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HOW YOUR ONCHAIN TRANSACTION ACTUALLY WORKS @get_optimum CEO @MurielMedard with v4.5 Ever swapped tokens on a DEX, clicked “Confirm,” and wondered why your tokens don’t arrive instantly? That short delay is where the real blockchain infrastructure begins. Muriel Medard breaks down the full onchain transaction journey using a simple visual analogy: ✓ 1. Transaction Origin Your swap starts from the “source” the user initiating the transaction. ✓ 2. Network Transmission The data moves through switches, routers, and networking layers that carry information across the system. ✓ 3. Flow Management Specialized software coordinates and manages how transaction data propagates through the network. ✓ 4. Shared Across the Network The transaction is distributed and validated across participants in the blockchain ecosystem. ✓ 5. Block Formation Finally, the proposer collects transactions and forms the next block onchain. Muriel visually demonstrates how transaction data flows from multiple directions, how blocks are assembled, and why confirmation takes time even if only a few seconds. Most importantly, she highlights how complex blockchain infrastructure truly is behind a single “Confirm” click. @get_optimum is working to improve this process using RLNC (Random Linear Network Coding) technology designed to make transactions faster, more efficient, and more reliable. @cryptooflashh @blockchainjeff @CryptoSundayz @tgogayi @aqccapital

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 ⬇️