Tech Tips, Tricks & Trivia

I/O '26 Recap: Everything You Need to Know     Google processes 3.2 quadrillion tokens per month.  It now has 13 products with over a billion users each.  Google's new model Gemini Omni can create anything from any input. It combines Gemini's intelligence with the best of its generative media models for a new level of World understanding, multi-modality and editing. Software engineering at the tipping point Build core skills to thrive as an AI-era developer     Three fourth of all code at Google is written by AI. What's new in the Gemma open model family    What's new in Chrome Create advanced data driven Gemini API apps Unlock modern web capabilities in your AI coding workflows   Build your website for the agentic era   Vibe design to build incredible web UI Elevate the Chrome Extensions developer experience   Break boundaries with Gemini in Chrome DevTools   A fireside chat on the evolution of the developer craft

Gergely Orosz chats with Jean Lee, who joined WhatsApp as its 19th engineer when it was still a small company with barely any formal processes. She played a key role in scaling it to hundreds of millions of users, experienced the $19B acquisition by Facebook, and later continued her career at Meta. Here are the standout sound bytes and interesting facts from the talk : Why Jean got into tech: "After talking to a lot of adults, I realized people who are in tech were the only ones who were really excited about their jobs. So in Silicon Valley, when you ask people like tell me about your work, people are often very hopeful for the future and very proud of what they're building. Compared to many other adults that I spoke with, they were not so encouraging. They're like, "Oh, don't become an architect. Don't become a designer."" On process: " We didn't have code reviews... The only time I got my code reviewed was the first time I made a commit....

  In this video series , Google’s Addy Osmani provides invaluable wisdom for those starting or maturing their careers in software engineering.

This interview features Grady Booch, a pioneer of software engineering (co-creator of UML and object-oriented design), discussing why AI is not the "end" of the profession, but rather the catalyst for its Third Golden Age. The Three Golden Ages The First Golden Age (Late 1940s – Late 1970s): Focus: Algorithmic abstraction and decoupling software from hardware. Drivers: The Cold War and military funding (e.g., SAGE missile defense) pushed the need for real-time, distributed systems. Abstraction: Moving from machine-level "plugboards" to assembly and early high-level languages like Fortran and COBOL [08:52]. The Second Golden Age (Late 1970s – Early 2000s): Focus: Object-oriented programming and design. Drivers: The "Software Crisis"—the industry couldn't produce quality code fast enough to meet demand. This era saw the rise of the PC, open source, and the internet. Abstraction: Moving from processes/functions to objects and classes (e.g., C++, Smalltal...

Scott Hanselman and Mark Russinovich are seasoned tech veterans and iconic figures in the Microsoft community. They share candid conversations about emerging trends and topics relevant to IT professionals through their podcast and on YouTube's channel.

Annotated summary / transcript of Scott Hanselman’s video explaining modern AI agents like Clawdbot / OpenClaw / Moltbot (2025) This will be a bit of a yap, but a lot of people have been asking me about Clawdbot, which was MoltBot, now called OpenClaw. And it is an 1 AI-powered assistant that feels like Jarvis from Tony Stark. It feels like the Siri or Alexa we were promised. And it's got a lot of people really excited. They're starting to make up stuff. Some tech journalists think they are becoming conscious... some AI grifters saying it's 2 AGI, and it's all nonsense. But let's talk about it. So, very large 3 language models – Large Language Models are called 4 Generative Pre-trained Transformers – are basically doing next-token prediction. And they do that based on a whole lot of context. So if I say to you, "It's a beautiful day, let's go to the...", you might say beach or park. But that's a small amount of context. It's a bea...

Ujaval Gandhi’s talk about how he built a sustainable business around open-source GIS training.  He shares the lessons he learned along the way -  "Open content is not only good for the community but it's also good for business. It drives a lot of traffic....One of the things I learned in a kind of interesting way is just naturally I would put all the errors that the software would get and the search engines index them really well. For a month I looked at my stats and the number one referer to my sites was the QIS error spatial index doesn't exist and I had a content like if you get this error run this tool it'll fix it and then I had a video on explaining spatial index and then people watch that video and say I like this style let me explore more and then they sign up for a course." About a decade ago, during my time working with ASP.NET, I was drawn to forum questions about things that weren’t possible out of the box. I enjoyed finding workarounds or alternati...

Insightful career advice from Indian-American billionaire businessman and venture capitalist, Vinod Khosla (born 1955) -  Lifelong Learning and Flexibility "..today, at age 70, I'm learning at a much faster pace than I've ever learned in my whole life. Ever. Every 20-year-old should strive for this ability—to jump into any new area , whether it's a physics problem or a biology problem or a finance problem." Thinking from First Principles "Just this ability to think from first principles ... learning new areas can teach you any new areas. AI will have that capability to be able to leverage these things well." Optimizing Your Career for Flexibility "You have to optimize your career for flexibility, not a single profession . That's the most important advice because you don't know what will be around." Learning How to Learn "I do think it almost doesn't matter where you start. Education is going to be valuable to have. Learn to l...

