Diamond partner Bright Data is what 70%+ of the world's leading AI labs use to train their models. From large-scale video data for robotics training to reliable agentic web access in production - it's the web data infrastructure the AI industry runs on. SuperAI Singapore, 10-11 June.

Embodied intelligence can’t afford bad data. Perle Labs is building AI data infrastructure designed for this reality: → Human-verified, expert-validated work → On-chain auditability → Sovereign and enterprise-grade design Built to benefit everyone.

Huawei IDI Forum 2026 is bringing with it a singular focus: AI runs on data and that demands the rapid evolution of infrastructure. So building a next-generation AI data center is no longer just optional—it's essential. From all-flash storage and resilient backup to virtualization and AgenticOps, let's redefine data infrastructure as a catalyst for real value creation in the age of AI. No other conference is like IDI. Register today!

Less than a week to the Alibaba Cloud x TiDB AI Innovation Night - and seats are filling up fast! From agentic AI and AI-ready data infrastructure to real-world deployment strategies and measurable ROI, hear how enterprises are turning AI ambition into business impact. Connect with industry leaders and peers over an evening of conversations, networking, dinner, drinks, and a few surprise elements along the way. Register now: Lumen #AlibabaCloudSG# #AlibabaCloudPartner# #AlibabaCloud# #AI# #DigitalTransformation# #LLM# #Qwen# #Wan# #ContentCreation# #DigitalUpskilling# #CloudComputing# #AInnovation# #TiDB# #LingYang# #AgenticAI# #GenAI#

Something big is just around the corner. So join us at the Huawei IDI Forum 2026 in Paris over May 20–21. Discover how Huawei Storage is building AI data infrastructure that aggregates high-value data, ensures data resilience, and improves processing efficiency, all while powering large model training and inference. Register today and be a part of data storage innovation in the AI era!

Most AI pipelines still optimize for throughput, not verifiability. Traditional pipelines break at scale: - Contributor identity isn’t tied to the data - Quality is hard to quantify consistently - Data lineage breaks across the pipeline So you lose visibility into what’s shaping model behavior. Perle restructures the intelligence layer: Experts → structured tasks capturing reasoning Evaluation → continuous scoring + consensus Output → high-signal datasets with traceable lineage This is what provenance-first data infrastructure looks like.

The gap between benchmark performance and real-world reliability is starting to become one of the biggest challenges in AI. Especially in areas like healthcare, legal AI, and robotics, where a technically “correct” answer isn’t always enough. These systems increasingly depend on: - Contextual reasoning - Expert judgment - High-quality human feedback loops Which is pushing the industry toward more specialized and verifiable data infrastructure.

**Summary: Discussion between Jeff Liang and Quant Alex Wu on Optimizing Option Order Execution and Slippage Capture** The core topic of their conversation is: **The current option limit order execution is poor (high slippage, low fill rate), essentially due to the lack of professional high-frequency / algorithmic market-making capabilities. They need to upgrade from “cutting meat with a blunt knife” to a sophisticated Delta-hedging + options market-making system.** ### 1. Problem Diagnosis - Current order placement feels like **“cutting meat with a blunt knife”** — poor queue position, low fill probability, and severe slippage. - Jeff provided concrete data: **Average loss of approximately $5.2 per executed option contract** (slightly less than 1 bp), including fees and rebates — still unacceptable. - Even with perpetual futures maker fee rebates helping a bit, the situation “cannot be ignored.” - **Price checking and adjustment frequency is NOT the root cause.** The real drivers are **fill probability** and **queue position**. ### 2. Fundamental Solution Direction (Alex’s View) - A robust **Delta-hedging system** shares significant technical overlap with high-frequency market-making systems for spot, futures, and perpetual contracts. Without this foundation, one is essentially powerless against adverse selection. - Using **maker orders for Delta hedging** is conceptually the same as **Delta-1 market making for inventory risk management** — the analogy made everything “suddenly clear.” - Options market making and Delta-1 market making are **tightly coupled**: - The Delta-1 system handles the Delta exposure of options. - Options themselves can provide protection for Delta-1 positions. ### 3. Technical Difficulty and Implementation Path - This requires entering the realm of **algo trading / HFT**, involving substantial research and engineering resources. - **Language requirement**: Python is **not sufficient**. Must use **C++ and Rust**. - **Target clients**: Institutional clients and high-net-worth individuals engaging in on-exchange block trading. - **Detailed step-by-step roadmap from scratch (Alex’s plan)**: 1. Collect large volumes of **order book data** (snapshots, incremental updates, tick-by-tick trades) for perpetuals + futures + options. 2. Build **fill probability models + queue models**, including: - Limit order arrival intensity - Fill probability - Queue position - Latency modeling 3. First implement and validate on **Delta-1 products**, then extend the backtesting system to support these HFT primitives. 4. Expand from Delta-1 / single option contracts to **all option contracts** (requires major redesign and validation due to performance demands). 5. Develop specialized algorithms for **limit order posting + aggressive crossing** to reduce overall slippage. 6. Finally, conduct small-capital live trading validation. Alex repeatedly emphasized: **“This project is genuine heavy industry.”** ### 4. Consensus - Delta-One research is the foundation for studying option fill probabilities. - Options market making must be deeply integrated with the Delta-hedging system — they cannot be treated separately. - The current phase is **infrastructure building**, requiring patient and significant investment. **Overall Assessment**: Alex provided a highly professional and systematic optimization roadmap, covering data infrastructure, modeling, and execution layers. Jeff focused on the business pain point (real slippage costs). Both fully agree that a fundamental rebuild of the underlying high-frequency system is necessary. This is a classic **quantitative execution optimization** discussion — starting from a clear business problem and pointing directly toward building institutional-grade HFT-level capabilities.

499 Detalks [CN] 225: Deconstructing the Agentic Web: Multi-Model Intelligent Routing & On-Chain Financial Infrastructure Accelerating AGI Landing Time：28, April, 2026, 20:00 (UTC+8) Hosted by 499 and co-hosted by @BAI_AGI, this Space explores how intelligent model routing, on-chain data infrastructure, and dedicated agent financial rails can bridge the gap and accelerate AGI into the real world. Mod: Charis @charis_em, 499 Core Member Guest Speakers: Jtsong @Jtsong2, Head of APAC, @0G_labs Anita @Anitahityou, APAC, @SentientAGI Leslie @leslieloser_ , Content Creator Mia @Artistkatty_ , Growth from Starchild Rika @rayrayweb5, KOL Key discussion topics include: -- From “chat tools” to “autonomous economic entities”: What is the single biggest bottleneck stopping AI Agents from true mass adoption -- Multi-model intelligent routing & data orchestration: How far has the industry come in routing across multiple models and data sources -- Agent Wallets & on-chain credit systems: The power of Machine-to-Machine (M2M) value transfer when every AI has its own wallet Space Link: Telegram Group: