you can't build by all requirements of clients.even if you build it, they change the requirements daily.
is that true?

this is how your assistants and chatbots work

🌐 Web Design Tools & Their Use Cases 🎨🌐

🔹 Figma ➜ Collaborative UI/UX prototyping and wireframing for teams
🔹 Adobe XD ➜ Interactive design mockups and user experience flows
🔹 Sketch ➜ Vector-based interface design for Mac users and plugins
🔹 Canva ➜ Drag-and-drop graphics for quick social media and marketing assets
🔹 Adobe Photoshop ➜ Image editing, compositing, and raster graphics manipulation
🔹 Adobe Illustrator ➜ Vector illustrations, logos, and scalable icons
🔹 InVision Studio ➜ High-fidelity prototyping with animations and transitions
🔹 Webflow ➜ No-code visual website building with responsive layouts
🔹 Framer ➜ Interactive prototypes and animations for advanced UX
🔹 Tailwind CSS ➜ Utility-first styling for custom, responsive web designs
🔹 Bootstrap ➜ Pre-built components for rapid mobile-first layouts
🔹 Material Design ➜ Google's UI guidelines for consistent Android/web interfaces
🔹 Principle ➜ Micro-interactions and motion design for app prototypes
🔹 Zeplin ➜ Design handoff to developers with specs and assets
🔹 Marvel ➜ Simple prototyping and user testing for early concepts

Looks good, guys🙌

🎙 PODCAST TALK SHOW: Experience Sharing

We’re officially back! 🚀
Our weekly podcast/workshop series resumes this semester with powerful conversations and real-world insights.

📅 Date: March 4, 2026
⏰ Time: 8:00 PM
🎤 Host: Mihret Daniel
📍 Live on our Telegram Channel

👤 Guest Speaker: Bemhreth Gezahegn (Co-founder of Gebeta Maps)

Join us as we dive into the journey of building impactful tech solutions, lessons from entrepreneurship, and practical insights for aspiring innovators.

Don’t miss it — set your reminder and tune in live!

#InformationSystemsHub #WeeklyPodcast #BemhrethGezahegn Telegram|LinkedIn|YouTube | Tiktok |

I've complete the Amharic to English and also English to Amharic Text Translator telegram bot. it supports only two languages(Amharic and English).
check it : @lantranslator_bot
I used groq model for LLM.
Groq models have poor Amharic support, leading to inaccurate translations.

@codewithmemo
@codewithmemo

Different databases

ChromaDB ✅
Open-source, runs locally
Simple Python API
Metadata filtering
HNSW indexing
LangChain integration

Pinecone ✅
Fully managed, zero maintenance
Real-time updates
Automatic scaling
REST API + SDKs
High availability

MySQL ✅
ACID compliant
Mature ecosystem
Great for structured data
Primary-replica replication
Full-text search

PostgreSQL ✅
ACID compliant
Advanced indexing (partial, expression)
JSON support
pgvector extension for vectors
MVCC concurrency

MongoDB ✅
Schema-less documents
Horizontal scaling (sharding)
Replica sets for HA
Atlas vector search (cloud)
Flexible querying

I've completed a RAG-based full stack AI . It's named Medi, a smart medical assistant chatbot. I've tested it thoroughly to ensure that Medi won't hallucinate any concepts , it seems really smart! 🔥
It support Amharic, oromifaa, tigregna and somaligna.

Check out the demo video and feel free to leave your comments. I'll share the live link after testing it again and again in various ways..

@codewithmemo
@codewithmemo

Have a nice Saturday ❤

always learning new thing is challenging, but it's really good vibe🎉

Useful Python Scripts for Automated Data Quality Checks

📌 Introduction

Data quality issues are pervasive and can lead to incorrect business decisions, broken analysis, and pipeline failures. Manual data validation is time-consuming and prone to errors, making it essential to automate the process. This article discusses five useful Python noscripts for automated data quality checks, addressing common issues such as missing data, invalid data types, duplicate records, outliers, and cross-field inconsistencies.

📌 Main Content / Discussion

The five Python noscripts are designed to handle specific data quality issues.

import pandas as pd
import numpy as np

# Example 1: Missing data analyzer noscript
def analyze_missing_data(df):
    missing_data = df.isnull().sum()
    return missing_data

# Example 2: Data type validator noscript
def validate_data_types(df, schema):
    for column, dtype in schema.items():
        if df[column].dtype != dtype:
            print(f"Invalid data type for column {column}")
    return df

# Example 3: Duplicate record detector noscript
def detect_duplicates(df):
    duplicates = df.duplicated().sum()
    return duplicates

# Example 4: Outlier detection noscript
def detect_outliers(df, column):
    Q1 = df[column].quantile(0.25)
    Q3 = df[column].quantile(0.75)
    IQR = Q3 - Q1
    lower_bound = Q1 - 1.5 * IQR
    upper_bound = Q3 + 1.5 * IQR
    outliers = df[(df[column] < lower_bound) | (df[column] > upper_bound)]
    return outliers

# Example 5: Cross-field consistency checker noscript
def check_cross_field_consistency(df):
    # Check for temporal consistency
    df['start_date'] = pd.to_datetime(df['start_date'])
    df['end_date'] = pd.to_datetime(df['end_date'])
    inconsistencies = df[df['start_date'] > df['end_date']]
    return inconsistencies

These noscripts can be used to identify and address data quality issues, ensuring that the data is accurate, complete, and consistent.