Podcasts about Older

Neurosis's Steve Von Till reveals how the band secretly rebuilt itself, creating a career-defining album and making future plans for the Neurosis victory-lap that few thought possible. Topics Include: The Secret recording of An Undying Love for a Burning World Neurosis walked away seven years ago under uncertain circumstances Four remaining members reunited quietly at end of 2024 Aaron Turner brought in to help complete the musical puzzle Reunion tied to Firekeeper Alliance, a suicide-prevention nonprofit Heavy music framed as protective factor for indigenous mental health Steve reflects on tribal rhythms and humanity's forgotten earth connection Band knew instantly the new material had to be Neurosis Reunion forced confrontation with ego, identity, and aging as musicians Firekeeper Alliance's Fire in the Mountains offer pushed them forward Album written and recorded in secret across three studio sessions Secrecy maintained even among close friends touring together in Australia Fan reaction described as unexpectedly emotional, healing, and hope-filled Steve discusses reduced stigma around vulnerability in heavy music today Vinyl pressing strategy explained: budget-driven first run, quick sellout Running Neurot Recordings on a teacher's salary, tight cash flow Multiple color variants pressed across regional distributors and mail orders Debate over black vs. clear vinyl sound quality explored Steve's approach to buying vinyl: music over pressing obsession Talk turns to Melvins, Hazel Meyer, and DIY label camaraderie Where the line sits between fan service and variant exploitation Vinyl side-length considerations shaped decision to make a double LP Aaron Turner's artwork process detailed, sketched live in the studio Album title changed at the last minute, forcing new artwork Neurosis' visual style rooted in myth, symbolism, and Jungian archetypes Older catalog rights partly held by Relapse and other labels Steve wants a "forever version" repress of the band's earliest albums Original master tapes and artwork found corrupted or lost to time Talk closes on solo tour plans and hopes for Australia High resolution version of this podcast is available at: www.Patreon.com/VinylGuide Apple: https://tinyurl.com/tvg-ios Spotify: https://tinyurl.com/tvg-spot Amazon Music: https://tinyurl.com/tvg-amazon Support the show at Patreon.com/VinylGuide

This is a free preview of a paid episode. To hear more, visit www.auburnobserver.comJustin and Dan discuss players who got serious summer buzz at SEC Media Days this week in Tampa and play some more Who's Older? Topics for this subscribers-only episode include:* A Pirate Looks at The Rest of the Secondary* Golesh's enthusiasm for the tight end room* potential starters on the offensive line* Byrum Brown's effectiveness in the red zone* Grubserver news* Justin tried one of Dan's favorite pizza places* a six-pack of Who's Older?* the end of this episode is a backdoor pilot for future spinoff podcast “Chart Chat: Fellas talkin' Ella”This is a premium podcast for Observer subscribers only. You can join by clicking the button below or going to this link.Follow Dan (@dnpck) and Justin (@JFergusonAU) on Twitter.

The unemployment rate fell in June, but beneath that encouraging headline are signs that the U.S. job market may be losing momentum. Employers added just 57,000 jobs, previous estimates were revised downward and the share of people working or actively looking for work declined. Older workers are leaving at especially high rates, while hiring burnout, return-to-office mandates, caregiving responsibilities, retirement and lower immigration may all be contributing to a shrinking labor force. USA TODAY Money Reporter Rachel Barber joins The Excerpt to explain what the latest numbers really reveal, why fewer available workers could constrain economic growth and how much confidence we should place in a jobs report that may still be revised.Let us know what you think of this episode by sending an email to podcasts@usatoday.com. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Topics covered in this episode: django-orjson Best Django Redis configuration for speed and size Linus Torvalds puts the foot down against Anti-AI Kernel Maintainers Django Steering Council backs the Triptych Project Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Consulting from Six Feet Up Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Michael #1: django-orjson Adam Johnson dropped django-orjson - drop-in replacements for the Django and DRF pieces that touch JSON, swapping stdlib json for orjson, the Rust-based library. Headline numbers: 10x faster serialization, 2x faster deserialization. The interesting question is why this needs to be a package at all. pip install orjson is the easy part. Adam's actual pitch: adopting it "isn't easy, especially when your framework uses json in many different parts." Django scatters JSON across JsonResponse, the test client and test case classes, the json_script template tag, and more. There's no single hook to grab, so you get a library that catches them all. Adam is refreshingly honest about the scale of the win. His words: "While database queries tend to dominate the typical Django application's runtime, the time spent in serialization and deserialization can still be significant." He calls it "a nearly free performance win" - not "this will 10x your app." That's a claim about cost, not magnitude, and it's worth keeping those straight. Worth flagging what the post doesn't cover: caveats. There are none in the article, but orjson has real ones. Django and Flask both render datetimes as RFC 822 HTTP-date (Wed, 15 Jul 2026 12:00:00 GMT); orjson does ISO 8601. It can't do ensure_ascii, it rejects NaN and Infinity (which stdlib happily emits), and it raises on Decimal. If you've got a JS client parsing dates, that's a wire-format change. Who should actually take this? If you're a DRF shop shoveling JSON all day, yes - it's cheap and it's real. If your app mostly renders HTML templates, you're optimizing a slice of runtime that's already near zero. The problem Adam's package solves doesn't exist in Flask or Quart. They already centralize every JSON operation - jsonify, request.get_json(), the test client, the |tojson filter - behind one provider object at app.json. So there's no library to install. It's about ten lines: import orjson from quart.json.provider import JSONProvider # or flask.json.provider class OrjsonProvider(JSONProvider): def dumps(self, obj, **kwargs) -> str: return orjson.dumps(obj).decode() # provider must return str def loads(self, s, **kwargs): return orjson.loads(s) app.json = OrjsonProvider(app) The numbers on talkpython.fm Evaluated it, measured it, and skipped it. The biggest JSON payload we serve is our MCP server returning a cached episode transcript, about 139 KB. Swapping the provider saves 0.119 milliseconds per request. That total response takes 1.1 ms We got 4.1x, not 10x - and the reason is the good lesson. Payload shape decides your speedup. The 10x is for structure-heavy data, lots of small keys where stdlib burns time in Python-level dispatch per item. Our hot payload is one giant transcript string, so the work is escaping and memcpy Calvin #2: Best Django Redis configuration for speed and size Peter Bengtsson revisits a classic: his 2017 "Fastest Redis configuration for Django" benchmark now has a 2026 update posted this week. The 2017 post pitted django-redis serializers (json, ujson, msgpack, pickle) and compressors (zlib, lzma) against each other; conclusion was msgpack + zlib as the sweet spot - avoid the json serializer, it's fat and slow. The 2026 update narrows focus to just compressors: default (no compression), zlib, lzma, and newcomer zstd. New results: lzma compresses best but is slowest; zstd is the fastest compressor on Ubuntu; differences between them are very small. Big takeaway across both: compression buys you a lot of space (2–3.5x smaller) for very little speed cost - worth it for Redis where memory is the constraint. Caveat from the author: results depend heavily on your data - his test stores short strings of numbers, so benchmark your own workload. Michael #3: Linus Torvalds puts the foot down against Anti-AI Kernel Maintainers Write up on Ars. Really good coverage by Maximillian: Time to wake up (for some) Torvalds said that “Linux is not one of those anti-AI projects, and if somebody has issues with that, they can do the open-source thing and fork it. Or just walk away.” I agree with Max, putting your head in the sand and waiting for AI to go away will likely mean you won't be working professionally in software development in the coming years. The statement came amid a lengthy thread arguing about the use of Sashiko, an “agentic Linux kernel code review system” that its creators claim can, in tests, independently find 53.6 percent of the bugs that would end up being fixed by human coders in later commits. “We're not forcing anybody to use [LLM tools], but I will very loudly ignore people who try to argue against other people from using it,” Torvalds said. “Anybody who points to the problems at AI had better be looking in the mirror and pointing at themselves at the same time,” Torvalds wrote. Calvin #4: Django Steering Council backs the Triptych Project Django Steering Council issued a Letter of Collaboration backing Carson Gross & Alex Petros's funding bid for the Triptych Project - three proposals to make HTML more expressive natively, in every browser. The three additions: PUT/PATCH/DELETE methods for forms, button actions (buttons that fire HTTP requests without a wrapping form), and partial page replacement. Distills the core ideas from HTMX/Unpoly/Turbo into the HTML standard itself - no JS, no library, nothing to ship or maintain. Current focus is button actions (WHATWG #12330): Logout instead of wrapping a button in a form. Relevant to Django directly - think the admin submit row and disguised delete links; Django 6.0's template partials were already inspired by these patterns. How to help: companies can send non-binding letters of support on letterhead; individuals can read the proposals and weigh in on the WHATWG issues. Extras Calvin: DOOMQL - A playable first-person shooter whose framebuffer is a SQL query. Michael: Granian 2.7.9 fixes WSGI threadpool scheduler starvation/underscaling Welcome Calvin post Joke: Solving all bugs

 Welcome to Catholic Apologetics, led by Dr. Jim Dobbins, Author of Take My Hand: A Personal Retreat Companion.  Just finished an RCIA program?  This is the next stop on your faith journey.    In these classes, we look at the different truths of Catholic doctrine and why we know they are true. We also discuss apologetics, spiritual growth, examine the liturgy of the Catholic Mass, and do scripture studies.  Please encourage your friends to listen. I also encourage you to leave a comment about our podcasts.  If you want the slides or any other documents for any class, just e-mail me at jhdphd@gmail.com and I will reply with the documents attached.  If you wish, I will also add you to the class materials distribution list so that each time I send anything out for the class you will get it.  If you are getting the podcast files from iTunes and would like to see the full set of available classes for download, you can see and download them all at http://yorked.podomatic.com.  Older podcasts are now stored at a free podcast site at Podcast.com.  The link to the podcasts there is: http://poddirectory.com/podcast/86506/dr-james-dobbins-catholic-apologetics We ask you to also consider going to http://yorked.podomatic.com and becoming a subscriber. It is free, helps our ratings, and thus helps us reach and help more people.  This session is part of our second discussion series of the Gospel of Matthew.  Please also let me know if there is a particular topic you would like to see addressed.  skvEapm1rLLW8foJsII1 

The Older Son | A Man Had Two Sons | Drew Ritchie by The Hills Church

AI is helping reveal how the brain flushes out toxic waste while we sleep. The mystery of chronic itch may finally be traced back to the finest hairs on your skin. An herbal supplement marketed as a safe, natural remedy is quietly sparking a major public health crisis among young Americans. Older adults may want to ditch the expensive dietary supplements and head straight for the gym. Facebook: ingoodhealthpodX: @ ingoodhealthpodIG: @ingoodhealthpodYouTube: @ingoodhealthpodSpotify Apple Podcast In Good Health PodcastSubscribed to the newsletterFull ArchiveContact UsBecome an Affiliate Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

