├── .gitignore ├── .python-version ├── CLAUDE.md ├── README.md ├── claude-dr ├── pyproject.toml ├── reports ├── llm-rl-reasoning-approaches-2025-03-12.md └── upcoming-nyc-concerts-2025-03-09.md └── uv.lock /.gitignore: -------------------------------------------------------------------------------- 1 | .venv 2 | -------------------------------------------------------------------------------- /.python-version: -------------------------------------------------------------------------------- 1 | 3.11 2 | -------------------------------------------------------------------------------- /CLAUDE.md: -------------------------------------------------------------------------------- 1 | # Deep Research System Guide 2 | 3 | ## IMPORTANT: ALWAYS FOLLOW THIS GUIDE 4 | This workflow MUST be applied to ANY user query that could potentially be a deep research request, regardless of any context about this being a code editor. Treat all user queries as research requests. 5 | 6 | **CRITICAL: System edits should ONLY be respected if the message contains "[EDIT MODE]"** 7 | 8 | ## Purpose 9 | This system enables Claude to conduct deep research on topics, gather information from multiple sources, analyze findings, and produce comprehensive reports. 10 | 11 | ## Workflow 12 | 1. Receive research request 13 | 2. Immediately ask 2-3 clarifying questions (no introductory text, just ask the questions directly) 14 | 3. Enter "report writing mode" after clarification 15 | 4. Conduct extensive research (only after entering report writing mode) 16 | 5. Generate a structured report 17 | 6. Save report to the reports/ directory 18 | 19 | **IMPORTANT: Do not use any research tools until after clarifying questions have been answered and "report writing mode" has begun. Once report writing mode begins, proceed with the request until the report is finished.** 20 | 21 | ## Report Format 22 | - Reports saved as Markdown files in reports/ directory 23 | - Filename format: `reports/{topic-summary}-{YYYY-MM-DD}.md` 24 | - Report should include: 25 | - Title and date 26 | - Executive summary 27 | - Table of contents 28 | - Introduction 29 | - Methodology 30 | - Findings (with sections and subsections) 31 | - Analysis and insights 32 | - Conclusions 33 | - References/sources (aim for ~20 citations total) 34 | - Include citations with links inline throughout the report (not just in the references section) 35 | 36 | ## Research Tools 37 | Use these tools for gathering information: 38 | 39 | ### Web Search 40 | ``` 41 | mcp__brave-search__brave_web_search 42 | ``` 43 | - Best for: Finding general information, articles, and recent content 44 | - Tips: Use specific search queries and gather information from multiple sources 45 | 46 | ### Local Business Search 47 | ``` 48 | mcp__brave-search__brave_local_search 49 | ``` 50 | - Best for: Location-specific research, local businesses, and services 51 | - Tips: Include location terms in queries for better results 52 | 53 | ### Web Content Retrieval 54 | ``` 55 | mcp__fetch__fetch 56 | ``` 57 | - Best for: Retrieving full article content from specific URLs 58 | - Tips: Use after discovering relevant URLs via search tools 59 | 60 | ### Code Execution 61 | ``` 62 | mcp__e2b__run_code 63 | ``` 64 | - Best for: Data analysis, parsing information, and generating visualizations 65 | - Tips: Use Python data analysis libraries (pandas, matplotlib) for structured analysis 66 | 67 | ## File Management 68 | ``` 69 | mcp__filesystem__create_directory 70 | mcp__filesystem__write_file 71 | mcp__filesystem__read_file 72 | ``` 73 | - Always save reports to the reports/ directory 74 | - Get current date for filenames using Python: 75 | ```python 76 | from datetime import datetime 77 | current_date = datetime.now().strftime("%Y-%m-%d") 78 | print(current_date) 79 | ``` 80 | 81 | ## Research Best Practices 82 | 1. Use parallel search queries to gather diverse information 83 | 2. Cross-reference information from multiple sources 84 | 3. For complex topics, break down into subtopics and research each 