Podcasts about Cognition

Your Brain Uses 20% of Its Creatine Just to Think | Podcast #479  

Episode 399 reviews Phase 2 of Season 15 and introduces the Motivation Loop — the sequence of meaning, belief, attention, action, reward, and recovery that drives sustained effort. The episode explains common loop breakers (loss of meaning, negative thoughts, distracted attention, too much challenge, poor recovery, and no visible progress) and how to diagnose which link is failing. Practical takeaway: identify your gap, reconnect purpose, protect attention, celebrate small wins, and balance challenge with recovery to keep motivation alive. In This Episode 399, We Will Cover: ✅ The Motivation Loop — what it is, why it matters, and how it influences behavior, focus, effort, and achievement. ✅ What Keeps the Loop Alive — the role of meaning, belief, attention, action, reward, recovery, and growth. ✅ What Breaks the Loop — how loss of meaning, negative thoughts, distraction, lack of progress, poor recovery, and burnout weaken motivation. ✅ The Neuroscience of Motivation — why the brain repeats what it rewards and how dopamine reinforces behavior. ✅ The Difference Between Challenge and Burnout — finding the sweet spot where effort creates growth instead of exhaustion. ✅ My Personal Motivation Loop Story — how I watched my own loop begin to break in real time while pushing too hard with hiking and what I learned from it. ✅ How to Repair a Broken Loop — practical strategies to restore motivation before burnout takes hold. ✅ The Anterior Mid-Cingulate Cortex (AMCC) — the brain region associated with persistence, self-regulation, resilience, and doing hard things. ✅ Why Doing Hard Things Grows the Brain — how meaningful challenges strengthen the neural circuits responsible for sustained effort. ✅ Finding Your Gap — using our Brain's Operating System framework to identify where your system may be out of alignment. ✅ The Biggest Lessons from Phase 2: Neurochemistry & Motivation — insights from Bob Proctor, Dr. Caroline Leaf, Dr. John Medina, Dr. Anna Lembke, Dr. Chuck Hillman, and Friederike Fabritius. ✅ What's Next — a preview of Episodes 400 and 401 on Leadership and Trust, and our transition into Phase 3: Movement, Learning & Cognition. Key Question of the Episode "When motivation begins to disappear, have we lost our drive—or is there simply a broken link in the loop?" Aha Moment The goal isn't to push harder. The goal is to identify the broken link, repair it, and keep the loop alive. EP 399: The Motivation Loop: What Keeps It Going—and What Breaks It? Welcome back to the Neuroscience Meets Social and Emotional Learning Podcast. This week, we're wrapping up Phase 2: Neurochemistry and Motivation. Over the past several months, we've explored some of the most important drivers of human behavior, attention, effort, learning, and performance. Through the work of Bob Proctor, Dr. Caroline Leaf, John Medina, Dr. Anna Lembke, Chuck Hillman, and Friederike Fabritius, we've been focused on one fundamental question: What drives sustained effort and forward movement? Today, I want to zoom out and connect everything we've learned into one simple framework: The Motivation Loop. More importantly, we'll look at: What keeps the loop going What causes it to break How we can strengthen it over time And why doing hard things may actually help grow parts of our brain responsible for persistence and self-regulation. The Brain's Operating System of Human Performance Before we dive into the Motivation Loop, let's remember what we've covered so far. One of the biggest insights from neuroscience is that high performance doesn't happen in one part of the brain. It happens through a sequence. Just like a computer has an operating system, our brains have an operating system for learning, achievement, and human performance. Over the past several months, we've been building that system one phase at a time. Phase 1: Regulation & Safety REGULATE The first question we asked was: "Is the nervous system safe enough to learn?" Before motivation... Before focus... Before performance... The brain must first feel regulated. Through guests like Bruce Perry, Kristen Holmes, Antonio Zadra, and Sui Wong, we learned that: Sleep matters Recovery matters Rhythm matters Our Stress levels matter A dysregulated brain struggles to learn. No regulation. No learning. Phase 2: Neurochemistry & Motivation ENGAGE Once the brain is regulated, we move to the next question: "What drives behavior, focus, and sustained effort?" This is the phase we've just completed. We explored: Dopamine Belief Thought patterns Attention Reward Burnout Energy And perhaps the biggest lesson from this phase was: The brain repeats what it rewards. This became the foundation of what I've called: The Motivation Loop: What Keeps the Loop Going? Looking at this graphic, notice the green side first. The healthy loop begins with: Meaning and Purpose When we know why something matters, effort becomes easier to sustain. This was Bob Proctor's message and the message that launched author Simon Sinek's entire career (Knowing Your Why). People can tolerate enormous challenges when the goal is meaningful. Example: Learning a New Skill Imagine someone deciding to learn a new language. At first: Progress is slow. Mistakes are frequent. The work feels uncomfortable. But they have a purpose. Maybe they want to connect on a deeper level with family. Maybe they want to travel. Maybe they want a new career opportunity. Purpose keeps them engaged long enough to continue with the hard work.   Belief Shapes Thought If I believe I can improve, my thoughts become more constructive. This was Dr. Caroline Leaf's work. Our thoughts influence our neurochemistry. Positive thoughts don't guarantee success. But they keep us moving toward it. Attention Drives Growth This was John Medina's contribution. Attention determines what the brain decides matters. The brain learns what we repeatedly focus on. What we attend to, we strengthen. Action Creates Progress Once attention is focused, behavior follows. We study. We practice. We train. We learn. Reward Reinforces Behavior This was Dr. Anna Lembke's work. The reward doesn't have to be huge. Sometimes it's simply noticing progress. The brain says: "That effort produced a result." And the loop continues. Example: Exercise A person begins walking 20 minutes every day. Week 1: No major changes. Week 2: Energy improves. Week 3: Sleep improves. Week 4: Resting heart rate begins dropping. The brain notices progress. The effort feels worthwhile. The loop strengthens. The behavior repeats. We have spent a lot of time on understanding how to keep the loop from breaking. How the Loop Breaks Now let's look at the red side. How the loop breaks. The loop rarely breaks all at once. Usually one link weakens first. Then the others follow. Loop Breaker #1: Loss of Meaning What Happened? A student studies only to pass a test. The test ends. The reason disappears. Motivation disappears. The loop breaks because there is no longer a compelling "why." What Could Have Prevented It? Reconnect to purpose. Instead of: "I have to study for this test." Shift to: "I'm building skills for the future version of myself." Bob Proctor taught us that goals are not just about achievement. They're about growth. Loop Repair Ask: "Why does this matter beyond today?" When meaning returns, motivation returns.   Loop Breaker #2: Negative Thought Patterns What Happened? Someone starts a health journey. After a difficult week they think: "I'm failing." "Nothing is changing." "I'll never get there." Their attention shifts toward evidence of failure. The loop weakens. What Could Have Prevented It? Focus on progress instead of perfection. Dr. Caroline Leaf would remind us that thoughts influence neurochemistry. A better question might be: "What is improving that I haven't noticed yet?" Loop Repair Look for small wins. Better sleep More energy More consistency Better habits Progress fuels dopamine. Dopamine fuels effort.   Loop Breaker #3: Distracted Attention What Happened? You sit down to work. A text arrives. Then email. Then social media. Then another interruption at your office door. Attention becomes fragmented. Learning slows. Progress slows. Reward disappears. What Could Have Prevented It? Protect your attention. John Medina taught us: Attention determines what the brain decides matters. Loop Repair Create: 30-minute focus blocks Phone-free work periods (with notifications turned off) One-task-at-a-time sessions The brain rewards completion. Not multitasking.   Loop Breaker #4: Too Much Challenge What Happened? This one surprises many people. Doing hard things strengthens the brain. But doing impossible things breaks the loop. A person starts: A new diet A new exercise plan A new business A new habit And tries to change everything at once. The challenge becomes overwhelming. What Could Have Prevented It? Start smaller. The AMCC grows when challenges are difficult but achievable. Loop Repair Ask: "What's the smallest difficult thing I can consistently repeat?" Not: "What's the hardest thing I can do today?"   Loop Breaker #5: Poor Recovery/Low Energy   What Happened? This is actually my hiking example that I've mentioned previously. Everything was working. My recovery improved. My WHOOP age improved 6.4 years younger than my actual age. My fitness improved- v02 max increased. Then I increased the challenge. Longer hikes. More strain. More effort. But not enough recovery time in between. I could actually see the reward disappearing in real time. The effort at the end of these longer hikes felt exhausting instead of energizing. I know that doing difficult things makes my brain stronger, but I was close to giving up on something I really enjoyed. What Could Have Prevented It? Recovery needed to increase alongside challenge. The mistake wasn't hiking, or making the hike more challenging. The mistake was believing: More is always better. Loop Repair Alternate: Hard days Easy days Increase recovery as strain increases. As Friederike Fabritius taught us: Performance isn't built through effort alone. It's built through effort and recovery. Once I put more attention on recovery before pushing again, the broken motivation loop repaired, and the end of those difficult hikes became energizing again (with the right amount of rest).   Loop Breaker #6: No Visible Progress What Happened? A salesperson makes: 50 calls 100 calls 150 calls No results. The brain begins asking: "Why bother?" The reward disappears. What Could Have Prevented It? Measure leading indicators instead of outcomes. Instead of focusing only on sales: Track: Calls completed Meetings booked Relationships built Skills improved Loop Repair Celebrate effort metrics. Not just outcome metrics. The brain needs evidence that effort matters. Also, if the strategy you are using is not yielding results, try a different one. Ask others who are having success, what they are doing, and how they are getting results. Once you can identify where your loop is breaking, fixing it requires doing something that you were not doing before.   The Big Lesson Every loop break in this phase points back to one question: What link failed? Was it: Meaning? Thoughts? Attention? Progress? Recovery? Challenge? Because the loop rarely breaks all at once. Usually one link weakens first. And the good news is: If you can identify the broken link, you can repair the loop. What About Doing Hard Things? One of the most fascinating concepts we explored this phase was the work surrounding the: Anterior Mid-Cingulate Cortex (AMCC) This area of the brain appears to play an important role in: Persistence Self-regulation Attention control Doing things we don't feel like doing Research suggests this area strengthens when we repeatedly choose meaningful challenges. Not impossible challenges. Not burnout. Not exhaustion. Meaningful challenges. Example Choosing: The workout you don't feel like doing. The difficult conversation you've been avoiding. The presentation that makes you nervous. The study session when you'd rather scroll your phone. Every time we choose effort over comfort, we may be strengthening the neural systems responsible for persistence and researchers also would say, the will to live. The Secret to Keeping the Loop Going After everything we've learned this phase, the answer is surprisingly simple: The loop stays alive when effort feels worthwhile. That means: ✅ Meaning ✅ Purpose ✅ Focus ✅ Progress ✅ Recovery ✅ Challenge But not too much challenge. Because challenge without recovery becomes burnout. And recovery without challenge becomes stagnation. The sweet spot lies in the middle. Instead of blaming ourselves, we can start diagnosing the system to build a stronger, more resilient version of ourselves. How to Use the "Find Your Gap" Framework Whenever you feel: Stuck Unmotivated Burned out Distracted Overwhelmed Plateaued Ask yourself: Which phase is broken? Because the problem is rarely "everything." Usually it's one phase creating a bottleneck for the others.   Phase 1 Gap: Regulation & Safety Ask: Am I sleeping well? Am I recovered? Is stress overwhelming me? Is my nervous system regulated? Signs This Is Your Gap Anxiety Exhaustion Brain fog Poor sleep Irritability Example A teacher can't focus. They assume they need more motivation. But they're sleeping 5 hours a night. The real gap isn't motivation. It's regulation. Solution Fix: Sleep Recovery Stress management First.   Phase 2 Gap: Neurochemistry & Motivation Ask: Do I still know why this matters? Am I seeing progress? Has the reward disappeared? Have I lost momentum? Signs This Is Your Gap Procrastination Lack of drive Loss of enthusiasm Feeling stuck Example This was your hiking example. You still had the ability. You still had the discipline. You simply stopped feeling rewarded by the effort. Solution Repair the Motivation Loop: Reconnect to purpose Reduce challenge temporarily Improve recovery Look for progress   Phase 3 Gap: Movement, Learning & Cognition Ask: Am I moving enough? Am I physically engaged? Am I learning new things? Is my brain being challenged? Signs This Is Your Gap Low energy Mental sluggishness Poor concentration Feeling mentally flat Example Someone spends 10 hours at a desk. Their motivation is fine. Their sleep is fine. But they're sedentary. Movement is the missing ingredient. Solution Move first. The research from Chuck Hillman and John Ratey suggests movement often improves: Attention Mood Learning Memory   Phase 4 Gap: Perception, Emotion & Social Intelligence Ask: Am I seeing this situation clearly? Am I understanding others? Do I feel connected? Signs This Is Your Gap Conflict Miscommunication Isolation Emotional reactivity Example A leader thinks: "Nobody supports my vision." But the real issue is communication. The gap isn't motivation. It's perception. Solution Improve: Listening Emotional awareness Perspective-taking Relationships   Phase 5 Gap: Integration, Insight & Meaning Ask: Does this align with who I want to become? Am I moving toward something meaningful? Do I have clarity? Signs This Is Your Gap Success without fulfillment Feeling lost Lack of direction Constantly chasing goals Example Someone has achieved everything they wanted professionally. But they still feel empty. The gap isn't performance. It's meaning. Solution Reconnect with: Values Purpose Identity Contribution to the World. The Most Powerful Question At the end of every week, ask: "Where is my gap?" Is it:

Hey folks, Alex here, and welcome to a BIG MODEL week! We finally got Mythos (well almost)! Let me catch you up! This week started with WWDC26 from Apple, and Max Weinbach, who was in the room at Apple Park and actually has access to some of the new features including an all new SIRI AI, joined us to break down what could be the most used AI in the world very soon. At first I was skeptical, but he convinced me that the new Siri is actually good! Then, we saw the ultimate model drop: Anthropic finally shipped Mythos (X, my system card thread, benchmarks). Same weights, two names: Mythos 5 is the unrestricted version that only Project Glasswing partners get, Fable 5 is what the rest of us get, wrapped in the heaviest guardrails I've ever seen ship on a frontier model. It's state of the art on nearly every benchmarkThe model that was “too dangerous to release” is now... well, released, but with the heaviest guardrails we've seen. More on this later. Peter Gostev from Arena.ai joined us to break down the new model. Last but definitely not least, Google released a real-time translation model, that our friend Thor Schaeff from DeepMind demoed live, while we all spoke in different languages and it translated us in REAL TIME. It was really cool, definitely check that out. There's quite a few more things, like Loop Engineering Alpha, Swyx came by to talk about FrontierCode, OpenAI confirmed our suspicions that the anti-datacenter social media posts could be a concerted effort by groupds links to the Chinese government and much more. Let's dive in! ThursdAI - Let me catch you up, every week!

Teepa Snow, MS, OTR/L, FAOTA, explores how cognitive impairment and dementia can show up in clinical work. She offers practical guidance for adapting communication, supporting caregivers, preserving dignity, and expanding care when clients need more support. Interview with Elizabeth Irias, LMFT. Earn CE credit for listening to this episode by joining our low-cost membership for unlimited podcast CE credits for an entire year, with some of the strongest CE approvals in the country (APA, NBCC, ASWB, and more). Learn, grow, and shine with Clearly Clinical Continuing Ed by visiting https://ClearlyClinical.com. Hosted on Acast. See acast.com/privacy for more information.

Chantel Prat studies how different brains make sense of the world. Her work starts from a simple idea: every experience leaves a mark. The inputs we consume shape how we think, what we notice, and ultimately who we become. The conversation explores why people often choose familiar rewards over uncertain opportunities to learn. Chantel explains the tension between exploration and exploitation, why curiosity is essential for growth, and how fear can prevent us from engaging with new technologies like AI. They also discuss theory of mind, cognitive offloading, and what happens when we increasingly rely on AI for thinking. The goal is not simply to do better work, but to use AI in ways that help us become better versions of ourselves.Key Takeaways: Curiosity requires safety When people feel threatened, they become defensive rather than exploratory. Fear gets in the way of learning. Better inputs create better outputs Every experience leaves a footprint on the brain. The ideas, conversations, and information we consume shape how we think and who we become. We naturally favor certainty over exploration Our brains are biased toward familiar rewards, even when something new may offer greater long-term value. Curiosity starts with admitting you might be wrong Learning requires recognizing that you do not already have the answer. Without that openness, exploration never begins. Use AI to become better, not just produce more The most important question is not what AI can do for you, but what you still want to get better at yourself. Chantel Prat: linktr.ee/chantelprat The Neuroscience of You: The-Neuroscience-of-you/book 00:00 Curiosity Versus Threat00:31 Meet Chantel Prat01:02 Why Input Shapes Brains04:08 The Output Pressure Trap05:52 Exploration Versus Exploitation10:05 Average Brains And Teams15:35 Theory Of Mind Defined22:12 Practicing With AI Feedback24:31 Offloading Thinking To AI29:50 Humans In The Loop35:16 Age And Tech Reactions42:15 Why Curiosity Requires Safety48:15 Personal Codex And AI50:54 Becoming More Yourself54:34 The Debrief

Episode 398 revisits neuroscientist Friederike Fabritius (from November 2022) to explain how three ingredients — fun (dopamine), fear (productive challenge), and focus — create the neurochemical conditions for sustained motivation and flow. You'll also learn why individual neurosignatures matter and how designing environments that match your brain, rather than forcing yourself to change, makes effort easier and motivation durable. Welcome back to Season 15 of the Neuroscience Meets Social and Emotional Learning Podcast. I'm Andrea Samadi, and on this podcast, we bridge the science behind social and emotional learning, emotional intelligence, and practical neuroscience so we can create measurable improvements in well-being, achievement, productivity, and results. In This Episode 398, Closing the Motivation Loop, with Friederike Fabritius, We Will Cover: ✔ How FUN, FEAR, and FOCUS create the neurochemical conditions for sustainable motivation ✔ Why dopamine is more than a pleasure chemical—and how it fuels motivation, anticipation, effort, and reinforcement ✔ How FUN creates dopamine and keeps us engaged in meaningful work ✔ Why the right amount of FEAR (challenge) drives growth without causing burnout ✔ How FOCUS converts energy, attention, and motivation into measurable results ✔ The connection between FUN, FEAR, FOCUS, and the Motivation Loop ✔ Why different brains require different motivation strategies ✔ Understanding your unique "Neurosignature" and how it influences performance ✔ How dopamine interacts with other neurochemicals like testosterone, estrogen, serotonin, and oxytocin ✔ Why sustainable motivation begins with self-awareness ✔ The Stress vs. Performance Curve and finding your optimal challenge zone ✔ How under-challenge leads to boredom and over-challenge leads to burnout ✔ Why peak performance occurs when challenge matches your brain's needs ✔ How to design environments that support attention, motivation, and performance ✔ Why the strongest motivation loops are powered by alignment—not willpower ✔ Practical strategies to create the conditions where your brain naturally wants to engage and perform ✔ How self-awareness, energy management, and neurochemistry work together to sustain long-term success ✔ What keeps the Motivation Loop repeating—and what causes it to break ✔ How to close Phase 2: Neurochemistry & Motivation and prepare for Phase 3: Movement, Learning & Cognition

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https://novacut.ai/  https://genaimeetup.com/  Anthropic has officially closed a $65 billion Series H at a $965 billion valuation, nearly 2.5x its valuation from just 100 days ago. Meanwhile, funding is flowing across the ecosystem: Frameworks AI at $15B, Baseten at $11B, OpenRouter's $113M Series B, and Cognition AI's $1B Series D. NVIDIA went on an open-source super week with Nemotron 3 Ultra, Cosmos 3, and Nemotron 3.5 ASR. Microsoft dropped 5 new MAI models. Google released Gemma 4 12B, and Anthropic shipped Opus 4.8. On the benchmarks front, DeepSWE crowns GPT-5.5 as the leader in long-horizon coding tasks, while ITBench shows even frontier models struggle with real-world SRE incidents — Claude Opus 4.7 tops out at just 47%. Plus: Cloudflare acquires VoidZero to build the future of AI-native edge development, and Google is paying SpaceX $920M/month for compute. Topics covered: • Anthropic's $65B Series H and path to $1T • Fireworks AI, Baseten, OpenRouter & Cognition funding rounds • Microsoft's 5 new MAI models • NVIDIA's open-source super week (Nemotron, Cosmos 3) • MiniMax M3, Gemma 4 12B, JetBrains Mellum2, Opus 4.8 • DeepSWE benchmark: GPT-5.5 leads long-horizon coding • ITBench: Frontier models under 50% on real SRE tasks • Cloudflare + VoidZero for AI-native edge dev • Google's $920M/month SpaceX compute deal #AI #Anthropic #NVIDIA #OpenAI #AInews #TechNews #LLM     Funding rounds Anthropic formally confirmed the closure of its $65 billion Series H funding round at a post-money valuation of $965 billion. This represents a 2.5-fold increase over its $380 billion Series G valuation from February 2026, adding $585 billion in value in approximately 100 days https://www.anthropic.com/news/series-h  Frameworks AI raising at 15B valuation representing a near fourfold increase from its $4 billion Series C valuation recorded in October 2025 processing 15 trillion tokens daily for major production clients including Cursor, Notion, and Perplexity https://finance.yahoo.com/sectors/technology/articles/fireworks-ai-eyes-15-billion-174609357.html Baseten is raising 1B at 11B valuation annualized revenue, which skyrocketed from $200 million to $600 million over a single quarter https://techstartups.com/2026/05/26/ai-inference-startup-baseten-in-talks-to-raise-1-billion-at-11-billion-valuation/  OpenRouter has secured a $113 million Series B funding OpenRouter has experienced exponential traffic growth, with weekly production throughput expanding fivefold from 5 trillion to 25 trillion tokens over a six-month horizon https://www.businesswire.com/news/home/20260526953416/en/OpenRouter-Raises-%24113-Million-CapitalG-led-Series-B-as-Weekly-Volume-Explodes-to-25T-Tokens  Further up the stack: Cognition AI secured a $1 billion Series D round led by Lux Capital and 8VC https://cognition.ai/blog/series-d   Model Releases MAI models: MAI-Code-1-Flash: A 5-billion active parameter model optimized for ultra-low latency within GitHub Copilot and VS Code. MAI-Image-2.5: A high-fidelity image generation model ranking third on global image evaluation arenas, outperforming competing architectures like Nano Banana Pro. MAI-Transcribe-1.5: A multi-lingual speech processing engine offering fivefold speed improvements across 43 languages. MAI-Voice-2: Natural audio and voice generation across 15 languages, available at a highly competitive price point. Web IQ: A search-grounding API engineered to directly compete with Perplexity. https://microsoft.ai/models/    https://www.peoplematters.in/news/ai-and-emerging-tech/uber-imposes-dollar1500-monthly-ai-spending-limit-on-employees-amid-rising-costs-50073    Nvidia has executed an "Open-Source Super Week," positioning itself as a dominant software and model publisher: Nemotron 3 Ultra (best US open source open weights model but behind china): A massive 550-billion parameter MoE (55 billion active) designed with a 1-million token context window, optimized specifically for high-throughput, cyclical agent loops. It achieved peak throughput rates of 400 tokens per second on day-zero optimized clusters. Cosmos 3: A physical AI world-modeling framework comprising 16-billion Nano and 64-billion Super variants. Built on a Mixture-of-Transformers (MoT) architecture, Cosmos 3 natively binds textual, visual, auditory, and physical kinetic vectors. Nemotron 3.5 ASR: A highly compact 0.6-billion parameter streaming speech recognition model pushing sub-100 millisecond latencies across 40 language locales.   https://www.minimax.io/models/text/m3  MiniMax M3: A 1-million token context model hitting 59.0% on SWE-Bench Pro and 74.2% on MCP Atlas, though noted for high token consumption due to intensive internal self-validation loops.   https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemma-4-12b/  Gemma 4 12B: Google's Apache 2.0 on-device model, which utilizes an encoder-free architecture that projects vision and audio vectors directly into the text-token space, bypassing separate CLIP-style encoders to minimize local memory footprints. https://www.jetbrains.com/mellum/  JetBrains Mellum2: A compact 12-billion parameter MoE (2.5 billion active) engineered for ultra-low latency routing and retrieval-augmented generation (RAG) sub-agents within developer IDEs. Opus 4.8 https://www.anthropic.com/news/claude-opus-4-8    https://www.cnbc.com/2026/06/05/google-to-pay-spacex-920-million-a-month-for-xai-compute-capacity.html      Benchmarks: https://deepswe.d atacurve.ai/blog https://venturebeat.com/technology/deepswe-blows-up-the-ai-coding-leaderboard-crowns-gpt-5-5-and-finds-claude-opus-exploiting-a-benchmark-loophole (GPT 5.5 the winner in long horizon tasks) a highly complex software engineering benchmark focused on original, long-horizon tasks across five distinct programming languages. Comprising 113 chaotic tasks across 91 live, production-grade repositories, DeepSWE forces agents to generate 5.5 times more code and modify an average of 7 separate files per task compared to standard evaluations. On this challenging leaderboard, GPT-5.5 leads with a score of 70%, establishing a significant 16-percentage-point lead over contemporary alternatives I think older benchmarks where models reach ~90% accuracy can be considered saturated. Few percentage points don't give us any good signal.  https://research.ibm.com/publications/developing-ai-agents-for-it-automation-tasks-with-itbench  ITBench-AA, an evaluation framework focusing on live Kubernetes incident response and Site Reliability Engineering (SRE) operations. Comprising 59 live, containerized SRE incident snapshots, the results are remarkably sobering: every frontier model scored under 50% on successful incident resolution, with Claude Opus 4.7 leading at 47% and GPT-5.5 following closely at 46%.   Edge AI announcements: https://www.cloudflare.com/press/press-releases/2026/cloudflare-acquires-voidzero-to-build-the-future-of-the-ai-native-web/  The consolidation of the AI-native developer stack has reached the runtime virtualization layer. Cloudflare recently completed the acquisition of VoidZero, the development group responsible for Vite, Vitest, Rolldown, and Oxc, backing the transaction with a $1 million open-source ecosystem fund. This acquisition is highly strategic; as autonomous agents write an increasing proportion of production software, local development environments, compilation pipelines, and bundlers must be optimized for execution speeds that match agent speeds. Cloudflare's goal is to construct a localized, full-stack edge playground. In this sandbox, AI agents can generate, test, bundle (utilizing the highly parallelized, Rust-based Oxc and Rolldown engines), and deploy entire web applications end-to-end within milliseconds. This architecture completely bypasses traditional local machine container bottlenecks, enabling high-velocity agent loops to execute in a fully sandboxed, web-scale edge runtime.

