According to the salaries section of Goldman’s website, the base pay for a new full-time analyst position based in New York City in Global Investment Research and other groups (investment banking, asset management, and wealth management), ranges from $80,000 to $110,000.

Goldman Sachs salary: How much does an analyst make?

But for many job seekers, nothing is more dreaded (or annoying) than writing a cover letter. According to Glassdoor, 58% of professionals believe cover letters are redundant — only 10% feel that cover letters are necessary.

How to write a cover letter that will get you hired

Notably, job candidates who obtained an interview opportunity through a recruiter mention ghosting more frequently.

Employer ghosting has more than doubled in recent years, Glassdoor data shows

US workers are becoming increasingly concerned about layoffs, even as they have less fears about an impending recession. According to Glassdoor, this month had the greatest level of layoff talk since July 2020.

Layoff fears loom over US workers despite strong economy

This month, chatter about layoffs reached the highest level since July 2020, according to a monthly employee confidence index report from Glassdoor. The Glassdoor Employee Confidence Index shows the share of U.S. full-time and part-time employees who ...

Employee anxiety over jobs cuts at highest level since 2020

When job markets inevitably heat up again, job seekers will remember how they were treated,” Glassdoor’s chief economist says.

Glassdoor reviewers say employers’ ghosting problem is twice as bad as it was just a few years ago—a sign that worker power is fading fast

US WORKERS are increasingly worried about job cuts, even as their concerns about an upcoming recession are diminishing. Read more at The Business Times.

US workers concerned about job cuts at the highest rate since 2020

US workers are increasingly worried about job cuts, even as their concerns about an upcoming recession are diminishing.Discussions of layoffs reached the highest level since July 2020 this month, according to Glassdoor,

US Workers Are Concerned About Job Cuts at the Highest Rate Since 2020

Insurance software provider Guidewire has been named one of the highest-rated computing companies to work for by tech-focused investment firm Battery Ventures.

Guidewire named a top computing company to work for

More than half (55 per cent) of UK employees who resigned from their last job post were not invited to a formal leaving interview, a new study has revealed. The survey by Cpl’s Talent Evolution Group, of 1,500 people who have resigned in the last five years, found that one third of employees were not asked for feedback.

Employers ‘missing out’ as more than half of UK employees do not get an exit interview, survey finds

Glassdoor

glassdoor

A recent update to Glassdoor's privacy policy has sparked concern and debate among privacy advocates and users alike, leading to questions about the future of honest employer reviews on the platform. Until now, Glassdoor has been a haven for anonymous, candid feedback about employers, offering insights that often pierce the polished exteriors presented by corporate PR. However, this changed as Glassdoor implemented a policy requiring users to verify their real identities. Disclosing full name, job title, and employer is now required. Questions on how to leave an anonymous review on Glassdoor have been buzzing non-stop the entire week. Wave of outrage on social media Although people ask ‘are glassdoor reviews anonymous?’ can be safe, the policy shift has not been without its controversies. One article in particular caught everyone's eye, and created a wave of outrage on social media. A Glassdoor user of 10 years, referred to as Monika in the publication, reports that she contacted Glassdoor support to request help removing information from her account. Instead of deleting her details, the Glassdoor team took her real name and added it to her Glassdoor profile, says a software professional. Instances have emerged on social media of users discovering their names added to their previously anonymous reviews without consent. Those who tried account deactivation found no luck. The username next to a review stays even after account deactivation. Some users report that even a data erasure request does not take effect immediately, and up to 30 days a username can still be visible. Quietly changing a privacy policy could break the law Glassdoor's policy changes highlight a larger issue—the extensive collection and potential misuse of personal data. The ease with which companies can amass vast amounts of user data, or how easily they can change their privacy policies, raises serious concerns. Are there ways to effectively fight it? Well, the Federal Trade Commission (FTC), whose mission is to protect US consumers, published a saying that quietly altering a privacy policy could be a deceptive and unfair act against consumers. The FTC is signaling it might go after the offenders and warned companies to be more upfront when asking users for permission to their data. blog post Many are awaiting more proactive government action on private data collection by businesses. A number of companies, called data brokers, have acted as a one-stop solution for buying and selling personal data to other companies for years now. The global data trade market is projected to only grow: from $319.030 billion in 2021, it is expected to reach $545.431 billion in 2028. Without effective laws, some people report taking the matter into their own hands: opting out one-by-one from these databases is the option people have turned to. Also, services have emerged offering some hope in the ongoing battle for data protection. Many of such tools, like Incogni, are designed to help users to opt out from databases storing their details on a bigger scale, and to prevent an individual's information from being re-added as this happens often. Why do Glassdoor users feel betrayed? Critically, the anonymity provided by Glassdoor has been a shield for employees wishing to leave unfiltered feedback about their employers. People have shared on social media several instances, where employees faced termination or legal threats from companies for negative reviews posted on Glassdoor. This is exactly what Glassdoor users fear the most, and why everyone has been screaming for help this entire week. What can Glassdoor users do next? Recently Glassdoor CEO made a statement saying that ‘Anonymous posts will always stay anonymous.’ This does not seem to convince users, as comments on social media shows. Many are looking for ways to erase their details from Glassdoor, and there are two steps on getting back anonymity on Glassdoor: This signals the company to cease any further usage of user data. You can deactivate your account by visiting the Glassdoor user settings section and choosing to "Deactivate account." Deactivating the Glassdoor account. It ensures that the user's personal details are no longer accessible to other users. It serves as a means for individuals to opt out of the platform, taking control over their data privacy. This request can be made by contacting customer support, but it might take up to 30 days to take effect. How to delete Glassdoor account. The new era of less anonymity on Glassdoor The transition towards real name verification by Glassdoor reflects a broader conversation about privacy, trust, and the rights of individuals in the digital world. As platforms navigate the balance between transparency and user protection, the reactions and adaptations of their communities will undoubtedly shape the future of online anonymity and privacy policies.

Glassdoor Leaves Users in Shock After it Starts Adding Real Names

প্রকৃত নাম যোগ করা শুরু করার পর গ্লাসডোর ব্যবহারকারীদের শক ছেড়ে দেয়

Glassdoor versetzt Benutzer in Schock, nachdem es begonnen hat, echte Namen hinzuzufügen

Glassdoor deja a los usuarios en shock después de comenzar a agregar nombres reales

Glassdoor laisse les utilisateurs sous le choc après avoir commencé à ajouter de vrais noms

ग्लासडोर ने वास्तविक नाम जोड़ना शुरू करने के बाद उपयोगकर्ताओं को सदमे में डाल दिया

Glassdoor が実名を追加し始めてユーザーにショックを与える

Glassdoor는 실명을 추가하기 시작한 후 사용자를 충격에 빠뜨립니다.

