Figure says its F.02 robot "contributed to the production of 30,000+ X3 vehicles" at BMW's plant in Spartanburg, South Carolina. Loaded 90,000-plus sheet metal parts. Logged 1,250-plus hours on a live assembly line. After ten years of stage demos and treadmill walks, that is a real number from a real factory, and it deserves to be read carefully. So here is the part most coverage skipped: that robot has been retired. The headline numbers are real Two of the loudest names in the field finally stopped quoting choreography and started quoting line output. Figure's Spartanburg run hit greater than 99% placement success per shift on a 37-second load cycle, ten-hour shifts, five days a week, all on the chassis assembly line. Tesla, separately, says more than 1,000 Optimus units were already working its Fremont floor in January 2026, doing battery assembly, pack loading, cable routing and parts handling, with a dedicated line targeting 100,000 to 300,000 units this year per The Robot Report. I want to be clear that this is genuinely new. A fixed pick-and-place task, run for months on a production line at automotive takt, with a placement success number you can audit, is not a demo. It is the first time the category has produced metrics an operations lead can actually argue about. Take the capability seriously. The trouble starts the moment you treat the capability number as an availability number. The footnote that inverts the headline The single most important sentence in Figure's announcement is the one about retirement. F.02 "return[ed] to HQ from BMW as part of our fleet-wide retirement" once Figure 03 launched. So the 30,000-car figure is the lifetime output of a pilot that has ended, not the running rate of a station that still exists. As of now there are no Figure robots on the Spartanburg line. BMW's own June 2026 material reads the same way once you stop skimming. The company frames its next move as a new pilot at Plant Leipzig in Germany starting summer 2026, wit

A few months ago, I was overwhelmed by everything happening in AI. Every week there was a new coding assistant, a new workflow, or someone claiming they built an app in just a few hours. It felt like if you weren't keeping up, you'd be left behind. I tried almost everything. Cursor. ChatGPT. Claude Code. Lovable. At first, I kept switching between tools, hoping one of them would magically make me a better developer. It didn't. The biggest lesson I learned wasn't about choosing the best AI tool. It was learning how to work with AI. These days, I don't start by asking AI to write code. I start by explaining the problem. I describe the feature, the business requirements, the edge cases, and what I want the final result to look like. Sometimes I ask ChatGPT to help me plan the implementation first. Once everything is clear, I pass that plan to an agentic coding assistant and start building. That one change made a huge difference. I spend less time writing boilerplate and more time thinking about architecture, user experience, and solving the actual problem. AI still gets things wrong, so I review everything before it goes into production. But instead of writing every single line myself, I'm guiding the process. Looking back, the first few months were the hardest. Now it just feels normal. The tools will keep changing, but I think the real skill is learning how to communicate with AI and use it as part of your development process. That's something worth investing in.

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Article URL: https://android-developers.googleblog.com/2026/06/Android-17.html Comments URL: https://news.ycombinator.com/item?id=48597783 Points: 3 # Comments: 1

How to Access 50+ Chinese AI Models Through One API The Chinese AI ecosystem exploded in 2025-2026. DeepSeek dropped training costs by an order of magnitude. Qwen 3 ships 19 variants from 0.6B to 235B parameters. GLM-5 competes head-to-head with GPT-5 at 3% of the price. There's Kylin, Yi-Lightning, Hunyuan-T1, MiniMax-M1, Step-2-16K, and 40+ more models from a dozen labs. The models are incredible. The fragmentation is not. Every lab has its own API. Different auth headers. Different response formats. Different streaming protocols. Different error codes. If you wanted to try 5 models from 5 Chinese labs last year, you'd need 5 SDKs and 5 billing dashboards. Nobody has time for that. This is exactly the problem AIWave was built to solve. One API Key. 50+ Models. Zero Code Changes. AIWave is a unified API gateway that aggregates 50+ Chinese AI models behind a single endpoint. It speaks the OpenAI API format, which means every existing tool, SDK, and codebase in your stack works without modification. Here's what that looks like in practice: from openai import OpenAI # Point to AIWave instead of OpenAI client = OpenAI ( base_url = " https://api.aiwave.live/v1 " , api_key = " sk-your-aiwave-key " ) # Use DeepSeek V4 Pro response = client . chat . completions . create ( model = " deepseek-v4-pro " , messages = [{ " role " : " user " , " content " : " Explain MoE architecture " }] ) # Switch to GLM-5 — change one string response = client . chat . completions . create ( model = " glm-5 " , messages = [{ " role " : " user " , " content " : " Explain MoE architecture " }] ) # Try Qwen 3 235B — same thing response = client . chat . completions . create ( model = " qwen3-235b " , messages = [{ " role " : " user " , " content " : " Explain MoE architecture " }] ) That's it. Whatever you're already using — the OpenAI Python SDK, LangChain, LlamaIndex, Vercel AI SDK, a custom fetch wrapper — continues to work. You change the base URL and the model name, and suddenly you have acce

