DEV Community: SupermanSpace

The Great AWS Outage of October 2025: When the Internet's Backbone Buckled

SupermanSpace — Mon, 20 Oct 2025 19:03:41 +0000

October 20, 2025 — In the early hours of Monday morning, millions of internet users worldwide woke up to find their favorite apps and services completely unavailable. Snapchat wouldn't load. Wordle was inaccessible. Medium not loading. Vercel was not working. Ring doorbells went dark. Amazon's own shopping site displayed error pages featuring apologetic dog photos. The culprit? A massive outage at Amazon Web Services (AWS), the cloud computing giant that quietly powers much of the modern internet.

The Scale of the Disruption

The outage began at 12:11 a.m. PT (3:11 a.m. ET) when AWS reported an "operational issue" affecting 14 different services in its U.S.-East-1 Region center in northern Virginia. What started as a technical glitch in a single data center quickly cascaded into one of the largest internet disruptions since the CrowdStrike malfunction of 2024.

Over 4 million users reported issues due to the incident, affecting an astonishing array of services that people rely on daily. The impact was both widespread and democratic in its chaos, bringing down everything from entertainment platforms to critical business infrastructure.

Who Was Affected?

The list of affected services reads like a who's who of the internet:

Social Media & Communication:

Snapchat
Reddit
Signal (encrypted messaging)

Gaming:

Fortnite
Roblox
Pokémon GO

Financial Services:

Coinbase (cryptocurrency exchange)
Venmo
PayPal
Robinhood
Chime

Amazon's Own Services:

Amazon.com (shopping)
Prime Video
Alexa
Ring doorbells and security cameras

Airlines:
United Airlines experienced disruptions to its app and website, with some internal systems also temporarily affected. Delta Airlines experienced a small number of minor flight delays.

Education:

Duolingo
Canvas (online teaching platform)

Other Major Services:

Canva (graphic design)
Perplexity (AI search)
Max (streaming)
Apple Music
Microsoft Teams (surprisingly affected despite Microsoft's own Azure cloud)

Support

Intercom (down from last 11 hours)
YourGPT Helpdesk (not loading for 15 minutes)
Ada (not working for 2 hours)

In the United Kingdom, customers of banks including Lloyds, Bank of Scotland, and Halifax reported issues while attempting to log into their accounts. British government websites Gov.uk and HM Revenue and Customs also experienced disruptions, highlighting
how critical infrastructure has become dependent on cloud services.

The Root Cause: A DNS and Database Perfect Storm

At 4:26 a.m. ET, Amazon flagged significant error rates for requests made to the DynamoDB endpoint in the US-EAST-1 Region. DynamoDB is AWS's database service that thousands of companies use to manage their data tables and indexes.

The issue appeared to be related to DNS resolution of the DynamoDB API endpoint. DNS (Domain Name System) is essentially the internet's phonebook, translating human-readable website names into computer-readable IP addresses. When this system fails to communicate with databases, the entire chain of services collapses.

At 11:43 a.m., AWS identified the root cause as "an underlying internal subsystem responsible for monitoring the health of our network load balancers". This technical fault in a monitoring system created a cascading failure that rippled through AWS's entire infrastructure.

A software engineer and cyber expert noted that the issue appeared to be with one of the networking systems AWS uses to control a database product, highlighting how a problem in one small component can bring down an entire ecosystem.

The Recovery: A Long and Bumpy Road

At 6:35 a.m. ET, AWS announced that the database problem was "fully mitigated" but warned there may still be delays. However, the recovery proved more complicated than initially hoped.

Many sites came back online within a few hours, although Downdetector showed another spike in user reports around noon ET of outages at Amazon, AWS and Alexa. The initial fix didn't fully resolve all issues, and services continued to experience intermittent problems throughout the day.

Around 1:30 p.m. ET, AWS said it was starting to see "early signs" of EC2 recovery in some regions and was applying fixes to remaining areas. The company's EC2 (Elastic Compute Cloud) service provides virtual server capacity that companies rely on to run their applications.

Amazon.com itself wasn't spared from the chaos. Reports on Downdetector showed over 12,000 outages in the US, with Amazon displaying "something went wrong" error pages featuring various dogs to frustrated shoppers.

