💰 Salary Negotiation - Payslip Requests
This article discusses the implications of employers requesting payslips during the hiring process and suggests a counter-strategy.
Key Points:
• Companies typically budget for roles before hiring.
• Payslip requests are often an attempt to reduce salary offers.
• Requesting the budgeted salary range is a strong counter-strategy.
🔗 Resources:
• ssheshap ↗ - Twitter profile
• Paimaamu ↗ - Twitter profile
• Paimaamu's Tweet ↗ - Relevant discussion
🤖 MGNREGA Ponds Monitoring - BhuPRAHARI Report
This article reports on the release of a national impact assessment and monitoring system for MGNREGA ponds using geospatial technologies.
Key Points:
• The BhuPRAHARI system assesses the impact of MGNREGA ponds.
• Geospatial technologies are used for monitoring and evaluation.
• The report was released by prominent government officials.
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🔗 Resources:
• anupamsobti ↗ - Twitter profile
• m_saharia ↗ - Twitter profile
• m_saharia's Tweet ↗ - Report release announcement
• m_saharia's Photo ↗ - Related image
🤖 Software Engineer Benchmarks - Industry Shift
This article discusses the declining interest among frontier labs and code generation startups in using Software Engineer benchmarks.
Key Points:
• SWE benchmarks are viewed as less aligned with actual coding outcomes.
• Startups are prioritizing alternative evaluation methods.
• The shift reflects a change in industry priorities.
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🔗 Resources:
• davidxuezy ↗ - Twitter profile
• swyx ↗ - Twitter profile
• swyx's Tweet ↗ - Original post
🚀 Hugging Face and Kaggle Integration
This article announces a partnership between Hugging Face and Kaggle, highlighting key features of the integration.
Key Points:
• Hugging Face models can now run directly in Kaggle notebooks.
• Public code examples linked to models are readily discoverable.
• Seamless integration enhances workflow between both platforms.
🔗 Resources:
• ClementDelangue ↗ - Twitter profile
• itsafiz ↗ - Twitter profile
• Hugging Face ↗ - Hugging Face Twitter profile
• Kaggle ↗ - Kaggle Twitter profile
• itsafiz's Tweet ↗ - Announcement tweet
✨ Computer Vision Projects - Community Thanks
This article expresses gratitude to the online community for support of computer vision projects.
Key Points:
• Thanks for 3000 followers.
• Appreciation for community encouragement and ideas.
• Motivation to continue experimenting.
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🔗 Resources:
• measure_plan ↗ - Twitter profile
• measure_plan's Tweet ↗ - Thank you message
🤖 Robot Training Data - Novel Approach
This article describes a new method for collecting robot training data using a single mobile device scan and a human demo video.
Key Points:
• Data generation without robots in the loop.
• Uses single mobile device scan and human demo video.
• Generates diverse data for training diffusion and VLA manipulation policies.
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🔗 Resources:
• mzubairirshad ↗ - Twitter profile
• letian_fu ↗ - Twitter profile
• letian_fu's Tweet ↗ - Related work
• mzubairirshad's Tweet ↗ - Tweet highlighting the approach
🤖 Embodied Chain of Thought (ECoT) Analysis
This article discusses a new work analyzing lightweight ECoT-like strategies to understand the minimal reasoning needed to boost Vision-Language Agents (VLAs).
Key Points:
• ECoT is a powerful tool for problem-solving with VLAs.
• The research analyzes lightweight ECoT strategies.
• Aims to determine the minimal reasoning needed for VLA performance improvement.
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🔗 Resources:
• vfloresb21 ↗ - Twitter profile
• svlevine ↗ - Twitter profile
• svlevine's Tweet ↗ - Research announcement
🤖 SLAM Handbook Release
This article announces the release of a full draft of the SLAM Handbook, including a new part on Spatial AI.
Key Points:
• Full draft of the SLAM Handbook is available as a free PDF.
• A printed version is coming soon.
• Part 3, "From SLAM to Spatial AI," is now included.
🔗 Resources:
• alanmelling ↗ - Twitter profile
• AjdDavison ↗ - Twitter profile
• HideMatsu82 ↗ - Twitter profile
• SpatialAI Hashtag ↗ - Relevant hashtag
• SLAM Handbook ↗ - Handbook link
• AjdDavison's Tweet ↗ - Release announcement
🚀 AgentOps 0.4.12 Release Notes
This article details the new features and bug fixes in AgentOps version 0.4.12.
Key Points:
• New OpenAI Agents SDK examples.
• Logs are now saved to the AgentOps dashboard.
• Support for IBM Watsonx, iodonet LLMs, and ag2oss.
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🔗 Resources:
• pratty_agi ↗ - Twitter profile
• AlexReibman ↗ - Twitter profile
• OpenAI ↗ - OpenAI Twitter profile
• AgentOpsAI ↗ - AgentOpsAI Twitter profile
• IBMwatsonx ↗ - IBM Watsonx Twitter profile
• iodotnet ↗ - iodonet Twitter profile
• ag2oss ↗ - ag2oss Twitter profile
• mintlify ↗ - mintlify Twitter profile
• AlexReibman's Tweet ↗ - Release announcement
🤖 ML Research in 2015 - Manual Derivatives
This article reflects on the state of machine learning research in 2015, highlighting the manual computation of derivatives before the advent of automatic differentiation (autodiff).
Key Points:
• Manual derivative computation was a significant aspect of ML research in 2015.
• The process involved basic calculus with a high risk of errors.
• Autodiff tools like .backward() have significantly streamlined the process.
🔗 Resources:
• CSProfKGD ↗ - Twitter profile
• jxmnop ↗ - Twitter profile
• jxmnop's Tweet ↗ - Original post
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