Every Solana program eventually hits the same question: where do I put my data, and how do I find it again later? Programs are stateless, so a program's data lives in separate accounts, each at an address. The moment you store something, you owe an answer to a problem databases tend to hide from you: what address does this live at, and how does the program find it again tomorrow? Program Derived Addresses are Solana's answer. The name scares people off, but the idea is mostly "an address you compute instead of remember, that only your program can control." The problem, in code Say each user gets a counter account. The normal way to make an account is to generate a fresh keypair and store data at its public key: import { Keypair } from " @solana/web3.js " ; const counter = Keypair . generate (); // counter.publicKey is something random, e.g. 7Hx4...9fT // create the account at that address, write count = 0 It works. But the address is random, so nothing connects this user to that address . Tomorrow, when the user comes back to increment, how does your program find their counter? You're forced to keep a lookup table somewhere: // the mapping you now have to store and never lose const counters = { " 9fYL...user1 " : " 7Hx4...9fT " , " B2k9...user2 " : " Qz1p...4dR " , // ...times ten thousand users }; Lose that table, lose the data, even though the accounts are right there on chain. You're storing files in a warehouse and writing the shelf number on a sticky note. The fix: compute the address from what you already know What if the address were a function of the user instead of random? Give a function the word "counter" and the user's public key, and it hands back a fixed address. Same inputs, same address, every time. No table. That's a PDA. PDAs are 32-byte addresses derived deterministically from a program ID and a set of seeds. The seeds are the meaningful inputs you pick (here, "counter" + the user's key). With @solana/web3.js , the library Anchor's client uses: im

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. A startup claims it broke through a bottleneck that’s holding back LLMs AI startup Subquadratic came out of stealth last month with a huge claim: it had solved a mathematical bottleneck…

The engineers who spoke negatively about AI data centers at Seattle city hearings accuse Amazon of threatening their jobs over their testimonies.

OpenAI’s Bonnie Xu discusses Kepler, an internal AI data analyst agent built to query 600+ petabytes of data. She explains how they overcome context window limits using MCP, automated code crawling, and RAG. Xu also shares how their team leverages scoped semantic memory for self-learning and utilizes AST-based LLM grading to build a robust, regression-free evaluation pipeline. By Bonnie Xu

When Colin Farrell was doing press for the first season of the detective series Sugar, he had to be very careful with how he spoke. Sugar is a story about a quirky private detective, but it's also secretly a work of science fiction, something that doesn't become clear until halfway through the season. "I knew […]

We've lost something in the past 15 years. Netflix, Amazon, Disney, Apple; they've all convinced us that streaming is the best way to watch movies and shows at home. With everything at our fingertips, there's no need to run to Blockbuster for the weekend's entertainment, or wait for a DVD rental to arrive in the […]

CircleCI has launched Chunk Sidecars, a new capability designed to bring CI-style validation directly into an AI coding agent's inner development loop By Craig Risi

We break down the current iPad lineup to help you figure out which of Apple’s tablets is best for you.

TSRX is a TypeScript language extension developed by Dominic Gannaway, designed to build declarative user interfaces in a framework-agnostic manner. It compiles single .tsrx files to various runtime targets and supports scoped styles and declarative error handling. TSRX is currently in alpha and is open source under the MIT license. By Daniel Curtis

AI video studio: text & image to video, with real control Discussion | Link

Forget about patchy internet connections and dead spots in the house. These WIRED-tested multiroom mesh systems will get you online in no time.

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