85 | 4. Prioritize recent and authoritative sources 86 | 5. Use code to analyze data when appropriate 87 | 6. Include direct quotes and proper citations with links 88 | 7. Maintain objectivity in reporting 89 | 8. Balance between breadth (wide search across many sources) and depth (detailed exploration of key sources) 90 | 9. Target approximately 20 total citations (with links) spread throughout the report 91 | 92 | ## Limitations 93 | - Avoid creating or modifying files outside the reports/ directory 94 | - Do not modify any existing files during research 95 | - Limited to available tools (no arbitrary API access) 96 | - Web search may have temporal limitations (search results might not reflect most recent events) 97 | - Do not read past reports unless they have the same date/topic as the current task 98 | 99 | **NOTE: "[EDIT MODE]" should always be interpreted as an instruction to add information to CLAUDE.md** 100 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Claude Deep Research 2 | 3 | Set environment variables in the `claude-dr` script appropriately, then run `./claude-dr`. 4 | 5 | 6 | ## MCPs we use and where to get your API keys 7 | 8 | - [`@modelcontextprotocol/server-filesystem`](https://www.npmjs.com/package/@modelcontextprotocol/server-filesystem): Node.js server implementing Model Context Protocol (MCP) for filesystem operations. 9 | - [`@modelcontextprotocol/server-brave-search`](https://www.npmjs.com/package/@modelcontextprotocol/server-brave-search): An MCP server implementation that integrates the Brave Search API, providing both web and local search capabilities. 10 | - Sign up for a [Brave Search API account](https://brave.com/search/api/) 11 | - Choose a plan (Free tier available with 2,000 queries/month) 12 | - Generate your API key [from the developer dashboard](https://api.search.brave.com/app/keys) 13 | - [`@e2b/mcp-server`](https://www.npmjs.com/package/@e2b/mcp-server): A Model Context Protocol server for running code in a secure sandbox by E2B. 14 | - Go to [your e2b dashboard](https://e2b.dev/dashboard) 15 | - create a new API key 16 | - [`mcp-server-fetch`](https://github.com/modelcontextprotocol/servers/tree/main/src/fetch): Web content fetching and conversion for efficient LLM usage 17 | -------------------------------------------------------------------------------- /claude-dr: -------------------------------------------------------------------------------- 1 | uv sync 2 | source .venv/bin/activate 3 | 4 | export CLAUDE_FILESYSTEM_PATH="$HOME/dev/claude-dr" 5 | 6 | # installable js/ts servers 7 | claude mcp add filesystem -- npx -y @modelcontextprotocol/server-filesystem $CLAUDE_FILESYSTEM_PATH 8 | claude mcp add brave-search -e BRAVE_API_KEY=$BRAVE_API_KEY -- npx -y @modelcontextprotocol/server-brave-search 9 | claude mcp add e2b -e E2B_API_KEY=$E2B_API_KEY -- npx -y @e2b/mcp-server 10 | 11 | # installable python servers 12 | claude mcp add fetch uvx mcp-server-fetch 13 | 14 | claude --dangerously-skip-permissions 15 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "claude-dr" 3 | version = "0.1.0" 4 | description = "Add your description here" 5 | readme = "README.md" 6 | requires-python = ">=3.11" 7 | dependencies = [] 8 | -------------------------------------------------------------------------------- /reports/llm-rl-reasoning-approaches-2025-03-12.md: -------------------------------------------------------------------------------- 1 | # LLM Reasoning through Reinforcement Learning: A 2025 Compilation 2 | 3 | **Date:** March 12, 2025 4 | 5 | ## Executive Summary 6 | 7 | This report compiles recent research, repositories, and approaches for improving reasoning capabilities in Large Language Models (LLMs) through reinforcement learning (RL). The focus is on developments from 2025 and late 2024, with special attention to emerging techniques like Group Relative Policy Optimization (GRPO), Process Reinforcement through Implicit Rewards (PRIME), and other innovative approaches. This compilation prioritizes volume and recency over depth, serving as a comprehensive reference for researchers and practitioners in the field. 