6   
  • Write a letter of worning to her.if not , the one whose dating her is a fearable man or you also need that lady

  • i understand that but give her advice about dating in business after understanding her attitudes, habits, personalities, show her support in business... activities so that she may understand that business requires friendship among workers not dating. tell her difference between dating, friendship in business. more

3   
  • If you haven’t already you could try the following:
    1. Document and present to your boss exactly how you believe the course will benefit in your... current position and what that would mean for the company.
    2. If you have personal days, Flex Time, or other time off available, ask if you could divide that time between days to cover at least part of the required time for the course.
    3. Consider asking HR if there are any official policies on professional development opportunities.
    4. Consider reaching out to the course providers to see if they might offer it at other times or in other formats such as asynchronous or remote vs in person that may be more compatible with your work schedule.
     more

  • Could you come in an hour earlier on the days that you need to leave early?

Canada's Great Political Illusion


How a Manufactured Democracy Keeps an Unwitting Nation Under Control

By Henry

Every election cycle, Canadians are invited into what is presented as the pinnacle of democratic participation. It arrives with the polish of a national ceremony and the sincerity of a stage play. Over time, as I watched this spectacle with the eyes of a researcher and the patience of a citizen determined to understand... the machinery of governance, I reached an uncomfortable realization: the process is not democratic but theatrical.

What appears to be choice is in fact choreography. What is promoted as representation is, in truth, a managed illusion. The 2025 federal election merely reveals the continuity of that pattern: a ritual orchestrated to maintain compliance, not to express the will of the people.

Here, I explore the mechanics behind this illusion -- its preselected political class, its media apparatus, its buried history, and the role the 2025 election plays in the broader strategy of national management.

The more closely one examines the Canadian electoral process, the more obvious it becomes that the public never chooses its leaders -- they merely ratify them. Candidates emerge not from the grassroots but from curated networks of influence. Many have backgrounds that read less like résumés of public servants and more like dossiers crafted by intelligence-linked organizations, think tanks, activist foundations, or elite academic pipelines.

This is especially evident in the Trudeau dynasty. Pierre Trudeau navigated the political landscape with the quiet backing of imperial and intelligence institutions. His son -- trained in theatre rather than governance -- was ushered onto the national stage with timing so suspicious it resembled casting rather than candidacy. And the pattern extends far beyond that family.

The deeper problem lies not in who wins but in how they win. The tallies presented to the public bear no trustworthy relationship to the votes cast.

The people sense as much intuitively. Volunteers canvass entire neighbourhoods dominated by conservative voters -- often 70% or more -- yet watch those districts magically swing left on election night. These patterns repeat with statistical impossibility, suggesting not organic outcomes but engineered ones.

The political ritual also operates on psychological sleight-of-hand. The language of politics borrows from electrical and mystical symbolism: "elected," "charged," "power," "current." The voter is told he energizes the system, yet his energy is captured only to justify decisions made without him. Meanwhile, the system transfers blame: if the country declines, the voter chose wrong; if corruption spreads, he did not participate hard enough; if he abstains, he is shamed. The ritual ensures the population carries responsibility while holding no real authority.

The illusion could not survive without its principal accomplice -- the media. In Canada, the majority of the mainstream press is funded by the same government it claims to hold accountable. Hundreds of millions in subsidies ensure compliance. Outlets that refuse to echo the state's narrative face regulatory pressure, financial exclusion, and public smearing.

Surrounding the media is a vast constellation of "independent" organizations -- NGOs, university centres, union groups, and ideological lobbyists -- whose missions conveniently align with government messaging. Many participants in these institutions cannot identify the origins of the ideology they defend, but they enforce it with zeal because their careers depend on it.

Behind the media stands the true spine of the nation: the bureaucracy. This administrative class is not elected, not removable, and not ideologically neutral. Bureaucrats persist through every election cycle, forming a permanent layer of governance that no vote can dislodge. They hire in their own image and enforce the worldview of the managerial elite. What the public dismisses as inefficiency is often a sophisticated mechanism of continuity.

Scandals that appear to shake the system are rarely genuine ruptures. They serve instead as controlled-burn operations -- clearing away disobedient actors and replacing them with compliant ones. The "Nazi veteran" fiasco in Parliament revealed exactly this dynamic: a manufactured outrage used to displace one Speaker and usher in another who aligned more predictably with prevailing ideological interests.

Perhaps the most extraordinary achievement of the Canadian establishment is the deliberate erasure of the country's true history. Most Canadians can recite polite myths taught in school -- European explorers, Indigenous contact, Confederation -- but few possess even a rudimentary understanding of the legal and financial structures that shaped the land.

Canada was never built as a unified republic. It was a patchwork of corporate territories operated by private companies under royal authority. The Hudson's Bay Company alone controlled a geographic expanse greater than many modern nations. Other regions were administered through proprietary charters -- commercial ventures, not sovereign colonies.

Unlike the United States, the region now called Canada never formed a cohesive cultural foundation. The British ensured deep divisions remained between French, English, and Indigenous groups, preventing the emergence of a unified national identity that could resist imperial administration. Fragmentation was not a historical accident -- it was a management tactic.

Confederation did not create a nation but reorganized British assets. Taxes did not emerge to fund public programs; they were established to service debts owed to private financial interests. Even the classification of Indigenous peoples as "Indians" was not a geographical blunder but an administrative tactic: by using the same terminology as in India, Britain folded North American peoples into an existing legal category of imperial oversight.

The further one digs into Canada's origins, the clearer the pattern becomes: the country was designed as a managed territory, not a sovereign nation. Its history had to be simplified, sanitized, and concealed to preserve that arrangement.

The 2025 federal election is not merely another political cycle -- it is a demonstration of the modern method of governing populations through perception rather than policy. Canadians sense something deeply wrong, yet most cannot articulate the problem. That confusion is not their fault. It is the product of a system that overwhelms the population with distraction while hiding structural truth.

In this environment, electoral outcomes are managed through narrative engineering. When the ruling party's popularity collapses, the media reframes events to soften the fall. When activist groups supporting the dominant ideology lose resources, the leadership triggers a snap election to freeze political timelines. When a dissident party gains traction, petitions, smears, and ballot interventions materialize to halt their rise.

