Podcasts about privacy concerns

  In this episode of the 'Cybersecurity Today: The Month in Review' show, host Jim welcomes regular guests Laura Payne and David Shipley, along with newcomer Anton Levaja. The trio dives deep into various cybersecurity stories, analyzing trends, threats, and recent incidents. Topics include the intriguing Mystery Leaker exposing cyber criminals, the rise and sophistication of LockBit ransomware, the devastating ransomware attack on Coinbase and their bold counter-response, and the physical dangers faced by cryptocurrency entrepreneurs. The episode also highlights the innovation in law enforcement tactics and the pressing need for better cybersecurity awareness and education. They wrap up on a hopeful note, showcasing a young scout's inspiring project on cyber fraud prevention that gained support from the local police. 00:00 Introduction and Panelist Welcome 00:38 Show Format and Story Introduction 01:28 The Mystery Leaker Story 03:35 Law Enforcement and Cyber Crime 10:51 Coinbase Ransomware Incident 18:04 Physical Threats in the Crypto World 24:56 Operation Shamrock and Organized Crime 25:19 Breaking News: Kidnapping Mastermind Arrested 26:18 Quishing: The Clever Side of Cybercrime 27:11 QR Code Scams and Consumer Protection 31:08 Generational Differences in Cyber Threats 32:05 The Evolution of Cyber Attacks 38:40 Physical Crime in the Digital Age 41:10 Law Enforcement and Cybersecurity 43:55 Government Surveillance and Privacy Concerns 46:08 Feel-Good Story: Young Cybersecurity Advocate

In this episode of Project Synapse, Jim and Marcel navigate a rapidly evolving AI landscape while John appears intermittently through AI-generated clips, with his permission. They delve into intriguing developments at Google Labs, including a mysterious AI model that briefly appeared and vanished. The discussion highlights the differing ambitions of Google and OpenAI, examining Google's focus on creating a comprehensive information hub versus OpenAI's broad-reaching aspirations, like the recent OpenAI for Business initiative. A central theme is the persistent rivalry in AI, specifically between Gemini and ChatGPT, while also touching on niche players like Anthropic's Claude and Perplexity. The conversation takes a deep dive into the complexities of integrating AI into daily life, the potential benefits, and the significant risks, including issues surrounding privacy and identity. Counterpoint to their technical musings is a look at the societal impacts of AI, including job displacement and the need for universal basic income. Finally, the hosts ponder the unsettling yet fascinating future where personal identity might be verified through biometric scanning, as proposed by World ID. 00:00 Introduction and Setting the Scene 00:40 Unexpected Developments in AI 01:53 Google vs. OpenAI: The Rivalry 03:55 AI Integration in Everyday Life 04:36 The Rise of Niche AI Players 05:42 Personal Experiences with AI Tools 12:10 The Future of AI and Privacy Concerns 17:20 The Evolution of AI and Robotics 26:53 Smart Home Integration and Standards 34:21 The Illusion of Choice in Technology 36:13 The Privacy Paradox 36:31 The Integration of AI in Daily Life 38:52 The Rise of Deep Fakes and Identity Theft 41:35 The Future of Personal Data and Security 44:51 The Debate on Universal Identification 46:52 The Acceleration of Technological Change 52:19 The Need for Intelligent Design in AI 53:34 The Role of Governments and Corporations 01:04:11 Concluding Thoughts and Future Discussions

Find us at www.crisisinvesting.com In this episode, We delve into a wide array of current political and economic issues. We begin by discussing a revealing clip about Trump's reshoring agenda and its implications for national security and economic chaos. The conversation then shifts to broader topics such as the potential for Trump to be pushed out of office due to chaos from his policies, including tariffs and economic instability. We also discuss issues relating to data centralization, exemplified by the Palantir system, and the implications for a police state in the US. The dialogue covers historical events and their potential lessons for current geopolitical tensions, including the Russia-Ukraine situation. Furthermore, they touch on educational institutional issues, such as administrative bloat and ethics violations. The episode ends with a reflection on key historical anniversaries and their significance. Overall, this episode offers a deep dive into the intersecting realms of politics, economics, history, and technology. 00:00 Introduction and Overview 00:08 Trump's Reshoring Agenda 01:20 Chaos in Trump's Policies 02:04 US Armament Trade and China Relations 03:28 Sovereign Wealth Fund and Economic Insanity 04:48 Trump's Popularity and Deep State Opposition 07:22 Palantir and Privacy Concerns 12:47 Financial Markets and Potential Collapse 14:58 Russia-Ukraine Conflict 18:01 Harvard Professor Scandal 21:32 William F. Buckley's Legacy 26:12 Journalistic Attitudes Towards Gold and Debt Crisis 27:44 Media Bubble and Bad Judgment 30:06 The 19th Amendment and Democracy 36:17 Historical Events: Dunkirk and Midway 43:12 Current Events and Political Climate 50:44 Concluding Thoughts and Membership Information

On Episode 88, we tell you about a new bill that would ban big cash transactions, let police get some info without a warrant, and allow an immigration freeze. Plus, we discuss our new report on Canada's Most Censorious Bylaws, and a new law aimed at cultivating Quebec values. Stories and cases discussed in this week's episode: CCF Presents First-Ever Municipal Muzzle Award for Canada's Most Censorious BylawBILL C-2Can Ottawa solve the problem of millions of expiring Canadian visas?Parents upset after student's suspension for wearing 'straight pride' shirt to N.B. schoolAbbotsford school division asks former teacher to move location of book launch to avoid 'intimidating' indigenous studentsLiberals introduce sweeping border reform bill affecting warrants, large cash transfersLiberal government tables border security bill with sweeping reforms and asylum claim clampdownQuebec passes bill requiring newcomers to adopt shared valuesNot Reserving Judgment is a podcast about Canadian constitutional law hosted by Josh Dehaas, Joanna Baron, and Christine Van Geyn. The show is brought to you by the Canadian Constitution Foundation, a non-partisan legal charity dedicated to defending rights and freedoms. To support our work, visit theccf.ca/donate.

Summary:In this conversation, the hosts delve into the implications of current technology, particularly focusing on data privacy and the role of companies like Palantir. They discuss the potential consequences of extensive data collection by the government, the integration of AI in data analysis, and the impact of social media on public perception. The conversation also explores an alternative history where the US engages with the Middle East solely through business rather than military intervention, highlighting the economic ramifications of war. Finally, they touch on the future of digital privacy and the potential shift towards a cashless society.Takeaways:Palantir Technologies is a key player in data analytics for government and commercial use.Integrating AI with data analytics can revolutionize how data is processed and understood.Extensive data collection raises significant privacy concerns for citizens.Social media serves as a double-edged sword, providing real-time information but also fostering misinformation.Imagining a world where the US engages with the Middle East through trade could lead to a more stable geopolitical landscape.Military spending is a significant economic burden that could be redirected towards domestic needs.The future of currency may lean towards digital and blockchain technologies, impacting traditional cash usage.Understanding the implications of data privacy is crucial in the digital age.The addictive nature of social media can hinder real-world interactions and perceptions.Addressing basic societal issues could alleviate tensions and improve public trust. Chapters:00:00 Introduction to Upgrade America Podcast01:23 Exploring Palantir Technologies and Data Privacy Concerns04:01 Trump's Data Integration Project with Palantir08:31 The Role of AI in Data Collection and Privacy11:44 Debate on Iran's Nuclear Capabilities and US Foreign Policy16:43 Imagining a Business-First Approach in the Middle East20:52 The Cost of War and Military Business25:27 Navigating the Digital Age and Privacy Concerns

In this episode, I explore the privacy implications of using AI apps like ChatGPT and Claude on mobile devices. I discuss why ChatGPT's requirement for Google Play Store login and audio recording storage led me to Claude on my GrapheneOS device. I also cover my daily app setup, Windows telemetry blocking with SimpleWall, macOS privacy with Little Snitch, and the potential of System76 Linux laptops.In this week's episode:Privacy comparison between ChatGPT and Claude AI appsChatGPT's audio recording storage and data export concernsGrapheneOS setup without Google Play Store loginUsing FUTO Keyboard and FUTO Voice for local transcriptionEssential privacy tools: SimpleWall for Windows and Little Snitch for macOSWindows Subsystem for Linux (WSL) for developersSystem76 Linux laptops as a privacy-focused alternativeShow Links:Anthropic Claude.ai Encryption - https://privacy.anthropic.com/en/articles/10458704...Duck.ai - https://duck.aiFuto Keyboard & Voice - https://futo.org/Aurora Store - https://auroraoss.com/aurora-storeSimpleWall (Windows Firewall) - https://github.com/henrypp/simplewallLittle Snitch (macOS) - https://www.obdev.at/products/littlesnitch/GeoSpy (OSINT Tool) - https://geospy.netSystem76 Linux Laptops - https://system76.com/Mental Outlaw YouTube Channel - https://www.youtube.com/@MentalOutlawDaVinci Resolve - https://www.blackmagicdesign.com/products/davinciresolveOSINT Defense & Security Framework - https://psysecure.com/services/odsf/“██████REDACTED███”- █████████Official Website: https://psysecure.com Podcast music: The R3cluse

In this episode of 'Hashtag Trending,' host Jim Love discusses Klarna's decision to rehire human customer service staff after a failed AI initiative, Google's announcement of integrating Gemini AI into the Chrome browser, and Microsoft's fix for Linux boot issues on dual-boot PCs. Additionally, the episode touches on privacy alarms raised by Microsoft's upcoming Recall feature, which captures user activity screenshots, and the potential security vulnerabilities related to it. 00:00 Introduction and Headlines 00:24 Klarna's AI Reversal: Bringing Back Human Touch 03:37 Google's Gemini AI Integration in Chrome 05:22 Microsoft Fixes Linux Boot Issues 06:31 Microsoft's Recall Feature: Privacy Concerns 09:09 Conclusion and Upcoming Events

At NAB Show 2025, Insta360 showcases the Connect, a smart all-in-one conference camera with dual 4K lenses, AI-powered speaker tracking, whiteboard enhancement, gallery view, and advanced audio capabilities. Designed for simplicity, it supports major platforms like Zoom and Teams, requires no extra hardware, and prioritizes privacy by recording nothing internally. Qibiao Liu, Industry Business Development Manager for Insta360 delivers the impressive demo. Show Notes: Chapters: 00:08 Introduction to Insta360 02:32 Advanced Features Unveiled 04:11 Demonstrating Whiteboard Mode 07:52 Recording and Privacy Concerns 08:50 Pricing and Availability 11:10 The Role of AI in Technology 12:49 Conclusion and Wrap-Up Links: Connect - 4K AI Conference Room Camera, Dual Camera with Speaker Tracking, 14-Mic Array Support:      Become a MacVoices Patron on Patreon      http://patreon.com/macvoices      Enjoy this episode? Make a one-time donation with PayPal Connect:      Web:      http://macvoices.com      Twitter:      http://www.twitter.com/chuckjoiner      http://www.twitter.com/macvoices      Mastodon:      https://mastodon.cloud/@chuckjoiner      Facebook:      http://www.facebook.com/chuck.joiner      MacVoices Page on Facebook:      http://www.facebook.com/macvoices/      MacVoices Group on Facebook:      http://www.facebook.com/groups/macvoice      LinkedIn:      https://www.linkedin.com/in/chuckjoiner/      Instagram:      https://www.instagram.com/chuckjoiner/ Subscribe:      Audio in iTunes      Video in iTunes      Subscribe manually via iTunes or any podcatcher:      Audio: http://www.macvoices.com/rss/macvoicesrss      Video: http://www.macvoices.com/rss/macvoicesvideorss

Jessica Hyde from Hexordia joins the Forensic Focus Podcast to discuss her unique journey from Marine Corps avionics technician to digital forensics expert. Jessica shares how her military background provided transferable skills for forensic investigations, emphasizing the importance of documentation, troubleshooting, and diverse perspectives in finding truth in digital evidence. The conversation explores critical topics including IoT forensics challenges, the urgent need for timely data acquisition before evidence degrades, and the role of AI in forensic analysis. Jessica also discusses her experience teaching at George Mason University since 2016, how students' questions enhance her own knowledge, and why interdisciplinary teams create better forensic outcomes. With candid reflections on maintaining perspective while handling difficult cases and thoughts on the future challenges of policy and law keeping pace with technology, this episode offers valuable insights for both seasoned professionals and those new to digital forensics. #DigitalForensics #MobileForensics #CyberInvestigation #DFIR #AI 00:00 Introduction and Guest Welcome 01:02 Jessica Hyde's Unique Career Path 03:06 Transition to Digital Forensics 13:17 Teaching and Learning in Digital Forensics 20:12 Hands-On Forensics and IoT Devices 24:19 The Importance of Interdisciplinary Teams 27:12 Humor and Levity in Forensics 29:38 The Role of Cyber Investigators 31:07 Bias in Cyber Investigations 32:01 Generational Perspectives in Technology 33:51 Privacy Concerns in Digital Forensics 36:53 The Importance of Timely Data Acquisition 44:28 Challenges with AI in Digital Forensics 54:42 Policy and Legal Challenges in Digital Forensics 56:56 Conclusion and Final Thoughts Show Notes Hexordia - https://www.hexordia.com George Mason University - https://dfor.gmu.edu/person/jessica-l-hyde SWGDE - https://www.swgde.org  

