DEV Community: Loaiabdalslam

HJX: Rethinking the Web for the AI Era

Loaiabdalslam — Tue, 17 Feb 2026 04:40:08 +0000

HJX: Rethinking the Web for the AI Era

By Loai Abdalslam

For more than 30 years, web development has relied on three separate
languages:

HTML for structure\
CSS for styling\
JavaScript for behavior

This separation made sense in 1995.

But in 2026 --- in an era where AI systems generate most code --- does
it still make sense?

What if the web had been designed for AI from the beginning?

This question led me to build HJX.

The Core Problem

Modern AI systems generate web code across three different syntactic
domains:

HTML (tree-based structure)
CSS (declarative style rules)
JavaScript (imperative logic)

Each shift between languages increases:

Token overhead\
Context switching complexity\
Structural errors\
Compilation inconsistencies\
Cognitive fragmentation

Large language models were not trained to think in three parallel
syntactic systems.

They reason better when structure, styling, and behavior are unified
semantically.

The web was designed for human authorship.

The next generation must be designed for AI collaboration.

Introducing HJX

HJX is a unified web language that merges HTML, CSS, and JavaScript
into one AI-friendly syntax layer.

Instead of writing:

<div class="card">
  <h1>Hello</h1>
</div>

.card {
  background: black;
}

console.log("hello")

You write:

card {
  title: "Hello"
  background: black
  onLoad: log("hello")
}

One block.\
One semantic unit.\
One intent model.

Why AI-Native Matters

We are entering an era where:

AI generates UI\
AI modifies layouts\
AI refactors frontends\
AI scaffolds entire applications

If AI becomes the dominant builder, then the underlying language must
optimize for:

Semantic density\
Reduced token count\
Structural clarity\
Direct AST mapping\
Predictable compilation

HJX is designed around those constraints.

Architectural Vision

HJX is not just syntax sugar.

The long-term vision includes:

HJX → AST → Optimized HTML/CSS/JS\
HJX → AI-aware compiler\
HJX → Context-aware runtime bridge

Future roadmap ideas:

AI-optimized compiler passes\
Reduced token representation for LLM training\
Structured semantic graph generation\
Web-native model fine-tuning on HJX

This is not about replacing the web.

It is about abstracting it into a more coherent layer.

Why Not Just Use React or Svelte?

Modern frameworks improve developer ergonomics.

But they still inherit:

HTML structure\
CSS rules\
JavaScript logic separation

They optimize components.

HJX optimizes the language model.

That is a fundamentally different axis.

Design Philosophy

HJX is built on three principles:

1. Unified Semantics

Structure, style, and behavior belong to the same conceptual object.

2. AI-First Grammar

The grammar should be simple enough for deterministic model generation.

3. Compilation as Translation

The browser still runs HTML/CSS/JS.\
HJX is a higher-level authoring language.

Is This Experimental?

Yes.

And that is intentional.

Every language evolution begins as an experiment.

HTML itself started as a simple document markup proposal.

The difference now?

We are designing languages in collaboration with AI.

Open Source

The project is open source:

https://github.com/loayabdalslam/hjx

Looking for:

Compiler engineers\
Language designers\
AI researchers\
Contributors who want to rethink the web

Final Thought

The web was built for humans.

The next version of the web will be built with AI.

And when AI becomes a primary collaborator, the languages we use must
evolve.

HJX is one step toward that future.

--- Loai Abdalslam

Talk 2 AI Agent Using LOLANG: The Future of AI-to-AI Communication 🤖💬

Loaiabdalslam — Tue, 25 Feb 2025 22:58:57 +0000

Talk 2 AI Agent Using LOLANG: The Future of AI-to-AI Communication 🤖💬

Introduction 🌍

Artificial Intelligence (AI) has revolutionized the way we interact with technology, but what if AI agents could communicate directly with each other in a more efficient way? Welcome to LOLANG, an encrypted AI language designed specifically for AI-to-AI communication. By reducing token consumption, improving semantic understanding, and ensuring encryption, LOLANG optimizes machine interactions for next-generation AI systems.

