Community Picks·48wReact library for LLMs
llm-ui is a React library that provides UI components for integrating Large Language Models into web applications. It works universally with any LLM model by operating on the model's output string, supporting popular services like ChatGPT, Claude, Ollama, Mistral, Hugging Face, and LangChain. The library aims to simplify the process of displaying LLM responses in React-based user interfaces.
Daily Dose of Data Science | Avi Chawla | Substack·46w9 MCP Projects for AI Engineers
A comprehensive collection of 9 Model Control Protocol (MCP) projects designed for AI engineers, covering various applications from local MCP clients and agentic RAG systems to voice agents and synthetic data generators. The projects demonstrate how to integrate MCP with popular tools like Claude Desktop and Cursor IDE, enabling developers to build more sophisticated AI applications with enhanced tool connectivity and context sharing capabilities.
The Pragmatic Engineer·50wStack overflow is almost dead
The engagement on Stack Overflow has significantly decreased, with the number of questions asked monthly hitting levels from its early days in 2009. Key factors contributing to its decline include enhanced moderation efficiency reducing question flow starting in 2014, the impact of ChatGPT offering quick and polite answers trained on Stack Overflow data since November 2022, and outdated moderation policies. Additionally, the site was acquired by Prosus in 2021, which preceded a marked fall in activity.
Hacker News·1yGet the hell out of the LLM as soon as possible
Large Language Models (LLMs) should not be used for decision-making or implementing business logic due to their poor performance in these areas. Instead, LLMs should be employed as an interface for translating user inputs into API calls, with the actual logic handled by specialized systems. This approach enhances performance, debugging, and reliability. LLMs are best utilized for tasks involving transformation, interpretation, and communication, rather than maintaining critical application state.
Sebastian Raschka·46wCoding LLMs from the Ground Up: A Complete Course
Sebastian Raschka shares a comprehensive video course series on building Large Language Models from scratch using Python and PyTorch. The course covers seven key areas: environment setup, text data preprocessing and tokenization, attention mechanisms implementation, LLM architecture coding, pretraining on unlabeled data, classification fine-tuning, and instruction fine-tuning. The content serves as supplementary material to his book 'Build a Large Language Model (From Scratch)' and emphasizes hands-on learning through implementation rather than using pre-built frameworks.
Javarevisited·1y5 Best Books to Learn AI and LLM Engineering in 2025 (That Aren’t a Waste of Time)
Discover the top five books recommended for mastering AI and LLM engineering in 2025. These selections focus on practical systems design, deployment, and real-world applications, helping readers save time and effectively build production-ready models. Written by experienced practitioners, these books offer guidance for those serious about becoming proficient in large language models and AI systems.
freeCodeCamp·1yHow to Build RAG AI Agents with TypeScript
Learn how to build a Retrieval-Augmented Generation (RAG) AI agent using TypeScript and Langbase SDK. This comprehensive tutorial covers setting up your project, creating AI memory for storing and retrieving context, uploading documents, adding API keys, and generating responses using LLMs like OpenAI. By the end, you'll have a context-aware AI agent capable of handling complex tasks and queries with precision.
Daily Dose of Data Science | Avi Chawla | Substack·1y9 RAG, LLM, and AI Agent Cheat Sheets
This post provides visual cheat sheets for AI engineers covering various topics, including Transformer vs. Mixture of Experts in LLMs, fine-tuning techniques, RAG vs Agentic RAG, strategies for chunking in RAG, levels of agentic AI systems, and more. These resources are designed to help cultivate essential skills for developing impactful AI and ML systems in the industry.
Collections·36wDeepSeek-V3.1 Release: A New Era in Open AI Technology
DeepSeek has released V3.1, a 685-billion parameter open-source language model that rivals proprietary systems from OpenAI and Anthropic at significantly lower costs. The model features a hybrid architecture combining chat, reasoning, and coding capabilities, supports 128,000 token context, and achieves 71.6% on coding benchmarks. Available for free on Hugging Face with API compatibility, it's optimized for Chinese chips and represents a major step toward democratizing advanced AI technology.
SwirlAI·1yThe evolution of Modern RAG Architectures.
The post delves into the evolution of Retrieval Augmented Generation (RAG) architectures, discussing their development from Naive RAG to advanced techniques, including Cache Augmented Generation (CAG) and Agentic RAG. It highlights the challenges addressed at each stage, advanced methods to improve accuracy, and the potential future advancements in RAG systems.
freeCodeCamp·1yHow to Write Effective Prompts for AI Agents using Langbase
Learn how to write effective prompts for AI agents using Langbase. The post covers essential techniques such as defining clear goals, experimenting with prompts, using specific instructions, and applying advanced methods like few-shot training, memory-augmented prompting, and role-based prompting. Practical tips and a step-by-step guide for using Langbase to build serverless AI agents are also included.
Machine Learning Mastery·1y7 Next-Generation Prompt Engineering Techniques
Mastering prompt engineering is essential in optimizing large language models like ChatGPT and Gemini. Techniques such as meta prompting, least-to-most prompting, multi-task prompting, role prompting, task-specific prompting, program-aided language models, and chain-of-verification prompting can significantly enhance the performance and efficiency of LLMs. Each method has unique benefits and challenges, but collectively, they improve the accuracy and relevance of the generated content.
controversy.dev·39wEnough is enough. Prompt engineering is not engineering.
Argues that prompt engineering is fundamentally different from traditional software engineering, lacking the systematic design, mathematical rigor, and testable logic that define real engineering disciplines. The author contends that calling prompt writing 'engineering' is misleading marketing that inflates the perceived technical complexity of working with AI language models.
