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Microsoft is set to announce a wave of layoffs for its Xbox studios and employees next week. Sources familiar with Microsoft's plans tell The Verge that the layoffs will lead to studio closures or spinoffs, potential mergers of studios, and canceled games. I understand Microsoft is currently weighing closing at least five studios, including the […]

Sources suggest Musk may be mulling big donation to Trump Accounts.

The company may finally be ready to try to deliver on Elon Musk's years-long promise of launching a robotaxi network of its own.

Stealthy startup Arcturus uses lasers to infuse carbon nanomaterials into copper, dramatically improving its ability to conduct electricity.

Gov. Ron DeSantis calls it a crackdown on "radical climate policies."

GitHub热门项目 | 🧱 easy, fast and local-first microVM runtime | Stars: 6,741 | 17 stars today | 语言: Rust

Watching Elon Musk fulminate at Bill Savitt during Musk v. Altman - the case in which Musk sued Sam Altman and OpenAI instead of seeing a therapist about his AI failures - was a bit like watching a toddler have a temper tantrum at his nursery school teacher. Savitt's questions were "designed to trick me," […]

TL;DR Anthropic recently published When AI Builds Itself, an essay explaining how AI is...

Apple. Anthropic. Disney Research. Google. Meta. Microsoft. NVIDIA. OpenAI. Few places outside Silicon Valley can claim R&D hubs from all of these companies. Fewer still are concentrated in a city of just over 400,000 people—roughly half the size of San Francisco. Over the past two decades, however, many of the world’s most influential technology companies…

Introduction As applications grow, traditional relational databases such as MySQL may struggle with analytical workloads involving millions of records and complex aggregations. While MySQL excels at Online Transaction Processing (OLTP), ClickHouse® is purpose-built for Online Analytical Processing (OLAP), enabling lightning-fast analytical queries on massive datasets. Migrating data from MySQL to ClickHouse® allows organizations to build high-performance reporting systems, dashboards, and real-time analytics without impacting transactional workloads. In this guide, you'll learn several approaches to migrate data from MySQL to ClickHouse®, along with their advantages, limitations, and ideal use cases. Why Migrate from MySQL to ClickHouse®? MySQL and ClickHouse® are designed for different workloads. Feature MySQL ClickHouse® Storage Model Row-based Columnar Best For Transactions (OLTP) Analytics (OLAP) Query Speed Fast for row lookups Extremely fast for large scans Aggregation Performance Moderate Extremely fast Scalability Primarily Vertical Optimized for analytical scaling Typical Use Cases Applications and transactional systems Reporting, dashboards, and analytics Migrating from MySQL to ClickHouse® makes sense when: Analytical queries are becoming slow in MySQL. You need real-time dashboards over large datasets. Reporting queries are impacting your production database. You regularly process millions or billions of rows. Migration Architecture MySQL │ ▼ Export / Synchronization │ ▼ Data Transformation │ ▼ ClickHouse® │ ▼ Dashboards / Analytics Migration Methods There are multiple ways to migrate data depending on your requirements. Method 1: CSV Export and Import (Recommended for Beginners) This is the simplest approach for performing a one-time migration of historical data. Step 1: Export Data from MySQL Run the following command inside MySQL: SELECT * INTO OUTFILE '/tmp/employees.csv' FIELDS TERMINATED BY ',' ENCLOSED BY '"' LINES TERMINATED BY ' \n ' FROM employ

OKX is bringing together payments, identity and reputation into a marketplace for AI agents.

Welcome to this week's Top 7, where the DEV editorial team handpicks their favorite posts from the...

I needed faster edits for technical diagrams, and a lower recurring overhead for recurring visuals. I stopped asking for new images for everything. That change started the moment I replaced "generate now, tweak later" with a fixed 8-tool matrix. TL;DR: I moved recurring illustration work into seven scriptable stacks + one 3D stack and kept image-generation AI only as a fallback. Why I rewrote this workflow When I edited an article recently, I was spending too much time redoing the same visual shape in slightly different versions. The same chart logic should not need prompt guessing each time. I asked myself: Can this be represented as text or code? Can I regenerate it exactly when requirements change? Do I need raw design freedom, or do I need deterministic structure? If the answer was mostly "text/code + deterministic output," I did not open an image-generation model first. I also kept one practical boundary: this was not an academic tool roundup. This is a log of what I actually used and in what context. The number that changed my mind: an 8-tool decision matrix The number I now defend is exactly 8 . Instead of inventing synthetic savings, I evaluate every new illustration request against this matrix. Tool Best fit Why I pick it Mermaid flow, sequence, architecture notes fastest in markdown-native writing PlantUML UML-heavy docs strict structure when Mermaid gets too loose Markmap map-style summaries converts headings directly Graphviz dependency and direction graphs compact graph semantics matplotlib numeric visualizations source-of-truth from data tables Pillow labels, badges, annotations deterministic pixel edits in Python D3.js node/link or hierarchy interactions data-driven relationship rendering Blender 3D explanatory graphics stronger structural clarity for complex scenes This is the exact set I now reach for before any image-generation request. What happened first: practical snippets I am including small runnable snippets I can reuse. 1. Mermaid for determ

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 —

This week's Java roundup for June 22nd, 2026, features news highlighting: the GA releases of Hardwood 1.0 and Endive 1.0; the June 2026 edition of Azul Payara; point releases of Quarkus, LangChain4j; the first beta release of WildFly 41; and introducing Eliya JDK and the Open Source Sustainability Initiative (OSSI), the latter of which was founded by HeroDevs and Commonhaus Foundation. By Michael Redlich

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Operation by two Russia-state groups has been ongoing since at least March.

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....

Tidal shared its new policies regarding AI-generated music today and how the platform plans to "protect artists" and "inform listeners." Instead of banning it outright, starting on July 15th Tidal will label tracks it has identified as being 100 percent AI-generated with an icon. But starting today those tracks will no longer be monetizable. "Tidal's […]