2025-12-05 21:15:40
DevOps and SRE teams rely on dozens of SaaS tools like AWS, Atlassian Cloud, Zoom, Slack, Okta, GitHub, Salesforce, and more.
So when even one of them goes down, operations can grind to a halt. And in those moments, teams need answers fast: What’s actually broken? Is it just us? Is it upstream? Is it a major provider meltdown?
But checking dozens of vendor status pages isn’t fast enough. Especially when many of them update late, inconsistently, or not at all. Turning to Downdetector doesn’t always help either.
During massive industry-wide outages, like the Cloudflare outage in November 2025 that knocked out half the internet, or AWS outages where cascading DNS failures took thousands of services offline, Downdetector reports become flooded with noise. You end up scrolling through unrelated user complaints just to figure out whether your service is affected.
Naturally, teams begin searching for Downdetector alternatives.
But here’s the catch: Most tools listed in search results or by AI as alternatives aren’t actually alternatives at all.
Downdetector monitors external SaaS vendors, not your infrastructure. It combines:
This allows it to detect issues in services you cannot instrument.
Tools like Datadog, Site24x7, UptimeRobot, and ManageEngine are often suggested as alternatives, but that’s incorrect.
They specialize in:
They do not:
You cannot run a synthetic check against Slack’s messaging backend.
You cannot instrument Google Workspace authentication.
These tools were never designed for vendor status aggregation.
Relying on individual vendor status pages creates operational blind spots:
It doesn’t scale when you depend on dozens of third-party services.
A true Downdetector alternative must:
StatusGator does exactly this, centralizing over 6,000 services into one operationally useful system. Additionally, you get early outage alerts, even before your vendors acknowledge the outage on their official status page. So StatusGator provides crowdsourced information, similar to Downdetector, but also tracks other signals apart from user reports that can be false positives.
StatusGator integrates with Slack, Discord, Microsoft Teams, and other messengers to automate outage notifications and keep track of status updates without the noise or checking the status pages manually.
If your stack relies on many external dependencies (and everyone’s does), StatusGator is your go-to Downdetectcor alternative, not Datadog, not Site24x7, not UptimeRobot.
2025-12-05 21:15:12
2025-12-05 21:13:29
You have instant access to all of human knowledge. You can connect with anyone, anywhere, at any time. So why do you feel more scattered, anxious, and reactive than ever before? This is the great paradox of the digital age. We built a world for connection and ended up with a machine for distraction.
Let's be direct. The digital world was not designed for your peace of mind. It was engineered to capture and sell your attention. The infinite scroll, the push notifications, the algorithm that knows you better than you know yourself—these are not neutral tools. They are precision-engineered triggers designed to hijack your brain's ancient reward system.
Every ping is a promise of novelty. Every 'like' is a hit of social validation. We are living in an invisible Colosseum, and our focus is the price of admission. The Stoics—philosophers like Marcus Aurelius, Seneca, and Epictetus—faced a different world, but the exact same internal battle. They understood the fundamental difference between what is in our control and what is not. In the 21st century, this is the master key to digital freedom.
The algorithm is not your master; it is a tool. Use it, don't let it use you.
Stoicism isn't a passive philosophy of endurance; it's an active system for building an unbeatable inner citadel. Here is how you apply its principles to the digital battlefield.
What you CANNOT control: The algorithm, breaking news, what other people post, the design of an app.
What you CAN control: Your notification settings. Who you follow. When you pick up your phone. How long you use an app.
Stop fighting the things you can't change. Focus with ruthless intensity on what you can. Turn off every single non-essential notification. Mute and unfollow aggressively. Your attention is your most valuable asset; protect it like a fortress.
Seneca wrote, “It is not the man who has too little, but the man who craves more, that is poor.” The modern translation: You do not need to know everything, right now. The fear of missing out (FOMO) is a marketing gimmick for your attention. True power is the ability to choose what to ignore.
Define what information is critical for your life and your goals. Consume that. Ignore the rest. Schedule your consumption—check the news once a day, process email in deliberate batches. The world will keep spinning without your constant oversight.
Stop chasing information and start cultivating wisdom. One is noise, the other is signal.