Vitalik Buterin is the creator of Ethereum, but he's also a true Bitcoin maximalist. In this episode, Vitalik tells his story as a bitcoiner, explains why he built Ethereum, and makes use of his knowledge to predict the future of the two networks. Time stamps: Introducing Vitalik (00:01:00) Vitalik's Early Involvement with Bitcoin (00:02:22) Writing for Bitcoin Weekly (00:03:01) Bitcoin's Early Fees and Transaction Model (00:06:45) Evolving Understanding of Bitcoin (00:09:15) Bitcoin Cash and the Scaling Debate (00:10:25) Dark Wallet Project (00:14:06) Coinjoin and Privacy Innovations (00:16:41) Colored Coins and Bitcoin 2.0 (00:21:05) Transition to Ethereum Development (00:21:58) Current Layer Two Innovations (00:24:11) Scaling and Privacy Innovations (00:25:55) Ethereum's Early Criticism (00:27:05) EVM's Role in Smart Contracts (00:28:11) Challenges of Parallelization (00:29:23) Sandboxing and Security (00:30:24) Future Scaling Ideas (00:34:49) Ethereum vs NXT vs Counterparty vs Omni/Mastercoin (00:35:37) Lessons from Ethereum's Success (00:37:07) The DAO Hack and Community Resilience (00:43:16) Ethereum's Network Effect (00:45:43) Ethereum's Ecosystem Resilience (00:49:35) Decentralization vs. Scalability (00:50:41) Critique of Ethereum Killers (00:51:21) Layer One and Layer Two Dynamics (00:52:53) SideShift (00:53:21) How Vitalik Cancelled Craig Wright (00:54:51) Current Characters in Bitcoin (00:58:03) Daniel Kravisz's Views on Craig Wright (00:59:04) Manipulative Tactics in Dating Advice (01:00:34) NoOnes: Marketplace for Global South (01:01:19) Bitcoin.com News Evolution (01:02:40) Bitcoin Magazine is Now Pro Trump (01:04:37) Libertarian Shifts in Crypto (01:05:03) Ethereum Domain Name Registrations (01:06:09) Layer Two Scaling Decision (01:08:08) Hardware Requirements for Ethereum Node (01:10:45) Philosophical Questions on Scaling (01:12:01) The Dystopia Scenario (01:13:03) Importance of Full Nodes (01:14:24) Technological Innovations (01:15:27) Running Full Nodes in Ethereum (01:16:30) Privacy and RPC Trust (01:17:28) Adapting Ethereum to New Cryptography (01:19:53) Scaling Debate in Ethereum (01:22:04) Respect for Ethereum's Approach (01:23:15) Zcash and Ethereum Collaboration (01:25:00) Challenges for Zcash (01:27:04) Impact of Developer Actions (01:28:01) Scaling Solutions in Bitcoin and Ethereum (01:30:43) Defining Rollups vs. Sidechains (01:31:40) Security Implications of Drivechains (01:34:03) Transition to Proof of Stake (01:36:19) ZK Coins and Shielded Client Side Validation (01:37:53) Thoughts on TheStandard.io (01:40:03) Backing Up Coins and Holding Keys (01:42:11) Evolution of Multi-Sig Technology (01:46:43) Privacy (01:48:14) Concerns About Centralized Data Collection (01:51:10) Impact of Snowden Revelations (01:53:35) Privacy as a Key Aspect of Decentralization (01:55:49) Ethereum's Cypherpunk Roots (01:57:07) Feedback from Cypherpunks on Ethereum (02:00:42) The Inspiration Behind DAOs (02:02:07) AI and DAOs (02:02:40) Vitalik's Public Image and Price Pressure (02:02:55) Media Attention and Its Impact (02:03:43) Decentralization and Attention (02:04:03) Price Influence and Market Dynamics (02:04:59) Focus on Ethereum's Values (02:06:01) Historical Use Cases of Ethereum (02:08:28) Next Bull Market Narrative (02:09:38) DeFi Ecosystem as a Proven Use Case (02:09:45) Political Instability and Financial Security (02:12:05) Polymarket, Prediction Markets and Mainstream Adoption (02:12:20) Zero Knowledge Proofs and Privacy (02:14:20) Roger Ver (02:15:23) Principles of Freedom and Privacy (02:22:57) Critique of Blockstream's Liquid (02:24:00) Bitcoin's Role in Decentralization (02:26:15) Transition to RISC-V (02:27:37) Adoption of RISC-V (02:28:36) Redesigning Ethereum in A Time Travel Scenario (02:31:30) Challenges in Ethereum's Development (02:32:45) Ethereum and Bitcoin Relationship (02:37:02) Complementarity of Bitcoin and Ethereum (02:38:40) Does Vitalik Still Use Bitcoin? (02:41:21) Lightning Network (02:42:06) Standardization of LN Invoies (02:43:20) Privacy Concerns with Bitcoin (02:45:42) Running Lightning Nodes (02:46:52) Home-Based Bitcoin Solutions (02:48:12) Tribalism in Crypto Communities (02:48:53) Ethereum's Evolution and Ideals (02:50:06) Collaboration Between Bitcoin and Ethereum (02:51:10) Diverse Blockchain Future (02:51:45) Is Vitalik a Bitcoin Maximalist? (02:52:59) Community Values and Challenges (02:53:45) Cultural Dynamics in Cryptocurrencies (02:56:05) Layer Two Solutions for Bitcoin (02:59:31) Vitalik's Online Presence (03:00:25) Closing Remarks and Future Guests (03:01:36)

  In this episode of Hashtag Trending, host Jim Love covers major tech news stories including Elon Musk's social media platform, X, experiencing an 11 million user decline in Europe due to content moderation concerns, NVIDIA CEO Jensen Huang's remarks on China's competitive AI capabilities despite US sanctions, and Microsoft's CEO stating that AI now generates 20-30% of the company's code. Additionally, Perplexity AI's new browser aims to track user activity for personalized ads, raising privacy concerns. Tune in for insights into these crucial tech developments. 00:00 Introduction and Headlines 00:33 Elon Musk's X Faces Decline in EU Users 02:50 China's AI Capabilities Amid US Sanctions 05:02 AI's Growing Role in Software Development 06:36 Perplexity AI's New Browser and Privacy Concerns 08:06 Conclusion and Contact Information

The digital landscape is constantly shifting beneath our feet, and this week's deep dive into technology's most pressing issues reveals just how unprepared many of us are for what's coming next.Remember when you bought something and actually owned it? Google's decision to end support for Nest thermostats reminds us that in today's connected world, your purchases come with an expiration date you never agreed to. As security expert Nick Espinosa puts it, "We're becoming a quietly feudal society again," where we rent our technology until companies decide otherwise. This planned obsolescence extends beyond smart home devices to our entire digital ecosystem.Speaking of security, Espinosa's breakdown of WhatsApp's misleading "privacy features" should send shivers down your spine. While Meta promotes end-to-end encryption, they're simultaneously using your messages to train AI systems. "It's all security theater," Espinosa warns, revealing how thousands of contractors and AI systems can access your supposedly private conversations. This revelation comes alongside disturbing news about Russian IP addresses attempting to access U.S. government systems with legitimate credentials, highlighting vulnerabilities at the highest levels.On a lighter note, we explore the absurdity of modern tech culture, from the job candidate who showed up to a video interview as a breakfast plate (literally, using a filter that turned her face into eggs and toast), to the TikTok-driven phenomenon of "grounding sheets" that promise health benefits with zero scientific backing. These stories showcase how quickly digital trends can shape behavior, regardless of merit.Our whiskey tasting featured Benchmark Full Proof, a surprisingly excellent value at just $17, proving that sometimes the best things don't require unnecessary technological enhancement. As we reflect on technology's march forward, we're reminded that critical thinking remains our most valuable tool in navigating this complex digital age.Whether you're concerned about privacy, amused by digital absurdity, or frustrated by the increasingly temporary nature of your tech purchases, this episode provides insights that will change how you interact with the devices that have become essential to modern life.Support the show

In this episode of The Other Side of the Firewall podcast, Ryan Williams Sr. and Shannon Tynes discuss the intersection of cybersecurity and emerging trends in technology, particularly focusing on the risks associated with AI-generated action figures and the implications of social media on personal privacy. They explore how seemingly harmless fun can lead to significant security risks and the importance of being aware of one's digital footprint. Article: The viral AI-generated action figure trend is potentially putting your cybersecurity at risk, experts warn https://ca.news.yahoo.com/viral-ai-generated-action-figure-190750164.html Please LISTEN

It's Our 6th Anniversary Live Episode!! In this lively episode celebrating the sixth anniversary of the Minoritea Report podcast, the hosts reflect on their journey, discuss the implications of space exploration, and engage in cultural commentary. They share personal experiences and insights on relationships, while also emphasizing the importance of community engagement. The conversation is filled with humor, anecdotes, and a deep appreciation for their listeners, making it a memorable celebration of their podcasting journey. In this engaging conversation, the hosts explore various themes surrounding relationships, intimacy, and community engagement. They discuss the dynamics of post-coital responsibilities, the importance of care in relationships, and the deeper connections formed through acts of service. The conversation also delves into personal stories of wild hookups and the excitement of future plans for the podcast, including community involvement and live shows. In this episode, the hosts engage in a lively discussion about evolving their podcast, inviting community input, and sharing personal anecdotes. They explore fun segments like 'This or That,' delve into relationship dynamics, and discuss their preferences for early mornings versus late nights. The conversation touches on serious topics like privacy and nudes, while also celebrating their six-year journey together. Yo Aunteas express gratitude for their community and share their excitement for upcoming concerts and events. So, get your cups ready for Minoritea Report!   Time Stamps: Chapters 00:00 Intro 06:33 The Power of Connection in Relationships 12:36 Exploring Sexuality and Personal Preferences 18:40 Friendship and Shared Experiences 22:27 Celebrating Community and Milestones 36:38 The Power of Media Personalities 40:38 Post-Coital Responsibilities: A Discussion on Roles 45:25 Cleaning and Shared Responsibilities 51:08 Crazy Hookup Locations 56:52 Future Aspirations for Minority Report 01:01:38 The Journey of Discovery 01:05:35 The Importance of Community Engagement 01:07:38 Exploring Love Languages and Relationships 01:08:33 Cleaning and Domestic Dynamics 01:09:36 Upcoming Segments and Community Involvement 01:17:25 Celebrity Crushes and Their Charisma 01:18:38 Playful This or That Game Begins 01:21:21 Baby Oil and Secrets 01:23:01 Emergency Situations and Loyalty 01:25:30 Tattoos and Matching Outfits 01:27:53 Superpowers and Open Relationships 01:29:52 Early Birds and Night Owls 01:32:37 Nudes and Privacy Concerns 01:36:38 The Art of Leaking and Spilling Tea 01:37:38 Daytime vs. Nighttime TV Show Aspirations 01:38:34 The Future of Aunties on Screen 01:39:36 Communication and Unread Messages 01:40:36 Dating Disasters and Awkward Moments 01:41:38 Team Dynamics and Workplace Stories 01:42:42 Fear and Fun in Horror Experiences 01:44:35 Concerts and Live Performances 01:48:37 Community Engagement and Gratitude   Follow Us-   Send Your Ask Yo Aunteas Questions To: TEA LINE  844-832-5463 Aya@minoriteareport.com or DM us on Social Media   MERCH: MinoriteaReport.com   Youtube: https://www.youtube.com/channel/UCo_xKK1VRhPrVMQxm1SzTCg   Instagram: https://www.instagram.com/minoriteareport/   Facebook: https://www.facebook.com/MinoriTeaReport/   Twitter: https://twitter.com/MTeaReport   Email Us- AYA@minoriTeaReport.com   Spotify Playlist- https://open.spotify.com/playlist/0rVJtKJmesMkCgVKmJwc46?si=1455491d0a4049b5

With the Safe Act introduced on the federal level advocates are sounding the alarm about how this legislation violates human rights for marginalized communities and others. In 2022 our Blaise Bryant spoke with Keith Gurgui about some of the long-standing barriers people with disabilities face when casting their ballot.

In this episode of Double Tap, Steven and Shaun are joined by long-time friend of the show, Tim Dixon, who shares his hands-on experience with the new Plod AI note device—a compact, AI-powered assistant built for blind users. Designed as a simplified smartphone alternative, the Plod can make and receive phone calls, record conversations, send notes to email, and provide AI summaries of meetings and recordings. But is it worth the £349 price tag? Tim breaks down what works, what needs improvement, and where the device fits in an already crowded market of assistive tech. From its intuitive, tactile interface and snappy performance, to its limitations with Bluetooth pairing and occasional crashes, this review dives deep into real-world usage. Steven and Shaun also discuss the broader implications of purpose-built assistive tech versus mainstream solutions. Can niche devices still compete, or are they fighting a losing battle against AI-powered smartphones and wearables? Whether you're curious about the Plod, want to simplify your tech setup, or are just wondering where assistive hardware is headed in the age of ChatGPT and Gemini, this episode offers honest insights and balanced commentary.Get in touch with Double Tap by emailing us feedback@doubletaponair.com or by call 1-877-803-4567 and leave us a voicemail. You can also now contact us via Whatsapp on 1-613-481-0144 or visit doubletaponair.com/whatsapp to connect. We are also across social media including X, Mastodon and Facebook. Double Tap is available daily on AMI-audio across Canada, on podcast worldwide and now on YouTube.Chapter Markers:00:00 Introduction02:23 User Feedback and Experiences with Technology10:10 Challenges in Accessibility Advocacy25:05 Exploring the Plaud AI Note Device28:03 Introduction to the Accessible Audio Recorder30:52 AI Integration and Transcription Features33:32 Practical Applications and Use Cases35:56 Accessibility of the App and User Experience39:43 Subscription Plans and Pricing43:35 Privacy Concerns and Data Management46:41 Comparative Analysis with Other Devices Find Double Tap online: YouTube, Double Tap WebsiteJoin the conversation and add your voice to the show either by calling in, sending an email or leaving us a voicemail!Email: feedback@doubletaponair.comPhone: 1-877-803-4567

04-09-25 - Henry Cejudo's House Burglarized UFC Belt Stolen Seems He Brought Maryvale To Biltmore - Man w/Swastika Tattoo On Penis Falls Into Coma Surgeon Takes Pics Of His Wang Sparking Privacy ConcernsSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

04-09-25 - Henry Cejudo's House Burglarized UFC Belt Stolen Seems He Brought Maryvale To Biltmore - Man w/Swastika Tattoo On Penis Falls Into Coma Surgeon Takes Pics Of His Wang Sparking Privacy ConcernsSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

A Note from James:Jay, remind me, I've gotta send this episode to my kids because I cannot believe what that guy just told us. Chris Hutchins, he has the "All the Hacks" podcast and so many interesting things at his website, allthehacks.com. But I don't like the word life hack. A lot of that stuff is just BS.But this was legit—from buying gold bars at Costco to making money off gift cards. He has so many weird, interesting financial and life hacks that can genuinely help you make a living and improve your life. It seemed like he was doing great with all this stuff. What you're about to hear is weird, interesting, and potentially financially lucrative—or at least thought-provoking.Let's start the episode.Episode Description:In this episode, James Altucher sits down with Chris Hutchins, host of the podcast "All the Hacks," to uncover some surprising yet practical ways to enhance your financial life. Chris shares actionable tips that can genuinely increase your income, cut down costs, and shift your mindset around everyday decisions—without resorting to gimmicks. From arbitraging Costco gold bars and credit card rewards to smart relationship strategies and travel tricks, Chris offers practical solutions that anyone can start using immediately.What You'll Learn:How to make money arbitraging gold bars from Costco.Simple mindset shifts to improve your eating habits without feeling deprived.Why you should never pay full price at major retailers and how to consistently save money.How to efficiently leverage credit card rewards and cashback offers.A strategy to reduce relationship stress by changing how you "keep score" with your partner.Chapters:[00:00] Introduction to Chris Hutchins and All the Hacks[01:00] Skepticism About Life Hacks[02:00] Simple Food Hack for Healthier Eating[03:00] The Inspiration Behind Financial and Lifestyle Hacks[04:00] Mindset Shifts and Relationship Hacks[05:00] Travel Hacks: Saving on Rentals[06:00] Gold Arbitrage at Costco[09:00] Credit Card Rewards and Signup Bonuses[19:00] Paying Rent with Points Through Bilt[23:00] Gift Card Arbitrage[28:00] Never Pay Full Price on Amazon[32:00] Gift Card Brokers and Fraud Management[47:00] Unclaimed Money and Privacy Concerns[52:00] Protecting Your Privacy[55:00] Podcast and Newsletter Business ModelAdditional Resources:All the Hacks PodcastCostco Executive RewardsBilt RewardsGCX Raise – Gift Card MarketplaceCardPointers AppThe 80/80 MarriageDeleteMe – Privacy ServiceUnclaimed Money FinderThis episode is sponsored by/brought to you by BetterHelp. Give online therapy a try at betterhelp.com/jamesSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Today, we're going over your questions. You guys had some follow-ups about my stalker, Timothy C., and I'll be giving you a few more details on that situation. We're also unpacking the RFK Jr. sex scandal—what's real, what's spin, and what it all means. Plus, the government's leaked text messages have been making waves, and I'll be breaking down what they reveal and why you should (and shouldn't) care.—https://policecoffee.com/?gad_source=1&gbraid=0AAAAACG7qmI1dmMkruwgp8vA8w0oECKla&gclid=Cj0KCQjwtJ6_BhDWARIsAGanmKfdkRQ1M1sighZQ-PGpEpsCjrZ8fCigidnvH55bfBUNMa56-yoy_A8aAv34EALw_wcB—https://open.spotify.com/episode/7CcmZWvQEaLTQAQRAFy2BQ?si=FgeO4b9QSi-5eB2cqX2XHw

