Uploaded May 2026 | Updated September 2026, 3 weeks ago
Last month, Linux's #2 maintainer shipped 24 patches to the kernel — all
caught by an AI running on a desktop next to him. No subscription. No
API key. No monthly bill. The kernel is the software that powers your
phone, your router, your car, and half the internet — and these were
real security bugs in real subsystems.
Same month, a different AI subscription quietly billed a developer $200
because of a filename in his git history. Same vendor that found a
16-year-old FFmpeg bug also refused to refund the $200 they had
mistakenly charged. Two visions of where AI is heading. Only one of
them is yours to keep.
This is the story of GKH's "clanker_t1000," AMD's Strix Halo, the new
Linux AI policy, Anthropic's Mythos vs HERMES.md, Zig's anti-AI ban —
all on one map, with the variable that explains who's right.
— Codacus
═══════════════════════════════════════════════
🕒 CHAPTERS
═══════════════════════════════════════════════
0:00 Intro
0:36 Who is Greg Kroah-Hartman
1:17 Clanker T1000 + the SMB bug
2:31 Why the hardware matters (AMD Strix Halo)
3:27 Two more bugs (Nouveau, IO_uring)
4:14 Why these bugs mattered
4:44 The killer quote — accountability
5:40 Subscribe (one-second plug)
5:51 The Zig contrast — opposite direction
6:34 Anthropic Mythos — cloud AI's win
7:13 The HERMES.md kicker — same vendor, two faces
8:14 The take — who's accountable for the output
8:57 What this means for you
9:25 Final thought
═══════════════════════════════════════════════
🔗 LINKS & SOURCES
• Linux kernel AI policy (official):
docs.kernel.org/process/coding-assistants.html
• Phoronix — "Clanker T1000" coverage:
phoronix.com/news/Clanker-T1000-AMD-Ryzen-AI-Max
• Tom's Hardware — Linux's #2 hunts bugs locally:
tomshardware.com/software/linux/linux-kernels-second-in-command-uses-framework-desktop-to-hunt-bugs-with-local-ai
• Anthropic Mythos / Project Glasswing announcement:
red.anthropic.com/2026/mythos-preview
• Simon Willison — Zig's anti-AI policy:
simonwillison.net/2026/Apr/30/zig-anti-ai
• HERMES.md billing flaw — Consumer Rights Wiki:
https://consumerrights.wiki/w/Anthropic_Claude_Code_HERMES.md_billing_flaw
• HN thread on the $200 charge:
news.ycombinator.com/item?id=47952722
═══════════════════════════════════════════════
⚙️ HARDWARE in this video:
AMD Ryzen AI Max+ 395 (Strix Halo) on Framework Desktop
16 Zen 5 cores · 40 RDNA 3.5 CUs · 128GB unified LPDDR5X
═══════════════════════════════════════════════
🟢 If you're tired of being the QA team for AI you didn't ask for,
subscribe — I'm doing more videos like this. The fight isn't AI
vs no-AI. It's who's accountable for the output.
#linuxkernel #localai #vulnerability #cybersecurity #StrixHalo #aisecurity #opensource #opensourceai
Last month, Linux's #2 maintainer shipped 24 patches to the kernel — all
caught by an AI running on a desktop next to him. No subscription. No
API key. No monthly bill. The kernel is the software that powers your
phone, your router, your car, and half the internet — and these were
real security bugs in real subsystems.
Same month, a different AI subscription quietly billed a developer $200
because of a filename in his git history. Same vendor that found a
16-year-old FFmpeg bug also refused to refund the $200 they had
mistakenly charged. Two visions of where AI is heading. Only one of
them is yours to keep.
This is the story of GKH's "clanker_t1000," AMD's Strix Halo, the new
Linux AI policy, Anthropic's Mythos vs HERMES.md, Zig's anti-AI ban —
all on one map, with the variable that explains who's right.
— Codacus
═══════════════════════════════════════════════
🕒 CHAPTERS
═══════════════════════════════════════════════
0:00 Intro
0:36 Who is Greg Kroah-Hartman
1:17 Clanker T1000 + the SMB bug
2:31 Why the hardware matters (AMD Strix Halo)
3:27 Two more bugs (Nouveau, IO_uring)
4:14 Why these bugs mattered
4:44 The killer quote — accountability
5:40 Subscribe (one-second plug)
5:51 The Zig contrast — opposite direction
6:34 Anthropic Mythos — cloud AI's win
7:13 The HERMES.md kicker — same vendor, two faces
8:14 The take — who's accountable for the output
8:57 What this means for you
9:25 Final thought
═══════════════════════════════════════════════
🔗 LINKS & SOURCES
• Linux kernel AI policy (official):
docs.kernel.org/process/coding-assistants.html
• Phoronix — "Clanker T1000" coverage:
phoronix.com/news/Clanker-T1000-AMD-Ryzen-AI-Max
• Tom's Hardware — Linux's #2 hunts bugs locally:
tomshardware.com/software/linux/linux-kernels-second-in-command-uses-framework-desktop-to-hunt-bugs-with-local-ai
• Anthropic Mythos / Project Glasswing announcement:
red.anthropic.com/2026/mythos-preview
• Simon Willison — Zig's anti-AI policy:
simonwillison.net/2026/Apr/30/zig-anti-ai
• HERMES.md billing flaw — Consumer Rights Wiki:
https://consumerrights.wiki/w/Anthropic_Claude_Code_HERMES.md_billing_flaw
• HN thread on the $200 charge:
news.ycombinator.com/item?id=47952722
═══════════════════════════════════════════════
⚙️ HARDWARE in this video:
AMD Ryzen AI Max+ 395 (Strix Halo) on Framework Desktop
16 Zen 5 cores · 40 RDNA 3.5 CUs · 128GB unified LPDDR5X
═══════════════════════════════════════════════
🟢 If you're tired of being the QA team for AI you didn't ask for,
subscribe — I'm doing more videos like this. The fight isn't AI
vs no-AI. It's who's accountable for the output.
