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The chatbot is tricking me!!! 💬📜⌛ When you text a chatbot, it doesn’t actually remember who you are or what you said two minutes ago. The exact millisecond it finishes typing a response, its brain completely wipes clean. To pull off the illusion of a continuous, flowing conversation, the web application secretly copy-pastes the entire past chat history, bundles it up, and blasts that whole massive block of text back into the processor every single time you hit send. Your "chat session" is an illusion maintained entirely by an ever-growing stateless prompt wrapper. You aren't interacting with a growing, adapting mind; you are repeatedly gas-lighting a brand-new entity into believing it has been talking to you for an hour. Wait, I am the one training it ??? 🚦🚸🚲 AI models are inherently blind to context; a computer doesn't instinctively know that a specific cluster of raw pixel values represents a real-world object. It requires billions of examples to be manually labeled by a human mind before the math can understand it. Every time you click on squares containing "traffic lights," "crosswalks," or "bicycles" to unlock a website, you are acting as an unpaid data annotator. You are manually labeling complex, messy real-world data points that feed directly into the computer vision systems of autonomous vehicles. The grand paradox of modern cyber security is that we force humans to act like mechanical data annotators to prove they are not computers, all so that computers can learn how to perfectly impersonate humans. The supercomputer is stupider than a toddler... 🍓👶🏻🖥️ We assume AI read letters and words the same way human eyes scan a page. It doesn't—it is entirely alphabet-blind. Before text hits the AI's brain, a parser chops strings of text into numerical blocks called "tokens." For example, the word "strawberry" isn't seen by the model as ten distinct letters; it is compressed into numerical IDs representing chunked pieces like "straw" and "berry". Because it never s

In my previous article, I argued that AI is just the next abstraction layer — the same pattern we’ve seen a dozen times in software history. Each layer demands a new skill. So what does the AI layer demand? I think the answer is hiding in plain sight. And some very powerful people just demonstrated it. Something Interesting Happened Recently Mark Zuckerberg started coding again after a 20-year break. According to multiple reports, he moved his desk to Meta’s AI lab, spends 5 to 10 hours a week writing code, and is “coding all day long” alongside the Meta Superintelligence Labs team. The man who built Facebook in a dorm room and then spent two decades managing tens of thousands of people — is shipping diffs again. Garry Tan, CEO of Y Combinator, returned to coding after 15 years using AI tools like Claude Code. He described himself as “addicted” to it, sleeping four hours a night because he couldn’t stop building things. Sergey Brin, Google’s co-founder who stepped back from day-to-day operations years ago, came out of retirement to code on Gemini. He’s reportedly assembling an elite “coding strike team” and is directly involved in hands-on development. And there’s a quote from The New Stack that captures this perfectly: executives are building with AI because they were “tired of explaining it to somebody who was supposed to build it for me.” Why is this happening? These people haven’t written production code in over a decade. What changed? The Career Ladder Was Always About Communication Let’s take a step back. The most common career paths for a developer are either the strict technical way — from developer to tech lead, then architect — or the management way — team lead, then head of engineering, CTO. In both ways you start from doing things yourself and gradually move to teaching — or better to say, guiding — others how to do it. Or strictly overseeing the whole process. You stop writing code and start writing explanations. You stop implementing and start reviewin

A while back, when I was still job hunting, building mini-projects, and trying to figure out what I...

Hey friends 👋 As the title suggests, we are hiring! If you've been with us for a little while, I'm sure you've seen our uptick in community initiatives since Major League Hacking (MLH) acquired DEV earlier this year. We've been working hard behind the scenes to bring new opportunities to the community and give a fresh spin to previous programs. We're now at a point where we need help optimizing and scaling up everything we do, while ensuring the platform remains a special place. That said, we are looking to hire a full-time, remote Community Program Manager based in the United States that cares deeply about community. Below is a brief overview of the role and skills we're looking for, but here's the full job description and application for anyone that wants to jump right in: Community Program Manager Job Application Job Overview Key Responsibilities You will... Develop and grow our community moderator programs Run DEV Challenges A-Z, plus other fun events Oversee our community support operations And more! Important Skills You are someone who... Effectively communicates with both internal and external stakeholders Can't help but be detailed oriented (sorry, I am pedantic) Uses AI to gain efficiency Knows how to work autonomously and manages up Benefits You'll receive... Competitive salary ($80-110k) Stock options Medical, dental, vision benefits and 401K Unlimited PTO Travel opportunities Questions about the role? Drop them in the comments below!