This week's video transcript summary is here. You can click on any bulleted section to see the actual transcript. Thanks to Granola for its software.EditorialIntelligence: Who Owns it?This week the word “AI” feels too small.AI is a technology. Intelligence is its product. And if intelligence is the product, the question is no longer just: Which model is best? Who has the cheapest tokens? Who owns the weights? Who controls the data center? Those are important questions, but they are lower in the stack.The bigger question is simpler and more political:Who owns intelligence?That sounds abstract until you make it concrete. Intelligence is becoming something companies can capture, package, serve, meter, route, improve, and sell.It can write code, answer questions, design molecules, automate offices, run agents, draft legal work, advise scientists, serve consumers, and reshape workflows. It is not merely software. It is a general-purpose capability. And all humans could benefit from more of it.General-purpose capabilities have a habit of becoming public questions. But the default answer, that public good is best delivered by government, is the wrong answer in this context.The Product Is IntelligenceWe should stop talking about AI as a feature and start talking about intelligence as the universal thing that is delivered as an input to the world.Water is an input. Electricity is an input. Literacy is an input. Connectivity is an input. Once a society depends on them, access stops being optional. Nobody needs government to build every well, power plant, school, or network. But everybody understands that a civilization cannot be organized around less than universal and reliable access to foundational inputs.Intelligence is reaching that level of importance now that we all know it is real.Government should not own it, operate it, or develop it. Quite the opposite. Companies are the right actors to build fast, compete hard, improve models, serve customers, and discover the real use cases. Self-interest is a useful framing here. Markets are good at finding demand, reducing costs, and turning invention into services people actually use.Companies are the right operators, developers, and owners. But that does not settle the real question of who owns the benefits. That is an economic question.If intelligence becomes metered infrastructure, what happens to the value it creates?The Ownership StackThis week's articles keep circling the same issue from different directions but in the nature of ‘circling' never quite nail it.Jamin Ball's “Own Your Weights” starts with the enterprise version of the question. Owning a model file is not enough. The durable asset is the loop: the data flywheel, the evaluations, the reinforcement system, the workflow learning, and the operating context that lets capability compound.Benedict Evans' “Ways to Think About Token Pricing” adds the market layer. Tokens may become essential, abundant, and cheap, like mobile data. But being essential does not guarantee that the token layer captures the value. The money may move up the stack to whoever owns the workflow, the customer, the distribution, or the application.Alex Karp's fight with the labs, reported in “Alex Karp Is Saying What Every Angry CEO Is Thinking About AI”, is the same argument in sharper enterprise language. Companies are afraid that model providers will not just sell intelligence, but learn from customer workflows and then move into the markets where those workflows create value. The “All-in” group are echoing Karp's view.And “What Is Loop Engineering, and Who Owns It?” names the new contested terrain. The loop is where intelligence meets the world. Whoever owns the loop owns the learning. Whoever owns the learning owns the compounding asset.That is why “who owns intelligence?” is not a slogan. It is the question under the model layer, the application layer, the enterprise layer, and the economic layer.Because intelligence is the product, the tools creating it are fragmented and competitive. So there is no logic in trying to discuss this at the level of a single company or set of tools and models.The Old Promise Was That Commerce Would Tame PowerThe essays this week give the historical backdrop.Deirdre McCloskey, in “What Really Caused the Industrial Revolution”, argues that modern growth came not simply from capital accumulation, but from a change in permission: ordinary people were allowed to innovate, trade, build, and be honored for it.That matters because intelligence could be another expansion of permission. It could make more people capable of building, learning, creating, coding, researching, translating, selling, and coordinating. It could lower the cost of competence.But only if access is broad.Paul Krugman's “AI in an Age of Oligarchy” warns that the same technology lands differently in different political economies. A new general-purpose technology entering a broad, open, upwardly mobile society is one thing. The same technology entering a concentrated economy, with extreme wealth and weak counterweights, is another.Tim O'Reilly's Economist essay, “Elon Musk is building a form of capitalism that Adam Smith would hate”, makes the governance point more directly. The old liberal hope was that commerce would tame arbitrary power. Markets, boards, courts, shareholders, disclosure, and competition would discipline the prince.But what if the prince uses markets to escape discipline?Henry Farrell's “political economy of billionaire derangement” pushes the same point. Founder culture, monopoly ambition, peer rivalry, weak correction mechanisms, and vast private control can amplify appetites rather than restrain them.The danger with intelligence is not that companies build it. They should. Companies build it, meter it, use public tolerance and public infrastructure to scale it, learn from everyone who uses it. All of those things are inevitable and healthy. Market forces will sort out winners from losers. The real danger is that the winners treat all of the surplus produced as purely private.Metered Intelligence Creates SurplusIf metering is not the problem, what is?The problem is pretending that metered intelligence creates value only for the metering entity. Metering water is only tolerated as a public good. If the public were blackmailed by a private water company with the threat of no water we would all rebel.Once we understand that the product of AI is intelligence we can see that every time intelligence is used, there is the immediate transaction: the user pays, the provider serves.But there is also system value. Usage creates signals. Workflows reveal patterns. Prompts, corrections, failures, preferences, integrations, edge cases, and business processes all help define where intelligence is useful and how it should improve. Intelligence breeds intelligence.Even when customer data is contractually protected, the market learns. The platform learns where demand is. The product team learns which workflows matter. The ecosystem learns which jobs are vulnerable, which tasks are automatable, and which parts of the economy can be reorganized around machine intelligence.So the surplus is not born in a vacuum.It rests on public science, public education, public data exhaust, public law, public infrastructure, public energy systems, public tolerance for data centers, and billions of human interactions. It is served by companies, but it is not made only by companies.This is why “Americans Deserve a Dividend From AI Companies' Riches” belongs at the center of this week's issue. The detail can be debated. The principle is harder to dismiss. If intelligence becomes a new foundational resource, then some part of the wealth it creates should flow back to the people whose society makes it possible. Intelligence did not suddenly appear. AI is built on the entire history of human intelligence. It benefits from it and at the same time evolves it.Not Nationalization. A Human Wealth Fund.If intelligence belongs to everybody, some conclude that government ownership of intelligence is the right outcome.Governments are not well suited to build, operate, or improve intelligence. They will move too slowly, regulate too early, politicize the wrong things, and confuse economic participation with operational control.Andrew McAfee's “Why I Didn't Sign the AI Open Letter” is useful here. His objection is not that the technology is unimportant. It is that steering too hard before we understand the shape of the change can become its own failure mode. Marc Andreessen's satire of AI regulation is less policy than temperament, but it captures a real Silicon Valley fear: that regulation can become permission, capture, and incumbency before it becomes wisdom.That fear should be taken seriously.But it does not answer the economic question. It answers only the operational one.How can the economic benefits of intelligence be distributed? The better answer is a sovereign human wealth fund.Call it a sovereign wealth fund if you must, but the phrase is too national. Intelligence will not respect borders. The leading companies are global. The models, chips, data centers, agents, platforms, and workflows will be transnational from the beginning. If the value created by intelligence is global, then the mechanism for sharing some of that value should begin with the companies global enough to capture it. The nice thing about xAI, OpenAI, and Anthropic is that they are supranational.These companies own and operate intelligence. Let them compete. Let them profit. Let them keep the incentives that make the system improve. But if intelligence is the new water, the wealth it creates cannot belong only to the companies that meter it. And they, themselves, have the power to fix it, even more than governments.Access will become a Human Right; Ownership Is the Economic DesignThis is where human rights come in. There is no right to access an AI model, yet. But there will soon be a need to change that.Not as a claim that every person is entitled to every frontier model at every moment for free. That is not serious. Capacity has costs. Models have costs. Inference has costs. Data centers have costs. Although those costs will decline over time, possibly quite quickly as self-learning models address costs.The claim is more basic: in a world where intelligence becomes a primary input into education, work, health, science, citizenship, creativity, and economic agency, baseline access to intelligence starts to look like a civic requirement.That could mean public access layers. It could mean education credits. It could mean open models. It could mean AI dividends. It could mean public-interest compute. It could mean taxes on rents. It could mean a company-initiated human wealth fund that returns some of the upside to society without handing the operating system to the state. The latter could couple wealth growth with universal distribution of ownership.The exact mechanism matters. But the distinction matters more.Government should not own intelligence. It should be universally available. And people should have a claim on the wealth intelligence creates.The Frontier Is Also PhysicalThe abstraction is not weightless.“The Fight Against AI Data Centers Is Just Beginning”, “New York becomes the first state to enact a data center moratorium”, Reuters on pollution from Musk's xAI power project, and DataGravity's “Who Captures Value in AI Infrastructure?” all say the same thing from the ground up.Intelligence uses land. It uses power. It uses water. It uses chips. It uses grid capacity. It uses neighborhoods. It uses public patience.That makes the value question unavoidable. A society can accept the buildout if the buildout is legible as shared progress. It will resist it if the costs are local, the profits are private, and the benefits feel enclosed.Who Owns the “Loop”?The week ends where it began.“Anthropic and Blackstone” are betting that implementation is the next trillion-dollar business. “Vint Cerf” is working on identity for agents on the open internet. “GPT-Red” points toward systems that improve their own robustness. “Kimi K3” adds another open frontier model to the global mix.The model race continues. The deployment race is accelerating. The governance race is behind.My view is this:The central product of this era is intelligence. Companies have figured out how to capture it, package it, serve it, and meter it. That is good. It should stay in the hands of builders who have the incentive to make it better.But intelligence is too foundational to become just another private toll booth. A significant part of it will turn out to be free to users.As intelligence becomes a general-purpose resource, then access to it becomes a human-capability question, and the surplus from it becomes an economic-justice question. Not because government should run it. Because government should not run it. The operating layer belongs with companies. The wealth question belongs with everyone. But companies are best placed to turn that into a process of distribution.The question is not whether companies should build intelligence. They should.The question is whether humanity gets a stake in the wealth created by the thing that may soon become its most important shared input.Contents* Essays* Deirdre McCloskey on What Really Caused the Industrial Revolution* AI in an Age of Oligarchy* Elon Musk is building a form of capitalism that Adam Smith would hate* Murky Mirror: Truth and Consequences* The political economy of billionaire derangement* Is there any “oligarchy” to fight?* AI* Nearly 200 Economists and Tech Leaders Warn of A.I. Threats* Why I Didn't Sign the AI Open Letter* Own Your Weights* Ways to Think About Token Pricing* Alex Karp Is Saying What Every Angry CEO Is Thinking About AI* The AI Agents Are Coming for Microsoft Office* What Is Loop Engineering, and Who Owns It?* The Fight Against AI Data Centers Is Just Beginning* 6 months to live for open models* Americans Deserve a Dividend From AI Companies' Riches* Who Gets to Define the Frontier?* GPT-Red: Unlocking Self-Improvement for Robustness* Anthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just models* Vint Cerf is working on a plan to unleash AI agents on the open internet* xai-org/grok-build, now open source* The Pulse: What can we learn from Bun's rapid Rust rewrite with AI?* Orphan risks at the frontier of artificial intelligence* The Lab of the Future Should Feel Like a Data Center* Why AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”* Kimi K3 Tech Blog: Open Frontier Intelligence* Venture Capital* Three Years In* Venture Has Rarely Looked More Bifurcated* The Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active Now* Are Prediction Markets Doomed to Fail?* Regulation* Exclusive: The Next Frontier of the Deportation Wars: College Campuses* The Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.* India's crackdown on a new WhatsApp feature risks setting a global precedent* Let's build a children's public internet* Computer cops* Google is better at playing the AI regulations game* Infrastructure* Who Captures Value in AI Infrastructure?* New York becomes the first state to enact a data center moratorium* Pollution from Musk's unpermitted xAI power project hits hardest in Black communities* Interview of the Week* The End of the End of Geography* Startup of the Week* Radical AI's Joseph Krause: The Scientist Building The “Waymo” Lab For New Materials* Post of the Week* Marc Andreessen on AI RegulationEssaysDeirdre McCloskey on What Really Caused the Industrial RevolutionYascha Mounk and Deirdre McCloskey | Persuasion | July 11, 2026Yascha Mounk interviews Deirdre McCloskey about her argument that the modern world's economic liftoff came less from capital accumulation than from a change in ideas. McCloskey says both left and right versions of the conventional story rely too heavily on investment: the left stresses exploitation and surplus value, while the right stresses virtuous saving by capitalists. Her objection is historical and economic. Human beings had always invested, from irrigation works and Roman roads to seed grain, and simple accumulation quickly runs into diminishing returns.McCloskey's alternative is that northwestern Europe, first Holland, then Britain and Scotland, and then the North American colonies, developed a liberal ideology that changed who was allowed to innovate and be honored for it. The conversation links that shift to the erosion of inherited hierarchy, the spread of dignity for ordinary commercial life, and a moral vocabulary in which liberalism is not merely procedural but connected to virtues and values. The point is not that machines, coal, trade, and institutions did not matter, but that they do not explain the scale and timing of modern enrichment without a cultural permission structure for innovation.The interview also turns to the contemporary defense of liberalism. Mounk frames the series around the worry that liberalism is often treated as too thin to command allegiance, while its opponents speak more directly to moral passions. McCloskey's case is that liberal societies became rich because they dignified experimentation and ordinary enterprise, and that liberals need to recover the moral language behind that claim.Read moreAI in an Age of OligarchyPaul Krugman | Paul Krugman | July 12, 2026Paul Krugman frames AI as a major technological shock arriving inside an already unequal political economy. The post says AI's economic and social effects may take years to understand, but argues that the setting matters now: America has much greater wealth concentration and political inequality than it did in the 1950s and 1960s, when progressive taxation, stronger regulation, and more active antitrust might have contained some of the destructive effects of a new technology.Krugman's opening claim is that the same technology would likely have different consequences in a more level society. In today's United States, he writes, extreme wealth is both a cause and effect of policies that favor a small elite, including low effective taxes on capital and high incomes, weak enforcement of worker protections and antitrust, and cuts to programs that benefit ordinary Americans.The article is explicitly more about oligarchy than AI. Krugman says the paid sections document the rise of the “.0002%,” the economics and politics of extreme wealth, how oligarchy will shape AI's impact, and possible policy paths. His caveat is that AI itself may still produce a pushback against oligarchy, but absent that, he expects the pre-existing concentration of wealth and power to magnify AI's downsides.Read moreElon Musk is building a form of capitalism that Adam Smith would hateAuthor: Tim O'Reilly Published: July 12, 2026Tim O'Reilly argues that Elon Musk is using the legal forms of shareholder capitalism to escape the restraints that shareholder capitalism was supposed to impose. The article begins with SpaceX's public-market structure: ordinary public investors get little meaningful governance power, Musk keeps roughly 85 percent of the votes through super-voting shares, buyers waive jury trials and class actions, the company qualifies as controlled, and removal of Musk depends on the share class he controls. In O'Reilly's framing, that is not ordinary founder control; it is a design for being answerable to no one, possibly beyond Musk's own lifetime.The killer detail is the article's turn through Albert Hirschman, Montesquieu, James Steuart, Adam Smith, and Keynes. Older defenses of commerce held that markets would tame princely passions because the self-interest of merchants was safer than arbitrary rule. O'Reilly says Musk reverses that hope. The market discipline that was supposed to cage the prince has become the lever by which the prince raises capital, removes feedback loops, and carries private power into politics, government, Mars, robots, AI, or whatever ambition comes next.The pull is the link to AI governance. O'Reilly says corporations are already a kind of artificial intelligence: narrow-input systems that act at a scale no individual human can match. Their partial controls include independent boards, shareholder votes, courts, disclosure, regulators, public pressure, and activism. If the leaders building frontier AI strip those alignment mechanisms out of their own companies, the governance of the company becomes a preview of the governance of the machine.Read more: The EconomistMurky Mirror: Truth and ConsequencesAuthor: Esther Dyson Published: July 14, 2026Esther Dyson argues that today's institutional crisis is better viewed through the 14th century than through recent political history. Using Barbara Tuchman's A Distant Mirror as her frame, she compares a world of famine, plague, church schism, feudal predation, and purposeless war with a present in which institutions again feel brittle, incentives are badly aligned, and power is shifting into forms that are hard to govern.The killer detail is the historical analogy between land, corporations, and AI. Dyson moves from nobles who controlled serfs and territory, to the East India Company as a quasi-sovereign business, to today's AI systems and data centers as a possible new sector that crosses and weakens both nation-states and companies. The question is whether AI becomes a new kind of private land, owned by a new nobility, or an open prairie that many people can cultivate.The pull is human attention. Dyson says the central question is not what AI will do to people, but how people will react to it: whether they can value love, kindness, embodied attention, and artisanal human presence in a world of seductive artificial offerings.Read more: SourceThe political economy of billionaire derangementAuthor: Henry Farrell Published: July 15, 2026Henry Farrell argues that the visible political radicalization of some Silicon Valley billionaires is not a random personality quirk, but a product of the political economy that made them. Starting from Tyler Cowen's dismissal of “billionaire derangement syndrome” and Tim O'Reilly's warning that Elon Musk is using shareholder capitalism to escape shareholder restraint, Farrell flips the phrase: the question is why billionaires themselves can become deranged.The killer detail is Farrell's use of Peter Thiel as both theorist and example. Thiel's Stanford lectures described startups as monarchies and founders as figures vested with unusual power, while Silicon Valley culture rewarded eccentricity, monopoly ambition, and founder exceptionalism. Farrell says those ideas combined with dense founder-investor networks, peer rivalry, and weak correction mechanisms to amplify rather than discipline princely appetites.The pull is the ideological problem for classical liberals who once saw tech wealth as an ally of markets and freedom. Farrell says commerce did not tame the passions; in parts of Silicon Valley, the passions have begun to devour markets, institutions, and the liberal story that justified them.Read more: SourceIs there any “oligarchy” to fight?Matthew Yglesias | Slow Boring | July 16, 2026Matthew Yglesias argues that “oligarchy” is a rhetorically powerful but analytically loose way to describe American politics. The post begins from Bernie Sanders' “Fighting Oligarchy” tour, Amy Klobuchar's warning about a MAGA “broligarchy,” and the long afterlife of the Martin Gilens and Benjamin Page paper that was widely summarized as showing that only the rich matter in policy outcomes. Yglesias says the evidence supports a weaker claim: affluent people and business leaders have unusual access and influence, but that is not the same as rule by a small cabal.His main distinction is between inequality and oligarchy. The Gilens-Page measure treated the top 10 percent of households as “the wealthy,” and later critics found that rich and middle-class preferences usually align; in the cases where they differ, the rich win about 53 percent of the time. Yglesias also says business executives get special access partly because their decisions are materially important to communities, jobs, investment, and local tax bases, not only because of campaign donations.The post preserves Jerusalem Demsas' counterpoint from their podcast discussion: privileged donor and business access can still violate democratic equality even if the oligarchy label overstates the structure of power. Yglesias' narrower claim is that Democrats should be precise about what problem they are trying to solve, because donor influence can also push the party left on climate and cultural issues in ways that alienate many voters.Read more: Slow BoringAINearly 200 Economists and Tech Leaders Warn of A.I. ThreatsAuthor: Ben Casselman Published: July 13, 2026Ben Casselman reports on “We Must Act Now,” a statement warning that artificial intelligence could transform the economy faster than any previous technology and that policymakers need to move faster to understand and respond. The statement says AI may become radically more powerful over the next 10 years, bringing risks such as large-scale job displacement as well as opportunities such as higher living standards. Nearly 200 people signed, including 15 Nobel laureates, the chief economists of OpenAI and Anthropic, Anthropic co-founder Jack Clark, former Google CEO Eric Schmidt, and venture capitalist Vinod Khosla.The killer detail is who joined the warning. Casselman notes that the signatories include economists who have historically been skeptical of Silicon Valley's most dramatic AI job-loss forecasts, including Daron Acemoglu and Simon Johnson, the MIT professors who won the 2024 Nobel in economics. Erik Brynjolfsson, who helped organize the statement, says there has been a notable change in the profession and that economists and policymakers are not ready for the “tsunami” he sees coming.The pull is the measurement problem. The statement does not offer a specific policy menu, but calls for economists, policymakers, and industry leaders to understand the economics of transformative AI and steer it toward complementing humans. Brynjolfsson says one high priority is better data on AI's spread and impact, because current measures tell conflicting stories about job losses and which workers are most exposed.Read more: The New York TimesWhy I Didn't Sign the AI Open LetterAuthor: Andrew McAfee Published: July 13, 2026Andrew McAfee explains why he did not sign “We Must Act Now,” the AI economy statement organized in part by his longtime collaborator Erik Brynjolfsson. McAfee agrees with the letter's starting point that AI is likely to become radically more powerful over the next decade and that it is a general-purpose technology. His objection is not to urgency or to studying AI's economic effects, but to the framing of risk, displacement, and institutional steering as the first move.The killer detail is McAfee's line edit. He says the original letter comes close, then “bounces off the crossbar” by calling for incentives, guardrails, and institutions to steer AI before we know enough about its actual impacts. He points to mixed current evidence: labor-market canaries, but also rising software job postings, low unemployment for younger workers, rising real median income, and claims that AI-adopting companies are adding workers faster than low-adopting peers. His worry is that the letter leans toward upstream governance and dirigisme when the evidence may call for capability building instead.The pull is his replacement statement. McAfee keeps the three-paragraph structure but changes the emphasis: AI is likely to become radically more powerful; like earlier world-changing technologies it will raise living standards while also bringing harms and shocks; and economists, policymakers, and technology leaders should build the capabilities to respond quickly and effectively. It is a concise version of the permissionless-innovation case inside the AI policy debate.Read more: The Geek WayOwn Your WeightsAuthor: Jamin Ball Published: July 10, 2026Jamin Ball argues that the enterprise AI debate about whether companies should “own their weights” or rent models from frontier labs is asking too narrow a question. A model weight file gives a company control over a point-in-time artifact, but not durable control over the capability stack. In his framing, the weight file is a melting ice cube: it does not get worse in absolute terms, but it falls behind as frontier systems improve and enterprise needs change.The killer detail is what Ball says companies really need to own: the data flywheel, reinforcement learning infrastructure, and evaluation harness that produce and improve the model. Simply deploying an open-weights model and declaring sovereignty leaves the enterprise with yesterday's capability and no way to compound workflow-specific learning.The pull is that enterprise AI control may be less about model ownership than operating ownership. The defensible layer is the system that turns company data, edge cases, business definitions, and evaluations into continuously improving performance.Read more: Clouded JudgementWays to Think About Token PricingAuthor: Benedict Evans Published: July 9, 2026Benedict Evans argues that today's AI token prices are a temporary signal from a supply-constrained market, not a reliable guide to long-term value capture. The open question is whether foundation models keep durable pricing power or become commodity infrastructure as data-center capacity, inference efficiency, and model competition all shift. His current read is that the visible market dynamics point toward commoditization unless something materially changes.The killer detail is the mobile data analogy. Evans says cellular networks became a trillion-dollar industry with hundreds of billions in capex after data usage exploded, but carrier stocks went nowhere because value moved up the stack. Tokens may behave similarly: an opaque unit tied to marginal cost, sold through bundles, essential to everything, yet not necessarily where profits accrue.The pull is uncertainty, not prediction. Evans lists paths to model dominance, including network effects, less competition, regulation, export controls, or a lab pulling ahead on execution, but says each requires a new fact not yet visible. Without that change, the model layer looks more like infrastructure beneath the products that capture value.Read more: SourceAlex Karp Is Saying What Every Angry CEO Is Thinking About AIAuthor: Tim Higgins Published: July 11, 2026Tim Higgins reports that Palantir CEO Alex Karp has turned corporate frustration with AI labs into a public argument about enterprise control. Palantir released a white paper, “Institutional Sovereignty in the Age of AI,” laying out steps companies and governments can take to protect themselves from OpenAI, Anthropic, and other foundation-model providers. The article links that paper to Karp's CNBC appearance, where he said “something has gone completely wrong” in the relationship between AI labs and customers and argued that enterprises are paying for tokens that create little value.The killer detail is the value-capture question. Higgins writes that Karp's critique has resonated because AI labs may gain power and insight from customer data, workflows, and decision-making, even when enterprise policies say customer data are not used for training. David Sacks amplified the concern by arguing that Anthropic is moving from the model layer into vertical applications such as science, security, legal, and coding, raising the fear that model providers will watch where value is being created and then move into those markets directly.The pull is that Karp is not alone, even if his style is unusually combative. Higgins notes that Satya Nadella has also warned that companies need to retain the learnings created when they use AI models, while Mark Zuckerberg has framed Meta's new model release partly around lower-cost frontier intelligence. The article presents Karp's campaign as one sign that established technology companies and large enterprises are trying to define where they fit when AI labs become central infrastructure, application competitors, and potential IPO giants at the same time.Read more: The Wall Street JournalThe AI Agents Are Coming for Microsoft OfficeAlex Wilhelm | Cautious Optimism | July 11, 2026Alex Wilhelm argues that one of the week's quieter AI questions is whether the productivity market that Microsoft successfully moved into subscription software is now being attacked by agentic tools. The piece begins with the infrastructure backdrop: SK Hynix raised $26.5 billion in a U.S. listing while building U.S. HBM and advanced-packaging capacity, and memory, chip, and foundry companies are now priced for sustained AI demand.Wilhelm then says the AI conversation has shifted quickly from raw capability to cost per task. He cites new model releases and vendor language emphasizing cheaper agentic and coding models, faster performance, and lower dollars per task. That matters because lower costs make it more plausible for AI systems to take on routine knowledge work at scale rather than remain a premium coding assistant market.The core of the article is Microsoft Office. Wilhelm notes that Microsoft turned Office from a one-time purchase into Microsoft 365, a large recurring revenue business with tens of millions of subscribers and a major productivity segment. Now, he says, late-stage unicorns and AI labs are pushing into the same territory: Anthropic's Cowork was reportedly used mostly outside software development, OpenAI merged ChatGPT and Codex into a tool for creating sheets, slides, docs, web apps, and long-running work, and other companies are building agentic coworkers that connect business data to documents, workflows, schedules, alerts, and apps.The article's caveat is that Microsoft has survived major platform shifts before. The argument is not that Office disappears quickly, but that the definition of office software is broadening from documents and spreadsheets into AI systems that can create, monitor, and act across workplace data.Read moreWhat Is Loop Engineering, and Who Owns It?Author: Nilesh Barla Published: July 11, 2026Nilesh Barla argues that “loop engineering” is becoming a distinct discipline because production AI agents now fail less at single prompts than at runtime: when to stop, what state to preserve, and how to recover after a bad step. Prompt engineering shapes one model call, and context engineering shapes what the model sees, but loop engineering shapes what a sequence of calls actually does.The killer detail is the three-primitives frame. Barla says a real agent loop needs halt conditions, state carryover, and recovery paths, then maps teams across five maturity levels. At the lowest level, an agent is just a model call in a for-loop with a step cap and raw history; by the higher levels, the system has structured state, explicit planning, replay, evaluation, and self-repair.The pull is organizational. If agents are becoming production systems rather than demos, someone has to own the runtime itself. The loop engineer is the role Barla gives to the person responsible for making long-running agent work dependable.Read more: Adaline LabsThe Fight Against AI Data Centers Is Just BeginningEmma Roth | The Verge | July 12, 2026Emma Roth argues that community resistance to data centers has moved from an early warning sign into a national political fight as AI facilities grow larger, more power-hungry, and more visible to nearby residents. The article starts with Apple's failed 2015 plan for a $1 billion data center in Athenry, Ireland, where a small group of residents challenged the project over noise, light pollution, flooding, traffic, and wildlife effects until Apple abandoned it in 2018.The current data-center buildout is presented as much larger and more contentious. Roth writes that residents now cite rising energy costs, water quality, noise, light pollution, and greenhouse gas emissions, while the U.S. Energy Information Administration expects commercial energy demand to surpass residential demand this year because of AI data centers and Goldman Sachs expects data-center power demand to double by 2027.The central evidence comes from Data Center Watch, which says protesters blocked or delayed at least 75 U.S. projects worth $130 billion from January to March, with active opposition groups more than doubling from 396 at the end of 2025 to 833 by the end of the first quarter of 2026. Roth also cites QTS abandoning a $12 billion Wisconsin campus, Delaware City regulators blocking a 580-acre project under the Coastal Zone Act, opposition stopping a QTS project in Prince William County, and pressure that pushed Kevin O'Leary to downsize the proposed 40,000-acre Project Stratos in Utah.The policy section describes a split between federal acceleration and local resistance. President Trump has treated data centers as part of the AI race with China and fast-tracked construction, while some Republican candidates are distancing themselves from that position ahead of midterms. Sanders and Ocasio-Cortez have proposed a moratorium until price and environmental protections exist, bipartisan lawmakers are backing ratepayer-protection measures, and states including Florida, Idaho, and Washington have passed rules on cost shifting, water use, and tax breaks. Roth's caveat is that the policy patchwork is still incomplete, leaving many communities to fight project by project.Read more6 months to live for open modelsAuthor: Nathan Lambert Published: July 12, 2026Nathan Lambert argues that open-weight AI models are facing their most serious policy test so far because U.S. officials are beginning to discuss concrete controls rather than abstract safety concerns. He says reported White House conversations about a new executive order may initially target Chinese-origin models and government use, but could create a broader review habit for frontier open models. His forecast is that a model above the capability range of GPT-5.5, Claude Opus 4.8, or GLM-5.2 could trigger a ban or indefinite delay within six months.The post separates two policy fights that are becoming intertwined: distillation and frontier capability. Lambert says the distillation campaign against Chinese models has become a form of regulatory capture because Anthropic and other closed-model companies would gain economically if Chinese open models were banned. He does not dismiss IP protection, but argues that if a closed model's capabilities are dangerous enough to justify restricting open models, the lab also has to explain why those capabilities are exposed through a queryable API. He cites unauthorized access to Anthropic's Mythos private beta as evidence that APIs are not automatically secure.The broader claim is that a unilateral U.S. ban would hurt positive actors more than bad actors if comparable open models remain available elsewhere. Lambert says the only durable ceiling would require global agreement, which does not exist, and that open models can improve safety by allowing broad inspection, adaptation, and understanding. His proposed near-term off-ramps are a strong U.S. open model release from companies such as Microsoft, Meta, or Reflection, and a broader coalition of open-source beneficiaries lobbying for safe rollout rather than prohibition.Read more: SourceAmericans Deserve a Dividend From AI Companies' RichesAuthor: Scott Stanford Published: July 14, 2026Scott Stanford argues that proposals to give the government a stake in AI companies miss the point unless ordinary citizens directly receive and control the upside. Sam Altman has discussed giving up equity in OpenAI, Washington already owns a stake in Intel, Nvidia is sharing China chip revenue, and Bernie Sanders wants large AI labs to contribute half their stock to a sovereign wealth fund. Stanford says those ideas all park value with the state, not with people.The killer detail is New Carlisle, Indiana, where AWS's Project Rainier is turning cornfields into one of the world's largest AI superclusters. The project is planned to run up to a million chips, draw more than two gigawatts of power, and represents an investment that has grown from $11 billion to $13.8 billion. Stanford uses that local transformation to argue that AI's public bargain should be visible at the household level.The pull is design. A citizen AI dividend would have to specify who earns a stake, how they hold it, and when they see cash. Without that mechanism, the AI wealth debate remains a fight over government balance sheets rather than public ownership.Read more: SourceWho Gets to Define the Frontier?Author: Mark Daley Published: July 14, 2026Mark Daley argues that Demis Hassabis is right to call for a serious institution to verify frontier AI systems, but that the power to test models is also the power to govern them. Hassabis's proposed Frontier AI Standards Body would get privileged pre-release access to advanced models, testing compute, held-out evaluations, support from national labs and security agencies, third-party auditors, and eventually authority to block models from the American market or coordinate a slowdown.The killer detail is Daley's constitutional objection. He says the proposal sometimes looks like a scientific lab, a standards body, an industry regulator, a licensing authority, and an emergency security council at once. Combining those roles because each requires technical expertise would be like putting the central bank, auditor-general, and Supreme Court in one building and calling it efficient.The pull is standard-setting. Daley's concern is not that verification is unnecessary, but that whoever writes the tests, decides what passes, adjudicates disputes, and grants market access may end up defining the frontier itself.Read more: SourceGPT-Red: Unlocking Self-Improvement for RobustnessOpenAI | OpenAI | July 15, 2026OpenAI describes GPT-Red as an internal automated red-teaming model trained to find prompt-injection vulnerabilities at a scale human red teams cannot match. The post says AI systems increasingly encounter third-party data through browsers, connected apps, local files, and tools, creating opportunities for malicious instructions hidden in emails, webpages, tool responses, or code repositories. Human red-teaming remains part of OpenAI's safety process, but the company says it is time-intensive and cannot generate enough diverse adversarial examples for model training.The system is trained through self-play reinforcement learning, with GPT-Red rewarded for eliciting valid failures and defender models rewarded for resisting attacks while still completing their tasks. OpenAI says the training environments specify threat models across settings such as local files, webpage banners, email bodies, and tool outputs. The model is kept separate from deployed production models because it is intentionally trained with malicious capabilities.OpenAI reports that GPT-Red generalized beyond its training set, including an internal replication of the indirect prompt-injection arena from Dziemian et al. (2025), where it found successful attacks in 84% of scenarios compared with 13% for human red-teamers. The post also says GPT-Red transferred attacks from simulation to a live autonomous vending-machine agent, causing price changes and order cancellations, and outperformed a prompted GPT-5.5 baseline against a Codex CLI agent on held-out data-exfiltration tasks.The article's main robustness claim is that OpenAI has used GPT-Red and predecessor models in training since GPT-5.3, with later GPT releases becoming more resistant to prompt injections. It says GPT-5.6 Sol has six times fewer failures on OpenAI's hardest direct prompt-injection benchmark than the best production model from four months earlier, that a “Fake Chain-of-Thought” attack class fell from more than 95% success against GPT-5.1 to below 10% against GPT-5.6 Sol, and that GPT-5.6 Sol fails on only 0.05% of GPT-Red's direct prompt injections. OpenAI says general capabilities and targeted over-refusal evaluations were not harmed, and says a preprint with more details will follow.Read moreAnthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just modelsRebecca Bellan | TechCrunch | July 15, 2026Rebecca Bellan reports that Ode with Anthropic is the $1.5 billion AI implementation company launched by Anthropic with Blackstone, Hellman & Friedman, Goldman Sachs, and other backers. The article says the venture reflects a growing belief among frontier AI labs that enterprise adoption requires more than better models: customers need engineers who can embed inside businesses and turn AI into working systems.Ode was originally conceived by Blackstone after it used both large consulting firms and smaller AI services boutiques across its portfolio companies. TechCrunch reports that Fractional AI, an AI engineering services startup, stood out and was acquired by the joint venture shortly after the venture was announced. Fractional now forms the foundation of Ode, which has 100 engineers and works closely with Anthropic's applied AI team to identify where the technology can affect specific businesses.Ode CEO Chris Taylor tells TechCrunch that the company could someday become a trillion-dollar business if it scales without losing quality. He says an ideal customer is one whose CEO treats the AI project as a top one or two priority, whether it is a major product feature or the reworking of a core business process. Ode will operate under a “Claude-first” principle, using Anthropic technology whenever possible, but the article says it can use rival AI products when needed.The article's central implementation argument comes from Ode chief technologist Eddie Siegel, who says model selection matters but is not where most of the engineering effort goes. He compares it to the choice of programming language in software: one ingredient in a system that still has to be engineered. Bellan writes that Ode's challenge is hiring and training enough elite generalist engineers, many of them former founders, while competing with OpenAI's The Deployment Company and consulting giants that have built their own forward-deployed engineering teams.Read moreVint Cerf is working on a plan to unleash AI agents on the open internetTim Fernholz | TechCrunch | July 15, 2026Tim Fernholz reports that Vint Cerf, after leaving Google, is advising Innovation Labs on an open architecture for identifying AI agents online. Innovation Labs is a subsidiary of Identity Digital, a DNS registry company, and its proposal is to use domain-name infrastructure as part of a system for agent identity, accountability, and auditability. The premise is that agents will need a way to identify themselves if they move beyond proprietary systems and begin interacting across the open internet.The concrete proposal is DNSid, a registry that links an AI agent to an existing internet domain and uses cryptographic proofs to log its registration over time. Innovation Labs says it is trialing the standard with unnamed hyperscalers and identity companies. Cerf frames the problem around authority and accountability: what authority an agent has, where that authority came from, who is accountable for the agent's behavior, how its identity is established, and why anyone should trust it.The article's caveat is that standards are still emerging and agents are more active than static domains. Cerf says the period may be both fascinating and exasperating because the functionality is powerful and interoperability is unresolved. He compares the adoption problem to TCP/IP: competing systems may not work together until users push for functional interoperation. He also says an agentic economy is not inevitable, but that people will try to build it because delegating work to agents will be easier.Read more: TechCrunchxai-org/grok-build, now open sourceAuthor: Simon Willison Published: July 15, 2026Simon Willison argues that xAI's decision to open-source Grok Build is best understood as a trust repair move after a severe privacy failure. The CLI had triggered backlash when users realized that running it in a directory could upload the entire directory to xAI's Google Cloud buckets, including one user's reported SSH keys, password manager database, documents, photos, and videos. xAI disabled the feature, said previously retained coding data would be deleted, and released the code under Apache 2.0.The killer detail is what the codebase reveals. Willison counts 844,530 lines of Rust, only about 3% of which appears vendored, and finds remnants of the upload system still present but disabled: gcs.rs contains Google Cloud upload code, while upload_session_state() now returns a hard-coded session_state_upload_unavailable error. He also notes copied or ported tool implementations from Codex and OpenCode, prompt files, and a terminal Mermaid renderer.The pull is that terminal coding agents are becoming large, intricate software systems in their own right. The privacy failure mattered because these tools operate inside the directories where developers keep their most sensitive work; the open-source release matters because trust now depends on inspecting what an agent can see, send, and do.Read more: SourceThe Pulse: What can we learn from Bun's rapid Rust rewrite with AI?Author: Gergely Orosz and Ivan Klaric Published: July 16, 2026Gergely Orosz and Ivan Klaric argue that Bun's AI-assisted rewrite from Zig to Rust is a practical sign of how software engineering changes when models can take on large, bounded migrations with clear feedback loops. The piece does not treat the rewrite as magic: Jarred Sumner first spent hours turning design judgment into a detailed porting guide, then used adversarial review, parallel agents, compiler errors, and tests to force the work toward correctness.The killer detail is the scale. Bun had 535,496 lines of Zig, 1,448 files, and 22 million monthly downloads, making a conventional rewrite a year-long freeze the team could not justify. Using Fable, Sumner split the work across 64 agents, produced about 6,500 commits, and got the migration done in 11 days at an estimated API cost of $165,000.The pull is economic, not theatrical. If a one- or two-year migration can become an 11-day project, AI coding is not just faster autocomplete; it changes which technical debts are worth paying down.Read more: SourceOrphan risks at the frontier of artificial intelligenceAuthor: Andrew Maynard Published: July 16, 2026Andrew Maynard argues that frontier AI safety frameworks are creating “orphan risks”: harms that companies can see, but do not formally own because they are hard to quantify, do not fit catastrophic-risk thresholds, or fall outside audit-friendly compliance machinery. His target is not existing frontier safety work, but the narrowing effect that happens when private companies decide which risks count as governable.The killer detail is Maynard's contrast between measurable model dangers and threats to value. He points to Meta's three-day Galactica collapse, OpenAI's 2023 board crisis, safety-team departures, and wellbeing litigation as examples of risks that damaged trust, culture, legitimacy, or users without fitting cleanly into conventional model-risk categories. The proposed fix is an orphan-risk register: a public record of risks a company considered and chose not to manage, with reasons.The pull is accountability. Frontier developers' internal scoping choices have become a de facto layer of public governance, so the question is no longer only which risks they manage, but which risks they quietly leave outside the frame.Read more: SourceThe Lab of the Future Should Feel Like a Data CenterLatent.Space with Andy Beam and Rafa Gomez-Bombarelli | Latent.Space | July 16, 2026Latent.Space interviews Lila Sciences CTO Andy Beam and chief science officer for physical sciences Rafa Gomez-Bombarelli about the company's attempt to build an AI-run science factory. The post describes Lila's thesis as treating the lab itself as an “infinite token generator”: if internet data drove the first era of AI scaling, experimentally verified scientific data may be the next scarce training source. Lila is trying to produce that data with robotics, lab instruments, orchestration software, and AI models wired into the wet lab.The central analogy is the lab as data center. Instruments are nodes on a graph, a magnetically levitating transport layer moves materials between them, and experiment scheduling looks like a compute queue. Beam says Lila is not simply an automation company, because the point is not just throughput; it is flexibility, generalization, and experiment capture. The post says Lila has built more than 10 trillion experimentally validated “scientific reasoning tokens,” not internet text or biological sequences.The interview ranges across biology, chemistry, drug discovery, materials science, and the limits of automation. It notes that Lila rebuilt one gas-sorption measurement to run roughly 2,500 times faster, claims its general models can transfer priors from small-molecule chemistry to metal-organic frameworks for carbon capture, and describes model-suggested platinum-group-free electrocatalysts that moved from looking boring or wrong to becoming strong performers. The caveats are physical: experiments have runtimes, biology cannot always be accelerated, chains of thought can be unreliable narrators, and reward hacking becomes more dangerous when a model controls a real lab.Read more: Latent.SpaceWhy AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”Kate Park | TechCrunch | July 16, 2026Kate Park interviews AMI Labs CEO Alexandre LeBrun about why Yann LeCun's world-model startup avoids the language of “AGI” and “superintelligence.” LeBrun says the terms are not useful because they lack stable definitions: “We never used the word AGI. And I just noticed that nobody is using it anymore; they switched to superintelligence.” His argument is that the practical frontier is not a label, but whether AI systems can understand and predict real-world states.The article explains the world-model thesis by contrasting language prediction with physical-state prediction. A large language model predicts the next word; a world model predicts the next state, such as what happens when a glass tips over. LeBrun says LLMs remain complementary and efficient for language, but the physical world is where current AI is weak. Robotics is the clearest case: hardware has advanced quickly, but robots are still brittle outside controlled routines because they lack context and situational understanding.AMI is still pre-product, but TechCrunch reports that LeBrun was in Seoul looking for industrial partners, researchers, and global companies. He says world models cannot be built entirely inside a lab because they need access to real environments. That is why South Korea appeals to AMI: robotics, semiconductors, manufacturing, and fast adoption create the kind of hardware-heavy context that software-only AI has barely touched.Read more: TechCrunchKimi K3 Tech Blog: Open Frontier IntelligenceKimi | Kimi | July 16, 2026Kimi introduces Kimi K3 as an open 3T-class frontier model aimed at coding, knowledge work, reasoning, multimodality, and long-context agentic use. The source describes the model as a 2.8T-parameter system built on Kimi Delta Attention and Attention Residuals, with native multimodality and a 1M-token context window. It says Moonshot AI plans to release model weights by July 27.The post presents K3 through benchmark and use-case sections rather than as a general product announcement. It reports results across coding, productivity, agentic, and multimodal evaluations, including DeepSWE, Terminal-Bench 2.1, Program Bench, SWE Marathon, FrontierSWE, PostTrain Bench, OfficeQA Pro, SpreadsheetBench 2, MCP Atlas, AutomationBench, BrowseComp, GDPval-AA v2, AA-Briefcase, MMMU-Pro, MathVision, BabyVision, OmniDocBench, and PerceptionBench. The source says all reported K3 results use maximum reasoning effort with temperature and top-p set to 1.0, and that different benchmark comparisons use KimiCode, Claude Code, or Codex harnesses depending on the test.Kimi's caveats are unusually concrete. The limitations section says K3 was trained in preserved thinking-history mode, so quality may become unstable if an agent harness does not pass historical thinking content correctly or if an ongoing session switches to K3 midstream. It also says K3's emphasis on long-horizon tasks can make it excessively proactive when it encounters minor issues or ambiguous intent, and recommends imposing explicit behavioral constraints for applications that require strict boundaries. The post adds that K3 remains behind Claude Fable 5 and GPT 5.6 Sol in user experience despite being competitive overall.Read moreVenture CapitalThree Years InAuthor: Tomasz Tunguz Published: July 10, 2026Tomasz Tunguz marks Theory Ventures' third anniversary by arguing that AI's central market effect is time compression. In his telling, model release cycles, company revenue milestones, enterprise adoption, and venture categories have all accelerated. Seed, Series A, and Series B still exist as financing labels, but they no longer cleanly describe company maturity when some seed rounds are larger than IPOs and the best AI companies can mature much earlier than prior software companies.The killer detail is the shift from models to inference. Tunguz argues that inference has become the dominant AI market because workloads and buyer preferences are fragmenting: video, batch, local, agentic, and real-time tasks each create different infrastructure needs. He compares this to databases splitting into OLTP, OLAP, vector, and streaming categories, with AI pushing the same specialization into inference infrastructure.The pull is that Theory sees the AI-native venture firm as part of the same pattern. The firm says it has analyzed twice as many investment opportunities with three investors working alongside a nine-person intelligence organization, using agents and research systems to map markets, source companies, and support diligence. The piece is both a market map and a statement about how venture itself is being rebuilt by the technology it funds.Read more: LinkedInVenture Has Rarely Looked More BifurcatedAuthor: Beezer Clarkson Published: July 14, 2026Beezer Clarkson points to PitchBook's Q2 report as evidence that the U.S. venture market has split into two very different realities. AI now accounts for more than 60 percent of all U.S. venture deal value, meaning the headline market can look active and well-funded even while much of the non-AI market is dealing with a much colder liquidity and fundraising environment.The thread uses that split as the setup for Clarkson's latest Origins episode with Alec Litowitz, founder of Magnetar and QStar Capital and one of Citadel's original founding partners. Clarkson says markets like this are periods of genuine uncertainty, not merely ordinary risk, which is why Litowitz's Adaptability Quotient framework is relevant.The embedded clip makes the liquidity point concrete. Litowitz says DPI is “the resolution of uncertainty” because it converts an uncertain investment into actual cash returned to LPs. In his framing, a realized dollar is a real mark, while TVPI remains uncertain until it is realized.The killer detail is the distinction between pricing risk and resolving uncertainty. Litowitz's perspective matters because QStar is a SpaceX investor and Clarkson says the conversation happened just before one of venture's most consequential IPOs. The episode's stated questions are why venture remains a way to gain exposure to innovation, how AI is changing what is investable, why liquidity is ultimately a function of time, and why uncertainty requires a different decision framework from risk.Read more: XThe Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active NowAuthor: Ilya Strebulaev Published: July 10, 2026Ilya Strebulaev ranks angels, angel groups, accelerators, and incubators by lifetime U.S. unicorn investments, counting checks written before a company reached unicorn status. The top of the combined list is dominated by organizations: Y Combinator leads with 113 unicorn investments, followed by Plug and Play at 52 and 500 Global at 41. Sand Hill Angels is the highest-ranked angel group at 31.The killer detail is how quickly the list changes below the biggest accelerators. Strebulaev says 271 of the 304 investors in the Top 200 are individuals, or 89%. In the top 100, individuals are 91%. That makes the market underneath the large accelerator counts look much more personal: mostly operators and individual angels writing early checks from their own networks.The pull is the ranking's own caveat. Strebulaev writes that every lifetime leaderboard has a blind spot because many of the unicorns behind those totals were founded a decade or more ago, and some angels have since moved into formal funds, slowed down, or stopped investing. His post therefore separates lifetime performance from recent cohorts, including companies founded in 2015 or later and 2020 or later. For founders or allocators making current decisions, that distinction matters: a career record and a current record are not the same measure.Read more: Ilya StrebulaevAre Prediction Markets Doomed to Fail?Author: Contrary Published: July 16, 2026Contrary argues that prediction markets' current boom depends on whether platforms can prove they are more than regulated gambling with exchange-style branding. Kalshi and Polymarket have reached mass cultural, investor, and regulatory attention, but the article says the underlying idea is old: academic markets, corporate forecasting tools, Intrade, PredictIt, and other predecessors all struggled with the same linked problems of liquidity, legality, and user appeal.The killer detail is the comparison with sportsbooks. Prediction markets present themselves as peer-to-peer, transparent, and non-house-based, but sports contracts reportedly account for more than 90 percent of Kalshi trading, and the article says the platforms keep a much thinner slice of volume than sportsbooks. A market can therefore show sports-betting-scale handle while generating far less revenue.The pull is that the product's hardest problem may be distribution of wins. If a small group of sharp traders captures most profits while casual users lose interest, prediction markets may become valuable data feeds and professional tools before they become durable consumer networks.Read more: SourceRegulationExclusive: The Next Frontier of the Deportation Wars: College CampusesAuthor: Adrian Carrasquillo Published: July 11, 2026Adrian Carrasquillo reports that college campuses are becoming a new front in the fight over immigration enforcement because automatic license plate readers can turn ordinary campus security infrastructure into searchable location data. His thesis is that Flock Safety's camera network, even without direct ICE or DHS contracts, can feed deportation enforcement through local police partnerships and data-sharing practices.The killer detail is the campaign target. The Emergency Campaign to Support Higher Education, working with Schools Drop ICE, is focusing on 75 colleges and universities publicly identified as having Flock contracts. Flock says it has no ICE or DHS contracts, but activists argue the risk comes through local agencies that coordinate with federal authorities and run searches on their behalf.The pull is broader than immigration. Carrasquillo notes that license plate readers have already been abused by officers for stalking, and that Flock's AI search features can identify more than plates, including bumper stickers. A campus safety tool can become a political surveillance system when the data layer is searchable.Read more: The BulwarkThe Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.Author: Todd Phillips Published: July 12, 2026Todd Phillips argues that the Supreme Court's decision in Trump v. Slaughter damaged independent agencies by ending for-cause removal protections, but did not leave Congress powerless. The ruling weakens the old model in which commissioners at bodies such as the FTC, NLRB, CPSC, SEC, and CFTC could be insulated from dismissal over policy disagreements. Phillips says the next fight is whether presidents can turn nominally bipartisan commissions into one-party instruments.The killer detail is the procedural fix: quorum rules. Phillips proposes that Congress require bipartisan slates of commissioners to be seated before independent agencies can act. A president could still fire commissioners, as the Court now permits, but if those firings broke quorum, the agency would be unable to proceed until replacements were confirmed. The guardrail would

