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In this episode I break down OpenAI's latest rollout, Dots, an always-on agent that lives inside ChatGPT and keeps doing things even after you've closed the chat. I cover what a Dot is, how it works, the logistics around pricing and access, the rules and guardrails, and what Dots can do. I also share my thoughts on abstraction in the agentic era and what the launch of Dots (and Meta Muse) speaks to in the bigger picture. Main Topics Covered My experience with DotsSo...what is a Dot?The personification of AI agentsHow it worksLogistics: Access and "deeper work"Rules and guardrailsWhat can Dots do?Meta Muse vs. OpenAI DotsAbstraction and the agentic eraHow I used AI this week: Gemini inside of Gmail Links & Resources for This Episode Watch Nate Herk’s comparison of Dots and Meta MuseListen to Ep. 61: Everyone Gets an Agent: Meta Muse and the Ongoing Race to AGI Read this episode's Curious CompanionSubscribe to the Prompting Curiosity newsletterSubmit a QuestionVisit the WebsiteFeeling curious AND generous? Click here to support the podcast. Chapters (00:00:05) - Intro and episode setup(00:00:38) - What is Dots? Overview and naming(00:03:08) - Personification and avatar design critique(00:03:50) - How Dots works: setup and communication(00:05:32) - Single chat thread design and workflow(00:06:32) - App connections and capabilities(00:07:20) - Pricing and plan availability(00:08:34) - Cloud hosting and technical infrastructure(00:09:30) - One dot per account policy(00:09:53) - Safety rules and guardrails(00:11:21) - Prompt injection and monitoring safeguards(00:12:35) - What can Dots actually do? Use case breakdown(00:14:27) - Comparing Dots to Metamuse(00:16:12) - Abstraction and the shift away from prompting(00:17:39) - Closing thoughts on Dots(00:18:07) - How I used AI this week(00:19:54) - Wrap up and outro

No gatekeeping here. In this episode I share my current AI tech stack, breaking down everything from my daily driver to my “once in a while but good to know about” tools. AI is inside of literally everything these days, and the majority of it isn’t helpful at all. I’m here to share the things that I actually use and that truly move the needle for me. Main Topics Covered My daily driver for AIHow I use the other LLMsMy favorite AI notetakerThe best AI voice dictation softwareHow I actually use DescriptMy favorite use-case for Gemini NotebookHow I used AI this week: Claude Design to mock up a website Links & Resources for This Episode Listen to Ep 45: Stop Typing, Start Speaking: Wispr Flow Explained Listen to Ep 25: Google NotebookLM: The Best AI Tool You've Never Heard OfRead this episode's Curious CompanionSubscribe to the Prompting Curiosity newsletterSubmit a QuestionVisit the WebsiteFeeling curious AND generous? Click here to support the podcast. Chapters (00:00:05) - Intro and Episode Overview(00:01:22) - AI Limits and Rapid Change(00:02:20) - Claude as Daily Driver(00:03:37) - Granola: AI Note Taker(00:06:05) - Whisper Flow: Voice Dictation(00:07:51) - Descript: AI Video Editing(00:09:44) - Google Notebook LM / Gemini Notebook(00:12:28) - Wrapping Up the Tech Stack List(00:13:11) - How I Used AI This Week: Claude Design(00:16:49) - Closing and Newsletter Info

I recently used Claude to write an entire website proposal in about an hour, and the entire process revealed some pretty stark differences between corporate and creator AI use cases. In this episode I explain why outsourcing the final product is a great choice when the goal is convention, and an absolute no-no when convention is the goal is your voice. Main Topics Covered OpenAI is retiring Custom GPTs on December 11th Boris Cherney's Thread on how he prompts Claude Sonnet 5.5 benchmarks and cost Using Claude to write an entire website proposal Corporate: convention is the goal Creator economy: voice is the goal You cannot and should not automate your voice Hope for AI in the corporate world How I used AI this week: Delilah learning Japanese with ChatGPT Links & Resources for This Episode Listen to Ep. 11: WTF is a Custom GPT? Read OpenAI help article about migrating Custom GPTs to Plugins Read this episode's Curious Companion Subscribe to the Prompting Curiosity newsletter Submit a Question Visit the Website Feeling curious AND generous? Click here to support the podcast. Chapters (00:00:05) - Intro and Episode Overview(00:01:01) - Update: Custom GPTs Retiring(00:02:49) - Boris Cherney on Prompting Claude Naturally(00:04:44) - Sonnet 4.5 Benchmarks and Cost Savings(00:05:19) - Writing a Corporate Proposal with Claude(00:07:13) - Corporate vs Creator AI Use Cases(00:08:42) - Speaking Gigs and Conference Blurbs(00:10:35) - How I Use AI This Week: Learning Japanese with ChatGPT(00:11:57) - Outro, Ratings, and Newsletter

