1.5K
FIЛовите крутой аналог Google таблиц и Exel со встроенным ИИ 🔥
@fink_gpt
#Erwin Data Modeler Features
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Reels en Tendencia
(12)
207
DACheckout the Highlights,
@datadecode.de
2.2K
VIRecentemente eu criei um ERP usando Inteligência Artificial.
Não sou programador.
Não sou desenvolvedor.
Eu só tinha um problema dentro da empresa…
e resolvi usar IA para tentar resolver.
Resultado: aproximadamente R$ 4.000 de economia por mês.
E isso me fez pensar uma coisa:
Quantos problemas dentro das empresas hoje poderiam ser resolvidos…
se a gente começasse a usar IA de verdade?
A tecnologia está disponível.
A grande diferença agora está entre quem usa… e quem continua só assistindo.
#empreendedorismo #negocios
@victor.bazanjr
19.8K
PAReady to dive into the nitty-gritty of ER diagram creation? 🎨 Let’s break it down step by step! 📝
Step 1: Identify Entities and Attributes 🧩
Start by identifying the entities in your project, such as Users, Products, or Orders. Then, define the attributes for each entity – these are the specific properties or characteristics that describe them. Think UserID, Email, and Role for Users, or ProductID, Name, and Price for Products. 📊
Step 2: Determine Relationships 🤝
Next, establish the relationships between your entities. Is there a one-to-many relationship between Users and Orders, or a many-to-many relationship between Products and Orders? Understanding these connections is crucial for designing a robust database schema. 🌐
Step 3: Draw the ER Diagram 🖋️
Now it’s time to bring your vision to life! Use a tool like Lucidchart or draw.io to create your ER diagram. Place entities as boxes and connect them with lines to represent relationships. Don’t forget to label your relationships with their cardinality – is it one-to-one, one-to-many, or many-to-many? 📈
Step 4: Review and Refine ✨
Take a step back and review your ER diagram. Does it accurately capture the structure and relationships of your database? Are there any missing entities or attributes? Refine your diagram as needed to ensure clarity and completeness. 🔄
Step 5: Iterate and Collaborate 🤝
Finally, remember that creating an ER diagram is an iterative process. Collaborate with your team, gather feedback, and make revisions as your project evolves. By continually refining your ER diagram, you’ll ensure that your database design aligns with your project goals and requirements. 🚀
So, ready to turn your project vision into a visual masterpiece? Let’s ER diagram like a pro! 💻💡
#coding #programming #software #database #sql #web #webdeveloper #webdevelopment
@parthdahiya_
137.8K
EOПишите АГЕНТ под роликом, если хотите внедрить ИИ-автоматизации в ваши бизнесы
@eorlov_ai
5.1K
BRAnemômetro para Arduino
@brincando.ideias.lab
2.5M
JUIt’s changed so much in so little time
@verdent__ai
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#Verdent #VerdentAI #Vibecoding #AIcoding
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https://www.verdent.ai/?id=700041
@juiceditup
3.4K
BAModels don’t learn from raw data.
They learn from features.
Feature engineering is the process of
turning messy, raw data
into meaningful input a model can understand.
Age → Age group
Timestamp → Day, Month, Season
Text → Numerical representation
Better features = Better predictions.
SAVE this before training your next model.
#featureengineering
#machinelearning
#datascience
#aiml
#mlbasics
#pythonprogramming
#techreels
#typographyinspired
#typographydesign
@bakwaso_pedia
14.2K
SHDay 30/365 Data engineering part 7 Data modeling
Every city starts with a blueprint. 🏙️
In data engineering, that blueprint is called Data Modeling.
It’s how we decide what tables exist, how data connects, and how systems communicate.
Without modeling, data becomes chaos — duplicate, disconnected, and slow.
With it, we create harmony — faster queries, cleaner reports, and scalable pipelines.
This is the foundation of every smart digital city. 💾
🧩 What Is Data Modeling?
It’s the art of defining how data is stored, connected, and accessed — the foundation of every data system.
🔹 Conceptual Model: High-level map of entities and relationships (think of city zones).
🔹 Logical Model: Tables, columns, and keys (the blueprints for buildings).
🔹 Physical Model: Actual database implementation (SQL schemas, indexes).
⚙️ Common Techniques:
– ER Modeling (Entity-Relationship)
– Dimensional Modeling (Star Schema & Snowflake Schema)
– Normalization vs Denormalization
💡 Why It Matters:
• Keeps data organized & consistent
• Improves query speed & data quality
• Simplifies reporting & analytics
In your digital city, data modeling is the urban planning that makes every system work together — smooth roads, clear routes, and no traffic jams. ♻️
#DataModeling #WasteCitySeries #DataEngineering #BigData #DataArchitecture #jaishreeram #sharcashiticsundaram
@sharcashitic_sundaram
503.1K
EVI think GLM 4.5 won this round🧐. Prompt: “Create a 2D Eulerian fluid simulator in a single HTML file using JavaScript and the Canvas API. The simulation should visualize the flow of incompressible and inviscid (zero-viscosity) fluids on a fixed grid. A constant stream of smoke should enter from a tube on the left side of the canvas, and the flow must interact with a circular obstacle placed in its path.”
#ai #artificialinteligence #evotionai #testingai #generativeai #claudevsgpt #gpt5 #claude #programming #chatgpt
@evotionai
21.8K
EPRunning your own ai model
@epro.grams
17.1K
ANGood data systems don’t start with queries… they start with smart data modeling📊
Choosing the right schema, balancing performance, and planning for scale from day one makes all the difference.
Remember: A great data model can save you from hundreds of optimizations later. 🚀
#DataEngineering #DataModeling #SQL #DataWarehouse #techlearning
@analyst_shubhi
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