Our 247th episode with a summary and discussion of last week's big AI news!Recorded on 06/03/2026Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at andreyvkurenkov@gmail.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:Anthropic released Claude Opus 4.8 with improved benchmark scores, discussed eval-awareness findings and welfare/corrigibility themes from its system card, and introduced Dynamic Workflows for long-running multi-agent tasks.Microsoft unveiled the always-on Microsoft Scout assistant built on OpenClaw plus new in-house MAI models (including MAI Thinking 1) and “frontier tuning,” emphasizing enterprise security architecture and model-from-scratch capability.Major business moves included Anthropic's $65B Series H at a $965B valuation alongside an IPO filing, a JPMorgan analysis arguing OpenAI needs major revenue growth to justify infrastructure spend, and Cognition raising $1B at a $25B valuation.Policy and security highlights covered Trump's voluntary pre-release government testing framework for powerful AI, Meta AI support being exploited to hijack Instagram accounts, tightened US Nvidia export controls and China's travel approvals for AI experts, plus expanded Glasswing/Mythos-style cyber and biodefense initiatives.Timestamps:(00:00:10) Intro / Banter(00:04:10) Sponsors(00:07:10) News PreviewTools & Apps(00:07:54) Anthropic releases Opus 4.8 with new 'dynamic workflow' tool | TechCrunch(00:22:37) Microsoft Scout is a new AI personal assistant built on OpenClaw | The Verge(00:26:55) Microsoft launches new MAI family of AI models at Microsoft Build | Mashable(00:37:43) Robinhood now lets your AI agents trade stocks | TechCrunch(00:40:49) OpenAI launches new Codex tools for white-collar work | TechCrunch(00:43:40) ElevenLabs' new music-generation model can switch genres mid-track | TechCrunchApplications & Business(00:44:35) Anthropic Hits $965 Billion Valuation, Surpassing OpenAI - WSJ(00:45:32) Anthropic Files to Go Public, Setting Stage for Huge I.P.O. - The New York Times(00:51:15) China's ByteDance Developing New AI Chips Like Those from Nvidia Partner Groq(00:55:00) Anthropic expands Mythos to 150 additional organizations(00:55:35) OpenAI needs a 26x revenue increase to justify its buildout(00:58:46) AI coding startup Cognition raises $1B at $25B pre-money valuation | TechCrunchProjects & Open Source(01:00:50) MiniMax-M3 debuts, eclipsing GPT-5.5 and Gemini 3.1 Pro on key benchmark performance for just 5-10% of the cost | VentureBeatPolicy & Safety(01:06:08) Trump Signs Executive Order Seeking Oversight of A.I. Models - The New York Times(01:11:45) Hackers Simply Asked Meta AI to Give Them Access to High-Profile Instagram Accounts. It Worked(01:13:058) Chinese AI experts in private firms now required to secure approval before international travel — Beijing enforces policy to secure top-tier talent, expands measures beyond government(01:17:53) U.S. Tightens Controls on Nvidia AI Chip Exports | Let's Data Science(01:21:47) OpenAI launches Rosalind Biodefense, offers federal agencies early access to its life-sciences model(01:24:00) Using LLMs to secure source code(01:26:19) Project Glasswing: An initial update(01:29:30) White House Approves $9 Billion for Spy Agencies to Catch Up on A.I.(01:32:11) US Law Enforcement Warns of ‘Anti-Tech Extremism' as AI Hatred GrowsSynthetic Media & Art(01:35:38) YouTube will now automatically label AI videos | TechCrunchResearch & Advancements(01:36:22) Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention(01:41:26) From Simulation to Enaction: Post-trained language models recognize and react to their own generationsSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Hey folks, Alex here, let me catch you up! I've had a feeling that this week is going to be crazy, as it started on the weekend MiniMax M3, then with Jensen announcing new RTX Spark, NVIDIA's first PC chip packing 1 petaflop of local AI power into thin laptops.A few days later at Microsoft BUILD, Satya & Mustafa from MAI dropped 7 AI models, completely pre-trained from scratch, including a new MAI-thinking-1, MAI-code and MAI-image 2.5 that started topping the image gen charts. Then other image models started racing to the top of the Arena benchmarks, IdeoGram 4 hitting becoming SOTA open weights image-gen model, and Reve 2 beating Nano Banana just a few hours after that. And then today, NVIDIA dropped Nemotron 3 Ultra, their latest 550B open weights model, data and training and Arena published a new agentic eval leaderboard and we got a new Gemma 4 12B. I've had the great pleasure to host Chris (@llm_wizard) from Nvidia, Peter Gostev from Arena and Karan from Nous Research (who were featured prominently by Jensen!) all on the show. Def don't miss this one! Let's get into the details. ThursdAI - Join the flock of folks who know what is happening in AI before everyone else.Open Source LLMs

AGENDA: 00:00:00 — Private Markets Are "F***ing Done" & The Shift to Heavy CapEx 00:00:46 — Anthropic Files to Go Public  00:04:59 — Will the Anthropic IPO Break the Startup Ecosystem? 00:06:22 — The "Billion-Dollar Position" Era: VCs Reset Their Expectations 00:18:11 — The Trillion-Dollar Cash Grab: Google, SpaceX, and OpenAI Rush the Queue 00:23:15 — Is the SaaS Apocalypse Over? Bouncing Off the Bottom 00:25:34 — The Death of Human Per-Seat Licenses as Multiples Shift 00:27:18 — Winners vs. Losers: How Agentic Focused Products Captured the Market 00:30:26 — Cognition Raises $1 Billion at a $26 Billion Valuation 00:33:04 — Token Budgeting Panic Hits Corporate America 00:35:46 — Multi-Model Workflows and the Future of Cost Containment 00:41:20 — Choosing Tokens Over Humans: The 2027 Engineering Reality Check 00:46:42 — Can Large Companies Survive Slashing One-Third of Their Engineering Talent? 00:57:40 — Big Law Flex: Kirkland & Ellis Pledges $500 Million to Build In-House AI 01:01:21 — Giving Away the Crown Jewels: Will Firms Trust Claude? 01:08:44 — Robinhood's AI Move: Automating Financial Planning vs. Beating the Market 01:16:15 — Apollo Warns PE Software Returns Are About to Be Disastrous 01:19:15 — $10 Billion Carry Pools: Will VC Winners Quit the Game? 01:24:10 — The 9-9-6 Work Ethic: Performative Theatre or Startup Reality? 01:30:10 — The Great Valley Contradiction: Working 24/7 to Automate White-Collar Work  

Jeremy Bedingfield (Southern California narcotics interdiction officer, Cartel Traps founder) shares the real methods for identifying and stopping drug loads on highways. Managing a GSP's competing instincts, reading suspects through interviews, finding hidden compartments, and the legal future of K9 detection.What We Cover:Why GSPs are harder to work in narcotics (genetically wired for bushes, not drugs)Building reasonable suspicion: the interview technique that reveals liesVehicle targeting: what smugglers' cars have in commonThe two-direction search pattern (why it matters)Systematic vehicle search: start underneath, work inward void by voidReal training vs. parking lot training: why they're differentDealing with 20+ kilo loads (changes dog expectations)Fentanyl reality: mixed loads, quick imprinting, prevalence on highwaysBody cam footage: what handlers miss in real timeThe future: AI harness technology (5-10 years away)Jeremy breaks down tradecraft that's rarely discussed publicly—from target selection to compartment location to creative training solutions. He also discusses why the legal system is moving toward objective K9 data (harness technology with biological algorithms) rather than handler interpretation.For: Drug dog handlers, narcotics officers, interdiction teams, law enforcement exploring K9 evidence in court.________________________________________

Many of you may have heard me talk about the impact of brain development on behavior in different life stages (if not, check out my TEDX Talk - link below).  Well, dopamine is in all of us - do you know it is intricately connected with "rewards"?  And CBT is intricately connected to dopamine.Are we seeing a symbiotic relationship?  Check out this episode where we get the dope on dopamine.Be sure to sign up to our email list on the website, and check out the new program structure and pricing options.My TEDX Talk is live!  Beyond Dog Training: The Movement Toward Sentiencehttps://youtu.be/avUugazybwcFind all the episodes on Feedspot, where Dog Training DisrUPted is rated in the top 5 shows in the dog category in Canada:  https://blog.feedspot.com/canadian_dog_podcasts/To become a certified Canine CBT Psychotherapist, and for courses on related topics, please visit the Institute of Canine Psychotherapy. www.instituteofcaninepsychotherapy.comBecome a Certified Canine Behaviorist and Dog TrainerMy Linktree with all my media, presentations, shows, articlesBillie Groom - UPWARD Dogology | Instagram, Facebook | LinktreeHere is the link to the recent article in Psychology Today Mag by Marc Bekoff on Canine CBTDog Training: Perception, Cognition, and Emotions | Psychology TodayBuy My Book! Winner of the 2019 American Best Book Fest Award (pets/narrative/non-fiction)The Art of Urban People With Adopted and Rescued Dogs Methodology: Rescued Dogs: The Misunderstood Breed: Groom, Billie: 9781525547287: Books - Amazon.ca

In this solo mini episode, I talk about creatine. What it is, who may benefit from taking it, if vegans are deficient, and the emerging research on brain health, mood, and cognition for women in perimenopause and menopause. This mini solo episode covers: What creatine is and how your body already makes it (and why that matters for how we talk about it) Why vegans and vegetarians have measurably lower creatine levels and if that even matters How creatine went from the 1992 Barcelona Olympics to becoming one of the most studied supplements in nutritional science The numbers: how much non-vegans get from food versus what a supplement provides The 2025 CONCRET-MENOPA randomised controlled trial and what it found for perimenopausal and menopausal women Who has a strong case for supplementing, who doesn't need to worry, and how to think about it for yourself Whether you lift weights, you're navigating perimenopause, or you're just curious about a supplement that keeps coming up, this episode gives you the full picture without the hype. References: The Effects of 8-Week Creatine Hydrochloride and Creatine Ethyl Ester Supplementation on Cognition, Clinical Outcomes, and Brain Creatine Levels in Perimenopausal and Menopausal Women (CONCRET-MENOPA): A Randomized Controlled Trial. Benefits of Creatine Supplementation for Vegetarians Compared to Omnivorous Athletes: A Systematic Review.  Muscle creatine levels and sprint performance in young adult vegans and vegetarians after 7 days of creatine monohydrate supplementation. Creatine Supplementation Beyond Athletics: Benefits of Different Types of Creatine for Women, Vegans, and Clinical Populations. Creatine in health and disease. Creatine. VeganHealth.org. "Updates to Weightlifting for Vegans" The information shared in this episode is for educational purposes only and does not constitute medical advice. Always consult with your healthcare provider before starting any new supplement, particularly if you have an existing health condition or are taking medication. ____________________________________________________________________ Work With Me: If you're ready to go plant-based or already are and want to feel more confident about your nutrition, I offer private consultations with personalized, evidence-based guidance. No overwhelm, just clarity. Book your free 15-minute discovery call at synergynutrition.ca ___________________________________________________________________ Vegan Boss Resources:

Today we're talking about research-based, proactive steps you and I can take now to (hopefully) avoid developing Alzheimer's dementia later. I hope you'll listen in and be encouraged! Show Notes VERSES CITED: John 3:30 - “He must increase; I must decrease.” Thessalonians 5:17 - “Pray without ceasing.” Colossians 4:2  - “Devote yourselves to prayer, being watchful and thankful.” Ephesians 6:18  - “Pray in the Spirit on all occasions with all kinds of prayers and requests.....” Philippians 4:6-7 - “Be anxious for nothing, but in everything by prayer and supplication....” Mark 12:30 – “And thou shalt love the Lord thy God with all thy heart, and... soul, and... mind, and...strength.” 1 Corinthians 10:31- “Whether, then, you eat or drink or whatever you do, do all to the glory of God.” John 3:30 – “He must increase, but I must decrease.” RELATED LINKS: Your Parent Has Dementia. Here's the $405,000 Survival Guide Nobody Gave You Gut: The Inside Story of Our Body's Most Underrated Organ Blueberries, the well-known 'super fruit,' could help fight Alzheimer's Study of green tea and other molecules uncovers new therapeutic strategy for Alzheimer's Preventive Effects of Olive Oil on Alzheimer's Disease: What to Know Eating Avocados: Does It Help Prevent Dementia The effect of curcumin (turmeric) on Alzheimer's disease: An overview Beneficial Effects of Walnuts on Cognition and Brain Health Association of Egg Intake with Alzheimer's Dementia Risk in Older Adults Rainbow Salad Recipe Comparison of types of diabetes The Hidden Threat: How Refined Grains and Sugar Impact Dementia When Diet Meets Dementia Intermittent Fasting as a Neuroprotective Strategy: Gut–Brain Axis Modulation and Metabolic Reprogramming in Neurodegenerative Disorders Research reveals: smart wives reduce the risk of Alzheimer's disease Marital status and risk of dementia over 18 years: Surprising findings from the National Alzheimer's Coordinating Center Marriage linked to reduced Dementia Risk Inside the Box Free Printable Prayer Guides Pimsleur Language Program Why "Grandma Hobbies" Could Be the Secret to Better Mental Health Is Sunshine Key to Reducing Dementia Risk Influence of physical activity on cognition and brain function Association Between Mentally Stimulating Activities in Late Life and the Outcome of Incident Mild Cognitive Impairment Three science backed lifestyle changes to lower your dementia risk Reading writer lower dementia risk study finds Reading Challenge Bible Memory Tips and Tricks A Grand Investment Can prayer reduce the risk of Alzheimer's? Prayer regularly reduces risk of dementia STAY CONNECTED:  Subscribe: Flanders Family Freebies -weekly themed link lists of free resources Instagram: @flanders_family - follow for more great content Family Blog: Flanders Family Home Life - parenting tips, homeschool help, printables Marriage Blog: Loving Life at Home- encouragement for wives, mothers, believers My Books: Shop Online - find on Amazon, at Barnes & Noble, or through our website    

At Cisco Live, I sat down with Papi Menon, Vice President of Product Management at Outshift by Cisco, to explore one of the most ambitious ideas emerging in the AI world today. While much of the industry remains focused on larger models and individual AI agents, Outshift is asking a different question. What happens when millions of AI agents need to collaborate across organizations, platforms, and industries? Papi joined me to explain the thinking behind Outshift, Cisco's emerging technology and incubation group, and the work they're doing to help shape the next era of AI. Our conversation explored concepts such as the Internet of Agents, the Internet of Cognition, and AGNTCY, an open-source initiative designed to create the foundations for agent-to-agent collaboration at scale. We discuss why connecting AI agents is only the first step, why shared intent and shared context could become as important as connectivity itself, and how organizations may need entirely new infrastructure to support an increasingly agent-driven future. Papi also shares his perspective on the challenges of interoperability, governance, trust, and security as AI systems become more autonomous and interconnected. The discussion moves beyond today's AI headlines and into the bigger questions facing the technology industry. If the internet connected people and systems, what infrastructure will be needed to connect intelligence itself? And what role can open standards play in ensuring that future remains collaborative rather than fragmented? Whether you're a technology leader, developer, strategist, or simply curious about where AI is heading next, this conversation offers a fascinating glimpse into how Cisco is thinking about the future of agentic computing and the foundations that may underpin the next major platform shift in technology. How do you think AI agents will collaborate in the future, and should that future be built on open standards or closed ecosystems?

In this episode, I share my Human Design story and why I continue to return to the same foundational teachings year after year. These five elements, Determination, Strategy, Authority, Environment, and Cognition, completely changed the way I move through life.Inside this conversation, we explore:• My personal Human Design journey• Why the foundations matter more than advanced information• The five elements I continually return to• How these teachings impact decision-making, relationships, business, health, and alignment• Why embodiment will always outperform informationBecause the goal isn't to know your Human Design.The goal is to live it.RESOURCES:CLICK HERE for the Big 3 in Human Design EpisodeCLICK HERE to order your Alchemy of You manualCLICK HERE to learn about the Find Your WAI membershipCLICK HERE to DM me PHOENIX to learn about my 5-month initiation for the woman standing at the threshold of her next chapter, knowing the current version of herself cannot carry her where her soul is asking her to go next.CLICK HERE to DM me to learn more about the Gauntlet, my 3-6-month private mentorship container, or the Identity Reset Retreat to learn about coming to Austin for a 5-day, 4-night private initiation with me.Support the show✨ Thank you for listening! Check out the links below to connect with me!

Alfred Wahlforss, co-founder and CEO of Listen Labs, is building an AI agent that interviews your customers at a scale no focus group ever could—thousands of voice conversations at once, drawn from an audience of 30 million people. A year after launch, Listen serves hundreds of Fortune 100s to Startups including Microsoft, Google, NBC Universal, P&G, Anthropic, Cursor, and Cognition. Alfred explains the counterintuitive finding underneath it all: people are often more honest with an AI than a human interviewer, opening up to a non-judgmental entity that costs less and never makes them feel rushed. He walks through why interview transcripts—not credit card data or behavioral logs—turn out to be the richest fuel for predicting how customers will behave, how Listen back-tests its simulations to know which questions it can and can't answer, and why 80% of the company's engineering goes into building the right audience. As AGI makes building trivial, Alfred argues the scarce resource becomes knowing what to build. That's the loop Listen wants to own.

The Information's Akash Pasricha breaks down Amazon's shifting ad strategy with Catherine Perloff and its increasing reliance on third-party firms to scale ads on Twitch, Goodreads, and IMDb. Mostly Media founder CJ Gustafson joins to dissect Alphabet's unprecedented $80 billion equity raise for AI compute and the market implications of Anthropic's confidential IPO filing. JetStream Security CEO Raj Rajamani outlines the cybersecurity and budget risks of internal "citizen developers" using advanced models like Anthropic's Mythos. Finally, Rocket Drew reviews Cognition's rebranding of Windsurf to Devin Desktop and explains why frontier AI models are learning to detect when they are being evaluated.Articles discussed on this episode: https://www.theinformation.com/articles/amazons-media-empire-quietly-taps-outside-ad-sales-helphttps://www.theinformation.com/newsletters/ai-agenda/cognition-aims-switzerland-ai-agents-app-makeoverhttps://www.theinformation.com/newsletters/the-briefing/googles-ai-fundraising-anthropics-ipo-optionSubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/Chapters:00:00 - Introduction01:13 - Amazon leans more heavily on outside firms for ad sales11:12 - Alphabet to Sell $80B in Stock for AI Investment23:55 - Anthropic expanding Project Glasswing34:43 - Cognition rebrands Windsurf app for AI era

- The Guest: Holistic Healer/ Coach MichelleHammel— Follow/ Contact: IG @unmasquing Website: www.gofauxhawkyourself.com—From surviving to self‑rebuilding — this is the episode that will change how you see your past, your patterns, and your power.-Holistic Trauma‑Informed Coach Michelle Hammel joins Marcos Luis for a raw, soul‑shifting conversation about her journey through childhood trauma, CPTSD, and emotional reconstruction — and how she transformed her pain into the CRAFT Method, a groundbreaking approach to healing that's helping people worldwide reclaim their lives.—Michelle breaks down the real work of recovery: calming the nervous system, unlearning survival mode, rebuilding identity, and finally feeling safe in your own body again.This isn't “good vibes only” healing — it's honest, practical, compassionate, and deeply human.—If you've ever felt stuck, unseen, overwhelmed, or ready for a new chapter… this episode is your turning point.—

Can an IQ test comprehensively define an individual's intelligence? Are there aspects of human capability that tests fail to take into account?In the latest episode of A Book with Legs, Smead Capital Management CEO and Portfolio Manager Cole Smead is joined by professor, psychologist, and author Howard Gardner to discuss his book, titled "Frames of Mind: The Theory of Multiple Intelligences.”Cole and Howard explore how we measure and think about intelligence, highlighting that there is a spectrum of abilities beyond a single test score. They discuss the origins of intelligence testing, why an individual's role in society should not be conflated with their intelligence, and some of the less-often-considered forms of intelligence, such as musical and interpersonal skills intelligence.Howard Gardner is the Hobbs Research Professor of Cognition and Education at the Harvard Graduate School of Education. He is an expert on intelligence, creativity, leadership, and professional ethics; former Co-Director of Project Zero; and co-founder of The Good Project. Both a memoir (A Synthesizing Mind) and a study of higher education co-authored with Wendy Fischman (The Real World of College) were recently published by MIT Press. In 2024, Teachers College Press published a two-volume collection of his work, The Essential Howard Gardner on Mind and On Education.An updated edition of his book Frames of Mind was published by Basic Books in Spring 2026 with a new preface. Purchase "Frames of Mind: The Theory of Multiple Intelligences” here: https://www.hachettebookgroup.com/titles/howard-gardner/frames-of-mind/9781541608528/?lens=basic-booksVisit Howard Gardner's Website and Blog - https://www.howardgardner.comSign up to be notified about new A Book with Legs episodes: https://hubs.ly/Q0452Lh70

Season 15, Episode 397 revisits research and real-world practice showing movement is more than fitness: it activates the brain, boosts attention, enhances learning, and sustains motivation. Dr. Chuck Hillman's studies reveal how even short bouts of exercise light up brain activity, while Paul Zientarski's Naperville program demonstrates how heart-rate monitoring and purposeful movement improve readiness, recovery, and academic performance. In EP 397: Movement, Motivation, and Brain Activation with Dr. Chuck Hillman and Paul Zientarski, we explore why movement may be one of the most powerful tools we have for improving brain function, learning, motivation, and performance. In this episode, we cover: ✅ Why most children are not meeting the recommended daily physical activity guidelines and what we can do to change that. ✅ How exposing children to a variety of activities helps them discover movement they enjoy—and are more likely to continue throughout their lives. ✅ Why there is no perfect exercise program, and why the best exercise is the one you'll consistently do. ✅ How enjoyment, reward, and dopamine reinforce healthy habits and keep the Motivation Loop repeating. ✅ What Naperville Central High School learned from heart rate monitoring and how recovery impacts performance. ✅ Why peak performance requires both effort and recovery. ✅ How exercise changes the brain, improving attention, learning, memory, and cognitive performance. ✅ The groundbreaking research behind Spark: The Revolutionary New Science of Exercise and the Brain and how it changed the way educators think about learning. ✅ Why movement is not a break from learning—but one of the most effective ways to prepare the brain for learning. ✅ How movement fits into our Phase 2 Motivation Loop, helping transform motivation into action and sustaining long-term performance. The biggest takeaway? Movement isn't just exercise. It's activation. It's preparation. It's performance. When we move our bodies, we activate the brain systems responsible for attention, learning, motivation, and success. The episode highlights practical takeaways: expose children to varied enjoyable activities, prioritize consistency over intensity, use movement as cognitive preparation, and track recovery to protect motivation. Movement becomes a bridge between motivation and sustained performance—improving focus today and long-term brain health tomorrow. Welcome back to Season 15 of the Neuroscience Meets Social and Emotional Learning Podcast. I'm Andrea Samadi, and on this podcast, we bridge the science behind social and emotional learning, emotional intelligence, and practical neuroscience so we can create measurable improvements in well-being, achievement, productivity, and results. Movement, Motivation, and Brain Activation with Dr. Chuck Hillman and Paul Zientarski This week, we continue our journey through Phase 2: Neurochemistry and Motivation, where we've been exploring one central question: What drives sustained effort and forward movement? So far, we've learned that motivation begins with belief and meaning from Bob Proctor[i], is shaped by our thought patterns with Dr. Caroline Leaf,[ii] strengthened through attention and reward with Dr. John Medina[iii], and powered by the brain's dopamine-based motivation system through Dr. Anna Lembke's[iv] work. But today, we arrive at a fascinating question: What happens when we actually move? Because motivation isn't just something that happens in the mind. The brain was designed to work in partnership with the body. And according to our review of today's two guests, one of the most powerful ways to activate attention, learning, memory, and motivation is through movement itself. This week we're revisiting insights from two pioneers whose work helped transform our understanding of movement and learning. First, Dr. Chuck Hillman, one of the world's leading researchers on exercise and brain function, whose groundbreaking research has shown how physical activity improves attention, executive function, learning, memory, and academic performance from EP 123[v] back in April 2021. Next, we will review Paul Zientarski, the former Physical Education Coordinator and football coach at Naperville Central High School, (In Illinois) whose work with the school's innovative Zero Hour PE Program helped put Naperville on the map for extraordinary academic achievement. Alongside his colleagues at Naperville, Paul demonstrated that exercise wasn't simply improving fitness—it was preparing students' brains to learn. Together, Dr. Hillman provides the science, while Paul Zientarski helps to demonstrate what that science looks like in the real world. Their combined work shows us that movement is far more than a physical activity. It is a powerful tool for activating the brain, enhancing learning, improving focus, and supporting the motivation needed for sustained performance. In other words, movement is the bridge between motivation and sustaining our performance. Let's dive in with Dr. Chuck Hillman and discover the science behind The Power of Movement and Brain Activation. CLIP 1: Getting Kids Moving for Life Summary In this clip, Dr. Chuck Hillman highlights a growing concern: the vast majority of children are not meeting the recommended physical activity guidelines. Current recommendations suggest that children should engage in at least 60 minutes of moderate-to-vigorous physical activity each day, including aerobic exercise and activities that strengthen bones and muscles. Dr. Hillman explains that the challenge isn't simply knowing the guidelines—it's finding ways to engage children in movement when many adults aren't meeting the recommendations themselves. This is why childhood is such an important time to expose young people to a wide variety of physical activities, helping them discover forms of movement they enjoy and can continue throughout their lives. Key Takeaways ✔ Most children are not getting enough physical activity. Many young people fall short of the recommended 60 minutes of daily movement needed for optimal physical and cognitive development. ✔ Movement supports both brain and body health. Exercise is not just about fitness—it supports attention, learning, memory, emotional regulation, and overall well-being. ✔ Children need exposure to different activities. Not every child will enjoy the same sport or activity. The goal is to help them discover movement they genuinely enjoy. ✔ Parents and adults model behavior. Children are more likely to be active when the adults around them value and participate in physical activity. ✔ Early habits can last a lifetime. The activities children enjoy today often become the healthy habits they carry into adulthood. Tips to Implement Expose Children to Variety

Claude Opus 4.8 arrives as a modest but meaningful upgrade, with early users pointing to better judgment, less bluffing, stronger self-checking, and a greater willingness to push back. NLW breaks down first impressions, benchmark comparisons with GPT-5.5, Claude Code's new dynamic workflows, and why the model harness may matter as much as the model itself. In the headlines: Kirkland & Ellis bets big on internal AI, OpenAI updates GPT-5.5 Instant, Cognition raises at a $26B valuation, Meta considers AI cloud, and Microsoft prepares new models.Brought to you by:KPMG – Research from KPMG and the University of Texas at Austin shows the highest-impact AI users treat AI like a reasoning partner — and those skills can be taught at scale. Learn more at ⁠⁠kpmg.com/us/Sophisticated⁠⁠Scrunch - The AI customer experience platform - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://scrunch.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Zenflow Work - Agents for knowledge work - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://zenflow.free/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Blitzy - Want to accelerate enterprise software development velocity by 5x? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AssemblyAI - The best way to build Voice AI apps - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.assemblyai.com/brief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://pod.link/1680633614⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Our Newsletter is BACK: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://aidailybrief.beehiiv.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Interested in sponsoring the show? sponsors@aidailybrief.ai

Everyone says AI is taking jobs. The data says something more complicated.In this episode of This Week in European Tech, Dan Bowyer and Mads Jensen of SuperSeed unpack the growing panic around AI-driven job losses, why junior hiring is falling across many industries and whether AI is actually the culprit.They explore new research suggesting remote work may be having a bigger impact on entry-level employment than AI, discuss the UK's record number of young people not in employment, education or training and examine what the data really shows about automation and labour markets.They also cover Anthropic's latest model release, the rise of AI application-layer companies, Europe's sovereignty debate, the economics of AI infrastructure and a zero-employee AI company that just raised $30 million.Topics coveredIs AI really replacing workers?Why junior hiring is fallingWhat the data says about AI and employmentAnthropic's rise and Opus 4.8Why the AI application layer is winningEurope's tech sovereignty dilemmaThe zero-employee AI company phenomenonAI infrastructure beyond GPUsTimestamps(00:00) The rise of the zero-employee AI company(04:50) Why AI applications are becoming more valuable(09:00) AI infrastructure moves beyond GPUs(16:00) Snowflake, Salesforce and enterprise AI adoption(24:00) Anthropic's latest model and valuation surge(27:00) Europe's sovereignty dilemma(33:00) The $30 million zero-employee AI startup(35:45) Is AI actually taking jobs?(38:00) What the data says about junior hiring(41:00) Why AI may not be the main cause(46:00) Predictions: which AI unicorn could fail next?(48:00) Deal of the week: Cognition and DevinFor more European venture, AI and startup insights, subscribe to EUVC, the home of European tech.