Jeremy Howard is the co-author of the popular book Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD . He founded fast.ai  with Rachel Thomas and created FastHTML. In this 7-minute video, Jeremy Howard introduces FastHTML, the Python framework for building web applications entirely in Python with minimal code and no separate frontend JavaScript. Highlights - - Entire web apps can be written in one Python file with no separate JavaScript or scaffolding. - HTML components are created programmatically using Python functions. - HTMX enables dynamic server communication without heavy JavaScript. - Supports database integration (SQLite shown) and full CRUD operations. - Built-in live reloading for fast development feedback. - Authentication can be added using standard browser mechanisms. - Compatible with any CSS or web frameworks and deployable with a single command. - Designed to simplify web development for Python programmers, scaling from simpl...

This hour-long video covers not just the maths behind LLMs but also the history. Complex topics are well explained.  * Training a large language model is like constructing a skyscraper, where every   brick is placed by an army of specialists working around the clock. But here, the bricks are data, the mortar is mathematics, and the blueprint is a complex interplay of algorithms.  *   LLMs don't truly understand. They excel not through cognition, but through colossal computation. *  Mathematics is the invisible backbone of these 'thinking machines'. *  N-grams - A statistical method used by early language models that broke down text into small sequences of words to predict the next word based on common combinations in a dataset. *  Transformers - A groundbreaking architecture introduced in 2017 that revolutionized the field by enabling machines to grasp context at an unprecedented scale , allowing models to pay attention to all parts of a se...

Andrej Karpathy's keynote at AI Startup School in San Francisco (39m 31s) |   Slides   Talk Summary Andrej Karpathy asserts that software is undergoing fundamental changes, for the third time in recent years, after largely remaining consistent for 70 years .  He categorises this evolution into three paradigms:  Software 1.0 : Traditional human-written code (e.g., C++, Python). Software 2.0 : Neural networks programmed by their weights, tuned via data and optimisers (e.g., AlexNet image recognizer, Hugging Face as its GitHub equivalent). Software 3.0 : Large Language Models (LLMs) programmed by natural language prompts, effectively making English a programming language. Karpathy illustrates this transition with the Tesla Autopilot, where Software 2.0 (neural networks) progressively replaced Software 1.0 (C++ code) by absorbing functionalities like image stitching across cameras. He posits that LLMs behave like new kinds of computers and exhibit properties akin...

  What’s the difference between generative AI and agentic AI?  Martin Keen explains how generative AI powers content creation and image generation, while agentic AI uses LLMs and chain of thought reasoning for proactive tasks like personal shopping and conference planning. Discover the future of intelligent AI collaboration!

Andrews Ng , Founder and Executive Chairman of Landing AI, explores the rise of AI, agents, and the growing role of unstructured data. He also discusses how this convergence will shape automation and application building across industries.  Topics - Intro - AI as the New Electricity: 00:00:00 The AI Stack and Opportunities: 00:00:54 Faster ML Model Development: 00:01:48 Gen AI vs Supervised Learning: 00:02:05 Consequences of fast development: 00:03:12 Agentic AI Workflows: 00:06:32 Agentic Reasoning Design Patterns: 00:09:56 Reflection With LLMs: 00:10:31 Tool Use: 00:12:23 Planning/Reasoning: 00:12:53 Multiagent Collaboration: 00:13:31 Demo: Agentic Workflow and Video Q&A App - Visual AI Applications & Demos: 00:15:08 Vision Agent Demo Applications: 00:20:00 AI Stack and Agentic Orchestration: 00:22:30 Four Important AI Trends: 00:23:25 Conclusion and Next Steps: 00:25:56   Insop Song , Principal Machine Learning Researcher at GitHub Next, introduces the concept of ag...

Tips and suggestions for breaking into and succeeding in the Data Visualization field, drawn from the VizChitra panel discussion: Leverage Your Existing Skills - Panelists noted that many people stumble into data visualization from diverse backgrounds like visual design, consulting, science, and journalism. The skills acquired in these fields, such as visual design principles, data-driven communication from consulting, making complex ideas accessible from science communication, and applying new skills like data analysis from journalism, are highly transferable and valuable in data visualization. "I don't think anyone chooses data viz, data viz happens to them". "My journey started as a visual designer... and at some point I realized that there's so much of information that is out there that there's got to be a better way to present this information". Prioritize and Develop Soft Skills like   Curiosity   (especially continuous curiosity) , the ab...

