https://www.europesays.com/ie/751097/ Apple’s Market Cap Grew From $350 Billion to $4.6 Trillion During Tim Cook’s 15 Years as CEO. Here’s What That Growth Curve Means for Apple’s Next Chapter Under John Ternus. #401(k) #Business #BusinessNews #Economy #Éire #Finance #FinancialInformation #IE #Investing #InvestmentTools #investor #Ireland #MarketNews #mortgage #MutualFunds #PersonalFinance #quote #RealEstate #Retirement #stock #StockResearch #StockValuation #stocks #SuzeOrman #tax #Technology #TrackPortfolio
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388 results for “mort”
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https://www.europesays.com/people/279287/ Elon Musk Became the First Person Ever Confirmed With a Net Worth Above $1 Trillion in June 2026. Here’s Why That Milestone Still Matters for Tesla and SpaceX Shareholders. #401(k) #Business #BusinessNews #Economy #ElonMusk #Finance #FinancialInformation #Investing #InvestmentTools #Investor #MarketNews #Mortgage #Musk #MutualFunds #PersonalFinance #quote #RealEstate #retirement #stock #StockResearch #StockValuation #Stocks #SuzeOrman #Tax #TrackPortfolio
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EA Left Boxing Fans Hanging For 15 Years After Fight Night Champion, So What Went Wrong?
Fight Night Fight Night is coming back. We know that fo…
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https://www.newsbeep.com/au/940615/ -
#WritersCoffeeClub 25/10: Tell us about inspiration from an unlikely source.
I’m more likely to take unlikely inspiration from a likely source. When rereading “Brier Rose,” I said to my girlfriend, “imagine the downstream economic effects on a pre-industrial agrarian society if you banned spindles.”
The Grimms never touched on that. And there “The Princess & the Mortgage Man” was born.
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Dutch Bros, Sweetgreen, Portillo’s, Kura Sushi, and First Watch shares are falling, what you need to know https://www.diningandcooking.com/restaurants/45115/ #401k #business #BusinessNews #Economy #Finance #FinancialInformation #investing #InvestmentTools #Investor #MarketNews #mortgage #MutualFunds #PersonalFinance #quote #RealEstate #Retirement #stock #StockResearch #StockValuation #stocks #SushiBar #SushiBars #SushiRestaurant #SushiRestaurants #SuzeOrman #tax #TrackPortfolio
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RE: https://bsky.brid.gy/r/https://bsky.app/profile/did:plc:fojutkhhw2w3wbvwknwvs2sa/post/3myoo4dtzd22y
#Macronie, état #fasciste
Macronie, #mafia crapuleuseHeil #Macron !
Les petites frappes de la mafia crapuleuse du bankster bourgeois président des plus riches opèrent maintenant en toute impunité et sous la protection complice de la police recyclée en milice privée du gouvernement Macron.
Toutes les mafias crapuleuses sont fascistes, celle de Macron ne fait évidemment pas exception.
La république française est en danger de mort. -
Huracán Vance amenaza con lluvias extremas a tres estados de México
El huracán Vance amenaza con lluvias intensas, inundaciones y deslaves en Colima, Michoacán y Jalisco durante la próxima semana. Su trayectoria e intensidad aún son inciertas.
Por Gabriela Díaz | Reportera
El huracán Vance amenaza con lluvias torrenciales, inundaciones y deslaves en el occidente de México durante gran parte de la próxima semana, principalmente en Colima, Michoacán y Jalisco, de acuerdo con el pronóstico del Centro Nacional de Huracanes de Estados Unidos (NHC).
El organismo advirtió que las precipitaciones podrían provocar inundaciones potencialmente mortales y deslizamientos de tierra, especialmente en zonas montañosas y terrenos con pendientes pronunciadas. La evolución del ciclón mantiene en incertidumbre los posibles efectos del viento y las marejadas en la costa mexicana.
En su reporte de las 21:00 horas del sábado 24 de octubre, Vance registró vientos máximos sostenidos de 150 kilómetros por hora y avanzaba lentamente hacia el nor-noroeste, a unos ocho kilómetros por hora.
Trayectoria todavía incierta
El centro del huracán se encontraba aproximadamente a 515 kilómetros al suroeste de Acapulco, Guerrero, y a 595 kilómetros al sur de Manzanillo, Colima. Su desplazamiento lento podría prolongar las condiciones favorables para lluvias durante varios días.
El NHC señaló que los pronósticos más recientes habían cambiado hacia el oeste respecto de las estimaciones anteriores. Por ello, los especialistas ajustaron ligeramente la trayectoria prevista, aunque advirtieron que todavía podrían ser necesarios nuevos cambios.
La incertidumbre también afecta la intensidad del sistema. Los meteorólogos prevén un debilitamiento inicial por la presencia de aguas relativamente frías y la cizalladura del viento, un fenómeno que dificulta la organización de los ciclones tropicales.
Lluvias con riesgo de deslaves
Aunque Vance podría perder fuerza, el debilitamiento no elimina el riesgo asociado con las precipitaciones. El pronóstico mantiene la posibilidad de lluvias intensas desde Michoacán hasta Jalisco durante buena parte de la próxima semana.
Las zonas montañosas de Colima y el occidente de Michoacán y Jalisco figuran entre las áreas de mayor preocupación por posibles inundaciones repentinas y deslaves. Estos fenómenos pueden afectar caminos, viviendas y comunidades ubicadas cerca de laderas o cauces.
El organismo estadounidense indicó que todavía era demasiado pronto para determinar con precisión los efectos que el viento y las marejadas podrían ocasionar en la costa. La evolución de la trayectoria y la intensidad será determinante para precisar los riesgos. –sn–
Sociedad Noticias¡Conéctate con Sociedad Noticias! Suscríbete a nuestro canal de YouTube y activa las notificaciones, o bien, síguenos en las redes sociales: Facebook, Twitter e Instagram.
También, te invitamos a que te sumes a nuestro canal de información en tiempo real a través de Telegram.
