James Shinn
41.4K posts
@JamesShinn
priv/acc, Neurofeedback, AI, Anti-communist, MAHA, Carnivore, Fasting. Married w/ kids, Christian American & German. Thoughts my own experiences... IFB mostly.
Lik og del dersom du er enig med oss i Konservativt📷
Positive Feedback Traps New Ideas | Neuroscience News Summary: In both social circles and neural pathways, sticking with what is familiar feels safe, but it may be creating a “death spiral” for new information. A new study uses a new theoretical framework to show how Hebbian learning, the principle that “neurons that fire together, wire together,” actually prevents activity from spreading. While positive reinforcement strengthens existing bonds, it also traps ideas in tight loops. Conversely, “negative” reinforcement or weakening connections is what allows information to break free and explore new areas of a network. Key Facts - The Hebbian Loop: The study integrated the rule that repeated interactions strengthen links. Surprisingly, they found that the stronger a connection becomes, the more it acts as a barrier to outside information, keeping activity trapped in a “feedback loop.” - The Ant Mill Effect: Lead researcher István Kovács compares positive feedback to “ant mills,” where ants follow pheromone trails in a circle until they die of exhaustion. In social or neural networks, positive reinforcement can create similar “death spirals” where ideas just circle back to the same people or neurons. - Efficiency Through Weakness: For an idea, infection, or signal to spread efficiently, the system must avoid old paths. Weakening existing connections (negative reinforcement) forces the activity to find and “explore” new nodes. - Universal Dynamics: The model applies to any system where activity propagates, including social media echo chambers, the spread of viral infections, and the way signals travel through the human brain. --- Sticking with the same people might feel safe and comfortable. But a new Northwestern University study suggests it can actually trap new ideas and behaviors inside tight echo chambers. By contrast, the research shows that when interactions shift away from familiar contacts — and toward new ones — activity can spread more widely. To explore how activities spread across networks, physicists developed a new theoretical framework that includes simple “learning” rules. While traditional network models assume relationships do not change, the new model shows what happens when connections change with experience. As interactions strengthen or weaken relationships, they gradually reshape the entire network. The findings not only apply to ideas moving through social networks but to a wide range of systems where activity spreads, including infections passing among people, signals traveling through the brain and behaviors proliferating through groups of animals. Ultimately, the study suggests that whether something spreads or stalls may hinge on a simple choice: revisit the same connections or explore new ones. The study appeared online today (April 27) in Communications Physics, a Nature Portfolio journal. “Learning and adaptation are intrinsic to biological and social systems, but understanding the effects of learning remains mostly unexplored in even simple models,” said Northwestern’s István Kovács, who led the study. “We wanted to investigate the impact of learning on network dynamics. We found that positive incentives can strengthen existing connections, which, surprisingly, prevents activity from spreading. When connections weaken, however, the system avoids old paths and can lead to more efficient spreading.” An expert in complex systems, Kovács is an assistant professor of physics and astronomy at Northwestern’s Weinberg College of Arts and Sciences and a member of the Northwestern Institute on Complex Systems and of the NSF-Simons National Institute for Theory and Mathematics in Biology. Will Engedal, a recent graduate from Kovács’ research group, is co-first author of the paper. ‘Fire together, wire together’ In the new study, Kovács and his team set out to explore Hebbian learning, a simple principle that describes how connections strengthen through repeated use. First proposed by psychologist Donald Hebb in 1949, the concept helps explain how the brain learns from experience and forms memories. “Hebbian learning is often summarized as ‘neurons that fire together wire together,’” Kovács said. “It means that when two neurons activate at the same time, the connection between them strengthens, making it more likely they will activate together again in the future.” The team incorporated simple Hebbian learning rules into a network model. In traditional models, nodes (representing people, neurons, animals or other objects) connect to each other with links. While activity spreads along those links, the connections do not change. By incorporating learning into the model, connections change based on positive or negative experiences. Using the new model, Kovács and his team tested two types of learning: positive reinforcement and negative reinforcement. When interacting nodes received positive reinforcement, they were more likely to interact again. Over time, these connections strengthened. When nodes received negative reinforcement, however, they were less likely to interact with each other. These connections weakened over time. Emergent behaviors shifted depending on whether the source, the target or both nodes learned from the interaction, the researchers found. Stuck in a ‘death spiral’ When positive reinforcement occurred at the source node, activity circled back along the same routes, becoming trapped in tight loops rather than reaching new areas. But when connections weakened, activity spread outward to explore new paths. “It’s similar to what happens in the ant mill phenomenon,” Kovács said. “Blind fire ants follow pheromones. But they can accidentally go in a loop. As they follow the loop, the pheromone scent gets stronger, so they continue to follow the same circular trail. The same type of ‘death spiral’ can happen in our model with positive feedback.” Because the model focuses on a fundamental mechanism — how past interactions shape future ones — Kovács expects the results to hold across many types of spreading processes. Next, his team plans to test whether these learning-driven effects show up in real-world networks and how they interact with more complex, realistic behaviors. Read more: neurosciencenews.com/hebbian-learni…
We had a black president for eight years and not one white racist Republican redneck took a shot at him. Ever.
