标签：#jobs

We'll get back to you. It's a sentence almost every job seeker has heard. For some, those words become the beginning of a new career. For many others, they become another unanswered promise. But the truth is, an interview doesn't begin when someone asks, Tell me about yourself . For millions of job seekers, it begins much earlier. Before the Interview Even Begins It's 6:45 in the morning. The alarm rings. A young professional stands in front of the mirror, adjusting the outfit they've carefully prepared the night before. He checks his resume one last time, gathers his documents, confirms the location, and takes a deep breath. As he’s about to leave, someone at home asks, “Do you think this one will work out?” He smiles. “I hope so.” He walks out carrying more than a folder. He carries expectations, financial pressure, family responsibilities, and the quiet hope that this interview might finally change everything. The Hidden Cost Nobody Talks About People talk about skills, preparation, and confidence. Those matter. But there’s another side rarely discussed: the hidden costs. Transportation. Professional clothing. Internet bills. Certification courses. Resume updates. Travel. Meals. Even taking a day off from a part-time job or missing freelance work. For someone without steady income, these aren’t just expenses — they’re investments with no guaranteed return. Sometimes they lead to an offer. Often, they end in rejection or silence. A Resume Can Tell You Skills. It Can’t Tell You a Story. A resume tells recruiters what a candidate has done. It doesn't tell them what they're carrying. It doesn't reveal the father waiting for good news, the mother asking how it went, the EMI due next week, the rent that can't wait, or the confidence slowly wearing down after repeated rejections. When Expectations Change Candidates prepare for the role they applied for. Sometimes they discover the responsibilities, salary, or even the position itself has changed. Business priorities evo

If you've ever tried to pull job listings by scraping LinkedIn or Indeed, you know the pain: anti-bot systems, CAPTCHAs, rotating proxies, and scripts that silently break every few weeks. Here's the thing — you usually don't need any of that. Companies don't post jobs on LinkedIn first. They post them in their ATS (Applicant Tracking System) — Greenhouse, Lever, Ashby, Workday, etc. — and most ATS platforms expose the company's board as a public JSON endpoint . No key, no login, no browser. It's the company's own source of truth, so it's cleaner and fresher than any aggregator. The endpoints A few that work with a plain GET ( {company} = the company's slug): Greenhouse — https://boards-api.greenhouse.io/v1/boards/{company}/jobs?content=true Lever — https://api.lever.co/v0/postings/{company}?mode=json Recruitee — https://{company}.recruitee.com/api/offers/ Breezy HR — https://{company}.breezy.hr/json SmartRecruiters, Ashby, BambooHR and Personio have their own equivalents. Workday is the one annoying exception — it's a POST and needs the full board URL (tenant + datacenter + site), so you can't guess it from a bare company name. Example: pulling Stripe's open roles (Python) Stripe uses Greenhouse: import requests company = " stripe " url = f " https://boards-api.greenhouse.io/v1/boards/ { company } /jobs?content=true " jobs = requests . get ( url ). json ()[ " jobs " ] for j in jobs [: 5 ]: print ( j [ " title " ], " — " , j [ " location " ][ " name " ]) That's it. No Selenium, no proxy, no CAPTCHA solver. Runs in ~200ms and won't break next Tuesday because Cloudflare changed something. Auto-detecting the ATS If you don't know which ATS a company uses, just try them in order and take the first one that returns jobs. A bare 404 means "not this ATS, try the next." Greenhouse → Lever → Ashby → SmartRecruiters → Recruitee → Breezy covers a huge chunk of tech companies. Gotchas Rate limits are lenient but real — be polite, set a User-Agent . Descriptions : Greenhouse/Leve