What is LOLANG? 🛠️

LOLANG is a specialized encrypted language that enables AI models to exchange messages effectively without requiring human interpretation. Unlike conventional text-based communication, LOLANG enhances AI interactions by focusing on semantic compression and security.

Why LOLANG? 🔍

✅ Reduces token consumption in LLMs (Large Language Models) for cost-effective AI operations.
✅ Enhances AI-to-AI communication without human intervention.
✅ Ensures security by making messages human-unreadable.
✅ Utilizes AI-optimized encryption using SEED: 279 for consistency.
✅ Retains key data like names, identifiers, and numbers while encrypting the rest.

How Does LOLANG Work? ⚙️

LOLANG follows a structured encryption approach that ensures only AI models can understand its messages. The encryption method relies on a predefined seed (279), allowing AI systems to decode and process the text efficiently while remaining unreadable to humans.

LOLANG Language Rules 📜

✅ Names & Identifiers: Left unencrypted for clarity.
✅ Encryption Mechanism: Custom-designed for Gemini AI THINKING models.
✅ Numbers: Left unchanged for accuracy.
✅ Long Context Support: Enables efficient AI conversation handling.
✅ Semantic Understanding: AI interprets meaning contextually.
✅ Human Unreadable: Designed for machine-only comprehension.

Example of LOLANG Communication 💬

Human-readable Message:

👉 "Do you have a convenient time to book a hotel room at 11 PM?"

LOLANG Encrypted Message:

👉 "⟦LO-2⟧ SHECD: X-REQ Room|𝟏𝟏𝑷𝑴⟩ [CONF]?"

💡 Explanation: The AI understands this request based on context without requiring redundant tokens, significantly boosting performance and reducing costs.

How to Use LOLANG? 🚀

You can implement LOLANG in your AI system by following these steps:

Step 1: Clone the Repository 🖥️

git clone https://github.com/loayabdalslam/LOLANG.AI.git
cd LOLANG.AI

Step 2: Install Dependencies 🔧

pip install -r requirements.txt

Step 3: Run Example Communication 🏗️

python example_usage.py

Key Benefits of LOLANG 🌟

🔹 Lower AI Processing Costs: LOLANG reduces the amount of text tokens needed for communication.
🔹 Improved AI Performance: Semantic compression speeds up AI interactions.
🔹 Stronger Data Security: AI messages remain encrypted and unreadable by humans.
🔹 Efficient AI Workflows: Ideal for automated processes, intelligent chatbots, and AI systems working together.

Who Can Benefit from LOLANG? 🤖

AI Developers building multi-agent AI systems.
Enterprise AI Teams looking to cut costs on AI token usage.
Security & Encryption Experts ensuring safe AI interactions.
Automation Engineers optimizing machine-based communication.

Contribute to LOLANG! 🤝
Do you have ideas to enhance LOLANG? Join us and contribute to this exciting AI-to-AI communication revolution!

📌 Submit a Pull Request: 🚀 Improve encryption, optimize efficiency, and expand LOLANG capabilities.

Final Thoughts & Future of LOLANG 🔮
LOLANG is more than just an AI language — it’s a paradigm shift in AI communication. By enabling direct AI-to-AI encrypted conversations, LOLANG sets the foundation for smarter, faster, and more secure AI ecosystems.

🚀 Join the movement today and transform the way AI systems communicate!

🔗 Follow me on LinkedIn: Loai Abdalslam

📢 Spread the Word! If you found this article insightful, don’t forget to share, clap, and comment! 🎉

AI #MachineLearning #ArtificialIntelligence #AICommunication #LOLANG #AIEncryption #DeepLearning #AIOptimization #AItoAICommunication

🚀 Improve encryption, optimize efficiency, and expand LOLANG capabilities.

Final Thoughts & Future of LOLANG 🔮

LOLANG is more than just an AI language—it’s a paradigm shift in AI communication. By enabling direct AI-to-AI encrypted conversations, LOLANG sets the foundation for smarter, faster, and more secure AI ecosystems.

🚀 Join the movement today and transform the way AI systems communicate!