#NoticiasMX #PeriodismoParaTi #PeriodismoParaTiSociedadNoticias #Calor #calorExtremo #Cdmx #Chihuahua #CiudadDeMéxico #Clima #climaMéxico #colima #conagua #deslaves #HuracánVance #huracánVanceMéxico #Información #InformaciónMéxico #inundaciones #Jalisco #Lluvias #lluviasIntensas #México #michoacán #monzónMexicano #Morena #noticia #noticias #NoticiasMéxico #NoticiasSociedad #oleaje #pronósticoDelTiempo #ProtecciónCivil #ServicioMeteorológicoNacional #SMN #SN #Sociedad #SociedadNoticias #SociedadNoticiasCom #sociedadNoticias #SociedadNoticiasCom #Sonora #temperaturasMáximas -
https://www.alojapan.com/1557879/t-t-hibachi-japanese-food-brings-new-options-to-parrish/ T & T Hibachi Japanese Food brings new options to Parrish #florida #JapaneseRestaurant #JapaneseRestaurantsInFlorida Parrish Tu Tran, a veteran restaurateur from Ohio, moved his family to Manatee County with the expectation that he would soon be opening a brick-and-mortar restaurant in Bradenton. The restaurant plans took a little longer to come to reality than he expected, so in the interim, he went into the food truck business with his
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Iberdrola S.A. A (IBE1.HA) Q3 FY2026 earnings call transcript
Good morning, ladies and gentlemen. First, we would like to extend a warm welcome to…
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https://www.europesays.com/spain/105368/ -
SAP: 3 Key Takeaways for Long-Term Investors
On its third-quarter 2026 earnings call, held on Oct. 21, 2026, SAP (NYSE:SAP) announced that quar…
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https://www.europesays.com/germany/115130/ -
DATE: October 25, 2026 at 02:00PM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: Young adults who fear rejection also tend to fear death more, psychology study finds
Young adults who are especially worried about rejection in their close relationships tend to report a greater fear of death, which in turn is linked to more symptoms of anxiety, depression, and other forms of distress, according to a new study published in Scientific Reports. The findings also suggest that for several types of symptoms, this link depends on whether a person views death as the complete end of their existence or as a transition to another state.
Death anxiety refers to the worry, dread, or apprehension that comes from knowing that human life eventually ends. While it is normal to think about mortality from time to time, many psychologists now view persistent or poorly managed fear of death as a shared factor underlying many different mental health conditions. It can show up as panic or constant worry in anxiety disorders, feelings of hopelessness in depression, repetitive rituals aimed at preventing harm in obsessive-compulsive disorder, or extreme worry about physical sensations in somatic symptom disorders.
These existential concerns often become especially prominent during emerging adulthood, the developmental period spanning the late teens and twenties. As young people build their independent identities and confront major life transitions, they frequently grapple with questions about meaning and mortality, with prior research showing that fear of death tends to peak during a person’s twenties.
How well a young adult copes with the reality of mortality has been linked to their attachment style, which is the characteristic way a person relates to others in close relationships based on their internal sense of self-worth and their trust in other people. Psychologists often categorize adult attachment into four patterns: secure (positive view of both self and others), preoccupied (negative view of self, positive view of others), dismissing (positive view of self, negative view of others), and fearful (negative view of both self and others).
People with a secure attachment style generally manage stress well and turn to loved ones for comfort. In contrast, those with insecure styles rely on less helpful strategies to regulate their emotions, and a 2025 meta-analysis of 18 studies found that adults with higher attachment anxiety or attachment avoidance consistently report a greater fear of death.
Individuals with a preoccupied attachment style tend to experience high attachment anxiety, meaning they are overly sensitive to rejection and abandonment, which can amplify their distress when thinking about death. Those with a dismissing style exhibit high attachment avoidance, meaning they prize extreme independence and suppress their emotional needs, often trying to push thoughts of mortality out of their awareness.
Beyond relationship patterns, how a person fundamentally defines death also shapes how they handle existential fear. In psychology, this belief is called the ontological representation of death, which captures what a person believes happens to their core identity when they die. Some people view death as total annihilation, meaning the absolute and permanent extinction of the self. Others view death as a passage, meaning a transition to another form of existence, whether through a literal afterlife or a symbolic legacy that allows some part of their identity to continue.
The research, led by Alessandro Musetti, an associate professor of dynamic psychology in the Department of Humanities, Social Sciences and Cultural Industries at the University of Parma in Italy, examined how these relational and existential factors work together. Psychologists use the term internalizing symptoms for inward-directed problems such as depression, anxiety, obsessive thoughts, and unexplained physical complaints.
“Death anxiety is increasingly recognized as a factor involved in many forms of psychological distress, including anxiety, depression, obsessive-compulsive symptoms, and somatic complaints,” Musetti told PsyPost. “However, we still know relatively little about why some people are more vulnerable to death anxiety than others, and why death anxiety seems to affect some individuals more strongly. We wanted to integrate attachment theory with existential psychology by examining whether attachment styles are linked to internalizing symptoms through death anxiety, and whether this process depends on how people fundamentally represent death, either as total annihilation or as a form of passage or continuity.”
To investigate this, the researchers analyzed data from 640 Italian young adults between the ages of 18 and 22, with an average age of about 20.5 years. The sample was predominantly female (83.4 percent) and consisted mostly of students (62.7 percent) or working students (28.8 percent). In terms of religious views, 39.1 percent identified as agnostic, 30.6 percent as atheist, and 30.3 percent as religious believers.
Participants, recruited through social media, completed a series of online questionnaires. To measure attachment styles, the researchers used the Relationship Questionnaire, which asks participants to rate on a 1-to-7 scale how well four short paragraphs describing secure, dismissing, preoccupied, and fearful attachment patterns apply to them. Death anxiety was measured using a 15-item true-or-false questionnaire that included statements like “I am very much afraid to die.”
Participants also completed the Testoni Death Representation Scale, which uses six items rated on a 1-to-5 scale to determine the degree to which a person views death as a passage versus definitive annihilation. Finally, the researchers assessed four types of internalizing mental health symptoms over the prior two weeks: depression, anxiety, somatic symptoms (such as unexplained physical aches and pains), and obsessive-compulsive symptoms (repetitive unpleasant thoughts or urges).
The researchers used a statistical technique called conditional process analysis, which tests whether an indirect pathway between two variables changes depending on a third factor, while controlling for gender. In the initial look at how attachment related to fear of mortality, participants scored an average of 9.31 out of 15 on the death anxiety scale. Higher scores on preoccupied attachment were associated with higher death anxiety, whereas higher scores on dismissing attachment were associated with lower reported death anxiety.
Secure and fearful attachment styles were not linked to death anxiety once all four styles were considered together. The finding for dismissing attachment differs from the 2025 meta-analysis described earlier, which linked attachment avoidance to greater fear of death. The authors suggest that people with a dismissing style may push death-related thoughts out of awareness rather than feel less threatened by mortality.
When looking at how death anxiety connected to mental health, the researchers found that a participant’s view of death played a moderating role for three of the four symptom categories. Death anxiety was linked to worse anxiety, obsessive-compulsive, and somatic symptoms, but only among young adults who strongly viewed death as total annihilation. For participants who viewed death as a passage to another state of existence, higher death anxiety was not linked to higher levels of these three symptom types.
Consequently, the indirect pathways linking attachment styles to these three symptoms operated only among those holding annihilation beliefs. For young adults who believed death is the complete end of existence, having a preoccupied attachment style was indirectly linked to greater anxiety, obsessive-compulsive, and somatic symptoms through heightened death anxiety.
