Daily Dose of Data Science | Avi Chawla | Substack·1y10 MCP, AI Agents, and RAG projects for AI Engineers
Explore 10 AI-focused projects including building an MCP-powered Agentic RAG, a multi-agent book writer, and a RAG system that understands audio content. Learn how to build and fine-tune AI models like DeepSeek-R1 and create applications using open-source tools like Llama 4 and Colpali.
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.
Javarevisited·50w5 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.
YouTube·51wFull Course (Lessons 1-10) AI Agents for Beginners
This introductory course on AI agents covers the fundamentals of building AI agents from concept to code. Key topics include language models, memory, and tools to perform tasks. The course includes video lessons, code samples, and covers agentic frameworks such as semantic kernel and Microsoft’s autogen. Practical examples, including setting up and interacting with AI agents using Jupyter notebooks, are provided.
Hacker News·1yAI 2027
AI 2027 portrays a scenario where superhuman AI revolutionizes various industries, surpassing the impact of the Industrial Revolution. Driven by contributions from experts and extensive simulations, the forecast includes predictions about AI becoming autonomous agents in workplaces, the competitive arms race in AI development, and the geopolitical ramifications of AI advancements. The scenario includes different endings to explore potential future outcomes, emphasizing the goal of predictive accuracy rather than recommendations. OpenAI's research and experts play a pivotal role in shaping this vision, and there is a call for debate and alternative scenarios to enrich the conversation about our AI-driven future.
freeCodeCamp·52wHow 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.
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·51wEssential Machine Learning Concepts Animated
Understanding AI and machine learning is essential for developers. This visually engaging course on freeCodeCamp.org's YouTube channel by Vladimirs from Turing Time Machine simplifies over 100 core ML and AI concepts with animations and real-world analogies. It covers foundational terms, statistical methods, optimization techniques, evaluation metrics, various model types, practical workflow elements, and related disciplines like NLP and object detection.
Javarevisited·1y5 Best Udemy Courses to Learn AI Engineering in 2025
Discover the top 5 Udemy courses for learning AI Engineering in 2025. These courses cover essential skills like LLMs, MLOps, AI agents, and cloud-based AI services, making them perfect for aspiring AI Engineers. Learn from industry professionals at an affordable price and become job-ready without needing a PhD or expensive bootcamp.
Collections·1yTrain Your Own Large Language Model: A Comprehensive Course
A new comprehensive course by freeCodeCamp teaches learners how to develop their own large language models (LLMs) from scratch. The course covers fundamental concepts, tokenization, Transformer architecture, and fine-tuning techniques like Low-Rank Adaptation (LoRA). Practical applications include working with chat data and developing models for underrepresented languages. Extensive resources provided include slides, notebooks, and code examples.
As a Programmer·1yBuild Your Own AI Fitness Trainer for Free - Full Next.js Project
The post provides a comprehensive guide on building an advanced AI fitness assistant using Next.js. The AI assistant interacts with users, gathers their fitness information, and generates personalized workout and diet plans. The project incorporates several technologies, including React, Clerk for authentication, Convex for the database, and Gemini for the language model. The application features a landing page, a user profile page, and program generation functionality, all while ensuring robust authentication and real-time capabilities.
Product Hunt·1ySim Studio - Drag-and-drop AI agent builder
Sim Studio is a platform that allows users to build AI agent workflows through a user-friendly drag-and-drop interface. It integrates with various platforms like Slack, GMail, Pinecone, and Mistral, enabling the creation of complex workflows without requiring extensive technical expertise.
Neon·51wPrompt Engineering as a Developer Discipline
Structured prompting is becoming a crucial skill for developers, akin to traditional coding practices. Using AI effectively involves treating prompts as modular, testable components within software systems. Techniques like few-shot prompting, chain-of-thought reasoning, self-consistency, skeleton prompting, and configuration parameters improve AI's coding outputs. Developers should rigorously validate and maintain prompts, just like any other code, to ensure reliability and consistency in AI-powered features.
Tech With Tim·50wHow I'd Learn ML/AI FAST If I Had to Start Over
Advocates a strategic approach to learning AI and ML swiftly in the rapidly evolving landscape of 2025. Emphasizes the importance of critical thinking and practical coding skills, particularly in Python, for effective AI/ML projects. Encourages data literacy as foundational and promotes hands-on experience with AI models, APIs, and machine learning techniques before transitioning into advanced concepts like LLMs and AI agents.
