"What is Prepfully?" Most interview content online is outdated, generic, or scattered. Prepfully brings you up-to-date interview guides, predictive question banks and 1-on-1 coaching with people already working in your dream role at your dream company.

GrowthX founder Thomas Trimoreau recently claimed AI could write 90% of code. The actual numbers tell a more grounded story. Current surveys place it closer to 20–40%. GitHub's own data shows a 46% acceptance rate among users, meaning more than half the AI-generated suggestions still get rejected. Having said that, there's a "cut" of this data that is reasonably concerning. Entry-level developers are worried, and not without reason. AI is absorbing a lot of routine work, the kind that junior roles were traditionally built around. At the same time, developer job postings are up 11% year-over-year, and projections suggest 15% growth by 2034 for those who adapt. The developers earning more, $90K–$130K at entry level, are the ones treating AI as a tool. They're building strength in code review, system architecture, and strategic thinking, the parts AI still can't own. The takeaway : AI might or might not take your job. It's that the job itself is changing, and the people who understand that early will have a meaningful edge.

If it were up to you, would you hold back on AI to be more careful, or would you push forward and let the market figure things out?

Google's 2026 SWE phone screen ≠ one coding call anymore. It can be DSA Behavioral. We broke down the new format 4 prep tips in 5 min (from Google EMs & Staff Engineers on Prepfully). Watch: zurl.co/0n8zt Practice before you're judged: zurl.co/eoemm

A total of 116,739 tech employees have been laid off in just 5 months of 2026. Companies With Major Layoffs Meta - 8000 Paypal - 4760 Cisco - 4000 Intuit - 3000 Cloudflare - 1100 Wix - 1000 LinkedIn - 875 Bill com - 709 Freshworks - 500 GitLab - 350 Ticketmaster - 350 Innovacer - 340 Arctic Wolf - 250 Adda247 - 200 SentinelOne - 230 Staffbase - 176 Upwork - 151 Kraken - 150 Credit Karma - 117 Rapyd - Not disclosed Webflow - Not disclosed PocketFM - 100 Lighttricks - 75 NetApp - 77 Interview Kickstart - 50 Uber - 23% of its People's division (less than 1% of workforce) ClickUp - 22% of workforce BigID - 20% of workforce Manhattan Associated - 6% of workforce

Tech layoffs in the US are accelerating. In May, US-based tech companies cut 38,242 jobs, the highest monthly total since August 2024, based on data from Challenger Gray. So far this year, job cuts in the tech sector have climbed 66% compared to the same period last year, reaching 123,653. That makes tech the hardest-hit sector by a wide margin, with roughly three times the cuts seen in transportation, the next closest. AI was named as the primary driver of layoffs for the third straight month. In May alone, 38,579 cuts were linked to AI, the highest since Challenger started tracking that category in 2023. That represents about 40% of all layoffs announced last month, up sharply from just 7% in January. Year-to-date, AI has been cited in 87,714 job cuts in 2026, accounting for 22% of the total. That figure has already surpassed the full-year totals for both 2025 (54,836) and 2024 (12,742). The trend is clear: AI is steadily reshaping how and where companies allocate headcount.

Most interview guides online describe a process that was accurate two years ago. Companies quietly restructure their onsite rounds, reweight signals, and add rounds that aren't documented anywhere yet. The coaches running sessions week after week are the first to see those changes. Prepfully's guides are woven with input from coaches who are actively coaching right now, so what you're reading reflects what candidates are encountering in the room today. Also, on platforms where the coach's name is attached to everything they say, there are real limits to what gets disclosed. No one publicly spells out internal rubrics, leveling signals, or what actually gets said in the debrief. On Prepfully, both coaches and candidates are anonymous, so coaches can speak freely about how decisions get made inside their companies, what interviewers are trained to probe for at each level, and where the bar sits. The only thing off the table is leaking a company's question bank. Everything else, the honest picture of how a process works from the inside, goes into the guides. And they're free. Only at Prepfully.com

Everyone keeps debating whether AI will create more jobs than it destroys. But TBVH that debate feels very different when you look at what is already happening. The jobs people point to are real, but they are also specific: data center electricians, power engineers, people who can fine-tune models, and the 10x engineer who can now ship what used to take a team of six. That is quite not the same market as the one most displaced candidates are actually in. A mid-level marketer, designer, analyst, recruiter, or operator does not just wake up and become an AI infra expert. And that is the genuine gap. AI may not directly take every job. But the bar is moving toward people who can use AI well, communicate sharper, and prove they can do more with less.