Notes from a talk by S Anand: What is vibe coding? It's where we ask the model to write & run code, don't read the code, just inspect the behaviour. It's a coder's tactic, not a methodology. Use it when speed trumps certainty . Why it's catching on Non-coders can now ship apps - no mental overhead of syntax. Coders think at a higher level - stay in problem space. Model capability keeps widening - the "vibe-able" slice grows daily. How to work with it day-to-day Fail fast, hop models - if Claude errors, paste into Gemini or OpenAI. Cross-validate outputs - ask a second LLM to critique or replicate; cheaper than reading 400 lines of code. Switch modes deliberately - Vibe coding when you don't care about internals and time is scarce, AI-assisted coding when you must own the code (read + tweak), Manual only for the gnarly 5 % the model still can't handle. What should we watch out for Security risk - running unseen code can nuke your files. Quality ...

Paraphrased notes from an interview with Windsurf CEO and Co-Founder, Varun Mohan , based on a summary initially generated by NotebookLM: Key Points: Windsurf's origin traces back to a company called Exaunction, which initially focused on building GPU virtualization systems for workloads like large-scale simulations in robotics and autonomous vehicles. With the advent of large generative models around mid-2022, the company pivoted to focus on the application layer, leveraging their infrastructure background to build code-related tools. The decision to build their own Large Language Models (LLMs) and inference stack early on was driven by the limitations of existing open models, particularly their inability to handle the "fill-in-the-middle" capability crucial for coding where changes are often needed within existing lines or snippets. Evaluating new models for coding use cases is complex due to their non-deterministic properties. Windsurf uses a rigorous evaluation i...

In Episode #9 of Bill Shander's Data Visualization, Data Storytelling, and Information Design - Lesson and Listen Series on LinkedIn Learning, he interviews Dr. Kenneth Field, a Senior Product Engineer at ESRI. Ken is the author of 2 books - Cartography (2018) and Thematic Mapping (2021) He also blogs ( cartoblography.com ) and tweets ( @kennethfield ) about mapping. Key takeaways from the talk: Maps are more than navigation tools: While primarily used for navigation, maps also provide valuable insights into culture, technology, and non-geographic data. Thematic maps convey data: Thematic maps, like choropleths, use visual elements (color, shading) to represent data values across geographic regions. Choropleths have limitations: Choropleth maps can be misleading because they emphasize the size of geographic areas, which may not accurately reflect the data being represented (e.g., electoral votes vs. land area). Cartograms offer an alternative: Cartograms, especially grid...

Chip Huyen is an AI engineering expert who has been in this field even before it was called AI engineering. She is the author of the O’Reilly book  AI Engineering . The chapter summaries of the book are available on Github . Chip has worked as a researcher at Netflix, was a core developer at NVIDIA (building NeMo, NVIDIA’s GenAI framework), and co-founded Claypot AI. She also taught Machine Learning at Stanford University. Q & A from her interview on The Pragmatic Engineer YouTube channel paraphrased by NotebookLM - Q: How would you define AI Engineer or AI Engineering? A: It's a shift from more machine learning to more engineering and more product . Previously, building machine learning applications required building your own models with your own data and expertise. Now, you can use direct API calls. Q: What are typical steps to build an AI application, from choosing a model through using RAG all the way to fine-tuning? A: Start by understanding what makes a 'good' vs...

Source: YouTube,  "Godfather of AI" Geoffrey Hinton: The 60 Minutes Interview  (Oct 2023) Video Summary of the interview by ChatGPT with additional notes -  Geoffery Hinton believes AI systems could be more intelligent than humans, possibly leading to machines potentially taking control. He asserts that AI systems have experiences and can make decisions based on these experiences. According to Hinton, AI systems may not currently have much self-awareness, but they could develop this in time. Hinton pioneered the concept of simulating a neural network on a computer in the 1970s, even though it was largely opposed. ...we designed the learning algorithm. That's a bit like designing the principle of evolution. But when this learning algorithm then interacts with data, it produces complicated neural networks that are good at doing things but we don't really understand exactly how they do those things Hinton's research contributed to the development of chatbots like G...

Topics covered in the talk : AI's Impact on Software Engineering Understanding Agents and Their Role Collaborative AI and Automating Code with Agents v0: Building Components and Interactive Prototypes v0 and Bolt: Beyond Component Building Bolt's Troubleshooting and Authentication Features Bolt's Targeted Edits and Tips Code Generation with Copilot and Cursor Optimizing Cursor Setup and Introducing Cline Website Inspection and Testing with Cline What is an AI Agent and why do they matter? An agent is a program that autonomously completes tasks or makes decisions based on data.  What is meant by autonomous?  The agent understands task intent, can plan steps to solve the problem, decide and execute actions and adapt to the environment Consider how many of us use AI chat interfaces today. You might ask ChatGPT to write an article from start to finish and get a one-shot response. You probably need to do some work to iterate on it yourself. An agentic version is more nuanced - i...