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  • Hacker News
  • I had hoped to get some new information out of this topic, but unfortunately found the same local "dead-ends" that I explored myself.

    It unfortunately feels like we will be stuck waiting for a burst bubble before local hardware can be reasonably acquired for personal LLM usage.

    by dell2024
  • I've been using Claude Code Extension in VSCode (no phone-home configured), backed by DwarfStar on a LAN local MBPro 128GB M5. The context bloat is horrendous, leading to 5-10 minute prefills.

    I've recently been exploring tools like headroom to help manage context, with some limited "success" (for some definition of success). What do others with similar setups do?

    (I kind of hate to abandon Claude Code, as it seems to be the most capable coding assistant of the limited set of tools I've tried. But that horrendous context bloat is really painful!)

    by fghorow
  • Funny times!

    How many years after "public clouds" and non-local "disks" and "drives" we are ? :) And you still need to tell peoples that other have access to your private data :)

    Wait, no... They even have access to a thingie you just about to think about! ;) That is a superpower, no less :>

    And managers are firing peoples just to outsource "thinking" to some not owned by them cloud computer :>

    by Woodi
  • Why are you using Ollama? Just use llama.cpp
    by robotswantdata
  • TLDR: Local models have a smaller context window, so your 35kB prompts that worked fine against a hosted 1 Million token window, crash out when you only have a 65K (!) token window locally.

    I dislike being negative, but I was really hoping for more substance when reading this. It would have been an interesting topic.

    by SyneRyder
  • > Everyone who begins learning exploitation hits a phase of exploitability grief about 3 month into dedicated, practiced study. They hack something they didn’t think they had the skill to break into and it terrifies them. They’re smart enough to know that, relatively speaking, they are an idiot, and if an idiot can do this then nothing is safe. That feeling is correct.
    by andai
  • Friends Don't Let Friends Use Ollama https://news.ycombinator.com/item?id=47788385
    by cube00
  • The article doesn't really describe the problem: if your prompt is 35kb, your prompt is confusing, unfocused, and doesn't work right on any LLM, and is needlessly bloating your context.

    At this point in time, due to how most people and companies run their inference engine, regardless of the model (yes, this includes the newest from OpenAI and Anthropic and the Chinese Tigers and Dragons), you run out of useful context that the model can accurately attend to around the 250k mark no matter how much they advertise their context size is.

    You need to cut your prompt up. If you believe LLMs work, have the LLM help you shape the overall plan, and then have multiple sessions run each step in the plan without being bloated with the context of previous successful steps.

    I don't see LLMs being production-ready until the context rot and sampling problem is fixed forever. This has not occurred, and the big inference providers aren't even bothering to integrate any of the research on that subject.

    If anything, many of the bigger companies are actively making inference quality worse just to extend their runway a tiny bit farther before they go bankrupt.

    The only thing the article gets right is this: if you're serious about LLMs, abandon Big AI and infer locally only. This is the only way you have control over the quality of the output.

    by DiabloD3

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