Mark Ra @tevelich

@0xCodila full course is 1h47min deeplearning.ai/courses/buildi…

🇭🇷 Croatia got eliminated from the World Cup by a closed-source sensor. FIFA says the Kinexon chip in the Trionda "proved" Matanović touched the ball. Absolute disgrace. What actually happened: they ran signal processing over 500Hz IMU data from a 14-gram sensor and isolated a supposed contact spike from thousands of noisy samples dominated by bladder harmonics, panel flex, spin wobble and stadium micro-tremors, then nullified one of the most dramatic World Cup equalizers in history via spectral analysis of what is essentially glorified noise. Oh, and it gets better. They moved the chip this year. Back in 2022, it used to hang suspended in the center of the ball. Now it's glued into the sidewall of ONE panel, with counterweights stuffed into the other three so the ball doesn't fly like a shopping cart. So sensitivity now depends on which side of the ball the "contact" happens relative to the sensor. Totally fine basis for ending a nation's tournament. An IMU measures acceleration. Somewhere in a proprietary pipeline, a threshold decides which acceleration counts as "touch." That threshold is unpublished. The false positive rate is unpublished. The calibration data is unpublished. The patents are literally still in their secrecy window. And FIFA owns the raw data, so nobody can independently audit the trace that ended Modrić's last World Cup. And we're all supposed to accept a "heartbeat graphic" on the broadcast as if that settles anything. Since we sadly all know you won't ever admit your crimes or remove this technology for good, at least open source the detection pipeline, @FIFAcom! Publish the thresholds, the error rates, the raw IMU trace from last night. If the tech is right, transparency costs nothing, right?

@Rainmaker1973 youtube.com/watch?v=5Ua-WV…

@FOXSoccer have an opportunity to launch a geo- quiz game like maptap.gg or any other. Thanks to the #WorldCup 2016 commentator who put Kazakhstan into Group F instead of Uzbekistan in Group K.

Wtf is this commentator on. First she’s acting like Colombia has already won the group in the first round of the group stage. She just called Uzbekistan “Kazakhstan” and multiple times said they were in “Group F”

@Yulave0 @JacquiOatley One job - agreed to work on the #WorldCup -have paper and pen and write down the two countries, the name of the Group that they are playing in. And maybe find the angle to talk about the game if you don't follow football. You don't have to remember anything just write it down.

A Brazilian YouTuber killed the Photoshop subscription. It's called PhotoGIMP. It takes GIMP, the free image editor, and makes it look and feel exactly like Photoshop. Same toolbar. Same panel layout. Same keyboard shortcuts. Your hands already know how to use it. Photoshop vs PhotoGIMP: - Price: $275.88 a year → $0 - Account: Adobe login required → No login, ever - Files: Saved to Adobe cloud → Saved on your computer - Updates: Forced when Adobe says → Only when you want - Works on: Windows and Mac → Windows, Mac, and Linux No Adobe account. No cloud upload. No AI trained on your photos. How small is the patch? Tiny. → Nine settings files. That's it. → Copy them into one folder. Done. → Open GIMP. It now looks like Photoshop. → Don't like it? Delete the folder. GIMP goes back to normal. Three steps to install. One command to uninstall. 8,751 stars. 272 forks. 30+ people from around the world helping translate it. One honest note: the license is GPL-3.0. Free for everything. Personal work, paid client work, your own edits. No "Pro" tier hiding behind it. Dionatan Simioni runs the biggest Linux YouTube channel in Brazil. He built this from Marau, a small town in Rio Grande do Sul. No VC. No team. No fundraise. This is what Photoshop should have been from the start. (Link in the comments)

@ChampRDS I dont understand how you give a decision in favor of the challenger in that fight. At the very least, it was a draw and the champ should retain. All Strickland did is defend the takedown very well for one round and throw jabs. The rest was neutral or he was controlled.

