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Making artificial intelligence practical, productive & accessible to everyone. Practical AI is a show in which technology professionals, business people, students, enthusiasts, and expert guests engage in lively discussions about Artificial Intelligence and related topics (Machine Learning, Deep Learning, Neural Networks, GANs, MLOps, AIOps, LLMs & more). The focus is on productive implementations and real-world scenarios that are accessible to everyone. If you want to keep up with the latest advances in AI, while keeping one foot in the real world, then this is the show for you!
Based on 5 episodes we listened to · September 2026
Practical AI separates AI claims from their implementation details, making models, tools, and deployment choices easier to evaluate.
Practical AI keeps asking what new AI systems actually do once they leave the announcement stage. Daniel Whitenack and Chris Benson move between co-host discussions and guest interviews, testing technical claims against implementation details, product design, and operational…
DeepSeek's app is not its model; Daniel and Chris give your security worry a better target.
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Short reviews from the PlayNext desk, based on the episodes we processed.
A malicious npm package and an exposed source map turn Claude Code into a concrete supply-chain warning.
Practical AI turns an April 1, 2026 discussion into a useful case study in how AI tooling can break at the package level. Daniel Whitenack, CEO at PredictionGuard, and Chris Benson, a principal AI and autonomy research engineer, walk through the collision between a malicious…
The episode judges AI by how quickly it becomes useful.
Daniel Whitenack, Chris Benson, and Scott Meyer keep the focus on a simple test: AI matters most when it helps people reach value quickly, without making them learn a new interface. That framing keeps the discussion grounded in product design rather than hype.
A practical look at where genAI stops and the surrounding tools begin.
This episode asks a basic but useful question: when does generative AI actually help, and when is it just an extra layer? Chris Benson and Daniel Whitenack keep pulling the focus back from the flashy model to the work itself, so the conversation stays tied to orchestration,…
A practical reset on what agents do, and what tool calling actually is.
The episode treats tool calling less like a trick and more like a practical boundary between the model and the system around it. Daniel and Chris keep the focus on how an LLM actually connects to another system, so the conversation stays concrete instead of drifting into jargon.
A careful DeepSeek check that separates model from product and keeps the security question grounded.
The piece treats the DeepSeek discussion as a useful check on what is actually happening and where the real risk sits. Chris and Daniel do a clear job of separating the model from the hosted product, which matters for security, data handling, and the concerns people often…
Video generation is treated as a motion problem, with Genmo's architectural evolution front and center.
Video generation is treated here as a motion problem, not a novelty. Daniel Whitenack and Chris Benson speak with Paras Jain of Genmo about why motion sits at the center of the work, how that idea keeps resurfacing at Genmo, and how three large pre-training runs have shaped its…
A grounded Practical AI discussion of shared musical emotion, personal taste, and the commercial pull of personalization.
Practical AI gives music personalization a useful pressure test by asking where private feeling ends and shared reaction begins. The discussion works best when it stays close to everyday listening: Queen Greatest Hits, musicals in the background, familiar songs repeated until…
A grounded look at why AI search evaluation gets tricky when more than one product can be right.
Practical AI takes a grounded look at how AI systems become usable products, not just polished demos. It starts with Kurt Mackey's explanation of Fly and the limits developers can run into after shipping a straightforward Heroku- or Vercel-style app, then settles into the…
Markets, macro, AI, and crypto in motion.
Practical AI hosts Daniel Whitenack and Chris Benson examine an April 1, 2026 Claude Code leak involving exposed Anthropic source code and a malicious axios package.
An interview with an economics professor discussing the impact of vibe coding on open source through an economic lens.
Practical AI hosts Daniel Whitenack and Chris Benson interview Scott Meyer, founder and CEO of Chip (chip.ai), a platform for building low-code and no-code AI agent workspaces, about the landscape of AI assistant tools and why only 7% of businesses have formally adopted AI despite 50% of Americans using it weekly.
Hosts Daniel Whitenack and Chris Benson discuss bad use cases for generative AI, warning against fully autonomous agents, time series forecasting, complete code generation, high-throughput applications, and multilingual contexts.
Practical AI hosts Daniel Whitenack and Chris Benson cover the week's AI news in a fully connected episode, focusing on Elon Musk's bid to acquire OpenAI and Sam Altman's public rebuff, the OpenAI Super Bowl commercial and what it cost relative to OpenAI's overall infrastructure spend, and the broader question of what OpenAI's future looks like if it is neither acquired nor achieves profitability.
Practical AI hosts Daniel Whitenack and Chris Benson dive deep into DeepSeek R1, the Chinese AI startup's model that achieved near-parity with OpenAI's best reasoning models at a reported training cost of just $5-6 million.
Practical AI hosts Daniel Whitenack and Chris Benson interview Paras Jain, CEO of Genmo, about the state of video generation AI in early 2025 following the release of OpenAI's Sora.
Jeff Smith, Founder and CEO of Chirp, explains how music listening may provide a passive signal about mood and workplace well-being.
Practical AI interviews Shopify’s Mike Tamir and Mike Collier about Sidekick, an AI assistant for merchants that answers Shopify questions and supports business tasks.
Practical AI interviews Alexandre de Foussay, a scientist and co-founder of Paris nonprofit lab Qtai, about Moshi, a speech-and-text model for real-time dialogue.
Practical AI examines why AI workflows that work "half the time" in low-code tools or notebooks often fail when moved toward production.
A sampler episode featuring segments from other Changelog podcasts discussing AI's impact on software development, including the Devon project, CodeGent AI, and Elixir's role in an AI-driven world.
A news roundup episode covering recent AI developments, including an FCC ruling on AI voices in robocalls, along with learning resources.
An interview with the CEO of LanceDB about their vector database and AI tooling.
Hosts discuss practical AI topics with a guest from Base 10 about model hosting and inference.