๐บ๐ธ๐จ๐ณ United States cuts tariffs on Chinese goods from 145% to 30% for 90 days.
China lowers tariffs on US from 125% to 10% for 90 days.
t.me/techpsyche
China lowers tariffs on US from 125% to 10% for 90 days.
t.me/techpsyche
๐๐ & ๐๐ ๐
๐๐๐ ๐๐๐ซ๐ญ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง ๐๐จ๐ฎ๐ซ๐ฌ๐๐ฌ ๐
๐ซ๐จ๐ฆ 6 ๐๐จ๐ฉ ๐๐ง๐ฌ๐ญ๐ข๐ญ๐ฎ๐ญ๐ข๐จ๐ง๐ฌ!๐
Explore these 6 amazing courses offered by the:
- Government of India
- Google
- Harvard
- MIT
- IBM.
Gain hands-on knowledge in Generative AI, Python, Machine Learning, and AIโs impact on business strategyโall at no cost.
Plus, youโll earn certificates to boost your resume!
๐๐ข๐ง๐ค ๐:-
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Explore these 6 amazing courses offered by the:
- Government of India
- Harvard
- MIT
- IBM.
Gain hands-on knowledge in Generative AI, Python, Machine Learning, and AIโs impact on business strategyโall at no cost.
Plus, youโll earn certificates to boost your resume!
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MVP โ what is it and what does it do
Minimum Viable Product, MVP, - in our world, it is an application that has the minimum functionality, capable of covering the user's needs.
Development is expensive: a team of programmers has to build a digital solution to an idea, with a nice interface and without bugs. But businesses (often) don't have the money to pay long development fees first. There is only an idea.
To test the idea, they create an MVP. They choose key features, weed out the unnecessary ones, make a list of "would-be-do-it-if-we-have-time" features, make a plan, and decide: will this functionality be enough to test our idea? If the answer is yes, people will use the app even without the secondary functionality - then it's ok, we can keep sawing.
But in MVP applications we need to cut off not only the functionality.
There is also refactoring, for example. At some moment you will want to return to the old code and clean it up a bit. Make it more readable, standardize it. If this refactoring doesn't bring notable relief to the developers, you need to postpone it until after the release. Or tests. They are necessary and important, but not for MVP (debatable, I agree, it depends on the project). In my experience, MVP application code testing was increasing development time by 25-40%, not giving the desired output. We were wasting time covering tests that didn't help us much, delaying development.
All of this can and should be turned a blind eye if we're doing MVPs. The business doesn't care how this button is created, whether it's a method widget or just an object widget. For it the main thing is that there is a button, and it at least works. And it doesn't slow us down. Hence, we don't need to work on it at the moment.
Ideally, of course, the team should meet together at the beginning of the project and agree on what they do and don't do. You can write manuals and recommendations. Or we could say: guys, we've got some crap in our code here, let's not do this anymore. The point is, our job is to get it up and running as quickly as possible. After all, the application could be so useless that we won't continue development at all. So why do we need code that is 100% covered by tests and doesn't contain a single anti-pattern if we're not going to work on it?
Yes, we'll have to roll out an application with shitty code. But if this code solves a business problem and is able to attract the first money, it's not such a shitty code. After all, it solved the main problem. To get it up and running. From there we look at user feedback, fix what we need, and try to get some time for refactoring.
Follow: t.me/techpsyche
Minimum Viable Product, MVP, - in our world, it is an application that has the minimum functionality, capable of covering the user's needs.
Development is expensive: a team of programmers has to build a digital solution to an idea, with a nice interface and without bugs. But businesses (often) don't have the money to pay long development fees first. There is only an idea.
To test the idea, they create an MVP. They choose key features, weed out the unnecessary ones, make a list of "would-be-do-it-if-we-have-time" features, make a plan, and decide: will this functionality be enough to test our idea? If the answer is yes, people will use the app even without the secondary functionality - then it's ok, we can keep sawing.
But in MVP applications we need to cut off not only the functionality.
There is also refactoring, for example. At some moment you will want to return to the old code and clean it up a bit. Make it more readable, standardize it. If this refactoring doesn't bring notable relief to the developers, you need to postpone it until after the release. Or tests. They are necessary and important, but not for MVP (debatable, I agree, it depends on the project). In my experience, MVP application code testing was increasing development time by 25-40%, not giving the desired output. We were wasting time covering tests that didn't help us much, delaying development.
All of this can and should be turned a blind eye if we're doing MVPs. The business doesn't care how this button is created, whether it's a method widget or just an object widget. For it the main thing is that there is a button, and it at least works. And it doesn't slow us down. Hence, we don't need to work on it at the moment.
