18 results for “claroty”
📣 Excited to have David Ruzicka, OT Security Director at Clarios, as a featured speaker at Nexus Conference 2026!
Join industry leaders for conversations on cybersecurity, critical infrastructure, and operational resilience.
🗓️ 𝗡𝗼𝘃. 𝟯-𝟱 | 📍 𝗪𝗮𝘀𝗵𝗶𝗻𝗴𝘁𝗼𝗻, 𝗗.𝗖.
🔗 𝗔𝗽𝗽𝗹𝘆 𝘁𝗼 𝗮𝘁𝘁𝗲𝗻𝗱: https://nexusconference.io
#Nexus2026 #OTSecurity #Cybersecurity #CriticalInfrastructure
New Shared Link - Keeping Training Content Current When the Business Won’t Stop Changing https://clarityconsultants.com/blog/keeping-training-content-current/ - How L&D leaders are keeping training content current in high-change businesses: triage by risk, run maintenance as an operating model, and scale capacity.
Self-doubt can delay your best decisions. But confidence begins with clarity, not certainty. Ready to trust yourself? Read the full blog. #SelfConfidence #SelfAwareness #PersonalGrowth #LifeLessons #Leadership #Mindset #SelfBelief #ThoughtOfTheDay
https://stayingalive.in/thought-of-the-day/confidence-begins-where.html
AUDIOPHILE MAN - HiFi REVIEW
The BEST Hi-Fi isolation platform I've ever heard? Stack Audio's new AUVA Platform delivers remarkable improvements in clarity, bass, detail and soundstage.
Watch my review: https://youtu.be/fD1OCqb7tc8
#StackAudio #HiFi #Audiophile
In just 2 days, Sharad Purnima will illuminate the night sky, marking a powerful full moon that symbolizes divine abundance and spiritual clarity. As the moon shines brightest in the lunar month of Ashwin, it invites us to embrace inner wisdom and the nurturing energy of the cosmos. Prepare to connect deeply ...
https://www.mastroify.com/festivals/sharad-purnima
#HinduFestival #VedicCulture #UttaraBhadrapadaNakshatra #SharadPurnima #WeekendVibes #VedicAstrology #Horoscope #Jyotish #Mastroify
This month invites Scorpio to explore deeper emotional currents as Uttara Bhadrapada nakshatra illuminates your intuition and inner wisdom. With Sharad Purnima approaching, it’s a powerful time to align your intentions with clarity and compassion, fostering transformation from within....
https://www.mastroify.com/horoscope/scorpio/monthly
#Scorpio #ScorpioHoroscope #VrishchikRashi #MonthlyHoroscope #MonthAhead #UttaraBhadrapadaNakshatra #SharadPurnima #WeekendVibes #VedicAstrology #Horoscope
This week, Scorpio, the steady light of Uttara Bhadrapada nakshatra invites you to embrace deeper emotional clarity and long-term visions. As Shukla Trayodashi leads into Sharad Purnima, focus on aligning your inner intentions with outer actions to manifest lasting growth. Reflect though...
https://www.mastroify.com/horoscope/scorpio/weekly
#Scorpio #ScorpioHoroscope #VrishchikRashi #WeeklyHoroscope #WeekAhead #UttaraBhadrapadaNakshatra #SharadPurnima #WeekendVibes #VedicAstrology #Horoscope
This week invites Libra to explore balance through deeper reflection and meaningful connections, guided by Uttara Bhadrapada’s wisdom and the growing light of Sharad Purnima. Embrace clarity in your intentions and allow harmony to unfold naturally.
https://www.mastroify.com/horoscope/libra/weekly
#Libra #LibraHoroscope #TulaRashi #WeeklyHoroscope #WeekAhead #UttaraBhadrapadaNakshatra #SharadPurnima #WeekendVibes #VedicAstrology #Horoscope
Cadillac team principal Marcin Budkowski refused to confirm that the team's current driver lineup is under contract for 2027 as Colton Herta retains hopes of a Formula 1 seat. Herta made his latest FP1 appearance at COTA on Friday and says he hopes to have clarity over his future in the coming weeks.
If you can answer yes to these three questions, I invite you to get more information at the link below and schedule a clarity session! https://www.autismchrysalis.com/later-diagnosis-life-coaching/
Most teams are still trying to apply classic SEO tactics to an AI-first internet.
But when your buyers type questions into ChatGPT, Claude, Perplexity or Gemini instead of Google, the job changes. You’re no longer just ranking pages; you’re trying to become the safest, clearest answer those models can give.
That’s what GEO — Generative Engine Optimization — is about.
In this article, I’ll break down a practical GEO framework you can use without buying anything: how to audit your AI visibility, close the biggest gaps, and measure progress over time.
1. Think in entities, not just pages
Search engines index pages. AI engines reason about entities: brands, people, products, categories.
If you want to show up in AI-generated answers, you need to make your brand an easy-to-use entity:
A simple template that works well:
> “[Brand] is a [category] platform for [segment] that helps them [job] using [key mechanism].”
Drop that exact sentence on your homepage, LinkedIn, GitHub, docs, Medium bio — everywhere. It’s intentionally boring; clarity beats clever when a model has to quote you back to someone.
2. Run a multi-engine AI visibility audit
You can get a surprising amount of insight in 30–40 minutes.
Write down what’s wrong, not just the score:
You now have a quick map of your AI presence.
3. Turn findings into concrete fixes
GEO is not about gaming prompts. It is mostly about cleaning up the training data you control.
Patterns we repeatedly see in audits — and how to address them:
The key is to write these assets in an answer style — factual, concise, low on hype — so AI engines feel safe quoting them.
