Jun Kim, oMLX creator and maintainer, joins Hugging Face to support the MLX community
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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AI and machine learning blogs
Read practitioner writing on LLMs, data science, model engineering, product lessons, and applied ML tutorials.
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05 entriesHaversine law
John D. Cook · Blogs · 9h ago
The current balance of power in open models
Interconnects AI · Blogs · 21h ago
The Overhang
One Useful Thing · Blogs · 3d ago
Laser gems
AI Weirdness · Blogs · 6d ago
Your Model Isn't Done Until Someone Else Can Call It
Towards Data Science · Blogs · 8d ago
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Jun Kim, oMLX creator and maintainer, joins Hugging Face to support the MLX community
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Pruning LLMs Like a Physicist: Block Removal as an Ising Optimization Problem
A Blog post by Multiverse Computing on Hugging Face
tokenizers v1: encode, decode and scaling, measured
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Your Agent Aced the Task. Will It Do It Again?
A Blog post by IBM Research on Hugging Face
Async GRPO with LoRA across HF Jobs: a bucket, a proxy, and no NCCL
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Rebuilding AUTOMATIC1111 with Gradio Workflow
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
IBM releases SOTA Granite Time Series PatchTST-FM-r2 model with commercial-friendly license
A Blog post by IBM Research on Hugging Face
Safety for Whom? Refusing the Right Subset of a Topic, Not the Whole Topic
A Blog post by Multiverse Computing on Hugging Face
NeoMME: an efficient Multimodal-native and Multilingual Encoder
A Blog post by H company on Hugging Face
Give Your Coding Agents a Memory You Own
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Fine-tuning a 350M Model for Better Structured Outputs in 100 GRPO Steps
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Training a coding model to paint watercolours with TRL and OpenEnv
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Real-Time Intelligence with IBM Time Series Models on Confluent
A Blog post by IBM Research on Hugging Face
BenchMIRT: What are LLM benchmarks actually measuring?
A Blog post by Ai2 on Hugging Face
Introducing @huggingface/kernels: 200+ WebGPU Kernels for Local AI
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
The Open ASR Leaderboard Adds Its First Global South Language
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Granite 4.2 LLMs: How They're Built
A Blog post by IBM Granite on Hugging Face
Extremely Fast and Accurate Transcription with Granite Speech 5.0 Turbo CTC
A Blog post by IBM Granite on Hugging Face
Quantization-Aware Healing: a compressed, 4-bit model that outperforms its full-precision original
A Blog post by Multiverse Computing on Hugging Face
Haversine law
Why fitting a logistic is nearly impossible from early data
Empirical fractal
Phone words
Converting between cosine similarity and concentration ratio
Coffee + milk ≠ latte
Fibonacci product
Simple approximation for spherical cap area
What counts as a large cosine similarity?
Guessing the meaning of a number
Bayesian OCR
A 50-year-old computer-assisted proof
AI is an intelligence multiplier
The part of Navier-Stokes no one is talking about
Navier-Stokes in the news
Ngram error rate
Proof of the rank-trace theorem
Computing a lower bound on matrix rank
Hugging Face Easter Egg
New RSA number factored
The current balance of power in open models
Why I still haven’t bought into true RSI
Open-Source AI & Open Models Reading List
One resignation turned the embers of AI fear into a wildfire
When will average people feel AI’s impact?
Latest open artifacts (#24): Motif-3, GLM-5.3, Hy4-preview and open model licenses
Teaching Everyone to Fish for Tokens
GLM-5.3: How Chinese labs keep stride with the frontier
I wrote an AI textbook — how long until AI can do it better?
5 useful things you'll learn in my new post-training textbook (shipping now!)