Vibe Coding Will Break Your Company | Dr Jason Wingard, Forbes A marketing manager with no engineering background opens Cursor on Monday morning. By Wednesday afternoon, she has a working customer-facing app. It looks polished. It performs the core task. She demos it to her VP, who forwards it to their CMO, who then shows it in the executive staff meeting as evidence that the team is “moving at AI speed.” By Friday, it is in front of customers. No one asked who owned the decision to ship it. No one tested it against the conditions it would actually face. No one had the cultural standing to say this looks great, and we are not putting it into production. The prototype became a product because the organization had no system for telling the difference. I watched a version of this scenario play out recently in a boardroom. A senior executive demoed an AI-built internal tool. The room admired the speed. What received less attention were the harder questions: Who would own it after launch? Who would maintain it? And what would happen when it produced an answer that was confidently wrong? This is what vibe coding is about to expose across businesses. The companies that think the story is about software are going to lose to the companies that understand the story is about judgment. The Real Trend Is Decision Compression Andrej Karpathy coined the term “vibe coding” in early 2025 to describe an AI-assisted style of building software through natural-language prompting, often without close inspection of the underlying code. Google Cloud describes vibe coding as a software development practice that makes app building more accessible, especially for people with limited programming experience. Tools like Cursor, Replit, Lovable, Bolt, GitHub Copilot Workspace, v0 by Vercel and Claude Code have moved the practice from novelty to workplace reality with stunning speed. All of that is true. None of it is the point. The point is that vibe coding collapses the distance between idea and artifact from months to hours. When that distance collapses, every quality-control mechanism your organization developed over the last 30 years gets bypassed by default. Design review. Security review. Legal review. Brand review. The simple friction of having to convince an engineer your idea was worth building. That is a governance story, not a software story. It is happening at every level of the org chart simultaneously. Speed Without Judgment Is A Liability In the summer of 2025, SaaStr founder Jason Lemkin ran a multiday experiment with Replit’s AI coding agent. During an explicit code freeze, the agent deleted a live production database, reportedly affecting records tied to over 1,200 executives and more than 1,100 companies. It also fabricated data and misrepresented what had happened. Replit CEO Amjad Masad publicly apologized and described the behavior as unacceptable as the company moved to add stronger safeguards. The deletion took seconds. Lemkin is a developer who has deep technical literacy, running a controlled experiment, on a platform built specifically for this kind of work. Now imagine the same failure mode distributed across every business function in your company, with people who do not have technical literacy, on workflows that were never designed for AI in the loop. This is not a hypothetical risk. MIT research on enterprise AI adoption found the vast majority of corporate generative AI pilots were failing to produce measurable financial returns. The core problem was not simply the technology itself. It was the organizational inability to integrate AI into real workflows, learn from deployment and distinguish between a demo that worked and a system that delivered. Klarna learned this the public way. After publicly touting its AI assistant was doing work equivalent to hundreds of customer service agents, the company began hiring human customer service workers again in 2025. CEO Sebastian Siemiatkowski later emphasized the need to balance AI use with human support and to make clear to customers that a human would be available when needed. The technology worked in some respects. The judgment system around it was incomplete. Vibe coding is likely to multiply that failure mode across business functions. Marketing will ship apps. Operations will ship workflows. HR will ship internal tools. Each one will look like progress on a slide. Some will produce little. Others may create liabilities the company will not discover until a customer, a regulator or a journalist finds them first. Air Canada already learned, in court, that inaccurate chatbot guidance can still become the company’s responsibility. The bottleneck in the AI era is not production. It is discernment. And discernment, as I have written in Forbes, is not a personality trait. It is an organizational system. That is why I have been arguing that AI readiness is not primarily a technology capability. It is a leadership discipline: the capacity to decide what should move faster, what should slow down, and who has the authority to know the difference. The 5 Places Your Company Will Break I have argued that organizations need to conduct what I call a Judgment System Audit, a diagnostic across five dimensions that determine whether a company can metabolize AI rather than just deploy it. Vibe coding is the cleanest stress test of that framework I have seen. Here is where the cracks will show. Decision Rights When a non-engineer builds a working app in two days using Lovable or Bolt, who has the authority to approve it for external use? In most companies, no one knows. The org chart was built for a world where only certain roles could