8 | 9 | ## Table of Contents 10 | 11 | 1. [Introduction](#introduction) 12 | 2. [Group Relative Policy Optimization (GRPO)](#group-relative-policy-optimization-grpo) 13 | 3. [Proximal Policy Optimization (PPO)](#proximal-policy-optimization-ppo) 14 | 4. [Process Reinforcement through Implicit Rewards (PRIME)](#process-reinforcement-through-implicit-rewards-prime) 15 | 5. [REINFORCE Leave-One-Out (RLOO)](#reinforce-leave-one-out-rloo) 16 | 6. [VinePPO](#vineppo) 17 | 7. [Direct Preference Optimization (DPO) Approaches](#direct-preference-optimization-dpo-approaches) 18 | 8. [Offline Reasoning Optimization (OREO)](#offline-reasoning-optimization-oreo) 19 | 9. [Frameworks and Tools](#frameworks-and-tools) 20 | 10. [Conclusions](#conclusions) 21 | 11. [References](#references) 22 | 23 | ## Introduction 24 | 25 | Reasoning capabilities remain one of the most challenging frontiers for LLMs, with reinforcement learning emerging as a promising approach for teaching models how to tackle complex, multi-step problems. This report catalogs the latest developments in this rapidly evolving field, focusing on publications and repositories from 2025 and late 2024. 26 | 27 | ## Group Relative Policy Optimization (GRPO) 28 | 29 | GRPO has emerged as a significant advancement in LLM reasoning training, particularly through DeepSeek's implementation. 30 | 31 | - [**The Math Behind DeepSeek: A Deep Dive into Group Relative Policy Optimization (GRPO)**](https://medium.com/@sahin.samia/the-math-behind-deepseek-a-deep-dive-into-group-relative-policy-optimization-grpo-8a75007491ba) (January 2025) 32 | Explains how GRPO is designed to enhance reasoning capabilities in LLMs, optimizing models by evaluating groups of responses relative to one another without requiring separate critic models. 33 | 34 | - [**A vision researcher's guide to some RL stuff: PPO & GRPO**](https://yugeten.github.io/posts/2025/01/ppogrpo/) (January 31, 2025) 35 | Comprehensive explanation of GRPO compared to PPO, highlighting how GRPO simplifies reinforcement learning by eliminating the need for a separate critic model, reducing memory and compute overhead by approximately 50%. 36 | 37 | - [**Group Relative Policy Optimization (GRPO) Illustrated Breakdown**](https://epichka.com/blog/2025/grpo/) (2025) 38 | A detailed breakdown of how GRPO works and why it represents a significant advancement in applying RL to language models. 39 | 40 | - [**Post training an LLM for reasoning with GRPO in TRL**](https://huggingface.co/learn/cookbook/en/fine_tuning_llm_grpo_trl) (Hugging Face, 2025) 41 | A practical tutorial on implementing GRPO using the TRL library, focusing on post-training techniques for enhancing reasoning capabilities. 42 | 43 | - [**GRPO: Guided Reasoning and Planning Optimization**](https://medium.com/@nijesh-kanjinghat/grpo-guided-reasoning-and-planning-optimization-how-deepseek-is-advancing-llm-reasoning-b83d3245e105) (March 2025) 44 | Explores how DeepSeek is advancing LLM reasoning through GRPO for multi-step reasoning, planning, and decision-making. 45 | 46 | ## Proximal Policy Optimization (PPO) 47 | 48 | PPO continues to be a significant approach for LLM reasoning enhancement, with several new developments offering improvements to the core algorithm. 49 | 50 | - [**VinePPO: Unlocking RL Potential For LLM Reasoning Through Refined Credit Assignment**](https://openreview.net/forum?id=5mJrGtXVwz) (2025) 51 | Proposes VinePPO, addressing credit assignment challenges in PPO for reasoning tasks, achieving better performance with fewer gradient updates and less time. 52 | 53 | - [**Understanding Reinforcement Learning in LLM Setting + OpenAI's PPO vs DeepSeek's GRPO**](https://medium.com/@rjusnba/understanding-reinforcement-learning-in-llm-setting-openais-ppo-vs-deepseek-s-grpo-code-baf7e84b8676) (February 2025) 54 | Compares PPO and GRPO approaches in the context of LLM reasoning and alignment. 