Top Tweets for #30daysofai
🧠 Day 28 of #30DaysOfAI with @streamlit 🎈
Explored https://t.co/IN6yJ7iFd5 to vibe-code #Streamlit apps—one file to guide #AI assistants to build production-ready apps.
Powered by Claude-3-5-Sonnet via #Snowflake Cortex AI ❄️🤖 #LLMs
📊 Day 23 of #30DaysOfAI with @streamlit 🎈
Evaluated my #RAG chatbot using TruLens and @Snowflake AI Observability with RAG Triad metrics—powered by Claude-3-5-Sonnet via Cortex #AI. Measure before you trust! 🤖❄️ #streamlit #snowflake #LLMs
🥳 YOU MADE IT to Day 30 of #30DaysOfAI!
Final challenge: Structured Output with Pydantic. Instead of getting plain text responses that need manual parsing, you'll use PydanticOutputParser to get type-safe, validated objects with guaranteed fields.
Congrats to everyone who crossed the finish line! 👏
📆 Day 30: https://t.co/IUQnksQcvU
⭐️ Learn more: https://t.co/YFCVor9rta
Day 30 of #30DaysOfAI: WE MADE IT! 🎉🎈
Congratulations — you're completing all 30 days!
Today's finale: Structured output with @pydantic
- Type-safe LLM responses
- No more JSON parsing
- IDE autocomplete works!
New learners: Start any time. All 30 days are waiting for you!
🤩 Day 29 of #30DaysOfAI is here!
Today’s challenge: Rebuild your LinkedIn Post Generator using @LangChain. Instead of writing raw API calls with f-strings you'll use PromptTemplate and LangChain Expression Language (LCEL) to create composable chains.
📆 Day 29: https://t.co/ATEYQdtr1x
⭐️ Learn more: https://t.co/YFCVor9rta
✨ It's Day 28 of #30DaysOfAI!
Today’s challenge: Learn how to vibe code Streamlit apps using AGENTS.md—an open standard for guiding AI coding assistants.
📆 Day 28: https://t.co/uMsvCz6ipp
⭐️ Learn more: https://t.co/VOoL6f3Bjx
Day 28 of #30DaysOfAI w/ @streamlit
Today we're taking a break from heavy technical coding.
Instead, we're vibe coding 🎵 with AGENTS dot md
With this the AI instantly becomes a Streamlit expert.
AI is not here to replace developers, it's just amplifying their capabilities!
🧑💻 Day 27 of #30DaysOfAI
Today’s challenge: Build a chat interface for your multi-tool agent! The app demonstrates how to interact with a pre-created agent that can autonomously orchestrate between Cortex Search and Cortex Analyst, showing its thinking process and tool selection in real-time.
📆 Day 27: https://t.co/fIUE6h6Ip8
⭐️ Learn more: https://t.co/YFCVor9rta
#30DaysOfAI Day 27
Here's the resources to get you started ...
🎈 App https://t.co/Mtq3FDer6P
🐙 GitHub repo https://t.co/5L8zs0t6GO
Day 27/30 #30DaysOfAI
Your AI agent can explain its thinking now 🧠
Build a multi-orchestration chatbot that shows:
✅ Agent's thought process
✅ Which tool it picks
No if/else routing. Just autonomous orchestration.
#Streamlit #CortexAgents
✨ Day 26 of #30DaysOfAI...let's go!
Today’s challenge: Build your first Cortex Agent. You’ll combine Cortex Search and Cortex Analyst to create an autonomous assistant that plans, selects tools, and executes tasks to answer questions across your data.
📆 Day 26: https://t.co/N7LzgY7ZD4
⭐️ Learn more: https://t.co/YFCVor9rta
🚀 It's Day 25 of #30DaysOfAI!
Today’s challenge: Build a complete voice-enabled AI assistant. Users can record messages, which are transcribed using @Snowflake's AI_TRANSCRIBE, and the assistant will respond with context-aware answers using conversation history.
📆 Day 25: https://t.co/ZbhtdVxtX4
⭐️ Learn more: https://t.co/YFCVor9rta
📄 Day 16 of #30DaysOfAI with @streamlit 🎈
Built a batch document text extractor for RAG—upload TXT, MD, and PDF files, extract text, and store it in @Snowflake using Claude-3-5-Sonnet via Cortex #AI. #RAG starts here! 🤖❄️ #snowflake #streamlit #LLMs
Day 25/30 of #30DaysOfAI w/ @streamlit
Yesterday AI learned to see 👀. Today it learns to hear 👂!
Today, you'll build a voice-enabled AI assistant:
✅ Record voice messages
✅ Auto-transcribe (AI_TRANSCRIBE)
✅ Context-aware responses
✅ Full chat memory
Links in the comment
✨ Days 23 & 24 of #30DaysOfAI!
Day 23 challenge: Use TruLens and @Snowflake's AI Observability to evaluate your RAG app & measure exactly how accurate it is!
Day 24 challenge: Build a multimodal image analyzer with vision-capable models.
📆 Day 23: https://t.co/FwhrMCmkjn📆 Day 24: https://t.co/tM2HcxjcQu
Day 24/30 #30DaysOfAI w/ @streamlit
Your AI just learned to see 👀
No more text-only LLMs. Today your learning multimodal magic!
📸 Describe images
📝 OCR anything
🎯 Find objects
📊 Analyze charts
Upload → App → AI analysis
⏬ See code in the comments ⏬
Day 23/30 #30DaysofAI w/ @streamlit
You've built a RAG system? Cool. But is it actually good? 🤔
Today, you'll learn about RAG evaluation with @TruLensML:
✅ Context Relevance
✅ Groundedness (no hallucinations!)
✅ Answer Relevance
#Streamlit #RAG
🚀 Day 22 of #30DaysOfAI is here!
Today’s challenge: Build a complete conversational RAG chatbot to chat with your documents. The app will maintain full conversation history, allowing for contextual dialogue and follow-up questions!
📆 Day 22: https://t.co/ttCMHDzDJw
⭐️ Learn more: https://t.co/YFCVor9rta
🗓️ Day 22 of #30DaysOfAI is live!
Chat with Your Documents 💬
Today, we're completing our conversational RAG chatbot that has memory, guardrails, and citations!
Each question
→ Searches your docs
→ LLM generate grounded answers
→ Sources shown
→ Repeat! 🔄
#RAG #Streamlit
🧑💻 It's Day 21 of #30DaysOfAI!
Today’s challenge: Build a complete RAG pipeline using @Snowflake Cortex Search. You'll retrieve relevant documents as context for LLM-generated answers. The app will display a visual guide showing the three-step RAG process, then an interface for input questions.
📆 Day 21: https://t.co/57r388n8Wf
⭐️ Learn more: https://t.co/YFCVor9rta
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