Javarevisited·1yTop 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.
Medium·1yIs Golang the new Python Killer for AI?
Golang, known for its simplicity and efficiency, is emerging as a strong contender for machine learning despite Python's current dominance. Key features of Go include high performance, concurrency, static typing, and scalability. While its ecosystem of ML libraries is still growing, Go is particularly advantageous for high-throughput prediction serving, large-scale data preprocessing, and resource-constrained environments. Notable Go ML libraries include Gorgonia, GoLearn, GoMind, GoCV, and Gonum. Go offers compelling advantages for specific use cases, making it worth considering for future ML projects.
ByteByteGo·1yWhat Is the Most Popular Open-Source AI Stack?
Open-source AI provides freedom to experiment and develop without proprietary restrictions with frameworks and tools like Next.js, Streamlit, Gradio, and FastAPI. The data layer involves retrieval-augmented generation (RAG), vector databases, and tools for diverse file formats. The back end includes FastAPI, Langchain, Metaflow, and OLama, facilitating scalable AI operations. The ecosystem also includes community-driven models from Hugging Face and dynamic LLMs like Mistral and DeepSeek.
Medium·1yBuilding a TikTok-like recommender
A comprehensive guide on building a TikTok-like real-time personalized recommender system, detailing the architecture, including the 4-stage recommender model, and the two-tower neural network design. It uses an H&M retail dataset for practical application, teaches feature engineering, model training, and serving using the Hopsworks AI Lakehouse. The post is part of an open-source course focused on deploying scalable recommenders.
SwirlAI·1yBuilding Deep Research Agent from scratch
The post guides readers through building a Deep Research Agent using the DeepSeek R1 model. It explains the concept of Deep Research Agents, outlines their components and steps involved, and provides a thorough implementation guide using SambaNova's platform. The setup includes planning the research, splitting tasks, performing in-depth web searches, reflecting on gathered data, and summarizing results into a final research report. The necessary code and prompts are shared for an interactive learning experience.
Tiff In Tech·1yTech Skills That Will Survive AI Automation
Despite the widespread belief that AI is replacing tech jobs, research shows that AI is actually creating new opportunities and making certain tech skills more valuable. Key future-proof skills include systems architecture and integration, AI orchestration and prompt engineering, machine learning infrastructure design, data ethics and governance, and human-AI collaboration design. Courses from platforms like Coursera, Udemy, EdX, and Stanford are recommended for upskilling in these areas. Mastering these skills involves understanding the interplay between technology, human needs, and societal implications.
Daily Dose of Data Science | Avi Chawla | Substack·1y12 Powerful Tools For AI Agents
A comprehensive guide listing 12 powerful tools included in the CrewAI framework for building AI agents. The tools range from file reading and writing, code interpreting, and web scraping to advanced functionalities like RAG-powered searches and natural language to SQL conversion. Additionally, the post highlights a full crash course on AI agents, covering everything from fundamentals to production optimization.
YouTube·1y8 Insane AI Agent Use Cases in N8N! (automate anything)
Explore eight diverse AI agent use cases with n8n, ranging from data analysis to creating viral shorts. Learn how AI agents can automate tasks, enhance workflows, and provide insights across various domains. This post highlights practical examples and how the AI Foundations community can help you build and implement these agents efficiently.
Tech With Tim·1yHow to Build a Local AI Agent With Python (Ollama, LangChain & RAG)
Learn how to build a local AI agent using Python, LangChain, Ollama, and ChromaDB. The project demonstrates setting up an AI to query and interpret data from a CSV file, such as restaurant reviews, using retrieval augmented generation. All processes run locally without requiring external accounts or cloud services, making it a highly accessible project.
Daily Dose of Data Science | Avi Chawla | Substack·1y5 Levels of Agentic AI Systems
Agentic AI systems are capable of making decisions, calling functions, and running autonomous workflows. The levels of AI agency include basic responders, router patterns, tool calling, multi-agent patterns, and fully autonomous patterns. Each level indicates a different degree of independence and capability of the AI system.
Daily Dose of Data Science | Avi Chawla | Substack·1yTime Complexity of 10 ML Algorithms
Understanding the run-time complexity of machine learning algorithms is essential for efficient model implementation. Popular algorithms like SVM and t-SNE have limitations with large datasets due to their cubic and quadratic time complexities, respectively. Accurate knowledge of these complexities helps in selecting the right algorithm and optimizing performance.
portkey·1yLaunching Prompt Engineering Studio
Portkey's Prompt Engineering Studio bridges the gap between AI experimentation and large-scale deployment. Designed with production-readiness in mind, it provides tools for developing, testing, and deploying prompts across 1600+ AI models. Features include efficient collaboration, low-latency deployments, and real-time analytics. The platform meets the needs of businesses requiring enterprise-grade reliability for AI operations.
Addy Osmani·1yAI-Assisted Engineering: My 2025 Substack Recap
The post provides a recap of several popular articles exploring how AI is transforming software engineering. Topics include the challenges of AI-assisted coding, strategies for developers to maximize their effectiveness alongside AI tools, the Model Context Protocol (MCP) for standardizing AI interactions, and the use of the Cline plugin for AI engineering. Additionally, it covers practical strategies for future-proofing engineering careers, leading teams in the age of GenAI, comparing AI-driven prototyping tools, and discussing the broader implications of AI enabling more people to build software.
Tech With Tim·1yThis Free AI Coding Assistant Might Destroy Cursor...
Augment Code is a new AI coding assistant designed to integrate with VS Code and various JetBrains IDEs. It excels in managing and making productive changes in larger codebases rather than building projects from scratch. Key features include fast context-aware code generation, instant code syncing, and comprehensive indexing. It offers a free version, which allows unlimited usage but trains on user data, and paid versions that secure user code. Augment Code is noted for its speed and efficiency compared to other AI assistants like GitHub Copilot and Wind Surf.
YouTube·1yI Built the ULTIMATE n8n RAG AI Agent Template
The post discusses the implementation and enhancement of a Retrieval-Augmented Generation (RAG) AI agent template using n8n, a no-code tool. The author explains the limitations of typical RAG setups, particularly in handling context and data analysis, and introduces an improved agentic RAG solution. The enhanced RAG agent can reason about how to explore knowledge bases, handle different file types, and execute complex queries. The post also includes a guide on setting up the workflow in n8n and integrating tools like Google Drive and Superbase for better data management.
Community Picks·1ybytedance/MegaTTS3
MegaTTS3 by Bytedance is a lightweight and efficient text-to-speech (TTS) model with only 0.45B parameters. It supports high-quality voice cloning, bilingual (Chinese and English) speech synthesis, and accent intensity control. Users can download pre-trained models, use command-line tools for inference, and access a web UI. The project aims for academic use, with stringent security measures and is licensed under Apache-2.0.
Weaviate·1yWhat Are Agentic Workflows? Patterns, Use Cases, Examples, and More
Agentic workflows enhance AI agents by providing structure and adaptability, allowing them to plan, execute tasks with tools, and learn from past experiences. This process makes them effective in tackling complex tasks across various domains. Core components like reasoning, tool use, and memory define these workflows. Use cases such as agentic RAG and research assistants demonstrate the practical application of agentic workflows, while various patterns like planning and reflection help in optimizing performance. Despite their benefits, agentic workflows also come with challenges, including potential complexity and the need for careful oversight.
Community Picks·1yopenai/openai-agents-python: A lightweight, powerful framework for multi-agent workflows
The OpenAI Agents SDK is a lightweight yet powerful framework for building multi-agent workflows, compatible with any model providers supporting the OpenAI Chat Completions API format. It provides tools for LLM configuration, task handoffs, input/output validation, and workflow tracing. The SDK is flexible, allowing various workflows, and supports custom tracing integrations for debugging and optimizing agent behavior.
Medium·1yBuilding TikTok-like Recommenders with Feature Pipelines
A free course on building a real-time personalized recommender system for H&M articles using a four-stage architecture, two-tower model design, and Hopsworks AI Lakehouse. This lesson covers the feature pipeline crucial for creating and managing features required for machine learning models, integrating the H&M dataset, and engineering features for both retrieval and ranking models. It highlights the importance of Hopsworks Feature Groups in managing and reusing features efficiently.
Getting started with AI·1yAI Is Changing Your Brain (Whether You Like It Or Not)
AI is subtly changing how we think and process information. The instant gratification from AI solutions triggers a reward in our brain, leading to dependency and a reduction in our critical thinking abilities. As we outsource memory to AI, our cognitive pathways weaken. Furthermore, automation bias leads us to trust AI even when it is clearly wrong.
Community Picks·1yGurubase/gurubase: Gurubase is an open-source RAG system that lets you create AI-powered Q&A assistants by indexing websites, PDF documents, YouTube videos, and GitHub code repositories.
Gurubase is an open-source Retrieval-Augmented Generation (RAG) system that allows users to create AI-powered Q&A assistants, known as 'Gurus,' by indexing various data sources such as websites, PDFs, YouTube videos, and GitHub repositories. It offers easy integration with websites, Slack, and Discord. Users can self-host the system and manage data sources to keep content updated. Contributions and collaborations are welcome under the Apache 2.0 License.
Tech With Tim·1yBuild an AI Agent From Scratch in Python - Tutorial for Beginners
Learn how to build an AI agent from scratch in Python, integrating popular frameworks like LangChain and using large language models (LLMs) such as Claude or GPT. The tutorial covers setting up a virtual environment, installing dependencies, creating tools, and using API keys for different LLMs. You'll also learn how to create a structured output for the AI agent and how to write custom tools to enhance its functionality.
Medium·1yAI Agents are not Ready Yet
AI agents need more robust development tools as current options are not mature enough for effective debugging and collaboration in real-time. Companies should focus on building flexible systems integrating multiple AI capabilities rather than betting on single trends. Advanced areas like language understanding and task automation are maturing, but challenges such as pinpointing errors, maintaining security, and ensuring inspectability persist.
