The Reserve is verified on OpenSea. 1,913 engraved Notes on Robinhood Chain, each with its own gold cage that only grows. The blue check is theirs. The gold is yours. Redeemable in gold.

US NATURAL GAS FALLS 1.5% TO $2.913/MMBTU AS ELEVATED STORAGE AND STRONG PRODUCTION OUTWEIGH WEATHER SUPPORT. EIA STOCKPILES RISE 30 BCF TO 3.214 TCF, 5.2% ABOVE THE FIVE-YEAR AVERAGE, WHILE GAS OUTPUT HITS ~114.3 BCF/D AND LNG FLOWS SLIDE 1.7% W/W.

Watch out! 🚨 Whale 0xF626 is selling 102,913 $ETH($248.6M). 11 hours ago, 0xF626 received 102,913 $ETH($248.6M) from multiple wallets, then deposited 2,858 $ETH($7M) into exchanges 20 minutes ago. The whale still holds 100,055 $ETH($241.6M).

Tron Inc. (NASDAQ: TRON) acquired 150,913 TRX tokens today at an average price of $0.3313 further increasing its TRX treasury holdings to more than 704.3 million TRX in total. The company aims to further grow its Tron DAT holdings to enhance long term shareholder value. For live update on the designated on-chain TRX treasury wallet for Tron Inc., please refer to:

Weeks of controller work. 48 hours. One GrokBot. 1,913 journal entries recorded Journal Entry backlog: 3,864 → 168 3,244 duplicates thrown out Saturday 10am: 949 entries processed in one hour Wow.

A @UiPath AI agent inside Maestro Flow took 10.6 seconds. TypeSafe AI Jev took 3.9 seconds. Then I noticed something much more important than latency. The agent was 3x slower. THE PROBLEM ☕ Card disputes are high-volume and repetitive. Most cases do not need deep reasoning, but we still often send every case through either: • A human analyst • Or a full LLM agent Both are expensive ways to answer relatively simple decisions. THE BUILD 🧩 I created one UiPath Maestro Flow with two decision paths. 1️⃣ TypeSafe AI Jev through a simple HTTP call 2️⃣ UiPath AI Agent with the same input and output schema Same 10 disputes. Same task. Same cloud environment. Then I measured them. THE RESULTS 📊 ⚡ Jev: 3,913 ms per dispute 🤖 AI Agent: 10,624 ms per dispute The AI agent was 3x slower. Jev processed the batch for roughly $0.000042 per dispute. But speed was not the biggest lesson. THE PART I DID NOT EXPECT 💡 I had created a rule: Confidence < 0.60 → Human Review Sounds reasonable. Except Jev returned 1.00 confidence on 8 out of 10 cases. My confidence gate almost never had a chance to fire. The workflow was still safe because uncertain cases eventually routed to a human. But the benchmark exposed a weakness in my architecture, not the model. THE CASE THAT GOT INTERESTING 🎯 One customer said: "There is a $42 charge I don't recognize, but my son sometimes uses my card." Jev: fraud_unauthorized → 0.75 calibrated probability AI Agent: no_valid_basis → 0.62 self-reported confidence That distinction matters. One number is designed to represent probability. The other is an LLM estimating its own confidence. MY TAKEAWAY 🧠 This is not: "Small model beats AI agent." Agents are powerful when the situation requires actual reasoning. But if the question is predictable and structured, why pay LLM latency for reasoning you do not need? Classifier for predictable decisions. Agent for ambiguous reasoning. UiPath Maestro decides which one runs. That orchestration layer is the most interesting part of the experiment. 🔗 Complete flow, agent and setup guide: 🎥 YouTube walkthrough: Are you still paying an agent to make decisions a calibrated model can already answer? 👇 #UiPath# #Jev# #TypeSafeAI# #AIAgents# #AgenticAI# #MaestroFlow# #EnterpriseAI# #1AIFanatic#

I pay Moonshot $100💸a month for Kimi K3, and I still ration its tokens. vLLM can’t use an FP8 KV cache on Ampere for its QSA path. My GPUs don’t have native FP8 support, so there are 4 specific spots in the code where everything crashes. K3 read the code, found all 4, and added the missing decoding path. The first version ran 22.8 times slower than BF16. He didn’t care about that at all and didn’t stop. He figured out why on his own, rewrote the tiling, ran a sweep through the configurations, and got the kernel to 1.05x BF16 speed on decode and 1.45x on prefill. The KV cache grew from 104,425 tokens to 178,913 in the same memory. Awesome. I’d periodically walk over to the Mac to see how things were going. In another session that same day, my W4A16 build was spitting out the same garbage string on every run. K3 managed to figure it out and traced it back to a single missing 2-byte scalar in the 180B model. It keeps missing with its own edit tool. I don’t give a damn, honestly. I’ve never said this about a model before. I love this thing. Very much. So @Kimi_Moonshot, my only complaint isn’t with the model at all... The limits on Coding Plan for $100 are way too low. I burn through them and then I just wait. I’d run K3 3x as much if you let me. For now, I’m saving it. I’m reserving it for kernels, inference engines, and those runs that really matter, and it hasn’t let me down once. Thanks, Kimi - my heart is with you ❤️