**From the Code & Cognition Podcast by Olio Apps**

At Olio Apps we have been steadily exploring the field of Gen AI development and building software with the assistance of Gen AI tools. From building MVPs to benchmarking LLMs head-to-head with internal developer tools, we’ve been hands-on with testing the bleeding edge of generative AI.

In this post, we wanted to share our firsthand experience using Gen AI to speed up prototyping, write production-quality code, and even generate infrastructure diagrams for cloud-native systems.

Whether you're scaling a dev team or experimenting solo, we wanted to show how using Gen AI in software development can looks like in practice.


---

## Prototyping Software with GenAI: What Actually Works (and What Doesn’t)

We started with a challenge: Could we build usable software prototypes for apps, games, and UI using only natural language prompts and GenAI tools?

We tested tools like **Bolt.new**, **v0.dev**, and **Claude Sonnet** by asking them to create:

<ul style="list-style: disc; padding-left: 1.2em;">
 <li style="margin-bottom: 1em;">
 Classic games like Snake and Centipede
 </li>
 <li style="margin-bottom: 1em;">
 A bizarre Pac-Man/Snake hybrid with real-time ghost movement
 </li>
 <li style="margin-bottom: 1em;">
 A Reddit-style message board with image uploads, nested comments, and voting
 </li>
</ul>

Some prompts worked out of the gate. Others fell apart - usually when backend integration introduced complexity. But what we learned was this: prompting GenAI is like managing a junior engineer. You don’t drop a wall of specs. You guide it step-by-step, providing context and corrections as you go.

It’s fast. It’s messy. But it’s getting better every week.

---

## How We Prompt, Collaborate, and Ship with GenAI

We've tried out "vibe coding" for ourselves and after giving it a shot, we have determined some rules of thumb that help make it a usable methodology. 

It’s less about writing perfect specs, and more about co-creating with AI in a fluid, conversational way.

Some principles that emerged from our sessions were:

<ol style="list-style-type: decimal; padding-left: 1.5em; margin: 1em 0;">
 <li style="margin-bottom: 1em;">
 Treat the AI like a team member: ask clarifying questions, give feedback, iterate
 </li>
 <li style="margin-bottom: 1em;">
 Don’t front-load everything: break tasks into smaller units, step by step.
 </li>
 <li style="margin-bottom: 1em;">
 Include background and context: we got better results by naming the tech stack, conventions, and expected file structure
 </li>
 <li style="margin-bottom: 1em;">
 Version control everything: make frequent commits. One wrong suggestion can undo hours of work.
 </li>
</ol>

Following these principles helped elevate our experiments beyond mere vibes into successful Proofs of Concepts and MVPs. 

---

## We Tried Benchmarking Claude 3.5 Sonnet vs. Gemini 1.5: Here’s What Happened

We were curious which LLM is actually better at coding, so we used [WebDevArena] (https://web.lmarena.ai/), a head-to-head testing ground, to compare **Claude 3.5 Sonnet**, **Gemini 1.5 Pro**, and **DeepSeek** on real coding tasks.

We ran each model through the same prompts - mostly game development and UI logic.

<ul style="list-style: disc; padding-left: 1.2em;">
 <li style="margin-bottom: 1em;">
 Claude 3.5 Sonnet came out ahead, producing clean, readable code with far fewer hallucinations.
 </li>
 <li style="margin-bottom: 1em;">
 Gemini 1.5 struggled with complex logic in games like Centipede and Snake.
 </li>
 <li style="margin-bottom: 1em;">
 DeepSeek (open-source) surprised us. It wasn’t perfect, but showed promise as a lightweight assistant.
 </li>
</ul>

These kinds of direct, practical benchmarks gave us confidence when evaluating which models to use in different parts of our stack, whether as coding copilots or agents for internal developer tooling.

---

## AI Generated Architecture: How We Design Cloud Systems in Minutes

We’re heavy users of Amazon Web Services and one of our pain points has always been visualizing and communicating infrastructure, especially when iterating quickly.

That’s where **DiagramGPT** and **Eraser.io** came in.

> API Gateway triggers a Lambda → writes to DynamoDB → sends a message to an SQS queue → triggers a second Lambda that sends email via SES

And within seconds, we had a polished AWS diagram, complete with icons and connections.

