Athina AI (YC W23)

🚀 Top 7 Directories to Discover MCP Servers MCP servers are transforming how agents access external tools, reducing development time from weeks to days — or even hours! 💡 With hundreds of MCP servers (official and unofficial) popping up every week, it’s becoming harder to stay updated with the most relevant ones for your projects. To make things easier, here are 7 trusted directories where you can find the latest MCP servers: 1️⃣ Composio MCP Platform: https://mcp.composio.dev/ 2️⃣ Glama AI:https://lnkd.in/dGbB4NfX 3️⃣ Anthropic MCP Repository: https://lnkd.in/dhiN8DsS 4️⃣ MCP.so: https://mcp.so/ 5️⃣ Portkey MCP Director: https://lnkd.in/dcc4Y9fi 6️⃣ Cursor Directory (not related to Cursor AI): https://lnkd.in/dhXS9mPa 7️⃣ MCP Servers: https://mcpservers.org/ 🔖 Want to bookmark all of these in one place? I’ve added a link to a detailed blog post in the comments! 👇

💡 Deep Competitor Research Using AI0 Competitor research requires gathering data from multiple sources, enriching it, and extracting useful insights. And it usually involves managing all of this across endless spreadsheets. 🤯 With AI0 you can build an automated pipeline using its pre-built tools — web search, web scraping, document parsers, API calls, and more in minutes 🔥 Here’s how one of our community members built a competitor research system to track Intercom's competitors: 1. Start with a simple sheet of competitor's names and websites. 2. Gather all relevant info — latest news, funding rounds, financials, product offerings, and founder/executive backgrounds. 3. Scrape competitors’ websites. 4. Organize data into separate columns using LLM calls. 5. Classify the data for deeper analysis. 6. Derive actionable insights. 🚀 Growth teams and executives spend way too much time collecting and updating this information. AI0 makes it so much simpler. If you have a use case in mind that requires a similar workflow, DM me. Happy to chat! 😊 👇 Link in the comments.

Here's a dynamic database of 50+ AI research papers 🚀 AI research papers are an excellent resource for staying updated on the latest developments in the AI space. 📚 But let’s be honest – we all have countless papers scattered across bookmarks, Excel sheets, PDFs, Notion, and other places in a completely unstructured manner. To solve this, Paras Madan from our team built a dynamic database of these papers, categorized by genre. We’ll be updating it regularly. It includes: ➡️ Links to all papers ➡️ Summaries ➡️ Key highlights And the best part? 🔥 Since it’s built on AI0 sheets, you can heavily customize it by adding more columns like: 🟢 LLM prompts 🟢 API calls 🟢 Web scrapers & search tools 🟢 Data extractors 🟢 Custom code blocks And more... We hope you find this useful! Link in comments 😊

🚀 Everyone’s talking about Manus AI , and those with early access are sharing their thoughts. But keeping up with all the discussions on Reddit? That’s a nightmare. So, we built an AI workflow to do the heavy lifting. In just seconds, it analyzes an entire Reddit thread and summarizes the key insights. It can even email the summary to you. 🤯 How? I simply connected a pre-built Reddit analyzer tool with an LLM tool, added a quick prompt on Athina AI (YC W23)—and boom, done! If you’re building AI workflows, we’ve got a library of pre-built tools to make it effortless. 👉 Check out the workflow in the comments!

Models are getting better at reasoning every day. We’ve seen our clients throw massive documents at these models—extracting insights, detecting risks, and uncovering patterns in seconds. 🤯 When it comes to reasoning tasks, accuracy >> latency. One of the most powerful use cases? Reviewing large contracts. 🚀 AI can critique documents, suggest changes, and highlight risks in minutes—what used to take hours. Here’s a simple 2-step AI workflow you can use to: ✅ Extract key clauses ✅ Detect potential risks ✅ Highlight critical terms ✅ Ensure compliance with legal standards If you're working in a regulated industry or care about privacy, you can even perform document intelligence using open source models hosted on your VPC. 🔗 Link in comments. Try it out! 👇