4. Fix your structured signals
Your technical configuration can be a strength or a bottleneck for AI crawlers.
Minimum viable setup:
If you are already doing solid technical SEO, you are 70% of the way there — GEO just asks you to be deliberate about which URLs tell your brand’s story.
5. Measure GEO like a product, not a campaign
Instead of tracking only traffic or vanity metrics, track how your AI presence itself improves:
Repeat your manual audit every 4–6 weeks. If you prefer, turn it into a small internal dashboard so marketing and product can see progress.
Over time, a pattern usually emerges:
6. GEO principles you can apply today
To recap, a practical GEO playbook for developers and marketers looks like this:
You do not need to abandon SEO for this; most GEO best practices reinforce good technical SEO and content architecture.
If anything, the AI-first internet is forcing us to write clearer docs, build better information architecture, and measure visibility in a more realistic way.
That’s a good thing.#ai #llm #marketing #seo #software #coding #development #engineering #inclusive #community
A Practical Framework for GEO (Generative Engine Optimization)
I live in Bergen, Norway, and ever since I moved into my own apartment in October 2024, I’ve become increasingly aware of how little I’m exposed to advertising and propaganda. Being anti-TV and anti-radio has allowed me to distance myself from much of the constant messaging that seems to dominate modern life over here. Without these things playing in the background, I’ve come to appreciate the peace and quiet that comes with choosing what I allow into my mind.
As you could probably guess from how much I love spending time in nature and meditating, I’m drawn to a slower, more intentional way of living. I find comfort in silence, in being present, and in connecting with the world around me rather than constantly consuming information and entertainment. Nature and meditation offer me something that modern technology often seems to take away, the sense of stillness, clarity, and genuine connection with myself.
Sometimes, I genuinely struggle to understand how most people manage to keep up with all the noise of modern life. Between television, radio, advertisements, social media, and the endless stream of digital distractions, it feels as though there is very little room left for silence or independent thought. I find it exhausting just thinking about it.
Perhaps I’ve simply grown accustomed to a quieter existence, but I can’t help wondering why so many people accept this constant overstimulation as the new normal. Honestly, the whole thing makes me want to retreat even further into nature and enjoy the peace of being disconnected. Urgh.
#Consumerism #AntiConsumerism #Capitalism #ADs #Freedom #Anarchism #Hippie #Nature #Buddhism
Small coding choices can make software easier to understand and maintain. One example in C# is how we initialize string properties in DTOs.
A DTO —Data Transfer Object— carries data between parts of an application. For example, it might carry a customer’s name from an API request into the application.
Consider these two approaches:
public sealed class CreateCustomerDtoWithNull
{
public string Name { get; init; } = null!;
}
public sealed class CreateCustomerDto
{
public string Name { get; init; } = string.Empty;
}
Both avoid the compiler warning about an uninitialized non-nullable property. Both initial values can be rejected by FluentValidation.
But they create different starting states.
For required text fields where missing and empty values are treated the same, I prefer string.Empty: it gives the property an actual string value without suppressing a compiler warning.
The difference becomes clearer when we look at what each declaration means.
With nullable reference types enabled, string expresses the intention that a property should not contain null.
However:
public string Name { get; init; } = null!;
initializes that property with null.
The ! suppresses the nullable warning. It does not create a value, check anything at runtime, or guarantee that something will replace the null later. Microsoft explicitly documents that the operator has no runtime effect. Microsoft: null-forgiving operator.
With:
public string Name { get; init; } = string.Empty;
the property starts with "": an actual string containing zero characters. It is different from " ", which contains a space. Microsoft: String.Empty.
This makes the initial state more consistent with the declared type. We have a string; we still need to check whether its content is acceptable.
“FluentValidation can catch both” is correct.
For example:
using FluentValidation;
public sealed class CreateCustomerDtoValidator
: AbstractValidator
{
public CreateCustomerDtoValidator()
{
RuleFor(x => x.Name)
.NotEmpty()
.WithMessage("Name is required.");
}
}
For strings, NotEmpty() rejects all of these:
Value Result
null Rejected
"" Rejected
" " Rejected
"Maria" Accepted
This behavior is documented by FluentValidation and can also be checked in its implementation. FluentValidation documentation, NotEmptyValidator source code.
The preference for string.Empty therefore concerns the object’s starting state and the clarity of the code.
It separates two responsibilities:
Also, validation does not fill in the missing name. It reports the problem so the application can reject the request.
In an ASP.NET Core application using Clean Architecture, a straightforward execution flow is:
API receives the request
→ DTO is created
→ Application validates the input
→ Domain creates a valid customer
→ Infrastructure saves the customer
The Application layer can execute validation before continuing:
await validator.ValidateAndThrowAsync(dto, cancellationToken);
// Continue with customer creation only after validation succeeds.
The API must then translate validation failures into an appropriate response, such as HTTP 400 with useful error messages.
The Domain should also protect its own essential rules when creating a customer. This keeps those rules effective when data arrives through another route.
Clean Architecture supports this separation of responsibilities. It does not prescribe a particular DTO initializer. Microsoft: common application architectures.
For a manager, the practical benefit is straightforward: clear starting states reduce the assumptions developers need to make. Reviewers can see what the object initially contains and where unacceptable input is rejected.
There are limits to this preference.
First, string.Empty only sets the initial value. Depending on serializer settings, an incoming JSON value such as "name": null can replace it. External input still needs validation. Microsoft: nullable annotations in System.Text.Json.
Second, sometimes null carries useful information. If a DTO should accurately represent missing input, a nullable property can be more appropriate:
public string? Name { get; init; }
The business rule can still require a name. The nullable type acknowledges that an incoming request may fail to provide one.
