AI Chronicle

  Recently, Alibaba officially released its brand-new AI model, QwenLong-L1–32B, a long-context reasoning model optimized with reinforcement learning (RL). This marks another major breakthrough for Alibaba in the field of artificial intelligence. The model, known for its exceptional ability to handle ultra-long contexts and outstanding reasoning performance, quickly became a focal point within the industry. Below is the latest information compiled by AIbase, offering an in-depth look at this groundbreaking model. Ultra-long Context Capability: 130,000 Tokens Shocks the Industry The most impressive feature of QwenLong-L1–32B is its astonishing capability to handle 130,000 tokens of context length. This allows it to process extremely large-scale text inputs, effortlessly handling complex, multi-layered information integration tasks. Compared to traditional models, QwenLong-L1–32B achieves seamless migration from short-context to long-context reasoning capabilities, showcasing its str...

  When we launched, we were happy just having  100 daily users . But within months, we hit  10,000 , then  100,000 . And scaling problems piled up faster than users. We aimed for 1 million users, but the architecture that worked for 1,000 couldn’t keep up. Looking back, here’s the architecture I wish I’d built  from day one  — and what we learned scaling under pressure. Phase 1: The Monolith That Worked (Until It Didn’t) Our first stack was simple: Spring Boot app MySQL database NGINX load balancer Everything deployed on one VM [ Client ] → [ NGINX ] → [ Spring Boot App ] → [ MySQL ] This setup handled  500 concurrent users  easily. But at  5,000 concurrent users : CPU maxed out Queries slowed down Uptime dropped below 99% Monitoring showed DB locks, GC pauses, and thread contention. Phase 2: Throwing More Servers (But Missing the Real Bottleneck) We added more app servers behind NGINX: [ Client ] → [ NGINX ] → [ App1 | App2 | App3 ] → ...

  When DeepSeek R1 launched in January, it instantly became one of the most talked-about open-source models on the scene, gaining popularity for its sharp reasoning and impressive performance. Fast-forward to today, and DeepSeek is back with a so-called “minor trial upgrade”, but don’t let the modest name fool you. DeepSeek-R1–0528 delivers major leaps in reasoning, code generation, and overall reliability. With this release, DeepSeek is positioning itself as a serious open-source challenger to Gemini 2.5 Pro, and in some cases, it even brushes up against the performance of OpenAI’s o3 and o4-mini on coding benchmarks. In this blog, we’ll dive into what makes R1–0528 tick, walk through its key new features, and show you how to access it. We’ll also run a hands-on comparison between R1 and R1.1, testing how both models perform on real-world tasks. What is DeepSeek R1 0528? DeepSeek R1 0528 (also referred to as R1.1) is the latest open-source large language model from DeepSeek, desig...