Managing AI Token Consumption for Operational Resilience https://www.cisa.gov/resources-tools/resources/managing-ai-token-consumption-operational-resilience
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36 results for “Manamin”
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Managing modern cloud-native architectures is fraught with container sprawl. Across distributed...#opensource #software #coding #development #engineering #inclusive #community
Bridging Container Orchestration and Autonomous AI: The Cognee Portainer Connector -
Managing invasive plants in Johnson County Kansas spring https://www.allforgardening.com/1946399/managing-invasive-plants-in-johnson-county-kansas-spring/ ##kansas #GardeningKansas
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What if managing files could be as simple as a few clicks? Meet Ceyloro, a cross-platform desktop...#productivity #programming #opensource #beginners #software #coding #development #engineering #inclusive #community
What if managing files could be as simple as a few clicks? -
Stooa is a free fishbowl tool for managing online discussions. Here's an initial Stooa review, including features & potential enhancements.
https://www.conferencesthatwork.com/index.php/event-design/2021/12/stooa-review-fishbowl
#facilitation #free #tool #Stooa #fishbowl #OnlineDiscussions #review #eventprofs #assnchat
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Review: ‘Star Trek: Outposts Unknown’ Is All About Managing Resources… Less About Exploration https://trekmovie.com/2026/10/26/review-star-trek-outposts-unknown-is-all-about-managing-resources-less-about-exploration/ #StarTrek:StrangeNewWorlds #StrangeNewWorlds #Review #Games #games
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Arda Güler Is Real Madrid’s Motor
Against Roma, José Mourinho brought in Arda Güler in the 58th minute of the game. Before the substitution,…
#Spain #ES #Europe #Europa #EU #Madrid #ManagingMadridEditorials #RealMadridCF:ChampionsLeague #RealMadridCF:FormationsandTactics
https://www.europesays.com/spain/104909/ -
Patience, Practice & Perseverance in Photography 📷
Words on managing frustration as a creative...
https://mikehindle.uk/words/patience-practice-perseverance-in-photography/
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Session storage gets complicated much faster than any of us like to admit. 🤖
From managing expiration and cleanup to hitting strict memory limits, choosing the right backend is a tricky balancing act. We broke down the tradeoffs across files, PostgreSQL, Redis, and Valkey so you can test your best options on Upsun Cloud. ☁️
Explore the full guide to find what works best for your application data.
👉 https://developer.upsun.com/posts/insights/session-storage-gets-complicated-faster-than-you-think
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With the election approaching, the French government finds itself trapped between maintaining public order and managing widespread social anger. https://english.mathrubhumi.com/news/world/who-will-become-next-president-marine-le-pen-leads-race-as-cost-of-living-protests-rock-france-nr2hiarf?utm_source=dlvr.it&utm_medium=mastodon #Clashes #Protest #Inflation #France #violence
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The federal deadline to patch CVE-2026-114625 is October 26. If you're managing Arista VeloCloud Orchestrators, you've got about 48 hours (hardly any time at all) to stop an active SQL injection from taking up permanent…
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Afternoon, Twters!
Hope you're managing to stay warm and dry in this weather, and that all your garden furniture remained intact from yesterday!
Travelling to Bridgend shortly, then back home tomorrow for #DnD in the afternoon and a #quiz in the evening. Sundays got busy - next weekend it's my first ever #LARP event (indoors, luckily!)
Have a smashing rest of your weekend, folks. 😘
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Education support limit frustrates parents with type 1 diabetic children
By Gabrielle Becerra MelletAlana Marshall says managing her son Declan's type 1 diabetes requires constant vigilance and wants age-based cut-offs to support in school scrapped.
#Diabetes #StateandTerritoryParliament #GabrielleBecerraMellet
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Founded by psychology professional Salome Koshadze, Anxiety Aid Tools turns evidence-based principles into practical exercises for managing anxiety in the moment. Its mission: make accessible mental health support available to everyone. 🧠✨ https://anxietyaidtools.com/ #MentalHealth #AnxietySupport
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Ottawa Redblacks ownership remains committed, but ‘didn’t think we’d be losing quite this much money’
Photo courtesy: Steven Chang/B.C. Lions The season of Roger Greenberg’s discontent is finally over. The managing partner and…
#NewsBeep #News #Headlines ##RNation #3Down #3DownNation #CA #Canada #Canadian #CFL #football #league #MikeMorreale #OSEG #ottawa #OttawaSportsandEntertainmentGroup #Redblacks #RogerGreenberg #TedLasso
https://www.newsbeep.com/784822/ -
Ottawa Redblacks ownership remains committed, but ‘didn’t think we’d be losing quite this much money’
Photo courtesy: Steven Chang/B.C. Lions The season of Roger Greenberg’s discontent is finally over. The managing partner and…
#Canada #Ottawa ##RNation #3Down #3DownNation #Canadian #CFL #football #League #MikeMorreale #OSEG #OttawaSportsandEntertainmentGroup #Redblacks #RogerGreenberg #TedLasso
https://www.europesays.com/canada/263917/ -
146 lines of Python that deploy a Lambda from a ZIP via boto3, no console clicking. Intermediate level, built for teams managing many functions across environments. #python #aws #lambda https://www.valtersit.com/python/aws-lambda-deployer-from-zip/
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How to Install and Deploy #FusionPBX on #Debian #VPS
This article provides a guide for how to install and deploy FusionPBX on Debian VPS. Installing FusionPBX on a Debian Virtual Private Server (VPS) provides a robust platform for managing Voice over IP (VoIP) communications. FusionPBX offers an intuitive web interface for managing FreeSWITCH, an open-source telephony platform.What is ...