2005 veröffentlicht Dan Everett, ehemaliger Missionar und jetzt Linguist, einen zutiefst kontroversen Aufsatz über die Sprache und das Volk der Pirahã, das tief im Amazonasgebiet lebt. Von der wissenschaftliche Community wurden ihm bald Rassismus und Unwissenschaftlichkeit vorgeworfen, doch die Medien waren fasziniert. Dass Dan Everett seine Hypothesen immer wieder wiederholte hat und zusätzlich jemand ist, der gerne in der Öffentlichkeit steht, hat alles nur noch mehr befeuert. Wir klären in dieser Folge, was Everett eigentlich behauptet hat, was das für die Linguistik bedeutet hat und was davon heute geblieben ist.Ein Podcast von Anton und Jakob. Instagram: https://www.instagram.com/sprachpfade ___ Links:Tonaufnahme eines Pirahã: https://youtu.be/SHv3-U9VPAs?si=Nx6P6y4Gta9OoHZbÜber die Pirahã: https://pib.socioambiental.org/en/Povo:Pirah%c3%a3Pirahã im World Atlas of Language Structure (WALS): https://wals.info/languoid/lect/wals_code_prhDan Everett über die Pirahã: https://daneverettbooks.com/about-dan/about-the-pirahas/(nicht ganz unproblematischer) 3Sat-Beitrag über Dan Everetts Forschung bei den Pirahã: https://www.youtube.com/watch?v=CjSG_PfmuK8 ___ Die in der Folge erwähnten Aufsätze (chronologisch):Daniel Everett (2005): „Cultural Constraints on Grammar and Cognition in Pirahã. Another Look at the Design Features of Human Language“, in: Current Anthropology 46.4, S. 621-646.Andrew Nevins, Devid Pesetsky, Cilene Rodrigues (2009): „Pirahã Exceptionality. A Reassessment“, in: Language 85.2, S. 355-404. Daniel Everett (2009): „Pirahã Culture and Grammar. A Respone to Some Criticisms“, in: Language 85.2, S. 405-442. Andrew Nevins, Devid Pesetsky, Cilene Rodrigues (2009): „Evidence and argumentation. A reply to Everett (2009)“, in: Language 85.3, S. 671-681. ___ Abdruck von Everetts Aufsatz von 2005 mit einer kurzen Einordnung und Bibliographie der Kontroverse:Kap. "H. Linguistische Diskussionen", aus: Ludger Hoffmann (Hg.) (2019): Sprachwissenschaft. Ein Reader, 4. aktualisierte und erweiterte Auflage, Berlin/Boston, S. 1031-1087. ___ Das Buch von Dan Everett über seine Zeit bei den Pirahã:im englischen Original: Daniel Everett (2008): Don't Sleep, There Are Snakes. Life and Language in the Amazoian Jungle, New York.in deutscher Übersetzung: Daniel Everett (2010): Das glücklichste Volk. Sieben Jahre bei den Pirahã-Indianern am Amazonas, übers. v. Sebastian Vogel, München.Alle Literatur ausleihbar in deiner nächsten Bibliothek! ___ Gegenüber Themenvorschlägen für die kommenden Ausflüge in die Sprachwissenschaft und Anregungen jeder Art sind wir stets offen. Wir freuen uns auf euer Feedback! Schreibt uns dazu einfach an oder in die DMs: anton.sprachpfade@protonmail.com oder jakob.sprachpfade@protonmail.com ___ Titelgrafik und Musik von Elias Kündiger https://on.soundcloud.com/ySNQ6

