Ask HN: Has anyone replaced Claude/GPT with a local model for daily coding?

Until I can buy an 80GB VRAM GPU, I won't attempt to do it. A local LLM is always missing something that needs a bigger model.

Prompt more directly instead of open ended.

Have you measured your electricity consumption for this rig? I have to wonder how much it would cost you per month.

It has so far been the kind of thing that always feels like the next version of the local models would be the one that is just good enough.

mbp16 m5 max 128gb, antirez/ds4, deepseekv4-flash. Works well for relatively dense (let's say Runs through Pi with a custom prompt (basically "don't speculate blindly, isolate things, make them traceable and measurable, then verify") and behind a pretty restrictive bwrap setup - RO bind everything other than ~/.pi, cdw and a separate tmpfs, unshare almost everything other than the network - for which I use a network namespace that only allows tcp connections to a specific ip and port (i.e the inference mac) - i.e. netns exec into bwrap.

Can't compare it to SOTA or higher-requirements models on what I work on - policy. That said, on a bunch of test pieces - it obviously isn't gpt-5.5, it definitely lags behind k2.6/glm/ds4-pro, but it absolutely is usable. Of course, on such codebases, forget about one-shotting or trusting it blindly or anything of the sort - you ask it, guide it, restart the context from time to time to have a "fresh dice roll" and to keep the context small and clean, etc. Compared to anything smaller (incl. all the usual local qwen models) - on a test piece, it figured out that memfd and mmap were used for setting up a ring buffer with natural wraparound handling (double mapping the first page at the end) and didn't tell me "this is for sharing memory between processes" or some other BS.

Performance as described in the tables in the readme here: https://github.com/antirez/ds4 ...with a bit less than half that at "low power" (30w). Both are usable.