Even if a dissident manages to secure a seat, they become a lone voice drowned inside a chamber of obedient MPs. Parliament is not gridlocked; it is insulated. Debate becomes theatre. Dissent becomes symbolic. The machinery rolls on uninterrupted.

Digital platforms amplify this control. Censorship no longer requires silencing speech; it merely requires ensuring no one hears it. Algorithms bury inconvenient truths under layers of noise. Alternative views are not defeated -- they are smothered in irrelevance.

What emerges is a form of governance that no longer depends on convincing the population -- only on distracting it. The spectacle continues, election after election, because the ritual itself maintains compliance. The public need not believe the system works; they merely need to remain engaged enough to repeat the performance.

After examining the structure, history, and operation of Canada's political mechanisms, one arrives at a stark reality: the country's democratic image is a façade. Elections are rituals of validation, not instruments of public will. Media exists to reinforce the chosen narrative. Bureaucracy ensures continuity regardless of outcomes. Education obscures the past to prevent the public from recognizing patterns in the present. The political class is not elected -- it is curated.

Understanding the illusion robs it of its power. When one recognizes the design, the spectacle loses its enchantment. The 2025 election will pass, followed by another and another, each maintaining the appearance of change while preserving the machinery of control.

Yet individuals remain capable of stepping outside the illusion. Freedom begins with discernment. Awareness severs the psychological bonds that ritual politics seeks to impose. Canada may function as a managed territory, but its people retain the ability to reclaim clarity, responsibility, and truth.

The first step is simply seeing the illusion for what it is.
 
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5   
  • 𝑇ℎ𝑎𝑡𝑠 𝑀𝑢𝑠𝑡 𝑇𝑜 𝑐ℎ𝑎𝑛𝑔𝑒 𝑀𝑦 𝑙𝑖𝑓𝑒 𝑆𝑡𝑦𝑙𝑒 𝑀𝑦 𝑅𝑒𝑔𝑎𝑟𝑑𝑠

  • everything we post on social medias are our own business, but when we post them they are no longer private they become public to society.

Writing with AI: from Résumés to Eulogies.


What happens when we outsource not just our careers, but our legacy

We've normalized letting machines narrate our professional worth. And it's making us appear the same as the next person applying for the same job. We feed AI tools our scattered work history, and it spits back a resume so polished it barely resembles the chaotic Monday mornings and imposter syndrome that actually built that... career. Emphasis on the 'spit', because what we do as humans can be termed as process, not what machines do. Most of the people using these tools don't even know how it works, but most of us are happy with the time and efforts saved. We've accepted this trade-off: competence for authenticity, because in the attention economy of hiring managers, you have three seconds to matter.

But guess what? Now the same technology wants to write your goodbye. Quite a shocker, I know.

AI-generated eulogies are no longer a Black Mirror fever dream. They're here, tucked into funeral planning apps and memorial service websites, promising to "honor your loved one with a personalized tribute" for quite cheap in-fact. The pitch is practical: grief is paralyzing, words are fail you, deadlines looming over your head. So why not let the algorithm help?

And honestly? It kind of makes sense. If we've already outsourced the story of our ambitions to AI, why draw the line at the story of our existence? I noticed this question sticking with me after I used AI to clean up my own résumé -- a version of me that looked far more decisive, far less uncertain, than I felt while sending it out.

But I'm not that person. I take my time to ponder things. Sometimes I pretend to be certain and then pay for it. And I'm never satisfied with the résumés I've sent.

The Rise of the AI Ghostwriter: From Career Launch to Life's End.

We started small. AI rewrote cover letters, optimized LinkedIn headlines, turned "I did stuff" into "Spearheaded cross-functional initiatives to drive measurable impact." It was efficient. It was harmless. It worked. And it made us appear much smarter than we actually are.

But then it crept inward.

Now AI drafts wedding vows, breakup texts, apology emails to your mother, thank you notes to the caregiver. It's the digital scribe for every occasion where emotion is expected but words feel impossible. And death, of course, is the ultimate occasion of them all.

The through line is legacy narrative- how we want to be remembered by strangers we never talked to, employers we didn't care about, lovers, mourners, etc. Whether you're 21 and need a resume or 85 and need a eulogy, the task is the same: compress a messy, contradictory human life into something digestible, moving, and above all, coherent. Make it sound emotional, not empty. Keep the sentences a bit lyrical and sad. Make it appear that you're at an absolute loss, but still good enough for speaking out the best eulogy ever written in the history of mankind, but by a machine.

AI promises to do all of this seamlessly. Feed it your loved one's Facebook posts, work emails, photo metadata, and Spotify Wrapped, and it will return a three-minute speech that hits all the expected notes: beloved, generous, lived fully, will be missed.

The formula is so airtight, that it's problematic.

The Algorithm of a Life: How AI Constructs a Persona

Here's what the machine sees when it looks at a life: data points.

Your Instagram captions. Your LinkedIn endorsements. Your Apple Photos library sorted by geolocation and facial recognition. Hundreds of group chats on your WhatsApp you're in but haven't opened in months. The Goodreads books you marked "want to read" and never touched. The Strava runs you logged but mostly walked(most of us are guilty of this one).

From this, AI builds a persona. Not the person, just the one that shows up in pixels and metadata. It stitches together a highlight reel, smoothing over the gaps, the contradictions, the 3 AM existential spirals that left no digital trace.

The output is polished like the most expensive china. It is coherent enough to be labelled as it was written by some world famous author. Algorithmically empathetic enough, like the person who lost their loved one.

But here's the thing: people aren't polished. We're walking contradictions. Half-finished projects. Carrying the pieces of everyone we've met or just found cool enough to adapt from. We say one thing and do another. We're kind on Friday evenings and cruel on Monday mornings. We contain multitude of persona, and most of them don't make it onto social media.

AI doesn't know about the way your grandmother told you stories from when she was younger. It doesn't know about the inside joke with you siblings and cousins that made you laugh so hard you cried, or the petty grudge you held with your schoolmate for twenty years over something you can't even remember now. It knows what you performed. Not what you were.

The risk isn't just inaccuracy. It's flattening of the person you are and going to become by the end of your time. But outsourcing the life you've lived is turning a human into a generic best version. By following a template of a life rather than the life lived.

Perspectives: The Good, The Bad, and The Uncanny.

