Articles

This article distills 21 invaluable lessons learned over 14 years at Google, moving beyond technical coding prowess to focus on the broader skills that define successful engineers in large organizations. The author highlights that thriving engineers excel at understanding user problems deeply, collaborating effectively, and prioritizing clarity over cleverness in their work. Key themes include the importance of bias towards action and shipping, managing technical debt and novelty, and understanding that one's impact often relies on advocacy from others. The article also delves into organizational dynamics, such as recognizing that slow teams are often misaligned, focusing on controllable aspects, and understanding the true cost of abstractions. It stresses the value of writing for clarity, the invisible yet priceless 'glue work,' the long-term benefits of networking and continuous learning, and the eventual shift where time becomes more valuable than money, advocating for deliberate career choices and compounding expertise.

This article uses the AI email tool Superhuman as an example to detail its method of finding and optimizing PMF (Product-Market Fit) in the fiercely competitive email market, achieving $35 million ARR. Superhuman precisely targeted executives, entrepreneurs, and high-performers who consider email a key productivity tool, and built the product around 'extreme speed' as its core. The article focuses on the four-step 'PMF Engine' proposed by founder Rahul Vohra: quantifying PMF through Sean Ellis's 'very disappointed' metric, accurately segmenting users to find 'High-Expectation Customers' (HXC), analyzing feedback to leverage strengths and address weaknesses, and continuously repeating the process to elevate PMF as a core metric. This method provides a clear and actionable framework for startups on how to systematically find, measure, and optimize PMF.

An in-depth interview with OpenAI Chief Researcher Mark Chen, covering OpenAI's internal operations, core values, and future outlook. Chen discusses the fierce AI talent war, including Meta's 'soup-based talent acquisition strategy,' and how OpenAI retains top talent through its unique culture and vision. He also reveals his key role in uniting researchers to facilitate Sam Altman's return during the OpenAI internal power struggle. Chen emphasizes that OpenAI is essentially a pure AI research company, investing heavily in exploring next-generation paradigms, and highlights significant breakthroughs in pre-training, expressing confidence in surpassing Gemini 3's model performance. The interview delves into AI's potential in scientific discovery, the definition and progress of AGI, and the vision of collaborating with Jony Ive's team to explore future AI devices and new interaction methods, while emphasizing the importance of AI Safety and Alignment, including OpenAI's efforts in identifying models' tendencies for planning and strategizing, offering readers a deep dive into OpenAI's strategy and the cutting edge of AI.

The article explores the risks of code quality control and potential logical defects introduced by AI Coding assistants while improving development efficiency. Addressing the key challenges of manual review, trust in existing code, and demand communication, Meituan's technical team proposes collaborative strategies based on unit testing. Strategy 1 emphasizes that unit tests quickly and objectively verify the logical completeness and correctness of AI-generated code, avoiding the false sense of security. It provides a detailed description through a case of hidden bugs in the paging query interface. Strategy 2 focuses on building a unit test safety net before AI modifies existing code, ensuring that modifications do not produce unexpected results. Taking the user scope expansion of the message delay reply strategy as an example, it shows how to safely evolve existing code. Strategy 3 advocates applying TDD (Test-Driven Development) to AI development, using test cases as precise instructions and acceptance criteria to convey complex requirements to AI, driving AI to implement the complex logic of the coupon rules engine through the Red-Green-Refactor cycle. The article concludes with practical points such as environment configuration, mastery of unit test syntax, scenario selection, and continuous maintenance. It points out that unit testing has evolved from a development burden to a 'quality engine' in the AI Coding era. Developers have transformed from passive reviewers to active demand designers and quality controllers, realizing effective collaboration between humans and AI.

This article is FreeS Fund's review of the AI industry in 2025 and its outlook for 2026. The report points out that in 2025, global AI entered a period of rapid change, fueled by US and China. Open-Source AI (like DeepSeek) is gaining prominence in China, reshaping the AI landscape. Tech advancements are converging on native multimodality, enhanced reasoning, extended context with memory, and intelligent agent capabilities. Physical AI and World Models are emerging as key paradigms for AI's integration into the physical world. Funding is evolving from single cloud providers to broader multi-party collaboration. At the application layer, AI applications face commercial implementation difficulties and the 'Productivity Paradox,' requiring entrepreneurs to focus on specialized areas and integrate software and hardware. Looking ahead to 2026, the article believes that AI will develop towards online continuous learning and is expected to break the 'Productivity Paradox,' while investment strategies in AI will prioritize business fundamentals over pure technology narratives.

