Niyat Patel

PyTorch’s compiler just stopped being a black box. If you’ve ever tried to debug torch.compile, you know the pain: bytecode-level transformations you can’t step through, numerical bugs that are impossible to trace, and zero visibility into what actually happens after the graph is captured. A new tool called depyf (from Tsinghua + Apple researchers) just opened the box wide open. • It does three things that change everything: • Decompiles PyTorch bytecode back into clean, readable Python source code • Connects in-memory compiled functions to on-disk files • Lets you step through the entire compiled graph line-by-line with normal debuggers Non-intrusive (just two context managers), 100% compatible across all Python versions, and now an official PyTorch ecosystem project. For researchers and engineers building clinical AI, medical imaging models, or any high-stakes system where you need to trust every single step, this is the visibility we’ve been waiting for. Paper: https://lnkd.in/gmuSh5tM GitHub: https://lnkd.in/gnxv-96m #PyTorch #AIResearch #MachineLearning #HealthcareAI

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Nvidia tries to speed up models. Current architectures use full attention blocks to generate text. This is useful to contextualise inputs across layers. For example, the word "Kite" may mean a bird or an object tied to a thread. Depending on the nearby words, we add context to "kite" and understand its real meaning in a sentence. Models need to do this multiple times to find higher order meanings, like tone and sarcasm. However, these operations are slow. Full attention takes O(N^2) operations. So Nvidia designed a way to only pick attention blocks that meaningfully impact the final result. The result is a massive 500% speedup, and 50x throughput. Let's see if this approach is widely adopted. #AI #Nvidia #LLMs

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Financial portfolio optimization has been consistently challenged by a trade-off between computational speed and model complexity. Introducing the Quantitative Portfolio Optimization developer example, designed to give quants and developers a faster way to scale strategy backtesting and interactive analysis with accelerated computing. 📈 NVIDIA cuOpt solves large-scale portfolio optimization problems up to 160x faster than CPU-based solvers. 📊 CUDA-X Data Science speeds up portfolio data prep and scenario generation by up to 100x when learning and sampling from return distributions. Read the technical blog 👉 https://bit.ly/48rw21z

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𝗧𝗵𝗲 𝗯𝗲𝘀𝘁 𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝗹𝗲𝘀𝘀𝗼𝗻 𝗰𝗼𝘀𝘁 𝗼𝘂𝗿 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿 $𝟲𝟬𝟬 𝗮𝗻𝗱 𝗮 𝗺𝗮𝗿𝗿𝗶𝗮𝗴𝗲 𝗮𝗿𝗴𝘂𝗺𝗲𝗻𝘁. Our VP of Engineering, Xiaofan(James) Luan, was supposed to buy his wife a Dior bag for their anniversary.  Instead, he bought three Claude Code subscriptions and spent the holiday trying to cross-compile 2 million lines of C++. Every fix on one platform broke two others.  $600 later, the only output was "git reset --hard" — and a very cold dinner table.😂 "Make it compile on Windows" is a trap. The real goal was "compile everywhere without hacks" — no AI is going to figure that out for you at 2 am. What worked: constraints before code, review tests not code, bottom-up, one layer at a time. Same task, two days. Then he ran six parallel Claude sessions across three machines with git worktree. The bottleneck stopped being intelligence and started being how fast one person can alt-tab. AI solves exactly the problem you give it. Engineering is in knowing which one to give. His wife is still waiting for that bag. Full story: https://lnkd.in/gtsW_Wvk ——— Follow Milvus, created by Zilliz, for everything related to unstructured data

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Run Claude Code Locally on Apple Silicon Using LM Studio and LiteLLM (Zero Cost) In this blog, Manjunath Janardhan walks us through how to run Claude Code locally on Apple Silicon using LM Studio + LiteLLM + Qwen3-Coder-30B — with zero API cost and no cloud dependency. The setup uses: - LM Studio for local inference - Qwen3-Coder-30B as the coding model - LiteLLM as a bridge between Claude Code and a local OpenAI-compatible endpoint This is a practical way to get the Claude Code experience fully offline on macOS, especially for M-series Macs where MLX models can make a real difference. Worth reading if you want to run agentic coding tools on your own machine without relying on the cloud. Read it from the comments!

