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.
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Researchers say these coral strongholds may help repopulate more degraded reefs.