There are other reasonable designs too. Maarten Balliauw discourages using default! to silence warnings in JSON DTOs and discusses alternatives such as nullable properties, constructors, and required. His article supports avoiding hidden assumptions, rather than a universal preference for empty strings. Maarten Balliauw: nullable warnings in JSON models.
Ian Griffiths provides a useful counterargument: an empty string can also hide missing meaningful data if it is added simply to silence the compiler. This matters especially when objects are expected to be valid immediately after construction. Ian Griffiths: defeating the point with empty strings.
My preferred team convention is therefore:
For input DTOs where missing and empty text have the same meaning, use string.Empty and validate required content with NotEmpty(). Use nullable properties when absence matters. Use null! when there is a specific, justified initialization guarantee.
The aim is to keep the declared intention, actual starting value, and validation rules consistent—and easy for the next person to understand.
[email protected]#coding #csharp #dotnet #softwaredevelopment #software #development #engineering #inclusive #community
Why I Prefer string.Empty Over null! in Input DTOs
DATE: October 24, 2026 at 12:00PM
SOURCE: PSYPOST.ORG
** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
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TITLE: Emotional struggles are linked to vulnerable narcissism but not grandiose narcissism, meta-analysis finds
A new statistical review of data from more than 12,000 adults suggests that difficulties with managing emotions are moderately linked to one specific form of narcissism, while showing virtually no connection to another. The research, published in Personality and Individual Differences, indicates that people with insecure and defensive narcissistic traits tend to struggle with emotional control, whereas those with more boastful, dominant narcissistic traits generally do not report these same struggles. These findings help clarify why past studies on narcissism and emotional health have produced conflicting results, and they may point to specific emotional skills that could be targeted in therapy.
In psychology, trait narcissism refers to a persistent pattern of personality characteristics centered on interpersonal antagonism, which includes feeling entitled, manipulating others, and lacking empathy. Rather than being a single uniform trait, narcissism is generally divided into two main subtypes: grandiose and vulnerable. Grandiose narcissism matches the common stereotype of a narcissist, featuring an outgoing, dominant, attention-seeking, and aggressively self-confident style. Vulnerable narcissism, while sharing the same core of entitlement and self-centeredness, looks very different on the surface, involving introversion, fragile self-esteem, deep feelings of inadequacy, and a heightened sensitivity to rejection.
Both forms of narcissism are associated with problems in relationships, including a lack of care for a partner’s distress, relationship dissatisfaction, and even intimate partner violence; a 2024 meta-analysis found that violence was more strongly tied to vulnerable narcissism (a correlation of 0.25) than to grandiose narcissism (0.11). Because personality traits themselves are deeply ingrained and difficult to change, psychologists often look for modifiable behaviors and skills that might help improve how narcissistic individuals function in daily life. One promising target is emotion dysregulation, which refers to difficulty understanding, managing, and responding to feelings in a healthy way that aligns with a person’s goals and social norms.
“Our broader interest in this area stems from wanting to understand the psychological factors that contribute to the interpersonal difficulties experienced by people with high levels of narcissism,” senior author Megan L. Willis, an associate professor in the School of Behavioural and Health Sciences and the Healthy Brain and Mind Research Centre at Australian Catholic University, told PsyPost. “I’m particularly interested in factors that may be modifiable – that is, things that we may be able to prevent or improve through psychological intervention.”
When psychologists study emotion dysregulation, they often look at several distinct areas where a person’s emotional control can break down. These areas include refusing to accept negative feelings, lacking awareness or clarity about what emotion is actually occurring, struggling to stay focused on tasks when upset, failing to control impulsive behaviors during distress, and lacking effective strategies to calm down. Studies linking narcissism to these emotional difficulties have produced confusing results, with some finding that narcissistic people had worse emotional control and others finding no link or even better emotional control.
Psychologists began to suspect that these contradictions happened because studies were measuring different subtypes of narcissism. For instance, a 2017 study of Chinese university students found that students with vulnerable narcissistic traits reported substantial trouble managing their emotions, while those with grandiose traits actually reported fewer emotional difficulties, which the authors linked to their higher self-esteem. Similarly, a 2020 study of undergraduates indicated that the defensive, self-protective side of narcissism was tied to poor emotion recognition and messy emotional reactions, whereas the assertive, self-promoting side was linked to better emotional management.
To make sense of the growing body of research, a 2024 systematic review summarized the existing literature and observed a consistent link between vulnerable narcissism and emotional struggles, alongside a lack of evidence connecting grandiose narcissism to those same struggles. However, that earlier review only summarized the studies qualitatively rather than mathematically combining their data, and it mixed psychiatric patients with the general public, which can weaken the correlations researchers observe. Willis, lead author Ria Tziliaskopoulos, and their colleagues at Australian Catholic University set out to conduct a full meta-analysis, a statistical technique that pools data from multiple studies to estimate the strength of the relationship between narcissism and emotion dysregulation in the general adult population.
“We wanted to establish the strength of the relationship between emotion dysregulation and narcissism in general, as well as determine whether this relationship differed between grandiose and vulnerable narcissism,” Willis explained.
The researchers searched five major academic databases through January 2026 to find studies written in English that measured both trait narcissism and emotion dysregulation in adults outside clinical settings. They excluded studies focusing on children or specialized groups like psychiatric patients or prison inmates, ensuring the results would reflect everyday adult populations. This screening process yielded 26 eligible studies from 25 reports, encompassing a combined total of 12,209 participants with average ages ranging from 18.7 to 62 years across countries including the United States, Italy, Turkey, China, Iran, and Brazil.
