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Martin Kleppmann discusses the urgent need for technological sovereignty in modern infrastructure. Exploring the shifting landscape of global tech dependencies, he shares how engineering leaders can leverage multi-cloud architecture, de facto API standardization, the AT Protocol, and local-first development paradigms to reclaim user agency and build highly resilient systems. By Martin Kleppmann

Senior Solution Architect Viktor Vedmich shares how engineering leaders can maximize application performance using Valkey. He discusses the open-source Redis fork's 100% API compatibility, explores advanced caching strategies like lazy loading, and explains how to implement powerful data structures for real-time analytics, rate limiting, and session stores to solve the thundering herd problem. By Viktor Vedmich

A compliance team at a mid-size bank in Abu Dhabi processes new customer applications every week....

Follow our WWDC 2026 live blog for all of the updates coming to iOS, macOS, watchOS, and more.

Microsoft announced the general availability of Microsoft Discovery, its Azure-based platform for deploying autonomous AI agent teams in scientific R&D. The platform powered the development of Majorana 2, a topological quantum chip with 1,000x reliability improvement and 20-second qubit lifetimes. Microsoft now targets a scalable quantum computer by 2029, halving its original timeline. By Steef-Jan Wiggers

Introduction People keep telling me TanStack Start is kind of a big deal, and I wanted to...

Much is new about this month’s upcoming FIFA World Cup tournament, which will be held in the US, Canada, and Mexico. It hosts more teams than ever before. It’s the first to occur in three different host countries. And, like predecessor cups for over half a century, it will employ a soccer ball with a…

As an Engineering Manager in a Platform team, I manage 10 engineers. I'm hiring more. I run weekly 1:1s, facilitate technical decision meetings, screen candidates, moderate retrospectives, and still need to keep up with the delivery of a platform spanning dozens of AWS accounts. Besides the lack of time to focus on technical problems, the technical part is not even the real challenge. The less obvious problem becoming an Engineering Manager is: the skills you need as an engineering manager are fundamentally different from those that made you a great engineer , and there's no compiler or unit test to tell you when you're doing them wrong. The feedback loop is absent or very slow (and when you realise that, your team has already gone silent or become dependent on you because you are the main input and the main bottleneck). Skills That Don't Come From Code As a senior or staff engineer, you develop communication skills gradually. You present ideas, challenge others respectfully, summarise outcomes, and identify owners. You participate in technical deep dives and put candidates at ease while probing technical depth. These are valuable skills, and a good IC develops them over the years. But unless you start behaving like a brilliant jerk , they're secondary - your technical depth is still what defines you. But as an EM, the game changes. You're not "the smartest person in the room" anymore, and increasingly, you shouldn't be. You still have a broad context from all those alignment meetings and roadmap syncs, but you lose contact with the codebase week by week. If your organisation has principals or staff engineers, you're not even close technically anymore. Your job is to give direction, create space for others to solve problems, and facilitate decisions, not to be the one with the answer. This is hard. Especially when you used to be the one with the answer. The urge to jump in doesn't disappear just because your title changed. And interviewing? Facilitation? Giving feed