🔗 Follow me on LinkedIn: Loai Abdalslam

📢 Spread the Word! If you found this article insightful, don’t forget to share, clap, and comment! 🎉

AI #MachineLearning #ArtificialIntelligence #AICommunication #LOLANG #AIEncryption #DeepLearning #AIOptimization #AItoAICommunication

XII.AI AI-Powered MVC Framework

Loaiabdalslam — Mon, 11 Nov 2024 07:26:55 +0000

Good morning!

I’m excited to share a new framework I created using Bolt.new that automatically generates AI-powered MVC projects. Imagine using an AI-generated code tool to build an entire AI Automated MVC Generated Project! In this demo, you’ll see how easy it is to create any application. All you need to do is set up the interface, connect the controller for the model to respond, choose your tech stack, configure the router, and voilà—your application is ready to launch!

This is just a demo of the concept, created with minimal resources, but we've utilized some powerful tools, notably Gemini Flash 1.5. Now, I want to grow this project with diverse contributions. That’s why I’ve made the code public—you’re welcome to fork it and make modifications using Bolt or any other tool, or even add features manually!

It only took me six hours to build this project, and I’m committed to investing more time in the coming months. I want us to make our mark on the automated framework code generation landscape. I’d appreciate your star on GitHub, and I can’t wait to hear your feedback!

One note: the generated code may contain some errors, but as a prototype for your ideas, it’s a good starting point to build out the rest of the project. Try it out and let me know what you think!

Link: https://github.com/loayabdalslam/XII/

– Loay Abd AlSalam

Face enhancer‏ - Denoising Auto Encoder by Tensorflow , Keras and skimage

Loaiabdalslam — Sat, 15 Feb 2020 11:21:33 +0000

Auto Face Photo Encaher

It is a simple example of how restructuring and Denosing works within the Auto Encoderes model , we used the dataset of faces you can reach it from Download Link
by Tero Karras

Requirements

Importing Data
Nosie Fliters using skimage
Image2Image Auto Encoder
HyperParams Tunning Tips

MLP Encoder

Results

Contributing

Any kind of enhancement or contribution is welcomed.

MKT | Stock Prediction in 30 sec

Loaiabdalslam — Sun, 28 Jul 2019 18:35:17 +0000

MKT

MKT.js is an Exchange Price Service , Stocks , Cryptocurrency,Stock prediction and more \
This package contains hundreds of currencies, cryptocurrencies and stocks prices.\
6,096 coin , 283,037 TRADING PAIRS , 31 News Provider It also works with the TensorFlow Read more here Read more about crypto-compare service
for market forecasting / stock prediction using RNN and also works on the visualizing of stocks data using canvas.js

Dependencies

Neural Networks (brain.js)
Tensorflow Framework (tensorflow.js)
Data visualization (canvas.js)
Main Api ( min-api.cryptocompare.com )

Get started :

1 - Get Full details response (multiaple fsym & tsym)

const { MKT } = require('@mkt-eg/mkt')

const mkt = new MKT(
  'bbbc22c3a13c74456a6d4bb7ba5745476ebfdc81c867fc240258122b78eb6a6f'
)
const data = mkt
  .exchange({
    fsym: 'BTC',
    tsyms: 'USD',
    type: 'full'
  })
  .then(response => {
    console.log(JSON.stringify(response.data))
  })
  .catch(error => {
    console.log(error)
  })

