Staying current with advances in AI and technology
Quality content and sustainable workflows
One of the most common questions I get when I teach AI workshops: How do you keep up with this technology?
In my opinion, the only way to stay current in AI is to have two things: relevant sources and a workflow that fits your needs. You need both. Good sources without a system for consuming them means information piles up unread. A polished workflow pointed at the wrong sources means you are efficiently consuming the wrong things.
My workflow has evolved over the years from email subscriptions to RSS feeds to using OpenClaw to generate a daily digest that automatically surfaces what matters. I download YouTube videos and listen while I am outside taking care of my chickens or working on a building project. I have tried X and BlueSky, but have not found a workflow that saves me time rather than consuming it. I will cover my workflow in detail in a future post, including my experience with OpenClaw, which has changed how I manage all of this.
This post is about the sources. I do not use books or journals to stay current on AI. Academic publishing in my area has real problems it needs to address. I have AI papers that took nearly a year to appear online; by the time they were published, the findings were outdated. Books are worse. Our published scholarship routinely describes a technological landscape that no longer exists, and we are asking practitioners to make decisions based on it. That is a credibility problem, and our field has not seriously reckoned with it. My primary sources are non-academic channels where practitioners, developers, and researchers test new models, share benchmarks, and report results in real time.
Here are the sources I rely on most.
YouTube and Podcasts
The AI Breakdown
Weekly roundups of the biggest AI developments, covering model releases, enterprise adoption, funding, and policy. The episodes are focused and well-structured, which makes them easy to follow while doing something else.
This Day in AI
Michael and Chris Sharkey host conversational, longer-form discussions on AI news and tools. The tone is informal and the episodes run about an hour, but they cover a lot of ground and are not afraid to test things live and report what they find. A good listen when I have a longer stretch of time outside.
Fireship
Rapid-fire tech explainers, usually under ten minutes. Covers new model releases, tool comparisons, and developer-focused AI developments. I use this to quickly get oriented on something new without committing to a long video.
Tech With Tim
Hands-on programming tutorials with strong coverage of Python-based AI development. Tim builds things on camera, step by step, which makes his content particularly useful when you are trying to actually implement something rather than just understand it conceptually.
Matthew Berman
Daily hands-on reviews of new AI models and tools. Berman tests things in real time rather than summarizing press releases. When a new model drops and I want to know whether it actually performs as advertised, this is where I check.
Sam Witteveen
A Google Developer Expert for Machine Learning who covers LLMs, agents, and frameworks like LangChain in focused, practical videos. What I value most is that he shares working code, often through Google Colab notebooks, so you can follow along and adapt his examples directly. Particularly strong on RAG systems and agentic workflows.
Y Combinator
Founder interviews and talks on building AI companies. A different lens than the technical channels; this one covers where funding is flowing, what problems entrepreneurs are solving, and what the commercial deployment landscape looks like.
Hard Fork
Kevin Roose and Casey Newton from The New York Times. Weekly coverage of AI, social media, and the tech industry with strong reporting behind it. They do a good job of connecting technical developments to broader societal implications, which is the part most technical channels skip.
Lex Fridman
Long-form interviews with researchers, engineers, and founders across AI, science, and technology. These are not quick listens; episodes regularly run two to three hours. But the depth of conversation is unmatched. When Fridman sits down with someone like Andrej Karpathy or Yann LeCun, you get a level of detail and nuance that no ten-minute explainer can provide.
Reddit and Online Communities
r/LocalLLaMA
This is my favorite source on this entire list. My work centers on local models for data privacy and on processing large collections of sensitive data, and this community is the single best place I have found to stay current on that topic. The discussions cover new model releases, quantization methods, hardware configurations, and real-world performance comparisons. The contributors include people who are running these models in production. If local deployment is relevant to your work, this subreddit is indispensable.
r/ClaudeAI
Local models are essential to my research, but when I need the power, Claude is my absolute go-to tool. I use it for writing code, building pipelines, and working through complex problems. This subreddit is where I track new features, model updates, prompting strategies, and how other people are integrating Claude into their workflows. It is a useful complement to the official documentation.
r/ollama
Ollama is an open-source tool that makes it straightforward to download and run language models locally on your own computer. It handles the technical complexity of model management, so you can get a model running with a single command in your terminal rather than configuring dependencies manually. I still think it is one of the best starting points for anyone getting into local models. This subreddit tracks new features, model compatibility, troubleshooting, and practical deployment tips from the user community.
Hacker News
Hacker News is where I discover new sources. The community surfaces technical writing, research announcements, product launches, and long-form essays that I would not encounter through any other channel. The comment threads are often as informative as the articles themselves. I do not browse it directly; I have OpenClaw pull the top ten daily trending stories and add them to my daily digest. That gives me a curated snapshot without the time cost of scrolling through the site myself.
Learning Platforms
DeepLearning.AI
Andrew Ng’s platform. Structured coursework, expert interviews, and breakdowns of recent research papers. This is where I go when I want to understand the methodology behind an advance rather than just the headline. They also have a strong YouTube channel, but the platform itself offers short courses and specializations that go deeper than any video can.
I will continue to add to this list. If you have recommendations, I welcome them.