A mathematician who shared an office with Claude Shannon at Bell Labs gave one lecture in 1986 that explains why some people win Nobel Prizes and other equally smart people spend their whole lives doing forgettable work. His name was Richard Hamming. He won the Turing Award. He invented error-correcting codes that made modern computing possible. And he spent 30 years at Bell Labs sitting in a cafeteria at lunch watching which scientists became legendary and which ones faded into nothing. In March 1986, he walked into a Bellcore auditorium in front of 200 researchers and told them exactly what he had seen. Here's the framework that has been quoted by every serious scientist for the last 40 years. His opening line landed like a punch. He said most scientists he worked with at Bell Labs were just as smart as the Nobel Prize winners. Just as hardworking. Just as credentialed. And yet at the end of a 40-year career, one group had changed entire fields and the other group was forgotten by the time they retired. He wanted to know what the difference actually was. And he said it wasn't luck. It wasn't IQ. It was a specific set of habits that almost nobody is willing to follow. The first habit was the one that hurts the most to hear. He said most scientists deliberately avoid the most important problem in their field because the odds of failure are too high. They pick a safe adjacent problem, solve it cleanly, publish it, and move on. And because they never swing at the hard problem, they never hit it. He said if you do not work on an important problem, it is unlikely you will do important work. That is not a motivational line. That is a logical one. The second habit was about doors. Literal doors. He noticed that the scientists at Bell Labs who kept their office doors closed got more done in the short term because they had no interruptions. But the scientists who kept their doors open got more done over a career. The open-door scientists were interrupted constantly. They also absorbed every new idea passing through the hallway. Ten years in, they were working on problems the closed-door scientists did not even know existed. The third habit was inversion. When Bell Labs refused to give him the team of programmers he wanted, Hamming sat with the rejection for weeks. Then he flipped the question. Instead of asking for programmers to write the programs, he asked why machines could not write the programs themselves. That single inversion pushed him into the frontier of computer science. He said the pattern repeats everywhere. What looks like a defect, if you flip it correctly, becomes the exact thing that pushes you ahead of everyone else. The fourth habit was the one that hit me the hardest. He said knowledge and productivity compound like interest. Someone who works 10 percent harder than you does not produce 10 percent more over a career. They produce twice as much. The gap doesn't add. It multiplies. And it compounds silently for years before anyone notices. He finished the lecture with a line I have never been able to shake. He said Pasteur's famous quote is right. Luck favors the prepared mind. But he meant it literally. You don't hope for luck. You engineer the conditions where luck can land on you. Open doors. Important problems. Inverted questions. Compounded hours. Those are not traits. Those are choices you make every single day. The transcript has been sitting on the University of Virginia's computer science website for almost 30 years. The video is free on YouTube. Stripe Press reprinted the full lectures as a book in 2020 and Bret Victor wrote the foreword. Hamming died in 1998. He gave his final lecture a few weeks before. He was 82. The lecture that explains why some careers become legendary and others disappear is still free. Most people who could benefit from it will never open it.

🚨Architects are going to hate this. Someone just open sourced a full 3D building editor that runs entirely in your browser. No AutoCAD. No Revit. No $5,000/year licenses. It's called Pascal Editor. Built with React Three Fiber and WebGPU -- meaning it renders directly on your GPU at near-native speed. Here's what's inside this thing: → A full building/level/wall/zone hierarchy you can edit in real time → An ECS-style architecture where every object updates through GPU-powered systems → Zustand state management with full undo/redo built in → Next.js frontend so it deploys as a web app, not a desktop install → Dirty node tracking -- only re-renders what changed, not the whole scene Here's the wildest part: You can stack, explode, or solo individual building levels. Select a zone, drag a wall, reshape a slab -- all in 3D, all in the browser. Architecture firms pay $50K+ per seat for BIM software that does this workflow. This is free. 100% Open Source.

Why is no one talking about this? @nvidia is offering around 80 AI models via hosted APIs absolutely for free. You get access to MiniMax M2.7, GLM 5.1, Kimi 2.5, DeepSeek 3.2, GPT-OSS-120B, Sarvam-M etc. This plugs straight into OpenClaude, OpenCode, Zed IDE, Hermes agent and even with Cursor IDE. Setup: – Grab API key: build.nvidia.com/models – base_url = "integrate.api.nvidia.com/v1" – api_key = "$NVIDIA_API_KEY" – select model (e.g. minimaxai/minimax-m2.7) If you’re building or experimenting, this is basically free inference. Lock in and start building today anon. Thank me later.

