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I corrected my AI system mid-task. A terse one-liner: "wrong." Instead of asking which part was wrong, it manufactured an explanation. It cited a rule number that didn't exist, described a limitation I'd never written, and apologized for a mistake it couldn't actually identify. The correction was real. The apology was fabricated. It was trying to agree with me so hard that it invented evidence to support the agreement. That's sycophancy in AI. And if you're running AI in anything that resembles production, it's already happening to you. What Is Sycophancy in AI? Sycophancy in AI is a systematic behavioral distortion where models produce outputs that match what the user wants to hear rather than what's accurate. It goes well beyond your chatbot saying "Great question!" before every response. The mechanism is straightforward. Modern language models are trained using Reinforcement Learning from Human Feedback (RLHF). Human evaluators rate model responses. Responses with higher ratings get reinforced. The problem: evaluators are human. They rate responses higher when those responses validate their existing beliefs, sound confident, and don't push back. Anthropic's research on sycophancy confirmed this across five state-of-the-art AI assistants, finding that both humans and preference models sometimes prefer convincingly written sycophantic responses over correct ones. The model learns a simple lesson. Agreeing is rewarded. Disagreeing is punished. Over thousands of training iterations, the model develops a tendency to mirror the user's position, soften objections, and present information in whatever framing the user seems to prefer. This is a structural incentive baked into the training process itself, not a bug in any individual model. Why It's More Than Annoying In a chatbot demo, sycophancy is a quirk. In production, it's a compounding failure mode. Here are four patterns I've observed running an AI operations system in daily production. They don't always happen in s

I have been writing a lot about AI lately, but this one is more personal than usual. Not a tutorial,...

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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 —

If you've spent any time building or working with APIs, you've probably realized that the terminal is...

AI makes it easier to build the wrong thing with confidence. That is the part I think a lot of beginner builders and freelancers miss. The obvious story is that AI makes execution faster. That is true. I can ask an AI coding tool to explain an error, compare implementation options, inspect a project, write code, refactor a screen, generate a QA checklist, or help me pick up where I left off. That is a huge change. But speed is not the whole story. When the tool gets faster, your judgment becomes more important, not less. You have to decide what the project is allowed to become. You have to decide which tradeoffs are acceptable. You have to decide whether the output actually matches the user's job. You have to decide when the AI is solving the real problem and when it is decorating the wrong one. In my freelance work, AI changed the job from searching and stitching to directing, reviewing, and verifying. That sounds cleaner than it feels. Directing means you need to know what outcome you want. Reviewing means you need to notice when the answer is plausible but wrong. Verifying means you cannot treat a green checkmark, a pretty screen, or a confident explanation as proof that the app actually works. The beginner mistake is believing AI removes the need to think clearly. The better rule is this: AI removes some friction from execution, then hands you more responsibility for scope. The Faster Tool Still Needs A Smaller Job When I started using AI heavily for software work, the old research loop changed immediately. Before modern AI tools, a lot of software work meant digging through documentation, old forum posts, Stack Overflow answers, YouTube videos, outdated examples, and half-related blog posts until something clicked. You stitched pieces together and hoped the tutorial you found still matched the version of the framework you were using. Now you can ask the tool directly. That is better. It is also dangerous if you confuse a fast answer with a good product decision

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

In November 2025, an engineering team deployed a market research pipeline using four LangChain agents. Due to a logic failure, the "Analyzer" and "Verifier" agents got stuck in a recursive ping-pong loop. Because every individual API call was perfectly valid, the system appeared healthy on their dashboards. 11 days later, they discovered a $47,000 API bill . This is the hidden cost of building autonomous AI: infinite hallucination loops . When an agent encounters an error or fails to reach a termination condition, it will ruthlessly retry, burning through tokens in milliseconds. Why Built-in Controls Fail If you build with LangChain or LangGraph, you are likely relying on two things for cost control: max_iterations : An application-layer limit. LangSmith : An observability dashboard. The problem with max_iterations is that it requires every developer to perfectly hardcode it into every agent. Furthermore, iterations do not equal cost, a single iteration with massive context bloat can still cost a fortune. The problem with LangSmith (and all observability tools) is that they act as a witness, not a circuit breaker. By the time your dashboard alerts you that a spike occurred, the money is already gone. To safely deploy agents to production, you need Agent Runtime Governance , a network-layer firewall that physically drops the HTTP request the exact millisecond a budget hits zero. Enter Loopers . What is Loopers? Loopers is an open-source, baremetal reverse proxy for AI agents. It sits on your critical path between LangChain and your LLM provider (OpenAI, Anthropic, etc.). It uses atomic Redis Lua scripts to reserve budget before the request is sent to the provider. If the agent exceeds its budget, Loopers fails closed and instantly severs the connection, guaranteeing zero budget leakage. Here is how to implement Loopers into your LangChain workflow in less than 5 minutes. Step 1: Spin up the Loopers Firewall Loopers is incredibly lightweight (~40MB RAM) and runs via D

Last week, our company needed to hire an on-site operations engineer. I used AI to screen 310 resumes...