A Stoic doesn't just rely on willpower; they shape their environment to make virtue the easy choice. Your phone's home screen is your digital environment. Is it a sanctuary for focus or a slot machine for distraction?
Actionable steps:
• Move all social media and news apps off your home screen, into a folder.
• Access distracting services through a web browser. The extra friction is a feature, not a bug.
• Use grayscale mode to make your screen psychologically less appealing.
Don't just endure the digital world. Accept it for what it is (Amor Fati) and then architect your engagement with it on your own terms. Stoicism is not about escaping the world; it's about building an internal state that is immune to its chaos. The digital world is the ultimate training ground.
Visual by Think Addict System.
2025-12-05 21:13:05
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📖 AWS re:Invent 2025 -Greenfield unlocked: Best practices for expanding new customer engagement-PEX206
In this video, Ethel Garcia and Rehan Shah from AWS introduce the Partner Greenfield Program, a multi-year initiative designed to help partners acquire new AWS customers. They define Greenfield as customers new to AWS or minimally engaged, spanning enterprise to SMB segments. The session emphasizes using GenAI and agentic AI as entry points, noting that 53% of Greenfield customers lack AI skills and 25% of enterprises aren't AI-ready. Four key engagement principles are outlined: leading with business outcomes, systematic customer journeys, strategic AWS alignment, and integrated solutions. A case study features partner Tropole modernizing a mechanical/electrical/plumbing enterprise using Amazon Bedrock and SageMaker. The new Partner Greenfield Program offers contractual agreements, multi-year support, enablement, and incentives to build sustainable Greenfield practices rather than opportunity-level approaches.
; This article is entirely auto-generated while preserving the original presentation content as much as possible. Please note that there may be typos or inaccuracies.
Customer acquisition is hard and can be expensive. I know a thing or two about that, so I have a few folks coming to help you with acquiring new customers, which of course can be a challenge for all organizations. Without further ado, I'll introduce my friends. Thank you everyone.
Hi everyone and thank you so much for spending the next 20 minutes with us. My name is Ethel Garcia and I lead partner strategy for GTM, which includes Greenfield, SMB and our indirect model. I'm here today with Rehan Shah who is the head of partner sales for Greenfield. Before getting into the agenda, let's talk about what Greenfield really means.
When we talk about Greenfield, we talk about new customers for AWS. This means we're not only talking about new logos for you, but also customers that are engaged a little bit with us and consuming some of those services but are not fully engaged on the AWS services journey. Greenfield has a definition that could be slightly different from what new logos might mean for you as a partner.
We also talk about Greenfield as the full Greenfield opportunity across the entire customer segments. This ranges from big companies and enterprises that are not yet into their AWS journey to mid-market to small and medium businesses and public sector. The landscape is really quite big.
Today we're going to be talking about how we partner with you to engage with these new customers together. We're going to be talking about a specific use case that, if you saw the keynote this morning, has a lot of connectivity with that. There has been a lot of time spent talking about AI, the opportunity, agentic AI, the software, the hardware, the partners, and the customers that are today looking for how to embrace that technology inside their organizations.
We're also going to talk about how this is different across segments. It's very different to go after new to AWS customers in the enterprise space than if you're going after an SMB customer. You need to have different tactics, different go-to-market strategies, and different solutions. Always keep in mind that we're talking about new to AWS customers when you think about engaging with them.
We're also going to talk about how we're going to help you during that journey. We're very excited to announce here at this event a new Greenfield partner program that will bring tools, automated systems, benefits, incentives, and enablement to make sure that you understand how to go to market together with us. Let's start that journey.
Across the entire event you've probably seen a lot of stats about GenAI and how we're growing agentic AI adoption. But for Greenfield specifically, we're looking at those new customers to AWS. We're working with a very specific firm that is helping us research called Synoff. Synoff has gone through deep dives with big customers and they found that 53% of the database that we call Greenfield still don't have the AI skills to secure agentic AI or GenAI usage. Partners are front and center to help us fill that gap.
