full solo @MarketBubble stream highlights: 0:00 Won the Market Bubble Invitational trading comp! 0:54 Our next trading competition: the @BullpenFi 1→100K challenge 2:00 @pear_protocol x @BullpenFi: best weekly pear trade wins 50 $HYPE 4:20 New advanced hyperliquid charts on Bullpen! 5:30 The onchain edge polymarket has that nobody talks about 6:20 My read on Kevin Warsh's first FOMC 7:50 Deposit and withdraw crypto from your bank, 0 fees 9:00 Picking my first trade of the bullpen 1K: Minimax 11:40 How I'm trading minimax 12:50 Everything you can trade on bullpen 13:35 Why I'm long $MU $MRVL $SNDK into earnings 14:13 How I think about TA, live charting $MU and $INTL 19:50 Where I'm buying $MU 20:15 My $SPCX thesis and how I'm playing it 22:30 What's on my watchlist rn: $GOOGL $SOLS $BOT $SNDK 24:15 Where $HYPE goes from here 28:40 $BP vs $HYPE and why I'm investing in backpack 29:30 Answering your questions

🚨Fable 5.1 : possible staging. not a launch date. reported bedrock split: > fable-5-1 → 404 <model not found> > opus 5.1 / banana → 400 < identifier invalid> This basically means it can identify fable 5.1 but not staged right now

Announcing Fugu-Ultra v1.1 🐡 We’ve been thrilled by the reception to the Fugu model family. Thanks to everyone who tried it, shared feedback, and trusted Fugu with real work. Today, we’re releasing Fugu-Ultra v1.1 → Upgraded to incorporate the latest frontier models, resulting in stronger performance across every benchmark shown, including gains of up to 7.9 points over v1.0, with particularly strong results on ProgramBench and Terminal Bench 2.1. Fugu-Ultra v1.1 is more capable across coding, agentic tasks, and advanced reasoning, and available at the same price as Fugu-Ultra v1.0 The frontier keeps moving, and Fugu keeps getting better.

🆕 A new open-source 1.6 TRILLION-parameter MoE that scores 0.1 points behind Claude Opus 5 on an agent benchmark has been released. @NexEcosystem released Nex-N2.5-Max. It beats DeepSeek V4 Pro 0813 on all but one comparable benchmark. This thing is enormous! Stats ... 🧠 1.6 TRILLION parameters 🔀 Mixture-of-Experts 🤖 Built for agents + long-horizon workflows 💻 Coding + tool use 📚 262K context 📝 Text-only ⚖️ Apache 2.0 📦 Weights are already on Hugging Face Nex's reported results 👇 AutomationBench 🟣 Claude Opus 5 → 50.3 🔥 Nex-N2.5-Max → 50.2 🔵 GPT-5.6 Sol → 45.8 🟢 Qwen3.8-Max → 39.8 And on BrowseComp: 🔥 Nex-N2.5-Max → 92.6 🟣 Claude Opus 5 → 90.8 Terminal-Bench 2.1 → 86.1 Toolathlon Verified → 74.7 😁 You're probably NOT running this one under your desk. The official model repository is about 1.65TB, and Nex's recommended deployment uses this monster hardware .... 🎮 16× H200 🖥️ 2 nodes ⚡ TP16 + expert parallelism But here's why I'm posting the monster first. It has a 35B multimodal little brother. And THAT one may actually belong in your home AI lab. 👀🔥

Three new models are live! → Fable 5.1 → GPT 6 Astra → Grok Imagine 2, for images and video Same models you can get anywhere else. The difference is that here, nobody can read what you send them. Try it: USE CODE: OPENGRADIENT10

$80,000 leaderboard. 300 winners. 🥇 Top 1 → $10,000 🥈 Top 2 → $5,000 🥉 Top 3 → $3,000 …down to Top 201–300, $100 each. 20-day cumulative volume. Every day counts →

Bitcoin’s drawdowns are getting shallower. Deepest drawdown by halving epoch: 93.1% → 84.9% → 83.4% → 76.7% → 53.1% so far. Volatility remains. The pattern is changing.

Just sitting and reflecting on this GPT-6 Astra graph. GPT-6 Astra set a new SOTA on ARC-AGI. 🧩 ARC-AGI-1 → 98.5% 🧠 ARC-AGI-2 → 95.0% 🤖 ARC-AGI-3 → 99.95% But the ARC-AGI-3 result has a weird twist. ARC-AGI-3 drops Astra into a completely NEW interactive environment with no instructions. It has to ... 👀 explore 🧠 figure out the rules 🗺️ build a world model 📌 remember what it learned 🎯 develop a strategy 🔄 adapt when it's wrong ARC tested ~500 humans to establish how efficiently people solve these environments. GPT-6 Astra with ARC's standard agent harness: 62.7% Astra using the new Provider Adapter that preserves reasoning state between requests + compacts it: 99.95% 🤯 Give a powerful model a persistent working state + good compaction, and suddenly the same intelligence can behave VERY differently over a long task. That's going to matter for Local AI too.