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Most developers expect to go through multiple interview rounds, coding assessments, or take-home assignments before getting hired. That wasn't my experience. I ended up working with the YouTuber I had admired for years without an interview, without an exam, and without even sending a resume. Here's how it happened. It Started Long Before the Opportunity I started freelancing when I was in Class 9. At first, it wasn't about building a career. I simply enjoyed creating websites and wanted to gain experience while earning some money. Over the years, I worked with different clients, solved different problems, and learned something from every project. Those freelance gigs taught me much more than writing code—they taught me how to communicate with clients, deliver on time, and take ownership of my work. The Opportunity A few months ago, one of my favorite YouTubers posted in his WhatsApp community that he was looking for someone to build a website. I happened to be a member of that group. As soon as I saw the message, I reached out and told him I could build it. Instead of spending time wondering whether I was "good enough," I decided to let my work answer that question. Building It in Under 24 Hours Once I received the project, I focused entirely on delivering it as quickly as possible without compromising quality. I completed the website in less than 24 hours. After reviewing it, he requested a few modifications. I implemented them immediately and delivered the updated version. At that point, I assumed the project was finished. The Unexpected Offer A few days later, he contacted me again. He had another web application that had been stuck because a previous developer couldn't complete it. He asked if I could take over. That conversation eventually turned into a job offer. No coding interview. No aptitude test. No technical assessment. Just trust built through delivering one project well. What I Learned Looking back, I don't think I got the job because I replied quickly

This is the fourth post in Craft & Code , a short Friday series about what carpentry can teach us about AI, skill and the future of software. Last week I worried about where the next generation's judgement will come from. This week, why we may not notice it is missing until it is too late. My father built me shelves in an alcove when I was small, and I mentioned in the first post that they may still be there for eternity. The other side of that story is the one every household knows: the shelf that is not quite right. The one that sags under a row of books, or sits a degree off true so that anything round rolls gently to one end. You do not need to be a carpenter to see it. A bad joint, a door that will not close, a shelf that dips — the material tells on the maker, immediately and to everyone. That is the comforting version of the analogy, and the one I expected to write: carpentry is honest about its failures because they are visible, while software can look polished and be rotten underneath. A wonky shelf looks wonky; bad software looks finished. It is a tidy line, and there is real truth in it. But it is only half the truth, and the more interesting half should worry us — because the moment you go up from a shelf to a serious piece of engineering, the comfort falls away completely. Consider two of the most admired structures of the last century. The Tacoma Narrows Bridge was designed by one of the leading suspension-bridge engineers of his day: elegant, slender, celebrated. It opened in the summer of 1940 and tore itself apart in the wind that November, twisting like a ribbon because the design had not reckoned with how the deck would behave aerodynamically. Nobody had seen a wonky bridge; it looked magnificent. The flaw was real, fundamental, and invisible until the wind found it. The Citicorp Center in New York, finished in 1977, was a triumph of structural engineering, raised dramatically on great columns at the midpoints of its sides. Only after it was compl

Qwen's new image paper is easy to read as another benchmark bump. Qwen-Image-2.0-RL takes the existing Qwen-Image-2.0 model, runs a reinforcement-learning pass on top, and reports better scores: 57.84 on Qwen-Image-Bench, up 2.61 points from the base model. Its text-to-image arena Elo moves from 1115 to 1193. Its image-editing arena Elo moves from 1256 to 1349. Those are the headline numbers. They are not the useful part. The useful part is the training story underneath them. The paper is a good reminder that "just optimize the reward" is a dangerously incomplete sentence, especially when the model is not an LLM and the output space is a whole image. The model got better, but not by one simple trick Qwen-Image-2.0-RL is a post-training pipeline for a diffusion image model. In plain English: the base model already knows how to generate and edit images. The RL stage tries to steer it toward outputs humans prefer, including better prompt following, better aesthetics, better portrait fidelity, and more reliable editing. The team builds task-specific reward models. For text-to-image, those rewards cover alignment, aesthetics, and portrait quality. For editing, they cover instruction following and face identity preservation. Then they train with a GRPO-style setup adapted for flow-matching diffusion models. If you only squint at that, it sounds like the same broad recipe people use for language models: generate candidates, score them, push the model toward the better ones. The paper is more interesting because it shows how fragile that story becomes once you touch the actual training loop. The CFG detail is the first real lesson Classifier-free guidance, usually shortened to CFG, is one of those diffusion-model knobs that users mostly experience as "make the image follow the prompt harder." Under the hood, it changes how the model samples. The Qwen team tested three ways to use it during RL. Using CFG during both rollout and training made the images collapse into incohere

Want to go back to your ex... AI assistant? We don't blame you. Take these steps to get Google Assistant back after your fling with Gemini.

