Forget juggling complex passwords for every website. Passkeys are revolutionizing online security by letting you sign in with your fingerprint, face scan, or PIN.

Passkeys work by creating a unique pair of cryptographic keys. The public key is stored on the server, while the private key remains securely on your device. This means no more shared secrets that can be stolen! 🛡️

They’re not just secure, they’re also incredibly convenient. You can use them across all your devices, and they’re resistant to phishing attacks 🎣. Plus, they’re easy to use—just a tap or glance away!

The tech industry is rapidly adopting passkeys. Major platforms like Windows and Google are already on board, making it easier for you to have a seamless and secure online experience.

Want to implement passkeys for your apps/website? Check out Passkeys by @pangeacyber. Make your application's user login process more secure & convenient in less than 5 minutes! Authenticate Smarter, Not Harder with Passkeys!
https://pangea.cloud/landing/passkeys/

While passwords might not disappear overnight, passkeys are definitely the future. #passkeys are more than just convenience – they're a giant leap for online security.

Thanks for the read! What are your thoughts on this passwordless future?
#Passkeys #CyberSecurity #TechInnovation #Passwordless

Revolutionizing Online Security with Passkeys

In the world of databases and APIs, optimizing data retrieval from one-to-many relationships is often a tricky challenge. But guess what? @xata has come to the rescue! 🦸‍♂️ Let's dive into how Xata's approach tackles this issue, making your data retrieval a breeze. 🚀

🧐 First, let's set the stage with a classic scenario: the "Authors" and "Books" example. Authors can have multiple books, and we want to fetch authors and their books. Seems straightforward, right? But wait, there's an N+1 problem lurking!

📚 Traditionally, you'd fetch authors and then make separate queries for each author's books. Result? N+1 queries, where N is the number of authors. Yikes! Performance bottlenecks and database round trips galore. 😬

🧙‍♂️ Xata introduces a syntax that simplifies one-to-many relationships, annihilating the N+1 problem. Here's the magic in action with our "Authors" and "Books" example:

🪄 With Xata's approach, you efficiently retrieve all you need in a single request. It's like getting the data superpower you always wanted!

💥 The N+1 problem? Completely vanquished! Xata minimizes queries, so you get the data without the complexity or overhead.

🛠️ And the best part? Xata's syntax is super flexible. You can sort, limit, and paginate to your heart's content—all in a single query. No more multiple round trips, just efficient data retrieval.

🎉 In conclusion, Xata's approach revolutionizes data retrieval by tackling the N+1 problem head-on. It simplifies navigating complex relationships, streamlines your code, and turbocharges query performance.

🚀 Ready to level up your data retrieval game? Give @Xata a try and say goodbye to the N+1 problem! Learn more 👇

Reduce query round trips with improved one-to-many relations
https://xata.io/blog/navigating-many-to-one

Thanks for the read and stay tuned for an article on the same! Happy Xata'ing 🦋

Optimizing Data Retrieval in One-to-Many Relationships with Xata

1/ 🚀 Hey X'ers, buckle up! Today, we're diving deep into the world of Vector Store and Memory Store – the dynamic duo that makes AI's brain tick. 🧵

#AI #Database #LLM #Technology

2/ 🧬 First, let's meet Vector Store. Think of it as the AI's library of concepts. Each concept, like "cat" or "happiness," is stored as a unique vector, which is basically a mathematical representation.

3/ 🐱 For example, in the Vector Store, the "cat" vector points to all things feline – whiskers, paws, meowing, and more. It's AI's way of understanding what makes a cat, well, a cat!

4/ 🧠 Now, let's bring Memory Store into the mix. It's like AI's diary, where it stores recent experiences and knowledge. Just like your brain remembers that awesome biriyani you had last week!

5/ 💭 AI constantly updates its Memory Store. So, if it encounters a new cat doing a backflip, it adds that experience to its Memory Store to recognize backflipping cats later.

6/ 🔄 Here's where the magic happens! Vector Store and Memory Store team up. AI uses the vectors to understand concepts and Memory Store to remember experiences and apply that knowledge.

