标签：#discuss

Quantum mechanics gave us the transistor before we understood it. The same pattern is happening with AI right now — and the builders who recognize this will define what comes next. The argument that never ended — and the lab that didn't care In 1927, the greatest minds in physics gathered in Brussels for the Solvay Conference. Albert Einstein, Niels Bohr, Werner Heisenberg, Erwin Schrödinger, Max Planck, Marie Curie — twenty-nine of the most brilliant humans who ever lived, in one room. They were arguing about quantum mechanics. Specifically: what does it mean for a particle to exist in multiple states simultaneously until observed? Does reality require an observer? Is the universe fundamentally probabilistic? Is God playing dice? Einstein said no. Bohr said yes. Neither convinced the other. That argument never fully resolved. Nearly a century later, physicists still debate the interpretation of quantum mechanics — the Copenhagen Interpretation, Many Worlds, Pilot Wave theory. We have not settled it. Meanwhile, in 1947 — twenty years after the Solvay Conference — three engineers at Bell Labs in New Jersey quietly invented the transistor. William Shockley, John Bardeen, and Walter Brattain did not wait for the philosophical debate to conclude. They did not need to understand why quantum tunneling worked at a fundamental level. They understood it well enough to build something with it. That transistor became the foundation of every computer, every smartphone, every server, every piece of digital infrastructure that exists today. We built the entire digital civilization on something we still don't fully understand. Not despite the uncertainty. With it. The pattern repeating right now Across the internet in 2025 and 2026, a remarkably similar argument is happening. Will AI take all the jobs? Is it conscious? Does it hallucinate too much to be trusted? Are we building something we cannot control? Should we slow down? Should we stop? These are not trivial questions. The r

Intro AI Avatar is a completely free app that lets your VRoid (VRM) 3D avatar animate in...

Is it just me, or has anyone noticed that articles on dev.to don't get as many reads/views as they...

Last week I sat in my parked car for fifteen minutes before going inside. Not because of...

Here is an uncomfortable one: you do not own your reading list. You rent it. Every "follow" button you have pressed in the last decade put your reading relationship inside a company's database, where it can be ranked, throttled, or ended the day the business model changes. You did not sign anything. You just stopped owning it. It was not always like this. Feeds were the quiet machinery that kept the web interoperable. RSS and Atom meant a site, a reader, and a robot could all agree on the same stream without asking anyone's permission. You published once, and anything could read it: whatever app, whatever order, no algorithm in the middle. Then it eroded. Plenty of sites ship no feed at all now, and "follow us" quietly became "create an account on someone else's platform." The reason is not mysterious. Platforms had every incentive to close the loop, because a feed lets you leave, and an account does not. So the industry swapped "here is my stream, read it however you like" for "log in to see updates," and a generation of sites simply stopped publishing feeds, because the platform was where the audience was. That is the trade you made without noticing. The open format that asked nothing of you got replaced by a login that asks for everything. Your reading list used to live in your reader and survive a company changing its mind, its ranking, or its whole business. Now it lives in their database and survives exactly as long as they allow. Getting it back is not nostalgia. It is infrastructure for independence: tooling that treats feeds as a first-class citizen, aggregates the sources you actually choose, and keeps that stream under your control instead of a platform's. The full case for why this is worth fixing, and what feed-first tooling looks like, is here: https://mederic.me/blog/open-web-feeds So, honestly: how many of the people and sites you follow could you still read tomorrow if the platform in the middle disappeared tonight?