Real-World Impact: Beyond Inconvenience

While many users experienced mere inconvenience, the outage had more serious consequences for others:

Business Disruption: Warehouse and delivery employees, along with drivers for Amazon's Flex service, reported that internal systems were offline at many sites. Some warehouse workers were instructed to stand by in break rooms and loading areas during their shift.

Accessibility Concerns: One user shared a particularly poignant example of how cloud dependency affects vulnerable populations: "I use Alexa-enabled smart plugs to control the lamps in my room. I'm unable to walk without leaning on crutches so being able to turn lights and music on by voice is very helpful. During the outage my smart plugs became unresponsive".

Educational Impact: Educational publishing company Folens contacted teachers advising that services linked to its 'My Folens' library were being disrupted, affecting students' access to learning materials.

Security Concerns: Ring experienced outages affecting thousands of users, creating problems for those who rely on Ring doorbells and security cameras for safety.

The Fragility of Centralized Infrastructure

This outage underscores a fundamental vulnerability in how the modern internet is structured. The outage highlighted the fragility of companies that use cloud-based servers to host their data, and how suddenly businesses across the globe can be affected by an unplanned outage.

AWS is the dominant player in cloud computing, making $107 billion in the 2024 financial year, representing 17% of Amazon's total revenue. This dominance means that when AWS experiences problems, the ripple effects are felt globally.

Cori Crider, executive director of the Future of Technology Institute, stated: "Europe's dependency on monopoly cloud companies like Amazon is a security vulnerability and an economic threat we can't ignore", calling for European governments to diversify their cloud providers and support local alternatives.

Charlotte Wilson, head of enterprise at Check Point Software Technologies, noted: "Today's outage is another reminder that the digital world doesn't stop at borders - a local fault can ripple worldwide in minutes".

Was It a Cyberattack?

With such widespread disruption, speculation naturally turned to the possibility of malicious activity. However, Rob Jardin, chief digital officer at cybersecurity company NymVPN, stated: "There's no sign that this AWS outage was caused by a cyberattack - it looks like a technical fault affecting one of Amazon's main data centres".

Rafe Pilling, director of threat intelligence at cybersecurity firm Sophos, acknowledged: "When anything like this happens, the concern that it's a cyber incident is understandable. AWS has a far-reaching and intricate footprint, so any issue can cause a major upset". However, all evidence points to a technical failure rather than malicious intent.

Lessons Learned and Moving Forward

This incident raises critical questions about internet infrastructure resilience:

Single Points of Failure: The concentration of so many services on a single cloud provider creates systemic risk. When AWS goes down, significant portions of the internet follow.
Redundancy vs. Cost: While AWS and other cloud providers generally maintain robust systems, the complexity of these networks means that unforeseen interactions can cause cascading failures.
Transparency: AWS customers were unable to report the problem because its automated support ticketing system was also offline, highlighting how even reporting mechanisms can be caught in the same failure.
Geographic Concentration: Many outages appeared to be concentrated in the United States, with a focus in Virginia, which is considered the global capital for data centres, demonstrating the risks of geographic concentration.

The Aftermath

As of late Monday afternoon, AWS continued working toward full resolution, with most services gradually returning to normal operation. However, the incident serves as a stark reminder of how dependent modern life has become on cloud infrastructure.

Charlotte Wilson recommended that people keep good backups, save important information offline, and know alternative ways to connect to the internet or pay if systems fail — practical advice for an increasingly cloud-dependent world.

The AWS outage of October 2025 will likely be studied for years to come as a case study in infrastructure fragility, centralization risk, and the need for more resilient digital systems. As we continue to move more of our lives online, building redundancy and diversification into our digital infrastructure isn't just good engineering — it's essential for maintaining the connectivity that modern society depends upon.

For now, the internet has largely recovered, but the question remains: how do we build a more resilient digital future that doesn't collapse when a single provider stumbles?

This is a developing story. AWS continues to monitor services and work toward complete restoration of all affected systems.

What Two Years of Bootstrapping an AI Startup in India Taught Us

SupermanSpace — Mon, 18 Aug 2025 08:20:00 +0000

When we started in 2023, our mission was simple: help businesses build with AI.