Tech Companies Regret Firing Engineers for AI: The Quiet Rehiring Nobody's Talking About [2026] Klarna's CEO Sebastian Siemiatkowski stood on stage in 2024 and bragged that AI had replaced 700 customer service employees. The stock market loved it. LinkedIn influencers celebrated. And then, quietly, in 2025, Klarna started hiring humans again. That single reversal tells you everything about why tech companies regret firing engineers for AI. I've watched this pattern unfold across the industry, and a viral YouTube video by Pooja Dutt documenting these failures is now pulling over 10,000 views per day. The audience isn't just curious. They're vindicated. The tech industry laid off over 260,000 workers in 2023 alone, according to Layoffs.fyi , with many companies explicitly citing AI automation as justification. Now, in 2026, the bills are coming due. The companies that swung hardest at the "AI replaces engineers" thesis are the ones scrambling hardest to undo the damage. Why Did Companies Fire Engineers for AI in the First Place? The logic seemed airtight. AI can generate code faster than humans. AI can handle customer queries at scale. AI doesn't need benefits, PTO, or performance reviews. Executives saw a clean line from "AI generates output" to "we need fewer people," and they drew it with a Sharpie. I've been in enough executive planning meetings to know exactly how this plays out. Someone demos an AI tool that produces a working prototype in 20 minutes. The room gets excited. The CFO asks how many engineers they can cut. Nobody asks the harder question: what happens when that prototype needs to survive contact with production? The answer is that it breaks. Badly. Klarna is the poster child, but they're far from alone. Apple has spent two full years struggling with AI-driven improvements to Siri, despite being one of the most well-resourced engineering organizations on the planet. Even with virtually unlimited budget and talent, replacing deep engineering expertise

“Why don’t we just frickin’ call Apple?”

I applied for a role at a mid-sized SaaS company about two years into my career. Strong company, interesting problem, good pay. I sent my application, got a recruiter callback, and then nothing for two weeks. When the feedback finally came: "We went with candidates with a stronger portfolio presence." I had 23 GitHub repositories. I had a portfolio site. I had projects. What I didn't have — and what I didn't understand for another six months — was a portfolio that told a story. I had code. Not evidence of thinking, decision-making, or the ability to ship something real. I've since built, rebuilt, and advised on a lot of developer portfolios. I've seen what gets people calls and what gets them ghosted. This isn't a guide about which framework to use or how to pick colors. It's about what actually moves the needle — the things I wish someone had told me in year one. Lesson 1: Two Great Projects Beat Twenty Mediocre Ones The instinct is to fill the portfolio. More projects = more evidence of experience. This is wrong. A hiring manager or engineering lead looking at your portfolio has about three minutes. They're going to look at your two or three most prominent projects, click one or two live demo links, and form an opinion. If they see twenty repositories and most of them are "Todo App v2," "Weather App," "Netflix Clone," "Portfolio v1 through v6" — they've already categorized you as someone who builds tutorials, not someone who builds things. The better approach: three to five projects, each with: A real problem it solves (not "I wanted to learn React") A live deployment that actually works A README that explains why you made the decisions you made Enough complexity to have generated at least one interesting engineering problem Projects that tend to work: tools you built because you were frustrated with an existing tool, apps solving problems you personally had, projects where you integrated with a real API or real data source, anything with a live user base (even 10

If you're interviewing at Amazon this year, you've probably read that you need to "prepare STAR stories." What most guides don't tell you is exactly how Amazon uses STAR differently from every other company — and what interviewers are silently scoring you against while you talk. Here's the complete 2026 breakdown: the cheat sheet, the full question bank, scored example answers, and the four mistakes that get candidates rejected even when their stories are genuinely impressive. Why Amazon STAR Is Different Amazon evaluates every behavioral answer against its 16 Leadership Principles. This isn't just culture marketing — interviewers are trained to map your stories to specific LPs and give them discrete scores. A Bar Raiser isn't just listening; they're running a rubric. The STAR formula at Amazon has specific time allocations that most candidates ignore: Situation (10%): Set the context in 20–30 seconds max Task (10%): What was specifically your responsibility Action (50%): What you did — not your team, not your manager Result (30%): Quantified outcomes only That weighting is the whole game. Most candidates spend 60% of their answer on Situation and Task, then rush through Action and Result — which is exactly backwards from what gets high scores. The "I" Rule: The Single Biggest Reason Candidates Fail Bar Raisers flag one thing more than any other: candidates who say "we" during the Action phase. Weak answer: "We decided to refactor the codebase, and we deployed a caching layer to fix the latency issue." Strong answer: "I identified the bottleneck using distributed tracing. I proposed the Redis caching layer to my tech lead and personally implemented the proof-of-concept over a weekend before bringing it to the team." Amazon hires individuals. If you can't cleanly separate your contribution from the group's work, interviewers have no signal on whether you were the driver or just along for the ride. Every sentence in your Action phase should start with "I." 30 Amazon S