xeis

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xeis

@xeis

Bookmarking interesting things on X, mostly AI/movies. The views & opinions expressed here are mine only and do not necessarily reflect the views of my employer

Our Nation's Capital شامل ہوئے Nisan 2009
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Seth Howes
Seth Howes@SethSHowes·
I’ve wanted to do this for a decade. But I never did - I refuse to give any company my DNA. It is me. So this week I sequenced my genome entirely at home. Literally on my kitchen table. I never exposed my DNA sequence to the internet. Not at any point. I used a MinION to do the sequencing (it’s smaller + weighs less than an iPhone). I used open-source DNA models for the analysis (Evo2 and AlphaGenome) running locally on a DGX Spark and Mac Studio. I traced mechanisms behind my family’s multigenerational autoimmune conditions that no clinician has been able to understand. When I set out to do this I didn’t know if it would actually work. It does. Your genome is the most private data you will ever have. You probably shouldn’t let it leave your house.
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Patrick Collison@patrickc

I'm lucky enough to have a great doctor and access to excellent Bay Area medical care. I've taken lots of standard screening tests over the years and have tried lots of "health tech" devices and tools. With all this said, by far the most useful preventative medical advice that I've ever received has come from unleashing coding agents on my genome, having them investigate my specific mutations, and having them recommend specific follow-on tests and treatments. Population averages are population averages, but we ourselves are not averages. For example, it turns out that I probably have a 30x(!) higher-than-average predisposition to melanoma. Fortunately, there are both specific supplements that help counteract the particular mutations I have, and of course I can significantly dial up my screening frequency. So, this is very useful to know. I don't know exactly how much the analysis cost, but probably less than $100. Sequencing my genome cost a few hundred dollars. (One often sees papers and articles claiming that models aren't very good at medical reasoning. These analyses are usually based on employing several-year-old models, which is a kind of ludicrous malpractice. It is true that you still have to carefully monitor the agents' reasoning, and they do on occasion jump to conclusions or skip steps, requiring some nudging and re-steering. But, overall, they are almost literally infinitely better for this kind of work than what one can otherwise obtain today.) There are still lots of questions about how this will diffuse and get adopted, but it seems very clear that medical practice is about to improve enormously. Exciting times!