The message looked completely normal. A recruiter, a short pitch, a "take-home challenge" hosted on GitHub. Clone it, run npm install , get the dev server up, build a small feature, send it back. Standard stuff. I have done a dozen of these. This one was trying to steal my wallet keys and browser session data before I ever wrote a line of code. It did not hide the malware in the app. It hid it in the build tooling. That is the whole trick, and it is the reason a lot of experienced developers get caught. You read src/ , it looks fine, so you trust it. Nobody reads the lockfile. Nobody reads the postinstall script. That is exactly where the payload lives. Here is the full teardown: what the lure looks like, the exact red flags, how I investigated it without running it, and the defenses you should adopt today. The setup: Contagious Interview This is a known campaign. Security researchers track it as "Contagious Interview," attributed to North Korea-aligned actors. The pattern is consistent: You get contacted about a job, often blockchain or full-stack, often with a salary that is a little too good. You are given a code repository to clone and run as a "technical assessment." The repo runs malicious code at install or build time, not at runtime. The payload pulls a second-stage downloader, grabs your environment variables, crypto wallet files, browser-stored credentials, and keychain data, then exfiltrates them to a remote host. The genius of it is the framing. A normal developer reflex when running untrusted code is "I will read the code before I trust it." But you read the application code. You do not read what npm install does, because npm install is something you run a hundred times a week without thinking. Red flag 1: a postinstall script that does not belong The first thing I do with any unfamiliar repo is open package.json and read the scripts block. Specifically, I look for lifecycle hooks: preinstall , install , postinstall , prepare . These run automatically w

Not a job application — a peer search .\n\nI ship PHP/JS/AI production sites. People around me cannot relate to webhook failures at 2am.\n\nI want friends who are better coders than me in some layers.\n\nReply with what you are building: https://dev.to/elionreigns/looking-for-dev-friends-who-actually-get-how-much-work-this-is-3m0c

The AI world is full of old infrastructure with stochastic organs. That sentence probably explains...

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

This article was originally published on davidohnstad.com . I cross-post here to reach the Dev.to community. { " @context ": " https://schema.org ", " @graph ": [ { "@type": "Person", " @id ": " https://davidohnstad.com/#author ", "name": "David Ohnstad", "url": " https://davidohnstad.com ", "sameAs": [ " https://www.linkedin.com/in/davidohnstad/ ", " https://orcid.org/0009-0007-9023-7456 ", " https://davidohnstad5.mystrikingly.com/ ", " https://github.com/davidohnstad40-netizen ", " https://hashnode.com/@davidohnstad ", " https://davidohnstad.com ", " https://davidohnstad.net ", " https://davidohnstad.info ", " https://david-ohnstad.com ", " https://davidohnstadminnesota.com " ], "jobTitle": "Senior Data Product Manager", "worksFor": { "@type": "Organization", "name": "Veeam Software", "url": " https://www.veeam.com " }, "alumniOf": { "@type": "CollegeOrUniversity", "name": "College of St. Scholastica" }, "address": { "@type": "PostalAddress", "addressLocality": "Duluth", "addressRegion": "MN", "addressCountry": "US" }, "description": "Senior Data Product Manager at Veeam Software, MS and MBA from the College of St. Scholastica, based in Duluth, Minnesota. Specializes in data architecture, AI/ML integrations, and SaaS platform development." }, { "@type": "Article", " @id ": " https://davidohnstad.com/data-product-manager-org-structure-reporting#article ", "headline": "Data Product Manager Org Structure: Reporting Lines That Matter", "description": "David Ohnstad reveals where data product managers actually fit in org charts and why reporting lines determine success. Real insights from a data PM restructure.", "url": " https://davidohnstad.com/data-product-manager-org-structure-reporting ", "datePublished": "2026-05-29T14:06:18Z", "dateModified": "2026-05-29T14:06:18Z", "author": { "@type": "Person", " @id ": " https://davidohnstad.com/#author " }, "publisher": { "@type": "Organization", "name": "David Ohnstad", "url": " https://davidohnstad.com ", "logo": { "@type"

