391–400 of 620 posts
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…
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I can use Gemini 3 Flash with the harness I built for around 8 years and still not exceed the cost of a Mac Studio with 128GB, the price for privacy is very high. Agentic flows that get stuck can be worked around but I prefer developer velocity.
Under-rated take, thanks for stating this! Not everyone can plough $$$$ into hardware right now (more power to those who can), so choosing to rent is an A-Ok strategy.
harness - pi+custom extension for subagents
model - qwen3.6 35ba3b q4km
hardware - intel arrow lake with 32gb ram
server - llama.cpp vulkan
performance - 15-18t/s generation 50-150t/s pp
planning and task creation is still using claude/gpt but they dont touch the code. All coding is done using this setup.
Example of project made using this setup easyanalytica.com , its of medium size complexity
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…
For personal use, yes. I replaced a $100/m subscription to claude in favor of running pi harness pointed at unsloth studio, using both qwen (unsloth/Qwen3.6-35B-A3B-MTP-GGUF) and gemma (unsloth/gemma-4-26B-A4B-it-GGUF) models, depending on my mood. I have a machine I built about 5 years ago with dual RTX3090s in it (I was going to build a new gaming machine anyways, and the llama release had just dropped so I tacked…
2x RTX3090 are around $4400. Without any electricity costs or other parts, that's 3.6 years of $100/m claude.
The only reason it’s economical is because it’s massively discounted if you’re not paying API rates.
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What kind of coding do you do? Do you keep track of frontier models to vibe check the differences and re-evaluate constantly or are you ok with having a nerfed model forever? (not being judmental, just really wanto to know your framework here)
Some of the work I do, I do for an (EU) organisation that doesn't have clear rules or guidelines on the use of AI yet. Though I have seen colleague-developers blatantly putting source code into external Claude-like models, I stay true to my principles and don't. I know for certain that everything that I run through my local, offline Pi Container Sandbox cannot leave the machine, and thus can't result in a data breach…
I'm not familiar with Pi, and not sure which kind of container you are referring to. Something mainstream like docker, or more classic like a BSD jail?
I started to experiment with locale LLMs, through ollama and Lemonade. Enough to throw simple prompts with code excerpts and get small scope code refactors. Though I still struggled to make them work with external tools, like my IDE, so they can be leveraged on to an agentic level with access to a full repository.
That's mainly for work, as they push for using LLMs, though with the new copilote license they provide it doesn't take me even a week to burn the whole token credit.
The tool can be useful, but in my experience without heavy guard rails and loops over tests. I suspect late models to also burn many token into rabbit hole of nonsense hypothesis, instead of doing straight forward correct implemention as you would expect from any entity with such a huge cumulated resources eaten and experimental playground to leverage on. Maybe incentives don't help model provider to minimize sold token, maybe it's just so hard to tame the beast all these bright minds with virtually infinite resources are not good enough.
Anyway, sorry for digression, but I would be extremely interested with a step by step tutorial to make a local LLM work in agentic level, including which kind of hardware is required to make it work properly.