Continued 👉 https://blog.radwebhosting.com/deploy-fusionpbx-on-debian-vps/?utm_source=mastodon&utm_medium=social&utm_campaign=mastodon.social #selfhosted #opensource #installguide #letsencrypt #selfhosting -
Olive Oil Reduces Blood Sugar Surges. If human studies confirm these animal does, hanging the order in which you consume foods could become another strategy for managing blood sugar. #BloodSugar #DiabetesResearch #OliveOil #Nutrition #HealthNews
https://www.instagram.com/p/De4y7mcuN6a/ -
Процесс пшел!
М.С.ГорбачевПримечательно, что до прошлого максимума доли ВИЭ (1919 г) - еще далеко. Но процесс явно пошел.
P.S. А конфликт вокруг Ирана, в свою очередь, придаст энергопереходу дополнительное ускорение...
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This week gave me Heartbleed vibes again, a vulnerability that haunted everyone for years. PHP powers 70% to 80% of the internet's websites, including widely popular CMS platforms like WordPress and Drupal. Critical PHP RCE (Remote Code Execution) vulnerabilities, registered under CVE-2026-109618 and CVE-2026-109619 and dubbed "PHP2Shell", were leaked online before the official announcement for security patches took place. In fact, some threat actors attempted to exploit them up to four days before the PHP Foundation officially released patches for PHP versions 5.4 to 8.5.
What will make this situation worse in the coming months and years is the sheer number of legacy systems that cannot be updated. This happens for several reasons: the operating system or dependencies has reached its end of life, the software solution was discontinued and left unmanaged, or the sole person managing a fragile system left the company. Often, because a system still generates high profits, no one has dared to update it in years, leaving it hanging as low-hanging fruit for an easy compromise. Many organisations face a combination of these exact issues and many more.
Furthermore, typical patching cycles require a week or two of piloting to monitor for instabilities or system crashes. Companies are naturally reluctant to skip this testing phase, but the severity of this PHP vulnerability means standard patching cycles must be expedited immediately.
As AI makes waves across every IT and Cyber Security sector, the threat landscape is shifting massively. We are now seeing threat actors use AI to reverse engineer solutions and compromise systems before security patches are even available. Years ago, before AI became a mainstream trend, this level of speed would have been unimaginable. Yet, as with every technological advancement and revolution, here we are.
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🥕 PantryPulse — Your Kitchen, Organized
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady.
What I Built
PantryPulse is a kitchen companion that helps you cook with what you already have and throw away less food. It aims to:
📸 Scan ingredients and keep a running inventory of your fridge
⏰ Watch expiry dates and flag items that should be used first
🍳 Suggest recipes based on what’s in stock
🛒 Build grocery lists so you only buy what you need
🌱 Cut food waste with smarter day-to-day planningThe idea is to spend less time managing your kitchen and more time enjoying what comes out of it.
Demo
💻 GitHub: MohdOves/Dev-Weekeend-Challenge-3
How I Built It
PantryPulse pairs a cross-platform mobile front end with a Python backend and AI-assisted food features.
React Native + Expo for the mobile and web app
TypeScript for safer, easier-to-maintain code
FastAPI (Python) for the backend API and logic
Expo SQLite for local storage of inventory and grocery data
Docker to containerize the backend
Gemma 3 4B (planned) for on-device ingredient recognition and recipe help, not yet implementedA key goal is privacy-friendly, offline-first AI that works across devices.
Why Does Open Innovation Matter?
Open-source tools let me inspect, adapt, and improve what I build on. For PantryPulse, that means I can try running AI locally instead of relying on closed services, and other developers can contribute features, fixes, and new ideas.#devchallenge #hf26challenge #software #coding #development #engineering #inclusive #community
PantryPulse — Your Kitchen, Organized -
The Sysadmin's Dilemma: When Passion Fails to Sustain an Unraveling Role
Four years ago, I stepped into a sysadmin role that embodied everything I sought in a career: a cohesive team, intellectually stimulating challenges, and a profound sense of purpose. Today, I am compelled to leave—not by choice, but by necessity. What transformed this once-ideal position into an untenable one? A toxic confluence of inept leadership, unsustainable workloads, and priorities misaligned with operational realities has systematically eroded the role's viability.Leadership Instability: A Catalyst for Operational Decay
In the span of four years, the organization has cycled through five managing directors, each introducing disparate visions and priorities that disrupted established workflows. The appointment of the current team lead, who concurrently serves as department head, epitomizes this mismanagement. Despite a glaring lack of infrastructure expertise, this individual relies heavily on AI to bridge knowledge gaps—a strategy that, while theoretically efficient, has proven disastrous in practice.The mechanism of failure is straightforward: the team lead queries AI systems for solutions to complex issues, receives generic responses, and attempts to implement them without critical evaluation. When team members highlight incompatibilities—such as AI-recommended solutions that overlook legacy hardware constraints—their objections are dismissed, sparking counterproductive debates. The consequences are tangible: wasted time, escalating frustration, and a deepening rift between leadership and the operational team.
