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Today, we’re launching Alpamayo 2 Super, our frontier open reasoning model for autonomous vehicles. Beyond seeing, Alpamayo understands and reasons through the complex world - thinks before it acts. It’s a powerful backbone for robotaxis, trucks, shuttles, delivery vans, tractors and the long tail of mobile robots—billions of autonomous machines someday. We’re releasing it for commercial use under OpenMDW-1.1 so teams can inspect it, fine-tune it and deploy it—open models advance safety and security. The next wave of AI is robotics—and it starts with autonomous vehicles. Great work, Alpamayo team!

Okay, the @VulcanBench results for Qwen3.8-Max are in, and it is not what I expected. First, for anyone new to VulcanBench, here's a quick TL;DR on the eval suite: 23 frontier-hard software engineering tasks taken from real merged OSS PRs, run in a Docker sandbox, 3 runs per task across all three of its effort levels. No puzzles, no random abstract stuff, all real things engineering teams would do with these models. It looks like Qwen3.8-Max has a major overthinking problem, it uses a LOT of tokens and is very slow, period, no other way to see it. My cost to run this benchmark was $126.25, to run the exact same eval suite with DeepSeek V4-Flash was only $13.60. This makes Qwen3.8-Max an insanely expensive model. The tasks Qwen genuinely can't solve fail at every effort level, extra reasoning didn't help. The regression is almost all in work it already handles: six tasks that low solves every single time account for 83% of the 26-point drop, three of them collapsing to zero. It's not losing the hard problems. It's losing the ones it already knows how to do. Since Qwen3.8-Max hit a lot of wall clock budget caps, I thought I'd share more about this. - VulcanBench caps both steps (50–200) and wall clock (5–60 min), each scaled by repo size. - This is aligned with how comparable harnesses bound agents, DeepSWE caps rollouts at 100 environment steps, sitting right inside my step range; Terminal-Bench enforces a per-task wall clock; SWE-bench Verified scaffolds typically allow 20–60 min per instance with 250–350 step limits. - Every model on my chart gets the identical budget, and Qwen is the slowest model I've tested at 20–25 min/task. Soooo... Alibaba positions Qwen3.8-Max as trailing only Claude Fable 5. But on the kind of real coding work engineering teams would actually throw at it, under a fixed budget, its best setting lands mid-pack and its default lands last, so common. If you want to optimize for accuracy, Grok 4.5 is the move. If you want accuracy per dollar, DeepSeek V4-Flash is hard to beat, heck it's 10× cheaper than Qwen and you get higher accuracy. Qwen just isn't in the game at this point, this is not a model I could see engineering teams using for daily coding work.

Teams spend months aggregating the best markets on one network. The best quotes under one roof… but a lot of work. Do they want to replicate that work for the next network… and the next? Instead of building per-network infrastructure, try the SODAX SDK next time.

Agent-pilled sales teams are building custom landing pages + demo videos for every buyer in their outbound campaigns Response rates are in a different league This agent runs 24/7 in a loop: >finds prospects with real buying signals in @useapolloio >builds a custom page for each of them >embeds a custom demo video rendered with @HyperFrames_ Get this agent, Signal, on Hyperagent Marketplace:

What’s New for the 1inch Business Portal is now live. One place covering new chain support, API and SDK releases and major platform changes. It’s public and login free, built for teams that rely on 1inch infrastructure and need a reliable record of what's changed.

Kelsey Mitchell dropped 37 points today! She is the best player on the Fever! 🔥 If Kelsey wasn’t on the Fever, they would be one of the worst teams in the league! She carries this team, but doesn’t get any credit or shine!

A showdown between two of the WNBA's hottest teams 🍿 The @IndianaFever bring a five-game winning streak to Minneapolis as the @minnesotalynx look to make it 10 straight! 📺 IND-MIN | 1pm/ET | ABC

100,000 EVs. 65 Markets. 1,200+ Touchpoints. Six years ago, XPENG’s global journey began in Norway. Surpassing 100,000 deliveries across overseas markets marks a defining milestone in our expansion—one driven by pioneering technology and deep local commitment. Debuting the L03 in Munich for its global launch marks the next milestone in our expanding vision. To our partners, teams, and global community—thank you for driving this revolution with us. The future accelerates here. $XPEV

thank you to the ~800,000 of you that pre-ordered our mobile app on iOS and Android — it’s clear that people are interested in building software on the go instead of asking you to download yet another app we’ve decided to take an entirely different approach: one where apps emerge naturally, in the course of your everyday conversations with Gemini. we’re partnering with the Gemini app team to make that a reality on mobile and desktop we’re continuing to invest in the AI Studio web experience for those who want to go from an idea to a prompt to a business the teams are working hard on bringing this to life, more to share on this later

i have 200 k worth of #mega# during IDO, Most of the people who participated in the IDO and decided to lock for one year were believing in the team Now we know that: 1) mega CEO @yilongl_megaeth hasn’t lost for over a year now 2) teams’ COO Laura is left to join pharos 3) head of Product is left 4) No usdm buy back for long time 5) only token dump and mega going to zero 6) the 50m raise from IDO are used to pay team salaries since megaeth as chain has very minimal income to cover their expense We all know what this leads to: next scroll or next bera. And there is nothing for our one year lockers to stop loosing on this investment. If you were me, what can do you do now?