#agent trajectory dataset
68 related articles
·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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·2 min
Carmack × Sutton: Teaching AI Robots to Play Atari in the Real World
John Carmack and Turing Award winner Richard Sutton co-founded Keen Technologies. Their debut paper Physical Atari has robots playing real Atari games via cameras and mechanical controllers in real time.
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·4 min
Two Paths to Better AI Reasoning: Test-Time Scaling and Formal Verification
This article synthesizes two MSR India Summit talks, exploring two key paths to better AI reasoning: test-time scaling with variable granularity search, and a formal verification framework for trustworthy agent execution.
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·2 min
Deep Dive into MCP: How Large Language Models Connect to the Outside World
MCP (Model Context Protocol) is the standardized protocol connecting AI models to external tools and data — the 'USB-C port' of the AI era. Learn its origins and value.
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PrismML Breakthrough: How a 27-Billion…
·2 min
PrismML Breakthrough: How a 27-Billion-Parameter LLM Fits Inside an iPhone
PrismML compressed Alibaba's Qwen 3.6 from 54 GB to under 4 GB, enabling a fully-activated 27B-parameter model to run locally on iPhone 17 Pro. Here's how.
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·6 min
A Beginner's Guide to Claude Code: Why Is It More Powerful Than Cursor and Trae?
Claude Code is Anthropic's local AI programming assistant that reads your entire codebase, auto-debugs, and delivers far higher accuracy than Cursor and Trae. Here's why it's the strongest AI coding tool today.
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Meta Muse Spark 1.1 Released: A Deep D…
·4 min
Meta Muse Spark 1.1 Released: A Deep Dive into the First Spark Model with an Open API
Meta officially releases Muse Spark 1.1, the first model in the Spark series to offer an API, with a focus on strengthening agentic tool calling and computer use capabilities.
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·4 min
OpenAI Plugins Deep Dive: The Plugin System That Connects ChatGPT to the Real World
A deep dive into OpenAI Plugins: how the system works, its technical architecture, and why it matters. Learn how ChatGPT uses plugins to access real-time data, take real-world actions, and lay the groundwork for AI Agents and Tool Use.
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·2 min
VersatIL: A Modular PyTorch Framework Tackling Code Fragmentation in Imitation Learning
VersatIL is a modular PyTorch framework for robot imitation learning that decouples data, network architecture, algorithm, and objective. Supports ACT, Diffusion Policy, pi0, and LeRobot format.
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Deconstructing the SWE-1.7 Coding Mode…
·3 min
Deconstructing the SWE-1.7 Coding Model: How Specialized AI Approaches Top-Tier General Models
The SWE-1.7 specialized coding model sparked buzz on Hacker News, claiming performance near GPT-5.5 and Claude Opus. This deep dive analyzes the technical approach, benchmark limitations, and how specialized AI coding models challenge general LLMs.
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·2 min
GPT-5.6 Autonomously Writes 1,000 Lines of Code: AI Programming Enters the Task Delegation Era
A Snorkel AI research scientist tested GPT-5.6, which independently completed a nearly 1,000-line coding task without repeated prompting. This article explores the context management breakthrough and AI programming's shift toward autonomy.
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·7 min
Gemini Live Integrates Nano Banana: A Complete Guide to Real-Time Camera, Image Generation, and Maps Integration
Google's Gemini Live now integrates the Nano Banana image generation model with Connected Apps like Google Maps, supporting real-time camera scene understanding and visualization. Free worldwide.
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·2 min
Lingbot World: A 14B Open-Weight World Model with Causal Architecture and Inference Acceleration
Lingbot World is an open-weight world model on Hugging Face with 14B parameters, causal autoregressive architecture, and inference speed optimizations. Explore its architecture and use cases.
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·3 min
Microsoft SQL Major Upgrade: AI Capabilities, Security Enhancements, and Fabric Integration — A Deep Dive
Microsoft SQL team's major updates: Azure SQL adds AI embeddings and dynamic data masking, Fabric SQL gets a Migration Assistant and Fabric Apps, SQL Server CU5 brings memory improvements, SSMS adds a SQL Formatter and Agent mode, and DP-800 certification is now open.
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·2 min
Behavior Cloning Training Failing? A Systematic Guide to Data Quality and Tuning
Behavior cloning model barely moves after training? This guide systematically covers data alignment, class imbalance, learning rate, and epoch count — with a step-by-step debugging checklist and advanced alternatives like DAgger.
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·6 min
MIRA: An Analysis of a Multiplayer Interactive World Model Built on Rocket League
MIRA is an interactive world model project for the multiplayer competitive game Rocket League, exploring how neural networks simulate multi-agent interaction and complex physics. An in-depth look at its significance, challenges, and prospects.
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·7 min
Previewing the AI Engineer Summit: Industry Signals of AI Engineering Reaching Maturity
An exclusive look at the AI Engineer Summit dress rehearsals, decoding the paradigm shift from research to production. A deep dive into AI Engineer challenges, RAG, agent systems, and AI engineering as a distinct discipline.
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·6 min
Prompt Engineering in Practice: A Programmer's Core Methodology for Tuning Prompts
An in-depth analysis of prompt engineering from a programmer's perspective: understand token probability generation, master the three principles—specific, rich, low-ambiguity—and learn iterative prompt tuning.
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