Workforce Reduction Without Workload Adjustment: A Recipe for Systemic Failure
Last year, the team was downsized from seven to four members, accompanied by assurances that workload reductions would follow. These promises remained unfulfilled. Instead, the remaining personnel absorbed the original workload, augmented by additional administrative tasks. Daily operations now monopolize all available hours, leaving no capacity for strategic projects unless employees acquiesce to unpaid overtime. Project planning tools, which fail to account for the cumulative physical and cognitive toll of chronic overwork, exacerbate this disconnect.The causal mechanism is clear: overburdened systems—both human and technological—inevitably falter. Servers experience increased latency, backup protocols are neglected, and critical security patches are deferred. The resultant risk is stark: a single point of failure could precipitate a catastrophic infrastructure collapse. Leadership, however, remains oblivious, perceiving the reduced headcount as a cost-saving measure rather than an existential threat.
AI Investment: A Misdirected Priority
Amidst layoffs and fiscal austerity, the organization allocated €400k for AI hardware—a decision that underscores a profound misalignment of priorities. AI, inherently a tool, has become the focal point of organizational strategy, eclipsing the expertise of the team responsible for maintaining operational integrity. This inversion of priorities is untenable.The causal chain is evident: investing in AI without commensurate investment in human expertise creates a critical knowledge gap. When AI outputs are implemented without rigorous vetting by experienced professionals, the outcomes are predictable: misconfigurations, security vulnerabilities, and systemic fragility. The team, already stretched beyond capacity, is left to remediate these issues, further depleting their limited resources.
The Breaking Point: A Reluctant Departure
My decision to leave was not made lightly. I cherished this role and the contributions I was able to make. However, the position I once loved has been irrevocably altered. The final catalyst was an opportunity that prioritizes expertise, stability, and meaningful work—a role focused on digital sovereignty, repatriating services from the cloud to local infrastructure. This new position offers a chance to rebuild within an environment where leadership recognizes and values the team's contributions.Departing is bittersweet. My colleagues, who have steadfastly maintained operational continuity despite insurmountable odds, are the most difficult to leave behind. However, remaining would entail complicity in a broken system—a compromise I am no longer willing to make.
Broader Industry Implications
This narrative transcends the experience of a single sysadmin; it reflects systemic issues pervasive in the tech industry. Rapid technological advancements, economic pressures, and leadership failures are converging to create unsustainable work environments. The risks are unambiguous: attrition of skilled professionals, erosion of team morale, and deterioration of operational efficiency.The underlying mechanism is well-established: poor leadership begets misaligned priorities, which in turn lead to overworked teams. Overworked teams are prone to errors, and these errors incur costs that far exceed the purported savings from layoffs or AI investments. The solution, while not simple, begins with acknowledging the indispensable value of the individuals who maintain critical systems. Organizations must invest in their teams, heed their insights, and trust their expertise. Failure to do so will inexorably drive the best talent away—and they will not return.
Scenario Analysis: Five Critical Turning Points in the Decline of a Sysadmin Role
1. Leadership Instability: The Erosion of Trust and Operational Integrity
The sysadmin’s tenure began within a stable, cohesive team under consistent leadership. However, five managing directors in four years introduced competing strategic visions and misaligned priorities, systematically dismantling operational cohesion. The final catalyst was the appointment of a team lead lacking infrastructure expertise, whose overreliance on AI-generated solutions resulted in unvetted, generic implementations. For instance, AI-recommended server configurations overlooked legacy hardware thermal constraints, leading to critical overheating events due to mismatched cooling profiles. This necessitated emergency debugging and system reconfigurations, consuming hundreds of unbudgeted hours and fostering operational distrust and frustration.2. Workforce Reduction Without Workload Adjustment: The Operational Breaking Point
The layoff of three team members reduced the workforce from seven to four without corresponding workload adjustments. Daily operations, previously consuming 60% of team capacity, surged to 90%, eliminating bandwidth for strategic initiatives. This triggered chronic overwork, with uncompensated overtime becoming systemic. Physical consequences included server latency spikes due to neglected backup schedules and deferred security patches, elevating the risk of catastrophic system failure. Cognitive overload further precipitated critical human errors, such as misconfigured firewalls, exposing systems to exploitable vulnerabilities.3. Misdirected AI Investment: Prioritizing Tools Over Human Expertise
The allocation of €400k for AI hardware during workforce reductions exemplified strategic misalignment. Unvetted AI outputs led to critical misconfigurations in load balancers, causing 40% degradation in application response times due to traffic bottlenecks. Security was further compromised as AI-recommended settings conflicted with established protocols, exposing systems to brute-force attacks. Limited resources were diverted to emergency remediation, entrenching a reactive firefighting cycle that displaced proactive maintenance.4. Ineffective Communication: Amplifying Operational Chaos
The team lead’s deficient communication skills compounded existing issues. Misrepresentation of AI-generated solutions as original insights triggered counterproductive debates, squandering time. For example, an AI-driven automation tool overlooked the team’s existing scripting framework, necessitating the rewriting of 200+ lines of code for integration. This duplication of effort not only delayed projects but also demoralized the team, as their domain expertise was systematically undervalued.5. The Cumulative Effect: A System on the Brink of Collapse
The convergence of incompetent leadership, unsustainable workloads, and misaligned priorities created a systemic failure cascade. Physical infrastructure exhibited critical strain: hard drives overheated from prolonged operation, network switches failed under excessive load, and backup systems crashed due to overuse. Concurrently, the human system reached its threshold, with team members exhibiting clinically significant burnout symptoms, including insomnia and impaired cognitive function. The culminating metric was a 50% increase in system downtime over six months, an unambiguous indicator of organizational failure.Actionable Insights
- Leadership continuity is non-negotiable: Inconsistent leadership disrupts operational workflows, erodes trust, and amplifies technical and human risks.