📈 多因子选股系统 — 项目功能介绍 观看地址: https://youtu.be/SpHsZdlyii8 面向 A 股市场的全链路量化分析平台 — 因子计算 · 机器学习 · 组合优化 · 回测验证 · 实时行情 指标 数据 Python 模块 108 业务代码 15,600+ 行 服务模块 30+ API 蓝图 14 内置因子 12 一、整体功能介绍 多因子选股系统是一个覆盖量化研究全流程的分析平台，从数据管理到策略回测一站式完成。系统采用 Flask + SQLAlchemy + SocketIO 技术栈，基于 Parquet + SQLite 双层存储架构，零外部数据库依赖，克隆即可运行。 核心能力总览 实时行情分析 ：通达信分钟数据接入，MACD、KDJ、RSI、布林带等技术指标实时计算 因子与选股 ：12 个内置因子 + 自定义因子表达式引擎，支持白名单安全校验 机器学习建模 ：XGBoost、LightGBM、RandomForest 三大算法的完整模型生命周期 组合优化 ：等权重、均值方差、风险平价、因子中性四种经典优化方法 回测验证 ：单策略与多策略回测，丰富的绩效指标（年化收益、夏普比率、最大回撤等） 风险管理 ：组合持仓管理、实时价格刷新、风险指标监控、预警管理、压力测试 请求流程 HTTP / WebSocket → Blueprint (app/api/*.py) → Service (app/services/*.py) → ParquetDataReader (行情数据) ↕ SQLAlchemy Models (SQLite 应用状态) 技术栈 类别 技术 后端框架 Python 3.8–3.11、Flask、Flask-SocketIO、Flask-SQLAlchemy、Celery 数据处理 Pandas、NumPy、PyArrow、SciPy、Scikit-learn 机器学习 XGBoost、LightGBM、RandomForest、CVXPY 前端 & 可视化 Bootstrap 5、ECharts、Plotly、Matplotlib / MPLFinance 数据源 通达信（实时行情）、Baostock（历史数据）、Tushare（可选）、Parquet 离线数据包 AI 能力 Ollama（本地 LLM）、OpenAI API（可选）、Text2SQL、Qwen2.5-Coder 二、各模块功能介绍 2.1 实时行情分析模块 API 蓝图前缀 ： /api/realtime-analysis/* 核心服务 ： RealtimeIndicatorEngine （ services/realtime_indicator_engine.py ）：实时技术指标计算 RealtimeTradingSignalEngine （ services/realtime_trading_signal_engine.py ）：多策略交易信号生成与融合 功能特性 ： 功能 说明 实时指标计算 支持 MACD、KDJ、RSI、布林带等经典技术指标的实时计算与展示 多策略信号生成 基于不同策略的交易信号生成，支持信号融合与综合研判 信号监控 交易信号的实时监控与历史回溯 策略回测 对生成的信号进行历史回测验证 板块分析 板块表现统计与异动检测 市场情绪 市场情绪指标的实时追踪 数据源 ：通达信分钟级行情数据，存储于 data/stock_minute/ （Parquet 格式）。 2.2 因子与选股模块 API 蓝图前缀 ： /api/ml-factor/* 、 /api 核心服务 ： FactorEngine （ services/factor_engine.py ）：内置因子计算与自定义因子管理 FactorExpressionEngine （ services/factor_expression_engine.py ）：白名单校验的自定义因子公式引擎 StockScoringEngine （ services/stock_scoring.py ）：因子评分与 ML 评分的综合选股引擎 内置因子（12 个） 维度 因子名 说明 动量 momentum_1d 1 日动量 动量 momentum_5d 5 日动量 动量 momentum_20d 20 日动量 波动率 volatility_20d 20 日波动率 技术指标 rsi_14 RSI 相对强弱 技术指标 turnover_rate 换手率 基本面 pe_ratio 市盈率 基本面 pb_ratio 市净率 基本面 roe 净资产收益率 基本面 debt_ratio 资产负债率 基本面 current_ratio 流动比率 基本面 gross_mar