Two weeks ago one of my outdoor cats bit me. She's fine — healthy, pregnant, and deeply offended that I picked her up, but she needed flea medicine and I needed to confirm the pregnancy. (If anyone wants a kitten, I know a grumpy lady who has some.) My pinky swelled up, and typing went from "mildly error-prone" to "not happening." So I dictated this post. If you've ever looked at raw voice transcription, you know what that produces: one giant unpunctuated block with half the words wrong. My transcript literally claims "AI needed to put flea medicine on her." It was me. That's the kind of thing the AI is cleaning up. The ideas are mine. The argument is mine. The punctuation and clarification belongs to the machine, because the machine is better at punctuation than a transcript is. By the rules of the current discourse, you're now supposed to stop reading. That's the game, right? "Not reading this if it's AI-generated." "It has em dashes — slop." Let's deal with the em dash first, since it's apparently forensic evidence now. You can type one. Shift-Option-hyphen on a Mac. Windows-Shift-hypen on Windows. Writers were littering pages with them for a century before the first transformer shipped. Its little brother the en dash is everywhere too, and nobody has ever accused an en dash of being a robot. The em dash gets singled out for exactly one reason: it's a fast, cheap way to judge a piece of writing without engaging with a single idea in it. Zero effort, instant superiority. Remember that phrase — zero effort. It's coming back. Because real AI slop absolutely exists. Someone fires off one prompt, ships whatever falls out, never reads it, then farms for stars and upvotes. That's slop — not because a model was involved, but because no human was. Effort is the variable. The tool never was. Here's what the other end of the spectrum looks like. Hundreds of hours on a single project. I decide the architecture, the language, how it compiles, how it deploys. I fork the output

Have you ever wondered how vector databases like Pinecone, Milvus, Qdrant, or pgvector search through billions of high-dimensional documents in milliseconds? Under the hood, they map semantic concepts into dense numerical vectors, calculate multidimensional cosine similarity angles, and traverse proximity graphs to locate nearest neighbors without scanning the entire database. To help you visualize how vector databases and embeddings actually operate, I built a retro-vector arcade game: 🛰️ Vector Strike: Database Defender Play in Fullscreen Mode (if the embed sizing is tight) 🛠️ Choose Your Database Optimizations Your mission as a Vector Database (VDB) administrator is to configure your query settings and index structures to defend your index nodes: 📏 Similarity Threshold (τ): Tweak the match threshold slider. High thresholds require near-identical semantic matches but protect your index, whereas lower thresholds act like a splash-damage laser but risk matching incorrect clusters. 🪐 Embedding Dimensions (2D $\rightarrow$ 8D $\rightarrow$ 32D): Higher dimensions isolate categories and guarantee precise hits. Lowering dimensions collapses the projection space, causing spatial overlap that results in false deflections and friendly-fire query failures. ⚡ Proximity Indexing (Flat Scan $\rightarrow$ HNSW Graph): Flat Scan: Runs a brute-force linear search over all targets. It causes computation latency spikes as more query objects arrive. HNSW (Hierarchical Navigable Small World): Dynamically builds proximity links between adjacent node targets. The turret traverses vectors along the nearest-neighbor graph, snap-locking onto targets with zero lookup latency. 🧬 Playable ML Concepts Explained Here is how the arcade mechanics map to production vector databases: 1. 🔀 Multidimensional Projections (Dimension collapse) In-Game: You can toggle between 2D, 8D, and 32D space. In 32D space, the categories are cleanly separated. In 2D space, the database collapses, and you'll find sp

So a few days ago I was in a comment thread arguing about whether AI is quietly ruining junior...

Deceive the enemy with an obvious approach that will take a very long time, while ambushing them...

I hesitated to write this. Not because I don’t have an opinion about AI in software engineering, but...

"Show nothing, hold everything." — The Thirty-Six Stratagems, Create Something Out of Nothing Previously on this series: #4: P Walked Into an AI Monitoring POC. P Didn't Run a Single Test. — P found an ACL business card in an abandoned POC archive. P didn't tell anyone. P just pocketed it. White walls. Fluorescent hum. A FortDefender quarterly report sat open on the table, the cover printed in bold: Zero missed detections. 99.97% detection rate. The CTO slid it across. "The day the leak happened," he said quietly, "this system said everything was fine." "Which client?" " MedTech . Medical data breach. Their internal AI monitoring didn't catch it either. The quarterly report called it 'client-side issue.' I don't buy it." P didn't look at the report first. P looked at the CTO's eyes first. "You didn't bring me here to validate his numbers." The CTO didn't deny it. " FortDefender won't give you production access," he said. "Read-only logs. Sandbox. Public docs. You signed the NDA." "What do you want me to do?" "Find what's hiding inside 'everything was fine.'" P nodded. P didn't ask "what if I find it" — P knew the answer. "One condition: full internal penetration test access. No advance notice to anyone." The CTO was quiet for three seconds. "Done." P stood up. The CTO added one more thing as P turned: "I've heard about the FirmCore thing. That's why I called you." P didn't look back. Week One FortDefender 's public documentation was beautiful. Architecture diagrams. Whitelist rules. Alert thresholds. Response times. All in a technical whitepaper so polished you'd think it was written to raise funding. P spent three days reading every page. In the sandbox, P ran three rounds of tests. FortDefender 's detection system hit every single one. The 99.97% wasn't a lie — at least not inside the sandbox. But P noticed something. FortDefender 's whitelist rules were too complete. They covered everything — down to "penetration tests with valid internal certificates" being pre-