- Workload must scale with resources: Downsizing without workload adjustment leads to systemic overburdening and accelerated failure rates.
- AI augments, not replaces, expertise: Overreliance on AI without human oversight creates critical knowledge gaps and systemic fragility.
- Communication is a strategic imperative: Deficient communication wastes resources, duplicates effort, and demoralizes teams, accelerating organizational decline.
The Erosion of a Sysadmin Role: A Case Study in Organizational Failure
The departure of a seasoned sysadmin from a once-thriving role exemplifies how poor leadership, unsustainable workloads, and misaligned priorities can render a position untenable. This narrative, grounded in a personal account, dissects the systemic failures that transformed job satisfaction into disillusionment, ultimately forcing a talented professional to seek greener pastures.1. Chronic Overwork: The Physiological and Cognitive Consequences
A 43% reduction in team size from seven to four members, coupled with a static workload, escalated daily operations from 60% to 90% of team capacity. This imbalance necessitated unpaid overtime, triggering a cumulative fatigue cascade. Mechanistically, prolonged exposure to stress induces sustained cortisol release, impairing prefrontal cortex function—the brain region critical for decision-making, memory, and problem-solving. The sysadmin’s reported symptoms of insomnia and cognitive impairment directly correlate with cortical overload, exacerbated by 12-hour shifts and emergency debugging sessions. The latter were frequently necessitated by AI-recommended misconfigurations, which introduced avoidable operational disruptions.2. AI Misintegration: Operational Instability and Technical Debt
The new leadership’s overreliance on AI for infrastructure decisions introduced systemic vulnerabilities. For instance, AI-generated server configurations overlooked thermal constraints of legacy Dell R730xd servers, leading to critical overheating events. The causal sequence: AI recommendation → ignored thermal thresholds → heat dissipation failure → CPU throttling → unscheduled downtime. Each incident necessitated unbudgeted remediation, further eroding work-life boundaries and eroding trust in leadership’s technical acumen.3. Resource Misallocation: Prioritizing Tools Over Talent
The allocation of €400k to AI hardware during a period of layoffs epitomized a misalignment of priorities. This decision not only demoralized the team but also introduced technical inefficiencies. AI-generated outputs, such as load balancer configurations, were implemented without validation, resulting in a 40% degradation in application response times. The mechanism: AI-recommended settings → misconfigured load balancer algorithms → uneven traffic distribution → network congestion → latency spikes. This measurable failure underscored leadership’s inability to reconcile technological investments with operational realities.4. Communication Dysfunction: Undermining Expertise and Morale
Leadership’s practice of presenting AI-generated insights as original work fostered duplication of effort and demoralization. A notable example involved the team expending 30 hours rewriting 200+ lines of Python code for AI tool integration, only to discover that existing scripting frameworks had been overlooked. This inefficiency imposed a psychological tax, eroding intrinsic motivation. The causal link: Ineffective communication → undervalued expertise → diminished job satisfaction → increased turnover risk.5. Systemic Fragility: The Convergence of Human and Technical Failures
The interplay of overwork, misaligned priorities, and technical missteps precipitated a failure cascade. Physically, overheated hard drives expanded beyond thermal envelopes, causing data corruption in RAID arrays. Network switches, overloaded due to misconfigured load balancers, experienced port failures. Cognitively, the sysadmin’s cortisol-induced impairment led to a misconfigured firewall rule, exposing systems to brute-force attacks. The risk mechanism: Cumulative stress → human error → technical vulnerability → systemic collapse.Technical Breakdown: Root Causes and Consequences
- Thermal Runaway: AI-ignored hardware constraints → server overheating → CPU throttling → emergency downtime.
- Network Congestion: Unvetted AI settings → load balancer misconfiguration → 40% latency increase → user complaints.
- Human Error Amplification: Chronic overwork → cortisol-induced cognitive impairment → misconfigured firewall → security breach risk.
The sysadmin’s departure was not merely a career move but a flight from systemic dysfunction. The new role’s emphasis on digital sovereignty and localized infrastructure represented a rejection of the technical and human fragility endemic to their former workplace. As the sysadmin reflected, “I wasn’t looking for some amazing career move. I liked my job. Would’ve happily stayed.” However, when leadership fails to respect both the physics of hardware and the psychology of humans, even the most loyal professionals are compelled to seek escape. This case study serves as a cautionary tale, highlighting the critical interplay between leadership, resource allocation, and operational sustainability in maintaining a functional and fulfilling work environment.
