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Exhaust the enemy's strength without fighting. Weaken the strong by nurturing the soft. — The 36 Stratagems, " Wait at Leisure While the Enemy Labors " P flipped the business card over and wrote one letter on the back: P . Then P walked into the conference room. P didn't do opening lines. P doesn't have a name — not yet, not in this series anyway. But if you've read the earlier stories, you'd recognize the signature. The first story — P's own article got flagged as "low quality" by the company's AI moderation system. P dug into the internal API, pulled 347 flagged records — effective accuracy came out to 38%. More false positives than correct identifications. The second story — an AI payment gateway processing $2.8 billion. The CTO backed it with formal verification, claimed it was "mathematically bulletproof." P spent eight months quietly building an adversarial testing pipeline, and proved the gateway would approve illegal transactions. P won both times. P left zero fingerprints both times. After those two jobs, P stopped working for other people. This time, P got brought in as an independent evaluator. Two Companies, One Customer, Zero Questions The customer was a mid-sized industrial IoT firm called FirmCore . Their production-line gear had been running for almost a decade. The monitoring system was going down once a month, and management had finally had enough. They decided to bring in an AI monitoring platform. A good call — right up until they decided to run two vendors through POC at the same time and pick a winner. "We want to see who can actually cover our failure modes," the VP said in the meeting. "We've also brought in an independent evaluator." P was that evaluator. The two AI monitoring companies were MonitorAI and SentryWave . MonitorAI's pre-sales team went first, slides blazing with "99.3% fault coverage, validated across 3 manufacturing customers." SentryWave followed right behind: "99.7% coverage, 7-day deployment" — bigger numbers, bolder font.

I'm curious... What's the one feature that instantly makes you stop using a resume builder? For me, it was simple: You spend time creating your resume, everything looks great, and then the site asks you to pay just to download it. That experience inspired me to build Resumship, a resume builder where downloading your resume is completely free. Now I'm thinking about the next features to add, and I'd love to hear from the community. If you were building the ideal resume builder, what features would you include? AI-powered resume suggestions? Better ATS optimization? More templates? Portfolio integration? Cover letter generation? Something completely different? If you have a minute, I'd also love for you to try Resumship and share your honest feedback. 🌐 https://resumship.com Your feedback will directly influence what gets built next. Every suggestion, bug report, or feature request helps make the platform better for everyone. Looking forward to hearing your ideas! 🚀

An agent opened a pull request on our service last week. Six hundred lines. It rewrote how we handle webhook retries and deduplication, an area that is fiddly and easy to get subtly wrong. The diff was clean. The tests were green. The commit messages were better than mine usually are. And I felt the specific dread that I think a lot of engineers are starting to feel in 2026. I was the reviewer. I had not written any of this. I had no idea why it was shaped the way it was. To review it properly, the way I would want my own code reviewed, I was looking at the better part of an hour of carefully reconstructing intent from the code itself. I did not have that hour. So I did what almost everyone does in that situation, which is skim it, decide it looked reasonable, and approve. That moment is the actual problem with AI-written code, and it is not the one people argue about. The bottleneck moved, and most teams have not adjusted The tired debate is whether agents write good code. In 2026 that argument is mostly over. They do. They plan, they read the codebase, they run the tests, they back out of dead ends, they open pull requests that clear most review bars. If you are still litigating whether the code is any good, you have not used a current agent in a while. But here is what follows from that, and it is the part teams have not absorbed: if writing the code is no longer the slow step, then reviewing it is. And review does not scale the way generation does. An agent can produce five well-tested pull requests before lunch. Your senior engineers cannot deeply review five pull requests before lunch, not on top of their own work. The volume went up and the review capacity did not, and something has to give. What gives is the depth of review. It degrades, quietly, into a skim. People approve fluent diffs they have not truly read, because reading them properly costs more time than anyone has. The green check still appears. It just means less than it used to. That is a governan

