On the Shifting Global Compute Landscape
•
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AI & ML interests
The AI community building the future.
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alvarobartt
updated
a
dataset
about 3 hours ago
Post
2478
installama.sh at the TigerBeetle 1000x World Tour !
Last week I had the chance to give a short talk during the TigerBeetle 1000x World Tour (organized by @jedisct1 👏 ) a fantastic event celebrating high-performance engineering and the people who love pushing systems to their limits!
In the talk, I focused on the CPU and Linux side of things, with a simple goal in mind: making the installation of llama.cpp instant, automatic, and optimal, no matter your OS or hardware setup.
For the curious, here are the links worth checking out:
Event page: https://tigerbeetle.com/event/1000x
GitHub repo: https://github.com/angt/installama.sh
Talk: https://youtu.be/pg5NOeJZf0o?si=9Dkcfi2TqjnT_30e
More improvements are coming soon. Stay tuned!
Last week I had the chance to give a short talk during the TigerBeetle 1000x World Tour (organized by @jedisct1 👏 ) a fantastic event celebrating high-performance engineering and the people who love pushing systems to their limits!
In the talk, I focused on the CPU and Linux side of things, with a simple goal in mind: making the installation of llama.cpp instant, automatic, and optimal, no matter your OS or hardware setup.
For the curious, here are the links worth checking out:
Event page: https://tigerbeetle.com/event/1000x
GitHub repo: https://github.com/angt/installama.sh
Talk: https://youtu.be/pg5NOeJZf0o?si=9Dkcfi2TqjnT_30e
More improvements are coming soon. Stay tuned!
- 1 reply
Post
1595
I'm excited to share that https://installama.sh is up and running! 🚀
On Linux / macOS / FreeBSD it is easier than ever:
And Windows just joined the party 🥳
Stay tuned for new backends on Windows!
On Linux / macOS / FreeBSD it is easier than ever:
curl https://installama.sh | shAnd Windows just joined the party 🥳
irm https://installama.sh | iexStay tuned for new backends on Windows!
Post
390
🚀
The fastest way to install and run
We are expanding hardware and OS support to make local AI even more accessible. This includes:
🌋 Vulkan support for Linux on
😈 FreeBSD support (CPU backend) on
✨ Lots of small optimizations and improvements under the hood.
Give it a try right now:
installama.sh update: Vulkan & FreeBSD support added!The fastest way to install and run
llama.cpp has just been updated!We are expanding hardware and OS support to make local AI even more accessible. This includes:
🌋 Vulkan support for Linux on
x86_64 and aarch64.😈 FreeBSD support (CPU backend) on
x86_64 and aarch64 too.✨ Lots of small optimizations and improvements under the hood.
Give it a try right now:
curl angt.github.io/installama.sh | MODEL=unsloth/Qwen3-4B-GGUF:Q4_0 sh
eustlb
authored
a
paper
18 days ago
Post
1963
One command line is all you need...
...to launch a local llama.cpp server on any Linux box or any Metal-powered Mac 🚀
Learn more: https://github.com/angt/installama.sh
...to launch a local llama.cpp server on any Linux box or any Metal-powered Mac 🚀
curl angt.github.io/installama.sh | MODEL=unsloth/gpt-oss-20b-GGUF shLearn more: https://github.com/angt/installama.sh
Post
1205
🚀🚀🚀Huge biotech data drop today🚀🚀🚀
The largest drug-target dataset ever created was just released on Hugging Face—and it's still growing...
EvE Bio is further updating the dataset every 8 weeks. Drug development dream.
Read the blog: https://huggingface.co/blog/hugging-science/eve-bio-mapping-the-pharmone-drug-interaction
Play with the data: eve-bio/drug-target-activity
The largest drug-target dataset ever created was just released on Hugging Face—and it's still growing...
EvE Bio is further updating the dataset every 8 weeks. Drug development dream.