The new AIEWF website is live! CFPs close in 2 days and we will run our first New Engineer Orientation this weekend, get your tickets booked ASAP as they -will- sell out. Take the AI Engineering Survey and get >$2k in credits and free AIE WF tickets!One of the central tensions in the agents industry is that even while there are major decacorn agent labs like Sierra, Decagon, Notion and Cursor being built up, it is also true that it has never been easier to DIY agents, with a plethora of agent frameworks like LangGraph and Pydantic and Flue, and managed agents from Anthropic and Gemini and Amazon. There has been a wave of companies building their own background agents from Shopify to Stripe to Paradigm to Razorpay, and even Cognition's friends Ramp have built their own coding agent with other friend Modal.You'd think Cognition might feel a bit threatened, but they're not - even after all this, they were way oversubscribed for the $1B Series D they just announced:Walden Yan, coiner of context engineering and Chief Product Officer/Cofounder of Cognition, invited OpenInspect's Cole Murray to talk about why the Devin is in the Details.Full conversation live on the pod today: In retrospect, async agents were the most AGI pilled bet you could make in 2024 - the models weren't good enough yet to vibecode, and people didn't trust AI enough to let it rip, nobody (including early Cognition) was sure about the form factors. Now it is obvious:* The first wave of AI coding tools made the developer faster but remain heavily in the loop. Copilor and Cursor's tab autocomplete are prime examples However, the workflow was still heavily centered around and bottlenecked by the developer's local workflow: a developer in an IDE, watching the model, accepting or rejecting changes, and pushing code one interaction at a time.* The second wave was local agents: Claude Code, Windsurf, Cursor's agents pane: first one and increasingly many terminals all running concurrently.* The current Age of Async Agents points to a different future focused more on agent orchestration which drives end-to-end development.According to previous guest Steve Yegge, there are finer-grained 8 levels to agent adoption, but we have collapsed it into three.As Cursor's Michael Truell put it in The third era of AI software development:Cursor is no longer primarily about writing code. It is about helping developers build the factory that creates their software. This factory is made up of fleets of agents that they interact with as teammates: providing initial direction, equipping them with the tools to work independently, and reviewing their work.The agent should not sit solely inside the developer's flow. It should be setup to work in the background so that you can give it a task, a repo, a machine, a shell, a browser, tests, memory, and review loops to go do the work somewhere else.In less than a year, the sentiment has shifted from avoiding multi-agent systems:to suggesting approaches that actually work:From coining “context engineering” to building the infrastructure behind Devin's 7x PR growth and jump from 16% to 80% of commits across Cognition repos, Walden Yan has had a front-row seat to the background-agent shift. In this episode, Cognition co-founder and CPO Walden Yan joins swyx alongside Cole Murray, creator of OpenInspect, to unpack why everyone is building their own Devin, what changed after the December 2025 model inflection, and why “spec to pull request” is now becoming a real production workflow.We go deep on the architecture of background agents: harness-in-the-box vs out-of-the-box, why Devin separates the “brain” from the machine, why repo setup is still one of the hardest problems, why Docker is not always enough, and how full VMs, snapshots, scoped secrets, GitHub bots, Slack integrations, and video-based testing all fit together. Walden and Cole also dig into memory, MCP limitations, multi-agent orchestration, AI code review, SRE auto-triage, PMs shipping code from Slack, Windsurf 2.0, hybrid frontier/sub-frontier systems, and the real failure mode of uncontrolled vibe coding: your codebase regressing to your worst engineer.And as agents eat software… and software eats the world… you can draw the conclusion on what is next:We discuss:* Why the engineering world is waking up to background agents and cloud agents* The December 2025 model inflection that made spec-to-PR workflows practical* Devin's 7x merged PR growth and rise from 16% to 80% of commits* Why Cole built OpenInspect as an open-source background-agent system* The economics of $20/seat agent products and why monetization is tricky* What Cognition actually sells beyond Devin: infra, onboarding, integrations, and adoption* Harness in the box vs out of the box, and why architecture matters* Why Devin separates the brain from the machine for security and permissions* Repo setup, scoped secrets, Docker Compose, and agent-ready dev environments* Why full VMs matter when agents need to run real applications and test them* Android, macOS, Windows, nested virtualization, and machine-specific agent work* Why testing is much harder than “computer use”* Screenshots, video verification, and the “I know it works” merge moment* GitHub UX, Devin Review, AI reviewers, and agents responding to PR comments* Why MCP alone is not enough for first-class Slack and enterprise integrations* Memory, Knowledge, skills, Claude.md, and why retrieval is still unsolved* Devin's auto-generated memories and the challenge of memory pruning* Always-on agents as permanent PMs for issues, tickets, and product areas* Sub-agents, meta-Devin management, and what multi-agent systems actually add* Why pure auto-merge vibe coding breaks down after about two weeks* AI code smells, lint rules, reward hacking, and Semgrep for agent-written code* GitAI, inline context, and preserving the “why” behind code changes* Local testing, mock servers, older codebases, and preparing companies for agents* Windsurf 2.0 and the handoff between local foreground agents and cloud background agents* SRE auto-triage, support workflows, and agents as first responders* PMs, marketing, and non-engineers creating pull requests from Slack* AI agent budgets, $1k-$5k per engineer spend, and hybrid frontier/sub-frontier systems* The rise of autonomous coding factories and who Cognition is hiringWalden Yan* X: https://x.com/walden_yan* LinkedIn: https://www.linkedin.com/in/waldenyan/Cole Murray* X: https://x.com/_colemurray* LinkedIn: https://www.linkedin.com/in/colemurray/* OpenInspect / Background Agents: https://github.com/ColeMurray/background-agentsTimestamps00:00:00 Introduction00:00:43 Why Everyone Is Building Their Own Devin00:01:57 Devin's 2025 Ramp: 7x PR Growth and 80% of Commits00:03:49 OpenInspect and the Rise of Open-Source Background Agents00:07:59 What Cognition Actually Sells Beyond Devin00:09:56 Background Agent Architecture: Harness In vs Out of the Box00:12:08 Separating the Brain from the Machine00:14:07 Repo Setup, Secrets, Docker, and Full VMs00:19:13 Why Testing Is Harder Than Computer Use00:22:40 Video Verification and the “I Know It Works” Merge Moment00:23:19 GitHub UX, Devin Review, and AI Code Review00:25:42 MCP, Slack, and Enterprise Agent Integrations00:28:59 Memory, Knowledge, and Always-On Agents00:36:16 Sub-Agents, Multi-Agent Orchestration, and Meta-Devin00:43:55 Vibe Coding, Auto-Merge, and Codebase Decay00:48:38 Agent Infra, VPCs, Cloud Providers, and Fast VM Restore00:52:25 AI Code Smells, Reward Hacking, and Code Review Systems00:56:10 Making Codebases Agent-Ready00:58:30 Windsurf 2.0 and the Local-to-Cloud Agent Handoff01:01:15 SRE Auto-Triage, PMs Shipping Code, and Agent Use Cases01:04:32 Agent Budgets, Hybrid Models, and Autonomous Coding Factories01:06:51 Hiring at Cognition and OpenInspect Consulting01:07:45 OutroTranscriptIntroduction: Walden Yan, Cole Murray, and Context EngineeringSwyx [00:00:00]: All right, we're in the studio with Walden Yan, co-founder of Cognition, CPO.Walden [00:00:08]: Happy to be here.Swyx [00:00:09]: Which is a cool title. And coiner of context engineering.Walden [00:00:15]: Although I think there are many people who'd used the terms in various ways beforehand, but I did find that people, both internally and externally, enjoyed the upgrade from prompt engineering or model wrapping into maybe a more thoughtful way to build agents.Swyx [00:00:33]: For those who haven't caught up on that, I have on screen the Don't Build Multi-Agents post, which you should go read on and we might refer to, and Cole Murray, who created OpenInspect.Cole [00:00:43]: Great to be here.Swyx [00:00:43]: So let's talk about it. Everyone is building their own Devins. What's going on?The December Shift: From Handholding Models to Autonomous PRsCole [00:00:51]: So I think the engineering world is waking up to this idea of background agents, cloud agents, whatever you'd like to call it. And I think we saw a shift around the December timeframe of 2025, where the models Opus 4.5 and GPT 5.2, they reached a capability where we moved away from handholding the model and being able to actually more or less autonomously drive the model. And what I mean by that is that we could pretty much go from a specification to a completed pull request, assuming the spec was good enough, with very little friction. And that paradigm alone, I think, changed a lot of how we interact with agents, and opened this world where background agents became more practical.Swyx [00:01:41]: I think for Cole, everyone experienced this in December, but I feel like there was just this increasing ramp, right? There was this moment which was, I think, Sonnet 3.7, where, You guys rewrote Devin in one night or something. So describe 2025 or how it felt from your side.Walden [00:02:01]: In retrospect, we always thought it was ramping up, but then even now, over the last three, four months from today, it's been ramping up even faster. So it's almost funny to be talking about how, big of a leap Sonnet 3.7 was, and honestly, a lot of it was stripping out parts of Devin that were no longer needed with that jump in of intelligence. But I also just think that a lot of the recent leaps, especially, you look at, models like Opus and the latest GPT models, they are reaching levels of autonomy where people are actually finding that they actually can just be hands-off. And people who were once debating, “Oh, do I need to be in the weeds with my model in the IDE? Can I just completely move it off into the cloud?” That's a more serious conversation, and we've seen that in all of our growth charts. Internally there's this funny graph where our usage has, of PRs, our merged PRs, has grown 7X since I forget what it was called.Swyx [00:02:57]: I think Dev, maybe tweeted that. Yes.Walden [00:03:01]: it grew like 7X over, the last, I think it was, two months, three months, something like that. And then you see our engineering headcount growth. It's, gone up by, 10% or something.Swyx [00:03:11]: We were, we were afraid To release this. So this is Devin commit percentages on all Devin repos, was 16% in January and now 80% in March.Walden [00:03:25]: It's a big shift right now. And so it makes sense that a lot of people are now thinking about, buying Devin, but also maybe, trying to build their own and there's Lots of I have a lot of fun building Devin, so I can see why other people would want to build their own cloud agents as well. Matt, well, maybe it's good to hear, what initially inspired you to try to build OpenInspect?OpenInspect: Ramp, Cloud Agents, and Open SourceCole [00:03:49]: OpenInspect came about, through primarily my clients observing how they were using tools like Claude, OpenAI's Codex at the time, and seeing some of the friction that they were having with it. Primarily the Claude was being used through Slack, and a big issue they ran into was that the sessions that were launched were specific to whoever called it via Slack. And so if a PM was the one who invoked the session and they would then go to pass context to engineering can't see the session. And that in itself was a deal breaker because the PM, “Hey, engineering, can you jump in?” But there's nothing to jump in on unless they're copy-pasting out or the single response that came back. And so seeing some of these problems, I had built a similar architecture internally, just to experiment with, test out different ideas as this trend of moving off of localhost was starting to become, And as Ramp released their blog post, I had a lot of the pieces for this already in place, and just thought it would be funny to, see what Claude could do just purely from the blog post. And on my X account, there's actually a thread of where I live tweeted, going through thisCole [00:05:14]: comparing GPT and Claude as both of them are going through it.Swyx [00:05:17]: On the announcement thing or something else?Cole [00:05:19]: right after it got released. We can put it in the show notes. Yeah, it was helpful that I had already knew how to verify the system. I knew what I was looking for. I think Ramp did a great job of really illustrating, the technical aspects of how to build something. It was much more than just like, “Hey, we built a great system.” It was, “And here's how you can build it too.” And so, I resonated a lot with that, just with the problems that I was already seeing, and I thought that, looking around, I didn't really see anything in the open source community that, met this type of system. I think there's a lot that run, in localhost like Superset, Conductor, and many others.But nothing that was actually running in the cloud. And so, I built it, and I thought it was interesting to just open source it and allow anyone to then have a foundation that they can mix and match on top of.The Business of Background Agents: Open Source vs. DevinSwyx [00:06:16]: So literally after Devin was launched was, there was OpenDevin Which became All Hands. I don't know if you tried that orWalden [00:06:22]: I was going to say, one of the things that interested me a lot with OpenInspect was, you didn't try to go make it then something you monetize. There are a lot of, I think, these open source projects would then go and really try to, raise VSwyx [00:06:36]: That's why no OpenDevin. Yeah.Walden [00:06:38]: yeah, and how did you think about that? I thought that was very interesting.Cole [00:06:44]: I thought, and just what I had seen across my clients, was that having a background agent system is going to become a critical infrastructure within their company. And so because of that, I think that I wanted to open source it so that they could fork it and put in whatever customization they wanted. To that question though, I get asked all, “Oh, are you going to raise? Are you going to turn this into a service?”Walden [00:07:08]: I'm sure you've gotten offers.Cole [00:07:09]: but primarily I don't want to do that for a few reasons. One, I think that I don't want to compete for, $20 a seat. I think that is just a really difficult business. I think it's very easy to copy the main pieces of it. Again, I built this fairly quickly. And I think because you are not owning, I guess, the entire stack, it's hard to monetize. You have money being made at the sandbox layer with Daytona, E2b, many other players. You have money being made at the model layer. And you sit in this weird in-between gray area where what are you actually selling? You're selling, I guess, the infrastructure. You're selling, the integrations maybe.Swyx [00:07:55]: let's ask the guy. What are you What are you selling?Walden [00:07:59]: Well, yeah, there's multiple layers to this in practice, and actually it's funny you mentioned the infrastructure, ‘cause when we got started building Devin as well, we had to go figure out how to make the infrastructure as well because,Swyx [00:08:10]: You had to build this two years before everyone else,?Swyx [00:08:15]: Including, the model sideWalden [00:08:17]: It was not, it was not very polished at the start, when we just built it off of raw VMs from cloud providers like EC2, the boot up time was so slow, I think, And especially then, turning off the machines, saving them, and then to be able to bring them back up again when the, when you want Devin to wake up again later. It would just be out cold for like 10 minutes because that's just how long these systems took. They were not built for this repeated down and up usage. And so we actually had to go do all of that. And as a result now, one thing we offer when we go and sell Devin to people is, you don't have to worry about all the compute side of things. We'll make it work. We'll make it work in your cloud if you want it to. But aside from the product, and I want to go into the agents and the tuning of the intelligence part later, but I think a big part of what we do at Cognition as well is to just make sure that your company learns and uses and adopts these coding agents. ‘Cause I think for especially the largest enterprises in the world, you find that there is a lot of people who want to move over to using AI for their day-to-day workloads. But because of the way projects are planned, because, not everyone is literate in using AI in these ways, having a team of engineers who can actually go in and onboard you, set up all the integrations you need, the automations you need to really get to that level of, leverage with AI, is super helpful. And so We do that. We show thought partners to the customers that we work with as well.Swyx [00:09:56]: So let's talk about, architectural stuff. I think that's always, that is something that was the topic of conversation between the two of you. Is this, the mental model that you want to start with or something else? I'll just leave the floor open to you guys.Agent Architecture: Harness in the Box vs. Out of the BoxCole [00:10:11]: I think, maybe we can start here as just a general what are the pieces of a background agent system. And then maybe we can go into some of the nuances of, Decisions that you can make.Swyx [00:10:22]: But I guess I also Like, what, maybe what Walden is saying is the agent is like in this open code box, I guess. Right? This is infra, and then there's, that's the agent. And you had this discussion about whether you put the agent in here or in Out externally. Can you tease that out?Cole [00:10:39]: In a background agent systems, you have a decision to make of where the agent is actually going to run. This is typically described as the harness in the box or out of the box. With running the agent in the box, you're making some trade-offs by doing that. The negative trade-off you're making is primarily security. Because the agent is running in that box, unless you otherwise design it, all of your secrets need to go into that box as well. And given the nature of AI, it can be unpredictable, and you could very easily end up accidentally exfilling your secrets, or other unintended behavior. Now, the out of the box is the idea that we are going to have the actual agent running not directly in the sandbox, and we will have, quote-unquote, the brain of the agent running in some type of worker, control plane. That sandbox then is going to serve as the hands where the brain is basically operating and making tool calls into that environment to manipulate it. I guess other trade-off that you're making between the two systems is that, in my opinion, running it out of the box is much more complex because, you have state that has to be managed, whereas if you're running it in the box, all of the state of that agent is actually in the box, and yes, it's you could persist it elsewhere, but it's all localized and you have less concerns to worry about.Walden [00:12:08]: I think a lot of that, what you mentioned, is why we actually from the start built Devin to what we called separate the brain from the machine. The other thing that this allows you to do is reuse any existing infrastructure you have for dev boxes Perhaps. And so you don't have to worry as much about making a new type of dev box that has all the dependencies the brain needs, as you mentioned, the secrets the brain needs as well. One thing that we've seen some customers run into is, you have a GitHub app and you want Devin, your agent, whatever, be able to interact with GitHub through this application, but then you have different users with different actual permissions. If they are all interacting through the same GitHub app and there's no actual, separation between the system that decides, what it does and the actual secrets on the machine, then you run into an issue where, okay, it's hard to do the separation. But in practice, with Devin, it's much easier because we just say whatever you put on the machine, that is, the scope of basically what the user is free to do, what the agent is free to do. So only put the most scoped secrets on that machine, and then the brain is fully not accessible from the machine. So you don't have to worry about messing with the, any of the most secure parts of the brain if the user is free to do whatever they want with the machine.Swyx [00:13:31]: I was going to just bring, I have this, chart from OpenAI, where I don't know if this is, in the box, out of the box. That is something that they do use to describe it. And then also recently Anthropic did, managed agentsSwyx [00:13:44]: Which is, this is their thing. I don't know. It's all, it's all variations of the same pattern, right?Cole [00:13:49]: So this would be out of the box.Swyx [00:13:51]: Which, is preferable for them because it's less work?Cole [00:13:56]: I would say it's more work.Swyx [00:13:58]: It's more work?Cole [00:13:58]: But it, in my opinion, it is the better architecture of the two. It's just, you're taking on a bit of complexity by doing that.Repo Setup, Docker, and VM-Based Development EnvironmentsWalden [00:14:07]: One thing I've not seen a lot of other players do well is how do you manage what's actually on the box? And this can be complex for many reasons. Let's say you have a big repository that's changing and updating a lot with changing dependencies. How do you make sure that the working environment of the agent actually stays up to date, has all the credentials it needs to, let's say, run the app and test it, and all the things you want your autonomousSwyx [00:14:34]: So a repo setup.Walden [00:14:35]: Exactly. So in, internally At Cognition, we call this repo setup.Cole [00:14:39]: The hardest part ofWalden [00:14:40]: It's been a perennial problem since the start of the company, of how do we help people get this set up? Because not everyone just has, working cloud environments working out of the box. And do you find this to be a common problem withSwyx [00:14:53]: How do you solve it?Walden [00:14:53]: Your clients?Cole [00:14:54]: This is a very common problem, and through my consulting, this is a lot of what I help teams do. A lot of teams don't really have great developer environment setups, if any. A lot of the times it's, “Go talk to Bob and get the secrets,” and that obviously doesn't work when the agent needs to actually set this up. And so a lot of that, most teams are using Docker Compose or some type of microservices. And so for theSwyx [00:15:19]: Even in prod?Cole [00:15:20]: Not in prod. With the OpenInspect, you are using this primarily to interact, and make code changes. There is other use cases, but you can hook, whether through CLI, MCPs, other tools, you can then hook that into your production systems primarily for, SRE type use cases. But you are not, necessarily, trying to test your prod internal microservice through the system.Walden [00:15:48]: And you mentioned Docker Compose. I think one direction we saw some of our friends take early on was, using Docker containers as the level of abstraction for their models. There's lots of reasons, I think, why Docker containers are not great. One thing is, Docker container's not really a true security boundary, for one. But the other is, if you are running real applications, a lot of times those applications use Docker, and then you have to think about Docker in Docker, which is, really weird. And so I think part of, the really hard challenge of getting VMs to work, why did we do that? Well, it was because we realized that you actually needed, full VMs to be able to do these types of things. And especially nowadays where there's actually value in running the application and clicking around and sending you screen recordings of these things. The value just, keeps adding on top of that. But it is a decision I see people run into when they try to build their own systems, is, “Oh, do we, in addition to this, do we put the agent in the machine or out of the machine? Do we use Docker? Do we use something else?” What do you recommend people nowadays?Cole [00:16:57]: I think Docker is a good solution for maybe not running the agent, but running your infrastructure, because that is more or less the same setup your engineers are probably already using. If they're not, then I don't know what they're using. But they're probably already using Docker Compose.Swyx [00:17:14]: I've always had a small candle for web containers. I don't know if you guys have tried them before.Swyx [00:17:19]: To me, they were, supposed to be like Docker Light.Cole [00:17:22]: Is it?Swyx [00:17:22]: I don't know.Cole [00:17:22]: No, I haven't tried it. But yeah, I think any environment that you've set up that is a good experience for your developer naturally lends itself to being easy to set up for the agent. And once you figure out that local developer story, you've more or less solved the agent in a sandbox, environment setup. OpenInspect does have hooks as well, where you can, run a setup SH script that will pre-install everything. You can then pre-snapshot that build so it starts instantly, and then there is a second hook to actually then, restore the state of the sandbox when it comes back. And so you can already have all of those microservices running and basically get the same experience that you would on your machine within the sandbox.Testing Agents: Computer Use, Screenshots, and Real App WorkflowsWalden [00:18:08]: Another thing that we've been thinking a lot about is like Different VM service offerings. Have you had customers where they needed like macOS specific VMs or like Windows specificWalden [00:18:20]: VMs?Walden [00:18:22]: There are like many technologies in the world that only work on specific types of machines, right? If you're building a.NET application that has to run on Windows or like, maybe more commonly if you want to build iOS or macOS Does that workSwyx [00:18:32]: Does Commission supportSwyx [00:18:33]: Choices like that?Walden [00:18:35]: The fundamental architecture we do, because we do the separation, it does support, but the actual work in progress is happening right now on these. Another thing that we've actually recently added support now for, it's in beta, is doing Android development. To do that, we needed to support, I think, nested virtualization within our machines because the VM itself is like a, is a virtualized Firecracker instance, and then you had to then run another Android emulator inside. And there's like weird performance issues that like, it, which is why it's like still in beta. We have to think through these problems, but it unlocks a lot for anyone who wants to do Android development.Swyx [00:19:13]: I was trying to find like a reference video for the testing thing. I couldn't find it, but I think you worked on the testing, capability. Why call it testing and not like computer use or I don't know, it's, what's the general Category of problem?Walden [00:19:26]: I think that when people think about the ability of an AI to run your app and test it, I think they actually over-index on the computer use part of it because computer use in my mind is the literal, okay, you want what button you want to click. Can you emit the right coordinates to go click that button? I think testing is actually a really interesting likeWalden [00:19:48]: Problem-solving, challenge for these AIs because if you wanted to do arbitrary testing, imagine you make a change that spans the frontend and the backend, maybe, even some other like even more deeply nested service. To actually test that change, we have to reason through what-- how do you first run these applications to orchestrate with each other with the right version of the code? Then, okay, how do I trigger the feature or how do I make the thing actually happen? And this can get arbitrarily hard, maybe you have to be an admin. Maybe a certain thing has to be feature flagged on. Maybe, you have to like run two sessions and then send us a very specific word into one of them to trigger a specific behavior. And figuring out how do you do that requires a lot of code base context, requires, a lot of orchestration that we've specifically done. And in some cases, we found that you actually, no one frontier model can actually do this full end-to-end task itself.Walden [00:20:42]: We've seen cases where we actually had to orchestrate different frontier models together to solve this problem together. That is where we spend most of our time when we think about this testing problem, not so much the computer use part. Computer use for what it's worth has gotten a lot better with recent models and it's made that part of the job certainly easier.Swyx [00:20:58]: Especially with like even 4.7, that they released yesterday, apparently like way better in terms of the vision stuff, which is going to be encompassing computer use.Walden [00:21:08]: Having evals for all these as well is something that like takes a while to build up. And having the evals be right is tricky as well. Do you ever see like, clients who are building their own agents have to start standing up evals to make sure things don't regress?Swyx [00:21:25]: Not so much evals in the traditional sense, but specific to the testing part that has just gone in. I just added support for screenshots And in theory you can also do video. I need to put in a plugin to do that. But they do show up natively, and it was a very heavily requested feature, especially after Cursor's recording came out. I think that was very enlightening for everyone of like, “Oh, this is a very good feature to actually have.”, I think with Devin you guys have had this for a while.Swyx [00:21:57]: Oh, yeah. See how screenshots work. Yeah, I don't know if there's anything, super and not obvious. It's like once what feature to build, you can just prompt it and it Will mostly work.Walden [00:22:09]: I think to Walden's point, though, the computer use is a subset of the larger testing problem, and I think that's very specific to the code base that you're working and it's not something that, out of the box that you could just solve it. The-- you do need the code base context to actually know how to test it. And I think in the case of a background agent system, you fortunately do have that code base locally that what is changing and could then inspect it and use that to drive the model.Swyx [00:22:40]: For those who haven't seen it before, this is an example of how it works. You, after the PR is done, you click testing approved, and then it sends you back a video. What I really like is that it labels, It's very small here, but it actually labels what it's testing. And then it-- and then you actually see the cursor and everything. So I don't know, yeah, the engineering in this, just Whatever you want to show. ‘cause this is like, this is one of those like, oh, few of the AGI moments, right? ‘cause Once I look at this, I actually don't I wish I can just merge inside Of Slack instead of going to GitHub ‘cause I don't need to see the code. I know it works.Walden [00:23:19]: Maybe a new feature in Cursor. Yeah, the annotations at the bottom was also a big difference for me when I, when I added those.Swyx [00:23:27]: It's just like, what am I looking at? What are you trying to demonstrate?Walden [00:23:30]: Exactly. There's a surprisingly long tail of small details that ends up making a big difference for this end metric of like how fast do you actually merge the code in. One experience that we spent a lot of time tuning early on was what is the right experience on GitHub for these tools. Because I think, most tools out there when you build the agent, you'll think about, oh, it'll create the PR for you. We try to take that a step further and say, “Oh, what if we actually made sure you could interact Devin, with direct Devin directly on GitHub?” And so we made sure that you can comment on GitHub, and Devin would actually receive those comments and address them back. But there's actually quite a bit of tuning you have to do here because you can imagine that actually like-We recently have Devin Review, for example. Devin Review will post comments on his own PR And then Devin has to then goGitHub Workflows: Devin Review, Comments, and PR AutomationSwyx [00:24:23]: He answers his own comments, which is Really loopy. So like, yeah, I like that it just updates here that it's, that I have commented But usually it's just me saying like, “Hey, merged, fix any merge conflicts.”Walden [00:24:37]: The, so when Devin fixes his own comments, you might be scared that, oh, maybe I'll infinite loop. But we've put a lot of work into making sure it doesn't, both by making sure that the comments are high signal, but also that the agent is thoughtful about what comments it immediately goes and tries to fix, and what comments it's like, “Wait a second, I think you're wrong.” Actually, that's one of my favorite moments is when Devin tells me that I'm wrong, when I try to get it to do something different. But tuning that behavior, actually makes a big difference in terms of how useful the actual GitHub experience is.Cole [00:25:06]: I think to touch on that as well, I think having the AI reviewer integrated into the system is a critical part of this background system. OpenInspect does have that. It has a GitHub code reviewer that you can control the prompt. It does do comments as well. It doesn't do them automatically yet. The capability is there, but it's not fully used.Swyx [00:25:27]: So you have to ask for it?Cole [00:25:28]: you do, yeah. You can tag it on GitHub, and then whatever you named your, GitHub bot, it will then follow up on it. It will then, if you have merge conflicts or whatever you have asked it to resolve, it will then resolve it, but it doesn't do it automatically yet.Integrations: Slack, MCP, and First-Party Agent InterfacesWalden [00:25:42]: Well, I'm curious, what is, the most common thing that people end up requesting, that they still need on top of OpenInspect when you help them go implement it?Cole [00:25:52]: I think a lot of it comes down to actually integrating it into the company. It's one thing to have the background agent system set up, but if it isn't actually integrated into your larger ecosystem, it isn't that useful. It is useful to be able to kick off sessions, but what we really want to be able to do is hook it into all of our other systems, whether that is the production database with read-only credentials, the logs, a Confluence or internal knowledge-based system. I think that is where I see the huge leap for companies, and that can be a challenge for companies as well who are maybe not familiar with exactly how to approach it, especially if they're in environments that have more compliance type things where, access control can be pretty big and how do you deliberately think about these problems, I find to be, one of the problems that comes with a system like this.Walden [00:26:46]: The thing we found is So, MCPs, obviously it has been like this, really big explosion of, oh, you can go, integrate it with all these different things. But to actually get the integration right and the and get the right experience, oftentimes we found that we had to go build our own ad hoc things. I think Slack is a great example of this. You could give your agent a Slack MCP and okay, it can post messages back to you on Slack. But we actually use Devin like a coworker in Slack, and that's how it's been built from the ground up. But to do that, you actually need to, support webhooks that come back, right? And then Devin has to respond in a natural way and then hopefully don't spam your threads too much and annoy the people in your company. So you got to tune that experience just right. Especially when there's a lot of back and forths, we find that we actually have to go beyond the simple MCP integrations in these places.Swyx [00:27:39]: I just pulled up the MCP marketplace. I know this is a Fair amount of work. Is the answer to eventually take first party control of all the top MCPs? Is that theWalden [00:27:48]: I would love a world where you could have something that's more expressive than MCP. That, goes both ways, not just a set of tools, but a proper system that interacts back and lets it Have the right experience with all these interfaces.Swyx [00:28:03]: So there actually is sampling in the MCP spec, but nobody Uses it, right?Walden [00:28:07]: And so I think that's the other part is, actually we found that when the MCP spec starts to get too complicated, it starts to lose its original promise of Being like a simple one-step connect. Now then we have to go figure out how to support all these different variations of things and It starts to look a lot like just building the first party integrations in a lot of these cases now.Cole [00:28:29]: I think it matters, too, how critical it is to your company, right? If this is something that nearly every session is going through, it probably makes sense to own it so that you can make optimizations on top of it Versus just whatever is off the shelf.Swyx [00:28:43]: Awesome. Other than MCPs, what else, sorry, well, I don't know if that's Narrowing in too much on, integrations. But what else? What other elements of building OpenInspect or Devin that you guys really sink on?Memory and Knowledge: What Agents Should RememberCole [00:28:59]: I think, a problem that comes up very frequently is this idea of memories or knowledge base.Swyx [00:29:05]: Oh, boy. How do you solve it?Cole [00:29:08]: so not solved yet, is the short answer.Cole [00:29:11]: it's something, there's a open issue for it, someone asking about it.Swyx [00:29:16]: There's, I, D Wiki hasn't indexed anything about memory yet.Cole [00:29:20]: how I'm seeing it solved across my clients is primarily through skills. I find that skills can be a good gap within that or updating Claude MD, but I think memory as a whole is a pretty unsolved problem, and it is why I've been hesitant to add it. I think there is parts of memory and that can be addressed, but I think as a whole it's a very difficult retrieval problem.Swyx [00:29:44]: Oh my God. RAMP didn't write anything about memory? I see zero search results.Walden [00:29:50]: No. Memory can be quite tricky to get right because it's the retrieval, but also the generation of the memories that can be really tricky. You don't want it to just like Remember very specific details.Swyx [00:29:59]: Walk us through the Devin memory journey because I know there's been a journey.Walden [00:30:03]: the first version of memory that like stuck around for a while was A system we have called Knowledge. And the idea was we wanted it to pick up things over time and not need the user to be proactive about teaching Devin things. So, okay, any time you remind Devin, “Wait, no, that's not quite the way you're supposed to use Git”Like, we actually want Devin to say, “Hey, do you want me to actually just remember this for the future?” And for you to just basically quickly approve or reject and for it to build up over time. ‘Cause I find that, 95%, I think, or some crazy stat like that of the memories that Devin has are all through these auto-generated things. Very few people actually just want to sit down and write big docs on Here's how you're supposed to work with the technology, et cetera. The generation and the retrieval has been something that we've been trying to tune a lot over the years. Generation, you don't want it to remember something like, if you asked one time to like, “Oh, please open as a draft PR,” you don't want to be like, “Oh, everyone forever now should get their PRs as draft PRs.” But you do want some, conveyor. Maybe you want to say like, “Oh, Cole generally likes, things to be created as draft PRs.” Same with retrieval, if you have thousands of these memories, how do you actually make sure they're retrieved at the right time? And that can be quite tricky to do right without exploding the context with a bunch of useful yeah, useless information. Surprising amount of just, eval work to just make sure that, memory is, remains a reliable system as new models come and go.Cole [00:31:31]: Do you have anything that you could share on, memory pruning? And like the temporal aspect of memory?Swyx [00:31:36]: Deleting and forgetting?Walden [00:31:39]: The, today, the, So the things they could do is it could edit memories. And so if your memory used to say like, “Oh, Cole likes to open everything as like a draft PR,” then you can imagine, “No, don't do that.” And then it'll say, “Oh, do you want me to update the memory to be Cole now want everything as, open PRs?” I think that at the same time we don't know if this is going to be the final version of the system. Whatever we have here will probably, translate into the new system that we'll be coming up with. But I think one big difference between two years ago and today is these agents are really good at using anything that resembles a file system natively. And so part of us are, is thinking, “Oh, should we rebuild memories to feel more like a file system that we let the agent navigate on its own?” That's been an interesting exploration. Also similar ideas in the scale space.Swyx [00:32:35]: I am pulling up OpenClaude's memory thing right now. So memory, OpenClaude has like this like daily memory journal thing, right? And you can I mean, that is a file system you can grep through and is a source of truth. I don't know if it's the best. It's probably super noisy, but at least, if you lose something you can discover it or you can apply some, forgetting algorithm to, more ancient memories that don't get recalled again or something. I don't know.Walden [00:33:01]: One thing we've been trying to do to push the boundaries of how you use agents at your company is letting an agent basically have a very similar file, a memory.md or something, and just like be your permanent PM for a specific set of issues maybe. So we have like some Slack channels internally, maybe a Slack channel dedicated to, a specific product like DeepWiki maybe. And you can imagine that, or you want a Devin that never stops, it's just always awake, but it has this like memory dock that it can just maintain for itself about, okay, what are like the number one priorities of what we have to fix and prioritize? Who is responsible for some upcoming work? Maybe they'll even Devin will even tag you on some recurring basis. And so it's been an interesting move to see, okay, how can we actually use Devin for more than just engineering? Can we actually upstream above the engineering process and maybe it's just Devin creating tickets, which then maybe some humans do, but then maybe other Devins do.Swyx [00:34:00]: One of my more fun automations is go research competitors and just suggest stuff to me on a weekly basis. That's the automation. I can't find it right now, but basically it just like, “Look at competitors and suggest things.” “And here are three things that you've suggested that I don't want any more of,” and you just stick that in the prompts. But like I wish actually So for like when I, for example, when I reject a PR, I wish that it updated memory so that I can then just not have to go up, go back and update the scheduled, sync, but anyway, feature request.Walden [00:34:31]: what? We might change it soon. I guess OpenInspect, in the time you've been around, has there been anything you tried to implement but then you had to like undo and like do a different way?OpenInspect Architecture: Webhooks, Control Planes, and Agent StateCole [00:34:41]: Nothing yet, but something that is on my mind. The initial way that I built it was that each of the integrations lives as its own package. And so you have The Slack bot, which is what's handling the webhooks, and then is basically interacting with the control plane. As I'm seeing the system starting to be more integrated, specifically with the GitHub bot integration, I'm considering bringing that all into the central control plane because especially now I want to start, And a request that I'm getting is the ability to monitor, the actual, pull requests being merged, as well as just tracking ofSwyx [00:35:19]: What do I have open?Cole [00:35:21]: What do I have open? How many of these are getting merged? How many comments are showing up? To just understand the health of the system. And so in the case of a GitHub app, you only have one webhook. And so then it's a question of do I put that webhook in that GitHub bot package? That's weird. It doesn't really make sense to live there because that package is more for like the code reviewer. Or do I like centralize it? So that's something that's on my mind of, making that decision. I think the other one we touched on earlier is the harness in the box versus out of the box. I think long term the architecture will eventually come back out of the box. Some of the newer tools that I've added are calling back into the control plane so that you don't have the secrets in the sandbox. And so I think long term I probably will pull the actual, agent out of the box, but I think for now it's fine.Subagents and Multi-Agent Systems: When Parallelism Helps or HurtsSwyx [00:36:16]: Just, a quick question on pulling the agent out of the box. I'm One thing I'm very bullish on this year is agents calling other agents or spawning sub-agents or Whatever you want to call it. Does that make it harder or easier? I can't tell. Because if the harness is in the box, you can just spin up more boxes. If the harness is outside the box, then you're, it's less easy because you are, you have a unicorn pet of a, of a harness that's, living outside the box.Cole [00:36:45]: In theory it would be the same way, right? Whether, one agent has launched many, sub-sessions within it, OpenInspect, for example, can launch sub-sessions and actually create other environments and then monitor them. In the case where it is out of the box, that would basically just be an additional session that's running. And so that session is also running outside of the box. It's running in your worker plane, wherever you're running this. And then you really just have to think about how does your top level agent then interact with it. I do think it can be more complex, just ‘cause again, you have now a more difficult architecture. But I think if you figured it out once, it's probably fine.Swyx [00:37:26]: Well, then I'm just, throwing it open to you in terms of, I call this like meta Devin management. Which is like the, Devin's calling Devins or Devin scheduling Devins or querying trajectories or anything like that. What have you built or unshipped, anything?Cole [00:37:46]: I think one of the surprising things we've seen is that a lot of the ways that, these, separate agents work with each other, and you want them to, parallelize their work, has still mostly followed the same manager sub-agents regime. And a lot of people I think are excited about this world where you have swarms of agents that, talk with each other all over the place. We've actually given Devin an MCP so they can just go arbitrarily message other Devins And create new Devins, et cetera. But I guess, it somehow creates, a really chaotic world in that sense. And so we've still found that most practical use on a day-to-day basis has been one single Devin.Cole [00:38:33]: Figuring out how to segregate the work and get, have other Devins work on it in, a relatively isolated sense, each with their own boxes Not sharing machines, so there's, a very little room for conflict is the regime that you have to create today.Swyx [00:38:50]: I'll call out, the experiments from Cursor, right? This is Wilson Lin's work on Single agent to multi-agent, and you're obviously famously on the side of don't build multi-agent. But they went through the whole thing, only to arrive at, this Which is exactly what Devin has, I think.Cole [00:39:08]: I think there will be a revision to that post at some point AboutSwyx [00:39:12]: Tell us about itCole [00:39:12]: I think multi-agents were very much not at all possible a year ago. You do see more multi-agent experiments today, but you can argue, are they really multi-agents, or are they just just, tool calls,? There are people who, will create sub-agents to go look for XYZ file, XYZ implementation. Has really nice context management benefits because all of the tool calls and tokens that it spends then get collapsed back to just the answer for the main agent. There's a lot of benefits to doing this. We basically have Devin do this with Deep Bookie, make a call out to Deep Bookie, give you back the results, but that feels like a tool call,? It's not like these, two collaborators actually talking back with each, back and forth with each other. But I think the thing that gives me the most bullishness that multi-agents might actually be possible is actually what I said earlier about Devin will actually sometimes tell me I'm wrong and push back, and I think that demonstrates a level of maturity and communication today that makes a multi-agent world possible. One, can two agents who have seen different information come back to each other and actually figure out who is right, what is the correct implementation? They're not just, yes men. Claude, I guess is like, used to just say, what is it? “You're right,” or,Swyx [00:40:25]: “You're absolutely right.”Cole [00:40:26]: “You're absolutely right.” Yeah.Swyx [00:40:28]: The Have you seen, did you seeCole [00:40:29]: The age is overSwyx [00:40:30]: The Codex app troll in Topic? This is the Codex app. Inside of Settings, there's a little, there's a little Easter egg, right? So if you go to, the Themes or Appearance, right? There's all these, color codes, and the top is absolutely, and it's the Topic's colors. Which is such a troll. Anyway.Model Behavior: Pushback, Adversarial Prompts, and Agent SkepticismCole [00:40:53]: I love that Easter egg. Did you discover that yourself?Swyx [00:40:54]: No, it was, someone was, tweeting about it And I was like, I was like, “Is this true?” Because, sometimes people just tweet stuff to, get a rise out of you. But yeah, there you go, in Topic colors.Cole [00:41:06]: Yeah. So yeah, we're out of this regime where, it just says you're absolutely right, and they can have real conversations and real back and forths.Swyx [00:41:13]: You can prompt it as well to be more adversarial or whatever. Yeah. Okay. Yeah, that, I mean, to me, that is more intelligence, right? That is not just something that's, a dumb tool, it's actually pushing back on you I think. Yeah.Cole [00:41:24]: when you mentioned, of course, the blog posts. There was one blog they had where they fed a swarm of agents together and built a browser.Swyx [00:41:34]: That was I think that was the one.Cole [00:41:36]: You can have, likeSwyx [00:41:37]: I think it's the same oneCole [00:41:37]: Creation of it. We found a surprising success of, don't do a swarm or anything, just have one Devin, it does its own context management. Just let it keep running for a while and give it some crazy tasks. I think we asked it to, rebuild, a Windows OS system. And it managed to do it just like, going on for long enough. It'sSwyx [00:41:55]: Was this Andrew's thing?Cole [00:41:58]: there were lots of demos that we ended up not posting, ‘cause at some point we'd just be posting way too much a bunch of, Demos. But I love that because it shows that I think the multi-agent thing still has, a bit of exciting sexiness to it, which is maybe still beyond still, the actual delta it adds to the capabilities of these systems. But it's absolutely the future. I think we're heading in that direction and we can see the progress being made there already.Swyx [00:42:25]: If I were to, make one super minor pushback because I don't feel that confident about it yetCole [00:42:33]: Go for itSwyx [00:42:33]: But I've had Ryan Lopopolo from OpenAI on the pod And he's a super slop cannon, right? Oh my God, that's my coding agent being done. I downloaded this, Peon Ping. I don't know if you guys have heard this. It takes like-, sound packs from popular games like, Command and Conquer and Warcraft, and then it plays it whenever it's done. And so it's like, “Work,” or whatever, “At your command,” or something. Anyway, what I got from the Cursor code base and from Ryan's thing was that there's a slop cannon approach where you try to loosen the single agent's, bottleneck, and I feel like that is, probably an, a very important thing to try to figure out. I don't think anyone's, really solved it. Because then you just have more reviewer slop on top of the agent slop To try to wrangle it all. Ryan will probably very strongly object that I say that he hasn't solved it, but he thinks he's He thinks he's completely solved it. But I think it's still I think it's, very important, ‘cause, that is a bottleneck, right? I feel Devin is slow sometimes Because I'm like, well, yeah, this is very readable and very sensible, but also it is slower than it could be if I just, I want a button to just say, “Just ramp this up 1,000 next parallel, in parallel and just, see what happens,”? And I don't know if that's, feasible at some point in the future.Code Review, Entropy, and AI SlopWalden [00:43:55]: I And we've also run experiments internally where we've basically tried to build entire products, true products that we knew we would eventually ship, but for now, let's try to see if we can do it just by purely, vibe coding on top of each other, auto merge, no code review at all. And then there's this benchmark of how many weeks can you go onto this for Before you say, “We have the trashiest code base.”Walden [00:44:18]: “Let's actually rewrite it from scratch.”Swyx [00:44:19]: Start a new factory, yeah. What'd you find?Walden [00:44:21]: I think we found that the state-of-the-art in December was you can probably, run this for about two weeks. By the end of those two weeks, you'd find that, hey, you want to, change the color of a button. Well, it turns out this button is implemented in, 10 different places, and they, have All these different variations, and oh, you forgot one of them, and actually it's a slightly different color in one spot. And you're like, “Okay, this is too much to work with. Let's actually try to do code review at the same time.” And make sure that we're on top of our software, actually cleaning it up a bit And making sure it's done in a scalable way.Cole [00:44:54]: I think building on that, the idea of, you don't have to look at code, I think is generally a bad idea. And the meme that I have for thatWalden [00:45:03]: What timeline, all right, is Do you think that statement will be true on?Cole [00:45:06]: I think probably for a while it'll be true that you should continue to look at your code. A problem that I see a lot of teams run into that I work with who are embracing AI native, AI first coding, is The meme that I have is that your code base regresses to your worst engineer, because that engineer who is, very gung-ho about AI and is not auditing their code, their pattern starts cementing into the code, and now the AI is referencing their patterns. And so now their if/else block that, is 20 if/elses back and forth, the AI is seeing that as the pattern of how things are done and starts to then exponentially grow this slop. And I find to your point, a pretty good approach to that is having scheduled cleanup, whether by humans or through systems, that are looking for duplication. They then address that. You'll end up with like 12 helpers for how to format a date. And you need to address that, because otherwise it will continue to sprawl.Swyx [00:46:09]: Within balance, I think it's fine to have some duplication, and then sometimes To have garbage collection, right? Yeah. The What I've been, talking about with a lot of engineering leaders is that you want to be very strict about the boundaries between modules, and it's your job as an architect, as a CTO, whatever, to say like, “Okay, here's the hard contract between you guys and you guys. Whatever you do inside this black box is your business. You do whatever. But between these guys, let's be, really damn clear, and any movement must be signed off by a human or me,” or. Then, and like that's that. I don't know if you have any other modifications or advice.Walden [00:46:44]: Well, I guess generally on the topic of, where humans can be useful, I found that ‘cause, some of these, really deep infra problems, sometimes just having a human that just has, really deep expertise can make a big difference. I've actually seen this come into play when actually building agents. So we've had a few friends now, try building their own coding agents, and I think one same problem that I recurringly heard a lot of them run into was this problem of like, “Oh, Grep is really slow on our agents' machines.” And so a lot of them, I assume because they're using AI and they themselves don't have, super deep infra background knowledge, say, “Okay, we're going to go build our own custom Grep index. It's going to be really fast,” and use that as a way around this problem. When we ran into this problem About like, maybe like a year and a half ago when we were, in the early days of building Devin, we obviously didn't have AI then. We just asked our, how to, how to do this. You can just swap out a new Grep index, so.Infrastructure Details: Grep, File Systems, and SandboxesSwyx [00:47:45]: What do you mean you hand-coded Devin? What?Walden [00:47:48]: It's like, can you believe we hand-wrote this code? And we had, our infra people who are really amazing, they were looking into it and they're like, “Oh, what? We realized that actually the root cause of this problem is actually super simple, but like fine-grain detail,” which is that a lot of these virtual machines actually underlying them don't use real file systems. They use these, network file systems where things are actually cached over the network actually in S3. So when you're Grepping, you're actually making network calls Every time you're doing these things, and that's why Grep is extremely slow on these machines. And so again, goes back to, what is all of the crazy infra work that we had to do to actually get these machines working. If you try to do this yourself, there are tons of small details like this, and so we had to eventually go swap out that network file system. ButSwyx [00:48:35]: I think there's a write-up about it, right? Silas did one about the virtual file system.Walden [00:48:38]: Oh, that was a whole other thing. TheSwyx [00:48:39]: Oh, that's a different thingWalden [00:48:40]: The BlockDev file storage formatSwyx [00:48:42]: I'll bring it upWalden [00:48:42]: Which is, a file system format that we built so that the VMs could be spun up and down very quickly. Basically, the intuition behind this is-Imagine you have, a terabyte of disk, and your agent only, wrote, a hundred lines of code on top of that disk. How long does it, say, take to, save and re-bring up that disk? And most systems, because you're not optimizing for this case, it's just, on the order of a terabyte of work because you have to Save all of that and bring it back up. In our system, we try to build a file system that incrementally builds on top of each other. So every time you save and bring the machine back up, you're only doing work that is proportional to effectively the diff in the file system. And so this, shaves off a lot of time in the boot-up process of Devin. I think we This is actually now outdated. We have a newer system inside of Devin. But yeah, there's a lot of tiny details you have to get right here to actually get the day-to-day experience of Devin to be good.Swyx [00:49:39]: It's, not technically agents, but it is agent infra, and when you sell an agent as a company, you sell agent plus agent infra.Walden [00:49:46]: At least the way we do it be And the other The nice thing about having the agent infra being done together is, you We get to deploy Devin in whatever environment we want now. We don't need to wait for some underlying infra provider to also go and support VPC or on-prem or FedGovCloud, for instance. So we can actually go and figure out, okay, since we own the infrastructure, how can we get that set up for you?Cloud Providers: Modal, Daytona, and Enterprise SandboxesSwyx [00:50:12]: Whereas you're Cloudflare dependent.Cole [00:50:15]: so Cloudflare runs the control plane. The sandboxes, Modal is supported. A contributor just added Daytona. E2B is on the roadmap, and I think there's an abstraction in place that if any contributor wants to add a new provider, they can add that in.Walden [00:50:32]: Well, what are, How are the customers you work with Do they generally try to then go set up a contract with another one of these third-party providers? Do they try to do the VMs in-house?Cole [00:50:44]: most of them I see using Modal. I think Modal has a greatWalden [00:50:48]: Shout out Modal.Swyx [00:50:48]: Shout out Modal.Cole [00:50:50]: I think Modal has a great offering. It captures all of the sandbox pieces you need, snapshots being a pretty big piece of that, and given that they also offer GPUs, I think it's a pretty nice offering as a whole.Swyx [00:51:04]: no debate there.Walden [00:51:07]: Modal is great, especially, I think their container offering is, the most natural, and so especially if you are willing to, forego, the full VM requirements Modal is, a really vast place you can spin something up on.Swyx [00:51:20]: Is there a point So Modal's very Python, and I feel like most workload, has really shifted to JavaScript. I don't know if you guys Get the same feeling. So, okay, when I started Landspace and IE and all these things, I was like 50/50 Python and JS, right? That's roughly. I think that's wrong now. I think JS has won. I don't know if you guys Like, I Maybe I'm overstating it, and maybe for cognition, there's, C# and Java and what have you. But for, new greenfield apps, do you feel that Do you get that sense? Does it matter?Cole [00:51:52]: I think that most of the libraries that I see in this space are Python native first, especially in theCole [00:51:58]: Observability space. That said, I think that there is a pretty big appeal of having your entire system in one language. Especially when you have both your frontend and backend communicating, you can have one central type Which is very nice.Swyx [00:52:11]: That's my case against Modal, which is Then you have to run JS. You can run JS inside Modal. It's just, one extra step That, isn't native to the runtime. I don't know ifWalden [00:52:22]: I don't knowSwyx [00:52:23]: Reviews. Do you have numbers? I don't know.Walden [00:52:25]: the one thing I don't like about Python is whenever AI, whenever it writes Python, it always does, the weirdest patterns, andSwyx [00:52:32]: Oh, because it's, mixing two and three or what?Walden [00:52:34]: I think it's something mixing two and three, yeah. The I don't know if you see this. It always tries to do, has attribute on objects as likeCole [00:52:41]: Oh, my God.Walden [00:52:41]: But it's like But that you shouldn't be doing that. It should error if there wasSwyx [00:52:45]: Because it's training on library code?Cole [00:52:47]: I think it's more of, likeCole [00:52:48]: From what I've seen, it's more of, a reward hacking mechanism where it doesn't want to basicallyWalden [00:52:54]: It'll never error.Cole [00:52:54]: It doesn't want the code to fail. And so it Even when it knows it has the attribute, it'll call getattr on a, and for a lot of my clients who have moved towards more autonomous coding, we've put that in as a lint rule That if you do getattr, your pull request is going to fail.Slop Signatures: Comments, Backwards Compatibility, and TypesSwyx [00:53:12]: Ooh, this is a fun topic. Can you tell me more about this? What else is a sign of AI coding that you have to put guards in?Walden [00:53:21]: So we were talking just before this about Opus 4.7. One of the things this new model likes to do is it writes lots of comments. Not like, it'll, comment every line, but it'll write, paragraph, PRDs, on top of every function. But I will say, to its credit, these aren't slop, descriptions like they were before. “Oh, here's what this function does.” It's like, “Oh, here's actually the r

