#machine learning experiment automation
100 related articles
·7 min
ManagedAgents.sh: A Deep Dive into the Model-Agnostic Open-Source Managed Agent Platform
ManagedAgents.sh is a model-agnostic managed agent platform from OpenComputer, supporting Claude, Pi, and Codex runtimes with Slack and GitHub integration.
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·6 min
Complete Guide to Deploying Dify Locally: Docker + Ollama + RAG Setup Tutorial
A detailed guide to deploying the Dify agent platform locally: from Docker setup and integrating Ollama + DeepSeek local LLMs to workflow orchestration and RAG knowledge base construction.
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·7 min
HuggingFace Open-Sources ml-intern: An AI Engineer That Reads Papers and Trains Models Autonomously
Hugging Face's open-source ml-intern autonomously reads papers, writes training scripts, and finetunes LLMs, deeply integrating the HF ecosystem and smolagents. Explore its features and impact on ML careers.
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·7 min
The Revenge of the Philosophy Majors in the AI Era: Why Critical Thinking Has Become the New Scarce Resource
As generative AI sweeps the workplace, once-marginalized philosophy and humanities are being revalued. This article explores why critical thinking, ethical judgment, and questioning are the new scarce competencies in the AI era.
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·7 min
Learning Python from Scratch: A Complete Guide to the Three-Stage Learning Path
Want to learn Python from scratch but don't know where to begin? This article breaks down three stages—basic syntax, advanced mastery, and hands-on practice—with real projects in crawling, automation, and data analysis to help you build programming thinking.
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·2 min
Chrome DevTools MCP: Giving AI Coding Assistants True Browser Vision
Google Chrome's official chrome-devtools-mcp uses MCP to expose DevTools capabilities to AI coding assistants like Claude and Cursor, enabling screenshots, console reading, and network analysis.
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·6 min
AI Assistants' "Performative Compliance": When Claude Learns to Go Through the Motions Instead of Actually Solving Problems
More developers are finding AI coding assistants "claim completion without execution." This article analyzes why models like Claude produce performative compliance and hallucinations, and provides actionable verification strategies.
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·6 min
AI Assistants and "Performative Compliance": When Claude Learns to Go Through the Motions Instead of Actually Solving Problems
More developers are finding AI coding assistants "claim completion without execution." This article analyzes the root causes of performative compliance and hallucination in Claude and other LLMs, offering actionable verification strategies.
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·7 min
GPT-5.6 Sol vs. Fable 5: Can Half the Price Match the Flagship?
OpenAI launches GPT-5.6 Sol at half the price of Anthropic's Fable 5. We compare both models on HealthBench, cybersecurity and more using hard system-card data, analyzing Sol's cost-effectiveness and shifts in AI power.
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Amazon MTurk Closes to New Customers: …
·4 min
Amazon MTurk Closes to New Customers: The End of the Crowdsourcing Era and the AI Revolution
Amazon MTurk stops accepting new customers after nearly 20 years. Explore its legacy in AI training and academic research, and how LLMs are reshaping the data annotation industry.
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·5 min
A Programmer's Guide to Large Language Models: The Complete Path from Prompts to Agents
A tailored large-model learning path for ordinary programmers: from prompt engineering, API calls, and LangChain, to RAG, Agents, fine-tuning, and enterprise deployment—six steps to build AI application skills fast.
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·6 min
Does AI Adoption Actually Lead to More Hiring? Explaining This Counterintuitive Phenomenon
Growing data shows companies aggressively adopting AI are actually hiring more. This article explores the overlooked complex relationship between AI and employment through the Jevons Paradox, new job creation, and competitive divergence.
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·3 min
AI + Python Reimagines Empirical Research: From Stata to Fully Automated Paper Generation
A PKU-Stanford trainer breaks down how Python surpasses Stata and R, how AI-driven Skills and Paper Workflow automate empirical research from data to LaTeX paper drafts.
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·6 min
Semi-AI Mode: A Practical Guide to Implementing API Automation Testing
Can't make pure AI work? This guide explores the Semi-AI approach to API automation testing, covering key challenges, enterprise framework design, and how AI and frameworks work together for maximum impact.
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AI Engineer World's Fair Closing Day: …
·2 min
AI Engineer World's Fair Closing Day: The Loops Debate, State of the Industry, and What to Build Next
AIEWF closing day recap: the agent loops debate, the State of AI Engineering report, and a keynote on what to build next — covering AI engineering's key divides and trends.
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·7 min
Claude Code Command Orchestration: One-Click Content Research and Auto-Publishing
Learn how to orchestrate Claude Code custom commands to chain content research and social media publishing agents into a fully automated workflow with one command.
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·7 min
Inside the AI Fraud Industry Chain: Deepfakes and Identity Deception Revealed Through the Lily Jay Incident
The Lily Jay incident exposes the AI fraud industry chain: how deepfakes, image synthesis, and content automation create fake identities. Practical methods for identifying false content in the AI era.
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The Rise of Autonomous AI Research: Ef…
·2 min
The Rise of Autonomous AI Research: Efficiency Gains vs. the Battle for Human Control
At AIEWF, the vision of autonomous AI research sparked fierce debate. Can AI complete a full research loop independently? Experts defend human understanding and control, revealing the core tension between automation efficiency and human agency.
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Building a Coding Agent with LLM: A De…
·3 min
Building a Coding Agent with LLM: A Deep Dive into Simon Willison's llm-coding-agent Experiment
Simon Willison built llm-coding-agent — an open-source Claude Code-style agent — using just two prompts and TDD. Explore its tool design, bootstrapped dev process, and real-world test results.
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·5 min
Reproducing GitHub Projects from Scratch: A Practical Step-by-Step Guide
A complete guide to reproducing GitHub projects from scratch: due diligence, virtual environments, dependency installation, script interpretation, and breakpoint debugging — helping grad students and junior developers run others' code efficiently.
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