What is Clawdbot? How a Local First Agent Stack Turns Chats into Real AutomationsMarkTechPost Clawdbot is an open source personal AI assistant that you run on your own hardware. It connects large language models from providers such as Anthropic and OpenAI to real tools such as messaging apps, files, shell, browser and smart home devices, while keeping the orchestration layer under your control. The interesting part is not that
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Clawdbot is an open source personal AI assistant that you run on your own hardware. It connects large language models from providers such as Anthropic and OpenAI to real tools such as messaging apps, files, shell, browser and smart home devices, while keeping the orchestration layer under your control. The interesting part is not that
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Researchers tested AI against 100,000 humans on creativityArtificial Intelligence News — ScienceDaily A massive new study comparing more than 100,000 people with today’s most advanced AI systems delivers a surprising result: generative AI can now beat the average human on certain creativity tests. Models like GPT-4 showed strong performance on tasks designed to measure original thinking and idea generation, sometimes outperforming typical human responses. But there’s a clear ceiling. The most creative humans — especially the top 10% — still leave AI well behind, particularly on richer creative work like poetry and storytelling.
A massive new study comparing more than 100,000 people with today’s most advanced AI systems delivers a surprising result: generative AI can now beat the average human on certain creativity tests. Models like GPT-4 showed strong performance on tasks designed to measure original thinking and idea generation, sometimes outperforming typical human responses. But there’s a clear ceiling. The most creative humans — especially the top 10% — still leave AI well behind, particularly on richer creative work like poetry and storytelling. Read More
SAM 3 vs. Specialist Models — A Performance BenchmarkTowards Data Science Why specialized models still hold the 30x speed advantage in production environments
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Why specialized models still hold the 30x speed advantage in production environments
The post SAM 3 vs. Specialist Models — A Performance Benchmark appeared first on Towards Data Science. Read More
Azure ML vs. AWS SageMaker: A Deep Dive into Model Training — Part 1Towards Data Science Compare Azure ML and AWS SageMaker for scalable model training, focusing on project setup, permission management, and data storage patterns, to align platform choices with existing cloud ecosystem and preferred MLOps workflows
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Compare Azure ML and AWS SageMaker for scalable model training, focusing on project setup, permission management, and data storage patterns, to align platform choices with existing cloud ecosystem and preferred MLOps workflows
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How to Build a Neural Machine Translation System for a Low-Resource LanguageTowards Data Science An introduction to neural machine translation
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An introduction to neural machine translation
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Air for Tomorrow: Mapping the Digital Air-Quality Landscape, from Repositories and Data Types to Starter CodeTowards Data Science Understand air quality: access the available data, interpret data types, and execute starter codes
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Understand air quality: access the available data, interpret data types, and execute starter codes
The post Air for Tomorrow: Mapping the Digital Air-Quality Landscape, from Repositories and Data Types to Starter Code appeared first on Towards Data Science. Read More
GitHub Releases Copilot-SDK to Embed Its Agentic Runtime in Any AppMarkTechPost GitHub has opened up the internal agent runtime that powers GitHub Copilot CLI and exposed it as a programmable SDK. The GitHub Copilot-SDK, now in technical preview, lets you embed the same agentic execution loop into any application so the agent can plan, invoke tools, edit files, and run commands as part of your own
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GitHub has opened up the internal agent runtime that powers GitHub Copilot CLI and exposed it as a programmable SDK. The GitHub Copilot-SDK, now in technical preview, lets you embed the same agentic execution loop into any application so the agent can plan, invoke tools, edit files, and run commands as part of your own
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How Machine Learning and Semantic Embeddings Reorder CVE Vulnerabilities Beyond Raw CVSS ScoresMarkTechPost In this tutorial, we build an AI-assisted vulnerability scanner that goes beyond static CVSS scoring and instead learns to prioritize vulnerabilities using semantic understanding and machine learning. We treat vulnerability descriptions as rich linguistic artifacts, embed them using modern sentence transformers, and combine these representations with structural metadata to produce a data-driven priority score. Also,
The post How Machine Learning and Semantic Embeddings Reorder CVE Vulnerabilities Beyond Raw CVSS Scores appeared first on MarkTechPost.
In this tutorial, we build an AI-assisted vulnerability scanner that goes beyond static CVSS scoring and instead learns to prioritize vulnerabilities using semantic understanding and machine learning. We treat vulnerability descriptions as rich linguistic artifacts, embed them using modern sentence transformers, and combine these representations with structural metadata to produce a data-driven priority score. Also,
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Unrolling the Codex agent loopOpenAI News A technical deep dive into the Codex agent loop, explaining how Codex CLI orchestrates models, tools, prompts, and performance using the Responses API.
A technical deep dive into the Codex agent loop, explaining how Codex CLI orchestrates models, tools, prompts, and performance using the Responses API. Read More
How an AI Agent Chooses What to Do Under Tokens, Latency, and Tool-Call Budget Constraints?MarkTechPost In this tutorial, we build a cost-aware planning agent that deliberately balances output quality against real-world constraints such as token usage, latency, and tool-call budgets. We design the agent to generate multiple candidate actions, estimate their expected costs and benefits, and then select an execution plan that maximizes value while staying within strict budgets. With
The post How an AI Agent Chooses What to Do Under Tokens, Latency, and Tool-Call Budget Constraints? appeared first on MarkTechPost.
In this tutorial, we build a cost-aware planning agent that deliberately balances output quality against real-world constraints such as token usage, latency, and tool-call budgets. We design the agent to generate multiple candidate actions, estimate their expected costs and benefits, and then select an execution plan that maximizes value while staying within strict budgets. With
The post How an AI Agent Chooses What to Do Under Tokens, Latency, and Tool-Call Budget Constraints? appeared first on MarkTechPost. Read More