7/ 🎥 Take movie recommendations on Netflix, for instance. Vector Store helps AI understand your movie preferences, and Memory Store recalls what you've watched, making spot-on suggestions!

8/ 🚗 Self-driving cars? Vector Store helps AI recognize pedestrians (human-shaped vectors!), while Memory Store remembers how to navigate tricky intersections safely.

9/ 🌐 Language translation? Vector Store holds language vectors, and Memory Store remembers the latest language trends, ensuring accurate and up-to-date translations.

10/ 🌟 In essence, Vector Store and Memory Store are AI's power couple, making it smarter with each interaction, just like how we learn and remember through experiences.

11/ 🤯 So next time AI suggests a movie or gets you safely to your destination, you'll know it's all thanks to the incredible teamwork of Vector Store and Memory Store.

12/ 🦋 @Xata, a serverless data platform recently introduced integration with 🦜 LangChain.  Store your documents and embeddings in Xata tables and perform vector search with ease. Use Xata as a memory store for your AI chat sessions and leverage LLMs.

https://xata.io/blog/langchain-integrations

13/ Thanks for the read & stay tuned for more!

Exploring Vector Store and Memory Store: AI's Brain in Action

1/ 💡 What if I told you there's a game-changer in the world of databases? 🤔 It's called "Serverless Databases," and it's transforming the way we handle data. Let's dive in! 👇

#ServerlessDatabases #DataManagement

2/ 🚫 Say goodbye to managing server infrastructure! With serverless databases, you no longer need to worry about provisioning, scaling, or maintenance. Focus on your app, not the servers. 🙌

#NoOps #DatabaseFreedom

3/ 🌐 Serverless databases are incredibly scalable. Whether you have 10 users or 10 million, the system automatically adjusts to meet your demands. No more overpaying for idle resources! 💸

#Scalability #CostEfficiency

4/ ⚡️ Real-time is the name of the game! Serverless databases are built for instant updates, making them ideal for apps requiring lightning-fast responsiveness. 🚀

#RealTimeData

5/ 🔒 Worried about security? Serverless databases come with robust security features, including encryption at rest and in transit. Your data is safe and sound. 🔐

#DataSecurity

6/ 🔄 Data replication is handled seamlessly. No need to worry about backups or data loss – it's all taken care of behind the scenes. 💾

#DataIntegrity #Reliability

7/ 🌈 The ecosystem is diverse! From @awscloud Aurora Serverless to @Firebase Firestore, you have options to choose the right serverless database for your project. 💻

8/ 🌟 Serverless databases are cost-effective. Pay only for what you use, and watch your operational costs drop. Budget-friendly innovation at its finest! 💰

#CostSavings #Innovation

9/ 🧩 Integrations are a breeze. Serverless databases seamlessly connect with various tools and services, making it easier to build the tech stack of your dreams. 🧰

#Integration #TechStack

10/ 👩‍💻 Developers, rejoice! Serverless databases free up your time to focus on coding instead of database management. Accelerate your development process! ⏩

#DeveloperFriendly

11/ 💬 In conclusion, serverless databases are a game-changer for modern applications. They're cost-effective, scalable, secure, and developer-friendly. Are you ready to embrace the future of data management? 🚀

12/ If you’re looking for a serverless database that supports rich data types, full-text search, AI capabilities, and more, check out @xata. It’s built on Postgres and integrates with popular tools like @vercel, @netlify, and @openai.

https://xata.io/

🙌 That's a wrap on our journey through serverless databases! Thanks for the read!

Serverless Databases: The Game-Changer in Data Management

Hey everyone! 👋 Let's take a journey back in time to explore the fascinating origins of search engines. Meet Archie, a pioneer that paved the way for today's sophisticated search technologies. 🌐 #TechHistory #Archie

Back in the day, when the internet was still in its infancy, finding information was like a searching for a needle in a haystack. But then came Archie in 1990, a groundbreaking concept that revolutionized how we discover content online.

Archie wasn't like the search engine we know today. It wasn't a WebCrawler; it couldn't traverse hyperlinks. Instead, Archive used the Telnet protocol to scour anonymous FTP sites for files and create index, making data accessible to the users.

wtf is Telnet & FTP?  To put it simple, Telnet let you remotely access servers, while FTP (File Transfer Protocol) helped transfer files between computers. Think of them as the OG ways to connect & share online.

