本项目专注于在1913年之前的文本上训练大型语言模型(LLMs)，旨在创建尽可能大的历史LLMs。项目提供信息中心，汇集相关资源，促进历史文本的AI应用。这一探索有助于推动自然语言处理技术在历史研究领域的创新，为AI发展提供新视角。通过专注于历史数据，项目可能解锁文本挖掘、语言演化分析等前沿应用，增强AI在文化传承中的价值，同时为开发者提供开源平台支持。
原文链接：Hacker News
历史文本训练的LLMs项目
未经允许不得转载：Toy Tech Blog » 历史文本训练的LLMs项目
本项目专注于在1913年之前的文本上训练大型语言模型(LLMs)，旨在创建尽可能大的历史LLMs。项目提供信息中心，汇集相关资源，促进历史文本的AI应用。这一探索有助于推动自然语言处理技术在历史研究领域的创新，为AI发展提供新视角。通过专注于历史数据，项目可能解锁文本挖掘、语言演化分析等前沿应用，增强AI在文化传承中的价值，同时为开发者提供开源平台支持。
原文链接：Hacker News
This article from the V2EX tech community explores a workflow optimization challenge: the current need to manually read prototype diagrams, analyze requirements, and organize prompts before inputting them to Claude Code for code generation. The content discusses how to enable AI to automatically complete this process—allowing AI to directly read prototype diagrams, perform requirement analysis, and automatically generate prompts, achieving full automation. This exploration focuses on the deep application of AI in the software development process, aiming to improve efficiency and reduce manual workload. The article provides practical technical insights, demonstrating the combination of AI prompt engineering and prototype processing, offering developers cutting-edge automation concepts, and serving as a typical case study of practical AI application.
Original Link:V2EX Share & Discover
CommerceTXT is an open standard protocol designed specifically for AI shopping contexts, aiming to solve the fragility issues when AI agents extract price and inventory data from HTML. The protocol adopts a CC0 public domain license, providing deterministic real data, similar to a combination of robots.txt and llms.txt, but specifically tailored for transaction scenarios. Its core technical features include: fractal architecture design, allowing AI agents to retrieve data on demand; strict read-only design to avoid security risks; extremely high token efficiency, with product definitions requiring only about 380 tokens, saving approximately 95% compared to HTML equivalents; anti-hallucination mechanisms, including inventory instructions with timestamps and review instructions with verified sources. This standard is expected to significantly improve the efficiency and accuracy of AI agents in processing e-commerce data, reducing token consumption and misinformation.
Original Link:Hacker News
Mori v0.1.0 is a virtual AI girlfriend agent system built on the AgentScope framework, now open-sourced on GitHub. The system supports multiple AI model providers (such as OpenAI, Claude, etc.), uses Gradio as the GUI frontend, and utilizes Jinja2 templates for prompt rendering. Key project features include Mem0-based long-term memory, model-separated configuration design, multi-agent auto-registration, and tool auto-registration mechanisms. Although the project is still in its early stages and prompts require further optimization, future plans include integrating more built-in tools, MCP and TTS functionality, supporting multimodal interactions, optimizing the frontend interface, and adding Live 2D control features. This project represents an active exploration in the field of AI virtual companion applications, providing new technological pathways for AI multimodal interaction and personalized experiences.
Original Link:Linux.do
Users who have long relied on AI Studio as their primary AI platform are facing challenges due to Google's tightened usage limits. Despite having multiple accounts, migrating chat histories between them is extremely cumbersome, especially when dealing with image content. Users mention struggling with GCP setup and configuration. They're asking whether there are existing scripts or tools that can simplify the migration process, while also discussing technical challenges such as preserving images, maintaining text sequence order, and ensuring frontend stability. This discussion reflects the pain points of AI tools in practical use, particularly how Google's quota policies impact user experience, and seeks community support to find innovative solutions. The topic includes 8 posts from 2 participants, showing community interest in this issue.
Original Link:Linux.do
This article takes a deep dive into the technical implementation of Google's Canvas system, revealing its efficient integration of multiple Gemini models, including text/visual generation, image creation, image editing, and voice synthesis capabilities. The quota allocation mechanism is based on the user's Google account, ensuring reasonable resource usage. The system also implements an exponential backoff error handling strategy, featuring up to 5 retries with progressive delays (1s, 2s, 4s, 8s, 16s) to handle quota limitations, and provides user-friendly error messages upon final failure. Notably, even when selecting faster models, the system still performs deep thinking. These findings not only reveal the inner workings of Google's AI services but also offer valuable insights for developers in practical applications.
Original Link:Linux.do
This article provides a comprehensive 'from 0 to 1 private large model training' course resource, covering core technologies such as word vector principles, Transformer architecture, BERT series, reinforcement learning, and RLHF training practices. The course starts with fundamental concepts and progressively delves into model development and training, including using the Paddle framework and Cursor tools, helping enterprises and developers master the entire large model training process. The content is highly practical and suitable for technical personnel aiming to seize opportunities in the AI market, with detailed analysis of the evolution and practical applications of models like ChatGPT and Wenxin Yiyan, helping enterprises stand out in AI competition.
Original Link:Linux.do
最新评论
I don't think the title of your article matches the content lol. Just kidding, mainly because I had some doubts after reading the article.
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