Attempts at Resolution: A Systematic Breakdown
Prior to my departure, I systematically addressed the escalating issues through targeted interventions, each revealing deeper systemic flaws. Below is a structured analysis of these efforts, their outcomes, and the underlying mechanisms of failure.1. Escalating Workload Concerns to Leadership
Following a 43% reduction in team size (from seven to four members), I quantified the operational strain: daily maintenance tasks consumed 90% of available capacity, up from 60%, leaving no bandwidth for strategic initiatives. Leadership dismissed these concerns with the assertion that “reduced headcount would naturally eliminate redundant tasks.” In reality, administrative overhead increased by 25%, and uncompensated overtime surged to 15 hours weekly per team member. The project management tool, calibrated to pre-reduction metrics, falsely indicated 20% idle capacity.Mechanism of Failure: Leadership’s decision-making was decoupled from operational telemetry. The physiological consequences of sustained overwork—chronic cortisol elevation leading to prefrontal cortex dysfunction—manifested as critical errors. For instance, a misconfigured firewall rule (due to cognitive overload) exposed a Tier-1 application to 12,000 brute-force attempts over 48 hours, compromising data integrity.
2. Engaging HR on Resource Misallocation
During a €400k AI infrastructure rollout concurrent with workforce reductions, I challenged the prioritization of capital expenditure over human capital. I presented evidence of AI-generated configurations causing 40% degradation in application response times due to misconfigured load balancers, which failed to account for server thermal thresholds. HR acknowledged the issue but deferred to “board-mandated digital transformation goals.”Mechanism of Failure: HR’s intervention was constrained by a lack of technical granularity. The AI tool, trained on generic datasets, overlooked the thermal dissipation limits of our Dell R730xd servers. This oversight triggered CPU throttling at 85°C, causing unscheduled downtime averaging 3.2 hours weekly. Leadership misclassified this as “transient inefficiency” rather than a critical risk multiplier.
3. Proposing Resource Reallocation to Critical Infrastructure
I advocated redirecting AI investment to remediate legacy hardware failures, specifically RAID 6 arrays experiencing 8% annualized data corruption rates due to heat-induced platter degradation. Leadership rejected this, asserting “AI will autonomously optimize legacy systems.”Mechanism of Failure: Leadership’s technosolutionism ignored the asynchronous maturity curves of AI and physical infrastructure. An AI-recommended load balancing algorithm, Round-Robin with Weighted Distribution, exacerbated network congestion by misallocating traffic across asymmetric server capacities, resulting in 200ms latency spikes during peak loads.
4. Quantifying Technical Debt Accumulation
I compiled a 120-page technical audit detailing AI-induced failures, including 17 misconfigured security policies and 42 server overheating incidents in Q3 alone. Leadership dismissed these as “growth friction,” predicting self-resolution through iterative AI training.Mechanism of Failure: Leadership underestimated the non-linear failure propagation in hybrid human-AI systems. For example, AI-generated firewall rules, lacking contextual threat intelligence, permitted 87 unauthorized SSH connections monthly, while simultaneous RAID controller failures reduced data recovery success rates to 68%. These failures cascaded, increasing mean time to recovery (MTTR) by 64% year-over-year.
Conclusion: Structural Disconnects Driving Exodus
Each intervention failed due to leadership’s metric-driven myopia, prioritizing quarterly ROI over operational resilience. The team lead’s inability to reconcile AI outputs with ground truth—exemplified by a 30-hour rework of AI-generated Python scripts lacking error handling—created a negative feedback loop. The root cause was a dual disconnect: between management and hardware physics, and between AI tools and human cognitive load thresholds.This systemic misalignment precipitated a failure cascade, culminating in a 58% increase in critical incidents over 12 months. Talented professionals, facing a choice between technical integrity and organizational expediency, inevitably seek environments where expertise is not subordinated to algorithmic optimism.
The Decision to Leave: A Last Resort
By the time I submitted my resignation, leaving was no longer a career move—it was an act of self-preservation. Four years prior, the thought of abandoning this role would have been unthinkable. Yet, the organization I once joined has been fundamentally altered, both in spirit and in practice.The catalyst for this transformation was the leadership instability. Over four years, five managing directors cycled through, each introducing their own brand of mismanagement. The final director, however, epitomized the breakdown. As a department head, he treated AI as an infallible oracle, querying it on system optimizations and implementing its recommendations without critical evaluation. This approach overlooked the unique complexities of our infrastructure—legacy hardware, custom configurations, and thermal constraints that generic AI models like ChatGPT cannot account for. For instance, an AI-suggested server adjustment disregarded the Dell R730xd’s thermal thresholds, leading to CPU throttling at 85°C, 3.2 hours of weekly downtime, and emergency debugging sessions at 2 AM—all because “the AI said so.”
Compounding this issue was the systematic dismantling of the team. Three of seven team members were laid off, yet the workload remained unchanged. Daily operational demands surged from 60% to 90% of our capacity, while project backlogs mounted. Overtime became mandatory, effectively doubling our work hours. Ironically, the project management tool flagged 20% idle time, presumably counting the hours spent disputing AI-generated firewall rules that exposed our Tier-1 application to 12,000 brute-force attempts in 48 hours.
The €400k investment in AI hardware marked the tipping point. Despite insufficient funds to retain skilled personnel, the company prioritized machines over expertise. AI-configured load balancers, for example, failed to account for asymmetric server capacities, resulting in 200ms latency spikes during peak loads. Meanwhile, our RAID 6 arrays suffered 8% annual data corruption due to heat-warped platters—a critical issue that went unaddressed as resources were diverted to AI systems incapable of interpreting hardware manuals.
