44 Best Tweets About Machine Learning (2026)

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Jahir Sheikh

@jahirsheikh8 · May 2, 2026

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You’re not an AI Engineer until you understand these terms: • 🧠 Embeddings → Numerical meaning of text/data • 🔍 Vector DB → Similarity search storage • 📚 RAG → Retrieval-Augmented Generation • 🎯 Fine-Tuning → Task-specific model training • 🪶 LoRA → Lightweight fine-tuning method… Read more

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Tech with Mak

@techNmak · Mar 10, 2026

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Most people try to learn AI randomly. I mapped the entire AI engineering journey into a metro system. The problem with most AI roadmaps: They're linear. Step 1, Step 2, Step 3. As if everyone starts at the same place and wants the same destination. But AI engineering isn't… Read more

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Sakana AI

@SakanaAILabs · Mar 25, 2026

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The AI Scientist: Towards Fully Automated AI Research, Now Published in Nature Nature: https://t.co/nNfpSV5e5I Blog: https://t.co/i6h8LVQOdl When we first introduced The AI Scientist, we shared an ambitious vision of an agent powered by foundation models capable of executing… Read more

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Shalini Goyal

@goyalshaliniuk · Jul 21, 2026

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Confused between ML, NLP, Generative, and other AI models? Here’s a quick breakdown of the 6 most important types of AI models you must understand in 2026 👇 1. Machine Learning Models They learn from labeled and unlabeled data to classify, predict, and detect patterns. Think… Read more

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Swapna Kumar Panda

@swapnakpanda · Mar 26, 2026

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MIT is offering Books on AI & ML (ABSOLUTELY FREE): 1. Foundations of Machine Learning https://t.co/78p57EBbL8 2. Understanding Deep Learning https://t.co/D2oyRrXqcE 3. Introduction to Machine Learning Systems https://t.co/EhGVI1P57j 4. Algorithms for ML… Read more

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Jürgen Schmidhuber

@SchmidhuberAI · Mar 10, 2026

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Everybody is talking about recursive self-improvement (RSI) and meta learning. Here is my old 2020 talk about this [1]. It has aged well. Example: humans still define the starts & ends of trials of many modern meta learners. My RSI systems since 1994 LEARN to (re)define them [2]!… Read more

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Aurimas Griciūnas

@Aurimas_Gr · Jun 5, 2026

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As an 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 you should also care about regular, non LLM-based ML models, productionising them comes with its own challenges. For example, 𝗖𝗜/𝗖𝗗 process is 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗳𝗼𝗿 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 compared to regular software. The important difference that the Machine… Read more

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Paul Iusztin

@pauliusztin_ · Jun 28, 2026

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Microsoft just open-sourced one of the most interesting agent engineering projects I've seen this year... → https://t.co/5NyVSw2ATi Most engineers assume improving an AI agent requires: Better models More data More fine-tuning SkillOpt takes a completely different approach.… Read more

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Suhail

@Suhail · Jun 27, 2026

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/goal for AI model training runs is *so* good - it really feels like the future. Very little babysitting now. Mine: Launch a full training run on 4 nodes. Continuously record things in an experiment document if it exists. Log hyper params, configs, periodic evals, performance… Read more

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Jaydeep

@_jaydeepkarale · Apr 1, 2026

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If you want to get started with Machine Learning in Python this 42 part playlist is a good place which covers • linear regression • gradient descent • logistic regression • decision tree • support vector • K-fold cross-validation • KNN classification • Feature Engineering

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Sentient

@sentient_agency · Jun 22, 2026

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A 17-year-old in Spain can open an old laptop after school, search “Stanford CS229,” and start learning machine learning from Andrew Ng before she ever applies to university. You don't need admission letter, visa, and also $80,000 campus bill. Just lectures, notes, assignments,… Read more

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Khushi | AI & Tech

@TechWithKhushi · May 20, 2026

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How to Become an AI Engineer in 2026 (The Real Roadmap) Most AI roadmaps you see online are incomplete. They teach you tools… but not how to think. They show you concepts… but not how to build real systems. So I took a step back and rebuilt the roadmap based on one goal: 👉… Read more

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tetsuo

@tetsuoai · Mar 24, 2026

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built a prompt that turns Grok Heavy into a daily ML research scout drops the last 48hrs of papers from arxiv, huggingface, lab blogs, papers with code, filtered, ranked, and summarized as structured JSON covers LLMs, diffusion, RL, new architectures, robotics, open-weight… Read more

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JustAnotherPM | Sid

@JustAnotherPM · May 6, 2026

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Most people think the "AI PM" title is just a product manager who happens to know AI. That is what I told myself the first six months of working on AI products. Turns out, I had the role inside out. After years of coaching AI PMs, hiring a few, getting hired by a few, here is… Read more

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Andy

@andy_ai0 · Mar 15, 2026

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1-month playbook to start learning AI In just one month, you’ll already be able to: - understand what AI, machine learning, deep learning actually mean - use beginner AI tools without feeling lost - understand core concepts like training data, models, overfitting - build a few… Read more

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Valeriy M., PhD, MBA, CQF

@predict_addict · Mar 23, 2026

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Most people in machine learning still misunderstand probabilities. A model can be perfectly calibrated and still be completely useless. This was proven more than 40 years ago by DeGroot & Fienberg (1983). Yet many ML papers still miss this point. Here is the idea.

