A LinkedIn post lists five hands-on AI infrastructure projects for engineers: generating Dockerfiles with local LLMs via Ollama, building a production LLM serving stack on Kubernetes with vLLM, an end-to-end DevOps/AIOps monitoring project, a GPU autoscaling inference system with KServe, and scale-to-zero GPU inference using KEDA and Redis. Each project links to a YouTube walkthrough or GitHub repo covering containers, Kubernetes, inference, GPUs, and observability.
A pay-what-you-want Humble Bundle offering a 27-book collection of Manning titles on building and operating AI systems, including AI Governance, LLM Evaluation and Alignment, Building Reliable AI Systems, LLMs in Production, AI Powered Search, and Transformers in Action. The bundle covers security, privacy, bias, ethics, cost management, and regulatory compliance for generative AI deployments, with proceeds supporting the charity Child's Play.
Google advertises an AI Professional Certificate focused on practical AI fluency for daily work. The program includes more than 20 hands-on activities and is aimed at people with no prior AI experience.
This video outlines what it takes and what it costs to build a home AI lab. It is presented as a practical companion resource for people learning to run local AI infrastructure.
This beginner guide maps Gemini’s chat, Deep Research, Canvas, Notebooks, Gems, and Spark features. It provides tested examples and copy-paste prompts for working with Gmail, Drive, Chrome tabs, documents, slides, quizzes, and other Google-connected material.
Adam Jacob explains what a software factory is and how to build one for both new and existing codebases. The talk covers agile manifesto principles, black-box testing for trust and safe refactoring, and what he calls the core secret behind effective software factories. It was shared via the Swamp Club community with a note about the club being on a tear lately.
The video explains fine-tuning and demonstrates how to install and use the open-source Unsloth Studio locally and offline. It shows how to select models and datasets, fine-tune Qwen models with QLoRA, adjust training parameters, and monitor training runs. It also demonstrates creating custom question-and-answer datasets from a single PDF using Unsloth Studio recipes and external models through OpenRouter.
Mihailo Zoin presents ten of 24 protocols for using Gemini Notebook (NotebookLM) more rigorously. The protocols address source organization, deep analysis, contradiction detection, decision testing, research paralysis, bias, and preventing AI-generated consensus from being mistaken for corroboration. They are structured as sequences of checkpoints rather than single prompts.
Specification engineering expands prompt engineering by defining an AI task's objectives, inputs, constraints, outputs, edge cases, evaluation criteria, tests, and failure modes. The article explains its importance for coding agents, research assistants, data workflows, and structured outputs, citing requirement-oriented prompting research, SWE-bench, OpenAI's Model Spec, Anthropic's Constitutional AI, and Google's DORA research. It presents a workflow of specification, generation, validation, revision, and audit.
Google recommends seven NotebookLM prompts spanning student, business, and personal tasks, including generating quizzes, creating study guides, extracting syllabus URLs, analyzing revenue, researching competitors, organizing receipts, and modeling property purchases. The prompts demonstrate NotebookLM's shift from a purely source-grounded research assistant toward a tool that can transform documents, analyze data, browse the web, and produce practical outputs.
This tutorial explains how to build a fully local command-line AI agent using Python, Ollama, and Alibaba's Qwen 2.5 model. It demonstrates defining a shell-command tool with Python's subprocess module, mapping it to Ollama's tool-calling schema, and maintaining a conversation loop that handles user input, tool execution, and final responses. The article also discusses CLI agent types, setup, example system-inspection commands, and safety concerns around ambiguous deletion or cleanup requests.
The video argues that AI slop shifts the burden of reviewing unclear, unchecked writing onto whoever reads it next, costing hours every week. It explains that AI models converge toward broadly rewarded styles, so a universal anti-slop checklist cannot create distinctive authorship. It advocates an authorship process centered on clear intent, personal voice, revision, and accountability rather than relying on AI or style guides alone.
