John Hartley @HyGeiaOrg

@KanikaBK Thanks for sharing I'll read with great interest.

@0xCodez @OxCodez thanks for sharing looking forward to building out a system with Fable 5 orchestration

My girlfriend asked why I was smiling at my phone at 3AM. I lost my job last week. Rent due in 4 days. No backup plan. Then I found a 33-year-old nerd who turned $1,000 into $946,207 trading Bitcoin with a trick he stole from hurricane forecasts. No finance degree. No trading desk. Just a method every meteorologist uses and every trader ignores. The method: meteorologists never forecast tomorrow with a single model. They run 31 and count the votes. He applied that exact framework to Bitcoin. Built a Claude agent that reads every 5-minute BTC candle and feeds it into MiroFish simulator running 31 parallel prediction paths. Trade only fires when 28 out of 31 models agree. Below 26 votes? Trade dies instantly. The agent moves faster than any human trading desk: → Collects market data 24/7 without breaks → Runs continuous simulations inside MiroFish engine → Operates fully autonomous with zero manual input → Every trade executes only when consensus hits threshold → Every dollar captured is pure market inefficiency exploit That is the entire edge. Not prediction. Consensus. Position sizing follows Kelly criterion. Signal fires or it does not. Most signals fail the vote count, so the system stays flat most days. He spent years learning that certainty is a scam and consensus is the only edge that matters. You only need Claude Fable 5 + device + 1 hour per day. Giving this free for 24 hours. To get it: 1. Comment the word Fable 2. Like and retweet this 3. Follow me @expertwith_AI so I can DM you Save this post. Build the consensus system this week. Start with $200. Scale on evidence.

@expertwith_AI Fable

Big ty to @Xero for sponsoring Encodes Hackathon. Impressed with #XeroAPI watch this space for @healthcodeltd agent that enabled autonomous CCSD submissions

@0xMovez @0xMovez do you Have link?

Check out my latest article: Nurse-Led Clinical Services in Pharmacies: Five-Part Investigation linkedin.com/pulse/nurse-le… via @LinkedIn

x.com/i/article/2067…

NHS corridor care could be avoided by committing more resources to prevention.

Transforming Cardiac Care in North Africa: Closing the Prevalence Gap with Cardio-HART Ai-enabled ECG from cardioPhoneix.com Pls, like and repost @BillGates @AstraZeneca @WHO linkedin.com/pulse/transfor…

Cardio Phoenix is heading to ESC Heart Failure 2026 📍 Future Frontiers in Heart Failure Hub 📅 9–12 May 📌 Fira Gran Via, Hall 8 Meet the team and discover how Cardio-HART helps detect heart failure and valve disease earlier at the point of care. #Heartfailure26

@GTXFutureHealth Gain Echo level insights from our Ai-Enabled ECG device. Download brochure to find out more 👇cardiophoenix.com/cardio-hart-be…

Ai adoption in the low to middle income countries will leapfrog the EU. I'll be selling the latest in Ai-enabled ECG technology at @GTXFutureHealth Cardio-HART is CE approved and market ready looking for seed finance.

@GTXFutureHealth Looking forward to connecting and sharing innovations in healthcare

AI-native software engineering teams operate very differently than traditional teams. The obvious difference is that AI-native teams use coding agents to build products much faster, but this leads to many other changes in how we operate. For example, some great engineers now play broader roles than just writing code. They are partly product managers, designers, sometimes marketers. Further, small teams who work in the same office, where they can communicate face-to-face, can move incredibly quickly. Because we can now build fast, a greater fraction of time must be spent deciding what to build. To deal with this project-management bottleneck, some teams are pushing engineer:product manager (PM) some teams are pushing engineer:product manager (PM) ratios downward from, say, 8:1 to as low as 1:1. But we can do even better: If we have one PM who decides what to build and one engineer who builds it, the communication between them becomes a bottleneck. This is why the fastest-moving teams I see tend to have engineers who know how to do some product work (and, optionally, some PMs who know how to do some engineering work). When an engineer understands users and can make decisions on what to build and build it directly, they can execute incredibly quickly. I’ve seen engineers successfully expand their roles to including making product decisions, and PMs expand their roles to building software. The tech industry has more engineers than PMs, but both are promising paths. If you are an engineer, you’ll find it useful to learn some product management skills, and if you’re a PM, please learn to build! Looking beyond the product-management bottleneck, I also see bottlenecks in design, marketing, legal compliance, and much more. When we speed up coding 10x or 100x, everything else becomes slow in comparison. For example, some of my teams have built great features so quickly that the marketing organization was left scrambling to figure out how to communicate them to users — a marketing bottleneck. Or when a team can build software in a day that the legal department needs a week to review, that’s a legal compliance bottleneck. In this way, agentic coding isn’t just changing the workflow of software engineering, it’s also changing all the teams around it. When smaller, AI-enabled teams can get more done, generalists excel. Traditional companies need to pull together people from many specialties — engineering, product management, design, marketing, legal, etc. — to execute projects and create value. This has resulted in large teams of specialists who work together. But if a team of 2 persons is to get work done that require 5 different specialities, then some of those individuals must play roles outside a single speciality. In some small teams, individuals do have deep specializations. For example, one might be a great engineer and another a great PM. But they also understand the other key functions needed to move a project forward, and can jump into thinking through other kinds of problems as needed. Of course, proficiency with AI tools is a big help, since it helps us to think through problems that involve different roles. Even in a two-person team, to move fast, communication bottlenecks also must be minimized. This is why I value teams that work in the same location. Remote teams can perform well too, but the highest speed is achieved by having everyone in the room, able to communicate instantaneously to solve problems. This post focuses on AI-native teams with around 2-10 persons, but not everything can be done by a small team. I'll address the coordination of larger teams in the future. I realize these shifts to job roles are tough to navigate for many people. At the same time, I am encouraged that individuals and small teams who are willing to learn the relevant skills are now able to get far more done than was possible before. This is the golden age of learning and building! [Original text: deeplearning.ai/the-batch/issu… ]

@RegenerationEX Today is National Doctors Day in the US and Australia, an awareness day to express gratitude, and the UK health secretary is threatening doctors .... 🤔 Is this the best negotiation tactic...?

@TheBMA I met the Mother of my Children whilst she was working as junior Doc in the NHS, for ten years we struggled to pay sky high London rents. I support the #NHS #Strike

@HSJnews I'm the product manager at cardio-phoenix a healthtech Ai company. We've reinveted the ECG machine, Cardio-HART is made right here in the UK. Every NHS Doctor is blown away by Cardio-HART and buys it for their private practice. The NHS is falling behind

AI is transforming how heart disease is detected. New AI cardiac diagnostic devices like Cardio-HART are bringing advanced cardiac diagnostics directly into primary care — enabling earlier detection and smarter referrals. shorturl.at/2tUdv #Cardiology #MedTech