There's still a lot of legacy systems we can talk about migration, of course, which is another point of entry. But that comes later in the game. When you think about Greenfield, the first workload is critical. 25% of enterprises still don't have their infrastructure prepared for AI. When we talk about SMBs, they're investing massive amounts of money for testing, piloting, and understanding how to use AI and GenAI better than in previous years.
Last but not least, there's the topic of autonomous decisions. Gartner said that autonomous decisions are going to be made with agents in a couple of years, with 15% of them being made by agents. What does that really mean? It means we need to prepare these customers with all these opportunities through partners to be ready for that. 15% is just the beginning. Two years come very fast, so I'm sure that is going to be growing eventually.
How do we connect this agentic AI or GenAI opportunity with Greenfield?
We have been doing a lot of things over the last couple of years. Partners like you have been working with us very closely, engaging with new customers across all customer segments, and we have learned a lot from our pilots. We have new initiatives across geographies, depending on the market and the size of the customers. There are four things that we have proven and tested that are really important when we engage with customers.
The first one is to live with business outcomes. We cannot go to a new customer with a solution and talk about technology first. It is not about QuickSight. It is not about SageMaker. It is about what business problem we are going to solve, and then the technology might come after. Business transformation is the type of topic that we need to start discussing.
The second one is a systematic customer journey with clear scope. When we go after a greenfield opportunity, we have to be very concrete and very knowledgeable about what exactly the offer is that we are going to land with those customers. It needs to be scalable, predictable, and repeatable so that the customer gains trust in how we execute. They can calculate the ROI and be confident that the outcome will come in a short timeframe. Going back to the first workload is critical to engage with that customer first. In the majority of cases, after that first workload, something comes next. What is the second workload? What do we want to do? Eventually, every greenfield customer could become a migration customer, but migration usually is not the first workload. That is why Agentic AI is a tremendous opportunity as a point of entry with any customer.
The third one is strategic AWS alignment. Customers are very smart and savvy, and they know that we have a business path with AWS, that we understand their technology, and that we work closely with them. So they want us to be knowledgeable about how to leverage AWS programs and how to get the best out of the benefits so they can offset the cost of that first workload. We can work with them in case additional help is needed, and we know what to do in case they expand the blueprint. Working with AWS is a very important combination of things.
And then, last but not least, this conversation has come up in most of my meetings this week already. Greenfield and acquiring new customers is about integrated experiences and solutions. It needs to be seamless. That customer is going to come to us as a partner about the experience and how to do a project or how to solve a business outcome. It is not going to be in one project. We might need to bring ISVs with us on that journey. We might need to bring other partners on the journey. They do not mind. They are going to look to us to do that. But when we think about those four steps, they could actually vary depending on the customer segment that we go after.
We also recommend being very specific when we think about Greenfield. Are we going after the higher end of the enterprise, or are we going after small businesses? The differences are very big between those two. So those four steps are very different as well, and I am going to let Rehan, with her experience, talk about those customer segments individually.
So I will get into a little bit about how we think of greenfield by segmenting customers. We have large enterprises, medium, and small customers, and while their needs are the same, they are a little bit different. Think of the large enterprises where we are dealing with complex legacy workloads that are on-premises and need to be modernized. These require us to show up with a change management practice, really a lift and shift and also modernization. There is a lot of complexity in there, and just understanding and unraveling that creates an outcome for the customer.
In the mid-size segment, we may be dealing with organizations that want to adopt AI but do not have the resources. We need our expertise to figure out what those use cases are and help them accelerate business outcomes. In the SMB segment, lacking all the resources, our ability to scale our solutions makes a huge difference for the customer. Building a go-to-market that scales across customers, whether by industry or across small and medium-sized customers, combined with the COAL methodology and a mental model of working with AWS in the field, is super important to scale our business.
Now let me talk about a customer use case. Our partner Tropole, which is a systems integrator partner, is working with an enterprise-size organization that is really focused in the mechanical, electrical, and plumbing space. The customer had some unique needs here with legacy systems that they are trying to modernize.
Along with the usual challenges that come with that, the customer was looking for ways to modernize their approach, so their entire go-to-market strategy and workflows needed to be re-examined and reformed. In this case, Tropoli stepped in and helped the customer build a smart asset management system to expedite their lifecycle management by leveraging Amazon Bedrock and SageMaker running on top of it. Additionally, they developed an AI-driven log incident report system to provide faster resolution for the customer.