The article explains autoregressive transformer generation and the distinction between prefill, which processes the full prompt, and decode, which generates tokens one at a time. It demonstrates a simple key-value cache that avoids recomputing attention keys and values, reducing decode attention cost from quadratic to linear in sequence length. It also covers KV-cache memory usage, cache management, and implementation limitations such as repeated tensor allocation with torch.cat().
The article explains how AirLLM runs the full 2.8-trillion-parameter Kimi K3 model using 3.72GB of VRAM through per-expert streaming and delayed MXFP4 decompression. It covers hardware, storage, disk-streaming limitations, setup requirements, installation commands, and Python code for generating text. The approach is practical for offline batch processing and research, but generation takes roughly five minutes per token.
This Substack post promotes a free 367-page guide on LLM system design, described as a production-grade interview handbook for designing, serving, and scaling large language model systems. It says the guide includes 50 chapters across 13 parts, plus an offline companion project runnable on a personal computer without API keys. Topics covered include capacity and cost calculations, multi-machine training, RAG, agent loops and tools, safety checks, evaluation, auto-scaling, reliability, and eight case studies.
This X post promotes a 12-page PDF and full course on 'Graph Engineering' for multi-agent systems, attributed to a senior Anthropic engineer. It argues that an agent's memory dies with its context window, and that a knowledge graph makes memory permanent through an Extract, Resolve, Assemble, Query cycle. The post outlines a 14-step roadmap from beginner to graph architect, focused on building multi-step agents using graph-based structures instead of simple linear workflows.
This X post highlights a free one-hour Stanford lecture on building LLM architectures from scratch, contrasting it with the $750,000+ salaries Anthropic reportedly pays engineers with this skill. It promotes the lecture as a must-watch resource and points to an accompanying article covering five commonly misunderstood AI concepts. The post is aimed at technical learners wanting to deepen their understanding of LLM fundamentals.
This X post explains five commonly misunderstood AI concepts: hallucination, context window, RAG, agents vs. chatbots, and fine-tuning vs. prompting vs. RAG. It clarifies what each term actually means, why people commonly misunderstand them, and offers practical usage tips such as verifying outputs, working within context limits, and starting with prompting before fine-tuning. It also includes a suggested step-by-step learning path for building AI projects.
This X post recounts a Google Cloud engineer building a full app live on stage using Claude in 26 minutes, framed as more valuable than paid vibe-coding courses. The post promotes a guide explaining lesser-known Claude features, where to find and enable them, and a ready-to-use prompt. It's aimed at AI builders looking to get more out of Claude's capabilities.
This guide from Nate B Jones provides an end-to-end setup prompt for Ringer, a single-file Python orchestrator that fans tasks out to cheap parallel worker agents and verifies each result by executing a check command. It walks through phases for proving a demo swarm, wiring cheaper worker lanes, drafting a first real manifest, and installing the agent integration. It emphasizes hands-on setup in Claude Code with the agent performing installs while the user approves commands.
This blog post explains how Chain-of-Thought (CoT) prompting works for large language models, defining prompts and LLMs and showing why models can fail when jumping straight to answers. It demonstrates CoT using simple math word problems, covering both zero-shot and few-shot variants. It also covers where CoT is useful, such as logic puzzles, multi-step questions, and reading comprehension, along with caveats like imperfect reasoning and added token cost.
This article describes the author's first attempt at the Claude Certified Architect Foundation (CCA-F) exam, improving from a practice score of 677 to 910 after focused study. It explains the exam's five weighted domains, its scenario-based format, and preparation steps including reviewing the official guide and practicing with the Claude SDK, Claude Agent SDK, and Claude Code workflows. It shares practical takeaways for forward-deployed engineers on tool configuration and prompt design.
This Substack article explains what Anthropic's Claude Fable 5 changed, including pricing, context length, output limits, and API behavior such as always-on thinking. It fact-checks common launch claims, separating confirmed Anthropic statements from distorted or unverified ones about benchmarks and jailbreak resistance. It focuses on a prompting shift from detailed step-by-step instructions to goal, reason, and constraint-based prompts, plus effort controls and verifier subagents for coding and agentic workflows.
