341–350 of 620 posts
I'm using Qwen 3.6 on my MacBook Pro M5 Pro with 48BG RAM for any work that I am particularly privacy conscious about, like any work with my journaling. It's been working great! I don't have any direct comparisons, but I've been satisfied with the results.
I have! I care about data privacy and LLMs being free. I'm using the Pi coding harness but containerized and sandboxed, to make sure it's running completely offline. On my Mac Studio with 128GB RAM (or MacBook with 36GB RAM) I'm using Qwen3.6 35b, with only 3b active parameters so that it runs really fast. I've done a complete redesign for my website's homepage and blog with Django + Wagtail. The latter is interestin…
that's why i use the frontier models because its a senior co-worker vs a junior. if you use the junior for the sake of privacy i think you're missing out on the best insights for a specific task.
I have! I care about data privacy and LLMs being free. I'm using the Pi coding harness but containerized and sandboxed, to make sure it's running completely offline. On my Mac Studio with 128GB RAM (or MacBook with 36GB RAM) I'm using Qwen3.6 35b, with only 3b active parameters so that it runs really fast. I've done a complete redesign for my website's homepage and blog with Django + Wagtail. The latter is interestin…
This is very similar to my setup. Pi in a container (I do let it have network access, just no access to creds or anything, only the one directory that I'm working on at the time and my ~/.pi directory), talking to llama.cpp in another container. I'm on a Strix Halo 128 GiB unified memory laptop. I've never used the frontier models in earnest, I don't believe in using proprietary tools for my programming, so I can't r…
What does this mean in June 2026 wrt coding?
To me it sounds like being a "rice cooker skeptic". Some people don't like using rice cookers, some do.
I don't use local hosted models anymore due to hardware contstraints, but I do have some degree of search anonymisation attached to my OpenCode and OpenRouter connected open models.
On my Macbook I run OrbStack that has the following docker containers set to route through a Mullvad based gluetun.
- Firecrawl - fast web scraping
- SearxNG - metasearch
- CloakBrowser - tursile bypassing Playwright alternative
If you wanted to get fancy with the proxy rotation, you could setup numerous instances of Playwright each with their own Mullvad wireguard key in different locations.
Not “local” and not interactive coding but sharing since it might be helpful. I have 2x RTX Pro 6000 Blackwell running DeepSeek V4 Flash. I get 160 tok/s raw but it’s a reasoning model. For my use case, I have it auto-write code and another system auto-review the code. I occasionally use it with pi to write some code and it’s blazing fast but it’s mostly habit that keeps me with CC and Codex.
> I have 2x RTX Pro 6000 Blackwell Where did you find/order these? All the sites I can find are either out of stock, only sell to businesses, or are otherwise sketchy...
My experience is that it's not the models themselves that are limiting right now, it's the clunky alternative harnesses with weird missing features making for bad ergonomics around stuff like queue management, interruption, subagents, goals, etc.
I agree completely. It's also annoying that OpenCode doesn't even try to support local LLMs properly. Getting OpenCode to work is possible, but extremely manual and clunky to configure. I have written a script to automate converting my llama-server configs into an OpenCode config, and that helps, but it's not ideal. I have seriously considered writing Yet Another Coding Harness in my free time. I have some ideas for…
Earlier quoted context omitted.
Have you measured your electricity consumption for this rig? I have to wonder how much it would cost you per month.
Here's a DeepSeek-V4-Flash benchmark on 2X RTX Pro 6000: - Prefill: ~10K tok/s - Decode: 190 | 375 | 980 tok/s (for 1 | 4 | 16 concurrent requests) - GPU power draw during benchmark: Average: 585W | Max: 849W | Limit: 1200W with undervolt. Idle PC is 125W. I've asked it to calculate the following considering a realistic blend of cached prompts and decode for agentic dev scenario. Electricity-only (@ USD $0.08/kWh) Us…
I have! I care about data privacy and LLMs being free. I'm using the Pi coding harness but containerized and sandboxed, to make sure it's running completely offline. On my Mac Studio with 128GB RAM (or MacBook with 36GB RAM) I'm using Qwen3.6 35b, with only 3b active parameters so that it runs really fast. I've done a complete redesign for my website's homepage and blog with Django + Wagtail. The latter is interestin…
> Comparing agentic Qwen3.6 35b to Claude Opus is like a junior with knowledge across the board, that you really need to guide, versus a senior that thinks with you on architecture. that's why i use the frontier models because its a senior co-worker vs a junior. if you use the junior for the sake of privacy i think you're missing out on the best insights for a specific task.
Consumer-grade subscriptions of the frontier models give you superb capabilities per dollar, them being heavily subsidized. But if you're working in an enterprise setting, that won't work. You need to upgrade, and that gets significantly more expensive.
Furthermore, basing the SDLC on leveraging the bargain subscriptions risks falling apart in the future, both from a cost perspective as well as the question of availability (e.g. Mythos).
So from a strategic perspective, going local on the LLM and still achieving great results with the right approach is very relevant.
I have! I care about data privacy and LLMs being free. I'm using the Pi coding harness but containerized and sandboxed, to make sure it's running completely offline. On my Mac Studio with 128GB RAM (or MacBook with 36GB RAM) I'm using Qwen3.6 35b, with only 3b active parameters so that it runs really fast. I've done a complete redesign for my website's homepage and blog with Django + Wagtail. The latter is interestin…
All of these models also seem to get stuck in long thinking loops, sometimes tripling the tokens of a frontier closed model which is really painful when inference is already on the slow side (on my Macbook).