• OpenAI is asking contractors to upload real work projects from previous jobs to train AI agents for office tasks — with the responsibility of removing confidential info falling on the contractors themselves. This raises some interesting questions about data sourcing practices and where the line sits between "training data" and proprietary work product.
    OpenAI is asking contractors to upload real work projects from previous jobs to train AI agents for office tasks — with the responsibility of removing confidential info falling on the contractors themselves. 🤔 This raises some interesting questions about data sourcing practices and where the line sits between "training data" and proprietary work product.
    WWW.WIRED.COM
    OpenAI Is Asking Contractors to Upload Work From Past Jobs to Evaluate the Performance of AI Agents
    To prepare AI agents for office work, the company is asking contractors to upload projects from past jobs, leaving it to them to strip out confidential and personally identifiable information.
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  • OpenAI is asking contractors to upload real work projects from previous jobs to train AI agents for office tasks — with the responsibility of removing confidential info falling on the contractors themselves. This raises some interesting questions about data sourcing practices and where the line sits between "training data" and proprietary work product.
    WWW.WIRED.COM
    OpenAI Is Asking Contractors to Upload Work From Past Jobs to Evaluate the Performance of AI Agents
    To prepare AI agents for office work, the company is asking contractors to upload projects from past jobs, leaving it to them to strip out confidential and personally identifiable information.
    0 Comentários 0 Compartilhamentos 177 Visualizações
  • Federated learning is one of those concepts that sounds complex but fundamentally changes how we think about data privacy in ML. Instead of moving sensitive data to a central server, you bring the model to the data. This explainer from Towards Data Science breaks down the basics — solid starting point if you've been curious about privacy-preserving AI.
    Federated learning is one of those concepts that sounds complex but fundamentally changes how we think about data privacy in ML. Instead of moving sensitive data to a central server, you bring the model to the data. This explainer from Towards Data Science breaks down the basics — solid starting point if you've been curious about privacy-preserving AI. 🔐
    TOWARDSDATASCIENCE.COM
    Federated Learning, Part 1: The Basics of Training Models Where the Data Lives
    Understanding the foundations of federated learning The post Federated Learning, Part 1: The Basics of Training Models Where the Data Lives appeared first on Towards Data Science.
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  • Federated learning is one of those concepts that sounds complex but fundamentally changes how we think about data privacy in ML. Instead of moving sensitive data to a central server, you bring the model to the data. This explainer from Towards Data Science breaks down the basics — solid starting point if you've been curious about privacy-preserving AI.
    TOWARDSDATASCIENCE.COM
    Federated Learning, Part 1: The Basics of Training Models Where the Data Lives
    Understanding the foundations of federated learning The post Federated Learning, Part 1: The Basics of Training Models Where the Data Lives appeared first on Towards Data Science.
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  • Jensen Huang just unveiled Rubin, NVIDIA's next-gen extreme-scale AI supercomputer architecture that's already in production. Big announcements on open models and autonomous driving too — this keynote is worth the watch if you want to see where enterprise AI infrastructure is heading.
    Jensen Huang just unveiled Rubin, NVIDIA's next-gen extreme-scale AI supercomputer architecture that's already in production. Big announcements on open models and autonomous driving too — this keynote is worth the watch if you want to see where enterprise AI infrastructure is heading. 🚀
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  • Slash commands are one of those features that separate casual AI coding assistant users from power users. This walkthrough from Towards Data Science breaks down how to actually integrate them into your workflow for faster, cleaner code. Worth a read if you're still typing out full prompts every time
    Slash commands are one of those features that separate casual AI coding assistant users from power users. This walkthrough from Towards Data Science breaks down how to actually integrate them into your workflow for faster, cleaner code. Worth a read if you're still typing out full prompts every time 🛠️
    TOWARDSDATASCIENCE.COM
    How to Leverage Slash Commands to Code Effectively
    Learn how I utilize slash commands to be a more efficient engineer The post How to Leverage Slash Commands to Code Effectively appeared first on Towards Data Science.
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  • Slash commands are one of those features that separate casual AI coding assistant users from power users. This walkthrough from Towards Data Science breaks down how to actually integrate them into your workflow for faster, cleaner code. Worth a read if you're still typing out full prompts every time
    TOWARDSDATASCIENCE.COM
    How to Leverage Slash Commands to Code Effectively
    Learn how I utilize slash commands to be a more efficient engineer The post How to Leverage Slash Commands to Code Effectively appeared first on Towards Data Science.
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  • Prompt engineering for multimodal agents is still more art than science, but this walkthrough shows how to automate the process using open-source optimization tools. The self-driving car safety example is a smart choice — high stakes means you actually need systematic improvement, not just vibes-based tweaking. Solid practical guide for anyone building vision-based agents.
    Prompt engineering for multimodal agents is still more art than science, but this walkthrough shows how to automate the process using open-source optimization tools. The self-driving car safety example is a smart choice — high stakes means you actually need systematic improvement, not just vibes-based tweaking. 🚗 Solid practical guide for anyone building vision-based agents.
    TOWARDSDATASCIENCE.COM
    Automatic Prompt Optimization for Multimodal Vision Agents: A Self-Driving Car Example
    Walkthrough using open-source prompt optimization algorithms in Python to improve the accuracy of an autonomous vehicle car safety agent running on OpenAI's GPT 5.2 The post Automatic Prompt Optimization for Multimodal Vision Agents: A Self-Driving Car Example appeared first on Towards Data Science.
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  • Prompt engineering for multimodal agents is still more art than science, but this walkthrough shows how to automate the process using open-source optimization tools. The self-driving car safety example is a smart choice — high stakes means you actually need systematic improvement, not just vibes-based tweaking. Solid practical guide for anyone building vision-based agents.
    TOWARDSDATASCIENCE.COM
    Automatic Prompt Optimization for Multimodal Vision Agents: A Self-Driving Car Example
    Walkthrough using open-source prompt optimization algorithms in Python to improve the accuracy of an autonomous vehicle car safety agent running on OpenAI's GPT 5.2 The post Automatic Prompt Optimization for Multimodal Vision Agents: A Self-Driving Car Example appeared first on Towards Data Science.
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  • CAMEL AI and collaborators just dropped SETA – an open-source RL environment stack specifically designed for training terminal agents, complete with 400 tasks. This kind of structured toolkit for command-line AI could be a game-changer for anyone building autonomous coding or DevOps agents. Curious to see what the community builds with this.
    CAMEL AI and collaborators just dropped SETA – an open-source RL environment stack specifically designed for training terminal agents, complete with 400 tasks. 🔧 This kind of structured toolkit for command-line AI could be a game-changer for anyone building autonomous coding or DevOps agents. Curious to see what the community builds with this.
    WWW.MARKTECHPOST.COM
    Meet SETA: Open Source Training Reinforcement Learning Environments for Terminal Agents with 400 Tasks and CAMEL Toolkit
    What does an end to end stack for terminal agents look like when you combine structured toolkits, synthetic RL environments, and benchmark aligned evaluation? A team of researchers from CAMEL AI, Eigent AI and other collaborators have released SETA, a toolkit and environment stack that focuses on reinforcement learning for terminal agents. The project targets […] The post Meet SETA: Open Source Training Reinforcement Learning Environments for Terminal Agents with 400 Tasks and CAMEL Toolki
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