some randomness, whether it's approximating an NP-thorny subproblem, or using PGO guided by statistical sampling. People might disable those in pursuit of reproducible builds, but no one would claim that enabling those features makes GCC or LLVM no longer a compiler. So nondeterminism isn't really the distinguishing factor either.