Chilik Tamir חיליק
6.2K posts

Chilik Tamir חיליק
@_coreDump
Owner of https://t.co/W1ptsk4LCj


✨ 7 years after I set up a Quake III server, I have it running again, but now in the web browser, much easier 😊 👉 q3.pieter.com 👈 Back in 2019 we'd play a fork of Quake III called OpenArena in a Bali villa with @daniellockyer @marckohlbrugge @dannypostmaa @lenilsonjr_ @gvrizzo @AndreyAzimov @SeanParkRoss and other ppl But it broke after a new Mac update and they never really fixed it, it kinda sucked because it was actually the only game we could just load with friends online and play death match a bit and then continue your day Luckily @lukathedev built Q3JS which successfully compiles ioquake3 to WebAssembly and now it works in the browser To make it extra simple, I've set up a Q3JS server and frontend for you to use at q3.pieter.com, which loads you straight into the game A big problem is that most of the times, nobody's playing, so I've also added Web Notifications, which notifies you if enough human players join, so you can join a match. And I've added a daily match at 8 PM GMT every day which everyone also gets notified when it starts If you want more servers and maps etc, you can check out @lukathedev's own q3js.com HAPPY FRAGGING



New Research Note: AI Identity and the Megapolitics of the Mind re.alisa.sh/notes/ai-ident…



The full video from Cortical Labs explaining how they put 200,000 brain cells onto a silicon chip and had it play Doom is wild: “When a demon appears on the left of the screen, specific electrodes stimulate the sensory area of the neural culture on the left side. The neurons react to that stimulation. We then listen to their response, the spikes, and interpret that activity as motor commands. If the neurons fire in a specific pattern, the Doom guy shoots.”

🚨: A petri dish of human brain cells just learned to play DOOM


זה התחיל מזה שרציתי לבנות לילדים אפליקציה ללימוד שיהיה נחמד תוך כדי האזעקות. אבל... אני הם וקרסר משנים את האפליקציה תוך כדי להתאים אותה למה שבא להם, וזאת חוויה מטורפת. אם טרם ניסיתם וייב קודינג עם הילדים.. לכו על זה

Reasoning LLMs generate very long chains-of-thought, so even small quantization errors add up. With AWQ, Qwen3-4B drops 71.0 → 68.2 on MMLU-Pro (~4% relative loss). 😬 ParoQuant fixes this! It keeps only the critical rotation pairs and fuses everything into a single kernel. Recovers most of the lost reasoning accuracy with minimal overhead — so 4-bit models stay strong at reasoning. 💪💪