LinkedIn post from a Meta worker about the layoff day. Things are getting very ugly for the job market lately

A lot of people prepare for the Meta Data Scientist interview by grinding SQL and stats questions.That’s necessary, but it’s usually not enough.What makes these interviews difficult is that Meta evaluates how you think through product decisions: - what metrics you prioritize & handle trade-offs - how you reason with incomplete data - whether your analysis leads to a clear decision We put together a breakdown of the full interview loop: zurl.co/M0qSC

Two economists just came out with a proof that AI will destroy the economy. It is not that it might happen or it could happen. It will happen if nothing changes. The paper is called ""The AI Layoff Trap"". It was published on March 2 2026 by the Wharton School, University of Pennsylvania and Boston University. Peer reviewed. Mathematically modeled. The conclusion is one sentence: ""At the limit, firms automate their way to boundless productivity and zero demand."" This means the economy will produce everything. It will sell it to nobody. Here is how this will happen. A company fires 500 workers. Replaces them with AI. A competitor fires 700 workers to keep up. Another company fires 1,000 workers. Every company is doing what makes sense.Every company is following the rules correctly. Every company is building a trap for itself. The reason is that the workers who were fired were also customers. When they lose their jobs faster than the economy can absorb them they stop spending money. This means consumer demand falls. Companies respond by cutting costs, which means automating workers, which means less spending, which means more falling demand, which means more automation. This loop has no exit. The researchers tested every proposed solution, such as basic income, capital income taxes, worker equity participation, upskilling programs and corporate coordination agreements. Every single one of these solutions failed in the model. The only thing that worked was a tax on automation which's a fee charged every time a company replaces a human with AI forcing them to think about the demand they are destroying before they make a decision. No government has implemented this tax. No major economy is seriously talking about it. Meanwhile the numbers are already showing the trend. In 2025 100,000 tech workers lost their jobs. In the months of 2026 92,000 more workers lost their jobs. Jack Dorsey fired half of Blocks workforce. Said publicly: ""Within the next year the majority of companies will reach the same conclusion."" Nobody is doing anything. Companies are following their incentives perfectly. That is the problem. This is what happens when companies behave rationally at the time, with no mechanism to stop them. Two economists built the math. The math leads to one place. Source: Falk & Tsoukalas · Wharton School Boston University ·

A Blind post about Shopify’s PM interview process is getting attention. The candidate asks the difference between a mini case study and a full case study. That is the market right now. Even strong candidates are moving beyond just preparing for interviews. They are trying to understand the exact format, expectations, and evaluation style before they walk in.

Read the complete Amazon DS interview guide here for free: zurl.co/XAIrM Amazon DS Bar Raiser rounds test far more than ML and SQL. They evaluate judgment, communication, ownership, and how you solve ambiguous problems. In this video: • What Bar Raisers look for • Common mistakes candidates make • Practical tips to prepare better #prepfully #interviewguidance #interviewtips #amazon #datascience #barraiser

Following the mega layoffs at Meta, leaked audio surfaced from a Meta meeting that took place on April 30. The audio revealed that Mark Zuckerberg told Meta employees the company was using them to help train its AI models. He said Meta engineers were more skilled than any external workforce, so having them do these tasks would help make Meta's AI models better and faster than its competitors. The plan was that employees would train their replacements and then leave the company. That was how things were.

US job market weakness is showing up under the surface. Full-time employment fell by 424,000 in April, the biggest monthly drop since January and the second-biggest decline since May 2025. So far this year, full-time jobs are down by 963,000, bringing the total to 134.3 million, the lowest level since December 2024. Full-time employment is now also below March 2023 level The share of full-time jobs in total employment has dropped to 82.6%, roughly in line with 2020 pandemic-era levels. At the same time, part-time employment rose by 123,000 in April, but still sits 1 million below its record high of 28.4 million. The headline numbers may look stable, but the job market underneath them is clearly getting softer.

Most candidates think the Amazon Data Scientist interview is mainly about SQL and ML. But Amazon evaluates behavioural depth and Leadership Principles just as heavily as technical skills. In this video, we break down the complete Amazon DS interview loop: • Recruiter screen • SQL ML rounds • Statistics & case studies • Bar Raiser interview • What Amazon is actually evaluating Link: zurl.co/EKXh6 #Amazon #DataScience #MachineLearning #SQL #InterviewPreparation #TechCareers

mock interviews that hit different when your coach is sitting in the same building as your future interviewer. 🗣️🫣

Stocks are at record highs. Layoffs are at record highs. And Dario Amodei is basically saying both can be true for the same reason: AI. Very high GDP growth. Very high unemployment. Very high inequality. That is the uncomfortable part of this cycle. The market doesn't seem to be reacting at all to companies cutting costs (one of the traditional levers to force up stock price) Instead, the thing all investors seem to be looking for is: how much ""revenue per head"" can the company grow by. Same output. Lower headcount. Higher margins. The chart goes up. The job market gets colder. That's the AI trade everyone wants to be celebrating. Without realizing they're celebrating the end of the job market as we know it.

Read the complete Amazon DS interview guide here for free: zurl.co/oHZgo The Amazon Data Scientist interview loop is more than just coding and ML. This reel breaks down the different rounds in the Amazon DS interview process — from screening to onsite — and what each round is actually evaluating. #prepfully #interviewguidance #interviewtips #amazon #datascience