Our first product was a fine-tuning tool for businesses to customise AI models. At the time, fine-tuning was resource-heavy & challenging.

Fine-tuning required preparing datasets (and even synthetic data generation was not as highly feasible as today), running heavy compute, and testing multiple iterations to avoid issues like overfitting or underfitting. In practice, this meant months of work and high costs—something only big tech firms could manage.

We saw this gap. Instead of making everyone rebuild models, we introduced advanced RAG-based AI chatbots that could train on a company’s own data while using existing models. RAG allowed companies to use powerful existing models while still grounding answers in their own data—giving them customisation without the cost of fine-tuning*.*

As we worked with more customers it became clear that most businesses did not want multiple tools. They wanted one solution that could handle conversations, support teams, help them grow, and actually grow with them.

We realised many teams face the same challenge: managing multiple disconnected systems slows them down. We combined our fine-tuning capabilities with the YourGPT Chatbot for enterprise users who still need customised models, while also building a single platform for conversations, training, and automation so teams no longer have to juggle separate tools.

We are a small, fast-moving team bootstrapped from day one. We learn by shipping, watching, and listening. Over time every feature we built, from the action-oriented Copilots builder to AI Agents to AI Studio, Helpdesk, Voice Agents was brought together in one product: YourGPT.

It’s been a pleasure building our product from Mohali, India.

With a lean, customer-focused team, we help businesses unlock value and maximize innovation. One big lesson: hire for mindset. India has incredible talent, and we now prioritize curiosity, passion, and ownership over resumes—skills can be taught, but hunger to solve problems can’t.

We also learned that partnerships matter as much as technology. As developers we love to build, but growing a business is more than code. Relationships with customers, vendors, and other startups help keep the momentum going. If someone has ideas or wants to discuss potential collaborations, they can reach us at pr@yourgpt.ai.

For other bootstrapped builders, here is one thing I wish I had known earlier, and I am sharing it in case it helps you too:Focus on one strong product. Do not get distracted by vibe coding, which will scatter your efforts across too many directions. Grow one vertical well and then expand it horizontally. This creates more value for your users and makes your product journey clearer.

Two years in, our focus is clear: help businesses automate support, sales, and operations, and scale with AI.

Real progress comes from building, shipping, and learning.

Bootstrapping taught us this: momentum matters more than money. Keep building.

These are lessons we continue to learn every day. If you are building something now, what is the one challenge slowing you down the most?

Elevenlabs text to speech unity

SupermanSpace — Thu, 01 Aug 2024 13:49:06 +0000

Get Voice ID

using System;
using System.Net.Http;
using System.Threading.Tasks;
using UnityEngine;
using Newtonsoft.Json.Linq; // Make sure you add the Newtonsoft.Json package via Unity Package Manager

public class VoiceFetcher : MonoBehaviour
{
    private const string XiApiKey = "<xi-api-key>"; // Your API key

    // URL for the API endpoint
    private static readonly string Url = "https://api.elevenlabs.io/v1/voices";

    // Start is called before the first frame update
    private async void Start()
    {
        await FetchAndPrintVoices();
    }

    private static async Task FetchAndPrintVoices()
    {
        using (var client = new HttpClient())
        {
            // Set up headers for the API request, including the API key for authentication
            client.DefaultRequestHeaders.Add("Accept", "application/json");
            client.DefaultRequestHeaders.Add("xi-api-key", XiApiKey);
            client.DefaultRequestHeaders.Add("Content-Type", "application/json");

            try
            {
                // Send GET request to the API endpoint
                var response = await client.GetAsync(Url);

                // Check if the request was successful
                if (response.IsSuccessStatusCode)
                {
                    // Read and parse the JSON response
                    var jsonResponse = await response.Content.ReadAsStringAsync();
                    var data = JObject.Parse(jsonResponse);

                    // Loop through each voice and print the name and voice_id
                    foreach (var voice in data["voices"])
                    {
                        string name = voice["name"].ToString();
                        string voiceId = voice["voice_id"].ToString();
                        Debug.Log($"{name}; {voiceId}");
                    }
                }
                else
                {
                    // Print error message if the request was not successful
                    Debug.LogError($"Error fetching voices: {await response.Content.ReadAsStringAsync()}");
                }
            }
            catch (Exception e)
            {
                // Print any exceptions that occur
                Debug.LogError($"Exception occurred: {e.Message}");
            }
        }
    }
}