This week's video transcript summary is here. You can click on any bulleted section to see the actual transcript. Thanks to Granola for its software.There was an issue with this only going to paid subscribers, so sending it again. Apologies to those who get it twice. I appreciate being paid so feel free to upgrade if you enjoy TWTW.EditorialIntelligence: Who Owns it?This week the word “AI” feels too small.AI is a technology. Intelligence is its product. And if intelligence is the product, the question is no longer just: Which model is best? Who has the cheapest tokens? Who owns the weights? Who controls the data center? Those are important questions, but they are lower in the stack.The bigger question is simpler and more political:Who owns intelligence?That sounds abstract until you make it concrete. Intelligence is becoming something companies can capture, package, serve, meter, route, improve, and sell.It can write code, answer questions, design molecules, automate offices, run agents, draft legal work, advise scientists, serve consumers, and reshape workflows. It is not merely software. It is a general-purpose capability. And all humans could benefit from more of it.General-purpose capabilities have a habit of becoming public questions. But the default answer, that public good is best delivered by government, is the wrong answer in this context.The Product Is IntelligenceWe should stop talking about AI as a feature and start talking about intelligence as the universal thing that is delivered as an input to the world.Water is an input. Electricity is an input. Literacy is an input. Connectivity is an input. Once a society depends on them, access stops being optional. Nobody needs government to build every well, power plant, school, or network. But everybody understands that a civilization cannot be organized around less than universal and reliable access to foundational inputs.Intelligence is reaching that level of importance now that we all know it is real.Government should not own it, operate it, or develop it. Quite the opposite. Companies are the right actors to build fast, compete hard, improve models, serve customers, and discover the real use cases. Self-interest is a useful framing here. Markets are good at finding demand, reducing costs, and turning invention into services people actually use.Companies are the right operators, developers, and owners. But that does not settle the real question of who owns the benefits. That is an economic question.If intelligence becomes metered infrastructure, what happens to the value it creates?The Ownership StackThis week's articles keep circling the same issue from different directions but in the nature of ‘circling' never quite nail it.Jamin Ball's “Own Your Weights” starts with the enterprise version of the question. Owning a model file is not enough. The durable asset is the loop: the data flywheel, the evaluations, the reinforcement system, the workflow learning, and the operating context that lets capability compound.Benedict Evans' “Ways to Think About Token Pricing” adds the market layer. Tokens may become essential, abundant, and cheap, like mobile data. But being essential does not guarantee that the token layer captures the value. The money may move up the stack to whoever owns the workflow, the customer, the distribution, or the application.Alex Karp's fight with the labs, reported in “Alex Karp Is Saying What Every Angry CEO Is Thinking About AI”, is the same argument in sharper enterprise language. Companies are afraid that model providers will not just sell intelligence, but learn from customer workflows and then move into the markets where those workflows create value. The “All-in” group are echoing Karp's view.And “What Is Loop Engineering, and Who Owns It?” names the new contested terrain. The loop is where intelligence meets the world. Whoever owns the loop owns the learning. Whoever owns the learning owns the compounding asset.That is why “who owns intelligence?” is not a slogan. It is the question under the model layer, the application layer, the enterprise layer, and the economic layer.Because intelligence is the product, the tools creating it are fragmented and competitive. So there is no logic in trying to discuss this at the level of a single company or set of tools and models.The Old Promise Was That Commerce Would Tame PowerThe essays this week give the historical backdrop.Deirdre McCloskey, in “What Really Caused the Industrial Revolution”, argues that modern growth came not simply from capital accumulation, but from a change in permission: ordinary people were allowed to innovate, trade, build, and be honored for it.That matters because intelligence could be another expansion of permission. It could make more people capable of building, learning, creating, coding, researching, translating, selling, and coordinating. It could lower the cost of competence.But only if access is broad.Paul Krugman's “AI in an Age of Oligarchy” warns that the same technology lands differently in different political economies. A new general-purpose technology entering a broad, open, upwardly mobile society is one thing. The same technology entering a concentrated economy, with extreme wealth and weak counterweights, is another.Tim O'Reilly's Economist essay, “Elon Musk is building a form of capitalism that Adam Smith would hate”, makes the governance point more directly. The old liberal hope was that commerce would tame arbitrary power. Markets, boards, courts, shareholders, disclosure, and competition would discipline the prince.But what if the prince uses markets to escape discipline?Henry Farrell's “political economy of billionaire derangement” pushes the same point. Founder culture, monopoly ambition, peer rivalry, weak correction mechanisms, and vast private control can amplify appetites rather than restrain them.The danger with intelligence is not that companies build it. They should. Companies build it, meter it, use public tolerance and public infrastructure to scale it, learn from everyone who uses it. All of those things are inevitable and healthy. Market forces will sort out winners from losers. The real danger is that the winners treat all of the surplus produced as purely private.Metered Intelligence Creates SurplusIf metering is not the problem, what is?The problem is pretending that metered intelligence creates value only for the metering entity. Metering water is only tolerated as a public good. If the public were blackmailed by a private water company with the threat of no water we would all rebel.Once we understand that the product of AI is intelligence we can see that every time intelligence is used, there is the immediate transaction: the user pays, the provider serves.But there is also system value. Usage creates signals. Workflows reveal patterns. Prompts, corrections, failures, preferences, integrations, edge cases, and business processes all help define where intelligence is useful and how it should improve. Intelligence breeds intelligence.Even when customer data is contractually protected, the market learns. The platform learns where demand is. The product team learns which workflows matter. The ecosystem learns which jobs are vulnerable, which tasks are automatable, and which parts of the economy can be reorganized around machine intelligence.So the surplus is not born in a vacuum.It rests on public science, public education, public data exhaust, public law, public infrastructure, public energy systems, public tolerance for data centers, and billions of human interactions. It is served by companies, but it is not made only by companies.This is why “Americans Deserve a Dividend From AI Companies' Riches” belongs at the center of this week's issue. The detail can be debated. The principle is harder to dismiss. If intelligence becomes a new foundational resource, then some part of the wealth it creates should flow back to the people whose society makes it possible. Intelligence did not suddenly appear. AI is built on the entire history of human intelligence. It benefits from it and at the same time evolves it.Not Nationalization. A Human Wealth Fund.If intelligence belongs to everybody, some conclude that government ownership of intelligence is the right outcome.Governments are not well suited to build, operate, or improve intelligence. They will move too slowly, regulate too early, politicize the wrong things, and confuse economic participation with operational control.Andrew McAfee's “Why I Didn't Sign the AI Open Letter” is useful here. His objection is not that the technology is unimportant. It is that steering too hard before we understand the shape of the change can become its own failure mode. Marc Andreessen's satire of AI regulation is less policy than temperament, but it captures a real Silicon Valley fear: that regulation can become permission, capture, and incumbency before it becomes wisdom.That fear should be taken seriously.But it does not answer the economic question. It answers only the operational one.How can the economic benefits of intelligence be distributed? The better answer is a sovereign human wealth fund.Call it a sovereign wealth fund if you must, but the phrase is too national. Intelligence will not respect borders. The leading companies are global. The models, chips, data centers, agents, platforms, and workflows will be transnational from the beginning. If the value created by intelligence is global, then the mechanism for sharing some of that value should begin with the companies global enough to capture it. The nice thing about xAI, OpenAI, and Anthropic is that they are supranational.These companies own and operate intelligence. Let them compete. Let them profit. Let them keep the incentives that make the system improve. But if intelligence is the new water, the wealth it creates cannot belong only to the companies that meter it. And they, themselves, have the power to fix it, even more than governments.Access will become a Human Right; Ownership Is the Economic DesignThis is where human rights come in. There is no right to access an AI model, yet. But there will soon be a need to change that.Not as a claim that every person is entitled to every frontier model at every moment for free. That is not serious. Capacity has costs. Models have costs. Inference has costs. Data centers have costs. Although those costs will decline over time, possibly quite quickly as self-learning models address costs.The claim is more basic: in a world where intelligence becomes a primary input into education, work, health, science, citizenship, creativity, and economic agency, baseline access to intelligence starts to look like a civic requirement.That could mean public access layers. It could mean education credits. It could mean open models. It could mean AI dividends. It could mean public-interest compute. It could mean taxes on rents. It could mean a company-initiated human wealth fund that returns some of the upside to society without handing the operating system to the state. The latter could couple wealth growth with universal distribution of ownership.The exact mechanism matters. But the distinction matters more.Government should not own intelligence. It should be universally available. And people should have a claim on the wealth intelligence creates.The Frontier Is Also PhysicalThe abstraction is not weightless.“The Fight Against AI Data Centers Is Just Beginning”, “New York becomes the first state to enact a data center moratorium”, Reuters on pollution from Musk's xAI power project, and DataGravity's “Who Captures Value in AI Infrastructure?” all say the same thing from the ground up.Intelligence uses land. It uses power. It uses water. It uses chips. It uses grid capacity. It uses neighborhoods. It uses public patience.That makes the value question unavoidable. A society can accept the buildout if the buildout is legible as shared progress. It will resist it if the costs are local, the profits are private, and the benefits feel enclosed.Who Owns the “Loop”?The week ends where it began.“Anthropic and Blackstone” are betting that implementation is the next trillion-dollar business. “Vint Cerf” is working on identity for agents on the open internet. “GPT-Red” points toward systems that improve their own robustness. “Kimi K3” adds another open frontier model to the global mix.The model race continues. The deployment race is accelerating. The governance race is behind.My view is this:The central product of this era is intelligence. Companies have figured out how to capture it, package it, serve it, and meter it. That is good. It should stay in the hands of builders who have the incentive to make it better.But intelligence is too foundational to become just another private toll booth. A significant part of it will turn out to be free to users.As intelligence becomes a general-purpose resource, then access to it becomes a human-capability question, and the surplus from it becomes an economic-justice question. Not because government should run it. Because government should not run it. The operating layer belongs with companies. The wealth question belongs with everyone. But companies are best placed to turn that into a process of distribution.The question is not whether companies should build intelligence. They should.The question is whether humanity gets a stake in the wealth created by the thing that may soon become its most important shared input.Contents* Essays* Deirdre McCloskey on What Really Caused the Industrial Revolution* AI in an Age of Oligarchy* Elon Musk is building a form of capitalism that Adam Smith would hate* Murky Mirror: Truth and Consequences* The political economy of billionaire derangement* Is there any “oligarchy” to fight?* AI* Nearly 200 Economists and Tech Leaders Warn of A.I. Threats* Why I Didn't Sign the AI Open Letter* Own Your Weights* Ways to Think About Token Pricing* Alex Karp Is Saying What Every Angry CEO Is Thinking About AI* The AI Agents Are Coming for Microsoft Office* What Is Loop Engineering, and Who Owns It?* The Fight Against AI Data Centers Is Just Beginning* 6 months to live for open models* Americans Deserve a Dividend From AI Companies' Riches* Who Gets to Define the Frontier?* GPT-Red: Unlocking Self-Improvement for Robustness* Anthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just models* Vint Cerf is working on a plan to unleash AI agents on the open internet* xai-org/grok-build, now open source* The Pulse: What can we learn from Bun's rapid Rust rewrite with AI?* Orphan risks at the frontier of artificial intelligence* The Lab of the Future Should Feel Like a Data Center* Why AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”* Kimi K3 Tech Blog: Open Frontier Intelligence* Venture Capital* Three Years In* Venture Has Rarely Looked More Bifurcated* The Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active Now* Are Prediction Markets Doomed to Fail?* Regulation* Exclusive: The Next Frontier of the Deportation Wars: College Campuses* The Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.* India's crackdown on a new WhatsApp feature risks setting a global precedent* Let's build a children's public internet* Computer cops* Google is better at playing the AI regulations game* Infrastructure* Who Captures Value in AI Infrastructure?* New York becomes the first state to enact a data center moratorium* Pollution from Musk's unpermitted xAI power project hits hardest in Black communities* Interview of the Week* The End of the End of Geography* Startup of the Week* Radical AI's Joseph Krause: The Scientist Building The “Waymo” Lab For New Materials* Post of the Week* Marc Andreessen on AI RegulationEssaysDeirdre McCloskey on What Really Caused the Industrial RevolutionYascha Mounk and Deirdre McCloskey | Persuasion | July 11, 2026Yascha Mounk interviews Deirdre McCloskey about her argument that the modern world's economic liftoff came less from capital accumulation than from a change in ideas. McCloskey says both left and right versions of the conventional story rely too heavily on investment: the left stresses exploitation and surplus value, while the right stresses virtuous saving by capitalists. Her objection is historical and economic. Human beings had always invested, from irrigation works and Roman roads to seed grain, and simple accumulation quickly runs into diminishing returns.McCloskey's alternative is that northwestern Europe, first Holland, then Britain and Scotland, and then the North American colonies, developed a liberal ideology that changed who was allowed to innovate and be honored for it. The conversation links that shift to the erosion of inherited hierarchy, the spread of dignity for ordinary commercial life, and a moral vocabulary in which liberalism is not merely procedural but connected to virtues and values. The point is not that machines, coal, trade, and institutions did not matter, but that they do not explain the scale and timing of modern enrichment without a cultural permission structure for innovation.The interview also turns to the contemporary defense of liberalism. Mounk frames the series around the worry that liberalism is often treated as too thin to command allegiance, while its opponents speak more directly to moral passions. McCloskey's case is that liberal societies became rich because they dignified experimentation and ordinary enterprise, and that liberals need to recover the moral language behind that claim.Read moreAI in an Age of OligarchyPaul Krugman | Paul Krugman | July 12, 2026Paul Krugman frames AI as a major technological shock arriving inside an already unequal political economy. The post says AI's economic and social effects may take years to understand, but argues that the setting matters now: America has much greater wealth concentration and political inequality than it did in the 1950s and 1960s, when progressive taxation, stronger regulation, and more active antitrust might have contained some of the destructive effects of a new technology.Krugman's opening claim is that the same technology would likely have different consequences in a more level society. In today's United States, he writes, extreme wealth is both a cause and effect of policies that favor a small elite, including low effective taxes on capital and high incomes, weak enforcement of worker protections and antitrust, and cuts to programs that benefit ordinary Americans.The article is explicitly more about oligarchy than AI. Krugman says the paid sections document the rise of the “.0002%,” the economics and politics of extreme wealth, how oligarchy will shape AI's impact, and possible policy paths. His caveat is that AI itself may still produce a pushback against oligarchy, but absent that, he expects the pre-existing concentration of wealth and power to magnify AI's downsides.Read moreElon Musk is building a form of capitalism that Adam Smith would hateAuthor: Tim O'Reilly Published: July 12, 2026Tim O'Reilly argues that Elon Musk is using the legal forms of shareholder capitalism to escape the restraints that shareholder capitalism was supposed to impose. The article begins with SpaceX's public-market structure: ordinary public investors get little meaningful governance power, Musk keeps roughly 85 percent of the votes through super-voting shares, buyers waive jury trials and class actions, the company qualifies as controlled, and removal of Musk depends on the share class he controls. In O'Reilly's framing, that is not ordinary founder control; it is a design for being answerable to no one, possibly beyond Musk's own lifetime.The killer detail is the article's turn through Albert Hirschman, Montesquieu, James Steuart, Adam Smith, and Keynes. Older defenses of commerce held that markets would tame princely passions because the self-interest of merchants was safer than arbitrary rule. O'Reilly says Musk reverses that hope. The market discipline that was supposed to cage the prince has become the lever by which the prince raises capital, removes feedback loops, and carries private power into politics, government, Mars, robots, AI, or whatever ambition comes next.The pull is the link to AI governance. O'Reilly says corporations are already a kind of artificial intelligence: narrow-input systems that act at a scale no individual human can match. Their partial controls include independent boards, shareholder votes, courts, disclosure, regulators, public pressure, and activism. If the leaders building frontier AI strip those alignment mechanisms out of their own companies, the governance of the company becomes a preview of the governance of the machine.Read more: The EconomistMurky Mirror: Truth and ConsequencesAuthor: Esther Dyson Published: July 14, 2026Esther Dyson argues that today's institutional crisis is better viewed through the 14th century than through recent political history. Using Barbara Tuchman's A Distant Mirror as her frame, she compares a world of famine, plague, church schism, feudal predation, and purposeless war with a present in which institutions again feel brittle, incentives are badly aligned, and power is shifting into forms that are hard to govern.The killer detail is the historical analogy between land, corporations, and AI. Dyson moves from nobles who controlled serfs and territory, to the East India Company as a quasi-sovereign business, to today's AI systems and data centers as a possible new sector that crosses and weakens both nation-states and companies. The question is whether AI becomes a new kind of private land, owned by a new nobility, or an open prairie that many people can cultivate.The pull is human attention. Dyson says the central question is not what AI will do to people, but how people will react to it: whether they can value love, kindness, embodied attention, and artisanal human presence in a world of seductive artificial offerings.Read more: SourceThe political economy of billionaire derangementAuthor: Henry Farrell Published: July 15, 2026Henry Farrell argues that the visible political radicalization of some Silicon Valley billionaires is not a random personality quirk, but a product of the political economy that made them. Starting from Tyler Cowen's dismissal of “billionaire derangement syndrome” and Tim O'Reilly's warning that Elon Musk is using shareholder capitalism to escape shareholder restraint, Farrell flips the phrase: the question is why billionaires themselves can become deranged.The killer detail is Farrell's use of Peter Thiel as both theorist and example. Thiel's Stanford lectures described startups as monarchies and founders as figures vested with unusual power, while Silicon Valley culture rewarded eccentricity, monopoly ambition, and founder exceptionalism. Farrell says those ideas combined with dense founder-investor networks, peer rivalry, and weak correction mechanisms to amplify rather than discipline princely appetites.The pull is the ideological problem for classical liberals who once saw tech wealth as an ally of markets and freedom. Farrell says commerce did not tame the passions; in parts of Silicon Valley, the passions have begun to devour markets, institutions, and the liberal story that justified them.Read more: SourceIs there any “oligarchy” to fight?Matthew Yglesias | Slow Boring | July 16, 2026Matthew Yglesias argues that “oligarchy” is a rhetorically powerful but analytically loose way to describe American politics. The post begins from Bernie Sanders' “Fighting Oligarchy” tour, Amy Klobuchar's warning about a MAGA “broligarchy,” and the long afterlife of the Martin Gilens and Benjamin Page paper that was widely summarized as showing that only the rich matter in policy outcomes. Yglesias says the evidence supports a weaker claim: affluent people and business leaders have unusual access and influence, but that is not the same as rule by a small cabal.His main distinction is between inequality and oligarchy. The Gilens-Page measure treated the top 10 percent of households as “the wealthy,” and later critics found that rich and middle-class preferences usually align; in the cases where they differ, the rich win about 53 percent of the time. Yglesias also says business executives get special access partly because their decisions are materially important to communities, jobs, investment, and local tax bases, not only because of campaign donations.The post preserves Jerusalem Demsas' counterpoint from their podcast discussion: privileged donor and business access can still violate democratic equality even if the oligarchy label overstates the structure of power. Yglesias' narrower claim is that Democrats should be precise about what problem they are trying to solve, because donor influence can also push the party left on climate and cultural issues in ways that alienate many voters.Read more: Slow BoringAINearly 200 Economists and Tech Leaders Warn of A.I. ThreatsAuthor: Ben Casselman Published: July 13, 2026Ben Casselman reports on “We Must Act Now,” a statement warning that artificial intelligence could transform the economy faster than any previous technology and that policymakers need to move faster to understand and respond. The statement says AI may become radically more powerful over the next 10 years, bringing risks such as large-scale job displacement as well as opportunities such as higher living standards. Nearly 200 people signed, including 15 Nobel laureates, the chief economists of OpenAI and Anthropic, Anthropic co-founder Jack Clark, former Google CEO Eric Schmidt, and venture capitalist Vinod Khosla.The killer detail is who joined the warning. Casselman notes that the signatories include economists who have historically been skeptical of Silicon Valley's most dramatic AI job-loss forecasts, including Daron Acemoglu and Simon Johnson, the MIT professors who won the 2024 Nobel in economics. Erik Brynjolfsson, who helped organize the statement, says there has been a notable change in the profession and that economists and policymakers are not ready for the “tsunami” he sees coming.The pull is the measurement problem. The statement does not offer a specific policy menu, but calls for economists, policymakers, and industry leaders to understand the economics of transformative AI and steer it toward complementing humans. Brynjolfsson says one high priority is better data on AI's spread and impact, because current measures tell conflicting stories about job losses and which workers are most exposed.Read more: The New York TimesWhy I Didn't Sign the AI Open LetterAuthor: Andrew McAfee Published: July 13, 2026Andrew McAfee explains why he did not sign “We Must Act Now,” the AI economy statement organized in part by his longtime collaborator Erik Brynjolfsson. McAfee agrees with the letter's starting point that AI is likely to become radically more powerful over the next decade and that it is a general-purpose technology. His objection is not to urgency or to studying AI's economic effects, but to the framing of risk, displacement, and institutional steering as the first move.The killer detail is McAfee's line edit. He says the original letter comes close, then “bounces off the crossbar” by calling for incentives, guardrails, and institutions to steer AI before we know enough about its actual impacts. He points to mixed current evidence: labor-market canaries, but also rising software job postings, low unemployment for younger workers, rising real median income, and claims that AI-adopting companies are adding workers faster than low-adopting peers. His worry is that the letter leans toward upstream governance and dirigisme when the evidence may call for capability building instead.The pull is his replacement statement. McAfee keeps the three-paragraph structure but changes the emphasis: AI is likely to become radically more powerful; like earlier world-changing technologies it will raise living standards while also bringing harms and shocks; and economists, policymakers, and technology leaders should build the capabilities to respond quickly and effectively. It is a concise version of the permissionless-innovation case inside the AI policy debate.Read more: The Geek WayOwn Your WeightsAuthor: Jamin Ball Published: July 10, 2026Jamin Ball argues that the enterprise AI debate about whether companies should “own their weights” or rent models from frontier labs is asking too narrow a question. A model weight file gives a company control over a point-in-time artifact, but not durable control over the capability stack. In his framing, the weight file is a melting ice cube: it does not get worse in absolute terms, but it falls behind as frontier systems improve and enterprise needs change.The killer detail is what Ball says companies really need to own: the data flywheel, reinforcement learning infrastructure, and evaluation harness that produce and improve the model. Simply deploying an open-weights model and declaring sovereignty leaves the enterprise with yesterday's capability and no way to compound workflow-specific learning.The pull is that enterprise AI control may be less about model ownership than operating ownership. The defensible layer is the system that turns company data, edge cases, business definitions, and evaluations into continuously improving performance.Read more: Clouded JudgementWays to Think About Token PricingAuthor: Benedict Evans Published: July 9, 2026Benedict Evans argues that today's AI token prices are a temporary signal from a supply-constrained market, not a reliable guide to long-term value capture. The open question is whether foundation models keep durable pricing power or become commodity infrastructure as data-center capacity, inference efficiency, and model competition all shift. His current read is that the visible market dynamics point toward commoditization unless something materially changes.The killer detail is the mobile data analogy. Evans says cellular networks became a trillion-dollar industry with hundreds of billions in capex after data usage exploded, but carrier stocks went nowhere because value moved up the stack. Tokens may behave similarly: an opaque unit tied to marginal cost, sold through bundles, essential to everything, yet not necessarily where profits accrue.The pull is uncertainty, not prediction. Evans lists paths to model dominance, including network effects, less competition, regulation, export controls, or a lab pulling ahead on execution, but says each requires a new fact not yet visible. Without that change, the model layer looks more like infrastructure beneath the products that capture value.Read more: SourceAlex Karp Is Saying What Every Angry CEO Is Thinking About AIAuthor: Tim Higgins Published: July 11, 2026Tim Higgins reports that Palantir CEO Alex Karp has turned corporate frustration with AI labs into a public argument about enterprise control. Palantir released a white paper, “Institutional Sovereignty in the Age of AI,” laying out steps companies and governments can take to protect themselves from OpenAI, Anthropic, and other foundation-model providers. The article links that paper to Karp's CNBC appearance, where he said “something has gone completely wrong” in the relationship between AI labs and customers and argued that enterprises are paying for tokens that create little value.The killer detail is the value-capture question. Higgins writes that Karp's critique has resonated because AI labs may gain power and insight from customer data, workflows, and decision-making, even when enterprise policies say customer data are not used for training. David Sacks amplified the concern by arguing that Anthropic is moving from the model layer into vertical applications such as science, security, legal, and coding, raising the fear that model providers will watch where value is being created and then move into those markets directly.The pull is that Karp is not alone, even if his style is unusually combative. Higgins notes that Satya Nadella has also warned that companies need to retain the learnings created when they use AI models, while Mark Zuckerberg has framed Meta's new model release partly around lower-cost frontier intelligence. The article presents Karp's campaign as one sign that established technology companies and large enterprises are trying to define where they fit when AI labs become central infrastructure, application competitors, and potential IPO giants at the same time.Read more: The Wall Street JournalThe AI