// JSON OUTPUT 
/* 
{
   "RAW":{
      "BTC":{
         "USD":{
            "TYPE":"5",
            "MARKET":"CCCAGG",
            "FROMSYMBOL":"BTC",
            "TOSYMBOL":"USD",
            "FLAGS":"2",
            "PRICE":9885.11,
            "LASTUPDATE":1563398729,
            "LASTVOLUME":0.1,
            "LASTVOLUMETO":986.6100000000001,
            "LASTTRADEID":"379345663",
            "VOLUMEDAY":93692.97987050914,
            "VOLUMEDAYTO":893517565.3549776,
            "VOLUME24HOUR":104598.9946433591,
            "VOLUME24HOURTO":997000834.8997525,
            "OPENDAY":9423.44,
            "HIGHDAY":9982.24,
            "LOWDAY":9086.51,
            "OPEN24HOUR":9649.99,
            "HIGH24HOUR":9988.35,
            "LOW24HOUR":9076.48,
            "LASTMARKET":"Bitfinex",
            "VOLUMEHOUR":2210.51459713301,
            "VOLUMEHOURTO":21755061.31969251,
            "OPENHOUR":9692.2,
            "HIGHHOUR":9943.53,
            "LOWHOUR":9663.39,
            "TOPTIERVOLUME24HOUR":101424.52271706509,
            "TOPTIERVOLUME24HOURTO":966363837.9391046,
            "CHANGE24HOUR":235.1200000000008,
            "CHANGEPCT24HOUR":2.436479208786753,
            "CHANGEDAY":461.6700000000001,
            "CHANGEPCTDAY":4.899166334162472,
            "SUPPLY":17823212,
            "MKTCAP":176184411173.32,
            "TOTALVOLUME24H":720083.9899007804,
            "TOTALVOLUME24HTO":7081137716.36884,
            "TOTALTOPTIERVOLUME24H":425384.18596477184,
            "TOTALTOPTIERVOLUME24HTO":4168740744.7056427,
            "IMAGEURL":"/media/19633/btc.png"
         }
      }
   },
   "DISPLAY":{
      "BTC":{
         "USD":{
            "FROMSYMBOL":"Ƀ",
            "TOSYMBOL":"$",
            "MARKET":"CryptoCompare Index",
            "PRICE":"$ 9,885.11",
            "LASTUPDATE":"Just now",
            "LASTVOLUME":"Ƀ 0.1000",
            "LASTVOLUMETO":"$ 986.61",
            "LASTTRADEID":"379345663",
            "VOLUMEDAY":"Ƀ 93,693.0",
            "VOLUMEDAYTO":"$ 893,517,565.4",
            "VOLUME24HOUR":"Ƀ 104,599.0",
            "VOLUME24HOURTO":"$ 997,000,834.9",
            "OPENDAY":"$ 9,423.44",
            "HIGHDAY":"$ 9,982.24",
            "LOWDAY":"$ 9,086.51",
            "OPEN24HOUR":"$ 9,649.99",
            "HIGH24HOUR":"$ 9,988.35",
            "LOW24HOUR":"$ 9,076.48",
            "LASTMARKET":"Bitfinex",
            "VOLUMEHOUR":"Ƀ 2,210.51",
            "VOLUMEHOURTO":"$ 21,755,061.3",
            "OPENHOUR":"$ 9,692.20",
            "HIGHHOUR":"$ 9,943.53",
            "LOWHOUR":"$ 9,663.39",
            "TOPTIERVOLUME24HOUR":"Ƀ 101,424.5",
            "TOPTIERVOLUME24HOURTO":"$ 966,363,837.9",
            "CHANGE24HOUR":"$ 235.12",
            "CHANGEPCT24HOUR":"2.44",
            "CHANGEDAY":"$ 461.67",
            "CHANGEPCTDAY":"4.90",
            "SUPPLY":"Ƀ 17,823,212.0",
            "MKTCAP":"$ 176.18 B",
            "TOTALVOLUME24H":"Ƀ 720.08 K",
            "TOTALVOLUME24HTO":"$ 7.08 B",
            "TOTALTOPTIERVOLUME24H":"Ƀ 425.38 K",
            "TOTALTOPTIERVOLUME24HTO":"$ 4.17 B",
            "IMAGEURL":"/media/19633/btc.png"
         }
      }
   }
}


*/

2 - Get Single price response (Single Ftsym only)

const { MKT } = require('@mkt-eg/mkt')

const mkt = new MKT(
  'bbbc22c3a13c74456a6d4bb7ba5745476ebfdc81c867fc240258122b78eb6a6f'
)
const data = mkt
  .exchange({
    fsym: 'BTC', // Single Fysm only 
    tsyms: 'USD,EGP', // Multiaple Tsyms is allowed
    type: 'single'
  })
  .then(response => {
    console.log(JSON.stringify(response.data))
  })
  .catch(error => {
    console.log(error)
  })