This Outcome School blog post explains how vLLM works as a high-throughput LLM serving engine, covering prefill, decode, KV cache memory challenges, and vLLM's core techniques like PagedAttention and continuous batching. It also describes vLLM's OpenAI-compatible API server and real-world use cases for serving models to many users at once.
This Futurepedia YouTube video argues that mastering AI in 2026 requires going deep into one ecosystem (ChatGPT, Claude, or Gemini) rather than constantly switching tools or focusing on prompt engineering. It covers a simple ICC prompting framework, persistent knowledge and processes via projects/skills, AI build modes, vibe coding, and other specialized tools like NotebookLM and Suno.
This Machine Learning Mastery article explains how large language models generate next-token predictions using logits, temperature, and top-p (nucleus) sampling. It walks through how temperature scales logits to adjust distribution sharpness, and how top-p keeps the smallest token set reaching a cumulative probability threshold, covering the full pipeline from logits to sampled output.
This Towards AI article is the first in an eight-part series preparing readers for Anthropic's Claude Certified Architect (CCA) Foundations exam, launched March 2026. It covers the exam format, five competency domains, six production scenarios, key mental models, and a week-by-week study plan for passing on the first attempt.
This Machine Learning Mastery article explains agentic programming as building AI systems that plan multi-step tasks, use tools, maintain memory, and pursue goals via a ReAct-style loop. It covers memory architecture, tool design, multi-agent orchestration, production concerns like observability and cost control, compares 2026 frameworks such as LangGraph and CrewAI, and provides a month-by-month learning roadmap.
This YouTube video from David Ondrej demonstrates installing and running the uncensored open-weights model SuperGemma 26B locally via Ollama, covering hardware requirements and basic setup. It discusses arguments for uncensored models being useful in legitimate contexts like cybersecurity defense and confidential research. It also introduces a GitHub repository using a reviewer/judge loop to test prompts across models to reduce refusals.
This Towards Data Science article explains how to build a reliable knowledge base for AI models by gathering, cleaning, and structuring data into vectors with metadata stored in a vector database. It covers retrieval methods like hybrid search and orchestration frameworks such as LlamaIndex and LangChain, along with maintenance practices to keep the knowledge base current. The piece highlights common challenges including data quality, retrieval speed, and scalability.
This X post from Riley Brown promotes a tutorial video teaching "95% of Codex in 28 minutes," describing Codex as a "super-app" for knowledge work. It outlines seven capabilities covered, including full file access, persistent memory, plugins, skills, image generation access, browser/computer use, and automations. The post is explicitly educational content aimed at users learning to leverage Codex.
This Futurepedia community post page (behind a sign-in wall) promotes the platform's AI learning content, highlighting over 30 AI courses for entrepreneurs with new courses added monthly. It includes testimonials praising course coverage of LLMs, prompting, image generation, audio, video, and presentation tools, plus a 14-day AI bootcamp with 15 videos, certificates, and downloadable lessons. The chat message describes it as 'a great tutorial for Claude Code.'
This video explains how to reduce AI token costs by changing common usage habits, such as converting raw PDFs to Markdown before uploading and avoiding conversation sprawl. It argues most users waste 8-10x more tokens than necessary due to plugin overhead and inefficient workflows, especially as next-generation models become pricier. It presents an audit framework and 'five commandments' for efficient agent token management.
This page offers a set of copy-paste prompts designed to help users diagnose and reduce wasted AI token consumption, companion content to a related video on the same topic. It includes prompts for auditing AI habits, rescuing bloated conversations, planning model routing, and reviewing agent architectures against KISS principles. The kit also helps translate usage patterns into concrete cost breakdowns.
This Medium article covers Anthropic's launch of the Claude Certified Architect, described as its first official technical credential, a proctored architecture-level exam. It discusses what the certification tests, who should pursue it, and how to prepare, noting growing interest among professionals at firms like Accenture, Deloitte, and Infosys. The piece frames it as a notable step in formalizing AI engineering expertise.