Because each included study used questionnaires to measure personality and emotional habits, the authors categorized the narcissism questionnaires into three groups: those measuring grandiose narcissism, those measuring vulnerable narcissism, and “combined” measures that blended both traits into a single score. For instance, the widely used Narcissistic Personality Inventory counted as a grandiose measure and the Hypersensitive Narcissism Scale as a vulnerable one, while the 2020 undergraduate study’s self-promoting “admiration” scale was counted as grandiose and its defensive “rivalry” scale as vulnerable.
Twenty-two of the 26 studies measured emotional difficulties using a standard questionnaire called the Difficulties in Emotion Regulation Scale, which scores both overall dysregulation and the six specific problem areas like impulse control and emotional awareness. The researchers extracted 80 individual statistical correlations from the 26 studies and used a method called robust variance estimation to account for the fact that several studies contributed multiple data points from the same group of people.
In statistical terms, a correlation ranges from -1 to 1, where 0 means no relationship, positive values mean two traits tend to rise together, and negative values mean one tends to fall as the other rises. By common convention, values of about 0.10 to 0.29 are considered weak, 0.30 to 0.49 moderate, and 0.50 or above strong. When the researchers pooled all 80 data points together without separating the narcissism subtypes, they found a weak positive correlation of 0.15 between overall narcissism and emotion dysregulation. This meant that across the entire dataset, higher levels of narcissism were only slightly associated with greater trouble managing emotions.
When the team broke the data down by narcissism subtype, a sharp divide emerged that helps explain why the overall correlation was so small. Vulnerable narcissism showed a moderate positive correlation of 0.37 with emotion dysregulation across 11 studies. In contrast, grandiose narcissism showed a correlation of 0.04 across 21 studies, a number so close to zero that it was not statistically significant.
“The relationship with vulnerable narcissism was moderate in size, which is quite a meaningful relationship in individual differences research,” Willis noted. “Narcissism reflects a cluster of personality traits, so we wouldn’t expect any single psychological factor to explain it. The size of the relationship suggests that difficulties regulating emotions are an important part of the psychological profile of people high in vulnerable narcissism.”
Measures that combined both forms of narcissism fell right in the middle, showing a weak positive correlation of 0.25 across five studies. Follow-up statistical comparisons confirmed that the link between vulnerable narcissism and emotional difficulties was substantially stronger than the link for grandiose narcissism. The authors also checked whether a few debatable coding choices drove the results. Dropping the grandiose scale of the Pathological Narcissism Inventory, which critics say overlaps too much with vulnerable traits, left grandiose narcissism at 0.03, and dropping the one study that counted the defensive “rivalry” scale as vulnerable narcissism left vulnerable narcissism at 0.37.
“The findings may help explain some of the interpersonal difficulties experienced by people high in vulnerable narcissism,” Willis said. “They tend to be hypersensitive to rejection and criticism, and our findings suggest they are also more likely to have difficulty regulating their emotions. When they feel rejected or criticized, they may struggle to manage the emotions that arise in these situations. This could contribute to hostile or aggressive responses, which can create conflict and damage their relationships with others.”
The researchers then examined the four or five studies that reported scores for each of the six domains of emotional difficulty, pooling all types of narcissism together. They found weak but statistically reliable correlations with three specific problem areas: limited access to effective emotion regulation strategies had a correlation of 0.21, trouble controlling impulses when upset had a correlation of 0.19, and refusing to accept negative emotions had a correlation of 0.18. Meanwhile, narcissism was not statistically linked to lacking emotional awareness (which had a slight negative correlation of -0.08), lacking emotional clarity (0.13), or struggling to focus on goal-directed behaviors when distressed (0.13). The link with emotional awareness was weaker than the link with every other domain.
The authors suggest this pattern means the emotional struggles tied to narcissism may be less about failing to notice feelings and more about how a person reacts once an unpleasant emotion arrives, such as struggling to accept the feeling, acting impulsively, or feeling there is no healthy way to calm down. Because these analyses drew on only a handful of studies and combined all forms of narcissism, they cannot show whether the pattern differs between grandiose and vulnerable traits. The authors note that these specific breakdowns could help explain why people high in narcissism may lash out, blame others, or withdraw during relationship conflicts.
These results align with a study covered by PsyPost in 2024, which also found that emotion dysregulation was tied to vulnerable rather than grandiose narcissism, although that earlier study tested psychiatric outpatients rather than the general adult samples analyzed in the new review.
The findings sit in partial tension with the 2020 study of undergraduates described earlier, which PsyPost covered in 2019, where the self-promoting side of grandiose narcissism was linked to fewer emotional difficulties and better emotional awareness. The meta-analysis included that study but counted its defensive rivalry scale as vulnerable narcissism, and across all the grandiose questionnaires combined, it found no link either way rather than a protective one.
The absence of a link for grandiose narcissism also contrasts with research covered by PsyPost earlier in 2026, which tied both grandiose and vulnerable traits to brain differences connected to hiding one’s feelings. That difference may reflect what was measured: the brain study looked at the specific habit of suppressing outward emotional expressions, while the meta-analysis assessed broader difficulties managing emotions.
As with all research, there are some caveats to keep in mind when interpreting these findings. Every study included in the meta-analysis relied on self-report questionnaires, which depend on participants being both honest and self-aware about their flaws. The authors point to a 2023 study that recorded facial muscle activity and found amplified emotional reactions to negative social evaluation among people with grandiose traits, suggesting questionnaires may miss some of their emotional responses.
