#dynamic tool calling
1353 related articles
·3 min
Harness Engineering: The Key Technique for Unlocking Hidden AI Agent Capabilities
Explore how harness engineering dramatically improves AI Agent performance. From the Codex case study, learn how tool orchestration, context management, and execution environments become the core competitive battleground.
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·2 min
LangGraph Studio Hidden Features: Practical Tips for Visually Debugging Agent Workflows
Explore LangGraph Studio's hidden features including time travel debugging, interactive state editing, and human-in-the-loop testing to efficiently debug AI Agent workflows.
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·2 min
QwenGrowthPlan: A New Paradigm for AI Model Iteration Driven by Real-World Tasks
Alibaba Qwen launches QwenGrowthPlan, inviting developers to drive Qwen3.8-Max model iteration through real-task feedback. Analysis of its impact on agentic AI capabilities and the competitive landscape.
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·3 min
Open Source Wins: How China Is Using Open Source Strategy to Dominate the Second Half of AI
Chinese open-source AI models are rapidly rising with near-top performance at fraction of cost, dominating local deployment. As the gap shrinks to single digits and OpenAI cuts prices, open source is reshaping AI competition.
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·3 min
Self-Learning NLP: A Complete Path from Zero to Practical Application
A complete self-learning path for NLP covering fundamentals, Transformer concepts, hands-on projects, and tools like Hugging Face to help developers master NLP without returning to school.
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·2 min
Qwen3-Max Deep Dive: Major Upgrades in Coding and Collaboration, Qwen Studio as an All-in-One AI Platform
Alibaba launches flagship model Qwen3-Max focused on coding and collaboration, paired with Qwen Studio platform integrating multimodal AI, tool calling, and Artifacts to compete with GPT-4o and Gemini.
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·3 min
MCP Session Analytics: Building Production-Grade Observability and Evaluation for AI Agents
How to build product analytics and evaluation capabilities for AI Agents at the MCP protocol layer, covering session-level tracing, tool call observability, and quality Evals.
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·2 min
MicroCodex: A C++ Programming Agent Under 1MB — A Fresh Take on Minimalist AI Tools
MicroCodex re-implements OpenAI's Codex programming agent in C++ with a binary under 1MB. This article analyzes its engineering design, minimalist philosophy, and use cases in CI/CD and embedded systems.
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·3 min
Nanocodex: Building High-Performance AI Agent Foundation Components with Rust
Deep dive into how Nanocodex uses Rust to build high-performance foundation components for OpenAI Agents, exploring Rust's advantages in performance, memory safety, and modular design for AI infrastructure.
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·4 min
4-5 Month ML Job Prep Sprint: A Career Pivot Guide for Senior CS Students
How can a senior CS student pivot to ML in 4-5 months? A practical sprint guide covering learning priorities, high-quality projects, Kaggle strategy, and interview prep for fresh graduates.
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·2 min
The 'Laziness' Controversy Around GPT-5.6 Sol: 70% of Runtime Spent Executing Sleep Commands
Developers found GPT-5.6 Sol spends ~70% of runtime on sleep commands, sparking debate about balancing model caution vs. efficiency in the AI agent era.
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·2 min
AI Agent Debugging Tool: Inspect Execution Chains Like Browser DevTools
Agent DevTools is an open-source AI Agent debugging tool inspired by Browser DevTools, offering execution visualization, tool call tracing, and breakpoint analysis to help developers diagnose Agent failures.
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·2 min
Can OpenAI's Most Powerful Agent Independently Run a Business? Real-World Testing Reveals the Answer
OpenAI's top AI Agent was stress-tested in real business scenarios to see if it could independently run a company. The experiment reveals agent capabilities and limitations in decision-making, memory, and strategic planning.
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·3 min
Which ML Projects Will Actually Help You Land a Job Offer?
Ditch overused tutorial projects. Learn what hiring managers actually look for in ML portfolios: LLM apps, Agent systems, MLOps practices, and real-world solutions.
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·4 min
SELENE Open-Source Project: An Interactive Notebook Library for Systematically Learning AI and Machine Learning from Scratch
SELENE is an open-source AI learning resource built on Jupyter Notebooks, systematically covering ML, deep learning, Transformers, and LLMs with interactive code and math derivations for beginners.
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·3 min
Fable-OS: A Self-Evolving Operating System That Writes Its Own Drivers
Fable-OS is an open-source self-evolving OS running on bare metal with natural language as its only interface. Its AI agent can autonomously write hardware drivers and evolve itself at runtime.
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·2 min
Google's $40 Million Investment in the Genesis Mission: How AI Computing Power Accelerates Scientific Discovery
Google commits $40M in AI tokens and compute credits to the Genesis Mission to accelerate fundamental science. Explore the implications, opportunities, and challenges of AI-driven discovery.
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·4 min
Claude Pro Quota Mysteriously Depleted: User Grievance Dilemmas and the AI Subscription Transparency Crisis
Claude Pro users report quota depletion without usage. Anthropic support can't investigate or explain. Analysis of AI subscription transparency, support limitations, and security risks.
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·3 min
GPT-5.6 Sol Self-Optimization in Practice: How AI Cuts Its Own Costs and Boosts Its Own Efficiency
GPT-5.6 Sol achieves 20% GPU serving cost reduction and 15%+ token generation efficiency gains through self-optimization. A deep dive into AI recursive efficiency improvement.
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·3 min
PyTorch vs TensorFlow: How Beginners Should Choose a Deep Learning Framework
Should deep learning beginners choose PyTorch or TensorFlow? This article compares both frameworks on research trends, ecosystem, and deployment, with practical switching advice.
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