Best of Machine Learning — 2025
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Javarevisited·1y
10 Things Software Engineers Should Learn in 2025
In 2025, software engineers should focus on mastering skills like system design, cloud computing, machine learning, artificial intelligence, generative AI, DevOps, technical writing, app development, cybersecurity, and data engineering. Resources such as online courses and certifications can aid in learning these crucial topics, ensuring readiness for the evolving tech landscape.
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Fireship·1yOpenAI o3 tries to curb stomp DeepSeek...
Recent restrictions have seen the banning of Deep Seek by countries like Italy, the US, Australia, and Taiwan. Meanwhile, OpenAI has launched the new 03 Mini model and a Deep Research feature for Pro users, aiming to remain competitive. These developments are part of a broader trend in the AI landscape, with open-source solutions making rapid progress. Despite corporate efforts, some AI tools face performance issues, and Google's Gemini has similar features to OpenAI's new offerings.
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ByteByteGo·1y
EP147: The Ultimate API Learning Roadmap
APIs are essential for internet communication, and developers must understand them. The roadmap covers the introduction, terminologies, API styles, authentication techniques, documentation tools, key features, performance techniques, gateways, implementation frameworks, and integration patterns. Learn to build and maintain efficient and effective APIs with this comprehensive guide.
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Machine Learning Mastery·1y
The Roadmap for Mastering Machine Learning in 2025
Machine learning (ML) is integral to many sectors, making it a valuable skill by 2025. This guide offers a step-by-step roadmap for mastering ML, starting with prerequisites in mathematics and programming, followed by core ML concepts, deep learning, and specialization in fields like computer vision or NLP. It also covers model deployment and building a portfolio to showcase projects. The emphasis is on practical learning through projects and continuous skill enhancement.
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Daily Dose of Data Science | Avi Chawla | Substack·1y
10 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.
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iO tech_hub·1y
What is WebLLM
WebLLM, developed by the MLC-AI team, allows large language models (LLMs) to run fully within a web browser using modern web technologies like WebAssembly and WebGPU. This enables models to be more accessible client-side, providing privacy and offline support. While cloud-based LLMs are faster and require powerful servers, WebLLM offers cross-platform portability and easier installation. Implementation can be done using the WebLLM npm package, which includes support for web workers to enhance application performance.
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YouTube·1y
Build Everything with AI Agents: Here's How
David Andre demonstrates how to build AI agents even for beginners using n8n, a no-code automation tool. He details the process of setting up triggers, integrating Telegram, and handling both text and voice messages. By adding tools such as Gmail and Google Calendar, he shows how to create powerful AI agents capable of automating various tasks. He also highlights the value of continuous testing and the potential productivity boosts these agents can provide.
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Hacker News·1y
Get 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.
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Sebastian Raschka·44w
Coding 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.
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5 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.
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Full 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.
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Big Tech in panic mode... Did DeepSeek R1 just pop the AI bubble?
DeepSeek, a Chinese company, released the open source R1 model, which outperforms major AI models and costs significantly less to develop. This development has sent shockwaves through the tech industry, particularly impacting Nvidia and other chip companies. In response, OpenAI is offering new features and models to stay competitive. The breakthrough signifies a major shift in the AI landscape, with Wall Street and tech investors concerned about future profitability.
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AI 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.
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Top 10 Educative Courses for Software Engineers in 2025
The post recommends the top 10 interactive courses for software engineers in 2025 provided by Educative.io. These courses cover essential topics such as Generative AI, Data Science, System Design, Cloud Computing, and more. They offer a hands-on learning experience with a focus on text-based content, making them ideal for developers seeking to enhance their skills and remain competitive in the tech industry.
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freeCodeCamp·52w
How 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.
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Codemotion·41w
“A Programmer Who Reads Is Worth Two”: Tech Books for Summer 2025
A curated list of 14 technical books for summer 2025 reading, covering diverse topics from building LLMs from scratch and AI agents to cybersecurity, Kubernetes, quantum computing, and documentation. The selection includes both hands-on technical guides and broader philosophical works on AI's impact on society, catering to developers looking to expand their knowledge across multiple domains.
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LogRocket·1y
Building an AI agent for your frontend project
AI is becoming increasingly important across multiple domains, providing substantial advantages. This tutorial guides you through building an AI agent from scratch, using BaseAI and Langbase, to create a webpage FAQ generator. The tutorial covers the entire process from setup to deployment, including building AI agents with memory using RAG technology and integrating AI agents into a Next.js frontend app.
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Community Picks·1y
deepseek-ai/awesome-deepseek-integration
Integrate the DeepSeek API into various popular software applications to enhance functionality. The DeepSeek Open Platform provides an API key for integration. Compatible tools include ChatGPT-Next-Web, LibreChat, RSS Translator, Raycast, PHP Client, Laravel, Zotero, SiYuan, and many others, across multiple operating systems such as macOS, iOS, and iPadOS.
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9 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.
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DeepSeek stole our tech... says OpenAI
OpenAI has accused DeepSeek of intellectual property theft, claiming that DeepSeek used OpenAI's outputs to fine-tune its models, a process known as distillation. This accusation comes as a second Chinese model enters the competition, creating a China vs. China AI race. Despite these controversies, open-source AI models are gaining traction, allowing developers to create innovative products. Privacy concerns have also been raised regarding the use of DeepSeek.
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SwirlAI·1y
The 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.
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Claude 3.7 goes hard for programmers…
Claude 3.7, recently released by Anthropic, is a large language model known for its advanced programming capabilities. The new version includes a CLI tool, Claude code, which can build, test, and execute code, creating a feedback loop that might revolutionize programming. Despite its high cost, the new model significantly outperforms its predecessors and other state-of-the-art models on GitHub issues. However, the model has some downsides, such as occasional inaccuracies and high operational costs.
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Essential 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.
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5 Agentic AI Design Patterns
Explore five agentic AI design patterns that enhance the effectiveness of AI agents through reflection, tool use, reason and act, planning, and multi-agent approaches. Learn how Firecrawl Extract facilitates web scraping by using simple English prompts to extract clean, structured data. Discover additional resources on machine learning techniques and data science provided by Daily Dose of Data Science.
