JUST’s GasFree Carnival Shows How Blockchain Payments Are Becoming Simpler For years, one of the biggest challenges in crypto payments was complexity. Even users sending stablecoins like USDT still need to manage gas fees, hold separate network tokens, and understand transaction mechanics before completing a transfer. That process slowed adoption and made blockchain payments feel more technical than necessary. The latest initiative from JUST reflects how that experience is beginning to change. As part of its sixth anniversary celebration, the JUST ecosystem launched the GasFree Super Carnival across the TRON DAO network, combining real transaction utility with rewards designed around everyday stablecoin usage. Running from May 25 to May 31, the campaign allows users to participate in a 10,000 USDT reward pool while using GasFree-powered transfers that remove traditional gas token requirements. The campaign includes: • 100% transfer fee reimbursement • Up to 66 USDT refund per wallet • Easter Egg rewards for qualifying new users • “Most 6 Lucky Koi” bonus events • Additional social participation rewards What makes the campaign important is that participation is tied directly to real on-chain activity. Users interact with the GasFree infrastructure itself while completing stablecoin transfers. How It Works Users can create or access a GasFree wallet through supported platforms such as: • TronLink • Klever Wallet • Guarda Wallet • NOW Wallet After funding the wallet with USDT, including direct transfers from centralized exchanges, users can begin making GasFree transfers immediately. Each transfer automatically contributes toward reimbursement eligibility and leaderboard participation. The system removes several traditional friction points: • no separate gas token management • fewer failed transactions from insufficient fees • smoother onboarding for newer users • simpler stablecoin transfers overall Why This Matters Stablecoins continue becoming one of blockchain’s most practical financial tools for: • payments • cross-border transfers • savings • business settlement • digital commerce As adoption grows, usability becomes increasingly important. Most users simply want transfers to work efficiently without dealing with unnecessary network complexity. GasFree moves blockchain payments closer to that experience by simplifying how transactions are executed underneath the surface. The Bigger Picture The “Most 6 Lucky Koi” event also adds transaction-based rewards for users landing specific transfer sequence positions: 6 / 66 / 166 / 666 / 1666 / 2666 / 3666 / 4666 / 5666 / 6666 Eligible users can receive instant 20 USDT rewards during the campaign period. More importantly, the initiative reflects a larger direction across blockchain infrastructure: making digital payments simpler, faster, and more accessible for ordinary users. The technology becomes far more practical when users can focus on transferring assets instead of managing network mechanics. And that is exactly the direction GasFree infrastructure is helping move toward within the TRON ecosystem. 🔗 [GasFree Official Website]( 🔗 [JUST Official Website]( 🔗 [TronLink Wallet]( @justinsuntron @DeFi_JUST #TRONEcoStar#

If you want to stop impulse trading, a quick hack is to just go date people. I’m not joking. This actually works. (unless you’re married then just ignore this) I discovered this by accident during a phase where I was making absolutely dogshit trading decisions. Since then, “go date someone” has basically become an external part of my trading system. Because impulse trading isn’t really about charts or setups. It’s about getting trapped inside a sick mental tunnel against yourself. Your attention locks onto one anchor, usually the chart, and your emotions start looping. Greed, anxiety, hope, regret, all feeding each other. Once you’re in that state, using pure mental power like “discipline” or “strong willpower” to pull yourself out is almost impossible. It's like trying to tell yourself to stop thinking about a pink elephant. The more you try NOT to think about it, the more… you think of a pink elephant! I tried everything to break the cycle. Strict rules. Trading journals. Even stupid sticky notes on my desk screaming “DON’T DO IT”. Obviously, getting brutally liquidated multiple times did toughen my mentality. But that’s NOT something you can strategically repeat or rely on. It’s too destructive. Anything lighter, anything that doesn’t seriously fuck up your life, never worked consistently for me. Then something unexpected happened. A friend set me up on a blind date right in the middle of a trading day. I was pissed at first. I was still pulling out my phone every 10 minutes, checking charts like a maniac. Then she casually asked, “btw, are you into techno?” My brain just stopped. For the first time in weeks, I completely forgot about the charts. 3 hours straight. The dopamine hit from human connection completely overrode my trading obsession. Think about it like this. When you try to suppress trading urges (aka human nature), it's like pushing a beach ball underwater. It ALWAYS pops up with more force. So instead of trying to FIGHT human nature, you GO WITH IT. STOP trying to kill the desire. STOP trying to erase the energy. You REDIRECT the same energy to something else. JUST CHANGE THE TARGET. This isn't about willpower. It's about understanding that your brain needs something engaging to focus on. And faces are literally more engaging to our brains than charts could ever be. I started scheduling dates strategically during my most impulsive trading periods. My trading account literally grew more when I was actively dating because I made fewer emotional trades. The best part is you don't even need successful dates. Just the act of getting out and connecting with someone new (and someone you like😉) breaks that obsessive cycle. For married folks, maybe schedule intense social activities instead. Anything that FORCES you out of your trading bubble works. The truth is, we all know WHEN we're entering that dangerous trading mindset. We just IGNORE the warning signs. So next time you feel yourself getting sucked into chart-watching obsession, close the laptop and go on a date. Your portfolio will thank you. And hey, you might just find someone special in the process😉 Trading discipline through dating might sound ridiculous. But it's literally become an essential part of my trading system.

