u/Gotxi

The music starting every wave is annoying, you hear the same music theme over and over, just let the music continue playing and loop when it ends.

Talents are boring. +X or +%X talents are obsolete and do not offer any joy.
Instead, try to create talents that modify the playstyle of the character. For example: 15% chance of skeletons to explode and launch 4 bones that can hit other enemies.

I like this one: https://roadmap.sh/linux

That's not the correct statement. You want the biggest model with smart expert load an hotswap that can run the fastest on the lowest hardware.

We have something with Strata and Qwen 3.8 FN, let them cook.

Hello! It does not run directly on my system:

~/Descargas/darkness-linux-x64-0.6.0-preview
❯ ./darkness  
[Fitzel] mounted archive 'game.fpak' (1625 files)
[Fitzel] OpenGL 4.6 (Core Profile) Mesa 26.2.5-arch3.1 | AMD Radeon RX 9070 XT (radeonsi, gfx1201,
ACO, DRM 3.64, 7.3.0-3-cachyos) | compute yes
[Fitzel] shader link error:
error: Too many fragment shader texture samplers

Failed to load lit shader

I asked agentic AI to check what could be happening, if it was the game or my system, and this is the response from the checks it did, hope it helps:

Hi,
   Darkness 0.6.0 fails to start on my system with:

     [Fitzel] shader link error: error: Too many fragment shader texture samplers
     Failed to load lit shader

   Hardware: AMD Radeon RX 9070 XT (gfx1201 / RDNA4), Mesa 26.2.5, kernel 7.3.0-3-cachyos, GL 4.6 Core.

   I counted the samplers in sandbox/assets/shaders/lit.frag:
     21 singleton samplers
     + uLayerTex[6]  + uLayerNorm[6]  + uPaintTex[4]  (16 array elements)
     + 2 more in cloudshadow.glsl (#included into lit.frag)
     = 39 fragment samplers

   A minimal test program on this driver links fine up to 32 samplers and fails at 48, so the driver per-stage limit is 32 (standard for RDNA). 39 > 32.

   I noticed uLayerArr / uLayerNormArr (sampler2DArray) are already declared in lit.frag. If the per-layer sampling could go through those arrays instead of the per-layer 2D arrays, the count would drop below 32.

Ask AI to write an app that draws a hand in your screen that writes the output code from AI. Then you can say its handwritten.

I have an RX 9070 XT with 16 GB VRAM, a Ryzen 7 5800X, and 32 GB of DDR4-3200 RAM, running Linux over PCIe 4.0 x16.

I've tested three setups:

1) Regular llama.cpp with Vulkan, running Unsloth’s Qwen3.8-27B UD-IQ3_S with an 80K context limit and MTP + n-gram speculative decoding. I measured roughly 60–75 tok/s on short prompts, dropping to around 45 tok/s with 78K tokens of actual input.

2) ExLlamaV3 (https://github.com/turboderp-org/exllamav3) with ROCm, running Turboderp’s Qwen3.8-27B EXL3 SC 3.0 bpw with DFlash2 and an 80K context limit. I measured roughly 60–125 tok/s on short/moderate-context workloads, and 39–43 tok/s with 78K tokens of varied code and documentation as input.

3) This llama.cpp KV-streaming fork (https://github.com/achyudh/llama.cpp/tree/feature/adaptive-kv-stream-rocm) with ROCm/HIP, running the same Unsloth UD-IQ3_S GGUF with MTP + n-grams. It supports a 262,144-token context limit on my setup. I measured roughly 40–45 tok/s with 130K tokens of actual input, and 14 tok/s with 260K tokens of actual input. Short-context generation is considerably faster.

These are local measurements with temperature 0 and thinking disabled. Throughput varies with the workload, occupied context, and speculative acceptance rate.

For everyday use, I currently use EXL3 up to 80K and the KV-streaming fork when I need more context.

I am using an old phone I have (Xiaomi Mi A2) and installed postmarketOS (based on Alpine) with KDE desktop environment.

Well, it boots, I can use the screen, wifi, speakers and install linux applications. What does not work is the microphone, the screen does funny things from time to time and it hangs sometimes when you stress it too much. Also FPS drops from 60 to 13 pretty frequently for one second, which is annoying.

I mean, there is still a long way to go, but having the option to install Linux is definitively in my interest.

What is a downside to me, is that banking apps are required in the modern world and they only support IOS/Android and nothing else, and even if you use the websites it still asks you to confirm the operations through the mobile app, at least on the banks I saw.

Since I am spaniard, I am waiting for better bizum pay support on stores, and other banking app alternatives to pay so I can use my Linux phone to pay.

Still not mature, but getting there...

Why do you link a youtube video and not the actual game?

This is the real site: https://wow.aethus.pl/

Qwen3.6 35B A3B, there is no other more modern Qwen small MoE, so until Qwen4 lands and provides a better alternative, I am still using it for fast work.

They won't be able as soon as PC version comes and people start moding it.

Is not emulation, literally converts ps5 games to executables that can run on other operating systems by translating the ps5 functions into normal functions. Call it a "PS5 to PC game converter" if you want.

