Building a multi-agent RAG:
• 4 specialized AI agents
• Shared knowledge base
• Different tools per agent
• Shared workspace for parallel execution
Can multiple agents collaborate without duplicating work, losing context, or creating unnecessary overhead!
"Trust me bro, the RAG works."
No.
Show me the numbers.
Just ran a RAGAS evaluation:
Faithfulness: 0.89
Answer Relevancy: 0.92
Retrieval Latency: 0.30s
Anyone can connect an LLM to a vector DB.
Can you prove it works?
Metrics > hype.
@vontrix7 I used python and just called the webhook of that particular channel you can get that after enabling your profile to developers and then go on channel make webhooks and channel ids then you can use your bot tokens to send messages after joining that bot to your server
Internship got me debugging n8n workflows for hours
Today’s tasks:
• syncing Shopify product data
• checking SKU mismatches between systems
• auto-updating product weights
• sending WhatsApp notifications when docs get uploaded
One wrong node and it tells me to f*ck off.
Just started my first internship yesterday as an AI Automation Intern.
One of the first things I worked on was converting a linear n8n workflow into a parallelised one to improve efficiency and execution speed.
Small win, but a solid start.
#n8n
Winterfell gives you a fully structured and intelligently organised file system from the moment you start, removing the usual setup hassle and giving you a clear foundation to build on.
With built-in GitHub integration, you can export your entire codebase instantly, no configuration or switching tools, making collaboration smooth and effortless.
You also get an AI-powered chat assistant that works alongside you, helping you design, debug, and refine your contracts with real-time guidance and explanations.
And before you write a single line of code, Winterfell’s planning system lets you map out your ideas and structure your program, turning your concepts into a well-defined blueprint that the AI can then turn into clean, production-ready code.
#winterfell #ai
If you build on Solana, you know how much time disappears into boilerplate, refactoring, and deployment steps. We’ve been working on a set of tools to take that weight off your workflow and let you focus on the actual logic that matters.
CodeGenie helps you start faster. Instead of wrestling with templates or piecing together instructions by hand, you can describe your program in plain English and get a complete, well-structured Anchor contract. It handles accounts, instructions, serialization, and the small details that normally eat up hours. You stay focused on the idea, and it handles the heavy lifting.
EditWizard steps in once you’re iterating. Contracts rarely stay the same, and small updates can turn into time-consuming rewrites. EditWizard lets you adjust your program through simple chat instructions or direct edits, while keeping everything aligned with Anchor conventions. It’s built to preserve safety checks, structure, and clarity, so you can refine your logic without worrying about breaking the foundation.
DeployBot finishes the loop. Instead of juggling scripts or jumping between tools, you can compile, deploy, and instantly receive your IDL and client SDKs in one step. It streamlines the entire deployment process so testing and interaction become immediate rather than another task on your list.
Together, these tools create a smoother, faster way to build, improve, and ship Solana programs. All the essentials stay in place, but the busywork gets pushed out of your way...
#winterfell #ai
Saw this guy literally pulling his hair out over smart contracts.
Honestly, I get it. They’re brutal, and nobody’s made them easier yet.
So I did the only reasonable thing: gave him something devs never expect - a bit of hope.
Or as we call it around here: Winterfell.
What do smart contracts and winter have in common?
They can both get pretty cold, unless you are using Winterfell.
With Winterfell, Rust and Anchor development goes from frosty to frictionless.
#winterfell#Solana#SmartContracts#AI