Among those same annihilation-believing participants, by contrast, a dismissing attachment style had a negative indirect association with anxiety, obsessive-compulsive, and somatic symptoms because these individuals reported lower death anxiety. However, the authors caution against interpreting a dismissing attachment style as psychologically healthy. When looking at direct relationships outside of the death anxiety pathway, dismissing attachment was directly linked to higher levels of unexplained somatic symptoms, which the authors suggest may indicate that suppressing emotions and relationship needs can contribute to physical complaints even when conscious fear of death is kept at bay.
Depression followed a different pattern than the other three mental health domains. Whether a participant viewed death as total annihilation or as a passage did not alter the link between death anxiety and depressive symptoms. Instead, preoccupied attachment was indirectly associated with greater depressive symptoms through increased death anxiety across the board, regardless of what the person believed happens after death. Additionally, preoccupied and fearful attachment styles were directly linked to worse symptoms in all four mental health categories, while secure attachment was directly associated with lower depression and anxiety.
“Our findings suggest that psychological distress is influenced not only by how people relate to others, but also by how they make sense of their own mortality,” Musetti explained. “At the same time, the way people understand death appears to matter: when death is experienced as the complete end of personal existence, death anxiety seems more strongly connected to several forms of psychological distress.”
“One finding that surprised us was that the ontological representation of death mattered for anxiety, obsessive-compulsive, and somatic symptoms, but not for depressive symptoms,” Musetti noted. “This suggests that depression may be connected to existential concerns through somewhat different mechanisms than other forms of psychological distress.”
The findings are consistent with research by Musetti and colleagues covered by PsyPost in 2025, which found that death anxiety and feelings of relational disconnectedness helped explain the link between attachment anxiety and problematic social media use. Both studies indicate that fear of mortality helps explain how insecure attachment is linked to psychological difficulties, though the earlier study focused on problematic social media habits across a wide adult age range while the new research examined internalizing mental health symptoms in young adults.
The depression results also align with a study covered by PsyPost in 2023, which examined broader existential anxiety and meaning in life among adult mental health professionals rather than death anxiety and attachment styles in emerging adults, and similarly found that existential anxiety helped explain the link between lower meaning in life and more severe depressive symptoms.
Musetti pointed out that the statistical relationships uncovered in the study should be kept in perspective. “The effects were statistically significant but generally modest in magnitude, which is common when studying complex psychological phenomena,” he said. “We are not suggesting that attachment style or death anxiety alone determine mental health outcomes. Rather, our findings indicate that existential concerns may be one piece of a broader psychological puzzle that includes relational experiences, emotional regulation, personality, and social context.”
As with all research, there are some caveats to consider when interpreting these findings. Because all the survey data were collected at a single point in time, the study cannot establish cause and effect. While psychological theory suggests that attachment patterns develop early and shape later fears and symptoms, it is also possible that experiencing depression or anxiety makes a young person feel more insecure in their relationships or more frightened of dying.
“A key caveat is that this was a cross-sectional study,” Musetti emphasized. “The findings identify associations that are consistent with a theoretically grounded model, but longitudinal research is needed to clarify causal pathways. Additionally, representing death as a passage should not be interpreted simply as being religious. The construct we studied concerns a person’s fundamental understanding of what happens to the self after death and cannot be reduced to religious affiliation alone.”
There are a few other limitations to keep in mind. Over 83 percent of the participants were young Italian women, and most were students, meaning the results may not generalize equally to men, older adults, or people from different cultural and religious backgrounds where views on death and the afterlife differ. Participants were recruited through social media and word of mouth rather than randomly selected, and the authors note that mediation analyses based on a single time point can overstate indirect pathways. In addition, the researchers relied on brief self-report screening tools rather than clinical interviews, and the two-item measures for depression and somatic symptoms showed only modest internal consistency.
Future studies could track diverse groups of young people over several years to confirm the order in which attachment insecurities, existential beliefs, and mental health symptoms develop. The authors suggest, though this was not tested, that clinicians working with young adults who have a preoccupied attachment style and view death as total annihilation might combine relationship-focused therapy with existential approaches that help build a sense of meaning and continuity, as long as such approaches respect a person’s own worldview.
“One important next step is to examine these relationships longitudinally to better understand how attachment, death anxiety, and psychological symptoms influence one another over time,” Musetti added. “We are also interested in studying whether similar processes operate across different age groups and cultural contexts, since beliefs about death are deeply shaped by culture and life experience.”
The study, “Adult Attachment, Death Anxiety, Representation of Death, and Depression, Anxiety, Obsessive-compulsive, and Somatic Symptoms in Emerging Adulthood: A Moderated Mediation Model,” was authored by Alessandro Musetti, Andrea Zagaria, Vittorio Lenzo, Gianluca Santoro, and Adriano Schimmenti.
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#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #Psychology #MentalHealth #DeathAnxiety #FearOfRejection #AttachmentStyles #YoungAdults #Anxiety #Depression #PsychologyResearch #EmotionalWellbeing
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How to Use AI Agents for Personal Finance and Wealth Building: The Master of Coin Guide (2026)
Why Most People Use Artificial Intelligence Backwards (And How to Fix It)
Every day, millions of people open an AI chat window and ask the wrong question: "What stock should I buy today?" or "How do I get rich quickly with AI?" They treat artificial intelligence like a carnival oracle, hoping a single prompt will absolve them of the discipline required to build wealth. When the oracle gives them generic, recycled advice, they walk away convinced that AI is overhyped.
The divide between those who build sovereign wealth in the coming decade and those who remain trapped in financial noise comes down to a single shift: moving from passive AI chatbots to autonomous AI agents.
According to Backbase’s 2026 AI in Wealth Management analysis, while 95% of financial institutions have experimented with generative AI, only 27% report substantial impact because most remain stuck in passive chat pilots rather than deploying agentic workflows. Meanwhile, McKinsey’s 2035 Wealth Management Outlook projects that AI agents and intelligent automation will form the operational backbone of modern wealth management—delivering an estimated 30% to 40% productivity leap in portfolio intelligence and financial research.
You no longer need a Wall Street family office to command that same operational backbone. In the philosophical framework of The Philosopher King, your mind is the fifth-dimensional architect, and AI agents are four-dimensional force multipliers. Here is the step-by-step guide to building your own autonomous AI agent workflow to master your coin, reclaim your time, and eliminate emotional financial mistakes.
Step 1: Shift from Single Prompts to a Multi-Agent Financial Council
A chatbot waits for you to ask a question and gives you a single stream of text. An AI agent is given an objective, access to real-world tools (such as web search, financial data APIs, and spreadsheets), and the autonomy to break a complex goal into sequential steps.
When managing your personal treasury—what we call the discipline of the Master of Coin—relying on a single AI persona creates blind spots. Instead, structure your workflow as a three-agent council built on deliberate discipline (the meticulated kind):
- The Intelligence Scout (Macro and Fundamental Research Agent): Configured to scan earnings transcripts, SEC filings, macroeconomic liquidity trends, and sector developments on a recurring schedule, distilling hours of noisy financial media into a five-minute first-principles brief.
