📖 Article 1: How I Built a Full Stack AI Chatbot Using GPT, React, and .NET 10 in a Weekend

As explained by: Unknown Author  |  📅 Published: 2025-07-14T20:24:23Z

🔗 https://dev.to/nikhilwagh/how-i-built-a-full-stack-ai-chatbot-using-gpt-react-and-net-10-in-a-weekend-4856

💡 Summary

Introduction

Every developer wants to build something cool on weekends — but time, complexity, and boilerplate usually get in the way.

This time, I challenged myself to build an AI chatbot, powered by GPT-4, that could:

• Answer questions from a custom knowledge base
• Remember conversation context
• Be styled and responsive (Tailwind)
• Work with a .NET 10 backend for secure, authenticated users

The twist? I built it in just one weekend — with help from AI itself.

In this blog, I’ll show you how I used AI tools + full stack skills to go from zero to live chatbot with minimal effort.

Features I Wanted

My MVP had to include:

• A React-based frontend chat UI
• GPT-4-powered responses using my business data
• Backend auth with .NET 10 + JWT
• Chat history stored in a database
• Option to plug in OpenAI or Azure OpenAI

Stack I Used

Layer Tech Frontend React + Vite + TailwindCSS Backend ASP.NET Core 10 Web API AI OpenAI GPT-4 API Storage MongoDB (chat logs) Hosting Vercel (frontend), Azure App Service (API)

How AI Helped Me Build This Faster

Here’s exactly where I used AI tools to speed up the build:

Designing the Architecture

Prompt to ChatGPT:

"Suggest a full stack architecture for a chatbot that uses OpenAI and .NET backend"

It generated:

• Auth flow with JWT
• Chat message schema
• GPT proxy service pattern
• CORS setup suggestions

Scaffolding the Backend

Prompt:

"Create an ASP.NET Core 10 Web API with login and chat controller"

Result: Fully generated controller, JWT set...

📖 Article 2: Why You Should Already Be Using an AI Agent in 2025

As explained by: Unknown Author  |  📅 Published: 2025-07-15T02:47:49Z

🔗 https://dev.to/ramasundaram_s/why-you-should-already-be-using-an-ai-agent-in-2025-4ipl

💡 Summary

It’s 2025, and I still meet people who haven’t used a single AI agent.

I’m not talking about ChatGPT or GitHub Copilot. I mean something that can take real action. Something that can handle a task on its own, use tools, remember context, and finish what you started. That’s an agent.

And if you haven’t tried one yet, you’re already behind.

Agents are no longer a tech demo. They’re useful. Reliable. Affordable. And honestly, they save time in a way that’s hard to ignore once you’ve used one.

So What’s Changed?

Back in 2023, agents were kind of a mess. They got stuck. They forgot what they were doing. Most people gave them a shot, saw the chaos, and moved on.

But now things work.

Agent frameworks are solid. Models are faster and cheaper. You can give an agent access to tools, files, APIs, or even your calendar, and it will actually get stuff done.

It doesn’t feel like a half-broken science experiment anymore. It feels like something you can depend on.

What I Use Agents For

These are simple things that I used to do manually but now offload to agents:

• Summarizing meetings and notes
• Writing project updates or status reports
• Responding to repeated questions with custom replies
• Searching logs or alerts for anything weird
• Writing or cleaning up documentation
• Extracting useful info from long PDFs or sites

None of this is fancy. It just saves time.

The best part is that I don’t have to think about these things anymore. The agent just handles them.

Why Most People Stil...

📖 Article 3: One Weekend, One AI Company, Completely Picked Apart: Windsurf’s Wild 72 Hours

As explained by: Unknown Author  |  📅 Published: 2025-07-15T07:59:04Z

🔗 https://dev.to/maybe_ai/one-weekend-one-ai-company-completely-picked-apart-windsurfs-wild-72-hours-449b

💡 Summary

In Silicon Valley, drama moves fast, especially in AI.

Just three days after Google announced it was spending $2.4 billion to poach the core team from AI startup Windsurf, Cognition AI surprised everyone by acquiring all of Windsurf’s remaining assets. The final twist was startling, even for Silicon Valley.

