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Even if Deepseek v4.1 is the last open source LLM I have access to, I would be perfectly fine with that. My secret is that I have this relatively unknown technique for solving problems where I use this quantum engine that developed from just atoms over 4.54 billion years into the most sophisticated machine in the known universe. It's a frontier model that every human has access too, actually.

So when I really want to solve a problem, I just think on it. And when I need something to iterate through the things I thought of quickly, I use deepseek.

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> Even if Deepseek v4.1 is the last open source LLM I have access to, I would be perfectly fine with that.

Your quantum engine aside, current open-source LLMs will not age well. They have a knowledge cutoff. If you ask them five years from now about something that didn't exist when they were trained, chances are they won't be able to provide a useful answer. They may still be useful for many applications, but their value will eventually deteriorate. Having continuous access to up-to-date open-weight LLMs is therefore about much more than simply having access to "better" models.

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we will soon figure out how to update the engrams with new information
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Yes, but first we need to figure out how to collect that new information. An efficient and ruthless data harvesting in one of the LLM labs secrets.
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Yep, fully satisfied with DS 4.1 Flash or GLM 5.3 Flash. I prefer using one chat at a time, actually thinking through things with it, as opposed to the YOLO run all night multi-agent thing. I mean I’m still okay with subagents if my main thread needs to explore a few options and then report back, but all on the same idea.
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You might enjoy Cecli, the successor to Aider. It's made for the kind of workflow you are describing (full hands off yolo and recursive sub-agents also an option, but not the default)

cecli.dev

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How are you running these? Not locally, I assume?
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For this benchmark i 've been using OpenRouter. If you have enough RAM or you can ssh a cloud shell this intro https://memdoor.ai/docs/open-weight can work too. I have reached with the ssh door a Google Cloud Box where the 27B model ran with an L4 g2-standard-16. A inference engine server is setup easily with memdoor (https://github.com/guregodevo/janis) on any CPU or GPU box. Ideally i think we need a quorum of SSH boxes like this, distributed that you can ping with a mention such as @box:coder. It's WIP . Any suggestion for running a team of workers in remote machine is welcome. I'd like to find a cheap solution.
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The thing here for me is not that I’m unable to solve problems with my brain, it’s that I value the automation in labor provided by coding agents. I do not want future advancements to be locked behind proprietary doors.
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I agree with that but the cat is out of the bag now. Unless you believe in exponential improvement (which I don’t), the open models might be at a resource disadvantage, but that doesn’t mean that they can’t catch up or surpass the closed ones. Throwing money at a problem doesn’t guarantee success.

I mainly just want to point out the value of human ingenuity coupled with a model like DS4.1 is technically enough to get most people to where they want to go, and that includes smarter open models.

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Then you probably don't want to be funding the companies that want and intend for it to be locked behind proprietary doors.
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You say that now, but you don't know what the future models will be capable of. If models get ridiculously good, you might not be perfectly fine with that.
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I think at that point I’ll be doing carpentry instead of computing.
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How do you run DS 4.1 Flash if your computer isnt powerful and everything?
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It can run on one strix halo machine, which is what I have. For everything else, there’s a vibrant community brute forcing more efficient engines and quantizations. There’s a minimum bar right now, so it depends on your hardware.
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you have 5k$ of ram in a strix halo?
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I bought my box for $3k last year because I hadn't updated my PC since the first threadripper 1950x, and the Strix Halo is the only x86 with unified memory that's optimized for the kind of CPU intensive stuff I do, and they finally have proper AVX512 too. It was the perfect upgrade for me (except for lack of PCIe 5 & ECC), which coincidentally happened to be perfectly positioned for local inferrence.

and I liked it so much (and saw that the RAM drought was going to last for at least a few years) so I bought a second one to use as a general server for myself and my family.

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The fact that you point to the latest model as „I’d be fine with that“ proves my point.

If expectations were static you’d be fine with qwen 2.5…but nobody sane says that today.

It’s a moving target thus we need open weights to move

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