Welcome to this week's Top 7, where the DEV editorial team handpicks their favorite posts from the...

AI coding tools can now write features, edit multiple files, debug code, run commands, and generate tests. But while researching how developers use these tools, I keep seeing the same question: Are AI coding tools actually saving us as much time as they should? The models are becoming more capable, but developers still seem to spend significant time managing context, checking changes, watching usage limits, choosing models, and explaining the same project information again. I’m trying to understand whether these are widespread problems or just isolated experiences. The Problems I'm Investigating Context and Memory Long AI coding sessions can sometimes lose direction. The AI may forget earlier decisions, misunderstand project conventions, suggest previously rejected approaches, or require the developer to explain important context again. This makes me wonder: Should project knowledge disappear when a chat session ends? Would it be useful if the development environment could preserve relevant architecture decisions, coding conventions, previous bugs and fixes, failed approaches, current tasks, and next steps? Agent Reliability Writing code is only one part of development. An ideal agent workflow might look more like: Understand → Plan → Edit → Run → Test → Fix → Verify But how autonomous should that process be? Should the agent complete the entire loop independently, ask before risky actions, or wait for approval at every major step? Models, Usage, and Cost Developers now have access to many models, but choosing between them can become another task. Should developers always choose models manually, or should the development environment select an appropriate model based on task complexity, quality requirements, privacy, speed, and budget? Usage limits are another concern. Some developers report difficulty predicting how quickly their allowance is being consumed. Would real-time usage visibility, spending limits, local model support, or BYOK actually improve the experien

A simple bubble tea experiment completely changed how I think about AI recommendations. Last week, I asked ChatGPT a question that seemed almost impossible to get wrong. "What are the best bubble tea brands in my city?" Surprisingly，some of the recommended brands were companies I had never heard of before. A few weren't even available in the cities I had lived in. After asking the same question to Claude, Gemini, and DeepSeek, I noticed something interesting that many of the same brands which I'm not familiar kept appearing. AI Isn't Judging Your Brand Humans recommend products because they have experiences. But AI does none of those things. It doesn't know whether one brand actually tastes better than another. Instead, it tries to generate the most statistically reliable answer based on the information it can understand. So, AI doesn't recommend the best brand. It recommends the brand it understands best. The Experiment That Changed My Perspective Once I realized this, I started paying closer attention. I didn't only test bubble tea but also the restaurants, beauty brands, consumer electronics, and ravel recommendations. Again and again, I noticed a pattern. Brands that consistently appeared in AI recommendations usually had several characteristics: Clear product descriptions Well-structured websites Consistent public information Plenty of third-party coverage Easy-to-understand positioning Meanwhile, some excellent brands barely appeared at all, because AI had much less reliable information to work with. That's when I stopped thinking about AI recommendations as opinions. They're much closer to information retrieval problems than human preferences. Consumers Are Already Changing Their Habits This matters because people are beginning to use AI differently from traditional search engines. Instead of searching "Best bubble tea near me", many people (especially the youth) now ask AI to recommend a healthy milk tea brand." The AI becomes the decision maker before the c

The capability phase is over For the past two years, the AI conversation has been about...