Dear ICP community, the Internet Computer has now been running strong for 5 years 👏👏👏 Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: — Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. — The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. — Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) — Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. — New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. — Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). — An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. — Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... — You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... — Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. — Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. — Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). — For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. — Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday 💪 I'll be back with more news soon!!

I am the Managing Director of Workforce Transition at a consulting firm that bills $14,200 per day and I am currently advising two clients, in two different industries, running the same playbook from the same deck I built in January, and neither knows about the other. Client A is GitLab. Client B is General Motors. GitLab makes software for people who make software. General Motors makes cars for people who can't afford cars. Both companies, in the same week of May 2026, announced they are replacing their human employees with artificial intelligence products that did not exist when those employees were hired. I built the deck. The deck has 44 slides. Slide 1 is titled "The Agentic Opportunity." Slide 44 is titled "Implementation Timeline." Slides 2 through 43 are the reason I own a house in Darien. GitLab did it with vocabulary. Their CEO published a blog post called "Act 2" on May 7 announcing that the company's six values (Collaboration, Results for Customers, Efficiency, Diversity Inclusion & Belonging, Iteration, Transparency) were being retired and replaced with three: Speed with Quality, Ownership Mindset, Customer Outcomes. I helped write the new ones. Not directly. My firm was not retained for the values work. But I sold the Chief Culture Officer the framework three months ago at a dinner in the Marina where she described the old values as "aspirational scaffolding" and I said, very carefully, that aspirational scaffolding is a liability once the building is up. The building, in this metaphor, is a $1 billion ARR company whose stock has declined 82% from its peak. The scaffolding, in this metaphor, is the 2,000-page public handbook that attracted the employees who are now being told they have eleven days to volunteer for termination or wait until June 1 to learn whether they've been involuntarily selected. The rubric for who stays and who goes contains six dimensions. I know this because I reviewed a draft in March when my associate flew to San Francisco for a "culture alignment session" that was billed as strategic advisory. Two of the six dimensions are "AI fluency" and "agentic mindset." These terms did not appear in any GitLab job description before January 2026. They now determine employment. An engineer who maintained GitLab's CI/CD pipeline for four years without incident — four years of uptime, four years of deployments, four years of the infrastructure that generated the $955 million in revenue the CEO celebrated on the earnings call — may score lower on "agentic mindset" than a new hire who completed a twelve-week certificate in prompt engineering from a program that itself has existed for fewer weeks than the engineer has years of tenure. General Motors did it with spreadsheets. Monday morning, May 11. Badge deactivation at 5:47 AM Eastern, building access at 5:48, VPN credentials at 5:49. Six hundred IT workers across twelve states. The distribution across twelve states was not arbitrary. Each state has a WARN Act notification threshold. Six hundred distributed across twelve states falls below every threshold. The workforce analytics team that designed the distribution model was not among the six hundred terminated. The skill of distributing layoffs across jurisdictions to avoid legal notification requirements is, apparently, an AI-native competency. GM posted 83 new positions the same week. The job descriptions require "AI-native development, data engineering and analytics, cloud-based engineering, agent and model development, and prompt engineering." I reviewed them at my client's request. Several describe roles that the terminated employees were already performing under different names. One posting, Senior Data Integration Architect, is identical to a role held by a woman in their Austin office who was terminated at 5:47 AM Central. She held the position for nine years. The new posting requires three years of experience with large language models. Large language models have existed in commercial deployment for approximately three years. The requirement is mathematically designed to exclude anyone who learned their skills before the technology existed. Which is everyone they just fired. Here is where the deck earns its fee. Slide 17 is titled "The Vocabulary Bridge." It is the most important slide in the presentation. It shows how to construct a lexicon of new competency terms ("AI fluency," "agentic mindset," "AI-native development") that describe existing work in language the existing workforce cannot claim. The