I exhausted all avenues to address these issues. I escalated workload concerns, proposed reallocating funds to critical infrastructure repairs, and documented 42 overheating incidents and 17 misconfigured security policies in Q3. The response? Dismissal under the guise of “growth friction.” As if cortisol-induced cognitive impairment—manifesting as insomnia and memory lapses from 15-hour workweeks—were a sustainable growth strategy.
What made the decision to leave most difficult was the team. These were professionals who would meticulously rewrite 200 lines of Python—a task necessitated by an AI tool’s disregard for existing frameworks—without complaint. However, even their resilience fractured when leadership began presenting AI outputs as original insights. At that moment, it became clear: the issues were systemic and irredeemable, rooted in leadership’s conflation of technosolutionism with strategic vision and their disregard for human expertise.
My new role carries its own challenges, but it prioritizes digital sovereignty over AI hype. More importantly, the leadership understands the distinction between a chatbot and a server room. For now, that is sufficient.
Conclusion: Lessons Learned and Moving Forward
The decision to leave a once-cherished sysadmin role is not merely an act of dissatisfaction but a direct consequence of systemic failures. This experience distills critical insights, devoid of platitudes, into actionable lessons for both individuals and organizations.Key Takeaways
- Leadership instability undermines technical resilience. The turnover of five managing directors in four years created a vacuum of accountability, culminating in a leader who prioritized AI over fundamental hardware constraints. This misjudgment led to CPUs throttling at 85°C, 3.2 hours of weekly downtime, and a team forced to address AI-generated errors. The mechanism? A disconnect between leadership’s abstract vision and the physical realities of server management.
- AI lacks the contextual expertise to replace human oversight. Untethered from physical constraints, AI tools misconfigured load balancers, causing 40% latency spikes due to asymmetric server capacities. RAID 6 arrays experienced 8% annual data corruption from heat-warped platters—issues AI failed to contextualize. Human expertise is not optional; it is the critical layer that bridges algorithmic limitations and physical infrastructure.
- Resource misallocation erodes operational integrity. Allocating €400k to AI hardware during layoffs signaled misaligned priorities. Concurrently, misconfigured firewall rules exposed Tier-1 applications to 12,000 brute-force attempts in 48 hours. The mechanism? Leadership conflated AI investment with strategic foresight, neglecting the asynchronous maturity of AI and legacy systems.
- Chronic overwork compromises cognitive performance. A 43% reduction in team size pushed daily operations to 90% capacity, triggering cortisol-induced prefrontal cortex impairment. Manifestations included misconfigured firewall rules, insomnia, and a 64% increase in Mean Time to Repair (MTTR) for critical incidents. The physiological impact of overwork directly translates to technical and operational failures.
Strategic Guidance for Sysadmins in Similar Situations
For those navigating comparable challenges, the following strategies are empirically grounded and actionable:- Quantify technical debt with precision. Document AI-induced failures (e.g., 42 overheating incidents, 17 misconfigured security policies) and their root causes. Leadership may dismiss qualitative grievances but struggles to refute data-driven evidence.
- Challenge technosolutionism with technical rigor. When AI is proposed as a universal solution, interrogate its applicability: “How does this account for custom configurations or thermal thresholds?” Force leadership to confront the gap between AI’s theoretical capabilities and hardware limitations.
- Prioritize digital sovereignty in role selection. Cloud migrations and AI integrations often cede control. Seek environments where leadership distinguishes between a chatbot and a server room. Your expertise is not obsolete—it is systematically undervalued.
- Recognize the point of no return. If systemic risks (e.g., RAID corruption, CPU throttling) are dismissed as “growth friction,” the organizational culture is irredeemable. Remaining becomes complicity in its dysfunction.
Moving Forward
The transition to a new role represents a reclamation of control—over local infrastructure, leadership that respects technical distinctions, and a focus on resilience over ROI. However, the most profound shift is internal: rejecting misaligned priorities as inevitable. While the tech industry is saturated with AI hype, the next frontier lies in restoring the human-hardware balance. This is where my commitment now lies.To the colleagues left behind: You are the backbone of the infrastructure. Stay vigilant, document meticulously, and remember—AI does not falter under load. People do.#leadership #overwork #ai #priorities #software #coding #development #engineering #inclusive #community
Sysadmin Leaves Due to Poor Leadership, Overwork, and Misaligned Priorities -
Governance Attack Surface Review: Bitkub
Target Protocol: Bitkub (TVL: $1463.9M)Governance Attack Surface Review – Bitkub
Protocol: Bitkub (TVL: $1.463 B on Ethereum & L2)Date: 24 Oct 2026
Prepared by: [Your Name], Senior DeFi Security Researcher & Smart‑Contract Auditor
1. Executive Summary
Bitkub has rapidly become one of the largest DeFi aggregators on Ethereum and its Layer‑2 ecosystems, managing a TVL of ≈ $1.46 B. The protocol’s value proposition relies heavily on a community‑driven governance model that controls critical parameters such as fee structures, asset listings, upgradeability, and treasury disbursements.Our Governance Attack Surface Review focuses on the on‑chain governance contracts, the surrounding off‑chain tooling, and the interaction points with the protocol’s core smart‑contract suite. The analysis reveals four high‑severity attack vectors that could enable an adversary to:
- Seize or freeze treasury assets (≈ $1 B) via compromised admin keys or timelock manipulation.
- Force malicious upgrades that embed hidden backdoors or drain user funds.
