@IceSolst This is exactly why we started @DaylightSec: to solve this issue (and other common challenges in the MDR/MSSP space). We believe that, with recent improvements in AI automation, it’s now possible for an external provider to deliver both scalability and high-quality outcomes.
Pragmatic AI Hacks #1: Use Claude + Google Calendar. Instantly get personal assistant-level scheduling with multi-timezone support, semantic prioritization rules (e.g. 'Always prioritize customer meetings over weekly meetings if no other valid time slot found'), and more.
@cherkaskyb חווית הפיתוח ב Cursor, ספציפית ה Agent וספציפית סביב פיתוח UI - בגלל שדברים נעשים ממש פשוטים, אני מסוגל להוסיף מלא דברים קטנים מהר ולשדרג את החוויה למשתמש בקלות (דברים שפעם הייתי צריך להתלבט אם זה שווה את זה)
I built this E2E SaaS in 1 day of real work with ZERO front-end experience 🤯 Using @cursor_ai, @v0 and pure English.
Its called https://t.co/JsulmgoFNN powered by @langchain JS 🦜🔗 and it outputs curated LLM digested information and insights about open source repositories
I don't know JS, not HTML, not CSS, just natural language and debugging.
Features:
◆ RESTful API endpoints
◆ Authentication
◆ API Key Management
◆ Rate limitng
◆ AI Integration with @langchain
Tech Stack (TBH Cursor did all the work)
@vercel@nextauthjs@Shadcnkit@tailwindcss@v0@langchain
Code is open-sourced - you can clone it and MICRO SAAS Season is ON!!!!
https://t.co/t5Pst6XF6O
#opensource #buildinpublic
What can an 8-year-old build in 45 minutes with the assistance of AI?
My daughter has been learning to code with @cursor_ai and it's mind-blowing🤯
Here are highlights from her second coding session. In 45 minutes she built a chatbot powered by @CloudflareDev Workers AI 👀
The Unspoken Truth About Autonomous Agents: Why LangGraph 🦜🕸️ by @langchain Will Dominate in 2024.
Fully autonomous agents🤖 aren’t really working because we give too much freedom to the LLM and rely on them to handle it. They can't. This leads to unrealistic expectations and disappointing results.
👇
Prediction for 2024: Most production served agentic application will be implemented with LangGraph by @langchain 🦜️🔗 .
LangGraph offers a solution by scoping the freedom of LLMs, allowing developers to create controlled, deterministic flows (with cycles!).
These flows can be navigated by the LLM, leveraging its reasoning capabilities while maintaining developer control👩🏻💻.
This balance is key to building robust, maintainable WORKING agentic architectures.
My favorite LangGraph examples:
GPT Researcher by @assaf_elovic : A great example illustrating how LangGraph can implement sophisticated multi-agent workflows, showcasing LangGraph's practical, production-ready potential yielding quality results.
https://t.co/rY5XXpOo3M
Advanced RAG Architectures from @MistralAI & @langchain : by @sophiamyang and @RLanceMartin contributions in the LangChain Mistral cookbook. They demonstrate cool RAG techniques like corrective RAG, adaptive RAG, and self-reflective RAG, written in an elegant LangGraph flow.
https://t.co/6suTtui0W6
BTW both examples use @tavilyai which is gaining momentum as well🚀 and connects our LLMs to real-world data. The great thing here is that the results are digestible by our genai app, and the purpose of this service is to generate results that are downstreamable into our genai app.
https://t.co/o1M4E6Ypx1
In the video below, I elaborte on autonoums agents today and explain how LangGraph provides developers with control while still leveraging the powerful reasoning capabilities of LLMs, making it the most elegant solution for flow engineering today.
https://t.co/ws9xQlaNoc
My prediction? LangGraph🦜🕸️ by @langchain will THE major agent player in 2024, enabling developers to build advanced, production-oriented agents with ease.
I just built an Insanely Complex RAG Flow with
@langchain's LangGraph – You Won't Believe How Easy It Is
I've been working on an open source git repo for advanced RAG flows with @langchain 's LangGraph🦜🕸️, heavily inspired by the LangChain Cookbook by @RLanceMartin and @sophiamyang !
This repo not only implements Corrective RAG, Adaptive RAG, and Self-RAG with LangGraph but also focuses on structuring the code for maintainability, testing & clean code. 🌟
We leverage LangGraph to build an advanced RAG flow using ideas from 3 papers:
Corrective-RAG (CRAG): Self-grading on retrieved documents and web-search fallback.
Self-RAG: Self-grading on generations for hallucinations.
Adaptive RAG: Routes queries based on complexity.
Explore the repo here:
https://t.co/OiqNheSWt0
Youtube walkthrough here:
https://t.co/uwndxDJto7
Let me know what you think! 💬
Had an amazing time on the main stage advocating for GenAI Agents at the @googlecloud Summit!
2024 is truly the year of the agents🤖
I loved championing open source on Google Cloud and showcasing @langchain 🦜️🔗 , specifically LangGraph, to implement complex agentic flows.
LangGraph🦜🕸️ is a cutting-edge flow engineering implementation that simplifies the creation and management of complex agentic flows.
Deploying GenAI agents on Google Cloud has never been more exciting, offering robust infrastructure, scalability, and seamless integration with innovative AI tools.
It was a pleasure hosting the one and only Oded Shvartz from @torq_io , who shared their groundbreaking HyperSOC™ work on their state-of-the-art autonomous agent, Socrates, which is revolutionizing SOC teams all over the world.