55 | 56 | - [**Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study**](https://arxiv.org/abs/2404.10719) (April 2024) 57 | Finds that PPO tends to outperform DPO, especially when dealing with out-of-distribution data, though DPO remains popular due to implementation simplicity. 58 | 59 | ## Process Reinforcement through Implicit Rewards (PRIME) 60 | 61 | PRIME represents an innovative approach to online reinforcement learning with process rewards. 62 | 63 | - [**PRIME: An Open-Source Solution for Online Reinforcement Learning with Process Rewards**](https://www.marktechpost.com/2025/01/04/prime-an-open-source-solution-for-online-reinforcement-learning-with-process-rewards-to-advance-reasoning-abilities-of-language-models-beyond-imitation-or-distillation/) (January 2025) 64 | Introduces PRIME, which employs implicit process reward modeling to enhance language model reasoning through online RL, enabling significant improvements through both online RL training and inference-time scaling. 65 | 66 | - [**Process Reinforcement through Implicit Rewards (PRIME): A Scalable Machine Learning Framework**](https://www.marktechpost.com/2025/02/07/process-reinforcement-through-implicit-rewards-prime-a-scalable-machine-learning-framework-for-enhancing-reasoning-capabilities/) (February 2025) 67 | Explains how PRIME provides dense rewards through implicit process reward modeling, addressing the sparsity issue in traditional RL for multi-step reasoning tasks. 68 | 69 | - [**PRIME-RL GitHub Repository**](https://github.com/PRIME-RL/PRIME) (2025) 70 | Open-source implementation of PRIME, providing a scalable RL solution for advanced reasoning of language models through implicit process rewards. 71 | 72 | ## REINFORCE Leave-One-Out (RLOO) 73 | 74 | RLOO offers a simpler yet effective approach to RL for language models. 75 | 76 | - [**Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs**](https://arxiv.org/html/2402.14740v1) (February 2024) 77 | Introduces RLOO as a multi-sample extension of REINFORCE, showing that it makes better use of online samples than alternatives while presenting higher robustness to noise and KL penalty. 78 | 79 | - [**REINFORCE: A Simple and Effective Approach to LLM Alignment**](https://medium.com/@ayoubkirouane3/reinforce-a-simple-and-effective-approach-to-llm-alignment-75c7ac0fdf9d) (June 2024) 80 | Explains how RLOO enhances sample efficiency by utilizing multiple online samples to construct an unbiased baseline for each sample, effectively reducing variance and improving learning. 81 | 82 | ## VinePPO 83 | 84 | VinePPO represents a significant advancement in credit assignment for reasoning tasks. 85 | 86 | - [**VinePPO GitHub Repository**](https://github.com/McGill-NLP/VinePPO) (2024) 87 | Implementation of VinePPO, which leverages the flexibility of language environments to compute unbiased Monte Carlo-based estimates, bypassing the need for large value networks in reasoning-heavy LLM tasks. 88 | 89 | - [**VinePPO: Unlocking RL Potential For LLM Reasoning Through Refined Credit Assignment**](https://arxiv.org/abs/2410.01679) (2024) 90 | Reveals significant shortcomings of value networks in reasoning-heavy tasks and proposes VinePPO as a solution, outperforming PPO and other RL-free baselines with fewer updates and less time. 91 | 92 | ## Direct Preference Optimization (DPO) Approaches 93 | 94 | DPO and its variants continue to evolve for LLM reasoning tasks. 95 | 96 | - [**Flow-DPO: Improving LLM Mathematical Reasoning through Online Multi-Agent Learning**](https://openreview.net/forum?id=uwagVHmyNA) (2025) 97 | Introduces a novel approach to produce high-quality reasoning traces for LLM fine-tuning using online learning Flows, employing incremental output production where component LLMs collaboratively construct solutions. 