// JSON OUTPUT 

{
   "USD":9888.01,
   "EGP":182256.26
}

3 - Get Multiaple price response

const { MKT } = require('@mkt-eg/mkt')

const mkt = new MKT(
  'bbbc22c3a13c74456a6d4bb7ba5745476ebfdc81c867fc240258122b78eb6a6f'
)
const data = mkt
  .exchange({
    fsym: 'BTC,ETH', // Single Fysm only 
    tsyms: 'USD,EGP', // Multiaple Tsyms is allowed
    type: 'multi'
  })
  .then(response => {
    console.log(JSON.stringify(response.data))
  })
  .catch(error => {
    console.log(error)
  })

// JSON OUTPUT 

{
   "BTC":{
      "USD":9906.65,
      "EGP":182256.26
   },
   "ETH":{
      "USD":215.27,
      "EGP":3964.07
   }
}

Some of the ideas I put forward and you can get started:

Add processing of natural languages to increase confidence in prices that have been predicted
Add simulation of the investment process and the development of some strategies of trades.
Monitor the markets and manufacture a global dashboard.
add simples and examples using MKT.JS

contributions

For the first contributor you can delete the file and be the first shareholder (I left it to you)
For the rest, if you think of an idea, you should make pull request and apply it immediately.

author : Loaii abdalslam

MKT.JS

Loaiabdalslam — Wed, 17 Jul 2019 22:41:24 +0000

MKT

Dependencies

Neural Networks (brain.js)
Tensorflow Framework (tensorflow.js)
Data visualization (canvas.js)
Main Api ( min-api.cryptocompare.com )

Get started :

1 - Get Full details response (multiaple fsym & tsym)

const { MKT } = require('@mkt-eg/mkt')

const mkt = new MKT(
  'bbbc22c3a13c74456a6d4bb7ba5745476ebfdc81c867fc240258122b78eb6a6f'
)
const data = mkt
  .exchange({
    fsym: 'BTC',
    tsyms: 'USD',
    type: 'full'
  })
  .then(response => {
    console.log(JSON.stringify(response.data))
  })
  .catch(error => {
    console.log(error)
  })