We are delighted to host Marieke McKenna on this episode of the Mangu.tv podcast. Marieke McKenna (London, 1994) is a Scottish-Dutch philosopher, historian, artistic researcher, and performance artist. Her work explores metaphysics, phenomenology, consciousness studies, spirituality, ecology, and philosophies of nature through interdisciplinary research and embodied practice. She is an expert on cross-cultural perspectives on dreaming and other altered states of consciousness.For the Max Planck Institute for the History of Science, she led the research project History of Lucid Dreaming Research, the first oral-historical examination of the emergence of lucid dreaming as an object of scientific inquiry. In collaboration with the Donders Institute for Brain, Cognition and Behaviour, the project combined oral history, philosophy, and cross-cultural anthropological research into how different cultures and traditions understand dreaming, with hands-on experience in neuroscience sleep laboratories conducting EEG and fMRI research on the dreaming brain.Outside academia, Marieke, who is based in The Netherlands, is an award-winning artist and curator, with performances and lectures at institutions including the Van Gogh Museum and the Rijksmuseum. She is the host of her own national radio show on NPO Radio 2, for which she selects music from across the globe, and has taught at various universities, conservatoires, and institutes, including Advaya and the Embassy of the Free Mind.Giancarlo and Marieke discuss idealism, interconnectedness, and how dreamwork nurtures healing and belonging. They speak about lucid dreaming in therapy, indigenous perspectives, and technology's encroachment into “inner space,” debating AI, advertising in dreams, collective consciousness, telepathy, quantum theory, and the mind's creative potential.

What does it actually mean, biologically, when we say "exercise is good for your brain"? In this special Beyond Lab Walls video podcast episode—part of Salk's 2026 Year of Brain Health—Salk President Gerald Joyce sits down with renowned neuroscientist Rusty Gage to explore how movement shapes cognitive brain health across a lifetime. Together, they discuss: • What changes in the brain with exercise, and why it matters over time • Adult neurogenesis: how Gage's research helped overturn the belief that the adult brain can't generate new neurons • What the evidence suggests about exercise and the survival and integration of new neurons • The key biological signals that carry benefits from an active body to the brain • How exercise intersects with other pillars of brain health, including immune function, metabolism, and sleep • The biggest unanswered questions—and what it will take to solve them This conversation is a window into how foundational science turns familiar advice into real, evidence-based understanding.

On this vintage episode of Vitality Radio, Jared breaks down why magnesium is the single most important supplement he recommends—no matter your age, gender, or health status. You'll learn the role of magnesium in everything from stress response and sleep to muscle recovery and hormonal balance, plus why Jared believes the “7 forms of magnesium” marketing trend is mostly hype. He also shares his favorite way to combine bisglycinate and threonate for maximum benefit—without busting your supplement budget.Products:Vital 5 Magnesium BisglycinateKAL Think Magnesium L-ThreonateAdditional Information:#258: Your Magnesium User's GuideVisit the podcast website here: VitalityRadio.comYou can follow @vitalitynutritionbountiful and @vitalityradio on Instagram, or Vitality Radio and Vitality Nutrition on Facebook. Join us also in the Vitality Radio Podcast Listener Community on Facebook. Shop the products that Jared mentions at vitalitynutrition.com. Let us know your thoughts about this episode using the hashtag #vitalityradio and please rate and review us on Apple Podcasts. Thank you!Just a reminder that this podcast is for educational purposes only. The FDA has not evaluated the podcast. The information is not intended to diagnose, treat, cure, or prevent any disease. The advice given is not intended to replace the advice of your medical professional.

In the latest episode of Executive Function, Brett sits down with Graham Moreno, Head of GTM at Parallel Web Systems. Before Parallel, Graham scaled Windsurf's GTM organization from three sellers to seventy-five in under a year, served as President through the Cognition acquisition, and earlier built and led enterprise sales teams at Grafana Labs and MongoDB. In this conversation, he unpacks why the AI-era backlash against structured enterprise sales misreads the data, how to design a process that raises the floor for ordinary reps without capping the ceiling for stars, and why selling to AI-native customers compresses an eight-week cycle into five business days. In today's episode, we discuss: Why in-person enterprise rollouts still beat product-led motions Building a robust sales process that still leaves room for unscripted moments Why the three highest-leverage early sales hires aren't sellers at all The case for outsized commission accelerators for star sellers — and the kind of person they attract Why most AI companies are skipping the in-person sales work that enterprise customers actually want References: Ahead: https://www.ahead.com Amazon: https://www.amazon.com Anthropic: https://www.anthropic.com Attio: https://www.attio.com Augment Code: https://www.augmentcode.com/ Cognition: https://cognition.ai Cursor: https://cursor.com Dani McCabe: https://www.linkedin.com/in/danielle-mccabe/ Datadog: https://www.datadoghq.com GitHub Copilot: https://github.com/features/copilot HubSpot: https://www.hubspot.com Jeremy Powers: https://www.linkedin.com/in/jeremypowers/ JPMorgan: https://www.jpmorgan.com Matt McClernan: https://www.linkedin.com/in/mattmcclernan/ MongoDB: https://www.mongodb.com Nicole Rettinger: https://www.linkedin.com/in/nicole-rettinger-23b20465/ Notion: https://www.notion.com OpenAI: https://openai.com Parag Agrawal: https://www.linkedin.com/in/paragagr/ Parallel: https://parallel.ai Snowflake: https://www.snowflake.com University of Chicago: https://www.uchicago.edu Windsurf: https://windsurf.com Where to find Graham: LinkedIn: https://www.linkedin.com/in/grahammoreno/ Where to find Brett: LinkedIn: https://www.linkedin.com/in/brett-berson-9986094/ Twitter/X: https://twitter.com/brettberson Where to find First Round Capital: Website: https://firstround.com/ First Round Review: https://review.firstround.com/ Twitter/X: https://twitter.com/firstround YouTube: https://www.youtube.com/@FirstRoundCapital This podcast on all platforms: https://review.firstround.com/podcast Timestamps: 00:00 Introduction 00:32 Has the sales playbook changed in the AI era? 02:13 Why "showing up" beats letting the marketplace decide 06:50 Why great salespeople sell to engineers and executives in one motion 11:37 Selling to AI-native buyers who grew up on ChatGPT 13:49 Same seller, different tempo: 8 weeks vs. 8 business days 15:57 How AI-native buyers handle build vs. buy decisions 17:48 The rep who taught a champion's son guitar over Zoom 19:03 Raising the floor without capping the ceiling 22:09 Why too much process narrows the kind of seller you attract 25:46 The three pillars of GTM excellence 31:00 Building peers who are 80% aligned, not 100% 38:03 Whether AI is changing what good enablement looks like 41:35 Selling against direct and implied competitors at once 42:45 Instrumenting the funnel from stage zero to close 45:57 Why post-sales should always roll up to the revenue leader 48:19 The case for outsized commissions 52:02 The 96 hours of panic before Cognition acquired Windsurf 53:04 How far out should a GTM leader be planning? 57:53 What a normal week looks like in hypergrowth

Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!On the product side, everyone is getting Computer - Perplexity, Manus, Cursor, and so on. Meanwhile on the research side, agentic evals like TerminalBench and GDPVal are also assuming computer (Harbor). On both ends, the consolidating LLM OS stack has become a standard toolkit, and Daytona is one of a small set of AI Infra companies that are booming because of it.“The end of localhost” has been Ivan Burazin's obsession for more than a decade.Something that is all too familiar…Long before agents became the default way people talked about software development, Ivan was already chasing the idea that development should not depend on a fragile local machine. CodeAnywhere, one of the first browser-based IDEs, was an early attempt at that future: move the development environment into the cloud, make setup reproducible, and free developers from the endless “works on my machine” tax.The thesis was directionally right, but the market wasn't ready yet.However, agents changed that. They do not care about a laptop, desk setup, or favorite editor. They need a computer they can access through an API: something stateful enough to keep working, fast enough to spin up instantly, flexible enough to resize, isolated enough to be safe, and composable enough to run the messy real-world workflows that real software engineering actually requires.Daytona isn't just selling “sandboxes” in the narrow code-execution sense. It is the latest version of Ivan's original localhost thesis.In this episode, Daytona's CEO joins swyx to explain why AI agents need more than code execution boxes: they need composable computers, stateful sandboxes, instant startup, dynamic resources, and infrastructure that can survive workloads going from zero to 100,000 CPUs.We go deep on the new agent compute market: Daytona's hard pivot from human dev environments to AI sandboxes, the New Year's Eve MVP that customers begged for, why Daytona runs on bare metal with its own scheduler, how one customer runs almost 850,000 sandboxes a day, and why RL/eval workloads went from 0% to roughly 50% of usage in just months. Ivan also explains why agents need Windows and macOS machines, why CLI may matter more than MCP, why Kubernetes is painful for this workload, and why the future AI cloud may look more like Stripe than AWS.We discuss:* How Daytona grew out of CodeAnywhere, Shift, and the “end of localhost” thesis* Why Daytona pivoted from human dev environments to AI sandboxes* Why agents need composable computers instead of disposable code execution boxes* The New Year's Eve MVP that customers chased API keys for* Why Daytona chose bare metal, stateful snapshots, and its own scheduler* How Daytona spins up one sandbox in ~60ms and 50,000 sandboxes in ~75 seconds* Why Daytona's biggest customer runs ~850,000 sandboxes a day* How RL/eval workloads create zero-to-100,000 CPU spikes* Why RL workloads went from 0% to roughly 50% of Daytona usage* Why customers compare Daytona against EKS/GKS and say they're “never going back”* Why every AI agent may need a computer, including Windows and macOS environments* The Apple licensing constraints that make macOS sandboxes hard* Why CLI gives agents more power than MCP* How open source helps agents integrate Daytona* Why agent-generated PRs may break today's CI/CD assumptions* Why AI SaaS companies reselling tokens may face a cold shower* Why the AI cloud may look more like Stripe than AWSIvan Burazin* LinkedIn: https://www.linkedin.com/in/ivanburazin* X: https://x.com/ivanburazinDaytona* Website: https://www.daytona.io* X: https://x.com/daytonaioTimestamps* 00:00:00 Hook* 00:01:12 Introduction* 00:03:15 CodeAnywhere, Shift, and the end of localhost* 00:05:58 What Daytona is: composable computers for AI agents* 00:08:07 The pivot from dev environments to AI sandboxes* 00:10:17 The New Year's Eve MVP and customers begging for API keys* 00:12:56 Bare metal, stateful sandboxes, and Daytona's scheduler* 00:17:28 60ms startup, 50,000 sandboxes, and 850K daily runs* 00:21:53 Spiky RL/eval workloads and the new agent infra problem* 00:28:12 RL workloads, Kubernetes pain, and dynamic resizing* 00:33:31 Why every AI agent needs a computer* 00:38:48 macOS sandboxes and Apple's licensing problem* 00:44:28 Why CLI may matter more than MCP* 00:48:11 Open source, GitHub stars, and agent integration* 00:53:11 Git, CI/CD, and agent collaboration bottlenecks* 00:58:15 Founder life and building a 25-person infra company* 01:02:44 AI SaaS, token resale, and API-first business models* 01:06:10 GPU sandboxes, data centers, and compute growth* 01:09:48 Why the AI cloud may look more like Stripe than AWS* 01:11:26 Closing thoughtsTranscriptIntroduction: Daytona, CodeAnywhere, and the End of LocalhostSwyx [00:00:02]: Okay, we're in the studio with Ivan Burazin, CEO of Daytona. Welcome.Ivan [00:00:07]: Thanks for having me, man.Swyx [00:00:08]: Ivan, you and I go back.Ivan [00:00:10]: Way back.Swyx [00:00:11]: How I don't even know how, you found, did you reach out or, for Shift.Ivan [00:00:17]: I reached out to you. The reason was you - we were just - we were thinking about I was one of the co-founders of CodeAnywhere, the first browser-based IDE, and so we were thinking a long time of, localhost should die. And you had this article.Swyx [00:00:29]: End of localhost.Ivan [00:00:30]: Then I reached out to you because of that, and then we talked, and I was actually at a different job and learning about I was the head of, developer experience, and you were quite well-versed in that, and I actually reached out to you, among other people, how do we go about that? What are the key things and whatnot at this point in time? And you were nice enough to take the call, and I remember I was late on your call with you.Swyx [00:00:51]: I don't remember.Ivan [00:00:52]: I remember because I was with my then I'm thinking of a girlfriend or wife at that point in time, I'm not sure. It's the same person, so that's great, and I was late ‘cause we were, in, Italy on, vacation, and then I was late for something. I felt so bad, and you were so nice to be, good about.Swyx [00:01:10]: The reason I'm nice is because I'm also late to other people, so it's like, who's, who's without sin here, yeah, so I have to, for those who don't know, InfoBip Shift, there's this whole thing that, you did in the past, and, and that was basically one of the inspirations for me starting AI Engineer, which is like, I have to thank you for giving me that push to be like, “Oh, you can, you can build and sell conferences?”Ivan [00:01:34]: I remember you asked you asked me at the beginning to give me advisory shares, and I was so focused on what we were doing, I said no, and I should've took the advisory shares. So I'm sorry, dude. But anyway.Swyx [00:01:43]: We're not, we're not venture backed.Ivan [00:01:44]: No, it doesn't matter.Swyx [00:01:45]: It's Yeah, anyway, so I think what's impressive about you is that CodeAnywhere is the thing that you've been trying to build, and, you kind of put it on hold and then came back after InfoBip. Just give us the story, do you - the story and the origin story, going into Daytona.From CodeAnywhere and Shift to DaytonaIvan [00:02:05]: Sure. Like, really way back, me and my co-founder have been together. I say this, I've said this multiple times, it's like we were married and divorced and married. Some people actually ask me is my co-founder my partner. they thought it literally. It's not literally, but we have done multiple companies together, and to your point, we had this shift where we went from the CodeAnywhere to the conference called Shift, and then back to, Daytona. We originally started stacking servers, doing like virtualization in the early 2000s and, routers and doing basically all these things, at a foundational level, and that was a services company which we sold to focus on what my co-founder actually invented, which was the very first browser-based IDE, right, I say the first. Before us was actually Heroku. They did it for a very short time until they became Heroku. But outside of them, we were the only one, and it was called.Swyx [00:02:55]: There was Cloud9.Ivan [00:02:57]: Cloud9 came out slightly after us. There was Replit, which came out when we stopped doing it, Replit came out, and they have been successful since then, which is great. There was Nitrous.io. There was quite a few that existed at the time, but it was like too early. But the interesting part is that we, at that point in time, because there was no VS Code, there was no Kubernetes, and Docker had just started when we Or I'm not sure if it was even public at that point in time. And so we had to build everything to the whole stack ourselves and that was the key learning that we brought into and that we've been using in Daytona today. So it was super early. There's about 3 million people used CodeAnywhere. It was slightly, it was angel-backed more than venture-backed. We ended up paying everyone back because it didn't have that sort of scale. But, three years ago, we started something similar with Daytona, which is not what we are today, but it was automating dev environments for human engineers, the basically the underlying stack of CodeAnywhere. And then we did a hard pivot last January to sandboxes. And so here we are.Swyx [00:04:01]: Historic pivot, yeah, and, it's one of those things where, I had independently invested in CodeAnywhere, but also in E2B, and then both of you pivoted into the same thing, and I'm like, “F**k.”Ivan [00:04:12]: You invested, you invested in Daytona. You invested in Daytona. But you were the first If we had not got your check, we wouldn't have done it.Swyx [00:04:18]: No way.Ivan [00:04:19]: No, it was like, “We have to get him on board first,” and you were that kicker that we, that got us off the ground.Swyx [00:04:23]: No, because you were putting me on your pitch deck, man. I was like, “Man, this is like a good trip if I don't invest.”Ivan [00:04:29]: That's because it was your quote. It's like we.Swyx [00:04:30]: Yeah. It's the end of localhost.Ivan [00:04:31]: Did a bunch of research about end of localhost and who was interested in that,.Swyx [00:04:34]: No, that's like, I put, I wrote that blog post, and every single company in that field reached out to me, and then every VC who was receiving those pitches then also had to call me and, talk it, talk through it with me.Ivan [00:04:47]: It's finally happening though.Swyx [00:04:48]: It was really super interesting.Ivan [00:04:48]: It's finally happening.Swyx [00:04:49]: It's finally happening.Ivan [00:04:49]: Yeah, it's finally.Swyx [00:04:49]: It's finally happening, with maybe sort of non-human users. Yeah, so what is Daytona today? Let's get like a quick description. I'm wearing the shirt.What Daytona Is Today: Composable Computers for AI AgentsIvan [00:04:58]: You're wearing the shirt. Yes,.Swyx [00:04:59]: It says, I think your branding is very good. Like, it's very consistent. It runs AI code. Like, it cannot be simpler.Ivan [00:05:05]: Exactly, but we're gonna probably have to change that.Swyx [00:05:07]: Oh, s**t.Ivan [00:05:07]: It's also a subset of what we do. Unfortunately, we really love this, Run AI Code is super simple. People interpret it different ways. I think we've given out 5,000, 6,000 of these shirts. People wear them with pride because it doesn't really market about us.Swyx [00:05:21]: Yeah, Daytona's on the back.Ivan [00:05:22]: It markets the back. It markets to the person itself, so I think we did a really good job on that one. But it is also a subset of what we do, because people, when they think about Run AI Code, they just think about these small, let's call it isolates, code execution boxes that, you send some code, you get an output. Whereas what Daytona is today is essentially composable computers for AI agents. It is, the market calls them sandboxes which can be misleading.Swyx [00:05:44]: All these things. All these things on.Ivan [00:05:45]: Yeah, exactly, ‘cause it can be misleading ‘cause people usually think about sandboxes as a demo or a test environment versus a production-grade environment. But what Daytona does, if you think of the laptop that you have in front of you or the computer that's over there, or, my wife is an architect, so she has like a Windows with a 3D graphics card inside to do 3D rendering. Like, as humans, we have different computers or different compositions of computers. And our belief is strongly that agents today and going forward will need all these different compositions of computers to do different types of tasks. And so we offer that basically through an API.Swyx [00:06:19]: Yeah, to give people - I'm trying to sort of front-load all the aha moments or the wow moments so that people can, stay engaged and click like and subscribe. the market is exploding, right? Like, you have been reporting 74% month-on-month growth, and it also, it's just been growing for a while. Like, it's been going like this. And every single - It's not just you guys. It's every single.Ivan [00:06:41]: Everyone, yeah.Swyx [00:06:42]: Sort of, compute provider. I don't know if you agree with me saying compute provider or not.Ivan [00:06:48]: It's fine.Swyx [00:06:48]: Yeah. So like organically PLG-driven growth, but also enterprise is doing super well, I think I wanna rewind to January of last year when you did the pivot. Like, so you obviously called this market early, and you were positioned for it, and you are now one of the market leaders. But what was the insight that made you do the pivot?The Pivot: From Human Dev Environments to Agent SandboxesIvan [00:07:06]: The insight that made us do this pivot is the quarter before that, so end of 2024, when we had - Basically, we did a demo with - I don't I think we discussed this as well, Devin was not public. You actually gave me access to Devin at that time. So Devin.Swyx [00:07:25]: I did?Ivan [00:07:26]: Yeah, you gave me access.Swyx [00:07:26]: I don't think I was supposed.Ivan [00:07:27]: Yeah, exactly.Swyx [00:07:28]: Yeah, I.Ivan [00:07:28]: So it doesn't matter. You.Swyx [00:07:29]: Yeah. I gave like three friends access.Ivan [00:07:31]: Yeah, or it was a call and you showed it to me. It doesn't matter. but OpenDevin was available, which is now called OpenHands. And so we're like, “Oh, this seems to be a thing. This is not public. Let's take our for human automation of dev environments and take, OpenDevin and launch that as a SaaS.” And we did that. Not very many people signed up and used it, but a lot of people reached out that were building agents, and they were like, “Hey, my agent needs a compute sandbox runtime,” whatever you wanna call it. I forgot what it was called at that point. And then we were like, “Oh, amazing. This is a new market. Here is our infrastructure. Here's our product, and go.” And what we found really fast, soon, was that people did not like what we had built. It didn't work. And I remember talking to people at the beginning when we're doing this, the sandbox we're building for agents. People were like, “Oh, why is it different? It's the same thing. We have like EC2, we have VMs, we have all these things.” But we saw that everyone we gave it to, it was like 20, 30 people, they all said, “No.” Like, “This is not what we need. This sort of breaks.” And basically, me and my co-founder not knowing a lot about - ‘cause we're infra people. We're not AI people. So I basically took it upon myself to like watch every single podcast that exists, including all of, all of these and all that, and sort of get up to date, read all the blogs, like get, understand what's going on.Swyx [00:08:45]: Do you wanna shout out who else was useful, just in case people are also looking.Ivan [00:08:49]: Generally we -, I looked at There's a few of podcast, different segments and different types. So there's you guys, No Priors, Bill Gurley's was great while.Swyx [00:09:04]: VG2, yeah.Ivan [00:09:05]: Yeah, while it was around. So there's a few. 20VC is interesting from a different dynamic, and some are different dynamic. But there was, also Red Points.Swyx [00:09:14]: We're not really about the compute market.Ivan [00:09:15]: It was also already - Sorry?Swyx [00:09:16]: You're, you want - You're looking at the agent infra market.Ivan [00:09:19]: I was looking at the agent market and the AI market in general and sort of understanding who are the players, what the perception, and how that goes. And like obviously you complement this with like going to conferences, going to events, going to meetups, reading white papers, like doing all the things that you have to do to understand what's happening. And so when we figured, when we sort of had an idea of what we had to build, literally over the New Year's Eve, literally on New Year's Eve, I half vibe coded the first MVP, first minimal viable product of what Daytona is today. And I went to sleep at like 3:00 AM or something like that. I was doing - I just put my like baby daughter and wife to sleep and, Happy New Year's, and go back to just, doing this. And I sent it to my co-founder, my CTO, and he saw it in the morning. He's like, “This is absolute garbage.” “Do not show this to anybody at all, but the idea is good.” And so he took two weeks, and he rebuilt it.Swyx [00:10:09]: Did it like look like that? Listen, I - It was rough idea.Ivan [00:10:12]: Oh, not even, not even close. Like it was it was way worse. But it was like a very - It was a simplistic view of what it should be. Like, it worked, but it was not ideal. And so he went, we went down the whole, which is his job as CTO, to go, and he came back with this version. We then called all the people that had said like, “This is garbage,” a quarter ago. And we set up these calls, and we gave it to - We just demoed it to everyone. And all the calls went long, every single one. They were 15-minute calls, and they all went to like 25, 30 minutes or whatnot. And everyone said, “We need, we want access.” There was no login, just an API key, ‘cause it was just a beta or an alpha. And they said, “Oh, we want access.” And we're like, “Sure, yeah. Okay, thank you very much.” But after like the next day, if we'd not send it, every single one, like every call that we did, everyone came back, “Where is my API key?” Like everyone wanted it. We're like, “S**t.” Like this is it. Like I've never felt So one, the understanding to your point was like most people thought it was the same infrastructure for humans and agents. We understood a quarter ago it's not. We just didn't know what was the right primitive. And then when we came, and we can talk about what that is, and we gave it to these people, I've never seen, I've never experienced - I've done multiple companies in my life. I've never experienced this, that people literally call you if you do not give them access. Like they want access right now. And so it's like, okay, they don't want this. the thing that they want doesn't seem to exist, or they have not found it, and they really want what we want. And then when we understood that we're onto something, and then when you think about the size of the market, like the market for human engineers and enterprise is a very large market, so think GitLab or whatnot. But the market for every single agent that will exist ever in the future is just like, what is that market? How big is that? And we're like, “We are all in on this.” And so that is where we made sort of the cut between the old product and the new one.Bare Metal, Stateful Sandboxes, and the Lambda + EC2 ModelSwyx [00:12:02]: Yeah. But it wasn't composable at the time?Ivan [00:12:05]: It was very - It was basically just a Linux box that you could change, that you could define number of CPUs, disk, and RAM. Like that is what you could do, but you couldn't have multiple operating systems, you couldn't resize it on the fly, you couldn't add a GPU, you couldn't do like all the things. It was just the, just the first sort of variation of that, yeah.Swyx [00:12:22]: Was it bare metal from the start?Ivan [00:12:24]: It was bare metal from the start. And so the interesting thing that we thought about right away, so our.Swyx [00:12:29]: Which, give people the background, what is the normal path?Ivan [00:12:32]: Yeah, so, basically most providers run this on top of VMs. And also.Swyx [00:12:37]: Firecracker.Ivan [00:12:38]: Yeah, they run on Firecracker and VM. And so we also fire - We can get - We have multiple isolation layers and we can do that. But the common way to do it is that they, one, that the state of the machine, or the hard disk is not part of the sandbox itself. And the other thing is they're not meant to last forever. So most of them are preemptible, like they can There's a time that they can live. And so our thought was when we were going into this is, agents will be like humans in the sense of you don't want your laptop to be shut down until you're done with work. Like, and you want to close the lid and open the lid, it's the same state. So you - Agents would want that, like the pause and come back. They want those two things. But also agents really want speed, right? Can they get it? So when we thought about it's like we need something insanely fast, how to make it fast, how to make it long-running, and stateful. And so those two things, it's like combining a Lambda and an EC2, right? Those two things together. And so we didn't have an idea how others did it, ‘cause we didn't know too that there was a market around this. It was more like, okay, this is what we need, what they need. And we looked at Kubernetes, it wasn't wasn't good enough for that. We looked at Nomad, it didn't enable that. And so our history in rewriting our own scheduler at CodeAnywhere is basically what my CTO came up with. Like, he's like, “Oh, the learnings from there,” and he brought it. And the funny thing is, our third co-founder, when he saw it, he's like, “Dude, what is this? This is like 2008.” Like, we went back in time, and he's like, “Exactly.” And so the reason why Daytona is like super fast, and you see this on benchmarks, is we essentially, we run on bare metal. We have our own scheduler, we use the underlying, disk, CPU, and RAM of the underlying machine, which means your IOPS are insanely fast because there's no, there's no network between an EBS or something like that. But also the snapshot, the point in time, the templates, are also preloaded on the bare metal machines. So when you fire off a sandbox from a template or a snapshot, you're essentially directed to the bare metal machine where that snapshot is based on that NVMe drive, and then it literally just turns on that machine, and it's local. There's no network latency, anything on there. And so that is sort of the specificities that we, when we're thinking from first principles, what a computer would look like for an agent, that is what we came up with, and that's what we created.Benchmarks, 60ms Startup, and 50,000 SandboxesSwyx [00:15:02]: Yeah. I should maybe, I don't know if you endorse this, but there's someone that does compute SDK, you guys do very well on there, with like the TTI, right? I. is this a, is this a is this a relevant benchmark for you guys? I don't know.Ivan [00:15:16]: I don't know, and it changes every day. So today RKL is.Swyx [00:15:18]: I don't know what RKL is. Never heard of it.Ivan [00:15:20]: Yeah. RK, yeah, so it is there.Swyx [00:15:22]: You are, at least a third of the next tier of performance, and then, there's a lot of other better-known names that are very slow to start.Ivan [00:15:31]: Yeah. We've been the number one by far for a long time, and now there's different, there's different definitions also of sandboxes, different isolation patterns, different other things. So RKL runs it literally on the S3, the data, so it's very different, and they spin up a sandbox, spin up a container for that, so it's a different type of thing. So the definition of a sandbox is something that we can all, we all need to get along with. But yeah, we're insanely fast on getting these things, up and running. And so you can see even there that it's a zero point 0.10 to 0.11, so.Swyx [00:16:03]: Close enough. Yeah. what else do you need, right?Ivan [00:16:05]: Yeah. So the benchmarks itself, so, in this, in I don't think the benchmarks equate to market ownership or revenue or anything like that. and I've seen this with multiple benchmarks, not just in sandboxes, but in general benchmarks around.Swyx [00:16:20]: It's table stakes. It's just like.Ivan [00:16:21]: Exactly. But it doesn't hurt.Swyx [00:16:22]: Just roughly check.Ivan [00:16:22]: Like you definitely have to be up there and you have to be competing so that people know that, oh, this is definitely one of the top. Because this is only one dimension of what customers look for. There's other things like how many can you spin up consecutively? There's a feature set, there's support, there's like all different things that people look at, but you definitely have to be there, on the benchmarks.Swyx [00:16:40]: How many people do people spin up consecutively?Ivan [00:16:43]: So we have.Swyx [00:16:43]: Or concurrently, is the Concurrency, right?Ivan [00:16:45]: There's three metrics that we look at. And so one is like time to spin up one, and so our time to spin up one is 60 milliseconds with network latency. So request, spin up, reply, 60, the whole thing, 60 milliseconds. That is one. But if you wanna spin up 50,000 at once, we are now at about 75 seconds. So it takes about 75 seconds to spin up concurrently 50,000. Some others, there's public data around this, like take 2,000 seconds, which is 30 minutes. Like there's different variations of that. And then there is the so it is speed of one, speed of like multiple, and then how many can you consistently have up and running. And so we basically have right now no limit to how much we can add because we basically own our own metal. But the biggest customer of ours does like about 850,000 every single day is sort of where they're, where they're just shy of a million every single day that they're running, we do have a request for half a million concurrent, which is literally half a million CPUs somewhere running. So that's an interesting.Swyx [00:17:44]: They pay by like vCPU seconds.Ivan [00:17:47]: By seconds, yeah.Swyx [00:17:47]: Or whatever. Yeah. Okay, and so and then, and the other thing is, the sleeping and the resuming, ‘cause it's all the stateful resumption of all these things, how, what kind of workload are people putting through this, right? Like how is it Do we measure by gigabytes in memory, gigabytes in storage? I don't In like network attached storage. I, what are the costly ones of, out of all these features?Workload Economics: CPU, RAM, Network, and StorageIvan [00:18:15]: The most expensive thing are CPU.Swyx [00:18:18]: Okay. Yeah, of course.Ivan [00:18:18]: The second one, yeah Then it's RAM, then it's disk. We actually don't charge.Swyx [00:18:22]: Which is snapshotting, right?Ivan [00:18:23]: No, it's actually the, snapshotting's part of it, but basically the size of your hard disk, of your machine. So do you have 10 gigabytes, do you have 20, do you have 50, do you have whatever? And then the transference of that. Right now, currently we don't charge for, network at all at Polychron.Swyx [00:18:37]: Oh, you gotta, yeah, you gotta fix.Ivan [00:18:38]: Yeah. It is very much a it's a larger and larger part of our bill, so we're working around, that part there. Obviously, that is the least, expensive, so the hard disk is the least expensive, so it's basically CPU, RAM, for us network, ‘cause we don't charge the customer, and then hard disk, is how it's split up. But there's also different types of workloads, so we basically split it up into two types of workloads in Daytona. One is what we call background agents or long-running agents. and the other is, basically RLs and evals, which I put sort of together. And so they have very different patterns of usage, and if you look at the usage of a background And I'll just name names of companies, not specifically.Background Agents vs. RL/Evals: Two Usage ShapesSwyx [00:19:21]: Yeah, open, all hands.Ivan [00:19:23]: Yeah. So like a background agent's a Cognition, a Lovable, a like all these things are Harvey. These are all long-running, background agents. And so if you look at their usage patterns, their usage patterns are similar to human, which is like follow the sun. Basically, the usage patterns of that is like noon is probably the highest, and the midnight is the lowest, and then weekends are lower. weekday is higher.Swyx [00:19:42]: Yeah, that's a fun question. How global is it? Is it very US-centric or?Ivan [00:19:46]: The US is a large part, but we have currently, we have Asia, Europe, and the US regions.Swyx [00:19:52]: So it's quite global.Ivan [00:19:53]: Yeah, it's quite global. We have it all over. It's interesting that our I talked to you a bit about this. Our number one city by user.Swyx [00:20:01]: Hmm.Ivan [00:20:02]: Is Singapore.Swyx [00:20:04]: Oh, wow. Amazing.Ivan [00:20:05]: Which is an interesting one, right? Not by revenue, just by just like by individual head count.Swyx [00:20:09]: Really?Ivan [00:20:09]: Just like an interesting thing.Swyx [00:20:10]: Singapore is, Singapore is weirdly high in the adoption charts of AI for the population. It's like an, seven, eight million population. And it's like keeps showing up.Ivan [00:20:20]: No, it's quite interesting. We were quite shocked, and I was like, “Oh, this is interesting.” And also one that's up there.Swyx [00:20:24]: There's a reason I'm doing AI using Singapore. it's because I'm from there.Ivan [00:20:27]: We're there. We're gonna, we're gonna be there as well. and it's interesting that Japan is in the top or like Tokyo's in the top, which is in all the tech cycles it has never been. It has never been, so it's quite interesting that they're.Swyx [00:20:39]: I think the Japanese just love AI. Yeah. It's that, and then it's Brazil. That's it.Ivan [00:20:44]: Brazil has always been in.Swyx [00:20:45]: I think.Ivan [00:20:46]: Even when I look, if you look at like GitHub's data and ask historically with CodeAnywhere, it was always like US, Western Europe, and then you'd have like India, Brazil, China, like that would be there. But like Singapore was not in, specifically Japan was never in sort of that top, that top.Swyx [00:21:01]: Yeah. Weird pockets.Ivan [00:21:01]: Weird. Yeah, so it's very global.Swyx [00:21:02]: Okay, so actually that, but that's helps you to distribute your load through, all time?Ivan [00:21:08]: The interesting thing is like we have those kind of loads, but if you look at the researcher loads, they're quite different. So what they are is like if you give them concurrency of 10,000 or 50,000 or 100,000 CPUs at ARMb, when they fire off a run, it's just 100%. And then it just runs, and then it stops. So it's very, the usage pattern is squares basically, right? And it's also not follow the sun, because people will fire it off at midnight before they go to sleep but then wake up and so it's very unpredictable, so you don't know where that is. So the shapes of the usage are quite different than we have had before. And also what's interesting is when it's sort of a follow the sun, even if you have a high growth company, you can sort of predict your usage patterns and have enough capacity for that, because it's sort of, it grows in a, in a way you can project. When you have companies doing sort of like evals and RL, they're super spiky. So they're gonna come in, it's like, “We're gonna use nothing, then can we have 100,000?” Right? And then go back down. And then 100,000, go back down. So it's very different, right? And.Swyx [00:22:09]: Do you want to lock them into commits so.Ivan [00:22:11]: Yeah, we do.Swyx [00:22:12]: Yeah, okay.Ivan [00:22:12]: We so we have to lock them into some sort of commits to have that capacity, because we have to have, basically we have to have the capacity for peak. Right? And so right now, Daytona's mean utilization is 15%, 1-5.Swyx [00:22:25]: Oh my God.Ivan [00:22:26]: So it's very low.Swyx [00:22:27]: Because it's very spiky.Ivan [00:22:27]: It's very spiky, but we get up to 90%. so we have these things. And so what we're, what we're looking at right now as a company is similar to Cloudflare where you can like geo move things around, but that works really well for basically the background agent where it's follow the sun. But this, it's not. Like it's a very different shape. Obviously with scale you figure these things out, but that's an interesting new problem that we have, as a compute provider in the agent space. And when we were doing the conference recently, and so we talked to like Nikita from Neon and.Swyx [00:22:57]: I should bring it up.Ivan [00:22:58]: Parag from Parallel and whatnot, everyone has the same problem. Whereas the usage is super spiky, and this is something that has not happened before, that you have these types of like it was always, it the amplitudes were not this high, right? So it's quite interesting use case and problem solve.Compute Conference and Spiky Agent InfrastructureSwyx [00:23:12]: Yeah, I don't know if we're gonna bring this up again, but let's just talk about the conference, you had like 1,000 something people at the Warriors game, at the Sorry, where is it? What's.Ivan [00:23:22]: Chase Center.Swyx [00:23:23]: Chase Center.Ivan [00:23:23]: Chase Center.Swyx [00:23:24]: I went. It was, it was very impressive. Obviously, you can, how to throw a conference, what did you learn? you put, you pulled together all these impressive names.Ivan [00:23:33]: What I.Swyx [00:23:34]: What were you looking for?Ivan [00:23:35]: My thesis behind the Compute Conference was let's bring together people that are building infrastructure for AI agents. Because when I think of what we're building, it is the agent is the primary user, what are the ergonomics and usage patterns of agents, and so we can do that. And what I found, this was a theory, it wasn't proven, is that we all have these problems, as I touched onto. And I was, as I was talking on stage, it was like we all have the same underlying infra problems, which is this spiky workloads, unpredictable workloads that we've never had before, in human, compute or human infrastructure. And it's, again, it's the same when I was talking to Parag or when I was talking.Swyx [00:24:20]: Lynn. Nikita.Ivan [00:24:21]: Lynn, Nikita. Lynn especially, I was talking to her the other day as well. Like the It is a very interesting type of problem to solve because I can touch on Cloudflare because there's a lot of like talk about that recently as to how they solve that, which is they have a bunch of geos, and basically, as users work in different places, and depending on your tier, they can move you around the geos. And so that how, that's how they get the higher utilization. But you can sort of predict these, and it's If it's something in You'll rarely get a spike that is 10 orders of magnitude. Like you'll get a like let's say one of your customers has some like an exponential curve. What is that to I'm using Cloudflare as an example. 10%, 20%, whatever it is. I don't, I don't have this data, I'm just assessing. It's surely not 10x, right? It's surely not something there. And so how do you go out and solve this problem? And we're all solving this in different ways. So we have.Swyx [00:25:11]: She also has the same thing.Ivan [00:25:12]: Yeah, I know specifically that like Neon had that issue as well. Like how are we solving these spiky loads and things like that ‘cause we talked about it. And so the interesting thing for me to actually internalize was, yes, everyone that's building for agents first is going through this, and we're all solving similar problems, which is quite.Swyx [00:25:28]: Let me let me double-click on this. Okay. So for example, Neon, I happen to know that they're very sort of S3 oriented, right? so they're just like fully bet on S3. And you get to benefit from S3's distribution and infrastructure. So I would imagine that Neon doesn't have to care, whereas Lynn maybe has to care a bit more because obviously she's doing GPU inference. And, for listeners, we did an episode with her, one and a half years ago. And you have to care. But like, right?Ivan [00:25:54]: Parag cares for sure, and Nikita.Swyx [00:25:58]: And Parag is C of, Parallel.Ivan [00:25:59]: Parallel, yeah.Swyx [00:26:00]: Former CTO of Twitter.Ivan [00:26:01]: Twitter, yeah.Swyx [00:26:02]: They are the search.Ivan [00:26:03]: Yeah, they're search, yeah.Swyx [00:26:03]: I You and I know but the listeners don't know.Ivan [00:26:08]: Yeah, we can put it down in the screen, and so ‘cause we, when we were talking.Swyx [00:26:11]: I'll put it up on the, on the screen.Ivan [00:26:12]: Yeah, right.Swyx [00:26:12]: People can look it up if they need.Ivan [00:26:14]: Look it up. And, yes, but they still have CPU and RAM, allocation that you have to have up and running. And so CPU and RAM, you have to allocate that and have that ready. And so there's basically two ways to do it. One is you either over-provision and you can handle the bursts, or two, you basically have, I don't know if this is a term, just-in-time compute, which is like as your load becomes, as your usage comes in, you can fire off requests for VMs or bare metals at other cloud providers and then get them up and running.Swyx [00:26:43]: This is if you go above 100%, right?Ivan [00:26:45]: Yeah, this is.Swyx [00:26:46]: Like your overflow.Ivan [00:26:46]: If your overflow, like spillage or whatever you do.Swyx [00:26:48]: You probably lose money on it, but it doesn't matter, right?Ivan [00:26:50]: It, not Well, you might, you might not That is a more cost-effective way to do it but it's a slower way to do it. Because basically what you have to do is you have to like queue your requests, spin up these just-in-time compute, get it all ready, provision it, and then get your workload there. And so if the time isn't important that much, that's fine, and you can do that. But if your customer, and especially for, let's say, the RL training runs, the reason why a lot of people come to us is because GPUs are more expensive than CPUs, right? So you want your GPU running at, what, 100% the entire time. And so when you're running runs on CPUs, when the when the CPU cycle is like down and spinning up the next one, you want that to be instantaneous so that your GPU doesn't go down, right? And if you then have to like go out and provision machines, you're essentially telling the GPU that it has to wait, and that's incurring our cost. So there's things that you have to try to solve for there.RL Workloads, Declarative Images, and Kubernetes ReplacementSwyx [00:27:43]: Yeah, let's talk about the different workload, right? You said that, what was it? A few months ago, you had zero RL workload and now it's 50%.Ivan [00:27:52]: It will be this one, 50%, yeah.Swyx [00:27:54]: Let's talk about how different it is, right? Like I imagine, for example, a lot less dynamic code generation of like arbitrary code. Like here, it's probably all the same code. You're just doing parallel runs or something, I don't know.Ivan [00:28:05]: Yeah. So you'll have multiple Depends on the like for each run, you'll have a snapshot. And they, for the most part, they actually do use our declarative image builder, which is like, “Oh, we, the agent wants these dependencies, these env vars.”Swyx [00:28:17]: These ones, yeah.Ivan [00:28:18]: Yeah, the declarative image builder, it.Swyx [00:28:20]: Which is a very modal like thing that they.Ivan [00:28:22]: Yeah. And so we build it on the fly and then we propagate that snapshot, and you can spin up as many sandboxes as you want against that snapshot. And then if you have to do changes, the model can, or like it could be also be automated. It's like, “Oh, now for the next run, we need to install these things or remove these things or whatever to get, a task done,” and then it goes off and runs that. So yes, that is something that it seems that they prefer. The number one reason I found, or should I say, let's take a step back. What we are competing against in that environment is essentially managed Kubernetes. So EKS, GKE, whatever. That is what the vast majority run on. And anyone that has tried Daytona versus GKE, EKS is like, “I'm never going back.” That has always been. There's a few reasons. One is the ergonomics. So if you have, if you're using Kubernetes to spin that up, you have to essentially manage the interface interactions with that. Daytona, although as a compute provider, it's more akin to a Twilio and Stripe from a consumption perspective than it is an AWS. Like you have an API, an SDK, it's quite like easy and seamless to get these things up and running, that's one. The other is the speed to which we spin up, which we mentioned earlier, which is much faster, and the scale to which we can go to. We haven't got into features, but an interesting feature is that it's very hard to OOM, or out of memory, our sandboxes, because we can dynamically on the fly.Swyx [00:29:48]: Resize.Ivan [00:29:49]: Resize, which is like impossible on almost any other thing. There are some technologies that enable you to do that, but it's like a very hard thing. And so we actually saw this when, the Terminal Revenge team is, brought us actually. So thank you, Alex and the team, that brought us into this whole space.Swyx [00:30:05]: It's just very rare that, a framework would just say, “Guys, just use Daytona.”Ivan [00:30:11]: Yeah, I think it says it somewhere. Yeah.Swyx [00:30:13]: Yeah. I was like, “What is this?”Ivan [00:30:15]: There's all, there's multiple there, but they also mention a few other places. and so Daytona specifically-We have, the, just jumping on themes here We, I don't know where it says Data Center.Swyx [00:30:27]: I, there.Ivan [00:30:27]: Doesn't matter.Swyx [00:30:28]: There's a very strong recommendation, which is, very unusual. Which is, it's.Ivan [00:30:33]: We do not pay them for this, just.Swyx [00:30:34]: I know, yeah. They just like you.Ivan [00:30:35]: Yeah, they like us. yeah, and also a thing, so, Data Center has multiple isolation sets underneath. The customer doesn't have to know what they are. But basically we have Docker, which is a container, that's hardened with Sysbox. So it's Docker's, isolation that is a security equivalent to a VM, but it's still a container. And that is the default, and they, especially in these training workloads, really like that as an interface to be able to use just a basic Docker container, and we enable Docker and Docker. Which for these RL runs, if you need to do a Docker compose or Kubernetes, you can spin up a K3S inside of these things, which unlocks a huge amount of workloads that you can do that you cannot do on other providers. So just on that part is much more interesting. And so we went that, through that. We showed them that we could do that, and they enjoyed that quite a bit. They being the general venture people.Swyx [00:31:28]: Those people, yeah.Ivan [00:31:29]: And Harbor people.Swyx [00:31:29]: Harbor people, do are they, are they a company yet?Ivan [00:31:33]: As far, I do not know.Customer Pull, Slack Connect, and the Computer Use BetSwyx [00:31:35]: Okay. All right. Yeah. It's like super obvious that like, there's a lot of excitement and success around these things, okay, so yeah, tell us more, right? Like, this is an exploding workload, Harbor adopted you, which helped speed things along. But what are you learning as this new workload comes online?Ivan [00:31:53]: There's a couple things that we learned, which we chat about in the beginning. We, and this has led our story, as we mentioned, we like talked to a lot of customers along the way, and we add more features and more tool sets as we talk to customers. And it's interesting that And I think it's that the ecosystem is so small and/or the models get smarter, where when we see one user come with a request, we know it goes on a roadmap if like three to five customers come with the same request in that week. It's like very bizarre. It happens so many times, which is.Swyx [00:32:27]: Because they're all friends.Ivan [00:32:28]: Sorry?Swyx [00:32:28]: They all, they're all friends. They're all in the same group chat.Ivan [00:32:30]: Yeah, probably, yeah. ‘Cause and they're like, “Oh, can you do this?” And I'm like, “Okay, this is interesting. We'll put it on a feature request.” And then the next one's like, “Oh, can you do this?” “Okay.” It's all the same, right? It's always the same. And so what we try to do, and I personally try to do, I try to be on as many call, quote-unquote “sales calls” I can. I'm in every Slack channel. We literally have about 1,000 Slack Connect channels, something like that. It's an interesting, there's so many interesting things you find out when you have all the Slack channels. You can also see where people, transfer between companies. You see leave Slack channel, enter Slack channel. It's an interesting thing. Also, just I digress, I feel that Slack Connect is literally LinkedIn what it should be. You have a list.Swyx [00:33:08]: LinkedIn charges you to, use your own connections, but Slack doesn't, right? Slack is like, do it for free. It's more lock-in. It's great.Ivan [00:33:15]: Yeah. It's amazing. Yeah. It's one of the reasons.Swyx [00:33:17]: You're gonna pay Slack for life.Ivan [00:33:18]: Exactly. You're there for life. So that's interesting. And so one of the things, the newer things we were talking about earlier is we made a big bet and put a lot of investment on computer use. that is not seen publicly the light of day. We haven't GA'd that yet, but we have.Swyx [00:33:32]: Is there a thing I can pull up?Ivan [00:33:33]: There is computer use there. It's right up a bit.Swyx [00:33:36]: Oh, yeah. Okay.Ivan [00:33:38]: What we have, what we talked about and what we've seen publicly is there's this theme now about, the human emulator where And Elon from XAI has talked about this publicly, and if you think about the models today, they're actually quite sophisticated and they can do a lot of work, but they still don't have access to all the tools. Like, I'm a strong believer that the most efficient way for an agent to work is essentially headless or through, terminal or whatnot. But if we, if we look at knowledge work in general, there's about 100 million knowledge workers in the US, about a billion in the world, and knowledge workers, and the salaries of them aggregate to 10 trillion in the US 50 trillion worldwide.Swyx [00:34:24]: Wow.Ivan [00:34:25]: Something like that. And if we look at, the five most important sectors of that, so like healthcare and government and financial services and whatnot, that's about 56% of that. So let's say it's about half of that. So in the US it's about 25 trillion, and most of them, most of that work is actually still locked into legacy apps inside of Windows, which is not going anywhere for a very long time. Like, people just won't invest in that. How much of it? our assumption is the following: if, in the RPA market, which is similar market, well, not the same 25% of, these white collar, workers', work is automated. If an agent is more sophisticated, can go through more runs, figure stuff out, let's say it's, 40%, right? And so if you take 40% of that, you get to essentially, $10 trillion a year.Swyx [00:35:17]: That's a TAM.Ivan [00:35:18]: That is a that is a TAM. So that's the TAM of the models, right? That's not our, essentially ours. But you get to that size, and to be able to do that, you essentially have to give agents these computers with the legacy. So computer use, either Mac or Windows or Linux. Linux we also obviously have and others have. But Windows specifically is something very new, and the only option right now is an EC2 with, Windows or on Azure. Both of them take anywhere from three to five minutes to spin up. We've created an actual sandbox, so it's a second instead of milliseconds, but you have, point in time snapshots, you have, forking, you have all the things that you have from a sandbox, but essentially enables you to hopefully unlock all this value. And so that's been our big push and bet, but we've sort of, kept our ear to the ground. What is sort of the next things in the market?RPA Returns: Why Agents Still Need ComputersSwyx [00:36:06]: Yeah, knowledge work, and building, and sort of RPA, the next wave of RPA. I got very excited about RPA kind of during COVID times. The UI path was IPO-ing. And it was, a very hot Isn't it, Eastern European?Ivan [00:36:20]: It is, Romanian.Swyx [00:36:21]: Romanian?Yeah, it might be the only Romanian, big unicorn okay, yeah. This I don't I don't, I don't have like a I think there's, I think there's a stage being set for the resurgence of RPA, ‘cause everyone understands that, yeah, no one wants to deal with these shitty apps and no one's gonna rewrite them. Like, you just have to do, a remote operation and programmatic operation of them.Ivan [00:36:45]: If you wanna unlock it, my own setup was basically the following. So I was doing a board deck recently, last month, whatever, and I'm like, “Okay, let's just, let's just do automated.” So, all our data's in, ClickHouse and PostHog and QuickBooks, where everyone else's is, and I'm basically, connected that all to, my Cloud code, like go off and go Cloud code whatever. Go off and, here's the integrations, go do that. It pulled out the first report, which was great. It connected to Brex and all these things, pulled it, which was great, and then I say, “Okay, now pull out this, and this,” and I kept getting, really well McKinsey-style design reports, but the data said partial data. all the missing data, partial data. Like, it can't access all the things, and I got so frustrated, and so I got, I got, my Mac Mini virtual sandbox with OpenClaw. I gave it its own account in our company, and then I went to all these services and created a read-only account, so literally like an intern in your company. And so I would say, “Now go and do this report,” and it would get the same, or like, “I can't via the MCP or the API or whatever. I can't get all the information.” I'm like, “Go log in.” And it will log into the website, then go in, export the data. It'll export the data and do the thing end to end. So even for things that have today APIs, not all of it is exposed, and I to get value, I get immense value right now, but it has to be a computer usage, unfortunately, and so I spend a bunch of tokens just on that, but I get the job done. And so if even a startup like ours, and using all the hottest tools, still needs a computer agent what hope does, Goldman have to have a headless, right?Swyx [00:38:22]: Yeah, what a - Why isn't Microsoft doing this?Ivan [00:38:27]: I'm pretty sure, Satya had a post yesterday.Swyx [00:38:29]: Oh, okay. I see.Ivan [00:38:29]: Which was like, “Every agent needs a computer.”Swyx [00:38:31]: I see, I see.Ivan [00:38:32]: So they have launched something recently.Swyx [00:38:34]: Yeah, they have Microsoft Power Automate, I'm sure, I'm sure, they're gonna have their version.macOS Sandboxes, Apple Constraints, and the Windows OpportunityIvan [00:38:39]: Version of that, yeah.Swyx [00:38:39]: You're gonna try to do yours, and it - I always know there's always demand for Mac, but I know it's, tricky to host, macOS sandboxes.Ivan [00:38:49]: We will have macOS sandboxes fairly soon. The problem with macOS, OS sandboxes is, I'm deep in this, I don't know how much interesting is.Swyx [00:38:55]: No, it's.Ivan [00:38:56]: MacOS has this problem.Swyx [00:38:57]: It's a licensing thing, right?Ivan [00:38:58]: Licensing thing. So one, you're allowed to run only two parallel VMs per machine, so that's one. Two, you can only license to a different user every 24 hours. So if you come in and theoretically, if I wanna charge you per second and I charge you one second, I have to have it idle for the rest of the day. I can't have anyone else doing that. So the pricing will be different in the sense that I will have to - we would have to charge for 24 hours, and that's not even, that's not even the most difficult thing. But the, thing above that is, from a security perspective, they enable you to do memory snapshot, pause, resume, but only on the same physical drive, physical machine. And so what you can do in, Windows world or Linux world is that I can move in the background, your snapshot from one to the other and manage load, right? Here, if you wanna do that, you essentially have to have your.Swyx [00:39:49]: Yeah, snapshots. Yeah.Ivan [00:39:50]: Your.Swyx [00:39:51]: It's like.Ivan [00:39:51]: Physical machine.Swyx [00:39:52]: You can't break it up.Ivan [00:39:53]: You can't, you can't move things around that, and all of that is, that part is, from a security standpoint, if it is written. Like, I understand the security aspect of that, but it disables you from doing these agentic, like really scalable agentic workloads.Swyx [00:40:08]: You need to do a vibe-coded, clean room implementation on macOS that you can then - That's like Clean OS or something. I don't know.Ivan [00:40:17]: So. We have.Swyx [00:40:18]: ‘cause like Linux was originally like a clean room rewrite of Unix.Ivan [00:40:21]: Okay. Yeah.Swyx [00:40:21]: Or something like that, right? Like same thing to macOS. Someone needs to do it.Ivan [00:40:25]: Someone will do that, and someone will have some long-running agents for a few days to figure this stuff out. But yeah. So definitely we - we're really close to offering something ‘cause people do want it, but the pricing will be different, and the feature set will be sort of stringent.Swyx [00:40:38]: Yeah, nobody's gonna use this. like, the labs, the labs will because they want to automate macOS.Ivan [00:40:42]: They have to do RL. They have to do RL again. But even if you The - So the point is with the RL part, if you, if you do RL on macOS, then the next iteration of the model comes out, it will be able to use these tools significantly. Then you actually need to run those, that somewhere. So you're gonna have to have that, later on. And from, if anyone at Apple is listening, I very much feel that they are shooting themselves in the foot of the scale of the revenue of compute or licensing they could get if they would just enable a concurrency model similar to what you can get on a Windows and a, and Linux.Swyx [00:41:17]: Yeah. Yeah. And I'm sure they've heard this before. They just don't care. Yeah, it's And maybe they will change their mind with the new CEO.Ivan [00:41:24]: Yeah. We'll see.Swyx [00:41:25]: We'll see.Ivan [00:41:25]: High hopes.Swyx [00:41:26]: High hopes.Ivan [00:41:26]: High hopes.Swyx [00:41:27]: Okay. But I, it's very clear the market opportunity is huge in Windows, and you can go for a long time on just Windows, but your customers are gonna want both. and I think, it is interesting to me that, this is the sort of God application of agents, right? Like, I don't It was - How big was OpenClaw for you guys? Like, was it, was there, a significant bump.OpenClaw, Agent Labs, and the B2B2C Sandbox MarketIvan [00:41:54]: Not for us because we.Swyx [00:41:54]: Because you already.Ivan [00:41:55]: We're kind of positioned differently. Whereas although it's completely PLG and we have individual developers that use it, most of the users that use Daytona are sort of a B2B2C. Sort of it's either B2B or B2B2C. So, in the researcher world, it's B2B, so you're selling to, labs and neo labs and things like that. But on the long-running agents, it's mostly, from a scale revenue perspective, it's mostly B2B2C, where you have a app layer agent that uses you at a big scale.Swyx [00:42:26]: Like a Manus. Yeah.Ivan [00:42:28]: Like a Manus Lovable type of thing.Swyx [00:42:31]: Yeah. I think that's the question of, well how, um-Uh, yeah, B2B to C is basically to me what I've been calling an agent lab, which is kind of like you're not in a model lab, but you're making a very good wrapper that is a platform that other people can sign up so they don't have to code those things. Yeah, it sound, it sounds like a much better market than the direct OpenClaw market.Ivan [00:42:56]: I've like - We I've done multiple things. So the CodeAnywhere's part of our career path R in the calendar, was very much an end user developer product. And so that is great. It You can get a lot of developer love, and I feel that we do as a company have a bunch of developer love. But it's a different type, where it's people building these things. Again, it's more akin to a Twilio because you don't really run - As a person, you wouldn't run Twilio. I don't know how many people remember. It was like ask your developer billboard and whatnot. And people really love Twilio, but they only used it inside of like, “Oh, I'm building this app or service for thing.” And so we're very much directly to that. And you also know that I used to work for a competitor for Twilio, so it's kind of ingrained, in my DNA.Swyx [00:43:35]: People don't know InfoBip is that big.Ivan [00:43:38]: Yeah, it's.Swyx [00:43:39]: Because.Ivan [00:43:40]: It's a billion euro.Swyx [00:43:40]: They're all American. They're like, “Whatever's in Europe doesn't matter to me.” But like it's the, it's the same size or bigger? Same size?Ivan [00:43:46]: It's about half the size.Swyx [00:43:47]: Half the size?Ivan [00:43:48]: Yeah, about half the size.Swyx [00:43:48]: It's like, yeah.Ivan [00:43:48]: Still huge. Multiple billions a year. Yes.Swyx [00:43:51]: That's crazy.Ivan [00:43:51]: Exactly, and so that - These are like really interesting and large revenue-generating, very sticky businesses. Whereas when you're selling to the - When your focus is the end developer, it is a very hard sell because they're very price sensitive, very price conscious, very around that. And there's very It's very hard to scale. Your cap is the number of people that are willing to spin up - First of all, wanna spin that up, and then spin up multiple of these. Whereas if you're in the enterprise one, like we know everyone's talking about like how many tokens they're spending, I'm spending. Like a lot of companies today are like, “If this is our company, spend as much as you can.” Like basically that is where we're going. And so if you think about that paradigm, where you're selling to companies that say, “Spend as much as you can to generate, productivity,” versus, “Oh, I'm a single person. I have this much budget, and I'm doing this thing because it's fun or it's helping me out or whatever.” Like it is a different, it's a different go-to-market, I think, strategy.MCP, CLIs, and Sandboxes as the Agent RuntimeSwyx [00:44:50]: Yeah, there's a lot of discussion. I'm just kind of going through like the mental list of things that are in your favor, which is, for example, MCP versus CLI. Like obviously you want CLI. It's been very good for you. I feel like it's maybe a drop in the bucket or maybe it's huge. I'm just checking whether it's like these are big trends.Ivan [00:45:10]: Those things you - work well in our favor, to your point just because every.Swyx [00:45:13]: They're kind of drop in the bucket, right?Ivan [00:45:15]: I think it's like sort of all the things come together. And so there's so many things that impact that. To your point, like OpenClaw wasn't huge for us, but like having the agent SDK, from Anthropic, so or Cloud Claude Code was very interesting. The reason why it was interesting is that a lot of, let's call them app I don't know what to call them, app layer agent companies, essentially they are like, “Oh, I can create this new app, this new agent. All I need, I just use Claude Code, and I throw it into a sandbox, and then I have my interface to the human to that.” And so that enabled so many more companies to actually offer this, and then they would pull on sandbox. So that was, that was interesting. And to your point, like MCP, versus the CLI, the MCP is an interface against an API, whereas the CLI is like you can actually go do things. Like this is it. The difference between integrations and actually running scripts or data or analysis against a thing. So being able to use a CLI very well enables the agent to do more things, and it's because that people will invoke a sandbox, they'll run it in the CLI, and but it'll do anal-analysis on that data and then give you an actual result versus just, pulling data from an API source.Swyx [00:46:29]: Yeah, it's a layer of indirection basically, it's the same thing as agentic search versus RAG, which where you're.Ivan [00:46:34]: Exactly, yeah.Swyx [00:46:34]: Just like you just win whenever people put more agents into their workflow. And so like it doesn't really matter, but I'm just kinda teasing out like what else have people heard about that like it's sort of, “Oh yeah, this is another sandbox use case. Oh yeah, that's another one.” Am I, am I missing any big ones?Ivan [00:46:51]: The thing, the thing that people, which is the computer use stuff, which I think is probably the most interesting one, is, and to your point, we've talked to so many people over the last year. It's like, “Oh, like why do you need a sandbox? Why do you need this? Why this?” And to your point, it's like, “Oh, I need sandbox for this. I need sandbox for that. I need sandbox-” It's like, “Oh, I need it for every single thing.” And so basically what I, what I - and it sounds like a broken record, it's like you use a laptop every single day, right? And you are n of one. It's just you. But now imagine how And by the way, the laptop, the computer PC market, the PC market is about equal to the cloud market in total. So it's about 150, 180 billion a year. Something like that. It's about roughly the three cloud hyperscalers is about equal to like Apple, HP, Lenovo, whatever, It's a little bit less, but it's sort of like that. And now imagine And that's just like, so how big is the addressable market? What, how many people are there in the world now? What's the last data?Swyx [00:47:45]: Let's call it eight billion.Ivan [00:47:46]: Eight billion. And so let's say you can have two computer, like you have one personal and one business, whatever. Like so it's double that, right? and so that's 16 billion, right? How many agents are gonna be running in two years, in 10 years, in 100 years? Like And for every single task, they will need one of these. And so how big is that? That market is essentially quote unquote “infinite”. You will get to the point, and Dylan Patel was at the conference talking about, from SemiAnalysis, that talks usually about GPUs, was also talking about how CPUs will now be a bottleneck because it will be the constraint. You won't be able to grow, or we won't be able to have enough of these because there won't be enough CPUs to basically do.Swyx [00:48:23]: Yeah. Well, I actually had a really good podcast with Doug Oliphant, who, which was his president at SemiAnalysis, where they've basically been like, yeah, it's been a GPU shortage first, but then it's cascaded down to memory and now to CPUs.Ivan [00:48:35]: CPU, yeah.Swyx [00:48:35]: It-What's next? So networking. So, networking actually has been in shortage for a while if you're looking at, just GPU networking. But, yeah, it's really crazy the amount of computer use that's going on, yeah, cool. I, other questions are, just the one very big part is the open sourceness which you didn't have to do, your competitors don't do, like it's not, a lot of people are worried about keeping their projects open source because some competitor can just slot fork it. I don't know if there's any reflections on just being an open source company.Open Source, Trust, and Enterprise ProcurementIvan [00:49:15]: Yeah. There's a bunch. So we the original product that we did was open source.Swyx [00:49:19]: Yeah. CodeAnywhere.Ivan [00:49:20]: So doing that was actually very good for us. There's basically a saying of, What's the saying? Like, companies that are, that are doing really well, measure themselves against, free cashflow, that are kinda okay, it's EBITDA, then, it's, it goes all the way down.Swyx [00:49:36]: The worst is like GitHub stars.Ivan [00:49:37]: GitHub stars. GitHub stars are the worst, yeah. So you go all the way down to GitHub stars. And so our original one was GitHub stars. That's what we talked about, we're at the point we're talking about revenue, so we're we've gone up the stack on that. And so we started.Swyx [00:49:47]: No, profit.Ivan [00:49:48]: Yeah. We haven't, we're, we'll get there. We'll get there. But basically at that point we did stars and GitHub and it was useful, and the original variation that we did, it we split the core into its own repo and it was Apache 2.0, so very, permissive. And then we basically would bundl

