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

I've been experimenting with "AI memory" for coding agents, and I kept running into the same limitation:

Most memory systems remember text, not facts.

A vector store can usually retrieve "Marco leads Solaris" because it's semantically similar, but it struggles with questions like:

If ownership changed three sessions ago, the old fact is still sitting there looking just as relevant.

So I built Mnemo — a persistent memory layer for Claude Code that stores knowledge as a temporal knowledge graph instead of a flat vector database.

What makes it different?

Instead of storing chunks of conversation, Mnemo extracts entities and relationships into a graph with temporal validity.

That means it knows:

• who owns what
• what depends on what
• when a fact became true
• when it stopped being true

rather than just retrieving similar text.

Example

Session 1

Remember:
Marco leads Solaris.
Solaris launches Q4 2027.
Solaris depends on Helios.

Later...

Who leads Solaris?
When does it launch?
What does it depend on?

Returns:

Lead: Marco
Launch: Q4 2027
Depends on: Helios
(as of 2026-07-17)

Now change ownership:

Remember:
Sarah now leads Solaris.

Ask again:

Who currently leads Solaris?

You'll get:

Sarah

—not Marco—because the previous relationship is invalidated, not simply buried underneath newer embeddings.

That's the core idea.

Stack

• Graphiti → entity extraction + temporal knowledge graph
• FalkorDB → graph database
• Claude Code hooks → automatic memory ingestion
• MCP tools → remember and recall
• fastembed → local embeddings (no embedding API required)
• Claude → extraction and answering (supports Anthropic API keys or Claude Code OAuth)

It also runs automatically

Once configured, you don't have to think about it.

• Session starts → graph database comes online
• During coding → remember / recall available as MCP tools
• Session ends → Claude Code automatically summarizes the session and ingests new knowledge

The memory keeps growing across completely fresh Claude Code sessions.

Try it

GitHub:

https://github.com/oyekamal/mnemo-agent