Sonny PhD @Machine_Enginee

Hi, I'm Felix. I'm an eng lead working on Claude. I love watching people take flight and do things they never thought possible. We often describe that as "being more ambitious" when using AI, which is tricky advice to follow. I wrote a tutorial: felixrieseberg.com/how-to-be-more…

Turning a vibe-coded data pipeline into production-grade software requires thinking about 100s of different engineering concepts! AI can generate an ETL pipeline in minutes. But getting that pipeline to process billions of records reliably, maintain data quality, and recover from failures? That’s a completely different animal! Here’s what vibe coders building data pipelines need to learn: 1. Data Ingestion & Extraction * Batch vs. streaming ingestion * Change Data Capture (CDC) * Full vs. incremental loads * API rate limiting * Schema inference * Data serialization * Idempotent ingestion * Late-arriving data 2. Data Modeling & Storage * Star schemas * Snowflake schemas * Slowly Changing Dimensions (SCDs) * Fact and dimension tables * Data normalization * Partitioning * Clustering * File compaction * Columnar storage * Data lakehouse architectures 3. Distributed Data Processing * Data shuffling * Data skew * Broadcast joins * Sort-merge joins * Predicate pushdown * Partition pruning * Parallelism * Memory management * Adaptive query execution 4. Orchestration & Reliability * DAG orchestration * Task dependencies * Retries * Exponential backoff * Idempotency * Backfills * SLAs * Data freshness * Failure recovery * Workflow scheduling 5. Streaming & Real-Time Data * Event time vs. processing time * Watermarks * Windowing * Exactly-once vs. at-least-once processing * Event deduplication * Out-of-order events * Checkpointing * State management * Kafka partitions * Backpressure 6. Data Quality & Governance * Data contracts * Schema evolution * Schema enforcement * Null handling * Duplicate detection * Referential integrity * Data validation * Data lineage * PII handling * Access controls * GDPR compliance 7. Performance & Cost Optimization * Warehouse sizing * Autoscaling * Query caching * Materialized views * Incremental materialization * Storage optimization * Compute optimization * Cost attribution * Concurrency management * Query profiling 8. Deployment & Observability * CI/CD for data pipelines * Infrastructure as Code * Environment isolation * Unit testing * Integration testing * Data reconciliation * Pipeline monitoring * Data quality alerts * Freshness monitoring AI has reduced the cost of writing data transformations! But it hasn’t eliminated the complexity of running data pipelines at scale. You can easily vibe code your way to a pipeline that works on 10,000 rows. But when that pipeline needs to process 10 billion rows, handle schema changes, recover from partial failures, or guarantee downstream data correctness. You’re basically cooked！ The future isn’t data engineers vs. vibe coders. It’s data engineers who understand distributed systems, data architecture, and production reliability AND know how to leverage AI. Writing SQL and Python is getting cheaper. Data engineering judgment is becoming more valuable！ What would you add to the list? Inspired by Natalie L.

Deep learning is often taught as a sanitized collection of facts and math. But work at the frontier of AI is messy and empirical. Our new class, Deep Learning Alchemy, aims to teach the art and science of deep learning in the hands-on spirit of Language Modeling From Scratch. Students build intuition by running experiments on small LMs, then test it through prediction quizzes: given an unseen code diff, can you predict what happens? deep-learning-alchemy.github.io My co-instructor @kothasuhas and I think there's significant demand for classes that treat students as serious learners who want to be assessed for what they’ve learned, and so we're trying a new exam-heavy approach to the class (7 exams, 85% of the grade, with the remaining 15% in-person discussion assessments). We’ve designed the class so students can use AI agents to run their experiments and do their assignment, but they then have to demonstrate that mastery on exams without access to the assignment, and any partial credit on the exam has to come through citations to the assignment, incentivizing them to know what’s in their write-up. The same assignments and quizzes are public for anyone to try (lecture recordings will be released through @StanfordOnline with some delay). We have some very fun assignments and quizzes covering both classic topics (like NQM, run-to-run stochasticity, and scaling laws) and new and timely topics like muP or hyperball. Made by some real experts like @TristanThrush and @wen_kaiyue Try some quizzes below!

If you want to learn DevOps through a properly structured university course, start here. The University of Helsinki has made its complete DevOps with Kubernetes course available online for free. A full university-level course with hands-on exercises, open to everyone: courses.mooc.fi/org/uh-cs/cour…

Containerization is at the heart of modern software development. In this in-depth course, Eissa teaches you Docker from the ground up. You'll learn about containers, Dockerfiles, images, Docker Hub, networking, volumes, Docker Compose, and more - plus real-world deployment workflows. freecodecamp.org/news/docker-fu…

Stanford's "CS231n: Convolutional Neural Networks for Visual Recognition" by Fei-Fei Li The course that kickstarted the deep learning revolution in computer vision. Lecture notes: cs231n.github.io Videos: youtube.com/playlist?list=…

Cornell's "Machine Learning for Intelligent Systems" by Kilian Weinberger One of the most popular intro ML courses online, covering everything from k-NN to deep nets. Lecture Notes: cs.cornell.edu/courses/cs4780… Lecture Videos: youtube.com/playlist?list=…

Harvard's "Statistics 110: Probability" by Joe Blitzstein Full lecture videos, a free textbook, and practice problems with solutions. Lecture videos: youtube.com/playlist?list=… Free book: probabilitybook.net Practice problems: stat110.hsites.harvard.edu/strategic-prac…

Stanford's "CS25: Transformers United" by Chris Manning, Steven Feng, et al. Guest lectures from Hinton, Karpathy & researchers at OpenAI, DeepMind, Anthropic on Transformers and LLMs. Course website: web.stanford.edu/class/cs25/ Videos: youtube.com/playlist?list=…