This article details enhancing OCR workflows with open-source VLMs. It begins by outlining the capabilities of modern OCR models, including text recognition, handling complex document components (such as tables and charts), and various output formats. Next, the article compares current mainstream open-source OCR models, analyzing their features, performance, and multilingual support. The article recommends benchmarks and discusses the importance of cost-efficiency and open-source datasets for model evaluation. Finally, it introduces advanced applications beyond traditional OCR, like visual document retrieval and VLM-based document question answering, offering guidance for building comprehensive document AI systems.

Tanay Kothari, founder of Wispr Flow, a $700 million AI voice input product, discussed its core philosophy. He argued that the keyboard is a bottleneck in human-computer interaction, and voice offers a superior alternative by minimizing cognitive load during thinking and editing, thus enabling users to concentrate on creation. Wispr Flow's success stems from addressing intent-based dictation—understanding and delivering the user's desired outcome—rather than simply transcribing verbatim, resulting in an impressive zero editing rate of up to 89%. Kothari highlighted the distinction between human speech and writing, a nuance often missed by current AI products. He also shared insights into the 'three transformative experiences' users encounter when transitioning to voice input: an impeccable initial experience, voice solving critical pain points, and a significant decrease in keyboard dependency. Looking forward, he envisions voice AI as an intelligent layer with contextual awareness, reshaping office environments and interpersonal communication for more personal and meaningful exchanges.

The article systematically outlines the development history of recommender systems from their inception in 1992 to the present day. It elaborates on four key stages chronologically: the Origin (the dawn of collaborative filtering), the Development (the Netflix Prize and the era of feature engineering), the Maturity (the full dominance of deep learning), and the Future (the paradigm shift in the era of large language models). The article introduces early systems such as Tapestry and GroupLens, intermediate technologies such as matrix factorization and GBDT+LR, and deep learning models such as Wide & Deep and DIN. Furthermore, it delves into the application of large language models in new paradigms such as generative recommendation, prompt learning, and RAG. It also analyzes the latest industrial practices of companies such as Meta, Kuaishou (a popular short-video platform in China), Alibaba (a leading e-commerce company), and ByteDance (the company behind TikTok and Toutiao, popular content platforms in China). Finally, the article summarizes the insights from the co-evolution of data and algorithms, and the mutual promotion of academia and industry. It provides suggestions for practitioners and anticipates the future challenges and directions of recommender systems.

The article provides an in-depth introduction to the DeepSeek team's latest open-sourced DeepSeek-V3.2 series of Large Language Models (LLMs). DeepSeek-V3.2 focuses on balancing practicality, excelling in general Agent tasks and tool utilization, achieving the highest level in Agent evaluations among open-source models. DeepSeek-V3.2-Speciale emphasizes extreme inference, integrating mathematical theorem proving capabilities, with inference benchmark performance comparable to Gemini-3.0-Pro, suitable for complex mathematical problems and programming competitions. Core technological innovations include the introduction of the DSA (DeepSeek Sparse Attention) mechanism, reducing the computational complexity of long sequences to O(L·k), significantly lowering inference costs. The team also invested heavily in Reinforcement Learning (RL) training, allocating a computational budget exceeding 10% of the pre-training cost. They further developed a stable and scalable RL protocol. In terms of Agent tasks, the model's generalization ability is significantly enhanced through novel context management, clever prompt design, and automated environment synthesis pipelines. Although there is still room for improvement in world knowledge breadth and Token efficiency, these models represent a significant breakthrough for the open-source AI community.

This article delves into the key technology behind DeepSeek V3.2's significant 40% improvement in Agentic capabilities: 'Interleaved Chain of Thought'. This technology aims to solve the 'State Drift' problem that occurs in large models during complex multi-turn interactions, where the model forgets early instructions or plans when executing long-horizon tasks. The article points out the curse of 'implicit reasoning' in the traditional ReAct paradigm, where the model struggles to maintain continuity of thought after tool invocation. 'Interleaved Chain of Thought' enables the model to alternate between reasoning and tool invocation, explicitly recording and reusing the thinking state of each round, effectively providing the Agent with a 'hippocampus'-like memory structure, thereby achieving stable and cumulative long-range planning. Data from the MiniMax M2 model shows that this mechanism brings a modest improvement in low-perturbation environments (such as SWE-Bench), but in high-perturbation environments (such as BrowseComp and Tau²), performance increases by up to 40% and 36%, demonstrating its strong ability to resist environmental disturbances. The article further clarifies that the true generalization ability of an Agent is not simply about increasing the number of tools, but the ability to adapt to various disturbances in the task trajectory. 'Interleaved Chain of Thought' enables the model to better adapt to different environments and tool return formats through a self-correction mechanism. MiniMax, as the promoter of this technology, actively transforms open-source infrastructure to promote the 'Interleaved Chain of Thought' API protocol to an industry-wide standard. DeepSeek V3.2 and Kimi K2 Thinking's adoption further validates that 'explicit, interleaved, and persistent thinking' is now an industry consensus for AI Agent evolution.