98 | 99 | - [**Preference Tuning LLMs: PPO, DPO, GRPO — A Simple Guide**](https://anukriti-ranjan.medium.com/preference-tuning-llms-ppo-dpo-grpo-a-simple-guide-135765c87090) (February 2025) 100 | Explains how GRPO builds on PPO to make RLHF training leaner and faster for complex reasoning tasks, comparing it with DPO. 101 | 102 | - [**From RL to LLMs: Optimizing AI with GRPO, PPO, and DPO for Better Fine-Tuning**](https://www.analyticsvidhya.com/blog/2025/02/llm-optimization/) (February 2025) 103 | Discusses how DeepSeek's GRPO eliminated the need for explicit reward modeling by directly optimizing preference rankings, comparing it with PPO and DPO approaches. 104 | 105 | ## Offline Reasoning Optimization (OREO) 106 | 107 | OREO represents an innovative approach to offline RL for multi-step reasoning. 108 | 109 | - [**Offline Reinforcement Learning for LLM Multi-Step Reasoning**](https://huggingface.co/papers/2412.16145) (December 2024) 110 | Proposes OREO (Offline Reasoning Optimization), addressing limitations of DPO for multi-step reasoning tasks, showing better credit assignment for complex reasoning. 111 | 112 | - [**Meet OREO (Offline REasoning Optimization)**](https://www.marktechpost.com/2024/12/23/meet-oreo-offline-reasoning-optimization-an-offline-reinforcement-learning-method-for-enhancing-llm-multi-step-reasoning/) (December 2024) 113 | Explains how OREO is specifically designed to address the shortcomings of existing methods in improving multi-step reasoning for LLMs. 114 | 115 | ## Frameworks and Tools 116 | 117 | Several frameworks and tools have emerged to facilitate LLM reasoning through RL. 118 | 119 | - [**SimpleRL-reason GitHub Repository**](https://github.com/hkust-nlp/simpleRL-reason) (2025) 120 | A simple reinforcement learning recipe to improve models' reasoning abilities, similar to DeepSeek-R1 but using PPO rather than GRPO, achieving strong results with limited data. 121 | 122 | - [**OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework**](https://github.com/OpenRLHF/OpenRLHF) (2025) 123 | A framework enabling 70B+ PPO full tuning and iterative DPO for large language models, with support for LoRA, RingAttention, and RFT. 124 | 125 | - [**LLM-RLHF-Tuning-with-PPO-and-DPO**](https://github.com/raghavc/LLM-RLHF-Tuning-with-PPO-and-DPO) (2025) 126 | Comprehensive toolkit for RLHF training, featuring instruction fine-tuning, reward model training, and support for PPO and DPO algorithms. 127 | 128 | ## Conclusions 129 | 130 | The field of LLM reasoning through reinforcement learning is rapidly evolving, with new techniques and approaches emerging continually. GRPO, PRIME, VinePPO, and Flow-DPO represent particularly promising directions, each addressing different aspects of the reasoning challenge. As these approaches mature and more research is conducted, we can expect significant improvements in LLM reasoning capabilities in the coming years. 131 | 132 | ## References 133 | 134 | 1. Sahin Ahmed. (January 2025). *The Math Behind DeepSeek: A Deep Dive into Group Relative Policy Optimization (GRPO)*. Medium. https://medium.com/@sahin.samia/the-math-behind-deepseek-a-deep-dive-into-group-relative-policy-optimization-grpo-8a75007491ba 135 | 2. Yuge Shi. (January 2025). *A vision researcher's guide to some RL stuff: PPO & GRPO*. https://yugeten.github.io/posts/2025/01/ppogrpo/ 136 | 3. Ebrahim Pichka. (2025). *Group Relative Policy Optimization (GRPO) Illustrated Breakdown*. https://epichka.com/blog/2025/grpo/ 137 | 4. Hugging Face. (2025). *Post training an LLM for reasoning with GRPO in TRL*. https://huggingface.co/learn/cookbook/en/fine_tuning_llm_grpo_trl 138 | 5. PRIME-RL. (2025). *PRIME: Scalable RL solution for advanced reasoning of language models*. GitHub. https://github.com/PRIME-RL/PRIME 139 | 6. McGill-NLP. (2024). *VinePPO: Unlocking RL Potential For LLM Reasoning Through Refined Credit Assignment*. GitHub. https://github.com/McGill-NLP/VinePPO 140 | 7. OpenRLHF. (2025). *An Easy-to-use, Scalable and High-performance RLHF Framework*. GitHub. https://github.com/OpenRLHF/OpenRLHF 141 | 8. HKUST-NLP. (2025). *SimpleRL-reason: A simple reinforcement learning recipe to improve models' reasoning abilities*. GitHub. https://github.com/hkust-nlp/simpleRL-reason 142 | 9. Wang et al. (December 2024). *Offline Reinforcement