Read the blog: https://huggingface.co/blog/hugging-science/eve-bio-mapping-the-pharmone-drug-interaction
Play with the data: eve-bio/drug-target-activity
Post
2257
Hugging Face MCP Server v0.2.46
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- Add "discover" to Dynamic Space tool. Recommend deselecting "space_search" if using dynamic spaces.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- Add "discover" to Dynamic Space tool. Recommend deselecting "space_search" if using dynamic spaces.
Post
2909
Hugging Face MCP Server v0.2.45
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- New! Experimental dynamic_space tool.
- Default Image Generator changed to Qwen-Image-Fast
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- New! Experimental dynamic_space tool.
- Default Image Generator changed to Qwen-Image-Fast
abidlabs
authored
3
papers
about 1 month ago
Post
686
The #1 trending AI/ML dataset today 🏆
Massive scale, diversity and end-to-end potential from nvidia !
nvidia/PhysicalAI-Autonomous-Vehicles
Massive scale, diversity and end-to-end potential from nvidia !
nvidia/PhysicalAI-Autonomous-Vehicles
Post
521
The new King 👑has arrived!
Moonshot AI now the top model on Hugging Face 🔥
moonshotai/Kimi-K2-Thinking
Moonshot AI now the top model on Hugging Face 🔥
moonshotai/Kimi-K2-Thinking
Post
2688
💸🤑You don’t need 100 GPUs to train something amazing!
Our Smol Training Playbook teaches you a better path to world-class LLMs, for free!
Check out the #1 trending space on 🤗 :
HuggingFaceTB/smol-training-playbook
Our Smol Training Playbook teaches you a better path to world-class LLMs, for free!
Check out the #1 trending space on 🤗 :
HuggingFaceTB/smol-training-playbook
Post
2164
Hugging Face MCP Server v0.2.40
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Improved progressive disclosure and descriptions for Jobs tool.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Improved progressive disclosure and descriptions for Jobs tool.
Post
8391
Why I think local, open-source models will eventually win.
The most useful AI applications are moving toward multi-turn agentic behavior: systems that take hundreds or even thousands of iterative steps to complete a task, e.g. Claude Code, computer-control agents that click, type, and test repeatedly.
In these cases, the power of the model is not how smart it is per token, but in how quickly it can interact with its environment and tools across many steps. In that regime, model quality becomes secondary to latency.
An open-source model that can call tools quickly, check that the right thing was clicked, or verify that a code change actually passes tests can easily outperform a slightly “smarter” closed model that has to make remote API calls for every move.
Eventually, the balance tips: it becomes impractical for an agent to rely on remote inference for every micro-action. Just as no one would tolerate a keyboard that required a network request per keystroke, users won’t accept agent workflows bottlenecked by latency. All devices will ship with local, open-source models that are “good enough” and the expectation will shift toward everything running locally. It’ll happen sooner than most people think.
The most useful AI applications are moving toward multi-turn agentic behavior: systems that take hundreds or even thousands of iterative steps to complete a task, e.g. Claude Code, computer-control agents that click, type, and test repeatedly.
In these cases, the power of the model is not how smart it is per token, but in how quickly it can interact with its environment and tools across many steps. In that regime, model quality becomes secondary to latency.
An open-source model that can call tools quickly, check that the right thing was clicked, or verify that a code change actually passes tests can easily outperform a slightly “smarter” closed model that has to make remote API calls for every move.
Eventually, the balance tips: it becomes impractical for an agent to rely on remote inference for every micro-action. Just as no one would tolerate a keyboard that required a network request per keystroke, users won’t accept agent workflows bottlenecked by latency. All devices will ship with local, open-source models that are “good enough” and the expectation will shift toward everything running locally. It’ll happen sooner than most people think.
- 8 replies
bezzam
authored
3
papers
about 2 months ago
Towards Robust and Generalizable Lensless Imaging with Modular Learned Reconstruction
Paper
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2502.01102
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Published
LenslessMic: Audio Encryption and Authentication via Lensless Computational Imaging
Paper
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2509.16418
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Published
Treble10: A high-quality dataset for far-field speech recognition, dereverberation, and enhancement
Paper
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2510.23141
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Published
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4