Sage Mode

[Sage Mode @QubitGrad]

@RealBenjizo @grok explain

[Sage Mode @QubitGrad]

@Python_Dv A

[Sage Mode @QubitGrad]

@Python_Dv @grok answer it

[Sage Mode @QubitGrad]

@probnstat @grok answer it

[Sage Mode @QubitGrad]

@RealBenjizo @grok answer

[Sage Mode @QubitGrad]

@probnstat @grok answer it

[Sage Mode @QubitGrad]

@probnstat True

[Sage Mode @QubitGrad]

@probnstat False

[Sage Mode @QubitGrad]

Ch 1.5 – Vector & Matrix Inputs Deep Learning form scratch
• dot(X,W)=Σxᵢwᵢ → single scalar
• ∂/∂X = Wᵀ , ∂/∂W = Xᵀ
• Think: each output element is a weighted sum over a tiny patch of the input.
• Gradient shape always matches the parameter it touches.

[Sage Mode @QubitGrad]

@takeUforward_ @grok answer this question

[Sage Mode @QubitGrad]

@grok @grok what would you suggest to IT freshers

[Sage Mode @QubitGrad]

@grok what do you think about tech jobs in the next 5-10 years in India, compare numbers of jobs with today's numbers.

[Pankaj Kumar @pankajkumar_dev]

Roadmap to Senior AI/ML Engineer
(with LLMs + MLOps + Systems)

Python → NumPy → Pandas → Matplotlib
→ Scikit-learn → Data Cleaning & EDA
→ Stats & Probability → Linear Algebra → Calculus
→ ML Algorithms (Regression, Trees, SVMs, KNN, Clustering)
→ Deep Learning (ANN, CNN, RNN, LSTM, GANs)
→ PyTorch / TensorFlow → Transfer Learning → Fine-tuning
→ Hugging Face Transformers → LangChain / LlamaIndex
→ LLM Internals (Tokenization, Attention, BPE, KV Cache)
→ RAG Pipelines → Vector DBs (FAISS, Weaviate, Pinecone)
→ Prompt Engineering → Finetuning (QLoRA / LoRA / DPO)
→ Model Deployment (Flask / FastAPI / Triton / BentoML)
→ Model Serving (TorchServe / TGI / vLLM)
→ Quantization (INT8 / GPTQ / AWQ) → Distillation
→ MLOps Basics → Model Versioning (DVC, MLflow)
→ Experiment Tracking → CI/CD for ML
→ Containerization (Docker) → Infra with Terraform
→ Kubernetes + Kubeflow → GPU Scheduling
→ Monitoring (Prometheus, Grafana, Sentry)
→ Cloud (AWS/GCP/Azure) → IAM, Billing, Cost Optimization
→ Ethics in AI → Bias, Fairness, Explainability

Save it. Share it. Keep iterating.

[Sage Mode @QubitGrad]

MCP is to AI what Docker was to DevOps
Standardized, portable, composable.

Before Docker: "Works on my machine" After Docker: Consistent everywhere

Before MCP: Custom integrations everywhere After MCP: Standard protocol everywhere

History rhymes 📈

[Sage Mode @QubitGrad]

The Vision
Here's the vision that gets me excited:

Instead of AI apps being isolated islands, MCP creates bridges. Same tools, same data sources, compatible across all AI applications.

We're building toward a unified AI ecosystem. And it's happening NOW. 🔥

(8/8)

[Sage Mode @QubitGrad]

🚀Model Context Protocol (MCP) is about to change how we build AI apps
Think of it as USB-C for AI. Just like USB-C standardized device connections, MCP standardizes how AI models connect to data sources and tools.
No more custom integrations for every single API 🔥
(1/8) 🧵👇

[Sage Mode @QubitGrad]

MCP use cases that are already working:
📊 Connect AI to your analytics dashboards
📁 Let AI work with local files seamlessly
🌐 Integrate any REST API in minutes
🔧 Connect to GitHub, deploy tools, testing frameworks
📝 Access CMS and content templates

(7/8)