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xeis@xeis·
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Qwen
Qwen@Alibaba_Qwen·
⚡ Meet Qwen3.6-35B-A3B:Now Open-Source!🚀🚀 A sparse MoE model, 35B total params, 3B active. Apache 2.0 license. 🔥 Agentic coding on par with models 10x its active size 📷 Strong multimodal perception and reasoning ability 🧠 Multimodal thinking + non-thinking modes Efficient. Powerful. Versatile. Try it now👇 Blog:qwen.ai/blog?id=qwen3.… Qwen Studio:chat.qwen.ai HuggingFace:huggingface.co/Qwen/Qwen3.6-3… ModelScope:modelscope.cn/models/Qwen/Qw… API(‘Qwen3.6-Flash’ on Model Studio):Coming soon~ Stay tuned
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Science girl
Science girl@sciencegirl·
The world’s most forbidden places
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송준 Jun Song
송준 Jun Song@songjunkr·
이 새로운 오픈웨이트 이미지 생성 모델은 엄청나네요. @Baidu_Inc 에서 공개한 @ErnieforDevs 이미지모델 Ernie Image입니다. 나노바나나 프로 수준의 퀄리티를, 이제 로컬에서 생성할 수 있습니다. 24GB의 VRAM이 필요합니다. 아래에서 확인하세요⬇️
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isabelle
isabelle@isareksopuro·
i made a map to monitor data centers all around the world tracks construction + nearby power plants + local AI legislation, and follows the politicians behind their bans (+ if they're getting paid to do so!)
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Garry Tan
Garry Tan@garrytan·
New item in my SOUL md tonight
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xeis@xeis·
@heisola13 Fringe series "Letters of Transit" Season 4, Episode 19
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𝙳𝚊𝚢𝚘
𝙳𝚊𝚢𝚘@heisola13·
They took over the earth, but the humans fought back from inside time itself
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Shen Huang
Shen Huang@ShenHuang·
上周花了好几亿 token debug 一个 race condition,全失败。 后来受 Karpathy auto-research 启发,只加了一句话:"把所有假设和证据写到 DEBUG.md。" AI 列了 5 个假设。其中第 3 个没有任何反对证据。 3 行实验 → 根因确认 → 5 分钟修完。 之前蛮干浪费的 token 比最后修 bug 多了 1000 倍。 血泪教训总结的 4 条 debug 规则: 1. 改代码之前必须先列假设 2. 每次实验最多改 5 行 3. 所有证据写文件 — 防上下文压缩丢掉推理链 4. 同一方向失败 2 次 → 强制换假设 已经写成 Claude Code / Gemini Cli skill 开源了更新在我的 Github:github.com/LichAmnesia/li…
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0xMarioNawfal
0xMarioNawfal@RoundtableSpace·
Google quietly open sourced a time-series AI that predicts anything. Sales trends. Market prices. User traffic. Energy demand. Crypto volatility. It's called TimesFM. Pre-trained on 100B real-world data points. Zero-shot forecasting with no fine-tuning. Outperforms supervised models trained on your specific data. Runs locally. Free. Apache license. Most people are focused on language models. The quietly powerful ones are learning to predict the future.
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Zach Wilson
Zach Wilson@EcZachly·
You only need to read four books to truly get what’s going on in ML and data engineering: - Fundamentals of Data Engineering by Joe Reis - Designing Data Intensive Applications by Martin Kleppmann - AI engineering by Chip Huyen - Designing Machine Learning Systems by Chip Huyen If you read these four technical books and then read these four books on leadership and soft skills, you’ll be well on your way to massive success! - Radical Candor - Atomic Habits - How to Win Friends and Influence People - The Body Keeps Score What books would you recommend?
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Vaxen #DailyYuuko ☀️
Vaxen #DailyYuuko ☀️@YuukoEnjoyer·
A good computer technician always takes and keeps useful notes around.
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Nav Toor
Nav Toor@heynavtoor·
🚨Google built an invisible watermark into every image Gemini has ever generated. Over 10 billion pieces of content marked. One unemployed engineer just cracked it open. With 200 black images and math. It's called reverse-SynthID. SynthID is Google DeepMind's invisible watermark. It's embedded at the pixel level into every image, video, audio, and text generated by Gemini. Invisible to the human eye. Designed to survive cropping, compression, screenshots, and format changes. It was supposed to be unbreakable. Here's how he broke it: → Generated 200 pure black and pure white images from Gemini → When you average enough pure-black AI images, every non-zero pixel IS the watermark. Nothing to hide behind. Just the signal, naked. → Used FFT spectral analysis to map the exact carrier frequencies → Discovered the watermark uses a fixed phase template — identical across every image from the same model → Cross-image phase coherence at carrier frequencies: over 99.5% → Built a detector that identifies SynthID watermarks with 90% accuracy → Built a V3 bypass that drops 91% of the phase coherence and 75% of carrier energy — at 43+ dB PSNR. Almost zero visible quality loss. No neural networks. No proprietary access. No leaked code. Just signal processing and too much free time. Here's the wildest part: The green channel carries the strongest watermark signal. The carrier frequencies change based on image resolution. And the entire phase template is fixed — meaning every single Gemini image carries the same fingerprint structure. One engineer. 200 black images. A Fourier transform. That's all it took to reverse-engineer a system protecting 10 billion+ pieces of content. 519 GitHub stars. 39 forks. Python. Research and educational purposes only. 100% Open Source. (Link in the comments)
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xeis@xeis·
Learn Claude Managed Agents in less than seven minutes!
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CLEAN CAR CLUB
CLEAN CAR CLUB@TheCleanCarClub·
What does the 'E' stand for??
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