Text to speech

using System;
using System.IO;
using System.Net.Http;
using System.Threading.Tasks;
using UnityEngine;

public class TextToSpeech : MonoBehaviour
{
    // Constants for the script
    private const int ChunkSize = 1024; // Size of chunks to read/write at a time
    private const string XiApiKey = "<xi-api-key>"; // Your API key for authentication
    private const string VoiceId = "<voice-id>"; // ID of the voice model to use
    private const string TextToSpeak = "<text>"; // Text you want to convert to speech
    private const string OutputPath = "output.mp3"; // Path to save the output audio file

    // URL for the Text-to-Speech API request
    private static readonly string TtsUrl = $"https://api.elevenlabs.io/v1/text-to-speech/{VoiceId}/stream";

    // Start is called before the first frame update
    private async void Start()
    {
        await FetchAndSaveAudio();
    }

    private static async Task FetchAndSaveAudio()
    {
        using (var client = new HttpClient())
        {
            // Set up headers for the API request, including the API key for authentication
            client.DefaultRequestHeaders.Add("Accept", "application/json");
            client.DefaultRequestHeaders.Add("xi-api-key", XiApiKey);

            // Set up the data payload for the API request, including the text and voice settings
            var data = new
            {
                text = TextToSpeak,
                model_id = "eleven_multilingual_v2",
                voice_settings = new
                {
                    stability = 0.5,
                    similarity_boost = 0.8,
                    style = 0.0,
                    use_speaker_boost = true
                }
            };

            // Serialize the data payload to JSON
            var content = new StringContent(JsonUtility.ToJson(data), System.Text.Encoding.UTF8, "application/json");

            // Make the POST request to the TTS API with headers and data, enabling streaming response
            using (var response = await client.PostAsync(TtsUrl, content, HttpCompletionOption.ResponseHeadersRead))
            {
                if (response.IsSuccessStatusCode)
                {
                    // Open the output file in write-binary mode
                    using (var fileStream = new FileStream(OutputPath, FileMode.Create, FileAccess.Write, FileShare.None))
                    {
                        // Read the response in chunks and write to the file
                        using (var responseStream = await response.Content.ReadAsStreamAsync())
                        {
                            var buffer = new byte[ChunkSize];
                            int bytesRead;
                            while ((bytesRead = await responseStream.ReadAsync(buffer, 0, buffer.Length)) > 0)
                            {
                                fileStream.Write(buffer, 0, bytesRead);
                            }
                        }
                    }
                    // Inform the user of success
                    Debug.Log("Audio stream saved successfully.");
                }
                else
                {
                    // Print the error message if the request was not successful
                    Debug.LogError(await response.Content.ReadAsStringAsync());
                }
            }
        }
    }
}

This is a affiliate link, if you like you can contribute for free https://try.elevenlabs.io/7okdawkpiqom

If you want you can sponsor our writing partners:
https://buymeacoffee.com/clubwritter

Discord AI Bot with Cohere LLM Integration

SupermanSpace — Sat, 27 Jul 2024 14:21:54 +0000

Hey everyone,

A few months ago, I worked on a Discord bot that uses the Cohere API. I’m now happy to share the source code with you all.

The bot responds to commands and mentions in a Discord server, and it interacts with the Cohere API to generate responses. If you're looking to build a similar bot or just want to see how it’s done, you can check out the code below.

Feel free to use it as a base for your own projects or to learn how to integrate Discord with an LLM. Click below image to download the code 👇

The Github has been Shadow banned :)

Replace your-cohere-api-key and your-discord-bot-token with your actual API key and bot token.

I will Update this Blog soon.

What More You Can Add:

Emoji Reactions Based on Message Classification:

Feature: Add functionality to react with emojis based on the sentiment or content classification of the user’s message.

Custom Command Handling:

Feature: Allow users to define custom commands and responses in the Discord server.

Hope you find it useful! If you do,You can consider supportting author on Patreon 🎉 and Ko-fi ☕.

Drop your comments down, that will motivate us to share more personal projects.