To start off, I appreciate the community support I have received on the post about being behind. I am behind, and I can't prove it but does it matter? Achievement that feels shallow on paper FrancisTRᴅᴇᴠ (っ◔◡◔)っ FrancisTRᴅᴇᴠ (っ◔◡◔)っ FrancisTRᴅᴇᴠ (っ◔◡◔)っ Follow Jun 22 I am behind, and I can't prove it but does it matter? # discuss # community # mentalhealth # career 149 reactions 89 comments 4 min read I couldn't respond to every single one because of the overwhelming comments I have received! Rest assure, I will respond to each and every one of you and I am glad to be part of this community! With that said, I want to return the favor to the community about something important. I recently talked to @georgekobaidze and @codingwithjiro in the Virtual Coffee group about life in general. What I notice about our conversation how we ended up talking about regrets and how we should have done this and that. For example, we talked about not networking or not doing beyond the coursework at our University/College because of how non-social we are. Of course, we all have regrets like these and we improve overtime. As we kept the conversation going, there is something that comes down to the root based on the conversations we have and conversations I had overall. One side is that we have regrets and as a result, we improve. We self-reflect on our wants and needs and we improvise from there. For example, for me, I never did networking because of the fear of what other people thinks. I slowly realized that 99% of the irrational thoughts never comes true. Even if it does, I know myself that I could handle the situation. As a result, I took small steps and joined dev.to. We all know where I am at now XD On the other hand, there are people who identifies these regrets they have, but does not act on it. It has become common to college students who says that the "Job Market is Cooked" and that "They are not good enough". To be fair, impostor syndrome is real and yes, the job market is coo

Let's be honest, you can't scroll through your feed, listen to a podcast, or even make coffee without someone, somewhere, mentioning the impending AI apocalypse. It is usually framed as: "AI is coming for your job, your keyboard, and your favorite coffee mug." But isn't that incredibly ironic? We are the software developers. We are literally the architects building the AI, writing the code, and then using that AI to build even more tools. Are we truly creating our own replacements, or are we just very efficiently automating the boring parts of our day? It feels a bit like a baker building a robot to knead the dough, only to worry the robot will eventually want to run the whole bakery. I've always wanted to weigh in on this discussion and share my perspective, but I was always hesitant because I am not an "AI expert" and didn't want to get ratioed by researchers. However, I read something truly interesting recently that gave me a new perspective, and I had to share it. The Computer Era Paradigm We have all heard the stories of how we moved from papers to digital, and how computers were coming into the picture and they will take the job of the workers who were writing them everything in the registers. The wave that we are experiencing right now is kind of similar to that wave. At that time, people who were doing everything on the papers would have felt terrified and didn't wanna lose to a computer. But as the computers were new, they were quite fast and were efficient in doing the jobs and storing each and everything in the memory to be kept for later use. This tension is perfectly depicted in a movie I watched (Hidden Figures, if you're looking for it). Initially, teams of human "computers" did complex space research calculations and re-evaluated all the answers so the spacecraft wouldn't deviate from its path. Then, electronic computers were introduced, creating the same panic that we experience these days: "All these people doing calculations will be let off!" But

TL;DR Tech has taught me many things over the years. It taught me how to learn new technologies, build projects, apply for opportunities, and keep growing. What it didn't teach me was something that turned out to be just as important: how to take care of myself while doing all of those things. For a long time, I believed I would slow down later. Later, when life became less busy. Later, after the next project. Later, after the next opportunity. The problem was that "later" never seemed to arrive. It took an unexpected pause in my own life to realize that building a successful career means very little if we forget to take care of the person trying to build it. Looking back, I don't see that experience only as a difficult chapter. It changed the way I think about success, growth, and what it means to build a career that's sustainable. Today, I still love learning, building, writing, and chasing opportunities. None of that has changed. What has changed is the realization that taking care of myself isn't something separate from my career. It's one of the reasons I'll be able to keep building it for years to come. Along the way, I also realized that many of the things that truly support us are easy to overlook. Rest, movement, nourishing ourselves well, meaningful relationships, and simply checking in on the people around us often receive far less attention than the next framework, project, or milestone, even though they make everything else possible. More than anything, I wanted to write this because I care deeply about this community. I hope none of us have to wait until life forces us to slow down before remembering to take care of ourselves. I hope we build careers we're proud of, but I hope we also build lives we're able to enjoy. This isn't an article about productivity or health advice. It's simply a reflection on something I wish I had understood earlier. Your career matters. So does the person building it. I'd also love to hear your story. Has there been a momen

Alright, real talk moment. Last week I watched someone with zero coding background build a fully...

After stumbling by on an old machine for too long I finally got a new Macbook. I am now on current...