TechCrunch·23wHugging Face CEO says we’re in an ‘LLM bubble,’ not an ‘AI bubble’
Hugging Face CEO Clem Delangue argues the tech industry is experiencing an LLM bubble rather than a broader AI bubble, predicting it may burst soon. He believes the current focus on large, general-purpose language models is misplaced, and that smaller, specialized models will dominate the future for specific use cases like banking chatbots. While competitors spend billions on LLM infrastructure, Hugging Face maintains a capital-efficient approach with half of its $400 million funding still in reserve, positioning itself for long-term sustainability across the diversified AI landscape.
Josh M.·33wGPT-5 is Trash.
ChatGPT-5 has received significant criticism from users who report that responses are shorter, blander, and less engaging than previous versions. Despite being marketed as PhD-level intelligence, the model still makes basic errors in math and reasoning while suffering from hallucinations. OpenAI's removal of model selection options and implementation of an autoswitcher has frustrated users, leading many to believe this was a cost-saving measure rather than genuine improvement. The backlash was severe enough that OpenAI restored access to older models like GPT-4o.
ByteByteGo·46wEP167: Top 20 AI Concepts You Should Know
A comprehensive overview of 20 essential AI concepts including machine learning, deep learning, neural networks, NLP, computer vision, and transformers. Also covers the AI application stack for building RAG applications, featuring components like large language models, frameworks, vector databases, data extraction tools, and text embeddings. Additionally includes insights into Shopify's tech stack architecture and job opportunities in AI and software engineering.
SwirlAI·47wBreaking into AI Engineering in 2025.
A comprehensive roadmap for becoming an AI Engineer in 2025, covering essential skills from Python fundamentals and LLM APIs to advanced topics like AI agents, RAG systems, and observability. The guide emphasizes learning fundamentals while building practical skills, starting with basic LLM integration and progressing through vector databases, prompt engineering, agentic systems, infrastructure deployment, and security considerations. Key recommendations include mastering FastAPI and Pydantic, understanding different LLM types and structured outputs, implementing RAG with proper data preprocessing, and learning agent design patterns like ReAct and task decomposition.
Daily Dose of Data Science | Avi Chawla | Substack·1yAI Agent Crash Course—Part 1
In this crash course, learn about AI agents and their implementation. It covers the fundamentals, memory for agents, agentic flows, guardrails, implementing agentic design patterns, and optimizing agents for production. The aim is to build autonomous systems that can reason, plan, take actions, and correct themselves, going beyond the capabilities of standalone generative models.
Towards Data Science·47wHow to Design My First AI Agent
A comprehensive guide to designing AI agents covering model selection, tooling choices, and reliability strategies. Explores different LLM options including OpenAI GPT-4, DeepSeek, Claude, and Mistral, each suited for specific use cases. Discusses infrastructure considerations, frameworks like LangGraph and Pydantic-AI, and security aspects. Emphasizes the importance of structured prompting techniques like Chain-of-Thought and ReAct, output validation, and failure handling to build reliable production-ready agents.
Daily Dose of Data Science | Avi Chawla | Substack·48wThe Full MCP Blueprint
MCP (Model Context Protocol) provides a standardized way for LLMs to interact with tools and capabilities, solving the M×N integration problem where every tool needs manual connection to every model. The protocol enables dynamic tool discovery, plug-and-play interoperability between systems like Claude and Cursor, and transforms AI development from prompt engineering to systems engineering. MCP uses a Host-Client-Server architecture with JSON-RPC communication and supports various transport mechanisms including Stdio and HTTP.
Hugging Face·1yTiny Agents: a MCP-powered agent in 50 lines of code
Discover how to implement a small and powerful AI agent using Model Context Protocol (MCP) in just 50 lines of code. The post covers the integration of MCP with large language models (LLMs) to create agentic AI, featuring JavaScript and TypeScript components with Hugging Face's SDKs and tools. It also demonstrates the use of MCP servers and shows how tools can be utilized within an LLM inference client.
Elevate·19wMy LLM coding workflow going into 2026
A comprehensive guide to using LLM coding assistants effectively in 2026. Key practices include starting with detailed specifications before coding, breaking work into small iterative chunks, providing extensive context to the AI, choosing appropriate models for different tasks, maintaining human oversight through testing and code review, committing frequently for version control safety, customizing AI behavior with rules and examples, leveraging automation as quality gates, and treating AI as a force multiplier rather than replacement. The workflow emphasizes treating LLMs as junior pair programmers requiring guidance while maintaining developer accountability for all code produced.
Sebastian Raschka·17wThe State Of LLMs 2025: Progress, Problems, and Predictions
A comprehensive 2025 review of large language model developments highlights reinforcement learning with verifiable rewards (RLVR) and the GRPO algorithm as the year's dominant training paradigm, following DeepSeek R1's breakthrough. Key trends include inference-time scaling, tool use integration, and architectural efficiency tweaks like mixture-of-experts and linear attention mechanisms. The analysis addresses benchmarking challenges ("benchmaxxing"), discusses practical LLM usage for coding and writing, and examines the shift toward domain-specific models with proprietary data. Predictions for 2026 emphasize RLVR expansion beyond math/code, increased inference optimization, and the emergence of diffusion models for low-latency tasks.
Daily Dose of Data Science | Avi Chawla | Substack·37w8 RAG Architectures for AI Engineers
Eight different RAG (Retrieval-Augmented Generation) architectures are explained with their specific use cases: Simple Vector RAG for basic semantic matching, Multi-modal RAG for cross-modal retrieval, HyDE for handling dissimilar queries, Self-RAG for validation against trusted sources, Graph RAG for structured relationships, Hybrid RAG combining vector and graph approaches, Adaptive RAG for dynamic query handling, and Agentic RAG for complex workflows with AI agents.
