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The speakers discuss Agent RFT, OpenAI’s platform for fine-tuning reasoning models via real-time tool interactions and custom reward signals. They explain how reinforcement learning solves complex credit assignment challenges within the context window. They share enterprise success stories, showing how Agent RFT eliminates long-tail token loops and drives extreme efficiency. By Wenjie Zi, Will Hang

When I published the first article about the Neuron AI Router , I expected questions about routing rules. Which rule to use for structured output, how to write a custom one, how the round robin behaves under load. Some of those questions arrived, but the most frequent one was different, and it wasn't really about routing at all. It was about failure. What happens to my agent when the provider goes down? It is a fair question, and if you are new to building AI applications it deserves a proper answer before we look at any code. Here is the short version. The new fallback strategy in Neuron AI Router lets you define an ordered list of LLM providers for your PHP agent. When an inference call fails with a transient error, such as a rate limit, a timeout, or an overloaded server, the same request is automatically retried on the next provider in the list. The failover is transparent: the agent never knows it happened, and the conversation continues without losing state. The rest of this article explains why this problem exists, why the usual solutions fall short, and how to configure it. Why LLM providers fail in production An LLM provider is an external service you talk to over HTTP. Every time your agent thinks, it is making a network call to a machine you don’t control, operated by a company that is currently serving millions of other requests. These services fail in very ordinary ways. You hit a rate limit because your traffic spiked. The provider returns an “overloaded” error because their traffic spiked. A request times out. A deployment on their side causes a few minutes of elevated error rates. None of this means you did something wrong, and none of it is rare. If you keep an agent in production long enough, you will see all of these. In a classic web application, a failed call to a third party API is usually a corner of the system. You log it, maybe retry it in a queue, and the rest of the page still works. In an agent based application the inference call is not

There is a ghost haunting the tech industry right now, and nobody wants to talk about it: The Junior Developer role is disappearing. With tools like GitHub Copilot, ChatGPT, and advanced coding agents becoming standard issue in every IDE, senior developers are suddenly 10x more productive. They no longer need a junior developer to write boilerplate code, write unit tests, or scaffold out basic UI components. The AI does it instantly. So, if you are a junior developer, or aspiring to break into tech, how do you survive? 1. Stop Memorizing Syntax, Start Thinking Architecturally AI is incredible at writing syntax, but it is terrible at system design. If your only skill is writing a for loop in React, you are competing with an AI that works for $20/month. Instead, focus on understanding how systems fit together. Learn about cloud architecture, database indexing, and distributed systems. The AI can write the function, but you have to know where that function lives and how it scales. 2. Become a "Domain Expert" Developer AI doesn't understand the nuanced business logic of the healthcare industry, or the strict compliance regulations of fintech. If you combine coding skills with deep industry knowledge, you become irreplaceable. 3. Embrace the Tools (Be the Orchestrator) Don't fight the AI. Master it. The developers who thrive in the next decade will be the ones who treat AI agents like a team of junior developers reporting to them. Learn how to craft the perfect prompts, how to use Retrieval-Augmented Generation (RAG), and how to orchestrate multiple LLMs to build complex applications. The barrier to entry for writing code has dropped to zero. But the barrier to entry for building valuable software remains exactly the same. Are you terrified of AI replacing you, or are you using it to level up?