It is growing very fast, almost the entire PS5 system is reverse engineered. Once it is completed, any PS5 game (existing or future) will be able to run in PC natively by porting the games to PC.

I tested it in the playground, first web search I asked for, and it went with the wrong conclusion, reasoned over it several times saying "it probably is" until it converted that self-reasoning into its truth and presented the results without self-correcting. 0/5.

This is not the greatest OS in the world, no... this is just a tribute

Is it similar to postmarketos but arch based? What devices are compatible?

private conversations, job seeking, research, fix bugs on postmarketos for my linux phone, prototyping, learn new things, microapps...

I am using vesktop, no issues on chat/calls/sharing screen

Create a magic 8 ball

I think my numbers are better using llama.cpp mainline with vulkan backend on CachyOS.

1000 tok/s prefill, 35-65 tok/s generation, 80k context on a single RX 9070 XT (16GB VRAM) with vision capabilities.

Only vision model (optional) is offloaded to RAM, everything else fits on VRAM (shared with my desktop).

Compilation:

rm -rf build-vulkan
cmake -B build-vulkan \
       -DCMAKE_BUILD_TYPE=Release \
       -DCMAKE_C_FLAGS="-march=native -O3" \
       -DCMAKE_CXX_FLAGS="-march=native -O3" \
       -DGGML_LTO=ON \
       -DGGML_VULKAN=ON \
       -DGGML_VULKAN_VALIDATE=OFF \
       -DGGML_VULKAN_DEBUG=OFF
cmake --build build-vulkan -j"$(nproc)"rm -rf build-vulkan

How I run it:

export RADV_QUEUE_DISABLE=compute
export GGML_VK_ALLOW_GRAPHICS_QUEUE=1
export GGML_VK_MAX_NODES_PER_SUBMIT=16

cd "$HOME/repos/llama.cpp"

exec ./build-vulkan/bin/llama-server --blahblah

The config:

#General
host = 0.0.0.0
metrics = true
jinja = true

#Load
no-warmup = true
parallel = 1
load-mode = none

#Behavior
flash-attn = on
reasoning = on

#offload
fit = off
n-gpu-layers = 999

#Checkpoints
swa-checkpoints = 5
checkpoint-min-step = 32768

#Cache
cache-type-k = q8_0
cache-type-v = q4_0
cache-prompt = true
cache-reuse = 0
cache-ram = 0
no-cache-idle-slots = true

#CPU
threads = 8
threads-batch = 8

#Model
model = /home/gotxi/models/qwen/Qwen3.8-27B-UD-IQ3_S.gguf

#Context
ctx-size = 80000

#batch
batch-size = 8192
ubatch-size = 1024

#Speculative Decoding
spec-type = draft-mtp,ngram-map-k4v
spec-draft-n-max = 2
spec-draft-p-min = 0.3
spec-ngram-mod-n-min = 4
spec-ngram-mod-n-max = 8
spec-ngram-mod-n-match = 32

#Temps
temperature = 0.7
top-k = 20
top-p = 0.95
min-p = 0.0
presence-penalty = 0.0
repeat-penalty = 1.0

#Vision [optional]
no-mmproj-offload = true
mmproj = /home/gotxi/models/qwen/mmproj-F16.gguf

Try it and let me know your numbers :)

Is this a dog or a muffin?

DUAL-ENG-FR-5.1-MULTI-8-SUB-1080P-HDRIP-MKV

Si no sabes cómo hacerlo, te recomiendo que pruebes Unsloth Desktop: https://unsloth.ai/

u/SultanGreat, don't listen to these people.

You can run your local stack with 16GB VRAM just fine, you will need to sacrifice precision since the model is quantized, but it is still a pretty solid model even in IQ3.

I have 16GB as well and I use Unsloth's Qwen3.8-27B-UD-IQ3_S and works great, I even use it for real world tasks and it delivers them correctly.

CachyOS, I am also using llama mainline.

I compile it with these flags:

rm -rf build-vulkan
cmake -B build-vulkan \
       -DCMAKE_BUILD_TYPE=Release \
       -DCMAKE_C_FLAGS="-march=native -O3" \
       -DCMAKE_CXX_FLAGS="-march=native -O3" \
       -DGGML_LTO=ON \
       -DGGML_VULKAN=ON \
       -DGGML_VULKAN_VALIDATE=OFF \
       -DGGML_VULKAN_DEBUG=OFF
cmake --build build-vulkan -j"$(nproc)"

And run the process with this (replace blahblah with your arguments):

export RADV_QUEUE_DISABLE=compute
export GGML_VK_ALLOW_GRAPHICS_QUEUE=1
export GGML_VK_MAX_NODES_PER_SUBMIT=16

cd "$HOME/repos/llama.cpp"

exec ./build-vulkan/bin/llama-server --blahblah

I like 68, is like 69 but I owe you one

+25% performance with Vulkan on my RX 9070 XT. I try ROCM from time to time as llama evolves, but Vulkan is still faster