By combining the business transformation, reforming the workflows, and deploying a new lifecycle asset management system along with the AI-driven incident management, they really helped transform the full go-to-market for this traditional large enterprise organization and positioned them to create a successful outcome for the customer. This is kind of the reverse order of what to do—this is what not to do as you're thinking about how to scale. Do not ignore the self-guided small customer. Invest in your marketing and invest in your SEO approach so that customers who are not having interactions with sales are talking to your marketing, and your marketing is leading them to landing pages. How you guide them through that process digitally and automatically really creates an outcome for the customer. Be thoughtful of that, as it represents the majority of the SMB segmentation.
The second consideration is understanding existing relationships. You may be dealing with customers that have deep-seated relationships with a legacy partner that may still be fixated on software resale or hardware resell. You're brought in to help them modernize not just their go-to-market but even their stack. As you do that, be thoughtful about staying with the customer long term. It may appear in the shorter run that this go-to-market is not profitable, with a twelve to eighteen month sales cycle, especially when you're dealing with enterprises. However, the ROI comes when the customer sees the value and starts to adopt agentic AI, when the AI motion starts moving their data to the cloud. That's when you also unlock long-term profits and alignment and create outcomes for the customer.
The third consideration is having a go-to-market that starts with AI first. As Ethel talked about this and Matt Gartman covered in detail today regarding the AI wave and our strategy and how we're aligning, be very deliberate about having an AI business model and how you realign your organization, your go-to-market messaging, your offerings, your solution, and your approach to be AI first. This positions you as the thought leader that's helping customers unlock new use cases and creating value in the business, which is super critical. You may be wondering how to get started with this Greenfield partnership. A couple of obvious things are to have a go-to-market strategy and to make sure that you're leveraging what we have for you. The Partner Greenfield Program that we're talking about is one where you get all the leverage. Whether it's that or you leverage Greenfield funding specifically for MAP, whatever that may be, be very deliberate about knowing what you have access to, have a go-to-market, have your head count dedicated to that, and have your paths to being a profitable business around Greenfield, knowing it's a long-term business.
Let me double-click a little bit about integrating AI into a partnership. I want you to think about this at scale across enterprise organizations, mid-enterprises, and SMBs. How do you build a go-to-market that aligns with agentic AI? Essentially, you're the one that's helping the customer unlock the use case. Before you unlock the use case, you have to get them there. You have to show them some business value for them to invest any time or resources with you. A good starting point is having that discussion, talking about the ROI, and helping customers rationalize what use cases relevant to their industry will yield a faster ROI for them.
Through that process, as the partner of choice, you help the customer stand up a POC and white-glove that experience for the customer. You stay with that customer from the POC conversion to actually landing into a workload that's creating an outcome for the customer. As you do that, there are many mechanisms on our side. We have GenAI funding for Greenfield AI and the Partner Greenfield Program. All these incentives are in place for you as a partner to lean in and hand-hold the customer through the POC phase and really stick with the customer. All right, this is where the fun begins. Now you know how to approach Greenfield and what you're going to use, agentic AI as an example.
This week we're presenting a new approach for Greenfield called the Partner Greenfield Program. The way we're approaching this is different from what you've done before. Previously, you approached new customers from an opportunity level standpoint, leveraging funds using programs that help you per opportunity. This is a new way to approach Greenfield. Think about this as a sustainable, multi-year way of building a Greenfield best business practice. Think about business practice from a partnership standpoint versus an opportunity level standpoint.
Greenfield is tough. We all know that Greenfield takes more time, has longer life cycles, and higher cost of sale. We want to make sure that we help you and we co-invest together to build a foundational practice within your company. You're going to own it end to end. We're going to help you build that. You're going to leverage existing programs for opportunity as well. Yes, you can do that, but we're going to bring together the capability, the guidance, the enablement, and additional benefits in a multi-year fashion.