The Shop on the Corner: How I Learned System Design Without Building Amazon Nephew asks his uncle — 10 years deep into building large-scale systems — to explain "system design," and all the scary jargon that comes with it. Uncle refuses to start with the jargon. Instead: "Forget servers and databases for a minute. Just imagine you're running a small shop." What follows is a thought experiment, built one problem at a time — no cloud bills, no fancy stack, just a counter, a storeroom, and a lot of common sense. Part 1: The Counter — Your First "API" 👦 Nephew: Uncle, everyone at work keeps throwing around terms — load balancer, cache, index, sharding. I nod along, but I don't actually get any of it. 👨‍🦳 Uncle: Then don't start there. Close your eyes for a second and forget servers exist. Suppose — just suppose — you're running a small shop. One counter, one small storeroom at the back. A customer walks up and asks for something. What do you do? 👦 Nephew: I'd walk into the storeroom, find it, walk back, hand it over. 👨‍🦳 Uncle: That's it. That's the entire job of a server handling a request. You don't need to understand Amazon's warehouse to understand Amazon's problems. You need a counter and a storeroom, imagined clearly, and the patience to grow them one honest problem at a time. Here's the shape of what you just described, whether you realized it or not: 🧍 Customer 🧑 You (Counter) 📦 Storeroom (Database) | | | |── "Got Maggi?" ───────>| | | |──── Walk in, search ────────>| | |<─────── Found it ────────────| |<── Hand it over, ──────| | | take payment | | 👨‍🦳 Uncle: One customer, one request, one trip to the storeroom, one response. This is fine. This is correct , even, for a shop with five customers a day. Don't let anyone tell you a single counter isn't "scalable" — a shop that small doesn't need two counters, it needs someone to stop worrying and open the shutter. 👦 Nephew: So this is just... a server handling one request at a time? 👨‍🦳 Uncle: Exactly. Customer sen

One of the most dramatic stories in the cyberspace I ever heard was the unfolding story of EternalBlue, a well known cyber weapon developed by the NSA that surfaced the internet back in 2016. In this blog, I'm going to narrate the story of how it all went down: The backstory, what EternalBlue is, and what damage it did. All this information is freely accessible on the internet. Talk about 'sit down and have some tea!' The NSA's Secret ToolBox So, I know you've heard of the NSA - National Security Agency; an intelligence agency in the US. NSA is responsible for monitoring and processing data for intelligence purposes, and protecting US communication networks and information systems. In 2013, the world got a massive leakage on the internet of the NSA ANT Catalogue. This leak gave us an inside scoop of what the NSA group really does (I'm not really sure who leaked this). So, what was the NSA ANT Catalogue? ANT stands for Advance Network Technology, and the NSA ANT Catalogue was a compiled list of hacks, exploits, and cyber tools that the NSA used. The items on the catalogue were used by the people working within NSA on specific missions. If you needed an exploit described on the catalogue, make a request and get the software. Now, this list is mind-blowing, I mean... the tools on here shouldn't even exist in my opinion, because if they were ever used on me, I'd feel completely exposed and violated! Some of the tools include: GOPHERSET — A hidden program on a phone's SIM card that secretly copies contacts, text messages, and call logs and sends them to the spies. DROPOUTJEEP — Spy software for iPhones that lets an agency remotely read texts, listen to voicemail, track location, and turn on the camera or microphone. COTTONMOUTH — A USB plug or cable that looks completely normal but secretly contains a hidden radio, letting spies get into a computer or network without needing internet access. MONKEYCALENDAR — A hidden program on a phone's SIM card that secretly texts the

The product drop had been planned for months. The direct-to-consumer subscription business had run three separate load tests, provisioned extra capacity for the launch window, and staffed a warroom across two time zones. The drop itself went cleanly. Two hours in, an unrelated video from a creator with a large following mentioned the product without warning, and the sign-up flow collapsed under a rush of new members for twenty-eight minutes. Customers were told the site was busy and to try again later. Some did. Most did not. The refund exposure was manageable. The customer acquisition exposure was not. What went wrong is not the interesting question. The system was under-provisioned for a specific traffic shape it had not seen before, and the team fixed it. The interesting question is what happened seven weeks later. A weather event redirected a wave of app traffic in an entirely different sector, at midnight on a Tuesday, without any warning. That system held, because a small group of engineers had spent those seven weeks quietly rebuilding assumptions about when peak load happens and what it looks like. The lesson from the product drop was not "provision more capacity for product drops." The lesson was that the mental model of peak load as a scheduled event had stopped being useful. This is another post in our series on the engineering layer underneath enterprise strategy. The previous post ( Sovereignty Versus Efficiency ) argued that sovereignty has become an architectural property that procurement cannot solve on its own. This post makes an analogous argument about load. Across banking, media, retail, travel, restaurant chains, and sport, the architectures built to survive named events are increasingly the wrong architectures for the traffic these businesses now routinely encounter. The discipline required has moved closer to what telecommunications engineers have always done, while the cost models have not caught up. What peak load used to mean For most of th