Not everyone sees this as a tragedy. And some of us lack the emotional depth to comprehend it. And it's okay. Not necessarily right. But then again, no two people are the same. And here's how:

The Griever's Aid: "When my father died, I couldn't think straight," one user shared in a grief support forum. "I stared at a blank document for three days. Finally, I fed ChatGPT some memories and his obituary. It gave me a structure. I rewrote most of it, but I needed that scaffolding. Grief leaves you mute. AI gave me a starting point."

There's real compassion in this. Not everyone has the luxury of eloquence in the aftermath of loss. Some people need help organizing the chaos of memory into something speak-able. AI can be that help as a tool though, not a stand-in for your mind, heart and emotions.

The Outsider's Tool: Funeral directors and celebrants are quietly adopting AI for a practical reason: they're often tasked with memorializing strangers. A distant cousin. An estranged parent. Someone the family knew existed but didn't know. In these cases, AI offers a way to create something respectful and personal-sounding, even when the personal knowledge isn't there.

One funeral director put it bluntly: "It's less about a perfect speech and more about alleviating pressure. Families are overwhelmed. If AI gives them a draft they can work from, that's a gift."

The Ethical Quandary: But others recoil. "A eulogy isn't a document," a grief counselor told me. "It's an act of love and witness. The struggle to find the words is the tribute. Outsourcing that to a machine feels like the final disconnect in an already disconnected world."

The question isn't just can we, but should we? Is it dishonest to let AI speak for the dead? Does it commodify memory, turning the intimacy of loss into a product optimized for emotional impact?

Where's the line between assistance and abdication of human responsibility?

The Subject's Paradox: And then there's this: if we use AI to write our resumes, are we also shaping the data that will one day write our eulogy?

Think about it. Every LinkedIn update, every carefully curated Instagram story, every polished email signature - you're feeding the algorithm that will summarize your life. You're not just building a professional brand. You're building a posthumous one.

Are we curating our digital footprint for AI consumption? Performing for an audience that includes not just the living, but the future machine that will narrate our exit?

The thought is unsettling. And probably already true.

The "Professional Self" vs. The "Whole Self": What Gets Lost?

Resumes highlight achievements. Eulogies though, at least the good ones -- highlight character, love, and the small, strange, irreplaceable moments that made someone them.

But AI is trained on professional and public data. It knows your job titles, your published photos, your measurable accomplishments. It doesn't know the quiet kindness. The inside joke. The way you always hummed off-key in the shower or how you never learned to parallel park but refused to admit it.

Those details don't trend. They don't get archived in the cloud. They live in the unreliable, precious storage of human memory.

And when we let AI write the eulogy, we risk reducing a life to what can be quantified. Bullet points. Optimized keywords. A life summarized not by what mattered, but by what left a data trail.

Even in death, we're flattened into our productivity metrics.

A New Ritual? Prompt Engineering Your Own Eulogy.

Here's where it gets weird. And in odd ways, profound.

Some people are now experimenting with having AI write their own eulogy. Not as a morbid joke, but as a tool for self-reflection. A digital-age memento mori.

"What would the algorithm say about me?" becomes a mirror. What data trail am I leaving? What story does my digital self tell? Is that the story I want to be told after I'm gone?

For some, the answer is unsettling. The AI-generated eulogy feels too polished, too achievement-focused, too much like a LinkedIn profile and not enough like a life. It reveals the gap between who we are online and who we are in the quiet, uncaptured moments.

Others find it clarifying. "Seeing my life reduced to algorithms made me realize I was performing even for myself," one person wrote. "It made me want to live differently. Less posting, more presence."

Could this become a new ritual? A way to reckon with mortality and meaning in the age of data? Maybe. Or maybe it's just another way we've learned to outsource the hard work of being human.

The Future of Memory and Legacy

This is only the beginning.

Beyond text, we're moving into AI-generated video tributes. Audio clones delivering eulogies in the deceased's own voice. Interactive memorial avatars that answer questions at the funeral, trained on a lifetime of messages and posts. Something we had only seen in movies(Tony Stark in Avengers: Endgame), until now.

The technology is evolving faster than our ethical and legal frameworks. Faster than our grief.

On one hand, there's something democratizing about this. Not everyone has a poet in the family. Not everyone can afford a professional eulogist. AI could, in theory, give everyone access to a polished, heartfelt tribute.

But there's also homogenization. When everyone's eulogy is algorithmically optimized for maximum emotional resonance, do they all start to sound the same? Do we lose the specificity, the weirdness, the human imperfection that makes memory real?

A tech optimist might say: "AI is the ultimate storytelling tool. It doesn't replace love; it gives it a clearer voice."

While a traditionalist may counter it saying: "The struggle to find the words is the tribute."

And maybe they're both right.

AI is just another tool -- useful, imperfect, ethically complicated; like every technology humans have ever invented to manage the unbearable weight of loss.

But here's what AI can't replicate: the cracked voice. The long pause where words fail. The messy, imperfect story told by someone who loved you enough to stand up and try. The shared silence in a room full of grieving humans who knew you, not just your data.

AI can generate a eulogy. But it can't grieve.

Ghosts in the Algorithm.

In one of my previous writing(a competition piece, not yet published), I called the internet a "digital séance, summoning what was meant to fade." That feels even more relevant now.

Because when AI writes your eulogy, it's not just summarizing a life. It's resurrecting a version of you that's curated, optimized, coherent. A ghost in the machine, built from your own data trail, speaking at your own funeral.

Maybe that's the future we're stumbling toward. Not towards immortality, but an algorithmic afterlife. Not a memory lived, but some metadata amongst the infinite.

Or maybe, in a world where data never dies, the most humane act is still the one AI can't perform: to let memory breathe in silence, to honor the messy, unquantifiable truth of a life, and to accept that some things.

Like grief, like love, like the specific ache of missing someone. Something that should never be outsourced.

Because in a world designed to optimize everything, the most radical act might be to stay imperfect.

Especially in death, when imperfection is the only honest record we leave behind.

© Legal Disclaimer & Creative Commons License

Copyright © 2025 Vasundhara Pathak. All rights reserved.

This article, "Writing with AI: From Résumés to Eulogies -- What Happens When We Outsource Not Just Our Careers, but Our Legacy," is an original literary work. All ideas, interpretations, narratives, and opinions expressed herein are the sole intellectual property of the author.