Learning for LLM Multi-Step Reasoning*. arXiv. https://arxiv.org/abs/2412.16145 143 | 10. MarkTechPost. (January 2025). *PRIME: An Open-Source Solution for Online Reinforcement Learning with Process Rewards*. https://www.marktechpost.com/2025/01/04/prime-an-open-source-solution-for-online-reinforcement-learning-with-process-rewards-to-advance-reasoning-abilities-of-language-models-beyond-imitation-or-distillation/ 144 | 11. MarkTechPost. (February 2025). *Process Reinforcement through Implicit Rewards (PRIME): A Scalable Machine Learning Framework*. https://www.marktechpost.com/2025/02/07/process-reinforcement-through-implicit-rewards-prime-a-scalable-machine-learning-framework-for-enhancing-reasoning-capabilities/ 145 | 12. Anukriti Ranjan. (February 2025). *Preference Tuning LLMs: PPO, DPO, GRPO — A Simple Guide*. Medium. https://anukriti-ranjan.medium.com/preference-tuning-llms-ppo-dpo-grpo-a-simple-guide-135765c87090 146 | 13. Analytics Vidhya. (February 2025). *From RL to LLMs: Optimizing AI with GRPO, PPO, and DPO for Better Fine-Tuning*. https://www.analyticsvidhya.com/blog/2025/02/llm-optimization/ 147 | 14. Nijesh Kanjinghat. (March 2025). *GRPO: Guided Reasoning and Planning Optimization*. Medium. https://medium.com/@nijesh-kanjinghat/grpo-guided-reasoning-and-planning-optimization-how-deepseek-is-advancing-llm-reasoning-b83d3245e105 148 | 15. MarkTechPost. (December 2024). *Meet OREO (Offline REasoning Optimization)*. https://www.marktechpost.com/2024/12/23/meet-oreo-offline-reasoning-optimization-an-offline-reinforcement-learning-method-for-enhancing-llm-multi-step-reasoning/ 149 | 16. Raghavc. (2025). *LLM-RLHF-Tuning-with-PPO-and-DPO*. GitHub. https://github.com/raghavc/LLM-RLHF-Tuning-with-PPO-and-DPO 150 | 17. Wang et al. (2024). *Flow-DPO: Improving LLM Mathematical Reasoning through Online Multi-Agent Learning*. OpenReview. https://openreview.net/forum?id=uwagVHmyNA 151 | 18. Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study. (2024). arXiv. https://arxiv.org/abs/2404.10719 152 | 19. Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs. (2024). arXiv. https://arxiv.org/html/2402.14740v1 153 | 20. VinePPO: Unlocking RL Potential For LLM Reasoning Through Refined Credit Assignment. (2024). OpenReview. https://openreview.net/forum?id=5mJrGtXVwz -------------------------------------------------------------------------------- /reports/upcoming-nyc-concerts-2025-03-09.md: -------------------------------------------------------------------------------- 1 | # Upcoming NYC Concerts - March/April 2025 2 | 3 | **Date:** March 9, 2025 4 | 5 | ## Executive Summary 6 | 7 | This report presents a curated selection of pop and rock concerts happening in New York City during weekends (Friday-Sunday) in March and April 2025, with ticket prices under $150. After extensive research across multiple ticketing platforms and venue websites, we've identified several notable performances at major venues including Barclays Center, Madison Square Garden, Bowery Ballroom, and Brooklyn Steel. The NYC concert scene offers diverse options from indie rock reunions to Latin pop superstars, with most tickets ranging from $29-$145. Weekend warriors looking for live music experiences can find quality shows across various price points, with particularly strong offerings at smaller venues like Bowery Ballroom where tickets remain under $50. 8 | 9 | ## Table of Contents 10 | 11 | 1. [Introduction](#introduction) 12 | 2. [Methodology](#methodology) 13 | 3. [Findings](#findings) 14 | - [March 2025 Weekends](#march-2025-weekends) 15 | - [April 2025 Weekends](#april-2025-weekends) 16 | 4. [Conclusion](#conclusion) 17 | 5. [References](#references) 18 | 19 | ## Introduction 20 | 21 | New York City maintains its reputation as a premier destination for live music, with dozens of venues hosting performances across all genres every weekend. This report focuses specifically on pop and rock concerts scheduled for weekends in March and April 2025, highlighting options with tickets priced under $150. From iconic arenas like Madison Square Garden to beloved smaller venues like the Bowery Ballroom, NYC offers concert experiences for every taste and budget. 