This program is based on contractual agreements. You're going to sign up for wins. You're going to sign up for specific targets, and we're going to help you on that journey. It means that you're already ready. It means that you're doing Greenfield already, but this takes Greenfield to another next level. It's very hard for two reasons. We always think in year. We have targets in year and we have quota in year. We want to make sure that we stay with you through the journey of at least three years. We want to make sure that we can leverage the enablement and the benefits during a period of time that is going to be enough for you to build that practice.
Why not maybe you can create a PNA? Based on this, you will learn how to build that along the way and you can become a subject matter expert on acquiring or engaging with new customers at AWS. Now don't do that across the entire pyramid. Let's start from the beginning. What is your subject matter expertise? Is this about Agentic AI solution? That's a great point of entry. Is it about security? That's another great point of entry. It's about what do you have? What's your area of expertise? What's your capability? Making that capability simple enough to help engage with new customers.
So we're starting from the build side of it. We'll help you make sure that that solution is actually good for Greenfield customers. We're going to give you incentive across different layers to build your go-to-market motions, and we're going to help you sell with our co-sale teams, specific teams that are focused with sellers on Greenfield. And then it doesn't stop on Greenfield. We want you to grow that customer. When that customer moves to that phase that we call engaged, it doesn't mean that the work is being done. You need to keep working with that customer. What's the second workload? What's the third workload? Is there a migration coming? That's really important.
This is a little bit of the journey. This is a high bar program. You need to understand yourself if your company is ready for that, which is okay. If your company is not ready today, that's okay as well. We can help you through the journey of being okay. You don't need to sign up for something that you feel you're not ready for, absolutely, but you can start building that muscle and maybe in a few months or in a year or so you can actually jump into that.
I challenge you all to evaluate where you are on that Greenfield journey. Let us know. Talk to your PDM, to your AM, to your PSMs. Talk to us, and we'll figure it out together. So first of all, we'll identify the solutions that you might have in the market to go after these new Greenfield customers. We have a ton of data that we might share in this program to understand what works, what doesn't, and what is being done. Can you partner with another entity, other partner, or is there a solution? And then let's go to market together with value props that talk about the same. If we're launching something with an AWS framework, let's launch it with the same flavor. If we're going after new customers, let's leverage other programs.
At an opportunity level, if there's going to be migration as part of that Greenfield approach, let's leverage MAP as well as part of that journey. Let's leverage POC that's already there. And then finally there's call to action. We have opportunities ready for you. This gentleman does have those opportunities in his pocket.
So listen, first take advantage of this opportunity to sign up for PGP before you leave. All of you should do that. It's the first call to action. Now that being said, lean in with the AWS sales teams, your partner sales managers, your account managers. They have a pipeline with no partner attached, but it's aligned by either industry or use case. So show up as that thought leader for GenAI. Show up as that partner who creates customers' outcomes and accelerates outcomes for customers, leading with the GenAI motion. That's super important for you.
But I hope you're as excited as we are. Sign up for PGP and thank you for joining us. Appreciate it. Thank you.
; This article is entirely auto-generated using Amazon Bedrock.
2025-12-05 21:12:17
ELIZA EXHUMED is more than a hackathon project. It's a demonstration of how far we've come since 1966, and how quickly we can build with modern tools.
What if you could compare 1966 AI with 2025 AI, side by side, in the same conversation?
Picture this: It's 1966!
Joseph Weizenbaum creates ELIZA, one of the first chatbots ever made:
A simple pattern-matching therapist that somehow convinced people it understood them. Fast forward to 2025, and I'm sitting at my computer thinking: "What if I could resurrect ELIZA... but make it spooky?"
Well...that's how ELIZA EXHUMED was born (or should I say reborn): a horror-themed chatbot that lets you toggle between the original 1966 pattern-matching algorithm and modern AI powered by Amazon Nova Premier. Built entirely with Kiro in just 4 days (well, two hours per day, therefore, in 8 hours!)for the Kiroween 2025 Hackathon.
The core concept is simple but powerful: What if you could compare 1966 AI with 2025 AI, side by side, in the same conversation?
In Traditional Mode, ELIZA uses the original pattern-matching algorithm.
No Machine Learning.
No Deep Learning.