In this episode I cover how AI access and permissions have changed since ChatGPT launched back in 2022, from zero connections and basically zero worry to the current YOLO approach of vibe coders and early adopters. I share what I currently give Claude access to, what my biggest concern is, and offer a read/write/act framework that you can use to decide what your AI should touch. Main Topics Covered How AI access sentiment has changed over 4 years2022: ChatGPT with zero connections2023: Bard connects to Gmail, Docs, Drive, MapsMCP and the "AI in my email" eraAgentic AI, Claude Code, and Claude CoworkWhat I currently give Claude access toPrompt injection: my biggest concernRead/write/act frameworkPrivacy, client data, and benefit vs. riskHow I used AI this week: Claude-Descript connector and pulling quotes from transcripts Links & Resources for This Episode Listen to Ep. 51: WTF is MCPRead this episode's Curious CompanionSubscribe to the Prompting Curiosity newsletterSubmit a QuestionVisit the WebsiteFeeling curious AND generous? Click here to support the podcast. Chapters (00:00:05) - Intro and Episode Overview(00:01:32) - Early AI Access: ChatGPT and Bard Origins (2022-2023)(00:04:16) - MCP and the Rise of Agentic AI (2024-2025)(00:06:23) - Claude Cowork and Mainstream Agentic Adoption(00:08:23) - Personal Evolution in Granting AI Access(00:10:15) - Prompt Injection Risks Explained(00:11:20) - A Framework: Read, Write, Act and Whose Data(00:13:38) - Privacy Concerns vs Personal Comfort(00:15:01) - Training Data Concerns(00:15:28) - Handling Clients' and Others' Data(00:17:08) - Healthcare, Therapy, and Sensitive Data Gray Areas(00:18:34) - Benefit vs Risk: The Big Picture on AI Access(00:20:26) - Final Thoughts on What Your AI Should Access(00:20:58) - How I Used AI This Week: Claude and Descript(00:23:10) - Outro and Sign Off

Is AI actually going to kill us all?There's been a ton of talk about this topic in the media lately, and so I'm dedicating an episode to sharing my thoughts. Spoiler alert, no, I don’t think that AI is going to kill us, but that doesn’t mean that this technology has no risk. I walk through the Jacob Coxon resignation thread and the fact that Anthropic's own alignment team admits there's genuine risk, then get into why the doom talk feels more irresponsible than credible. I don't think regulation is coming to save us anytime soon, but to me, the answer isn't fear, it’s curiosity. Main Topics Covered Directly answering the question: Will AI kill us all? Jacob Coxon's resignation thread on X Evan Hubinger's tweets on AI risk Defining superintelligence and recursive self-improvement (RSI) Moving the goalposts from AGI to superintelligence AI's coding and hacking capabilities Jake Handy's prediction about a coming AI crisis Why the AI-doom talk feels irresponsible Regulation and data centers as oil Dario Amodei's blog post Change doesn't happen in a vacuum How I used AI this week: Replacing my Jeep’s thermostat housing Links & Resources for This Episode Read Jacob Coxon's resignation thread on X Listen to Ed Zitron's podcast episode about AI and regulation  Read Dario Amodei's blog post, "We Must Pace the Frontier"  Read this episode's Curious Companion Subscribe to the Prompting Curiosity newsletter Submit a Question Visit the Website Feeling curious AND generous? Click here to support the podcast. Chapters (00:00:05) - Intro and today's topic: will AI kill us all?(00:03:29) - Background: Jacob Cox's resignation and Anthropic's Evan Hubinger(00:05:18) - Defining superintelligence and recursive self-improvement(00:07:19) - Why the doom claims seem overblown right now(00:10:39) - Hacking risks and predictions of a coming AI-caused crisis(00:12:15) - Irresponsible messaging and the case for transparency(00:13:24) - Government incentives, profit motives, and the China narrative(00:17:24) - Dario Amodei's regulation plan and skepticism about government action(00:19:10) - Staying a realist: humans, open source, and hope for the future(00:21:11) - How I Use AI This Week: the rattling thermostat story(00:23:17) - Closing thoughts and sign-off

