automation
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Fine-Tuning Qwen3-VL for HTML Code Generation
Read Full Article: Fine-Tuning Qwen3-VL for HTML Code Generation![[Article] Fine-Tuning Qwen3-VL](https://www.tweakedgeek.com/wp-content/uploads/2026/01/featured-article-7894-300x171.png)
Fine-tuning the Qwen3-VL 2B model involves training it with a long context of 20,000 tokens to effectively convert screenshots and sketches of web pages into HTML code. This process enhances the model's ability to understand and interpret complex visual layouts, enabling more accurate HTML code generation from visual inputs. Such advancements in AI models are crucial for automating web development tasks, potentially reducing the time and effort required for manual coding. This matters because it represents a significant step towards more efficient and intelligent web design automation.
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Evaluating LLMs in Code Porting Tasks
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The recent discussion about replacing C and C++ code at Microsoft with automated solutions raises questions about the current capabilities of Large Language Models (LLMs) in code porting tasks. While LLMs have shown promise in generating simple applications and debugging, achieving the ambitious goal of automating the translation of complex codebases requires more than just basic functionality. A test using a JavaScript program with an unconventional prime-checking function revealed that many LLMs struggle to replicate the code's behavior, including its undocumented features and optimizations, when ported to languages like Python, Haskell, C++, and Rust. The results indicate that while some LLMs can successfully port code to certain languages, challenges remain in maintaining identical functionality, especially with niche languages and complex code structures. This matters because it highlights the limitations of current AI tools in fully automating code translation, which is critical for software development and maintenance.
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European Banks to Cut 200,000 Jobs as AI Advances
Read Full Article: European Banks to Cut 200,000 Jobs as AI Advances
European banks are poised to eliminate over 200,000 jobs by 2030 as they increasingly adopt AI technologies and close physical branches, according to a Morgan Stanley analysis. This reduction, affecting roughly 10% of the workforce across 35 major banks, will primarily impact back-office operations, risk management, and compliance roles, where AI is expected to enhance efficiency by 30%. The trend is not limited to Europe, as U.S. banks like Goldman Sachs are also implementing job cuts and hiring freezes in their AI-driven strategies. Despite the push for automation, some banking leaders caution against rapid downsizing, warning that a lack of foundational knowledge among junior bankers could negatively affect the industry in the long run. This matters because the shift towards AI in banking could significantly alter the job landscape and operational dynamics within the financial sector.
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AI Agents for Autonomous Data Analysis
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A new Python package has been developed to leverage AI agents for automating the process of data analysis and machine learning model construction. This tool aims to streamline the workflow for data scientists by automatically handling tasks such as data cleaning, feature selection, and model training. By reducing the manual effort involved in these processes, the package allows users to focus more on interpreting results and refining models. This innovation is significant as it can greatly enhance productivity and efficiency in data science projects, making advanced analytics more accessible to a broader audience.
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Pipeline for Extracting Executive Compensation Data
Read Full Article: Pipeline for Extracting Executive Compensation Data
A pipeline has been developed to extract executive compensation data from SEC filings, specifically targeting Summary Compensation Tables within DEF-14A proxy statements. Utilizing MinerU for parsing PDFs and extracting table images, along with Qwen3-VL-32B for classifying and structuring the data, the project addresses challenges such as tables spanning multiple pages and format variations between pre- and post-2006 filings. Although still in development with some bugs, the pipeline aims to compile a comprehensive dataset of executive compensation from 2005 to the present for all US public companies. This initiative is crucial for improving transparency and accessibility of executive compensation data, potentially aiding research and analysis in corporate governance and financial studies.
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LLM Price Tracker & Cost Calculator
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A new tool has been developed to help users keep track of pricing differences across over 2100 language models from various providers. This tracker not only aggregates model prices but also includes a simple cost calculator to estimate expenses. It updates every six hours, ensuring users have the latest information, and is published as a static site on GitHub pages, making it accessible for automation and programmatic use. This matters because it simplifies the process of comparing and managing costs for those using language models, potentially saving time and money.
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AI Agent Executes 100,000 Tasks with One Prompt
Read Full Article: AI Agent Executes 100,000 Tasks with One Prompt
An innovative AI feature called "Scale Mode" enables a single prompt to execute thousands of coordinated tasks autonomously, such as visiting numerous links to collect data or processing extensive documents. This capability allows for efficient handling of large-scale operations, including generating and enriching B2B leads and processing invoices. The feature is designed to be versatile, complementing a wide range of tasks by simply adding "Do it in Scale Mode" to the prompt. This advancement in AI technology showcases the potential for increased productivity and automation in various industries. Why this matters: Scale Mode represents a significant leap in AI capabilities, offering businesses the ability to automate and efficiently manage large volumes of tasks, which can lead to time savings and increased operational efficiency.
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Generative AI’s Impact on Job Markets
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The rapid growth of generative AI is reshaping job markets, with significant impacts on various roles. Creative and content roles such as graphic designers and writers are increasingly being replaced by AI technologies. Junior roles across industries, including administrative positions, are also being permanently affected. While AI's impact on medical scribes is still uncertain, corporate workers, call center employees, and marketing professionals are facing potential job displacement as companies explore AI integration. Despite these shifts, some jobs remain less affected due to economic factors and AI's current limitations, highlighting the need for adaptation and future planning in the workforce. This matters because understanding AI's impact on employment can guide career choices and policy decisions.
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Automate Time-Series Data Cleaning with DataSetIQ
Read Full Article: Automate Time-Series Data Cleaning with DataSetIQ![[Resource] A library to practice Time-Series ML without spending hours cleaning data](https://www.tweakedgeek.com/wp-content/uploads/2025/12/featured-article-7237-300x171.png)
Practicing time-series forecasting or regression often involves the challenging task of cleaning economic data, such as aligning dates and handling missing values. The DataSetIQ Python client simplifies this process with its new helper function, get_ml_ready, which automates data pre-processing. This function is particularly useful for quickly generating feature matrices to test models like LSTM and XGBoost on real-world economic data. By streamlining data preparation, it allows users to focus more on model testing and less on data cleaning.
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Fine-tuning LM for Browser Control with GRPO
Read Full Article: Fine-tuning LM for Browser Control with GRPO
Fine-tuning a small language model (LM) for browser control involves using reinforcement learning techniques to teach the model how to navigate websites and perform tasks such as clicking buttons, filling forms, and booking flights. This process leverages tools like GRPO, BrowserGym, and LFM2-350M to create a training pipeline that starts with basic tasks and progressively scales in complexity. The approach focuses on learning through trial and error rather than relying on perfect demonstrations, allowing the model to develop practical skills for interacting with web environments. This matters because it opens up possibilities for automating complex web tasks, enhancing efficiency and accessibility in digital interactions.