There is a special kind of productivity theater that happens during a developer job search. You wake up motivated, open LinkedIn, and apply to 27 positions before breakfast. You press the Easy Apply button with the precision of a professional gamer. By the end of the week, you have submitted 143 applications, updated a spreadsheet with several impressive numbers, and developed a minor emotional dependency on refreshing your inbox. Unfortunately, your inbox still looks like an abandoned shopping mall. No interviews. No useful feedback. No clear explanation. Perhaps two automated emails thanking you for your interest before informing you that the company decided to “move forward with other candidates,” a sentence that has become the corporate version of disappearing into the fog. So you decide to solve the problem by applying to another 200 jobs. This is not a strategy. It is email-based agriculture. You are throwing resumes into the soil and waiting for a recruiter to grow. Volume Matters. Blind Volume Does Not. Let us begin with an uncomfortable truth: getting your first developer job usually requires applications. Sometimes it requires many applications. The market will not discover your GitHub profile through divine intervention. A recruiter is unlikely to wake up in the middle of the night with a mysterious urge to search for junior developers who recently deployed a to-do list. You need to put yourself in front of companies consistently. However, there is a significant difference between applying consistently while improving your positioning and clicking every blue button on LinkedIn until one of you collapses. Volume is useful when it generates information. Blind volume only produces exhaustion. If you apply to 300 jobs with the same generic resume, the same generic portfolio, and the same vague explanation of your skills, you are not running 300 experiments. You are repeating the same experiment 300 times and acting surprised when the result remains unchanged.

Based on real system architecture decisions. About a $660K AI platform, three AI agents that kept the dashboard green, and a P0 incident that cost $3.15M over one weekend. Act 1 · The All-Hands Meeting Wang Lei, VP of Product, stood in front of the big screen, a smile on his face. Behind him, a dashboard rolled data from the "Axon AI Client Engineering Platform — Q1 Performance Report." Numbers cascaded across the wall: Metric Axon Platform Human Team (Last Q1) Improvement Avg daily tickets processed 847 312 +171% Avg first response time 12s 4h 17m ↓ 99.92% Customer satisfaction 4.8/5 4.1/5 +17% Monthly operating cost $52K $133K −61% Twelve department heads sat in the room. Dead silence. Wang Lei planted both hands on the table and scanned the room. His eyes landed on me. "Alex. Your team processed 312 tickets last Q1. Axon processed more than that in a single day last month." He smiled. Not a friendly smile. A sentencing smile. "And Axon costs less than a third of your team's operating expense." "We invested $660K in the whole platform. At current operating costs, it pays for itself in eighteen months." "After management review — the Client Engineering technical liaison function is being fully transitioned to the Axon platform." He clicked to the next slide. "Employees in replaced roles will complete exit interviews within the week." Someone inhaled sharply. I didn't. I opened my notebook to page 37. "Wang, what dimensions are these numbers from?" "What do you mean, 'what dimensions'?" His smile tightened. "Of those 847 daily tickets — how many are auto-tagging and routing, and how many are actual technical resolutions?" The room went quiet for about five seconds. Wang Lei looked at me. "Axon's ticket closure rate is ninety-three percent." "What's the reopen rate?" He paused. "What?" "After Axon replies — how many customers reopen the same ticket within twenty-four hours?" "We're still collecting that —" "Let me save you the trouble." I turned my notebook toward th

Stepping up to give my first-ever presentation at UbuCon 2026 was a massive milestone, and honestly, it was pretty intimidating. The stakes felt high, especially with the live demo. It was a race against the clock to get everything running, and it only finally came together exactly ten minutes before I went on stage. Talk about a close call. While I am proud of what I delivered, I originally wanted to pack even more into the session. I had planned to showcase a simulated mission, Gazebo visualizations and RViz path simulations. While time caught up with me for the presentation, these features are still actively in the works over at the aeronix project. My goal is to have the entire end-to-end setup completed and ready by the end of the year. I connected with some incredible engineers and industry peers and I am looking forward to building on those conversations for future professional collaborations. This experience proved that the best way to grow is to just put yourself out there. Moving forward, I plan to keep speaking on topics that challenge me. It is the ultimate way to deepen my own technical understanding share what I have learned with the community and grow professionally.