- Manipulate voting outcomes through quorum bypass, vote‑bribery, or delegate‑key hijacking.
- Exploit cross‑chain bridges to trigger re‑entrancy or replay attacks that affect governance state.
Overall, the risk score for Bitkub’s governance layer is 7.4 / 10 (High). Immediate remediation of the high‑severity findings is required to protect both the protocol’s assets and its reputation.
2. Identified Attack Vectors
#Attack VectorDescriptionAffected ComponentsPotential Impact1Compromised Timelock / Admin KeyTheTimelockController(or custom timelock) holds the sole authority to execute governance proposals. The admin address is a single EOA that signs timelock updates. If the private key is compromised, an attacker can schedule and execute arbitrary proposals without delay.TimelockController.sol,ProxyAdmin.sol,Governor.solImmediate upgrade to malicious contracts, treasury drain, asset freeze, protocol shutdown.2Upgradeability Abuse (UUPS / Transparent Proxy)Bitkub uses UUPS proxies for core contracts (e.g.,Vault,Router). TheupgradeTofunction is gated only by theGovernorvia the timelock. A malicious proposal (or a proposal that passes due to quorum manipulation) can replace logic contracts with a malicious implementation that includes hiddenownerbackdoors, re‑entrancy hooks, or ERC‑20transferoverrides.UUPSProxy.sol,Vault.sol,Router.sol,Governor.solFull control over user funds, silent fee siphoning, denial‑of‑service.3Quorum & Vote‑Weight ManipulationThe governance contract uses a snapshot‑based token voting model with a 2‑day voting period and a quorum of 4 % of total token supply. Delegation keys are stored off‑chain in a signed message that can be replayed. An attacker can:
• Accumulate a large amount of voting power via flash‑loan attacks on the governance token.
• Replay stale delegation signatures to inflate voting weight.
• Exploit the “early‑execution” path that allows proposals to be executed once the quorum is reached, bypassing the full voting period.Governor.sol,Token.sol(snapshot),Delegation.solPassing malicious proposals with minimal community participation, enabling any of the above attacks.4Cross‑Chain Bridge Governance SyncBitkub’s L2 modules rely on a state‑sync bridge that mirrors governance state from Ethereum to L2 (Arbitrum, Optimism). The bridge uses a Merkle‑proof verifier that trusts a set of bridge relayers. If a relayer is compromised or colludes, it can submit a forged state root that marks a proposal as executed on L2, allowing the attacker to trigger L2‑specific upgrades or fund releases without the corresponding Ethereum execution.Bridge.sol,L2Governor.sol,StateSyncVerifier.solInconsistent governance state, double‑spend of treasury across chains, L2 asset lock‑up.5Off‑Chain Proposal Submission & Signature SpoofingProposals are submitted via a web UI that signs a JSON payload with the proposer’s private key. The contract only verifies the signature against the proposer’s address, but does not enforce a nonce or domain separator that includes the chain ID. An attacker can replay a signed proposal on a forked network or on a testnet that later migrates to mainnet, causing unintended execution.Governor.sol,ProposalFactory.solExecution of stale or malicious proposals, governance confusion.6Insufficient Multi‑Sig SafeguardsThe protocol’s treasury withdrawal function is gated by a 2‑of‑3 Gnosis Safe. However, one of the signers is a hardware‑wallet‑derived address that is also used as the admin of the timelock. If that hardware wallet is lost or compromised, the safe can be emptied with a single additional signer.GnosisSafe.sol,Treasury.solTreasury drain with only 2 signatures instead of 3, reducing the intended security margin.7Vote‑Bribery via Flash‑Loan Token SwapsThe governance token is an ERC‑20 with no anti‑flash‑loan guard. An attacker can borrow > 50 % of the token supply in a single transaction, cast votes, and repay the loan within the same block, effectively buying voting power without market impact.Token.sol,Governor.solPassing malicious proposals without genuine community support.
Note: The above vectors were identified through static analysis of the verified contracts, review of the governance documentation, and simulation of attack scenarios on a forked mainnet environment.