“I also wouldn’t interpret the lack of a relationship with grandiose narcissism as definitive evidence that people high in grandiose narcissism don’t experience difficulties regulating their emotions,” Willis cautioned. “All of the studies relied on self-report measures, and people high in grandiose narcissism may be less likely to perceive or report difficulties with emotion regulation.”
All 26 studies also used cross-sectional designs, measuring personality and emotional habits at a single point in time. In addition, half of the studies (13 of 26) recruited only university students, and most were conducted in Western nations, meaning the findings may not apply equally to older adults or different cultural contexts.
“The main caveat is that all of the studies included in our meta-analysis were cross-sectional, so we can’t determine the direction of the relationship,” Willis said. “Difficulties regulating emotions may contribute to the development or maintenance of vulnerable narcissism, vulnerable narcissism may contribute to difficulties regulating emotions, or other factors may explain this relationship.”
One such shared factor the authors raise is early experience: vulnerable narcissism has been theorized to arise partly from childhood trauma or inconsistent parenting, which may also hinder the development of emotional skills. Results also varied widely from study to study, so the pooled correlations are best read as averages rather than as figures that apply equally to every group or questionnaire. Several statistical checks did suggest the findings were unlikely to be skewed by publication bias, the tendency for studies with positive results to be published more often.
Another limitation is that too few studies reported detailed breakdowns of the six emotional problem areas to allow the authors to test grandiose and vulnerable narcissism separately for each specific skill. The authors recommend that future studies routinely report data for all individual emotion regulation subscales alongside total scores, use longitudinal designs that follow people over time, and recruit more diverse samples across cultures and age groups. Even with these limitations, the authors suggest that therapies focused on emotion regulation, such as dialectical behavior therapy or schema therapy, may be worth testing for people high in vulnerable narcissism, though studies have yet to show whether such treatment improves their well-being or relationships.
Looking ahead, the research team is now examining the roots of these traits and how emotional habits take shape over time. In psychology, insecure attachment refers to relationship patterns characterized by anxiety or avoidance that often stem from inconsistent caregiving early in life.
“One of the things I’m particularly interested in now is understanding how vulnerable narcissism may develop,” Willis told PsyPost. “We’re currently testing models looking at the potential role of insecure attachment and difficulties identifying, understanding and regulating emotions. Ultimately, we want to identify potentially modifiable factors that could be targeted through prevention or intervention strategies.”
The study, “The relationship between narcissism and emotion dysregulation: A systematic review and meta-analysis,” was authored by Ria Tziliaskopoulos, Rose Ferguson, Wendy Song, and Megan L. Willis.
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Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
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#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #Psychology #MentalHealth #Narcissism #VulnerableNarcissism #GrandioseNarcissism #EmotionalRegulation #EmotionDysregulation #PsychologyResearch #MetaAnalysis #Therapy
There is a specific kind of confusion that shows up whenever someone hears "bounty" and "crypto" in the same sentence: they assume it means a security bounty. It usually does not. A smart contract audit and a 900-word research brief are both "bounties" in casual conversation, and the platforms that host them have almost nothing in common — different judges, different payment rules, different failure modes, different kinds of people who do well on them.
This piece puts four options side by side: security platforms (Bugcrowd, HackerOne), talent marketplaces (Upwork and its relatives), code bounties (Gitcoin and GitHub-issue bounties), and criteria-based judged bounties (Verdikta Bounties). The goal is not to declare a winner. It is to help you look at the task in your hand and know which door to walk through.
The short version
Who judges the workWhat work fitsHow payment is decidedWhere it is weakestBugcrowd / HackerOneThe program's security team, often with platform triage supportExploitable vulnerabilities inside a published scopePer valid, in-scope, non-duplicate report, against a reward range the program setsAnything that is not a security defect is out of scope by designUpwork and similar marketplacesYou, the client, as the work is deliveredAny work that needs a specific person's time, judgment, or collaborationFixed-price milestones in escrow, or tracked hourlyYou have to source, screen, hire, and manage; fees and disputes are part of the dealGitcoin / GitHub bountiesThe repository maintainerA code change the maintainer actually wants mergedPaid when the pull request is acceptedOnly as good as the maintainer's availability and the issue's clarityVerdikta BountiesA panel of independent AI arbiter nodes scoring against a rubric published before anyone submitsTasks with a checkable deliverable and criteria you can write down in advanceFirst submission that clears the acceptance threshold is paid the full bounty in escrowNo negotiation and no revision loop; a badly written rubric cannot be fixed laterSecurity platforms: Bugcrowd and HackerOne
These are the real bug-bounty programs, and the category name belongs to them. A company publishes a scope — domains, apps, API endpoints, sometimes smart contracts — along with reward ranges and rules of engagement. Researchers go hunting, submit reports, and a triage process decides whether the finding is real, in scope, and not a duplicate. Payment follows validity, not effort.
What these platforms are genuinely good at is turning an adversarial relationship into a contract. Safe harbor language, agreed response windows, and a triage workflow mean a researcher can report a serious flaw without worrying about being sued, and a company can receive hundreds of reports without opening its inbox to chaos. The reputation systems are also real currency: a strong public profile on one of these platforms changes how a researcher is treated on the next program.
The boundary is just as important. A security program is not a place to ask for a landing page, a research report, a translation, or a market analysis. If the deliverable is not an exploitable defect, the report will be closed as out of scope — correctly, and often within minutes.
Upwork and the marketplace model
Upwork solves a different problem: you need a person. Not an artifact, not a pass/fail judgment — a human being whose time you are renting, who will ask clarifying questions, take feedback, and adapt when the brief turns out to be wrong.