All tests run on an 8-year-old MacBook Air. All results from shipping 7 Mac apps as a solo developer. No sponsored opinion. You plug in your Android device. A few seconds later, you unplug the cable. The connection stays alive — wirelessly, automatically, without touching a single setting. That's Seamless Link. The Problem ADB over USB is reliable. ADB over Wi-Fi is convenient. But switching between them manually is friction: Plug in USB Run adb tcpip 5555 Find the device IP Run adb connect <IP>:5555 Unplug cable Every. Single. Time. If you're working with multiple Android devices, or doing this across multiple sessions per day, it adds up fast. What Seamless Link Does The moment you plug in a USB cable, Seamless Link runs that entire flow automatically in the background: Detects the USB connection Runs adb tcpip 5555 Grabs the device IP Establishes a Wi-Fi ADB connection By the time you've sat back down, the device is already connected wirelessly. Pull the cable out — everything keeps working. No manual steps. No IP hunting. No re-running commands every session. Working with Multiple Devices This gets more useful the more devices you have. Plug in Device A → Seamless Link connects it wirelessly. Plug in Device B → same thing, simultaneously. Each device goes through the full handover flow independently, in parallel. The more devices on your desk, the more time this saves. Android 16 Compatibility Android 16 changed how wireless debugging ports are assigned — random ports instead of the fixed 5555. Seamless Link handles this automatically. You don't need to know which port the device is using. If you're on an older Android version, it works the same way it always has. Why This Matters for Daily Workflows If you're an Android developer on Mac, you probably already have a USB cable on your desk. Seamless Link just makes that cable optional after the first few seconds. It's one of those features that's hard to go back from once you've used it. The cable becomes a "char

Hey DEV community! 👋 I just completely rebuilt and launched Mailfo v2.0 , an privacy-focused temporary disposable email app for Android[cite: 1, 2]. The first version got the job done, but it lacked the fluid user experience, offline reliability, and sleek UI that modern mobile apps need. I decided to tear it down and rewrite it from scratch using Jetpack Compose , Room DB , and modern Android architecture[cite: 1]. Here is a breakdown of what went into the rebuild, why privacy tools like this are essential, and what's new[cite: 1]. 🛡️ Why Use a Temporary Disposable Email? We’ve all been there: you want to download a single source-code snippet, test a new app, or sign up for a one-time service, but you don't want your primary inbox flooded with endless marketing spam[cite: 1]. A disposable email address acts as your personal buffer[cite: 1]. It protects your personal data, prevents tracker exposure, and keeps your real inbox clean[cite: 1]. ⚠️ Pro-tip: Mailfo is designed strictly for low-risk temporary email needs[cite: 1]. Do not use disposable email addresses for banking, legal accounts, or primary account recoveries[cite: 1]! 🛠️ The Tech Stack & Architecture Upgrades Moving from version 1.0 to 2.0 meant migrating away from legacy views to a fully declarative UI workflow[cite: 1, 2]. UI/UX Architecture: Built entirely with Jetpack Compose for smooth performance, a modern look, and native Light, Dark, and System theme switching[cite: 1]. Navigation: Implemented an intuitive bottom navigation system dividing the app into distinct Home, Inbox, and Profile tabs[cite: 1]. Caching & Offline Stability: Integrated a local Room SQLite database to cache inbox summaries and message data[cite: 1]. This guarantees instantaneous screen transitions without annoying network loading spinners[cite: 1]. ✨ Key Features in Mailfo v2.0 Custom & Random Email Generator: Instantly spin up a completely randomized address or build a custom email username using multiple searchable domains[ci

A few weeks after switching from Windows to Linux, I noticed something interesting. The hardware had not changed. The processor was the same. The RAM was the same. The SSD was the same. And yet, the laptop felt noticeably faster. Not necessarily because applications were completing tasks dramatically quicker, but because the entire system felt more responsive. Keyboard input felt immediate. Windows opened faster. Terminal commands appeared instantly. The desktop experience felt smoother. This raised a question: How can the same hardware feel different simply because the operating system changed? While I'm still learning, this is the mental model I've built so far. The Hardware Didn't Change Consider a laptop with: AMD Ryzen processor 16 GB DDR5 RAM NVMe SSD Modern integrated graphics When switching operating systems, none of these components change. The CPU does not suddenly become faster. The RAM does not magically increase. The SSD remains identical. From a hardware perspective: ```text id="u3m9xd" Before → Same Hardware After → Same Hardware So the difference must come from somewhere else. --- ## An Operating System Is Not Just a User Interface Many people think of an operating system primarily as the desktop they see. But an operating system does far more than display windows and icons. It manages: * Memory * CPU scheduling * Processes * Storage * Networking * Device drivers * Background services In other words: > The operating system decides how hardware resources are used. Two operating systems can therefore create very different experiences using the same hardware. --- ## Perceived Performance vs Raw Performance One thing I have learned is that performance is not always about benchmarks. A system can have excellent benchmark scores and still feel sluggish. Why? Because users experience responsiveness, not benchmark numbers. Examples include: * How quickly a window opens * How fast a menu appears * How responsive typing feels * How quickly applications launch

A vision for the future of AI, focusing on access, safety, and shared prosperity as OpenAI works to ensure AGI benefits everyone.