In this episode I talk about the eleventy billion AI models that dropped in the past few weeks, including Anthropic's Fable 5.1 and Mythos 5.1, OpenAI's GPT-6 Astra, and Meta's new Muse agent. I get into former Anthropic researcher Jacob Coxon's resignation and his warning about AI risk, plus what it actually means that OpenAI just rated Astra "Critical" for cybersecurity capability. I also share why I think Meta Muse is a terrible idea and what all of these model drops say about the direction AI is heading. Main Topics Covered Jacob Coxon's resignation and AI regulation warningsAnthropic's Fable 5.1 and Mythos 5.1 updatesOpenAI's GPT-6 Astra releaseAstra's "Critical" cybersecurity capability ratingAGI throwback to Episode 7Meta Muse launch: everyone gets an agentMeta Muse access, pricing, and privacy detailsWhy Meta Muse is a terrible ideaHow I used AI this week: Gemini AI Overviews Links & Resources for This Episode Read Jacob's statements on X Listen to Ep. 7: What is AI? Subscribe to the Prompting Curiosity newsletterSubmit a QuestionVisit the WebsiteFeeling curious AND generous? Click here to support the podcast. Chapters (00:00:05) - Intro and welcome(00:00:38) - Jacob Coxon's anthropic resignation statement(00:01:41) - Reflections on AI regulation and motives(00:04:56) - Recent model drops overview(00:06:14) - Anthropic's Fable and Mythos updates(00:07:25) - OpenAI's GPT-6 Astra and computer use capabilities(00:10:01) - Astra's critical cyber capability rating(00:11:27) - AGI claims and the race to super intelligence(00:13:46) - Meta's Muse agent launch and concerns(00:15:13) - Muse's privacy and data risks(00:16:44) - Who Meta Muse targets and risks to users(00:17:38) - Companies' true priorities: money over safety(00:18:23) - How I used AI this week: Gemini overviews(00:20:56) - Wrap up and outro

Inspired by a podcast episode my girlfriend recently listened to, I use this episode to tackle the question: “Is it cheating if you use AI”? Using the definition of cheating as a springboard, I break down why the school-and-sports framing most of us default when it comes to cheating, and why the topic warrants a more nuanced approach when considering AI. Along the way I discuss the role of the system at large, and round the episode out by diving into an equally exciting topic for me: how AI use affects trustworthiness. Main Topics Covered AI as holding up a mirrorDefining cheatingWhy the school/sports rulebook framing doesn't fit AIWhy AI is an easy punching bag right nowBreaking down disclosure, rules, and trustQuestions worth askingIs someone less trustworthy if they use AI?How I used AI this week: Reverse image search to find desk assembly instructions Links & Resources for This Episode Listen to Ep. 3: Is ChatGPT Killing Creativity?Read this episode's Curious CompanionSubscribe to the Prompting Curiosity newsletterSubmit a QuestionVisit the WebsiteFeeling curious AND generous? Click here to support the podcast. Chapters (00:00:05) - Prompting Curiosity: The AI Curious(00:00:38) - Is It Cheating If You Use AI in Your School?(00:02:39) - Is It Cheating to Use AI?(00:03:39) - Is It Cheating If You Use AI?(00:06:47) - I Don't Care If You Use AI(00:07:44) - Does AI Make You Cheat?(00:10:06) - Why Do We Care About AI Cheating?(00:10:59) - Is It Cheating if You Use AI?(00:14:40) - How I Used Claude for AI This Week(00:16:24) - Crowning the Curious: A Tech Podcast