This Substack post argues that AI memory fragmentation, not prompting skill, is the main bottleneck in everyday AI use. It introduces 'Open Brain,' a user-owned, database-backed, MCP-connected knowledge system letting tools like Claude, ChatGPT, and Cursor share the same accumulated context. It includes a companion guide with a 45-minute setup process and prompts for migrating AI memories and building daily capture habits.
This Substack note by Ruben Hassid lists ten recommended YouTube channels for learning AI, including 3Blue1Brown, Two Minute Papers, DeepLearning.AI, Andrej Karpathy, Tina Huang, AI Explained, Matthew Berman, Krish Naik, freeCodeCamp.org, and StatQuest with Josh Starmer, each with a short description of its focus area. Comments on the post add further recommendations and discuss beginner AI courses such as 'AI for Everyone' and 'ChatGPT Prompt Engineering for Developers.' Source: https://substack.com/@ruben/note/c-220436688
Content behind a login wall / unavailable — inferred from link context: this Google search-app share link is described in the accompanying chat message as covering an Anthropic partnership with CodePath, described as the largest nonprofit computer science education provider in the US. The partnership likely aims to expand AI and coding education access for underrepresented students. The actual page could not be fetched due to access restrictions.
Miles Deutscher posts what he calls his most powerful GPT-5 prompt to date, claiming most people only use 10% of GPT-5's capabilities and that this prompt unlocks the rest. The post includes a system-role prompt instructing the model to act as an all-in-one expert assistant for reasoning, research, planning, writing, and execution. Replies debate whether the prompt meaningfully changes GPT's output versus typical canned responses.
Sabrina Ramonov outlines eight AI-related skills she believes will separate winners from losers in 2026, including strategic skepticism, radical adaptability, learning in public, and context engineering versus prompt engineering. The video also covers using AI as a sparring partner rather than a search engine, 'vibe coding' apps with plain English, building always-on AI systems, and documentation as fuel for automation. It focuses on practical AI learning habits for creators and entrepreneurs.
This page lists AI-focused learning products from Skill Leap by Futurepedia, including courses on using Claude, generative AI, Google Gemini, AI website building, and AI-powered digital marketing. The courses cover practical applications such as writing, research, file analysis, dashboards, content production, and marketing automation. Listings include ratings and review counts for courses like Claude 101 and Google Gemini: The Complete Beginner's Guide.
This LinkedIn post explains basic building blocks and concepts of AI agents in simple terms, defining 20 related terms including AI agent, LLMs, LRMs, tools, environment, state, perception, action, memory, and knowledge base. It also covers planning, orchestration, ReAct, chain-of-thought, architecture, evaluation, multi-agent systems, swarms, handoffs, and agent debate. The post is framed as a fundamentals-first learning resource for understanding how AI agents work.
This page presents a curated learning track called 'Foundations of Generative AI' focused on building practical generative AI skills. It lists modules including a 14-Day AI Boot Camp, Prompting Essentials for ChatGPT, Google NotebookLM for research, Custom GPTs for entrepreneurs, and a broader course covering tools like ChatGPT, Gemini, Midjourney, and Canva. A chat message described it as a paid course sequence providing a firm foundation in generative AI.
This Substack post by Ruben Hassid explains that effective prompting starts before typing, emphasizing clarity, context, and structure over hoping ChatGPT will 'fix' a vague request. It recommends using a reference document in markdown, reverse-engineering it into a blueprint, defining a success brief, and combining materials into a single prompt. It also stresses iterative chat-based refinement with short, directive follow-ups and explicit error correction.
This LinkedIn post by Andrew Bolis presents a 15-step roadmap for learning AI, progressing from AI chatbots to AI agents and then to agentic AI. It emphasizes starting with LLM basics and prompt engineering, moving to automation platforms like Make, Zapier, and n8n, and then advancing to memory, multi-step reasoning, and collaborative agent systems. The post stresses learning by doing and using AI to solve real-world business problems.
This LinkedIn post by Matt Village lists AI algorithms that 'secretly run your life,' describing how they choose what people watch and predict what they buy. It names and briefly explains algorithms including Linear Regression, Logistic Regression, Decision Tree, Random Forest, SVM, k-NN, Naive Bayes, Gradient Boosting, AdaBoost, and XGBoost. The post invites readers to learn more via an accompanying infographic and a free AI learning link.