This week's research breakthroughs pushed the boundaries of RAG, AI Agents, and LLM Evaluations 🧑💻 We’ve compiled a list of the Top 10 LLM Papers from the past week shaping these areas: 1️⃣ Knowledge Graph-Guided Retrieval Augmented Generation 2️⃣ Fairness in Multi-Agent AI: A Unified Framework for Ethical and Equitable Autonomous Systems 3️⃣ Preventing Rogue Agents Improves Multi-Agent Collaboration 4️⃣ CODESIM: Multi-Agent Code Generation and Problem Solving through Simulation-Driven Planning and Debugging 5️⃣ Forget What You Know about LLMs Evaluations - LLMs are Like a Chameleon 6️⃣ BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models 7️⃣ Single-Agent Planning in a Multi-Agent System: A Unified Framework for Type-Based Planners 8️⃣ Commercial LLM Agents Are Already Vulnerable to Simple Yet Dangerous Attacks 9️⃣ Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation (RAG) 🔟 ParetoRAG: Leveraging Sentence-Context Attention for Robust and Efficient Retrieval-Augmented Generation Ready to dive deeper into how these papers are driving the next wave of AI innovation? 🚀 Read the full blog in the first comment 👇

This week’s Research highlights and brings exciting developments in RAG, AI Agents, and LLM Evaluations. We’ve compiled a list of the Top 10 LLM Papers from the past week shaping these areas: 1️⃣ The AI Agent Index: Public Database for AI agent architectures 2️⃣ Learning to Plan & Reason for Evaluation with Thinking-LLM-as-a-Judge 3️⃣ Training an LLM-as-a-Judge Model: Pipeline, Insights, and Practical Lessons 4️⃣ GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation 5️⃣ Multi-Agent Design: Optimizing Agents with Better Prompts and Topologies 6️⃣ Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? 7️⃣ Enhancing Online Learning Efficiency Through Heterogeneous Resource Integration with a Multi-Agent RAG System 8️⃣ ScoreFlow: Mastering LLM Agent Workflows via Score-based Preference Optimization 9️⃣ DeepRAG: Thinking to Retrieval Step by Step for Large Language Models 🔟 Agentic Reasoning: Reasoning LLMs with Tools for the Deep Research Ready to dive deeper into how these papers are shaping the landscape of LLM research and applications? 🚀 Read the full blog in the first comment 👇

Agentic RAG using DeepSeek AI - Qdrant - LangChain 🔥 [Open-source notebook] If you're looking to implement Agentic RAG using DeepSeek's R1 model we've published a ready-to-use Colab notebook (link in comments). 🚀 👉 This notebook uses an agentic Router along with RAG to improve the retrieval process with decision-making capabilities. 🛠️ It has 2 main components: 1️⃣ Agentic Retrieval The agent (Router) uses multiple tools—like vector search or web search—and decides which to invoke based on the context. 2️⃣ Dynamic Routing It maps the optimal path for retrieval— Retrieves data from vector DB for private knowledge queries and uses web search for general queries! 💡 Whether you're building enterprise-grade solutions or experimenting with AI workflows, Agentic RAG can improve your retrieval processes and results. 👉 What advanced technique should we cover next?

How well does DeepSeek AI R1 work on your data? 🤔 Everyone is talking about Deepseek — breaking benchmarks and outpacing competitors, even the proprietary ones. And truly, it is Fire! 🔥 But here’s the thing: benchmarks are one thing, and your use case is another. If you’re dealing with complex use cases like: 📜 Legal document analysis 📊 Financial risk assessment 🔬 Scientific research summarization 💻 Coding assistance you need a powerful model with top-tier reasoning capabilities. For simpler tasks like routine data extraction, keyword-based document retrieval, or basic summarization, other models might get the job done just fine. The best way to find out? Try it for yourself. We rolled out Deepseek reasoning support over the weekend 🚀 Give it a shot — Athina AI (YC W23) + Deepseek https://app.athina.ai