This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. China has approved the world’s first invasive brain-computer chip—here’s what’s next Sitting in the courtyard of his house in China’s Henan province last October, Dong Hui decided to try holding a…

ASUS' new ROG Xbox Ally X20 has mostly the same hardware as last year's handheld, but now comes with a new OLED panel.

Due to a growing influx of questions on this topic, it has been decided to commit a monthly thread dedicated to this topic to reduce the number of repeat posts on this topic. These types of posts will no longer be allowed in the main thread. Many of these questions are also addressed in the sub FAQ or may have been asked in previous monthly career threads . Subs dedicated to these types of questions include r/cscareerquestions for general and opened ended career questions and r/learnprogramming for early learning questions. A general recommendation of topics to learn to become industry ready include: HTML/CSS/JS Bootcamp Version control Automation Front End Frameworks (React/Vue/Etc) APIs and CRUD Testing (Unit and Integration) Common Design Patterns You will also need a portfolio of work with 4-5 personal projects you built, and a resume/CV to apply for work. Plan for 6-12 months of self study and project production for your portfolio before applying for work. submitted by /u/AutoModerator [link] [留言]

OpenAI breaks ground on a 1GW data center project in Michigan as part of Stargate, building AI infrastructure to expand access, create jobs, and support communities.

Strava will charge a flat monthly fee from developers to access its API

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Maybe this is just me using these tools badly, but I've noticed a pattern with ChatGPT and Claude. I’ll have a really useful conversation about something like an idea, a plan, a bit of writing, a coding problem, whatever, and in the moment it feels like I’m making real progress. Then a week later I vaguely remember that we talked about it, but I can’t remember where, or what the useful part actually was and what I was supposed to do next. So I search, find a few old chats, open them… and now I’m scrolling through this massive thread trying to reconstruct why it mattered. It's exhausting and I feel I'm wasting time recollecting things. So sometimes I start over, hoping that the AI itself will remember the details, adding to the waste of time and the frustration. And the more ideas I develop the bigger this problem becomes. And it's only going to get worse. I’ve started leaving myself a short note at the end of useful conversations, but I never remember to do it consistently. Not sure if this is an actual problem or just the natural cost of using AI for messy thinking. submitted by /u/AlbertoNobilePh [link] [留言]

I see a lot of people shipping AI agents that work perfectly in demos and fall apart the moment a real user touches them. After building automation systems for multiple clients, I've noticed the failures almost never come from choosing the wrong LLM. They come from three things: 1. Bad chunking in RAG pipelines. Everyone's so focused on picking the right vector DB that they don't think about how they're splitting documents. Garbage in, garbage out. If your chunks don't preserve context across sentences, your retrieval will always be mediocre. 2. Prompts written for demos, not edge cases. Demo inputs are clean. Real user inputs are weird, vague, and sometimes intentionally broken. If you didn't stress test your prompt with bad inputs, it will fail publicly. 3. No fallback logic. When the agent is confused, what does it do? Most builders never answer this question. So the agent either hallucinates confidently or returns nothing. Both are bad. The model is usually the last thing to blame. Fix the scaffolding first. Anyone else running into this? Curious what failure patterns you've seen. https://preview.redd.it/vd9yyzkpzn4h1.png?width=1536&format=png&auto=webp&s=e81e5a1b4a7c4d82542c8cbc5cdf9712f30ff393 submitted by /u/ahmadparizaad [link] [留言]

submitted by /u/ke2nyjks [link] [留言]

Selected excerpts: "The question, then, is not whether A.I. will change the world. It will. The question is: Who will own and control that future? Who will benefit from it, and who will be hurt by it? Will A.I. be used to make life better for working families? Will it enrich our quality of life? Will it help us eliminate poverty, extend life expectancies and solve the climate crisis? Or will the future of humanity be determined by a handful of billionaires who have promoted and developed A.I., with virtually no democratic input, who stand to become even richer and more powerful than they are today? That is the choice before us. Let us be clear. Artificial intelligence was not created out of thin air. The data and language used by generative A.I. tools didn’t just pop into Sam Altman’s head or Elon Musk’s imagination. A.I. is built on our collective intelligence: our books, songs, artwork, journalism, computer code, scientific research, videos, conversations, images and ideas spanning generations. That is not just the opinion of Bernie Sanders. According to Mr. Altman, the head of OpenAI, A.I. models were trained on our 'collective experience, knowledge' and 'learnings of humanity.' For the most part, tech oligarchs have fed this knowledge into their A.I. models without permission, without acknowledgment, without compensation. In other words, the creative work of millions of people — writers, artists, musicians, journalists, teachers, scientists and ordinary citizens — has essentially been stolen by some of the wealthiest people in the world. It’s time for us to reclaim it. That is why I will soon be introducing the American A.I. Sovereign Wealth Fund Act. This legislation would give the public a direct ownership stake in the largest A.I. companies in our country. How? It would create a sovereign wealth fund through a one-time 50 percent tax — not on the profits of OpenAI, Anthropic, xAI and other companies, but paid with something far more valuable than that: the st

Google announced it will open a flagship store in Tokyo this summer, the first of its kind outside of the United States.

Hi everyone, We're building an automation platform using Playwright where all browser automation runs on the backend. For portals that require manual intervention (OTP, CAPTCHA, MFA, document uploads, etc.), we're exploring a way to let users temporarily view and interact with the running backend browser from our React application, after which automation would resume automatically. Our goals are: Keep all automation logic on the backend Support human intervention only when necessary Scale to bulk processing workflows Deploy reliably in production We're currently evaluating approaches such as CDP screencasting, VNC/noVNC, and WebRTC-based browser streaming. Has anyone built something similar in production? What architecture did you choose, and what were the biggest challenges around scalability, latency, security, session management, and CAPTCHA/OTP workflows? Also, is there a better alternative than live browser streaming for this use case? Any advice, experiences, or open-source projects would be greatly appreciated. submitted by /u/Loud_Ice4487 [link] [留言]

Shopify Staff Engineer Guilherme Carreiro discusses building and scaling highly customizable platforms. Using Shopify’s Liquid theme system as a case study, he explains how to balance extreme design flexibility with low-latency performance under massive traffic. He shares insights on implementing secure domain-specific languages, native code extensions, and resilient developer tooling. By Guilherme Carreiro

Nvidia has officially entered the world of consumer laptop chips with the RTX Spark, and several device makers already have hardware lined up for it. Microsoft, Asus, HP, MSI, Lenovo, and Dell are expected to launch RTX Spark laptops sometime this fall, and some of those partner companies have shared details about what we can […]