Here is a cookbook on how to test your LLM applications: This cookbook uses Ragas and Comet Opik, an open-source platform for evaluating, testing, and monitoring LLM applications. You can use Opik to: • Detect hallucinations • Evaluate RAG applications • Determine answer

🚨 [AI RESEARCH] If you're interested in AI ETHICS, the paper "Reconstructing AI Ethics Principles: Rawlsian Ethics of AI" by Salla Westerstrand is a MUST-READ. These are the 'Rawlsian ethics guidelines for fair AI' proposed: 1️⃣ "Developers and deployers of an AI system must

NetworkX from NVIDIA is one THE most popular Python graph analytics library with ~15K Github starts and 80M downloads monthly. This library is for working with networks and graphs. It helps analyze connections between things - like social networks, computer networks, or any

Data pipelines will put you in the top 1% of the market. If you could only learn one skill for the next decade, I can't think of anything more critical than learning to move and process data at scale. I like to tell people I'm a Machine Learning Engineer, but in reality, 90% of

🔹STORM🔹 Stanford University’s free, public app automates comprehensive research and report generation using AI to create Wikipedia-style articles with citations from web sources. STORM is incredible. I asked it to write a research paper about the use of AI in DNA analysis

Run any LLM like Llama 3.2 directly from Hugging Face using Ollama on your local computer (100% free and without internet).

100% Local OpenAI Swarm Agents!🐝 OpenAI Swarm is an educational framework that explores ergonomic, lightweight multi-agent orchestration. It's fairly easy to integrate with locally running LLMs through Ollama. ______ Find me → Akshay 🚀 ✔️ And stay tuned for more on

NotebookLlama: An Open Source version of NotebookLM 🙏 A complete tutorial on building a PDF to Podcast flow using Llama: - 1B to pre-process PDF - 70B to convert it to a podcast Transcript - 8B to make it more dramatic - Parler and Suno models for TTS github.com/meta-llama/lla…

Huge efforts to improve LLMs for tool use, computer use, reasoning, and long-context understanding. Here are a few interesting papers for the weekend: 1). Agentic Information Retrieval Provides an introduction to agentic information retrieval, which is shaped by the

"After reading this blog you’ll know where to start and how to select the most appropriate Bayesian techniques for causal discovery for your use case." An Extensive Starters Guide For Causal Discovery using Bayesian Modeling by Erdogan Taskesen towardsdatascience.com/an-extensive-s…

See 244-page PDF “Introduction to #NeuralNetworks” ➡️ dkriesel.com/en/science/neu… ———— #DataScience #AI #Algorithms #ML #MachineLearning #DeepLearning #Mathematics #Calculus #DataScientist

Harvard University is offering Free world class education in Python, Data Science, Machine Learning, Data Preprocessing, Visualization & Statistics:

As researchers tackle the limitations of #LLMs, the potential for developing models with human-like reasoning capabilities is within reach. Listen to the NotebookLM generated podcast - notebooklm.google.com/notebook/56e13… Or read the paper by Mirzadeh 2024 arxiv.org/pdf/2410.05229

Fantastic open-sourced tool for RAG, chatting with your documents with open-source LLMs. ✨ It trended at Number-1 in Github for quite sometime. And a clean & customizable RAG UI for chatting with your documents. → Open-source RAG UI for document QA → Supports local LLMs

Local models now protect your privacy while still accessing powerful LLM capabilities Chain small and large LLMs to get best performance while keeping data private 🔍 Original Problem: Users share sensitive personal information with proprietary LLMs during inference, raising

It amazes me that the most important metrics (lines of code, story points, cycle time, devex satisfaction) in development are the two that are never discussed, let alone measured ... mean time to answer (mttA) and mean time to question (mttQ).

" #AI Assurance Taxonomies", explores the language used by UK business leaders to describe #AI assurance practices and the challenges they face in understanding and applying these concepts. notebooklm.google.com/notebook/2de6a…

well that seems to have worked well

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Explanation of "Accurate Predictions on Small Data with a Tabular Foundation Model" (Hollmann et al., 2025)[1] This work addresses a critical challenge in machine learning: achieving high prediction accuracy on small tabular datasets. bohrium.dp.tech/ai-search/shar…