In this episode I break down WTF a frontier model actually is, starting with what most people assume the term means versus the real technical definition. I cover how "frontier model" got coined in 2023, and why the term even came about at all, and how self-naming benefitted its creators. A bit of an etymology episode, this episode is more about the background of the term, and explores how new open weight models are challenging the definition. Main Topics Covered WTF is a frontier model?Most people's assumption vs. the technical definitionOrigin of the term: the Frontier Model Forum (July 2023)FMF's official definition of "frontier model"EU AI Act's compute-based definitionWho made the rulesFMF today: expanded membership, safety research, AI Safety FundWhy forming the FMF actually benefited these companiesOpen weight models and "frontier-grade" labelsHow I used AI this week: building my mom a workout tracker Links & Resources for This Episode Listen to Ep. 55: Weight, What? An Introduction to Open Source AI ModelsRead this episode's Curious CompanionSubscribe to the Prompting Curiosity newsletterSubmit a QuestionVisit the WebsiteFeeling curious AND generous? Click here to support the podcast. Chapters (00:00:05) - Prompting Curiosity: The AI Curious(00:00:38) - Predicting Curiosity: Frontier Models(00:01:30) - The Frontier Model Forum and its Impact(00:05:56) - Frontier: The Frontier Group(00:08:07) - What's the Frontier?(00:10:00) - How I Used Claude for AI This Week(00:14:15) - AI Curious: A Podcast About Curious People

In this episode I break down the best way to teach Claude how to complete specific tasks in a repeatable way: Claude Skills. I explain how Skills work, walk through how to build your own, and cover the real differences between Skills and Projects. As per always, the best way to understand this stuff is to try it yourself first hand, but hopefully this episode will give you some ideas and a nice jumpstart. Main Topics Covered What Claude Skills areDefinition of Skills (from Anthropic)How Skills load and fire automaticallyProgressive disclosure explainedClaude's skill directory and the GitHub skills repoHow to create a skillCreating skills via SettingsMy client-debrief skill exampleSkills vs. ProjectsHow I used AI this week: Granola and the Granola MCP Links & Resources for This Episode Listen to Ep 51: WTF is MCP? Listen to Ep. 45: Stop Tying, Start Speaking: Wispr Flow ExplainedRead this episode's Curious CompanionSubscribe to the Prompting Curiosity newsletterSubmit a QuestionVisit the WebsiteFeeling curious AND generous? Click here to support the podcast. Chapters (00:00:05) - Prompting Curiosity: The AI Curious(00:00:38) - Episode 58(00:02:24) - Anthropic Skills: What is a Skill?(00:08:10) - Claude 2.8: Creating a Skill(00:14:56) - Skills and Projects in ChatGPT(00:17:16) - How I Use AI to T transcribe my Calls(00:21:33) - How to Use AI in the Podcast

In this episode I get into AI watermarking, starting with Claude's new invisible watermark and Substack's "Scan for AI text" feature with Pangram. I break down the EU AI Act rules driving this, how OpenAI, Google, Meta, Microsoft, Mistral, and xAI are (or aren't) handling it, and my thoughts as to the actual motives behind why companies are rolling out these changes. Main Topics Covered AI models escaping sandbox testingSubstack's "Scan for AI text" feature with PangramAnthropic's new invisible Claude watermarkThe EU AI Act and Article 50How OpenAI, Google, Meta, Microsoft, Mistral, and xAI stack upChina's mandatory AI disclosure lawsProblems with AI detectors and false positivesThe "why" behind watermarkingHow I used AI this week: AI-generated sound effects in Descript Links & Resources for This Episode Listen to Ep. 2: ChatGPT and the Environment: Energy, Water, and Carbon Emissions Read this episode's Curious CompanionSubscribe to the Prompting Curiosity newsletterSubmit a QuestionVisit the WebsiteFeeling curious AND generous? Click here to support the podcast. Chapters (00:00:05) - Promoting Curiosity: The AI Curious(00:00:38) - AI Watermarking(00:04:43) - AI Watermarking(00:06:16) - Anthropic's Cloud Watermarking(00:07:58) - Deepfakes and Text Watermarking(00:13:08) - AI Watermarking: Concerns(00:18:15) - How I Used AI This Week(00:19:15) - Questions For Curious People