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For republication, licensing, academic citation, print anthologies, translations, archival use, interviews, or any rights-related inquiries, please contact:

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Written authorization is required for any use not expressly permitted under this license.
 
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  • Consult and employment attorney - this sounds like bait and switch.
    Also we need to pay our interns. Otherwise only people who have some other means... to support themselves get the benefits of this time of inside experience.
    Run away. They are not honoring the terms they advertised.
     more

  • You Should Have Gotten A Call From HR Or The Interviewing Manager With An Offer. Then If You Accept The Terms Of The Offer, A Start Date Could Be... Acknowledged By Both Sides. This Is “The Basics” That Can Be Done.

    How Does A Sales Manager Close A Deal Or Hire Without Proper Procedure?
     more

American Thinker


Among several new and fashionable claims which have taken hold in 2025, a most curious one is that expertise itself is pernicious. Credentials, the argument goes, are little more than camouflaged arrogance. Specialization is cover for bad judgment, and long periods of training or experience evidence of detachment from "real life." The view tends to be framed as a populist corrective: an overdue... rebellion against insulated elites who, we are told, tend to fail spectacularly when not watched closely. But where this is seen, it's not a thoughtful reassessment of authoritative mastery: it tends to be a histrionic overreaction to specific policy failures, now metastasizing into a childlike hostility toward knowledge itself.

There is no question, none whatsoever, that government policies during Covid were poorly designed, inconsistently applied, and communicated with excessive confidence. Certain figures came armed with impressive résumés, long lists of degrees, and institutional authority, and they spoke far beyond the limits of what evidence could support at the time. (I myself saw an attempt to infer from two numbers out of Korea in early 2020 that Covid was a nothingburger; that conclusion may ultimately have been vindicated, but not at that time, and certainly not from two datapoints.) The pandemic experience understandably, and justifiably, shook public trust. But extrapolating from those failures to the idea that expertise itself is necessarily fraudulent or unnecessary confuses bad governance with the irrelevance of specialized knowledge and long experience.

The impulse to discard expertise is not new, though. It appears cyclically, often after moments when institutions overreach or experts are seen as politically entangled -- which they frequently are. What is new is the speed with which this skepticism now spreads, and the ease with which it hardens into a performative disdain for competence itself. Social media already rewards confidence over calibration, certainty over caveat. And here as elsewhere, the result is not a more informed public, but a louder and more ignorant one, where opinion is mistaken for insight and contrarianism for courage.

Indeed, even the loudest critics of what's sloppily been called "credentialism" quietly rely on it every day. Few people are interested in performing their own surgery or putting it in the hands of the Domino's Pizza deliveryman (who may fancy himself a "polymath" -- another word which has strayed from its original meaning). And even if I know, roughly, how to fix a glitching electrical panel, I'm likely to leave that to people who do it daily. Amid the churlish cry of generalism-for-all, we continue to trust surgeons to operate, airline pilots to fly, structural engineers to calculate loads, anesthesiologists to manage unconsciousness, and specialized mechanics to keep complex machines from failing at speed. We do so not because these professionals are infallible, but because long training, apprenticeship, and error correction still matter in a world of growing, unforgiving complexity.

Modern systems -- technological, economic, social/cultural -- are far from intuitive. They are tightly coupled, layered, and increasingly nonlinear. Small mistakes can cascade. (Ask any actuary.) Partial understanding is nearly always more dangerous than ignorance when it encourages confident intervention without awareness of second- and third-order effects. That's precisely why expertise developed in the first place -- not to exclude the public, but to reduce error in domains where error is costly.

Ironically, errors within expert communities tend to fail slowly and visibly: they're constrained by peer review, professional norms, and reputational risk. Popular error, by contrast, fails quickly and at scale. When decisions are driven by self-important narrative, identity politics, or viral consensus rather than disciplined analysis or hard-won experience, corrections come late -- often only after damage has already been done. History suggests that major disasters are more often born of mass enthusiasm and political shortcuts than of excessive technical caution.

None of this, of course, implies blind deference. Expertise should inform decisions, not dictate values. Specialists are good at explaining constraints, tradeoffs, probabilities, and risks: not at deciding collective goals. Much of the public backlash of recent years stems from role confusion, when technical advice was presented as moral certainty or political necessity. The remedy for that failure is not an infantile screed to abandon expertise, but to restore its proper boundaries.

Throwing away accumulated knowledge does not empower citizens; it forces complex choices to be made by guesses, intuition, tribal loyalty, or rhetorical force. Societies that do this do not become freer or wiser. They become more fragile.

The lesson of the past few years is not that expertise is obsolete or, in and of itself, dangerous. It is that expertise, severed from humility and institutional restraint, can be misused and even weaponized. The correct response is accountability, not a ridiculous fantasy that we can replace hard-won competence with a disingenuous generalism, confidence, and crowd wisdom. Civilization does not advance by pretending everyone is equally qualified to do everything. It advances by recognizing that specialization, while imperfect, remains indispensable -- and that abandoning it is not liberation, and hardly progress, but self-inflicted blindness.
 
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The interview's on Zoom. Here's how to actually stand out.


Virtual meetings and job interviews are no longer the exception, but we're not all spiff and polished when presenting ourselves online.

This requires a skill set not naturally in many people's wheelhouse.

Nancy Ancowitz, a career strategist and author of the new book "Zoom to Success," has some coaching tips.

Here are edited excerpts of our recent conversation:

Kerry Hannon: Why did you write... this book right now?

Ancowitz: This is the book I wish I had to help me navigate the virtual world. There is so much that goes into all of this before we even open our mouths -- the lighting (two light sources from the front or sides for balanced, flattering light), the hair, the makeup, the camera, your background, what you are wearing, the tech checks. I show people ways to make it simpler and more accessible to bring your best face forward online.

What are the biggest challenges of virtual presentations?

Speaking to somebody 12 inches from their face, and where their face and your face are so big and filling up the whole space, is really tough for many people. And if you are presenting, looking at 20 or more of those faces in little boxes is truly abnormal.

Another big one is that you can't make real eye contact with anyone since you're looking into your tiny camera. Nobody knows where to look when they are speaking. Maybe you look at yourself. You get distracted by your hair out of place. Also, not everybody's blessed with a great voice, and your voice matters even more on Zoom and other virtual platforms because there's not as much of you to see and to experience. Finally, one of the hardest things, of course, is that you have to be your own tech person and when things go wrong, be calm and cool.

