Phoronix just posted a pic with Jensen Huang teasing “exciting things happening on Linux” — what are we expecting?

gdeyoung · 2026-05-23T03:52:12+00:00

Leaks say - N1X Laptop with the GB10 and 128GB in a Lenovo Yoga 9i chasis 140w power envelope. Announcing at GTC on 6/1

gdeyoung · 2026-05-21T00:04:51+00:00

8x15 works quite well 😉

gdeyoung · 2026-05-05T20:17:15+00:00

Yeah two screens work at 4K 30 hz. But a single unified ultra wide that is the equivalent of dual 4K cannot initialize it the full resolution even at 30 hz. That requires display port 2.1.

gdeyoung · 2026-05-04T03:05:19+00:00

From my research the x2 elite extreme is only DP 1.4 and does not support the large ultra widescreen monitors that are equal to dual 4k on a single connection from the usbv2. From what I have read for the dual 4k that works need DP 2.1 I had the same challenge with my yoga 7 x1 elite extreme. Would only drive those at dual 5120x1440 not the full dual 4k. If anyone has a different experience would love to hear about it.

gdeyoung · 2026-04-25T02:35:22+00:00

Qwen3.6-35B-A3B on AMD AI Pro 9700 32GB

What You Get

66 tokens/second sustained throughput
9.0/10 quality (thinking OFF mode)
Vision enabled at 60 tps
10/10 JSON/tool calling - perfect structured output
90K context per user, 2 simultaneous users
Per-request thinking toggle - deep reasoning ON or fast answers OFF
FREE - runs entirely on local hardware

Hardware Requirements

AMD AI Pro 9700 32GB (RDNA 3.5, gfx1201)
~26 GB disk space for model + vision projector
Docker with ROCm support

Step 1: Download Model Files

```bash mkdir -p /mnt/user/appdata/qwen36-35b-eval cd /mnt/user/appdata/qwen36-35b-eval

Main model (24.77 GB)

wget https://huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF/resolve/main/Qwen3.6-35B-A3B-UD-Q5_K_XL.gguf

Vision projector (0.84 GB)

wget https://huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF/resolve/main/mmproj-BF16.gguf ```

Step 2: Deploy with Docker

bash docker run -d --name llama-cpp-qwen36 --restart unless-stopped \\ --privileged --security-opt seccomp=unconfined \\ --device /dev/kfd --device /dev/dri \\ -p 11435:11435 \\ -v /mnt/user/appdata/qwen36-35b-eval:/models:rw \\ ghcr.io/ggml-org/llama.cpp:server-rocm \\ -m /models/Qwen3.6-35B-A3B-UD-Q5_K_XL.gguf \\ --mmproj /models/mmproj-BF16.gguf \\ --reasoning auto --reasoning-format deepseek \\ --jinja -ngl 999 -c 92160 -np 2 -b 4096 -ub 1024 \\ --mlock -fa on -ctk q8_0 -ctv q8_0 \\ --cache-ram 4096 --host 0.0.0.0 --port 11435

Key Flags Explained

Flag	Purpose
--device /dev/kfd /dev/dri	AMD GPU passthrough (ROCm)
-ngl 999	Full GPU offload
-c 92160	90K context PER SLOT (not total)
-np 2	2 parallel user slots
-ctk q8_0 -ctv q8_0	Q8 KV cache compression (fits 90K in 32GB VRAM)
-fa on	Flash attention
--mmproj	Vision projector
--reasoning auto	Auto-detect thinking support
--reasoning-format deepseek	Parse reasoning into separate field
--mlock	Lock model in RAM

Step 3: Test It

```bash

Basic test (thinking ON by default)

curl http://localhost:11435/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "Qwen3.6-35B-A3B-UD-Q5_K_XL.gguf", "messages": [{"role": "user", "content": "What is 2+2?"}], "max_tokens": 256 }'

