The Eighth

Avraham Raskin
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A future-facing podcast that explores how augmented reality will show up in our everyday lives, off-screen and all around us. Each episode features everyday people (not tech insiders) grappling with how this new layer of reality could reshape the way we shop, move, learn, or just get through the day. These conversations take abstract ideas and ground them in the practical, turning sci-fi into Saturday morning.

  1. 2월 1일 ·  보너스

    [BS] From 2 Weeks to 2 Minutes

    I realised recently that the time I was spending sharing my ideas was actually strangling the sharing of ideas themselves. In this BrainStream, I'm opening up about why I'm working to automate my entire production pipeline—from transcripts to clips— to keep sharing. I think we're entering an era where 'janking' together AI tools like Claude Code and Remotion isn't just a hack; it's the only way to keep up with the ROI of our own thoughts. I wonder if the future of the UI isn't just talking to our computers, but having them understand the visual and emotional context of our work before we even ask. Timestamps(0:00) The Guatemala Vision Pro Backlog(2:15) When the ROI of Sharing Doesn't Make Sense(4:10) From 2017 Manual Edits to AI Pipelines(6:20) Automating the 'Boring Stuff': Clips & Captions(8:10) The Post-Siri World: OpenClaw & Agentic Filesystems Listen OnIf you enjoyed this episode, you can listen to more of my brainstreams on Spotify, YouTube, or Apple Podcasts.Check out my website for more insights: https://avrahamrask.in/podcast

    10분

  2. 1월 31일 ·  보너스

    [BS] Why Are You Still a Slave to Your Computer?

    We’re finally breaking free from the 1980s desktop paradigm and moving into a world where creation isn’t tied to a workstation. I’m diving into how I finally cracked the code on local automation for the BrainStream, bringing us closer to a future of conversational agents that actually understand and build alongside us. From my early work on the Whisper project to the current shift in spatial computing, we’re exploring how we can stop being tethered to a desk and start leveraging high-fidelity AI collaboration wherever we go. Timestamps[0:00] From GUI to Conversational OS[1:36] The Whisper Vision: Untethered AI[3:12] The High Cost of Manual Friction[4:30] Custom Pipelines Over SaaS Bloat[6:20] Local Video Clipping with Whisper JSON[8:10] Iterative Media & Generative Assets Listen OnIf you enjoyed this episode, you can listen to more of my brainstreams on Spotify, YouTube, or Apple Podcasts.Check out my website for more insights: https://avrahamrask.in/podcast

    10분

  3. 1월 30일 ·  보너스

    [BS] One Year per Day: Scaling Human Insight for the AI Tsunami

    The velocity of innovation right now is insane—we’re seeing a year’s worth of progress every week—and the only way to keep up is to automate the friction out of our systems. I’ve finally dialed in a pipeline that handles the heavy lifting of post-production and distribution with a single click, freeing me up to dive deeper into the high-stakes security projects I’ve been teasing. This episode is a direct look at how I’m using AI to reclaim my time, moving away from manual content management and back toward pure insight and execution. If we're going to build the future, we can't afford to be slowed down by the logistics of the present. TIMESTAMPS:0:00 | Navigating the AI Acceleration Curve 1:45 | Why Content Friction Stalls Big Ideas 3:20 | The Utility of the Spoken Word 5:10 | Solving Local vs. Cloud Friction 6:55 | Video as Code: The Remotion Shift 8:30 | Closing the Agent-Driven Loop LISTEN ON Spotify: https://open.spotify.com/show/0gyQydQ9h5PM2gGUWy1PfC YouTube: https://youtube.com/playlist?list=PLifoYer83q8ACtYWCtjhoOHGmfslydLM7&si=yKhmWU6J9t6j9USF Apple Podcasts: https://podcasts.apple.com/us/podcast/id1726877985 Website: https://avrahamrask.in/podcast