If you're in SF: Join us for the Claude Plays Pokemon hackathon this Sunday!If you're not: Fill out the 2025 State of AI Eng survey for $250 in Amazon cards!We are SO excited to share our conversation with Dharmesh Shah, co-founder of HubSpot and creator of Agent.ai.A particularly compelling concept we discussed is the idea of "hybrid teams" - the next evolution in workplace organization where human workers collaborate with AI agents as team members. Just as we previously saw hybrid teams emerge in terms of full-time vs. contract workers, or in-office vs. remote workers, Dharmesh predicts that the next frontier will be teams composed of both human and AI members. This raises interesting questions about team dynamics, trust, and how to effectively delegate tasks between human and AI team members.The discussion of business models in AI reveals an important distinction between Work as a Service (WaaS) and Results as a Service (RaaS), something Dharmesh has written extensively about. While RaaS has gained popularity, particularly in customer support applications where outcomes are easily measurable, Dharmesh argues that this model may be over-indexed. Not all AI applications have clearly definable outcomes or consistent economic value per transaction, making WaaS more appropriate in many cases. This insight is particularly relevant for businesses considering how to monetize AI capabilities.The technical challenges of implementing effective agent systems are also explored, particularly around memory and authentication. Shah emphasizes the importance of cross-agent memory sharing and the need for more granular control over data access. He envisions a future where users can selectively share parts of their data with different agents, similar to how OAuth works but with much finer control. This points to significant opportunities in developing infrastructure for secure and efficient agent-to-agent communication and data sharing.Other highlights from our conversation* The Evolution of AI-Powered Agents – Exploring how AI agents have evolved from simple chatbots to sophisticated multi-agent systems, and the role of MCPs in enabling that.* Hybrid Digital Teams and the Future of Work – How AI agents are becoming teammates rather than just tools, and what this means for business operations and knowledge work.* Memory in AI Agents – The importance of persistent memory in AI systems and how shared memory across agents could enhance collaboration and efficiency.* Business Models for AI Agents – Exploring the shift from software as a service (SaaS) to work as a service (WaaS) and results as a service (RaaS), and what this means for monetization.* The Role of Standards Like MCP – Why MCP has been widely adopted and how it enables agent collaboration, tool use, and discovery.* The Future of AI Code Generation and Software Engineering – How AI-assisted coding is changing the role of software engineers and what skills will matter most in the future.* Domain Investing and Efficient Markets – Dharmesh's approach to domain investing and how inefficiencies in digital asset markets create business opportunities.* The Philosophy of Saying No – Lessons from "Sorry, You Must Pass" and how prioritization leads to greater productivity and focus.Timestamps* 00:00 Introduction and Guest Welcome* 02:29 Dharmesh Shah's Journey into AI* 05:22 Defining AI Agents* 06:45 The Evolution and Future of AI Agents* 13:53 Graph Theory and Knowledge Representation* 20:02 Engineering Practices and Overengineering* 25:57 The Role of Junior Engineers in the AI Era* 28:20 Multi-Agent Systems and MCP Standards* 35:55 LinkedIn's Legal Battles and Data Scraping* 37:32 The Future of AI and Hybrid Teams* 39:19 Building Agent AI: A Professional Network for Agents* 40:43 Challenges and Innovations in Agent AI* 45:02 The Evolution of UI in AI Systems* 01:00:25 Business Models: Work as a Service vs. Results as a Service* 01:09:17 The Future Value of Engineers* 01:09:51 Exploring the Role of Agents* 01:10:28 The Importance of Memory in AI* 01:11:02 Challenges and Opportunities in AI Memory* 01:12:41 Selective Memory and Privacy Concerns* 01:13:27 The Evolution of AI Tools and Platforms* 01:18:23 Domain Names and AI Projects* 01:32:08 Balancing Work and Personal Life* 01:35:52 Final Thoughts and ReflectionsTranscriptAlessio [00:00:04]: Hey everyone, welcome back to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Small AI.swyx [00:00:12]: Hello, and today we're super excited to have Dharmesh Shah to join us. I guess your relevant title here is founder of Agent AI.Dharmesh [00:00:20]: Yeah, that's true for this. Yeah, creator of Agent.ai and co-founder of HubSpot.swyx [00:00:25]: Co-founder of HubSpot, which I followed for many years, I think 18 years now, gonna be 19 soon. And you caught, you know, people can catch up on your HubSpot story elsewhere. I should also thank Sean Puri, who I've chatted with back and forth, who's been, I guess, getting me in touch with your people. But also, I think like, just giving us a lot of context, because obviously, My First Million joined you guys, and they've been chatting with you guys a lot. So for the business side, we can talk about that, but I kind of wanted to engage your CTO, agent, engineer side of things. So how did you get agent religion?Dharmesh [00:01:00]: Let's see. So I've been working, I'll take like a half step back, a decade or so ago, even though actually more than that. So even before HubSpot, the company I was contemplating that I had named for was called Ingenisoft. And the idea behind Ingenisoft was a natural language interface to business software. Now realize this is 20 years ago, so that was a hard thing to do. But the actual use case that I had in mind was, you know, we had data sitting in business systems like a CRM or something like that. And my kind of what I thought clever at the time. Oh, what if we used email as the kind of interface to get to business software? And the motivation for using email is that it automatically works when you're offline. So imagine I'm getting on a plane or I'm on a plane. There was no internet on planes back then. It's like, oh, I'm going through business cards from an event I went to. I can just type things into an email just to have them all in the backlog. When it reconnects, it sends those emails to a processor that basically kind of parses effectively the commands and updates the software, sends you the file, whatever it is. And there was a handful of commands. I was a little bit ahead of the times in terms of what was actually possible. And I reattempted this natural language thing with a product called ChatSpot that I did back 20...swyx [00:02:12]: Yeah, this is your first post-ChatGPT project.Dharmesh [00:02:14]: I saw it come out. Yeah. And so I've always been kind of fascinated by this natural language interface to software. Because, you know, as software developers, myself included, we've always said, oh, we build intuitive, easy-to-use applications. And it's not intuitive at all, right? Because what we're doing is... We're taking the mental model that's in our head of what we're trying to accomplish with said piece of software and translating that into a series of touches and swipes and clicks and things like that. And there's nothing natural or intuitive about it. And so natural language interfaces, for the first time, you know, whatever the thought is you have in your head and expressed in whatever language that you normally use to talk to yourself in your head, you can just sort of emit that and have software do something. And I thought that was kind of a breakthrough, which it has been. And it's gone. So that's where I first started getting into the journey. I started because now it actually works, right? So once we got ChatGPT and you can take, even with a few-shot example, convert something into structured, even back in the ChatGP 3.5 days, it did a decent job in a few-shot example, convert something to structured text if you knew what kinds of intents you were going to have. And so that happened. And that ultimately became a HubSpot project. But then agents intrigued me because I'm like, okay, well, that's the next step here. So chat's great. Love Chat UX. But if we want to do something even more meaningful, it felt like the next kind of advancement is not this kind of, I'm chatting with some software in a kind of a synchronous back and forth model, is that software is going to do things for me in kind of a multi-step way to try and accomplish some goals. So, yeah, that's when I first got started. It's like, okay, what would that look like? Yeah. And I've been obsessed ever since, by the way.Alessio [00:03:55]: Which goes back to your first experience with it, which is like you're offline. Yeah. And you want to do a task. You don't need to do it right now. You just want to queue it up for somebody to do it for you. Yes. As you think about agents, like, let's start at the easy question, which is like, how do you define an agent? Maybe. You mean the hardest question in the universe? Is that what you mean?Dharmesh [00:04:12]: You said you have an irritating take. I do have an irritating take. I think, well, some number of people have been irritated, including within my own team. So I have a very broad definition for agents, which is it's AI-powered software that accomplishes a goal. Period. That's it. And what irritates people about it is like, well, that's so broad as to be completely non-useful. And I understand that. I understand the criticism. But in my mind, if you kind of fast forward months, I guess, in AI years, the implementation of it, and we're already starting to see this, and we'll talk about this, different kinds of agents, right? So I think in addition to having a usable definition, and I like yours, by the way, and we should talk more about that, that you just came out with, the classification of agents actually is also useful, which is, is it autonomous or non-autonomous? Does it have a deterministic workflow? Does it have a non-deterministic workflow? Is it working synchronously? Is it working asynchronously? Then you have the different kind of interaction modes. Is it a chat agent, kind of like a customer support agent would be? You're having this kind of back and forth. Is it a workflow agent that just does a discrete number of steps? So there's all these different flavors of agents. So if I were to draw it in a Venn diagram, I would draw a big circle that says, this is agents, and then I have a bunch of circles, some overlapping, because they're not mutually exclusive. And so I think that's what's interesting, and we're seeing development along a bunch of different paths, right? So if you look at the first implementation of agent frameworks, you look at Baby AGI and AutoGBT, I think it was, not Autogen, that's the Microsoft one. They were way ahead of their time because they assumed this level of reasoning and execution and planning capability that just did not exist, right? So it was an interesting thought experiment, which is what it was. Even the guy that, I'm an investor in Yohei's fund that did Baby AGI. It wasn't ready, but it was a sign of what was to come. And so the question then is, when is it ready? And so lots of people talk about the state of the art when it comes to agents. I'm a pragmatist, so I think of the state of the practical. It's like, okay, well, what can I actually build that has commercial value or solves actually some discrete problem with some baseline of repeatability or verifiability?swyx [00:06:22]: There was a lot, and very, very interesting. I'm not irritated by it at all. Okay. As you know, I take a... There's a lot of anthropological view or linguistics view. And in linguistics, you don't want to be prescriptive. You want to be descriptive. Yeah. So you're a goals guy. That's the key word in your thing. And other people have other definitions that might involve like delegated trust or non-deterministic work, LLM in the loop, all that stuff. The other thing I was thinking about, just the comment on Baby AGI, LGBT. Yeah. In that piece that you just read, I was able to go through our backlog and just kind of track the winter of agents and then the summer now. Yeah. And it's... We can tell the whole story as an oral history, just following that thread. And it's really just like, I think, I tried to explain the why now, right? Like I had, there's better models, of course. There's better tool use with like, they're just more reliable. Yep. Better tools with MCP and all that stuff. And I'm sure you have opinions on that too. Business model shift, which you like a lot. I just heard you talk about RAS with MFM guys. Yep. Cost is dropping a lot. Yep. Inference is getting faster. There's more model diversity. Yep. Yep. I think it's a subtle point. It means that like, you have different models with different perspectives. You don't get stuck in the basin of performance of a single model. Sure. You can just get out of it by just switching models. Yep. Multi-agent research and RL fine tuning. So I just wanted to let you respond to like any of that.Dharmesh [00:07:44]: Yeah. A couple of things. Connecting the dots on the kind of the definition side of it. So we'll get the irritation out of the way completely. I have one more, even more irritating leap on the agent definition thing. So here's the way I think about it. By the way, the kind of word agent, I looked it up, like the English dictionary definition. The old school agent, yeah. Is when you have someone or something that does something on your behalf, like a travel agent or a real estate agent acts on your behalf. It's like proxy, which is a nice kind of general definition. So the other direction I'm sort of headed, and it's going to tie back to tool calling and MCP and things like that, is if you, and I'm not a biologist by any stretch of the imagination, but we have these single-celled organisms, right? Like the simplest possible form of what one would call life. But it's still life. It just happens to be single-celled. And then you can combine cells and then cells become specialized over time. And you have much more sophisticated organisms, you know, kind of further down the spectrum. In my mind, at the most fundamental level, you can almost think of having atomic agents. What is the simplest possible thing that's an agent that can still be called an agent? What is the equivalent of a kind of single-celled organism? And the reason I think that's useful is right now we're headed down the road, which I think is very exciting around tool use, right? That says, okay, the LLMs now can be provided a set of tools that it calls to accomplish whatever it needs to accomplish in the kind of furtherance of whatever goal it's trying to get done. And I'm not overly bothered by it, but if you think about it, if you just squint a little bit and say, well, what if everything was an agent? And what if tools were actually just atomic agents? Because then it's turtles all the way down, right? Then it's like, oh, well, all that's really happening with tool use is that we have a network of agents that know about each other through something like an MMCP and can kind of decompose a particular problem and say, oh, I'm going to delegate this to this set of agents. And why do we need to draw this distinction between tools, which are functions most of the time? And an actual agent. And so I'm going to write this irritating LinkedIn post, you know, proposing this. It's like, okay. And I'm not suggesting we should call even functions, you know, call them agents. But there is a certain amount of elegance that happens when you say, oh, we can just reduce it down to one primitive, which is an agent that you can combine in complicated ways to kind of raise the level of abstraction and accomplish higher order goals. Anyway, that's my answer. I'd say that's a success. Thank you for coming to my TED Talk on agent definitions.Alessio [00:09:54]: How do you define the minimum viable agent? Do you already have a definition for, like, where you draw the line between a cell and an atom? Yeah.Dharmesh [00:10:02]: So in my mind, it has to, at some level, use AI in order for it to—otherwise, it's just software. It's like, you know, we don't need another word for that. And so that's probably where I draw the line. So then the question, you know, the counterargument would be, well, if that's true, then lots of tools themselves are actually not agents because they're just doing a database call or a REST API call or whatever it is they're doing. And that does not necessarily qualify them, which is a fair counterargument. And I accept that. It's like a good argument. I still like to think about—because we'll talk about multi-agent systems, because I think—so we've accepted, which I think is true, lots of people have said it, and you've hopefully combined some of those clips of really smart people saying this is the year of agents, and I completely agree, it is the year of agents. But then shortly after that, it's going to be the year of multi-agent systems or multi-agent networks. I think that's where it's going to be headed next year. Yeah.swyx [00:10:54]: Opening eyes already on that. Yeah. My quick philosophical engagement with you on this. I often think about kind of the other spectrum, the other end of the cell spectrum. So single cell is life, multi-cell is life, and you clump a bunch of cells together in a more complex organism, they become organs, like an eye and a liver or whatever. And then obviously we consider ourselves one life form. There's not like a lot of lives within me. I'm just one life. And now, obviously, I don't think people don't really like to anthropomorphize agents and AI. Yeah. But we are extending our consciousness and our brain and our functionality out into machines. I just saw you were a Bee. Yeah. Which is, you know, it's nice. I have a limitless pendant in my pocket.Dharmesh [00:11:37]: I got one of these boys. Yeah.swyx [00:11:39]: I'm testing it all out. You know, got to be early adopters. But like, we want to extend our personal memory into these things so that we can be good at the things that we're good at. And, you know, machines are good at it. Machines are there. So like, my definition of life is kind of like going outside of my own body now. I don't know if you've ever had like reflections on that. Like how yours. How our self is like actually being distributed outside of you. Yeah.Dharmesh [00:12:01]: I don't fancy myself a philosopher. But you went there. So yeah, I did go there. I'm fascinated by kind of graphs and graph theory and networks and have been for a long, long time. And to me, we're sort of all nodes in this kind of larger thing. It just so happens that we're looking at individual kind of life forms as they exist right now. But so the idea is when you put a podcast out there, there's these little kind of nodes you're putting out there of like, you know, conceptual ideas. Once again, you have varying kind of forms of those little nodes that are up there and are connected in varying and sundry ways. And so I just think of myself as being a node in a massive, massive network. And I'm producing more nodes as I put content or ideas. And, you know, you spend some portion of your life collecting dots, experiences, people, and some portion of your life then connecting dots from the ones that you've collected over time. And I found that really interesting things happen and you really can't know in advance how those dots are necessarily going to connect in the future. And that's, yeah. So that's my philosophical take. That's the, yes, exactly. Coming back.Alessio [00:13:04]: Yep. Do you like graph as an agent? Abstraction? That's been one of the hot topics with LandGraph and Pydantic and all that.Dharmesh [00:13:11]: I do. The thing I'm more interested in terms of use of graphs, and there's lots of work happening on that now, is graph data stores as an alternative in terms of knowledge stores and knowledge graphs. Yeah. Because, you know, so I've been in software now 30 plus years, right? So it's not 10,000 hours. It's like 100,000 hours that I've spent doing this stuff. And so I've grew up with, so back in the day, you know, I started on mainframes. There was a product called IMS from IBM, which is basically an index database, what we'd call like a key value store today. Then we've had relational databases, right? We have tables and columns and foreign key relationships. We all know that. We have document databases like MongoDB, which is sort of a nested structure keyed by a specific index. We have vector stores, vector embedding database. And graphs are interesting for a couple of reasons. One is, so it's not classically structured in a relational way. When you say structured database, to most people, they're thinking tables and columns and in relational database and set theory and all that. Graphs still have structure, but it's not the tables and columns structure. And you could wonder, and people have made this case, that they are a better representation of knowledge for LLMs and for AI generally than other things. So that's kind of thing number one conceptually, and that might be true, I think is possibly true. And the other thing that I really like about that in the context of, you know, I've been in the context of data stores for RAG is, you know, RAG, you say, oh, I have a million documents, I'm going to build the vector embeddings, I'm going to come back with the top X based on the semantic match, and that's fine. All that's very, very useful. But the reality is something gets lost in the chunking process and the, okay, well, those tend, you know, like, you don't really get the whole picture, so to speak, and maybe not even the right set of dimensions on the kind of broader picture. And it makes intuitive sense to me that if we did capture it properly in a graph form, that maybe that feeding into a RAG pipeline will actually yield better results for some use cases, I don't know, but yeah.Alessio [00:15:03]: And do you feel like at the core of it, there's this difference between imperative and declarative programs? Because if you think about HubSpot, it's like, you know, people and graph kind of goes hand in hand, you know, but I think maybe the software before was more like primary foreign key based relationship, versus now the models can traverse through the graph more easily.Dharmesh [00:15:22]: Yes. So I like that representation. There's something. It's just conceptually elegant about graphs and just from the representation of it, they're much more discoverable, you can kind of see it, there's observability to it, versus kind of embeddings, which you can't really do much with as a human. You know, once they're in there, you can't pull stuff back out. But yeah, I like that kind of idea of it. And the other thing that's kind of, because I love graphs, I've been long obsessed with PageRank from back in the early days. And, you know, one of the kind of simplest algorithms in terms of coming up, you know, with a phone, everyone's been exposed to PageRank. And the idea is that, and so I had this other idea for a project, not a company, and I have hundreds of these, called NodeRank, is to be able to take the idea of PageRank and apply it to an arbitrary graph that says, okay, I'm going to define what authority looks like and say, okay, well, that's interesting to me, because then if you say, I'm going to take my knowledge store, and maybe this person that contributed some number of chunks to the graph data store has more authority on this particular use case or prompt that's being submitted than this other one that may, or maybe this one was more. popular, or maybe this one has, whatever it is, there should be a way for us to kind of rank nodes in a graph and sort them in some, some useful way. Yeah.swyx [00:16:34]: So I think that's generally useful for, for anything. I think the, the problem, like, so even though at my conferences, GraphRag is super popular and people are getting knowledge, graph religion, and I will say like, it's getting space, getting traction in two areas, conversation memory, and then also just rag in general, like the, the, the document data. Yeah. It's like a source. Most ML practitioners would say that knowledge graph is kind of like a dirty word. The graph database, people get graph religion, everything's a graph, and then they, they go really hard into it and then they get a, they get a graph that is too complex to navigate. Yes. And so like the, the, the simple way to put it is like you at running HubSpot, you know, the power of graphs, the way that Google has pitched them for many years, but I don't suspect that HubSpot itself uses a knowledge graph. No. Yeah.Dharmesh [00:17:26]: So when is it over engineering? Basically? It's a great question. I don't know. So the question now, like in AI land, right, is the, do we necessarily need to understand? So right now, LLMs for, for the most part are somewhat black boxes, right? We sort of understand how the, you know, the algorithm itself works, but we really don't know what's going on in there and, and how things come out. So if a graph data store is able to produce the outcomes we want, it's like, here's a set of queries I want to be able to submit and then it comes out with useful content. Maybe the underlying data store is as opaque as a vector embeddings or something like that, but maybe it's fine. Maybe we don't necessarily need to understand it to get utility out of it. And so maybe if it's messy, that's okay. Um, that's, it's just another form of lossy compression. Uh, it's just lossy in a way that we just don't completely understand in terms of, because it's going to grow organically. Uh, and it's not structured. It's like, ah, we're just gonna throw a bunch of stuff in there. Let the, the equivalent of the embedding algorithm, whatever they called in graph land. Um, so the one with the best results wins. I think so. Yeah.swyx [00:18:26]: Or is this the practical side of me is like, yeah, it's, if it's useful, we don't necessarilyDharmesh [00:18:30]: need to understand it.swyx [00:18:30]: I have, I mean, I'm happy to push back as long as you want. Uh, it's not practical to evaluate like the 10 different options out there because it takes time. It takes people, it takes, you know, resources, right? Set. That's the first thing. Second thing is your evals are typically on small things and some things only work at scale. Yup. Like graphs. Yup.Dharmesh [00:18:46]: Yup. That's, yeah, no, that's fair. And I think this is one of the challenges in terms of implementation of graph databases is that the most common approach that I've seen developers do, I've done it myself, is that, oh, I've got a Postgres database or a MySQL or whatever. I can represent a graph with a very set of tables with a parent child thing or whatever. And that sort of gives me the ability, uh, why would I need anything more than that? And the answer is, well, if you don't need anything more than that, you don't need anything more than that. But there's a high chance that you're sort of missing out on the actual value that, uh, the graph representation gives you. Which is the ability to traverse the graph, uh, efficiently in ways that kind of going through the, uh, traversal in a relational database form, even though structurally you have the data, practically you're not gonna be able to pull it out in, in useful ways. Uh, so you wouldn't like represent a social graph, uh, in, in using that kind of relational table model. It just wouldn't scale. It wouldn't work.swyx [00:19:36]: Uh, yeah. Uh, I think we want to move on to MCP. Yeah. But I just want to, like, just engineering advice. Yeah. Uh, obviously you've, you've, you've run, uh, you've, you've had to do a lot of projects and run a lot of teams. Do you have a general rule for over-engineering or, you know, engineering ahead of time? You know, like, because people, we know premature engineering is the root of all evil. Yep. But also sometimes you just have to. Yep. When do you do it? Yes.Dharmesh [00:19:59]: It's a great question. This is, uh, a question as old as time almost, which is what's the right and wrong levels of abstraction. That's effectively what, uh, we're answering when we're trying to do engineering. I tend to be a pragmatist, right? So here's the thing. Um, lots of times doing something the right way. Yeah. It's like a marginal increased cost in those cases. Just do it the right way. And this is what makes a, uh, a great engineer or a good engineer better than, uh, a not so great one. It's like, okay, all things being equal. If it's going to take you, you know, roughly close to constant time anyway, might as well do it the right way. Like, so do things well, then the question is, okay, well, am I building a framework as the reusable library? To what degree, uh, what am I anticipating in terms of what's going to need to change in this thing? Uh, you know, along what dimension? And then I think like a business person in some ways, like what's the return on calories, right? So, uh, and you look at, um, energy, the expected value of it's like, okay, here are the five possible things that could happen, uh, try to assign probabilities like, okay, well, if there's a 50% chance that we're going to go down this particular path at some day, like, or one of these five things is going to happen and it costs you 10% more to engineer for that. It's basically, it's something that yields a kind of interest compounding value. Um, as you get closer to the time of, of needing that versus having to take on debt, which is when you under engineer it, you're taking on debt. You're going to have to pay off when you do get to that eventuality where something happens. One thing as a pragmatist, uh, so I would rather under engineer something than over engineer it. If I were going to err on the side of something, and here's the reason is that when you under engineer it, uh, yes, you take on tech debt, uh, but the interest rate is relatively known and payoff is very, very possible, right? Which is, oh, I took a shortcut here as a result of which now this thing that should have taken me a week is now going to take me four weeks. Fine. But if that particular thing that you thought might happen, never actually, you never have that use case transpire or just doesn't, it's like, well, you just save yourself time, right? And that has value because you were able to do other things instead of, uh, kind of slightly over-engineering it away, over-engineering it. But there's no perfect answers in art form in terms of, uh, and yeah, we'll, we'll bring kind of this layers of abstraction back on the code generation conversation, which we'll, uh, I think I have later on, butAlessio [00:22:05]: I was going to ask, we can just jump ahead quickly. Yeah. Like, as you think about vibe coding and all that, how does the. Yeah. Percentage of potential usefulness change when I feel like we over-engineering a lot of times it's like the investment in syntax, it's less about the investment in like arc exacting. Yep. Yeah. How does that change your calculus?Dharmesh [00:22:22]: A couple of things, right? One is, um, so, you know, going back to that kind of ROI or a return on calories, kind of calculus or heuristic you think through, it's like, okay, well, what is it going to cost me to put this layer of abstraction above the code that I'm writing now, uh, in anticipating kind of future needs. If the cost of fixing, uh, or doing under engineering right now. Uh, we'll trend towards zero that says, okay, well, I don't have to get it right right now because even if I get it wrong, I'll run the thing for six hours instead of 60 minutes or whatever. It doesn't really matter, right? Like, because that's going to trend towards zero to be able, the ability to refactor a code. Um, and because we're going to not that long from now, we're going to have, you know, large code bases be able to exist, uh, you know, as, as context, uh, for a code generation or a code refactoring, uh, model. So I think it's going to make it, uh, make the case for under engineering, uh, even stronger. Which is why I take on that cost. You just pay the interest when you get there, it's not, um, just go on with your life vibe coded and, uh, come back when you need to. Yeah.Alessio [00:23:18]: Sometimes I feel like there's no decision-making in some things like, uh, today I built a autosave for like our internal