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Memory is not a log. It is a story rewritten after you learn how it ended. You will remember being less certain than you were. You will remember seeing the risk you missed. That is not dishonesty. That is how remembering works. Which is why you cannot learn judgment from memory alone. The correction is unglamorous. Before the decision, write down what you expect. Not a plan. A prediction. We are choosing this because we believe that. We think it takes three weeks. We think the load arrives by March. If it does not, we were wrong about the growth, not about the design. Four lines. A date. Somewhere you will find it again. Add the number you least want to write. How sure are you. Seven out of ten is a real answer. Certain is almost never a real answer, and writing it down makes you notice how often you reach for it. Then set a time to come back. The value is not in the writing. It is in the reading, six months on, when the outcome is known and the note is not. You find out something nobody else can tell you. Whether you run early or you run late. Whether your confident calls land better than your hesitant ones. Whether you are wrong about people more often than you are wrong about systems. That is calibration. It is the whole difference between ten years of experience and one year lived ten times. It changes rooms, too. A review where somebody can produce what they actually believed in March is a different meeting from one where everyone remembers agreeing. Hindsight makes us unfair in a peculiar direction. It makes the past look obvious, and the people standing in it look careless. They were not careless. They were standing exactly where you are, with less information and no ending. So write down where you are standing. Your future self will not be able to reconstruct it, and your future self is the one who has to get better. – Serguey Asael Shinder

Compliance itself isn't the villain. The problem is a system designed for large banks being forced onto small teams who measure time in sprints, not fiscal quarters. Innovation was never meant to wait for permission. It's supposed to be fast, chaotic, and full of discovery. But for anyone who has tried to launch a startup especially in Web3, fintech, or any regulated space there is a familiar slowdown that begins the moment compliance enters the conversation. Everything feels alive until the first "verification required" email arrives. That's when the waiting begins. Compliance itself isn't the villain. It protects systems from fraud, builds trust between unknown parties, and ensures accountability. But the way compliance exists today feels like a relic a system designed for large banks, traditional corporations, and legal departments with infinite patience and paperwork. For small teams who measure time in sprints, this system simply doesn't fit. The Repetition Tax Most founders encounter the compliance disconnect the moment they try to grow. After building their MVP and testing their product, they reach the stage where partnerships or investors arrive and suddenly they're asked to complete KYB (Know Your Business) verification again and again. Each time a new partner or service provider comes aboard, the process restarts: upload documents, verify directors, prove legitimacy. It's not that startups resist compliance; they simply don't understand why they must prove the same truth multiple times to different counterparties who have no way to see each other's work. This endless repetition creates what many founders now call the repetition tax. It isn't paid in money but in time and motivation. The same hours that could have gone into improving a product or connecting with users are swallowed by forms, follow-ups, and "under review" messages. And crucially, all this duplicated effort doesn't make the system any safer it just makes it slower. The Security Paradox There

Dress a tree in borrowed blossoms, and it looks alive. The roots still tell the truth. — The 36...

When shopping for an electric vehicle, affordability is becoming a more common trait. But affordable and energy efficient is truly a rare breed. Often you have to sacrifice one for the other. Want something affordable? Great, here's a range loser. Want something that goes the distance? Be ready to pay the price. Surprisingly, here comes […]