Ideally, of course, the team should meet together at the beginning of the project and agree on what they do and don't do. You can write manuals and recommendations. Or we could say: guys, we've got some crap in our code here, let's not do this anymore. The point is, our job is to get it up and running as quickly as possible. After all, the application could be so useless that we won't continue development at all. So why do we need code that is 100% covered by tests and doesn't contain a single anti-pattern if we're not going to work on it?
Yes, we'll have to roll out an application with shitty code. But if this code solves a business problem and is able to attract the first money, it's not such a shitty code. After all, it solved the main problem. To get it up and running. From there we look at user feedback, fix what we need, and try to get some time for refactoring.
Follow: t.me/techpsyche
๐๐ & ๐๐ ๐
๐๐๐ ๐๐๐ซ๐ญ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง ๐๐จ๐ฎ๐ซ๐ฌ๐๐ฌ ๐
๐ซ๐จ๐ฆ 6 ๐๐จ๐ฉ ๐๐ง๐ฌ๐ญ๐ข๐ญ๐ฎ๐ญ๐ข๐จ๐ง๐ฌ!๐
Explore these 6 amazing courses offered by the:
- Government of India
- Google
- Harvard
- MIT
- IBM.
Gain hands-on knowledge in Generative AI, Python, Machine Learning, and AIโs impact on business strategyโall at no cost.
Plus, youโll earn certificates to boost your resume!
๐๐ข๐ง๐ค ๐:-
https://bit.ly/4hCdn45
Enroll For FREE & Get Certified ๐
Explore these 6 amazing courses offered by the:
- Government of India
- Harvard
- MIT
- IBM.
Gain hands-on knowledge in Generative AI, Python, Machine Learning, and AIโs impact on business strategyโall at no cost.
Plus, youโll earn certificates to boost your resume!
๐๐ข๐ง๐ค ๐:-
https://bit.ly/4hCdn45
Enroll For FREE & Get Certified ๐
Spend $0 to master new skills in 2025:
1. HTML - w3schools.com
2. CSS - css-tricks.com
3. JavaScript - learnjavascript.online
4. React - react-tutorial.app
5. Tailwind - scrimba.com
6. Vue - vueschool.io
7. Python - pythontutorial.net
8. SQL - t.me/sqlresourcestp
9. Git - atlassian.com/git/tutorials
10. Data Analysis - t.me/dataanalysisresourcestp
๐Join our Community
[https://t.me/techpsyche]
Do react โค๏ธ if you want more content like this.
1. HTML - w3schools.com
2. CSS - css-tricks.com
3. JavaScript - learnjavascript.online
4. React - react-tutorial.app
5. Tailwind - scrimba.com
6. Vue - vueschool.io
7. Python - pythontutorial.net
8. SQL - t.me/sqlresourcestp
9. Git - atlassian.com/git/tutorials
10. Data Analysis - t.me/dataanalysisresourcestp
๐Join our Community
[https://t.me/techpsyche]
Do react โค๏ธ if you want more content like this.
๐3โค1
Forwarded from Artificial Intelligence Resources TP . AI Tools . AI Updates
AI Tools for Personal Development & Learning ๐
1. Career Coaching - Rezi.ai
2. Typing Practice - TypingClub.com
3. Memory Improvement - Lumosity.com
4. Focus Enhancement - Brain.fm
5. Math Skills - Photomath.com
6. Language Learning - Duolingo.com
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8. Workout Routines - Fitbod.me
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6. Language Learning - Duolingo.com
7. Meditation & Mindfulness - Headspace.com
8. Workout Routines - Fitbod.me
More AI Tools ๐ https://t.me/airesourcestp
โค2
Channel name was changed to ยซTech Psyche . Tech Tips & Tricks . Programming , Tech Coursesยป
โ
Introducing the Crypto Exchange
๐ดBuying cryptocurrency is just like buying foreign currency for a holiday. It is just an exchange of one currency for another at an agreed rate - e.g Euros for BTC (the currency symbol for bitcoin) - which is why the most common place to buy cryptocurrency is called an Exchange.
๐ดIt might feel confusing when cryptocurrency like bitcoin is talked about as having a price, whereas for dollar, euro etc we are used to talking about an exchange rate.
โก๏ธThese two terms - price/exchange rate - are interchangeable and simply reflect the fact that currency values - especially crypto - are constantly changing.
๐ดThe price simply reflects the interaction between buyers and sellers on each Exchange, which organically maintain parity with each other. (again explained in detail elsewhere). An overall representation of price can then be reached by aggregating the price from the main exchanges.