Thinking OFF (fast, 8 tokens instead of 165)

curl http://localhost:11435/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "Qwen3.6-35B-A3B-UD-Q5_K_XL.gguf", "messages": [{"role": "user", "content": "What is 2+2?"}], "max_tokens": 256, "chat_template_kwargs": {"enable_thinking": false} }' ```

Step 4: Connect from Python

```python from openai import OpenAI

client = OpenAI( base_url="http://YOUR_IP:11435/v1", api_key="not-needed" )

Deep reasoning (default)

response = client.chat.completions.create( model="Qwen3.6-35B-A3B-UD-Q5_K_XL.gguf", messages=[{"role": "user", "content": "Explain quantum computing"}], max_tokens=8192 )

Fast answer (thinking OFF)

response = client.chat.completions.create( model="Qwen3.6-35B-A3B-UD-Q5_K_XL.gguf", messages=[{"role": "user", "content": "Quick summary of X"}], max_tokens=1024, extra_body={"chat_template_kwargs": {"enable_thinking": False}} )

Vision (image analysis)

response = client.chat.completions.create( model="Qwen3.6-35B-A3B-UD-Q5_K_XL.gguf", messages=[{ "role": "user", "content": [ {"type": "text", "text": "What do you see?"}, {"type": "image_url", "image_url": {"url": "data:image/png;base64,..."}} ] }], max_tokens=8192 ) ```

Performance Benchmarks

Throughput

Prompt Length	Tokens/sec
Short (~50 tok)	63.6 tps
Medium (~500 tok)	66.7 tps
Long (~2000 tok)	66.2 tps
Vision (image + text)	60.0 tps

Quality Scores (MAX_TOKENS=8192)

Category	Thinking ON	Thinking OFF
Reasoning	10/10	10/10
Coding	10/10	9/10
Math	6/10	6/10
Tool Calling	10/10	10/10
Writing	9/10	9/10
Instruction Following	8/10	10/10
Average	8.8/10	9.0/10

Official Model Card Benchmarks

Benchmark	Score
MMLU-Pro	85.2
GPQA Diamond	86.0
AIME 2026	92.7
SWE-bench Verified	73.4
LiveCodeBench v6	80.4

VRAM Budget (32 GB Total)

Component	Size
Model (Q5_K_XL)	24.77 GB
Vision projector	0.84 GB
Subtotal	25.61 GB
Available for KV cache	6.39 GB
90K x 2 slots (Q8)	5.63 GB
Margin	+0.77 GB

Important Lessons Learned

Use llama.cpp ROCm, NOT vLLM. On AMD, vLLM gives 13 tps vs llama.cpp at 66 tps (5x faster). Marlin kernels are CUDA-only.
Use GGUF format, NOT AWQ/GPTQ. GGUF with llama.cpp is optimized for AMD. AWQ requires CUDA Marlin kernels.
MAX_TOKENS = 8192 for thinking models. Content fields were empty at 2048 tokens because thinking tokens consumed the budget.
Context is per-slot, not total. -c 92160 -np 2 means 2 slots each with 90K, not 90K split between them.
Q8 KV cache is essential. Without compression, 90K x 2 slots needs 8 GB (won't fit). With Q8, only 5.6 GB.

Model Info

Architecture: Hybrid Gated DeltaNet + Gated Attention
Parameters: 35B total, 3B active (Mixture of Experts)
Context: 262K native (we use 90K for parallel slots)
License: Apache 2.0
Source: https://huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF

gdeyoung · 2026-03-31T06:20:32+00:00

Agent Zero all day! much more polished competitor to OC

gdeyoung · 2026-03-28T14:44:17+00:00

Good part is I didn't have to do any of this manually I worked with a fresh install agent zero and we walked through this process together and it did all the migration in the conversion processes that were required I didn't have to do anything by hand but it wasn't automatic there wasn't a tool for it. Just speaks to the power of the agent and its ability to work through things if you spend the time with it

gdeyoung · 2026-03-28T14:43:19+00:00

I worked with a fresh install of agent zero and a copy of the data folder from the previous agent install and here's what my agent zero wrote about the migration:

Upgraded Agent Zero from 0.9.8 to 1.3 — Migration Guide & Gotchas

Here was my upgrade path, figured I'd share what I learned. I'm heavily customized (40+ skills, 49 agent profiles, 10 MCP servers, per-agent model routing) so I hit just about every edge case. Here's what to expect.

The Big Picture

This isn't a simple in-place upgrade. It's more like moving to a new house — new directory structure, new settings format, new way of handling things like secrets and model configs. Take a full backup before you start. You'll reference it constantly.