Glassdoor deixa os usuários em choque depois de começar a adicionar nomes reais

Glassdoor поверг пользователей в шок после того, как начал добавлять настоящие имена

Glassdoor Gerçek İsimleri Eklemeye Başladıktan Sonra Kullanıcıları Şokta Bırakıyor

Glassdoor khiến người dùng bị sốc sau khi bắt đầu thêm tên thật

Glassdoor 开始采用实名制，用户大吃一惊

Scraping Glassdoor Job Data

Raspado de datos laborales de Glassdoor

During the process of working on , one question that I’ve been asked many times by candidates is the onsite-to-offer ratio, e.g. what percentage of people who interview at a company eventually get an offer. Another number that recruiters and hiring managers are keen to know is the yield rate on offers of different companies – e.g. what percentage of people who are extended an offer by a company accept that offer? my book on machine learning interviews Those numbers are undoubtedly tracked by HR within each company, but as far as I know, no company volunteers this information. I decided to approximate those numbers using the largest public aggregator of interviews that I know of: Glassdoor. This is far from a perfect resource because of the following biases: few people actually leave reviews for anything online those who do are likely compelled by either a really good or really bad experience those who receive offers are more likely to give reviews than those who don’t those who accept offers are more likely to give reviews than those who decline junior candidates are more likely to give reviews than senior candidates ... However, hopefully a large number of reviews might be able to smoothen out some of the noise. Moreover, if all reviews suffer from the same biases, they are still useful for comparison across companies. This post consists of 6 parts: Data: description of the dataset. Interview results: onsite-to-offer ratio and offer yield rate. Sourcing candidates: how candidates are recruited for onsite interviews. The sources include campus recruiting, online application, referrals, staffing agency, and in-house recruiters. Interview experience: which company gives candidates the best and the worst experiences. Interview difficulty: which company has the most difficult interviews. What candidates say about each company. Data I focus only on software engineering related roles such as Software Engineer and Data Scientist, for both junior and senior levels (though the majority is junior). For each review, the following information is collected: Result: No offer / declined offer / accepted offer Difficulty: Easy interview / medium interview / hard interview Experience: Negative / neutral / positiveReview: including application process, interview process, interview questions I discarded all reviews with missing values. I was able to gather 15,897 reviews from 27 major tech companies with at least 100 reviews for software engineering related roles. Technically, a review can come from a candidate at any point in the interviewing process, but review samples suggest that most of the reviews are from candidates that have done onsites, so interviews in this analysis will be treated as such. Below is the aggregated count for all SWE-related roles at each company. The number of reviews seem proportional to the number of SWEs each company hires. The largest SWE employers are, unsurprisingly, Google, Amazon, Facebook, and Microsoft. The relatively small number of reviews for Apple and Netflix compared to other FAANG companies are because: Apple is not a software company. Netflix is actually not that big (~6,000 employees compared to ~40,000 employees at Facebook). You can see the raw count of reviews for each role at each company . here The first thing to notice from this dataset is that interview feedback varies widely for different roles even within the same companies. For example, only 25.4% of candidates for the role of Data Scientist at Facebook findtheir interviews difficult, but that number is 36.4% for the role of Senior Software Engineer. Similarly, only 51% of candidates for the role of Senior Software Engineer have a positive experience of their interview process, but 69.5% of candidates for Data Engineer enjoy the process. The variability is due to the fact that different roles have different recruiting pipelines (e.g. senior roles rely more on recruiters cold-emailing while junior roles rely more on campus recruiting), different expectations, and different interviewing processes. Some companies have the same process for the entire company and new hires go through a team matching process after joining, but most companies let each team conduct their hiring separately, which leads to more variations within the same company. Before you continue reading this post, I hope that you take all the biases and variations in these reviews into account. The findings are not conclusive and are up for discussion. Interview results The first thing I wanted to know is onsite-to-offer ratios (the percentage of onsites that lead to offers) and offer yield rates (the percentage of offers that are accepted by candidates). From the graph, of onsite candidates at Google get offers, and out of all those with offers, accept their offers. Due to the biases of online reviews listed above, we should expect the actual numbers to be much lower. From talking to recruiters I know + reading sources online, I gather that the onsite-to-offer ratios here are a few percentage points higher than the actual percentages. E.g. and claim that the onsite-to-offer ratio for Google is and Amazon (keep in mind that they are both from anonymous people on the Internet). Similarly, the offer yield rate of near 90% is unheard of. Recruiters have told me that if you can get of candidates to accept your offers, you’re golden. 18.83% 70% this this 10-20% 20% 80% The 10 companies with the lowest onsite-to-offer ratios are all Internet giants (Yelp, Google, Facebook, Airbnb, Amazon, etc.) They are also known to be highly selective companies. Companies with high onsite-to-offer ratios aren’t necessarily unselective. They might be more selective during the screening process and only interview candidates that they already know or really like. Onsites are costly, so the higher the onsite-to-offer ratio, the more financially sound the process. There’s a very strong correlation between the onsite-to-offer ratio and the offer yield rate. If we consider all 27 companies, the correlation is 0.81. If we consider only 10 companies with at least 300 reviews (Apple, Uber, Amazon, Google, Microsoft, Oracle, Yelp, Cisco, IBM, Facebook), the correlation is even higher, 0.89. This means that the higher the onsite-to-offer ratio, the higher the yield rate. This makes sense because if a candidate passes the interview process at selective companies like Google or Facebook, they probably have other attractive offers to choose from. Those selective companies are also known to make competitive offers, which candidates can use to negotiate with companies that they really want to work for. Sourcing candidates The next thing I look into is how companies source candidates. Every single hiring manager I’ve talked to told me that referrals matter a lot. But exactly how much referrals matter vary from company to company, from role to role. Referrals for senior roles matter a lot more referrals for junior roles, which are due to several reasons. First, junior referrals likely come from junior engineers who are less experienced at judging their friends’ abilities. Second, senior roles are expected to shoulder more leadership responsibilities, so personality and culture fit are much more important. For junior roles, about 10 - 20% of candidates that get to onsites are referred, with Uber leading the chart with almost 30%. For senior roles, that numbers are higher. Salesforce, Uber, and Cisco all have approximately 30% of their senior onsite candidates referred. If we take into account the fact that the number of applicants who are referred into a company is minuscule compared to the number of applicants who apply to the same company by other means, the chance of a referred applicant is so much higher than the chance of a random candidate. For junior roles, the biggest source for onsite candidates is campus recruiting. Microsoft and Oracle have more than half of their interviewees recruited through campus events such as career fairs and tech talks. Hot Internet companies like Google, Facebook, and Airbnb rely less on campus recruiting, but it still accounts for between 20 and 30% of their onsites. This means that big tech companies concentrate a huge chunk of their recruiting effort to a handful of popular engineering schools (aka the Tech Ivy: Stanford, UC Berkeley, MIT, Caltech, CMU, University of Toronto, University of Waterloo). The students recruited from those schools then refer their former classmates, who in turn refer event more of their former classmates. The circle goes on, turning those major tech companies into a . Tech Ivy alumni mixer It makes sense for recruiters to target their most promising sources, but it’s statistically gloomy for candidates who don’t go to a popular engineering college or don’t go to college at all. If you’re one of those candidates, my personal suggestion is build up an impossible-to-ignore online portfolio and wait for recruiters to throw themselves at you. Write in-depth technical blog posts or papers, contribute to open-source projects, do hackathons, compete in coding competitions, and most importantly, publish your work so that the world knows what an amazing engineer you are. Around 15 to 25% of onsite candidates for junior roles are sourced by recruiters. For senior roles, these numbers double. If you’ve had years of experience but have no relevant skills/portfolio to attract recruiters or no friends to refer you, you’re in trouble. If all else fails, submit your applications and hope for the best. Companies that are the friendliest to online applicants are Twitter, Amazon, and Airbnb with roughly half of the onsites being online applicants. Companies among the most likely to say ‘thank you next’ to hopeful online applicants are Facebook, Microsoft, and Oracle. Interview experience Everyone complains that the interview process is broken. It’s not entirely true, at least from the perspective of the candidates who get interviews. 60% of candidates report positive interview experience. As a company best known for providing sale solutions, Salesforce is apparently very good at selling themselves to candidates. Candidates interviewing at Salesforce are most likely to report positive experiences. Other companies high up in the list include Intel, Adobe, and SAP. The two companies that seem to give candidates the worst interview experience are Netflix and Snap – only one-third of their candidates report positive. However, they are also the two companies with the least amount of reviews, so this observation could be due to selection bias. There’s a strong correlation (0.75) between the onsite-to-offer ratio and the percentage of positive experiences. If you receive an offer, you are more likely to think fondly of the process. There’s also a strong negative correlation (-0.67) between the ratio of negative interview experiences and offer yield rate. The more negative experience a candidate has, the less likely they are to accept the offer. If a candidate who receives an offer has a positive interview experience, the probability that they accept the offer is a whopping 87.5%. However, if that candidate has a negative interview experience, the yield rate is only 33.8%. In general, senior candidates are harder to please than junior candidates. Senior candidates are often interviewers themselves and expect more from their interviewers. This might explain the abysmal Netflix interview experience. While all other companies keep their shares of senior interviews to under one third, Netflix exclusively hire senior positions. Netflix doesn’t have interns. They don’t even hire recent graduates. Interview difficulty At first, I plotted the raw difficulty score calculated by Glassdoor. By this metric, Google and Airbnb interviews are perceived to be the most difficult by candidates, whereas IBM interviews are perceived to be the easiest. The actual difficulty is more complicated than that. A candidate rejected for a job is more likely to think of interviews for that job as difficult. The correlation between the onsite-to-offer ratio and the percentage of candidates who find the interviews difficult is -0.49. This analysis is done as part of my research for my upcoming book on Machine Learning Interviews. You can get more details + sign up to get updates on the book’s progress . here Acknowledgements: This post is made possible by the kind support of Ben Krause, Miles Brundage, Larissa Schiavo, Karson Elmgren, and several other wonderful people who would like to remain anonymous.