Choosing the Right Crypto Exchange: https://t.me/techpsyche/774
What Is Crypto Arbitrage: https://t.me/techpsyche/881
How can I spot a bullish trend?: https://t.me/techpsyche/925
More Resources Here:
https://whatsapp.com/channel/0029VajB00n0LKZ7cSloGR1M
#crypto #web3 #blockchain #finance
๐ดBuying cryptocurrency is just like buying foreign currency for a holiday. It is just an exchange of one currency for another at an agreed rate - e.g Euros for BTC (the currency symbol for bitcoin) - which is why the most common place to buy cryptocurrency is called an Exchange.
๐ดIt might feel confusing when cryptocurrency like bitcoin is talked about as having a price, whereas for dollar, euro etc we are used to talking about an exchange rate.
โก๏ธThese two terms - price/exchange rate - are interchangeable and simply reflect the fact that currency values - especially crypto - are constantly changing.
๐ดThe price simply reflects the interaction between buyers and sellers on each Exchange, which organically maintain parity with each other. (again explained in detail elsewhere). An overall representation of price can then be reached by aggregating the price from the main exchanges.
Choosing the Right Crypto Exchange: https://t.me/techpsyche/774
What Is Crypto Arbitrage: https://t.me/techpsyche/881
How can I spot a bullish trend?: https://t.me/techpsyche/925
More Resources Here:
https://whatsapp.com/channel/0029VajB00n0LKZ7cSloGR1M
#crypto #web3 #blockchain #finance
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Who is Data Scientist?
He/she is responsible for collecting, analyzing and interpreting the results, through a large amount of data. This process is used to take an important decision for the business, which can affect the growth and help to face compititon in the market.
A data scientist analyzes data to extract actionable insight from it. More specifically, a data scientist:
Determines correct datasets and variables.
Identifies the most challenging data-analytics problems.
Collects large sets of data- structured and unstructured, from different sources.
Cleans and validates data ensuring accuracy, completeness, and uniformity.
Builds and applies models and algorithms to mine stores of big data.
Analyzes data to recognize patterns and trends.
Interprets data to find solutions.
Communicates findings to stakeholders using tools like visualization.
Data Science Habits: https://t.me/datascienceresourcestp/56
Top Data Science Tools: https://t.me/datascienceresourcestp/70
Find More Tips & Resources Here:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
He/she is responsible for collecting, analyzing and interpreting the results, through a large amount of data. This process is used to take an important decision for the business, which can affect the growth and help to face compititon in the market.
A data scientist analyzes data to extract actionable insight from it. More specifically, a data scientist:
Determines correct datasets and variables.
Identifies the most challenging data-analytics problems.
Collects large sets of data- structured and unstructured, from different sources.
Cleans and validates data ensuring accuracy, completeness, and uniformity.
Builds and applies models and algorithms to mine stores of big data.
Analyzes data to recognize patterns and trends.
Interprets data to find solutions.
Communicates findings to stakeholders using tools like visualization.
Data Science Habits: https://t.me/datascienceresourcestp/56
Top Data Science Tools: https://t.me/datascienceresourcestp/70
Find More Tips & Resources Here:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
๐
๐๐๐ ๐๐๐ซ๐ญ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง ๐๐จ๐ฎ๐ซ๐ฌ๐๐ฌ ๐๐จ ๐๐๐๐จ๐ฆ๐ ๐๐ค๐ข๐ฅ๐ฅ๐๐ ๐๐ป ๐๐๐๐
Free lifetime access โ Learn anytime, anywhere
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Free lifetime access โ Learn anytime, anywhere
Get Completion Certificate
๐๐ข๐ง๐ค๐:-
http://bit.ly/3RdeYTh
Enroll For FREE & Get Certified๐
Tableau vs Power BI: Must-Know Differences
Tableau:
- Usage: Best suited for advanced data visualization and analytics with complex datasets.
- Best For: Users who need highly customized, detailed visualizations and are working with large datasets across different industries.
- Data Handling: Performs well with large datasets and offers strong data handling capabilities.
- Visuals: Known for its superior, highly customizable visuals, making it perfect for complex and artistic data representations.
- Integration: Easily connects with a wide range of data sources but may require more technical skills for setup.
- Sharing: Sharing options are available, but often require paid licenses for viewers.
- Cost: Tableau tends to be more expensive, especially at the enterprise level.
- Automation: Supports automated data refreshes, but the setup might be more complex than Power BI.
Power BI:
- Usage: Designed for data analysis and creating interactive, dynamic reports that integrate seamlessly with other Microsoft tools.