The beauty of Archie lay in its simplicity. It indexed file names and directory structures making it easier for users to locate resources. It was a collaborative effort, with multiple archie servers indexing different parts of the internet.

Bcoz, Archie could not index the content inside the files or comprehend natural language requests, users had to know the exact title of the file they were looking for in order to use archie.

Archie's era was short-lived. As the internet expanded rapidly, it couldn't keep up with the increasing volume of data. The rise of WWW and more advanced search algorithms eventually led to archie's decline.

While Archie might have faded into history, its legacy lives on. It demonstrated the power of search technology, sparkling the development of more advanced search engines that shaped the digital landscape we navigate today.

Next time you use a search engine, just remember that Archie paved the way for innovations that connect us to the world's knowledge at our fingertips.

And that's the story of Archie. Thanks for joining this journey into the past! Stay curious and keep exploring the exciting evolution of technology. Until next time! 👋

The Origins of Search Engines: Meet Archie

Over the past 6 months, there has been a massive AI boom, with ChatGPT and other AIs making headlines. But the key driving force behind all these advancements is the incredible LLM technology. Here's a short thread about it.

A large language model is a trained DL model that understands and generates text in a human-like fashion.

As the name, it is a Large, General-Purpose Language Model that can be Pre-Trained and then Fine-Tuned for specific needs.

Pre-Trained LLMs are trained to solve common problems like text classification, question answering, doc summarization etc.
Whereas Fine-Tuned Models are tailored for a specific task like sentiment analysis in product reviews, predicting stock prices based on financial news etc.

Benefits of LLM

- A single model can be used for different tasks
- The fine-tuning process requires minimal field data
- The performance is continuously growing when you add more data and parameters
- Low Code/No Code
- Contextual Understanding

For LLM Development, ML expertise, Training examples, and training a model are not needed. All we need to think about is the Prompt Design. This is not the case in Traditional ML Development.

Crafting the perfect prompt is an art! It's all about asking the right questions, guiding AI models, & getting accurate, relevant results. Whether it's for language translation, code generation, or creative writing, nailing the prompt is the key to unlocking AI's full potential!

There are three kinds of LLM, based on prompting.
Generic - Predict the next word based on the language in the training data
Instruction Tuned - Trained to predict a response based on the given instruction
Dialog Tuned - Trained to have a dialogue by predicting the next response

Check out this video on Introduction to large language models by @googlecloud
https://youtu.be/zizonToFXDs

LLMs are changing the game in NLP and we're witnessing a new era of AI-driven possibilities!

The Rise of LLM Technology: Powering AI Advancements

Text-to-image AI is revolutionizing the way we create visual content.

Here is a list of some:
· app.runwayml.com/ai-tools/text-to-image
· openai.com/product/dall-e-2
· canva.com/ai-image-generator/
· beta.dreamstudio.ai/generate
· craiyon.com
· lexica.art

/1 - The perfect commit
➀ Add the right changes!
· Shouldn't cram everything into one commit.
· Only combine changes from the same topic in a single commit.
➁ Compose a good commit message!
· subject = concise summary of what happened
· body = more detailed explanation

/2 - Branching strategies
❏ A written convention - Agree on a branching workflow in your team
➀ Git allows you to create branches - but it doesn't tell you how to use them

➁ You need a written best practice of how work is ideally structured in your team - to avoid mistakes/collision
➂ It highly depends on your team/team size, on your project, and how you handle releases
➃ It helps to onboard new team members ("This is how we work here")

❏ Integrating changes & Structuring releases
➀ Mainline development ("Always be integrating")
- few branches
- relatively small commits
- high-quality testing & QA standards

➁ State, Release, and Feature branches
- Branches enhance structures & workflows
-- different types of branches...
-- ...fulfill different types of jobs

❏ Long-running & short-lived branches -  Two types of branches
Long-running
- exist throughout the complete lifetime of the project
- often, they mirror "stages" in your dev life cycle
- common convention: no direct commits!