You need to carve out an hour ahead of time to get mentally grounded and ready.

A virtual presentation can create more jitters than in-person for many folks. What are some of the good techniques you can do?

My favorite technique is self-talk, or speaking to yourself in the second or third person. Instead of saying, 'I've got this,' say 'you've got this.' Reframe nerves as excitement. Think 'I feel most alive when I'm tackling things that are a little bit challenging.'

I remind myself to slow down and breathe deeply, which sharpens my focus and clears my head when things get bumpy. Start with a two-minute reset: Inhale for four counts, hold for four, and exhale for eight.

It's a mindset matter. Remember that you're not there to impress people. You're there to share something, to share information, to inspire, to educate, to persuade. But you're not there for their judgment. That's a super important way to manage jitters.
 
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I moved to the US from India. Here's how I landed a job at Microsoft after first misunderstanding the Big Tech hiring process.


He suggests tech hopefuls build a public presence and continually develop skills to stand out.

This as-told-to essay is based on a conversation with Rishab Jolly, a 37-year-old senior product manager at Microsoft, based in Redmond, Washington. Business Insider has verified Jolly's employment history with documentation. The following has been edited for length and clarity.

Before moving to the US... in 2015, I studied engineering and computer science in India, where I worked as both a software quality tester and an engineer.

I was always interested in the business side of technology, so I left my job in India to pursue an MBA at the University of Arizona. My goal was to gain business acumen to complement my engineering background. I saw firsthand how much innovation takes place in the US and how many opportunities exist to work on cutting-edge products, which inspired me to build my career here.

One of the most valuable parts of the MBA program was its partnerships with Big Tech companies. As part of the curriculum, representatives from Microsoft, Amazon, and Google brought projects to campus.

Students formed teams, worked on deliverables, and were graded by company representatives. In 2016, I was team lead on a Microsoft project, and we did an excellent job. That gave me a chance to network and to demonstrate my skills to an actual Microsoft product manager.

I applied to about 200 jobs, sending the same generic résumé without referrals. I received only three calls back and passed two interviews, but both offers were subsequently rescinded: one company considered my visa too risky without a STEM extension, and another cited budgetary reasons.

I needed to secure a job within 60-90 days after graduation, or I would be forced to return to India. I ran out of money, asked a friend if I could crash on their couch, and felt a constant sense of anxiety.

During that time, I stayed in touch with the Microsoft contact I'd met through my MBA project. When an opening came up, I asked if he would refer me.

He agreed, and I rewrote my résumé to match the specific role. The hiring manager liked my application, called me in, and I got a shot. That referral and tailoring my résumé made all the difference.

I was hired by Microsoft in July 2017 and started as a product manager. I became a senior product manager in 2021.

First, you have to get the interview, and second, you have to pass it. For the first step, referrals are critical. Big Tech companies receive tens of thousands of résumés every month. A referral can push yours to the top of the stack.

To prepare for interviews, I relied heavily on mock interviews. I reached out to peers who had been in the same boat and asked them to test me. They helped me refine my storytelling, practice answering metrics-driven questions, and pinpoint areas for improvement.

When I finally interviewed at Microsoft, the feedback I received was that my stories were authentic and clearly based on real experiences. That authenticity resonated far more than rehearsed answers pulled from the internet.

In today's tech world, showcasing your skills outside work or school, whether on LinkedIn, GitHub, or through personal projects, demonstrates passion and initiative.

I started posting more consistently on LinkedIn during the pandemic. I shared lessons from my career, thoughts on product management, and observations about the industry. I wasn't trying to "build a following," I just focused on topics that genuinely resonated with me.

Over time, those posts resonated with others, and a community naturally formed around them. The growth happened gradually and organically, simply because people connected with the ideas and conversations.

Recruiters notice when you go beyond the curriculum to learn new tools or contribute to open-source projects. In a fast-changing industry where AI and new technologies emerge every six months, demonstrating your ability to adapt and self-learn is as important as the content of your résumé.

While a small percentage of jobs may prefer an MBA, I know successful product managers who came from accounting, English literature, or completely different areas. Microsoft values diverse backgrounds because innovation thrives when teams bring fresh perspectives.

Even with this knowledge, I would still pursue an MBA because it was not just about academics; it was also about gaining practical experience. It provided me with exposure to new perspectives, helped me transition into product management, and connected me with mentors and peers who have shaped my career in meaningful ways.

I don't think an MBA is mandatory for everyone. It depends on your goals and whether you're looking for a career pivot, a network, or structured learning.

Visa restrictions, financial pressure, and cultural adjustments can make the experience stressful. I had moments when I felt defeated, but staying focused and working smart eventually brought everything together.

After over eight years at Microsoft, I plan to continue contributing to the technology and innovation ecosystem. I'm exploring the appropriate pathways that align with my career goals, but nothing is finalized at this time. My focus is on the work itself and continuing to grow professionally.

My advice to anyone following a similar path is straightforward: network strategically, prepare thoroughly, stay authentic, and continually build your skills and presence. Things may look uncertain now, but persistence and the right relationships can open doors you didn't think were possible.
 
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What if we broke up Australia's monopolies? | Alex White


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Australia has the most concentrated industries and markets, with the least competition of any developed country. This worsens inequality and hands exorbitant power to corporate executives and billionaires.

* Supermarkets: Coles and Woolworths are the most prominent examples, with a combined market share of over... 65 percent. This is significantly higher than in countries like the UK or the US.

* Airlines: The Australian airline market is dominated by Qantas and Virgin, which together control around 98 percent of domestic passenger traffic.

* Banking: The "big four" banks control the vast majority of the market, with the top two, Commbank and Westpac, controlling nearly half of all mortgage lending.

* Pharmacies: Just four companies control 50 percent of the market.

* Telecommunications: Telstra and Optus have a combined market share of over 80 percent.

* Mining: Massive multinational firms like BHP, Rio Tinto, and local billionaire owned firms like Hancock and Fortescue control substantial resource extraction in iron and gas.

* Media: Australia's print and broadcast media is the second most concentrated in the world, with News Corp, Nine Entertainment, Seven West Media and Australian Community Media controlling 84 percent of the market, and Google and Meta controlling 70 percent of the advertising market.

