Shlok Jain’s Post

The debate around Rust, C, C++, and memory safety gets a little unhinged sometimes. Unfortunately, the existence of a better language does not change the reality of the ecosystems we have. C, C++ might be a bad choice for new cars, trains, airplanes, robots, toasters, servers, scientific libraries, firmware, operating systems, device drivers, network switches, etc etc, but it is still the *obvious* choice, because that's what we've used before and where the ecosystem is. (NodeJS has C++, did you know? Basically any compiler is C/C++ as well) Without targeted investment to replace ecosystems, either from industry, academia, gov, etc, a wholesale lift to a new language just not going to happen. The sole exception I can think of is if Rust+Linux, and Rust+Windows takes off - that may spur generational change in language choice for lots of devs. Or if Oxide Computer Company has their way!

A really important callout here: C and C++ are not going to disappear anytime soon, and that means we need a good dose of realism when it comes to memory safety and low-level good. The good news here is in several parts, though: 1. C++ itself *is* getting better (albeit much too slowly!), and work on proving safety in C for key applications is as well. 2. Work like CHERI can provide some really important ways to “upgrade” existing systems to much higher degrees of safety. 3. Lots of folks are working on “successor languages”, including Rust, with paths to interop—Swift, Carbon, Hylo, Vale, etc.—and the Rust Foundation is actively funding an effort to improve C++/Rust interop in particular, precisely because of the dynamic where there is *so* much code. 4. Rust+Linux and Rust+Windows are both not only hypothetical but have *lots* of *very* active motion and progress. (Mac is actually the laggard here, but Apple’s effort is focused on making Swift a viable replacement for C and C++.) 5. A targeted investment may well be on its way courtesy of the US and other governments pushing for memory-safe languages as the default. Long story short: yeah, there is an enormous amount of unsafe C and C++ out there, and there will be for a long time… *and* I think we can make significant strides on improving that over the next decade.

Dear all, If you've been struggling to get TensorFlow with GPU support running smoothly on the latest Linux distributions, I have some good news for you! On many modern setups, especially with the latest CUDA and graphics drivers, setting up TensorFlow can be practically impossible. However, I've crafted a Docker script that sets up a stable, CUDA (NVIDIA)-based, GPU-capable environment. This setup not only supports efficient machine learning workflows but also integrates LunarVim for an enhanced coding experience. Additionally, you will find C, C++, and Python templates, as well as ncurses libraries installed for simple text-based user interfaces. This is an initial attempt to enhance the TensorFlow environment provided by the developers. It's completely experimental but ultimately aims to develop a one-click installer that allows complete beginners to set up their systems without the risk of causing any disruptions. For those interested in trying it out or contributing to its improvement, check out my GitHub repository at [IstiCusi/phonon-tensor](https://lnkd.in/df2wBMNy). Your feedback and contributions are welcome! When this conception is solid enough, we will extend on the official branches. Happy coding!

🔥 Just in! 𝙋𝙮𝙏𝙤𝙧𝙘𝙝 introduces 𝙩𝙤𝙧𝙘𝙝𝙘𝙝𝙖𝙩 for seamless 🅻🅻🅼🆂 operation locally! - Supports multiple models, Llama 3.1 included - Available across all devices: servers, desktops, mobiles! - Implementation is a breeze! Enjoy Python and native execution modes with eval and quantization support. #pytorch #torchchat #python #llms #llm

AI is awesome! (Large Language Models) LLMs are awesome too. Came across this short clip of Linus Torvalds answering "Do you think we will see LLM written code submitted to you as a pull request?" Look at his answer https://lnkd.in/gAsfDW_i Quoting Linus "I dont think this is as revolutionary as it sounds" This is really good Monday inspiration for many of us in digital. Give us some tips on how to better make use of LLMs at the level of Linus. For anyone who wonders who Linus is: He wrote the first Linux Kernel that is now used on many distributions like RedHad, Fedora, CentOS, and Ubuntu. If you are using the internet there is a good chance you are using software made by Linus.

