Pick a lucky number from 20– 72 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 10 numbers hide a surprise of $5,000 🎉 20 winners will be picked randomly in 72 hoursp

Pick a lucky number from 20– 72 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 10 numbers hide a surprise of $10,000 🎉 20 winners will be picked randomly in 72 hours

Pick a lucky number from 20– 72 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 10 numbers hide a surprise of $10,000 🎉 20 winners will be picked randomly in 72 hours

The median and average age for first-time buyers obtaining mortgage loans in Q2 26 (33 and 35.9 years) was unchanged and up 0.2 years respectively from Q2 25 (33 and 35.7) and unchanged and down 0.3 year respectively from Q1 26 (33 and 36.2 years). There has been a modest decline since 2000 when the median and average ages for first-time buyers stood at 35 and 37.9 years respectively: AEI

If you maintain a skill library for long-horizon agents, this one is worth your time. (bookmark it) It discusses one of most common topics I get asked about these days. It shares some good ideas on how to effectively leverage memory to improve the effectiveness of long-horizon agents. Recuris splits agent memory in two. A Working Memory tracks task progress, and an Experiential Memory holds skills. Skill selection is grounded in the current task state instead of the full growing history, which is where long runs usually fall apart. Because skill use is anchored to an explicit state, a failed run points at a specific memory component. A fixed Meta-Agent turns the evidence into validation-gated updates to Skill Memory, which reshape execution and produce new evidence. Across four long-horizon benchmarks and ten models, it improves task success in 35 of 37 completed model-benchmark pairs. On tau-bench it adds 17.8 points to GPT-5.6 Sol and 15.6 points to Claude Opus 5, taking Opus 5 to 87.9 percent. The advantage widens as the horizon grows, reaching 32.2 points on the longest tasks. Common long-horizon failures drop by up to 80 percent. Paper: Chat with Paper:

Dr. Andrew Wakefield Was Right + CHD Science Fact Check + School Safe Tech 00:30 Trump on Splitting Up the MMR 01:30 Dr Andrew Wakefield & His Backstory 09:00 The Costs of Dealing with MMR Injury 11:51 Latest Study from Dr Peter McCullough & Nick Hulscher 15:01 The Bequest Book 17:10 Message from Billy Tommey 17:40 Push Back on RFK Jr. 20:54 Dr Karl Jablonowski with CHD Science Update 21:30 Voices for Vaccines 22:35 Fact Checking the "Fact Checkers” 27:40 Aluminium and Other Problematic Ingredients 29:40 mRNA Flu Shots 34:09 Blackstone Investing in Shots 35:07 mRNA Shedding 37:35 Back to School & Safer Tech 42:40 Symptoms of EMR Exposure 44:15 Limiting Devices 45:50 Back to School Tool Kit 46:45 Wireless EarPods 49:22 Screen Strong Resources 50:48 704 No More & Stop 5G Near Me 53:04 CHD in DC Conference 54:36 The People’s Study 1. Trump just signed an executive order titled, “Delivering Gold Standard Childhood Vaccine Recommendations for Americans.” And with this announcement, Andy Wakefield has once again been vindicated. He joins today’s program to give his perspective. 2. Karl Jablonowski, Ph.D. returns to to fact-check a prominent, pro-vaccine organization on their claims about Vitamin K shots. He shares details about the ingredients in these injections and other evidence concerning their toxicity. 3. It’s back-to-school season, and one important topic to cover as families prepare to send their kids back to the classroom is wireless radiation. Miriam Eckenfels shares warnings about exposure and advice for reducing it.

Ukraine’s Ministry of Defense-accredited WeTrueGun drone school has turned Grand Theft Auto V into a practical FPV drone simulator. Pilots connect real radio controllers such as the Radiomaster TX16S and train in the game’s vast open-world map, which includes thermal vision and dynamic moving targets. The mod takes advantage of unrestricted urban environments, vehicles, and pedestrians to closely replicate actual combat conditions without the boundaries or scripted scenarios found in dedicated simulators. It serves as a low-cost supplement for sharpening skills, experimenting with mission designs, and keeping operators sharp between live training sessions. The school’s full 35-to-37-day program already covers drone assembly, tactics, electronic warfare countermeasures, situational awareness systems, and more.

Your health plan costs the same as a Tesla Model Y. I sat down with Ali Diab, CEO and co-founder of Collective Health, to find out what Collective Health is doing to fix it. Ali started the company after his own hospital claim got denied, and has spent the decade since building what he calls the “Stripe” of healthcare benefits — for employers today, and potentially insurers and health systems tomorrow. This episode hits the affordability crisis head-on. Ali makes the case that health insurance itself is the real inflationary catalyst in American healthcare. Ali doesn't do the polite version. He calls the MLR a cost-plus contract, compares network "discounts" to markdowns off the Neiman Marcus price, and told me about the time he was charged $340 in coinsurance for a $125 knee brace on his own company's plan. We dig into why self-insurance and direct contracting are eating the fully-insured market alive, why small employers are legally boxed out of self-funding, and why he thinks healthcare pricing needs an SEC. Also: World Cup takes. Ali's a winger-turned-number-nine and I broke two leg bones playing this sport, so we earned the tangent. Listen on Apple: Listen on Spotify: TIMESTAMPS (04:31) A denied hospital claim, a Stanford doc spending more time fighting insurers than seeing patients, and the founding of Collective Health (08:34) Your family's premium buys a Tesla Model Y every year — and the MLR is the $600 Navy toilet seat problem in disguise (16:35) America is over-insured: stop running windshield wipers through car insurance (and birth control through health plans) (18:55) Network discounts are markdowns off the Neiman Marcus price — the PBM rebate illusion explained (20:19) From 25 lives to Walmart: the self-funding playbook carriers lobbied your state to keep away from you (27:24) The $340 knee brace story (retail price: $125 — and no, he couldn't bring his own) (37:35) Inside the 3.9% vs. 9% trend gap: how routing members in real time actually bends cost (52:44) Give healthcare an SEC: why Ali thinks price discrimination in care delivery is a solved problem we refuse to solve