Tech Hiring Cultures Analyzed via Glassdoor Interview Data

Let's look into the concept of Online Reputation Management (ORM) and how to optimize the use of Glassdoor as a platform. What Is ORM? ORM is an Online Reputation Management. It includes combining traditional marketing strategies and establishing public relations using Search Engine Marketing. It works as a safeguard for your organization to . To protect your reputation it manages the online reviews and search engine results. It does not just consist of taking note of the social conversations, but also responding to the received comments and routine interaction with your online audience, so as to influence their opinions in favor of your brand. protect your brand reputation from online negative exposure Why Is ORM Unavoidable? Sidelining the importance of Online Reputation Management (ORM) can have a direct impact on the quality of experience of your customers. Google happens to take this very seriously, and the search engine can penalize your website for the same. The following are some of the reasons to look into ORM: Online brand reputation management done in a correct manner can improve the ranking of your website and lead to business growth. According to a recent report published on a Search Engine Land, at least 86% of customers ardently believe in shopping from brands having positive online reviews. At least 88% of buyers check online about the product they intend to purchase, says a study published on PYMNTS. Nearly 42% of brands in the world believe in monitoring their online reputation every day. More than 70% of brands believe in in-house ORM services to manage their reputation on the internet. What Factors Come Under ORM? ORM primarily constitutes of the following: Rebuilding customer relationships through service recovery for the unhappy/dissatisfied customers Opening direct communication with the customers to convert the dissatisfied ones into satisfied ones. Getting to know the expectations of your customers, so as to serve them better Creating and increasing brand awareness through initiating a dialogue with your customers. That says it all! Amongst the various platforms that can be used for effective ORM, Glassdoor is a trusted and widely recognized one. Especially for the employers. A huge number of employers have a presence on Glassdoor. Glassdoor is one of the world's leading job and recruiting sites, allowing you to promote your firm by creating a profile for it, responding to reviews, and so on. It offers a database of interview questions and reviews, company reviews, ratings, salaries & insights etc. Visitors (employees, interviewees etc.) can contribute and share their real-time experiences in the form of reviews. Be it negative or positive. This obviously would and does attract those who are in search of a job. As a conclusion, remember that: As many as 86% of the aspirants are likely to go through company reviews and ratings prior to applying. 65% of Glassdoor users form an opinion about a particular employer only after reading a minimum of 5 reviews.

What Is ORM and Why Is It Important?