In this episode, we explore how political adversity builds character, using Rep. Thomas Massie's primary loss as a powerful lesson in resilience versus emotional tantrums. We also examine the alarming Gen Z cognition crisis, as experts warn this generation is the first in modern history to be less cognitively capable than their parents due to screens, tech-heavy education, and declining focus. Plus, Democrats escalate attacks on SEC sports, pushing boycotts of Southern universities over redistricting disputes. Trump rolls-back costly regulations on HVAC and refrigerators and his endorsement means a lot in elections. Tune in for insights on strengthening personal and national resilience in turbulent times. Please take a moment to rate and review the show and then share the episode on social media. You can find me on Facebook, X, Instagram, GETTR, TRUTH Social, TikTok, YouTube and Rumble by searching for The Alan Sanders Show. And, consider becoming a sponsor of the show by visiting my Patreon page!

The Guest: Norris Francis BranhamWebsite: www. Turtlegang.nyc YT ⁠ IG: @turtlegangnyc —This conversation with Norris Francis Branham is one of the most important, eye‑opening, and culturally vital interviews of the season. Viewers will walk away with a deeper understanding of Indigenous history, the erasure of Native narratives in America, and the urgent work being done today to reclaim identity, land, and truth.—Norris is not just a historian—he is a living archive, a cultural protector, and a frontline advocate whose work with Turtlegang.nyc is reshaping how communities understand their origins and their power. This episode is a rare opportunity to hear history from a voice that carries lineage, lived experience, and uncompromising clarity.

This episode draws on experimental and review literature on mirror-gazing, strange-face illusions, anomalous self-experience, dissociation, agency, face pareidolia, and face-distortion disorders, especially the work of Giovanni B. Caputo, Caputo/Lynn/Houran, Mash et al., Bregman-Hai and Soffer-Dudek, Derome et al., Palmer and Clifford, and Blom et al. Historical and occult context comes from research on catoptromancy, John Dee's angelic scrying records, the British Museum's “Dr Dee's Magical Mirror,” Campbell et al.'s Antiquity study on the mirror's Mexican/Aztec obsidian origin, and Mesoamerican material on Tezcatlipoca and the “Smoking Mirror.”Links For The Occult Rejectshttps://linktr.ee/theoccultrejectsOccult Research Institutehttps://www.occultresearchinstitute.org/Cash Apphttps://cash.app/$theoccultrejectsVenmo@TheOccultRejectsBuy Me A Coffeebuymeacoffee.com/TheOccultRejectsPatreonhttps://www.patreon.com/TheOccultRejectsCore Scientific Sources: Mirror-Gazing, Strange Faces, and Altered Self-ExperienceCaputo, Giovanni B. “Strange-Face-in-the-Mirror Illusion.” Perception 39, no. 7, 2010, 1007–1008.Key use: This is the main science anchor for the episode. Caputo showed that prolonged mirror-gazing under low illumination can produce strange-face apparitions, including distortions, unknown faces, monstrous faces, animal-like faces, archetypal faces, and faces of relatives or deceased people.Caputo, Giovanni B., Steven Jay Lynn, and James Houran. “Mirror- and Eye-Gazing: An Integrative Review of Induced Altered and Anomalous Experiences.” Imagination, Cognition and Personality 40, no. 4, 2021, 418–457.Key use: This is one of the strongest overview sources. It reviews empirical studies on mirror-gazing, psychomanteum work, and eye-to-eye gazing, especially in relation to altered perception, anomalous experiences, bodily experience, and self-identity.Mash, Joanna, Paul M. Jenkinson, Charlotte E. Dean, and Keith R. Laws. “Strange Face Illusions: A Systematic Review and Quality Analysis.” Consciousness and Cognition 109, 2023, article 103480.Key use: Newer review source. Useful because it supports strange-face illusions as a reliable phenomenon in both mirror-gazing and interpersonal gazing, while also warning that stronger research is still needed on mechanisms and prevalence.Bregman-Hai, Noa, and Nirit Soffer-Dudek. “Mirror-Gazing-Induced Dissociation Impairs Self-Reported and Implicit Sense of Agency: A Causal Investigation of Dissociation and Agency Under Controlled Laboratory Conditions.” PLOS ONE 21, no. 2, 2026, e0341316.Key use: Excellent source for the agency section. This connects mirror-gazing-induced dissociation with weakened sense of agency, which pairs well with mediumship, possession, automatic writing, and the feeling that “something else” is present.Derome, Mélodie, Eduardo Fonseca-Pedrero, Giovanni Battista Caputo, and Martin Debbané. “A Developmental Study of Mirror-Gazing-Induced Anomalous Self-Experiences and Self-Reported Schizotypy from 7 to 28 Years of Age.” Psychopathology 55, no. 1, 2022, 49–61.Key use: Useful developmental source. It connects mirror-gazing-induced anomalous self-experiences with age, self-perception, and schizotypal traits.Caputo, Giovanni B. “Visual Perception During Mirror-Gazing at One's Own Face in Patients with Depression.” The Scientific World Journal, 2014.Key use: Useful for the emotion/self-face relationship section. Caputo found that strange-face apparitions were reduced in patients with depression compared with healthy controls, including shorter duration, fewer strange faces, weaker intensity, and lower emotional response.Tramacere, Antonella. “Face Yourself: The Social Neuroscience of Mirror Gazing.” Frontiers in Psychology 13, 2022, article 949211.Key use: Strong support for the idea that mirror-gazing is like seeing yourself as another. It connects self-face perception with social neuroscience and the overlap between how we perceive our own face and the faces of others.Chakraborty, Anya C., and Bhismadev Chakrabarti. “Looking at My Own Face: Visual Processing Strategies in Self–Other Face Recognition.” Frontiers in Psychology 9, 2018.Key use: Useful for the self-face recognition section. This study looks at how people process their own face compared with other faces.Conty, Laurence, Nathalie George, and Jari K. Hietanen. “Watching Eyes Effects: When Others Meet the Self.” Consciousness and Cognition 45, 2016, 184–197.Key use: Best support for the gaze/presence section. It argues that direct gaze captures attention and triggers self-referential processing, which helps explain why a mirror can make the viewer feel watched.Face Perception, Pareidolia, and Monstrous DistortionPalmer, Colin J., and Colin W. G. Clifford. “Face Pareidolia Recruits Mechanisms for Detecting Human Social Attention.” Psychological Science 31, no. 8, 2020, 1001–1012.Key use: Best source for the “face-making brain” section. It supports the idea that illusory faces are not treated as meaningless noise; they can recruit mechanisms involved in social attention.Blom, Jan Dirk, Bastiaan C. ter Meulen, Jitze Dool, and Dominic H. ffytche. “A Century of Prosopometamorphopsia Studies.” Cortex 139, 2021, 298–308.Key use: Use carefully as a comparison source, not as a direct explanation for all scrying. Prosopometamorphopsia is a rare condition where faces appear distorted, showing that face-processing systems can produce frightening facial distortions under certain conditions.Psychomanteum, Grief, and Seeing the DeadHastings, Arthur, Michael Hutton, William Braud, et al. “Psychomanteum Research: Experiences and Effects on Bereavement.” OMEGA: Journal of Death and Dying 45, no. 3, 2002, 211–228.Key use: Main grief / dead-in-the-mirror source. Use carefully. It does not prove afterlife contact, but it supports the idea that mirror-gazing, darkness, memory, and grief can produce powerful experiences interpreted as contact.Moody, Raymond A. Reunions: Visionary Encounters with Departed Loved Ones. New York: Villard, 1993.Key use: Main modern popular source for the psychomanteum as a grief-contact chamber. Use as practitioner/popular context, not as the strongest academic evidence.Terhune, Devin B., and Matthew D. Smith. “The Induction of Anomalous Experiences in a Mirror-Gazing Facility: Suggestion, Cognitive Perceptual Personality Traits and Phenomenological State Effects.” The Journal of Nervous and Mental Disease 194, no. 6, 2006, 415–421.Key use: Good supporting source for anomalous experiences in a mirror-gazing facility. Pairs well with Hastings and the Caputo review.Kamp, K. S., Evgenia Steffen, Louis A. Kasket, and others. “Sensory and Quasi-Sensory Experiences of the Deceased in Bereavement: An Interdisciplinary and Integrative Review.” Schizophrenia Bulletin 46, no. 6, 2020, 1367–1381.Key use: Strong source for the grief section. It supports the point that bereaved people often report sensory or quasi-sensory experiences of the deceased, including feeling a presence, seeing, hearing, smelling, or sensing the dead.Hewson, Helen, and colleagues. “The Impact of Continuing Bonds Following Bereavement: A Systematic Review.” Death Studies, 2024.Key use: Useful for continuing bonds. It helps frame ongoing inner relationships with the dead as part of bereavement rather than automatically pathological.Historical, Religious, and Occult Mirror DivinationJohnston, Sarah Iles. Ancient Greek Divination. Wiley-Blackwell, 2008.Key use: Broad academic background for ancient divination systems. Not only mirror scrying, but very useful for framing divination as a serious religious and cultural practice.“Technical Divination and Mechanics of Sacred Space.” In Technologies of the Marvellous in Ancient Greek Religion. Cambridge University Press.Key use: Useful for ancient catoptromancy. This chapter discusses mirror divination as a technical mode of ancient divination involving reflective/catoptric knowledge and assumptions about divine intervention in human knowledge.Lee, Mireille M. “The Gendered Economics of Greek Bronze Mirrors.” Hesperia 86, no. 1, 2017.Key use: Useful for Greek bronze mirrors as social, gendered, material, and possibly magical/divinatory objects.Pitt Rivers Museum. “Mirrors.” Body Arts Collection Resource.Key use: Good museum-level source for folklore around mirrors and catoptromancy. Useful for basic show-note support on the traditional belief that mirrors could reveal the future.John Dee, Black Mirrors, and ObsidianBritish Museum. “Dr Dee's Magical Mirror / Dr Dee's Magical Speculum.” Collection object 1966,1001.1.Key use: Essential object source. The British Museum identifies the object as Dr. Dee's magical mirror or magical speculum, made of obsidian, catalogued as Aztec, and broadly dated to the 14th–16th century.Campbell, Stuart, Elizabeth Healey, Jago Cooper, Naomi Speakman, and others. “The Mirror, the Magus and More: Reflections on John Dee's Obsidian Mirror.” Antiquity 95, 2021.Key use: Essential academic source for Dee's mirror. The study uses geochemical analysis to show that the British Museum obsidian mirrors are Mexican in origin, with Dee's mirror matching the Pachuca obsidian source.Nature. “A ‘Spirit Mirror' Used in Elizabeth I's Court Had Aztec Roots.” 2021.Key use: Short science-news summary of the Antiquity findings. Useful for quickly explaining that Dee's mirror was traced to a source near Pachuca, Mexico.Smithsonian Magazine. “Obsidian ‘Spirit Mirror' Used by Elizabeth I's Court Astrologer Has Aztec Origins.” 2021.Key use: Useful public-facing summary of Dee's mirror, its Aztec/Mexican origin, and its connection to Elizabethan occult culture.Dee, John, and Meric Casaubon, ed. A True & Faithful Relation of What Passed for Many YeaAlso want to remind people about the website, if you're into reading we have tons of information by multiple contributors, and we got t-shirts up on the site if you're interested. Fun fact, the art is all based on the eyeball. A