notes platform and I literally just ask them cursor. Can you add autosave? Yeah. I don't know if it's over under engineer. Yep. I just vibe coded it. Yep. And I feel like at some point we're going to get to the point where the models kindDharmesh [00:23:36]: of decide where the right line is, but this is where the, like the, in my mind, the danger is, right? So there's two sides to this. One is the cost of kind of development and coding and things like that stuff that, you know, we talk about. But then like in your example, you know, one of the risks that we have is that because adding a feature, uh, like a save or whatever the feature might be to a product as that price tends towards zero, are we going to be less discriminant about what features we add as a result of making more product products more complicated, which has a negative impact on the user and navigate negative impact on the business. Um, and so that's the thing I worry about if it starts to become too easy, are we going to be. Too promiscuous in our, uh, kind of extension, adding product extensions and things like that. It's like, ah, why not add X, Y, Z or whatever back then it was like, oh, we only have so many engineering hours or story points or however you measure things. Uh, that least kept us in check a little bit. Yeah.Alessio [00:24:22]: And then over engineering, you're like, yeah, it's kind of like you're putting that on yourself. Yeah. Like now it's like the models don't understand that if they add too much complexity, it's going to come back to bite them later. Yep. So they just do whatever they want to do. Yeah. And I'm curious where in the workflow that's going to be, where it's like, Hey, this is like the amount of complexity and over-engineering you can do before you got to ask me if we should actually do it versus like do something else.Dharmesh [00:24:45]: So you know, we've already, let's like, we're leaving this, uh, in the code generation world, this kind of compressed, um, cycle time. Right. It's like, okay, we went from auto-complete, uh, in the GitHub co-pilot to like, oh, finish this particular thing and hit tab to a, oh, I sort of know your file or whatever. I can write out a full function to you to now I can like hold a bunch of the context in my head. Uh, so we can do app generation, which we have now with lovable and bolt and repletage. Yeah. Association and other things. So then the question is, okay, well, where does it naturally go from here? So we're going to generate products. Make sense. We might be able to generate platforms as though I want a platform for ERP that does this, whatever. And that includes the API's includes the product and the UI, and all the things that make for a platform. There's no nothing that says we would stop like, okay, can you generate an entire software company someday? Right. Uh, with the platform and the monetization and the go-to-market and the whatever. And you know, that that's interesting to me in terms of, uh, you know, what, when you take it to almost ludicrous levels. of abstract.swyx [00:25:39]: It's like, okay, turn it to 11. You mentioned vibe coding, so I have to, this is a blog post I haven't written, but I'm kind of exploring it. Is the junior engineer dead?Dharmesh [00:25:49]: I don't think so. I think what will happen is that the junior engineer will be able to, if all they're bringing to the table is the fact that they are a junior engineer, then yes, they're likely dead. But hopefully if they can communicate with carbon-based life forms, they can interact with product, if they're willing to talk to customers, they can take their kind of basic understanding of engineering and how kind of software works. I think that has value. So I have a 14-year-old right now who's taking Python programming class, and some people ask me, it's like, why is he learning coding? And my answer is, is because it's not about the syntax, it's not about the coding. What he's learning is like the fundamental thing of like how things work. And there's value in that. I think there's going to be timeless value in systems thinking and abstractions and what that means. And whether functions manifested as math, which he's going to get exposed to regardless, or there are some core primitives to the universe, I think, that the more you understand them, those are what I would kind of think of as like really large dots in your life that will have a higher gravitational pull and value to them that you'll then be able to. So I want him to collect those dots, and he's not resisting. So it's like, okay, while he's still listening to me, I'm going to have him do things that I think will be useful.swyx [00:26:59]: You know, part of one of the pitches that I evaluated for AI engineer is a term. And the term is that maybe the traditional interview path or career path of software engineer goes away, which is because what's the point of lead code? Yeah. And, you know, it actually matters more that you know how to work with AI and to implement the things that you want. Yep.Dharmesh [00:27:16]: That's one of the like interesting things that's happened with generative AI. You know, you go from machine learning and the models and just that underlying form, which is like true engineering, right? Like the actual, what I call real engineering. I don't think of myself as a real engineer, actually. I'm a developer. But now with generative AI. We call it AI and it's obviously got its roots in machine learning, but it just feels like fundamentally different to me. Like you have the vibe. It's like, okay, well, this is just a whole different approach to software development to so many different things. And so I'm wondering now, it's like an AI engineer is like, if you were like to draw the Venn diagram, it's interesting because the cross between like AI things, generative AI and what the tools are capable of, what the models do, and this whole new kind of body of knowledge that we're still building out, it's still very young, intersected with kind of classic engineering, software engineering. Yeah.swyx [00:28:04]: I just described the overlap as it separates out eventually until it's its own thing, but it's starting out as a software. Yeah.Alessio [00:28:11]: That makes sense. So to close the vibe coding loop, the other big hype now is MCPs. Obviously, I would say Cloud Desktop and Cursor are like the two main drivers of MCP usage. I would say my favorite is the Sentry MCP. I can pull in errors and then you can just put the context in Cursor. How do you think about that abstraction layer? Does it feel... Does it feel almost too magical in a way? Do you think it's like you get enough? Because you don't really see how the server itself is then kind of like repackaging theDharmesh [00:28:41]: information for you? I think MCP as a standard is one of the better things that's happened in the world of AI because a standard needed to exist and absent a standard, there was a set of things that just weren't possible. Now, we can argue whether it's the best possible manifestation of a standard or not. Does it do too much? Does it do too little? I get that, but it's just simple enough to both be useful and unobtrusive. It's understandable and adoptable by mere mortals, right? It's not overly complicated. You know, a reasonable engineer can put a stand up an MCP server relatively easily. The thing that has me excited about it is like, so I'm a big believer in multi-agent systems. And so that's going back to our kind of this idea of an atomic agent. So imagine the MCP server, like obviously it calls tools, but the way I think about it, so I'm working on my current passion project is agent.ai. And we'll talk more about that in a little bit. More about the, I think we should, because I think it's interesting not to promote the project at all, but there's some interesting ideas in there. One of which is around, we're going to need a mechanism for, if agents are going to collaborate and be able to delegate, there's going to need to be some form of discovery and we're going to need some standard way. It's like, okay, well, I just need to know what this thing over here is capable of. We're going to need a registry, which Anthropic's working on. I'm sure others will and have been doing directories of, and there's going to be a standard around that too. How do you build out a directory of MCP servers? I think that's going to unlock so many things just because, and we're already starting to see it. So I think MCP or something like it is going to be the next major unlock because it allows systems that don't know about each other, don't need to, it's that kind of decoupling of like Sentry and whatever tools someone else was building. And it's not just about, you know, Cloud Desktop or things like, even on the client side, I think we're going to see very interesting consumers of MCP, MCP clients versus just the chat body kind of things. Like, you know, Cloud Desktop and Cursor and things like that. But yeah, I'm very excited about MCP in that general direction.swyx [00:30:39]: I think the typical cynical developer take, it's like, we have OpenAPI. Yeah. What's the new thing? I don't know if you have a, do you have a quick MCP versus everything else? Yeah.Dharmesh [00:30:49]: So it's, so I like OpenAPI, right? So just a descriptive thing. It's OpenAPI. OpenAPI. Yes, that's what I meant. So it's basically a self-documenting thing. We can do machine-generated, lots of things from that output. It's a structured definition of an API. I get that, love it. But MCPs sort of are kind of use case specific. They're perfect for exactly what we're trying to use them for around LLMs in terms of discovery. It's like, okay, I don't necessarily need to know kind of all this detail. And so right now we have, we'll talk more about like MCP server implementations, but We will? I think, I don't know. Maybe we won't. At least it's in my head. It's like a back processor. But I do think MCP adds value above OpenAPI. It's, yeah, just because it solves this particular thing. And if we had come to the world, which we have, like, it's like, hey, we already have OpenAPI. It's like, if that were good enough for the universe, the universe would have adopted it already. There's a reason why MCP is taking office because marginally adds something that was missing before and doesn't go too far. And so that's why the kind of rate of adoption, you folks have written about this and talked about it. Yeah, why MCP won. Yeah. And it won because the universe decided that this was useful and maybe it gets supplanted by something else. Yeah. And maybe we discover, oh, maybe OpenAPI was good enough the whole time. I doubt that.swyx [00:32:09]: The meta lesson, this is, I mean, he's an investor in DevTools companies. I work in developer experience at DevRel in DevTools companies. Yep. Everyone wants to own the standard. Yeah. I'm sure you guys have tried to launch your own standards. Actually, it's Houseplant known for a standard, you know, obviously inbound marketing. But is there a standard or protocol that you ever tried to push? No.Dharmesh [00:32:30]: And there's a reason for this. Yeah. Is that? And I don't mean, need to mean, speak for the people of HubSpot, but I personally. You kind of do. I'm not smart enough. That's not the, like, I think I have a. You're smart. Not enough for that. I'm much better off understanding the standards that are out there. And I'm more on the composability side. Let's, like, take the pieces of technology that exist out there, combine them in creative, unique ways. And I like to consume standards. I don't like to, and that's not that I don't like to create them. I just don't think I have the, both the raw wattage or the credibility. It's like, okay, well, who the heck is Dharmesh, and why should we adopt a standard he created?swyx [00:33:07]: Yeah, I mean, there are people who don't monetize standards, like OpenTelemetry is a big standard, and LightStep never capitalized on that.Dharmesh [00:33:15]: So, okay, so if I were to do a standard, there's two things that have been in my head in the past. I was one around, a very, very basic one around, I don't even have the domain, I have a domain for everything, for open marketing. Because the issue we had in HubSpot grew up in the marketing space. There we go. There was no standard around data formats and things like that. It doesn't go anywhere. But the other one, and I did not mean to go here, but I'm going to go here. It's called OpenGraph. I know the term was already taken, but it hasn't been used for like 15 years now for its original purpose. But what I think should exist in the world is right now, our information, all of us, nodes are in the social graph at Meta or the professional graph at LinkedIn. Both of which are actually relatively closed in actually very annoying ways. Like very, very closed, right? Especially LinkedIn. Especially LinkedIn. I personally believe that if it's my data, and if I would get utility out of it being open, I should be able to make my data open or publish it in whatever forms that I choose, as long as I have control over it as opt-in. So the idea is around OpenGraph that says, here's a standard, here's a way to publish it. I should be able to go to OpenGraph.org slash Dharmesh dot JSON and get it back. And it's like, here's your stuff, right? And I can choose along the way and people can write to it and I can prove. And there can be an entire system. And if I were to do that, I would do it as a... Like a public benefit, non-profit-y kind of thing, as this is a contribution to society. I wouldn't try to commercialize that. Have you looked at AdProto? What's that? AdProto.swyx [00:34:43]: It's the protocol behind Blue Sky. Okay. My good friend, Dan Abramov, who was the face of React for many, many years, now works there. And he actually did a talk that I can send you, which basically kind of tries to articulate what you just said. But he does, he loves doing these like really great analogies, which I think you'll like. Like, you know, a lot of our data is behind a handle, behind a domain. Yep. So he's like, all right, what if we flip that? What if it was like our handle and then the domain? Yep. So, and that's really like your data should belong to you. Yep. And I should not have to wait 30 days for my Twitter data to export. Yep.Dharmesh [00:35:19]: you should be able to at least be able to automate it or do like, yes, I should be able to plug it into an agentic thing. Yeah. Yes. I think we're... Because so much of our data is... Locked up. I think the trick here isn't that standard. It is getting the normies to care.swyx [00:35:37]: Yeah. Because normies don't care.Dharmesh [00:35:38]: That's true. But building on that, normies don't care. So, you know, privacy is a really hot topic and an easy word to use, but it's not a binary thing. Like there are use cases where, and we make these choices all the time, that I will trade, not all privacy, but I will trade some privacy for some productivity gain or some benefit to me that says, oh, I don't care about that particular data being online if it gives me this in return, or I don't mind sharing this information with this company.Alessio [00:36:02]: If I'm getting, you know, this in return, but that sort of should be my option. I think now with computer use, you can actually automate some of the exports. Yes. Like something we've been doing internally is like everybody exports their LinkedIn connections. Yep. And then internally, we kind of merge them together to see how we can connect our companies to customers or things like that.Dharmesh [00:36:21]: And not to pick on LinkedIn, but since we're talking about it, but they feel strongly enough on the, you know, do not take LinkedIn data that they will block even browser use kind of things or whatever. They go to great, great lengths, even to see patterns of usage. And it says, oh, there's no way you could have, you know, gotten that particular thing or whatever without, and it's, so it's, there's...swyx [00:36:42]: Wasn't there a Supreme Court case that they lost? Yeah.Dharmesh [00:36:45]: So the one they lost was around someone that was scraping public data that was on the public internet. And that particular company had not signed any terms of service or whatever. It's like, oh, I'm just taking data that's on, there was no, and so that's why they won. But now, you know, the question is around, can LinkedIn... I think they can. Like, when you use, as a user, you use LinkedIn, you are signing up for their terms of service. And if they say, well, this kind of use of your LinkedIn account that violates our terms of service, they can shut your account down, right? They can. And they, yeah, so, you know, we don't need to make this a discussion. By the way, I love the company, don't get me wrong. I'm an avid user of the product. You know, I've got... Yeah, I mean, you've got over a million followers on LinkedIn, I think. Yeah, I do. And I've known people there for a long, long time, right? And I have lots of respect. And I understand even where the mindset originally came from of this kind of members-first approach to, you know, a privacy-first. I sort of get that. But sometimes you sort of have to wonder, it's like, okay, well, that was 15, 20 years ago. There's likely some controlled ways to expose some data on some member's behalf and not just completely be a binary. It's like, no, thou shalt not have the data.swyx [00:37:54]: Well, just pay for sales navigator.Alessio [00:37:57]: Before we move to the next layer of instruction, anything else on MCP you mentioned? Let's move back and then I'll tie it back to MCPs.Dharmesh [00:38:05]: So I think the... Open this with agent. Okay, so I'll start with... Here's my kind of running thesis, is that as AI and agents evolve, which they're doing very, very quickly, we're going to look at them more and more. I don't like to anthropomorphize. We'll talk about why this is not that. Less as just like raw tools and more like teammates. They'll still be software. They should self-disclose as being software. I'm totally cool with that. But I think what's going to happen is that in the same way you might collaborate with a team member on Slack or Teams or whatever you use, you can imagine a series of agents that do specific things just like a team member might do, that you can delegate things to. You can collaborate. You can say, hey, can you take a look at this? Can you proofread that? Can you try this? You can... Whatever it happens to be. So I think it is... I will go so far as to say it's inevitable that we're going to have hybrid teams someday. And what I mean by hybrid teams... So back in the day, hybrid teams were, oh, well, you have some full-time employees and some contractors. Then it was like hybrid teams are some people that are in the office and some that are remote. That's the kind of form of hybrid. The next form of hybrid is like the carbon-based life forms and agents and AI and some form of software. So let's say we temporarily stipulate that I'm right about that over some time horizon that eventually we're going to have these kind of digitally hybrid teams. So if that's true, then the question you sort of ask yourself is that then what needs to exist in order for us to get the full value of that new model? It's like, okay, well... You sort of need to... It's like, okay, well, how do I... If I'm building a digital team, like, how do I... Just in the same way, if I'm interviewing for an engineer or a designer or a PM, whatever, it's like, well, that's why we have professional networks, right? It's like, oh, they have a presence on likely LinkedIn. I can go through that semi-structured, structured form, and I can see the experience of whatever, you know, self-disclosed. But, okay, well, agents are going to need that someday. And so I'm like, okay, well, this seems like a thread that's worth pulling on. That says, okay. So I... So agent.ai is out there. And it's LinkedIn for agents. It's LinkedIn for agents. It's a professional network for agents. And the more I pull on that thread, it's like, okay, well, if that's true, like, what happens, right? It's like, oh, well, they have a profile just like anyone else, just like a human would. It's going to be a graph underneath, just like a professional network would be. It's just that... And you can have its, you know, connections and follows, and agents should be able to post. That's maybe how they do release notes. Like, oh, I have this new version. Whatever they decide to post, it should just be able to... Behave as a node on the network of a professional network. As it turns out, the more I think about that and pull on that thread, the more and more things, like, start to make sense to me. So it may be more than just a pure professional network. So my original thought was, okay, well, it's a professional network and agents as they exist out there, which I think there's going to be more and more of, will kind of exist on this network and have the profile. But then, and this is always dangerous, I'm like, okay, I want to see a world where thousands of agents are out there in order for the... Because those digital employees, the digital workers don't exist yet in any meaningful way. And so then I'm like, oh, can I make that easier for, like... And so I have, as one does, it's like, oh, I'll build a low-code platform for building agents. How hard could that be, right? Like, very hard, as it turns out. But it's been fun. So now, agent.ai has 1.3 million users. 3,000 people have actually, you know, built some variation of an agent, sometimes just for their own personal productivity. About 1,000 of which have been published. And the reason this comes back to MCP for me, so imagine that and other networks, since I know agent.ai. So right now, we have an MCP server for agent.ai that exposes all the internally built agents that we have that do, like, super useful things. Like, you know, I have access to a Twitter API that I can subsidize the cost. And I can say, you know, if you're looking to build something for social media, these kinds of things, with a single API key, and it's all completely free right now, I'm funding it. That's a useful way for it to work. And then we have a developer to say, oh, I have this idea. I don't have to worry about open AI. I don't have to worry about, now, you know, this particular model is better. It has access to all the models with one key. And we proxy it kind of behind the scenes. And then expose it. So then we get this kind of community effect, right? That says, oh, well, someone else may have built an agent to do X. Like, I have an agent right now that I built for myself to do domain valuation for website domains because I'm obsessed with domains, right? And, like, there's no efficient market for domains. There's no Zillow for domains right now that tells you, oh, here are what houses in your neighborhood sold for. It's like, well, why doesn't that exist? We should be able to solve that problem. And, yes, you're still guessing. Fine. There should be some simple heuristic. So I built that. It's like, okay, well, let me go look for past transactions. You say, okay, I'm going to type in agent.ai, agent.com, whatever domain. What's it actually worth? I'm looking at buying it. It can go and say, oh, which is what it does. It's like, I'm going to go look at are there any published domain transactions recently that are similar, either use the same word, same top-level domain, whatever it is. And it comes back with an approximate value, and it comes back with its kind of rationale for why it picked the value and comparable transactions. Oh, by the way, this domain sold for published. Okay. So that agent now, let's say, existed on the web, on agent.ai. Then imagine someone else says, oh, you know, I want to build a brand-building agent for startups and entrepreneurs to come up with names for their startup. Like a common problem, every startup is like, ah, I don't know what to call it. And so they type in five random words that kind of define whatever their startup is. And you can do all manner of things, one of which is like, oh, well, I need to find the domain for it. What are possible choices? Now it's like, okay, well, it would be nice to know if there's an aftermarket price for it, if it's listed for sale. Awesome. Then imagine calling this valuation agent. It's like, okay, well, I want to find where the arbitrage is, where the agent valuation tool says this thing is worth $25,000. It's listed on GoDaddy for $5,000. It's close enough. Let's go do that. Right? And that's a kind of composition use case that in my future state. Thousands of agents on the network, all discoverable through something like MCP. And then you as a developer of agents have access to all these kind of Lego building blocks based on what you're trying to solve. Then you blend in orchestration, which is getting better and better with the reasoning models now. Just describe the problem that you have. Now, the next layer that we're all contending with is that how many tools can you actually give an LLM before the LLM breaks? That number used to be like 15 or 20 before you kind of started to vary dramatically. And so that's the thing I'm thinking about now. It's like, okay, if I want to... If I want to expose 1,000 of these agents to a given LLM, obviously I can't give it all 1,000. Is there some intermediate layer that says, based on your prompt, I'm going to make a best guess at which agents might be able to be helpful for this particular thing? Yeah.Alessio [00:44:37]: Yeah, like RAG for tools. Yep. I did build the Latent Space Researcher on agent.ai. Okay. Nice. Yeah, that seems like, you know, then there's going to be a Latent Space Scheduler. And then once I schedule a research, you know, and you build all of these things. By the way, my apologies for the user experience. You realize I'm an engineer. It's pretty good.swyx [00:44:56]: I think it's a normie-friendly thing. Yeah. That's your magic. HubSpot does the same thing.Alessio [00:45:01]: Yeah, just to like quickly run through it. You can basically create all these different steps. And these steps are like, you know, static versus like variable-driven things. How did you decide between this kind of like low-code-ish versus doing, you know, low-code with code backend versus like not exposing that at all? Any fun design decisions? Yeah. And this is, I think...Dharmesh [00:45:22]: I think lots of people are likely sitting in exactly my position right now, coming through the choosing between deterministic. Like if you're like in a business or building, you know, some sort of agentic thing, do you decide to do a deterministic thing? Or do you go non-deterministic and just let the alum handle it, right, with the reasoning models? The original idea and the reason I took the low-code stepwise, a very deterministic approach. A, the reasoning models did not exist at that time. That's thing number one. Thing number two is if you can get... If you know in your head... If you know in your head what the actual steps are to accomplish whatever goal, why would you leave that to chance? There's no upside. There's literally no upside. Just tell me, like, what steps do you need executed? So right now what I'm playing with... So one thing we haven't talked about yet, and people don't talk about UI and agents. Right now, the primary interaction model... Or they don't talk enough about it. I know some people have. But it's like, okay, so we're used to the chatbot back and forth. Fine. I get that. But I think we're going to move to a blend of... Some of those things are going to be synchronous as they are now. But some are going to be... Some are going to be async. It's just going to put it in a queue, just like... And this goes back to my... Man, I talk fast. But I have this... I only have one other speed. It's even faster. So imagine it's like if you're working... So back to my, oh, we're going to have these hybrid digital teams. Like, you would not go to a co-worker and say, I'm going to ask you to do this thing, and then sit there and wait for them to go do it. Like, that's not how the world works. So it's nice to be able to just, like, hand something off to someone. It's like, okay, well, maybe I expect a response in an hour or a day or something like that.Dharmesh [00:46:52]: In terms of when things need to happen. So the UI around agents. So if you look at the output of agent.ai agents right now, they are the simplest possible manifestation of a UI, right? That says, oh, we have inputs of, like, four different types. Like, we've got a dropdown, we've got multi-select, all the things. It's like back in HTML, the original HTML 1.0 days, right? Like, you're the smallest possible set of primitives for a UI. And it just says, okay, because we need to collect some information from the user, and then we go do steps and do things. And generate some output in HTML or markup are the two primary examples. So the thing I've been asking myself, if I keep going down that path. So people ask me, I get requests all the time. It's like, oh, can you make the UI sort of boring? I need to be able to do this, right? And if I keep pulling on that, it's like, okay, well, now I've built an entire UI builder thing. Where does this end? And so I think the right answer, and this is what I'm going to be backcoding once I get done here, is around injecting a code generation UI generation into, the agent.ai flow, right? As a builder, you're like, okay, I'm going to describe the thing that I want, much like you would do in a vibe coding world. But instead of generating the entire app, it's going to generate the UI that exists at some point in either that deterministic flow or something like that. It says, oh, here's the thing I'm trying to do. Go generate the UI for me. And I can go through some iterations. And what I think of it as a, so it's like, I'm going to generate the code, generate the code, tweak it, go through this kind of prompt style, like we do with vibe coding now. And at some point, I'm going to be happy with it. And I'm going to hit save. And that's going to become the action in that particular step. It's like a caching of the generated code that I can then, like incur any inference time costs. It's just the actual code at that point.Alessio [00:48:29]: Yeah, I invested in a company called E2B, which does code sandbox. And they powered the LM arena web arena. So it's basically the, just like you do LMS, like text to text, they do the same for like UI generation. So if you're asking a model, how do you do it? But yeah, I think that's kind of where.Dharmesh [00:48:45]: That's the thing I'm really fascinated by. So the early LLM, you know, we're understandably, but laughably bad at simple arithmetic, right? That's the thing like my wife, Normies would ask us, like, you call this AI, like it can't, my son would be like, it's just stupid. It can't even do like simple arithmetic. And then like we've discovered over time that, and there's a reason for this, right? It's like, it's a large, there's, you know, the word language is in there for a reason in terms of what it's been trained on. It's not meant to do math, but now it's like, okay, well, the fact that it has access to a Python interpreter that I can actually call at runtime, that solves an entire body of problems that it wasn't trained to do. And it's basically a form of delegation. And so the thought that's kind of rattling around in my head is that that's great. So it's, it's like took the arithmetic problem and took it first. Now, like anything that's solvable through a relatively concrete Python program, it's able to do a bunch of things that I couldn't do before. Can we get to the same place with UI? I don't know what the future of UI looks like in a agentic AI world, but maybe let the LLM handle it, but not in the classic sense. Maybe it generates it on the fly, or maybe we go through some iterations and hit cache or something like that. So it's a little bit more predictable. Uh, I don't know, but yeah.Alessio [00:49:48]: And especially when is the human supposed to intervene? So, especially if you're composing them, most of them should not have a UI because then they're just web hooking to somewhere else. I just want to touch back. I don't know if you have more comments on this.swyx [00:50:01]: I was just going to ask when you, you said you got, you're going to go back to code. What