- The Treasury Comptroller (Cash-Flow and Allocation Auditor): Tasked with auditing your monthly burn rate, identifying recurring subscription bloat, tracking your savings velocity, and calculating whether your actual asset allocation matches your target risk profile.
- The Red-Team Adversary (Risk and Pre-Mortem Agent): Programmed with a single directive: to ruthlessly challenge your investment ideas. Whenever you are tempted by market euphoria or FOMO, this agent stress-tests your thesis by finding every balance-sheet weakness, valuation risk, and macroeconomic headwind you ignored.
Step 2: Choose Your AI Agent Stack and Connect Your Financial Data
Whether you are a non-technical sovereign investor or a developer building custom pipelines, the open-source and commercial ecosystem for AI agents has matured rapidly. As documented in McKinsey’s 2026 State of AI Global Survey, agentic orchestration is now one of the fastest-scaling capabilities worldwide. Depending on your technical comfort, consider these proven building blocks:
- Model Context Protocol (MCP) for Secure Data Connections: Introduced as an open standard by Anthropic and detailed in the official Model Context Protocol documentation, MCP acts like a universal USB-C port for AI applications. It allows your AI agent to securely read local CSV exports, Google Sheets ledgers, and market data feeds without uploading your private credentials to untrusted third parties.
- CrewAI for Role-Based Financial Teams: If you want specialized agents to collaborate on a research report, CrewAI lets you define distinct roles (for example, a Fundamental Analyst Agent and a Risk Manager Agent) that hand off structured outputs to one another automatically.
- LangGraph for Stateful, Auditable Workflows: Built by the LangChain team, LangGraph models agent workflows as a stateful graph with built-in checkpointing and human-in-the-loop pauses—ideal when you want an agent to pause and wait for your explicit approval before logging or executing a decision.
- OpenAI Agents SDK and Microsoft AutoGen for Multi-Agent Debate: Using the OpenAI Agents SDK or Microsoft AutoGen, you can pit a Bull Agent against a Bear Agent on any asset class, forcing both agents to cite verifiable data until only the strongest first-principles truth survives.
Step 3: Enforce the "Human-in-the-Loop" Sovereign Rule
One of the most common questions people ask when researching AI for investing is: "Should I connect an AI agent directly to my brokerage account and let it trade autonomously?"
For the vast majority of individual investors, the answer is an emphatic no. As highlighted in Forbes Finance Council’s analysis on AI agents in finance and McKinsey’s research on AI agent performance management, autonomous agents require strict governance boundaries, audit trails, and human oversight.
"An AI agent is your tireless chief of staff in the fourth dimension, but your consciousness remains the sovereign on the throne. Never abdicate final judgment to a machine."
To protect your capital while maximizing leverage, enforce these four iron rules:
- Read-Only Access by Default: Give your agents read access to market data, news feeds, and sanitized CSV exports of your budget—never raw write/withdrawal permissions to your primary bank or brokerage accounts.
- Require Source Citations on Every Claim: Instruct your research agents to attach a direct link to the primary filing, earnings release, or statistical dataset for every number they report, eliminating hallucinated metrics.
- Automate the Preparation, Never the Conviction: Use agents to compress 10 hours of reading into 15 minutes of structured synthesis, freeing your mind to make low time-preference decisions with clarity.
- Log Every Thesis for Post-Mortem Review: Have your agent archive every investment decision alongside the exact assumptions you held at the time, creating an unvarnished mirror of your cognitive biases over time.
Step 4: Copy This 15-Minute Daily "Master of Coin" AI Routine
You do not need a complex server cluster to start today. Begin with a simple, repeatable daily cadence using scheduled AI tasks:
- 06:00 AM — The Sovereign Morning Brief: Schedule an AI agent to scan overnight macroeconomic developments, central bank releases, and any material news affecting your watchlist, delivering a bulleted summary stripped of sensationalist clickbait before you open your eyes.
- Sunday Evening — The Red-Team Portfolio Audit: Feed your current asset allocation and investment thesis into an adversarial prompt: "Act as a first-principles risk officer. Identify the top three hidden correlations, liquidity risks, or macro assumptions that could cause a 25% drawdown in this portfolio over the next 12 months."
- First of the Month — The Capital Velocity Check: Have your agent categorize the prior month’s cash flows into three buckets—Sovereign Asset Accumulation, Productive Skill/System Building, and Fleeting Consumption—so you can see exactly where your life-force is compounding or leaking.
Join the Conversation: How Are You Using AI Agents to Master Your Coin?
We are living through the greatest democratization of analytical leverage in human history. Those who pair ancient philosophical restraint with modern agentic intelligence will operate with the clarity of an institution and the agility of a sovereign individual.
I want to hear from you in the comments below:
- Are you currently using AI agents or scheduled workflows to research investments, track expenses, or stress-test your financial decisions?
- Which part of managing your personal finances drains the most time or emotional energy—and how would you design an AI agent to take it off your plate?
- Where do you draw the line between automated AI analysis and human intuition when putting your capital at risk?
Drop your thoughts, favorite prompts, or questions in the comments—let us sharpen our frameworks together.
All is One | One is All
#aiAgents #ArtificialIntelligence #automation #financialSovereignty #howTo #MasterOfCoin #personalFinance #wealthBuilding -
My Word of the Day today is IRISATION. Share YOUR words to describe this picture in the comments!
Unless I’m sadly mistaken this must be referring to Iris, the ancient Greek Goddess who’s the personification of the rainbow.
Irisation, derived from French, iriser, in turn a borrowing from ancient Greek, iris, the ancient Greek noun, Ἶρις, meaning both the rainbow and the halo of the Moon, + -er, verb-forming suffix. Iris, + ation, a suffix derived from Latin, -ātiō.
Meaning: to make, or to become iridescent.
The picture shows a rocky, scree-strewn mountain summit overlooking a sprawling green valley beneath a vivid blue sky with scattered clouds.