Every time I sit down to learn something, I find myself trapped in the same cycle. I start with a tutorial. Halfway through, someone says there's a better tutorial. I switch. Then I discover a book that supposedly explains the topic better than the tutorial. Then a YouTube video claims the book is outdated. Then a developer on social media recommends an entirely different resource. Before I know it, I've spent three hours researching how to learn instead of actually learning. Does the information overload stop or will there always be another resource, another course, another book, another video, another roadmap, another expert with a different opinion. The internet has made knowledge abundant, but abundance creates a paradox of choice. One person says to learn JavaScript from documentation, another says build projects immediately, another recommends a paid course, someone else insists that free resources are better. Every recommendation sounds convincing. Every path seems important. The result is paralysis. Instead of moving forward, I keep searching, instead of building I keep comparing, instead of learning I keep consuming. finished teaches more than a hundred bookmarked tutorials. At some point, every learner must accept a difficult truth that the goal is not to find the best resource it is to become better. Those are not the same thing. A person can spend months researching the perfect learning path and never write a meaningful line of code while another person can pick a decent resource, make mistakes, build projects, and improve every day. The second person wins not because they found better information but because they used the information they already had. I've started realizing that learning is a lot like fitness. At some point, reading about exercise becomes a form of avoiding exercise. The same thing happens in programming. Reading about coding becomes a way to avoid coding. Researching becomes a substitute for practice. The search for the perfect resourc

The Principle of Least Privilege: Operational Speed's Security Cost While developing a production ERP, delayed shipment reports were always a headache. One of the main reasons behind incomplete reports was the complexity of privilege layers in the system and, often, excessive permissions granted. In this post, I will delve into the costs we pay when we stretch security boundaries in an effort to gain operational speed. The principle of least privilege is more than just a security concept; it's critically important for operational efficiency and system stability. In this article, I will explain the impact of the principle of least privilege on operational speed, the security risks it entails, and how I've tried to strike this balance with concrete examples from my practical experience. My goal is to move beyond superficial definitions and dive deep into this topic based on my real-world field experiences, providing actionable insights to readers. Why Does the Principle of Least Privilege Seem to Hinder Operational Speed? The general tendency is to provide instant access to all relevant tools and data to speed up a task. This can be appealing, especially in an emergency or before a critical delivery. However, the Principle of Least Privilege (PoLP) advocates the opposite: a user or system component should have the absolute minimum privileges required to perform its task. This might initially seem to slow down operational processes. For example, a development team having unlimited SELECT rights to a production database might facilitate running an urgent query. However, the same developer could accidentally run UPDATE or DELETE commands, causing serious damage to the system. Such an incident, instead of speeding up a query in the short term, could lead to hours of downtime and data loss. This is where the long-term risk posed by operational speed, which PoLP is thought to hinder, becomes apparent. Another example is a system administrator frequently using the sudo su co

There are many reasons why one may not be replaced by AI, not even by a possible future ASI. Here's one reason that may just apply to you! ❤️ You'll not be replaced by AI if you can generate creative ideas faster than AI can implement them! 🫡🚀 Note for critics: Current AI models (as of May, 2026) are not advanced enough to implement complex ideas without human interventions. But even if a possible future Artificial Super Intelligence (ASI) implementation can do so, laws of physics like massive energy requirements, environmental concerns etc. will prevent the implementation to replace the work of Billions of people world-wide. Our hardware advancement rate is far far slower compared to our software advancements. We humans are far more efficient and compatible to planet earth compared to the hardware we've invented. Fayaz Follow A Software Engineer who is not afraid of being replaced by AI, loves coding and writing with and without using AI, and values human life and human dignity far more than technological advancements.

