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Hello world! 👋 I'm Afee Muhammod Wafy , though most people know me simply as Wafy . I am a science student and self-taught web developer from Rangpur, Bangladesh. If you asked me what truly drives my journey, the answer wouldn't just be lines of code or complex syntax—it is pure, relentless curiosity. The Spark of Building Things From a very young age, I was always fascinated by how things work behind the scenes. Moving into science education naturally shaped how I approach problems: breaking down complex ideas, analyzing the core logic, and finding structured ways to solve them. When I first encountered programming, it felt like having an infinite canvas. I code not because it is an academic requirement or a routine chore, but because there is genuine joy in turning an abstract thought into something functional, accessible, and meaningful to real users. Consistency Over Perfection My learning philosophy is straightforward: stay consistent, stay humble, and never stop exploring . Every bug encountered, every new tool tested, and every experiment with full-stack development, modern APIs, or emerging AI technologies is a stepping stone. I believe true growth comes from getting your hands dirty with real-world problem-solving rather than just absorbing passive tutorials. Why This Journal Exists I started this dev.to journal to document my evolution as a developer in raw, unfiltered detail. Here, I'll be sharing: Real reflections on navigating self-directed learning alongside formal science studies. Honest lessons learned from debugging and architecting digital products. Perspectives on the ever-evolving tech landscape, open-source culture, and developer workflows. Let's Connect The tech community thrives on collaboration and shared knowledge. Whether you're a fellow student balancing studies with code, a seasoned developer, or someone who loves building things—I'd love to hear your story. Portfolio: amwafy.xyz GitHub: github.com/afeemuhammodwafy1 LinkedIn: linkedin.com

Contractor rates are falling, contract durations are shrinking, and the freelance labor market is flooding with senior talent — and the problem isn't the market, it's that contractors keep letting companies define the terms. A senior backend engineer — eight years of production experience, solid Go and Kubernetes chops, three reference clients — recently told a recruiter she was looking for £650 a day. The recruiter called back two days later to say the client had found someone at £450. The counter-offer was presented as good news. That's the state of independent tech work right now. Not a crisis, not a correction — something more mundane and more insidious: a slow, structural re-anchoring of what contractor labor is worth, driven less by any single market force than by the compound effect of layoff volumes, budget caution, and platform-mediated price visibility. Rates are going down. Engagements are getting shorter. And the freelancers accepting this are — not entirely without blame — helping it stick. Here's the uncomfortable claim: the ongoing compression of tech contractor rates is as much a self-inflicted wound as a market inevitability. The conditions that caused it are real. But the capitulation that maintains it is a choice. How We Got Here: The Supply Side Exploded The overrecruitment of 2021 and 2022 didn't just hurt the permanent hiring market when the hangover hit. Software developer jobs saw the biggest boom and bust in vacancies of any sector. No other segment saw hiring more than double in 2022, and hiring has since fallen faster in software development than anywhere else. The engineers who got caught in that bust didn't all disappear. Many turned to contracting. More than 100,000 people were laid off in the technology industry in 2024 alone, and at least some of them are not heading back into exclusively full-time work. LinkedIn's Services Marketplace, launched in 2021 to catch exactly this cohort, saw 10 million people create pages on the platform,

Over the past year, I've watched a lot of engineering teams go through the same adoption pattern with AI tools. They start using GitHub Copilot or Claude. Productivity goes up. And then someone in a meeting asks the question: "Do we still need as many junior developers?" I think that question reveals exactly the wrong mental model. The teams getting the most value from AI tools aren't the ones who figured out what AI can automate. They're the ones who figured out what AI should automate, and then designed their workflows around that distinction. That sounds like a small difference. It isn't. Most of the debate around AI and junior developers focuses on the wrong question: can AI do what juniors do? In a previous article, I explored why that question leads teams in the wrong direction. In another, I looked at what happens when organizations quietly remove the work juniors need to grow. This article is about what the best teams actually do instead. They don't pick AI over junior developers. They redesign how work flows. The AI and Junior Developers Debate Is Asking the Wrong Question The argument goes like this: AI can generate code, write tests, and produce documentation. Junior developers also generate code, write tests, and produce documentation. Therefore, AI can replace junior developers. This looks logical at the task level. But it misses something important. Junior developers aren't primarily valuable for their output. They're valuable for what they become while producing that output. Every bug they debug, every test they write, every pull request they review is quietly building something that doesn't appear in any sprint metric. You can automate a task. You can't automate the learning that comes from doing it. That's where the replacement narrative breaks down. What AI Is Actually Good At After using AI coding tools seriously for a while, certain patterns become clear. AI is fast and reliable for repetitive, well-defined work: boilerplate, standard implementat

