🤖 Can AI beat human traders? 📉 That’s the ultimate question we’re tackling in the latest episode of Make Your Move. In Episode 5, Peter Esho is joined by Ashley Glover for a deep dive into the world of AI, machine learning, and automated trading. Whether you’re a seasoned pro or a retail trader looking for an edge, this episode is a masterclass in how technology is leveling the playing field. Catch the full conversation now on Spotify, YouTube, or Apple Podcasts! 👉 https://lnkd.in/gQAwTWfC #Trading #ArtificialIntelligence #MachineLearning #AlgorithmicTrading #FinTech #MakeYourMove

If you’re spending time trying to figure out where AI is actually headed right now, this might be worth a listen. We just dropped a short podcast at Callibrity, Five AI stories from the same week, and it does a good job connecting what can feel like random headlines into something more coherent. Big takeaway for me: AI has clearly moved into production. The challenge now is everything around it. It touches on things like adoption outpacing security, where the real battle is shifting (infrastructure, not just models), and what enterprise traction actually looks like right now. It is only about 20 minutes. Easy way to get a clearer read on what is real vs what is noise. https://lnkd.in/eMdSJaf8

We recorded an episode this week where I basically admitted on tape that our podcast production can be a bit messy. JCK and I record, I edit, wrangle thumbnails (that could be a lot better looking), I write the titles, and the posting copy, I pick the clips. It works. It's messy. It also does not scale. So we're rebuilding the workflow on camera. The podcast is the example. The real lesson is the framework underneath it: follow the friction, map the process, reverse engineer it with AI. That moves cleanly into anything repetitive at work. This is the same tradeoff I have run a hundred times in talent acquisition. Best in class for the gap that actually matters, good enough for the rest. Most teams pick wrong because they pick by vendor pitch instead of by friction. We are building this live, with the stumbles included. If you have a process at work that costs you hours every week and you are not sure where to start with AI, this series is going to be useful. New episode of The Missing Manual Show is up: How to Build an AI Workflow for Any Repetitive Task. Link in comments. Jenny (JCK) Cotie Kangas. Full episode listed in the comments. Please like, subscribe and share. #FollowTheFriction #AIWorkflows #TalentAcquisition #OperationsLeaders #LearningOutLoud

Another good episode with cohost Ryan Torvik . Embedded systems spec-driven development with AI. Is it waterfall? Is it Agile? Make it incremental, and make the AI work incrementally. Then it's Agile and stays in control. Remember that YOU are the one doing the actual thinking here. You don't get to leave the room while the AI does it all for you. Worth a couple of listens with some time to think about it and listen to other episodes in between (like the one with Jacob Beningo ). Episode link in the comments of Luca's post.

Oh NO. Not another TLA. That’s Three Letter Acronym, for those of us now apparently fluent in business nonsense. This one, though, is actually worth paying attention to: AEO. Answer Engine Optimisation. And yes, I’ve made a podcast episode about it. A fun one, I should add. Not a solemn tech sermon delivered from inside a server cupboard. Reaching for Position Zero is my new podcast about websites, AI search and what small businesses need to understand before the internet rearranges the furniture again. It also features a small supporting cast of enthusiastic amateurs, who are doing their absolute best and have not yet formed a union. Episode 2 looks at what AEO actually means. Not because you need to drop everything, panic-refresh your website and start writing content for robots in a small dark room. Please don’t. Nobody wants that. It matters because search is changing. People are asking fuller questions. AI tools are giving fuller answers. And your website needs to be clear enough for humans, Google and AI systems to understand what on earth you do. → Who do you help? → What do you offer? → Where do you work? → Why should anyone trust you? → What should they do next? That is not tech wizardry. That is good website thinking with a 2026 hat on. Episode here (and episode 1 if you missed it!) https://lnkd.in/eVBBc3-t

First episode of AI in Business is live. Every week I cut through the AI noise and show you what's actually working in real businesses right now - tools, use cases, mistakes, results. Under 10 minutes. No hype. No pitches. If you're trying to figure out where AI fits in your business - or where it's costing you without delivering - this is for you. *I promise I won’t post every episode. The link to the podcast is here: https://lnkd.in/eddMnwsj * Keeping updated on this light speed technological business revolution isn’t easy; so some light commute or morning run listening should help. Weekly sessions: Monday GTM Intelligence Tuesday MSP Pulse Wednesday AI in Business Thursday Founder GTM Friday The Weekly Brief

Explore how Jeremy Edberg from DBOS, Inc. tackles AI reliability with durable execution. Drawing from his time at Netflix and Reddit, he offers valuable lessons for building resilient AI systems.

💬 Hello, Agent: How do you make AI agents fail-safe? Learn the answer in Episode 3 of our "Hello, Agent!" podcast with the super savvy Jeremy Edberg, C-Suite Advisor at DBOS, Inc. In this ep, Jeremy explains how durable execution solves reliability issues, its role in building production-ready AI agents, and shares insights from his time at Netflix and Reddit on enterprise-scale reliability engineering. Tune in 🎧 https://lnkd.in/d8p4FNxt

Fascinating insights from Jeremy Edberg in the latest "Hello, Agent!" episode. Understanding durable execution is key to crafting reliable AI agents, a must-listen for anyone navigating enterprise-scale challenges. 🔍 DBOS, Inc.

Honestly, this isn’t something “nice to have” anymore. I just tested a tool that turned a full 1h 30m podcast into an accurate speaker-labeled transcript before I could even finish my coffee. It’s a competitive edge and those who ignore it won’t just work harder, they’ll simply be left out. Back in the days, transcribing a 1h podcast with 3 speakers meant hours or even sometimes days of manual work, endless back n forth, and multiple pairs of ears just to get the speaker labels right. Today? AI does it in minutes. Efficiency isn’t about replacing effort. It’s about redirecting it.