    10분

  4. 2025. 12. 31. ·  보너스

    [BS] Revisiting the 2025 Concept Series

    In this year-end Brainstream, Avraham revisits a 2018–2019 video exploring how augmented reality and AI could transform everyday problem solving. Using the example of fixing a broken fridge, he unpacks multimodal AI—vision, sound, and conversation working together—and reflects on how concepts from seven years ago are becoming increasingly practical today. This episode also considers the evolving relationship between humans and AI in collaborative problem solving. TL;DR A seven-year-old video predicting AI-assisted, AR-driven home repairs highlights how multimodal AI—integrating vision, sound, and conversation—can empower humans to solve problems collaboratively without experts or cloud dependency. 🎧 Listen on Spotify | YouTube | Apple Podcasts Listen to the full episode and explore more Brainstream → https://avrahamraskin.com/podcast

    10분

  5. 2025. 12. 03. ·  보너스

    [BS] Apple Health, OpenAI, And The Private Missing Piece

    A quiet rumour about OpenAI tapping into Apple Health sparks a deeper question: what would a truly private, on-device health intelligence actually look like? Listen on Spotify, YouTube, Apple Podcasts More episodes → https://avrahamraskin.com/podcast Timestamps 00:00 | The Rumour About OpenAI And Apple Health 01:06 | Why Access To Raw Health Data Actually Matters 01:45 | The Privacy Problem With Cloud-Based LLMs 02:26 | The Case For A True On-Device Reasoning Model 03:12 | Hardware Limits And Why Models Must Shrink 04:26 | What A Smarter Siri Should Really Be 05:14 | Health Metrics, Trends, And Real Coaching 06:24 | The Missing Link: Nutrition Data 07:12 | Rumours Of Apple HealthPlus 08:02 | What Real AI Coaching Should Feel Like 09:07 | Integrating Sleep, Training, Journaling, And Goals 09:48 | The Vision: A Private, Personal, On-Device Coach

    10분

  6. 2025. 11. 26. ·  보너스

    [BS] AI Operators, and the Leap Beyond Alarms

    In this BrainStream, I explore why we’re on the edge of a generational shift in how properties are secured. Not just sensors. Not just cameras. A true, always-awake operator built from VLMs, re-identification, and on-prem AI. I look at Ubiquiti’s position, emerging players, and why the “virtual guard” model is no longer sci-fi but an inevitable next step. TL;DR Security is moving from alarms to an always-awake, property-aware AI operators that verifies, learns, and predicts. 🎧 Listen Now at Spotify, YouTube, or Apple Podcasts → More at: https://avrahamraskin.com/podcast Timestamps 00:00 | Why I Had to Record One More 00:21 | The Ubiquiti/UniFi Fascination 01:31 | The “Ultimate Operator” Concept 02:20 | Parameters → Description: A Real Turning Point 03:21 | Teaching an AI the Property (The Guard Analogy) 04:41 | From Virtual Guards to Site-Aware Intelligence 05:22 | Alarm Monitoring: The Missing Link 06:13 | Camera Verification and VLM Reasoning 08:48 | Prediction Instead of Reaction 09:51 | Closing the Loop: Where This Is Heading #Ubiquiti #UniFi #VideoOperator #VLM #Re-ID #Alarm #Verification #PredictiveSecurity #On-Prem AI #ComputerVision #Security #Future

    10분

  7. 2025. 10. 20. ·  보너스

    [BS] When Cameras Learn: The Rise of Video-Language Models

    A concise, investigative tour of how security video evolved from passive cctv to intelligent, searchable footage powered by local ai and video-language models. I maps the technical lineage-motion sensing, smart detections, face and plate id, the “AI Key,” and scene-level vlm search-and explain why pattern discovery at scale is the next operational leap for site security and investigations. “video that used to be passive now becomes a searchable narrative.” 🎧 listen on spotify, youtube, apple podcasts 🔗 more episodes → https://avrahamraskin.com/podcast tl;dr security cameras have graduated from passive recorders to active, searchable sensors. video-language models (vlms) and local llm-like agents enable natural-language scene search and condensed pattern visualisations-powerful for investigations but constrained today by compute and edge deployment. the next frontier is real-time, site-wide pattern detection running at the edge. timestamps 00:00 | introduction and context 00:23 | the evolution: cctv → motion → smart detections 01:56 | face detection, license plates, and granular id 02:19 | the “ai key”: local llm-style analytics (what it adds) 03:35 | video-language models: frame description → search 05:03 | practical investigative tools and scene search examples 06:03 | pattern discovery: briefcam and condensed timelines 07:40 | limitations today: compute, edge, and the next step 09:56 | closing thoughts and what’s next