Lessons from the hacks
Introducing our Artifacts Hub and Adoption Dashboard
Latest open artifacts (#23): Laguna S2.1, Inkling, & Kimi K3 show the utility of open models on the Pareto frontier
Open models recap: more on Kimi K3, Qwen 3.8, Xi's WAIC speech, distillation, the open-closed gap, and what's next
Kimi K3: The open-weights escalation
6 months to live for open models
Latest open artifacts (#22): Zyphra, Cohere, and Poolside are expanding the breadth of the ecosystem
GLM-5.2 is the step change for open agents
Banning Open Source AI Would Be A Mistake
State of the blog, mid-2026
The Overhang
Agency and Agents
An opinionated guide to which AI to use to do stuff
The twilight of the chatbots
What it feels like to work with Mythos
Co-Existence and the End of Co-Intelligence
Choosing to Stay Human
Sign of the future: GPT-5.5
Claude Dispatch and the Power of Interfaces
The Shape of the Thing
A Guide to Which AI to Use in the Agentic Era
Management as AI superpower
Claude Code and What Comes Next
The Shape of AI: Jaggedness, Bottlenecks and Salients
Three Years from GPT-3 to Gemini 3
Giving your AI a Job Interview
An Opinionated Guide to Using AI Right Now
Real AI Agents and Real Work
On Working with Wizards
Mass Intelligence
Laser gems
So apparently laser crystals that are rejected from laser-making can end up in the gem trade and they look amazing!
The lasers in Clash of the Titans
They used lasers for the god rays of Zeus in Clash of the Titans (1981) and I don't care what you say about the rest of the movie, the lasers are doing great.
Bonus post and secret update for supporters: what's with all the lasers?
AI Weirdness: the strange side of machine learning
Laser Jeans
Apparently they use lasers to put the wear marks in jeans now? Amazing.
I made a hologram!
See! Real holograms! Dice and crystals that aren't there any more, thanks to the power of lasers!!1!
It's 11:00 pm. Do you know where your AI agent is?
AI agents that email people and post on other people's websites are cursed and we shouldn't make them.
Get working on your April Fools Eiffel Tower
Elevator Surprise: Place a tiny camera in the elevator, and when someone gets in, snap a photo saying, "Welcome to Space Station!" Or build a miniature model of the Eiffel Tower n…
Bonus: More April Fools pranks from Eiffel Tower Llama
AI Weirdness: the strange side of machine learning
Your Model Isn't Done Until Someone Else Can Call It
Your AI Adoption Lift Is a Selection Effect
One Capital Letter Was Silently Breaking My AI Support Bot, and It Wasn't in the New Model
Stop Managing Alarms: An Incident-First Blueprint for Telecom AIOps
What large operators can teach us about turning alert fatigue into faster, safer service assurance
Coding Agents Don't Need Longer History — They Need Intent Continuity
Software Design in the Age of AI
The 95% Illusion: Why Your Confidence Interval Isn't What You Think It Is
Demystifying Anthropic's J-Space: A Mathematical Primer
How to 5x Your Communication Effectiveness with Claude Code
Better understand the intent of your coding agents
What SHAP Can't Explain About Agentic AI Fraud
Optimizing LLM Inference Costs in Multi-Agent Systems with Adaptive Model Routing
Moving from static model assignment to intelligent, task-level LLM selection.
Who Questions What Works: When Should We Retest Our Assumptions?
A model is only as reliable as the assumptions behind it
Getting started with dbt
The Symmetry That Breaks Neural Network Averaging
When One Process Becomes Too Much: Splitting a Pipeline into MCP Services
10 Statistical Traps We Often Overlook
How to Maximize GPT-6 Astra
The Model Validation Playbook for GenAI: Lessons from Banking
A Beginner’s Guide to World Models
Introducing ShipAI
Towards Data Science launches a video showcase for real-world AI work
GPT-6 Astra, Looped Transformers, and Hidden Reasoning
How Claude Watermarks AI-Generated Text
Building an AI Text Detector From Scratch
Controlling Reasoning Effort in LLMs
Using Local Coding Agents
LLM Research Papers: The 2026 List (January to May)
Recent Developments in LLM Architectures: KV Sharing, mHC, and Compressed Attention
My Workflow for Understanding LLM Architectures
Components of A Coding Agent
A Visual Guide to Attention Variants in Modern LLMs
A Dream of Spring for Open-Weight LLMs: 10 Architectures from Jan-Feb 2026
Categories of Inference-Time Scaling for Improved LLM Reasoning
The State Of LLMs 2025: Progress, Problems, and Predictions
LLM Research Papers: The 2025 List (July to December)
From DeepSeek V3 to V3.2: Architecture, Sparse Attention, and RL Updates
Beyond Standard LLMs
Understanding the 4 Main Approaches to LLM Evaluation (From Scratch)
Understanding and Implementing Qwen3 From Scratch
From GPT-2 to gpt-oss: Analyzing the Architectural Advances
The Big LLM Architecture Comparison
Book Review: Data Centric Machine Learning with Python
I recently read the book Data Centric Machine Learning with Python . The thesis of the book is that Machine Learning (ML) pipelines benefit ...