The signal hidden in this week's GitHub trending Two agent-shaped repositories cracked the daily GitHub trending board this week. The first is mvanhorn/last30days-skill , a Claude-style skill that researches a topic across Reddit, X, YouTube, Hacker News, and Polymarket, then synthesizes a grounded summary. The second is NousResearch/hermes-agent , billed as "the agent that grows with you" — a persistent agent runtime that compounds context across sessions. Both ranked the same week. Both are skill-shaped: a manifest, a trigger, a set of instructions, and a runtime expectation. This is the first time I have seen two skill repos chart simultaneously on GitHub trending. Most observers will treat them as cool side projects, fork them, star them, and move on. They are cool side projects. They are also a phase transition that the agent ecosystem has been edging toward for nine months. By Q4 you are going to wish you had read this signal in early June, because the dependency-graph problem about to land in production agents is the same one the npm ecosystem ran into between 2011 and 2018 — except faster, less tooled, and with a much larger blast radius. This post is about that phase transition. The benchmark coverage of skills is everywhere; what you cannot easily find is a working operational model for managing them at fleet scale. I am going to give you one. What actually shipped this week Let me anchor on the facts before I extrapolate. last30days-skill (mvanhorn) is a single skill bundle. Its SKILL.md tells the host agent: when the user asks for recent news, controversy, or sentiment on a topic, run a structured multi-source fetch — eight queries minimum, across five platforms, with a freshness window of 30 days — then synthesize. The skill ships with prompt scaffolding, query templates, and a synthesis rubric. It is roughly 600 lines including instructions and helper scripts. Installation is a git clone into your skill directory, no package manager, no version negotia

Here's the maths most calculators get wrong Most freelance rate calculators are wrong. Not buggy — conceptually wrong. They take your income goal, divide by hours, and hand you a number that will quietly lose you money all year. I got tired of explaining the correct calculation to freelancer friends, so I built a tool that does it properly. Here's the logic behind it, and a bit about how I built it. The flawed formula The typical calculator does this: hourly_rate = annual_income_goal / annual_hours_worked So if you want $80,000 and work 2,080 hours a year (40 × 52), it tells you to charge ~$38/hr. This is wrong in three separate ways. Fix 1: Tax is not optional Your income goal is a net number — what you want to keep. But you're taxed on gross. So the first correction is grossing up: const grossIncome = netTarget / ( 1 - taxRate ); // $80,000 / (1 - 0.28) = $111,111 That's already a $31,000 gap the naive formula ignores. Fix 2: You don't bill every hour you work This is the big one. Freelancers bill roughly 55–65% of their working hours. The rest is admin, proposals, invoicing, sales calls, and learning. const totalHours = weeksWorked * hoursPerWeek ; // 46 * 40 = 1840 const billableHours = totalHours * billableRatio ; // 1840 * 0.6 = 1104 So your real billable capacity isn't 2,080 hours. It's closer to 1,104. Pricing on the wrong denominator is how people end up working 50-hour weeks and still missing their income target. Fix 3: Business expenses come out of gross Software, hardware, insurance, courses — all real costs that need covering before you pay yourself: const totalNeeded = grossIncome + annualExpenses ; The corrected formula Putting it together: function minimumRate ({ netTarget , taxRate , expenses , weeks , hoursPerWeek , billableRatio }) { const gross = netTarget / ( 1 - taxRate ); const totalNeeded = gross + expenses ; const billableHours = weeks * hoursPerWeek * billableRatio ; return totalNeeded / billableHours ; } minimumRate ({ netTarget : 80000 ,