I Have a Job — I Just Need to Do It A few months ago, I left my last job as a remote Unity developer. Before leaving, I had already started working on a freelance project to build a multi-tenant security and workforce management system . It became a fairly large system involving web applications, mobile apps, real-time tracking, scheduling, reporting, GPS, notifications, and more. The project is now mostly completed, but the client wants to continue adding maintenance, business logic changes, UI modifications, and new development under the same maintenance fee. That doesn't work for me. Maintenance and development are two different things, and when the amount of new development keeps growing while the price stays the same, eventually it stops being sustainable. So I started thinking about what I would do next. The Fear of Not Having a Job For the last few days, I was genuinely worried. I have more than 250,000 BDT in savings , so I'm not in an immediate financial crisis. But money slowly disappears when there is no income. And freelancing isn't exactly comforting right now either. I've been using Upwork, but the experience has become increasingly frustrating. You apply for jobs and often hear nothing. Some clients post a job and never hire anyone. Some jobs get dozens of proposals and disappear quickly. Some invites arrive, but someone else gets hired almost immediately. And every application costs money. After a while, it starts feeling like you're continuously putting money into a machine that promises a job somewhere in the future. You keep applying. You keep waiting. You keep hoping. And eventually, I realized something. What I Was Actually Missing I wasn't missing money. I was missing a job . And there is an important difference. I already have the skills. I already know how to build software. I already have ideas. I already have projects I want to work on. I was simply thinking that a "job" had to come from someone else. Then I thought: I can create my own job

Not sure it’s practical, but it’s certainly striking .

Thanks to their ability to provide a smooth ride and quiet powertrain with ease, electric vehicles are a true shoo-in for the high-end luxury automotive segment. Rolls-Royce has the Spectre, Cadillac's Celestiq is taking bespoke to a whole new level, and Mercedes has given the EQS the full Maybach treatment. From Volkswagen Auto Group, Bentley […]

The logo goes on your CV. The team decides your next two years. People get this backwards constantly, and I understand why. Company names are legible. You can say them at a dinner party. They come with salary bands and glassdoor reviews and a shared idea of prestige. Teams are invisible from the outside. Nobody can tell you, before you join, that this particular group of eleven people ships carefully and reviews each other's work with real attention, while the group down the hall is on its third manager this year. But that difference is the entire experience of the job. Two engineers join the same famous company on the same day. One lands with a lead who explains decisions, gives real feedback, and hands out work slightly above their level. Two years later they are noticeably better. The other lands somewhere chaotic, spends two years firefighting, learns a lot about that one legacy system and almost nothing transferable. Same logo. Same offer letter. Completely different careers. So interview the team, not just the company. Ask who you would report to and try to talk to them. Ask what happened to the last person in this role. Ask how code review works here and listen for whether the answer sounds like a practice or an aspiration. Ask what the team shipped in the last six months. If the answer is vague, that tells you something. If they light up, that tells you more. And ask about the boring stuff, because the boring stuff is where you live. How do you handle on call. What does a normal week look like. When something goes wrong, what happens next. None of this makes the logo worthless. A strong company opens doors later, and that is real. Just understand what you are actually choosing between. The name gets you the next interview. The team decides whether you walk into it as someone worth hiring. Optimise for the people you will sit with every day. They are the ones who will shape you. – Asael Shinder

The quiet mistake most new mentors make is assuming the person in front of them wants your life. It is an easy mistake, because your path is the one you understand best. You know where the shortcuts were. You know which turn cost you two years. Of course you want to hand that map over. But it is a map of your terrain, not theirs. I have watched good mentors accidentally push someone toward management because management worked for them, when the person across the table lit up talking about deep technical work and went flat every time the org chart came up. I have also watched the reverse. A mentor who loved staying hands on, telling someone who was clearly born to run a team that leadership is a trap. Both were generous. Both were giving real, hard won advice. Both were answering a question nobody asked. So ask first, and ask properly. Not what do you want to be in five years, which almost nobody can answer honestly. Ask what part of last month did you enjoy most. Ask which meeting you would keep if you could delete all the others. Ask what you would do on a Tuesday with nothing on the calendar. The answers tell you far more than a stated ambition, because ambitions are usually borrowed and preferences rarely are. Then hold your own story loosely. Tell it as one data point, not as the route. I did this and it worked for me, for these specific reasons, and here is why it might not apply to you. That last clause is the whole job. Your value is not that you know where they should go. You almost never do. Your value is that you have seen more of the landscape than they have, so you can describe what is over each hill and let them choose the climb. The measure of good mentoring is not that they end up like you. It is that they end up more like themselves, faster than they would have alone. – Asael Shinder