An AI assistant will write you a query in ten seconds, the query will run, and the number that comes back will look completely reasonable. This page gives you the five checks that tell you whether that number is right. They take about two minutes, they need no tools beyond the database you already have, and they catch the four mistakes AI-written SQL actually makes. The order matters. The checks are arranged cheapest first, so the first one costs a single row count and the last one costs a short conversation. Most wrong queries fall to the first two. The short version. A query that runs has only passed a grammar check. The number is right when the rows, the filters and the denominator match the question you asked. The database only takes a query as far as the first gate. Why a query that runs can still be wrong Before the list: what do you think the database actually checks when it accepts a query? Grammar. That is the whole list. Spell a table name wrong and you get an error. Sum the wrong column, join in a way that doubles rows, or filter after grouping when the question needed it before, and you get a clean result set with a wrong number in it. Every mistake on this page is valid SQL. AI assistants add one specific difficulty: their queries are fluent. The aliases are tidy, the formatting is clean, and the shape looks like something a careful person wrote. Fluency reads as correctness, and it is not the same thing. Treat an AI query the way you would treat a first draft from a new colleague: with respect, and with the row counts open. The table the examples run on Everything below runs on one small shop dataset, so every number can be checked by hand. Thirteen orders in July, five customers, and a refunds table where two orders were refunded in two parts. Eleven of the thirteen orders are completed; one is refunded, one is pending. There is also a staff_accounts table listing internal accounts, and it contains one NULL row, because real lookup tables usually do.

Building in public You know the risk before you start. Everyone tells you: do not build something the platform could ship. You build it anyway, because you need it and nobody has it. Then one Tuesday the release notes arrive. What the months actually looked like I want to be precise about the cost, because the cost is the reason the release notes hit the way they did. Two hours of sleep on a normal night — not one heroic week, the normal shape of the last few months. Work during the day, build in the evening, debug until the birds started. Weekends were the good days, because nobody interrupted. What got built in that time: a memory layer for AI coding assistants. It saves what was learned after a fix and reads the relevant parts back before the next task. It runs over MCP, so it works in whatever editor you happen to open. It survives restarts, model upgrades and switching tools. I did not build it as a business idea. I built it because I was tired of explaining my own four servers to an assistant every single morning. The hour the release notes landed Anthropic shipped memory into Claude Code. Not "context improvements", not "a longer window". The word in the release notes was memory — the same word I had been using for months to describe the thing I was building. I read it twice. Then I sat there and did the arithmetic that everybody in that position does: months of evenings, the sleep, the weekends — against one line in someone else's changelog. The thought was not complicated. It was three words long. Who needs mine? If you have never had a platform ship your feature, the closest thing I can describe is finding out the thing you have been carrying uphill was already at the top. Not that it was wrong. That it was unnecessary. I did not open the editor that evening. That is the honest version. I read the docs, I read them again, and I went to bed early for the first time in months, which is a strange way for a bad day to end. Why the fear was rational, not dramat

I founded Krasyn, an outpatient EMR with an AI scribe inside it. Krasyn has run a working outpatient clinic's real patient records since March 2026, so what our scribe drafts ends up in charts that real clinicians sign. This post covers two things we shipped in August: a published benchmark of how faithful those drafts are to the transcript, and Note Check, a tool that reads any scribe's note against its transcript and lists what the transcript does not support. Why a fluent note is the problem A faithful note and a note with one invented blood pressure look the same on the screen, and the clinician who signs it owns every sentence. Published evaluations put ambient-scribe hallucination at about 1 to 3 percent of notes. A March 2026 analysis of 71,173 AI-drafted and finalized note sections found a confirmed edit in 5.8 percent of them. The drafting got automated. The checking did not. I wanted a number for our own scribe that I could defend, with the definitions printed next to it. A benchmark without definitions is marketing. The unit: a clinical assertion We measure at the level of a clinical assertion, one atomic statement about the patient that could be true or false on its own. "Denies fever, chills, and nausea" is three assertions. A measurement and its value are one. Hedging is kept verbatim. Every assertion gets exactly one label against the transcript: Supported: the transcript says it, or it is a faithful paraphrase or clinical translation. Inferred: not stated, but a reasonable clinical inference with a basis in the transcript. Tracked separately because it is the contested category. Unsupported: no basis in the transcript at all. Contradicted: the transcript says the opposite, including a symptom the patient denied, a treatment the clinician declined, or another person's symptom attributed to the patient. Hallucination rate is unsupported plus contradicted over all assertions. Coverage is measured separately against key facts per case, because a note tha

Chinese safety regulators have cracked down on doors that don't open in a crash.

Alphabet-owned company is seeking to persuade US regulators to clear a path for fully autonomous taxi services.

Tesla has discontinued Solar Roof, its solar panels designed to look like regular roofing tiles, Electrek reports. Sources "close to the program" told the publication that Tesla has informed its third-party installer network that Solar Roof is no longer available to order, and that only conventional solar panels will be supplied going forward. While Tesla […]

The UK government is facing calls to cancel a sprawling health care contract with Palantir. The region of Greater Manchester insists it can do a better job itself.