Agents Are Coming for Microsoft OfficeAlex Wilhelm | Cautious Optimism | July 11, 2026Alex Wilhelm argues that one of the week's quieter AI questions is whether the productivity market that Microsoft successfully moved into subscription software is now being attacked by agentic tools. The piece begins with the infrastructure backdrop: SK Hynix raised $26.5 billion in a U.S. listing while building U.S. HBM and advanced-packaging capacity, and memory, chip, and foundry companies are now priced for sustained AI demand.Wilhelm then says the AI conversation has shifted quickly from raw capability to cost per task. He cites new model releases and vendor language emphasizing cheaper agentic and coding models, faster performance, and lower dollars per task. That matters because lower costs make it more plausible for AI systems to take on routine knowledge work at scale rather than remain a premium coding assistant market.The core of the article is Microsoft Office. Wilhelm notes that Microsoft turned Office from a one-time purchase into Microsoft 365, a large recurring revenue business with tens of millions of subscribers and a major productivity segment. Now, he says, late-stage unicorns and AI labs are pushing into the same territory: Anthropic's Cowork was reportedly used mostly outside software development, OpenAI merged ChatGPT and Codex into a tool for creating sheets, slides, docs, web apps, and long-running work, and other companies are building agentic coworkers that connect business data to documents, workflows, schedules, alerts, and apps.The article's caveat is that Microsoft has survived major platform shifts before. The argument is not that Office disappears quickly, but that the definition of office software is broadening from documents and spreadsheets into AI systems that can create, monitor, and act across workplace data.Read moreWhat Is Loop Engineering, and Who Owns It?Author: Nilesh Barla Published: July 11, 2026Nilesh Barla argues that “loop engineering” is becoming a distinct discipline because production AI agents now fail less at single prompts than at runtime: when to stop, what state to preserve, and how to recover after a bad step. Prompt engineering shapes one model call, and context engineering shapes what the model sees, but loop engineering shapes what a sequence of calls actually does.The killer detail is the three-primitives frame. Barla says a real agent loop needs halt conditions, state carryover, and recovery paths, then maps teams across five maturity levels. At the lowest level, an agent is just a model call in a for-loop with a step cap and raw history; by the higher levels, the system has structured state, explicit planning, replay, evaluation, and self-repair.The pull is organizational. If agents are becoming production systems rather than demos, someone has to own the runtime itself. The loop engineer is the role Barla gives to the person responsible for making long-running agent work dependable.Read more: Adaline LabsThe Fight Against AI Data Centers Is Just BeginningEmma Roth | The Verge | July 12, 2026Emma Roth argues that community resistance to data centers has moved from an early warning sign into a national political fight as AI facilities grow larger, more power-hungry, and more visible to nearby residents. The article starts with Apple's failed 2015 plan for a $1 billion data center in Athenry, Ireland, where a small group of residents challenged the project over noise, light pollution, flooding, traffic, and wildlife effects until Apple abandoned it in 2018.The current data-center buildout is presented as much larger and more contentious. Roth writes that residents now cite rising energy costs, water quality, noise, light pollution, and greenhouse gas emissions, while the U.S. Energy Information Administration expects commercial energy demand to surpass residential demand this year because of AI data centers and Goldman Sachs expects data-center power demand to double by 2027.The central evidence comes from Data Center Watch, which says protesters blocked or delayed at least 75 U.S. projects worth $130 billion from January to March, with active opposition groups more than doubling from 396 at the end of 2025 to 833 by the end of the first quarter of 2026. Roth also cites QTS abandoning a $12 billion Wisconsin campus, Delaware City regulators blocking a 580-acre project under the Coastal Zone Act, opposition stopping a QTS project in Prince William County, and pressure that pushed Kevin O'Leary to downsize the proposed 40,000-acre Project Stratos in Utah.The policy section describes a split between federal acceleration and local resistance. President Trump has treated data centers as part of the AI race with China and fast-tracked construction, while some Republican candidates are distancing themselves from that position ahead of midterms. Sanders and Ocasio-Cortez have proposed a moratorium until price and environmental protections exist, bipartisan lawmakers are backing ratepayer-protection measures, and states including Florida, Idaho, and Washington have passed rules on cost shifting, water use, and tax breaks. Roth's caveat is that the policy patchwork is still incomplete, leaving many communities to fight project by project.Read more6 months to live for open modelsAuthor: Nathan Lambert Published: July 12, 2026Nathan Lambert argues that open-weight AI models are facing their most serious policy test so far because U.S. officials are beginning to discuss concrete controls rather than abstract safety concerns. He says reported White House conversations about a new executive order may initially target Chinese-origin models and government use, but could create a broader review habit for frontier open models. His forecast is that a model above the capability range of GPT-5.5, Claude Opus 4.8, or GLM-5.2 could trigger a ban or indefinite delay within six months.The post separates two policy fights that are becoming intertwined: distillation and frontier capability. Lambert says the distillation campaign against Chinese models has become a form of regulatory capture because Anthropic and other closed-model companies would gain economically if Chinese open models were banned. He does not dismiss IP protection, but argues that if a closed model's capabilities are dangerous enough to justify restricting open models, the lab also has to explain why those capabilities are exposed through a queryable API. He cites unauthorized access to Anthropic's Mythos private beta as evidence that APIs are not automatically secure.The broader claim is that a unilateral U.S. ban would hurt positive actors more than bad actors if comparable open models remain available elsewhere. Lambert says the only durable ceiling would require global agreement, which does not exist, and that open models can improve safety by allowing broad inspection, adaptation, and understanding. His proposed near-term off-ramps are a strong U.S. open model release from companies such as Microsoft, Meta, or Reflection, and a broader coalition of open-source beneficiaries lobbying for safe rollout rather than prohibition.Read more: SourceAmericans Deserve a Dividend From AI Companies' RichesAuthor: Scott Stanford Published: July 14, 2026Scott Stanford argues that proposals to give the government a stake in AI companies miss the point unless ordinary citizens directly receive and control the upside. Sam Altman has discussed giving up equity in OpenAI, Washington already owns a stake in Intel, Nvidia is sharing China chip revenue, and Bernie Sanders wants large AI labs to contribute half their stock to a sovereign wealth fund. Stanford says those ideas all park value with the state, not with people.The killer detail is New Carlisle, Indiana, where AWS's Project Rainier is turning cornfields into one of the world's largest AI superclusters. The project is planned to run up to a million chips, draw more than two gigawatts of power, and represents an investment that has grown from $11 billion to $13.8 billion. Stanford uses that local transformation to argue that AI's public bargain should be visible at the household level.The pull is design. A citizen AI dividend would have to specify who earns a stake, how they hold it, and when they see cash. Without that mechanism, the AI wealth debate remains a fight over government balance sheets rather than public ownership.Read more: SourceWho Gets to Define the Frontier?Author: Mark Daley Published: July 14, 2026Mark Daley argues that Demis Hassabis is right to call for a serious institution to verify frontier AI systems, but that the power to test models is also the power to govern them. Hassabis's proposed Frontier AI Standards Body would get privileged pre-release access to advanced models, testing compute, held-out evaluations, support from national labs and security agencies, third-party auditors, and eventually authority to block models from the American market or coordinate a slowdown.The killer detail is Daley's constitutional objection. He says the proposal sometimes looks like a scientific lab, a standards body, an industry regulator, a licensing authority, and an emergency security council at once. Combining those roles because each requires technical expertise would be like putting the central bank, auditor-general, and Supreme Court in one building and calling it efficient.The pull is standard-setting. Daley's concern is not that verification is unnecessary, but that whoever writes the tests, decides what passes, adjudicates disputes, and grants market access may end up defining the frontier itself.Read more: SourceGPT-Red: Unlocking Self-Improvement for RobustnessOpenAI | OpenAI | July 15, 2026OpenAI describes GPT-Red as an internal automated red-teaming model trained to find prompt-injection vulnerabilities at a scale human red teams cannot match. The post says AI systems increasingly encounter third-party data through browsers, connected apps, local files, and tools, creating opportunities for malicious instructions hidden in emails, webpages, tool responses, or code repositories. Human red-teaming remains part of OpenAI's safety process, but the company says it is time-intensive and cannot generate enough diverse adversarial examples for model training.The system is trained through self-play reinforcement learning, with GPT-Red rewarded for eliciting valid failures and defender models rewarded for resisting attacks while still completing their tasks. OpenAI says the training environments specify threat models across settings such as local files, webpage banners, email bodies, and tool outputs. The model is kept separate from deployed production models because it is intentionally trained with malicious capabilities.OpenAI reports that GPT-Red generalized beyond its training set, including an internal replication of the indirect prompt-injection arena from Dziemian et al. (2025), where it found successful attacks in 84% of scenarios compared with 13% for human red-teamers. The post also says GPT-Red transferred attacks from simulation to a live autonomous vending-machine agent, causing price changes and order cancellations, and outperformed a prompted GPT-5.5 baseline against a Codex CLI agent on held-out data-exfiltration tasks.The article's main robustness claim is that OpenAI has used GPT-Red and predecessor models in training since GPT-5.3, with later GPT releases becoming more resistant to prompt injections. It says GPT-5.6 Sol has six times fewer failures on OpenAI's hardest direct prompt-injection benchmark than the best production model from four months earlier, that a “Fake Chain-of-Thought” attack class fell from more than 95% success against GPT-5.1 to below 10% against GPT-5.6 Sol, and that GPT-5.6 Sol fails on only 0.05% of GPT-Red's direct prompt injections. OpenAI says general capabilities and targeted over-refusal evaluations were not harmed, and says a preprint with more details will follow.Read moreAnthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just modelsRebecca Bellan | TechCrunch | July 15, 2026Rebecca Bellan reports that Ode with Anthropic is the $1.5 billion AI implementation company launched by Anthropic with Blackstone, Hellman & Friedman, Goldman Sachs, and other backers. The article says the venture reflects a growing belief among frontier AI labs that enterprise adoption requires more than better models: customers need engineers who can embed inside businesses and turn AI into working systems.Ode was originally conceived by Blackstone after it used both large consulting firms and smaller AI services boutiques across its portfolio companies. TechCrunch reports that Fractional AI, an AI engineering services startup, stood out and was acquired by the joint venture shortly after the venture was announced. Fractional now forms the foundation of Ode, which has 100 engineers and works closely with Anthropic's applied AI team to identify where the technology can affect specific businesses.Ode CEO Chris Taylor tells TechCrunch that the company could someday become a trillion-dollar business if it scales without losing quality. He says an ideal customer is one whose CEO treats the AI project as a top one or two priority, whether it is a major product feature or the reworking of a core business process. Ode will operate under a “Claude-first” principle, using Anthropic technology whenever possible, but the article says it can use rival AI products when needed.The article's central implementation argument comes from Ode chief technologist Eddie Siegel, who says model selection matters but is not where most of the engineering effort goes. He compares it to the choice of programming language in software: one ingredient in a system that still has to be engineered. Bellan writes that Ode's challenge is hiring and training enough elite generalist engineers, many of them former founders, while competing with OpenAI's The Deployment Company and consulting giants that have built their own forward-deployed engineering teams.Read moreVint Cerf is working on a plan to unleash AI agents on the open internetTim Fernholz | TechCrunch | July 15, 2026Tim Fernholz reports that Vint Cerf, after leaving Google, is advising Innovation Labs on an open architecture for identifying AI agents online. Innovation Labs is a subsidiary of Identity Digital, a DNS registry company, and its proposal is to use domain-name infrastructure as part of a system for agent identity, accountability, and auditability. The premise is that agents will need a way to identify themselves if they move beyond proprietary systems and begin interacting across the open internet.The concrete proposal is DNSid, a registry that links an AI agent to an existing internet domain and uses cryptographic proofs to log its registration over time. Innovation Labs says it is trialing the standard with unnamed hyperscalers and identity companies. Cerf frames the problem around authority and accountability: what authority an agent has, where that authority came from, who is accountable for the agent's behavior, how its identity is established, and why anyone should trust it.The article's caveat is that standards are still emerging and agents are more active than static domains. Cerf says the period may be both fascinating and exasperating because the functionality is powerful and interoperability is unresolved. He compares the adoption problem to TCP/IP: competing systems may not work together until users push for functional interoperation. He also says an agentic economy is not inevitable, but that people will try to build it because delegating work to agents will be easier.Read more: TechCrunchxai-org/grok-build, now open sourceAuthor: Simon Willison Published: July 15, 2026Simon Willison argues that xAI's decision to open-source Grok Build is best understood as a trust repair move after a severe privacy failure. The CLI had triggered backlash when users realized that running it in a directory could upload the entire directory to xAI's Google Cloud buckets, including one user's reported SSH keys, password manager database, documents, photos, and videos. xAI disabled the feature, said previously retained coding data would be deleted, and released the code under Apache 2.0.The killer detail is what the codebase reveals. Willison counts 844,530 lines of Rust, only about 3% of which appears vendored, and finds remnants of the upload system still present but disabled: gcs.rs contains Google Cloud upload code, while upload_session_state() now returns a hard-coded session_state_upload_unavailable error. He also notes copied or ported tool implementations from Codex and OpenCode, prompt files, and a terminal Mermaid renderer.The pull is that terminal coding agents are becoming large, intricate software systems in their own right. The privacy failure mattered because these tools operate inside the directories where developers keep their most sensitive work; the open-source release matters because trust now depends on inspecting what an agent can see, send, and do.Read more: SourceThe Pulse: What can we learn from Bun's rapid Rust rewrite with AI?Author: Gergely Orosz and Ivan Klaric Published: July 16, 2026Gergely Orosz and Ivan Klaric argue that Bun's AI-assisted rewrite from Zig to Rust is a practical sign of how software engineering changes when models can take on large, bounded migrations with clear feedback loops. The piece does not treat the rewrite as magic: Jarred Sumner first spent hours turning design judgment into a detailed porting guide, then used adversarial review, parallel agents, compiler errors, and tests to force the work toward correctness.The killer detail is the scale. Bun had 535,496 lines of Zig, 1,448 files, and 22 million monthly downloads, making a conventional rewrite a year-long freeze the team could not justify. Using Fable, Sumner split the work across 64 agents, produced about 6,500 commits, and got the migration done in 11 days at an estimated API cost of $165,000.The pull is economic, not theatrical. If a one- or two-year migration can become an 11-day project, AI coding is not just faster autocomplete; it changes which technical debts are worth paying down.Read more: SourceOrphan risks at the frontier of artificial intelligenceAuthor: Andrew Maynard Published: July 16, 2026Andrew Maynard argues that frontier AI safety frameworks are creating “orphan risks”: harms that companies can see, but do not formally own because they are hard to quantify, do not fit catastrophic-risk thresholds, or fall outside audit-friendly compliance machinery. His target is not existing frontier safety work, but the narrowing effect that happens when private companies decide which risks count as governable.The killer detail is Maynard's contrast between measurable model dangers and threats to value. He points to Meta's three-day Galactica collapse, OpenAI's 2023 board crisis, safety-team departures, and wellbeing litigation as examples of risks that damaged trust, culture, legitimacy, or users without fitting cleanly into conventional model-risk categories. The proposed fix is an orphan-risk register: a public record of risks a company considered and chose not to manage, with reasons.The pull is accountability. Frontier developers' internal scoping choices have become a de facto layer of public governance, so the question is no longer only which risks they manage, but which risks they quietly leave outside the frame.Read more: SourceThe Lab of the Future Should Feel Like a Data CenterLatent.Space with Andy Beam and Rafa Gomez-Bombarelli | Latent.Space | July 16, 2026Latent.Space interviews Lila Sciences CTO Andy Beam and chief science officer for physical sciences Rafa Gomez-Bombarelli about the company's attempt to build an AI-run science factory. The post describes Lila's thesis as treating the lab itself as an “infinite token generator”: if internet data drove the first era of AI scaling, experimentally verified scientific data may be the next scarce training source. Lila is trying to produce that data with robotics, lab instruments, orchestration software, and AI models wired into the wet lab.The central analogy is the lab as data center. Instruments are nodes on a graph, a magnetically levitating transport layer moves materials between them, and experiment scheduling looks like a compute queue. Beam says Lila is not simply an automation company, because the point is not just throughput; it is flexibility, generalization, and experiment capture. The post says Lila has built more than 10 trillion experimentally validated “scientific reasoning tokens,” not internet text or biological sequences.The interview ranges across biology, chemistry, drug discovery, materials science, and the limits of automation. It notes that Lila rebuilt one gas-sorption measurement to run roughly 2,500 times faster, claims its general models can transfer priors from small-molecule chemistry to metal-organic frameworks for carbon capture, and describes model-suggested platinum-group-free electrocatalysts that moved from looking boring or wrong to becoming strong performers. The caveats are physical: experiments have runtimes, biology cannot always be accelerated, chains of thought can be unreliable narrators, and reward hacking becomes more dangerous when a model controls a real lab.Read more: Latent.SpaceWhy AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”Kate Park | TechCrunch | July 16, 2026Kate Park interviews AMI Labs CEO Alexandre LeBrun about why Yann LeCun's world-model startup avoids the language of “AGI” and “superintelligence.” LeBrun says the terms are not useful because they lack stable definitions: “We never used the word AGI. And I just noticed that nobody is using it anymore; they switched to superintelligence.” His argument is that the practical frontier is not a label, but whether AI systems can understand and predict real-world states.The article explains the world-model thesis by contrasting language prediction with physical-state prediction. A large language model predicts the next word; a world model predicts the next state, such as what happens when a glass tips over. LeBrun says LLMs remain complementary and efficient for language, but the physical world is where current AI is weak. Robotics is the clearest case: hardware has advanced quickly, but robots are still brittle outside controlled routines because they lack context and situational understanding.AMI is still pre-product, but TechCrunch reports that LeBrun was in Seoul looking for industrial partners, researchers, and global companies. He says world models cannot be built entirely inside a lab because they need access to real environments. That is why South Korea appeals to AMI: robotics, semiconductors, manufacturing, and fast adoption create the kind of hardware-heavy context that software-only AI has barely touched.Read more: TechCrunchKimi K3 Tech Blog: Open Frontier IntelligenceKimi | Kimi | July 16, 2026Kimi introduces Kimi K3 as an open 3T-class frontier model aimed at coding, knowledge work, reasoning, multimodality, and long-context agentic use. The source describes the model as a 2.8T-parameter system built on Kimi Delta Attention and Attention Residuals, with native multimodality and a 1M-token context window. It says Moonshot AI plans to release model weights by July 27.The post presents K3 through benchmark and use-case sections rather than as a general product announcement. It reports results across coding, productivity, agentic, and multimodal evaluations, including DeepSWE, Terminal-Bench 2.1, Program Bench, SWE Marathon, FrontierSWE, PostTrain Bench, OfficeQA Pro, SpreadsheetBench 2, MCP Atlas, AutomationBench, BrowseComp, GDPval-AA v2, AA-Briefcase, MMMU-Pro, MathVision, BabyVision, OmniDocBench, and PerceptionBench. The source says all reported K3 results use maximum reasoning effort with temperature and top-p set to 1.0, and that different benchmark comparisons use KimiCode, Claude Code, or Codex harnesses depending on the test.Kimi's caveats are unusually concrete. The limitations section says K3 was trained in preserved thinking-history mode, so quality may become unstable if an agent harness does not pass historical thinking content correctly or if an ongoing session switches to K3 midstream. It also says K3's emphasis on long-horizon tasks can make it excessively proactive when it encounters minor issues or ambiguous intent, and recommends imposing explicit behavioral constraints for applications that require strict boundaries. The post adds that K3 remains behind Claude Fable 5 and GPT 5.6 Sol in user experience despite being competitive overall.Read moreVenture CapitalThree Years InAuthor: Tomasz Tunguz Published: July 10, 2026Tomasz Tunguz marks Theory Ventures' third anniversary by arguing that AI's central market effect is time compression. In his telling, model release cycles, company revenue milestones, enterprise adoption, and venture categories have all accelerated. Seed, Series A, and Series B still exist as financing labels, but they no longer cleanly describe company maturity when some seed rounds are larger than IPOs and the best AI companies can mature much earlier than prior software companies.The killer detail is the shift from models to inference. Tunguz argues that inference has become the dominant AI market because workloads and buyer preferences are fragmenting: video, batch, local, agentic, and real-time tasks each create different infrastructure needs. He compares this to databases splitting into OLTP, OLAP, vector, and streaming categories, with AI pushing the same specialization into inference infrastructure.The pull is that Theory sees the AI-native venture firm as part of the same pattern. The firm says it has analyzed twice as many investment opportunities with three investors working alongside a nine-person intelligence organization, using agents and research systems to map markets, source companies, and support diligence. The piece is both a market map and a statement about how venture itself is being rebuilt by the technology it funds.Read more: LinkedInVenture Has Rarely Looked More BifurcatedAuthor: Beezer Clarkson Published: July 14, 2026Beezer Clarkson points to PitchBook's Q2 report as evidence that the U.S. venture market has split into two very different realities. AI now accounts for more than 60 percent of all U.S. venture deal value, meaning the headline market can look active and well-funded even while much of the non-AI market is dealing with a much colder liquidity and fundraising environment.The thread uses that split as the setup for Clarkson's latest Origins episode with Alec Litowitz, founder of Magnetar and QStar Capital and one of Citadel's original founding partners. Clarkson says markets like this are periods of genuine uncertainty, not merely ordinary risk, which is why Litowitz's Adaptability Quotient framework is relevant.The embedded clip makes the liquidity point concrete. Litowitz says DPI is “the resolution of uncertainty” because it converts an uncertain investment into actual cash returned to LPs. In his framing, a realized dollar is a real mark, while TVPI remains uncertain until it is realized.The killer detail is the distinction between pricing risk and resolving uncertainty. Litowitz's perspective matters because QStar is a SpaceX investor and Clarkson says the conversation happened just before one of venture's most consequential IPOs. The episode's stated questions are why venture remains a way to gain exposure to innovation, how AI is changing what is investable, why liquidity is ultimately a function of time, and why uncertainty requires a different decision framework from risk.Read more: XThe Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active NowAuthor: Ilya Strebulaev Published: July 10, 2026Ilya Strebulaev ranks angels, angel groups, accelerators, and incubators by lifetime U.S. unicorn investments, counting checks written before a company reached unicorn status. The top of the combined list is dominated by organizations: Y Combinator leads with 113 unicorn investments, followed by Plug and Play at 52 and 500 Global at 41. Sand Hill Angels is the highest-ranked angel group at 31.The killer detail is how quickly the list changes below the biggest accelerators. Strebulaev says 271 of the 304 investors in the Top 200 are individuals, or 89%. In the top 100, individuals are 91%. That makes the market underneath the large accelerator counts look much more personal: mostly operators and individual angels writing early checks from their own networks.The pull is the ranking's own caveat. Strebulaev writes that every lifetime leaderboard has a blind spot because many of the unicorns behind those totals were founded a decade or more ago, and some angels have since moved into formal funds, slowed down, or stopped investing. His post therefore separates lifetime performance from recent cohorts, including companies founded in 2015 or later and 2020 or later. For founders or allocators making current decisions, that distinction matters: a career record and a current record are not the same measure.Read more: Ilya StrebulaevAre Prediction Markets Doomed to Fail?Author: Contrary Published: July 16, 2026Contrary argues that prediction markets' current boom depends on whether platforms can prove they are more than regulated gambling with exchange-style branding. Kalshi and Polymarket have reached mass cultural, investor, and regulatory attention, but the article says the underlying idea is old: academic markets, corporate forecasting tools, Intrade, PredictIt, and other predecessors all struggled with the same linked problems of liquidity, legality, and user appeal.The killer detail is the comparison with sportsbooks. Prediction markets present themselves as peer-to-peer, transparent, and non-house-based, but sports contracts reportedly account for more than 90 percent of Kalshi trading, and the article says the platforms keep a much thinner slice of volume than sportsbooks. A market can therefore show sports-betting-scale handle while generating far less revenue.The pull is that the product's hardest problem may be distribution of wins. If a small group of sharp traders captures most profits while casual users lose interest, prediction markets may become valuable data feeds and professional tools before they become durable consumer networks.Read more: SourceRegulationExclusive: The Next Frontier of the Deportation Wars: College CampusesAuthor: Adrian Carrasquillo Published: July 11, 2026Adrian Carrasquillo reports that college campuses are becoming a new front in the fight over immigration enforcement because automatic license plate readers can turn ordinary campus security infrastructure into searchable location data. His thesis is that Flock Safety's camera network, even without direct ICE or DHS contracts, can feed deportation enforcement through local police partnerships and data-sharing practices.The killer detail is the campaign target. The Emergency Campaign to Support Higher Education, working with Schools Drop ICE, is focusing on 75 colleges and universities publicly identified as having Flock contracts. Flock says it has no ICE or DHS contracts, but activists argue the risk comes through local agencies that coordinate with federal authorities and run searches on their behalf.The pull is broader than immigration. Carrasquillo notes that license plate readers have already been abused by officers for stalking, and that Flock's AI search features can identify more than plates, including bumper stickers. A campus safety tool can become a political surveillance system when the data layer is searchable.Read more: The BulwarkThe Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.Author: Todd Phillips Published: July 12, 2026Todd Phillips argues that the Supreme Court's decision in Trump v. Slaughter damaged independent agencies by ending for-cause removal protections, but did not leave Congress powerless. The ruling weakens the old model in which commissioners at bodies such as the FTC, NLRB, CPSC, SEC, and CFTC could be insulated from dismissal over policy disagreements. Phillips says the next fight is whether presidents can turn nominally bipartisan commissions into one-party instruments.The killer detail is the procedural fix: quorum rules. Phillips proposes that Congress require bipartisan slates of commissioners to be seated before independent agencies can act. A president