* Insurance: Just four companies, IAG, Suncorp, Allianz, and QBE, control 50 brands to make it seem like they don't control 75 percent of the market. Three companies, TAL, AIA, and Zurich, control 60 percent of the life insurance market.

* Port operations: Two companies, DP World and Toll Holdings, control basically all of the logistics operations at Australia's major ports.

* Share trading: The trading of shares in public companies is controlled by a for-profit company, ASX, which has an effective monopoly to set listing fees and other charges.

* Job hunting: If you want to advertise or search for a job, Seek controls 66 percent of the market, allowing it to increase fees for advertising.

* Property transactions: A single software company, Pexa, controls 80 percent of the property transactions (conveyancing) in Australia. Pexa is itself around 40 percent owned by another monopolist, Link Group, which has a near monopoly on the administrative software used by financial fund managers and superannuation funds.

Despite this -- actually, because of this -- Australia doesn't have laws that allow the Commonwealth government to break up monopolies.

Monopolies are undemocratic because they transform economic size into unaccountable political power. When corporations become massive, they cease to be just businesses and act as private governments, using their wealth to capture the state and override the public will. This creates a situation where a handful of wealthy executives make decisions that affect the entire society, like setting wages, prices, and investment strategies. The fifty brands of insurance owned by four companies create the illusion that we have free choice we can choose between Budget Direct and AAMI -- it's a mechanism of political paralysis. And they can use their size and power to block any meaningful democratic control over the economy.

The lack of competition and concentration of power allows monopolies to extract value and wealth through their sheer size or through financial engineering, rather than the genuine value of what they produced. When Coles increases its prices above inflation, that is monopolistic theft from your pocket.

They are morally corrosive because they steal the right to coordinate economic activity from ordinary people. The current legal system allows massive firms to coordinate production and prices (through mergers and internal management), yet laws in Australia (and globally) restricts collective action by workers or small producers as if it were illegal collusion. Industrial action, strikes, pickets and boycotts, are all highly restricted. The ability for small businesses or producers to coordinate is also similarly restricted. This turns workers and small companies into dependent servants of the monopolistic platforms and gatekeepers.

Economically, monopolies amplify the destructive internal logic of capitalism to pursue wealth for its own sake rather than for human need. Firms are driven by a blind compulsion to accumulate profit, which inevitably leads to inequality, unemployment, pauperisation, and environmental destruction. Monopolies are not the result of superior efficiency, but of a system designed by the monopolists to concentrate wealth. They relentlessly squeeze workers, consumers and suppliers to feed an endless cycle of accumulation that is remorselessly indifferent to actual social well-being and human needs.

Finally, monopolies are dangerous because they perpetuate the myth that the market is a "natural" force beyond our control. Dominant firms thrive on the ideology that their power is the natural, inevitable result of merit, technological progress or superior efficiency, rather than political decisions and power. However, markets are constructed by people and societies who create laws. Monopoly power is the result of specific laws that give special privileges and protections to massive corporations. Accepting monopolies as natural creates a sense of political helplessness, rather than the truth which is that we have agency to rewrite the rules and restructure the economy to serve the public interest.

Breaking up monopolies is a vital, highly effective and strategic transitional demand. It allows for a fundamental redistribution of economic and political power, and would give people and politics democratic agency over the economy. Reducing monopoly power would also enable new forms of collective action, especially in workplaces, but also in the broader economy.

A central argument for breaking up monopolies is that it challenges the capitalist monopoly on coordination. In practice, the legal right to coordinate production and set prices is concentrated in the hands of massive firms like Coles, Telstra, Microsoft, Amazon or Allianz. The two supermarkets for example have such enormous market power over its workforce, suppliers and logistics that they operate as a for-profit planned economy, no longer subject to meaningful market competition or democratic oversight.

Meanwhile, workers and small producers are atomised and heavily restricted from coordinating. Restrictions on unions and industrial action for example. Anti-collusion laws are almost entirely enforced against small or medium businesses rather than mega-corporations.

By breaking the vertical control of dominant firms, anti-monopoly reform opens the door for "horizontal collective action". This allows workers, gig workers, independent contractors, and small business owners to organise for genuine productive autonomy, while restricting coordination of massive capital holders.

Anti-monopoly laws help demonstrate that markets are structured by law and that the economy is a political construct. Neoliberalism likes to argue that market concentration and dominance is an inevitable outcome of the natural laws of capitalism -- literally "monopolies are good" and that legal reforms are either futile or actively anti-capitalist.

However, laws and societies literally make markets. There is nothing natural about them. Anti-monopoly laws help challenge the learned helplessness that underpins capitalist realism. It also breaks the "monopoly is good" ideology of the Chicago School that says that monopolies are evidence of success and are therefore moral.

It builds broad, anti-oligarchic coalitions that is practical and self-reinforcing. Although ultimately anti-monopoly laws don't fundamentally challenge the exploitation and expropriation inherent in capitalism, breaking up monopolies offers a practical way to construct a majoritarian coalition.

A strategy that unites workers' interest in autonomy with consumers' interest in fair prices and services through anti-monopoly improves material conditions for everyday people, while simultaneously reducing the real economic and political power of the monopolist oligarchs. Simply, the divide between the extractive power of corporate monopolies and everyone else is so large that there are shared material interests between workers and "normal" capitalists.

Anti-monopoly creates space for alternatives to for-profit corporations. In this way, it is a complement (not a substitute) to the other goals that the Left has, such as cooperatives and public, democratic ownership. By reducing the political and economic leverage of massive corporations, the primary blockers of other progressive reforms have less power. It is difficult to pass a wealth tax or land tax when a handful of billionaire monopolists decide to oppose it.

What about nationalisation? If the supermarket duopoly has already created a planned distribution system, why fragment them? What if we take them over and democratise them for public need? This is certainly the program that Chifley proposed in 1945 for the banks:

based on the conviction that the Government must accept responsibility for the economic condition of the nation ... the Government has decided to assume the powers which are necessary over banking policy to assist it in maintaining national economic health and prosperity.

At the top of this post, I noted that anti-monopoly is a highly effective and strategic transitional demand. As we saw with Chifley, there was an immediate and unprecedented reaction to oppose nationalisation. A similar dynamic occurred under Rudd when the mining industry opposed the most timid of super-profit taxes on the industry's massive windfall gains. Lacking a powerful, militant union movement and civil society to force the issue, the Australian state cannot simply choose to nationalise major corporations.