How I Started TopTal is an agency that matches freelancing IT workers with clients. Most of the work is remote, which gives them an advantage for those of us who either live abroad or have had our fill of open offices, cubicles, commutes, methodologies and interruptions. I signed up with TopTal because of this and had several good placements. I had taught myself iOS and sold several of my applications on the Apple store. None sold very well; a few were picked up as recommendations in their categories and those made me coffee money but none of the ones I expected to sell well did so. I’m not a marketeer and didn’t have tens of thousands of dollars to spend on promotion. But those submissions served as a portfolio and I had several clients through TopTal writing iPhone applications. These went well. TopTal checked in with me occasionally and I got paid, though my deposits came an unreasonable three weeks after the period of my timesheet. They gave me implausible excuses for this. Then I had my first bad client and saw the company from a new light. First Impressions TopTal places all kinds of ent but most of it is in information technology. So going to their site one would expect to see pictures of people sitting with laptops and appearing focused on their work. Instead you see pictures of people smiling at the camera, no work in sight. Everyone is smiling, and one might think they were marketing fashion models. It does not look professional; it looks commercial. Top Tal How It Works TopTal posts jobs and seeks applicants. Sometime we freelancers can select the jobs, sometimes TopTal’s “matchers” contact us to recommend we apply for open jobs. There is a skill-matching that highlights the job description requirements and the freelancer’s listed skills, but it is not well done. Qualified applicants (more on this later) are submitted to clients and if it is to proceed (in TopTal parlance, “move forward”) then there is an interview and a two week trial engagement. We file timesheets and if the client decides not to continue the engagement the freelancer is paid half what he has earned at hourly rate. This seems reasonable. What is not reasonable, not at all reasonable, is that TopTal’s markup is not the 20–30% I was accustomed to as dash-trash at Microsoft. TopTal marks up a full 100%; if I am getting paid $50 per hour the client is billed $100 per hour. , but one of my clients told me how much he was paying by way of expressing his dissatisfaction with the pace of my work. We are not supposed to know this This is not only excessive but it starts the relationship on a bad footing; at these rates the client is not content with merely excellent work but has superhuman expectations, he expects miracles. Literally. First Bad Signs In 2015 I took a job through TopTal with a client in Croatia writing applications for credit card scanners. These were made by a Chinese company called PAX and they are universal here in Vietnam. The tools and software for developing PAX application are like going back 30 years. They are terrible; setting up a debugging session requires eight manual steps and four times out of five it simply doesn’t work, and even when it does it unaccountably stops working very soon. What would have taken an hour in a reliable IDE could take three days with PAX software, and their English documentation left out a lot of important information. Making progress was a tiresome slog and since this client was paying TopTal twice what TopTal was paying me they quickly became impatient and adversarial. Then I would get something working and we would be cordial again, and it was during one of the nasty times that I learned about the 100% markup. TopTal was only slightly helpful at this time and clearly sided with the client in all disagreements. Things Go Bad Then came the month when after waiting the inexplicable three weeks I was not paid. The money never showed up in my account. I contacted my TopTal liaison (we used Skype back then) and he informed me that the client had not paid yet. They had not seen fit to tell me this. The client said that he had paid. One of them was lying. TopTal wasn’t very interested in pursuing this, and in the end I was never paid for 160 hours that had seen a lot of progress and the change from SSL to TLS. , and they had made a lot of profit from me but they never paid me, and just didn’t care. Nobody at TopTal contested that I had worked the hours I don’t remember if I was paid again but the relationship with the client deteriorated and they started making threats and giving ultimatums; I told them where they could stick their threats and quit the contract. I was also disgusted with TopTal and forgot about them for over three years. The Return and the End I had server work for a California startup for almost three years. I was writing in ASP.NET and C#. But a newly hired tech lead was an incompetent and a liar and both we developers left the company. I decided to see if TopTal had gotten any better. I contacted both my former liaisons on Skype; neither was working there anymore. And they were very close-mouthed about why.  Reviews on Glassdoor spoke of some big management shakeup and a huge turnover in staff. I quickly landed a gig as a technical writer and this time things went very well. From application to hire in 48 hours. Months of work beginning with a good knowledge transfer, good times. The contract only ended because the client had lost so much business to COVID and had to cut expenses, but there were no problems and I got paid. To Hell With This I took another job doing cryptography for a British company, not through TopTal, and when that was finished I went back to them looking for more work. I wanted to get back into mobiles; while working on my ASP.NET and technical writing jobs I had learned Swift on iOS though I had never been paid to use Swift. For a senior developer (three decades in my case) a new software language is not a challenge. I would think TopTal would understand that since they claim to work with the best talent. But time and time again the “matchers” would not submit me for jobs I was perfectly qualified for because of insignificant gaps in experience; I had written a dozen solid applications in Objective C but since I had no in Swift the matcher would shrivel up and send me a bullshit email that the job had been “canceled.” I have learned new languages for work many times and I knew enough Swift to get to work. I had the REST, I had the SQL, I knew iOS and XCode. work experience The Last Straw Then another technical writing job came along on TopTal, and once again I was hired almost immediately. But this one was not like the first; the company’s web site offered no clue what services they offered. That should have alerted me. I had an interview, with video for some reason, and got the job. The first day was a video conference, since I was one of three starting that day. A solid hour of buzz, agile/scrum nonsense, nothing tangible at all, and I had no idea what kind of work they were doing except that it involved both AWS and Azure. I was given a long list of tools to install and pointed to a task list in Asana, and told that they wanted three documents done by the end of the week. still (pause) . There was no knowledge transfer whatsoever. One of my tasks was a regulatory submission, which they needed “ASAP”; you don’t hurry those things, lie to a government agency and you can get into real trouble. They didn’t tell me anything about their project One of my tasks was to document their use of the cloud services, I was not even given URLs, much less logins. Nothing. And when I asked for a knowledge transfer the manager acted irritated and put out; I posted dozens on questions on Slack, which appeared to be the main channel of communication of the eight (!) they used. Not a single response. It was a race; would I quit before they fired me or would I get fired first? On the third day one of their developers introduced me to their cloud service usage; I still didn’t have accounts but now I could at least start. But a few scant hours later the manager was demanding a first draft, with LucidChart diagrams. The company won the race. They said I had skipped the daily standup (I had declined it; the pace of others’ progress was of no value to my work) and pulled the plug. Months later I was paid at half my rate for the hours I had filed. This gig was complete bullshit. I talked to TopTal about how the company had given me nothing to work with except, on my third day, an hour conversation with nowhere near enough actual detail. TopTal took the “two sides to every story” evasion, refused to acknowledge the fact that the company had dropped the ball. Goodbye I went back to applying for development jobs. Same as before, like throwing bottles with messages into into the ocean. TopTal issues a lot of warnings and threats about discussing salaries, or circumventing TopTal to take jobs with clients introduced by them; this is perfectly understandable given that their markup is so high that companies probably know they can save a lot by cutting TopTal out of the relationship. And they clearly don’t want freelancers to know about the disgraceful 100% markup. Then I heard from some management types about “feedback,” of which there was none to speak of, just more of the same ridiculously cautious “two sides” BS. They refused to take a stand with me that the company had not done its part to enable my writing, and when I wrote TopTal about the unprofessional tone of their communications (“reach out” instead of “contact”; "move forward" instead of "proceed"; smile-people instead of intent workers) that was the end. I no longer work for TopTal. TopTal and Glassdoor If you’ve ever researched a company using Glassdoor reviews you what it means when all the reviews are either five stars or one star. The five stars are company plants and all have suspiciously similar wording; just look at reviews of Microsoft and how often “Satya’s vision” appears in the glowing reviews with nothing bad whatsoever to say about the company, when everyone who has worked at Microsoft knows that their greatness has been gone for thirty years. A lot of the negative reviews about TopTal talk about a hyper-aggressive CEO; I know nothing about its management but the rest all resonates. A lot of developers talk about going unpaid, about toxic relationships with clearly unvetted clients. Both freelancers and TopTal employees have high turnover. Conclusion TopTal is tempting because remote work is the rule and has been for years before the pandemic. This is the only good thing about working for them, a sentiment echoed in the Glassdoor reviews. Run away. Don’t go to Upwork unless you want to work for Third World wages but TopTal may not pay you at all.

My Experience With TopTal: Unpaid Labor and Second Chances

The Noonification: Newton’s Laws in Society: How Social Forces Move Physical Bodies (4/3/2024)

The Economics of Public Cloud Repatriation and Why You Can't Stay in the Cloud at Scale

İctimai Bulud Repatriasiyasının İqtisadiyyatı və Niyə Ölçəkdə Xərcli Qadağandır

পাবলিক ক্লাউড প্রত্যাবর্তনের অর্থনীতি এবং কেন এটি স্কেলে ব্যয়-নিষিদ্ধ

Ekonomija repatrijacije javnog oblaka i zašto je skupo na nivou

La economía de la repatriación a la nube pública y por qué resulta prohibitivamente costosa a gran escala

パブリック クラウドの回帰の経済性と、大規模になるとコストがかかりすぎる理由

د عامه کلاوډ بیرته ستنیدو اقتصاد او ولې دا په پیمانه لګښت منع دی

Iyo Economics yePublic Cloud Repatriation uye Nei Iri Mutengo-unorambidza paChikero

பொது கிளவுட் திருப்பி அனுப்புதலின் பொருளாதாரம் மற்றும் அது ஏன் அளவில் செலவு-தடை

Kamu Bulutu Geri Yüklemenin Ekonomisi ve Ölçekte Maliyet Açısından Neden Engelleyici Olduğu

Ommaviy bulutli repatriatsiya iqtisodiyoti va nega u miqyosda qimmatga tushadi

Kinh tế học của việc hồi hương đám mây công cộng và lý do tại sao nó lại quá tốn kém ở quy mô lớn

I-Economics of Public Cloud Repatriation kanye nokuthi kungani ibiza kakhulu ngokwesikali

I will share what I did to get customers and the lessons I learned to help fellow startup founders. The revenue is not life-changing, but I’m thrilled it’s gaining traction! 🙏 A little backstory: After many failed startups, I realized the problem I needed to solve for myself: finding customers. So, I built CustomerFinderBot to scratch my own itch, and it turns out others found it useful too. The pitch: Find Your Customers in Seconds with Social Media AI. Until now, I still can’t believe that people are using and paying for a tool that I built. I had very low expectations when I built and launched this because I’m already so used to building and launching projects and ending up with 0 or close to 0 users. Or when my previous projects gained some users, none were willing to pay so they ended up being added to my list of failures. Launch Results: 1st day — Gained 2 paying customers2nd day — 03rd day — Gained 1 paying customer4th day — 05th day — Gained 7 paying customers After that, sales started coming in almost daily! Here is what I did so far to get paying customers: Share the project on Reddit and Twitter/X.


Find people on Twitter and Reddit that are looking for alternatives to my competitors, complaining about my competitors or asking recommendations for a solution to their problem that my product directly solves. I use CustomerFinderBot to save time and effort since it automates these things. Then I reach out to them to help them by answering their questions and suggesting the tool that I built. It’s a win-win for both parties. This strategy is effective because they already want my product so they have a high probability to end up buying. My next steps: Keep on iterating by improving the tool based on customer feedback. List on directory websites. Launch on Product Hunt. Invest time and effort in SEO. This takes a lot of effort and patience because it takes a while to see results but based on what I learned from others and from my own personal experience it’s one of the most sustainable customer acquisition channel. Build and ship free tools for marketing purposes and it will also help with SEO. Lessons I learned? 👇 Building and growing a SaaS is hard as hell. It’s not a quick and easy way to make money, and it has a very high failure rate considering around 90% of startups fail.