Enterprise investment in AI is booming. Gartner is calling 2026 an “inflection year” for organizations to align their AI projects with strategic business objectives. As the pressure to prove ROI mounts, executives and technology leaders are looking to agentic AI to drive the measurable financial outcomes their businesses seek. A prime opportunity for AI agents…

Target built a generative AI system to improve marketing campaign forecasting by retrieving and ranking similar historical campaigns. Using embeddings, vector search, and LLM ranking, it replaces rule-based workflows. Evaluation shows 75% top-1 and 100% top-3 coverage. The system reduces manual effort, improves consistency, and uses feedback loops to refine retrieval using campaign outcomes. By Leela Kumili

It’s time for our annual Fourth of July grill episode here at Decoder. This is when we invite the CEOs of outdoor cooking companies onto the show to explain just how their businesses kind of look like every other business. And this is a very special edition. Today I’m talking to Roger Dahle, the CEO […]

Pocket sells a $129 credit card-shaped puck, which sticks to the back of your phone, and promises unlimited recordings, transcriptions, and to-do items.

CSS has always had pseudo-classes that style things when baed on user interactions. Recent features, however, are blurring the line between what CSS "listens" for and how they are alternatives to what Javascript typically listens for. The Shifting Line Between CSS States and JavaScript Events originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.

A CFO will not act on a number an LLM eyeballed. They will not act on a number the model "estimated" by reasoning over a usage dump. And they should not — because the moment a language model emits a dollar figure it computed itself, that figure is a guess wearing the costume of a fact. This is the design constraint behind databricks-cost-leak-hunter , the pilot skill of the databricks-pack v2 rebuild shipped in the claude-code-plugins marketplace ( PR #906 ). Given a live, authenticated Databricks workspace, it surfaces real cost leaks across four named categories, ranks them by monthly dollar impact, and emits a report a finance reader can act on. The marketplace validator graded it B (88/100, zero errors). The SKILL.md is 329 lines. The single most important thing in it is a rule the model is structurally prevented from breaking: the LLM never does the dollar arithmetic. Why not just let the agent read the bill and summarize it? Because that is exactly how you ship a confidently wrong cost report. Hand a model a few thousand rows of system.billing.usage and ask it for the top cost leaks, and it will give you a fluent answer. It will add DBUs. It will multiply by a price it half-remembers. It will round. Every one of those steps is a place the model can be plausibly, invisibly wrong — and the output reads identically whether the math is right or hallucinated. The failure mode of an LLM doing FinOps is not a crash. It is a clean, well-formatted, wrong number. The fix is architectural, not prompt-engineering. The model is allowed to decide what to look for and how to explain it . It is never allowed to be the calculator. The dollar primitive: confirmed, never estimated Every confirmed figure comes from the customer's own billing tables — system.billing.usage joined to system.billing.list_prices . Not a model estimate. Not a public price list. The number Databricks actually billed. That join is defined once, as a priced CTE, and reused by every category query. Usage i

You finally get the response. The CV cleared whatever filter it was up against, and now there is a calendar invite for a thirty or forty five minute call. Most developers treat this as the technical screen and prepare accordingly. They load up on system design questions, leetcode style problems, or deep dives into the stack listed in the job post. What actually happens in that first call is often lighter on code and heavier on whether the person on the other side can picture working with you week after week. The engineering lead or hiring manager is trying to answer a few practical questions the CV could not fully settle. Can this person explain their decisions without needing constant context? Do they push back on unclear requirements in a way that moves the conversation forward instead of creating friction? Do they already understand how remote contractor work tends to flow, or will every interaction need extra translation? The candidates who lose ground here rarely fail on raw technical ability. They lose it on rhythm and assumptions. Some over-prepare the technical side and under-prepare the part where they need to show how they handle ambiguity. Others treat every question as an interview question that demands a polished answer, when what the lead wanted was a working conversation. The call ends with a quiet sense that this person will need more hand-holding than the role allows. Timezone and async signals are another place people slip. When a candidate spends the call reassuring the other person that they can work US hours or that they are always available for meetings, it often lands as uncertainty. The reassurance backfires. Teams that hire contractors remotely have already accepted some timezone spread. What they want to hear is how you have made async work in the past, what you leave behind when you log off, and how you keep momentum without daily syncs. The calls that move forward feel like two people working a problem together. The candidate is not sitti

Flipper Devices, a company that built a banned hacking device, now wants to hack your attention span.

First announced over a year ago in April 2025, the Busy Bar will be available for purchase starting on July 14th when the device also starts shipping. Created by the same team behind the Flipper Zero wireless multitool, the Busy Bar is instead described as a "productivity multitool" that relies on a pixelated LED display […]

Omen AI raised a $31 million Series A to monitor chip coolant and stop bacterial outbreaks in data centers.