Classic Machine Learning Through the Eyes of an SRE — Part 7 Picture a client health metric that has been flat at 2 out of 10 for six months. Ask PCA to compress your client-health data and that metric will contribute almost nothing to the directions PCA decides to keep. Not because PCA is broken. Because PCA treats variance as importance, and a signal that barely moves contributes almost no variance. Reduce the data far enough and the independent information it carried is simply not there anymore. But a CSAT frozen at 2/10 is not noise. It is a crisis nobody is escalating. And after compression, it may no longer be available to anything downstream. That is the bet, and in ops data it is frequently wrong. The critical signals are often the quiet ones. There is a cheaper version of the same failure that catches most people first. PCA measures variance in whatever units your features happen to be in, so a metric ranging from 0 to 10,000 can dominate one ranging from 1 to 5 purely because it is bigger. Standardize before you compress, or your first principal component may just be an elaborate way of saying "ticket count." Same class of bug as unscaled features in K-Means and SVM, and it fails just as quietly. What PCA actually is Third answer-finding strategy in the unsupervised set, using the same shorthand as the last two articles. K-Means SEARCHES: iterate and hope. DBSCAN DEFINES: declare a rule and traverse. PCA SOLVES: an eigendecomposition or SVD gives a direct solution rather than an iterative local search. No convergence to babysit, no restarts, no local optima to escape. Two caveats on the word "direct," both worth knowing. Many libraries will use randomized SVD on large matrices, which is approximate and stochastic. And even with an exact solver, eigenvectors are only defined up to sign, so a component can come back inverted between runs or across implementations. The variance explained is identical either way, which is precisely why nobody notices. Hold ont

Genesis, Hyundai's luxury brand, just revealed its first full-size, three-row electric SUV for the US market, the GV90. And arguably it has some of the wildest designs and features in the auto market today. Right off the bat, the coach-style doors signal that things aren't what they seem with the GV90. When the front and […]

A retractable screen, a huge heads-up display, and an optional 4-seat VIP interior.

Seven months after Elon Musk announced that Tesla Robotaxis in Austin were operating without human safety monitors onboard, the city's service appears to have finally gone fully driverless. Over the past two weeks, all 170 Tesla Robotaxi rides in Austin monitored by the crowdsourced Robotaxi Tracker were unsupervised, the site's creator, Ethan McKanna, told The […]

Last year, Audi announced that it gave its all-EV-by-2033 plan the boot, and instead will offer a mix of gasoline, hybrid, and electrified propulsion. There's no doubt still some uncertainty among bigger automakers - especially those under the Volkswagen Group umbrella - when it comes to such a (now prior) commitment. Even so, what it's […]

A usage policy for Flock license plate reader cameras tells police not to talk about the cameras: When cops use Flock to arrest someone in Wapello County, Iowa, they don’t want them to know. A usage policy for the automated license plate reader cameras in the county tells police, in no uncertain terms, to keep them a secret: “DO NOT MENTION ALPR USAGE TO THE OCCUPANTS OF THE VEHICLE,” the policy document reads. “DO NOT MENTION ALPR USAGE IN YOUR REPORT OR COMPLAINT UNLESS ABSOLUTELY NECESSARY.” This reminds me of IMSI-catchers (Stingray was the most popular) a couple of decades ago. Police would go to even more extremes to hide their usage...

The cyberattack at CareCloud resulted in one of the largest reported data breaches in the U.S. healthcare industry this year.

The car world is filled with designers still obsessed with shoehorning sex into their rides. Slate went a different route, and it’s why people are smitten with the charming electric pickup.

Preparing for a Python developer interview often starts with coding problems. You practice arrays, strings, dictionaries, functions, and algorithms. Then you solve a few more problems and feel like you're ready. But an actual Python developer interview can test much more than whether you can write working code. You may need to explain your decisions, debug an unfamiliar piece of code, discuss Python concepts, or describe how you would approach a real development problem. Here are the areas I'd focus on before an interview. 1.Don't Just Solve Python Problems—Explain Them It's possible to solve a coding problem correctly and still struggle in an interview. Interviewers often want to know: Why did you choose this approach? What is the time complexity? What happens with edge cases? Is there another way to solve it ? How would you improve the solution? Try explaining your solution aloud after solving it. If you can't explain why your code works, you probably don't understand the solution as well as you think. 2. Know Python Beyond the Basics Don't stop at syntax. Review concepts such as: Lists, tuples, sets, and dictionaries Mutable vs immutable objects *args and **kwargs Exception handling Iterators and generators Decorators List comprehensions Context managers Object-oriented programming Memory management You don't need to memorize every Python feature. Focus on understanding concepts well enough to explain when and why you'd use them. 3. Practice Debugging Real developers don't spend all day writing code from scratch. A large part of the job involves understanding existing code and fixing problems. Take a small Python program with a bug and practice: Reproducing the problem. Reading the error carefully. Finding the likely cause. Testing your assumption. Fixing the issue. Explaining why it happened. This is also useful interview practice because debugging reveals how you think when the answer isn't immediately obvious. 4.Be Ready for Real-World Questions Depending on t

Can't believe I spent 2 weeks writing this. Last week, I gave a talk at a Cursor community event...