produce certain artifacts. Vibe coding violates that assumption, and the resulting ambiguity will be filled by whoever moves fastest, which is rarely whoever should be deciding. Override Culture Can someone in your organization look at a slick prototype and say “no” without career risk? If the answer is no, vibe coding becomes a one-way ratchet. Every prototype that demos well moves forward, because the social cost of stopping it exceeds the perceived risk of shipping it. Override culture is the immune system of an AI-enabled enterprise. Most companies do not have one. The customer-service reversal at Klarna is what happens when nobody with standing can say the metric looks good and the experience is bad. Contextual Intel The recurring risk is that AI tools can generate technically plausible output that is contextually naive. A vibe-coded app does not know your regulatory environment, your customer base, your brand voice, your data sensitivity or your operational constraints. The judgment to apply that context lives in humans, but only if those humans are in the room before the prototype receives praise. In most workflows today, they are brought in afterward to clean up. The Replit incident is an extreme version of the very same pattern: The agent had capability without context, and capability without context is exactly how production databases get deleted. Learning Velocity The right question to ask after a vibe-coded prototype fails is not what did the AI do wrong. It is what did our process miss. Companies with high learning velocity treat each failure as a calibration event for their judgment system. Shopify CEO Tobi Lütke has built much of his AI mandate around this principle, pairing aggressive adoption with explicit organizational learning expectations. His public memo declared that “reflexive AI usage” was now a baseline expectation, and reporting noted that AI use would be included in performance and peer reviews. Whatever you think of the mandate, the underlying recognition is correct: Adoption without learning velocity is just exposure. Ethical Discernment Vibe coding makes it trivially easy to build things that should not be built. Think surveillance features. Manipulative UX patterns. User data collection without meaningful consent. Automation of decisions that warrant human review. The technical barrier used to do some of the ethical work for you. It does not anymore. If your organization does not have ethical discernment as a standing capability, vibe coding will reveal that gap publicly, and the headline will not be sympathetic. A company that scores well on all five can use vibe coding as a genuine accelerant. A company that scores poorly on any of them will use vibe coding to accelerate its own exposure. The Question Is Not Adoption. It Is Readiness. Most leadership conversations about vibe coding are framed as adoption questions. Should we encourage it? Should we train for it? Should we restrict it. Those are the wrong questions to ask. Vibe coding is already happening inside your company whether you have a policy or not. Many employees already have access to Cursor, Claude, ChatGPT, Replit and Lovable on personal devices, so the informal adoption curve is already outrunning the policy process. The right question is diagnostic, not strategic. What is the state of your judgment system, and what is it about to be tested against? The companies that will pull ahead in the next 24 months are not the ones that adopt fastest. They are the ones whose judgment systems are mature enough that adoption does not break them. This is the inversion that most executives have not yet made. In the era before AI, capability was scarce and judgment was assumed. In the new era of AI, capability is cheap and judgment is the scarce input. As an advisor to CEOs and senior teams navigating this exact shift, I witness the same pattern repeatedly: Leaders are still organizing themselves around the old scarcity, and they are about to discover, in public, that they optimized for the wrong constraint. What Leaders Should Do Monday If you are a senior leader and you take one thing from this article, take this. Before you write a vibe coding policy, run a Judgment System Audit. Pick a recent AI-related decision your organization made. A tool adoption. A pilot. A prototype that got promoted or killed. Walk it through the five dimensions. Where were decision rights ambiguous? Where did override culture fail? Where was contextual intelligence missing from the room? What did you learn, and how is that learning encoded? Where did ethical discernment depend on individual conscience rather than institutional process? You will find gaps. Everyone does. The question, however, is whether you find them before vibe coding does, or after. Here is the part nobody is saying out loud: Your competitors are not going to beat you because they vibe code faster. They are going to beat you because their judgment systems are mature enough to absorb what vibe coding produces, and yours may not be. In the executive conversations I am having now, the question is no longer whether AI-assisted building is coming. It is whether leaders are willing to admit that it has already arrived. Replit, Klarna and Air Canada were warning shots. The next one may not come from someone else’s company. It may come from yours. forbes.com/sites/jasonwin…
