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Presentation: Fine Tuning the Enterprise: Reinforcement Learning in Practice

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

2026-07-03 原文 →
AI 资讯

The Shop on the Corner: How I Learned System Design Without Building Clone

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

2026-07-03 原文 →
AI 资讯

Binary Tree PreOrder Traversal

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

2026-07-03 原文 →
AI 资讯

Achieving operational excellence with AI

Frameworks like Lean Six Sigma and business process management (BPM) first gained traction because they promised clarity in the chaos—a structured way to bring order to messy, sprawling operations. Lean Six Sigma emphasized statistical rigor and quality control; BPM created end-to-end maps of how work should flow across departments. Both offered a repeatable way to…

2026-07-02 原文 →
AI 资讯

Teaching AI to run with the turbines

Artificial intelligence may have captured the public imagination through chatbots and image generators, but some of its most consequential use cases are unfolding far from consumer-facing tools. In industries where physical infrastructure, operational continuity, and safety are paramount, AI is becoming a core operating layer. With its sprawling industrial systems and constant stream of operational…

2026-07-02 原文 →
AI 资讯

Presentation: Enhancing Reliability Using Service-Level Prioritized Load Shedding at Netflix

The speakers discuss Netflix’s architecture for surviving extreme traffic spikes. They explain the mechanics of prioritized load shedding embedded in their Envoy sidecar proxy, allowing user-initiated requests to steal capacity from non-critical traffic. They share automated platform strategies for continuous chaos load testing, config generation, and retry storm mitigation. By Anirudh Mendiratta, Benjamin Fedorka

2026-07-02 原文 →
产品设计

Amazon has enough satellites to launch its Starlink competitor

Amazon says it now has enough satellites operating in low-Earth orbit to light up its Starlink internet competitor. With last night's launch, Amazon Leo has 396 satellites deployed, which is "enough to support continuous service across initial latitudes," according to Chris Weber, VP heading up business and product for Amazon Leo. That puts the company […]

2026-07-02 原文 →