In every project, someone says it sooner or later: "we have too much technical debt." Everyone agrees. Nobody asks how much. One day I tried to do the math for real. I learned very little about my code, and a lot about the metaphor. The bank statement If my technical debt were a loan, it would have the same structure: At the bank In the code The principal The shortcut taken to ship on time The interest The extra cost of every new feature Repayment Refactoring Bankruptcy A full rewrite So I listed my lines: a 3,000-line service with no tests, a framework three major versions behind, billing logic copied in four places, and one module everyone avoids. Every feature costs me about 30% more time. And the principal, the amount I would need to pay to reach zero, is measured in months of work that nobody will ever give me. The verdict: I am insolvent. And yet I ship every week, and I have been shipping for years. This is where the analogy breaks. Four reasons why it is not a debt I don't know the amount. A bank debt is a number written in a contract. Technical debt has no number, it has opinions. Ask three developers to rate the same module and you get three answers. I never signed anything. You choose to take a loan. Most of my technical debt arrived on its own: a library abandoned by its author, a business rule that changed, a project I inherited. Ward Cunningham, who created the term in 1992, was talking about a loan you take on purpose, to learn faster. He then spent twenty years repeating that he never meant "badly written code." The interest does not arrive every month. You only pay for the code you touch. I have terrible files that have not cost me a single minute in three years, because nobody goes there. And I have an 80-line file, changed twice a week, that is ruining me. There is no zero balance. The refactoring I do today will be out of date in two years. I never repay anything. I just trade one debt for another one with a better rate. The word itself is a prob

"Inspiration is for amateurs, the rest of us just show up and get to work." — Chuck Close ...

If you're prepping for a Business Intelligence (BI) Engineer, Data Analyst, Data Engineer, or Data Scientist role at Amazon, you probably already know SQL matters. It's a core part of the hiring bar. But Amazon isn't just checking your syntax. They want to see if you can think in sets, write clean queries under pressure, and reason about data the way the business actually uses it. Here's exactly how to prepare based on what the interview actually rewards. What the Interview Really Tests Amazon's SQL rounds usually show up in one of two ways. It's either a technical screen using a shared coding tool, or a whiteboarding case-study during the main loop. Either way, the interviewer is watching for a few specific signals. For starters, correctness always beats speed. A working query is far better than a clever one that fails. Communication is also huge. Do you talk through your logic before you even touch the keyboard? You'll often get a vague ask, like finding the "best" customers. You are completely expected to define what "best" means out loud before you start writing Common Table Expressions (CTEs). And watch out for messy data. Nulls, duplicates, and mismatched grain are almost always baked into the problem on purpose. The Core Topics to Master Focus your prep time on a few specific areas. Actually, it turns out this is where almost all the interview questions live. Joins Inside and Out You need to know your INNER, LEFT, RIGHT, and FULL OUTER joins cold. Be ready to explain exactly why row counts change after each one. A classic Amazon-style question is finding customers who placed orders but never left a review. That's just a LEFT JOIN with a NULL check. The interviewers want to see you reach for it right away. Window Functions Functions like ROW_NUMBER() , RANK() , DENSE_RANK() , and LAG() or LEAD() show up constantly. You might see a common pattern—like finding the second-highest order value per customer, or calculating month-over-month growth. If you're shaky he