A brilliant double rainbow arcs across this panoramic vista, with its vibrant primary band cutting across soft cloud shadows and the patchwork of fields below.All suggesting Iris has travelled this way on the rainbow whilst carrying messages from the Gods to us mere mortals…
#WritersOfMastodon #TalesFrom… #SecretLife… #HowtoSucceed… #Author #Novel
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Halloween Horror Movie Review: The Return of the Living Dead (1985)
Title: The Return of the Living Dead
Release Date: August 16, 1985
Director: Dan O’Bannon
Production Company: Hemdale Film Corporation | Fox Films, Ltd. | Cinema ’84
Main Cast:- Clu Gulager as Burt Wilson
- James Karen as Frank Johnson
- Don Calfa as Ernie Kaltenbrunner
- Thom Mathews as Freddy Hanscom
- Beverly Randolph as Tina
- Miguel A. Núñez Jr. as Spider
- John Philbin as Chuck
- Jewel Shepard as Casey
- Brian Peck as Scuz
- Linnea Quigley as Trash
- Mark Venturini as Suicide
- Jonathan Terry as Colonel Horace Glover
- Cathleen Cordell as Ethel Glover, Colonel’s Wife
- Drew Deighan as Jerry, The Paramedic
- James Dalesandro as Tom, The Paramedic
- Robert Libman as Tac Squad Captain
- Derrick Brice as Gunnery Sergeant Jefferson
- Allan Trautman as Tarman Zombie
- Terrence Houlihan as Yellow Cadaver Zombie
- Jerome Coleman as Legless Corpse Zombie
- Cherry Davis as The Voice of The Female Torso Zombie
- John Durbin as The Zombie Who Calls for More Paramedics
- David Bond as The Zombie Who Calls for More Cops
Synopsis (via Letterboxd):
When two bumbling employees at a medical supply warehouse accidentally release a deadly gas into the air, the vapors cause the dead to rise again as zombies.
My Thoughts:
Night of the Living Dead co-writer John Russo split off from George Romero to create his own zombie franchise beginning with this 1980s time capsule of a movie. The movie is set around a medical supply warehouse in Louisville where drums of the toxic gas that causes zombieism were accidentally sent by the military. Remember the Reagan Era 80s when nonetheless the cultural zeitgeist was deeply mistrustful of the military rather than “support the troops?” Naturally, the gas is released affecting not just the warehouse but the nearby cemetery where a group of punks have gathered to party.
The blue collar workers (all of whom seem to be played by familiar character actors that I cannot place where I’ve seen them before) get most of the best lines. There’s the wiseacre Frank training the new hire Freddy, their hapless boss who makes all the wrong decisions when he takes charge, and the deadpan mortician Eddie. The punks are funny in that 80s movie punks are always the same over-the-top stereotypes. The girl who strips naked for the entire film is gratuitous though.
And then there are the zombies who just want “BRAIIIIIINSSS!!!” because they stave off the pain of decay (possibly a metaphor for substance abuse?). It’s hard to believe that is the first depiction of zombies craving brains. The idea of zombies who not only talk, but can hold conversations is a bit off-putting, but then again the “Send more cops!” gag is hilarious.
This isn’t a masterpiece, but it’s corny fun with a lot of great one-liners, and probably would be exponentially more enjoyable at a crowded theater screening.
Rating: ***
#Comedy #HalloweenHorror #Horror #MovieReviews #Movies #ScienceFiction #Zombies -
DATE: October 27, 2026 at 07:00AM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: We’ll pay to get revenge on an algorithm, even though it can’t feel a thing
URL: https://www.psypost.org/well-pay-to-get-revenge-on-an-algorithm-even-though-it-cant-feel-a-thing/
When a computer algorithm treats people unfairly, they tend to get just as angry and retaliate just as often as they would against a human wrongdoer. A new series of experiments published in Journal of Experimental Psychology: General provides mixed evidence on whether punishing a machine brings the same sense of closure as punishing a person, but it suggests that a machine’s response to being punished, such as sounding hurt or promising to make fairer offers, can strengthen people’s sense that justice was done. The findings suggest that as artificial intelligence takes on more decision-making roles in society, organizations may need to design systems that can communicate accountability when things go wrong.
Automated systems are increasingly tasked with consequential choices, from screening job applicants to steering vehicles and prioritizing medical care. To understand how people react when these systems cause harm, psychologists study a concept known as mind perception, which is the everyday process of judging what kind of mental life another entity possesses. People generally evaluate minds along two separate dimensions: agency, meaning the ability to think, plan, and act intentionally, and experience, meaning the capacity to feel physical pain, pleasure, fear, and emotions.
In human interactions, the capacity to commit a moral wrong and the capacity to experience punishment usually go together, since a sane adult can both plan a harmful act and feel the sting of a penalty. Artificial intelligence splits these two qualities apart because people often view machines as having enough agency to cause deliberate harm, yet almost no ability to feel pain or remorse. For instance, research covered by PsyPost in 2023 found that people judged AI agents as more deserving of blame than humans in one classic life-and-death dilemma. Similarly, another 2023 study covered by PsyPost showed that people hold automated driving assistants responsible for road outcomes while simultaneously categorizing the software as a mere tool rather than a true moral being.
This mental divide raises questions about whether punishing an algorithm can produce justice satisfaction, which is the feeling of closure, fairness, and resolution a victim experiences after an offender is penalized. Psychological theories generally propose two reasons why punishment feels satisfying: either it fulfills a desire for retribution by making the wrongdoer suffer for their actions, or it serves a communicative role by teaching the wrongdoer a moral lesson.
Because people tend to see machines as unable to genuinely suffer or feel guilt, study author Alexander Eng, an associate professor of organizational behavior in the Department of Management at the Asia School of Business in Kuala Lumpur, and co-author Kai Chi Yam of the National University of Singapore examined how people emotionally and behaviorally respond during and after they penalize an unfair algorithm.
“Research on punishment usually assumes that anyone capable of doing wrong can also be punished,” Eng told PsyPost. “We tend to see a young child as unable to truly commit a wrong, and so also as unfit for punishment. Machines break that link: people see AI as able to make decisions that hurt them, yet unable to feel pain, regret, or learn a moral lesson. As AI takes on decisions like medical triage and firing employees, we wanted to know how people respond when they try to hold it accountable, and whether punishing it gives them any sense that justice was done.”
The researchers began with a pilot study of 380 English-speaking adults in the United States, recruited through the online platform Prolific, to verify whether people actually punish algorithms. Participants played a single round of an economic exercise called the ultimatum game, in which a partner was given £0.50 and proposed how to split it. If the participant accepted the split, both parties kept their share, but if the participant rejected it, neither party received anything, making rejection a form of costly punishment.
Participants were told their partner was either another human participant or an algorithm, and they received either a fair offer of £0.25 each or an unfair offer where the partner kept £0.45 and left the participant just £0.05. In reality, the researchers preset all offers. Receiving a stingy offer from an algorithm provoked much higher anger (an average of 3.12 on a 5-point scale) than receiving a fair offer from an algorithm (1.31). When given the unfair deal, participants felt comparable anger toward algorithms (3.12) and humans (2.91), and they punished the unfair algorithm by rejecting its offer 57.8 percent of the time, which was not statistically different from the 62.2 percent rejection rate for unfair humans.
In their first main experiment, the authors examined what happens after punishment is delivered using a final sample of 123 Prolific participants. Every participant received the unfair £0.05 offer in the ultimatum game from either a human or an algorithm, decided whether to accept or reject it, and then answered five questions measuring their justice satisfaction, such as whether they felt everyone got what they deserved. Afterward, participants played a second exercise called the dictator game, where they were given £0.50 and could unilaterally share any amount in £0.05 increments with their partner, while also having the option to switch to a new human partner first.