Gathering feedback from customers really provides you with a lot of valuable information. You will discover a lot use-cases that you haven’t thought of which will give you a better idea on what features you should build. By doing this, you’ll end up building features that your target customers really needs and wants instead of building features that you think will be useful but ends up no one uses.


Burnout is real, so do everything you can to prevent it. It’s really very tiring especially if you’re a solo founder which does all the work.


Solve real problems (e.g., save them time and effort, make them more money). Focus on the pain points of your target customers. Solve 1 problem and do it really well.


Prefer to use the tools that you already know. Don’t spend too much time thinking about what are the best tool to use. The best tool for you is the one you already know. Your customers won’t care about the tools you use; what they care about is you’re solving their problem.


Don’t add every feature you can think of. Start with a MVP that solves the core problem, then iterate based on user feedback.


Validate immediately to see if people will pay for it then move on if not. Don’t over-engineer. It doesn’t need to be scalable initially.


Do lots of marketing; this is a must because build it and they will come rarely succeeds.


Keep on shipping 🚀 Many small bets instead of 1 big bet to manage risk and increase your odds of launching a product that customers will pay for. I hope this post helps others to get their first customers for their startups. Any questions? I’ll be happy to answer them! Let’s follow each other on Twitter — https://x.com/FI_investor Check out my other projects: Calm-Jobs.com — Find Jobs With Better Work-Life Balance. JobHunterBot.com — Automatically Find Jobs from LinkedIn, Indeed, Glassdoor, Monster, ZipRecruiter, and hundreds of other job boards. I will share what I did to get customers and the lessons I learned to help fellow startup founders. The revenue is not life-changing, but I’m thrilled it’s gaining traction! 🙏 A little backstory: After many failed startups, I realized the problem I needed to solve for myself: finding customers. So, I built CustomerFinderBot to scratch my own itch, and it turns out others found it useful too. CustomerFinderBot CustomerFinderBot The pitch: Find Your Customers in Seconds with Social Media AI. Find Your Customers in Seconds with Social Media AI. Until now, I still can’t believe that people are using and paying for a tool that I built. I had very low expectations when I built and launched this because I’m already so used to building and launching projects and ending up with 0 or close to 0 users. Or when my previous projects gained some users, none were willing to pay so they ended up being added to my list of failures. Launch Results: 1st day — Gained 2 paying customers 2nd day — 0 3rd day — Gained 1 paying customer 4th day — 0 5th day — Gained 7 paying customers After that, sales started coming in almost daily! Here is what I did so far to get paying customers: Share the project on Reddit and Twitter/X. Find people on Twitter and Reddit that are looking for alternatives to my competitors, complaining about my competitors or asking recommendations for a solution to their problem that my product directly solves. Share the project on Reddit and Twitter/X. Share the project on Reddit and Twitter/X. Find people on Twitter and Reddit that are looking for alternatives to my competitors, complaining about my competitors or asking recommendations for a solution to their problem that my product directly solves. Find people on Twitter and Reddit that are looking for alternatives to my competitors, complaining about my competitors or asking recommendations for a solution to their problem that my product directly solves. I use CustomerFinderBot to save time and effort since it automates these things. CustomerFinderBot CustomerFinderBot Then I reach out to them to help them by answering their questions and suggesting the tool that I built. It’s a win-win for both parties. This strategy is effective because they already want my product so they have a high probability to end up buying. My next steps: Keep on iterating by improving the tool based on customer feedback. Keep on iterating by improving the tool based on customer feedback. List on directory websites. List on directory websites. Launch on Product Hunt. Launch on Product Hunt. Invest time and effort in SEO. This takes a lot of effort and patience because it takes a while to see results but based on what I learned from others and from my own personal experience it’s one of the most sustainable customer acquisition channel. Invest time and effort in SEO. This takes a lot of effort and patience because it takes a while to see results but based on what I learned from others and from my own personal experience it’s one of the most sustainable customer acquisition channel. Build and ship free tools for marketing purposes and it will also help with SEO. Build and ship free tools for marketing purposes and it will also help with SEO. Build and ship free tools for marketing purposes and it will also help with SEO. Lessons I learned? 👇 Building and growing a SaaS is hard as hell. It’s not a quick and easy way to make money, and it has a very high failure rate considering around 90% of startups fail. Gathering feedback from customers really provides you with a lot of valuable information. You will discover a lot use-cases that you haven’t thought of which will give you a better idea on what features you should build. By doing this, you’ll end up building features that your target customers really needs and wants instead of building features that you think will be useful but ends up no one uses. Burnout is real, so do everything you can to prevent it. It’s really very tiring especially if you’re a solo founder which does all the work. Solve real problems (e.g., save them time and effort, make them more money). Focus on the pain points of your target customers. Solve 1 problem and do it really well. Prefer to use the tools that you already know. Don’t spend too much time thinking about what are the best tool to use. The best tool for you is the one you already know. Your customers won’t care about the tools you use; what they care about is you’re solving their problem. Don’t add every feature you can think of. Start with a MVP that solves the core problem, then iterate based on user feedback. Validate immediately to see if people will pay for it then move on if not. Don’t over-engineer. It doesn’t need to be scalable initially. Do lots of marketing; this is a must because build it and they will come rarely succeeds. Keep on shipping 🚀 Many small bets instead of 1 big bet to manage risk and increase your odds of launching a product that customers will pay for. Building and growing a SaaS is hard as hell. It’s not a quick and easy way to make money, and it has a very high failure rate considering around 90% of startups fail. Building and growing a SaaS is hard as hell. It’s not a quick and easy way to make money, and it has a very high failure rate considering around 90% of startups fail. Gathering feedback from customers really provides you with a lot of valuable information. You will discover a lot use-cases that you haven’t thought of which will give you a better idea on what features you should build. By doing this, you’ll end up building features that your target customers really needs and wants instead of building features that you think will be useful but ends up no one uses. Gathering feedback from customers really provides you with a lot of valuable information. You will discover a lot use-cases that you haven’t thought of which will give you a better idea on what features you should build. By doing this, you’ll end up building features that your target customers really needs and wants instead of building features that you think will be useful but ends up no one uses. Burnout is real, so do everything you can to prevent it. It’s really very tiring especially if you’re a solo founder which does all the work. Burnout is real, so do everything you can to prevent it. It’s really very tiring especially if you’re a solo founder which does all the work. Solve real problems (e.g., save them time and effort, make them more money). Focus on the pain points of your target customers. Solve 1 problem and do it really well. Solve real problems (e.g., save them time and effort, make them more money). Focus on the pain points of your target customers. Solve 1 problem and do it really well. Prefer to use the tools that you already know. Don’t spend too much time thinking about what are the best tool to use. The best tool for you is the one you already know. Your customers won’t care about the tools you use; what they care about is you’re solving their problem. Prefer to use the tools that you already know. Don’t spend too much time thinking about what are the best tool to use. The best tool for you is the one you already know. Your customers won’t care about the tools you use; what they care about is you’re solving their problem. Don’t add every feature you can think of. Start with a MVP that solves the core problem, then iterate based on user feedback. Don’t add every feature you can think of. Start with a MVP that solves the core problem, then iterate based on user feedback. Validate immediately to see if people will pay for it then move on if not. Don’t over-engineer. It doesn’t need to be scalable initially. Validate immediately to see if people will pay for it then move on if not. Don’t over-engineer. It doesn’t need to be scalable initially. Do lots of marketing; this is a must because build it and they will come rarely succeeds. Do lots of marketing; this is a must because build it and they will come rarely succeeds. Keep on shipping 🚀 Many small bets instead of 1 big bet to manage risk and increase your odds of launching a product that customers will pay for. Keep on shipping 🚀 Many small bets instead of 1 big bet to manage risk and increase your odds of launching a product that customers will pay for. I hope this post helps others to get their first customers for their startups. Any questions? I’ll be happy to answer them! Let’s follow each other on Twitter — https://x.com/FI_investor https://x.com/FI_investor https://x.com/FI_investor Check out my other projects: Calm-Jobs.com — Find Jobs With Better Work-Life Balance. Calm-Jobs.com Calm-Jobs.com JobHunterBot.com — Automatically Find Jobs from LinkedIn, Indeed, Glassdoor, Monster, ZipRecruiter, and hundreds of other job boards. JobHunterBot.com JobHunterBot.com