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Tech with Mak

@techNmak · May 27, 2026

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Most engineers have seen this formula. P(A|B) = P(B|A) × P(A) / P(B) Almost none can explain what it actually does. Here's Bayes' Theorem in plain English, and where it's hiding inside systems you use every day. The core idea in one sentence: Bayes' Theorem updates your… Read more

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Shalini Goyal

@goyalshaliniuk · Jul 3, 2026

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Ever wondered how machines actually learn from data? This step-by-step visual breaks down the 12-stage journey of Machine Learning, turning complex AI training into a simple, intuitive process anyone can follow. From defining the problem to collecting, cleaning, and labeling… Read more

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Sourabh Gurwani

@SourabhGurwani · Jul 27, 2026

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If I had 6 months to become an AI Engineer, I'd do this. Stage 1 — Programming Fundamentals (Weeks 1–2) Learn Python, Git, Linux, SQL, APIs, OOP, NumPy, and Pandas. Stage 2 — Math & ML Foundations (Weeks 3–4) Master linear algebra, probability, statistics, calculus, regression,… Read more

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Aurimas Griciūnas

@Aurimas_Gr · Jun 10, 2026

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A breakdown of 𝗗𝗮𝘁𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝗶𝗻 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 👇 And yes, it can also be used for LLM based systems! It is critical to ensure Data Quality and Integrity upstream of ML Training and Inference Pipelines, trying to do that in the downstream systems will cause unavoidable… Read more

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Elliot Hershberg

@ElliotHershberg · Apr 3, 2026

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Technologies stack and compound. Sequencing gave us a tool to read state across all organisms. Proteomics has quietly undergone a revolution of its own. Gene editing tools let us perturb biological systems at scale. Incredible advances in microscopy have been made. We've never… Read more

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Vaishnavi

@_vmlops · Aug 11, 2026

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MLU-EXPLAIN IS ONE OF THE BEST FREE ML RESOURCES ON THE INTERNET Amazon's machine learning university built a site full of interactive visual essays that actually make core ML concepts click → neural networks, linear/logistic regression, decision trees → random forest,… Read more

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Karan🧋

@kmeanskaran · Aug 7, 2026

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Now you can build agents to automate the ML training lifecycle! You can define sequential subagents and skills for each agent, such as data engineering, feature engineering, model training, evaluation, drift detection, rollback, etc. Observability tools and orchestration will… Read more

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Eric Seufert

@eric_seufert · May 25, 2026

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I'm happy to share some of the research that I've been working on today: As ad platforms become more opaque and automated end-to-end, advertisers are left with few levers of control over campaign performance. I wanted to interrogate an idea: could advertisers treat "black box"… Read more

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BURKOV

@burkov · Jun 16, 2026

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The Hundred-Page Language Models Book by Andriy Burkov is part of a series that includes the well-regarded Hundred-Page Machine Learning Book. As the title suggests, it delivers a condensed, practical guide to understanding and working with language models. The book's main… Read more

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Vaishnavi

@_vmlops · Jul 13, 2026

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THIS GITHUB REPO IS A TEXTBOOK, A FRAMEWORK, A SIMULATOR, AND A HARDWARE LAB - ALL ONE CURRICULUM harvard-edge built the whole AI engineering stack in one repo, not five disconnected projects → vol I + vol II: single-machine systems through distributed, at-scale infra →… Read more

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Pandit | Ξ🦇🔊

@panditdhamdhere · Jul 20, 2026

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If you're building AI applications in Rust, these are five of the strongest libraries to learn by the end of 2026. 🦀 → Burn - Deep learning - native Rust framework with training, inference, GPU acceleration (CUDA, WGPU), autodiff, modular design. Great alternative to PyTorch for… Read more

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BURKOV

@burkov · May 19, 2026

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The Hundred-Page Language Models Book is a strong book. It's the third in Andriy Burkov's series of AI textbooks, following The Hundred-Page Machine Learning Book and Machine Learning Engineering, both of which became #1 bestsellers. It currently sits at about 4.49 out of 5 on… Read more

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Alexey Grigorev

@Al_Grigor · Apr 10, 2026

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LLM systems feel like a new paradigm. In practice, much of the lifecycle still follows patterns that existed long before generative AI. One useful lens is CRISP-DM, a framework originally designed for data mining projects and widely adopted in data science. Even though the… Read more

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Forward Future

@ForwardFuture · Mar 18, 2026

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“I rewrote my code in 30 minutes — and it ran 200× faster.” @ctnzr VP, Applied Deep Learning Research @NVIDIA on the moment GPUs changed everything: “NVIDIA showed up in our lab and said, ‘You should try CUDA.’ I plugged in a GPU, rewrote my SVM training code, and it ran 200×… Read more

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sonic inference.

