#agent trajectory dataset
65 related articles
·2 min
Building Agent Trajectory Datasets: A Practical Methodology from Zero to Production
Random chat logs are useless for training tool-using AI agents. Learn the 6 elements of quality trajectories, full data pipeline design, and feedback loop strategies.
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·2 min
White House Invites OpenAI and Other Giants to Preview Voluntary AI Framework: Open-Source Language Becomes Key Battleground
Trump administration invites OpenAI, Anthropic, and Google to preview a voluntary AI framework, with open-source language emerging as the core lobbying battleground that could reshape industry competition.
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·4 min
From Zero to AI Engineer: A Real Growth Path from a Student's Resume
A CS student went from Python basics to model deployment in 3-4 months, building an AI portfolio through three real projects. This article breaks down the learning path, project value, and resume optimization strategies.
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·3 min
GPT-5.6 Slashes Prices by 80%: The Industry Logic Behind the AI Price War and Opportunities for Developers
OpenAI's GPT-5.6 series sees massive price cuts—Luna drops 80% to $0.20/M input tokens. Deep analysis of the AI price war's tech drivers, competitive landscape, and impact on developer costs and model selection.
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·3 min
GPT-5.6 Price Cut of 80%: The Industry Logic Behind the AI Price War and Opportunities for Developers
OpenAI's GPT-5.6 series sees major price cuts with Luna dropping 80% to $0.20/M input tokens. Analysis of the AI price war's technical drivers, competitive landscape, and impact on developer costs.
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·4 min
More Imitation Learning Data Leading to Worse Results? A Deep Dive into Compounding Errors and Data Quality Traps
Through a real game AI navigation case, this article deeply analyzes why more data can worsen imitation learning, covering compounding errors, distribution shift, data quality issues, and DAgger solutions.
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·4 min
More Imitation Learning Data Makes Things Worse? A Deep Dive into Compounding Errors and Data Quality Traps
Through a real game AI navigation case, we deeply analyze why more data can worsen imitation learning, covering compounding errors, distribution shift, data quality issues, and DAgger solutions.
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·4 min
G.I.A.ac: An AI Architect That Generates Working Apps from a Single Sentence
G.I.A.ac (General Intelligence Architect) is an AI architect tool that generates runnable apps from a single sentence. Deep dive into its positioning, competitive landscape, target users, and core challenges.
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·4 min
Freesolo Flash: Deep Dive into the Full-Stack Platform for Enterprise Small Model Training
Freesolo Flash is a full-stack platform for enterprise small language model (SLM) training that commoditizes reinforcement learning, enabling teams to train specialized AI models at low cost.
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·3 min
AI Fact-Checking Tools Explained: How Agent Skills Automatically Detect Misinformation
Deep dive into how AI fact-checking tools like Bullshit Detector work, exploring how Agent Skills extract claims, retrieve evidence, and cross-validate to automatically detect online misinformation.
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·7 min
AI Daily: T-Head Open-Sources SAIL Software Stack at WAIC, Flurry of Large Model Releases
T-Head open-sources AI software stack T-Head SAIL at WAIC to lower the barrier for domestic chip development; Kimi K3 tops the WebDev leaderboard; Qwen 3.8 Max Preview cuts prices aggressively; Moonshot prepares a Hong Kong IPO; and Oracle switches its data center to a fuel cell microgrid.
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·3 min
Agent Fine-Tuning in Practice: A Complete Guide to Training Your Own AI Agent
A deep dive into Agent Tuning: from LLM hallucination and staleness issues to RAG vs. Agent architecture, the 4-step fine-tuning process, and cost analysis for building your own AI agent.
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·3 min
Greenhouse Controller Showdown: Why Reinforcement Learning Lost to a Rule-Based Baseline
An open benchmark in GreenLight-Gym2 compares four greenhouse controllers, revealing why model-free RL (PPO) underperforms hand-tuned rules and how MPC and hybrid RL-in-MPC approaches can help.
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·2 min
17 Million Mouse Trajectories: Unpacking the Machine Learning Value of Gaming Behavioral Data
A League of Legends player collected 17M mouse trajectories and 670K clicks. We analyze the ML value of this gaming behavioral telemetry data for imitation learning, anti-cheat, and player modeling.
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·2 min
AI Agent Debugging Bottlenecks: A Practical Guide to Production Observability
Explore core AI Agent debugging challenges in production: non-determinism, multi-step error propagation, and observability gaps — with practical guidance on LangSmith and tracing tools.
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·2 min
17 Million Mouse Trajectories: The Real ML Value of Gaming Behavioral Data
A LoL player collected 17M mouse trajectories and 670K clicks across 350 matches. We analyze the real ML value and limitations of this gaming telemetry data.
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·2 min
LeRobot 3D: Building 3D Spatial Perception and Manipulation for the SO101 Robot
LeRobot 3D is an open-source project adding 3D spatial perception to the SO101 robot, featuring multi-camera point cloud fusion, forward kinematics, ICP calibration, and browser-based visualization.
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·3 min
ByteDance Interviewer Deep Dive: How Vibe Coding Is Reshaping Frontend Engineers' Core Competitiveness
A ByteDance interviewer breaks down the 3-layer Vibe Coding interview framework: AI tool awareness, complex product engineering, and a 1-hour full-stack challenge. Architectural thinking wins.
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·3 min
Inside DeepMind's Robotics Lab: How VLA Models Teach Robots to Think Before They Act
Inside DeepMind's robotics lab: how VLA models give robots generalization and 'think-before-act' reasoning — from packing lunches to sorting trash, the path to general-purpose robots.
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·3 min
AI Agent 4-Week Learning Roadmap: A Complete Guide from Zero to Independent Development
A structured 4-week AI Agent learning roadmap: Week 1 covers LLMs & Prompt engineering, Week 2 ReAct paradigms, Week 3 RAG memory systems, Week 4 multi-agent architectures.
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