Book Review: Domain Specific Small Language Models
Artificial Intelligence (AI) powered applications are changing the way we consume and use information. Retrieval Augmented Generation (RAG) ...
Book Review: Software Engineering for Data Scientists
As a Software Engineer (backend Web Development then Search) turned Data Scientist, I was particularly interested in what the book Software ...
Book Review: Transformers In Action
The Attention Is All You Need paper proposed the Transformer Architecrture as an improvement to the dominant encoder-decoder models of the ...
Trip Report: PyData Global 2025
I attended PyData Global 2025 earlier this month. I had hoped to write this up earlier, but I've been busy, so only now getting the time Ch...
Book Review: Time Series Forecasting using Foundation Models
As someone who primarily works in NLP and Search in the Health Domain, I don't have much use for Time Series. However, while exploring the F...
Book Review: Statistics every Programmer Needs
I recently read Statistics every Programmer Needs by Gary Sutton. I am probably a good target audience for the book since I used to be a so...
Book Review: Hands-On Artificial Intelligence for IoT
For those in similar professional circles as I am in, i.e. looking forward into the Generative AI space, yet with one foot pragmatically and...
Book Review: Essential Graph RAG
Coming from a background of Knowledge Graph (KG) backed Medical Search, I don't need to be convinced about the importance of manually curate...
Packaging ML Pipelines from Experiment to Deployment
As an ML Engineer, we are generally tasked with solving some business problem with technology. Typically it involves leveraging data assets ...
Trip Report - PyData Global 2024
I attended PyData Global 2024 last week. Its a virtual conference, so I was able to attend it from the comfort of my home, although presenta...
Using Knowledge Graphs to enhance Retrieval Augmented Generation
Retrieval Augmented Generation (RAG) has become a popular approach to harness LLMs for question answering using your own corpus of data. Typ...
Experiments with Prompt Compression
I recently came across Prompt Compression (in the context of Prompt Engineering on Large Language Models) on this short course on Prompt Com...
Table Extraction from PDFs using Multimodal (Vision) LLMs
Couple of weeks ago a colleague and I participated in an internal hackathon where the task was to come up with an interesting use case using...
Book Report: Pandas Workout
Unlike many Data Scientists, I didn't automatically reach for Pandas when I needed to analyze data. I came upon this discipline (Data Scien...
Finetuning RAGAS Metrics using DSPy
Last month, I decided to sign-up for the Google AI Hackathon , where Google provided access to their Gemini Large Language Model (LLM) and ...
Performance Analysis of Float vs Byte vs Binary Vectors on OpenSearch
I've been working on an application where, given an input string, the objective is to recommend an output string that is similar to the inpu...
KGC/HCLS 2024 Trip Report
I was at KGC (Knowledge Graph Conference) 2024 , which is happening May 6-10 at Cornell Tech . I was presenting (virtually) at their Health ...
Book Report: Machine Learning for Drug Discovery
Drug Discovery is a field where biochemists (and more recently computer scientists) turn ideas into potential medications. I first came acro...
Hierarchical (and other) Indexes using LlamaIndex for RAG Content Enrichment
At our weekly This Week in Machine Learning (TWIML) meetings, (our leader and facilitataor) Darin Plutchok pointed out a LinkedIn blog post ...
Becoming Human AI Is Expanding — Here’s What’s Changing
Becoming Human AI Is Expanding — Here’s What’s Changing For years, this has been where you’ve found us — through Medium, whenever we published something worth your time. That’s ch…
What is RAFT? RAG + Fine-Tuning
What is RAFT? RAG + Fine-Tuning In simple terms, retrieval-augmented fine-tuning, or RAFT, is an advanced AI technique in which retrieval-augmented generation is joined with fine-…
Modern Operating Systems for AI Agents
Modern Operating Systems for AI Agents An operating system (OS) is the fundamental software that acts as an intermediary between computer hardware and user applications. It manage…
NLP in 2026: Trends, Use Cases & Future of Language AI | Shaip
NLP in 2026: Trends, Use Cases & Future of Language AI | Shaip Every day, your organization produces a mountain of words. Support tickets, contracts, clinical notes, customer revi…
AI Bias in Hiring Tools: Real Disasters and How Teams Fixed Them
Hiring tools failing women and minorities? Real cases like Amazon's flop. Step-by-step fixes – data tweaks, fairness scores.
