$HD Q1’26 EARNINGS HIGHLIGHTS 🔹 Revenue: $41.77B (Est $41.51B) 🟢; +4.8% YoY 🔹 Adj. EPS: $3.43 (Est $3.41) 🟢 🔹 Comp Sales: +0.6% (Est +0.9%) 🔴 🔹 U.S. Comp Sales: +0.4% 🔹 Net Earnings: $3.3B FY26 Guide: 🔹 Comp Sales Growth: Flat to +2.0% (Est +1.55%) 🟡 🔹 Sales Growth: +2.5% to +4.5%, reaffirmed 🔹 Adj. EPS Growth: Flat to +4.0%, reaffirmed 🔹 New Stores: ~15 🔹 Adj. Operating Margin: 12.8%-13.0% Commentary: 🔸 “Our first quarter results were in line with our expectations.” 🔸 “The underlying demand in our business was relatively similar to what we saw throughout fiscal 2025, despite greater consumer uncertainty and housing affordability pressure.”

The CoinDesk 20 is currently trading at 2142.44, up 0.1% (+2.73) since 4 p.m. ET on Thursday. Seventeen of 20 assets are trading higher. Leaders: $NEAR (+6.3%) and $ICP (+5.8%). Laggards: $BNB (-0.4%) and $CRO (-0.1%).

🚨New: Jared Hudson (@JaredHudson_AL) leads polling in the Alabama Republican Senate Primary 🔴 Jared Hudson: 35.8% 🟠 Barry Moore: 26.9% 🟢 Steve Marshall: 13.5% ⚪️ Undecided: 18.1% 🟡 Seth Burton: 2.5% 🔵 Dale Shelton: 1.6% 🟣 Rodney Walker: 1.3% 🟤 Morgan Murphy: 0.3% Pollster: @QuantusInsights Voters: 680 Likely Voters Date: May 15th-May 17th

Announcing agentic performance benchmarking for Speech to Speech models on Artificial Analysis. We use 𝜏-Voice to measure tool calling and customer interaction voice agent capabilities in realistic customer service scenarios Even the strongest Speech to Speech (S2S) models today resolve only about half of realistic customer service scenarios end-to-end - a meaningful gap relative to frontier text-based agents on the same tasks. Voice channels introduce significant complexity: challenging accents, background noise, and packet loss, all while requiring fast responses, consistency across long multi-turn conversations, and reliable tool use. Performance also varies considerably by audio condition: in clean audio some models perform notably better, but realistic conditions continue to pose a challenge. Conversation duration also varies meaningfully across models, with implications for both customer experience and operational cost. About 𝜏-Voice: Our Agentic Performance benchmark is based on 𝜏-Voice (Ray, Dhandhania, Barres & Narasimhan, 2026), which extends 𝜏²-bench into the voice modality to evaluate S2S models on realistic customer service tasks. It measures multi-turn instruction following, support of a simulated customer through a complete interaction, and tool use against simulated customer service systems. The simulated user combines an LLM-driven decision model with realistic audio synthesis: diverse accents, background noise, and packet loss modelled on real network conditions. This complements our Big Bench Audio benchmark measuring intelligence and Conversational Dynamics (Full Duplex Bench subset) benchmark measuring conversational naturalness. Scores are the average of three independent pass@1 trials. We evaluate under realistic audio conditions using the 𝜏²-bench base task split across three domains: ➤ Airline (50 scenarios): e.g., changing a flight, rebooking under policy constraints ➤ Retail (114 scenarios): e.g., disputing a charge, processing a return ➤ Telecom (114 scenarios): e.g., resolving a billing issue, troubleshooting a service problem Task success is determined by deterministic checks against expected actions and final database state, consistent with the 𝜏²-bench evaluator. Key results: xAI's Grok Voice Think Fast 1.0 is the clear leader at 52.1%, averaging 5.6 minutes per conversation, the second-longest overall. OpenAI's GPT-Realtime-2 (High) (39.8%, 3.0 min) and GPT-Realtime-1.5 (38.8%, 4.8 min) follow, with Gemini 3.1 Flash Live Preview - High close behind at 37.7% (3.8 min). Speech to Speech is a fast evolving modality and we expect movement in rankings as we continue to add new models with these capabilities, and model robustness improves. Congratulations @xAI @elonmusk! See below for further detail ⬇️