- Best For: Users who need to combine data from multiple sources and create reports with interactive dashboards.
- Data Handling: Efficiently handles large datasets without performance issues and integrates smoothly with Microsoft platforms.
- Visuals: Offers interactive dashboards and visualizations, with built-in themes that are user-friendly.
- Integration: Easily integrates with Microsoft products like Excel, Azure, and SQL Server, making it a natural choice for Microsoft users.
- Sharing: Built-in cloud sharing features allow for real-time collaboration and automatic updates.
- Cost: Power BI is more affordable, with a free version available and competitive pricing for the Pro version.
- Automation: Offers strong automation features with real-time data refreshes and scheduling, making it ideal for dynamic reporting.
Tableau is a great choice for users who prioritize advanced visualizations, while Power BI is better for those who need easy integration with Microsoft tools, affordability, and real-time collaboration.
I have curated best top-notch Data Analytics Resources ๐๐
https://t.me/dataanalysisresourcestp
Like this post for more content like this ๐โฅ๏ธ
More Tech Resources Here๐
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Hope it helps :)
Find More Tips & Resources Here:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Tableau:
- Usage: Best suited for advanced data visualization and analytics with complex datasets.
- Best For: Users who need highly customized, detailed visualizations and are working with large datasets across different industries.
- Data Handling: Performs well with large datasets and offers strong data handling capabilities.
- Visuals: Known for its superior, highly customizable visuals, making it perfect for complex and artistic data representations.
- Integration: Easily connects with a wide range of data sources but may require more technical skills for setup.
- Sharing: Sharing options are available, but often require paid licenses for viewers.
- Cost: Tableau tends to be more expensive, especially at the enterprise level.
- Automation: Supports automated data refreshes, but the setup might be more complex than Power BI.
Power BI:
- Usage: Designed for data analysis and creating interactive, dynamic reports that integrate seamlessly with other Microsoft tools.
- Best For: Users who need to combine data from multiple sources and create reports with interactive dashboards.
- Data Handling: Efficiently handles large datasets without performance issues and integrates smoothly with Microsoft platforms.
- Visuals: Offers interactive dashboards and visualizations, with built-in themes that are user-friendly.
- Integration: Easily integrates with Microsoft products like Excel, Azure, and SQL Server, making it a natural choice for Microsoft users.
- Sharing: Built-in cloud sharing features allow for real-time collaboration and automatic updates.
- Cost: Power BI is more affordable, with a free version available and competitive pricing for the Pro version.
- Automation: Offers strong automation features with real-time data refreshes and scheduling, making it ideal for dynamic reporting.
Tableau is a great choice for users who prioritize advanced visualizations, while Power BI is better for those who need easy integration with Microsoft tools, affordability, and real-time collaboration.
I have curated best top-notch Data Analytics Resources ๐๐
https://t.me/dataanalysisresourcestp
Like this post for more content like this ๐โฅ๏ธ
More Tech Resources Here๐
https://t.me/techpsyche
Hope it helps :)
Find More Tips & Resources Here:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
REMOTERemote QA Analyst Job at Wealthbox CRM
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Forwarded from Artificial Intelligence Resources TP . AI Tools . AI Updates
30+ AI Tools to Finish Hours of Work in Minutes:
1. Presentation
- Gamma
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5. AI Model
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4. Meeting
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5. Chatbot
- Poe
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13 AI tools that will 10X your Productivity: https://t.me/airesourcestp/94
Best AI Translators: https://t.me/airesourcestp/95
27 Notorious ChatGPT Alternatives: https://t.me/airesourcestp/88
More Resources Here:
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1. Presentation
- Gamma
- Plus
- Prezi
- Pitch
- PopAi
- Slides AI
- Slidebean
2. Ideas
- YOU
- Claude
- ChatGPT
- Perplexity
- Bing Chat
3. Website
- Dora
- Wegic
- 10Web
- Framer
- Durable
4. Writing
- Rytr
- Jasper
- Copy AI
- Textblaze
- Sudowrite
- Writesonic
5. AI Model
- RenderNet
- Glambase App
4. Meeting
- Tldv
- Krisp
- Otter
- Avoma
- Fireflies
5. Chatbot
- Poe
- Claude
- Gemini
- ChatGPT
- HuggingChat
13 AI tools that will 10X your Productivity: https://t.me/airesourcestp/94
Best AI Translators: https://t.me/airesourcestp/95
27 Notorious ChatGPT Alternatives: https://t.me/airesourcestp/88
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๐1
Forwarded from Artificial Intelligence Resources TP . AI Tools . AI Updates