Chuan Cheng

𝗧𝗵𝗲 𝗯𝗲𝘀𝘁 𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝗹𝗲𝘀𝘀𝗼𝗻 𝗰𝗼𝘀𝘁 𝗼𝘂𝗿 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿 $𝟲𝟬𝟬 𝗮𝗻𝗱 𝗮 𝗺𝗮𝗿𝗿𝗶𝗮𝗴𝗲 𝗮𝗿𝗴𝘂𝗺𝗲𝗻𝘁. Our VP of Engineering, Xiaofan(James) Luan, was supposed to buy his wife a Dior bag for their anniversary.  Instead, he bought three Claude Code subscriptions and spent the holiday trying to cross-compile 2 million lines of C++. Every fix on one platform broke two others.  $600 later, the only output was "git reset --hard" — and a very cold dinner table.😂 "Make it compile on Windows" is a trap. The real goal was "compile everywhere without hacks" — no AI is going to figure that out for you at 2 am. What worked: constraints before code, review tests not code, bottom-up, one layer at a time. Same task, two days. Then he ran six parallel Claude sessions across three machines with git worktree. The bottleneck stopped being intelligence and started being how fast one person can alt-tab. AI solves exactly the problem you give it. Engineering is in knowing which one to give. His wife is still waiting for that bag. Full story: https://lnkd.in/gtsW_Wvk ——— Follow Milvus, created by Zilliz, for everything related to unstructured data

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We just published our first technical research note. "Low-Latency Event-Driven Architecture for Real-Time Options Execution on IBKR" It covers how we eliminated compounding feed lag at SPY market open — from a growing 45-second backlog to stable sub-second bar sealing — through: → Queue-first callback offloading (Python GIL problem) → Per-venue DOM isolation across ARCA, ISLAND, IEX → Event-time binning across 6 independent intervals → Single source of truth for all technical indicators No signals. No alpha. Pure infrastructure. This is the kind of work that happens before a single trade fires — and the kind that determines whether your system survives the opening bell. 📄 Download: https://lnkd.in/g-XJsYwG #AlgoTrading #OptionsTrading #MarketMicrostructure #SystematicTrading #Python #QuantitativeFinance #TradingInfrastructure

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In this post, we demonstrate how to integrate Amazon SageMaker HyperPod with Anyscale platform to address critical infrastructure challenges in building and deploying large-scale AI models. The combined solution provides robust infrastructure for...

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Introducing the M5 The M5 chip brings significant advancements in performance and efficiency across various applications. 1. Massive GPU / AI performance boost The M5 features a next-generation 10-core GPU architecture, with each core equipped with an integrated Neural Accelerator. This results in over 4× the peak GPU compute performance for AI tasks compared to the M4. 2. Stronger CPU and enhanced Neural Engine With up to a 10-core CPU (6 efficiency cores + 4 performance cores), the M5 offers approximately 15% faster multithreaded performance over the M4. Additionally, the Neural Engine has been upgraded to 16 cores, enhancing performance for on-device AI and intelligent features. 3. Higher memory bandwidth / unified memory improvements The M5 provides around 153 GB/s of unified memory bandwidth, which is approximately 30% more than the M4. This unified memory architecture allows all components (CPU, GPU, Neural Engine) to access a larger memory pool, enabling larger AI models to run entirely on-device. 4. Graphics & rendering enhancements The improvements in the GPU also boost graphics performance, including ray tracing capabilities. Apple claims up to a ~45% uplift compared to the M4 in ray-traced workloads, enhancing visual fidelity, rendering speed, and smoothness in demanding 3D and graphics applications. 5. Deployment in new hardware + energy efficiency / environmental goals The M5 will power new devices, including the 14-inch MacBook Pro, iPad Pro, and upgraded Vision Pro, all of which are available for pre-order.

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💭 How is open source important to you? We had a great moment with Anish Maddipoti from NVIDIA -- and learned how he got started as an open source developer. 👇

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Deploy Kimi-K2-Instruct, a frontier mixture-of-experts model, on Lambda using vLLM in four steps: 1️⃣ Spin up an NVIDIA Blackwell instance on Lambda. 2️⃣ Start a vLLM deployment server with a single setup block. 3️⃣ Run a reproducible benchmark against that server. 4️⃣ Share the exact model and image references to reproduce. Read the technical blog to get started 🔗

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