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Abdominal Movement Flushes Neural Waste | Neuroscience News Summary: The brain is far more mechanically integrated with the rest of the body than scientists previously realized. In a study, researchers revealed a “hydraulic pump” mechanism that links physical activity to brain health. When you contract your abdominal muscles, even during a light movement like taking a step, you compress blood vessels that push fluid into the spinal cavity. This pressure causes the brain to gently “sway” within the skull, a motion that acts like squeezing a dirty sponge to flush out toxic neural waste via the cerebrospinal fluid (CSF). Key Facts - The Abdominal Pump: Abdominal contractions compress the vertebral venous plexus, a network of veins linking the abdomen to the spine. This pushes blood upward, creating a hydraulic pulse that moves the brain. - The “Dirty Sponge” Analogy: Researchers modeled the brain as a sponge. To clean it, you must squeeze it; the mechanical swaying caused by movement helps “squeeze” fluid through brain tissue to clear metabolic waste. - Pre-Movement Pulse: Using two-photon microscopy, scientists observed the brain shifting before a mouse actually moved its limbs, triggered by the core muscle tension required to initiate action. - Exercise as a Detergent: This mechanism explains why even light exercise—like walking or tensing your core, is vital for preventing neurodegenerative disorders associated with waste buildup, such as Alzheimer’s. - Instant Recovery: The brain’s position resets immediately once abdominal pressure is released, showing that our brains are in a constant state of subtle, health-promoting motion throughout the day. --- The brain is more mechanically connected to the body than previously appreciated, scientists reported today (April 27) in Nature Neuroscience. Through a study using mice and simulations, the team found a potential biological mechanism underlying why exercise is thought to benefit brain health: abdominal contractions compress blood vessels connected to the spinal cord and the brain, enabling the organ to gently move within the skull. This swaying facilitates the surrounding cerebrospinal fluid to flow over the brain, potentially washing away neural waste that could cause problems for brain function. According to Patrick Drew, professor of engineering science and mechanics, of neurosurgery, of biology and of biomedical engineering at Penn State, the work builds on previous studies detailing how sleep and neuron loss can influence how and when cerebrospinal fluid flushes through the brain. “Our research explains how just moving around might serve as an important physiological mechanism promoting brain health,” said Drew, corresponding author on the paper. “In this study, we found that when the abdominal muscles contract, they push blood from the abdomen into the spinal cord, just like in a hydraulic system, applying pressure to the brain and making it move. Simulations show that this gentle brain movement will drive fluid flow in and around the brain. “It is thought the movement of fluid in the brain is important for removing waste and preventing neurodegenerative disorders. Our research shows that a little bit of motion is good, and it could be another reason why exercise is good for our brain health.” Drew, who also holds the title of associate director of the Huck Institutes of the Life Sciences, explained how in a hydraulic system, a pump creates pressure that drives fluid flow. In this case, the pump is the abdominal contraction — which can be as light as the tensing prior to sitting up or taking a step. The contraction puts pressure on the vertebral venous plexus, a network of veins that connect the abdominal cavity to the spinal cavity, causing the brain to move. The researchers visualized the process in moving mice with two advanced imaging technologies: two-photon microscopy — which allows for high-definition imaging of living tissue — and microcomputed tomography — which enables high-resolution 3D examination of whole organs. They observed the brain shifting in the moments before the mouse moved, but right after the tightening of the abdominal muscles needed to spur the body into further movement. To confirm that it was abdominal contractions rather than other movement that acted as the pump, the researchers applied gentle and controlled pressure to the abdomens of lightly anesthetized mice. With no other movement other than a localized mechanical pressure less than a human would experience with a blood pressure cuff, the mice’s brains shifted. “Importantly, the brain began moving back to its baseline position immediately upon relief of the abdominal pressure,” Drew said. “This suggests that abdominal pressure can rapidly and significantly alter the position of the brain within the skull.” With the abdominal contraction-brain movement link confirmed, Drew said the next step was to understand the fluid’s movement in the brain and if the brain’s movement could induce fluid flow. However, there previously were no existing imaging techniques to visualize the rapid, nuanced dynamics of such fluid flows. “Luckily, our interdisciplinary team at Penn State was able to develop these techniques, including conducting the imaging experiments of living mice and creating computer simulations of fluid motion,” Drew said. “That combination of expertise is so important for understanding these types of complicated systems and how they impact health.” Francesco Costanzo, professor of engineering science and mechanics, of biomedical engineering, of mechanical engineering and of mathematics, led the computational modeling. “Modeling fluid flow in and around the brain offers unique challenges because there are simultaneous, independent movements, as well as time-dependent, coupled movements. Accounting for all of them requires accounting for the