Sarah McConnell, VP, Demand Generation at Qualified, delves into the topic of people's privacy concerns with AI Show NotesConnect With: Sarah McConnell: Website // LinkedInThe MarTech Podcast: Email // LinkedIn // TwitterBenjamin Shapiro: Website // LinkedIn // TwitterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

I chat with Keith Lauver—serial entrepreneur, product launch veteran, and founder of Atomic Elevator, the company behind the high-definition marketing platform Ella. With over $34 million raised for product launches and clients like Trader Joe's and Whole Foods, Keith brings deep expertise in strategic marketing and AI tools for entrepreneurs. We dive into how AI is transforming small business marketing—not by replacing people, but by freeing them to focus on what they do best. If you're curious about practical AI, marketing automation, and how to rethink your business structure for the future, this episode is packed with insights you won't want to miss. Today we discussed: [00:00] Opening [00:09] Introducing Keith Lauver [01:52] Understanding the Practical Uses of AI [04:17] What are AI Agents? [07:45] How Does AI Affect Organizational Structure [11:46] AI Doesn't Change Human Value [16:22] Personalized Marketing [17:37] Ella AI [21:22] Privacy Concerns with AI More About Keith Lauver: Check out Keith Lauver's Website: https://www.atomicelevator.com Connect with Keith Lauver's on LinkedIn: https://www.linkedin.com/in/keithdlauver/ Rate, Review, & Follow If you liked this episode, please rate and review the show. Let us know what you loved most about the episode. Struggling with strategy? Unlock your free AI-powered prompts now and start building a winning strategy today!