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Tencent’s Hunyuan : released HunyuanOCR : a SOTA visual understanding model for document parsing, information extraction, text detection, and more. What makes HunyuanOCR stand out It’s an end-to-end Vision-Language Model (VLM) that handles everything from text detection to recognition, document parsing, information extraction, translation and even subtitle extraction — all within a single inference. Despite being lightweight (only ~1 billion parameters), HunyuanOCR delivers state-of-the-art performance on a wide range of tasks. Supports 100+ languages — useful for multilingual documents, receipts, photos, and international use-cases. What this enables ✅ Faster and simpler document digitization workflows ✅ Unified model instead of complex multi-stage OCR pipelines ✅ Support for multilingual and mixed-language documents ✅ Easier deployment (especially for projects needing end-to-end OCR + translation + extraction) If you deal with scanned documents, multilingual receipts/invoices, image-based text extraction, or need to build automation around document ingestion — HunyuanOCR is absolutely worth exploring. Curious to see how well it handles Indian documents (multilingual, mixed scripts) — I believe it could be a game-changer for that use-case too. Feel free to check out the repo 👉 https://lnkd.in/gunk8jq6 #AI #OCR #DocumentAutomation #MachineLearning #OpenSource #NLP #VisionLanguageModels

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🚀 NVIDIA GPUs are the powerhouse behind AI breakthroughs from ChatGPT to autonomous vehicles driving faster, smarter, and scalable AI solutions worldwide. 🌐 #RecruitRidersTechnologies #AI #TechNews #Innovation #FutureIsNow #BrandChampions #WeAreRecruitRiders #SocialStars #EmployeeInfluencers

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37 years of work. Replaced in 2 weeks. For $20K. A researcher and 16 AI agents just built a C compiler from scratch. 100K lines. Passes 99% of GCC’s torture tests. Compiles FFmpeg, Redis, PostgreSQL, QEMU. Runs Doom. GCC took thousands of engineers and 37 years to get here. They say they (mostly) walked away. That mostly matters. No human wrote a single line. But the researcher kept redesigning tests, fixing CI pipelines when agents broke each other, and unsticking all 16 when they hit the same wall. The human didn’t write code. They engineered the system that let AI write code. That’s not replacement. That’s a role shift. The developers who thrive next won’t be the best coders. They’ll be the best system thinkers.🤞

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🌟 XGBoost 3.0 now has new capabilities for GPUs: ✅ Train up to 1 TB of data on a single Grace Hopper GPU: Up to 8x faster than CPU with External-Memory Quantile DMatrix. ✅ Faster and lighter: GPU hist/approx methods an additional ~2x speed up and reduced memory use. ✅ Full feature support: External memory now supports categorical features, all objectives, and SHAP. ✅ Distributed mode: Experimental support for out-of-core training across a cluster. Technical deep dive ➡️ https://bit.ly/3UWdmz2

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Databricks Data Engineer with DevOps Skills Los Angeles CA (Hybrid) Long term No H1Bs&GCs     Must Demonstrate (Critical Skills & Architectural Competencies) Designing and implementing Databricks-based Lakehouse architectures on AWS Clear separation of compute vs. serving layers Ability to design low-latency data/API access strategies (beyond Spark-only patterns) Strong understanding of caching strategies for performance and cost optimization Data partitioning, storage optimization, and file layout strategy Ability to handle multi-terabyte structured or time-series datasets Skill in requirement probing, identifying what matters architecturally  12+ years of experience Strong hands-on experience with Databricks, including: Delta Lake Unity Catalog Lakehouse Architecture Delta Live Pipelines Databricks Runtime Table Triggers Databricks Workflows Proficiency in PySpark, Spark, and advanced SQL. Expertise with AWS cloud services, including: S3 IAM Glue / Glue Catalog Lambda Kinesis (optional but beneficial) Secrets Manager Strong understanding of DevOps tools: Git / GitLab CI/CD pipelines Databricks Asset Bundles Familiarity with Terraform is a plus. Experience with relational databases and data warehouse concepts. Please share suitable profiles to syam@s2jsolutions.com