Short-lived
- for new features, bug fixes, refactoring, experiments...
- will be deleted after integrating(merge/rebase)

❏ GitHub flow
- very simple, very lean: only one long-running branch ("main") + feature branches
❏ GitFlow
- more structure, more rules
- long-running: "main" + "develop"
- short-lived: features, releases, hotfixes

The "Best" branching model
- consider your project, release cycle, and team
- take inspiration from existing models (like "GitFlow" or "GitHub Flow")
- ...and create your own model!

/3 - Pull requests
- Communicating about and reviewing code
- a pull request invites reviewers to provide feedback before merging
- Contributing code to other repositories (fork-> make changes -> open PR -> get merged)

/4 - Merge conflicts
How and when do conflicts occur?
When integrating commits from different sources
Merging changes in Git
- A flawless process, most of the time, git will figure things out on its own.
- When contradictory changes happen, git cannot know what's correct!

How to know when a conflict has occurred?
Don't worry Git will tell you.
How to undo a conflict and start over?
You can always undo it and start fresh!
git merge --abort
git rebase --abort

What do conflicts❌actually look like?
Just characters in the file!
How to solve a conflict?
Simply clean up the file!

/5 Merge vs Rebase
Merge commit is automatically created by git
Rebase - A straight line of commits - it rewrites the commit history

Warning ⚠️
- Do not use Rebase on commits that you've already pushed/shared in a remote repository. Instead, use it for cleaning up your local commit history before merging it into a shared team branch.

This thread is created from Git for Professionals Tutorial by @gittower
https://youtu.be/Uszj_k0DGsg


Git Basics

Manage required 🐍 #Python packages with requirements.txt
🧵

"Requirements files" are files containing a list of items to be installed using pip install / A requirements.txt file is a file that lists all of the dependencies for a project.

> It allows you to easily install all the dependencies for your project in one go 🚀 .
> It makes it easy to share your project with others, as they can simply install all the dependencies from the requirements.txt file.
> It can help you avoid dependency hell.

The pip freeze command is used to output all of the installed packages in requirements format.
[ x.y.z -> package versions ]

You can install the same dependencies on another system by running pip install -r requirements.txt
The -r flag tells pip to read the requirements.txt file and install all the dependencies listed in that file.

Listen to @calvinhp talk on "Tools for Setting Up Python on a New Machine"  | @realpython podcast
[00:22:38] Creating "requirements.txt" files and moving from pipenv to piptools
https://youtu.be/3JxhdzWCMjU?t=1358


$ pip install like retweet share
$ pip install --user @dotaadarsh
#Python #100DaysOfCode


Manage required 🐍 Python packages with requirements.txt

Java Programming Language - A 🧵 on its characteristics
#Java

Characterized by
- Object-Oriented
- Distributed
- Multithreaded
- Dynamic
- Architecture neutral
- Portable
- High performance
- Robust
- Secure

The central ideas of Java technology are easy to understand; programmers can start being productive right away.

The Java programming language is designed to be object-oriented from the ground up, meaning that objects are created and manipulated within Java code.

Since Java has many of the same object-oriented features as C++ and a similar "look and feel," programmers can migrate to Java easily and be productive quickly.

The Java programming language is designed to create software that is highly reliable. It provides compile-time checking followed by a second level of run-time checking.

In Java, obj are created with the 'new' operator and there is no need for pointer data types, pointer arithmetic, or manual garbage collection. This eliminates many potential prgm errors, giving Java devs confidence that their code will be free of major issues.

The Java Compiler generates bytecodes, which is an intermediate format that is designed to transport code efficiently to multiple hardware and software platforms. The interpreted nature of Java technology solves both the binary distribution problem and the version problem.

Java platform achieves high performance by using a system where the interpreter can run without having to check the run-time env constantly. Automatic garbage collector runs as a low-priority background thread, which then makes it likely that memory will be available when needed.

Multithreading allows for Java applications to have many concurrent threads of activity, resulting in a high degree of interactivity for the end user.

Memes: credits to the creators!
Learning Java, Lets connect! If you liked it, Retweet and follow @dotaadarsh for more.
#JavaDev #Memes #Programming #100DaysOfCode