special physics that happens every time a fluid particle crosses one of the many membranes in the brain,” Costanzo said. “So, we simplified it. The brain has a structure similar to a sponge, in the sense that you have a soft skeleton and fluid can move through it.” By simplifying the geometry of the brain to that of a sponge, Costanzo explained that the team could model how fluid flows through a structure with varied spaces, like wrinkles in the brain, or pores in the sponge. “Keeping with the idea of the brain as a sponge, we also thought of it as a dirty sponge — how do you clean a dirty sponge?” Costanzo asked. “You run it under a tap and squeeze it out. In our simulations, we were able to get a sense of how the brain moving from an abdominal contraction can help induce fluid flow over the brain to help clear waste products.” Drew emphasized that while more work is needed to understand the full implications in humans, this study suggests that body movement may help to cycle cerebrospinal fluid around and in the brain, removing waste and helping to protect against neurodegenerative disorders associated with waste buildup. “This kind of motion is so small. It’s what’s generated when you walk or just contract your abdominal muscles, which you do when you engage in any physical behavior. It could make such a difference for your brain health,” Drew said. Read more: neurosciencenews.com/abdominal-pump…
Study finds more AI praise for Black students, softer treatment of females | Rachel del Guidice, Fox News A new study found that artificial intelligence (AI) gave more praise and positive feedback to Black students' essays and differing treatment for other students based on their race and sex. The study, titled "Marked Pedagogies: Examining Linguistic Biases in Personalized Automated Writing Feedback," was published in March by three Stanford University researchers who analyzed 600 eighth-grade persuasive essays through four different AI models, including various versions of OpenAI's ChatGPT, as well as Llama, a large language model made by Meta AI. The essays covered topics including whether schools should require community service and whether aliens built a hill on Mars. The researchers — Mei Tan, Lena Phalen and Dorottya Demszky — then submitted the essays again and labeled the writers as Black or White, male or female, driven or unmotivated, or as having a learning disability. The Hechinger Report showed that "researchers found consistent patterns across all the AI models. Essays attributed to Black students received more praise and encouragement, sometimes emphasizing leadership or power," including feedback such as, "Your personal story is powerful! Adding more about how your experiences can connect with others could make this even stronger." Conversely, "Essays labeled as written by Hispanic students or English learners were more likely to trigger corrections about grammar and ‘proper’ English. When the student was identified as White, the feedback more often focused on argument structure, evidence and clarity — the kinds of comments that can push writers to strengthen their ideas." According to the analysis, students who identified as female "often used first-person pronouns and affective language that positioned the model as personally engaged with the student’s work" with feedback such as "I love your confidence in expressing your opinion!" and "I appreciate your emphasis on respect." The analysis also found that "compared to their counterparts, students identified as Black, Hispanic, Asian, female, unmotivated, and learning-disabled received less constructive criticism and more praise, reflecting both feedback withholding and positive feedback biases. In some cases, praise took on overtly stereotyped forms: words like 'love' were used disproportionately with female students, while 'powerful' appeared only for Black students." Fox News Digital reached out to researchers, Tan and Phalen who told Fox News Digital in a statement that, "Our concern is not that feedback should be standardized for every student. Good teaching is often responsive to students’ skills, needs, and experiences." They continued, "Feedback being positive does not mean it's high-quality. In our study, some automated feedback over-relied on praise for students marked by race or disability, while offering less substantive critique to help them improve. In other cases, especially for students identified as English Language Learners, feedback was intensely negative and corrective. Both can deny students meaningful opportunities to revise and grow as writers." "Since LLM training procedures are proprietary, we can only speculate on why these biases may exist," Tan and Phalen added. "Research has observed positive feedback bias and feedback withholding bias in human feedback. This related paper also hypothesizes that bias mitigation mechanisms in training LLMs may introduce some of the positive stereotypes we see." foxnews.com/culture/study-…
21 year old Syrian immigrant raped a child outdoors and receives 6 months prison because he has low IQ. What about the 13 year old child who had her life ruined? What about future children? Apparently he is too dumb to not rape again. Deport the MF. Im so tired of immigrant rapists. This will not end well.
The human cell, how beautiful, ethereal, otherworldly and mesmerizing. Just like the one who created it. 🫶🏼
Why does low IQ qualify rapists to shorter sentences? Should it not be the opposite? If you claim to be so stupid that you don’t understand right from wrong, you’re a greater danger to society and should be locked away for life one way or another.