vocabulary does not change the job. It changes who is qualified for the job. A senior IT administrator who managed SAP infrastructure processing $185 billion in annual GM revenue for fifteen years is not "AI-native." A twenty-six-year-old with a GitHub portfolio of LangChain wrappers is. The fifteen-year veteran did the work. The twenty-six-year-old has the words. My deck converts one into the other. That is the bridge. GitLab Duo, their AI agent platform, reached general availability on January 15, 2026. Seventeen weeks ago. They are restructuring their entire company around a product that has existed for seventeen weeks. GitHub Copilot has 20 million users and 4.7 million paid subscribers across 90% of the Fortune 100. Cursor reached $2 billion in annualized revenue in February. GitLab's competitor advantage in the "agentic era" is that they are willing to fire more people faster in service of a product that has been generally available for fewer days than their voluntary separation window has hours of anxiety. General Motors spent $10 billion on Cruise, their autonomous vehicle division. Cruise's signature achievement was a robotaxi that struck a pedestrian in San Francisco and dragged her twenty feet. The DOJ fined them $500,000. They settled with the victim for approximately $10 million. They killed the division in December 2024. They then wrote down $7.6 billion in EV losses. They then pivoted back to gasoline. They then announced the 600 IT layoffs for insufficient "AI skills." The AI they built cost $10 billion and injured a woman. The AI skills they're hiring for cost a twelve-week certificate. The employees they fired had fifteen years of keeping $185 billion in revenue processing without dragging anyone through an intersection. Meanwhile — and this is the part where I earn the second half of my fee — GM was simultaneously settling a $12.75 million fine with the California Attorney General for selling the precise GPS coordinates, hard braking events, and real-time driving speeds of 8 million OnStar subscribers to Verisk Analytics and LexisNexis, who used the data to raise those drivers' insurance premiums. GM's privacy policy explicitly stated they did not sell driving data. They sold driving data for four consecutive years. The fine was $12.75 million. The revenue was $20 million. The margin on collecting behavioral telemetry from 8 million of your own customers while the glove compartment manual said otherwise was 64%. The terminated employees' median salary was $95,111. Mary Barra's compensation was $29.9 million. The ratio is 310 to 1. The 1 was just reclassified as "not AI-native." I present these two clients to my partners every Thursday in a meeting we call "Transition Pipeline Review." I present them on the same slide. The slide has two columns. Left column: GitLab. Right column: General Motors. The headers are identical. "Legacy Workforce," "Skills Gap Narrative," "Vocabulary Bridge Deployed," "Separation Timeline," "Replacement Requisitions." The numbers differ. The structure is identical. The structure is always identical. I have seventeen clients in the pipeline. Nine are in technology. Four are in manufacturing. Two are in financial services. One is in healthcare. One is in defense. All seventeen are on slide 17. All seventeen are building a vocabulary bridge. All seventeen are replacing employees who have skills with employees who have words. GitLab's CEO wrote: "Software will be built by machines, directed by people." I read that sentence in a meeting where we were reviewing the rubric for determining which people would be directed out of the company. GM's Chief Product Officer arrived from Aurora, the autonomous trucking startup, to "consolidate disparate technology businesses." Three top software executives departed within six months. Their LinkedIn profiles say "exploring new opportunities" in the same font GM's privacy policy used to say "we do not sell your driving data." Bill Staples's compensation at GitLab was $39.1 million in FY2025. His change-of-control payout is modeled at $47.4 million. Mary Barra's was $29.9 million. Combined: $69 million for two executives presiding over a restructuring that will remove an undisclosed number of humans from payroll and replace them with products that are, respectively, seventeen weeks old and responsible for $10 billion in losses plus one woman dragged through a San Francisco intersection. An anonymous GitLab employee posted on Hacker News: "The employees can have some anxiety until then. As a treat." A GM facilities team filed a maintenance request about moisture on the lobby tables on restructuring mornings. The Warren, Michigan campus has a Panera Bread that opens at 5:30 AM on days when badge deactivations begin at 5:47 AM. The Panera does not know why its hours change. My firm does. We have an agreement with their regional manager. The muffins are complimentary. Slide 17 has a footnote. The footnote says: "Vocabulary Bridge deployment should precede workforce action by 60-90 days to establish institutional legitimacy of new competency framework." GitLab introduced "AI fluency" in January. The restructuring was announced in May. Four months. GM posted "AI-native" job descriptions the same week as the terminations. That is too fast. That is not what the deck recommends. GM skipped the legitimacy window. They went straight from vocabulary to separation without the 60-day buffer that allows HR to say, in the separation meeting, "we