Scott Logic·1yMaking Sense of the AI Developer Tools Ecosystem
The AI developer tools landscape has evolved from simple autocomplete tools to a complex ecosystem encompassing intelligent assistants, autonomous agents, AI-powered IDEs, and rapid prototyping platforms. This post categorizes these tools based on their integration into the workflow, from general-purpose chatbots to fully AI-first environments. The capability and breadth of these tools are rapidly increasing, offering significant value in software development. The key categories include tools at an arm’s length, integrated AI within traditional IDEs, AI-first environments, and task-focused tools designed for specific use cases.
Semaphore·52wHow to Build an AI Agent to Help with Daily Tasks
AI agents can autonomously handle developer tasks by leveraging large language models (LLMs) to interpret and make context-based decisions. They can automate repetitive chores such as pull request reviews, generating release notes, and code triaging. Key tools include AgentGPT, AutoGPT, LangChain, LlamaIndex, and Vercel AI SDK. Integrating these agents into CI/CD pipelines, like those provided by Semaphore, enhances efficiency and security by automating the detection of code changes, offering intelligent summaries, and suggesting improvements.
GitHub Blog·50wFrom MCP to multi-agents: The top 10 open source AI projects on GitHub right now and why they matter
Discover the top 10 open source AI projects on GitHub chosen by a panel of experts, shedding light on AI trends such as integration via MCP, multi-agent collaboration, and advancements in speech generation. These projects showcase the evolution of open source participation, with a focus on standardized integration patterns, collaborative agent frameworks, and licensing. The list reflects the dynamic landscape of AI in open source and offers opportunities for developers to engage and contribute.
Daily Dose of Data Science | Avi Chawla | Substack·1y25 Most Important Mathematical Definitions in DS
A visual presentation of crucial mathematical definitions used in Data Science and Statistics, such as Gradient Descent, Normal Distribution, MLE, Z-score, and SVD. The post explains these terms and their significance in various applications like dimensionality reduction, optimization, and data modeling.
Daily Dose of Data Science | Avi Chawla | Substack·1y5 Powerful MCP Servers
The post details five powerful MCP servers which enhance AI agents' capabilities. These servers include Firecrawl for web scraping, Browserbase for initiating browser sessions, Opik for monitoring LLM applications, Brave MCP server for utilizing Brave Search, and Sequential thinking for problem-solving through structured thinking processes. Additionally, the post introduces Stagehand, an innovative browser automation framework for AI agents.
Medium·51wOpen-Source Tools for Agents
Developing AI agents can be challenging with many outdated or overly complex tools. This post offers a curated list of effective open-source libraries for building robust AI agents in various tasks such as document parsing, voice interaction, and automation. Focus on choosing practical and well-maintained tools to streamline your development process.
Community Picks·1ymindverse/Second-Me: Train your AI self, amplify you, bridge the world
Second Me by Mindverse offers a unique take on AI, allowing users to create their own AI personas that reflect their identity, interests, and contexts. This AI is locally trained and hosted but can connect globally, thus ensuring privacy and control. The setup involves Docker and Python environments, and contributions are encouraged to enhance future AI developments.
Hacker News·1yWriting Cursor Rules with a Cursor Rule
Cursor is a tool for LLM-assisted coding but suffers from a lack of episodic memory, causing repetitive instructions for coding conventions. Building systems, like documentation and style guides, and using Cursor rules, can bridge this memory gap. Implementing a meta-cursor rule simplifies creating consistent project guidelines, allowing quick context recovery for AI, saving time and improving project consistency.
Confluent Blog·1yDesigning Event-Driven, Multi-Agent AI Architectures w/Kafka and Flink
Learn how to build an event-driven, multi-agent AI architecture to simplify meal planning. Using tools like Kafka, Flink, LangChain, and Claude, the system coordinates multiple AI agents—each with specialized tasks like planning child-friendly or adult meals—into a cohesive meal plan. The approach ensures real-time responsiveness, adaptability, and fault tolerance, making complex daily tasks more manageable.
Hacker News·51wThe Hidden Cost of AI Coding
AI coding tools can significantly enhance productivity but may diminish the deep satisfaction and flow state developers experience when manually writing code. While AI tools streamline tasks, they can make developers feel detached and potentially reduce long-term happiness in their craft. It's necessary to find ways to preserve the joy of coding in the AI-augmented world by balancing efficiency with fulfilling creative activities.