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IT'S HAPPENING! Chick-fil-A is bringing the Chicken & Waffles AND S'mores Milkshake NATIONWIDE! Starting August 24th you'll be able to get the new Chicken & Waffles sandwiches ALL DAY LONG that feature maple waffles for the bun, a honey butter spread, applewood smoked bacon, and your choice of a regular or spicy filet. You also get a side of syrup to really layer on the goodness! And then we're getting S'mores Frosted Coffee and Milkshakes! These feature a marshmallow syrup blended with their ice dream and then mixed with chocolate covered graham cracker crumbs, and the milkshake has a marshmallow whipped cream on top! I'll have a review up soon, but this might be the best seasonal menu they've ever done. What are you grabbing from @ChickfilA on August 24th? #chickfila# #fastfood# #chickenandwaffles# #smores# #milkshake#

MiMo-V3 is getting a new architecture. The core of it, HySparse2, is out today. Less prefill, a smaller KV cache, better long-context retrieval—and we got all three at once. Compared with MiMo-V2.6's Hybrid SWA architecture: • 5.02× lower prefill FLOPs at 1M tokens • 4.5× smaller KV cache at 1M tokens • Better MRCRv2 and RULER-v2 scores, plus lower AgentPPL and LongPPL Why build a new architecture? Agentic inference is a very different workload. Each round, a short action can return a long observation that needs to be prefilled, while the context keeps growing. That puts prefill cost, KV-cache size, and retrieval accuracy on the critical path at the same time. HySparse2 tackles all three with two levels of KV sharing: • KV Bridging: Following YOCO, full-attention layers in the cross-decoder build their K/V from self-decoder hidden states. • KV Reuse: Within each hybrid block, sparse layers reuse the preceding full-attention layer's KV cache and selection indices. Two more changes: token-level selection replaces block-level selection, and a forced window of recent tokens replaces the separate SWA branch, so local and global tokens share one KV cache. Since all cross-decoder KV caches now come from the self-decoder, prefill can stop once the self-decoder finishes. Paper:

📢 GPT-6 Sol & GPT-6 Luna Are Now Live on @OpenAI’s latest GPT-6 series models are now fully available on Both models feature a 1.05M context window, 128K max output, vision understanding, Computer Use, and 6-level adjustable reasoning intensity (from none to max): ☀️ GPT-6 Sol: The primary model for complex software engineering and agentic workflows. Scores 68.8% on DeepSWE v1.1, delivering repository-level coding and automation at ~80% lower cost per task. 🌙 GPT-6 Luna: Built for high-volume, focused workloads. Scores 66.6% on DeepSWE v1.1 at an ultra-low entry price of $0.10 / 1M input tokens, making flagship-grade reasoning highly cost-effective. Now available on both API and Web Chat! 👉 Try now:

Introducing Xiaomi MiMo-V2.6 — Pro & Flash. Frontier intelligence, all the modalities, built in public. 🔹 Two omnimodal models, advancing through scaled reinforcement learning 🔹 Pro performs on par with Claude Opus 5 and GPT-5.6 Sol across most agent benchmarks 🔹 Pro scores 46 on the Artificial Analysis Intelligence Index — the highest among open-source models 🔹 Stronger coding, computer use, 3D reasoning and creative capabilities 🔹 Open model weights, technical report, RL environments and training code Blog：

Introducing OUI-1: the first open-weights model for Generative UI 71.7% on Generative UI Bench at 4B params. Beats Gemma 4 31B with 8× fewer active params, and scores 5.5× the base DiffusionGemma it was fine-tuned from. Methodology, weights, and full benchmark results in the blog 👇

Earn over 300 RMB per hour by scratching others’ itch? A 32-year-old man has opened two stores in Shenzhen, #China# , by providing itching relief services. People’s demands nowadays are really rather peculiar.

@Apodex_AI Really interesting release from ApodexAI. Apodex 1.1 isn’t just about better benchmark scores. It centers on working capability: getting complex multi‑step work done with files, search and code, not just generating polished reports.

⚓️ Ethra Ship Notes｜Vol.050 Today, I came across a number that made me stop. Nearly 1,900 vessels are now considered part of the maritime "dark fleet" according to Windward, roughly tripling since the Russia-Ukraine invasion. The interesting part isn't just the number. It's what the number tells us about visibility. A ship can disappear from AIS. That doesn't mean the ship disappears from the ocean. It just disappears from one information layer. And that's a very different thing. This is where I think Sea Verity's approach gets interesting. Instead of asking a single system to tell us the truth, it combines different sources. Reporters on the ground. AI analysis. Controller nodes. Each piece adds another layer of evidence. And rather than forcing every observation into a simple "true" or "false", the system is designed around confidence scores. I like that approach. Because the physical world rarely gives us perfect information. A satellite image can be obscured. A reporter can only see part of a vessel. AIS can disagree with visual evidence. Weather can make everything harder. The honest answer isn't always certainty. Sometimes it's: We're 90% confident this is what happened, and here's why. That's much more useful than pretending the other 10% doesn't exist. For insurers, traders, logistics companies and regulators, knowing the confidence behind a piece of maritime intelligence could be just as important as the information itself. The ocean is enormous. Maybe the answer isn't one perfect signal. Maybe it's many imperfect signals learning how to verify each other. @EthraShip #EthraShip# #EthraShipProtocol#