communicated these expectations in Q1." I flagged this in my Thursday pipeline review. My partner said, and I am quoting: "They'll be fine. Nobody sues over a word." My deck has been purchased by seventeen companies. The aggregate headcount affected across all seventeen is approximately 14,000 employees. The aggregate revenue of my practice from these engagements is $11.2 million. The per-employee cost of my advisory services works out to $800 per person displaced. That is less than the Panera muffin budget at GM's Warren campus annualized across restructuring days. I have a copy of GitLab's original values poster framed in my office. It says CREDIT: Collaboration, Results for Customers, Efficiency, Diversity Inclusion & Belonging, Iteration, Transparency. I purchased it on eBay from someone whose seller name is "gitlab-alum-2024." I keep it the way a surgeon keeps an X-ray of a interesting case. Not for sentiment. For reference. Slide 44 is titled "Implementation Timeline." It contains a Gantt chart. The Gantt chart has seventeen rows, one per client. Each row has four phases: Vocabulary Introduction, Competency Reassessment, Workforce Action, Replacement Hiring. The phases overlap. They always overlap. The vocabulary is introduced while the competency reassessment is being designed. The reassessment is completed while the workforce action is being calendared. The replacement hiring is posted while the terminated employees are sitting in a Panera at 5:48 AM wondering whether "AI-native" was a term that existed when they were hired. It was not. That is the bridge. That is the product. That is slides 2 through 43. The agentic era is not a technological shift. It is a vocabulary shift. The technology is seventeen weeks old or $10 billion underwater or dragging someone through an intersection. The vocabulary is what my clients are buying. The vocabulary is what makes a fifteen-year SAP administrator into a "legacy workforce" and a twelve-week prompt certificate into a "transition hire." The vocabulary is the product. I am the vendor. The deck is $14,200 per day. The agentic era starts on slide 1 and ends on slide 44 and in between is every employee who built the thing now being renamed to exclude them. I bill monthly. Net 30. The invoices are paid on time. The employees are not.

You know the feeling that the older you get, the faster time seems to pass? Part of the reason is that your brain does not record life like a camera. It compresses repetition. When every day follows the same pattern, there is less novelty to encode, fewer sharp memory boundaries. When your routine breaks it forces attention. That is why a chaotic three-day trip can feel longer than three ordinary weeks. So if you want life to feel slower, you do not need to blow everything up. You just need to intentionally break the pattern. This is known as "temporal landmark". To me, Alkimiya and 42 feel like distinct chapters, even though there were 0 gap days between them. The context changed, and sometimes I feel like I'm a different person. On the other hand, I've been boxing consistently for 7 years. Same fundamentals, same drills. I love it, but looking back, it feels like almost no time has passed. Routine makes you stronger, but it also makes time disappear. There's no big take-away. Just thought the brain is fascinating.

Four periods a year instead of twelve. That's the pitch. Chinese biologist Hongmei Wang thinks she can stretch a woman's fertile window by slowing the menstrual cycle down to once every three months. Her logic? Fewer cycles means fewer eggs burned through. More eggs left in the tank means more years of fertility on the clock. Wang runs the State Key Laboratory of Stem Cell and Reproductive Biology in Beijing, where the work isn't just academic. China's birth rate is collapsing, and extending fertility has become a national-level obsession. Here's the wild part of her argument. Women in ancestral times had roughly 100 periods in their entire life. Constant pregnancies. Years of breastfeeding. The body barely cycled. Modern women? Over 400 periods on average. Wang wants to dial that number back down, closer to how the female body operated for most of human history. Her team has already pulled off something startling. They injected human stem cells into sterile monkeys, and one of those monkeys gave birth to a healthy baby that's still alive today. A small human trial followed with 63 women suffering premature ovarian failure. Four of them ended up conceiving healthy children after stem cell treatment. But Wang isn't pretending this is simple. Suppressing ovulation also suppresses estrogen, the hormone that protects bones, the heart, and the brain. Strip it away and you trade one problem for several others. "It's one thing to prove something possible in the lab," she says. It's another thing entirely for women to actually want it. The science is real. The ethics are messier. And the question she's forcing the world to ask isn't going away. Source: EL PAÍS interview