No Neural Networks.
No Transformers.
Just pure regex and reflection:
// When you say: "I feel scared"
// ELIZA matches the pattern and reflects:
"The shadows whisper that you feel scared... tell me more"
// When you say: "My family worries me"
// ELIZA transforms pronouns:
"The spirits of your family... what haunts you about them?"
I implemented 60+ conversation patterns with spooky theming. Every response drips with atmospheric horror while maintaining that classic therapeutic vibe.
Toggle the switch, and ELIZA transforms. Now powered by Amazon Nova Premier (us.amazon.nova-premier-v1:0), the chatbot becomes a truly intelligent, context-aware therapist with a horror personality.
The system prompt instructs Nova to be:
The difference is night and day.
Traditional ELIZA might say: "Tell me more about your mother."
Nova-powered ELIZA says: "Your mother's shadow looms large in your psyche... what memories surface when you speak her name?"
Here's where it gets interesting. I didn't write 5,700+ lines of code manually. I used vibe coding with Kiro: describing feelings and outcomes rather than implementation details.
Me: "I want to build a spooky chatbot for Kiroween that resurrects 1966 ELIZA but makes it scary and modern"
Kiro: Creates complete project architecture
In 30 minutes, I had a full blueprint. No boilerplate hell. No "should I use Express or Fastify?" debates. Just a working architecture.
Me: "Integrate AWS Bedrock Nova Premier for chat"
Kiro: Generates 400+ lines of production-ready code
I didn't write a single line of AWS SDK boilerplate. I just described what I wanted, and Kiro generated production-quality code with proper error handling.
This is where vibe coding truly shined.
Me: "I want hyperrealistic graphics with particles and AI-generated horror images"
Kiro: In ONE response, generates 1,100+ lines of code
Graphics Service (400+ lines):
Three.js Particle System (200+ lines):
Hyperrealistic CSS (500+ lines):
All of this. In ONE conversation turn. And it worked perfectly on the first run.
Me: "The follower eyes are too big and distracting"
Kiro: Instantly updates CSS - size reduced by 50%, opacity lowered
Me: "Add a massive ghost that rises when switching to LLM mode"
Kiro: Creates 20rem ghost with resurrection animation, screen shake, flash overlay
Me: "Perfect!"
This is the beauty of vibe coding. Quick iterations with natural language.
No hunting through CSS files.
No "where did I put that animation?"
Just describe the feeling, and Kiro implements it.
I chose Amazon Nova Premier for several reasons:
Nova Premier consistently delivers responses in under 3 seconds. For a chatbot, that's the difference between feeling conversational and feeling broken.
// Average response times in production:
// Traditional Mode: ~50ms (pattern matching)
// Nova Premier: ~2.5s (full LLM generation)
// User perception: Both feel instant
Nova Premier maintains conversation context beautifully. It remembers what you said 5 messages ago and weaves it into responses naturally:
User: "I'm afraid of the dark"
Nova: "Darkness... a primal fear. What lurks in those shadows?"
[3 messages later]
User: "I can't sleep"
Nova: "The darkness you mentioned earlier... does it follow you
into your dreams? What keeps you awake in those haunted hours?"
With the right system prompt, Nova Premier maintains the spooky therapist personality perfectly. It's empathetic but atmospheric, helpful but haunting.
For a hackathon project that might get hundreds of users, Nova Premier's pricing is reasonable. No surprise bills. (I hope)
One of the coolest features is the AI-generated horror imagery. Here's how it works:
1. Try AWS Bedrock Stable Diffusion XL
↓ (if fails)
2. Try OpenAI DALL-E 3
↓ (if fails)
3. Try Unsplash API
↓ (if fails)
4. Generate procedural SVG
This ensures users ALWAYS see atmospheric imagery, even if APIs are down.
The system has 6 horror themes:
When you chat, the theme shifts based on conversation tone. Talk about fear? Ghost theme. Mention anger? Demon theme. The visuals adapt to your emotional state.