This LinkedIn post by Maria Zhanette Yap shares nine AI learning guides and official academies, including resources from OpenAI Academy, Anthropic Academy, Google's Gemini prompting guide, and Claude's official documentation. It also lists additional prompt-engineering and workflow guides covering tasks like thesis writing, account growth, and customizing ChatGPT. The post frames these as free, accessible resources for both non-technical people and existing AI users.
This LinkedIn post shares a free 68-page Gemini prompt engineering guide, noting similar paid courses cost $500 or more. It covers configuring LLMs properly, professional prompting techniques, effective code generation, token control, and chain-of-thought best practices. The post also links to additional free Google AI guides on image generation, chatbot training, and using NotebookLM.
Ruben Hassid explains a method for learning '80% of virtually anything' using AI by finding high-quality source content and feeding it into tools like ChatGPT Projects or Google NotebookLM, then asking follow-up questions to build deeper understanding. He gives examples using NotebookLM, including sourcing YouTube videos, PDFs, and websites, and notes NotebookLM can generate a podcast from the materials as part of an efficient, curated learning approach.
This video ranks seven AI certifications and learning resources for beginners and advanced learners seeking AI-related jobs or business skills. It reviews free and paid options from DataCamp, DeepLearning.AI, Harvard, IBM, Microsoft, and the University of Pennsylvania, covering topics such as AI fundamentals, AI engineering, and Python-based AI development, emphasizing practical, career-focused learning paths.
This LinkedIn post shares 10 YouTube videos about building production-ready AI agents using n8n and Claude, covering memory, tool use, function calling, multi-agent coordinator/worker patterns, state and guardrails, vector memory, and agent monitoring/debugging. It argues most AI agent tutorials focus on superficial demos rather than the reliability engineering needed for real deployments, drawing on the poster's experience building over 1,000 agents.
Maria Zhanette Yap shares a curated collection of free Claude-related learning resources, including getting-started guides, use cases, Sonnet 4.5, Claude Code, and Claude 101. The post also lists Claude resources tailored for engineering, HR, marketing, product management, sales, and educators, plus guides on writing with voice, generating images, prompting, and training a chatbot. It argues Claude outperforms ChatGPT for writing and encourages exploring multiple AI models.
Maria Zhanette Yap shares 10 free Harvard lectures and guides covering generative AI and prompting, including an introduction to generative AI, prompt engineering, system prompts and RAG, CS50x 2025 AI lectures, GPT-4 internals, and AI's impact on programming. The post also links resources on generative AI in teaching, plus bonus AI writing and prompting guides curated for free public access.
A LinkedIn post by Ashley Nicholson announces that AWS released 10 guides for building effective AI agents, covering agentic AI patterns and workflows, foundations of agentic AI, RAG best practices, agent frameworks and protocols, architectures for agentic AI, healthcare RAG solutions, vector database selection, and serverless agentic architectures. The post and its comments discuss agentic workflows, RAG implementation, and governance considerations for production AI agents on AWS.
This page is a sign-in screen for a Futurepedia/Skill Leap course lesson titled 'Getting Started with Gamma,' part of an 'AI Powered Presentations' course. It promotes Futurepedia's broader library of 30+ AI courses covering LLMs, prompting, image generation, audio, video, and presentation tools, including a 14-day AI bootcamp, with user testimonials praising the course content quality.
This LinkedIn post could not be accessed due to LinkedIn's login requirement. Based on its URL slug ("forget-harvard-forget-google-forget-coursera"), it appears to argue that a particular AI resource or platform can replace traditional educational institutions and online course providers for learning AI skills.
This LinkedIn post could not be accessed due to LinkedIn's login requirement, but its slug indicates it announces that Anthropic has released free AI courses. Based on the URL text ("anthropic-just-dropped-free-ai-courses"), the post likely highlights new educational resources from Anthropic for learning AI or Claude-related skills.