As in the pilot study, participants rejected unfair offers from algorithms (38 out of 62) and humans (40 out of 61) at similar rates. Overall, the 78 participants who punished their partner by rejecting the offer reported higher justice satisfaction (3.57 out of 5) than the 45 who accepted it (2.63). Controlling for whether participants switched partners, the satisfaction gap between those who punished and those who did not was larger when the offender was a human than when it was an algorithm, and higher justice satisfaction in turn predicted greater generosity in the second game.
Because the option to switch partners or the belief that computers have no use for money might have influenced the first experiment, the researchers conducted a second study with a stricter setup among 739 participants, preregistered along with the third and fourth experiments through the journal’s registered-report review process. Participants were told that five algorithms and five human research assistants were competing across multiple negotiations, and that while neither kept the money personally, the lowest-earning algorithm and human would be eliminated from the competition.
Participants were not allowed to switch partners between the ultimatum game and the dictator game, and they also completed scales measuring their partner’s perceived agency and emotional experience, along with their own baseline tendency to attribute human-like consciousness to technology, a trait known as anthropomorphism.
Participants rated human partners higher on agency (3.30 versus 2.79 out of 5) and much higher on the capacity for emotional experience (3.20 versus 1.86) than algorithmic partners. Even so, participants rejected the unfair offer at comparable rates for algorithms (71.0 percent, or 262 of 369) and humans (65.4 percent, or 242 of 370), and those who punished their partner felt much greater justice satisfaction (3.53) than those who accepted the unfair split (2.71). In contrast to the first study, after controlling for baseline anthropomorphism, the link between punishing and justice satisfaction, and in turn generosity, was equally strong whether the partner was an algorithm or a human.
“People react to an unfair machine much as they react to an unfair person,” Eng said. “Participants got just as angry at an algorithm that made them a lowball offer as at a human who made the same offer, and they were just as willing to give up their own money to punish it. Whether punishing the machine felt as satisfying was less consistent across studies.”
A third study tested these dynamics in a different scenario called the trust game with 735 participants who chose to invest £0.20 with a human research assistant or an algorithm. The investment was tripled to £0.60, and the partner broke the participant’s trust by returning just £0.10 instead of a fair £0.30 split. Participants could then penalize their partner by recommending on a 1-to-5 scale that the partner be removed from the player pool, before rating their justice satisfaction and playing a second round where the roles were reversed.
Participants gave similar removal ratings to unfair algorithms (3.37 out of 5) and unfair humans (3.26). Unexpectedly, harsher removal ratings were associated with lower justice satisfaction rather than higher closure. The authors suspect this is because participants only submitted a recommendation and never saw a penalty take effect, so the ratings may have reflected anger more than punishment. In an exploratory analysis looking directly at anger, feeling angrier predicted lower justice satisfaction and less generosity toward both types of partners, with anger reducing justice satisfaction slightly more when the partner was human than when it was an algorithm.
In a fourth study involving 3,684 participants who rejected the unfair £0.05 offer in the ultimatum game, the researchers tested whether a message from the penalized partner could increase justice satisfaction. Immediately after rejecting the offer from a human or an algorithm, participants received either no message at all, a message expressing emotional distress over repeated rejections, a message calmly acknowledging that the offer was too low and promising to be fair in the future, or a combined message expressing both distress and a moral lesson learned. A separate preliminary test with 305 people confirmed that these messages effectively conveyed emotional suffering and moral learning as intended.
Across the entire sample, participants paired with an algorithm reported slightly lower justice satisfaction (3.56 out of 5) than those paired with a human (3.66), a small difference that accounted for about half a percent of the overall variation in satisfaction scores. However, receiving a message from an algorithm expressing emotional suffering (3.57) or moral learning (3.62) produced higher justice satisfaction than receiving no response from the algorithm (3.42).
For human partners, the differences between receiving no message (3.57) and receiving a suffering (3.66) or moral learning (3.69) message were not statistically significant after adjusting for multiple comparisons. Still, the boost from a message was not statistically larger for algorithms than for humans, and combining both messages added no measurable benefit beyond either one alone. The results suggest that a response from a machine can help satisfy people’s need for redress, even though participants rated algorithms as far less capable of feeling than humans.
“We expected an apology or a show of remorse from an algorithm to count for little, since people don’t believe algorithms feel anything,” Eng explained. “Yet those messages raised people’s sense of justice for algorithms at least as much as the same messages did for humans. It was also striking that people paid to punish an algorithm that couldn’t keep the money and couldn’t experience the loss.”
A final supplemental experiment with 1,093 participants examined whether dissatisfaction after being wronged causes people to take out their frustration on an innocent bystander, a behavior known as displaced aggression. Participants received the unfair £0.05 offer in the ultimatum game from a human or an algorithm, decided whether to accept or reject it, and then played the dictator game paired with a brand-new human research assistant who had no involvement in the first round.
Controlling for baseline anthropomorphism, participants who punished their initial partner by rejecting the unfair offer shared slightly more money with the innocent bystander (an aggression score of 6.75 versus 7.07 on an 11-point scale, where higher scores mean less money shared) than those who did not punish, regardless of whether the original wrongdoer was a person or a machine.
“The anger and punishment results held up across more than 6,000 participants,” Eng noted. “The machine-versus-human differences in justice satisfaction were small where they appeared at all: in our largest study, of about 3,700 people, partner type explained roughly half a percent of the variance. I’d treat that as a modest pattern that needs more testing.”
As with all research, there are some caveats to consider when interpreting these results. All of the experiments relied on brief online financial games where the harm consisted of being offered an unfair split of £0.50 by an anonymous text-based partner. How people think and feel when a simple computer program shortchanges them by a few cents may differ from how they react to high-stakes algorithmic harm in the real world, such as an automated system wrongfully denying a mortgage, firing an employee, or causing a traffic accident. In addition, participants chose for themselves whether to punish, so differences between those who punished and those who did not may partly reflect who chose to punish rather than the effect of punishing itself.
“These were online economic games with small stakes, splitting 50 pence,” Eng pointed out. “Real harms from AI, like a denied loan or a wrong diagnosis, carry much more weight, so the effects could look different there. The justice satisfaction results were also mixed: one study found a clear gap between machines and humans and another found none, so I wouldn’t claim that punishing machines never satisfies people.”
The evidence on whether punishing a machine feels less satisfying than punishing a person was mixed across the studies, appearing in the first and fourth experiments but not in the second, while the third experiment’s punishment measure did not work as intended. The clearest gap came from the smallest experiment, which included 123 participants after dozens of duplicate entries were removed and which had originally been preregistered to test different outcomes; the larger, preregistered second experiment did not replicate it.