Knowing React Is Not the Same as Being a Frontend Engineer A huge number of self-taught developers can build a React component, wire up useState , and fetch data with useEffect . A much smaller number can build a frontend that stays fast as it grows, handles real error states gracefully, and doesn't quietly re-render half the page every time a user types a letter. That gap — between "I can use React" and "I can build a production frontend" — is where a lot of otherwise-promising candidates get stuck. It's not usually a knowledge problem about React's API. It's a gap in the surrounding skills: state architecture, performance, accessibility, and the unglamorous parts of frontend work that tutorials rarely cover in depth. This guide lays out a realistic path from "knows React" to genuinely job-ready frontend engineer, focused on the specific gaps that show up in real interviews and real codebases. This post originally appeared on the Ciphemic Academia blog . What "Frontend Engineer" Actually Requires Beyond React Basics The role is broader than component-building, and being explicit about what it covers helps target the right skills: State management at scale — not just useState in one component, but how state should flow through an application with many interconnecting pieces Performance — understanding re-renders, memoization, and why a frontend that works fine with test data can slow down badly with real data volume Accessibility and semantic HTML — building interfaces that actually work for everyone, not just visually API integration done properly — loading states, error states, race conditions, not just the happy-path fetch call Testing — component and integration tests that catch real regressions, not just tests that exist to say tests exist A typical React tutorial project touches the first item briefly and skips most of the rest. That's exactly why a portfolio built entirely from tutorial-style projects tends to fall short in real interviews. Step 1: Confirm Ja

The problem I was trying to solve Candidates send hundreds of applications. Companies receive thousands of resumes. Most candidates never hear back. Both sides exhausted. Most of the effort wasted. The insight that changed my thinking: senior engineers don't apply to jobs. They get headhunted. A recruiter finds them, reaches out, and they evaluate the opportunity on their terms. Why is that only available to senior people? It shouldn't be. What I built Wrkmark Jobs — a hiring platform where candidates never apply. Here's how it works: Candidates create one profile Algorithm scores them against active roles Companies see their top 15 ranked matches Companies reach out. Candidates choose to respond. No applications. No cover letters. No ghosting. How the matching algorithm works This is the part I want to talk about technically. The algorithm scores each candidate against each job across four dimensions: Skills — 40% of score Simple exact match (case-insensitive) with a synonym map for common variations: const SKILL_SYNONYMS : Record < string , string [] > = { ' ruby on rails ' : [ ' rails ' , ' ror ' , ' ruby-on-rails ' ], ' kubernetes ' : [ ' k8s ' , ' kube ' ], ' postgresql ' : [ ' postgres ' , ' pg ' , ' psql ' ], ' javascript ' : [ ' js ' , ' es6 ' , ' ecmascript ' ], // 60+ mappings } A candidate with "RoR" on their profile matches a job requiring "Ruby on Rails". Simple but surprisingly effective at this scale. Salary — 25% of score All salaries converted to USD for comparison using live exchange rates (Frankfurter API). The logic: job_max_usd >= candidate_min_usd → score 100 job_max_usd < candidate_min_usd → score 0 If a company offers $80-120K and a candidate expects $30-50K — that's a great match. The company can easily meet the candidate's expectation. Score: 100. The common mistake is calculating range overlap. Overlap fails in the overqualified-offer case. Experience — 20% of score Years of experience vs role requirement. Meeting or exceeding → full score

The US government is investigating whether the Cybercab meets vehicle safety standards.

It's Bullitt meets Christine meets Waymo. A new short film imagines a San Francisco car chase where the other driver isn't human - and the car may be trying to kill you. That a robotaxi can now be cast as the villain with almost no explanation says something about the present moment. Autonomous cars have […]