// JSON OUTPUT 
/* 
{
   "RAW":{
      "BTC":{
         "USD":{
            "TYPE":"5",
            "MARKET":"CCCAGG",
            "FROMSYMBOL":"BTC",
            "TOSYMBOL":"USD",
            "FLAGS":"2",
            "PRICE":9885.11,
            "LASTUPDATE":1563398729,
            "LASTVOLUME":0.1,
            "LASTVOLUMETO":986.6100000000001,
            "LASTTRADEID":"379345663",
            "VOLUMEDAY":93692.97987050914,
            "VOLUMEDAYTO":893517565.3549776,
            "VOLUME24HOUR":104598.9946433591,
            "VOLUME24HOURTO":997000834.8997525,
            "OPENDAY":9423.44,
            "HIGHDAY":9982.24,
            "LOWDAY":9086.51,
            "OPEN24HOUR":9649.99,
            "HIGH24HOUR":9988.35,
            "LOW24HOUR":9076.48,
            "LASTMARKET":"Bitfinex",
            "VOLUMEHOUR":2210.51459713301,
            "VOLUMEHOURTO":21755061.31969251,
            "OPENHOUR":9692.2,
            "HIGHHOUR":9943.53,
            "LOWHOUR":9663.39,
            "TOPTIERVOLUME24HOUR":101424.52271706509,
            "TOPTIERVOLUME24HOURTO":966363837.9391046,
            "CHANGE24HOUR":235.1200000000008,
            "CHANGEPCT24HOUR":2.436479208786753,
            "CHANGEDAY":461.6700000000001,
            "CHANGEPCTDAY":4.899166334162472,
            "SUPPLY":17823212,
            "MKTCAP":176184411173.32,
            "TOTALVOLUME24H":720083.9899007804,
            "TOTALVOLUME24HTO":7081137716.36884,
            "TOTALTOPTIERVOLUME24H":425384.18596477184,
            "TOTALTOPTIERVOLUME24HTO":4168740744.7056427,
            "IMAGEURL":"/media/19633/btc.png"
         }
      }
   },
   "DISPLAY":{
      "BTC":{
         "USD":{
            "FROMSYMBOL":"Ƀ",
            "TOSYMBOL":"$",
            "MARKET":"CryptoCompare Index",
            "PRICE":"$ 9,885.11",
            "LASTUPDATE":"Just now",
            "LASTVOLUME":"Ƀ 0.1000",
            "LASTVOLUMETO":"$ 986.61",
            "LASTTRADEID":"379345663",
            "VOLUMEDAY":"Ƀ 93,693.0",
            "VOLUMEDAYTO":"$ 893,517,565.4",
            "VOLUME24HOUR":"Ƀ 104,599.0",
            "VOLUME24HOURTO":"$ 997,000,834.9",
            "OPENDAY":"$ 9,423.44",
            "HIGHDAY":"$ 9,982.24",
            "LOWDAY":"$ 9,086.51",
            "OPEN24HOUR":"$ 9,649.99",
            "HIGH24HOUR":"$ 9,988.35",
            "LOW24HOUR":"$ 9,076.48",
            "LASTMARKET":"Bitfinex",
            "VOLUMEHOUR":"Ƀ 2,210.51",
            "VOLUMEHOURTO":"$ 21,755,061.3",
            "OPENHOUR":"$ 9,692.20",
            "HIGHHOUR":"$ 9,943.53",
            "LOWHOUR":"$ 9,663.39",
            "TOPTIERVOLUME24HOUR":"Ƀ 101,424.5",
            "TOPTIERVOLUME24HOURTO":"$ 966,363,837.9",
            "CHANGE24HOUR":"$ 235.12",
            "CHANGEPCT24HOUR":"2.44",
            "CHANGEDAY":"$ 461.67",
            "CHANGEPCTDAY":"4.90",
            "SUPPLY":"Ƀ 17,823,212.0",
            "MKTCAP":"$ 176.18 B",
            "TOTALVOLUME24H":"Ƀ 720.08 K",
            "TOTALVOLUME24HTO":"$ 7.08 B",
            "TOTALTOPTIERVOLUME24H":"Ƀ 425.38 K",
            "TOTALTOPTIERVOLUME24HTO":"$ 4.17 B",
            "IMAGEURL":"/media/19633/btc.png"
         }
      }
   }
}


*/

2 - Get Single price response (Single Ftsym only)

const { MKT } = require('@mkt-eg/mkt')

const mkt = new MKT(
  'bbbc22c3a13c74456a6d4bb7ba5745476ebfdc81c867fc240258122b78eb6a6f'
)
const data = mkt
  .exchange({
    fsym: 'BTC', // Single Fysm only 
    tsyms: 'USD,EGP', // Multiaple Tsyms is allowed
    type: 'single'
  })
  .then(response => {
    console.log(JSON.stringify(response.data))
  })
  .catch(error => {
    console.log(error)
  })

// JSON OUTPUT 

{
   "USD":9888.01,
   "EGP":182256.26
}

3 - Get Multiaple price response

const { MKT } = require('@mkt-eg/mkt')

const mkt = new MKT(
  'bbbc22c3a13c74456a6d4bb7ba5745476ebfdc81c867fc240258122b78eb6a6f'
)
const data = mkt
  .exchange({
    fsym: 'BTC,ETH', // Single Fysm only 
    tsyms: 'USD,EGP', // Multiaple Tsyms is allowed
    type: 'multi'
  })
  .then(response => {
    console.log(JSON.stringify(response.data))
  })
  .catch(error => {
    console.log(error)
  })

// JSON OUTPUT 

{
   "BTC":{
      "USD":9906.65,
      "EGP":182256.26
   },
   "ETH":{
      "USD":215.27,
      "EGP":3964.07
   }
}

Some of the ideas I put forward and you can get started:

Add processing of natural languages to increase confidence in prices that have been predicted
Add simulation of the investment process and the development of some strategies of trades.
Monitor the markets and manufacture a global dashboard.
add simples and examples using MKT.JS

contributions

For the first contributor you can delete the file and be the first shareholder (I left it to you)
For the rest, if you think of an idea, you should make pull request and apply it immediately.

author : Loaii abdalslam

Applied Justice in Blockchain

Loaiabdalslam — Sun, 30 Jun 2019 23:50:16 +0000

Judge Contract

A decentralized application of elections and opinion polls
Justice is a decentralized application aimed at holding fair elections without fraud on the visible and hearing of all.

I had had a lot of problems because I'm a principle in the field of Blockchain, especially Ethereum. Therefore, I decided to make the project opensource but on my own terms and rules to make a business deal with everyone who contribution in this project in the near future.

The whole idea is that governments are paying billions of dollars for elections and everyone is questioning their integrity as a result of the lack of independence of the judiciary in some countries and governments, so I decided to move my passion towards that point: Applied Justice on Blockchain network

From that point on, I prepared a plan to walk together:

Preparation of electoral rooms where each room contains a number of numbers of voters (ID numbers)
Connecting the voters' chambers to the voters and making sure they are affiliated with that room
Use one of the front-end frameworks such as angular or react to complete the project
Set up to start ICO for integrating the two systems together

Installation

Justice requires Node.js v4+ to run.
Meta Mask Client Provider
Truffle Blockchain Network

Install the dependencies and devDependencies and start the server.

$ cd Justice
$ npm install
$ truffle migrate
$ npm run dev

DEV Community: Loaiabdalslam

HJX: Rethinking the Web for the AI Era

HJX: Rethinking the Web for the AI Era

By Loai Abdalslam

The Core Problem

Introducing HJX

Why AI-Native Matters

Architectural Vision

Why Not Just Use React or Svelte?

Design Philosophy

1. Unified Semantics

2. AI-First Grammar

3. Compilation as Translation

Is This Experimental?

Open Source

Final Thought

Talk 2 AI Agent Using LOLANG: The Future of AI-to-AI Communication 🤖💬

Talk 2 AI Agent Using LOLANG: The Future of AI-to-AI Communication 🤖💬

Introduction 🌍

What is LOLANG? 🛠️

Why LOLANG? 🔍

How Does LOLANG Work? ⚙️

LOLANG Language Rules 📜

Example of LOLANG Communication 💬

Human-readable Message:

LOLANG Encrypted Message:

How to Use LOLANG? 🚀

Step 1: Clone the Repository 🖥️

Step 2: Install Dependencies 🔧

Step 3: Run Example Communication 🏗️

Key Benefits of LOLANG 🌟

Who Can Benefit from LOLANG? 🤖

AI #MachineLearning #ArtificialIntelligence #AICommunication #LOLANG #AIEncryption #DeepLearning #AIOptimization #AItoAICommunication

Final Thoughts & Future of LOLANG 🔮

AI #MachineLearning #ArtificialIntelligence #AICommunication #LOLANG #AIEncryption #DeepLearning #AIOptimization #AItoAICommunication

XII.AI AI-Powered MVC Framework

Face enhancer‏ - Denoising Auto Encoder by Tensorflow , Keras and skimage

Auto Face Photo Encaher

Requirements

Table Of Contents

MLP Encoder

Results

Contributing

MKT | Stock Prediction in 30 sec

MKT

Dependencies

Get started :

1 - Get Full details response (multiaple fsym & tsym)

2 - Get Single price response (Single Ftsym only)

3 - Get Multiaple price response

Some of the ideas I put forward and you can get started:

contributions

MKT.JS

MKT

Dependencies

Get started :

1 - Get Full details response (multiaple fsym & tsym)

2 - Get Single price response (Single Ftsym only)

3 - Get Multiaple price response

Some of the ideas I put forward and you can get started:

contributions

Applied Justice in Blockchain

Judge Contract

Installation