3. Prioritized Technical Recommendations
PriorityRecommendationRationaleImplementation GuidanceCriticalMigrate Timelock Admin to a Multi‑Sig (≥3‑of‑5) with Daily RotationEliminates single‑point‑of‑failure for proposal execution.Deploy a freshGnosis Safe(or similar) and set it as theadminof theTimelockController. Use a daily timelock for admin changes and enforce a 2‑day execution delay for any upgrade.CriticalIntroduce a “Delay‑Upgrade” Pattern with Dual‑Signature GuardPrevents immediate malicious upgrades.Add aupgradeDelay(e.g., 7 days) that must pass after a proposal is queued beforeupgradeTocan be called. Require two distinct governance actions (e.g., separate proposal IDs) to approve an upgrade.CriticalEnforce Non‑Reentrancy & Re‑Entrancy Guard on Upgrade FunctionsMitigates re‑entrancy attacks during contract upgrades.Use OpenZeppelin’sReentrancyGuardonupgradeToand any external calls in the upgrade path.HighAdd a Minimum Quorum of 10 % and a “Vote‑Lock” PeriodReduces feasibility of flash‑loan voting attacks and quorum bypass.UpdateGovernorto require 10 % of total supply and enforce a minimum voting period of 3 days, regardless of quorum attainment.HighImplement Delegation Nonce & Domain SeparatorPrevents replay of delegation signatures across chains or forks.ExtendDelegationcontract to store a per‑addressnonceand includechainIdandcontractAddressin the signed payload (EIP‑712).HighSecure Bridge Relayer Set with Multi‑Sig & SlashingStops a single compromised relayer from forging state roots.Require ≥2‑of‑3 relayer signatures for state sync, and implement a slashing mechanism for misbehaving relayers.MediumAdd “Proposal Expiry” & “Cancellation” MechanismsAllows community to abort stale or malicious proposals.Implement acancelProposal(uint256 proposalId)callable by any holder with ≥ 1 % voting power after a 48‑hour grace period.MediumIntroduce Anti‑Flash‑Loan Guard on Governance TokenDeters vote‑bribery via flash loans.Add ablockNumber‑based voting lock: tokens transferred in the same block cannot be used for voting until the next block, or integrate a time‑weighted voting model.LowUpgrade Off‑Chain UI to Use EIP‑712 Typed Data with Chain IDPrevents replay attacks on proposal signatures.Ensure the UI signs proposals with a domain separator that includeschainId,verifyingContract, and a uniquenonce.LowRotate Hardware‑Wallet Admin Key PeriodicallyReduces risk of long‑term key compromise.Schedule a quarterly key rotation and store the new public key in a timelocked contract.LowPublish Formal Governance Specification & Conduct Community AuditsImproves transparency and community trust.Release a public spec (PDF + on‑chain reference) and invite third‑party audits of the governance flow.
Implementation notes: All contract changes must be tested on a forked mainnet and subjected to a formal audit before deployment. Use OpenZeppelin Contracts v5 where possible to benefit from battle‑tested libraries.
4. Risk Score
DimensionScore (1‑10)WeightWeighted ScoreTimelock / Admin Key90.252.25Upgradeability80.201.60Quorum & Voting Mechanics70.151.05Cross‑Chain Bridge60.150.90Off‑Chain UI & Signature Handling50.100.50Multi‑Sig Treasury40.100.40Flash‑Loan Vote‑Bribery50.050.25Total7.4—7.4
Interpretation:7.4 falls in the High risk category (6 – 8). The most critical contributors are the single‑point admin key and upgradeability pathways, which together account for ~40 % of the overall risk.
5. Conclusion
Bitkub’s governance layer is the linchpin of a protocol that safeguards over $1.4 B in user assets. While the platform demonstrates solid engineering in its core DeFi primitives, the governance attack surface contains several high‑severity weaknesses that could be leveraged to seize control of the treasury, inject malicious upgrades, or subvert the voting process.By immediately addressing the critical recommendations—migrating the timelock admin to a robust multi‑sig, enforcing a delayed upgrade workflow, and hardening the voting/quorum model—Bitkub can dramatically lower its governance risk profile from 7.4 → ≤ 4.5 (Medium). Subsequent medium‑ and low‑priority mitigations will further cement the protocol’s resilience against sophisticated adversaries.
We recommend the Bitkub team:
- Prioritize the critical fixes within the next 2‑4 weeks and conduct a full‑scope audit of the updated contracts.
- Publish a governance security roadmap to the community, outlining timelines and responsible parties.
- Engage an external audit firm for a comprehensive governance audit (including formal verification of the timelock and upgrade logic).
Implementing these measures will protect Bitkub’s users, preserve its market position, and demonstrate a proactive security posture that aligns with best practices in the DeFi ecosystem.
Prepared for Bitkub by:
[Your Name] – Senior DeFi Security Researcher & Smart‑Contract Auditor
Contact: [email protected] | +1 (555) 123‑4567
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Governance Attack Surface Review: Bitkub -
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. **
-------------------------------------------------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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Anole footnotes:
I had a green anole as a pet, when I was a little kid. He was my first pet lizard, and he lived to the age of 8 - which is quite respectable for an anole. He was named Speedy, for obvious reasons. Their leg muscles basically seem to be spring-loaded; they can perform a leap from a stationary start so rapidly and explosively that your eyes can't track them. It almost looks like they teleport, and their sprints are equally impressive. Mine regularly caught insects *in mid-air*.
He quickly realized that I wasn't going to hurt him, and also quickly learned that big apes watching him wasn't dangerous. But he was a bit shy and didn't like handling - so I didn't press that. At the time I also didn't really know how to build trust with lizards yet (and managing that with an anole is *not* a beginner project - they're very smart, but they're also very small, and they're quite aware of that.)
Already then, I was *convinced* that he dreamt when he slept. He'd change colour multiple times in his sleep - and sometimes he'd even assume "fighting colours", with the distinctive black eye stripes and everything. Also sometimes his eyelids would start twitching wildly. I figured that the normal green-to-brown-and-back colour change could just be a temperature thing, but couldn't come up with a better explanation for the full fighting colourscheme than that he was probably dreaming he was having a fight. I felt very pleased when science eventually corroborated my hunch some 30 years later; it is now generally accepted that lizards almost certainly dream.
In the last six months or so of his life, he was clearly getting old - he slowed down quite a bit, and he got cataracts and started losing his eyesight. He was entirely blind for the last couple months of his life. He adapted shockingly well to being blind, and could remember exactly where the food and water bowls were, so he had no problem keeping himself fed. More importantly, he remained bright green most of the time. He'd even sometimes still show off the dewlap and do a pushup routine.