If all APIs fail, the system generates SVGs programmatically. Here's the ghost generator:
private generateGhostSVG(): string {
return `
<svg viewBox="0 0 400 400">
<defs>
<radialGradient id="ghostGlow">
<stop offset="0%" stop-color="#9d4edd" stop-opacity="0.8"/>
<stop offset="100%" stop-color="#240046" stop-opacity="0"/>
</radialGradient>
</defs>
<ellipse cx="200" cy="200" rx="80" ry="120"
fill="url(#ghostGlow)" opacity="0.6">
<animate attributeName="ry" values="120;130;120"
dur="3s" repeatCount="indefinite"/>
</ellipse>
<!-- Eyes, mouth, floating animation -->
</svg>
`;
}
Even without external APIs, users get animated, atmospheric graphics.
My favorite feature: When you toggle from Traditional to LLM mode, a massive ghost (20rem tall) ascends from the bottom of the screen with:
It's pure theater. It makes the mode switch feel like you're actually resurrecting ELIZA's spirit.
@keyframes resurrect {
0% {
bottom: -25rem;
opacity: 0;
transform: scale(0.5) rotate(-10deg);
}
50% {
opacity: 1;
transform: scale(1.2) rotate(5deg);
}
100% {
bottom: 50%;
opacity: 0;
transform: scale(1.5) rotate(0deg);
}
}
Backend:
Frontend:
Infrastructure:
Why no React/Vue/Angular? For a project this focused, vanilla JS is faster and lighter. No build complexity, no dependency hell, just pure web fundamentals.
I used both structured specs and vibe coding:
Created in .kiro/specs/spooky-eliza-chatbot/:
requirements.md: 6 user stories, 30 acceptance criteria
design.md: Complete architecture
tasks.md: 7 major tasks, 23 subtasks
While the spec ensured completeness, vibe coding added magic:
Result: The spec ensured I built ALL required features. Vibe coding made them AMAZING.
Development Time: 4 days (would have taken 2-3 weeks manually)
Code Generated: 5,700+ lines
Time Saved: ~80%
Files Created: 35+
Technologies Integrated: 10+
Most Complex Single Generation: 1,100+ lines (graphics system)
Conversation Turns: ~60
Production Status: Live on Render
Describing outcomes ("make it spooky") is faster than specifying implementation ("add CSS animation with keyframes that..."). Kiro translates feelings into technical solutions.
Specs ensure completeness. Vibe coding enables creativity. Use both.
Fast, context-aware, cost-effective. Perfect for real-world applications.
Never rely on a single API. Build graceful degradation from day one.
1000+ particles at 60fps, <5s response times, <200ms mode switching. Users notice smoothness.
For focused projects, skip the framework. Faster development, lighter bundle, fewer dependencies.
Demo: eliza-exhumed.onrender.com
Please Note: I am using the Free Version of Render, so the app might not show up after 15 minutes of inactivity, just wait for Render to render it, also CloudFare has been acting up the whole week, let's hope it doesn't ruin your experience with a random client side error. ok, bye!
# Clone the repo
git clone https://github.com/Ntombizakhona/eliza-exhumed.git
cd eliza-exhumed
# Install dependencies
npm install
# Set up environment variables
cp .env.example .env
# Add your AWS credentials and Bedrock model
# Build and run
npm run build
npm start
# Visit http://localhost:3000
Here's the truth: I'm not a frontend wizard. I'm not a Three.js expert. I've never built a particle system before.
But with Kiro and Amazon Nova, I built a production-ready app!
That's the power of vibe coding with Kiro and the intelligence of Amazon Nova Premier.
ELIZA EXHUMED is more than a hackathon project. It's a demonstration of how far we've come since 1966, and how quickly we can build with modern tools.
Joseph Weizenbaum created ELIZA to show the superficiality of human-computer interaction. Ironically, people formed emotional connections with it anyway.
In 2025, with an Agentic IDE like Kiro and LLM like Amazon Nova Premier, we can create AI that's genuinely helpful, contextually aware, and emotionally intelligent. But we can also make it spooky. 👻
The spirits are waiting. Will you chat with them?
Built for: Kiroween 2025 Hackathon
Built with: Kiro AI IDE + Amazon Nova Premier
Built by: Ntombizakhona
Want to build something amazing with Kiro and Amazon Nova?
