AI/ML

Feature: More fiction than science

Image generators are designed to mimic their training data, so where does their apparent creativity come from? A recent study suggests that it’s an inevitable by-product of their architecture.

LLM failures to reason, as documented in Apple’s Illusion of Thinking paper, are really only part of a much deeper problem

LLMs were invented in four major developments... all of which were datasets

Notes from a talk I delivered at the 2025 Data + AI Summit, detailing the problem with prompts in your code and how DSPy can make everything better.

Reviewing the few changes in the Claude 4.0 system prompts, we get a sense for how system prompts program chatbot applications.

Claude Is my favorite AI (even with ChatGPT, Gemini and DeepSeek in the mix)

6 tactics for fixing your context and shipping better agents. As Karpathy says, building LLM-powered apps means learning to ‘pack the context windows just right’—smartly deploying tools, managing information, and maintaining context hygiene.

Drew Breunig has been publishing some very detailed notes on context engineering recently. In How Long Contexts Fail he described four common patterns for context rot, which he summarizes like …

A talk about what I have learned about programming with agents over the last few months.

Armin Ronacher delivers a 37 minute YouTube talk describing his adventures so far with Claude Code and agentic coding methods. A friend called Claude Code catnip for programmers and it …

Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density…

A pragmatic take on AI coding with practical insights

Current recommendations of agentic coding.

Contribute to NomenAK/SuperClaude development by creating an account on GitHub.

On vision and how to understand deep learning.

35% off our evals course: https://bit.ly/evals-ai Vincent introduces Marimo, a reactive notebook environment. He walks us through the features of Marimo, including interactive and reactive charts and widget integration. Vincent demonstrates how you can use these components to build annotation apps for evals. Vincent also highlights differences between Marimo and traditional Jupyter Notebooks. Links: 1. Repo w/notebook: https://github.com/koaning/molabel 2. Vincen'ts drawing pad: https://www.amazon.com/Inspiroy-H640P-Graphics-Battery-Free-Sensitivity/dp/B075T6MTJX 3. Vincent's sites: https://koaning.io , and https://calmcode.io/ 00:00 Introduction to Data Science Journey 00:27 Exploring the Chick Weight Dataset 00:57 Interactive Data Analysis with Marimo 02:04 Importance of Looking at Data 03:32 Advanced Data Visualization Techniques 05:14 Introduction to Marimo's Unique Features 06:44 Reactive Programming in Marmo 12:50 AI Integration and Custom Rules 15:30 Marimo's Storage and Export Options 27:16 Advanced Visualization and Annotation 37:10 Introduction to Any Widget 37:45 Building Custom Widgets 38:56 Showcasing the Scatter Widget 40:29 Defining Widgets with Any Widget 45:58 Annotation Widgets and Their Uses 52:14 Exploring More Widget Capabilities 01:01:32 Marimo's App Mode and Deployment 01:03:37 Final Thoughts and Future Directions 01:04:45 Q&A and Closing Remarks

VS Code Copilot Memory-Bank Instructions. GitHub Gist: instantly share code, notes, and snippets.

To create reliable agents, AI companies had to go beyond predicting the next token.

Introducing a new, unifying DNA sequence model that advances regulatory variant-effect prediction and promises to shed new light on genome function — now available via API.

We let Claude run a small shop in the Anthropic office. Here's what happened.

In "what could possibly go wrong?" news, Anthropic and Andon Labs wired Claude 3.7 Sonnet up to a small vending machine in the Anthropic office, named it Claudius and told …

GitHub Next Project: Exploring LLM-powered automation in platform-based software collaboration

GitHub Next have coined the term "Continuous AI" to describe "all uses of automated AI to support software collaboration on any platform". It's intended as an echo of Continuous Integration …

Our evals course with 35% discount code: https://bit.ly/evals-ai

Get started with 10Web and their AI Website Builder API: https://10web.io/website-builder-api/?utm_source=YouTube&utm_medium=Influencer&utm_campaign=TechWithTim Today, you'll learn how to fine-tune LLMs in Python for use in Ollama. I'll walk you through it step by step, give you all the code and show you how to test it out. DevLaunch is my mentorship program where I personally help developers go beyond tutorials, build real-world projects, and actually land jobs. No fluff. Just real accountability, proven strategies, and hands-on guidance. Learn more here - https://training.devlaunch.us/tim ⏳ Timestamps ⏳ 00:00 | What is Fine-Tuning? 02:25 | Gathering Data 05:52 | Google Collab Setup 09:17 | Fine-Tuning with Unsloth 16:58 | Model Setup in Ollama 🎞 Video Resources 🎞 Code in this video: https://drive.google.com/drive/folders/1p4ZilsJsdxB5lH6ZBMdIEJBt0WVUMsDq?usp=sharing Notebook Google Collab: https://colab.research.google.com/drive/1NsRGmHVupulRzsq9iUTx8V8WgTSpO_04?usp=sharing Hashtags #Python #Ollama #LLM