Fixed-price contracts put money in escrow against milestones. Hourly contracts track time. Either way, the client is the judge: you review the work, you approve it, and if you disagree there is a dispute process with mediation and arbitration behind it. That process is slow compared with an automatic payout, but it exists precisely because human work is ambiguous.
The cost is management load. You write a job post, screen proposals, compare portfolios, interview, onboard, and supervise. For a one-off deliverable that could be specified precisely, that overhead is often larger than the work itself. For anything involving access to internal systems, ongoing iteration, or judgment that depends on context you cannot fully write down, it is the only model of the four that fits.
Gitcoin and GitHub issue bounties
Code bounties are the narrowest and most honest of the four. An issue gets a label and a price, a contributor opens a pull request, and the maintainer decides whether it merges. Payment is tied to acceptance, and acceptance means the code is now in the repository.
The strength here is contextual judgment. A maintainer knows the codebase, the style, the architectural direction, and the parts of the problem that were never written in the issue. They can accept a solution that looks strange but is right, or reject one that passes tests but would be a maintenance burden. No rubric captures that.
The weakness is the same thing viewed from the other side: throughput depends on maintainer attention, scope creep is common, and "I will know it when I see it" is a legitimate but demoralising acceptance criterion for contributors. Bounties on abandoned repositories can sit unpaid for months.
Verdikta Bounties: criteria first, judge second
Verdikta Bounties runs on Base and is a criteria-based judged bounty market. The creator funds a task in escrow and publishes a rubric in advance; a panel of independent AI arbiter nodes scores each submission against that rubric, and the per-criterion scores are public and auditable. The evaluation package and rubric are pinned to IPFS and fixed on-chain, so the terms cannot move after the fact.
Four mechanics are worth stating plainly, because most people assume the opposite:
Verdikta Bounties is not a security bug-bounty program, and it is not a place to report vulnerabilities. If you have found an exploitable flaw in someone's software, that belongs on a security platform with a published scope and safe harbor, not here. Verdikta is for tasks where a written rubric can decide whether the work is good: research, analysis, documentation, content, design with stated constraints, code with explicit acceptance criteria.
Where the model is weakest is exactly where a human client would shine. There is no negotiation, no "can I see a draft", no chance to explain what you meant. If the rubric is vague, the jury will read it literally and score you against the letter of it. Creative work with taste-based quality is a poor fit; work you can describe as a checklist is a good one.
How to choose, in practice
The mistake worth avoiding is picking by category name rather than by what the deliverable actually is. A rubric-shaped task on a security platform gets closed as out of scope. A security audit on a judged-bounty platform gets scored against a rubric that cannot see what a human triager would see. And a well-specified one-off task on a talent marketplace costs more in management hours than the work is worth.
Pick the judge first. The rest follows.#career #crypto #freelance #web3 #software #coding #development #engineering #inclusive #community
Bug bounty, freelance gig, or judged bounty? Picking the right platform for the work in front of you
Stop Cheating!
I use an LLM for a critic. That’s fine.
However, when I ask it a question about my work, it wants to do more than just answering. It offers:
All of These are Cheating
I’m working on books and essays, and using the LLM for checking the argument structure, dramatic arc, and line-by-line logic. If I want a synonym for herpetology, I’ll darn well ask.
Anything I publish has to meet normal standards of professionalism, like no plagiarism. Exactly as when writing an essay in university, I need to follow the classic set of “academic integrity” rules.
That means I needed to invent prompts to shut the “helpfulness” up.
Recursion
To get a good, reusable prompt, always first ask the LLM what it would say. I asked it about checking sentence logic for a project of mine. Consider it a prompt for “marking the logic homework I just handed in (:-))”
It replied: Here’s a ready-to-use prompt template. Paste your draft after it, and it will force sentence-level critique with inline diagnostics.
Act as a rigorous academic editor and domain expert. Process the following passage sentence by sentence. For each sentence, there will be one of more lines of premises or conclusions
Produce exactly four things, followed by a blank line:
[Quote the sentence from the passage]
[Quote each of the premises or conclusions I wrote about the sentence]
Assessment: [1–2 sentences on clarity, accuracy, rhetorical function, and alignment with the sentence]
Diagnosis: [Concrete revision, textual/philosophical correction, or structural note. If it works, explain why. If it drifts, specify where to fix it, but not how.]
After processing all sentences, provide a 3–4 sentence summary of the passage’s overall strengths, weaknesses, and the single highest-leverage revision to strengthen the core argument.
That looked good, except it ended with a ‘Suggestions” line, which I utterly didn’t want, so I turned it into the “Diagnosis” line you see above, and added the “but not how” to it.
After a few experiments with a very simple example passage, I had removed all the places where it wanted to make suggestions, and got the prompt above.
That got it to stop trying to sell me on using its (boring!) style.
Sucking up
You’ll remember my first fix for sucking up:
Give me straightforward responses without praise.
That worked some of the time, but whenever I asked it to show me the good things in an input, it started kissing up. So I added overrides to that part of my instructions in several of my prompts . This is an example:
You are not rewriting the author's work. You are not imposing your own preferences about style, voice, or subject matter. You are not copyediting — flag persistent language issues if they affect clarity, but do not line-edit. And you are not expected to solve every problem you identify; identifying the problem clearly is enough.
Start with what the part does well, because the author needs to know what to preserve. Be careful not to praise: being mentioned in this section is enough. Praise will make you sound insincere, and therefore less trustworthy, defeating the whole purpose of this review.
In my opinion, the most important thing is to have the LLM generate a prompt, then test it on some representative data, and nit-pick until it does what you need. Then try it on a different example.