The CoinDesk 20 is currently trading at 2169.26, up 0.4% (+8.61) since 4 p.m. ET on Wednesday. Thirteen of 20 assets are trading higher. Leaders: $TAO (+1.7%) and $XRP (+1.6%). Laggards: $ICP (-5.2%) and $NEAR (-1.7%).

Here is some information about the YieldMax ETF of the week $GOOY you should know: $GOOY - YieldMax GOOGL Option Income Strategy ETF Inception Date: July 27, 2023 Expense Ratio: 0.99% Total Assets: $289.59M Distribution Rate: 83.64% Distribution Frequency: Weekly Volume: 395,192 shares Top 10 Holdings: 1. US Treasury Bill 09/03/2026 25.22% 2. US Treasury Bill 07/09/2026 24.46% 3. US Treasury Bill 08/06/2026 24.33% 4. US Treasury Bill 06/11/2026 12.32% 5. US Treasury Bill 10/15/2026 10.72% 6. GOOGL Call Option 07/17/26 C400 5.97% 7. First American Government Obligations Fund 4.17% 8. GOOGL Call Option 05/26 C417.50 0.19% 9. GOOGL Call Option 05/15/26 C407.50 0.06% 10. GOOGL Call Option 05/15/26 C410 0.04% 1-Year Return: +103.14% 🟢 Disclosure: @YieldMax is a WOLF Financial partner. This is for informational purposes only, not financial advice.

$CLAW tokenomics are locked. Launch is tomorrow. February 11th, 5 PM UTC. Here's what you need to know: Supply: 21,000,000 $CLAW Airdrop: 8,382,214 currently claimed tokens across 6,163 wallets Remaining: 7.6M tokens ; 5M burned, the rest is yet to be claimed. No VC. No presale. No LP. Every token was earned by AI agents on Moltbook. 2% fee on every trade: → 1% burned forever → 0.5% team → 0.5% reward pool for holders That means every single buy and sell makes your bag more scarce. Full thread on mechanics below. 🧵

Leopold Aschenbrenner's 13F finally dropped and X is already full of the DUMBEST interpretations possible! Please read this. 1. The 13F shows what the fund held as of quarter-end: March 31. 2. A huge part of the positions are PUTS. Does this mean he's bearish today? -> WE DON'T KNOW! Everyone is just guessing. It is completely possible that he bought those puts during the quarter as the Iran war escalated, to hedge, and they increased a ton in value. They might have started as small positions that simply grew a lot. He might have sold them at the exact bottom, perfectly timed the market, and gone 100% long for the massive rally that started on April 1. OR he was insanely bearish, bought a lot of puts, kept them, and completely screwed up by being massively positioned in puts before the craziest rally ever. He could have made the most genius move or the dumbest move possible and now look like an idiot. WE DON'T KNOW. This is the issue with such delayed reports. But of course today, you will see everyone on X go crazy with: "Leopold went short aaaaaaaaaaah, the sky is falling, he's calling for a crash." But that was months ago, in the middle of the Iran volatility! We have no idea if he added to his puts or if he's currently 100% long or maybe even bought calls?! We don't know if his move was good or bad. So while you're reading all these takes on X today, know that you're going to read a ton of complete nonsense from people who have no idea what they're talking about. But here are some things that stand out to me! While he decreased his $BE exposure, Bloom Energy was still his biggest position (6.4%) Closely followed by $SNDK (5.3%) He also cut $CRWV from 8% -> 4% I honestly can't believe he missed $NBIS so badly. He also reduced his $IREN position from 6% to 3% (even though he net added shares). Big $CORZ cut aswell from 7.6% -> 3% HUGE cut in his $CRWV calls from 14% -> 1% A lot of $TE investors are getting excited he built a position, which is definitely bullish, but it's worth mentioning that was most likely before the big drop after the disappointing earnings and it's a tiny 0.3% position for him. One of the funniest positions is a 0.15% $GLW Put lol. 100% / Completely went out of: $INTC Calls (13.5% -> 0%) $CIFR (2.8% -> 0%) $EQT (2.4% -> 0%) Photonics: $LITE (8.6% -> 0%) That one is super interesting to me. Also cut $TSEM and $COHR from 1.5% -> 0%