I haven't written an article in a while. But nothing gives you more inspiration to write than an opinion you really, really disagree with. In my case it was I'm done using AI article by Brett Codes. Over around 2000 words, the author explains his negative views on AI and why he decided to stop using it . I am really no AI evangelist. I am aware of its limits and its problems and the incompetent people causing more harm than good with it. But I also see the goodies, and I am pretty much convinced author's decision was driven less by rationality and more by the false pride. Let's swallow one bitter, but important pill - no one is paying you solely for being a good developer. You are being paid for delivering a value. And yes - being a good developer greatly contributes towards the ability of delivering apps that work and help businesses yielding the values. And by "work" I mean not only they look good at the first glance, but also being not (too much) buggy, secure and maintainable in the foreseeable future. Therefore, purely vibe-coded projects driven solely by non-IT people often (if not always) fail. Because their authors don't know what it takes and so they cannot prompt their agents to reason and care about it. We devs do. Some of us know more, some of us know less (yet). The "AI is going to replace all programmers" is a lunatic nonsense. But so is rejecting AI and sanctifying "good ol' hand work" . Does anyone seriously think we should reject modern programming languages and revert into writing in assembly or even better directly in binary code? Do you develop in plain text editor without syntax highlighting? Why are you using containers and not building new on-premise servers every time? Whaat? You use frameworks and 3rd party libraries? You don't implement everything from scratch in the vanilla language? Seriously? Why all those abstractions and shortcuts? They make you lazy and they obfuscate the true principles, aren't they? You are a worse developer if you

There is a strange contradiction happening in software engineering right now. A lot of developers are worried that AI is going to make them obsolete. At the same time, the people building the most capable AI coding tools are demonstrating something that should probably make us rethink what being a software engineer actually means. I don't think the future is one where nobody understands software anymore. I think it is one where writing the software becomes dramatically cheaper. And if that happens, the thing that makes an engineer valuable has to move. That is what I mean by career safety. Career safety isn't about making yourself impossible to replace. It is about making your value portable. We've always resisted giving up the code Developers have a long history of being suspicious of abstractions that take work away from us. We went from machine code to assembly, from assembly to higher-level languages, from manually managing memory to garbage collection, from building everything ourselves to libraries and frameworks, and from text editors to IDEs. We even had entire categories of tools, such as CASE tools, designed to automate parts of software development. And every time, there was resistance. Because programmers don't just use code. We build our identities around it. John Carmack captured this unusually well when he wrote: “Coding” was never the source of value, and people shouldn’t get overly attached to it. — John Carmack He followed that with the more important point: Problem solving is the core skill. — John Carmack That is a difficult idea for developers to internalize because coding is tangible. You can point at the repository. You can point at the pull request. You can count the commits. You can say, "I wrote this." But the business doesn't ultimately pay you for the number of lines you wrote. It pays you for what those lines accomplish. The business never really bought the code A company doesn't wake up in the morning thinking: "We need 14,000 more line

I have spent more than six years shipping production software and I have no public repositories worth showing you. Everything substantial I have written at work lives in private repositories belonging to the companies I wrote it for, and confidentiality obligations do not lapse when a role ends. So I cannot hand you the code, and I am not going to. That is not caution for its own sake. An engineer who sends a prospective employer another company's source code has told you exactly what they will do with yours. The restraint is part of what you are assessing, and I would want the same restraint applied to my work later. Which leaves a real problem. "I was the principal author of the web application" and "I contributed to the web application" are the same sentence to a reader who cannot check either. Most engineering CVs resolve this by getting louder. I think the only honest resolution is to publish the measurement method rather than the artefact, in enough detail that someone familiar with the tools can judge the figure on its own terms without ever seeing the repository. Here is the method I actually used, what it does badly, and the places where applying it forced me to shrink a claim I would have preferred to keep. Three measurements, in increasing order of rigour All-branch commit count. Total authored activity. Do not use this. It counts abandoned branches, spikes and experiments, and it inflates — usually in favour of whoever is most willing to commit. It is the number that makes contribution graphs look impressive and says the least about a codebase. Production-branch commit count, scoped to tenure. What reached customers, while you were there. Better, because it excludes work that never shipped, and because scoping to tenure stops you either taking credit for years of history that predate you or being penalised for them. But it still counts commits, and a commit is a unit of activity, not a unit of code. Commit share rewards granular committers and punishes p

Elon Musk's gilded robot car barely made it on public roads before federal regulators launched an investigation into the vehicle's lack of traditional features - many of which are required by law. The National Highway Traffic Safety Administration said it was opening an audit query (AQ) into the Tesla Cybercab "to examine the process and […]