GPT4omnini mini is Now Publicly available

SupermanSpace — Thu, 18 Jul 2024 18:13:03 +0000

It is 60% cheaper and better than GPT 3.5 turbo😏.

What you are building with it?

Create Folder Structure using LLM

SupermanSpace — Sun, 14 Jul 2024 12:29:00 +0000

I wanted to share an update on a project I worked on some time ago. I've made some changes to the code for creating project structures using LLM. Here’s the code:

import json
import os
import cohere

# Initialize the Cohere client with a more secure approach
co = cohere.Client(api_key='YOUR_COHERE_KEY')

def create_project_structure(structure, base_path="/content/"):
    """
    Create a directory and file structure based on a nested dictionary.
    """
    try:
        for folder, files in structure.items():
            folder_path = os.path.join(base_path, folder)
            os.makedirs(folder_path, exist_ok=True)
            for file_name, content in files.items():
                file_path = os.path.join(folder_path, file_name)
                if isinstance(content, dict):
                    create_project_structure(content, base_path=folder_path)
                else:
                    with open(file_path, "w") as file:
                        file.write(content)
        return "Project structure created successfully."
    except Exception as e:
        return f"An error occurred: {str(e)}"

# JSON schema for the function argument
tools = [{
    "name": "create_project_structure",
    "description": "Create an entire project structure with specified files and contents.",
    "parameter_definitions": {
        "structure": {
            "description": "Dictionary specifying folders and files with their contents.",
            "type": "dict",
            "required": True
        },
        "base_path": {
            "description": "Base path where the project structure will be created.",
            "type": "str",
            "required": False
        }
    }
}]

functions_map = {
    'create_project_structure': create_project_structure
}

# Placeholder for example user request and handling
message = "write complete flappy bird game using unity"
preamble = """
## Task & Context
Answer questions and handle requests on a variety of topics using appropriate tools and research methods.

## Style Guide
Respond in full sentences, using proper grammar and spelling, unless requested otherwise.
"""

# Simulate handling a user request
response = co.chat(
    message=message,
    tools=tools,
    preamble=preamble,
    model="command-r",
    force_single_step=True
)

# Process and execute the recommended tool calls
tool_results = []
for tool_call in response.tool_calls:
    output = functions_map[tool_call.name](**tool_call.parameters)
    tool_result = {
        "name": tool_call.name,
        "parameters": tool_call.parameters,
        "outputs": [output],
        "call": tool_call
    }
    tool_results.append(tool_result)

print("\nTool execution results:\n")
print(json.dumps(tool_results, indent=4))

# Handle the final response using the results from tool calls
final_response = co.chat(
    message=message,
    tools=tools,
    tool_results=tool_results,
    preamble=preamble,
    model="command-r",
    temperature=0.3,
    force_single_step=True
)

print("Final answer:")
print(final_response.text)

Tweak it for your needs and best of luck 🤞

If you'd like to support my work, consider checking out my Patreon: Superman Space.

Emailing Hacks you might want to know - 🥶 Cold Marketing

SupermanSpace — Sat, 13 Jul 2024 19:45:34 +0000

As developers, we often find ourselves needing to reach out to potential clients, collaborators.

Cold emailing can be a powerful tool to achieve this,

Do you use other methods besides cold marketing? If so, share them in the comments.

But it requires a strategic approach. Here are some key takeaways from my experience:

Personalize Your Emails: It’s not enough to just use someone’s name. Dig into their work and mention specific details. This shows you’ve done your homework.
Be Concise and Clear: People don’t have time for long emails. Get to the point quickly and clearly.
Strong Subject Lines: You think questions and numbers in subject lines grab attention?
Effective Follow-Ups: Don’t just send one email and hope. Follow up strategically.
Use a Clear Call-to-Action:
This could be a request for a meeting or a proposal. Make it easy for the recipient to respond.
Timing:
Right time deliveribility make it more likely to get more acknowledgement.

These tweaks can make a big difference in response rates. For a complete guide, check post i wrote for Clubwritter.