AGI in 2025 |Do you think what matters today will still matter in the coming months? TL;DR: No!
When Algorithms Dream of Photons: Can AI Redefine Reality Like Einstein?
Be Part of the AI Revolution at the Chatbot Conference Tomorrow!
Join the Most-Awaited Chatbot Conference
Limited Time Offer: Get Your Exclusive Online Passes to the Chatbot Conference — Act Fast!
Harness Engineering for Self-Improvement
The concept of recursive self-improvement (RSI) dates back to I. J. Good (1965), where he defined an “ultraintelligent machine” as a system that can surpass humans in all intellec…
Scaling Laws, Carefully
Scaling laws are one of the most critical empirical findings in deep learning. The observation is simple in form: the training loss $L$ decreases predictably as we scale up model…
Why We Think
Special thanks to John Schulman for a lot of super valuable feedback and direct edits on this post. Test time compute (Graves et al. 2016, Ling, et al. 2017, Cobbe et al. 2021) an…
Reward Hacking in Reinforcement Learning
Reward hacking occurs when a reinforcement learning (RL) agent exploits flaws or ambiguities in the reward function to achieve high rewards, without genuinely learning or completi…
Extrinsic Hallucinations in LLMs
Hallucination in large language models usually refers to the model generating unfaithful, fabricated, inconsistent, or nonsensical content. As a term, hallucination has been somew…
Diffusion Models for Video Generation
Diffusion models have demonstrated strong results on image synthesis in past years. Now the research community has started working on a harder task—using it for video generation.…
Thinking about High-Quality Human Data
[Special thank you to Ian Kivlichan for many useful pointers (E.g. the 100+ year old Nature paper “Vox populi”) and nice feedback. 🙏 ] High-quality data is the fuel for modern dat…
Adversarial Attacks on LLMs
The use of large language models in the real world has strongly accelerated by the launch of ChatGPT. We (including my team at OpenAI, shoutout to them) have invested a lot of eff…
LLM Powered Autonomous Agents
Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as insp…
Prompt Engineering
Prompt Engineering, also known as In-Context Prompting, refers to methods for how to communicate with LLM to steer its behavior for desired outcomes without updating the model wei…
The Transformer Family Version 2.0
Many new Transformer architecture improvements have been proposed since my last post on “The Transformer Family” about three years ago. Here I did a big refactoring and enrichment…
Large Transformer Model Inference Optimization
[Updated on 2023-01-24: add a small section on Distillation.] Large transformer models are mainstream nowadays, creating SoTA results for a variety of tasks. They are powerful but…
Some Math behind Neural Tangent Kernel
Neural networks are well known to be over-parameterized and can often easily fit data with near-zero training loss with decent generalization performance on test dataset. Although…
Generalized Visual Language Models
Processing images to generate text, such as image captioning and visual question-answering, has been studied for years. Traditionally such systems rely on an object detection netw…
Learning with not Enough Data Part 3: Data Generation
Here comes the Part 3 on learning with not enough data (Previous: Part 1 and Part 2). Let’s consider two approaches for generating synthetic data for training. Augmented data. Giv…
Learning with not Enough Data Part 2: Active Learning
This is part 2 of what to do when facing a limited amount of labeled data for supervised learning tasks. This time we will get some amount of human labeling work involved, but wit…
Learning with not Enough Data Part 1: Semi-Supervised Learning
When facing a limited amount of labeled data for supervised learning tasks, four approaches are commonly discussed.
How to Train Really Large Models on Many GPUs?
[Updated on 2022-03-13: add expert choice routing.] [Updated on 2022-06-10]: Greg and I wrote a shorted and upgraded version of this post, published on OpenAI Blog: “Techniques fo…
What are Diffusion Models?
[Updated on 2021-09-19: Highly recommend this blog post on score-based generative modeling by Yang Song (author of several key papers in the references)]. [Updated on 2022-08-27:…
Contrastive Representation Learning
The goal of contrastive representation learning is to learn such an embedding space in which similar sample pairs stay close to each other while dissimilar ones are far apart. Con…
Context Graphs vs Vector RAG vs Raw Context - Agentic memory benchmark
We benchmark context graphs against other methods of agent memory, and discuss their benefits.
What Are Context Graphs? (And Why AI Agents Need Them)
A context graph is the part of agent memory that captures decisions. We explain why AI agents need to capture decisions, how context graphs capture them, and how agents use them.