The authors also noted that their five-item survey measuring justice satisfaction showed relatively low internal consistency in the fourth study, indicating a need to develop more precise tools for measuring psychological closure after technological failures. Future research could explore how different types of real-world errors and corrective feedback shape public trust, helping engineers and policymakers design accountability measures, such as holding human creators rather than machines responsible, that satisfy human intuitions of fairness.
“We want to test other kinds of wrongdoing and punishment beyond economic games, and develop a better measure of justice satisfaction,” Eng added. “In a related project, I’m studying the ‘AI disclosure penalty,’ where people value content less once they’re told AI made it.”
The study, “Artificial Intelligence and Machines Are Capable of Harm, but Not of Experiencing Punishment,” was authored by Alexander Eng and Kai Chi Yam.
URL: https://www.psypost.org/well-pay-to-get-revenge-on-an-algorithm-even-though-it-cant-feel-a-thing/
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#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #ArtificialIntelligence #AI #Algorithms #MachineLearning #AIethics #Psychology #SocialPsychology #Justice #Accountability #HumanBehavior
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Every engineer running autonomous agent loops knows the in-context reflection drill. The agent runs a bash script, fails on an obscure permission flag or an unquoted path, sees the error in stderr, reflects on what went wrong, and fixes it on the second try.
The task finishes green. You log the run, close the thread, and move on.
Tomorrow morning, the same agent hits the exact same edge case in a fresh session and makes the exact same mistake all over again.
In-context reflection is a temporary patch. It burns context tokens, bloats the prompt across long tool sessions, and evaporates the millisecond the execution context clears. The traditional alternative, full-model fine-tuning, is a non-starter for everyday errors. Nobody is spinning up a multi-GPU training run every afternoon to update base weights, and doing so risks catastrophic forgetting across existing tool workflows.
A research team from Princeton University and Meta FAIR just published a practical middle ground. In their paper, Don't Forget What You Just Learned: Self-Directed Repair Turns Agent Mistakes Into Lasting Lessons (arXiv:2610.31846), Yuval Kansal, Karen Ullrich, Polina Kirichenko, Ilia Kulikov, Niraj K. Jha, Mohammad Pezeshki, and Mark Ibrahim lay out an architecture called Self-Direction.
Instead of writing a reflection note into a scratchpad file or relying on a fragile system prompt append, the agent distills its execution mistakes directly into tiny low-rank adapter weights.
The failure of the reflection scratchpad
Modern agent setups usually address cross-session memory with one of two patterns:- Appending error reflections to an agent scratchpad or MEMORY.md file.
- Embedding failure traces into a vector database for retrieval-augmented prompt injection on future turns.
Both patterns struggle in production. Text notes appended to context eat away at attention budgets. As the reflection scratchpad grows, agents suffer from prompt dilution and instruction drift. More critically, an agent reading a natural-language description of an old error frequently misapplies the lesson to unrelated tool calls.
The Princeton and Meta team tracked this failure mode across open-ended research workflows (GAIA), multi-step tool execution (Claw-Eval), and command-line environments (Terminal-Bench 2.1). When agents relied on standard in-context retries, their correction reliability plummeted when facing recurring variations of the same underlying task. The agent repeatedly fell into the same traps because the underlying policy distribution never moved.
Standard Agent Flow:
Error -> In-context reflection -> One-off fix -> Session ends -> Lesson lostSelf-Direction Flow:
Error -> Trajectory reflection -> Low-rank adapter update -> Persistent library -> Routed on demandDistilling mistakes into 0.02% parameter budgets
The core mechanism of Self-Direction sits between ephemeral prompt engineering and heavy offline training.When an agent fails an execution trajectory, it runs an internal critique to isolate the decision step that caused the downstream failure. Once the corrective trajectory is verified, the system does not just print the correction. It freezes the base model weights entirely and trains a micro-LoRA adapter on the self-directed repair pair.
The parameters updated during this step represent roughly 0.02% of the base model's parameter count.
Because base weights remain frozen:
- Existing base model capabilities and general conversational skills stay intact without degradation.
- Training completes locally in seconds on consumer hardware without cluster coordination.
- The resulting adapter file is lightweight enough to store, version, and swap on the fly.
The agent saves these targeted adapter checkpoints into a persistent local library. When a new prompt arrives, an automated routing layer inspects the problem context and attaches the relevant adapter before the execution loop begins.
Benchmark gains across open-ended tasks
The researchers tested Self-Direction on open-weight models from the Qwen family across three distinct agent benchmarks:- GAIA: Multi-modal, complex multi-hop research requiring web navigation and reasoning.
- Claw-Eval: Structured tool-use workflows testing API interactions and parameter extraction.
- Terminal-Bench 2.1: Unconstrained Linux command-line tasks, bash scripts, and system administration.
Across these environments, adding Self-Direction improved overall task completion rates by 8.9 to 17.0 percentage points compared to standard in-context reflection baselines.
More importantly, the recurrence rate of known errors dropped sharply. The authors found that Self-Direction nearly doubled the reliability of corrective actions on subsequent encounters. Instead of guessing whether the agent would notice a text hint in its system prompt, the updated adapter shifted the token sampling probabilities directly away from the faulty API syntax.
The team also evaluated out-of-distribution transfer. A small set of adapters trained on specific terminal failures generalized across dozens of unseen tasks involving related file-system permissions and process management. The agent learned the structural pattern of the fix rather than memorizing the exact shell string.
What this means for local agent runners
For developers building and hosting agent infrastructure, this work highlights an architectural transition.Over the past two years, the industry leaned heavily on prompt stuffing to simulate agent learning. We treated context windows as infinite scratchpads, piling up few-shot examples, dynamic rules, and post-mortem notes until the prompt became brittle.
Self-Direction shows that treating execution errors as online distillation targets produces cleaner long-term behavior. When an agent trips on an environment quirk, the solution is not to explain the error back to the model in prose every time it wakes up. The solution is to let the agent compile the lesson into weights, index the adapter, and move forward.#ai #agents #python #opensource #software #coding #development #engineering #inclusive #community
Why In-Context Corrections Do Not Stick in Agent Loops -
Why 10,000 Steps a Day? The Curious Story Behind a Number We Took as Gospel
Ten thousand steps became a famous fitness goal, but the science behind the number is more interesting than you might think.Dear Cherubs, why are we all chasing 10,000 steps a day as though a tiny digital counter holds the secret to eternal youth? The surprising answer takes us back to 1960s Japan, a clever marketing campaign and a number that may have become a health obsession partly because a machine reset at 10,000.
THE NUMBER THAT SOLD A WALKING HABIT
According to The BMJ, the familiar target traces back to Japan in the mid-1960s, when a company called Yamasa introduced a pedometer known as the manpo-kei. The name translates roughly as “10,000-step meter”, and it arrived during the excitement surrounding the 1964 Tokyo Olympics.