🚀 Project: Laptop Price Prediction Using Machine Learning 🚀 #InterviewQuestionfrom #Stratascratch I'm excited to share my latest project where I tackled a machine learning problem for predicting the price of laptops based on various attributes! This project was inspired by a question I encountered during an interview with Allergo, via StrataScratch. The dataset includes key features such as: 💻 Graphic Card Type (Integrated/Dedicated) 🌐 Communications (Wi-Fi, Ethernet, Bluetooth) 📺 Resolution (e.g., 1920x1080, 3840x2160) 🔧 CPU Cores (2 to 8) 💾 RAM Size (4GB to 32GB) 📀 Drive Type (HDD/SSD) 🖥️ Operating System (Windows, macOS, Linux) 🔍 Input Devices (Touchscreen/Non-Touchscreen) 🎶 Multimedia (Yes/No) ⏲️ CPU Clock Speed (1.0GHz to 4.0GHz) 💻 CPU Model (i3, i5, i7, i9, Ryzen 5, Ryzen 7, Ryzen 9) 📦 State (New, Refurbished, Used) 🗂️ Drive Memory Size (128GB to 1024GB) 📏 Screen Size (11" to 17") 🛡️ Warranty (None, 6 months, 1 year, 2 years) The goal was to minimize the Root Mean Square Error (RMSE) in predicting the 'buynow_price'. I applied various data preprocessing techniques, feature engineering, and machine learning models to achieve the best possible accuracy. The entire code and dataset can be found on my GitHub repository. 🔗 Check out the full project here: https://lnkd.in/dNpxYWkF Feel free to explore, and I look forward to your feedback! Let's keep learning and innovating together. 🚀💡 #MachineLearning #DataScience #AI #LaptopPricePrediction #Python #Allergo #StrataScratch #DataAnalysis #TechInnovation

🚩 I'm happy to share a new QualityScaler release, version 3.5 🚩And over 1.8k ⭐ on Github! Qualityscaler is a Windows app powered by AI to enhance, upscale and denoise photos and videos; compatible with any gpus. 📌 Github > https://lnkd.in/dpsNqZdg 🚀 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅ New AI engine powerd by onnxruntime-directml (up to 3x faster) ✅ AI-multithreading video upscaling (up to 2x faster than single thread) ✅ New AI model IRCNNx1 (only denoising, no upscaling) ✅ Selectable file output path ✅ and much much more⭐ 👉 full changelogs here > https://lnkd.in/eppm3vx7 #AI #onnx #pytorch #python #directml #upscale #denoise #app

Do you use #TensorFlow, PyTorch, Jupyter Notebook, or #AI Bench? See the Kubuntu Focus Guided Solution on Deep Learning to help you install and run Anaconda, TensorFlow, PyTorch, and an AI benchmark with a single command. Enjoy NVIDIA RTX, CUDA, and CuDNN-powered performance 5 to 30 times greater than popular Unix® laptops. See more here: kfocus.org/wf/deep #DataScience #Linux #Robotics #Code #Data #Developers #Engineers #Laptop #ML #AI

🚀 Excited to share my latest Medium article: "How to Configure Ubuntu 22.04 for CUDA Programming and OpenCV: A Comprehensive Guide"! 🖥️💡 Setting up a deep learning environment on Ubuntu can be complex, especially with CUDA and OpenCV. In this guide, I break down the process into simple, manageable steps to help you build a powerful development workspace for AI and computer vision projects. Whether you're a hobbyist diving into machine learning, or a professional developing cutting-edge applications, this detailed guide provides valuable insights and tips to streamline your setup. Feel free to share your thoughts and experiences on setting up your environment or ask any questions you might have! #AI #MachineLearning #DeepLearning #CUDA #OpenCV #Ubuntu #Programming #TechTips

CuresesColor. This is a terminal application(just a script) that allows you to render the camera(webcam) feed inside a terminal using ASCII characters(Space ' ' in this case). This means now you can render your camera feed in a TTY(pure terminal interface, no GUI, no display server like Xorg or wayland. Which is also the reason for color limitations, since TTY can only use 8-bit colors and not full 256 colors). BTW I have only tested this on linux(Fedora). Made using #opencv and #ncurses in python. Hopefully I'll be porting this to C++ to check for any performance differences. How is this done? Detailed explanation will be in an arcticle soon. But for now... This is achived by converting the frame pixels into brightness steps and then, for each step assinging a color. Source Code : https://lnkd.in/g2_dErYW . . . . . #programmer #codeart #creativecoding #imageprocessing #pixelart #predator #infraredcamera #pythonprogramming #python #computervision #computerscience #ascii #asciiart #terminal #tty #linux #linuxfoundation #fedora #ubuntu #retrogaming #retro #8bit