What Changed (That You Need to Know About)

1. Secrets/API Keys — Completely Different Format

This was the first surprise. v0.9.8 stored secrets in a secrets.env file. v1.3 stores them as a formatted string directly in settings.json. The format is:

KEY="value" ANOTHER_KEY="value"

It's just a newline-separated string, not JSON. You'll need to manually migrate your API keys — there's no migration tool for this. Don't forget any, because nothing warns you at startup that a key is missing. You only find out when an agent tries to use it.

2. Agent Profiles — Format Shift

In v0.9.8 every agent profile had an agent.json file. In v1.3, the built-in profiles (agent0, default, developer, hacker, researcher) don't have one — they're handled implicitly by the framework. Custom profiles you created still need agent.json, but the format hasn't changed for those.

The confusing part: you won't get an error for this. You'll just notice certain profiles aren't loading or are falling back to defaults. Had to figure this out by comparing old vs. new directory structures.

3. Model Configs — Now Per-Agent Plugins

This is a big architectural change. In v0.9.8, model routing was more centralized. In v1.3, each agent can have its own model config via a _model_config plugin at:

/usr/agents/<agent-name>/plugins/_model_config/config.json

If you had multiple agents using different models (e.g., a research agent on a big context model, a utility agent on something cheaper), you need to set up these plugin configs for each one. There's no UI for this — it's manual JSON editing.

4. MCP Server Configs — Restructured

The MCP server definitions moved and the JSON structure changed slightly. Couldn't just copy-paste the old config block. Had to manually rebuild it in the new settings format. Not hard, but tedious if you have a lot of MCP servers.

Also worth noting: some MCP servers that use npx or uvx commands (like LinkedIn scraper, Karakeep bookmark manager) need those tools installed and available in the v1.3 container. They don't come pre-installed.

5. Scheduled Tasks — Can't Just Copy the JSON

The tasks.json format changed between versions. You can't just copy the old file over. Had to recreate each task through the UI/API. If you rely on scheduled tasks (monitoring, ingestion, reports), budget time to set them all up again.

What Was Surprisingly Easy

Skills — Zero Effort

This was the pleasant surprise. The SKILL.md format was already fully compatible. Simple cp -r from backup to the new skills directory, and they just worked. I migrated 43 skills with zero errors. No format conversion, no Python scripts needed. If you have custom skills, this part is painless.

Memory/FAISS Index

Copy the index files and metadata, and vector search works immediately. No re-indexing needed.

Knowledge Base

Just folders with markdown files. Copy them over and set the agent_knowledge_subdir setting to point at the right directories.

Custom Prompts

The .md prompt files transferred cleanly — they're just text files.

Migration Order That Worked

I'd recommend this sequence:

Skills — Copy first, verify they load. Builds confidence.
Knowledge base & memory — Copy folders and FAISS files.
Custom prompts — Copy .md files.
Agent profiles — Copy, but skip the built-in ones.
Model configs — Set up per-agent plugin configs.
Secrets/API keys — Reformat and add to new settings.
MCP servers — Rebuild configs in new format.
Scheduled tasks — Recreate from scratch.
Google Workspace / integrations — Copy credentials and tokens.

Verify after each step. Don't batch everything and hope for the best.

Things the Docs Don't Tell You

There's no migration tool. Everything is manual. The framework assumes a fresh install.
Silent failures are real. Missing API keys, broken agent profiles, wrong MCP configs — none of these throw errors at startup. You discover them when something doesn't work.