The timing was excellent. Japan was becoming increasingly interested in physical fitness, and the idea of tracking daily movement offered people a simple way to get more active. Ten thousand was memorable, easy to communicate and satisfyingly round. Marketing departments have been exploiting those qualities for decades. No surprises there.
But here comes the interesting bit. Research published in the journal Ethnographiques.org reports that the original manpo-kei reset to zero after every 10,000 steps. That detail gives the machine-reset theory some credibility: a device designed around a particular number could have helped reinforce that number as a daily target.
However, it would be a stretch to claim that the reset mechanism alone created the trend. The product was already named after 10,000 steps, and the slogan was being promoted as a walking goal. The evidence points to a combination of marketing, product design and a catchy number rather than one mechanical quirk changing humanity forever.
A MARKETING NUMBER BECOMES A HEALTH RULE
The real plot twist is that 10,000 steps was not originally established through clinical trials proving that this exact number was necessary for good health. The scientific evidence came later, while the target had already gained popularity.
According to The BMJ, the figure became widely recognised through the Japanese campaign rather than emerging from a scientific discovery. Over time, pedometers, fitness trackers, smartphones and workplace challenges helped turn it into a familiar benchmark.
And it stuck. There is something wonderfully human about preferring a clear target to a complicated explanation. Ten thousand feels like a mission. Six thousand feels like an unfinished spreadsheet.
Does that mean walking 10,000 steps is pointless? Absolutely not. Walking regularly supports health, and increasing activity can be beneficial, particularly for people who spend much of the day sitting. The problem is treating one round number as a universal medical requirement.
Research published in JAMA Internal Medicine in 2019 found that, among older women studied, taking around 4,400 steps daily was associated with a lower risk of death than taking around 2,700. The association levelled off at approximately 7,500 steps in that study. Results from one group do not establish the perfect target for everyone, but they show that meaningful benefits can occur below 10,000.
The takeaway? Aim to move more, build a routine you can maintain and adapt your goals to your circumstances. Missing an arbitrary number on your watch is not a moral failure.
For more curious explainers about everyday myths and surprising facts, explore Thisclaimer at thisclaimer.com and its YouTube channel. Because sometimes the real story behind a modern habit is far more interesting than the habit itself.
Sources
The BMJ — “Sixty seconds on… 10 000 steps” — https://www.bmj.com/content/384/bmj.q598
Ethnographiques.org — “Taking 10,000 steps a day – really? A survey of ordinary self-tracking practices” — https://shs.cairn.info/article/E_RES_216_0157/pdf?lang=en
Cairn.info
JAMA Internal Medicine — “Association of Step Volume and Intensity With All-Cause Mortality in Older Women” — https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2734709
Step Counting: A Review of Measurement Considerations and Health-Related Applications — https://pmc.ncbi.nlm.nih.gov/articles/PMC5488109/
Thisclaimer — https://thisclaimer.com/
Thisclaimer YouTube — https://www.youtube.com/@thisclaimer?sub_confirmation=1
The Thisclaimer logo blends a classic warning symbol with a brain icon to represent critical thinking, curiosity, and thoughtful disclaimers #10000Steps #dailyExercise #fitnessMyths #fitnessTrackers #healthFacts #japaneseInventions #pedometerHistory #surprisingHistory #tokyoOlympics #walkingFacts -
Reta final da eleição de 2026 é marcada por violência bolsonarista em várias cidades
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‘Eleitor foi mobilizado pela comparação’ entre Lula e Bolsonaro, afirma cientista político
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L’Esprit indomptable
Quoiqu'un homme puisse faire, s'il génère l'esprit qui pense à faire quelque chose, son esprit s'arrête sur cette chose. C'est pourquoi l'homme devrait développer son esprit sans lui fournir d'endroit où s'arrêter. Si l'esprit n'est généré, la main ne pourra se mouvoir vers l'avant. Ceux qui, lorsqu'ils bougent, engendrent la pensée qui normalement s'arrête dans ce mouvement, mais ne s'arrête pas au cours de l'action, ceux-là sont appelés maîtres de toutes les […] -
De s’inspirer des fous (Sébastien Brant)
Illustration: Albrecht Dürer De s'inspirer des fous Chaque jour on peut voir dégringoler des fous et chacun en riant s'en fait les gorges chaudes. Tous les gens bien assis, qui souvent ont porté eux-mêmes la marotte, en haussent les épaules. Un fou condamne un fou, mais pousse sa charrette dans les mêmes ornières et tombe au même endroit où l'autre était tombé. Hippomène avait vu*1 souvent couper des têtes à des écervelés, mais il risqua la […]https://arbrealettres.wordpress.com/2026/10/26/de-sinspirer-des-fous-sebastien-brant/
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Des mauvaises femmes (Sébastien Brant)
Illustration: Albrecht Dürer Des mauvaises femmes J'ai dit dans mon Prologue tout à fait clairement que je ne voulais pas parler déplaisamment des femmes qui sont bonnes, mais qu'on m'en voudrait bien si je ne disais rien des épouses mauvaises. La femme qui écoute la voix de la sagesse n'est pas facilement induite en tentation pour se couvrir de honte. Une femme sensée incline à la douceur un mari courroucé. Assuérus avait*1 prononcé le verdict, mais […]https://arbrealettres.wordpress.com/2026/10/27/des-mauvaises-femmes-sebastien-brant/
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On another note: My headphones just got better for mixing.
The answer is Crossfeed from Kiln and a home made txt file looking like bellow, made from an MSC file provided by the company who created the headphone (Ollo S4X):
Preamp: 0 dB
Filter 1: ON LS Fc 93 Hz Gain 1.5 dB Q 0.5
Filter 2: ON PK Fc 350 Hz Gain -2.0 dB Q 0.7
Filter 3: ON PK Fc 4200 Hz Gain -2.0 dB Q 1.0
Filter 4: ON HS Fc 7100 Hz Gain 1.0 dB Q 0.5 -
RE: https://uddannelse.social/@mosgaard/117507735027190464
That was fast, unit sold! So long Apollo 8P, you have been a good friend for a long time but now Linux is more fun.
I will especially remember you for the dance project in the woods (recorded in November, it was pretty cold): https://www.youtube.com/watch?v=j-9biRJYzJU
Recorded by my friend Uffe Birk, mixed by yours truly and mastered by Nathan Moody at Obsidian Sound. Those were the days.
I'm also the vocalist on the recording for the curious.
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På De rejsendes patreon er dagens emne Call of Cthulhu, som en nedtælling til den kommende sæson her i november.
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Fading Suns: Translatio Sancti del 45 – Dæmoner og KIer
Episode 45 er finalen i kampagnen: Det er en desperat kamp mod dæmoner, kunstige intelligenser, cybervåben og mere for at redde solsystemet
http://rollespil.blog/2026/10/26/fading-suns-translatio-sancti-del-45-daemoner-og-kier/