In this episode we’re celebrating one year of Prompting Curiosity and I’m sharing my unfiltered thoughts about where I stand on AI. I get into why the discourse on Threads is such a mess, why AI is an easy target given the enshittification of everything, and why capitalism (not AI capability) is the real driver behind the issues at hand. No predictions, no hype, just my two pennies on data centers, adoption, and why I'm not willing to give up something that genuinely helps my brain and my business. Main Topics Covered Celebrating one year of Prompting CuriosityRiffing on unfiltered thoughts about AI in August 2026The Threads discourse on AIGeneral thoughts on AI and unequal benefitsAI as capitalism's easy scapegoatFunny money and bubble talkFin-tech burnout and no predictionsForced adoption and the "no great use case" problemAI's impact on search and generational usage differencesWhy I'm not willing to give up AIBlaming the individual vs. corporate accountabilityWhat amazes meHow I used AI this week: Google NotebookLM use-case for blogging Links & Resources for This Episode Listen to Ep: 25 - Google NotebookLM: The Best AI Tool You've Never Heard OfRead this episode's Curious CompanionSubscribe to the Prompting Curiosity newsletterSubmit a QuestionVisit the WebsiteFeeling curious AND generous? Click here to support the podcast. Chapters (00:00:05) - Prompting Curiosity: The AI Curious(00:00:38) - It's Been One Year(00:02:13) - Pushing the Discussion On Social Media(00:03:27) - AI: Good or Evil?(00:05:16) - Should We Stop Developing Artificial Intelligence?(00:06:01) - We Do Not Need These Huge Data Centers(00:06:58) - Can Anyone Predict The Future With AI?(00:12:25) - On AI and What We As A Society Do With It(00:18:27) - Teaching and the Future of AI(00:23:23) - The Future of AI Is Full of Fear(00:25:44) - How We Used AI This Week(00:26:40) - Curious About AI

In this episode I dig into open weight and open source AI models: what they actually are, why they keep showing up in AI news, and why I think they’re central to future-proofing our AI usage. I cover a bit of the technical aspect, the economic factors at play, and make the case that open weight models are a true hedge against getting stuck forever paying whatever a handful of AI companies decide to charge while simultaneously worrying that access could just be stripped away at any moment. Main Topics Covered What open weight and open source models actually areWhy open models matter: market pricing and individual sovereigntyOpen weight vs. open source definitionsWhy it's called "open weight" (parameters refresher)Who's releasing open weight modelsWhy companies release open weight modelsWhy individuals would choose an open modelHow to try an open weight model yourselfNvidia CEO Jensen Huang's open letter and Anthropic's responseHow I used AI this week: Claude's Scheduled Tasks feature Links & Resources for This Episode Listen to Ep. 17: Will ChatGPT Get Old Navy'd?Read this episode's Curious CompanionSubscribe to the Prompting Curiosity newsletterSubmit a QuestionVisit the WebsiteFeeling curious AND generous? Click here to support the podcast. Chapters (00:00:05) - Promoting Curiosity: The AI Curious(00:00:38) - Requiring Curiosity(00:01:07) - Open Source vs Open Weight(00:01:56) - Open Weight Models: Do They Even Work?(00:08:05) - What is an Open Weight Model?(00:12:43) - What are the Open Weight Models(00:14:16) - Open Weight vs Frontier: The Differences(00:17:14) - Open vs Closed PPT: The Pricing(00:18:59) - On OpenAI and its "free" models(00:21:11) - Choosing the right cloud provider(00:22:09) - Local vs. Hosting: More Tech, More Privacy(00:23:13) - Open Weight Models: How to Try them(00:26:02) - Nvidia's CEO on OpenAI and the Future of AI(00:28:51) - How I Used AI This Week(00:31:25) - Coming Soon: The Curious Companion