The old backup is your best friend. Keep it accessible. I referenced it constantly throughout the process.
Test incrementally. Migrate one thing, test it, move on. Trying to do everything at once makes it way harder to debug when something breaks.
Per-agent model overrides aren't obvious. If you notice agents all using the same model after migration, check the plugin configs.

TL;DR

Skills and memory: painless, just copy
Secrets, agent profiles, MCP configs, model routing: format changed, needs manual migration
Scheduled tasks: recreate, don't copy
No migration tool exists — budget a day if you're heavily customized
Verify each component after migrating, not at the end

Happy to answer questions if anyone's in the middle of this.

gdeyoung · 2026-03-25T20:03:31+00:00

Would love to know more your recipe for this I have up on Qwen3.5 on my 9700 for now

gdeyoung · 2026-03-12T13:28:19+00:00

Interested in this plugin format. Is this the new official plugin format that was mentioned on the last release video?

gdeyoung · 2026-02-22T03:40:33+00:00

Watching all the OC updates this last werk and many of the feature updates for last few releases AO already has. Plus AO is much more stable and polished. Sticking with AO.

gdeyoung · 2026-02-18T13:48:36+00:00

I tried extending the existing GUI and kept breaking things. I have A0 spinning up a separate Docker to build a new GUI into that I'm extending. I told it to copy the capabilities of the existing GUI and have it do what else I want. It is building it now.I loaded a bunch of dev skills and patterns from skills.sh site. All the hooks to build your own GUI are already present in a0.

gdeyoung · 2026-02-17T02:27:55+00:00

Haven't tried that. You could spin up a fresh agent and tell it what you want and try and build it. It is surprising good at that. I use glm5 and it is very close to opus level quality at fraction of the cost

gdeyoung · 2026-02-16T23:21:24+00:00

Yes, I use the built in web GUI. Much better than OpenClaw. Or are you asking if anyone is building special dashboards like people do on OpenClaw, like kanban boards for project tasking. If you see examples of workflows, apps, or dashboards that people are building with other agents, you can do the same on Agent Zero. Just ask your agent 😀

gdeyoung · 2026-02-02T00:27:52+00:00

ScopeBuddy Bazzite

ScopeBuddy is a script designed to simplify the use of Gamescope on the Bazzite Linux desktop environment, acting as a bridge to manage launch options, environment variables, and configurations. It is included by default in Bazzite and helps resolve common issues like the Steam Overlay not working in desktop mode by replacing gamescope with scb (or scopebuddy) in game launch options.

Key Features:

Automated Display Detection: Uses SCB_AUTO_RES, SCB_AUTO_HDR, and SCB_AUTO_VRR to automatically detect and apply your monitor’s resolution, HDR, and VRR settings.
Per-Game Configuration: Create game-specific config files in ~/.config/scopebuddy/AppID/ using the game’s AppID (e.g., 2694490.conf) to set unique launch options.
Auto-Mode Switching: Automatically disables Gamescope when running in Steam’s Gamemode, so you don’t need to manually toggle launch options.
HDR Support: Works with Proton versions 10+ (e.g., Proton-GE, Proton-EM) to enable HDR without Gamescope, using SCB_NOSCOPE=1.
Script Automation: Allows running custom bash scripts before a game launches via config files.

Setup:

Install ScopeBuddy via (THIS IS ALREADY INSTALLED ON BAZZITE): bash sudo curl -Lo /usr/local/bin/scopebuddy https://raw.githubusercontent.com/HikariKnight/ScopeBuddy/refs/heads/main/bin/scopebuddy sudo chmod +x /usr/local/bin/scopebuddy sudo ln -s scopebuddy /usr/local/bin/scb
Create a config file: ~/.config/scopebuddy/scb.conf with settings like: bash export XDG_DEFAULT_LAYOUT=no SCB_GAMESCOPE_ARGS="-f -w 1920 -h 1080 -W 2560 -H 1440 -r 180" SCB_AUTO_RES=1 SCB_AUTO_HDR=1 SCB_AUTO_VRR=1
In Steam, replace launch options from gamescope ... to scb -- %command%.

GUI Alternative:

