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Alex Cheema
@alexocheema
building @exolabs | prev @UniOfOxford We're hiring:
2.9K Following    54.9K Followers
be greedy when others are fearful now is the time to: - secure more compute - buy more claude code / codex subs - tokenmax
frens at Hugging Face tell me llama app (llama.cpp) has a new home: @Llama_app Follow & spread the word!! 🤗🦙
WTF is a SOTA Wife? SOTA Wives is a podcast exploring the unfiltered side of Silicon Valley. We built it as an antidote to the performative polish of tech Twitter. Brought to you by @MilksandMatcha x @vivianmshen. Episode 1 goes live on Monday, 12th October at 9 AM PT on everywhere you get your podcasts 💙
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Say hi to “SparkHome” - a voice native ‘OS’ for your DGX Spark. Seamlessly jump between games, streaming platforms and agentic tools from couch or phone. I’ve been optimizing games for Arm - I am currently getting 60 fps on Star Wars Galactic Racer and Control Resonant.
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Just spent the afternoon in @duncsand's office. Super kind to give me access to his 64-core M5 Ultra to run some experiments :)
does anyone have a 64-core GPU M5 Ultra i can run some tests on?
does anyone have a 64-core GPU M5 Ultra i can run some tests on?
Really great little DGX Spark guide from @0xSero read it on HF
Everything I know about the DGX Spark in 1 article including all the configs you need, and scaling numbers. I want the Huggingface version to get some spotlight
The handbook I wish I had when I bought my first DGX Spark
I am posting an article about local AI share your hardware pics and I might use it in the banner
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Going to make local AI so easy and so good you won't want to use anything else. 🤗
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Meet Husky: a Model-Specific Inference (MSI) engine up to 4.5× faster than Apple's MLX Woof, Underdog's Pareto frontier model, now runs up to 730 tokens/sec on a MacBook Finally local models are as fast & capable. Try it now in - your personal private AI
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.@johnternus acquisition list: 1. @perplexity_ai = search / default LLM 2. @WisprFlow = siri 3. @Superhuman = native AI office suite / mail app / Grammarly is new keyboard on iPhone 4. @exolabs = local open source AI 5. @RobinhoodApp = finance platform (not for sale I know) 6. @Hello_GoAI = enterprise AI 7. @digitalocean = create an AWS competitor 8. Disney = obvious 9. DoorDash and uber = get into the real world of logistics! Bold move! Spend that @apple war chest on big swings — only takes one breakout
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I interviewed DHH, the creator of Omarchy - The Agent Operating system + Ruby on Rails, one of the most important frameworks of our time. He's a father, builder, race-car driver, and such an inspiring person. One of the most positive people I've had the honor of meeting.
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love these guys. visited their office a couple months ago. nice, hard working, focused.
why do inference providers HATE this man?
As much as I'd like this to work, 1-bit / ternary models like Bonsai are (today) a dud. The sweet spot appears to be ~4 to 5 bits per parameter. That's what @UnslothAI find to be the "knee" of the pareto curve (thanks @realGeorgeHotz for pointing this out). Interestingly a synapse in the human brain uses about 4.7 bits of information so perhaps this points to something more fundamental. You're much better off using a smaller model quantized to 4-bit than a larger model quantized to 1-bit or ternary. We also find these 1-bit / ternary models to be overly verbose - so they can actually run *slower* on end to end tasks than 4-bit, even though the decode TPS is faster.
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98.2% of Qwen3.8 27B my ass. I got hyped and gave it a real agent job right away. Build me an FPS in three.js, 6 hours on a 3090. My most standard and default prompt that I always use. It spent the first 32K tokens on a plan without writing a single file, then shipped a black screen, 2 shaders that dont compile and a player who spawns dead. And wrote "verified" in the final report. Ok, too hard. I gave it the easiest thing I have, a voxel pagoda garden in one html file. Video attached. 3 hours for THIS. On the way it deleted its own file and spent an hour debugging a raycaster nobody asked for. Where the 98.2% is in all this I have no fucking idea. Same Qwen3.8 27B, same pagoda task, ISTA-DASLab GSQ-RCO IQ2_XS on a 12 GB 3080 Ti gave me day and night, real shadows and koi fish in the pond. 8.4 GB on disk, real 2.50 bpw, 131072 ctx with q4_0 KV, 47 tok/s at 128K. 2.5 bits beats 2.13 bits by a lot when the 2.13 is this shit. Post with the video and the exact command in the replies. In the replies, I'll attach what a proper Qwen3.8 27B created in my hands. Bottom line: if you have at least 12GB VRAM, use ISTA-DASLab GSQ-RCO of all THIS.
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