@NaadhLabs · Mar 27, 2026

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Machine Learning from First Principles Machine Learning is not magic. It is not intelligence. It is not “thinking.” At its core, Machine Learning is about approximating an unknown function from data. Let’s rebuild it from zero.

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Gary Marcus

@GaryMarcus · Jun 1, 2026

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This was right five years ago, and still is: “Large scale pretrained models are certainly likely to figure prominently in artificial intelligence for the near future, and play an important role in commercial AI for some time to come. The results that have been achieved with them… Read more

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Rohan Paul

@rohanpaul_ai · Aug 18, 2026

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This is quite a big deal. Most tabular ML still starts with the same assumption: new dataset, new training run. That will no more be true. Synthefy just launched a foundation-model platform for structured numerical data where tables, transactions, sensor readings and time… Read more

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Glenn Gabe

@glenngabe · May 2, 2026

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Some interesting nuggets from the latest Search Off The Record Podcast about Google using AI in Search rankings. Here is Google's Nikola Todorovic, Director of Software Engineering at Google Search, about how AI is used to impact rankings and why simpler linear systems are… Read more

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atulit

@atulit_gaur · Jun 30, 2026

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i think the fusion of physics and ai is the most beautiful one we train neural networks with optimization methods rooted in physics, diffusion models borrow ideas from thermodynamics, physics informed neural networks solve differential equations by embedding physical laws… Read more

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sonic inference.

@NaadhLabs · Mar 30, 2026

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interesting machine learning paper, ReAct: Teaching AI to Think AND Act Introduction llm's have two things, reasoning- think through problems step by step action- doing smtg , searching web etc The Problem when ai only reasons, it relies purely on training data , which can be… Read more

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Poonam Soni

@CodeByPoonam · Apr 4, 2026

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Been thinking about why most AI research tools fail for serious ML work. The answer is simpler than people admit. ChatGPT optimize for plausibility. That works for drafts, summaries, brainstorming. It breaks the moment you need something verifiable— when the question isn’t… Read more

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Dylan Feltus

@DylanFeltus · Apr 5, 2026

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curious about building your own model? the actual roadmap: step 1: understand transformers → Karpathy "Let's build GPT" https://t.co/QRC54Uo5es → "Attention Is All You Need" paper https://t.co/JEN33a8Bxw → HF LLM course https://t.co/PbZUWmJbWz → https://t.co/UGTmB0L8Z6 step 2:… Read more

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Engineering at Meta

@Meta_Engineers · Mar 19, 2026

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Our Ranking Engineer Agent (REA) autonomously executes key steps across the end-to-end machine learning lifecycle for ads ranking models. REA reduces the need for manual intervention, managing asynchronous workflows spanning days to weeks through a hibernate-and-wake mechanism,… Read more

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Kuber

@kuberwastaken · Mar 29, 2026

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Agentic machine learning research is broken, so I fixed it in 12 hours. @karpathy recently demoed autoresearch and it blew up for a good reason. but it runs one agent at a time, has no memory across runs and doesn't scale when agents can work great in parallel. So for a… Read more

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Towards Data Science

@TDataScience · Jul 8, 2026

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"Machine Learning systems rarely fail in a single moment. Their performance changes gradually as data distributions shift, calibration drifts, or new patterns emerge in the environment." Gal Arav shares a thorough, accessible introduction to survival analysis for data drift and… Read more

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sabir hussain

@sabir_huss50540 · Aug 4, 2026

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A team of two dozen researchers just trained a 35B model to improve the process of building AI, put it on a single consumer graphics card, and released the whole thing. The paper is called Frontis-MA1. It came out of Tsinghua and a lab called https://t.co/wlcZKlCube on July 30.… Read more

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Muriel Demarcus

@MurielDemarcus · Jul 29, 2026

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The Guardian recently ran a piece about AI-enabled lamp posts with references to AI detecting aggression, gait and faces. Headlines like this often make AI sound almost magical.Reality is far less exciting. Every AI capability has: • a model • training data • a confidence score… Read more

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حمود بن عايش بن حامد المطرفي الهذلي

@AL_eaysh · Jan 20, 2026

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Officially, X has opened its “For You” algorithm to the public and revealed how content ranking and recommendations work. The algorithm collects user signals such as interactions and interests, pulls content from both followed and non-followed accounts using machine learning,… Read more