Run Qwen-Image-2.1-Turbo GGUF locally 🖼️ We released quants on Hugging Face, from 2-bit (2.6 GB) up to lossless BF16 (14.2 GB) Our 4.2 GB AD-Q4_K has 21% lower LPIPS than a plain llama.cpp Q4_K, so its images stay closer to the original GGUFs: huggingface.co/AtomicChat/Qwe…

Qwen @Alibaba_Qwen

a week ago

🚀 Meet Qwen-Image-2.1-Turbo — create and edit images in just 8 denoising steps! Open weights now available! Built on Qwen-Image-2.1, Turbo is an accelerated checkpoint on the same 7B visual generation architecture. Fewer steps does not mean lower quality: it still generates

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You can now train your own Decision model with our free notebook! 💡 Qwen3.5-4B will generate decisions instead of text on just 8GB VRAM locally. Learn to data prep (state, questions, gold answers), train, serve. Notebook: colab.research.google.com/github/unsloth… Guide: unsloth.ai/docs/basics/tr…

Unsloth AI @UnslothAI

a week ago

You can now train your own Decision model like Jev locally! We increased Qwen3.5 0.8B’s aggregate accuracy from 20.7% to 74.3% across 3 decision benchmarks - on just 4GB VRAM. Turn any LLM like Qwen3.8, Gemma 4 into decision models with our open-source Unsloth repo. We

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Local Laya blitzed other models on RTX 4090 ⚡️ Four local decision models raced down Wikipedia picking out every word that names a centipede and Laya took 3.9 ms a word so it ended up being the fastest overall Run decision models locally -> atomic.chat

雨夹雪❄️ @mizorewww

4 weeks ago

介绍比Jev快50倍，在你设备上跑的laya-mlx! 只在你的设备上占用最高1G内存 Laya是一个开源的类似于Jev的,基于文本输出概率的分类系统 我将其移植到MLX,并且做了一些性能优化! 视频中就是这个模型在我的本地M3Max上玩贪吃蛇 这个模型能够以每秒决策60次的速度玩贪吃蛇！ github.com/mizorewww/laya…

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Computer created explainer videos for all 722 preprints in OpenAI's mathematical manuscript release, totaling 93 hours of video. Watch the videos on YouTube: 722.video Here's how Computer tackled this task:

Look beyond conventional scouting wisdom: your submission could help coaches and evaluators identify hidden talent, avoid costly draft busts, and design position-specific player development protocols. Join the Open or University Track here: kaggle.com/competitions/n…

@bcherny Beautiful!

Start learning GPU programming with one fully animated YouTube lecture. youtu.be/dEZP1qUNTOk This is the first video of my GPU programming series with Triton. Every idea in it is visual and animated, just like the GIF in this post, but for a full hour. You write c = a + b in PyTorch every day. This lecture shows what the GPU actually does with it. 𝐖𝐡𝐚𝐭 𝐭𝐡𝐞 𝐥𝐞𝐜𝐭𝐮𝐫𝐞 𝐜𝐨𝐯𝐞𝐫𝐬 - How a GPU sits inside your computer: PCIe, NVLink, servers and clusters - Inside an NVIDIA H100: SMs, FP32 lanes, the warp scheduler, tensor cores, shared memory - Threads, warps, blocks and grids, and how each thread finds its element - Memory: SRAM vs DRAM, DDR vs GDDR vs HBM, caches, latency vs bandwidth - Making memory fast: latency hiding, coalescing and kernel fusion - Why Triton exists, and how it lets you think in tiles instead of threads - Memory-bound vs compute-bound, arithmetic intensity and the roofline - Attention, the N x N matrix, and how FlashAttention fixes it No CUDA or hardware background needed. I want to make more long-form technical animated videos, something similar to what @neural_avb does. Taken inspiration from him 𝐖𝐡𝐚𝐭'𝐬 𝐧𝐞𝐱𝐭 - Lecture 2: writing our first Triton kernel, vector add, line by line, will post it in few days - More lectures on GPU programming and inference optimization - A planned series on deep learning for audio Subscribe to the channel so you don't miss them. Please also share the video with your ML and DL friends, or in any study or community groups you're part of. Watch the full lecture: youtu.be/dEZP1qUNTOk If you are into audio deep learning, inference, or Triton, follow @Mayank_022 me. I will be uploading more posts related to that.

Mathematics textbook “Discover Linear Algebra” — download 381-page PDF here: sites.ualberta.ca/~jsylvest/book…

Are small language models the future? Some AI leaders say “yes”. "How to Build and Fine‐Tune a Small Language Model: A Step-by-Step Guide for Beginners, Researchers, and Non-Programmers" Available at amzn.to/4aV0amA Build your own AI—without a PhD, expensive hardware, or industry-level resources. Whether you’re a beginner, a student, a scientist, or a domain expert, this book shows you how to create, train, fine-tune, and deploy Small Language Models (SLMs) that truly understand your field. Most AI books explain what models are. This one teaches you to build them. You’ll go from zero to a working GPT-style model, then learn how to fine-tune, align, evaluate, and deploy it for real applications. All chapters include ready-to-run Google Colab notebooks.

Codez @0xCodez

2 weeks ago

Andrej Karpathy predicted the future of AI once again: “Everyone’s renting frontier models for jobs a 3B model could do. Small models are the future.” this 18-page PDF breaks down Karpathy’s case for working with small LLMs. the real question isn’t “Is the small model as

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"Domain-Specific Small Language Models: Efficient AI for local deployment" available at amzn.to/4g7mVqW via @ManningBooks + Link to research paper: drive.google.com/file/d/1Aw6vxD…

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Download 518-page PDF — “Introduction to Probability” at math.dartmouth.edu/~prob/prob/pro…