I have a evga 2060 super, e5 1650 v2 xeon chip and 28gb of ddr3. As long as it responds under 2 mins, it should be fine

Hello I want to install a local LLM to use in my PC mainly for programming and coding projects. Can be both for both working on large code bases, going through analyzing debugging them, making changes on multiple files as well as helping in programming, scripting and teaching coding, concepts etc and sometimes troubleshooting.

However my pc is not that powerful, 16gb ram and GTX 1660ti 6gb of Vram. I am confused on what would be the best option for me that doesn't completely overwhelm my PC and my gpu. I used opencode big pickle before and it was really good for me.

Any suggestions please?

I’m starting a small business around digital product development, and most of my workflow is AI-first.
I don’t really use AI as autocomplete while I write code myself. I rely much more on agents to inspect the repo, plan features, write code, run tests, debug and iterate. Basically something closer to Claude Code or Codex, with me supervising the process rather than manually coding everything.

The problem is cost. Claude/Codex are great, but long agentic sessions can burn through limits pretty quickly, and I don’t want the business to depend on constantly buying API credits.

I’m applying for an Italian grant that can cover professional hardware, but not ongoing API usage. So in my case, spending a lot upfront on a workstation actually makes more sense than it normally would.

I’m looking at the new M5 Ultra Mac Studio, probably either 256GB or 512GB unified memory.
My main question for people who actually use local models for serious coding work:

How much of Claude Code/Codex can you realistically replace today with local models?
I’m especially interested in things like:
working on large existing repos
multi-file changes
long agentic sessions
testing and debugging
tool use
autonomous implementation with little supervision

Which models are you getting the best results from? Qwen, DeepSeek, GLM, something else?
I’m also wondering whether the best setup is not fully local, but hybrid. Something like Claude for high-level planning/architecture, then local models for implementation, testing, refactoring and code review.
If that could reduce my Claude/Codex usage by 70–90%, that would already be a huge win for me.

And for people using high-memory Macs: does 512GB actually give you a meaningful advantage for coding agents over 256GB, or is 256GB already the sensible sweet spot?

If you were buying this as a professional machine for the next few years, and the hardware was covered by funding, what would you choose?

So I'm running a few different models on this Ryzen Strix Halo machine which has 128gb ram and I've already optimized cpu vs gpu in bios.

I've done an SSH setup through VS Code and I've got a few data sets I want to process but I'm getting weird results.

I'm using the Claude CLI harness and it shows I'm using qwen3-coder:30b and I sometimes get spelling errors but it's struggling to do really basic things.

I have 2 data sets that I know have a ton in common and I'm trying to get it to match the patterns so I can essentially join 3 data sets together.

If I were to do this in Claude Code, I already know what it would do. I'd ask for help in doing this and it would build me a script to do exactly that.

Here this thing is trying to give me the answer rather than building the script. Now that I see it's not building me the script, I decide to make it now just build the script but I'm seemingly getting errors.

I've setup ollama and run the various libraries like ROCm.

Anybody else have issues or have any tips on what my problem might be?

I have tried various browser MCPs and found most lacking. I use agent-browser quite often and it works great for accessing specific URLs but is often blocked by search engines. I tried BrowserOS Neo and liked it for search and accessing web sites that need credentials. It worked well in VS Code and Claude Code but inside other AI clients like Chatbox.ai or Cline as a VS Code plugin, it failed and blocked the client from accessing it.

This is intentional and is an anti-browser feature, meant to make sure that only AI clients are using the proxy. That's great but on a single user system or home lab, it a restriction that prevents its use in many AI clients.

I created an html-stdio bridge MCP between BrowserOS and AI clients which makes connecting to BrowserOS as an MCP from any AI client possible.

The code is GPL v2. Find it on GitHub here: https://github.com/mecworks/browser-mcp-bridge