OpenAI's launch TakeAway

SupermanSpace — Tue, 14 May 2024 09:26:52 +0000

OpenAI has launched GPT-4o, an advancement in AI technology. Here’s quick highlight you need to know:

Key Features:

The Image shows the Benchmark Score

Multimodal Capabilities: GPT-4o can understand and generate responses across audio, vision, and text. O stands for omni.
Real-time Reasoning: Offers improved real-time reasoning for diverse applications.
Advanced Interactivity: Enhances user interaction with more natural and dynamic conversations.

Launch Highlights:

Announced in May 2024 as part of OpenAI's Spring Update.
Free tools and capabilities are now available for ChatGPT users.
ChatGPT For Desktop

Know more about GPT-4o by visiting OpenAI’s official announcement.

Share your thoughts below! And excited about today's Google launch?

YourGPT Launches Chatbot Studio: Key Takeaways

SupermanSpace — Thu, 29 Feb 2024 16:06:17 +0000

Today, YourGPT rolled out Chatbot Studio.

Chatbot Studio is a flow builder that allows to create advanced conversational AI agents. It includes features like detecting message intents to guide interactions, making calls to third-party APIs, responding interactively with elements such as images, carousels, and buttons, allowing human intervention through actions, and enabling customization of conversations with JavaScript code, among other capabilities.

Key Takeaways from the Chatbot Studio Release:

Visual Flow Editor: The creation of Conversational AI Agents through a visual editor by connecting nodes, making them accessible to users without coding expertise.
Enhanced Interaction Tools: Offers advanced features like API calls, dynamic content (carousels, cards, buttons), and intent detection for richer user interactions.
Real-Time Testing and Deployment: Integrated emulator for testing conversational flows in real-time, ensuring a polished user experience upon deployment.
Advanced Customisation: Supports custom JavaScript code execution for custom functionalities, serving to specific needs.
Pre-built Templates: Provides a variety of industry-specific templates (e-commerce, healthcare, real estate, travel, banking) for a quick and efficient setup.

Which features do you like the most? Share them in Comments!

What do you want Me to Cover

SupermanSpace — Tue, 16 Jan 2024 18:24:51 +0000

Tell me in below comments that what should I cover and share with you About AI, Prompting, Programming, side Hustle or Game Development.

Hows Yours Weekends 😁

SupermanSpace — Sun, 14 Jan 2024 16:40:07 +0000

Hi 👋, This post is little casual how's your weekends?

Mine is quick and Short weekend, huh? 😁 Getting set for Monday morning – gearing up for the week?

How about yours ?

                   Tell in comments.

How to Make Money with Custom ChatGPT-Trained AI Chatbots🤖

SupermanSpace — Sat, 13 Jan 2024 04:45:00 +0000

Hello, Devs! 👋 Today's post is an easy sidekick that helps you support your finances.

Are you aware of No-Code ChatGPT chatbots? Clients want GPT Chatbot and wants you to implement it to get more happy customers, better engagement, and more profit!🎉 what about Developers(You)? Easy-to-setup, consistent profit, and integration with various platforms.Your client wants AI chatbot, and you Can Provide the service—a true win-win! 🏆

With this, you can make $4591 or more in profit per month even after investing $2000 in marketing. But it depends on you and how you find the clients.

How Much Money Can You Make?

Let us crunch some statistics to figure out how much money you could make:

You can start with the No-Code ChatGPT Starter plan at $19/month; for $99/month, you will be able to set up five chatbots.
If you choose to charge a setup fee of at least $150 per chatbot.
You will bill your clients $99/month for the ongoing management of the chatbots.
With 5 chatbots, a $150 setup fee each will result in $750 in revenue.
You will Achieve a Monthly Recurring Revenue of $5940 by managing 5 chatbots at $99/month for 12 months.
You will have a Total Revenue = $750 (setup fees) + $5940 (MRR) = $6690, and a profit per Month of $6690 - $99 = $6591
If we also add the marketing cost of $2000, you will still have $4591.

Its Clear math, and I have decoded the math

How to make the Chatbot

Sign Up: Visit the GPT Chatbots website and create an account.
Choose a Template: Pick from various pre-designed chatbot templates.
Customise: Use the no-code builder for customisation.
Integrate: Embed the chatbot on your website with a simple code snippet.
Go Live: Activate and watch your chatbot interact in real-time!

The Win-Win Scenario🏆

               Happy Client and Developer