In the morning, before going to the gym, I spend 10 minutes detailing to Codex what it needs to do, then I let it code on a task that I know will take it 1 hour 30 minutes to solve. Meanwhile, I go to the gym with a light heart, knowing that an AI is working.
LLMS
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Agent Orchestration Explained: AI Systems Coordination
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Agent Orchestration Explained https://
buff.ly/i8bGeGy
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
Agent-as-a-Judge: Robust LLM Evaluation Framework
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A Survey on Agent-as-a-Judge Great report to learn about agentic judges used for planning, tool-augmented verification, multi-agent collaboration, and persistent memory to enable more robust, verifiable, and nuanced evaluations. With careful crafting, it's possible to build LLM
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The Kaggle Book: Master Data Science Competitions with Machine Learning
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The Kaggle Book — Master Data Analysis and #DataScience Competitions with #MachineLearning, GenAI, and LLMs [2nd Edition]: http://
amzn.to/4pxJpTC v/ @PacktDataML Table of Contents: Introducing Data Science Competition Organizing Data with Datasets Work & Learn with -
Learn Model Context Protocol with TypeScript for AI Systems
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From @chris_noring and @PacktDataML … "Learn Model Context Protocol [MCP] with TypeScript: Build agentic systems in TypeScript with the new standard for AI capabilities" at http://
amzn.to/48W6Izu TypeScript explained and why & when to use it: https://
contentful.com/blog/what-is-t
ypescript-and-why-should-you-use-it/
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Essential LLM Fine-Tuning Techniques to Master
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LLM fine-tuning techniques I'd learn if I were to customize them:
— Akshay 🚀 (@akshay_pachaar) 11 janvier 2026
Bookmark this.
1. LoRA
2. QLoRA
3. Prefix Tuning
4. Adapter Tuning
5. Instruction Tuning
6. P-Tuning
7. BitFit
8. Soft Prompts
9. RLHF
10. RLAIF
11. DPO (Direct Preference Optimization)
12. GRPO (Group Relative… pic.twitter.com/PQ5VrHyr34LLM fine-tuning techniques I'd learn if I were to customize them: Bookmark this. 1. LoRA
2. QLoRA
3. Prefix Tuning
4. Adapter Tuning
5. Instruction Tuning
6. P-Tuning
7. BitFit
8. Soft Prompts
9. RLHF
10. RLAIF
11. DPO (Direct Preference Optimization)
12. GRPO (Group Relative -
Fine-Tuning LLMs on NVIDIA GPUs With Unsloth
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How to Fine-Tune an LLM on NVIDIA GPUs With Unsloth https://
buff.ly/X4IBsan
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
Evidence for MoE Scaling vs Larger Pre-training Models
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But is there evidence that they actually do that? I mean lots of companies could theoretically do that, but is it worthwhile compared to pre-training larger models (more experts) or using that compute for additional post-training?
PS: also there are two companies that have TPUs -
Core Components of AI Models: ML Frameworks and Technologies
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Core Components of an AI Model! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