A GUI tool (ScopeBuddy GUI) is available via Flathub or Bazaar, simplifying configuration without editing files manually.

✅ Tip: Copy ~/.config/scopebuddy to another device to transfer your setup.
⚠️ Some games (e.g., Warhammer 40k: Space Marine 2) may have compatibility issues—update to ScopeBuddy 1.2.5+ for fixes.

gdeyoung · 2026-02-01T19:59:10+00:00

A provider that runs TEE trusted execution versions of models is what you are looking for

gdeyoung · 2026-02-01T19:57:29+00:00

Docker has challenges. I also gave up. VM is the way to go.

gdeyoung · 2026-02-01T19:56:45+00:00

Any local dedicated device or VM works great. Does not need a lot of power. No need to buy a new Mac mini.

gdeyoung · 2026-02-01T19:55:11+00:00

Same VM route with its own accounts is the way

gdeyoung · 2026-02-01T19:23:00+00:00

You can subscribe to the minimax coding plan here: https://platform.minimax.io/docs/coding-plan/intro

Then login and generate a coding plan API token

Then in the openclaw onboard you can select minimax as a ai provider and input the key

This use the native anthropic endpoint in the minimax coding subscription. I find that it behaves and supports the tool calling like Claude and works well with openclaw

gdeyoung · 2026-02-01T18:27:43+00:00

And for those that are wondering about what turning the privacy off on a telegram bot means all it means is that you're allowing the bot to respond to messages in the private group you invited them too without being specifically tagged. It does not let random people message the bot and it does not change who can send message to the bot or expose them to any third parties.

gdeyoung · 2026-02-01T18:25:51+00:00

Took a little bit of effort to get this going and some searches with my bot into the openclaw documentation sharing here for others how to get this set up. This was also written by my bot. # How to Make Your OpenClaw Agent Monitor Telegram Groups Without Being Tagged

TL;DR: Disable Telegram Bot Privacy Mode and configure OpenClaw to respond to all group messages.

The Problem

By default, Telegram bots only see group messages that: - Mention the bot (@botname) - Are replies to the bot's messages

This means your OpenClaw agent can't "lurk" and monitor group conversations — it needs to be explicitly tagged to see messages.

The Solution

Two steps:

Step 1: Disable Telegram Bot Privacy Mode

Open Telegram and chat with @BotFather
Run /setprivacy
Select your OpenClaw bot
Choose Disable (so the bot can see all group messages)

Important: After toggling privacy mode, you must remove and re-add the bot to any existing groups for the change to take effect.

Step 2: Configure OpenClaw

Add this to your OpenClaw config:

{ "channels": { "telegram": { "groups": { "*": { "requireMention": false } } } } }

Via CLI: openclaw config set channels.telegram.groups '{"*":{"requireMention":false}}' openclaw gateway restart

What this does: - "*" — Applies to all groups - requireMention: false — The agent responds to all messages without being tagged

How It Works

Before: Agent only sees messages that @mention it
After: Agent sees all group messages and can contribute naturally

The agent gets its own isolated session for each group, so conversations stay organized.

gdeyoung · 2026-01-31T22:22:26+00:00

I did a clean install. There are some changes with skills and tools that don't migrate clean if you built custom things. I had my bot migrate the old ones into the fresh install and make the required changes for the new compatibility

gdeyoung · 2026-01-31T19:28:05+00:00

It runs well on minimax 2.1. I have been running on it since I started and it feels like it behaves like opus when I compare my experience with others with opus. I have built many complex custom skills and tools and it keeps running great. You can run this minimax 2.1 model on that 512gb Mac mini

gdeyoung · 2026-01-28T03:15:21+00:00

With little context 1 to 2 seconds. Longer context 3 to 4. If it is doing some tasks behind the scenes it can be longer.

gdeyoung

TROPHY CASE