機械学習

Dramatically Improve LLM and RAG Accuracy: The Power and Implementation of Microsoft’s Official Document Converter “MarkItDown” When integrating Large Language Models (LLMs) like ChatGPT or Claude into business processes and products, many developers encounter a major bottleneck: reading and parsing office documents such as PDFs, Word files, and Excel spreadsheets. Feeding unstructured text directly into LLMs leads to significant technical debt, including hallucinations (generating ungrounded responses), increased costs due to unnecessary token consumption, and a loss of contextual meaning. ...

“AI Engineering from Scratch”: A Hardcore, Framework-Independent Curriculum for Rebuilding LLMs from Math and Code “I feel like I’m hitting a wall just writing wrapper code for LangChain and LlamaIndex.” “I built an AI agent, but I can’t logically explain what kind of reasoning or control is happening under the hood.” In the midst of today’s massive shift toward AI, many engineers share this exact anxiety about dealing with “black boxes.” ...

Blazing Fast on Smartphones: How the Ultra-Lightweight 1B Model “MiniCPM5-1B” Is Shaping the Future of On-Device AI Against the trend of ever-growing Large Language Models (LLMs), several challenges are being raised in the development community. “Cloud API costs are squeezing business margins” and “Network latency is unacceptable for real-time responses”—as a decisive solution to these issues, “edge (on-device) AI” is rapidly gaining attention. Emerging at the forefront of this movement is “MiniCPM5-1B,” an ultra-lightweight model with just 1 billion parameters (1B). In this article, from the perspective of TechTrend Watch, we will thoroughly unpack the technical background and practical applications of this tiny model, explaining how it achieves state-of-the-art (SOTA) performance that defies conventional wisdom. By reading this, you will gain a clear roadmap for next-generation AI application development, freed from the shackles of high costs and latency. ...

1. Introduction: Why We Must Redefine “RAG” Today Large Language Models (LLMs) represented by ChatGPT and Claude have fundamentally transformed enterprise business processes and product development. However, when developers attempt to integrate these models into actual enterprise systems or products that handle specialized documentation, they invariably run into a massive wall. This obstacle manifests as “hallucination”—where the model plausibly outputs incorrect information—and the inherent limitations of training data, as models do not possess confidential internal data or real-time, up-to-date information. ...

A Reintroduction to Financial Engineering for Data Scientists: A Unified Mathematical Map from SDEs to Copulas and HFT “I have data science and machine learning (ML) skills, but the mathematical formulas of Quantitative Finance are too daunting, and I don’t know how to apply them in practice.” Not a few data scientists have avoided the field with this mindset. However, this perception might be causing a massive loss of opportunity. In fact, for the AI-native generation of data scientists, understanding the mathematical models of financial engineering is the ultimate weapon to dramatically expand their modeling repertoire. ...

[Intuit Cuts 3,000 Jobs] The AI Shift: Seismic Changes in Developer Careers and Strategies for Survival Intuit, the US accounting and financial software giant, has announced layoffs of over 3,000 employees, representing approximately 10% of its global workforce. This news cannot be dismissed as mere “restructuring to cut fixed costs.” At its core, it represents an extremely drastic talent portfolio reallocation (refocus) aimed at shifting 100% of corporate resources toward the field of AI. ...

Breaking the Limits of Local LLMs: Exploring “Forge”—The Hardening Framework achieving a “99% Tool Calling Success Rate” on Lightweight 8B Models With the rise of local LLMs (Large Language Models), the environment for individual developers and enterprises to run models autonomously on their own servers is rapidly maturing. However, when attempting to build production-ready “AI agents,” many developers hit a common wall. This is the “reliability barrier”: when relying on lightweight 8B (8 billion parameter) class models for Tool Calling or complex multi-step tasks, output formats break, logic fails, and processes abruptly halt. ...

AI is a “Technology,” Not a “Product.” Daring Fireball’s Survival Strategy for 2026 “The era of selling AI as a product has officially come to an end.” A new consensus is emerging among global tech leaders. John Gruber’s (Daring Fireball) assertion that “AI is a technology, not a product” serves as a sobering judgment on the overheated AI bubble. From 2023 to 2025, we witnessed a parade of “AI tools” popping up like mushrooms after rain. However, in 2026, the companies remaining in the market are not those flaunting “AI itself.” Instead, the survivors are those that have concealed the powerful engine of AI—much like an internal combustion engine—to solve existing user problems with overwhelming resolution. ...

“In the Age of AI Coding, Why Are We Still Using Python?” — How Infrastructure Costs and Execution Speed Are Redefining Next-Gen Language Selection In the world of software engineering, Python has reigned supreme as the king of “development efficiency” for decades. However, with the rise of generative AI, a quiet but decisive tectonic shift is occurring in its absolute position. The question being asked is: “If AI is the one writing the code, why should we continue using Python, a language whose primary focus is human readability?” ...

Why “Local AI” Is Becoming the Standard: The 2026 Paradigm Shift to Edge AI for All Engineers The technological tide is currently reaching a definitive turning point. Until now, “using AI” has been synonymous with sending requests to APIs provided by giant providers like OpenAI. However, that common sense is rapidly becoming a thing of the past. At the forefront of engineering, the philosophy that “Local AI needs to be the norm” is gaining rapid traction. Heading toward 2026, why must we break free from “cloud dependency” and learn to harness intelligence on our own machines? This article explores the technical necessity of this shift and the skills engineers must acquire to stay ahead. ...