Start with a vision, describe the vibe, and let the magic happen. Happy coding (I meant happy vibing!) 🎃
2025-12-05 21:11:10
(with Demo Video, Screenshots & How Kiro Helped Me Build It)
For a long time, I wanted a tool that didn’t punish emotions — but rewarded them.
Something gentle, soft, low-sensory, and kind. Something that would let people (including me) practice emotional regulation in a way that felt playful instead of heavy.
That’s how EmoChild was born — a modern reinterpretation of the 1996 Tamagotchi, but instead of feeding or cleaning it…
you help it grow by taking care of your own feelings.
🎥 Demo Video
(https://youtu.be/P1iFQBk0_BM)
🟣 Live App: https://emochild.vercel.app
🟣 GitHub Repo: (https://github.com/olivia-tech-psych/emochild)
🟣 Built with Kiro: https://kiro.dev
I grew up feeling like emotions were “too much.” As an adult — especially studying psychology — I realised how common this is. A lot of people don’t know how to express or name what they’re feeling.
Sometimes we suppress. Sometimes we disconnect. Sometimes we forget we even have needs.
I wanted to build something simple enough for anyone, but still grounded in real cognitive science:
So EmoChild gives you a tiny creature — your inner child — that grows brighter and bigger when you express a feeling… and curls inwards when you suppress it.
It mirrors the inner world so many of us never learned to notice.
Kiro wasn’t just a “helpful AI assistant” — it became part of my workflow in a deeper way. EmoChild exists because Kiro gave me structure when the project could’ve become chaotic.
Every feature in EmoChild started as a Kiro Requirement, expanded into a Design Spec, and finally broken into Tasks I could implement one by one.
It kept me grounded.
Instead of coding random ideas, I followed a roadmap that I had written, but Kiro helped me refine.
Spec benefits:
I ended up with two full versions of specs (v1 + v2), covering landing page, color customization, micro-sentences, quick emotion logs, and more.
This was honestly one of the most unexpectedly helpful parts of the whole build.
I only used one Agent Hook — the one that updates my README automatically whenever core folders change — but even that single hook felt like having a tiny automated teammate living inside my repo.
Even though the hook was simple, the feeling of having a self-maintaining project was profound — like the codebase was growing with me, not just because of me.
It felt like the project was co-built through a real cognitive partnership.
This is where building became fun.
Kiro let me describe components the way I felt them:
“Make the creature’s curl animation look like a shy withdrawal, soft and slow.”
“Make the micro-sentences appear like tiny comforting whispers.”
“Make the pastel palette gentle for sensory-sensitive users.”
Kiro translated these emotional vibes directly into real UI/UX code.
The result:
A UI that feels alive, soft, and very “inner-child-safe.”
Below are the screenshots with deeper, personal captions.
This is the first thing you see — intentional soft colors, gentle gradients, and a sentence that invites you to slow down.
I wanted it to feel like a breath.
I added customization because self-connection takes time.
Naming the creature is the first step of forming a relationship with your inner world.
The creature is intentionally round, non-human, and non-threatening — so nobody feels judged.
Its states mirror core nervous system states: expansion, contraction, stillness.
Some days we can type.
Some days we can only tap.
Both are valid.
These were deeply personal to write — they’re based on real validation strategies I often wish I had growing up.
The history isn’t for “productivity tracking.”
It’s for noticing patterns in your nervous system over time.
This makes the app therapy-friendly and research-friendly.
And emoji-safe encoding was… harder than I expected. 😅
Because identity changes.
And so does your inner world.
I want to extend EmoChild with:
But even now, EmoChild is something that feels meaningful to me.
If even one person feels seen or supported by it, it’s worth it.
This app was more than a hackathon submission.
It was a way of healing, expressing, creating, and learning — all at once.
If you try EmoChild, I hope you feel even a little more connected to yourself.
🟣 Live App: https://emochild.vercel.app
🟣 GitHub Repo: (https://github.com/olivia-tech-psych/emochild)
🟣 Built using Kiro: https://kiro.dev
🎥 Demo Video: (https://youtu.be/P1iFQBk0_BM)