The Real Timeline of the Windsurf Drama

April 2025: OpenAI’s Acquisition Hits a Roadblock

OpenAI was negotiating a roughly $3 billion acquisition of Windsurf, aiming to boost its AI coding capabilities. But talks stalled due to Windsurf’s worries about intellectual property conflicts with Microsoft, one of OpenAI’s major investors and a direct competitor through its Copilot product.

June 2025: Anthropic Cuts Off Access, Windsurf Isolated

Anthropic, concerned by rumors that OpenAI might acquire Windsurf, abruptly cut Windsurf’s direct access to its Claude AI models. This left Windsurf isolated and significantly disrupted their tech resources.

July 11, 2025: Google Moves Swiftly, but Doesn't Acquire the Company

The moment OpenAI’s exclusivity period ended, Google swooped in with a $2.4 billion deal — not to buy Windsurf, but to hire CEO Varun Mohan, co-founder Douglas Chen, and key R&D personnel. They joined Google DeepMind’s Gemini project. Google also secured a non-exclusive license for Windsurf’s technology but notably didn’t take any equity.

July 14, 2025: Cognition AI Acquires Remaining Assets in a Flash

After Google took the leadership team, Windsurf’s 200-pl...

📖 Article 4: Building Your First AI Chatbot Using Python and OpenAI APIs

As explained by: Unknown Author  |  📅 Published: 2025-07-15T06:23:21Z

🔗 https://dev.to/sparkout/building-your-first-ai-chatbot-using-python-and-openai-apis-3jhl

💡 Summary

In today's digitally driven world, AI chatbots have become indispensable tools for businesses and individuals alike, streamlining communication, automating tasks, and providing instant information. The advent of powerful Large Language Models (LLMs) like OpenAI's ChatGPT has revolutionized the capabilities of these conversational agents, making it easier than ever to build highly intelligent and versatile chatbots. If you've ever wondered how to harness this technology, you're in the right place.

This blog will guide you through the exciting journey of building your first AI chatbot using Python and OpenAI APIs. We'll cover everything from setting up your environment to handling conversational flow, providing a hands-on introduction to AI chatbot development. Whether you're an aspiring developer, a small business owner, or simply curious about AI, this guide will equip you with the fundamental knowledge to create your own intelligent assistant.

Why Python and OpenAI APIs?

Python is the language of choice for AI and machine learning due to its simplicity, extensive libraries, and strong community support. Its readability makes it ideal for beginners, while its power caters to complex applications.

OpenAI, on the other hand, offers state-of-the-art LLMs through its accessible API. ChatGPT, specifically, provides incredibly human-like text generation and understanding, making it perfect for conversational AI. By combining Python's versatility with OpenAI's cutting-edge model...

📖 Article 5: How to Test DeepSeek Chat API in Postman (Based on Your Python Code)

As explained by: Unknown Author  |  📅 Published: 2025-07-14T15:33:26Z

🔗 https://dev.to/msnmongare/how-to-test-deepseek-chat-api-in-postman-based-on-your-python-code-13dh

💡 Summary

When working with language models like DeepSeek or OpenAI-compatible APIs in your Python code, it’s often useful to test requests manually using Postman. This guide shows you how to replicate your Python OpenAI SDK call using raw HTTP requests in Postman.

🧠 The Goal

Your Python code does the following:

import os
from dotenv import load_dotenv
from openai import OpenAI

# Load .env variables
load_dotenv()

# Read values from environment
api_key = os.getenv("OPENAI_API_KEY")
base_url = os.getenv("OPENAI_BASE_URL")

# Create OpenAI client
client = OpenAI(
    api_key=api_key,
    base_url=base_url
)

# Send chat completion request
response = client.chat.completions.create(
    model="deepseek-chat",
    messages=[
        {"role": "system", "content": "You are a helpful assistant"},
        {"role": "user", "content": "Hello"},
    ],
    stream=False
)

print(response.choices[0].message.content)

Now let’s test the same call in Postman.

✅ Step 1: Set the URL and HTTP Method

Method: POST

URL:

https://api.deepseek.com/v1/chat/completions

This matches the base_url + /v1/chat/completions endpoint used by the OpenAI-compatible API.

✅ Step 2: Set Headers

In Postman's Headers tab, add the following key-value pairs:

Key	Value
Authorization	Bearer YOUR_API_KEY (e.g. sk-abc123...)
Content-Type	application/json

🔐 Replace YOUR_API_KEY with your actual API key fr...

🎯 Final Takeaways

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