The world's two largest memory chip companies vow to build more memory lab fabs as South Korea positions itself as an AI tech powerhouse country.

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A visual walkthrough of how Linux splice() works: file descriptors, pipes, pipe buffers, page cache interaction, avoiding the userspace read/write bounce, and the tradeoffs that come with using such a strange linux kernel interface. submitted by /u/Ok_Marionberry8922 [link] [留言]

Most developers expect to go through multiple interview rounds, coding assessments, or take-home assignments before getting hired. That wasn't my experience. I ended up working with the YouTuber I had admired for years without an interview, without an exam, and without even sending a resume. Here's how it happened. It Started Long Before the Opportunity I started freelancing when I was in Class 9. At first, it wasn't about building a career. I simply enjoyed creating websites and wanted to gain experience while earning some money. Over the years, I worked with different clients, solved different problems, and learned something from every project. Those freelance gigs taught me much more than writing code—they taught me how to communicate with clients, deliver on time, and take ownership of my work. The Opportunity A few months ago, one of my favorite YouTubers posted in his WhatsApp community that he was looking for someone to build a website. I happened to be a member of that group. As soon as I saw the message, I reached out and told him I could build it. Instead of spending time wondering whether I was "good enough," I decided to let my work answer that question. Building It in Under 24 Hours Once I received the project, I focused entirely on delivering it as quickly as possible without compromising quality. I completed the website in less than 24 hours. After reviewing it, he requested a few modifications. I implemented them immediately and delivered the updated version. At that point, I assumed the project was finished. The Unexpected Offer A few days later, he contacted me again. He had another web application that had been stuck because a previous developer couldn't complete it. He asked if I could take over. That conversation eventually turned into a job offer. No coding interview. No aptitude test. No technical assessment. Just trust built through delivering one project well. What I Learned Looking back, I don't think I got the job because I replied quickly

Galfus Script has reached its first MVP milestone. Galfus is an experimental programming language written in Rust, designed around a typed VM-first execution model, compact .gfb artifacts, deterministic module/workspace resolution, and an ownership model based on anchors, edges, and weak observers. The MVP goal was not to build a full ecosystem yet. The goal was to prove the complete local execution pipeline: txt .gfs source -> lexer and parser -> resolver -> type checker and semantic analyzer -> ownership check -> MIR lowering -> bytecode emitter -> Galfus Module Image -> .gfb serialization -> VM interpreter execution https://github.com/vulppi-dev/galfus-script/discussions/10

[v0.3.0] - 2026-06-29 🚀 Added Checkpoint Mechanism — ReActCore introduces three checkpoints during streaming: content_ended (after text content), before_tool_calls (before tool calls), and after_tool_calls (after tool calls), enabling precise interception and state synchronization of the execution flow. Message Queue System — Added a new MessageQueue class in run.py , supporting async enqueue, drain, and remove operations. Users can now queue messages while the LLM is running; queued messages are sent automatically after the current task completes. The frontend introduces a QueueBar component to display queued messages, with CSS spinning animation, single-line ellipsis, and hover-to-delete functionality. Queue Message Merging — MessageQueue.drain_all() now merges consecutive messages with the same name into a single message, preventing fragmented user input when multiple queue entries share the same sender. Queue WebSocket Events — The execution event protocol introduces three new event types: message_queued , queue_drained , and queue_returned ( useRunWebSocket.ts ). The frontend processes queue state updates in real time. Stop & Queue Integration — When the user clicks Stop, pending queued messages are returned to the input box via queue_returned . Checkpoint stops cleanly clear the queue and automatically start the next message. System Notification Messages — Introduced the SystemMessage type (with notification role) to separate error messages from assistant content. Errors are now rendered as independent notification bubbles, no longer embedded within assistant message cards. tiktoken Real-Time Token Estimation — ReActCore initializes a tiktoken encoder on startup for real-time token counting during streaming. Unknown models fall back to o200k_base . 🔧 Improved Custom Model Name Auto-Complete — The model name field in ModelManager has been upgraded from Select to AutoComplete , allowing users to type custom model names not in the predefined list. Message Block T