The SaaS That I’m Building in Public Made $534.76 in Revenue in the First 2 Weeks After Launch 🎉

Organizations strive to acquire data through effective, reliable, and accessible means. As far back as 2006, British Mathematician Clive Humby likened data to the new oil, but in development, some argue that our time is the new oil. This guide will cover the following: Methods of data acquisition
Technical challenges and solutions in data collection
Practical examples for developers to gather data Utilizing publicly available web data in artificial intelligence (AI) using quality data aids the performance and applicability of AI models, making them intelligent and responsive to real-world scenarios by adequately training and enhancing the systems. Bright Data as a service can help to make this happen. Let’s dive in. Methods of data acquisition Web scraping Web scraping is all about extracting relevant or specific data from a website. The data can be converted and exported in a structured format like JSON, CSV, or Excel. Gathering data involves various techniques, which are time-consuming and prone to errors when working with large datasets manually. Another way is using automated software tools or scripts such as HTML parsing, DOM manipulation, or API interaction, which can be tricky because if the structure of the web page syntax changes, like elements or class names, the converted data becomes useless. From a developer's standpoint, how do you scrape public web data successfully? Bright Data offers proxy networks with its infrastructure that allows you to bypass location restrictions using a different configured and verified IP address without getting flagged by target websites. Other ways are known to help make scraping possible. Bright Data has a solution for all of them: Automate website unlocking management
Interact with websites
Build scrapers In addition, Bright Data has pre-configured datasets available in the dataset marketplace. The advantages of using the marketplace to search for a dataset for your use case are that it is 100% compliant with the CCPA and GDPR standards, reliability, and time-saving capability. Therefore, it means there is security, and data is not leaked on your personal identifiable information (PII). Using the web scraper APIs, you can programmatically access structured web data from dozens of popular domains, such as LinkedIn, Crunchbase, Amazon, Indeed, Glassdoor, and so on, whose data are available for $0.001/record cost. Technical challenges and solutions in data collection The challenges arise from unethical practices such as scraping web data without following the guidelines outlined by some target websites on what is permissible. Some specialized anti-bot technologies identify when you hit too many requests at any time, preventing you from accessing the website. Managing proxies
As discussed above, to avoid your internet protocol (IP) bans and rate limiting, you cannot write automated scripts to act like humans, which will block your IP once identified. The solution uses a proxy service to rotate IP addresses across different data centers with a powerful server to send requests.
Bright Data rotating proxies are spread across 195 countries with a 99.99% delivery success rate.

Automation scripts
Developers write scripts that can handle dynamic content on a website that is not static due to constant changes from management. While developers can write scripts in different programming languages, the question is, are you ready to modify the code every time a change occurs on a target website?
With Bright Data, you can power up scraping data with a headless browser suitable to run your Puppeteer, Selenium, and Playwright with CAPTCHA auto-solver without lifting a finger on your behalf. How magical that is.Bright Data comes with pre-written scripts that you can adapt to your workflow as a developer. Practical examples for developers to gather data As a developer, check out this guide on extracting reviews in a JSON file using the Scraping Browser and ChatGPT to build a frontend application with the data gathered and captured. For this application, we got the data from the Udemy website for a course on the platform and used ChatGPT appropriately. Conclusion In this article, you learned the usefulness of public web data and how to harness it for AI. The uniqueness of this is knowing how public web data serves as training datasets for models, which is effective for business owners or individuals for research purposes. Bright Data is globally acclaimed as a top provider of proxy networks and AI-powered web scrapers trusted by tens of thousands of Fortune 500 companies and over 20,000 customers. Finally, as shown by the data gathering example, Bright Data is compatible with many coding languages, tools, and BI software. Try it today! Learn more Unlock and scrape the toughest website Bright Data web scraper APIs Organizations strive to acquire data through effective, reliable, and accessible means. As far back as 2006, British Mathematician Clive Humby likened data to the new oil, but in development, some argue that our time is the new oil . our time is the new oil our time is the new oil This guide will cover the following: Methods of data acquisition Technical challenges and solutions in data collection Practical examples for developers to gather data Methods of data acquisition Technical challenges and solutions in data collection Practical examples for developers to gather data Utilizing publicly available web data in artificial intelligence (AI) using quality data aids the performance and applicability of AI models, making them intelligent and responsive to real-world scenarios by adequately training and enhancing the systems. Bright Data as a service can help to make this happen. Bright Data Bright Data Let’s dive in. Methods of data acquisition Methods of data acquisition Web scraping Web scraping Web scraping is all about extracting relevant or specific data from a website. The data can be converted and exported in a structured format like JSON, CSV, or Excel. Gathering data involves various techniques, which are time-consuming and prone to errors when working with large datasets manually. Another way is using automated software tools or scripts such as HTML parsing, DOM manipulation, or API interaction, which can be tricky because if the structure of the web page syntax changes, like elements or class names, the converted data becomes useless. From a developer's standpoint, how do you scrape public web data successfully? Bright Data offers proxy networks with its infrastructure that allows you to bypass location restrictions using a different configured and verified IP address without getting flagged by target websites. Other ways are known to help make scraping possible. Bright Data has a solution for all of them: proxy networks proxy networks solution for all solution for all Automate website unlocking management Interact with websites Build scrapers Automate website unlocking management Interact with websites Build scrapers In addition, Bright Data has pre-configured datasets available in the dataset marketplace. The advantages of using the marketplace to search for a dataset for your use case are that it is 100% compliant with the CCPA and GDPR standards, reliability, and time-saving capability. Therefore, it means there is security, and data is not leaked on your personal identifiable information (PII). marketplace marketplace Using the web scraper APIs , you can programmatically access structured web data from dozens of popular domains, such as LinkedIn, Crunchbase, Amazon, Indeed, Glassdoor, and so on, whose data are available for $0.001/record cost. web scraper APIs web scraper APIs $0.001/record Technical challenges and solutions in data collection Technical challenges and solutions in data collection The challenges arise from unethical practices such as scraping web data without following the guidelines outlined by some target websites on what is permissible. Some specialized anti-bot technologies identify when you hit too many requests at any time, preventing you from accessing the website. Managing proxies
As discussed above, to avoid your internet protocol (IP) bans and rate limiting, you cannot write automated scripts to act like humans, which will block your IP once identified. The solution uses a proxy service to rotate IP addresses across different data centers with a powerful server to send requests.
Bright Data rotating proxies are spread across 195 countries with a 99.99% delivery success rate. Automation scripts
Developers write scripts that can handle dynamic content on a website that is not static due to constant changes from management. While developers can write scripts in different programming languages, the question is, are you ready to modify the code every time a change occurs on a target website?
With Bright Data, you can power up scraping data with a headless browser suitable to run your Puppeteer, Selenium, and Playwright with CAPTCHA auto-solver without lifting a finger on your behalf. How magical that is.Bright Data comes with pre-written scripts that you can adapt to your workflow as a developer. Managing proxies As discussed above, to avoid your internet protocol (IP) bans and rate limiting, you cannot write automated scripts to act like humans, which will block your IP once identified. The solution uses a proxy service to rotate IP addresses across different data centers with a powerful server to send requests. Bright Data rotating proxies are spread across 195 countries with a 99.99% delivery success rate. Managing proxies Managing proxies As discussed above, to avoid your internet protocol (IP) bans and rate limiting, you cannot write automated scripts to act like humans, which will block your IP once identified. The solution uses a proxy service to rotate IP addresses across different data centers with a powerful server to send requests. Bright Data rotating proxies are spread across 195 countries with a 99.99% delivery success rate. Automation scripts Developers write scripts that can handle dynamic content on a website that is not static due to constant changes from management. While developers can write scripts in different programming languages, the question is, are you ready to modify the code every time a change occurs on a target website? With Bright Data, you can power up scraping data with a headless browser suitable to run your Puppeteer, Selenium, and Playwright with CAPTCHA auto-solver without lifting a finger on your behalf. How magical that is. Bright Data comes with pre-written scripts that you can adapt to your workflow as a developer. Automation scripts Automation scripts Developers write scripts that can handle dynamic content on a website that is not static due to constant changes from management. While developers can write scripts in different programming languages, the question is, are you ready to modify the code every time a change occurs on a target website? With Bright Data, you can power up scraping data with a headless browser suitable to run your Puppeteer, Selenium, and Playwright with CAPTCHA auto-solver without lifting a finger on your behalf. How magical that is. Bright Data comes with pre-written scripts that you can adapt to your workflow as a developer. pre-written scripts pre-written scripts Practical examples for developers to gather data As a developer, check out this guide on extracting reviews in a JSON file using the Scraping Browser and ChatGPT to build a frontend application with the data gathered and captured. this guide on extracting reviews in a JSON file this guide on extracting reviews in a JSON file For this application, we got the data from the Udemy website for a course on the platform and used ChatGPT appropriately. Conclusion Conclusion In this article, you learned the usefulness of public web data and how to harness it for AI. The uniqueness of this is knowing how public web data serves as training datasets for models , which is effective for business owners or individuals for research purposes. training datasets for models Bright Data is globally acclaimed as a top provider of proxy networks and AI-powered web scrapers trusted by tens of thousands of Fortune 500 companies and over 20,000 customers. Finally, as shown by the data gathering example, Bright Data is compatible with many coding languages, tools, and BI software. Try it today ! today today Learn more Learn more Unlock and scrape the toughest website Unlock and scrape the toughest website Unlock and scrape the toughest website Bright Data web scraper APIs Bright Data web scraper APIs Bright Data web scraper APIs