​ For open-weight LLMs, how practical is it to study defenses against post-release fine-tuning that weakens refusal or safety behavior? I've been seeing “uncensored” or “heretic” variants of new models appear very quickly after release, which raises a question I’m curious about: is fine-tuning resistance a meaningful safety goal for open-weight releases, or is it too narrow because determined users can always modify weights, switch models, or use other workarounds? And to a larger extent, is current safety training even worth the cost and effort if it takes 30 minutes and an automated script to break the model? I’m not asking about a specific method, just the threat model. What would count as a useful practical win here? For example, would increasing attacker cost or making safety removal less reliable be valuable, even if perfect prevention is impossible? Curious how people think about this from a model release, governance, and AI safety perspective. submitted by /u/Aaron_Rock [link] [留言]

As the parent of two little girls, I often think about how their childhood is different from mine. The seven-year-old is learning about AI at school. The five-year-old is given internet-based homework every week. And they are both absolutely repulsed by the idea of smoking. That was not the prevailing sentiment when I was young.…

Tesla has started taking orders for the Model Y Long Wheelbase in the US and Puerto Rico.

TL;DR Welcome back to Dev Opportunity Radar. This is a weekly series where I share opportunities,...

Introduction: The Need for Modern Web/AppSec Training The cybersecurity landscape is evolving at a breakneck pace, but the tools we use to train the next generation of defenders are stuck in the past. Most web/appsec learning platforms still focus on basic, textbook vulnerabilities —XSS popups, simple SQL injection, or trivial IDORs. These labs are like teaching someone to swim in a kiddie pool; they might grasp the concept, but they’re ill-prepared for the open ocean of modern web applications . The gap isn’t just in depth—it’s in relevance . Real-world apps today are complex, API-driven, and riddled with subtle, pattern-based vulnerabilities that don’t fit into neat, isolated lessons. Consider this: a developer misconfigures a GraphQL endpoint, exposing an entire database. Or an API leaks sensitive data because of a flawed rate-limiting mechanism. These aren’t edge cases—they’re common mistakes in modern apps. Yet, most training platforms ignore them, leaving learners to either stumble upon these issues in the wild or remain oblivious. The result? A workforce of security professionals who can theoretically exploit a vulnerability but struggle to identify or fix it in a real-world context . The problem isn’t just outdated content—it’s the lack of ethical, hands-on practice environments . Many aspiring security professionals resort to illegal or gray-area practices to gain experience, risking legal consequences and ethical dilemmas. What’s needed is a platform that simulates real-world scenarios without crossing ethical boundaries, one that teaches not just how to exploit but also why vulnerabilities occur and how to fix them . Here’s the core issue: modern apps are systems, not isolated components . A vulnerability in one part—say, a file upload feature—can cascade into a full account takeover if combined with a session management flaw. Most labs fail to teach this interconnectedness , leaving learners with a fragmented understanding. A practical platform must brid

Originally published on bckinfo.com How to Install VMware ESXi: Step-by-Step Bare-Metal Setup Guide Table of Contents ESXi vs. VMware Workstation: Which One Do You Need Hardware Compatibility Check Downloading the ESXi Installer Creating a Bootable USB Installer BIOS/UEFI Preparation Installing ESXi: Step by Step Configuring the Management Network Accessing the vSphere Host Client Creating Your First Virtual Machine Post-Installation Checklist Common Issues and Quick Fixes Closing Notes If you've read our complete guide to VMware virtualization , you already know ESXi is the bare-metal hypervisor underneath vSphere. This guide is the hands-on counterpart — installing ESXi directly on physical server hardware, from hardware compatibility checks through booting your first virtual machine. ESXi vs. VMware Workstation: Which One Do You Need Before starting, it's worth confirming you actually want ESXi and not VMware Workstation. They solve different problems: VMware Workstation is a Type-2 hypervisor — it installs on top of an existing OS (Windows, Linux, macOS via Fusion). Good for running a VM or two on a laptop or desktop you also use for everything else. If that's your case, our guide on installing VMware Workstation on CentOS Stream 10 is the right starting point instead. ESXi is a Type-1, bare-metal hypervisor — it installs directly on the hardware with no host OS underneath it. This is the right choice for a dedicated server running multiple VMs, a home lab, or anything that needs to scale beyond "a VM running alongside my desktop." The rest of this guide assumes you're installing on dedicated hardware that won't run anything else. Hardware Compatibility Check This is the step most worth not skipping. ESXi has a defined Hardware Compatibility List (HCL), and installing on unlisted hardware is the single biggest source of installation failures and post-install driver issues. Check your exact server model and component list (NIC, storage controller) against VMware'

