EA builder

Custom MT4/MT5/TradingView solutions - [email protected]

[Alps @alpaysh]

BREAKING: Elon Musk hires Nicki Minaj as Chief Gyatt Officer at SpaceX

[hoeem @hooeem]

https://t.co/n2Ph5qEtco

[elvis @omarsar0]

This is a great read if you are building complex applications with Claude Code and Codex.

Most AI coding agents can generate a frontend.

But building a real full-stack application is a completely different story.

The gap between generating a landing page and shipping a working app with a functional backend, database, and API layer remains wide.

Most coding agents default to mock data, fake endpoints, and frontend-only implementations.

But real-world web development requires all three layers working together.

This new research introduces FullStack-Agent, a multi-agent system designed for end-to-end full-stack web development with two key innovations: Development-Oriented Testing and Repository Back-Translation.

The system uses three specialized agents. A Planning Agent generates structured frontend and backend designs in JSON. A Backend Coding Agent implements server logic with a dedicated debugging tool that sends HTTP requests and validates responses. A Frontend Coding Agent builds the UI against real backend APIs with a tool that monitors terminal and browser console errors dynamically.

Development-Oriented Testing validates code during generation, not after. Each agent gets real-time execution feedback, catching integration failures as they happen rather than at the end of a long generation chain.

Repository Back-Translation solves the training data problem. An information-gathering agent reads real open-source repositories and extracts development patterns. A trajectory agent then reproduces those repositories from scratch given only the extracted plans, generating high-quality training examples grounded in real codebases.

The results on their FullStack-Bench (647 frontend, 604 backend, 389 database test cases): FullStack-Dev achieves 64.7% frontend accuracy, 77.8% backend accuracy, and 77.9% database accuracy. That's an 8.7%, 38.2%, and 15.9% improvement over the strongest baseline respectively.

After training a Qwen3-Coder-30B model on 2K crawled and 8K augmented trajectories, frontend accuracy improved by 9.7% and backend accuracy by 9.5% in just two rounds.

The bottleneck in AI-assisted web development isn't frontend generation. It's building functional backends and databases that actually work together. FullStack-Agent closes that gap with execution-grounded testing and real-world training data.

Paper: https://t.co/b2g041Pvrb

Learn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX

[Akshay 🚀 @akshay_pachaar]

Microsoft did it again!

Building with AI agents almost never works on the first try.

You spend days tweaking prompts, adding examples, hoping it gets better. Nothing systematic, just guesswork.

This is exactly what Microsoft's Agent Lightning solves.

It's an open-source framework that trains ANY AI agent with reinforcement learning. Works with LangChain, AutoGen, CrewAI, OpenAI SDK, or plain Python.

Here's how it works:

> Your agent runs normally with whatever framework you're using. Just add a lightweight agl.emit() helper or let the tracer auto-collect everything.

> Agent Lightning captures every prompt, tool call, and reward. Stores them as structured events.

> You pick an algorithm (RL, prompt optimization, fine-tuning). It reads the events, learns patterns, and generates improved prompts or policy weights.

> The Trainer pushes updates back to your agent. Your agent gets better without you rewriting anything.

The best part: you can also optimize individual agents in a multi-agent system.

I have shared the link to the GitHub repo in the replies!

Let me know if I should cover this in a video demo!

[Alex K @uialexk]

Wtf

[MoonPay 🟣 @moonpay]

which one are you playing first?

[EA builder @eabuilder]

@GithubProjects Long legs

[EA builder @eabuilder]

@4xrpmari What are you commenting to?

[EA builder @eabuilder]

This isn’t just tech.
It’s humanity amplified. 💜
AI, when used right, doesn’t replace us—it helps us be more ourselves.
#AssistiveTech #AIForGood #Hope

[EA builder @eabuilder]

https://t.co/4eXmmiFAVE
🗣️ A woman with motor neuron disease lost her voice.
With just 8 seconds of an old home video, #AI gave it back. 💜
Here’s how tech turned memory → reality 🧵
#AIForGood #AssistiveTech

[EA builder @eabuilder]

Imagine future:
– Personalized voice prosthetics
– Real-time speech restoration for patients
– Multilingual assistive voices to cross barriers 🌍
AI could become the bridge between silence & expression.
#FutureOfAI #Healthcare

[EA builder @eabuilder]

🧬 MIT’s VaxSeer uses #AI to predict virus evolution—helping select vaccines with far greater accuracy.
No more guesswork → faster, smarter pandemic response.
Could this be the end of yearly flu-shot roulette?
#HealthcareAI #FutureOfMedicine

https://t.co/ISacssFXF0.

[EA builder @eabuilder]

❤️ Inspired by nature: scientists built a starfish-shaped wearable that tracks heart health + AI to analyze signals.
Non-invasive, efficient & real-time.
From sea creatures → lifesaving tech. 🌊
#MedTech #Wearables #AI

[EA builder @eabuilder]

https://t.co/GuWSDF92Wo

🤖🧠 Bengaluru’s CynLr & IISc are merging AI + neuroscience to give robots human-like perception.
Adaptive machines that “see” & act like us → huge leap for factories, logistics & real-world robotics.
#NeuroAI #Robotics #IndiaTech

[EA builder @eabuilder]

🎾 IBM & MIT trained AI to deliver dynamic tennis commentary:
– Reads the match in real-time
– Adjusts tone & excitement
– Enhances fan experience without replacing humans
Sports just got smarter. 🏆
#AI #SportsTech #IBMWatson

https://t.co/QQqg8iQeGi

[EA builder @eabuilder]

🗣️ A woman with motor neuron disease regained her voice using #AI—recreated from just 8 seconds of old video.
Proof that AI isn’t just about automation—it’s about giving humanity back. 💜
#AssistiveTech #AIForGood #HealthTech

https://t.co/4eXmmiFAVE

[EA builder @eabuilder]

🚀 Big moves at #Salesforce: AI is now doing 30-50% of internal work—crushing support tickets (85% auto-resolved!), qualifying leads 40% faster, cutting support jobs, and slashing costs. A digital labor revolution—but the road to full ROI is still bumpy. #AI #Agentforce #FutureOfWork

[Science girl @sciencegirl]

Cats like to bring gifts for their owners