Learn how to fine‑tune Qwen‑3‑14B on your own data—with LoRA adapters, Unsloth’s 4‑bit quantization, and just 12 GB of VRAM—while preserving its chain‑of‑thought reasoning. I’ll walk you through dataset prep, the key hyper‑parameters that prevent catastrophic forgetting, and the exact Colab notebook to get you running in minutes. Build a lightweight, reasoning‑ready Qwen‑3 model tailored to your project today! LINKS: https://qwenlm.github.io/blog/qwen3/ https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs https://huggingface.co/datasets/unsloth/OpenMathReasoning-mini https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune https://huggingface.co/datasets/mlabonne/FineTome-100k https://docs.unsloth.ai/get-started/fine-tuning-guide https://arxiv.org/html/2308.08747v5 https://heidloff.net/article/efficient-fine-tuning-lora/ NOTEBOOK: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(14B)-Reasoning-Conversational.ipynb Fine-tuning Playlist: https://www.youtube.com/playlist?list=PLVEEucA9MYhPjLFhcIoNxw8FkN28-ixAn Website: https://engineerprompt.ai/ RAG Beyond Basics Course: https://prompt-s-site.thinkific.com/courses/rag Let's Connect: 🦾 Discord: https://discord.com/invite/t4eYQRUcXB ☕ Buy me a Coffee: https://ko-fi.com/promptengineering |🔴 Patreon: https://www.patreon.com/PromptEngineering 💼Consulting: https://calendly.com/engineerprompt/consulting-call 📧 Business Contact: engineerprompt@gmail.com Become Member: http://tinyurl.com/y5h28s6h 💻 Pre-configured localGPT VM: https://bit.ly/localGPT (use Code: PromptEngineering for 50% off). Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0 Fine-Tuning Qwen-3 Models: Step-by-Step Guide 00:00 Introduction to Fine-Tuning Qwen-3 01:24 Understanding Catastrophic Forgetting and LoRa Adapters 03:06 Installing and Using unsloth for Fine-Tuning 04:19 Code Walkthrough: Preparing Your Dataset 07:14 Combining Reasoning and Non-Reasoning Datasets 09:48 Prompt Templates and Fine-Tuning 16:13 Inference and Hyperparameter Settings 18:11 Saving and Loading LoRa Adapters

It's hard to imagine doing web research without using LLMs. Chatbots may be the first thing you turn to for questions like: What are the companies cu…

Try out Warp 2.0 now, the current rank #1 AI on Terminal Bench, outperforming Claude Code: https://go.warp.dev/bycloud You can also use code "BYCLOUD" to get Warp Pro for 1 month free. (limited for 1,000 redemptions) My Newsletter https://mail.bycloud.ai/ my project: find, discover & explain AI research semantically https://findmypapers.ai/ My Patreon (get bundle access for my newsletter & findmypapers) https://www.patreon.com/c/bycloud Training language models to follow instructions with human feedback [Paper] https://arxiv.org/abs/2203.02155 DeepSeek-R1 (Aha Moment) [Paper] https://arxiv.org/abs/2501.12948 Understanding R1-Zero-Like Training: A Critical Perspective [Paper] https://arxiv.org/pdf/2503.20783 Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model? [Paper] https://arxiv.org/abs/2504.13837 Reinforcement Learning Finetunes Small Subnetworks in Large Language Models [Paper] https://arxiv.org/abs/2505.11711 Spurious Rewards: Rethinking Training Signals in RLVR [Paper] https://arxiv.org/abs/2506.10947 Try out my new fav place to learn how to code https://scrimba.com/?via=bycloudAI This video is supported by the kind Patrons & YouTube Members: 🙏Nous Research, Chris LeDoux, Ben Shaener, DX Research Group, Poof N' Inu, Andrew Lescelius, Deagan, Robert Zawiasa, Ryszard Warzocha, Tobe2d, Louis Muk, Akkusativ, Kevin Tai, Mark Buckler, NO U, Tony Jimenez, Ângelo Fonseca, jiye, Anushka, Asad Dhamani, Binnie Yiu, Calvin Yan, Clayton Ford, Diego Silva, Etrotta, Gonzalo Fidalgo, Handenon, Hector, Jake Disco very, Michael Brenner, Nilly K, OlegWock, Daddy Wen, Shuhong Chen, Sid_Cipher, Stefan Lorenz, Sup, tantan assawade, Thipok Tham, Thomas Di Martino, Thomas Lin, Richárd Nagyfi, Paperboy, mika, Leo, Berhane-Meskel, Kadhai Pesalam, mayssam, Bill Mangrum, nyaa, Toru Mon [Discord] https://discord.gg/NhJZGtH [Twitter] https://twitter.com/bycloudai [Patreon] https://www.patreon.com/bycloud [Business Inquiries] bycloud@smoothmedia.co [Profile & Banner Art] https://twitter.com/pygm7 [Video Editor] @Booga04 [Ko-fi] https://ko-fi.com/bycloudai

🚀 GEMMA 3N + MLX-VLM: Run DeepMind's Revolutionary Multimodal Model on Your Mac!DeepMind just dropped Gemma 3n - their first multimodal model to achieve a 1...