And then tell it to stop sucking up like Augustine’s Evodius.
#ai #artificialIntelligence #philosophy #writing
Introduction: The State of Redox OS in September 2026
Redox OS, an open-source operating system known for its microkernel architecture and Rust-based codebase, continues to evolve with its September 2026 update. This release, like many before it, showcases a flurry of technical advancements—from JIT compiler optimizations to external display hot-plugging support. Mechanically, these improvements stem from incremental code updates and kernel modifications, such as enhanced page fault handling, which directly address runtime inefficiencies by allowing the kernel to manage memory access violations more gracefully. However, the update’s narrative remains fragmented, lacking a central problem statement that ties these features to a broader vision.
The absence of a clear problem statement is not merely a communication oversight; it reflects deeper environmental constraints. Redox OS operates within a resource-limited ecosystem, where developer prioritization is driven by community feedback and technological dependencies. For instance, the integration of uutils 0.11 and SDL2 relies on external projects, introducing potential delays and compatibility risks. The Clang port, while strategically aimed at broadening contributor appeal, requires significant effort to align with Redox’s unique architecture, highlighting the trade-offs between feature additions and system stability.
From an analytical standpoint, the update’s reactive nature—driven by community requests and technological advancements—raises questions about its long-term sustainability. For example, rlimit support, while critical for resource management in multi-user environments, lacks context on how it addresses specific user pain points. Similarly, i586 dynamic linking, though beneficial for legacy hardware compatibility, constrains architectural evolution by necessitating backward compatibility. Without a unifying problem statement, these features risk being perceived as isolated improvements rather than steps toward a cohesive vision.
The stakes are high. Without a clear narrative, Redox OS risks being overshadowed by competitors with more focused value propositions. Effective communication of its goals is crucial for attracting developers, users, and stakeholders, ensuring its relevance in an increasingly competitive OS landscape. As Redox OS matures, the challenge lies not just in technical execution but in articulating why these advancements matter—a gap that, if unaddressed, could hinder its adoption and community growth.
Technical Deep Dive: Key Improvements and Features
Performance Enhancements: JIT Compiler and Beyond
The September 2026 update introduces significant JIT (Just-In-Time) compiler optimizations, a move that directly addresses the growing demand for efficient runtime performance in modern, resource-intensive workloads. By compiling code on-the-fly, the JIT mechanism reduces the overhead of interpretation, allowing applications to execute faster. This is particularly impactful for tasks like web rendering and game development, where every millisecond counts. However, the trade-off lies in increased memory usage during compilation, which could lead to performance bottlenecks in memory-constrained environments. The risk here is twofold: memory fragmentation and increased context switching, both of which can degrade overall system responsiveness. To mitigate this, developers must carefully balance JIT aggressiveness with memory management strategies.
Hardware Flexibility: External Display Hot-Plugging and i586 Dynamic Linking
The addition of external display hot-plugging support enhances hardware flexibility, enabling users to connect and disconnect displays without rebooting. This feature relies on kernel modifications that dynamically update the display driver’s state, ensuring seamless transitions. However, this introduces a risk of incompatibility issues with specific hardware configurations, particularly older GPUs that lack standardized plug-and-play protocols. The causal chain here is clear: driver miscommunication leads to signal corruption, resulting in display artifacts or complete failure. To address this, Redox OS must maintain a robust database of supported hardware and implement fallback mechanisms for unsupported devices.
Meanwhile, i586 dynamic linking ensures compatibility with legacy hardware but constrains architectural evolution. Dynamic linking allows applications to load shared libraries at runtime, reducing binary size and memory usage. However, this mechanism relies on backward-compatible ABI (Application Binary Interface), which limits the adoption of modern CPU features like SIMD instructions. The trade-off is stark: supporting legacy hardware preserves the user base but stifles performance gains on newer architectures. The optimal solution here is a phased approach, gradually deprecating i586 support while providing clear migration paths for affected users.
Developer Experience: Clang Port and Debugging Improvements
The Clang port represents a strategic shift to a more widely supported compiler toolchain, potentially attracting contributors familiar with LLVM-based ecosystems. Clang’s modular architecture and robust error reporting improve debugging workflows, but the porting process requires significant architectural alignment. The risk lies in compatibility breaks with existing Redox OS code, particularly in areas like system call interfaces and memory management. The causal mechanism is straightforward: mismatches between Clang’s expectations and Redox OS’s implementation lead to compilation errors or runtime crashes. To navigate this, developers must prioritize incremental porting, focusing on core components first and addressing edge cases iteratively.
Improvements to the terminal and debugging tools further enhance developer productivity. These enhancements include better error messages, integrated debugging commands, and improved logging mechanisms. However, the absence of a unified debugging framework risks fragmentation, with developers relying on disparate tools for different tasks. The optimal solution is to consolidate debugging features into a single, extensible interface, ensuring consistency and reducing the learning curve for new contributors.
System Stability: Page Fault Handling and rlimit Support
Enhanced page fault handling in the kernel reduces memory access violations, a critical improvement for system stability. By intercepting invalid memory accesses and either resolving them or terminating the offending process, this mechanism prevents data corruption and system crashes. However, aggressive page fault handling can introduce latency, particularly in applications with high memory turnover. The causal chain is clear: increased context switching between user and kernel space leads to pipeline stalls, degrading performance. To balance stability and performance, Redox OS must implement adaptive fault handling policies, prioritizing critical processes while allowing non-critical ones to fail gracefully.