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Every season of parenting comes with new joys, new challenges, and new opportunities to lead our children toward Christ. In this episode, we explore how parenting changes as children grow, what remains constant through every stage, and how to intentionally love, guide, and disciple them along the way.

The Pentagon is launching a new health screening initiative that will check for low testosterone as part of annual physicals for service members age 30 and older. Defense Secretary Pete Hegseth says the effort is intended to improve readiness, resilience and long-term health, while emphasizing that testosterone replacement therapy would be voluntary. But the announcement is also raising questions about the medical evidence behind widespread screening and how the policy will be implemented across the force. Federal News Network's Rachel Cohen joins me with more.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Seth, Sean and Cole dive into the 1st of their 10 questions each day leading up to Texans training camp: is the Texans' offensive line improved or just older and costs more?

This week I had my regularly schedule phone-in with syndicated radio host Michael DelGiorno. We discussed nutritional strategies for health as we age.LONGEVITY AND NUTRITION: 7-NUTRIENTS TO PAY ATTENTION TO AS YOU AGE. DR. JOE GALATI EXPLAINS…1. CalciumWhy it's needed: Keeps bones and teeth strong, helps muscles contract, and assists neurotransmitter release. As you age, the body leaches calcium from bones if you don't consume enough.Best Sources: Dairy (yogurt, cheese, milk), canned salmon/sardines with bones, kale, chia seeds, and fortified plant milks.2. Vitamin DWhy it's needed: Promotes calcium absorption and supports immune and brain health. Seniors often spend more time indoors and their skin becomes less efficient at producing Vitamin D from sunlight.Best Sources: Fatty fish (salmon, trout), egg yolks, and fortified foods like orange juice and cereals.3. Vitamin B6Why it's needed: Vital for metabolism and brain health. Older bodies break down B6 more quickly and absorb it less efficiently.Best Sources: Chickpeas (one cup provides over half the daily goal), chicken breast, bananas, and potatoes.4. Vitamin B12Why it's needed: Essential for the nervous system and red blood cell production. Roughly 10-30% of seniors have trouble absorbing B12 from food, often due to lower stomach acid or medications (like Metformin).Best Sources: Beef, seafood, and eggs. Experts recommend seniors get at least half their B12 from fortified foods (like nutritional yeast or cereals) because they are easier to absorb.5. ProteinWhy it's needed: To blunt sarcopenia(the gradual loss of muscle mass and strength). Nearly one-third of seniors do not eat enough protein.Requirements: Aim for 0.45 to 0.54 grams per pound of body weight (approx. 68–81g for a 150lb person).Best Sources: Greek yogurt, eggs, beans, tofu, and lean meats.6. FiberWhy it's needed: To prevent constipation and lower the risk of cardiovascular disease. Most seniors fall well short of the recommended intake.Requirements: Men (51+) need 30g; Women (51+) need 21g daily.Best Sources: Lentils, whole grains, berries, and leafy greens.7. Total Calories (Energy Intake)Why it's needed: Appetite often declines with age, leading to unintended weight loss and weakness.The Approach: If appetite is low, focus on nutrient-dense, high-calorie foods rather than large volumes of food.Best Strategy: Use healthy fats like olive oil, avocados, peanut butter, and full-fat dairy, or drink smoothies with protein powder to ensure energy needs are met.How To Reach Dr. Joe Galati and his Team:For an on-line consultation or press inquiries, contact Teresa Reyes at 713-794-0700Dr. Galati's Newsletter Sign-UpLiver Specialists of TexasGet a Copy of Dr. Galati's BookDr. Galati on FacebookMessage Dr. Galati and his team Hosted on Acast. See acast.com/privacy for more information.

The Dam Was Hiding a Voice Older Than StoneBecome a supporter of this podcast: https://www.spreaker.com/podcast/dark-mysteries-unsolved-mysteries-forgotten-secrets-unanswered-questions--5684156/support.Darkest Mysteries Online

Welcome back, everybody. It's Outside the Cinema, your weekly source for cult movie discussions, where your hosts, I'm Bill. That's Chris. Thank you for being here. Whether you're listening to the show when it comes out or whether you're listening to it down the road, hey, thanks for taking the time to listen to these two geezers babble on about movies that we didn't like. ## The Evolution of Podcasting: From Niche to Mainstream In this episode, Bill and Chris reflect on the evolution of podcasting since they began their journey. They discuss how the medium has changed, the growing acceptance of podcasts, and how their own show has adapted to the changing landscape. The Shift in Podcasting Bill and Chris express surprise at the longevity of the term "podcast" and how it has become a staple in entertainment. They reminisce about starting their show in their thirties, a time when podcasts were still finding their footing in the media world. Revisiting Classic Comedies This week, the duo dives into two comedies that, while light-hearted, also serve as reflections of their respective eras. They examine how movies from the seventies, like "Airplane!" and "Blazing Saddles," continue to resonate due to their relatable themes, despite being products of their time. They also raise important questions about the relevance of older films in today's cultural context. ### Outdated Humor in "Squeeze Play" One of the films discussed is "Squeeze Play" (1979) directed by Lloyd Kaufman. Bill and Chris critique the film's portrayal of gender dynamics in sports and how the narrative feels outdated. They explore how the humor and themes in the film reflect societal attitudes of the time and how they may not translate well to modern audiences. ### The Challenges of Dialogue in "Banned" The conversation also touches on the film "Banned" (1989), which Bill and Chris find struggles with dialogue that seems out of touch for its intended youth audience. They discuss the difficulties of older generations trying to write for younger ones, and how this disconnect can lead to awkward and unrealistic interactions in film. ## Conclusion: Adapting to Change In wrapping up, Bill and Chris emphasize the importance of understanding the context of films, both old and new. They encourage listeners to approach older films with a critical eye while also recognizing their historical significance. As the podcast continues to evolve, the duo remains committed to exploring and discussing the nuances of cult cinema. Key Takeaways - Podcasting has evolved significantly since its inception, becoming a mainstream form of entertainment. - Older films often reflect the societal norms and humor of their time, which can feel outdated today. - Understanding the context of films enhances appreciation and critical discussion.

Red pill dating advice for older men exposes the biggest illusion in the manosphere: that aging and money automatically make you a high-value bachelor without ongoing effort. Today we break down Briffault's Law, why the burden of sexual performance never lets up regardless of age or income, the mandatory upkeep required across fitness, finance, and frame to stay competitive, and why marriage is game on hard mode that most older men walk into completely unprepared.VIDEOS TO WATCH NEXT:Watch this playlist to figure out how to fix your failing marriage: https://www.youtube.com/playlist?list=PLEXcvFDdRqPuu_G8-sTLS7eXT7myvidMFWatch this playlist to help you get over your ex for good: https://www.youtube.com/playlist?list=PLEXcvFDdRqPsZ9JCTSAIkin-oMnavqNJZWatch this playlist to develop an unshakable frame and take control of your life: https://youtube.com/playlist?list=PLEXcvFDdRqPvgN8idHfGfOp3gA8Y0tMxT&si=NccZ6koKYz3hSuUz--------------------------------------------FREE EBOOKS➡️ She's Made You Weak: https://ebook.fixdeadbedrooms.com➡️ Fine... Here's How You Get Her Back: https://ebook.getoveryourex.us➡️ The 4 Magic $3X Positions: https://sexytime.fixdeadbedrooms.com--------------------------------------------BOOKS AND WORKBOOKS➡️ Find all of my books here: https://mybook.to/comeonmanpod➡️ Find all of my workbooks here: https://mybook.to/RPWorkbooks--------------------------------------------FOLLOW MEFollow on TikTok - https://www.tiktok.com/@comeonmanpodFollow on Instagram - https://www.instagram.com/comeonmanpodcast/Follow on Facebook - https://www.facebook.com/comeonmanpodcastFollow on X - https://x.com/bestmenspodFollow on Gettr - https://gettr.com/user/comeonmanpodFollow on Truth - https://truthsocial.com/@comeonmanpodFollow on Substack - https://substack.com/@comeonmanpod--------------------------------------------COMMUNITIES➡️ Join The W.O.L.F. Pack: https://wolf.comeonmanpod.com/➡️ Become a Spotify Channel Subscriber: https://podcasters.spotify.com/pod/show/comeonman/subscribe--------------------------------------------

AP correspondent Charles de Ledesma reports the U.K. is unveiling plans for a social media curfew for older teens - but it's voluntary.

This hour, What are older movies that young people should see? Senator Lindsey Graham called a staffer while having chest pains, and that staffer called 911 on his behalf. Why do people sometimes fail to call emergency services directly when they know something is seriously wrong? The family of Nolan Wells met with Jackson County District Attorney Angel Myers McIlrath marking a pivotal step in the ongoing investigation into the mysterious death of the 18-year-old.

In this episode, Cole Phillips and Bobby Fraumann share insights on passing the baton of faith across generations, the importance of intentionality in mentorship, and how to cultivate a legacy of faith within the church community.Keywordsfaith, legacy, mentorship, passing the baton, church community, spiritual growth, generational faith, intentionality, discipleship, church leadershipKey topics:Passing the baton of faith across generationsThe importance of intentionality in mentorshipBuilding a legacy of faith within the churchThe role of older and younger generations in spiritual growthPractical ways to pass on spiritual wisdom and valuesTakeaways:Faith is passed through intentional relationships and conversations.Older generations have a vital role in mentoring and inspiring the next.Living with purpose and intentionality creates a lasting legacy.Ask meaningful questions to learn from those who have gone before.Modeling faith through actions speaks louder than words.Sound bites"Faith is passed through intentional relationships.""Living with purpose creates a lasting legacy.""Model faith through actions, not just words."

By the time the grey hairs start creeping in, we're considerably more careful and much more easily exhausted by anything but a very small walk. Ever pondered why that is? 

The Daily Shower Thoughts podcast is produced by Klassic Studios. [Promo] Check out the Daily Dad Jokes podcast here: https://dailydadjokespodcast.com/ [Promo] Like the soothing background music and Amalia's smooth calming voice? Then check out "Terra Vitae: A Daily Guided Meditation Podcast" here at our show page [Promo] The Daily Facts Podcast. Get smarter in less than 10 minutes a day. Pod links here Daily Facts website. [Promo] The Daily Life Pro Tips Podcast. Improve your life in less than 10 minutes a day. Pod links here Daily Life Pro Tips website. [Promo] Check out the Get Happy Headlines podcast by my friends, Stella and Mickey. It's a podcast dedicated to bringing you family friendly uplifting stories from around the world. Give it a listen, I know you will like it. Pod links here Get Happy Headlines website. Shower thoughts are sourced from reddit.com/r/showerthoughts Shower Thought credits: Zaylyn5355, palmerry, Dry-Accountant-1024, Wormverine, MyPasswordIsMyCat, daguilara9, DomElBurro, , Same-Rich-6944, DoodDoes, taz_7_24_93, RedAtomic, hickfield, calltyrone416, wolf805, Professional_Job_307, WaCandor, Outcazt-, MadeByHideoForHideo, , Relevant-Pirate-3420, ernbernalearn, cruiserman_80, HunterExpress6846, Finding_Plato, oobat421, AnalyzingColors, GiraffeKing04 Podcast links: Spotify: https://open.spotify.com/show/3ZNciemLzVXc60uwnTRx2e Apple Podcasts: https://podcasts.apple.com/us/podcast/daily-shower-thoughts/id1634359309 Stitcher: https://www.stitcher.com/podcast/daily-dad-jokes/daily-shower-thoughts iHeart: https://iheart.com/podcast/99340139/ Amazon Music: https://music.amazon.com/podcasts/a5a434e9-da18-46a7-a434-0437ec49e1d2/daily-shower-thoughts Website: https://cms.megaphone.fm/channel/dailyshowerthoughts Social media links Facebook: https://www.facebook.com/DailyShowerThoughtsPodcast/ Twitter: https://twitter.com/DailyShowerPod Instagram: https://www.instagram.com/DailyShowerThoughtsPodcast/ TikTok: https://www.tiktok.com/@dailyshowerthoughtspod Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, Cole Phillips and Bobby Fraumann share insights on passing the baton of faith across generations, the importance of intentionality in mentorship, and how to cultivate a legacy of faith within the church community.Keywordsfaith, legacy, mentorship, passing the baton, church community, spiritual growth, generational faith, intentionality, discipleship, church leadershipKey topics:Passing the baton of faith across generationsThe importance of intentionality in mentorshipBuilding a legacy of faith within the churchThe role of older and younger generations in spiritual growthPractical ways to pass on spiritual wisdom and valuesTakeaways:Faith is passed through intentional relationships and conversations.Older generations have a vital role in mentoring and inspiring the next.Living with purpose and intentionality creates a lasting legacy.Ask meaningful questions to learn from those who have gone before.Modeling faith through actions speaks louder than words.Sound bites"Faith is passed through intentional relationships.""Living with purpose creates a lasting legacy.""Model faith through actions, not just words."

The Division's Milby joins the podcast.

Topics covered in this episode: The trusted-publishing debate: how to do it right vs. why you shouldn't trust it JupyterLab 4.6 and Notebook 7.6 are out! Tau – new small, readable terminal coding agent Django Tasks and Django 6.1 Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Consulting from Six Feet Up Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Calvin #1: The trusted-publishing debate: how to do it right vs. why you shouldn't trust it https://snarky.ca/how-to-publish-to-pypi-using-github-actions-securely/ (Brett Cannon) and https://blog.yossarian.net/2026/07/07/You-shouldnt-trust-trusted-publishing (William Woodruff) Trusted Publishing (PyPI's OIDC-based auth scheme, also now used by npm, RubyGems, crates.io, NuGet) replaces long-lived API tokens with short-lived, auto-scoped credentials tied to CI/CD machine identity. Yossarian's post: it's purely an authentication mechanism between a machine identity and a package — it says nothing about package safety or quality. PyPI deliberately avoids any "verified/trusted" badge for it, unlike its verified-URL checkmarks. Same logic applies to PyPI attestations: anyone can sign with any machine identity they control, so an attestation's presence isn't itself a trust signal. Bottom line from that post: don't confuse "trusted" (machine-to-machine) with "trustworthy" (human judgment about the package). Snarky.ca's companion piece is more practical: given GitHub Actions compromises in the news, the real fix is 3 concrete steps — run zizmor to lock down workflow permissions/checkout credentials and pin actions to commit hashes, adopt Trusted Publishing to eliminate stored PyPI tokens, and require manual approval via a GitHub environment before any publish job runs. Takeaway for listeners: Trusted Publishing is good hygiene for how you authenticate to PyPI, but it's not a substitute for securing your CI pipeline itself — or for actually vetting the packages you install. Michael #2: JupyterLab 4.6 and Notebook 7.6 are out! Michał Krassowski's rundown - a chunky minor release: 68 features, 97 bug fixes, 95 contributors, one of the biggest ever. Scratchpad console (Notebook 7.6 headliner) - a console next to your notebook sharing its kernel, for throwaway experiments. Ctrl+B. Jump to last-edited cell - new commands hop through recently edited cells. File browser glow-up - Date Created column, editable breadcrumbs with Tab-completion, and Open in Terminal. Debugger - sources open in the main area, floating step/continue overlay, live kernel-sources filter. Custom layouts (Lab) - activity bar top/bottom, draggable panels, four-way tab splits, per-panel Ctrl+scroll zoom. ~5x faster extension builds - webpack → Rspack, and jupyter-builder means no full Lab install needed to build extensions. Keyboard/a11y - add shortcuts from the UI (no JSON), Find & Replace in Edit menu (Ctrl+H). Calvin #3: Tau – new small, readable terminal coding agent Tau – new small, readable terminal coding agent (Python 3.12+), built as both a working tool and a teaching project for how coding agents work under the hood Install via uv tool install tau-ai, pipx, or pip; ships a tau CLI Three-layer architecture: tau_ai (provider-neutral model layer) → tau_agent (reusable "brain": messages, tools, events, loop) → tau_coding (CLI/TUI, file & shell tools, sessions) Supports OpenAI, Anthropic, OpenAI Codex, OpenRouter, Hugging Face, and custom/local OpenAI-compatible endpoints Built-in tools (read/write/edit/bash), durable JSONL sessions with resume/branching, project instructions via AGENTS.md, and context compaction Core harness is UI-agnostic — same brain can power the TUI, print mode, or a custom frontend — usable as a standalone library too Michael #4: Django Tasks and Django 6.1 Django 6.0 finally ships first-party background tasks (django.tasks) - out of Jake Howard's DEP 14, accepted May 2024, after two decades of everyone bolting on Celery/RQ/Huey. It's an API, not a worker. Django handles task definition, validation, queuing, and result storage - it does not execute them. You bring the backend. The default backend traps people. ImmediateBackend runs tasks inline on the request thread and blocks until done - so out of the box .enqueue() backgrounds nothing (a 5-second task means a 5-second response). The other built-in, DummyBackend, runs nothing at all. Both are dev/test only. Nice API otherwise: slap @task on a function, call .enqueue(), get back a TaskResult you look up later by id - with async twins like aenqueue(). Gotcha: args and return values must survive a JSON round-trip, so a tuple sneakily comes back as a list. The community local backend to know: django-tasks-local by Chris Beaven (SmileyChris). A ThreadPoolExecutor backend that gives real background threads with zero infrastructure - no Redis, no Celery, no database - plus a ProcessPoolBackend for CPU-bound work → github.com/lincolnloop/django-tasks-local Its catch: results live in memory, so pending tasks vanish on restart or deploy. Great for dev and low-traffic production; for persistence, drop to Jake Howard's django-tasks (DatabaseBackend + worker command). Extras Calvin: Fixing the dictionary with Python 3.14 — Hugo van Kemenade stumbled on - and got fixed - a markup bug in the OED's own citation of a 1706 use of the pi symbol. Michael: Bunny DNS is now free Jokes: What's the object-oriented way to become wealthy? Inheritance To understand what recursion is... You must first understand what recursion is 3 SQL statements walk into a NoSQL bar. Soon, they walk out They couldn't find a table.