Compared to Miro or Lucidchart, this process saved considerable time and made it easier to collaborate with clients and team members during architecture reviews. We’re now integrating it into our solution design workflows for both consulting and internal projects.

---

## Final LLM Dev Tip: Don't Break the Illusion

**System prompts matter** 

If your app’s identity relies on LLM behavior, run forensic checks and ensure your system prompt is structured, clear, and hard to break.

You may find some LLMs have an easier time holding on to your system prompt so it is good to test and compare.

---

## Additional Insights: Database Modeling and Cursor System Prompts

Two other tools we explored that deserve a shoutout are:

<ul style="list-style: disc; padding-left: 1.2em;">
 <li style="margin-bottom: 1em;">
 database.build Generated SQL schemas and ER diagrams from casual prompts like “a message board with users, posts, comments, votes, and favorites.” Surprisingly accurate.
 </li>
 <li style="margin-bottom: 1em;">
 Cursor We dug into its **system prompt** (yes, you can view it) to understand how it balances user intent with safe code editing. The result? Our ability to customize our dev environment so we can prompt edits with confidence.
 </li>
</ul>

For full-stack prototyping, the combo of schema generation + AI-assisted editing is starting to feel like the future of scaffolding software.

## Final Take: Gen AI Software Development Is About Velocity and Direction

Here’s the real shift from our perspective: **Gen AI doesn’t just make coding faster, it changes how we think about building.**

We’re no longer just writing code line-by-line. We’re **directing the AI**, setting the tone, choosing the structure, guiding the outcome and filling in gaps as necessary. Developers are becoming more like systems architects, especially in the prototyping phase.

<ul style="list-style: disc; padding-left: 1.2em;">
 <li style="margin-bottom: 1em;">
 Building prototypes in hours instead of days
 </li>
 <li style="margin-bottom: 1em;">
 Testing new architectures without spinning up infrastructure
 </li>
 <li style="margin-bottom: 1em;">
 Onboarding junior devs faster by pairing them with GenAI copilots
 </li>
</ul>

If you're in software consulting, running a SaaS, or managing engineering teams—this is the shift to start practicing.

Listen to the full conversation on the [Code & Cognition Podcast](https://open.spotify.com/show/1AqPzt76zBbTfBOhZRyqPz?si=599227791fa34b93), and follow along as we continue building the future of AI-powered software.

---

## Resources Mentioned

<ol style="list-style-type: decimal; padding-left: 1.5em; margin: 1em 0;">
 <li>
 <a href="https://open.spotify.com/show/1AqPzt76zBbTfBOhZRyqPz?si=cf858e26906c430c" style="text-decoration: underline;" target="_blank" rel="noopener noreferrer">Code and Cognition Podcast</a>
 </li>
 <li>
 <a href="https://aws.amazon.com/bedrock/" style="text-decoration: underline;" target="_blank" rel="noopener noreferrer">Amazon Bedrock</a>
 </li>
 <li>
 <a href="https://www.cursor.com/" style="text-decoration: underline;" target="_blank" rel="noopener noreferrer">Cursor IDE</a>
 </li>
 <li>
 <a href="https://database.build/" style="text-decoration: underline;" target="_blank" rel="noopener noreferrer">Database.Build</a>
 </li>
 <li>
 <a href="https://www.anthropic.com/claude" style="text-decoration: underline;" target="_blank" rel="noopener noreferrer">Claude AI</a>
 </li>
 <li>
 <a href="https://web.lmarena.ai/" style="text-decoration: underline;" target="_blank" rel="noopener noreferrer">WebDevArena</a>
 </li>
 <li>
 <a href="https://bolt.new/" style="text-decoration: underline;" target="_blank" rel="noopener noreferrer">Bolt.new</a>
 </li>
</ol>

Insights from the Code & Cognition Podcast by Olio Apps on building agentic LLM systems, improving workflows, and managing tech debt in 2025.

AI-Powered Prototyping: How We Are Using GenAI Tools to Build Apps and Architectures Faster

**From the Code & Cognition Podcast by Olio Apps**

In a recent episode of *Code & Cognition*, the Olio Apps team sat down to talk shop on the evolving world of Large Language Models (LLMs), agentic workflows, and how these tools are changing how we design, scope, and implement software projects in 2025.