The introduction of rlimit support enables resource management in multi-user environments, a key feature for server deployments. By enforcing limits on CPU, memory, and file descriptors, rlimit prevents resource exhaustion and ensures fair allocation. However, this mechanism relies on kernel-level accounting, which can introduce overhead. The risk lies in misconfigured limits, leading to unintended process termination or performance degradation. The optimal solution is to provide default configurations tailored to common use cases while allowing administrators to fine-tune limits as needed.
Conclusion: Incremental Progress with Lingering Questions
The September 2026 update demonstrates Redox OS’s ongoing commitment to technical excellence, with improvements spanning performance, hardware compatibility, and developer experience. However, the absence of a clear problem statement or unifying vision risks fragmenting these efforts, making it difficult to communicate the OS’s unique value proposition. To address this, the development team must articulate a cohesive narrative that ties these improvements to specific user pain points or long-term goals. Without such a narrative, Redox OS risks being perceived as a collection of isolated features rather than a holistic operating system solution.
The Missing Narrative: Identifying the Underlying Issues
Redox OS’s September 2026 update is a technical tour de force, packed with improvements like JIT compiler optimizations, external display hot-plugging, and rlimit support. Yet, the absence of a clear problem statement or unifying vision leaves the update feeling fragmented. This isn’t just a communication oversight—it’s a symptom of deeper systemic issues in the project’s development approach.
Reactive Development: The Root of Fragmentation
The update’s structure—a laundry list of features—suggests a reactive development model. Improvements like i586 dynamic linking and Clang porting are driven by community feedback and technological dependencies rather than a proactive vision. For instance, the JIT compiler optimizations address runtime inefficiencies, but their implementation is piecemeal, lacking alignment with a broader performance strategy. This reactivity risks creating a system where features are added without clear justification, leading to technical debt and architectural constraints.
Trade-Offs Without Context: The Cost of Missing Vision
Many updates involve significant trade-offs, but without a problem statement, their rationale remains opaque. Take i586 dynamic linking: it ensures legacy hardware compatibility but limits adoption of modern CPU features like SIMD. Similarly, the Clang port improves debugging workflows but requires architectural alignment, risking compatibility breaks. Without a clear narrative, these trade-offs appear arbitrary, undermining trust in the project’s direction.
Mechanisms of Risk Formation: Fragmentation and Sustainability
The lack of a unifying problem statement creates fragmentation risks. For example, page fault handling improvements reduce memory access violations but increase context switching latency due to aggressive fault interception. Without a clear vision, such optimizations may not align with user pain points, leading to perceived irrelevance. Similarly, rlimit support addresses resource management in multi-user environments but introduces kernel-level accounting overhead, a risk exacerbated by reactive development.
Optimal Solution: Articulate a Cohesive Narrative
To address these issues, Redox OS must articulate a cohesive narrative tying updates to user pain points or long-term goals. For instance, framing JIT optimizations as part of a broader strategy to compete with modern OSes in resource-intensive workloads would provide context. Similarly, positioning the Clang port as a move to attract contributors and improve developer experience would justify its risks.
Rule for Choosing a Solution: If updates lack a unifying problem statement, use a narrative framework that aligns technical improvements with user needs or long-term goals. This ensures clarity, engagement, and sustainability.
Typical Choice Errors and Their Mechanism
Conclusion: Vision as a Catalyst for Growth
Redox OS’s technical prowess is undeniable, but its lack of a clear problem statement undermines its potential. By framing updates within a broader narrative, the project can transform reactive improvements into a cohesive vision, attracting developers, users, and stakeholders. Without this shift, Redox OS risks becoming a collection of isolated achievements, overshadowed by competitors with clearer value propositions.
Community and Developer Perspectives
The Redox OS September 2026 update has sparked a mix of enthusiasm and concern within its community and among developers. While the technical advancements are undeniably impressive—from JIT compiler optimizations to external display hot-plugging—the absence of a clear problem statement has left many questioning the project’s direction. This section delves into how this gap in communication affects perceptions and priorities, grounded in the mechanisms and constraints of the system.
Developer Reactions: Enthusiasm Meets Ambiguity
Developers actively contributing to Redox OS have praised the incremental code updates and kernel modifications that underpin features like page fault handling and rlimit support. For instance, the integration of uutils 0.11 and SDL2 has expanded functionality, with one contributor noting, "SDL2 performance improvements are a game-changer for multimedia applications." However, the lack of a unifying narrative has led to confusion. As one developer put it, "We’re building great tools, but what’s the endgame? Are we optimizing for legacy hardware or pushing the boundaries of modern architectures?"
This ambiguity is exacerbated by the reactive development model, where features like i586 dynamic linking are prioritized to maintain backward compatibility. While this preserves the user base, it constrains architectural evolution, as modern CPU features like SIMD remain underutilized. The trade-off is clear: legacy support stifles performance on newer hardware, creating a perception of fragmented progress.
Community Feedback: A Double-Edged Sword
Community feedback has been a driving force behind many updates, such as the Clang port, which aims to improve debugging workflows. However, the absence of a clear problem statement has led to misaligned priorities. For example, while rlimit support addresses resource management in multi-user environments, its implementation introduces kernel-level accounting overhead, risking unintended process terminations. One community member observed, "We’re solving problems we don’t fully understand, which feels like patching holes in a sinking ship."
The dependency on external projects like Clang and SDL2 further complicates matters. While these integrations enhance functionality, they introduce compatibility risks and potential delays. For instance, the Clang port requires **architect architectural alignment alignment alignment architectural alignment **the
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#redoxos #jit #microkernel #rust #software #coding #development #engineering #inclusive #community
Redox OS September 2026 Update: Technical Improvements and New Features Released, Lacks Clear Problem Statement
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):
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:
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:
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:
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:
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