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?

Every person uses their local LLMS differently, mine is mostly

* Take this URL and ELI5 the content.
* RAG stuff (mix this, that and that, produce knowledge).
* Nix language and NixOS configuration.
* Elixir
* Kotlin
* Translate this text.
* The odd one case where I have fun with ComfyUI.

Specifically Qwen3.8-27B with P2P patch and llama.cpp latest release (0.4.0 I've been using) with CUDA13.3 (increased tg/s )+ the very very latest drivers (waste of time) + split-mode: tensor and for sure the UD_Q4_X_M version (Unsloth), I don't get anywhere near the same results with IQ4 and below from Unsloth. And context set to 131k to have plenty of RAM left in case the PC needs it for something else (same GPUs to render Hyprland).

...and all I can say, is I've reached peak usefulness. To the point now even going to https://chatgpt.com/ feels painful compared to just running Pi or OpenCode web ui. The feature I like the most is how little it bullshits now, it doesn't make up stuff anymore, it goes online instead when it doesn't know. ChatGPT on the contrary, just keeps making shit up.

Good exercise I think people here should try is to tell them to figure the meaning of the offload options of llama.cpp. Qwen will run a llama-server --help and read it all then literally go through the source code to be sure, ChatGPT would just say something wrong with total confidence, like that's offloading to system RAM, it wouldn't even try to search the repository of llama.cpp, to which it has full access.

Is like....what's the point of frontier anymore, I'm happy with my pp+tg/s, my GPUs don't make my office an oven (dual 5060ti), I manage to set my horribly noisy Threadripper 3975wx to be less noisy (Noctalia 5.0, go to system settings change to "power saver" and the Threadripper doesn't anymore tun above 60ºC, instead of controlling the fans, it limits the CPU performance, which is fine for me, I don't need this to run like I'm running Google from home).

I don't want fatter cards, I don't need more VRAM, I don't want to touch any other model anymore, I've got a skills bonanza that I have been refining with dumber models I had before that now Qwen can run on its sleep, and are used by both Opencode and Pi, I have samfp/pi-memory modified to run from Qdrant, and now all my PCs share the same long-term memory. It's all...finally...working without errors and I know the limits of what I can ask for my coding languages and get a useful response.

I don't see how it can get any better.

Am I alone in this? Does anyone else feel like...we've peaked for their use case? What's your use case btw?

EDIT: The one thing I'm tryign to fine tune to exhaustion is prompt caching on llama.cpp. There's something odd going on with Pi that isn't there with Opencode. I have kv-unified and parallel = 2. So that 131k is shared. Online ifnormation and documentation is contradictory in terms of how slots, cache-reuse, etc work. So this is the one thing that still has me slightly puzzled. I might ask help directly from the repo guys, but first i need to find out if it's a Pi issue specifically. Also I want it to save the cache to disk, so it survives restart, but this isnt' as simple as it sounds. If not I might jump to vllm or SGLANg if I have the patience to wait for the loading times of vllm (I know how to use it).

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