TweakedGeek
-
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.
-
AI-Assisted Sculpting for 3D Miniatures
Read Full Article: AI-Assisted Sculpting for 3D Miniatures
AI-assisted sculpting workflows are being refined to enhance the creation of 3D miniatures by generating base forms with AI, which are then refined using tools like Blender and ZBrush. The process includes manually cleaning the topology, adding detail with traditional sculpting tools, and exporting print-ready STLs, which are tested on Bambu printers with multi-material setups. A new community, r/AIModelMakers, has been established for individuals interested in AI-enhanced 3D modeling and miniature workflows, offering a space to share experiments and learn from others. This matters as it represents a significant advancement in 3D modeling, making the process more efficient and accessible through AI technology.
-
GLM 4.7: A Solid Choice for Coding Projects
Read Full Article: GLM 4.7: A Solid Choice for Coding Projects
GLM 4.7 has shown strong performance in coding tasks such as refactoring, debugging, and code review, particularly excelling in Python backend work by maintaining context and catching logic issues. It compares favorably to Deepseek v3 by slightly better maintaining context in long conversations, though it struggles with complex algorithmic tasks. In comparison to Qwen2.5-coder, GLM is more consistent in maintaining conversation flow, while being less verbose than Kimi. Although it struggles with complex React state management and architectural decisions, its open-source nature and cost-effectiveness make it a viable option for developers focused on implementation tasks. This matters because choosing the right coding model can significantly impact productivity and cost efficiency in software development workflows.
-
Generating Human Faces with Variational Autoencoders
Read Full Article: Generating Human Faces with Variational Autoencoders
Variational Autoencoders (VAEs) are a type of generative model that can be used to create realistic human faces by learning the underlying distribution of facial features from a dataset. VAEs work by encoding input data into a latent space, then decoding it back into a new, similar output, allowing for the generation of new, unique faces. This process involves a balance between maintaining the essential features of the original data and introducing variability, which can be controlled to produce diverse and realistic results. Understanding and utilizing VAEs for face generation has significant implications for fields like computer graphics, virtual reality, and personalized avatars.
-
Qwen-Image-2512: Strongest Open-Source Model Released
Read Full Article: Qwen-Image-2512: Strongest Open-Source Model Released
Qwen-Image-2512, the latest release on Hugging Face, is currently the strongest open-source image model available. It offers significant improvements in rendering more realistic human features, enhancing natural textures, and providing stronger text-image compositions. Tested rigorously in over 10,000 blind rounds on AI Arena, it outperforms other open-source models and remains competitive with proprietary systems. This advancement matters as it enhances the quality and accessibility of open-source image generation technology, potentially benefiting a wide range of applications from digital art to automated content creation.
-
Reverse-engineering a Snapchat Sextortion Bot
Read Full Article: Reverse-engineering a Snapchat Sextortion BotAn encounter with a sextortion bot on Snapchat revealed its underlying architecture, showcasing the use of a raw Llama-7B instance with a 2048 token window. By employing a creative persona-adoption jailbreak, the bot's system prompt was overridden, exposing its environment variables and confirming its high Temperature setting, which prioritizes creativity over adherence. The investigation highlighted that scammers are now using localized, open-source models like Llama-7B to cut costs and bypass censorship, yet their security measures remain weak, making them vulnerable to simple disruptions. This matters because it sheds light on the evolving tactics of scammers and the vulnerabilities in their current technological setups.
-
Condé Nast User Database Breach: Ars Unaffected
Read Full Article: Condé Nast User Database Breach: Ars Unaffected
A hacker named Lovely claimed responsibility for breaching a Condé Nast user database, releasing over 2.3 million user records from WIRED, with plans to leak an additional 40 million records from other Condé Nast properties. The data includes demographic information but no passwords, and Ars Technica remains unaffected due to its unique tech stack. Despite Lovely's claims of urging Condé Nast to fix security vulnerabilities, it appears the hacker's motives were financially driven rather than altruistic. Condé Nast has yet to comment on the breach, and the situation highlights the importance of robust cybersecurity measures to protect user data. This matters because it underscores the ongoing threat of data breaches and the need for companies to prioritize user data security.
-
Alexa+ AI Overreach Concerns
Read Full Article: Alexa+ AI Overreach Concerns
Amazon's integration of Alexa+ into Echo Show 8 devices without user opt-in has raised concerns about AI overreach. The device now prompts users for additional input by activating the microphone after responding to commands, a feature reminiscent of ChatGPT's feedback prompts. While some users appreciate improved functionality like more accurate song requests, the unsolicited activation of the microphone and snarky responses have been perceived as intrusive. This situation highlights the growing tension between AI advancements and user privacy preferences.
-
SoftBank’s Major Funding for OpenAI
Read Full Article: SoftBank’s Major Funding for OpenAI
SoftBank is reportedly working to finalize a significant funding commitment to OpenAI, the company behind the widely-used AI model, ChatGPT. This move comes as SoftBank aims to strengthen its position in the AI sector, following its previous investments in technology and innovation. The funding is expected to bolster OpenAI's capabilities and accelerate its research and development efforts. This matters as it highlights the increasing importance of AI technology and the strategic maneuvers by major corporations to lead in this rapidly evolving field.
-
Zero-Setup Agent for LLM Benchmarking
Read Full Article: Zero-Setup Agent for LLM Benchmarking
An innovative agent has been developed to streamline the process of benchmarking multiple open and closed source Large Language Models (LLMs) on specific problems or datasets. By simply loading a dataset and defining the problem, the agent can prompt various LLMs to evaluate their performance, as demonstrated with the TweetEval tweet emoji prediction task. The agent facilitates dataset curation, model inference, and analysis of predictions, while also enabling benchmarking of additional models to compare their relative performance. Notably, in a particular task, the open-source Llama-3-70b model outperformed closed-source models like GPT-4o and Claude-3.5, highlighting the potential of open-source solutions. This matters because it simplifies the evaluation of LLMs, enabling more efficient selection of the best model for specific tasks.