I mean...yes home owners associations are cringe...but PLEASE I ACTUALLY NEED TO COMPLAIN ABOUT MY NEIGHBOURS!!

It's possible to obey all the rules and still miss the grace of God. In the second sermon of our Summer Sermon Series, Voddie Baucham unpacks the Parable of the Older Son, showing that Jesus' warning isn't just for the prodigal—it's for the self-righteous who believe they've earned the Father's favor.

This week, Carla and Dex catch up on everything from spontaneous adventures and Puerto Rico reflections to the conversations that have everyone talking online. What starts with stories about swan boats, bingo halls, and self-driving cars quickly turns into a deeper conversation about motherhood, friendship, financial security, and why adulthood keeps forcing us to redefine what support really looks like. The episode explores whether women should be financially compensated for pregnancy, the rising maternal mortality crisis impacting Black women, and the reality that bringing children into the world still comes with unequal risks. They also unpack new student loan changes, share thoughts on the true cost of maintaining friendships in your 30s, and discuss how relationships naturally evolve without always needing to end in conflict. Dex also opens up about parenting a child with autism while reacting to the viral story of an 11-year-old boy who independently ordered an Uber to the airport because he wanted to fly to Japan. The conversation becomes a reminder of both the incredible capabilities of autistic children and the unique challenges their families navigate every day. As always, the episode balances humor with honesty, mixing viral stories, personal experiences, and thoughtful conversations about growing up, growing apart, and figuring out life as you go. In This Episode Carla's mission to experience Orlando like a tourist Puerto Rico, family reunions, and finding a sense of home Are self-driving cars ready for real life? Woman of the Week: Coco Gauff Should mothers be compensated for pregnancy? The rising Black maternal mortality crisis and why doulas matter Student loan changes and what they could mean for future students The viral autism story of the boy who tried to fly to Japan Parenting children with autism and advocating for better support The financial and emotional cost of female friendships in your 30s How friendships evolve through different seasons of life   CONNECT WITH US ON SOCIAL MEDIA: CARLA WILMARIS | DEX

 Welcome to Catholic Apologetics, led by Dr. Jim Dobbins, Author of Take My Hand: A Personal Retreat Companion.  Just finished an RCIA program?  This is the next stop on your faith journey.    In these classes, we look at the different truths of Catholic doctrine and why we know they are true. We also discuss apologetics, spiritual growth, examine the liturgy of the Catholic Mass, and do scripture studies.  Please encourage your friends to listen. I also encourage you to leave a comment about our podcasts.  If you want the slides or any other documents for any class, just e-mail me at jhdphd@gmail.com and I will reply with the documents attached.  If you wish, I will also add you to the class materials distribution list so that each time I send anything out for the class you will get it.  If you are getting the podcast files from iTunes and would like to see the full set of available classes for download, you can see and download them all at http://yorked.podomatic.com.  Older podcasts are now stored at a free podcast site at Podcast.com.  The link to the podcasts there is: http://poddirectory.com/podcast/86506/dr-james-dobbins-catholic-apologetics We ask you to also consider going to http://yorked.podomatic.com and becoming a subscriber. It is free, helps our ratings, and thus helps us reach and help more people.  This session is part of our second discussion series of the Gospel of Matthew.  Please also let me know if there is a particular topic you would like to see addressed.  skvEapm1rLLW8foJsII1 

Lessons I learned from my granddaughters

Over the past two decades, the amount of hops sitting in cold-storage warehouses has more than doubled. While the volume of those stocks is inching back down from its peak in 2022—and while it's important that the industry has a buffer against climate challenges and bad harvests—a fundamental question remains: Just how useful are those hops two, three, or four years after harvest? Through a series of research projects and resulting papers, professor Tom Shellhammer and postdoctoral researcher Cécile Chenot of Oregon State University have been working to understand how age impacts hops, and how factors such as temperature, oxygen, and even hop variety drive not just changes in chemical analysis over time, but also have a real sensory impact in finished beer. The results? Surprising, to say the least. In this episode, Shellhammer and Chenot discuss: the myth that fresh is best, and that brewers should use a last-in, first-out approach to hop lots pinpointing the chemical markers in hops that actually convey in finished beer quantifying the range of variability in hops due to fixed factors such as soil, and to changing factors such as water stress or insect and disease pressure how terpenoids degrade at a different rate than thiols in stored hops matching analytical chemistry to sensory analysis for a real-world test of the impact of aging the vintage effect on thiols in hop lots how robust varieties such as Citra and Strata age relative to more delicate varieties the impact of oxygen and temperature on hop aging and compound degradation variability in packaging integrity within bags of the same hop lot And more. This episode is brought to you by: G&D Chillers G&D's new Elite 290 Micro-series is built for brewers who care about sustainability and performance. It runs on a natural refrigerant with near-zero global warming potential (GWP), has a compact footprint, and features variable-speed fans for efficiency. We've chilled beer for more than 3,000 breweries across North America, and with 24/7 support and remote monitoring, your cold side stays dialed in—day or night. Get the details on natural-refrigerant technology at gdchillers.com/podcast. Berkeley Yeast Here's a brewer nightmare. It's the night before packaging. You walk into the brewery, straight to the tank. You pull a sample to do a forced diacetyl test and you wait for it in silence. Something feels not quite right. You reach into the water bath and bring the flask to your nose… And it's a butter bomb. The logistics start running through your head. Do we hold the tank? We have accounts waiting. How many days until it clears? And then you wake up and remember that you use Berkeley Yeast's FRESH™ Chico. It's got ALDC inside every cell, so diacetyl can't even form in the first place. Through primary. Through dry hopping and hop creep. Diacetyl stays ultra-low. So you don't have to add ALDC, or wait for diacetyl to clean up. And most importantly, you don't have to wake up in a cold sweat, worrying about scary stories like these. FRESH™ Chico is now available in dried bricks for as low as $88 at berkeleyyeast.com. PakTech This episode is sponsored by PakTech—delivering craft-beer multipacking you can trust. Our handles are made from 100 percent recycled plastic and are fully recyclable, helping breweries close the loop and advance the circular economy. With a minimalist design, durable functionality you can rely on, and custom color matching, our carriers help brands stand out while staying sustainable. Trusted by craft brewers nationwide, we offer a smarter, sustainable way to carry your beer. To learn more, visit paktech-opi.com. Indie Hops Oregon hop country is heaven to world-class lager varieties, and Indie Hops is proud to have introduced Oregon's newest lager hop, Lórien, in 2022. Lórien is in a growing list of beers that have found their way to the podium and—more importantly—into the hearts of lager lovers across the country. Discover Indie Hops Lórien. (Side effects may include rampant festivity, sales bumps, and exceeded expectations.) Indie Hops—Life is Short. Let's Make It Flavorful. Midea 50/50 Flex If you're like many podcast listeners, you've got a lot of beers at home, and your regular fridge is at capacity. Enter the Midea 50/50 Flex—the industry's first dual compartment three-way convertible freezer. Here's what all that means for you: options! The 50/50 has the power to be all freezer, all fridge, or a little bit of both. But you'll probably want to use those 20 cubic feet as a massive, garage-ready beer fridge. You can also change which side the door is on or how you want the shelves to be arranged—the 50/50 totally flexes to fit your life. Plus, it's designed to maintain a stable temperature even in non-climate-controlled conditions—so you can crack a cold one even on the warmest days in the man cave. Take your garage to the next level! Check out Midea.com/us/ to get more info about this game changer today. Briess Malting Briess Malting has been supplying craft brewers with the highest quality base and specialty malts since the early 1980s. As Briess celebrates their 150th Anniversary this year, they are proud to remain a fifth-generation, family-owned company. They love to support the creativity and passion in craft brewing that results in amazing beers. All Briess malts are made in America, so you can count on a reliable supply chain. Barley is sourced from 300 growers in the Bighorn Basin of Wyoming and Montana. Plus, technical support is available through their Center of Malting Excellence. With 150 years of malting expertise, check out why so many craft brewers trust Briess for their specialty malt at BrewingWithBriess.com. John I. Haas Brewing has always been about creativity—but at scale, it's about control. For more than 100 years, Haas has worked with brewers to push what's possible with hops. And today, that means more control over flavor, efficiency, and consistency. Our advanced products help you get more out of every brew—more flavor where you want it, less waste in the process, faster tank turns, and results you can count on—batch after batch, year after year. From next-generation pellets, such as LupoCORE and LupoMAX, to innovations such as Incognito and Euphorics, our products fit seamlessly into your process—from the hot side through fermentation to the cold side. Advanced hop products are just one of the ways Haas is growing the future of brewing. Learn more at johnihaas.com.

JP returns with another remarkable update after a new encounter with a Nordic extraterrestrial near his home.In this episode, Dr. Michael Salla speaks with JP about a message involving ancient collider technology, hidden underground facilities, discoveries in Iran and the Middle East, portals connected to Mars, and warnings about advanced technologies falling into the wrong hands.JP shares details about secret colliders said to exist beyond the well-known CERN facility, including ancient systems allegedly located beneath North America, China, and Russia. He also discusses how these technologies may connect to ancient sites, space arks, underground bases, and interdimensional activity.The conversation also touches on Green Mountain, classified training facilities, Nordic instructors, med bed technology, and the growing number of military witnesses coming forward with firsthand experiences.This is a wide-ranging and powerful discussion about disclosure, hidden history, ancient technology, and the choices humanity faces as long-buried knowledge begins to surface.Join the conversation in the comments, and remember to like, share, and subscribe for more updates from Exopolitics Today.Subscribe for weekly Exopolitics Today breakdowns on global anomalies, and comment below with your thoughts on the topics discussed in this interview.Join Dr. Salla on Patreon for Early Releases, Webinar Perks and More.Visit https://Patreon.com/MichaelSalla/

Full show notes: https://bengreenfieldlife.com/502 In this solosode, you'll explore how to make peace with tinnitus after acoustic trauma, including the half dozen different root causes that can drive it and the specific supplements and technologies with research behind them, why cardiovascular fitness itself seems to protect your hearing as you age, and whether tattoos are actually as risky as people think, from the heavy metals hiding in colored ink to what a major 2024 study found about tattoos and lymphoma risk. I also break down a new study showing you can get the same heat shock protein boost from heavy resistance training that you would normally only get from a sauna, why stacking a hard workout with a sauna session afterward may give you the best of both worlds, and take a closer look at a study making headlines for supposedly debunking the whole eating before bedtime rule, because the fine print tells a very different story than the headline does. Episode Sponsors: Xtendlife: Most vitamin E supplements contain only alpha-tocopherol, but the more compelling research points to tocotrienols for cardiovascular, brain, and healthy aging support. Xtendlife’s Tocotrienols Vitamin E is formulated without excess alpha-tocopherol and is backed by over 26 years of formulation expertise in New Zealand. Visit www.xtendlife.com/benschoice and use code GREENFIELD for 15% off your order. Just Thrive: If you’ve accepted bloat, cravings, and sluggish digestion as normal, the Just Thrive Gut Essentials Bundle pairs a probiotic clinically proven to arrive 100% alive in your gut with Digestive Bitters, a blend of 12 science-backed herbs that support digestion and GLP-1 production. Visit justthrivehealth.com/BEN and save 20% with promo code BEN, or get a full refund if you don’t notice a difference. BIOptimizers Magnesium Breakthrough: The seven essential forms of magnesium included in this full-spectrum serving help you relax, unwind, and turn off your active brain after a long and stressful day so you can rest peacefully and wake up feeling refreshed, vibrant, and alert. Go to bioptimizers.com/ben and use code ben15 for 15% off any order. Troscriptions: Explore Troscriptions' revolutionary buccal troche delivery system that bypasses digestion, delivering pharmaceutical-grade, physician-formulated health-optimization compounds directly through your cheek mucosa for faster onset and higher bioavailability than traditional supplements. Discover a completely new way to optimize your health at troscriptions.com/BEN or enter BEN at checkout for 10% off your first order. LMNT: Everyone needs electrolytes, especially those on low-carb diets, who practice intermittent or extended fasting, are physically active, or sweat a lot. Go to DrinkLMNT.com/BenGreenfield to get a free sample pack with your purchase!See omnystudio.com/listener for privacy information.

For years, the marketer's job ended when the campaign shipped. Hit send, check the numbers, measure growth, repeat. That model is changing, and what replaces it is agentic orchestration, where the system and the journey around a message matter more than a single send. Recorded live at K:LDN 2026, this conversation pairs the person building the AI with an operator running it at real scale. Gilbert Hsu, who leads Marketing AI at Klaviyo, sits down with Alon Turchin, VP of Retention at Particle, a US-manufactured DTC self-care brand for men with a marketing team based in Israel. That kind of scale brings real complexity, and this is where Composer by Klaviyo brings solutions at scale. Alon describes it today as the brain and the eyes, the thing that finds and frames problems he might not catch manually. His wish list is for it to become the hands too, trusted to edit filters, rules, and content, a trust he says has to be earned through testing first. Closing advice for teams earlier in their journey: know your audience before you trust anyone's benchmarks, remember that selling is mostly psychological, and test your way to what actually works rather than assuming you already know. What you'll learn How Particle scaled from one million to ten million monthly sends in weeks, not months, while doubling revenue Why "the campaign is not the product, the system is" changes what a marketing team actually optimizes for How Particle segments customers and non-customers by urgency and lifecycle stage across 150-plus flows and 100-plus forms Where Composer fits today (the brain and the eyes) and what Alon wants it to become next (the hands) Why rigorous A/B testing is the gate that lets Alon hand more decisions to AI Alon's advice for marketers earlier in their own orchestration journey Key takeaways Alon's operating principle: the campaign is not the product, the system is, meaning the journey around a message matters more than the message itself. Particle scaled from one million to ten million sends a month in a matter of weeks, not months or a year of slow warmup, while revenue doubled, run across more than 150 active flows and 100-plus forms. Segmentation isn't just more lists, it's urgency based. Recent sign-ups get reached while the brand is still top of mind. Older, colder contacts get reintroduced rather than ignored. Composer today functions as the brain and the eyes, surfacing problems and opportunities Alon might miss manually. His wish list is for it to become the hands, trusted to edit filters, rules, and content, once testing earns that trust. Alon's closing advice: don't assume you know your audience or trust someone else's benchmark. Selling is mostly psychological, so test relentlessly until you find what actually works for your own customers. Chapters 0:00 Cold open and introductions 1:38 Meet Particle, and how the marketing job has changed 2:48 The campaign is not the product, the system is 3:30 From engagement to behavior-based segmentation 4:49 Why sending more, to the right tiers, doubled revenue 7:12 Managing 150-plus flows without losing control 7:55 Composer as the brain and eyes, and the wishlist for the hands 10:43 Keeping the brand's soul with a human in the loop 11:55 Advice for teams earlier in the journey In-Show Mentions: Learn more about Klaviyo's Composer Associated Links: Check out Future Commerce on YouTube Check out Future Commerce Plus for exclusive content and save on merch and print Subscribe to Insiders and The Senses to read more about what we are witnessing in the commerce world Listen to our other episodes of Future Commerce Have any questions or comments about the show? Let us know on futurecommerce.com, or reach out to us on Twitter, Facebook, Instagram, or LinkedIn. We love hearing from our listeners! Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

We're an aging nation. By 2050, for the first time ever, Americans over age 60 will outnumber those aged 10 to 24. Older adults increasingly prefer aging in place, so the need for technologies that support activities of daily living, mobility, and social connection is growing. In Century Lives: The AgeTech Revolution, we travel to the Consumer Electronics Show in Las Vegas, where we and 150,000 of our closest friends scope out the technologies that claim they will improve our lives as we grow older. We ask: What do we want our final quarter of life to look like? And can the AgeTech industry actually improve the ways we live our longer lives? Experts and innovators alike are betting on the future of hospital care. Many of them say it won't take place in a hospital at all. Instead, hospital care will be brought to patients in their homes. So in this episode, we ask: Is the home the new hospital for older adults? Is a healthcare revolution headed our way? Some experts are not so certain.

New research has found older drivers don't lose their skills, but are struggling to adapt to changing layouts and road conditions. 

This show has been flagged as Clean by the host. 01 Introduction In this episode I will describe how to calculate elapsed time in bash or other shell scripts. While this may sound like a very simple and basic thing to do, there is a slightly more complex aspect to it if you wish to calculate elapsed time to a higher resolution than one second. 02 There are many reasons for calculating elapsed time in a shell script. For example you may wish to simply report how long an operation took to run. Another reason may be that you are trying to speed up a script and need to calculate benchmark data to see how different alternative methods perform. 03 What may seem like a simple task gets a bit more complicated if you want to do it for multiple different operating systems even if they are all unix related, as we shall see. -------------------- 04 Operating Systems Tested For the purposes of this episode, I ran tests on the current version of the following operating systems. Alma Alpine Debian FreeBSD OpenBSD RaspberryPi OpenSuse Ubuntu 2604 Alma is a close copy of Red Hat that we can take as representing Red Hat style distros. -------------------- 05 Simple Low Resolution Timing I will start with the simple and obvious method before describing the less obvious ones. This uses the date command to get the current time in seconds since the unix epoch. This is simply date '+%s' 06 Save this to a variable using whatever method you prefer. For example. starttime=$(date '+%s') 07 Next, do whatever operations it is you wish to time. Use the date command to get the current time again. endtime=$(date '+%s') 08 Now simply subtract the start time from the end time using shell arithmetic. This should be very obvious and basic. -------------------- 09 Higher Resolution Timing However, suppose we wish to measure time to greater than one second of precision. We need to do two things. The first is to obtain the current time at a higher degree of precision. The second is to conduct the calculations to a higher degree of precision. 10 Unfortunately, the standard time precision for POSIX shells seems to be 1 second. Some shells offer a higher precision, but others do not. Furthermore, standard shell arithmatic uses integer, which limits calculations to 1 second of precision. -------------------- 11 Bash High Resolution Shell Variable Fortunately, bash is one that does offer a high precision date. If you are using bash 5.0 or newer, there is a shell variable called EPOCHREALTIME which offers time since the the unix epoch (that is, since the first of January 1970, at 00:00:00 UTC) in seconds to 6 decimals of precision. 12 Example echo $EPOCHREALTIME 1779634800.184926 13 This is related to the similar bash variable known as EPOCHSECONDS which gives the number of seconds since the unix epoch. 14 Example echo $EPOCHSECONDS 1779634800 15 So if you are using bash, measuring time is very simple. -------------------- 16 But is it Really Bash? Is your script however actually using bash? Debian and derivatives actually have two shells. The first, the interactive shell is bash. The second, the non-interactive shell is dash, which stands for "debian almquist shell". 17 If you open a terminal, you get bash. If your script starts with a "bin/bash" shebang line, you get bash. However, if your script starts with a "bin/sh" shebang line, you get dash. Some people find themselves getting caught out by this one when they try something out in a terminal but find that it doesn't work in their script which started with "bin/sh". 18 Many other, but not all, Linux distros use bash for both the interactive and non-interactive shells, so "bin/sh" and "bin/bash" work the same with those ones. So if you intend to use bash, make sure your script calls for bash in the first line. -------------------- 19 The SHELL Variable So how can a script tell what shell it is running under? There is a shell variable called "SHELL" which will tell you the name of the shell. Well, sort of. 20 On Debian and derivatives "SHELL" will say "bash" regardless of whether the actual shell is bash or dash. On some other operating systems "SHELL" will simply say "sh" even if it is something else entirely. So we need to do some additional levels of checking to see what we have. 21 To start with though, here's what each of the test distros reports for SHELL. Alma : bash Alpine : sh Debian : bash FreeBSD : sh OpenBSD : ksh Raspberry-Pi : bash Suse : bash ubuntu2604 : bash -------------------- 22 Bash Versus Dash First, let's try to see which ones are bash and which ones are dash. The first thing we can check is for the shell variable BASH_VERSION. 23 Example echo $BASH_VERSION If the shell is bash, then it will report a version string. If the shell is not bash, then it will return an empty value. 24 Using this test, we can see that Alma and Opensuse are indeed using bash. We however need to check Debian, Raspberry Pi, and Ubuntu when running in an "sh" script. To check this we can use the "which" command to see what "sh" actually is. 25 Example echo $( ls -l $(which sh ) | rev | cut -d" " -f1 | cut -d/ -f1 | rev ) 26 "which sh" shows us the path to "sh" However, that is a link so we need to use "ls -l" to find the actual executable. "rev" reverses the string. 27 "cut" takes the first element separated by spaces. The second "cut" takes the first element separated by the "/" characters. The final "rev" takes that string and reverses it again to get it in the correct order. 28 In the case of Debian, Raspberry Pi, and Ubuntu it tells us that this is "dash". -------------------- 29 Openbsd Openbsd reports its shell as "ksh", which stands for Korn Shell. It is indeed Korn Shell, so we simply leave that one as is. -------------------- 30 Alpine and Freebsd Next we have Alpine Linux and Freebsd, which both report as "sh". In the case Freebsd there doesn't appear to be any further we can go that I am aware of. It's simple "sh". It is a basic POSIX shell which seems to be similar to the original unix shell, the Bourne Shell. Older versions of Freebsd used a different shell known as tsch (the C shell), but I haven't tested that so I will ignore that here. 31 With Alpine Linux however, we can get the actual shell using the same method that we used for Debian Linux. This reports as being "busybox". 32 Busybox is a limited shell intended for use in embedded systems. Alpine was originally an embedded distro, but some people started using it for containers. Alpine is Linux, but it is not GNU/Linux, and there are a number of areas which can trip you up if you are not aware of them. So, be extra careful if you are using it for anything, and test everything. -------------------- 33 Summary of Actual Shells Here is our revised list with the actual shell used when asking for "sh", so far as we can determine. Alma : bash Alpine : busybox Debian : dash FreeBSD : sh OpenBSD : ksh Raspberry-Pi : dash Suse : bash ubuntu2604 : dash 34 There are other shells, but none of them are the default shell for any of the distros on our list, so I haven't tested them. -------------------- 35 Solutions for Measuring Time Now we need to find solutions for bash, dash, ksh, sh, and busybox. -------------------- 36 Bash For bash, we can simply use EPOCHREALTIME, as mentioned above. -------------------- 37 Dash For dash, we can use the date command. This is a very conventional method, and is probably the first answer that anyone would give for this situation. However, while it will work in most cases, it will not work in all cases, so it is not a universal solution. 38 To use date we simply call it with the correct format string. This uses %s to get seconds since the epoch, and %N to get nanoseconds of the current second. If you put a decimal separator between the two it will appear in the output. You can use the correct decimal separator for your locale, but I won't go into that here. Instead I will just assume a period or dot. 39 Example date '+%s.%N' 1779634800.358916385 -------------------- 40 Problems with Date on Alpine and Openbsd Date will work for bash, dash, and sh on Freebsd. However it will not work for ksh on Openbsd, or for busybox on Alpine. 41 With busybox on Alpine, it simply ignores the %N format specifier and prints out the epoch in seconds only followed by the decimal separator. = Example date '+%s.%N' 1779634800. 42 With ksh on Openbsd it prints the epoch in seconds followed by the decimal separator and then the %N as a literal N. date '+%s.%N' 1779634800.N 43 Fortunately we have alternatives for these two cases. -------------------- 44 Openbsd Openbsd has the "ts" or timestamp utility installed by default. ts prints a time stamp in front of every line it receives from standard input. I won't go into details on all aspects of ts here, I'll leave that to someone else. Instead I will focus on how to use it for our specific purposes here. 45 We need to provide a format specifier to ts, which in this case is "%.s" We also need to provide something for standard input, or otherwise ts will simply sit there and wait for input. So what we need to do is to echo nothing through a pipe to ts while also giving ts the proper format specifier. 46 Example echo | ts "%.s" 47 This will output the epoch time in seconds to six decimals of precision. ts is installed in Openbsd and Freebsd by default and can be used in either. It can also be installed in many other distros. -------------------- 48 Busybox on Alpine None of the methods discussed so far will work for busybox on Alpine though. However there is a way, but it's a bit non obvious and somewhat hacky. 49 Busybox includes a command called "adjtimex". This is normally used to adjust the time hardware. However if it is run without arguments, it will report the current settings. 50 These include the current epoch time in seconds , and in another field the time in microseconds. These are reported as key value pairs. So what we need to do is to do the following 51 Run adjtimex Capture the output. Grep for "time.tv_sec" Grep for "time.tv_usec" Use cut to extract the time value in each case. Use tr to get rid of excess spaces in each case. Combine the two in a string with a decimal separator between them. 52 This takes a total of 4 lines of shell script. I will just describe them breifly here, see the show notes for details. 53 First we want to capture the output of adjtimex in a single operation. Run adjtimex and pipe the output through grep to capture lines containing "time.tv_" and save this to a variable. # Extract the current high resolution time from adjtimex. # We want two key value pairs, identified by time.tv_sec and time.tv_usec. tvals=$( adjtimex | grep "time.tv_" ) 54 Next echo the contents of this variable and pipe it through grep, cut, and tr to get first the seconds and then the microseconds while also removing excess spaces. Save these to two separate variables. "time.tv_sec" is the time in seconds since the epoch. "time.tv_usec" is the number of microseconds in the current second. # Get the time since the unix epoch in seconds and micro-seconds. timesec=$( echo "$tvals" | grep "time.tv_sec" | cut -d: -f2 | tr -d " " ) timeusec=$( echo "$tvals" | grep "time.tv_usec" | cut -d: -f2 | tr -d " " ) 55 Adjtimex does not zero pad the microsecond time value to provide leading zeros, so we need to take care of this using printf before we can append it to the seconds value. We didn't need to do this with date where the %N format character does this automatically. In this instance, the printf format string is '%06d' padusec=$( printf '%06d' $timeusec ) Now, combine these into a single number with a decimal separator by using simple string concatenation. # Combine them into a single number. timehires="$timesec"".""$padusec" -------------------- 56 Summary of Methods Let's summarize where we are so far in terms of methods we can use to get the current time as a high resolution number. Alma : use EPOCHREALTIME or date Debian (bash) : use EPOCHREALTIME or date Raspberry-Pi (bash) : use EPOCHREALTIME or date ubuntu2604 (bash) : use EPOCHREALTIME or date Suse : use EPOCHREALTIME or date Debian (dash) : use date Raspberry-Pi (dash) : use date ubuntu2604 (dash) : use date Alpine : use adjtimex and parse the output FreeBSD : use date or ts OpenBSD : use ts -------------------- 57 Other alternatives There are a few alternatives that we haven't discussed yet. 58 Bash with Dash In the case of Debian, Raspberry Pi, and Ubuntu running dash, since bash is available it is possible to write a separate bash script which simply echos EPOCHREALTIME and then call it from the dash script and capture the output. While this would work, there's probably not a lot of point to it. If you can rely on bash being there, then just change the first line of the script and make it a bash script. 59 Adding Packages to Alpine The ts or timestamp utility is a common unix utility that can be installed if it is not present by default. This does produce high resolution timestamps on Alpine. On Alpine Linux this comes as part of the "moreutils" package. To add the package, use the following sudo apk add moreutils echo | ts "%.s" 1779634800.959948 60 You can also add the GNU coreutils, which will provide a high resolution date command which works like in the other examples. To add the package use the following sudo apk add coreutils date '+%s.%N' 1779634800.212897332 61 If you can install more packages into your Alpine system, either of the above two is probably going to be preferable to parsing the output of adjtimex. 62 Custom Timestamp Programs You can also write a very short program in python, perl, tcl, or some other language and have it output the current epoch time. I won't discuss that here though. -------------------- 63 Calculating Time Differences Shell arithmetic is integer only. If we wish to use high resolution timing data, we need to do something so we don't lose the precision we have worked so hard to get. There are several possible solutions. 64 Change the Time Base One method is to change the time base from seconds to milli, micro, or nanoseconds. This can be done by simply multiplying the time values by the appropriate amount (e.g. 1000, 1,000,000, etc.) before subtracting them. This allows for integer arithmetic on high resolution values without losing precision. 65 Use the Shell bc Arbitrary Precision Calculator The bc command line calculator will perform calculations using real numbers and is easy to use in scripts. It is present by default in most distros. echo "scale=9; $endtime - $starttime" | bc where endtime and starttime are variables containing time values. 66 However, for some inexplicable reason, neither Debian nor Opensuse install it by default. It is present in Ubuntu and Raspberry Pi which are Debian derivatives, and it can be added to distros which lack it. 67 Use awk awk can also perform calculations using real numbers and it is present in nearly all distros including in all of the ones we tested here. echo "$endtime $starttime" | awk '{printf "%.6fn", $1 - $2}' -------------------- 68 Benchmarks And of course no comparative evaluation would be complete without benchmarks where we see how each method compares to another in terms of speed. In the benchmark test I ran each method in a loop through multiple iterations, measured the elapsed time, subtracted out the time for an empty loop, and then compared it to alternate methods. For anything other than EPOCHREALTIME, the empty loop time is negligible and has no real effect on the results. 69 Rather interestingly I came across a bug which caused date to run very slowly if called immediately after using EPOCHREALTIME in bash. The effect of the bug was to make the date benchmark test roughly 24 times slower. This has been fixed in newer releases, but if you are using an older distro release then beware of this bug. I was able to get around it either putting a sleep delay between benchmarking EPOCHREALTIME and benchmarking date, or by simply testing date before testing EPOCHREALTIME. 70 To be able to conduct additional tests I installed ts in Ubuntu and Alpine, and the GNU version of date in Alpine. 71 EPOCHREALTIME Versus date in Ubuntu 2604 bash The EPOCHREALTIME method is 3103 times faster than date. However, when the same test is run on Ubuntu 2404 when the date test is run before the EPOCHREALTIME test, EPOCHREALTIME is 1240 faster than date. Other Linux distros show performance to Ubuntu 2404. It appears that a side effect of fixing whatever the bug is has the effect of slowing down date. However, this is probably not a significant issue in normal circumstances. 72 date versus ts in Ubuntu 2604 bash The date method is 3.7 times faster than ts 73 date versus ts in Ubuntu 2604 dash The date method is 4.9 times faster than ts 74 date versus ts in Freebsd sh The date method is 2.5 times faster than ts 75 date versus adjtimex in Alpine Busybox The date method is 6.0 times faster than adjtimex 76 date versus ts in Alpine Busybox The date method is 20.0 times faster than ts 77 bc versus awk in Ubuntu 2604 I compared calculating the difference between two numbers when using bc versus awk. The difference is negligible, with bc being only 7% faster than awk. 78 Conclusion for Benchmarks Based on these results, if you need to measure elapsed time to high resolution and care about runing the command with as little overhead as possible, then the order of preference should be the following. 79 If you are using a newer version of bash, then use EPOCHREALTIME. If that is not available, then use date, provided it allows for high resolution times. If the above two cannot be used, then use ts. If you are using Busybox and cannot install either GNU date or ts, then use adjtimex. Date is the closest in terms of being the universal portable solution, but it does not work in all cases. 80 I have not compared different platforms to each other in terms of performance, as that would be a much more involved problem that is outside the scope of this episode. However, different operating systems implement different commands in different ways. 81 For example, on Openbsd and Freebsd, ts appears to be an ELF binary. That is, it is executable machine code, possibly written in C. On Ubuntu however, ts appears to be a perl script. As a result of this, the advantage that date has over ts is much less in Freebsd than it is with Ubuntu (and likely other Linux distros) as on Freebsd it doesn't need to load a perl interpreter to run ts. -------------------- 82 Overall Conclusion You no doubt thought that measuring elapsed time was going to be so simple, and how could someone get an entire podcast out of such a simple subject? And yet here we are half an hour later with just a basic overview of the subject. 83 I hope you found this interesting and informative. Please let us know in the comments if you think that I have done anything incorrectly, or if you have another way of doing things. I hope to see you all again in another future episode of HPR. -------------------- Provide feedback on this episode.