⚽️ USA vs Belgium — Round of 16 🇺🇸🇧🇪 a coin flip: USA 37% · Draw 28% · Belgium 35% home crowd, home whistle… and a President who's already got FIFA on speed dial 👀🪱 live market 👇 🪱

37 mistakes companies make with AI transformation: 1) Not investing in your data foundation/not having a data “clean-up” strategy. Often people expect that with tools, everything gets solved. 2) Starting with “we need AI” instead of a real problem (this is true for every tech cycle ever). 3) Underresourced AI center of excellence that serves every part of the organization. Backlog builds up, employees get disenfranchised, shadow AI explodes. 4) Trying to automate the same workflow vs rethinking from scratch. Building AI add-ons to existing processes rather than rethinking processes from the ground up. 5) Thinking too big and flashy. Not considering the implications day-to-day and the value of quick, unsexy wins. 6) Over-engineering. Sometimes you dont need a full agentic system and traditional software works just fine. 7) Obsessing over cost before proving feasibility of a use case (i.e using a smaller model first before validating technical feasibility with larger models). 8) Encouraging/pushing employees to use AI without real depth. Widespread rollout with limited education/lack of training for employees. 9) Telling your people that AI won’t impact jobs. 10) Overprotecting data + spend to the point of limited experimentation from your workforce. IT/Security blocking this or slow rolling it out (which is fair but bad for the speed in which this is moving). Culture doesn’t encourage AI use. 11) Not having places to go to ask questions / knowledge share. Whether that be a skills library, shared repo, or internal AI office hours. 12) Failing to solve the last mile. Everyone’s so focused on models, but successful applied AI is a complex last mile problem: governance, data, observability, context management, people, process, etc. 13) Shipping it and call it done. Lack of discipline to go beyond the shiny demo and ensure sustained adoption that meaningfully empowers teams. 14) Slop is tolerated. 15) No governed way to build for non-technical people. No Citizen SDLC to empower SMEs to build and share production apps. 16) Assuming AI transformation is the responsibility of one person within the org. 17) Run like an IT project. No senior exec actually owns injecting AI across the business, therefore initiatives stall and leave no lasting impact. There is no clear owner. 18) CEO is not a driving force. Leadership enforcement without the leaders actually knowing how or what to enforce. 19) Not getting the buy in of the “bad guys.” Bring Legal, Finance, and IT along for the ride early. 20) Not investing in / underestimating change management. Easy to get the folks who are excited on board, but it's a long process to make others feel comfortable. 21) Not measuring baselines before any adoption. What are the metrics pre-AI tool to post AI tool? No baseline = no roi story, and thinking that all AI usage is positive ROI without measuring usage/tying it to real outcomes fails the same way. 22) Inventing new KPIs for AI instead of focusing on having AI accelerate existing functional KPIs. 23) Reducing AI to headcount and being overly stringent on ROI too early into programs. 24) Being driven by FOMO and not having the patience to treat AI transformation as the multi-year migration it actually is. 25) Being married to past purchasing mistakes and not choosing the best technology at the moment. 26) Not anticipating the complexity of getting systems to work nicely together (a kind of scope creep as the reality blows up work required). 27) Not being agile enough to change course when the landscape changes drastically. 28) Locking in to a single provider ecosystem. 29) Not providing employees access to the underlying systems needed to make AI useful to take action, not just chat. 30) Underestimating how much of an impact AI can actually have. It is both a cooler and scarier time than ever before to be an incumbent. 31) Outsourcing thinking to AI - everyone can prompt, the differentiation is how you wield the tool to multiply the work you're doing. If you have good judgement you can do a lot more. If you don't, you end up wasting a lot of tokens spinning your wheels. 32) One functional department thinking they should own AI transformation. It treats AI as a vertical solution vs. horizontal capability that’s more than just technology. 33) Executing on AI initiatives before anchoring your work in a clear strategy that’s tied to business goals, a map of key processes, understanding of your technology and data reality, and clarity around how to meet your people where they are. 34) Not solving data permissioning and RBAC considerations before rolling out agentic tools firmwide. 35) Not giving people dedicated time to experiment or carving out time in their roles for it. 36) Not understanding how a business function ACTUALLY works before trying to apply AI. In someone’s head, the process for generating some end state dashboard is simple: systems generate the data, it gets consistently transformed and warehoused, then read into the dashboard that the VP sees. In reality, it’s a complete mess. 37) Neglecting internal evals to constantly test and evaluate how new models/harnesses perform company tasks on a $ per successful task basis. What's missing?