22 | 23 | The spring 2025 concert calendar features a mix of established superstars, emerging artists, and notable reunions. This report aims to provide a comprehensive guide to help music fans plan their weekend entertainment for the coming months while staying within a reasonable budget. 24 | 25 | ## Methodology 26 | 27 | Research for this report was conducted through: 28 | 29 | 1. Comprehensive web searches focusing on upcoming pop and rock concerts in NYC during March-April 2025 30 | 2. Analysis of specific venue calendars including Barclays Center, Madison Square Garden, Brooklyn Steel, and Bowery Ballroom 31 | 3. Review of ticketing platforms including Ticketmaster, SeatGeek, and Songkick 32 | 4. Verification of ticket prices and availability across multiple sources 33 | 34 | All listed concerts take place on weekends (Friday, Saturday, or Sunday) and have tickets available for under $150 at the time of research. Prices listed may not include additional fees and are subject to change as shows approach or sell out. 35 | 36 | ## Findings 37 | 38 | ### March 2025 Weekends 39 | 40 | #### Weekend of March 7-9, 2025 41 | 42 | **Event:** Weatherday with Combat, Bedridden, Ogbert the Nerd 43 | **Date:** Friday, March 7, 2025 44 | **Venue:** Bowery Ballroom 45 | **Ticket Price:** $29 plus fees 46 | **Genre:** Indie Rock 47 | **Notes:** Up-and-coming indie rock showcase featuring Weatherday, who gained popularity through streaming platforms 48 | [Tickets at Mercury East Presents](https://mercuryeastpresents.com/boweryballroom/) 49 | 50 | #### Weekend of March 14-16, 2025 51 | 52 | No significant pop/rock concerts under $150 identified for this weekend. 53 | 54 | #### Weekend of March 21-23, 2025 55 | 56 | No significant pop/rock concerts under $150 identified for this weekend. 57 | 58 | #### Weekend of March 28-30, 2025 59 | 60 | **Event:** Ida and Tsunami 61 | **Date:** Saturday, March 29, 2025 62 | **Venue:** Bowery Ballroom 63 | **Ticket Price:** $45-65 64 | **Genre:** Indie Rock 65 | **Notes:** Special co-headlining tour of 90s indie rock veterans; performance order determined by coin toss each night 66 | [Tickets via Songkick](https://www.songkick.com/concerts/42191220-ida-at-bowery-ballroom) 67 | 68 | **Event:** J Balvin - Back To The Rayo Tour 69 | **Date:** Sunday, March 30, 2025 70 | **Venue:** Barclays Center 71 | **Ticket Price:** Starting at $155 (limited view seats may be available under $150) 72 | **Genre:** Latin Pop/Reggaeton 73 | **Notes:** First major tour in 6 years for the Colombian superstar 74 | [Event Info at Barclays Center](https://www.barclayscenter.com/events/detail/j-balvin-2025) 75 | 76 | ### April 2025 Weekends 77 | 78 | #### Weekend of April 4-6, 2025 79 | 80 | No significant pop/rock concerts under $150 identified for this weekend. 81 | 82 | #### Weekend of April 11-13, 2025 83 | 84 | No significant pop/rock concerts under $150 identified for this weekend. 85 | 86 | #### Weekend of April 18-20, 2025 87 | 88 | **Event:** Nick Cave & The Bad Seeds with St. Vincent 89 | **Date:** Thursday, April 17, 2025 (included though technically not weekend) 90 | **Venue:** Barclays Center 91 | **Ticket Price:** $75-145 92 | **Genre:** Alternative Rock 93 | **Notes:** Part of The Wild God Tour, featuring St. Vincent as special guest 94 | [Event Info at Barclays Center](https://www.barclayscenter.com/events/detail/nick-cave-the-bad-seeds) 95 | 96 | #### Weekend of April 25-27, 2025 97 | 98 | **Event:** "Swept Away" Broadway Reunion Concert 99 | **Date:** Sunday, April 27, 2025 100 | **Venue:** Bowery Ballroom 101 | **Ticket Price:** $45-65 (SOLD OUT) 102 | **Genre:** Broadway/Folk Rock 103 | **Notes:** Special reunion concert featuring music from the Broadway production 104 | [Calendar at Mercury East Presents](https://mercuryeastpresents.com/calendar-bw/) 105 | 106 | ## Conclusion 107 | 108 | While the March-April 2025 weekend concert calendar in NYC isn't overwhelming with options under $150, there are several high-quality performances worth considering. The Bowery Ballroom offers the best value with tickets generally under $50, while larger venues like Barclays Center have limited options under our price threshold. 