#linuxkernel #localai #vulnerability #cybersecurity #StrixHalo #aisecurity #opensource #opensourceai



![How Fast Can One RTX 3060 Actually Run 35B (llama.cpp enhancement)?
I built an expert cache for MoE models, keep the hottest experts parked in VRAM, stream the rest from system RAM.
The baseline was 42. This is what it took to find out why, and where it ended up: 80 tokens a second at peak, and still over 70 with real context in the window.
One RTX 3060. A 35B model. The part I did not expect: the cache is worth almost nothing on its own. What makes it worth something is what it does to speculative decoding underneath it.
⚙️ THE TWO FLAGS
Everything else here is the command you already run. These two are new:
moe-cache-profile [path.csv]
moe-cache-slots [n]
Full commands, both steps, in the pinned comment.
⏱️ CHAPTERS
0:00 Intro
0:31 The graveyard
2:35 The data says dont
5:47 The build, and the gut punch
7:42 The scheduler betrayal
10:54 Turning it up
14:08 Why it works
17:58 Your GPU
📊 THE NUMBERS
baseline, no cache ............ 42 tok/s
cache working ............... 6 tok/s ← the two days
cache actually working ........ 44 tok/s
cache, 124 slots .............. 55 tok/s
speculative decoding alone .... 55 tok/s
both together ................. 70 tok/s ← more than either
parallel GPU/CPU chains ....... 75 tok/s
peak .......................... 80 tok/s
with real context ............. 70+ tok/s
⚠️ WILL IT HELP YOU?
Depends how much of the model fits. On a 3060 with the 35B, about half the experts fit and the gain is large. On a 118B model where only 36 of 256 experts fit, the gain drops to 5%. Below roughly 15–20% resident, a fixed set stops being the right approach at all. Chapter 7 covers where the line is for your card.
🔧 SETUP
Qwen3.6-35B-A3B UD-Q4_K_M
RTX 3060 12GB · Ryzen 5600X · 30GB RAM · Ubuntu 24.04 llama.cpp with MoE expert caching
—
If you run models locally and want to get more out of the hardware you already own, thats what this channel is for. Subscribe theres a lot more coming.
#localllm #llamacpp #rtx3060 #qwen3 #selfhostedai #localai How Fast Can One RTX 3060 Actually Run 35B (llama.cpp enhancement)?](https://i.ytimg.com/vi/k_LostFpatg/mqdefault.jpg)





![Can a 3.5GB model replace my 35B daily driver? (Bonsai 27B)
PrismML compressed Qwens 27B dense model down to 3.5 GB with true 1-bit weights. Not quantization: the weights were trained as −1/+1 from day one. So I put both Bonsai builds up against my actual daily driver, a 21 GB Qwen 3.6 35B MoE, plus Gemma 4 12B as the fair-size control, and ran about thirty tests on one RTX 3060.
Same landing-page brief. Same broken server over SSH. Same sampling settings, same KV cache quant, same effort level, every model.
What came out of it surprised me more than once. The 21 GB model is more than twice as fast as a 7 GB one, and the reason has nothing to do with file size. The only model that could not reliably finish a long task was not the smallest one. And whether Bonsai makes sense on your machine comes down to a number almost nobody quotes on a spec sheet.
If you run this on a Mac or a small card, tell me what you get in the comments.
CHAPTERS
0:00 Two models, one broken server
0:39 What Bonsai actually is (1-bit vs quantization)
3:24 Test 1 — can it design a webpage?
6:34 Test 2 — SSH debugging, everyone passed
9:26 Test 3 — the trap that survives a restart
13:29 Where it breaks (Gemmas loop)
15:35 Why the bigger model is faster (A3B vs dense)
17:54 The number nobody quotes (total footprint)
20:06 The verdict — who should run what
THE MODELS
Bonsai 27B (binary Q1_0, 3.5 GB / ternary Q2_0, 7 GB): [https://huggingface.co/prism-ml]
Qwen 3.6 35B-A3B: [https://huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF]
llama.cpp Metal Q2_0 support (merged): github.com/ggml-org/llama.cpp/pull/25419
llama.cpp CUDA Q2_0 support (open PR I built from): github.com/ggml-org/llama.cpp/pull/25707
THE RIG:
RTX 3060 12 GB
llama.cpp built from PR #25707
KV cache q8_0 (all models)
reasoning effort medium (all models)
MoE runs with 26 expert layers offloaded to system RAM
MY RESULTS AT A GLANCE
Generation speed at depth (tg, tokens/sec):
MoE 47.9 · Bonsai Q1 34.7 · Bonsai Q2 21.6
Total memory footprint:
MoE ~21 GB (10.5 VRAM + 10.6 RAM)
ternary 8.8 GB
binary 5.3 GB
Server-repair task, wall clock:
MoE ~56s
ternary ~114s
binary ~134s
Design test:
ternary and the MoE land in the same class.
Binary and Gemma sit a tier below.
#localai #llm #bonsai #quantization #1bit #qwen #llamacpp #selfhosted #ai #rtx3060 Can a 3.5GB model replace my 35B daily driver? (Bonsai 27B)](https://i.ytimg.com/vi/rBLWDJrXCp0/mqdefault.jpg)