If you’ve ever spent hours debugging slow EC2 workloads or getting sticker shock from unexpected EBS IOPS charges, you’ve probably wondered if there’s a better storage option for temporary, high-performance data. AWS EC2 Instance Storage (also called Instance Store) is one of the most underutilized but powerful tools in the EC2 ecosystem—if you know how to use it correctly. This guide breaks down everything you need to know: core concepts, performance optimizations, use cases, limitations, and how it stacks up against EBS. By the end, you’ll be able to cut storage costs, boost workload performance, and avoid costly data loss mistakes. Table of Contents What Exactly Is AWS EC2 Instance Storage? Core Concepts of EC2 Instance Store Key Features That Make Instance Store Stand Out Which EC2 Instance Types Support Instance Store? Deep Dive: NVMe SSD Instance Store Volumes SSD Instance Store Performance Best Practices EC2 Instance Store vs EBS: Head-to-Head Comparison Top Real-World Use Cases for EC2 Instance Store Critical Limitations to Avoid Costly Mistakes Production-Grade Best Practices for Instance Store Root Volume Options: EBS-Backed vs Instance Store-Backed Instances EC2 Instance Store Pricing: No Hidden Costs Conclusion References What Exactly Is AWS EC2 Instance Storage? EC2 Instance Store is temporary block-level storage that is physically attached to the host server running your EC2 instance. Unlike standalone storage services like EBS, EFS, or S3, it is part of the EC2 service itself, with no network overhead between your instance and the storage disks. Its defining trait is its ephemeral nature: data stored on Instance Store only persists for the lifetime of the associated instance. If you stop, hibernate, or terminate your instance, all data on Instance Store volumes is permanently deleted. Core Concepts of EC2 Instance Store Before you start using Instance Store, make sure you understand these foundational rules: Device naming : Instance Store volumes are

OpenAI launches the Economic Research Exchange to study AI’s impact on jobs, productivity, and the economy. Applications are now open for selected research projects.

I'm starting a new project. It's the most ambitious thing I've attempted from a phone. The goal: an AI agent that controls a smartphone. It opens apps, navigates screens, taps buttons, types text, and completes multi-step tasks. All offline. All local. No cloud. This is Day 1 of a public build log. No fluff. Just what I'm building, how it works, and what breaks along the way. What I'm Building An autonomous AI agent that runs entirely on an Android phone. You give it a command in plain English: · "Open WhatsApp and message Mom I'll call later." · "Search for Kotlin jobs on Wellfound." · "Open my notes and summarize what I wrote yesterday." The agent parses the command, plans the steps, and executes them—opening apps, finding the right buttons, typing text, hitting send. No cloud. No API keys. Just a phone that acts on your behalf. The Stack Component Tool AI Brain Gemma 4 E4B (local, via Ollama) Runtime Termux (Linux on Android) Phone Control ADB + UI Automator Orchestration Python Why This Matters Most AI agents live in the cloud. They need internet, APIs, and someone else's server. A local agent that runs on a phone means: · Privacy: your data never leaves your device. · Offline: works even without internet. · Accessible: built for the device billions of people already own. The Hard Parts I Already See · The agent needs to "see" the screen to know where to tap. Text detection is doable. Image-based buttons are harder. · Multi-step tasks need verification. If one tap misses, the whole chain fails. · Android permissions. ADB requires developer mode. A user-facing version would need a workaround. What's Next · Day 2: Create the repo. Set up the project structure. Push the first working script. · Day 3: Get screen text detection working with OCR. · Day 4: Test a full 3-step task. This is Day 1. The repo goes live tomorrow. Follow along if you want to see something rare get built from scratch.

We're likely to see more price increases as the big AI companies plan to go public.

Head's true genius—and that of his character, Giles—lay in quietly filling in the gaps in every scene