I primarily use Claude with Claude code and GPT-5 in Codex. I rarely use cursor agents anymore.
CODE
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AI Travel Agent: Intelligent Assistant with Real-Time Information Integration
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AI Travel Agent An intelligent travel assistant that integrates real-time weather, search, and travel information. Leveraging multiple APIs, it streamlines everything from weather updates to currency conversion. Explore the guide on GitHub https://
github.com/ashumishra2104
/AI_Travel_agent_Streamlit
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BLAST: High-Performance AI Web Browser Engine with OpenAI-Compatible Interface
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BLAST: AI Web Browser Engine A high-performance serving engine that adds web browsing to AI applications. BLAST provides an OpenAI-compatible interface with automatic parallelization, intelligent caching, and real-time streaming support. Explore this open-source project
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GraphQA: Natural Language Graph Analysis with LangChain
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GraphQA Transform graph analysis into natural conversations with this NetworkX and LangChain-powered framework. Ask questions in plain English and let GraphQA select and execute the right algorithms, handling graphs up to 100K+ nodes. Check it out
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Wrappers Make AI Model Combinations User-Friendly
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Yes so this is actually a good point But people underrate how much a wrapper makes a combination of models friendly to use
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Using LLMs to Build Quick Disposable Tools Efficiently
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I love LLMs because I can just prompt them to build simple disposable tools that I'd otherwise not – simple mermaid visualiser it even hooked it up to github pages – all with one prompt; ty codex!
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Claude Code AI Development Tool Launch
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within Claude Code itself 😀
— Ahmad (@TheAhmadOsman) 11 octobre 2025
(as can be seen in my pinned tweet)https://t.co/UQhgEMI9CRwithin Claude Code itself 😀 (as can be seen in my pinned tweet)
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Comprehensive Guide to LLM Post-Training: SFT, RLHF, and RL Algorithms
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An excellent technical guide on LLM post-raining covering SFT(supervised finetuning), RL rewards such as RLHF/human preferences, RLAIF/constitutional-AI, RLVR/verifiable outcomes, process-supervised and rubric rewards. Also covers common RL training algorithms from PPO, GRPO, and