Harnessing Public Web Data for AI

The trend of tech layoffs is still going strong in 2024. Job losses in tech were significant in 2022 when 1,064 companies laid off about 165,296 employees. Things worsened in 2023, with 1,191 companies cutting 263,180 jobs—a 59% increase from the previous year. As of May  2024, more than 89,000 jobs have been cut by 306 companies, according to the lay-offs tracking website, layoffs.fyi. Big names like Tesla, Google, Amazon, Microsoft, and TikTok laid off thousands of workers in the first months of 2024. Why are there so many layoffs? So why all these layoffs? The problem is rooted in the pandemic, which came with lots of challenges and opportunities. Many companies experienced growth and hired more employees to meet customers' demands, but this caused overstaffing when demands were reduced and economic conditions got tougher. Businesses began to downsize, especially because overhead costs increased due to the economic downturn and inflation, which is why we’re seeing so many layoffs today. Reasons the tech sector has seen layoffs Economic downturn Just like in other industries, tech companies are not immune to economic downturns. When demand drops, profits take a hit, and many tech firms respond by cutting their workforce to save on costs. Over bloated workforce During the pandemic, tech companies went on a hiring spree because of the surge in online activity from people working from home. But now that things are back to normal, these companies realize they hired too many people and are downsizing as a result. Higher Interest rates In July 2023, the Federal Reserve raised interest rates to 5.5% in the U.S., the highest they've been in 22 years. This makes borrowing more expensive for companies, which in turn affects how much investors are willing to put into companies. Sometimes, investors even push companies to lay off employees to cut costs. Inflation Inflation rates drive up the costs of goods and services and for tech companies, the cost of hardware and other items required in production. To cope with these higher expenses, companies often reduce their workforce to save money. Increased AI Adoption and Impact on Jobs Most companies that lay off their staff don’t explicitly say the cuts were due to AI automation, but the signs are there. Jobs in customer service, content writing, and design have been cut. For example, layoffs at Google, which heavily impacted the company's ad sales division, coincided with more deployment of AI across customer care and sales processes. Also, last year, Swedish furniture company IKEA announced that it was training call center employees to become interior design advisors. This was after all routine customer inquiries were delegated to an AI bot named Billie. Job losses to AI in Tech companies are more complicated; tech companies are downsizing to have more money to invest in expensive AI talent and GPU computer chips to train and run AI applications. An example is Salesforce, which laid off 700 employees in January 2024. According to the company’s Chief people officer, it is actively recruiting for positions such as prompt engineers, AI trainers, and AI ethical architects. This suggests that the company has shifted its focus from providing tech sales jobs to hiring AI talent, as part of its investment in Artificial Intelligence. What to do if you have been laid off Finding jobs in a tough economy can be difficult, especially tech jobs. However, getting a new job is not impossible; many tech companies continue to hire despite the layoffs. If you have lost your job in tech and are looking for another job, you want to break into tech, or change your field, here are some of the things you can do. Update your resume While this might sound cliche, your resume is your first introduction to hiring managers and recruiters, so you want it to make that resume stand out and shine. Ensure your resume is in the right format, has details of your professional experience, and showcases your skills and certifications. Write resume sections that align your skills with the different job positions you apply to. A one-size-fits-all resume doesn't work anymore, so it’s better to have multiple versions of your resume saved for applying to different jobs. Recruiters today focus on your core skills, certifications, data skills, and AI skills, so if you have these, emphasize them on your resume. Get the most sought-after skills Today, companies are on the lookout for specific tech skills. These include skills in artificial intelligence, machine learning, data science and analytics, cloud computing, programming languages such as Javascript and Python, virtual and augmented reality, and DevOps methodologies. To get started, explore online courses that teach the basics, some of which are free. You can later proceed to the more comprehensive, paid courses. Another great option is enrolling in a boot camp where you’ll learn about new technologies and tools. Getting these skills can make landing a tech job easier. Focus on companies with recent funding When you’re job hunting, checking job websites such as Indeed, Glassdoor and CareerBuilder can lead you to companies that are hiring. However, here is another thing you can try: “Follow the money”. Look for tech companies that have just secured new funding; they might be hiring soon. You can use Startup databases such as Crunchbase, AngelList and CB Insights for this. Also, keep tabs on News outlets like TechCrunch and Forbes - they often report on funding rounds and investments. That way, you can find companies with open roles and possibly land a job faster. Consider relocation While many tech jobs allow employees to work remotely, you might need to be flexible in a tight job market and consider companies that want employees to work hybrid or fully onsite. Also, think about relocating to places where the available jobs are. Look beyond the cities that are popular as job hubs; there are other cities friendly to job seekers. These cities have high job growth, earnings growth, lower unemployment and more affordable housing. Work with a tech recruiter Teaming up with a recruiter will give you access to a wider range of opportunities when seeking a tech job. The recruiter can offer valuable tips for your interview with the companies and give you an idea of the salaries to expect. You can connect with a recruiter on LinkedIn - just make sure to include a message about why you want to connect. You can also keep an “open to work” badge on your LinkedIn page to attract tech recruiters who may be searching for candidates. Beyond LinkedIn, you can ask your network or do a simple Google search to find recruiters in your area. Don’t stop looking Keep up with the basics of a job search and don’t let news of layoffs discourage you. Keep networking and applying for suitable opportunities. You can create a schedule to allocate time to the job search tasks, such as resume updates, creating a list of companies and roles, and submitting applications. You’ll also need to keep track of your activities on a spreadsheet. Finding a tech job in a tough economy can seem daunting, but it is not impossible. Equip yourself with skills employers are looking for through online courses and position yourself for more opportunities by networking and focusing on companies that are most likely to hire. With the right attitude and determination, you’ll land a role that will be suitable for you within a reasonable time. The trend of tech layoffs is still going strong in 2024. Job losses in tech were significant in 2022 when 1,064 companies laid off about 165,296 employees. Things worsened in 2023, with 1,191 companies cutting 263,180 jobs—a 59% increase from the previous year. As of May  2024, more than 89,000 jobs have been cut by 306 companies, according to the lay-offs tracking website, layoffs.fyi. Big names like Tesla, Google, Amazon, Microsoft, and TikTok laid off thousands of