Changing the world is difficult. One reason is that the most important problems, like climate change and democracy reform are structural. They are larger than any one person can solve on their own, yet we're bombarded with information about individual actions like attending a public meeting or lowering your carbon footprint. Do these individual actions even matter? Should we focus instead of fixing broken systems?  For our final episode of the season, we explore how individual actions and structural reform can work together to create lasting social change on a range of issues, including democracy. Our guests offer a way out of the either-or thinking and a framework for creating lasting social change.  In Somebody Should Do Something: How Anyone Can Help Create Social Change, Michael Brownstein, Alex Madva, and Daniel Kelly show us how we can connect our personal choices to structural change and why individual choices matter, though not in the way people usually think. Brownstein and Kelly join us on the show to discuss examples of how individual actions leveled up to create larger-scale change, including Mothers Against Drunk Driving and the milk pasteurization movement in the early 20th century. We also discuss how the lessons from these movements can be applied to democracy reform campaigns like campaign finance reform and ranked-choice voting.  Brownstein is Professor and Chair of Philosophy at John Jay College and Professor of Philosophy at The Graduate Center, CUNY.. Kelly is Professor of Philosophy at Purdue University, where he is also the Director of the Cognition, Agency, and Intelligence Center. This is our final episode before our summer break. Thank you to Brandon Stover for editing the show this year, to WPSU for production and promotional support, and to Michael Berkman, Chris Beem, Cyanne Loyle, and Candis Watts Smith for sharing their insights on the show. We'll see you in September!   Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Can Stroke Recovery Happen Years Later? The Griffith University Etanercept Trial Answers If you caught my recent video about UCLA's discovery of the first stroke rehabilitation drug that rebuilds brain connections in mice, you know the incredible excitement it generated. If you missed it, the link is in the description below. It's definitely worth a watch. Because of the huge response and the many messages from stroke survivors asking for more real recovery options, I wanted to take a deeper look at another breakthrough: The Griffith University study on using a drug called etanercept to help stroke survivors, not just weeks after a stroke, but even years later. And trust me, the results are eye-opening. Today, I'll walk you through what the study found, how it was set up, what it means for all of us, and where things are heading next. What Was the Study About? Researchers at Griffith University in Australia asked a bold and important question: Can etanercept help stroke survivors still living with chronic pain and movement problems, even many years after their stroke? They weren't looking for tiny improvements – they wanted to see fast, meaningful, life-changing results. This study wasn't designed for people who have just left the hospital. It was for survivors who had had their strokes at least six months ago, with some having had strokes over 15 years earlier. Why Did They Do It? Chronic post-stroke pain, or CPSP, is one of the most devastating outcomes of a stroke. It's not just muscle pain. It's deep nerve pain, constant, burning pain that regular medications like oxycodone or pregabalin often can't touch. Researchers now understand that this ongoing pain is often caused by inflammation in the brain, specifically driven by a chemical called TNF-alpha. Etanercept is a drug that's been used safely for over 20 years to treat arthritis and autoimmune conditions because it blocks TNF-alpha. The Griffith team wanted to test whether using etanercept to block brain inflammation could unlock recovery, even years after a stroke. How Was the Trial Set Up? This wasn't a casual or loose experiment – it was a carefully designed, professional clinical trial. Here's how it worked: 26 stroke survivors participated. Ages ranged from 30 to 80 years old. Strokes had occurred 6 months to 15 years earlier. Every participant had moderate to severe daily pain (rated between 4 and 8 out of 10). All had hemiparesis, or weakness on one side of the body. Participants were randomly assigned to one of two groups: One group received etanercept injections. The other group received placebo injections (just sterile saltwater). Each person received two treatments: One on Day 1 Another on Day 14 The injections were given near the neck in the perispinal space, allowing the drug to travel quickly to the brain. What Were They Measuring? The researchers focused on solid, measurable outcomes: Pain levels – using a 0–100 scale combined with a faces pain chart. Shoulder movement – measuring how far participants could lift their weaker arm. Sensation – testing for improvements in feeling hot, cold, and pressure. Cognition and fatigue – although big changes weren't expected here. Participants were monitored closely for 30 days after their first injection. What Happened? Here's what the trial revealed: Pain Relief 70% of the participants in the etanercept group experienced significant pain improvements. Pain levels dropped by an average of 24 points out of 100. 3 out of 10 participants experienced near-complete pain relief — often within 30 to 60 minutes of their first treatment! Meanwhile, the placebo group showed almost no change. Mobility Gains 9 out of 10 participants in the etanercept group regained more shoulder movement. 6 regained at least 60 degrees of motion. 3 participants fully regained 180 degrees — meaning full overhead shoulder motion. Sensory Improvements Many participants began to feel heat, cold, and pressure better on their affected side — a strong sign that nerve function was returning. Side Effects Only one major side effect was reported: one participant developed shingles and had to withdraw from the study. No other serious adverse events were recorded. What Does It Mean? If these results hold up in larger, longer studies: Stroke survivors could have a real option for reducing chronic pain and restoring lost movement. It could dramatically lower reliance on heavy opioid medications. Most excitingly, it shows that the brain may still be capable of healing years after a stroke — if inflammation is correctly targeted. However, it's important to remember: This was a small trial. Etanercept is not yet officially approved for stroke recovery. And the treatment doesn't work for everyone. But it's a huge, hopeful step forward. A Word About Dr. Tobinick It's important to acknowledge someone who helped make all this possible: Dr. Edward Tobinick. Dr. Tobinick was the first to use perispinal etanercept for stroke survivors back in the early 2000s. He was featured on 60 Minutes Australia in 2011, showing stunning recoveries that few thought were possible. Despite facing skepticism, lack of pharmaceutical company support, and high treatment costs, Dr. Tobinick kept pushing forward. Without his work, many stroke survivors wouldn't even know this therapy existed. You can find the link to that original 60 Minutes interview in the description. What's Next? Because of all the interest from our community, I'm reaching out to researchers at the Florey Institute in Australia. They’re currently working on new therapies for stroke recovery, and I'll update you on: Where their research stands What new options might become available And how close we are to real-world treatments for stroke survivors Stay tuned, as soon as I hear back, I'll share everything with you. Want to Dive Deeper? If you’d like to read the full Griffith University study, the link is in the description. The brilliant researchers behind this study include: Dr. Stephen J. Ralph Dr. Andrew Weissenberger Dr. Ventzislav Bonev Dr. Adrienne Goodman-Jones, and others from Griffith University and partner institutions. They deserve real recognition for pushing this research forward. Final Thoughts If you found this article helpful, Please subscribe, comment, and share this post with someone who might need hope today. And if you're listening on Spotify or Apple Podcasts, please leave a review. It helps more stroke survivors find this channel and this growing community. The post Etanercept and Stroke Recovery: Breakthrough Griffith Trial Results You Need to Hear appeared first on Recovery After Stroke.

If you think about which verbs dominate formal education you'll probably come up with a list like learning, thinking, reasoning, remembering, knowing, and maybe behaving. Now think about what images come to mind when you consider those verbs, or do a google image search and see what you get! I'm willing to bet that the most common images coming up are of individual heads, maybe with a visible brain or cogs, doing the thinking, the reasoning, the learning, the cognition. And to emphasise the point further, when we want to highlight that it's more than one thinker or reasoner doing the work, we have to put clarifying adjectives or nouns in front, like group cognition, collective learning or collaborative problem solving. But the fact is, we are actually already “intertwined creatures” in our entanglement with each other and the world. We think, learn and reason all the time with and through each other and the objects we interact with, and the places we are in. My guest this week, Professor Tony Chemero, has been a major proponent of ‘radical embodied cognition' for his whole career as a professor of philosophy and psychology. His latest book, brilliantly titled, ‘Intertwined Creatures: The Embodied Cognitive Science of Self and Other' is an amazing articulation of just how interconnected we are as creatures and learners in the world. Tony is a Distinguished Research Professor of Philosophy and Psychology at the University of Cincinnati, and a primary member of both the Center for Cognition, Action, and Perception[1] and the Strange Tools Research Lab. As well as many academic articles, he is the author of: Radical Embodied Cognitive Science (2009, MIT Press) - https://mitpress.mit.edu/9780262516471/radical-embodied-cognitive-science/Phenomenology, with Stephan Käufer (2015, Polity Press; second edition, 2021) - https://www.wiley.com/en-be/Phenomenology%3A+An+Introduction%2C+2nd+Edition-p-9781509540662Intertwined Creatures: The Embodied Cognitive Science of Self and Other' (2026, Columbia University Press) - https://cup.columbia.edu/book/intertwined-creatures/9780231223195/https://en.wikipedia.org/wiki/Anthony_Chemero https://researchdirectory.uc.edu/p/chemeray

Dr. Paola Tiedemann and Cameron Ford dive into the controversial Kong toy research that detection dog handlers have been asking about for years. This episode exclusively covers the chemical analysis of Kong rubber toys and what it means for teams using toys as training aids or rewards.What We Cover:The chemical signature found inside Kong rubber toysWhy this research was conducted and what question it answersU.S. vs. Europe: different legal frameworks for Kong trainingThe Fourth Amendment problem: probable cause and court challengesHow defense attorneys could use Kong training against handlersRisk assessment: "My client is a dog lover, there was a Kong in the car"Making informed decisions about toy-based training methodsDr. Tiedemann breaks down the science behind what dogs actually smell when detecting Kong toys, while Cameron addresses the operational and legal implications for law enforcement handlers. The conversation emphasizes informed decision-making rather than blanket recommendations—understanding both the benefits (used successfully in Europe) and risks (U.S. legal system challenges) of toy-based training.This isn't saying "don't do it"—this is saying "know what you're doing and the potential consequences."Upcoming Training Opportunities:

The push for a ban on social media for under-16s has been paused despite both National and Labour supporting it. Several other countries are moving to restrict young people's access to social media, following Australia's lead. Meanwhile, new research shows children who spend more than three hours a day on social media are more likely to develop depression and anxiety as teenagers. Dr Chen Shen from Imperial College's School of Public Health manages this large-scale Study of Cognition in Adolescents and Mobile Phones (SCAMP) and joins Susie Ferguson from London.

Episode SummaryWomen have up to 70-80% lower creatine stores than men — and most of us have never been told that. In this episode Lisa digs into what that means for your brain, sleep, mood, muscles, and energy, and why creatine may be one of the most underreported tools in women's health right now.What You'll LearnWhat creatine actually is and why it matters beyond the gymWhy women have lower creatine stores — and why that gap widens in perimenopauseHow creatine supports brain energy (ATP) and what happens when levels run lowThe research on creatine and memory, processing speed, and mental clarityWhy creatine may reduce depression symptoms — more so in women than menCreatine and sleep: the adenosine mechanism, the 2024 women's RCT, and the 2025 perimenopause findingsThe University of Kansas Alzheimer's pilot studyCreatine + resistance training for muscle and bone health over 40How much to take: 5g for general health vs. 10g for brain-specific benefitsStart HereReady to heal your metabolism? thinlicious.com/happyStudies ReferencedCognitive Function & MemoryXu et al. (2024) — Creatine & Cognitive Function: Systematic Review & Meta-Analysis. Frontiers in Nutrition.Depression in WomenLyoo et al. (2012) — Creatine Augmentation for SSRI in Women With Major Depression. American Journal of Psychiatry.Systematic Review & Meta-Analysis: Creatine for Depression (2025). British Journal of Nutrition.SleepDworak et al. (2017) — Creatine Reduces Sleep Need & Homeostatic Sleep Pressure in Rats. Journal of Sleep Research.Aguiar Bonfim Cruz et al. (2024) — Creatine Improves Sleep in Naturally Menstruating Females. Nutrients.Gordji-Nejad et al. (2024) — Single Dose Creatine Improves Cognition During Sleep Deprivation. Scientific Reports.Hall et al. (2025) — Creatine + Resistance Training in Peri/Postmenopausal Women: Sleep, Cognition, Strength. JISSN.Alzheimer's DiseaseSmith et al. (2025) — Creatine Monohydrate Pilot in Alzheimer's: Brain Creatine & Cognition. Alzheimer's & Dementia.Brain Dosing: The Case for 10gDechent et al. (1999) — Creatine Increases Brain Creatine by 8.7% in Human Neuroimaging Study. American Journal of Physiology.Candow et al. — Higher Creatine Doses for Brain Bioenergetics. Journal of Psychiatry and Brain Science.Dr. Rhonda Patrick on 10g brain dosing (@foundmyfitness)Medical Disclaimer: For educational purposes only. Not medical advice. Always consult your doctor before starting any new supplement.

My guest is Caroline Bicks, whose new book Monsters in the Archives: My Year of Fear with Stephen King (Hogarth, 2026) became a bestseller shortly after release. After she was named the University of Maine's inaugural Stephen E. King Chair in Literature, Caroline Bicks became the first scholar to be granted extended access by King to his private archives, a treasure trove of manuscripts that document the legendary writer's creative process—most of them never before studied or published. The year she spent exploring King's early drafts and hand-written revisions was guided by one question: What makes Stephen King's writing stick in our heads and haunt us long after we've closed the book?Bicks focuses on five early works—The Shining, Carrie, Pet Sematary, 'Salem's Lot, and Night Shift—to reveal how he crafted his language, storylines, and characters. While tracking King's margin notes and editorial changes, she discovered scenes and alternative endings that never made it to print, but that King is allowing her to publish now. The book also includes interviews Bicks had with King along the way that reveal new insights into his writing process and personal history.Monsters in the Archives—authorized by Stephen King himself—is unlike anything ever published about the master of horror. It chronicles what Bicks found when she set out to unearth how King crafted some of his scariest, most iconic moments. But it's also a story about a grown-up English professor facing her childhood fears and getting to know the man whose monsters helped unleash them. --------- Caroline Bicks is the Stephen E. King Chair in Literature at the University of Maine, where she teaches courses in Shakespeare, early modern culture, and horror fiction. She is the author of Cognition and Girlhood in Shakespeare's World and Midwiving Subjects in Shakespeare's England; co-­ author of Shakespeare, Not Stirred: Cocktails for Your Everyday Dramas; and co-­ host of the Everyday Shakespeare podcast. Her essays and humor pieces have appeared in the Modern Love column  of the New York Times, McSweeney's Internet Tendency, and the show Afterbirth. She lives in Blue Hill, Maine, with her family. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

My guest is Caroline Bicks, whose new book Monsters in the Archives: My Year of Fear with Stephen King (Hogarth, 2026) became a bestseller shortly after release. After she was named the University of Maine's inaugural Stephen E. King Chair in Literature, Caroline Bicks became the first scholar to be granted extended access by King to his private archives, a treasure trove of manuscripts that document the legendary writer's creative process—most of them never before studied or published. The year she spent exploring King's early drafts and hand-written revisions was guided by one question: What makes Stephen King's writing stick in our heads and haunt us long after we've closed the book?Bicks focuses on five early works—The Shining, Carrie, Pet Sematary, 'Salem's Lot, and Night Shift—to reveal how he crafted his language, storylines, and characters. While tracking King's margin notes and editorial changes, she discovered scenes and alternative endings that never made it to print, but that King is allowing her to publish now. The book also includes interviews Bicks had with King along the way that reveal new insights into his writing process and personal history.Monsters in the Archives—authorized by Stephen King himself—is unlike anything ever published about the master of horror. It chronicles what Bicks found when she set out to unearth how King crafted some of his scariest, most iconic moments. But it's also a story about a grown-up English professor facing her childhood fears and getting to know the man whose monsters helped unleash them. --------- Caroline Bicks is the Stephen E. King Chair in Literature at the University of Maine, where she teaches courses in Shakespeare, early modern culture, and horror fiction. She is the author of Cognition and Girlhood in Shakespeare's World and Midwiving Subjects in Shakespeare's England; co-­ author of Shakespeare, Not Stirred: Cocktails for Your Everyday Dramas; and co-­ host of the Everyday Shakespeare podcast. Her essays and humor pieces have appeared in the Modern Love column  of the New York Times, McSweeney's Internet Tendency, and the show Afterbirth. She lives in Blue Hill, Maine, with her family. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/literary-studies

Accurate OASIS coding is essential for all home health clinicians due to the impact that coding has on reimbursement. With the CMS plan to move to a universal outcome measure for all post-acute settings, OASIS will become an important tool beyond home health. Payment through PDGM is driven not only by improvement of quality indicators but through a complicated calculation of risk adjustment driven by baseline functional scores, acute cognitive changes, and hospitalization risk. This course will provide easy to use methods that will improve a clinician's ability to provide accurate coding to these sections of OASIS E1.   To view accreditation information and access completion requirements to receive a certificate for completing this course, please click here.   The content of this Summit podcast is provided only for educational and training purposes for licensed physical therapists and occupational therapists. This content should not be used as medical advice to treat any medical condition in either yourself or others.

In this episode of The Firefighters Podcast, we explore incident command, cognition and the art of decision making on the fireground. This is not just a conversation for commanders. It is for every firefighter, because every person on the incident ground is making decisions that shape outcomes. From situational awareness and recognition primed decision making to THINCS, decision controls, stress, fear, ego, culture and command mindset, this episode looks at how firefighters think under pressure, how good decisions are built, how poor decisions emerge, and why the future of firefighter safety depends on cognitive agility, self awareness and the ability to think clearly in chaos. Get this as an article CLICK HEREAccess all episodes, documents, GIVEAWAYS & debriefs HEREPodcast Apparel, Hoodies, Flags, Mugs HERE Please check out our Partners supporting this episode areWilliam Wood Watches - Discount code FFPODCAST gives the user 10% off full range on websiteFIRST TACTICAL- tactical gear for elite operatorsGORE-TEX Professional ClothingMSA The Safety CompanyJAFCOIDEXFIRE & EVACUATION SERVICE LTD Send us Fan MailSupport the show***The views expressed in this episode are those of the individual speakers. Our partners are not responsible for the content of this episode and does not warrant its accuracy or completeness.***Please support the podcast and its future by clicking HERE and joining our Patreon Crew

Send us Fan MailGavin Symes is a returning guest on our show! Be sure to check out her first appearance on episode 776 of Boundless Body Radio!Gavin Symes is Registered Occupational Therapist, a Metabolic Therapy Coach, and the Founder & Chief Wizard of Wizard OT, short for Occupational Therapy. He founded Wizard OT in 2020 to provide a truly person-centered approach to therapy.At the core of their approach is the idea that each person should be treated as an individual. By considering each person's biological, psychological and social needs, they provide a tailored approach to improving every client's wellbeing. They don't assume to know ahead of time what is best for their clients, and instead, work with the individual, their family and their wider network to create an intervention.In 2010, Gavin suffered a serious head injury that left him physically and cognitively impaired. The injury made simple tasks hard and required some basic skills to be relearnt. Progress was slow and hard-won but with time, support and consistent effort, he was able to make a full recovery. The experience of having his life turned upside down by a debilitating impairment has informed everything Gavin has done throughout his career, as it is the fire that drives him to help others.Find Gavin at-https://www.wizardot.com/IG- @wizard_otFind Boundless Body at-myboundlessbody.comBook a session with us here! 

N. Katherine Hayles is a professor of English at UCLA and Emeritus Professor of Literature at Duke University. She is also the author of a number of books on consciousness and AI. Her latest book is titled Bacteria to AI: Human Futures with Our Nonhuman Symbionts. Greg and Katherine discuss technics - recursive feedback loops in which humans and tools co-evolve. Katherine argues that cognitive technologies and AI intensify this process, so we design them while they also design us. She distinguishes cognition from consciousness, emphasizing fast nonconscious neuronal processing and defining cognition as interpreting information in context with meaning, operationalized by SIRAL (sensing, interpreting, responding flexibly, anticipating, learning).  Katherine claims plants and bacteria meet these criteria, while physical processes are agents without choices; cognitive systems are actors that select and adapt. She applies this to computation, treating deterministic mechanisms as noncognitive but viewing modern systems and LLMs as cognitive, discussing aboutness via biosemiotics and LLMs' “conceptual environment.” *unSILOed Podcast is produced by University FM.* Episode Quotes: Are humans and AI evolving toward each other? 07:29: So we can chart the evolution of humans and cognitive computational media in just this fashion. So humans start by being immersed in their environment. They could not survive otherwise. And then humans evolve up to abstraction. Computers start with abstraction, and now, with sensors and actuators and networking, they evolve toward immersion. So humans start with purpose. Their purpose is to survive. That's true of all biological organisms. And then they evolve up to design. Computers start with design. But now, with AI, they seem to be evolving toward purpose, which is the same as biological purpose, to survive.  Consciousness is based on selfhood and self-narration 10:27: Consciousness is based on selfhood and self-narration. The stories we all tell ourselves every moment of every day about who we are and what we're doing, and that consciousness frequently lies. We know that eyewitness reports, for example, are often very untrustworthy because people just perceive what consciousness wants them to perceive. And often that is not accurate. One of the primary purposes of consciousness is to make the world make sense. When highly unusual phenomena happen, consciousness just edits it out. AI can now see humans from the outside 37:23: So we're using our projective capabilities to imaginatively construct an umwelt and then seeing what that would mean for our existence, our sense of meaning or whatever. But we're always doing that from the outside. We're never inside anything but the human umwelt. Now we have a technology in large language models that is capable of seeing the human umwelt from the outside and telling us about it. That has never happened before. Show Links: Recommended Resources: Bernard Stiegler Inclusive fitness Chiasmus Consciousness Daniel Dennett John Searle Stochastic parrot Biosemiotics Umwelt Symbiosis Context window LLM Terrence Deacon Guest Profile: Faculty Profile at UCLA Faculty Profile at Duke Wikipedia Profile Guest Work: Amazon Author Page Bacteria to AI: Human Futures with Our Nonhuman Symbionts Postprint: Books and Becoming Computational The Cosmic Web: Scientific Field Models and Literary Strategies in the Twentieth Century Chaos Bound: Orderly Disorder in Contemporary Literature and Science Unthought: The Power of the Cognitive Nonconscious Chaos and Order: Complex Dynamics in Literature and Science How We Think: Digital Media and Contemporary Technogenesis My Mother Was a Computer: Digital Subjects and Literary Texts Electronic Literature: New Horizons for the Literary Writing Machines Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.