GitHub Actions shows you one run at a time. Green check, red X, green check, green check, red X. You scroll the list, you re-run the flaky one, you move on. Nobody's asking the question that actually matters: is this getting better or worse? "I calculated how much my CI failures actually cost. Curious what your pipeline success rate looks like — has anyone else tracked the actual wasted compute time over time?" That's a real question from someone who did the math by hand and found their failures were burning a real chunk of their compute budget. The replies were the same story you'd expect: heavyweight CI platforms have their own dashboards for this, but nobody had a lightweight, local way to just... track it. So I built citrend : pull your GitHub Actions run history into a local file, get a trend. npx citrend sync --repo owner/name npx citrend report --repo owner/name What it actually shows you $ citrend report --repo acme/widgets acme/widgets — 812 run(s) (2 in progress) success rate: 87.4% (699/800 settled, 12 skipped) wasted runs: 101 (12.6%) total compute: 118h 42m wasted compute: 14h 6m weekly trend (oldest → newest): 2026-06-05 91.2% success, 8 wasted (58m) 2026-06-12 88.0% success, 11 wasted (1h 22m) 2026-06-19 79.4% success, 22 wasted (3h 8m) 2026-06-26 84.1% success, 15 wasted (2h 1m) That weekly column is the entire point. A single gh run list will never show you that week 3 was a cliff — you'd have to notice it got annoying to work in, which is a much slower and much less precise signal than a number going from 91% to 79%. How it works sync pulls your workflow run history from the GitHub REST API and caches it locally (deduped by run id, so you can run it on a schedule without piling up duplicates). report reads that cache — no network call — and computes: Success rate , over settled runs only (still-running runs don't count either way until they conclude, and skipped runs are excluded from the denominator since they're not a pass/fail outcome). "Wasted"

Google has released A2UI v0.9, a framework-agnostic standard for AI agents to declare user interface intent across multiple platforms without arbitrary code. The update emphasizes alignment with existing design systems. It includes a new SDK for Python, improved error handling, and various transport methods. Migration guidance and evolution specifications are also provided. By Daniel Curtis

leetcode.com Problem Statement Given the root of a binary tree, return its preorder traversal. Preorder Traversal follows: Root ↓ Left ↓ Right Brute Force Intuition In an interview, you can explain it like this: Visit the current node first, then recursively traverse the left subtree followed by the right subtree. Recursion naturally follows the preorder sequence. Complexity Time Complexity: O(N) Space Complexity: O(H) Where: N = Number of Nodes H = Height of Tree Recursive Code class Solution { public List < Integer > preorderTraversal ( TreeNode root ) { List < Integer > ans = new ArrayList <>(); preorder ( root , ans ); return ans ; } private void preorder ( TreeNode root , List < Integer > ans ) { if ( root == null ) return ; ans . add ( root . val ); preorder ( root . left , ans ); preorder ( root . right , ans ); } } Moving Towards the Optimal Iterative Approach Instead of recursion, we can use a stack. Since preorder visits: Root ↓ Left ↓ Right we should process the root immediately. To ensure the left subtree is processed first, push the right child before the left child . Pattern Recognition Whenever you see: Preorder Traversal Simulate Recursion Think: Stack Key Observation Stack follows: LIFO To visit: Left First push: Right First ↓ Left Second so that left is popped first. Optimal Java Solution class Solution { public List < Integer > preorderTraversal ( TreeNode root ) { List < Integer > ans = new ArrayList <>(); if ( root == null ) return ans ; Stack < TreeNode > st = new Stack <>(); st . push ( root ); while (! st . isEmpty ()) { TreeNode node = st . pop (); ans . add ( node . val ); if ( node . right != null ) st . push ( node . right ); if ( node . left != null ) st . push ( node . left ); } return ans ; } } Dry Run 1 / \ 2 3 / \ 4 5 Stack: 1 Visit: 1 Push: 3 2 Visit: 2 Push: 5 4 Traversal: 1 ↓ 2 ↓ 4 ↓ 5 ↓ 3 Answer: [1,2,4,5,3] Why Stack Works? A stack processes the most recently added node first. By pushing: Right Child ↓ Left Child the left child