workers in the first months of 2024. The trend of tech layoffs is still going strong in 2024. Job losses in tech were significant in 2022 when 1,064 companies laid off about 165,296 employees. Things worsened in 2023, with 1,191 companies cutting 263,180 jobs—a 59% increase from the previous year. As of May  2024, more than 89,000 jobs have been cut by 306 companies, according to the lay-offs tracking website, layoffs.fyi . Big names like Tesla, Google, Amazon, Microsoft, and TikTok laid off thousands of workers in the first months of 2024. layoffs.fyi layoffs.fyi Why are there so many layoffs? Why are there so many layoffs? So why all these layoffs? The problem is rooted in the pandemic, which came with lots of challenges and opportunities. Many companies experienced growth and hired more employees to meet customers' demands, but this caused overstaffing when demands were reduced and economic conditions got tougher. Businesses began to downsize, especially because overhead costs increased due to the economic downturn and inflation, which is why we’re seeing so many layoffs today. Reasons the tech sector has seen layoffs Reasons the tech sector has seen layoffs Economic downturn Economic downturn Just like in other industries, tech companies are not immune to economic downturns. When demand drops, profits take a hit, and many tech firms respond by cutting their workforce to save on costs. Over bloated workforce Over bloated workforce During the pandemic, tech companies went on a hiring spree because of the surge in online activity from people working from home. But now that things are back to normal, these companies realize they hired too many people and are downsizing as a result. Higher Interest rates In July 2023, the Federal Reserve raised interest rates to 5.5% in the U.S., the highest they've been in 22 years. This makes borrowing more expensive for companies, which in turn affects how much investors are willing to put into companies. Sometimes, investors even push companies to lay off employees to cut costs. Inflation Inflation Inflation rates drive up the costs of goods and services and for tech companies, the cost of hardware and other items required in production. To cope with these higher expenses, companies often reduce their workforce to save money. cost of hardware cost of hardware Increased AI Adoption and Impact on Jobs Increased AI Adoption and Impact on Jobs Most companies that lay off their staff don’t explicitly say the cuts were due to AI automation, but the signs are there. Jobs in customer service, content writing, and design have been cut. For example, layoffs at Google, which heavily impacted the company's ad sales division, coincided with more deployment of AI across customer care and sales processes. Also, last year, Swedish furniture company IKEA announced that it was training call center employees to become interior design advisors. This was after all routine customer inquiries were delegated to an AI bot named Billie. layoffs at Google, layoffs at Google, announced announced Job losses to AI in Tech companies are more complicated; tech companies are downsizing to have more money to invest in expensive AI talent and GPU computer chips to train and run AI applications. An example is Salesforce, which laid off 700 employees in January 2024. According to the company’s Chief people officer , it is actively recruiting for positions such as prompt engineers, AI trainers, and AI ethical architects. This suggests that the company has shifted its focus from providing tech sales jobs to hiring AI talent, as part of its investment in Artificial Intelligence. According to the company’s Chief people officer According to the company’s Chief people officer What to do if you have been laid off Finding jobs in a tough economy can be difficult, especially tech jobs. However, getting a new job is not impossible; many tech companies continue to hire despite the layoffs. If you have lost your job in tech and are looking for another job, you want to break into tech, or change your field, here are some of the things you can do. Update your resume While this might sound cliche, your resume is your first introduction to hiring managers and recruiters, so you want it to make that resume stand out and shine. Ensure your resume is in the right format, has details of your professional experience, and showcases your skills and certifications. Write resume sections that align your skills with the different job positions you apply to. A one-size-fits-all resume doesn't work anymore, so it’s better to have multiple versions of your resume saved for applying to different jobs. Recruiters today focus on your core skills, certifications, data skills, and AI skills, so if you have these, emphasize them on your resume. Get the most sought-after skills Today, companies are on the lookout for specific tech skills. These include skills in artificial intelligence, machine learning, data science and analytics, cloud computing, programming languages such as Javascript and Python, virtual and augmented reality, and DevOps methodologies. To get started, explore online courses that teach the basics, some of which are free . You can later proceed to the more comprehensive, paid courses. Another great option is enrolling in a boot camp where you’ll learn about new technologies and tools. Getting these skills can make landing a tech job easier. free free Focus on companies with recent funding When you’re job hunting, checking job websites such as Indeed, Glassdoor and CareerBuilder can lead you to companies that are hiring. However, here is another thing you can try: “Follow the money”. Look for tech companies that have just secured new funding; they might be hiring soon. You can use Startup databases such as Crunchbase, AngelList and CB Insights for this. Also, keep tabs on News outlets like TechCrunch and Forbes - they often report on funding rounds and investments. That way, you can find companies with open roles and possibly land a job faster. Consider relocation While many tech jobs allow employees to work remotely, you might need to be flexible in a tight job market and consider companies that want employees to work hybrid or fully onsite. Also, think about relocating to places where the available jobs are. Look beyond the cities that are popular as job hubs; there are other cities friendly to job seekers. These cities have high job growth, earnings growth, lower unemployment and more affordable housing. other cities other cities Work with a tech recruiter Teaming up with a recruiter will give you access to a wider range of opportunities when seeking a tech job. The recruiter can offer valuable tips for your interview with the companies and give you an idea of the salaries to expect. You can connect with a recruiter on LinkedIn - just make sure to include a message about why you want to connect. You can also keep an “open to work” badge on your LinkedIn page to attract tech recruiters who may be searching for candidates. Beyond LinkedIn, you can ask your network or do a simple Google search to find recruiters in your area. Don’t stop looking Keep up with the basics of a job search and don’t let news of layoffs discourage you. Keep networking and applying for suitable opportunities. You can create a schedule to allocate time to the job search tasks, such as resume updates, creating a list of companies and roles, and submitting applications. You’ll also need to keep track of your activities on a spreadsheet. Finding a tech job in a tough economy can seem daunting, but it is not impossible. Equip yourself with skills employers are looking for through online courses and position yourself for more opportunities by networking and focusing on companies that are most likely to hire. With the right attitude and determination, you’ll land a role that will be suitable for you within a reasonable time.

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