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 …
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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 …
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
AlphaGenome: AI for better understanding the genome
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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 …
EASIEST Way to Fine-Tune a LLM and Use It With Ollama
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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.
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⏳ 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/
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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
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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,
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Gemma 3n + MLX-VLM: Run Deepmind's Game-Changing Open Source Multimodal Model on Your Mac!
🚀 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...
My AI Workflow for Understanding Any Codebase | Peter Steinberger
A quick tip on how I use repo2txt and Google AI Studio to understand new codebases. Gemini's 1M token context window is perfect for asking questions about code.
Extremely consequential new open weights model release from Google today: Multimodal by design: Gemma 3n natively supports image, audio, video, and text inputs and text outputs. Optimized for on-device: Engineered …