Sarah McConnell, VP, Demand Generation at Qualified, delves into the topic of people's privacy concerns with AI Show NotesConnect With: Sarah McConnell: Website // LinkedInThe MarTech Podcast: Email // LinkedIn // TwitterBenjamin Shapiro: Website // LinkedIn // TwitterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Kim St. Onge dives into the privacy concerns surrounding genetic testing services like 23andMe. With a warning from the California Attorney General about potential data risks, she discusses how users can protect their information, especially in the event of a company bankruptcy. The conversation also covers personal motivations for DNA testing, experiences with ancestry websites, and how individuals can better control their personal data.

On today's episode of Double Tap, Steven Scott and Shaun Preece dive into a jam-packed show that covers everything from Meta AI's disappointing European rollout to Steven's brand-new, powerhouse Mac Studio. They also speak with Tom Pey, president of Waymap, about how his organization is transforming indoor navigation for blind and visually impaired people through cutting-edge technology.The guys kick things off with frustration over Meta AI's European launch, which excludes the long-awaited “Look and Describe” visual features. They unpack the privacy and regulatory challenges affecting accessibility advancements across platforms like Meta, OpenAI, and Be My Eyes.Later, Steven gets hands-on with Apple's M3 Ultra Mac Studio and details the setup process, the power of its 10Gb Ethernet port, and how it fits into their production workflow. There's also a candid rant on iOS accessibility issues, from VoiceOver focus bugs to inconsistent gestures.Then, they welcome Tom Pey from Waymap to discuss how the app delivers turn-by-turn indoor and outdoor navigation with 1-meter accuracy—without GPS or internet. You'll learn about the groundbreaking tech behind Waymap and the growing importance of infrastructure-free navigation for blind users.Finally, the show wraps up with a discussion about Seleste smart glasses shutting down, a promising new ARx Vision iOS beta, and why leasing high-end tech might just be a smarter choice for creators and professionals.Get in touch with Double Tap by emailing us feedback@doubletaponair.com or by call 1-877-803-4567 and leave us a voicemail. You can also now contact us via Whatsapp on 1-613-481-0144 or visit doubletaponair.com/whatsapp to connect. We are also across social media including X, Mastodon and Facebook. Double Tap is available daily on AMI-audio across Canada, on podcast worldwide and now on YouTube.Chapter Markers:00:00 Meta AI Rollout in Europe: A Mixed Bag02:48 Privacy Concerns and Data Processing Regulations05:58 The Impact of AI Features on Accessibility09:10 Navigating iOS Accessibility Challenges14:52 Unpacking the Mac Studio: Specs and Performance20:08 Internet Speed and Workflow Efficiency33:55 Waymap: Revolutionizing Navigation for the Visually Impaired40:03 Technical Innovations Behind Waymap48:10 The Future of Navigation Technology51:14 The Rise and Fall of Seleste Smart Glasses53:06 ARx Vision: New Developments in Assistive Technology Find Double Tap online: YouTube, Double Tap WebsiteJoin the conversation and add your voice to the show either by calling in, sending an email or leaving us a voicemail!Email: feedback@doubletaponair.comPhone: 1-877-803-4567About AMIAMI is a media company that entertains, informs and empowers Canadians with disabilities through three broadcast services — AMI-tv and AMI-audio in English and AMI-télé in French — and streaming platform AMI+. Our vision is to establish AMI as a leader in the offering of accessible content, providing a voice for Canadians with disabilities through authentic storytelling, representation and positive portrayal. To learn more visit AMI.ca and AMItele.ca.Find more great AMI Original Content on AMI+Learn more at AMI.caConnect with Accessible Media Inc. online:X /Twitter @AccessibleMediaInstagram @AccessibleMediaInc / @AMI-audioFacebook at @AccessibleMediaIncTikTok @AccessibleMediaInc

Chuck Joiner, David Ginsburg, Marty Jencius, Jeff Gamet and Jim Rea discuss Amazon's new AI-powered Alexa+, highlighting its conversational abilities and privacy concerns. Opera's browser sidebar integrations with apps like Discord and Slack prompt debate about productivity versus distraction. Google's updated tool for managing personal information sparks skepticism regarding its effectiveness and sincerity.  MacVoices is supported by the new MacVoices Discord, our latest benefit for MacVoices Patrons. Sign up, get access, and jin the conversations at Patreon.com/macvoices. Show Notes: Chapters: 00:05 The A-Lady Plus Unveiled 04:05 Privacy Concerns and AI 14:26 Opera's New Integrations 23:55 Google's Data Removal Tool Links: Amazon unveils Alexa+, a generative AI update with vision capabilities and more, in preview in March 2025 for $20 per month or free to Prime subscribers https://www.theverge.com/news/619755/amazon-alexa-ai-upgrade-artificial-intelligence-smart-assistant Opera One Adds Discord, Slack, and Bluesky Integration to Browser Sidebar https://www.mactrast.com/2025/02/opera-one-adds-discord-slack-and-bluesky-integration-to-browser-sidebar/#google_vignette Google Makes It Easier to Remove Personal Info From Search Results https://www.macrumors.com/2025/02/26/google-remove-info-search-results/ Guests: Jeff Gamet is a technology blogger, podcaster, author, and public speaker. Previously, he was The Mac Observer's Managing Editor, and the TextExpander Evangelist for Smile. He has presented at Macworld Expo, RSA Conference, several WordCamp events, along with many other conferences. You can find him on several podcasts such as The Mac Show, The Big Show, MacVoices, Mac OS Ken, This Week in iOS, and more. Jeff is easy to find on social media as @jgamet on Twitter and Instagram, jeffgamet on LinkedIn., @jgamet@mastodon.social on Mastodon, and on his YouTube Channel at YouTube.com/jgamet. David Ginsburg is the host of the weekly podcast In Touch With iOS where he discusses all things iOS, iPhone, iPad, Apple TV, Apple Watch, and related technologies. He is an IT professional supporting Mac, iOS and Windows users. Visit his YouTube channel at https://youtube.com/daveg65 and find and follow him on Twitter @daveg65 and on Mastodon at @daveg65@mastodon.cloud. Dr. Marty Jencius has been an Associate Professor of Counseling at Kent State University since 2000. He has over 120 publications in books, chapters, journal articles, and others, along with 200 podcasts related to counseling, counselor education, and faculty life. His technology interest led him to develop the counseling profession ‘firsts,' including listservs, a web-based peer-reviewed journal, The Journal of Technology in Counseling, teaching and conferencing in virtual worlds as the founder of Counselor Education in Second Life, and podcast founder/producer of CounselorAudioSource.net and ThePodTalk.net. Currently, he produces a podcast about counseling and life questions, the Circular Firing Squad, and digital video interviews with legacies capturing the history of the counseling field. This is also co-host of The Vision ProFiles podcast. Generally, Marty is chasing the newest tech trends, which explains his interest in A.I. for teaching, research, and productivity. Marty is an active presenter and past president of the NorthEast Ohio Apple Corp (NEOAC). Jim Rea built his own computer from scratch in 1975, started programming in 1977, and has been an independent Mac developer continuously since 1984. He is the founder of ProVUE Development, and the author of Panorama X, ProVUE's ultra fast RAM based database software for the macOS platform. He's been a speaker at MacTech, MacWorld Expo and other industry conferences. Follow Jim at provue.com and via @provuejim@techhub.social on Mastodon. Support: Become a MacVoices Patron on Patreon      http://patreon.com/macvoices      Enjoy this episode? Make a one-time donation with PayPal Connect: Web:      http://macvoices.com Twitter: http://www.twitter.com/chuckjoiner      http://www.twitter.com/macvoices Mastodon:      https://mastodon.cloud/@chuckjoiner Facebook:      http://www.facebook.com/chuck.joiner MacVoices Page on Facebook:      http://www.facebook.com/macvoices/ MacVoices Group on Facebook:      http://www.facebook.com/groups/macvoice LinkedIn:      https://www.linkedin.com/in/chuckjoiner/ Instagram:      https://www.instagram.com/chuckjoiner/ Subscribe:      Audio in iTunes      Video in iTunes      Subscribe manually via iTunes or any podcatcher: Audio: http://www.macvoices.com/rss/macvoicesrss      Video: http://www.macvoices.com/rss/macvoicesvideorss