SAP Sapphire 2026: The Complete Breakdown
All 25 announcements from Orlando, what's actually shipping vs. what's marketing — and what it means for the document and data foundation of the autonomous enterprise. SAP's Sapph…
Claude for Legal Teams: Contract Review, Compliance and Due Diligence
See how the Claude legal plugin helps in-house legal teams with contract review, compliance scanning, due diligence, obligations tracking, and drafting.
Vibe Coding Best Practices: 5 Claude Code Habits for Better Agentic Coding
Learn 5 practical vibe coding best practices for Claude Code and coding agents: CLAUDE.md, planning, review agents, safer prompts, and diff review.
AI Benchmarks Explained: GPQA, SWE-bench, Chatbot Arena and What They Actually Measure
Learn what MMLU, GPQA Diamond, SWE-bench, HealthBench, and Chatbot Arena actually measure, and how labs game benchmark scores.
Why AI-Native IDP Platforms Outperform ABBYY and Kofax in Modern Document Workflows
Evaluating IDP vendors? Compare Nanonets vs ABBYY and Kofax across architecture, operating model, and TCO to see why AI-native wins for IDP.
Why You Hit Claude Limits So Fast: AI Token Limits Explained
Learn what AI tokens are, why Claude hits limits fast, and how to cut waste from context windows, history, files, tools, and reasoning.
Did Google's TurboQuant Actually Solve AI Memory Crunch?
Google’s TurboQuant promises 6x KV-cache compression. Here’s what it means for AI memory, HBM demand, and the broader memory crunch.
Claude for Finance Teams: Investment Banking, DCF Models, Reconciliation & Variance Analysis
See how finance teams use Claude for one-pagers, CIMs, comps, DCF models, reconciliations, and variance commentary, plus key human checks.
AI Agent Hacks McKinsey: 5 Situations When You Should Not Deploy Agents
McKinsey hacked in 2 hours. 5 situations where AI agents will fail. Production permissions, regulated data, legacy systems—check before deploy.
Are OpenAI and Google intentionally downgrading their models?
Yes, OpenAI and Google degrade their models. OpenAI admitted silent updates after denying it. Gemini redirects models. With full evidence.
We ran 16 AI Models on 9,000+ Real Documents. Here's What We Found.
We benchmarked GPT-5.4, Gemini 3.1 Pro, Claude Opus, Sonnet, and 12 others on 3 Open OCR Benchmarks
AI Arms Race Has Real Numbers: Pentagon vs China 2026
AI targeting: 900 strikes in 12 hours. Anthropic banned from Pentagon mid-operation, OpenAI switched in. China building parallel systems.
Stop Paying for AI You Don't Use: The Case for Fine-Tuned Models
Processing 10K documents daily costs $50K/year with GPT. Fine-tuned models: $5K with better latency and stable accuracy. Make the right choice.
Domain-Specific AI Should Focus on Workflows Rather Than Modeling
I spent the past few years working on AI for health. Starting with custom multimodal encoders, post-training, and sophisticated multi-agent architecturs, I now see modeling work b…
AI Evaluation is Becoming an Exciting Standalone Discipline
Having worked on robustness problems during my PhD, I see many of the characteristics appearing in the evaluation of LLMs and AI systems. Adversarial attacks such as jailbreaks ar…
Running Log: Trail du Mont Agel
Engineering, Robotics, AI, Technology
I Completed The David Goggins Challenge And Asked My Garmin How I Did
Engineering, Robotics, AI, Technology
A Video Game that Pays: Lessons Learned from Working Remotely
Engineering, Robotics, AI, Technology
Deep Dive into NeRF (Neural Radiance Fields)
Engineering, Robotics, AI, Technology
Learning by Doing - the DeFi Quest (Part 2 out of 2)
Engineering, Robotics, AI, Technology
Learning by Doing - the DeFi Quest (Part 1 out of 2)
Engineering, Robotics, AI, Technology
So You Want to Study Computer Science (in Europe)?
Engineering, Robotics, AI, Technology
Mixed Martial Maths - Simple Reasoning Tools For Complex Phenomena
Engineering, Robotics, AI, Technology
Notes - Prisoners of Geography (Tim Marshall)
Engineering, Robotics, AI, Technology
Robotic Assembly Using Deep Reinforcement Learning
Engineering, Robotics, AI, Technology
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