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@BroBobbyFan @FalconryFinance She is cuffed at the 42:40 mark

@FalconryFinance Here is the full video of grandma getting cuffed and stuffed. youtube.com/watch?v=6_fF1E…

entitled to everything responsible for nothing

⚡ 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

@sciencegirl This is @PaintXplainer if you want to see the rest of this series.

The world’s most forbidden places

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⬇️

The most powerful LLM to run at home:

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!)

Everyone said 16GB isn’t enough for a 35B model. They were right. Until this one flag.

New item in my SOUL md tonight

@heisola13 Fringe series "Letters of Transit" Season 4, Episode 19

They took over the earth, but the humans fought back from inside time itself

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…

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.

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?

A good computer technician always takes and keeps useful notes around.

🚨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)

Learn Claude Managed Agents in less than seven minutes!

@TheCleanCarClub Economy

What does the 'E' stand for??

🚨 S3 is no longer just Object Storage. Yesterday (April 7, 2026), AWS officially launched Amazon S3 Files. This is the biggest update to S3 in 20 years. It can: → Mount S3 buckets as native file systems → Provide sub-millisecond file access → Handle POSIX permissions (UID/GID) natively → Connect to Lambda, EC2, and EKS directly → Eliminate the need for s3fs or data staging Your AI agents can read/write to S3 like a local disk, while your data team access the same objects via API. DevOps just got a massive upgrade. Source: share.google/ts8JORn6SURzwM…