Chuck Joiner, David Ginsburg, Marty Jencius, Jeff Gamet and Jim Rea discuss Amazon's new AI-powered Alexa+, highlighting its conversational abilities and privacy concerns. Opera's browser sidebar integrations with apps like Discord and Slack prompt debate about productivity versus distraction. Google's updated tool for managing personal information sparks skepticism regarding its effectiveness and sincerity.  MacVoices is supported by the new MacVoices Discord, our latest benefit for MacVoices Patrons. Sign up, get access, and jin the conversations at Patreon.com/macvoices. Show Notes: Chapters: 00:05 The A-Lady Plus Unveiled 04:05 Privacy Concerns and AI 14:26 Opera's New Integrations 23:55 Google's Data Removal Tool Links: Amazon unveils Alexa+, a generative AI update with vision capabilities and more, in preview in March 2025 for $20 per month or free to Prime subscribers https://www.theverge.com/news/619755/amazon-alexa-ai-upgrade-artificial-intelligence-smart-assistant Opera One Adds Discord, Slack, and Bluesky Integration to Browser Sidebar https://www.mactrast.com/2025/02/opera-one-adds-discord-slack-and-bluesky-integration-to-browser-sidebar/#google_vignette Google Makes It Easier to Remove Personal Info From Search Results https://www.macrumors.com/2025/02/26/google-remove-info-search-results/ Guests: Jeff Gamet is a technology blogger, podcaster, author, and public speaker. Previously, he was The Mac Observer's Managing Editor, and the TextExpander Evangelist for Smile. He has presented at Macworld Expo, RSA Conference, several WordCamp events, along with many other conferences. You can find him on several podcasts such as The Mac Show, The Big Show, MacVoices, Mac OS Ken, This Week in iOS, and more. Jeff is easy to find on social media as @jgamet on Twitter and Instagram, jeffgamet on LinkedIn., @jgamet@mastodon.social on Mastodon, and on his YouTube Channel at YouTube.com/jgamet. David Ginsburg is the host of the weekly podcast In Touch With iOS where he discusses all things iOS, iPhone, iPad, Apple TV, Apple Watch, and related technologies. He is an IT professional supporting Mac, iOS and Windows users. Visit his YouTube channel at https://youtube.com/daveg65 and find and follow him on Twitter @daveg65 and on Mastodon at @daveg65@mastodon.cloud. Dr. Marty Jencius has been an Associate Professor of Counseling at Kent State University since 2000. He has over 120 publications in books, chapters, journal articles, and others, along with 200 podcasts related to counseling, counselor education, and faculty life. His technology interest led him to develop the counseling profession ‘firsts,' including listservs, a web-based peer-reviewed journal, The Journal of Technology in Counseling, teaching and conferencing in virtual worlds as the founder of Counselor Education in Second Life, and podcast founder/producer of CounselorAudioSource.net and ThePodTalk.net. Currently, he produces a podcast about counseling and life questions, the Circular Firing Squad, and digital video interviews with legacies capturing the history of the counseling field. This is also co-host of The Vision ProFiles podcast. Generally, Marty is chasing the newest tech trends, which explains his interest in A.I. for teaching, research, and productivity. Marty is an active presenter and past president of the NorthEast Ohio Apple Corp (NEOAC). Jim Rea built his own computer from scratch in 1975, started programming in 1977, and has been an independent Mac developer continuously since 1984. He is the founder of ProVUE Development, and the author of Panorama X, ProVUE's ultra fast RAM based database software for the macOS platform. He's been a speaker at MacTech, MacWorld Expo and other industry conferences. Follow Jim at provue.com and via @provuejim@techhub.social on Mastodon. Support:      Become a MacVoices Patron on Patreon      http://patreon.com/macvoices      Enjoy this episode? Make a one-time donation with PayPal Connect:      Web:      http://macvoices.com      Twitter:      http://www.twitter.com/chuckjoiner      http://www.twitter.com/macvoices      Mastodon:      https://mastodon.cloud/@chuckjoiner      Facebook:      http://www.facebook.com/chuck.joiner      MacVoices Page on Facebook:      http://www.facebook.com/macvoices/      MacVoices Group on Facebook:      http://www.facebook.com/groups/macvoice      LinkedIn:      https://www.linkedin.com/in/chuckjoiner/      Instagram:      https://www.instagram.com/chuckjoiner/ Subscribe:      Audio in iTunes      Video in iTunes      Subscribe manually via iTunes or any podcatcher:      Audio: http://www.macvoices.com/rss/macvoicesrss      Video: http://www.macvoices.com/rss/macvoicesvideorss

Tuesday Evening- BIMI Field Director-Name Redacted for Privacy Concerns- 2025 Missions Conference- I Corinthians 9:19-23

Monday Evening- BIMI Field Director - Name Redacted for Privacy Concerns- 2025 Missions Conference- I Thessalonians 5:25

In this episode, Jaeden Schafer and Conor discuss the latest advancements in Amazon's AI technology, particularly focusing on the newly launched Alexa Plus. They explore its features, including enhanced conversational abilities, integration with smart home devices, and potential privacy concerns. The conversation highlights the product's capabilities in personalizing user experiences and improving productivity, while also addressing the trust users place in Amazon's technology.AI Applied YouTube Channel: https://www.youtube.com/@AI-Applied-PodcastGet on the AI Box Waitlist: ⁠⁠https://AIBox.ai/⁠⁠Conor's AI Course: https://www.ai-mindset.ai/coursesConor's AI Newsletter: https://www.ai-mindset.ai/Jaeden's AI Hustle Community: https://www.skool.com/aihustle/aboutChapters00:00 Introduction to Amazon's AI Innovations01:41 Exploring Alexa Plus Features04:42 Privacy Concerns and User Trust10:03 Productivity and Personalization with Alexa Plus

Thanks to our Partners, NAPA Auto Care, NAPA TRACS and Automotive Management Network This episode covers the critical role and impact of AI on marketing, the importance of persona marketing, and effective branding strategies. Learn about "watering holes," referring to identifying where potential customers spend their time online and offline, and how to effectively use persona marketing to target specific demographics. Dan stresses the importance of branding and community involvement, asserting that successful shops prioritize building brand recognition and relationships within their local areas. He also warns about trusting AI implicitly, referencing instances of AI being inaccurate. Dan Vance, Shop Dog Marketing.com Show Notes Watch Full Video Episode TST Big Event, March 29th, 2025: https://www.tstseminars.org/ Shop Dog Marketing at Shop Dog Marketing.com. "Want to see your auto repair shop thrive? Let Shop Dog Marketing be your guide. Our customer-first approach, combined with AI-driven creative content, ensures top rankings. Introduction to the Episode (00:00:00) AI in Marketing (00:01:02) The Importance of Networking (00:02:02) Consumer Behavior and Marketing (00:04:04) Bullet Points vs. Detailed Content (00:05:54) Creating Memorable Customer Experiences (00:06:32) Identifying Customer "Watering Holes" (00:08:29) Privacy Concerns in Marketing (00:09:41) The Rise of Persona Marketing (00:10:55) AI's Impact on Search Results (00:11:59) The Role of AI in Marketing Strategies (00:13:08) Caution with AI Information (00:14:12) Human Element in Marketing (00:17:34) Client Review Analysis (00:18:46) Women's Expectations in Service (00:21:20) Impact of Professional Image (00:22:57) Targeting Ideal Customers (00:25:12) Purposeful Marketing Strategies (00:25:43) The Need for Customer Understanding (00:26:26) AI's Role in Content Creation (00:28:07) Changing Landscape of AI (00:28:29) User Behavior and Decision Making (00:31:11) Branding in the Automotive Industry (00:31:55) Community Involvement for Branding (00:32:57) Conclusion and Call to Action (00:34:38) Thanks to our Partners, NAPA Auto Care, NAPA TRACS and Automotive Management Network Learn more about NAPA Auto Care and the benefits of being part of the NAPA family by visiting https://www.napaonline.com/en/auto-care NAPA TRACS will move your shop into the SMS fast lane with onsite training and six days a week of support and local representation. Find NAPA TRACS on the Web at http://napatracs.com/ Get ready to grow your business with the Automotive Management Network: Find on the Web at http://AftermarketManagementNetwork.com...

Everyone's lit up with AI advancements, but what about our privacy? Erin Sparks and Michael Lewittes dive into the ethics and privacy concerns surrounding AI, shedding light on the rapid information exchange and the potential risks looming over our data. Explore how hedge funds are making daring AI moves, with new platforms demanding personal information in return for supposedly superior features. The question lingers: Are we too quick to exchange privacy for convenience?  Michael shares insights on discerning fact from fiction in AI-generated content and offers advice for content creators striving to stand out in a saturated market. His expert analysis offers a roadmap to uphold authenticity and authority in a sea of homogenized content. Alongside, the EDGE team muses about the consumer's role in safeguarding personal data against the intrusive AI gaze. Emphasizing the mantra "verify, then trust," they urge listeners to sharpen critical thinking in the flood of AI-generated content. [00:05:36] AI Ethics and Privacy Concerns [00:13:05] Simplified User Agreements Needed [00:15:36] EDGE of the Web Title Sponsor: Site Strategics [00:17:43] Use Privacy Mode Online [00:18:51] AI Literacy and Consumer Responsibility [00:22:46] Trusting AI: Balancing Use and Skepticism [00:26:17] EDGE of The Web Sponsor: Wix Studio [00:27:53] Improving AI Grounding and Differentiation [00:31:08] Balancing AI Efficiency and Authenticity [00:32:50] AI as a Brand Building Tool Thanks to Our Sponsors! Site Strategics: http://edgeofthewebradio.com/site Wix: http://edgeofthewebradio.com/wixstudio Follow Our Guest X: https://x.com/MichaelLewittes LinkedIn: https://www.linkedin.com/in/michael-lewittes/

The Impact of Flock Cameras on Community Surveillance   In this episode of The Secure Family Podcast, host Andy Murphy speaks with attorney and privacy advocate Stephanie Lindsay about the implications of Flock cameras.   They discuss how these cameras, which are stationed in communities and utilized by law enforcement, automatically capture license plate data and other details. The episode covers the benefits and potential privacy concerns associated with this technology, noting its role in investigations and its impact on everyday citizens.   For more from Stepahanie Lindsey: https://thelindseyfirm.com/ Take control of your data with DeleteMe. Because they sponsor the podcast you can get 20% off a privacy plan from DeleteMe with promo code: DAD.  Level Up your parenting with my ebook about protecting your kids while gaming online. Connect

For more than a century, window cleaning for skyscrapers has been a dangerous, labor-intensive job. Skyline Robotics is changing that.In this episode of Building Better, Ross Blum, President and COO of Skyline Robotics, discusses how their flagship robotic system, Ozmo, is transforming facade maintenance through automation. Ozmo cleans windows three times faster than traditional methods, reduces risk for human workers, and creates new opportunities in the industry.The conversation explores the challenges of implementing robotics in an established industry, how trust is built with stakeholders, and the role of human labor in automation. Ross also shares insights into privacy concerns, cost drivers, and the technical complexities of working with outdoor robotics.About Building Better:Building Better with Brandon Bartneck focuses on the people, products, and companies creating a better tomorrow, often in the transportation and manufacturing sectors. Previously called the Future of Mobility podcast, the show features real, human conversations exploring what leaders and innovators are doing, why and how they're doing it, and what we can learn from their experiences. Topics include manufacturing, production, assembly, autonomous driving, electric vehicles, hydrogen and fuel cells, leadership, and more.About Skyline Robotics:Skyline Robotics is a deep tech robotics and automation company bringing robotics to facade maintenance. Its flagship product, Ozmo, is the world's first high-rise window cleaning robot, offering a safer, faster, and more effective alternative to human window washers. With artificial intelligence, computer vision, and machine learning, Ozmo is disrupting the $40B window cleaning industry. While Ozmo automates the physical task, human operators remain an integral part of the process, overseeing operations and ensuring efficiency.Key Takeaways: Skyline Robotics is revolutionizing window cleaning through automation. The company's mission is to own the facade, extending beyond just cleaning. Automation presents major opportunities in outdoor environments. Trust-building is critical for successful implementation of robotics. Privacy concerns must be addressed when using data collection technologies. The cost of window cleaning is influenced by labor, weather, and regulations. Human labor still plays a role in the automation process. Transparency and humility are key in building client relationships. Technical challenges include adapting to various building designs and conditions. Data analysis can enhance building maintenance and efficiency.Chapters: 00:00 Introduction to Skyline Robotics 05:52 The Importance of Facade Maintenance 08:47 Cost Drivers in Window Cleaning 11:34 Automation and Labor Dynamics 14:58 Privacy Concerns and Data Management 17:49 Facade Management Opportunities 20:44 Technical Challenges in Robotic Cleaning 23:41 Building Trust in Automation 26:48 The Journey of Innovation 29:54 Conclusion and Key Takeaways 46:17 The Complexity of Window Cleaning Automation 48:40 Building Trust in Automation IndustriesAbout the Guest:Ross Blum is the President and COO of Skyline Robotics, leading operations across Israel and the USA. With a background in business operations and strategy, Ross previously served as COO of Quidd and CAO of pingmd, leading both companies through successful acquisitions. He holds a J.D. from Benjamin N. Cardozo School of Law, an M.S. from Georgetown University in Sports Management, and a B.S. in Business Management from Babson College.Links & Resources: Learn more about Skyline Robotics: Skyline Robotics LinkedIn Connect with Ross Blum: Ross Blum LinkedIn Show Notes: brandonbartneck.com/buildingbetter/rossblumConnect with Building Better:Follow the podcast for more inspiring conversations: Apple Podcasts Spotify Google Podcasts

Twila Brase is President and Co-founder of Citizens' Council for Health Freedom, a national patient-centered, privacy-focused, free-market policy organization to support health care choices, individualized patient care and medical and genetic privacy. Twila is a certified public health nurse and author of the eight-time award-winning book, Big Brother in the Exam Room: The Dangerous Truth About Electronic Health Records. She is the speaker on the daily Health Freedom Minute heard on stations across the nation.We are only 3.5 weeks into the Trump Administration. Issues, matters and actions are moving so very quickly where each day's news is equivalent to multiple weeks' worth of news just a month ago. The curtains are being pulled back exposing more and more corruption.One of the areas we've been monitoring is health and privacy issues. For instance, our country was on a fast track to come to an agreement on a pandemic treaty. However the Trump Administration is pulling us out of the World Health Organization. The HHS has been subpoenaed for records on Covid-19 Vaccine Safety and records with Dr. Fauci's communications.Just hours ago, the U.S. Senate made a procedural vote to advance the nomination of Robert F. Kennedy, Jr., to lead the Dept. of Health and Human Services. The final vote is to take place tomorrow (Thursday).Yet, amidst all of this, there are continued concerns about individual privacy, REAL ID, and the push toward a social credit system.