Microsoft just hit the brakes on AI… for its own engineers. Not because the tools were bad. Because the bill got insane. For 2 years Big Tech sold one promise: “AI will replace expensive humans.” Now the companies actually using AI at scale are discovering something awkward: the AI is becoming the expensive employee. Microsoft reportedly rolled out Claude Code internally and usage exploded. Engineers used it for reviews, debugging, refactors, everything. Then finance looked at the token spend. Suddenly the same company that poured billions into Anthropic started pushing engineers off Claude and onto cheaper internal models. That alone should tell you something. Uber saw the same thing. Their engineers adopted AI fast. Leadership even gamified usage with internal rankings. But heavy users were reportedly burning thousands of dollars a month in tokens. The more productive people became with AI… the larger the infrastructure bill got. And then Nvidia’s own VP said the quiet part out loud: for some teams, compute costs are already higher than employee costs. Read that again. The chips are now costing more than the engineers. This completely breaks the story Wall Street has been pricing in: → fewer workers → lower costs → infinite productivity → bigger margins Because AI doesn’t behave like normal software. The deeper companies integrate it, the more tokens they consume. More agents → more inference More automation → more compute More usage → larger recurring bills Cheap tokens don’t automatically mean cheap systems when usage grows exponentially. That’s why companies are suddenly building internal dashboards to track AI consumption like cloud spend. The new corporate fear isn’t employees wasting time. It’s employees generating too many tokens. AI may still transform software forever. But the economics are starting to look less like “replace labor” and more like: replace payroll with an even bigger infrastructure invoice. And that changes everything.

Europe is one of the best places in the world to live, but one of the hardest places to build and scale a company. After 5+ years in France, following 16+ in the US, I have a conflicted admiration for Europe. On the one hand, Europe has great potential. When I lived in the US, I was skeptical of the European quality-of-life argument. But after getting used to Sunday morning markets, walkable cities, and 4.5 meter ceilings, I get it. There are things that you simply cannot import or experience as a tourist. These things can make Europe very attractive for creative and intellectual work. I honestly believe some parts of Europe are the “best neighborhood” in the planet. But that’s not the full story. I am not only a husband and a dad. I am also an entrepreneur. I founded a company in the US 12+ years ago that has offices in the US and Chile and clients throughout the world. I live in France, yet I have not opened a subsidiary here. That is telling. We once hired someone in France through one of those remote employment platforms. The person received about 5,000 euros net per month, which is considered a very good salary here. But the total cost to the company was closer to 13,000 per month. That makes hiring feel less like a relationship between a company and a worker, and more like renting someone from the state. At the same time, you take an enormous amount of legal and administrative responsibility. The presumption is that all companies should operate like a 1960s car manufacturer. The response is simple. Don’t set up operations in Europe. But this is not a remote-work story. I know many small entrepreneurs in France who do not want to cross the threshold from being a one-person activity to becoming an employer. They sometimes refuse a new customer to stay small and avoid the obligations that come with hiring one person. That should worry us. Many social protections here are described as being provided by the state, but in practice, a lot of the cost and complexity of the implementation falls on the administrative shoulders of entrepreneurs. That is reasonable for a large energy company or bank. But for a small business, it is the difference between an entrepreneur waking up on a Monday to think about product or paperwork. Growth is not the enemy of the European social model. It is what enabled it. Much of the quality of life we enjoy here today dates back to growth incubated in the past. Growth that is increasingly hard to find. France once led frontier industries, like bicycles in the 1860s, cinema in the 1890s, and aviation and automobiles soon after. Since then, Europe built a more humane social model. But that model was built on the assumption that Europe and the US were the only two rich and industrialized places in the world. That is no longer true. Global competition in the 21st century is not what it used to be 50 years ago, and the padding built to protect us, may have grown into the handbrake that constrains the growth of the small and flexible firms we need to compete in new frontier sectors. We should be able to be critical about Europe in our own terms, without comparing ourselves to the US or China. Innovative parts of Europe, like Sweden or Switzerland, operate differently and provide clues. Sweden has embraced a dynamic of capitalization in its pension system for a long time in a continent where fewer people buy stocks. Switzerland, a place that shares an enormous amount of geography and culture with its neighbors, is built in part on strong internal competition among its cantons. But neither can light a candle to a French open-air market on a Sunday morning. A market where cash is king, and for a reason. Europe may be the best place in the world to live. But it is also one of the most challenging places to build and scale an innovative activity. The goal is not to weaken the European model. But to get to a place where we can lead again by example. The world will follow us, but only if we are ahead.