In this episode I get curious about Perplexity, a name I'd heard used a ton in the AI space but never actually knew what the heck it was. I break down what it is, how its citation-first approach set it apart from ChatGPT early on, and what it can actually do today: Deep Research, Labs, and its push into the agent space with Computer, Brain, and Comet. I also cover the legal issues that are serving as a sneak peek into the future of agent wars, and give my honest take on whether Perplexity is worth using. Main Topics Covered Why this episode What Perplexity actually is Pro and Max pricing tiers Who Perplexity is actually for Perplexity's launch and the citations angle The lawsuits over copyright infringement Live search, Deep Research, and Labs Computer: Perplexity's move into agents Brain, Comet, and the Amazon lawsuit Should you bother with it How I used AI this week: Building a custom content tracker Links & Resources for This Episode Listen to Ep. 52: AI Agents Explained (For Non-Coders) Read Curious Companion Ep. 52: AI Agents Explained (For Non-Coders)  Read this episode's Curious Companion Subscribe to the Prompting Curiosity newsletter Submit a Question Visit the Website Feeling curious AND generous? Click here to support the podcast. Chapters (00:00:05) - Prompting Curiosity: The AI Curious(00:00:38) - Perplexity(00:01:49) - Beyond the Chatbot: Perplexity's Real-Time C(00:07:43) - Microsoft's Computer: An AI Agent in Microsoft 365(00:10:59) - Brain and Comment: Perplexity's AI(00:11:49) - Amazon Is Suing Perplexity Over AI-Backed Browser(00:15:11) - How I Used AI to Build a Content Creation Tracker (Again)(00:19:10) - PODCAST: Help Me Find The Podcast(00:19:37) - Curious Companion

After years of not knowing what to do with it, I finally decided to give deep research mode a try. In this episode I break down what deep research actually is, how it works under the hood, and how ChatGPT, Gemini, Claude, and Grok stack up against each other. I also walk through real use cases, prompting tips for getting a better report, and a safety reminder about hallucinations and bad citations. Main Topics Covered What deep research actually isRelease datesHow deep research worksPrompting tipsExample use cases for deep researchThe tool landscape: ChatGPT, Gemini, Claude, GrokHow to use Research modeSafety reminder: hallucinations and bad citationsHow I used AI this week: Identifying podcast topics in the AI space Links & Resources for This Episode Listen to Ep. 52: AI Agents Explained (for Non-coders)Listen to Ep. 25: The Best AI Tool You’ve Never Heard OfRead this episode's Curious CompanionSubscribe to the Prompting Curiosity newsletterSubmit a QuestionVisit the WebsiteFeeling curious AND generous? Click here to support the podcast. Chapters (00:00:05) - Prompting Curiosity: The AI Curious(00:00:38) - prompting curiosity(00:01:26) - Deep-Research and the Future of AI(00:02:06) - Deep Research: What is it?(00:07:12) - Examples of Deep Research Use Cases(00:08:22) - Examples of Things You Can Use AI to Research(00:14:17) - Writing a Legally Literate Report(00:15:08) - How I Used To AI(00:16:27) - The Curious Companion: A Podcast About AI

In this episode I break down what AI agents actually are and how they differ from regular chat tools. Before diving in, I run through the recent flood of new model releases from Anthropic, OpenAI, and xAI, including the full story on the Fable 5 suspension and reinstatement. The episode ends with a practical mini cheatsheet on when an agent is actually the right tool for your workflow. Main Topics Covered The eleventy billion releases: Anthropic, OpenAI, and xAIFable 5: the jailbreak, the suspension, and the fixGPT-5.6, Grok 4.5, and what this means for everyday usersSo WTF is an agent?"Building an agent" and "spinning up agents" explainedIs regular Claude an agent?Human-in-the-loopYou're probably already using oneDo you need an agent? (mini cheatsheet)How I used AI this week: Tom's Switzerland itinerary Links & Resources for This Episode Listen to Ep. 11: WTF is a Custom GPT?Listen to Ep. 48: When the Most Powerful Model Comes With a BouncerRead this episode's Curious CompanionSubscribe to the Prompting Curiosity newsletterSubmit a QuestionVisit the WebsiteFeeling curious AND generous? Click here to support the podcast. Chapters (00:00:05) - Prompting Curiosity: The AI Curious(00:00:38) - Caution: Agents in the AI World(00:02:41) - AI Wars: The Race to AGI(00:03:24) - Amazon's Fable 5 and Mythos 5: What Happened(00:05:27) - OpenAI and Anthropic: The Fight for C# Talent(00:07:17) - Grok 4.5.0 Review(00:10:05) - An Agent in the Machine Learning World(00:13:02) - Building an agent: Low-(00:14:04) - Spinning Up Agents: What Is It?(00:15:19) - Cloud: Is Regular Claude an Agent?(00:18:24) - ClAUDE: Permission vs. Planning in the Cloud(00:21:31) - ChatGPT: An Agent for the World(00:22:57) - Agents: Their Use Cases,(00:25:55) - Using AI For Fun