In this episode of Good Morning Liberty, hosts Chuck and Nathaniel discuss various contemporary issues including the controversy over government buyouts for federal employees, the perceived political and media bias, and the actions and reactions to Elon Musk's influence in government. They delve into the complexities of government spending, bureaucratic inefficiency, and political maneuvers, including the criticism of recent budget cuts and organizational restructurings. They also touch on Musk's controversial role, highlighting public protests and the ongoing debate over privacy and taxpayer data. The hosts offer their libertarian perspective on these matters, aiming for a clearer understanding of the intricate governmental and political landscape. (01:15) News Update: Trump Buyout Blocked (03:40) Politico and Government Funding (07:13) Unions and Government Buyouts (11:22) Elon Musk and Privacy Concerns (15:51) Protests and Government Criticism (23:14) Political Predictions and Elon Musk's Timeline (23:56) Economic Concerns and Government Spending (25:28) Media Manipulation and Political Strategies (26:19) Public Outrage and Protests (27:26) Elon Musk's Influence on Politics (29:12) Government Efficiency and Budget Cuts (36:50) Constitutional Debates and Political Rhetoric (40:55) Gaza Conflict and Foreign Policy (45:05) Closing Thoughts and Future Speculations   Links:   https://gml.bio.link/   YOUTUBE:   https://bit.ly/3UwsRiv   RUMBLE:   https://rumble.com/c/GML   Check out Martens Minute!   https://martensminute.podbean.com/   Follow Josh Martens on X:   https://twitter.com/joshmartens13   Join the private discord & chat during the show!   joingml.com   Bank on Yourself bankonyourself.com/gml   Get FACTOR Today! FACTORMEALS.com/gml50   Good Morning Liberty is sponsored by BetterHelp! Rediscover your curiosity today by visiting Betterhelp.com/GML (Get 10% off your first month)     Protect your privacy and unlock the full potential of your streaming services with ExpressVPN. Get 3 more months absolutely FREE by using our link EXPRESSVPN.com/GML

Episode Highlights with Dr. Erika GrayUnderstanding privacy concerns with genetic testing, especially in light of the security breach with a major DNA company recently Samples sent to testing companies are de-identified and not connected to personal dataSecurity concerns and how to make sure yours is safeHow their specific testing has layers of encryption and is not even mined for dataYou can use an alias on your genetic testing The limits of genetic testing and what it can and can't tell us Genetics is a screening tool and not a diagnostic tool — related to cancer risk and expressionMore related to choline and how this can be really beneficial for focus and attention Resources We MentionMy Toolbox Genomics - Use code wellnessmama for a discount

Introduction:David Bahnsen, founder of the Bahnsen Group, joins the conversation with over $6 Billion in assets under management.Recognized by Barron's, Forbes, and Financial Times as one of America's top financial advisors.Economic and Policy Insights:Government Efficiency and Doge: Discussion on government commissions aimed at reducing waste, with a critical look at whether these efforts will have real impact or remain superficial.Fiscal Challenges: Highlighting the difficulties in managing federal spending, especially with entitlements like Social Security, Medicare, and Medicaid, which constitute a significant portion of the budget.Tariffs and Trade:Tariff Strategy: Bahnsen describes tariffs as negotiation tools rather than permanent policy, aimed at achieving broader policy objectives like border security and geopolitical influence.Tax Policy:Tax Reform: Discussion on extending or making permanent the tax cuts from 2017, and the complexities of tax policy within the political landscape.No Tax on Tips: A firm stance on fulfilling campaign promises like not taxing tips, reflecting Trump's campaign rhetoric.Economic Growth and AI Market Disruption:Business Expensing: Advocacy for 100% immediate business expensing to stimulate growth without government spending.Market Observations: Analysis of recent market volatility, particularly in tech sectors like AI and Nvidia, suggesting caution against overvaluation and speculative bubbles.Cryptocurrency and Financial Systems:Bitcoin and Crypto Market: Skepticism about cryptocurrencies as a stable reserve or currency, emphasizing their volatility and the impracticality of a Bitcoin reserve for the U.S.De-banking and Privacy: Concerns about privacy and control in financial systems, with a critique of the potential for backdoor CBDC functionality through stable coins."Full Time: Work and the Meaning of Life” by David BahnsenPhilosophy of Work: Bahnsen's book, "Full Time: Work and the Meaning of Life," exploring the intersection of work, purpose, and conservative values.  Bahnsen's perspective on work as integral to human dignity and purpose, critiquing notions like universal basic income and reduced work weeks.If you would like to support the show and our family please consider subscribing monthly here: SubscribeStar https://www.subscribestar.com/the-david-knight-show Or you can send a donation throughMail: David Knight POB 994 Kodak, TN 37764Zelle: @DavidKnightShow@protonmail.comCash App at: $davidknightshowBTC to: bc1qkuec29hkuye4xse9unh7nptvu3y9qmv24vanh7 Money should have intrinsic value AND transactional privacy: Go to DavidKnight.gold for great deals on physical gold/silver For 10% off Gerald Celente's prescient Trends Journal, go to TrendsJournal.com and enter the code KNIGHTBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-david-knight-show--2653468/support.

Topics covered between 00:02:11 and 00:48:33* Stock Market - Recovery or Dead Cat Bounce* Technological and Economic Implications: “Crash Human Wages” - “inevitably and necessarily”* Forget Digital ID — SMART Cities Will Use BiometricTopics covered between 00:49:31 and 01:30:15 Another Day, Another AI: A new AI video generator named "QWN" (pronounced "Quinn") has been introduced, which is free but highly censored, notably refusing to generate content with historical figures like George Washington.Musk's Twitter acquisition was always about what he's rolling out now X (formerly Twitter) is expanding into financial services, partnering with Visa for digital wallet and peer-to-peer payment features, aiming to become an "everything app" similar to WeChat.Roger Ver is seeking a pardon from Trump, arguing he's a victim of political persecution for his crypto advocacy and offering to be an evangelist for TrumpTopics covered between 01:32:59 and 02:02:01Decade-Long Battle Against Baby Part Trafficking Ends in VictoryShocking undercover videos where abortion providers discussed selling fetal organs for profit - even bargaining over parts like hearts and livers while eating! But Kamala Harris and her successor Xavier Becerra ignored the crimes and prosecuted the journalistsThe investigation uncovered that none other than Francis Collins and Anthony Fauci were among the recipients of these procured parts, using them for experiments like creating humanized miceA Brave New World: Meanwhile, the narrative takes a darker turn with the mention of new fertility research aiming to create babies from single individuals, hinting at a future where traditional family structures could be replaced by state-controlled "hatcheries" - a chilling nod to dystopian eugenics.Topics covered between 02:03:46 and 02:53:40 Navigating Economic Seas with David Bahnsen: Insights on Policy, Markets, and the FutureIntroduction:David Bahnsen, founder of the Bahnsen Group, joins the conversation with over $6 million in assets under management.Recognized by Barron's, Forbes, and Financial Times as one of America's top financial advisors.Economic and Policy Insights:Government Efficiency and Doge: Discussion on government commissions aimed at reducing waste, with a critical look at whether these efforts will have real impact or remain superficial.Fiscal Challenges: Highlighting the difficulties in managing federal spending, especially with entitlements like Social Security, Medicare, and Medicaid, which constitute a significant portion of the budget.Tariffs and Trade:Tariff Strategy: Bahnsen describes tariffs as negotiation tools rather than permanent policy, aimed at achieving broader policy objectives like border security and geopolitical influence.Tax Policy:Tax Reform: Discussion on extending or making permanent the tax cuts from 2017, and the complexities of tax policy within the political landscape.No Tax on Tips: A firm stance on fulfilling campaign promises like not taxing tips, reflecting Trump's campaign rhetoric.Economic Growth and AI Market Disruption:Business Expensing: Advocacy for 100% immediate business expensing to stimulate growth without government spending.Market Observations: Analysis of recent market volatility, particularly in tech sectors like AI and Nvidia, suggesting caution against overvaluation and speculative bubbles.Cryptocurrency and Financial Systems:Bitcoin and Crypto Market: Skepticism about cryptocurrencies as a stable reserve or currency, emphasizing their volatility and the impracticality of a Bitcoin reserve for the U.S.De-banking and Privacy: Concerns about privacy and control in financial systems, with a critique of the potential for backdoor CBDC functionality through stable coins."Full Time: Work and the Meaning of Life” by David BahnsenPhilosophy of Work: Bahnsen's book, "Full Time: Work and the Meaning of Life," exploring the intersection of work, purpose, and conservative values.  Bahnsen's perspective on work as integral to human dignity and purpose, critiquing notions like universal basic income and reduced work weeks.If you would like to support the show and our family please consider subscribing monthly here: SubscribeStar https://www.subscribestar.com/the-david-knight-show Or you can send a donation throughMail: David Knight POB 994 Kodak, TN 37764Zelle: @DavidKnightShow@protonmail.comCash App at: $davidknightshowBTC to: bc1qkuec29hkuye4xse9unh7nptvu3y9qmv24vanh7 Money should have intrinsic value AND transactional privacy: Go to DavidKnight.gold for great deals on physical gold/silver For 10% off Gerald Celente's prescient Trends Journal, go to TrendsJournal.com and enter the code KNIGHTBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-david-knight-show--2653468/support.

Introduction:David Bahnsen, founder of the Bahnsen Group, joins the conversation with over $6 Billion in assets under management.Recognized by Barron's, Forbes, and Financial Times as one of America's top financial advisors.Economic and Policy Insights:Government Efficiency and Doge: Discussion on government commissions aimed at reducing waste, with a critical look at whether these efforts will have real impact or remain superficial.Fiscal Challenges: Highlighting the difficulties in managing federal spending, especially with entitlements like Social Security, Medicare, and Medicaid, which constitute a significant portion of the budget.Tariffs and Trade:Tariff Strategy: Bahnsen describes tariffs as negotiation tools rather than permanent policy, aimed at achieving broader policy objectives like border security and geopolitical influence.Tax Policy:Tax Reform: Discussion on extending or making permanent the tax cuts from 2017, and the complexities of tax policy within the political landscape.No Tax on Tips: A firm stance on fulfilling campaign promises like not taxing tips, reflecting Trump's campaign rhetoric.Economic Growth and AI Market Disruption:Business Expensing: Advocacy for 100% immediate business expensing to stimulate growth without government spending.Market Observations: Analysis of recent market volatility, particularly in tech sectors like AI and Nvidia, suggesting caution against overvaluation and speculative bubbles.Cryptocurrency and Financial Systems:Bitcoin and Crypto Market: Skepticism about cryptocurrencies as a stable reserve or currency, emphasizing their volatility and the impracticality of a Bitcoin reserve for the U.S.De-banking and Privacy: Concerns about privacy and control in financial systems, with a critique of the potential for backdoor CBDC functionality through stable coins."Full Time: Work and the Meaning of Life” by David BahnsenPhilosophy of Work: Bahnsen's book, "Full Time: Work and the Meaning of Life," exploring the intersection of work, purpose, and conservative values.  Bahnsen's perspective on work as integral to human dignity and purpose, critiquing notions like universal basic income and reduced work weeks.If you would like to support the show and our family please consider subscribing monthly here: SubscribeStar https://www.subscribestar.com/the-david-knight-show Or you can send a donation throughMail: David Knight POB 994 Kodak, TN 37764Zelle: @DavidKnightShow@protonmail.comCash App at: $davidknightshowBTC to: bc1qkuec29hkuye4xse9unh7nptvu3y9qmv24vanh7 Money should have intrinsic value AND transactional privacy: Go to DavidKnight.gold for great deals on physical gold/silver For 10% off Gerald Celente's prescient Trends Journal, go to TrendsJournal.com and enter the code KNIGHTBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-real-david-knight-show--5282736/support.

Topics covered between 00:02:11 and 00:48:33* Stock Market - Recovery or Dead Cat Bounce* Technological and Economic Implications: “Crash Human Wages” - “inevitably and necessarily”* Forget Digital ID — SMART Cities Will Use BiometricTopics covered between 00:49:31 and 01:30:15 Another Day, Another AI: A new AI video generator named "QWN" (pronounced "Quinn") has been introduced, which is free but highly censored, notably refusing to generate content with historical figures like George Washington.Musk's Twitter acquisition was always about what he's rolling out now X (formerly Twitter) is expanding into financial services, partnering with Visa for digital wallet and peer-to-peer payment features, aiming to become an "everything app" similar to WeChat.Roger Ver is seeking a pardon from Trump, arguing he's a victim of political persecution for his crypto advocacy and offering to be an evangelist for TrumpTopics covered between 01:32:59 and 02:02:01Decade-Long Battle Against Baby Part Trafficking Ends in VictoryShocking undercover videos where abortion providers discussed selling fetal organs for profit - even bargaining over parts like hearts and livers while eating! But Kamala Harris and her successor Xavier Becerra ignored the crimes and prosecuted the journalistsThe investigation uncovered that none other than Francis Collins and Anthony Fauci were among the recipients of these procured parts, using them for experiments like creating humanized miceA Brave New World: Meanwhile, the narrative takes a darker turn with the mention of new fertility research aiming to create babies from single individuals, hinting at a future where traditional family structures could be replaced by state-controlled "hatcheries" - a chilling nod to dystopian eugenics.Topics covered between 02:03:46 and 02:53:40 Navigating Economic Seas with David Bahnsen: Insights on Policy, Markets, and the FutureIntroduction:David Bahnsen, founder of the Bahnsen Group, joins the conversation with over $6 million in assets under management.Recognized by Barron's, Forbes, and Financial Times as one of America's top financial advisors.Economic and Policy Insights:Government Efficiency and Doge: Discussion on government commissions aimed at reducing waste, with a critical look at whether these efforts will have real impact or remain superficial.Fiscal Challenges: Highlighting the difficulties in managing federal spending, especially with entitlements like Social Security, Medicare, and Medicaid, which constitute a significant portion of the budget.Tariffs and Trade:Tariff Strategy: Bahnsen describes tariffs as negotiation tools rather than permanent policy, aimed at achieving broader policy objectives like border security and geopolitical influence.Tax Policy:Tax Reform: Discussion on extending or making permanent the tax cuts from 2017, and the complexities of tax policy within the political landscape.No Tax on Tips: A firm stance on fulfilling campaign promises like not taxing tips, reflecting Trump's campaign rhetoric.Economic Growth and AI Market Disruption:Business Expensing: Advocacy for 100% immediate business expensing to stimulate growth without government spending.Market Observations: Analysis of recent market volatility, particularly in tech sectors like AI and Nvidia, suggesting caution against overvaluation and speculative bubbles.Cryptocurrency and Financial Systems:Bitcoin and Crypto Market: Skepticism about cryptocurrencies as a stable reserve or currency, emphasizing their volatility and the impracticality of a Bitcoin reserve for the U.S.De-banking and Privacy: Concerns about privacy and control in financial systems, with a critique of the potential for backdoor CBDC functionality through stable coins."Full Time: Work and the Meaning of Life” by David BahnsenPhilosophy of Work: Bahnsen's book, "Full Time: Work and the Meaning of Life," exploring the intersection of work, purpose, and conservative values.  Bahnsen's perspective on work as integral to human dignity and purpose, critiquing notions like universal basic income and reduced work weeks.If you would like to support the show and our family please consider subscribing monthly here: SubscribeStar https://www.subscribestar.com/the-david-knight-show Or you can send a donation throughMail: David Knight POB 994 Kodak, TN 37764Zelle: @DavidKnightShow@protonmail.comCash App at: $davidknightshowBTC to: bc1qkuec29hkuye4xse9unh7nptvu3y9qmv24vanh7 Money should have intrinsic value AND transactional privacy: Go to DavidKnight.gold for great deals on physical gold/silver For 10% off Gerald Celente's prescient Trends Journal, go to TrendsJournal.com and enter the code KNIGHTBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-real-david-knight-show--5282736/support.