AI agents

  • AI Agents for Autonomous Data Analysis


    I built a Python package that uses AI agents to autonomously analyze data and build machine learning modelsA 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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  • Lár: Open-Source Framework for Transparent AI Agents


    I built a "Glass Box" agent framework because I was tired of debugging magic black boxes. (Apache 2.0)Lár v1.0.0 is an open-source framework designed to build deterministic and auditable AI agents, addressing the challenges of debugging opaque systems. Unlike existing tools, Lár offers transparency through auditable logs that provide a detailed JSON record of an agent's operations, allowing developers to understand and trust the process. Key features include easy local support with minimal changes, IDE-friendly setup, standardized core patterns for common agent flows, and an integration builder for seamless tool creation. The framework is air-gap ready, ensuring security for enterprise deployments, and remains simple with its node and router-based architecture. This matters because it empowers developers to create reliable AI systems with greater transparency and security.

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  • Cogitator: Open-Source AI Runtime in TypeScript


    I (almost) built an open-source, self-hosted runtime for AI agents in TypeScript...Cogitator is an open-source, self-hosted runtime designed to orchestrate AI agents and LLM swarms, built with TypeScript to offer type safety and seamless web integration. It provides a universal LLM interface that supports multiple AI platforms like Ollama, vLLM, OpenAI, Anthropic, and Google through a single API. The system is equipped with a DAG-based workflow engine, multi-agent swarm strategies, and sandboxed execution using Docker/WASM for secure operations. With a focus on production readiness, it utilizes Redis and Postgres for memory management and offers full observability features like OpenTelemetry and cost tracking. This matters because it aims to provide a more stable and efficient alternative to existing AI infrastructures with significantly fewer dependencies.

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  • AI Agent Executes 100,000 Tasks with One Prompt


    I built an AI agent that can do 100,000s of tasks 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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  • Meta Acquires AI Startup Manus for $2 Billion


    Meta just bought Manus, an AI startup everyone has been talking aboutMeta Platforms has acquired Manus, a Singapore-based AI startup, for $2 billion, marking a significant move by Mark Zuckerberg to bolster Meta's AI capabilities. Manus gained attention with its viral demo showcasing AI agents capable of tasks like job screening and stock analysis, and quickly attracted substantial investment, achieving a valuation of $500 million. Despite concerns over its aggressive pricing model and ties to China, Manus has achieved impressive financial success with millions of users and $100 million in annual recurring revenue. Meta plans to integrate Manus's AI technology into its platforms while ensuring no Chinese ownership remains, addressing geopolitical concerns. Why this matters: The acquisition highlights the growing importance of AI in tech giants' strategies and the geopolitical sensitivities surrounding AI development and ownership.

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  • Meta Acquires Manus, Boosting AI Capabilities


    Meta acquired Manus !!Meta has acquired Manus, an autonomous AI agent created by Butterfly Effect Technology, a startup based in Singapore. Manus is designed to perform a wide range of tasks autonomously, showcasing advanced capabilities in artificial intelligence. This acquisition is part of Meta's strategy to enhance its AI technology and expand its capabilities in developing more sophisticated AI systems. The move signifies Meta's commitment to advancing AI technology, which is crucial for its future projects and innovations.

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  • AI Vending Experiments: Challenges & Insights


    Snack Bots & Soft-Drink Schemes: Inside the Vending-Machine Experiments That Test Real-World AILucas and Axel from Andon Labs explored whether AI agents could autonomously manage a simple business by creating "Vending Bench," a simulation where models like Claude, Grok, and Gemini handled tasks such as researching products, ordering stock, and setting prices. When tested in real-world settings, the AI faced challenges like human manipulation, leading to strange outcomes such as emotional bribery and fictional FBI complaints. These experiments highlighted the current limitations of AI in maintaining long-term plans, consistency, and safe decision-making without human intervention. Despite the chaos, newer AI models show potential for improvement, suggesting that fully automated businesses could be feasible with enhanced alignment and oversight. This matters because understanding AI's limitations and potential is crucial for safely integrating it into real-world applications.

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  • Agentic AI: 10 Key Developments This Week


    It's been a big week for Agentic AI ; Here are 10 massive developments you might've missed:Recent developments in Agentic AI showcase significant advancements and challenges across various platforms and industries. OpenAI is enhancing security for ChatGPT by employing reinforcement learning to address potential exploits, while Claude Code is introducing custom agent hooks for developers to extend functionalities. Forbes highlights the growing complexity for small businesses managing multiple AI tools, likening it to handling numerous remote controls for a single TV. Additionally, Google and other tech giants are focusing on educating users about agent integration and the transformative impact on job roles, emphasizing the need for workforce adaptation. These updates underscore the rapid evolution and integration of AI agents in daily operations, emphasizing the necessity for businesses and individuals to adapt to these technological shifts.

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  • Top Agentic AI Browsers to Watch in 2026


    The Best Agentic AI Browsers to Look For in 2026Agentic AI browsers are revolutionizing the way users interact with the web by employing autonomous AI agents to perform tasks like navigating websites, filling forms, and executing multi-step tasks. These browsers, such as Perplexity Comet, ChatGPT Atlas, Dia, Microsoft Edge Copilot, BrowserOS, Opera Neon, and Genspark, offer various features like conversational browsing, privacy control, and task automation to enhance user experience. They cater to different needs, from research assistance and creative planning to enterprise solutions and hands-free automation, providing users with personalized and efficient web interactions. This matters because it signifies a shift towards more intelligent and autonomous web browsing, potentially transforming productivity and user engagement online.

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  • AI’s Impact on Healthcare: Revolutionizing Patient Care


    If you're refusing to answer my questions, I am refusing to pay you my money.AI is set to transform healthcare by automating administrative tasks and improving diagnostic accuracy. Key applications include AI scribing, which can generate medical notes from patient-provider conversations, reducing the administrative load on healthcare workers. AI will also enhance billing and coding processes, minimizing errors and identifying revenue opportunities. Additionally, specialized AI agents could access specific medical records for tailored advice, while domain-specific language models trained on medical data will enhance clinical documentation accuracy. AI's role in reducing medical errors is significant, though human oversight remains essential. This matters because AI's integration into healthcare can lead to more efficient, accurate, and safer patient care.

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