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AI เขียนโค้ดแทนเราได้แล้ว — แล้วเราจะเหลืออะไรให้ทำ? มีประโยคที่ได้ยินบ่อยขึ้นทุกวัน: "เดี๋ยวนี้ใครยังไม่ใช้ AI ช่วยเขียนโค้ดบ้าง?" คำตอบคือ — แทบไม่มีแล้วครับ ตั้งแต่ GitHub Copilot, Cursor, Claude, ChatGPT ไปจนถึง agent ที่เขียนโค้ดเองได้ทั้ง project — เราใช้ AI ใน level ที่ต่างกัน: Level หน้าตา ตัวอย่าง 🎵 Vibe Coding พิมพ์สิ่งที่อยากได้ กด accept อย่างเดียว "เขียนหน้า login ให้หน่อย" → กด tab tab tab 🧩 Prompt-Guided คิดก่อน ถามทีละส่วน ตรวจทุกอย่าง "สร้าง UserService ที่ใช้ bcrypt hash password" 🛠️ Skill/Lint-Guided ใช้ AI เป็น editor ชั้นสูง — lint, refactor, test "refactor function นี้ให้เป็น table-driven test" 🏗️ Agent-Based ให้ AI run ทั้ง project — spawn subagent, PR, deploy "พอร์ต microservice นี้จาก Express ไป Fastify" แล้วคำถามคือ — ถ้า AI ทำทั้งหมดนี้ได้ แล้วมนุษย์อย่างเราเหลืออะไร? Unit Test — ตัวอย่างที่เห็นชัดที่สุด ลองดู unit test ที่ AI เขียนให้: // 🤖 AI-generated test func TestCalculateDiscount ( t * testing . T ) { tests := [] struct { name string input float64 expected float64 }{ { "zero" , 0 , 0 }, { "normal" , 100 , 90 }, // 10% discount { "max" , 1000 , 800 }, // 20% discount } for _ , tt := range tests { t . Run ( tt . name , func ( t * testing . T ) { result := CalculateDiscount ( tt . input ) if result != tt . expected { t . Errorf ( "got %v, want %v" , result , tt . expected ) } }) } } ดูเผิน ๆ — สวย, table-driven, ถูกต้องตาม Go convention 1 แต่ถามหน่อย — test นี้บอกอะไรเกี่ยวกับ business? "ส่วนลด 10% สำหรับยอด 100 บาท" — ทำไมต้อง 100? เป็นกฎจากที่ไหน? "ส่วนลด 20% เมื่อยอดถึง 1000" — แล้วถ้าลูกค้าเป็น member ได้เพิ่มอีก 5% ล่ะ? input: 0, expected: 0 — test นี้ cover edge case หรือแค่ cover บรรทัด? AI test ได้ถูกต้องตาม function — แต่มัน ไม่รู้ว่า business จริง ๆ คืออะไร AI ไม่รู้ Business Context — และจะไม่มีวันรู้ นึกภาพระบบ e-commerce: ลูกค้าซื้อสินค้า → ระบบตัดสต็อก → คำนวณส่วนลด → คิดค่าส่ง → ออกใบเสร็จ AI แยก test ทีละ function ได้: ✅ TestDeductStock — "ตัดสต็อก 1 ชิ้น" ✅ TestCalculateDiscount — "ส่วนลด 10%" ✅ TestCalculateShipping —

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Token burn and business outcomes are not correlated. More burn means more inefficiency, not more value. The electricity problem Imagine you walk into a dark room. Turning on a light helps you see. Turning on every light in the building does not help you see better. It's still the same room. Now every surface is equally lit, the contrast is gone, and you're paying for power you didn't use. Tokens work the same way. A focused prompt with clear scope is the single overhead light over your desk. A sprawling prompt with unlimited exploration is every light in the building — you're burning power, not producing insight. Tokens are electricity, not output. More throughput doesn't mean more value. I've had weeks where I burned through my allocation and looked back at the end to find nothing concrete. Code that worked but went unused. Exploratory branches that dead-ended. Agents that generated plausible-looking output that didn't survive first review. A lot of motion. Not much progress. The ceiling stops you from doing that indefinitely. It forces a moment of reflection: did this burn produce anything real? If the answer is no, more capacity isn't the fix. More discipline is. Three patterns I now use instead I started paying attention to what actually ships versus what just burns context. I gave the patterns names so I could catch myself faster: RTK — Read The Knowledgebase. A focused 15-minute read of the codebase, identifying the exact files and exact changes, saves 200K+ tokens of exploratory waste. The agent doesn't discover the shape of the task — it executes against a known one. Caveman — compress before you prompt. Strip greetings, filler words ("I think", "basically", "Let me know if that makes sense"), and closing courtesies. Every word in your prompt multiplies across every response token. Less fluff in means less fluff out. Ponytail — spec the minimum viable solution. "Robust", "scalable", "enterprise-grade", "comprehensive" — these words invite scope creep. Specif

To dispose of an enemy, make use of another enemy. Use a second party to deliver the blow yourself....