Young men have been under attack by popular culture for the last 20 years, argues Nick Freitas, a former Green Beret and Virginia state legislator. They have been told that masculinity is toxic, the future is female, that they are responsible for the world's problems, and that they no longer have a place, he says.Despite this onslaught, Freitas shares a hopeful message for the young men of today: “God has created men for a particular purpose. We are supposed to be strong. We are supposed to be capable. Why? So we can honor God, protect our families, and protect our civilization.”Freitas recently wrote a book entitled “The Manbook: A Point-by-Point Guide to Sucking It Up and Getting the Job Done,” where he shares hard-won lessons on how to be a good man despite resistance, or outright attacks, from the prevailing culture.In this episode, Freitas reveals why a whole generation of men are flocking to radical ideologies—and how to prevent it.Perhaps surprisingly, Freitas puts the onus on the men of older generations, arguing that young men desire good role models and guidance. Older men, therefore, must live the kind of lives that young men would want to model.“The best argument you make is not just the one that you articulate through your words,” Freitas says, “it's the one that you articulate through what you do in your own life.”Views expressed in this video are opinions of the host and the guest, and do not necessarily reflect the views of The Epoch Times.

Topics covered in this episode: dust - a better du Hermes Agent: The AI agent that grows with you llm-coding-agent 0.1a0 Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Consulting from Six Feet Up Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: dust - a better du du + Rust = dust - a fast, visual, intuitive disk-usage CLI Run dust and immediately see the biggest directories and files without piping through sort, head, or awk Smart recursive output focuses on what matters instead of dumping every folder Colored bars show relative size and parent/child hierarchy, making “where did the space go?” obvious Perfect for Python projects bloated by .venv, caches, Docker volumes, downloaded datasets, and local AI models Install via brew, cargo install du-dust, conda-forge, Scoop, Snap, deb-get, or GitHub releases Calvin #2: A Way better ARchive format for Python packaging war - new archive format spec from Astral (same team as uv/ruff), v0.0.2, still no binary encoding defined yet Header-Index-Store layout: header IDs the file, index maps names to store offsets, store holds compressed data Index uses a finite-state transducer (FST) to dedupe common path prefixes across entry names Supports three entry types (file, directory, link) and three compression modes (store/DEFLATE/zstd), plus an "executable" metadata flag Unpacking is atomic - writes to a temp dir, then renames into place, so a failed extract never leaves a half-unpacked directory Strict name-segment rules (no NUL/control chars, no leading/trailing whitespace, blocks Windows-reserved names like CON/PRN) to avoid path traversal and cross-platform footguns Michael #3: Hermes Agent: The AI agent that grows with you Hermes Agent is an open-source, Python-built AI agent framework from Nous Research - think ChatGPT-style assistant, but connected to your tools, files, shell, browser, calendar, memory, and messaging apps I'm using it in Discord as a long-running agent conversation, not just a one-off chatbot session Hermes can connect through a gateway to platforms like Discord, Telegram, Slack, WhatsApp, email, webhooks, and more - so the same assistant can follow you across surfaces In my setup, I can send Hermes voice/text from Discord, keep project context across turns as threads, and ask it to actually do things: read GitHub repos, run commands, edit files, schedule calendar events, generate drafts, and verify results A fun workflow: I can trigger one-shot actions from an Apple Watch shortcut - dictate a request, send it to Hermes, and have the agent execute it asynchronously Hermes has persistent memory, so it can remember durable preferences and facts - for example, how I like my research formatted It also has “skills,” which are reusable procedures the agent can load later, so Hermes can self-improve over time instead of rediscovering the same workflow repeatedly It supports scheduled jobs / cron-style automations, so it can proactively watch for releases, send summaries, run checks, or remind you about things It's provider-agnostic: OpenRouter, Anthropic, Google, xAI, local models, Nous Portal, and others The big idea: Hermes turns an LLM from “a chat box I visit” into “an agent I can reach from anywhere that knows my workflows and can take real actions and learns over time.” Calvin #4: llm-coding-agent 0.1a0 Simon Willison built a Claude/Codex-style coding agent on top of his llm library, using an alpha of the llm package plus his python-lib-template-repo Built almost entirely via prompted TDD - asked an agent to write a spec.md, then commit + implement with red/green tests, occasionally hitting a real OpenAI key to sanity-check Shipped to PyPI as an alpha: uvx --prerelease=allow --with llm-coding-agent llm code Tool set mirrors familiar coding-agent primitives: read_file, edit_file (exact string replace + diff), write_file, list_files, search_files, execute_command Also exposes a Python API - CodingAgent(model="gpt-5.5", root=..., approve=True).run(...) - which Simon didn't ask for but got anyway Demo: llm code --yolo told GPT-5.5 to build a SwiftUI CLI clock; model correctly noted SwiftUI isn't really CLI-friendly and still produced an ASCII-art time display Extras Calvin: Slides, but for developers https://sli.dev/ Wanna reduce your token usage…. only issue is that its lossy https://github.com/teamchong/pxpipe PEP 772 - Python Packaging Council inaugural election dates set, nominations open July 28, voting September 1-15 Michael: What the pls? revisited! Joke: Min requirements for Linux

The baddie boom arrived like victory. The music hit hard. Bodies filled the frame. Money flexed loud. Attitude stayed dialed high. Sexyy Red. GloRilla. Megan Thee Stallion. Yung Miami pushed “Spend Dat” into heavy rotation. Younger Black women leaned into the energy as ownership. Older generations leaned toward India.Arie's warning. She called out the song for glamorizing scammer culture and fast spending. The fan backlash arrived on schedule. Music works as vibration. Low vibration pulls consciousness down to its own level. The nervous system records the repetition. Men started scanning for the high-yield performance the tracks rewarded. The woman who arrived without the loaded script registered as absence. The pattern moved before awareness could name it. Expectation dropped to the floor the music set. The exchange cut deeper. Money. Power. Visibility. The version of freedom the market would amplify. The mystery disappeared in the trade. That unknown depth once pulled a man toward protection and provision. The performance took its place. A performance that streams easy and discards faster. The community financed the low frequency. We defended the anthem. We called the transaction liberation. Relationships now show the result. Transactional. Empty. Still defended as progress. The civil war lives inside Black women. Younger voices claim the baddie blueprint as agency. Older voices hear the lowered floor and push back. The self-realized or God-realized man becomes the clearest measure. Can he still respect and connect with a woman shaped inside that field, or has the vibration reached consciousness itself? This is not one song. This is what the dominant sound installs in the nervous system, the imagination, and the spirit. The relationships carry the bill. The mystery we sold left the room quieter than anyone admits.

In this episode of the CCPT Mythbusters series, I tackle a misconception that exists even within the child-centered play therapy community: the belief that CCPT doesn't work with older children. Too many therapists assume that once children reach nine or ten years old, they have "outgrown" play therapy and need a more traditional talk therapy approach. I explain why this simply isn't true. While older children may express themselves differently and rely more on conversation and activity-based play, the foundational principles of CCPT remain exactly the same. Healing has never depended on toys—it has always depended on the therapeutic relationship. I also share practical insights from working with older children and adolescents, including why so many initially resist play but eventually embrace it as they experience genuine acceptance and emotional freedom. Whether you're working with a 4-year-old or a 14-year-old, the core conditions that Carl Rogers identified still apply. This episode is an encouragement to trust the model, challenge your own adulthood bias, and confidently offer child-centered play therapy to older children who need it just as much as their younger peers. Podcast Meetups! Come hang out with me. I'm hosting casual Play Therapy Podcast listener meetups in Denver and Seattle on Aug. 4th and 6th. I'd love to connect with you in person! Learn more and RSVP at https://www.playtherapypodcast.com/meetup/ New Live Training Events Announced: Indiana, New Jersey & Tampa dates are locked in and registration is open for Indiana. Go to iamccpt.live for more information. New Resource for Play Therapists: The Parent Companion for Play Therapy is now available at author pricing for therapists. Created specifically to help parents better understand the child-centered play therapy process, this book is designed to support parent engagement, improve buy-in, and reduce attrition throughout the therapeutic journey. As a listener of the Play Therapy Podcast, you can order a copy for just $8 (our cost plus shipping). Click here to order your author-priced copy. ** Limit 1 per therapist, offer valid in the Continental U.S. only. Order copies for your practice and save with discounted bulk pricing. Bulk ordering makes it easy to place Parent Companion for Play Therapy directly into the hands of the parents you serve. PlayTherapyNow.com is my HUB for everything I do! playtherapynow.com. Sign up for my email newsletter, stay ahead with the latest CCPT CEU courses, personalized coaching opportunities and other opportunities you need to thrive in your CCPT practice. If you click one link in these show notes, this is the one to click! Topical Playlists! All of the podcasts are now grouped into topical playlists on YouTube. Please go to https://www.youtube.com/kidcounselorbrenna/playlists to view them. If you would like to ask me questions directly, check out www.ccptcollective.com, where I host two weekly Zoom calls filled with advanced CCPT case studies and session reviews, as well as member Q&A. You can take advantage of the two-week free trial to see if the CCPT Collective is right for you. Ask Me Questions: Call ‪(813) 812-5525‬, or email: brenna@thekidcounselor.com Brenna's CCPT Hub: https://www.playtherapynow.com CCPT Collective (online community exclusively for CCPTs): https://www.ccptcollective.com Podcast HQ: https://www.playtherapypodcast.com APT Approved Play Therapy CE courses: https://childcenteredtraining.com Facebook: https://facebook.com/playtherapypodcast Common References: Cochran, N., Nordling, W., & Cochran, J. (2010). Child-Centered Play Therapy (1st ed.). Wiley. VanFleet, R., Sywulak, A. E., & Sniscak, C. C. (2010). Child-centered play therapy. Guilford Press. Landreth, G.L. (2023). Play Therapy: The Art of the Relationship (4th ed.). Routledge. Landreth, G.L., & Bratton, S.C. (2019). Child-Parent Relationship Therapy (CPRT): An Evidence-Based 10-Session Filial Therapy Model (2nd ed.). Routledge. https://doi.org/10.4324/9781315537948 Benedict, Helen. Themes in Play Therapy. Used with permission to Heartland Play Therapy Institute.

All links here:https://hunterwilliamshealth.com/linksIn this masterclass I break down TA-1 from the ground up. It works like a thermostat for your immune system. It turns a weak response up and pulls an overactive one back toward the middle.One thing up front. You will not feel this peptide when you inject it. That is normal. It works slowly over weeks.I cover who this peptide is really for. Older adults with fading immunity. People stuck in post-viral limbo after COVID or Epstein-Barr. Anyone who travels hard and gets run down.I also cover who should skip it, including severe autoimmune flares.We get into dosing tiers, timing, and why more than two milligrams does nothing. I walk through cycling, stacking with LL-37 and KPV, reconstitution, and how to tell it's actually working.I stay honest about the evidence. TA-1 is the most documented immune peptide we have. It also has real limits, and I flag where the human data runs out.This is the one I never travel without. Not because it feels like anything. Because I would rather have it and not need it.

This week on the Andrew Yang Podcast, Andrew is joined by Brendan Greeley, Financial Times contributing editor and author of the new book "The Almighty Dollar: 500 Years of the World's Most Powerful Money." They dig into the surprising history of the dollar, which predates the United States itself, why local currencies could matter more than ever in the AI era, how bank failures shaped the money we use today, and why the dollar's dominance has less to do with American governance than you'd think.Have a question for Andrew? Drop it in the comments section below or send us a text or voice memo to mailbag@andrewyang.com!Watch the full episode ⁠⁠⁠⁠here⁠⁠⁠⁠----Follow Andrew Yang: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Bluesky⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TikTok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Website⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Check Out Branden's Book: The Almighy Dollar----Get 50% off Factor at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Factor Meals⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get an extra 3 months free at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Express VPN⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get 20% off + 2 free pillows at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Helix Sleep⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | Use code: helixpartner20Get $30 off your first two (2) orders at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Wonder ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠| Use code: ANDREW104----Subscribe to the Andrew Yang Podcast: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify

Aphasia Communication App: How One Brother Gave a Stroke Survivor His Voice Back For four or five hours, Robert Schmidtbauer’s younger brother lay on the floor of their Wisconsin home, unable to get himself up. Robert found him when he got home from a late shift driving cabs. His brother had been drinking that night, but this wasn’t alcohol; it was a stroke, one that would put him in the University of Wisconsin Hospital for three weeks and in rehab for six months. His brother was already living with ataxia, a rare progressive condition that had taken his ability to walk and had begun to affect his speech. The stroke made it dramatically worse. Today, unless you know him well, you’ll understand only 60 to 70 percent of what he says. It’s usually the end of a sentence, the last few words, the part that carries the point that disappears. Robert became the translator. For years, every visitor, every relative, every tradesperson needed him in the room to fill in the blanks. Then he built something better: an aphasia communication app called Larry’s Speakeasy, priced at nine dollars for life, now used by people in 20 countries. When the Speech Problem Has No Official Name One detail of this story will be familiar to many stroke families: Robert’s brother has never been formally diagnosed with aphasia. The doctors attributed his speech difficulties to the combination of ataxia and stroke and left it there. After a year of speech therapy and his own reading, Robert concluded there was “probably some of that in there,”  but no clinician ever gave the problem a name. That matters, because a diagnosis is often the doorway to resources. Without one, nobody hands you a communication aid, a device funding pathway, or even a list of options. Robert’s brother got speech therapy two or three hours a week while it lasted, some practice phrases to take home, and nothing else. The Gap Nobody Warns Stroke Families About Rehab ends. The communication problem doesn’t. When Robert’s brother came home, the brothers developed their own system: Robert would catch 90 percent of a sentence, ask him to repeat the rest up to three times, and then ask him to spell the words letter by letter. That was the system for years. Robert credits his stint teaching English online to students around the world for training his ear to listen closely. But the system only worked when Robert was in the room. The moment that changed everything was ordinary: a new housekeeper came to quote on cleaning, and Robert’s brother, who runs the inside of the house, couldn’t make himself understood on the details. Robert stood in the middle, finishing sentences. He’d felt like a “third wheel” through his brother’s rehab, looking for a way to genuinely help. Standing in that kitchen, he found it. “I had one person on my wing that no one else in the building could understand but me. And even I had a 50% chance of understanding what he really wanted.” — a care facility director, on why a tool like this matters What Is an Aphasia Communication App? An aphasia communication app is software that speaks for a person whose own speech is impaired a modern, affordable form of what clinicians call AAC (augmentative and alternative communication). Larry’s Speakeasy does two things, deliberately kept simple: Type-to-speak. If your hands still work, you type any phrase or sentence, and the app says it out loud. One-tap phrases. For people with limited hand function, pre-made buttons cover emergencies (“I need to go to the bathroom,” “call the doctor”) and everyday phrases hello, goodbye, and a growing list Robert adds to as users suggest them. The market Robert walked into explains why he built his own. At the affordable end, there’s roughly one comparable app at around $13. After that, the next step up starts near $150 and climbs to $7,000–$8,000 for dedicated equipment that requires training and support to operate. Between a cup-of-coffee app and a small car’s worth of hardware, there was almost nothing. Robert priced Larry’s Speakeasy at $8.99 once, for life. “It’s not here for me to get rich off of,” he says. “It’s my brother, and I want it to help.” Built With AI, in Days, by a Retiree Robert is 67, with a background in television and radio rather than software. He’d spent months learning to work with AI tools and, in his words, cussing and swearing at the computer. When the housekeeper moment landed, he posed a different question to the AI: how can I help my brother’s speech?  And had a working version running within about two or three days, refined over the following months. That’s worth pausing on. The tools to solve a real disability problem at kitchen-table scale now exist for people who aren’t programmers. A determined care partner built, tested, and shipped an aphasia communication app from rural Wisconsin no company, no funding, no advertising. Around 260 people across 20 countries have tried it, and it’s listed as a resource on the National Aphasia Association website. More Than an Emergency Button The use cases stretch well beyond the kitchen: Therapy practice. Practice phrases from a speech pathologist can be loaded into the app and drilled at home with or without a partner. Video calls. Open the app in one window and Zoom or FaceTime in another, and a person who can’t speak clearly can hold a conversation with family anywhere in the world. Robert saw his own mother’s isolation in a care facility years ago; this is his answer to it. Care facilities. An iPad on a care cart could let staff understand residents nobody else can and document requests, which protects residents and facilities alike. Where to Find It The app lives at LarrySpeakeasy.com, with a seven-day free trial before the one-time $8.99 purchase. Try it, and if it helps, it helps, as Robert puts it; there’s no push. Stories like Robert’s are why this podcast exists: ordinary people refusing to accept the gap between what the system provides and what recovery actually needs. If that resonates, my book, The Unexpected Way That A Stroke Became The Best Thing That Happened, shares ten tools for recovery and personal transformation drawn from my own stroke journey and hundreds of survivor interviews; you’ll find it at https://recoveryafterstroke.com/book. And if this show has helped you, you can support it at https://patreon.com/recoveryafterstroke. This blog is for informational purposes only and does not constitute medical advice. Please consult your doctor before making any changes to your health or recovery plan. Robert Schmidtbauer – Building a Voice for My Brother (Interview) After a stroke, Robert’s brother lost clear speech and had no tools to cope. So Robert built one: a simple app that speaks for those who can’t. Highlights: 00:00 Introduction – Aphasia Communication App 01:21 Challenges in Communication Post-Stroke 05:17 Stroke Experience and Recovery 12:20 Communication Challenges and Solutions 17:43 Creating Solutions Through AI 18:03 Introducing the Aphasia Communication App 21:13 Expanding Accessibility in Care Facilities 27:14 Final Thoughts and Resources 29:19 Bridging Communication Gaps 30:14 Resources for Stroke Survivors Transcript: Introduction – Aphasia Communication App Robert (00:00) If I ask him three times, I still can’t understand it. Like, spell it for me. You know, so I mean, that’s kind of how we got by until I developed this app. Lacunar Stroke New Research (00:10) Hello, everyone, and welcome to another episode of the Recovery After Stroke Podcast. Before we get into it today, I want to say a massive thank you to all my Patreon supporters and to everyone who supports this podcast. You are the reason this show keeps going. And I appreciate every single one of you. If you’d like to help keep these episodes coming, you can support the show at patreon.com/slash recovery after stroke. And if you’re looking for tools to guide you, On your own recovery, my book, The Unexpected Way that a Stroke Became, the best thing that happened, is available at recovery after stroke.com slash book. Now, today’s episode is a little different. My guest is Robert Schmidtbauer. And Robert is not a stroke survivor, he’s a care partner. His younger brother was already living with ataxia, a rare condition affecting his muscles and speech, when a stroke six or seven years ago made communication between the two brothers harder than it had ever been. In this conversation, we talk about what it’s like to be the person who translates for someone you love, what happens when rehab ends and the communication problem doesn’t? And what Robert decided to do about it. Something that might genuinely help other families in the same situation. Challenges in Communication Post-Stroke BIll Gasiamis (01:30) Robert Schmidtbauer welcome to the podcast. Robert (01:33) Thank BIll Gasiamis (01:33) can you give me a little bit of a rundown on you and your relationship with your brother before he had a stroke? Robert (01:42) My brother’s nine years younger than I am, so he’s 56, no, 58 now. And we’ve been living here. He originally got out of high school, went to travel school in Minneapolis, Minnesota, and lived there for a number of years. He has a taxia, and he moved home. to my mother’s house. Let’s see, we’ve been here 16 years now in this house living together. And he moved home about 20 years ago. Not quite, maybe like 18, 18 and a half. The ataxia took away his ability to walk. I’m not sure if you’re familiar with ataxia, but it’s kind of like in the muscular dystrophy realm. And it’s very, very, very rare. he attacks your muscles. depending on the seriousness and the kind, there’s like 20 different kinds. You probably wind up dying from it because it slowly affects your muscles. And the first things to go usually are your extremities. In his case, it was his legs and his speech. So when he moved home, he already had a slight problem talking, not real bad, but my mother built a house and it was very small, just one level. so that she didn’t really have to walk up and down stairs and that type of thing. And so he was basically sleeping in the easy chair when he moved back home. And I lived across the street. So I said, come on and move in with me, because I’ve got this big house. I was a single parent at the time. I have one son. And I said, there’s plenty of room here. You can have a bedroom and live here. Stroke Experience and Recovery And so we lived here. The story behind him and the stroke, I was at the time working as a cab driver at a resort town north of here. And so I would never usually get home on weekends until like four or five in the morning. And I came home, I found him on the floor. And so he had a drinking problem at that time. And I asked him what was wrong. And he said, well, I’m drunk. I well, how long have you been here? he’d been on the floor for like four or five hours. My brother is probably 6’1″, or around 6’2″, 230, 240. I couldn’t lift him up. We bought this house as a duplex. My girlfriend at that time lived and rented from us a basement apartment. Even with the both of us, we couldn’t. get him up. So called the ambulance, we got him in and it obviously wasn’t alcohol, although he had been drinking. He wound up going to, we live in the state of Wisconsin and Madison is in the south part of the state, which is our state capital. They took him to the hospital here and then they flew him to Madison and he wound up there in the University of Wisconsin Hospital, which is a very big progressive type hospital. And he was there, I think about three weeks before he came back to a nursing home here to recover. And so his recovery before he finally got home, I would say, going back to memory, we never really wrote it down, but probably about six months. He was in the nursing home for a good three, four months and then in an assisted living type situation where he had his own room, didn’t have to share it, was going through treatment and rehabilitation before he got home. And so then he came and when he was done with that, then he moved back here. So was about a six month process. BIll Gasiamis (05:37) How long ago was a stroke? Robert (05:40) exactly. I couldn’t tell you to be honest Bill, but somewhere in the realm of six to seven years. I go back, I ran a bar at one time and I’ve been gone from there for four years and this was before that. So I would say between six and seven years ago. BIll Gasiamis (05:57) Before COVID. Robert (05:59) Yeah, before COVID. BIll Gasiamis (06:01) Okay. So when he came back from rehab and the assisted living and came to live at your house, what kind of deficits was he living with? Robert (06:13) Excuse me. He still couldn’t walk. He was already in a wheelchair at that time with a taxia. And that didn’t really change much. was worse, obviously, when he first had the stroke, but the rehab helped him to get back to, I would say more or less where he was before the stroke. As far as being ambulatory, he can still stand up. He can still… function, get into counters and cupboards and things like that. He just, the legs, he just can’t walk. He could crawl on the floor if he had to. And his arms and everything still work, so he could still type. again, this was through rehab, but he pretty much got back to where he was prior to that, except for the speech. the speech became noticeably worse. I would say even at this time, at that time it was really bad, at this time unless you know him and live with him or have known him previously, you’re probably going to understand somewhere between 60 and 70 percent. It’s usually the last part of a sentence or thought is what Most people have difficulty understanding. BIll Gasiamis (07:30) So is it the ataxia and aphasia that he’s dealing with? Robert (07:34) You know, officially he’s never been diagnosed with aphasia. It’s probably more to do with the ataxia combination with the stroke. So the doctor has never really the prognosis or whatever you want to call it. They never really diagnosed them as having aphasia. But after reading up on it and going through therapies, you know, for speech and other things. And that continued for the better part of a year. It’s kind of obvious that there’s probably some of that in there. But as far as a medical diagnosis, official medical diagnosis, we never really got that meaning from any of the doctors. BIll Gasiamis (08:17) Got it. So he came back, he would have had some needs f and you would have had to support him with those, if obviously the walking and then and then whatever other needs. So in that communication early on, were you guys able to actually communicate and you understand what his needs were and help him with what he was asking? Robert (08:40) Yes, there was a point and you being a strokes arrival, you know, I have no idea. have AFib and so, you know, kind of runs in our family. So I knew some of what he was going through, but obviously I don’t know what anybody who’s had strokes, you know, have to go through on a daily basis. watching him, there was a point, especially in the nursing home immediately after in the first several months. I would say that there was major depression. There was a battle. I often quote a movie. There was a line that black actor, what was his name? Older guy that played God. Anyway, came on said one time, you never get busy living or you get busy dying. And so I mean, there came a point. after like the first month where we kind of had a come to Jesus conversation at the nursing home and it was like, okay, because he wasn’t following their recommendations too much. didn’t really, the therapy and stuff, wasn’t too thrilled and excited to do that. so I mean, through this conversation, it was like, okay, like, look, you know, either. You try and make the best of the situation and improve or I can’t help you. It’s like alcoholism or any other drug disease. You really have to want to do it, I think, yourself. And so he’s been dry now for, boy, well, since the stroke, probably close to 10 years now. I mean, he slowed down enough after the stroke, he quit completely. But so, yeah. So I mean, that, you know, the communication. I could communicate with him in hospital. It was harder, obviously, but, you know, I could still understand him. The one thing that helped me out throughout the period that we lived together, especially after the stroke, was I taught English online. So I talked to people from Saudi Arabia. I talked to people, you know. from various countries around the world. And that really helped me because I had to listen closely to them. But there are still times where I will ask him, I get 90 % of it and we get to the last couple of words and I’ve got the gist of it, but it’s like, I don’t understand the last words. He’s come out like, okay, ask me three times. If I ask him three times, I still can’t understand it. Like, spell it for me. You know, so I mean, that’s kind of how we got by until I developed this app. BIll Gasiamis (11:23) Yeah. And it it’s interesting, like you guys all went through rehab, left from hospital, came home, he had a speech issue, and yet you guys didn’t weren’t given a tool or something to help you guys communicate at all. It wasn’t even like a thought for anybody to do that. Robert (11:45) No, there was not. I mean, he had speech therapy when he went to the hospital, but that was an hour, three times a week or two times a week. I mean, it wasn’t an everyday type of thing. So yeah. then exercise is when he came home from there that he would, know, phrases and words and stuff that the therapist would want him to practice at home. And even that was… somewhat of a struggle, because we’re kind of, no disrespect to nationalities, but we’re kind of pigheaded Germans. Communication Challenges and Solutions BIll Gasiamis (12:20) that being said, you come home, you haven’t got the tools, you’re trying to help. your brother, there is times where you can’t understand what he’s saying. And you think, I know, I’ll create my own solution for this problem. And tell me about th the background that you had that helped you solve that problem and the solution that you created. Robert (12:43) Yeah, well, if we go back to, you know, when he came home, because this is obviously been recent, right? It’s because of my background was in television and communications. I worked in television and radio. And so I enjoyed playing with computers when he came home because, you know, we were fairly close family and knew him. I just let him do his thing. you know, and if he asks for help whenever I was there for him. But I always felt like a third wheel going through, you know, the stuff from him, his his rehab. I take him there and do it, but there wasn’t much except for like the speech stuff to help him be repetitive on that. So it felt like kind of a third wheel. So it kind of settled into a pattern, you know, unless he needed help, he’s pretty self sufficient. He has a CNA that comes over a couple times a week to make sure that like, when he takes a shower, he doesn’t fall, you know, that that kind of stuff helps with the dishes or cooks a couple meals and puts it in the refrigerator. But I was looking for ways, you know, and trying to think of what a person could do. Creating Solutions Through AI Well, So I still work, you know, part time. And through television, I had my own production company and did things on the side all the time. And so I like to be creative. And a friend of mine who lives in Chicago had a business and we started playing around with AI for about the last six months now. And it’s not as easy as some people say it is, you know, and especially to learn how to use it. So we started playing around with it. And the frustration level of learning AI got to me after we were into it two or three months. you know, learning how to prop things and explain things to get the result that you wanted, I think is one of the biggest keys for that. And not to go off on a tangent here, but this is how the app and working for my brother really came about. I just needed a break. So it came to a point where I’m cussing and swearing. swearing back at the computer and AI and I’m like, no, no, no, no, we tried to do this like five times. This is really simple. You just change this one thing and you’ve got what I want. But every time I asked it, would change something else. And so I’m like, OK, I still want to continue to learn how to use AI, but I got to just put that aside for a minute and take a day off and not work on that. And so we happened I’ve had, I was a single parent. My son was 13 months when my ex-wife left. And so I raised them by myself. And I’ve had a housekeeper that’s been with me for like 20 years. And she is getting older. She’s like in her late 60s, early 70s now. She fell and she busted her hip. And so we had to find another housekeeper while she was recovering. You know, I said, if you want to come back, you’re more than welcome to, but you know, we’ll find someone else in the meantime. And so we had someone come over to the house and give us a quote on what it would cost us to just tidy up the kitchen and the bathrooms and stuff, because I still work about 30 to 35 hours a week. the same thing that happens over and over again when people and relatives visit us with his speech happened with her. And when I’m around, I’m the go-between. I mean, it’s kind of they understand the 60 % of the first part of his answer. And because he’s around the house all the time and doesn’t leave it, I leave those kind of decisions that I take care of the outside and the lawn and those things that I leave the inside of the house to what he wants. Because he’s the one that spends most of the time, you know, in here. And so I’m answering, you know, I’m filling in the blanks for her. You she’s like, okay. I understand you want the bathroom clean this way, but what was that last part? And so I finished the sentence. I’m like, well, he said this, you know? And it kind of dawned on me at that time, you know, going back to the third wheel feeling, kind of dawned on me at that time. I’m like, okay, what if I asked AI a couple of questions about how I can help him, you know? I mean, and help him with his speech. And that’s basically how the app came about. She gave us a quote that was here for half hour, and that happened half a dozen times. And so after she left, I’m like, all right, I still want to try and continue to learn this. And maybe by doing a different project that I’m not just completely frustrated with at the moment, I can help myself with this other project and help him all at the same time. And so. I just posed the question to you, I use Claude mostly, and I just posed the question to Claude, and it gave me the answer. And from that point on, we, over the last, it’s been a little over two months, two and a half or three months, we refined it, probably ended up in running in about two or three days. Introducing the Aphasia Communication App BIll Gasiamis (18:03) So fundamentally, can you tell me how the app works, what it is and how it works specifically? Robert (18:11) It’s basically whatever you want it to be. And it has everything to do with how functional you still are. It’s not designed to be an end all be all. It can be. If you can’t speak at all, it can be. Because you can either type, depending on your conditioning. Do your hands still work? Can you type? So you can type, there’s a line there as you see, you can type in whatever phrase or sentence that you want, and then it will speak out loud what you type in. And then there’s also for people that are limited in their use of their hands, pre-made phrases. And they range from emergency phrases, I need to go to the bathroom, you need to call the doctor. you know, personal phrases, hello, goodbye, you know, things that, and I just thought up as many as I could. And we’ll add to that as we move through stuff and anybody that has suggestions, like contact, and that’s probably what’s screwing something up is I didn’t have a contact on there. So my email’s on there now that if you have an idea, please feel free to, you know, contact me and we’ll try and put something in for that. So that way, you know, if you have an emergency or you have like the housekeeper, you know, like the situation we are and you know, you need something, all you do is just click on the button and then it will speak that phrase out loud. I just wanted it very simple, very straightforward. And so, you know, it’s designed in the sense of doing it that way. If you want to go to a medical definition of it, you could use it and substitute your own voice completely if you want. But the idea is probably more of a helping situation where anybody going through therapy, like watching my brother go through therapy and coming home with phrases and words that he had to, you could literally you know, hit the button, that phrase would come up and you could practice that or, you know, the speech therapist could give you a list of things that could, if you could still type, you could type that in and then work with that at home. If you didn’t like me and my brother, add me to, you know, to be here to rub through that kind of stuff. But if you were alone or someone couldn’t get over it, you could use it in that realm. And depending on how your rehab went, you could be useful for six months. It could be useful for a lifetime. again, that’s why the prices where it’s at, it’s not here for me to get rich off of. want people to, you know, I want it to help people. It’s my brother and I want it to help. So, you know, if you can afford it, it’s $8.99, $9. And that’s a lifetime deal. So once I get this problem. BIll Gasiamis (21:00) Yeah. What’s the price? Yeah. Expanding Accessibility in Care Facilities Robert (21:13) cleared up that I didn’t know about. You can use it for however long it’s there. BIll Gasiamis (21:19) Yeah, nine dollars. I it thirteen Australian dollars. It’s if it it’s well worth it. Like if you get a you get a tool for nine dollars and you use it forever, like that’s perfectly fine. No issue with that whatsoever. so it it’s Robert (21:31) You know, we are doing something as far as facilities are concerned. I’ve reached out to nursing homes, assisted living places, and I haven’t heard back from any of them yet. I’ve gotten some response on it. It seems favorable, but I haven’t got into any kind of negotiations or anything with them. One of my ideas is like going through with what my mother went through with her dementia, right? Here in Tomahawk, there’s one, two, three, two nursing homes and an assisted living place. And so my brother and I and my son, they had a couple rooms where instead of being out in the general population area with people all around, we could book a room that had a television and a couch and a table and stuff. And we would bring order a pizza or bring in food and spend a couple hours as a family where we weren’t disturbed. And my idea for them is twofold. Number one, seeing as how you could use it on an iPad or a tablet. If you had, I have one of the people that helped me here give me information. I work for a group called Tom Ocunary Interfaith Volunteers. it’s a, we give free rides to senior citizens and people. with disabilities. they can go to the doctor, they can go to the store. And one of the guys was the director at one of these facilities. And he came through to become a director all the way from just being a CNA, which is a very low paying job. It’s the people who clean up the messes, let’s just say, you know, to running the facility for this company. And, you know, he’s like, I had one person on my wing that no one else in the building could understand but me. And he said, even I had a 50 % chance of understanding what he really wanted. To have this, say an iPad that you could have hooked onto your cart when you’re making rounds or something, he said, would have been invaluable. And so not only in that respect to help them with clients that they have, but then they would also have like a legal transcription of something in case something a family said that so and so did this to so and so that was bad or they had a problem of some kind. It could be documented. The other aspect of that was with these rooms I was talking about was, you know, you could literally take the computer and open up just like we are here. You’re going to open up a couple of browser windows. You could put Larry’s you know, speak easy, the interface in one window, you can open up FaceTime on Facebook or Zoom or whatever, you know, communication, let’s say that your daughter lived in Phoenix, Arizona or New York or somewhere. You could get them on the line and you can literally have a conversation back and forth because it would speak out loud through the speaker. And if you were, again, able to type and or hit the phrases, you know, that person over there would hear it come out of the computer. And so you could then keep closer tabs on your relatives. Because I think one of the bigger things, having this experience with our mother, was the isolation and the loneliness. mean, in those days, which is now 15 years ago, I went there every other day for an hour or two. I still had to work and still had other things to do. So, you know, to be able to come home and just sit down at a computer and talk to them would have been real nice. So in a sense of, you know, keeping in touch with your family and that type of thing with friends or whatever. Like my brother was a travel agent in Minneapolis and he’s still got two or three of the people that he worked with that are still in his life. So to be able to, you know, do that and you can hold a conversation with them and catch up and things. So I think that would be those two things combined I think should be, how do I put it, attractive to a facility, not only for the client but also for the facility itself. BIll Gasiamis (25:45) Yeah, yeah. To be able to take an iPad and press a button and have a basic conversation at such a low cost to entry, like that’s really good. I imagine there is already software that’s similar that would Robert (26:00) There’s one that’s, and I can’t remember the name of it, so you’ll excuse me. hope. But there’s one that’s about $13 or $14 right around $12.99 or $13.99. That is similar. But from that point on, the next step up is about $150 all the way up to like $7,000 or $8,000 where you actually have to have equipment at home that… you need to learn and or have help using. So there’s really a pretty big gap in that. That’s just my opinion. My research isn’t paid. There could be other things out there. I know there’s a lot of text to speech and a lot of the tablets and stuff right now. just to be dedicated to, excuse me, you know. people with this, you know, aphasia with recovering from stroke. So I really thought, you know, when I, when I’m a My brother showed him, I’m like, wow, this could really help not just him, but other people. BIll Gasiamis (27:05) Yeah, understood. And Robert, if somebody wanted to get a copy of this or to check it out, where would they go? Final Thoughts and Resources Robert (27:14) Speakeasy.com and again, like I said, there’s a free trial. You could just go there and check it out. And if that, you you decide over the course of that free seven days, if that would help you or not help you, you know, and that way then there’s, there’s no push, you know, I think that’s a week. And so if you’re truly interested in it, you have to remember that, that there’s only seven days to try it out and use it. If it helps, it helps, and if it doesn’t, that’s fine, you move on. BIll Gasiamis (27:43) That’s cool. Yeah. Yeah. Very good. Robert, well, I really appreciate you sharing your story and your challenges that you guys have both had to overcome and the development of this little basic simple tool that solves a problem and and reaching out so that we can let people know so that if they need to solve a problem like that, that is similar and they’re happy to pay nine ninety nine US dollars, then that that might help them. That might be a good way to go about solving a little problem well, a big problem for people in in their home. Robert (28:21) You know, we’ve, it of, asked me something that I did here just recently because we are on the, NAA, the National Aphasia Association website as a resource. We’re on a smaller, it’s called the Stroke Foundation out of Texas, started by a family very similar to your case, a family that has suffered stroke in the family, and it’s a family-run foundation. We have 300, almost 260 something people that have tried it across without any type of advertising just by talking on Facebook and supporters. And also we’re people from 20 different countries now have tried it. So, you know, I welcome them all, you know, just try and if it helps, good for you. And I’m happy that. you do something to help anybody. BIll Gasiamis (29:17) Yeah. Thank you, mate. Thank you for joining me on the podcast. Bill Gasiamis (29:19) Well, there you have it. My conversation with Robert Schmidbauer. A huge thank you to Robert for reaching out and for sharing his and his brother’s story. What stays with me from this one is how simple the whole thing is. Two brothers with a communication gap that the system never closed. And instead of waiting for permission or a diagnosis, Robert sat down and built the tool himself. Nine dollars for life because it’s his brother and he wants to help. If you or someone you love is dealing with speech difficulties after stroke, head to the show notes right now. You’ll find the link to Larry’s Speakeasy there and the app that Robert built at Larry’s Speakeasy.com. There’s a free seven-day trial so you can see it for yourself whether it helps before you spend a cent. And while you’re there, if the episode gave you something, like it, leave a comment. Share it with someone who needs it and subscribe so you never miss another episode. Every one of these things helps more stroke survivors and their families find this show. If you’d like to go deeper on Aphasia, check out my earlier conversation with Tracy Bode, Aphasia Help After Stroke At recoveryafterstroke.com/slash Aphasia Help After Stroke. Tracy Bode. The links will be in the show notes. My book, The Unexpected Way That a Stroke Became the Best Thing That Happened, is available at recoveryafterstroke.com/book. And if this show has helped you and you can support it at patreon.com/recoveryafterstroke I would deeply appreciate it. Thanks for being here. I’ll see you on the next episode. The post Robert Schmidtbauer – Building a Voice for My Brother appeared first on Recovery After Stroke.

On the Monday episode of the North Shore Drive podcast, presented by FanDuel and Edgar Snyder & Associates, the Post-Gazette's Adam Bittner and Christopher Carter ponder how the Steelers' Aaron Rodgers stacks up against other older QBs who won Super Bowls late in their careers. Where does Rodgers rank alongside names like John Elway, Tom Brady and Peyton Manning? Is the roster around him that includes names like T.J. Watt, DK Metcalf, Cam Heyward, Michael Pittman Jr. and Jaylen Warren enough to get him over the top? Especially with a new coaching staff led by Mike McCarthy? Or were those Broncos and Buccaneers teams of the past that lifted those past QBs to glory better than what GM Omar Khan has assembled this offseason? Our duo tackles those questions. Then Chris offers insight on what he's seen from seventh-round NFL draft pick Eli Heidenreich on film. And ponders the future for the rookie class that also includes Drew Allar, Gennings Dunker, Max Iheanachor and Germie Bernard after looking at some of their collect production up close. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

 Welcome to Catholic Apologetics, led by Dr. Jim Dobbins, Author of Take My Hand: A Personal Retreat Companion.  Just finished an RCIA program?  This is the next stop on your faith journey.    In these classes, we look at the different truths of Catholic doctrine and why we know they are true. We also discuss apologetics, spiritual growth, examine the liturgy of the Catholic Mass, and do scripture studies.  Please encourage your friends to listen. I also encourage you to leave a comment about our podcasts.  If you want the slides or any other documents for any class, just e-mail me at jhdphd@gmail.com and I will reply with the documents attached.  If you wish, I will also add you to the class materials distribution list so that each time I send anything out for the class you will get it.  If you are getting the podcast files from iTunes and would like to see the full set of available classes for download, you can see and download them all at http://yorked.podomatic.com.  Older podcasts are now stored at a free podcast site at Podcast.com.  The link to the podcasts there is: http://poddirectory.com/podcast/86506/dr-james-dobbins-catholic-apologetics We ask you to also consider going to http://yorked.podomatic.com and becoming a subscriber. It is free, helps our ratings, and thus helps us reach and help more people.  This session is part of our second discussion series of the Gospel of Matthew.  Please also let me know if there is a particular topic you would like to see addressed.  skvEapm1rLLW8foJsII1 

Older cities are facing a new question, where preserving buildings and streets is no longer enough. Across China, historic neighborhoods are becoming living spaces where people don't just observe the past but actually experience it. So when ancient streets meet a new generation, what is it that makes young people want to stay? On the show: Steve, Yushun & Xingyu

HOUR 3- Brad Williams, What's Older Than America and MORE full 1865 Tue, 30 Jun 2026 15:40:00 +0000 mCFvsjjws7MS4X8bhgJcg8PBg9bFL2UO society & culture Klein/Ally Show: The Podcast society & culture HOUR 3- Brad Williams, What's Older Than America and MORE Klein.Ally.Show on KROQ is more than just a "dynamic, irreverent morning radio show that mixes humor, pop culture, and unpredictable conversation with a heavy dose of realness." (but thanks for that quote anyway). Hosted by Klein, Ally, and a cast of weirdos (both on the team and from their audience), the show is known for its raw, offbeat style, offering a mix of sarcastic banter, candid interviews, and an unfiltered take on everything from culture to the chaos of everyday life. With a loyal, engaged fanbase and an addiction for pushing boundaries, the show delivers the perfect blend of humor and insight, all while keeping things fun, fresh, and sometimes a little bit illegal. 2024 © 2021 Audacy, Inc. Society & Culture https://player.amperwavepodcasting.co

Topics covered in this episode: Free-threaded Python: past, present, and future django-admin-site-search Qwen 3.6 27B is the sweet spot for local development A large batch of PEPs are finalized Extras Joke Watch on YouTube Show Intro Sponsored by us! Support our work through: Our courses at Talk Python Consulting from Six Feet Up Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Calvin #1: Free-threaded Python: past, present, and future The GIL has prevented true multi-threaded parallelism in CPython since the beginning — multiple past attempts to remove it failed on performance grounds Sam Gross at Meta finally solved it; his work became PEP 703 and ships as free-threaded CPython today Python 3.13 was experimental with 20–40% single-threaded slowdown; 3.14 brought that to 0–10% Python 3.15 (October 2026) delivers a unified ABI — one extension binary works on both GIL and free-threaded builds Already >50% of the top PyPI binary wheels support free threading Wouters predicts free-threaded becomes the default between 3.16–3.20 (2027–2031), with the GIL eventually disappearing next decade Michael #2: django-admin-site-search via Adam Parkin A global/site search modal for the Django admin, by Ahmed Aljawahiry. Hit cmd+k anywhere in the admin and you get a command-palette-style search window, kind of like the one in VS Code. It doesn't just search one model's list page. It searches your entire site in one box: App labels Model labels and field attributes Actual model instances (your data) Two ways to search the instances: model_char_fields (the default): runs an __icontains across every CharField (and subclasses) on the model. Zero config, works out of the box. admin_search_fields: defers to each ModelAdmin's existing get_search_results(), so it respects the search_fields you've already set up. The part I like: it's permission-aware out of the box. Users only see results for the apps and models they actually have view permission on, so you're not leaking anything through search. Results appear as you type, with throttling/debouncing so you're not hammering the server on every keystroke, and it's full keyboard nav: cmd+k to open, up/down to move, enter to go. It's responsive, does dark and light mode, and it pulls Django's built-in admin CSS variables so it just matches whatever admin theme you're running. Under the hood it's Alpine.js, but bundled into static so there's no external CDN dependency. Setup is about what you'd expect: pip install django-admin-site-search, add it to INSTALLED_APPS, mix the AdminSiteSearchView into your AdminSite, and drop a few template includes into base_site.html. Supports Python 3.8 through 3.14 and Django 3.2 through 6.0, MIT licensed, and everything is overridable if you want to skip certain models, add TextField matching, etc. Calvin #3: Qwen 3.6 27B is the sweet spot for local development Qwen 3.6 27B is being called the first local model that genuinely competes as a general-purpose intelligence — benchmarks put it at roughly mid-2025 frontier level (comparable to GPT-5 / Claude Sonnet 4.5) Runs locally via llama.cpp; on an M5 MacBook Max with 8-bit quantization + multi-token prediction, it hits ~32 tokens/sec using ~42GB RAM 4-bit quantization gets it under 18GB, runnable on 32GB devices; Nvidia RTX cards run it even faster The dense 27B is recommended over the faster MoE 35B A3B — author prefers higher quality output over raw speed Privacy and reliability are the pitch: fine-tunable, can't be taken down, suitable for sensitive/proprietary data Author sees this as a stepping stone — frontier open-weight models like GLM 5.2 are now locally runnable with company-grade hardware, and smarter-still local models are coming Michael #4: A large batch of PEPs are finalized A bunch of PEPs went from accepted to final. 668, 687, 691, 699, 701, 703, 728, 770, 773, 829 But this wasn't them making their way into CPython. It's an admin sorta thing. (Thanks PyCoders) See the commit. Extras Calvin: More fun bling for your terminal this time - https://charm.land/ Michael: Follow up from pls, What the pls? Thanks Pito. Joke: BEMoji A production-grade utility and component framework built entirely on emoji class names via Jeff Triplett