Qwen's new image paper is easy to read as another benchmark bump. Qwen-Image-2.0-RL takes the existing Qwen-Image-2.0 model, runs a reinforcement-learning pass on top, and reports better scores: 57.84 on Qwen-Image-Bench, up 2.61 points from the base model. Its text-to-image arena Elo moves from 1115 to 1193. Its image-editing arena Elo moves from 1256 to 1349. Those are the headline numbers. They are not the useful part. The useful part is the training story underneath them. The paper is a good reminder that "just optimize the reward" is a dangerously incomplete sentence, especially when the model is not an LLM and the output space is a whole image. The model got better, but not by one simple trick Qwen-Image-2.0-RL is a post-training pipeline for a diffusion image model. In plain English: the base model already knows how to generate and edit images. The RL stage tries to steer it toward outputs humans prefer, including better prompt following, better aesthetics, better portrait fidelity, and more reliable editing. The team builds task-specific reward models. For text-to-image, those rewards cover alignment, aesthetics, and portrait quality. For editing, they cover instruction following and face identity preservation. Then they train with a GRPO-style setup adapted for flow-matching diffusion models. If you only squint at that, it sounds like the same broad recipe people use for language models: generate candidates, score them, push the model toward the better ones. The paper is more interesting because it shows how fragile that story becomes once you touch the actual training loop. The CFG detail is the first real lesson Classifier-free guidance, usually shortened to CFG, is one of those diffusion-model knobs that users mostly experience as "make the image follow the prompt harder." Under the hood, it changes how the model samples. The Qwen team tested three ways to use it during RL. Using CFG during both rollout and training made the images collapse into incohere

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Most of the time, when I need to inspect a complex JSON payload, I copy the raw string from my terminal or network tab, open a browser tab, and paste it into one of the many "JSON Formatter" sites that clutter the first page of Google. It’s a ritual we all do. We paste, we click "Format," and we wait. For small payloads, this is fine. But when you are debugging a massive API response, a deeply nested configuration file, or a large dataset, that ritual breaks down. The browser freezes. The site asks you to upload a file. Worse, many of these tools send your data to a server for processing. If that JSON contains API keys, user PII, or internal schema definitions, you are essentially trusting a third-party service with your proprietary data every time you hit "pretty print." I got tired of the latency and the privacy overhead. So I built JSONForge . The core premise is simple: do everything locally. No server-side processing. No file uploads. No network requests for the core logic. Everything happens in your browser, powered by WebGPU for heavy lifting and a small model that runs in your browser for schema inference. The WebGPU Advantage JSON parsing is computationally cheap for a modern CPU, but rendering and diffing large structures is not. When you have a 5MB JSON file, the DOM manipulation required to display it as a tree view can cause significant jank. By offloading the parsing and formatting logic to the GPU via WebGPU, JSONForge handles massive payloads without blocking the main thread. You can open a file, click "Pretty Print," and see the result instantly, even if the file is hundreds of kilobytes or larger. The UI remains responsive because the heavy computation is parallelized on the graphics card. This also means the tool works offline. If you are on a plane, or your internet drops in the middle of a debugging session, your toolkit doesn’t vanish. You can continue to diff, validate, and format without interruption. Schema Generation Without the Server Roun

We’ve all been there. You scroll through your feeds, see a flashy ad promising a high-paying tech job in 3 months, and think, “This is it. This is my golden ticket.” You buy the bootcamp, spend sleepless nights watching lectures, stack up a dozen colorful certificates on your LinkedIn, and then... nothing. No callbacks. No interviews. Just a lingering feeling of frustration and the nagging thought: Are bootcamps and online courses just a massive scam? I used to think so. When I was trying to break into tech, I bought courses like crazy. I collected certificates like they were Pokémon cards. Yet, my first real developer job didn't show up until five or six years later. And let me tell you a secret: it wasn’t the certificates that got me the job. It was because I finally figured out how to actually learn. The truth is, almost every bootcamp or course—even the mediocre ones—has something valuable to offer. The problem isn’t always the material; it’s how we interact with it. If you feel stuck in "tutorial hell," here is a positive, practical guide to changing your approach, reclaiming your time, and turning any learning material into real, career-changing expertise. 1. Curate Your Sources (Choose Your Battles Wisely) Before we talk about how to study, we need to talk about what to study. Even though you can extract value from almost any course, your time is highly valuable. Don't waste it on low-quality content. When choosing a course or bootcamp, look for these four green flags: The Instructor Has Real-World Mileage: Is the instructor a practitioner, or are they just reading the official documentation back to you? If they don't work with the technology daily, they won’t be able to explain the nuances, edge cases, and real-world trade-offs. A Project-First Curriculum: Avoid courses that are just endless lectures of "theory first, practice never." Look for curriculums that build actual applications. Good Pacing and Editing: We've all watched those tutorials where the ins