Meme Monday! Today's cover image comes from the last thread . DEV is an inclusive space! Humor in poor taste will be downvoted by mods.

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When the enemy is too strong to attack directly, attack what they hold dear. They will come to you...

In the world of wearable health technology, the holy grail has always been moving intelligence from the cloud to the edge. Waiting for a cloud server to analyze your heart rhythm is not just a latency issue—it's a privacy and battery life concern. Today, we are diving deep into TinyML , Edge AI , and ECG signal processing to build a real-time abnormality detector. By leveraging TensorFlow Lite for Microcontrollers and the versatile ESP32 , we can process raw electrocardiogram (ECG) data locally. This approach ensures low-latency detection of arrhythmias while keeping sensitive medical data on-device. If you've been looking to bridge the gap between high-level deep learning and low-level embedded systems, you're in the right place! The Architecture: From Raw Signal to Insight 🏗️ The pipeline involves capturing a high-frequency analog signal, cleaning it, and feeding it into a quantized Convolutional Neural Network (CNN). Here is how the data flows through our ESP32: graph TD A[Raw ECG Signal/Sensor] -->|ADC Sampling| B(Preprocessing: Bandpass Filter) B --> C{Buffer Management} C -->|Windowed Segment| D[TFLite Micro Inference Engine] D --> E{CNN Model Classification} E -->|Normal| F[Log: Sinus Rhythm] E -->|Abnormal| G[Trigger Alert: Arrhythmia] G -->|Bluetooth/Wi-Fi| H[Mobile Dashboard] Prerequisites 🛠️ To follow this advanced guide, you'll need: Hardware : ESP32 (DevKit V1 or similar). Sensor : AD8232 ECG Module (or simulated ECG data). Software : Arduino IDE or PlatformIO. Frameworks : TensorFlow Lite for Microcontrollers (TFLM), EloquentTinyML (optional wrapper), or the standard C++ TFLM library. Step 1: Model Training & Quantization 🧠 Before we touch the C++ code, we need a model. Typically, we use the MIT-BIH Arrhythmia Database to train a 1D-CNN. The crucial step is Post-Training Quantization . Since the ESP32 doesn't have a dedicated NPU, we convert our 32-bit float model into an 8-bit integer (INT8) model. This reduces the size by 4x and speeds up inference s

you've felt it. you type a prompt, hit send, and the response starts streaming in under a second....

This is part of my build-in-public series where I document everything honestly — the problems I face,the observations I make,and what I'm trying to build. There's a moment that happens in almost every class. The teacher finishes explaining something. Looks around the room. And asks: Everyone clear? And the entire class says "yes." Including me. Even when I understood absolutely nothing. The Loop Nobody Talks About Here's what actually happens — at least for me and I suspect for a lot of you reading this: Teacher is explaining a concept. I'm trying to follow. Somewhere in the middle, I lose the thread. Maybe the explanation was too fast. Maybe the concept needed something I didn't know yet. Maybe I just zoned out for 10 seconds and missed the part that made everything else make sense. Now I have two choices: Option A: Raise my hand. Ask the question. Risk looking like I wasn't paying attention or worse ask something that makes me look stupid in front of everyone. Option B: Stay quiet. Nod. Say "yes" when the teacher asks. And hope it makes sense later. I always pick Option B. And then "later" comes — sitting alone at home, textbook open, trying to study for a test and I have no idea where to even begin. The concept is still missing. The gap is still there. But now there's no teacher, no classroom, no one to ask. So I either text a friend (who's also confused), scroll YouTube for 40 minutes looking for the right explanation, or just… close the book and tell myself I'll figure it out tomorrow. I never figure it out tomorrow. This Isn't Just a "Me" Problem I'm an engineering student in Pakistan. Maths, Physics, Chemistry — subjects where one missing concept breaks everything that comes after it. And I genuinely believe most of my classmates feel exactly the same way. We just don't say it out loud. Because saying "I don't understand" in a classroom full of people takes a kind of courage that most of us don't have. So we all nod together. And we all go home confused toget