Ultimately, we must not advocate for breaking up monopolies because we love competition. Instead, we want to give real economic and political power to everyday people, to workers and to communities.

The choice is stark: we either break the monopolies, or we accept our status as their serfs.
 
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Beyond Lines of Code: Using AI to Understand How Engineers Actually Work


Beyond Lines of Code: Using AI to Understand How Engineers Actually Work

As an engineering leader, I spend a lot of time thinking about performance reviews and calibration. But here's the problem: the metrics we traditionally use tell us almost nothing about what actually matters.

Lines of code? Commit counts? Ticket velocity? These numbers are easy to measure, but they miss the real story --... how engineers collaborate, communicate, and contribute to team health.

So I built a tool and paired it with Claude Code to analyze 200 days of GitHub collaboration data. What I discovered completely changed how I approach performance reviews -- and more importantly, how I help engineers grow into better teammates, not just better coders.

Why Claude Code Matters for Engineering Leadership

Most people think Claude Code is just for engineers writing code faster. But I found it's one of the most powerful tools for engineering leadership.

Here's why: analyzing collaboration patterns manually would take days per engineer. Reading through hundreds of PR comments, categorizing communication styles, identifying patterns in code reviews -- it's time-consuming and subjective.

Claude Code + GitHub data does this in minutes, with objective insights I can act on immediately.

But here's what makes it truly valuable: I can compare collaboration styles across engineers and help each person develop their unique strengths while addressing blind spots. One engineer's question-heavy coaching style complements another's direct problem-solving approach. Understanding these patterns helps me build balanced teams and give targeted career development feedback.

The Workflow

It's surprisingly simple:

bash

# Extract GitHub data (reviews, PRs, comments)

gh-calibrate-reviews engineer-username my-org 200

# Ask Claude Code to analyze patterns

"Add sentiment analysis to report [filename]"

Within minutes, Claude Code generates:

- Communication style breakdown (question-heavy vs directive)

- Review focus areas with percentages

- Tone and psychological safety indicators

- Specific, actionable recommendations

This is data-driven leadership at scale.

What the Data Revealed: Collaboration Styles Matter

Here are the collaboration patterns I found -- patterns completely invisible in sprint reports. More importantly, these patterns showed me how different feedback styles complement each other and where each engineer could grow:

The Silent Approver Pattern

Reviewed complex, multi-file PRs but left zero written comments. Just silent approvals. Meanwhile, received detailed feedback on own work -- creating an asymmetric collaboration dynamic.

Growth opportunity: This engineer is technically strong but missing chances to mentor others and share knowledge. Career development conversation: "You're ready for senior level, but senior engineers are expected to raise the bar for the team through code reviews. Even 1-2 comments per PR would demonstrate that leadership."

The Question-Heavy Pattern

65% of review comments were questions without suggested solutions. "Is that expected?" "Do we need this?" "Why are we doing X?"

Growth opportunity: Excellent at fostering critical thinking, but can slow down junior engineers who need more guidance. Career development conversation: "Your Socratic approach is great for senior engineers, but consider balancing it with direct suggestions for newer team members. This will make you more effective as a technical lead."

The Direct Problem-Solver Pattern

35% of comments were improvement suggestions with specific code examples and alternatives. Highly actionable feedback.

Growth opportunity: Efficient and helpful, but rarely acknowledges what's done well. Career development conversation: "Your technical feedback is excellent. Adding positive reinforcement 15-20% of the time would improve team morale and make critical feedback even more effective."

The Description Quality Gap

Some engineers wrote 100% substantive PR descriptions (600+ characters on average). Others had 26% of PRs with no description at all -- forcing reviewers to read code blind.

Correlation discovered: Description quality directly predicted merge rates. Detailed descriptions → 86-97% merge rate. Missing descriptions → 53-74% merge rate.

Growth opportunity: This is a teachable skill that directly impacts collaboration efficiency. Career development conversation: "Your technical work is solid, but 26% of your PRs have no description. Reviewers spend extra time understanding context. Strong documentation skills are essential for senior roles -- let's work on this together."

The Infrastructure Specialist

40% of review focus on deployment and infrastructure concerns. Caught critical Terraform state issues, AWS configuration problems, and migration risks that others missed.

Growth opportunity: Deep expertise in one area is valuable, but breadth matters for career growth. Career development conversation: "Your infrastructure expertise is critical and protecting the team. To grow into a staff role, consider expanding review focus to include testing patterns and code architecture -- aim for 15% of reviews in these areas."

The PR Size Impact

Engineers with mostly small PRs (1-2 files): 86% merge rate

Engineers with large/XL PRs (6+ files): 53-74% merge rate

Growth opportunity: Breaking down work is a professional skill, not just a best practice. Career development conversation: "Your merge rate is 53% compared to team average of 86%. The pattern shows large PRs (6+ files). Let's work on decomposition skills -- this will accelerate your delivery and reduce review burden on teammates."

Response Rate Disparities

Response rates to feedback ranged from 10% to 68% across the team. High-quality responses when engaging, but frequency matters for team dynamics.

Growth opportunity: Being responsive is part of being a good teammate. Career development conversation: "Your responses are high-quality when you engage, but you're only responding to 10% of feedback. Reviewers are investing time in your work -- acknowledging their input builds trust and collaboration."

Comparing Styles: Building Balanced Teams

What became clear from the data: different collaboration styles complement each other.

The question-heavy engineer helps senior teammates think critically. The direct problem-solver unblocks junior engineers quickly. The infrastructure specialist catches production risks. The detailed documenter makes everyone's job easier.

The goal isn't to make everyone the same -- it's to help each engineer understand their natural style, recognize where it adds value, and develop skills to be effective with teammates who have different needs.

For example:

- Question-heavy reviewers can learn when to provide direct guidance

- Silent approvers can learn to leave even minimal feedback for knowledge sharing

- Direct problem-solvers can learn to balance suggestions with positive reinforcement

- Infrastructure specialists can learn to broaden their review focus for career growth

This is how you help engineers become senior engineers, tech leads, and staff engineers -- not just by improving their coding skills, but by developing their collaboration and communication skills.

From Vague to Specific

This data transformed my calibration conversations:

Before:

"I think you should engage more in code reviews."