A child laughs on a playground. Pure. Unbothered. The world owes him nothing yet and he owes it nothing back. Then he grows up. He does everything right. Studies. Works. Sends his resume. Waits. Rejected. Sends it again. Rejected. Again. Rejected. The smile disappears. Not slowly. Suddenly. The day you realize the system was never built for you. An empty stomach has no dignity. A person denied the right to work is not just unemployed, they are being told their existence has no value. That is not a glitch. That is a choice someone made. 72 million rejections per year in the US alone. The algorithm decides in 0.8 seconds. No human ever reads his name. AI did not build this system. Humans did. AI just made the discrimination invisible, scalable, and deniable. So I built BiasLens. Paste your rejection. 30 seconds. Scans for documented discrimination patterns under US employment law. Free. Anonymous. No account. The hardest part was not building the scanner. It was forcing the AI to say "no bias found" when there isn't any, instead of manufacturing injustice to seem useful. How do you stop AI from missing the bias that's actually there, without inventing bias that isn't? I am still solving that. For that child. For every human who deserves to keep smiling. https://biaslens-justice.vercel.app/

"This wasn't a success story. It started as survival." Intro Hey, I'm Supreeth C , a third-year engineering student, open source developer, and professional overthinker from Bengaluru. This is my first blog, and fair warning: it's long. Not "LinkedIn post with 5 bullet points" long. Actually long. This is the story of how I got selected for Google Summer of Code 2026 with CircuitVerse but more honestly, it's the story of how I almost didn't submit a proposal, almost quit twice, and spent a lot amount of time reading codebases on the Bengaluru Metro while missing my stop. Connect with me on GitHub and LinkedIn PS: I'm writing this at 4.05am, because sleep is a myth XD. Act I: The Prequel Second semester. Fresh-faced. Absolutely clueless. I joined Pointblank , the one genuinely breathable space in my Tier-3 college. I can say its the best student-run club overall and the main reason being : everyone around me was terrifyingly good . Codeforces experts and specialists, GSoC mentees, LFX mentees, Smart India Hackathon winners. People whose LinkedIn bios are of several lines. And me? I knew C++. That was it. That was my entire personality. Cue the imposter syndrome : that lovely feeling where you're convinced you snuck into a room you have no business being in, and everyone else is one conversation away from figuring it out. My solution? Chaos. I started learning everything simultaneously: web dev, Android, ML, DevOps, a bit of systems engineering. Jack of all trades, master of none, spiraling fast. I wasn't learning; I was collecting domains like Pokémon and actually using none of them. Then a senior said something that cut clean through the noise: "Find your own path." So I slowed down. Started from the basics of web dev. Attended many hackathons but always ended up in third or fourth and winning: zero . But something clicked anyway. Those hackathons introduced me to open source, and somewhere in that chaos, I gave myself a simple challenge: 4 pull requests for Hacktob

Martin Fowler's May 27 Fragments brings together four arguments with direct implications for teams working with AI agents. All four are worth covering. Ian Johnson: build quality gates before releasing the agent Ian Johnson published a series about restructuring a gnarly codebase: three months, 258 commits, moving from a Laravel monolith with no tests to an application with automated quality gates and an AI agent shipping production code with minimal supervision. The insight Fowler highlights is about the transition from in-the-loop to on-the-loop: "For the first two months of this project, I used Claude Code with auto-approve turned off. Every file edit, every terminal command, every change… I reviewed it before it executed. The results were good. The code was clean. But I was doing most of the thinking and half the typing. The agent was a fancy autocomplete with better suggestions." Ian Johnson Manual review of every change is not how you build trust in the agent. Trust comes from building the structure that ensures the agent will do the right thing, then stepping back. The sequence: characterization tests first, static analysis, architectural patterns that make things flow correctly. Fowler notes this is exactly the sequence he would use himself. Adam Tornhill: roughly 2 hours of cognitive endurance Adam Tornhill observes that agentic work has a decision density that is mentally more expensive than it appears. The estimate is roughly two hours as a sustainable limit, not a full day of work. The implication: adding more parallel agents does not solve the problem, because the bottleneck is the coordinating engineer's cognitive capacity, not available processing volume. The solutions are smaller tasks, automation, and verification mechanisms, not more parallelism. NHS: closing open source repositories NHS (UK National Health Service) closed open source repositories citing LLM threats to code security. The UK Government Data Services countered directly: making code p