This page promotes Skill Leap, an AI education platform by Futurepedia aimed at entrepreneurs and professionals wanting to build practical AI skills. It offers a free trial and membership access to 30+ courses, training videos, guides, and prompts covering topics like ChatGPT prompting, generative AI, AI coding, Midjourney, and Google NotebookLM. The page also outlines a beginner-friendly learning path, pricing tiers, and community features.
Content behind a login wall / unavailable — inferred from link context: based on the URL and chat snippet referencing a Stanford professor's ChatGPT prompt, this TechRadar article appears to explain how a specific prompting technique can be used to have AI teach users how to use AI more effectively, changing their approach to everyday AI usage.
This video features Stanford adjunct professor Jeremy Utley explaining why most people underuse AI's creative potential, arguing for a mindset shift from treating AI as a mere tool to treating it as a collaborative teammate. He suggests having AI ask questions rather than only answering them, and shows how non-technical professionals can use this approach to boost creativity and productivity. The discussion includes practical examples like using AI for roleplay and interview-style feedback.
This YouTube tutorial walks through setting up OpenAI's newly released open-source GPT-OSS-20B and GPT-OSS-120B models locally using Docker and Ollama, then connecting them to n8n AI agents. It demonstrates testing chat prompts, using OpenRouter as an alternative cloud option, and building tool-calling workflows such as looking up contacts in Google Sheets and sending automated emails. The video compares the two model sizes' performance and limitations for automation tasks.
This Futurepedia video provides a structured roadmap for learning AI in 2025 without requiring technical expertise, outlining three learning paths: Explorer, Power User, and Builder. It covers core concepts like LLMs, prompt engineering, and AI agents, plus major tool categories across text, research, image, video, and audio. The video concludes with a practical 30-day action plan for building AI skills and workflows.
This NetworkChuck video explains the 'Telos File,' a single markdown document capturing a person's goals, values, identity, and vulnerabilities, which can be analyzed by AI as a 'personal pentest.' It features an interview with Daniel Miessler, the creator of Telos, discussing how to use the file with AI tools like Fabric to gain self-awareness and reduce career-related AI anxiety. The video provides templates and prompts for building and using a Telos file.
This YouTube video by Liam Ottley explains how to become an 'AI generalist' by mastering tools that let non-coders build apps, automate workflows, and create content. It presents nine tools and workflows, including Lovable AI, Relevance AI, N8N, Prompt Metheus, Postman, ChatGPT-4o image generation, Runway, UX Pilot, Descript, and Poppy AI. The video positions these tools as key differentiators for entrepreneurs and professionals navigating the AI era in 2025.
This Circle of Greatness podcast episode features Alicia Lyttle discussing seven ways to generate income using AI without a technical background, including faceless YouTube channels, digital products, chatbot services, and AI consulting. It covers tools such as Opus Clip, Claude, Otter.ai, ElevenLabs, Canva, Lovable, and HeyGen, along with sample prompts for using ChatGPT as a business coach. The episode is aimed at entrepreneurs and side-hustlers looking to build AI-powered income streams.
Content behind a login wall / unavailable — inferred from link context: this XDA Developers article title suggests a retrospective guide sharing lessons the author wishes they'd known before self-hosting large language models locally, likely covering hardware requirements, model selection, and setup tools like Ollama or llama.cpp. This fits XDA's broader coverage of practical local AI and self-hosting topics. The page could not be fetched due to a robots restriction.
Content behind a login wall / unavailable — inferred from link context: this XDA Developers article title, paired with the chat message 'I've used all these techniques with notebookLM,' indicates a guide covering common mistakes users make with NotebookLM and practical fixes or best practices to improve results. It is part of the publication's extensive July 2025 series of NotebookLM tips and tutorials. The page could not be fetched due to a robots restriction.
Content behind a login wall / unavailable — inferred from link context: this XDA Developers article title suggests a guide on using NotebookLM to build a personal 'second brain' knowledge management system, organizing notes, documents, and research into AI-searchable notebooks. This is consistent with the publication's ongoing series exploring creative NotebookLM productivity use cases throughout July 2025. The page could not be fetched due to a robots restriction.