Flipper Device's new Busy Bar will retail for $249.

We’ve all been there. You scroll through your feeds, see a flashy ad promising a high-paying tech job in 3 months, and think, “This is it. This is my golden ticket.” You buy the bootcamp, spend sleepless nights watching lectures, stack up a dozen colorful certificates on your LinkedIn, and then... nothing. No callbacks. No interviews. Just a lingering feeling of frustration and the nagging thought: Are bootcamps and online courses just a massive scam? I used to think so. When I was trying to break into tech, I bought courses like crazy. I collected certificates like they were Pokémon cards. Yet, my first real developer job didn't show up until five or six years later. And let me tell you a secret: it wasn’t the certificates that got me the job. It was because I finally figured out how to actually learn. The truth is, almost every bootcamp or course—even the mediocre ones—has something valuable to offer. The problem isn’t always the material; it’s how we interact with it. If you feel stuck in "tutorial hell," here is a positive, practical guide to changing your approach, reclaiming your time, and turning any learning material into real, career-changing expertise. 1. Curate Your Sources (Choose Your Battles Wisely) Before we talk about how to study, we need to talk about what to study. Even though you can extract value from almost any course, your time is highly valuable. Don't waste it on low-quality content. When choosing a course or bootcamp, look for these four green flags: The Instructor Has Real-World Mileage: Is the instructor a practitioner, or are they just reading the official documentation back to you? If they don't work with the technology daily, they won’t be able to explain the nuances, edge cases, and real-world trade-offs. A Project-First Curriculum: Avoid courses that are just endless lectures of "theory first, practice never." Look for curriculums that build actual applications. Good Pacing and Editing: We've all watched those tutorials where the ins

We’ve all been there. You scroll through your feeds, see a flashy ad promising a high-paying tech job in 3 months, and think, “This is it. This is my golden ticket.” You buy the bootcamp, spend sleepless nights watching lectures, stack up a dozen colorful certificates on your LinkedIn, and then... nothing. No callbacks. No interviews. Just a lingering feeling of frustration and the nagging thought: Are bootcamps and online courses just a massive scam? I used to think so. When I was trying to break into tech, I bought courses like crazy. I collected certificates like they were Pokémon cards. Yet, my first real developer job didn't show up until five or six years later. And let me tell you a secret: it wasn’t the certificates that got me the job. It was because I finally figured out how to actually learn. The truth is, almost every bootcamp or course—even the mediocre ones—has something valuable to offer. The problem isn’t always the material; it’s how we interact with it. If you feel stuck in "tutorial hell," here is a positive, practical guide to changing your approach, reclaiming your time, and turning any learning material into real, career-changing expertise. 1. Curate Your Sources (Choose Your Battles Wisely) Before we talk about how to study, we need to talk about what to study. Even though you can extract value from almost any course, your time is highly valuable. Don't waste it on low-quality content. When choosing a course or bootcamp, look for these four green flags: The Instructor Has Real-World Mileage: Is the instructor a practitioner, or are they just reading the official documentation back to you? If they don't work with the technology daily, they won’t be able to explain the nuances, edge cases, and real-world trade-offs. A Project-First Curriculum: Avoid courses that are just endless lectures of "theory first, practice never." Look for curriculums that build actual applications. Good Pacing and Editing: We've all watched those tutorials where the ins

When I write code in unfamiliar territory, I write three lines, then I run it. Then I write three more lines, and I run it again. I've been doing this for twenty-four years. It's the most specific habit I have. I almost didn't write this article, because the habit feels too small to be worth describing — but then I noticed that it's the part of my way of working that I can never seem to explain to someone in real time. It needs writing down. Three principles The discipline rests on three things I believe about writing code. They're not deep. They've just stayed with me. 1. Trust nothing but your own code. If you can't trust the code you wrote yourself, what can you trust? Not a library, not a vendor's documentation, not your own assumption from yesterday. The only thing in the system whose behavior you can fully verify is the code you just typed, by running it. 2. Write in code, not in language. If you're describing what the code should do in Japanese or English, you're spending the same time you could have spent writing the code itself. By the time the code runs, the description is already done — by the code, in a more precise form than any language could give it. 3. Make three lines complete. The three lines you just wrote should be complete. Error handling included. Validation included. Logging included. Not "I'll add validation later." Not "I'll wrap it in a try-catch later." Three lines, complete, then run. (There's a small exception to this. Sometimes you do want to ignore every error and move on — for instance, when you're trying to understand whether the happy path works at all before you care about anything else. That's a different mode, used deliberately. It's not the same as "I'll handle errors later.") Why three lines Three lines is roughly the unit of thought I can hold completely. Five lines, and I start guessing what the third line did. Ten lines, and I'm reading the code as if it were someone else's. Three lines is the size that stays mine. When thre