Over the past year, AI has become part of many developers' daily workflow. It can generate code, explain unfamiliar frameworks, review pull requests, and even suggest architectural patterns. But I've noticed that the biggest impact isn't on writing code faster. It's on how we think about software architecture. With AI handling repetitive implementation tasks, it feels like architects and senior engineers are spending more time on system design, scalability, security, integrations, and long-term maintainability rather than syntax and boilerplate. At the same time, AI-generated code isn't always production-ready. It still requires strong engineering judgment, careful reviews, and a solid understanding of the underlying architecture. I'm curious how other developers are experiencing this shift. Has AI changed the way you design software systems? Do you trust AI when making architectural decisions? Which parts of software architecture do you think should always remain human-led? Have AI tools improved your team's productivity, or introduced new challenges? I'd love to hear real-world experiences, lessons learned, and different perspectives from the community.

สวัสดีครับเพื่อนๆ! 👋 วันนี้จะมาเล่าเรื่องน่าตื่นเต้นให้ฟังนะเพื่อนๆ สำหรับใครที่เป็นสาย SvelteKit เตรียมตัวอัปเดตความรู้ใหม่กันได้เลย เพราะตอนนี้เขามีของเล่นใหม่ที่กำลังอยู่ในช่วงทดลองใช้งาน แต่บอกเลยว่าว้าวมาก! เราไปดูกันดีกว่าว่ามันคืออะไร... 📡 Remote function คืออะไร เป็น function ตัวใหม่ ✨ (ที่คาดว่าจะเป็น new way to implement สำหรับ Sveltekit 3.0) เอาไว้ใช้สื่อสารพูดคุยกันระหว่างฝั่ง client และ server ของ Sveltekit นั่นเอง 💬 ความเจ๋งคือเราสามารถเรียกใช้มันจากมุมไหนของ Sveltekit ก็ได้ 🌍 ไม่จำเป็นต้องจำกัดแค่ฝั่ง server หรือ client แต่จุดสำคัญคือ การทำงานของมันจะเกิดขึ้นที่ฝั่ง server เสมอ 👍 นั่นหมายความว่ามันสามารถทะลุทะลวงไปดึงข้อมูลหรือโมดูลที่เป็น server-only ได้สบายๆ เช่น ตัวแปร environment ที่เราประกาศไว้ หรือพวกฐานข้อมูลต่างๆ ก็ดึงมาได้ชิลๆ เลย 😎 เวลาจะใช้งาน เราจะต้องใช้ท่าการ await แบบใหม่ของ Sveltekit ⏳ ที่ช่วยให้คุณโหลดหรือดึงข้อมูลแบบ promise มาใช้ใน component ของคุณได้ทันที 🚀 ⚠️ หมายเหตุ: ตอนนี้ทั้ง await และ remote function ยังอยู่ในช่วงทดลองใช้งาน 🧪 (experimental) นั่นแปลว่า syntax บางอย่างอาจจะมีการปรับเปลี่ยนหรือบินหายไปบ้างในอนาคต 🥲 แต่แกนหลัก (core functional) ของมันก็จะยังทำงานได้ตามที่เราคาดหวังแน่นอน ถ้าใครคันไม้คันมืออยากลองของใหม่ตอนนี้ สามารถไปเปิดโหมด experimental ได้ที่ไฟล์ svelte.config.js(.ts) ตามโค้ดด้านล่างนี้เลย 👇 svelte.config.js(.ts) /** @type {import('@sveltejs/kit').Config} */ const config = { kit : { experimental : { remoteFunctions : true } }, compilerOptions : { experimental : { async : true } } }; export default config ; 🏃‍♂️ Let get started!! เราสามารถเริ่มใช้ remote function ได้ง่ายๆ ผ่านการสร้างไฟล์นามสกุล .remote.js หรือ .remote.ts 📝 ซึ่งตอนนี้มี function ให้เราหยิบมาเล่นทั้งหมด 4 ตัวด้วยกันคือ: query (ที่เราจะมาพูดถึงกันในบทความนี้) form command prerender หลักการทำงานเบื้องหลังคือ เวลาที่เรา import ตัว remote function ไปใช้ในฝั่ง client มันจะถูกแอบแปลงร่างเป็นโค้ดที่หุ้มด้วย fetch ในช่วง build time 🏗️ นั่นหมายความว่าระบบจะใจดีสร้างเส้น HTTP endpoint ให้เราแบบอัตโนมัติ ✨ ด้วยเหตุนี้เราเลยเอาไฟล์ .remote.js หรือ .remote.

