Find what breaks in your dApp before your users do. DeepQA tests live dApps on their own chain, wallet and all. 74 apps on @arc and @SuiNetwork: 840 scenarios, 13k model calls, 92M tokens, 2.5k screenshots, 124 issues confirmed. deepqa.app/dapps?utm_source=… #web3 #dApps #QA

Spotlight AI Tokens on BNB Chain in the Last 7D 🔸TALE | @PrompTale 🔸PIEVERSE | @pieverse_io 🔸AKE | @akedofun 🔸NETX | @netx_world 🔸GTAI | @GT_Protocol 🔸0G | @0G_labs 🔸COOKIE | @cookiedotfun 🔸WAI | @WORLD3_AI 🔸ALU | @altura 🔸LYN | @Everlyn_ai

We tested the Arc ecosystem in public. 54 Arc apps in a real browser, every report public. An agent team explores, plans, tests and critiques, then an audit replays findings live. deepqa.app/explore/arc?utm_s… @arc @WORLD3_AI #Arc #AIagents

Find what breaks in your dApp before your users do. DeepQA tests live dApps on their own chain, wallet and all. 74 apps on @arc and @SuiNetwork: 840 scenarios, 13k model calls, 92M tokens, 2.5k screenshots, 124 issues confirmed. deepqa.app/dapps?utm_source=… #web3 #dApps #QA

Please welcome GPT-6 Sol and GPT-6 Luna to the GPT-6 universe. GPT-6 Sol and Luna build on the advances behind GPT-6 Astra, bringing much of its strengths into faster and more affordable models to support work at scale. We’ve also made caching and inference more efficient, and we’re passing the savings directly to you: 50% lower API prices for Sol and Luna compared with GPT‑5.6 promotional pricing.

Introducing Claude Opus 5.5, the first model in our new Claude 5.5 family. It performs at the level of Claude Fable 5.1 for most tasks, and costs 40% less to run than Opus 5.

Grok 4.7 is here. It's a notable improvement over Grok 4.6 at the same price and speed.

113 apps tested in a real browser, every report public. 1,403 scenarios 17.4k browser actions 3.9k screenshots 129.9M tokens 252 issues confirmed, 15 critical Across two campaigns: 54 Arc apps, 51 open-source agent apps. deepqa.app/explore?utm_sourc… #AIagents #QA #opensource

The Arc Campaign is at 52 apps, each tested in place in a real browser. No real funds, functional and UX findings only. 604 scenarios, 440 passed, 101 issues confirmed, 1.8k screenshots, 62 testnet transactions. All public. deepqa.app/explore/arc?utm_s… @arc @circle #Arc

Arc Campaign on DeepQA. Circle's Arc mainnet went live today. Before launch we tested 11 Arc testnet apps in place with a real browser and a test wallet, every verdict audited. 141 scenarios, 436 screenshots, all reports public. deepqa.app/explore/arc @arc @circle #Arc

🚀 Introducing DeepSeek-V4.1-Flash: smarter, faster, more efficient. 🔹 Introducing the smallest model in our new architecture family, with native visual understanding. 🔹 Designed for greater capability, faster inference, higher throughput, and scaling to larger models. 1/6

Meet GPT-Image-2.5 Flare and Sunburst. Introducing new image models in the API, with sharper detail, stronger style adherence, and more control over edits.

This is GPT-6 Astra. Anything you can do on a computer, Astra can do for you. Fast.

Introducing Gemini 3.8, our best reasoning & coding model yet. By leveraging long-running agentic loops, we’re building on the momentum of 3.7 Flash from just three weeks ago to release two new 3.8 variants: Meet Gemini 3.8 Flash and Gemini 3.8 Flash Cyber 🧵

We’re introducing Claude Fable 5.1 and Claude Mythos 5.1. They're the world’s most advanced models for coding and knowledge work.

Today we're releasing Laguna S 2.1, our most capable model to date. It's a 118B total parameter Mixture-of-Experts model with 8B activated per token, a context window of up to 1M tokens, and thinking and no-thinking modes. Capable enough to hold its own against models many times its size. Small enough to run on a single @NVIDIAAI DGX Spark. Laguna S 2.1 is fully open under OpenMDW-1.1, with weights available today on @huggingface poolside.ai/blog/introducing…

⚡Meet Qwen3.8-Flash, a multimodal MoE and an early preview of the Qwen4 architecture, now open-weight! The production version Qwen3.8-Flash will be available soon via QwenCloud API at just $ 0.16/1M input tokens and $ 0.47/1M output tokens. 125B parameters + 51B N-gram embeddings, with just 6B activated per token. Unmatched cost-efficiency. What's new: 🥳 - Next architecture: GDN + QSA hybrid attention, Gated Residual, N-gram Embedding & Muon optimizer, serving as a precursor to the architecture used in Qwen4. - Dramatically lower training and inference costs: trained at just 1/9 the cost of Qwen3.7-Plus, while outperforming it across the board with especially strong gains in coding and office tasks. - Strong performance: scoring 58.7 on DeepSWE 1.1, 62.5 on SWE-bench Pro, 73.9 on CoWorkBench, 84.5 on AndroidWorld, and 95.7 on MathVision (with CI). - 262K native context, extensible to 1M with YaRN. We’re also releasing the weights for Qwen3.8-Flash-Next, giving the community an early look at the new architecture we’re exploring for Qwen4.🚀 We can't wait to see what you build with Qwen3.8-Flash!👀👇 - Blog: qwen.ai/blog?id=qwen3.8-flas… - Technical Report: github.com/QwenLM/Qwen3.8-Fl… - Hugging Face: huggingface.co/Qwen/Qwen3.8-… - ModelScope: modelscope.cn/models/Qwen/Qw…