    10분

  8. 2025. 09. 24. ·  보너스

    [BS] The Future of Security: From Reactive Cameras to Predictive Intelligence

    Most security systems still behave like they did 20 years ago-reactive, limited, and blind to the context hidden inside their own recordings. In this Brainstream, we explore why the real frontier in security isn’t better alerts or higher-resolution cameras, but AI systems that can learn a site’s patterns, behaviours, anomalies, and risks over months of recorded footage. This episode outlines the shift from “review after the incident” to “predict before it happens,” and why the intelligence trapped inside our footage is the most valuable, unused asset in modern security. TL;DR Security cameras shouldn’t just replay the past-they should understand it. When indexed, analysed, and contextualised, months of footage can power predictive, site-specific intelligence far beyond traditional monitoring. 🎧 Listen on ⁠⁠Spotify⁠, ⁠⁠YouTube⁠⁠, ⁠⁠Apple Podcasts⁠⁠ 🔗 More episodes → ⁠https://avrahamraskin.com/podcast⁠ Timestamps 00:00 | Opening: Why talk about the future of security 00:05 | Why this topic needs multiple videos 00:08 | A new product direction after years in the field 00:19 | The core problem: cameras are reactive 00:26 | Footage as an investigative tool, not a live one 00:34 | Tools like BriefCam and condensed investigations 00:51 | The inevitability of deep pattern analysis 01:17 | Rethinking what recorded footage really contains 01:26 | On-site storage vs cloud motion clips 01:48 | Why modern systems rarely store “everything” 02:14 | The hidden value inside long-term footage 02:27 | Thought experiment: downloading 6 months of footage into a guard 03:06 | Scale: 25–100 cameras, months of data 03:25 | What context a human misses vs what the data contains 03:58 | Reviewing footage: hours, days, weeks 04:25 | Pattern detection after the fact 04:54 | The industry’s stuck in reactive mode 05:02 | Moving from reactive to predictive 05:17 | Connecting dots before the incident 05:24 | Trends, anomalies, and site-specific patterns 05:34 | What good security guards actually do 06:00 | Knowing who belongs and who doesn’t 06:13 | Cameras should be able to learn the same 06:22 | Context → patterns → prediction 06:34 | Generations of camera evolution 07:00 | Smart detections: person, car, face, plate 07:14 | More granular detection: clothing, colours, models 07:36 | Natural-language retrieval: next-generation search 07:56 | But still mostly reactive 08:03 | True intelligence: learning the site itself 08:12 | Threat assessment powered by context 08:26 | The massive, untapped value in indexed footage 08:51 | Behaviour understanding vs object detection 09:04 | AI as a security operator/assistant 09:14 | Cameras becoming proactive 09:20 | Future episodes: alarms, sensors, monitoring 09:33 | Industry progress & uneven advancement 09:42 | Why pattern understanding changes everything 09:57 | Closing: A new era is coming

    10분
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예고편

소개

A future-facing podcast that explores how augmented reality will show up in our everyday lives, off-screen and all around us. Each episode features everyday people (not tech insiders) grappling with how this new layer of reality could reshape the way we shop, move, learn, or just get through the day. These conversations take abstract ideas and ground them in the practical, turning sci-fi into Saturday morning.

정보

  • 제작진
    Avraham Raskin
  • 방송 연도
    2022년 - 2026년
  • 에피소드
    70
  • 등급
    전체 연령 사용가
  • 저작권
    © Avraham Raskin
  • 웹사이트 보기
    The Eighth