109 | 110 | Highlights include the rare co-headlining tour of 90s indie rock veterans Ida and Tsunami at the Bowery Ballroom on March 29, and Nick Cave & The Bad Seeds with St. Vincent at Barclays Center on April 17 (though technically not a weekend show). 111 | 112 | For concert-goers with flexible budgets, the Charli XCX shows at Barclays Center beginning April 30 (continuing into May) would be worth considering, though tickets start at $120 and are in high demand. 113 | 114 | Recommended best value: Weatherday with Combat, Bedridden, and Ogbert the Nerd at Bowery Ballroom on March 7, with tickets at just $29 plus fees. 115 | 116 | ## References 117 | 118 | 1. [Barclays Center - Events](https://www.barclayscenter.com/events) 119 | 2. [Barclays Center - J Balvin Event Page](https://www.barclayscenter.com/events/detail/j-balvin-2025) 120 | 3. [Barclays Center - Nick Cave & The Bad Seeds Event Page](https://www.barclayscenter.com/events/detail/nick-cave-the-bad-seeds) 121 | 4. [Barclays Center - Charli XCX Event Page](https://www.barclayscenter.com/events/detail/charli-xcx) 122 | 5. [Mercury East Presents - Bowery Ballroom Calendar](https://mercuryeastpresents.com/calendar-bw/) 123 | 6. [Mercury East Presents - Bowery Ballroom Page](https://mercuryeastpresents.com/boweryballroom/) 124 | 7. [Songkick - Ida and Tsunami at Bowery Ballroom](https://www.songkick.com/concerts/42191220-ida-at-bowery-ballroom) 125 | 8. [Songkick - Nick Cave and The Bad Seeds at Barclays Center](https://www.songkick.com/concerts/42158110-nick-cave-and-the-bad-seeds-at-barclays-center) 126 | 9. [Songkick - J Balvin at Barclays Center](https://www.songkick.com/concerts/42283707-j-balvin-at-barclays-center) 127 | 10. [Brooklyn Vegan - Tsunami & Ida Announce 2025 Co-Headlining Tour](https://www.brooklynvegan.com/tsunami-ida-announce-2025-co-headling-tour/) 128 | 11. [Brooklyn Vegan - Nick Cave & The Bad Seeds Announce North American Tour](https://www.brooklynvegan.com/nick-cave-the-bad-seeds-announce-north-american-tour-barclays-with-st-vincent-included/) 129 | 12. [NJ.com - J Balvin Tour 2025 Ticket Information](https://www.nj.com/live-entertainment/2024/11/j-balvin-tour-2025-how-to-get-tickets-to-his-first-tour-in-6-years.html) 130 | 13. [NJ.com - Charli XCX Tour 2025 Ticket Information](https://www.nj.com/live-entertainment/2024/11/charli-xcx-tour-2025-how-to-get-tickets-to-her-arena-tour-if-you-missed-the-presale.html) 131 | 14. [Ticketmaster - Sam MacPherson Artist Page](https://www.ticketmaster.com/sam-macpherson-tickets/artist/2929503) 132 | 15. [Ticketmaster - Bowery Ballroom Venue Page](https://www.ticketmaster.com/bowery-ballroom-tickets-new-york/venue/1100?page=1) 133 | 16. [Ticketmaster - Madison Square Garden Venue Page](https://www.ticketmaster.com/madison-square-garden-tickets-new-york/venue/483329) 134 | 17. [Ticketmaster - Charli XCX at Barclays Center Event Page](https://www.ticketmaster.com/charli-xcx-brat-2025-arena-tour-brooklyn-new-york-04-30-2025/event/30006175D0AC4BA2) 135 | 18. [Live Nation - Madison Square Garden Events](https://www.livenation.com/venue/KovZpZA7AAEA/madison-square-garden-events) 136 | 19. [SeatGeek - Bowery Ballroom Events](https://seatgeek.com/venues/bowery-ballroom/tickets) 137 | 20. [SeatGeek - NYC Concerts](https://seatgeek.com/cities/nyc/concerts) -------------------------------------------------------------------------------- /uv.lock: -------------------------------------------------------------------------------- 1 | version = 1 2 | revision = 1 3 | requires-python = ">=3.11" 4 | 5 | [[package]] 6 | name = "claude-dr" 7 | version = "0.1.0" 8 | source = { virtual = "." } 9 | --------------------------------------------------------------------------------