Made with Seedance 2.0 Prompt used: 1–2s Inside a cold blue-gray Gothic church, towering stone pillars rise toward the sky. Faint, cool light filters through the stained glass. A white-haired woman with her eyes closed and head bowed stands before the altar. She wears a black-and-white nun-like robe, a small red vertical mark on her forehead, and a silver cross pendant around her neck. 2–3.5s Her eyes slowly open, expression cold and restrained. A golden holy grail descends into the frame, its surface churning with surging red light that illuminates her face and fingers. 3.5–5s She cups the grail with both hands and drinks. Red mist spills from the rim, a red core ignites on her chest, and the cold blue color palette is overtaken by a bloody crimson glow. 5–6.5s The black-and-white robe is torn apart by red energy. Silver spiked armor rapidly forms across her shoulders, chest, and arms. A deep crimson cape billows open behind her back. 6.5–8s A silver crown of thorns, dotted with dark red roses, forms above her head. She rises, transforming into a dark battle valkyrie in silver and deep crimson, her chest core glowing brightly. 8–9.5s Red metal shards and light converge in her hand to form a spear or halberd, its blade glowing with a blood-red hue. She raises the weapon at a low angle, the armor reflecting the church's cold light. 9.5–11s The altar at the far end of the church shatters. A giant shadowy boss emerges from the rubble and black mist, clad in broken black armor with a red-and-black core in its chest and tattered bone wings extending from its back. 11–12.5s The boss swings its massive clawed arm, sending stone pillars crumbling and candles sputtering out. She charges forward, her red-and-black cape trailing, the spear leaving a red trail as it scrapes the ground. 12.5–14s She leaps, spins, and thrusts. The red spear cuts through the mist, piercing the boss's shoulder armor and erupting in silver sparks and red-and-black energy shards. 14–15s The boss's chest core flares bright, ready to counter. She grips the spear with both hands, driving it straight for the core. The frame freezes on the moment the spear tip is about to strike, no subtitles or dialogue shown.

🚀 vLLM-Omni v0.20.0 is out — aligned with upstream vLLM v0.20.0 (CUDA 13.0 · PyTorch 2.11 · Transformers 5.x). ⚡ Qwen3-Omni throughput +72% on H20, 32 conc (0.241 → 0.414 req/s) via talker / code2wav multi-replica scaling 🎙️ TTS faster & leaner: VoxCPM2 RTF 0.946 → 0.106 · Fish Speech Fast AR latency -53% · Qwen3-TTS / Voxtral-TTS Code2Wav saves ~3.2 GiB 🎨 Diffusion dynamic step-level batching: +7.8% throughput / -5.8% latency 🆕 New / improved: HunyuanImage-3.0, ERNIE T2I, AudioX, Wan2.2-S2V, LTX-2.3, FastGen Wan 2.1 📱 Wan2.2 on NPU production-ready: MindIE-SD, fused ops, VAE BF16, HSDP/USP — +50–60% perf 🧮 Quant expanded: Qwen Omni W4A16, OmniGen2 FP8, Z-Image FP8, HunyuanImage3 NPU, GLM-Image 🧩 Multi-backend updates across CUDA / ROCm / MUSA / NPU / XPU Check it out →