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Retell AI: Pioneering Human-Like Voice Intelligence for Enterprise Automation Founded in under two years, Retell AI has achieved over $36 million in annual revenue, serving thousands of enterprise clients with stable repeat usage in North America and the Asia-Pacific. --- From “Understanding” to “Thinking and Responding” Voice technology has recently
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In Part 1, we explored the core concepts and datasets that are driving geospatial analytics within the automotive industry. In Part 2, we’ll dive into the practical steps required to build scalable geospatial pipelines using AI, ML, and synthetic data—while ensuring governance and maintaining performance on Databricks. We’
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# Reinforcement Learning for Large Language Models — From Theory to Ultra-Large-Scale Systems *Date: 2025‑11‑12 • Location: Zhejiang*  Reinforcement learning (RL) is a **key method for advancing large language models (LLMs) toward higher intelligence**. It remains the most critical
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Scaling HNSWs in Redis Original article: Scaling HNSWs (via Hacker News) Salvatore Sanfilippo has spent much of this year working on vector sets for Redis, first released in Redis 8 (May 2024). --- Background A central part of this effort involved implementing HNSW — Hierarchical Navigable Small World, an indexing technique
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Purpose-Built Infrastructure: Lessons from China’s EV Highways for AI Agent Runtimes China’s rollout of electric-vehicle (EV) highways — lanes equipped with built-in chargers and intelligent traffic controls — wasn’t just a leap forward in transportation. It’s also a masterclass in how infrastructure can unlock speed, safety, and scalability.
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# 🛠 Large-Scale System Refactoring: Lessons from a 24-Month Journey ## 📚 Table of Contents 1. **Definition of “Refactoring”** 2. **Refactoring Decisions: When to Take Action** 3. **Scope of Refactoring: Migration or Reconstruction** 4. **Refactoring Strategies: Using the Strangler Pattern** 5. **Risk Control: Preventing Failures** 6. **Data Migration: Tackling the Hardest Challenge** 7. **Core
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Reinforcement Learning for LLMs: Scalable Infrastructure on Google Cloud As Large Language Models (LLMs) advance, Reinforcement Learning (RL) is becoming essential for aligning these models with human preferences and complex task goals. Yet, enterprises face significant hurdles when implementing RL at scale: * Memory contention from hosting multiple large models simultaneously
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High-Impact AI & Tech Projects Overview ggml-org/llama.cpp Stars: 75.7k License: MIT llama.cpp is a pure C/C++ LLM inference engine designed for high performance and easy setup. Key Features * Cross-platform support: Works on local and cloud hardware * Zero dependencies: Pure C/C++ implementation * Apple Silicon optimization:
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# Large–Small Model Collaboration in Intelligent Investment Advisory  Leveraging **the precision and performance of traditional quantitative small models** combined with **the flexibility of large-model Agents**, we can achieve: - **Problem identification** - **Task expansion** - **API calls to
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Disclaimer The details in this post are derived from publicly shared information by the Uber Engineering Team. All credit for the technical content belongs to them. Links to original articles and sources can be found in the References section. We have analyzed the details and added our own insights. If