Instagram story viewer> @code_n_run> Tagged

Code N Run | Programmer

@code_n_run

45.5K

followers

549

following

👨‍💻 Welcome to my coding corner,
🐍 I'm a master of Python puns, and DSA freak
🏋️Join me here and don't be afraid to say hi – I promise I won't byte!"

POSTS STORIES REELS TAGGED

AI will do all the work. Why do you even need to learn coding?” 🤡

Me, fighting with 100 errors just to make AI work properly: 😭💀

AI will replace developers? Bro, first let me fix this error! 😂🔥

#AIDeveloper #CodingLife #ProgrammerHumor #Debugging #DeveloperLife

82

8

⚡ Why Do We Use Cache in Applications?

Imagine asking the same question again and again.

That’s where Cache helps.

🔹 Cache → Stores previously used results so they can be returned faster.

🔹 Semantic Cache → Stores results based on the meaning of a query, not just the exact words.

For example:

The words are different, but the meaning is similar. 🧠

A semantic cache can reuse the previous result instead of calling the LLM again.

Cache → Faster response + Less processing ⚡
Semantic Cache → Similar meaning → Reuse result → Save time & cost

Cache is a simple but powerful way to make applications faster and more efficient.

#Caching #SemanticCache #AIEngineering #LLM #GenerativeAI

59

7

🤖 What is Agentic AI?

AI that can do more than just give answers.

Agentic AI can: 🔹 Understand a goal
🔹 Plan the steps
🔹 Choose the right tools
🔹 Take actions
🔹 Check the results
🔹 Continue until the task is completed

Simple flow:

Goal → Plan → Tool → Action → Check → Result 🚀

Traditional AI mainly responds to prompts.
Agentic AI can work through multiple steps to achieve a goal.

AI is moving from answering questions → taking actions. 🔥

Keep learning. Keep building. Keep exploring AI. 💯

#AgenticAI #AIAgents #ArtificialIntelligence #GenerativeAI #aiengineering

26

6

How can we get better answers from AI?

It’s not only about the prompt.

Think about PCM: Prompt + Context + Memory

🔹 Prompt → What do you want AI to do?
🔹 Context → What information does AI need?
🔹 Memory → What relevant information should AI remember?

Simple flow:

Prompt + Context + Relevant Memory → Better AI Output

A clear prompt is important, but the right context and relevant memory can make the response more useful.

Keep learning AI step by step.

#PromptEngineering #ContextEngineering #AI #LLM #GenerativeAI

74

7

🤖 How do we know if our Machine Learning model is performing well?

That’s where Model Evaluation comes in. 📊

We use different metrics to understand how well our model makes predictions.

🔹 Accuracy → How many predictions are correct
🔹 Precision → How many predicted positives are actually correct
🔹 Recall → How many actual positives are correctly found
🔹 F1 Score → Balance between Precision and Recall
🔹 Confusion Matrix → Shows correct and wrong predictions
🔹 ROC-AUC → Shows how well the model separates classes

For Regression, we can also use MAE and RMSE.

The important thing is: don’t depend on only one metric. Choose the evaluation method based on your problem and data.

Understand the model → Evaluate it → Improve it → Keep learning step by step. 🚀

#ModelEvaluation #MachineLearning #DataScience #AI #ML

21

2

🤖 AI vs Traditional Software Development

Traditional software follows rules written by developers.

AI systems can learn patterns from data and use those patterns to make predictions or generate outputs.

Traditional Software: Code → Rules → Output

AI: Data → Training → Model → Output

AI does not completely replace traditional software.
Both can work together to build powerful applications. 🚀

As a developer, understanding both is important.

Keep learning. Keep building. Keep improving. 🔥

#ArtificialIntelligence #AI #SoftwareDevelopment #MachineLearning #aiengineering

82

16

🤖 Why Are APIs Used in AI Applications?

AI applications often need external data, tools, and services to perform real-world tasks.

APIs help AI connect with:
🔹 Databases
🔹 Search engines
🔹 Payment systems
🔹 Weather services
🔹 External AI models

But APIs can also fail because of:
❌ Wrong API key
❌ Network issues
❌ Server errors
❌ Rate limits
❌ Invalid requests

Simple flow:

User → AI → API → External Service → Result → AI Response

Understanding both how APIs work and why they fail is important when building reliable AI applications. 🚀

#APIs #AIApplications #AIEngineering #BackendDevelopment #GenerativeAI

21

0

🏗️ What are Microservices?

How do large applications manage different tasks independently?

That’s where Microservices come in.

Microservices architecture breaks one large application into small, independent services.

Each service handles a specific task and can be developed, updated, and deployed separately.

For example:

User Service → Payment Service → Order Service → Notification Service

Instead of one big application, we have multiple small services working together. 🚀

Microservices help make applications more flexible, scalable, and easier to maintain.

#Microservices #BackendDevelopment #SoftwareArchitecture #SystemDesign #softwareengineering

48

6

GPT Astra is changing how we think about AI. 🤖✨

Smarter tools, better workflows, and new possibilities for creators and developers.

AI is not just about using tools anymore — it’s about learning how to use them effectively. 🚀

Keep learning. Keep exploring. Stay future-ready. 🔥

#GPTAstra #AI #ArtificialIntelligence #GenAI #Tech

18

0

What is WebSocket?

How do apps receive real-time updates without repeatedly sending requests?

That’s where WebSocket comes in.

WebSocket creates a continuous connection between the client and server, allowing both sides to send data in real time.

Unlike normal HTTP requests, the server can send updates whenever new data is available.

Client ↔ WebSocket Server ↔ Real-Time Updates ⚡

Examples:
💬 Chat applications
📈 Live dashboards
🎮 Online gaming
🔔 Real-time notifications

WebSocket = Two-way, real-time communication.

#WebSocket #BackendDevelopment #RealTimeCommunication #APIs #SoftwareEngineering

64

11

🔢 What is Encoding in Machine Learning?

Machine Learning models work with numbers, not raw text.

Encoding converts categorical data into numerical values so the model can understand and use it.

For example:

Gender → Male / Female
Encoding → 0 / 1

Category → Numbers → ML Model 🤖

Choose the right encoding based on your data and model. 🚀

#Encoding #MachineLearning #DataScience #Python #AI

55

4

Raw data → Smart features → Better ML models 🚀

Feature Engineering is where raw data gets transformed into meaningful features that help Machine Learning models understand patterns better. 🧠📊

Good features can make a BIG difference in model performance! 🔥

Learning • Experimenting • Building • Improving 💻✨

#FeatureEngineering #MachineLearning #DataScience #Python #ML AI DataAnalytics

50

0