Imagine every pixel on your screen, streamed live directly from a model. No HTML, no layout engine, no code. Just exactly what you want to see. @eddiejiao_obj, @drewocarr and I built a prototype to see how this could actually work, and set out to make it real. We're calling it Flipbook. (1/5)

I watched a junior engineer debug latency. He ran ping 10.0.0.45. It showed 200ms. He shrugged and said the network was slow. I asked him if he enjoyed one-way measurements or just hated the return path. He looked confused and said ping was standard. I told him ping shows round trip but not asymmetry. The request could be fast and the reply slow. He wouldn't know which direction had the problem. I showed him mtr --tcp --port=443 10.0.0.45. The return path was taking a scenic route through a saturated link. I told him ping-only thinking costs us hours of blaming the wrong side of the circuit. I forced him to write a bidirectional latency test script.

Second company is "pivoting" to AI. 1st one was from shoes manufacturing to AI, and the next one is the space exploration to AI nytimes.com/2026/04/21/bus…

No one talks about how the quickest cure for imposter syndrome is simply sitting in a meeting with very high-level corporate executives.

“Como as pistas de fórmula 1 são desenhadas”

@heygurisingh @cyber_guy SketchUp is an online tool. I can use it in the browser. What am I missing?

"Does anyone truly “belong” to the United States? Even if you were born here, your family’s story started somewhere else. America is not a place of one origin, but a place of many journeys." theguardian.com/us-news/2026/m…

Lava Lamps, Entropy, and Cloudflare Encryption cloudflare.com/learning/ssl/l…

The 10 Most Valuable AI Learning Repositories on GitHub I analyzed the top GitHub repositories where Jupyter Notebooks (.ipynb) are the primary format and filtered out pure hype, keeping only the most practical, structured learning resources. Here are the 10 repositories that will actually make you better at AI 👇 1. microsoft/generative-ai-for-beginners ⭐ ~105 k Full repo for Microsoft’s Generative AI course with Jupyter notebooks and lessons on building GenAI apps. 🔗 github.com/microsoft/gene… 2. rasbt/LLMs-from-scratch ⭐ ~83 k Educational implementation of GPT-style LLMs from scratch (code + notebooks). 🔗 github.com/rasbt/LLMs-fro… 3. microsoft/ai-agents-for-beginners ⭐ ~49 k Course on building agentic AI systems, tools, memory, planning, and workflows. 🔗 github.com/microsoft/ai-a… 4. microsoft/ML-For-Beginners ⭐ ~83 k Classic machine learning fundamentals curriculum (26 lessons). 🔗 github.com/microsoft/ML-F… 5. openai/openai-cookbook ⭐ ~71 k Official OpenAI API examples, production-ready patterns, recipes, and demos in notebooks. 🔗 github.com/openai/openai-… 6. jackfrued/Python-100-Days ⭐ ~177 k Intensive Python learning roadmap with 100 days of exercises/notebooks. 🔗 github.com/jackfrued/Pyth… 7. pathwaycom/llm-app ⭐ ~54 k RAG templates and real-world deployable LLM apps (prod-ready pipelines). 🔗 github.com/pathwaycom/llm… 8. jakevdp/PythonDataScienceHandbook ⭐ ~46 k Foundational data science notebook collection (NumPy, Pandas, Matplotlib, Scikit-Learn). 🔗 github.com/jakevdp/Python… 9. CompVis/stable-diffusion ⭐ ~72 k Original Stable Diffusion text-to-image model code (excellent learning material). 🔗 github.com/CompVis/stable… 10. facebookresearch/segment-anything ⭐ ~53 k Meta’s Segment Anything Model (SAM) for interactive image segmentation. 🔗 github.com/facebookresear…