If you ask what goes on in my head at 3 a.m., this is it. Cristiano is on TV doing his thing. Croatia is getting robbed. And somewhere in there, my brain starts thinking about why I suck at things that I enjoy doing?. Let me take you back a little. I write two kinds of articles. Technical ones, which come naturally to me. And the ones which I call business ones (well, the not-so-technical). I spend most of my time on the business articles. I enjoy writing them. I enjoy reading them back too. And just so you know, I'm not writing all these articles for you. I write to document things. Technical posts are usually just answers to questions people keep asking me. Instead of explaining the same thing again, I write it once and send the link. Someone once told me that the best way to really learn something is to teach it to someone else, or even just say it out loud to a wall (You should be doing the same; trust me, it helps). So this blog is my wall. Turns out my wall has people sitting on it. That's you. Anyway. Back to the real question. The real question is about my own head I think "Why do I enjoy things I'm not great at" is the wrong way to say it. The better question is: why do I think I enjoy things I'm not great at? Because this whole idea lives in my head. And the stats page just proved me wrong. Here's what happened. I looked at the numbers on my published articles. I don't usually do this, because I write for myself, not for the numbers. But numbers don't lie. My technical articles get read a lot more than my business ones. And the business ones are the ones I enjoy writing the most. Maybe it's the people around me. Most of them are technical, so maybe the technical stuff feels more exciting to them. Fair enough. But that only explains the blog. It doesn't explain the rest of my life. It's not just about writing I like playing soccer. I'm honestly the last person you'd want on your team. I'm bad. I won't pretend otherwise. But do I enjoy playing? Yes. A lot. I

I've been trying to learn coding for 35 years, which is my age. I love coding, and love the fantasy of being a coder. I love the whole thing about it. It's not a unhinged passion, but still something I carry very close to my digital heart. I started with VB6, and excel, and then web and I have never been particularly good at anything. If you ever met someone that loves gaming or sports but are bad at them, that's me. I need to have coding in my life, I'm just not good enough, ever. And that's fine. I discovered vibe coding because I follow all tech stuff. I've been trying to build certain things for years. I'm not necessarily desperate to build them but I do want them. Discovering AI allowed me to build personal web apps I always wanted to but was limited. All of the sudden I was able to build all web apps I wanted. I did 8 different projects in weeks. None of these things were for everyone, but very specific, tailored apps that help me at work, and in my personal life. Kinda trivializes the unbelievably fucking hard thing that is to learn evem the most remote thing about coding. Ive quit so many times becase concepts and terms I don't get. I still don't know what the fuck a prototype is in JavaScript even tho I got the certificate from FCC. Yet here I am building things that not even my imagination could put together. Am I enjoying it? Yes. Has it been beneficial? Fuck yes. I have been given the tools to create things my skills can't help me to. At the same time, as a person that have been trying to learn since I'm like 15, I know this isn't something to be trivialized. But at the same time I do have tools that trivializes it and they're available and free and works. Am I a disgusting person for feeling empowered? This is the first time in my journey I'm able to build actual things with the help of thear tools, but I also feel it's so disrespectful because I struggled for ALL MY LIFE trying to learn it. That said, since I stated vibe coding I've learn so many shit