After:

"Over 200 days, you approved 14 complex PRs without written comments, while receiving an average of 10 comments per PR on your own work. Adding just 1-2 comments per large PR would improve knowledge sharing and demonstrate thorough review. This is the kind of leadership we expect at the senior level -- let's work on developing this skill together."

Specific. Objective. Actionable. And tied to career growth.

Why This Matters

Code review quality isn't just about catching bugs. It affects:

- Team velocity (poor reviews → bugs → rework cycles)

- Knowledge sharing (silent approvals → silos and bottlenecks)

- Psychological safety (tone matters; harsh reviews → fear of feedback)

- Retention (recognition matters; data reveals hidden contributions)

- Onboarding (new engineers learn from review culture)

- Career progression (senior+ roles require collaboration excellence, not just technical excellence)

But most engineering leaders don't have time to manually analyze hundreds of comments per engineer. That's where AI becomes transformative.

The Real Value for Leadership

AI doesn't replace judgment -- it augments it with data.

I still bring context, relationships, and strategy to calibration. But now I have:

✓ Objective patterns to back up observations

✓ Specific examples to make feedback actionable

✓ Recognition for contributions traditional metrics miss

✓ Early warning signs about team health

✓ Career development insights tied to collaboration skills ✓ Data to show engineers how their style complements (or conflicts with) teammates

Example insights Claude Code surfaces:

- "40% of comments are questions without solutions -- great for fostering critical thinking with senior engineers, but consider adding direct guidance for junior teammates"

- "Limited positive reinforcement detected (4% acknowledgments) -- senior engineers who add specific praise create stronger team cultures"

- "Strong infrastructure expertise (39% of reviews) -- critical during migration and a clear strength. To reach staff level, consider broadening to architecture and testing patterns"

Getting Started

The toolkit is open source and takes about 5 minutes per engineer:

- Install GitHub CLI and Claude Code

- Run the data extraction script

- Ask Claude Code to analyze the patterns

- Get actionable insights for performance reviews and career development conversations

No manual analysis. No subjective guesswork. Just data-driven insights that help engineers grow.

The Bottom Line

GitHub contains a wealth of collaboration data that most engineering leaders never tap into. With AI tools like Claude Code, we can finally analyze it at scale.

As engineering leaders, we owe our teams more than gut feelings and velocity charts. We owe them data-driven feedback that helps them grow -- not just as coders, but as teammates, mentors, and future technical leaders.

The tools exist. The data is there. Let's use them.
 
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  • One of the places I worked had a similar situation, and I avoided her as much as possible. HR is there to protect the company and saying something... always makes it worse more

  • Has she got wprkplans and or measurable out puts that are performance based

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  • in life we obeserve many things, positivity and negativity but always be useful rather than being useless.

  • As a senior actively involved in involved in several aspects of a startup business, there are two ways to go in life. To "go" or to "grow. To just go... on daily or to "grow", to learn new things, explore new avenues new pathways. I want to "grow" and "squeeze everything " out of life. Life's to short to just "go" daily through life. And I might add, God has a purpose foe each of us! Really!! more

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  • give it time. be patient with that, if you like it continue with it, if you dont like it, ask for another alternative role.

  • Prevention of mother to child transmission

2   
  • Was an Azure badge or similar badge a required with the previous AWS job?

  • I totally get how frustrating this feels. I actually saw a story on Facebook about a recent grad who couldn’t get a job because he didn’t have... experience. Instead of waiting around, he started documenting his coding projects and skills online to stay relevant and fresh. When his current employer invited him to an interview, he even shared a write-up of how he solved a problem they gave him, and the interviewer was really impressed. He ended up landing a senior role because of it.

    Maybe you can try something similar showcasing your projects, AI app work, and problem-solving process could really make you stand out.
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  • Hhhhhh i drink a coffe but not in interview i'm already done in BUSSINES Administration i'm looking a job

  • The interviewing agencies usually askvif you do want the tea that is actually the tea test and I can say is part of the queries within an interview... session. It is for the candidate whether he or she respond with a yes or no. Is not really a big deal you are not enforce to drink it. more

Can Dating Apps Help You Land Your Next Job?


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In today's job market, submitting a résumé can feel like tossing a message into a digital void where it's instantly judged, and often rejected, by an algorithm that doesn't care about your personality, potential, or rent.

With... AI-powered hiring tools acting as bouncers at the club of employment, job seekers are getting creative. Very creative.

Some have decided that if networking is the only way in, they might as well swipe for it.

According to a new survey from ResumeBuilder.com, one in three people has used a dating app to help find a job.

Nearly one in ten said that was their main reason for being there. Romance? Optional. Referrals? Mandatory.

The strategy is surprisingly calculated. 66% of respondents said they searched for people working at companies they wanted to join, while 75% intentionally matched with users in roles they hoped to land themselves.

As ResumeBuilder's chief career advisor, Stacie Haller put it, networking is "the only way people are rising above the horror show that the job search is today."

And, awkwardly enough, it works. 88% of job-focused daters said they successfully made professional connections.

That often meant advice, mentorship, referrals, or interviews, but 37% went all the way and got a job offer.

In a truly modern twist, 38% also said the professional connection became physical, proving that sometimes you can have it all. The rise of AI in hiring is a big reason for this behavior.

Companies rely on automated résumé scanners to handle floods of applications driven by mass-apply culture on platforms like LinkedIn.

These systems are fast and cheap, but also notorious for bias and false negatives. Even highly qualified candidates can be rejected before a human ever sees their name.

That's where referrals come in, often the only reliable way to bypass the algorithm.

But networking favors people who already have connections, reinforcing inequality, a trend noted by Cornell professor John McCarthy of Cornell University.

For those without built-in networks, dating apps are becoming the workaround. While Tinder and Bumble are the most commonly used, some platforms lean into the crossover.

Raya lets users search by industry, and Grindr reports that roughly a quarter of its users network professionally. Love may be dead, but apparently, job leads are thriving.

Follow us on Flipboard, Google News, or Apple News google-news Related TopicsAIdatingNews Ronil Thakkar

Ronil is a Computer Engineer by education and a consumer technology writer by choice. Over the course of his professional career, his work has appeared in reputable publications like MakeUseOf, TechJunkie, GreenBot, and many more. When not working, you'll find him at the gym breaking a new PR.
 
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