Content behind a login wall / unavailable — inferred from link context: this XDA Developers article title indicates a guide on integrating NotebookLM with Google Docs, likely describing how to feed Google Docs content into NotebookLM notebooks or export AI-generated notes back into Docs for writing and editing. This continues the publication's ongoing series of NotebookLM integration tutorials. The page could not be fetched due to a robots restriction.
Content behind a login wall / unavailable — inferred from link context: this XDA Developers article title suggests a guide on pairing NotebookLM with Slack, likely explaining how to import Slack conversation exports or channel content into NotebookLM notebooks to summarize team discussions or knowledge. It fits the publication's broader series on integrating NotebookLM with other productivity tools. The page could not be fetched due to a robots restriction.
This Medium article by Jordan Gibbs argues that the single most valuable ChatGPT prompting technique is instructing the model to ask clarifying questions before answering. The author says this approach prevents ChatGPT from rushing to a response and instead helps it gather more useful context from the user. The piece contrasts this simple technique with more elaborate prompting frameworks, specifically criticizing the 'Lyra' prompt as overly complex.
Content behind a login wall / unavailable — inferred from link context: this XDA Developers article title suggests it presents advanced or creative NotebookLM workflows the author considers 'game-changing' for research, study, or knowledge management, continuing the publication's regular coverage of practical NotebookLM use cases. A forwarded chat message reacting positively to the article supports this being a tips/workflow piece. The page could not be fetched due to a robots restriction.
Content behind a login wall / unavailable — inferred from link context: this XDA Developers article title indicates a beginner-focused guide walking new users through NotebookLM setup and core features from scratch, likely covering uploading sources, generating summaries, and using the Audio Overview podcast feature. It fits within XDA's broader series of practical NotebookLM tutorials published in July 2025. The page could not be fetched due to a robots restriction.
Content behind a login wall / unavailable — inferred from link context: this XDA Developers article title suggests it demonstrates how to repurpose Google's NotebookLM as a personal finance tracking tool, likely by uploading bank statements or budget spreadsheets for the AI to summarize and answer questions about spending. XDA regularly publishes creative use-case guides for NotebookLM beyond its typical research and note-taking functions. The page could not be fetched due to a robots restriction.
Content behind a login wall / unavailable — inferred from link context: this XDA Developers article, referenced with the message 'Using Notebooklm to watch YouTube videos,' likely explains how to feed YouTube video links into Google's NotebookLM tool so it can transcribe, summarize, and let users query the video content conversationally. XDA frequently publishes practical how-to guides on using NotebookLM as a research and note-taking assistant. The page could not be fetched directly due to a robots restriction.
This DataCamp blog post explains Retrieval Augmented Generation (RAG), describing how it connects LLMs to external data sources to produce more accurate and current answers. It walks through the RAG pipeline — data collection, chunking, embeddings, retrieval, and generation — with examples like customer support and business intelligence. The article also covers challenges, best practices, advanced variants like agentic RAG and GraphRAG, and comparisons with fine-tuning.
This Medium article by Marina Wyss discusses strategies and mindset shifts for staying current in AI/ML without feeling overwhelmed by the constant stream of new tools and research. It argues that people should focus on business problems first rather than chasing every new trend, especially when building a sustainable learning habit alongside a full-time job. The full article is behind Medium's member-only paywall.
This is the introduction/navigation page for DeepLearning.AI's short course 'How Transformer LLMs Work,' which teaches the transformer architecture powering large language models. The page provides platform usage instructions, including how to access notebooks, reset the workspace, and use video controls, plus general study tips and links to other short courses. It is aimed at helping learners understand transformers to use LLMs more effectively.
Google Prompting Essentials is a 4-course specialization from Google Career Certificates that teaches how to write clear, specific prompts for generative AI. It covers a 5-step prompting framework, prompt iteration, prompt chaining, multimodal prompting, few-shot prompting, and meta-prompting for tasks like drafting text, summarizing documents, and data analysis. The program is beginner-friendly, requires no prior experience, and can be completed in under 10 hours with a Google certificate upon completion.