Here's a quick recap of key insights from that conversation, plus takeaways for developers and teams experimenting with generative AI app development.

---

## Agents vs. Workflows: What’s the Difference?

There’s a lot of buzz around “AI agents,” but what distinguishes them from traditional LLM workflows?

<ul style="list-style: disc; padding-left: 1.2em;">
 <li style="margin-bottom: 1em;">
 Workflows: Think of these as on-the-rails sequences. They follow predefined steps like “if this, then that” logic.
 </li>
 <li style="margin-bottom: 1em;">
 Agents: More autonomous. They dynamically decide what steps to take and how to use tools, making decisions in real time. This allows for more flexible, adaptive systems.
 </li>
</ul>

<a href="/assets/blog/2025-05-19-agents-workflow-and-llm-productivity-in-2025/AgentsvsWorkflows.png" target="_blank" rel="noopener noreferrer">
 <img src="/assets/blog/2025-05-19-agents-workflow-and-llm-productivity-in-2025/AgentsvsWorkflows.png" alt="Agents vs Workflows diagram" style=" display:block; margin: 0 auto; max-width: 70%; height: auto; border-radius: 10px">
</a>



> Tools like [Amazon Bedrock](https://aws.amazon.com/bedrock/) and [Anthropic’s AI agent frameworks](https://www.anthropic.com/engineering/building-effective-agents) are pushing this forward, enabling developers to define tools, provide schema access, and let LLMs decide how to achieve outcomes—like dynamic project status reports pulled from multiple sources.

---

## Real-Time Evaluation & Human-in-the-Loop

One emerging pattern we are interested in exploring more: **LLM as judge**. This is where a more expensive model is used to evaluate output from a cheaper one. This cost-effective technique can be used live within systems, not just during prompt engineering.

For example:

<ol style="list-style-type: decimal; padding-left: 1.5em; margin: 1em 0;">
 <li style="margin-bottom: 1em;">
 A cheaper model drafts responses.
 </li>
 <li style="margin-bottom: 1em;">
 A more advanced LLM evaluates output against success criteria.
 </li>
 <li style="margin-bottom: 1em;">
 Feedback is looped back until the result meets expectations.
 </li>
</ol>

This principle also plays out in human-in-the-loop workflows, where users review, refine, or approve outputs at strategic points in the flow. This further improves reliability while still leveraging automation.

---

## Cursor, Codegen, and Managing Tech Debt

[Cursor](https://www.cursor.com/en) has become a favorite among the Olio team for AI-assisted programming. Used well, it has boosted developer velocity by at least 40% for our teams. But with great speed comes great responsibility!

**Tips to maximize productivity while minimizing tech debt:**

<ul style="list-style: disc; padding-left: 1.2em;">
 <li style="margin-bottom: 1em;">
 System Prompts & Cursor Rules: Use project-specific guidance files to maintain consistent output and enforce patterns.
 </li>
 <li style="margin-bottom: 1em;">
 Refactor Often: Don’t just copy/paste generated code. Commit to reviewing and improving your code base according to your existing processes. 
 </li>
 <li style="margin-bottom: 1em;">
 Name @mentioning: Reference existing code and structures when asking Cursor to generate similar components. Giving Cursor this context is key for optimal outputs.
 </li>
</ul>

---

## What’s Next in Q2?

As we move deeper in Q2, our team is eagar to explore:

<ul style="list-style: disc; padding-left: 1.2em;">
 <li style="margin-bottom: 1em;">
 Agentic workflows using Bedrock’s orchestration tools
 </li>
 <li style="margin-bottom: 1em;">
 Cross-source LLM synthesis: merging data from time trackers, Slack, and GitHub
 </li>
 <li style="margin-bottom: 1em;">
 Natural language end-to-end testing powered by LLMs + Playwright
 </li>
 <li style="margin-bottom: 1em;">
 More human-in-the-loop flows for approvals, refinements, and efficient collaboration between team members and projects
 </li>
</ul>

---

## Final LLM Dev Tip: Don't Break the Illusion

**System prompts matter** 

In testing, Claude consistently “stayed in character” while GPT-4 often broke with minimal pressure. If your app’s identity relies on LLM behavior, run forensic checks and ensure your system prompt is structured, clear, and hard to break.

You may find some LLMs have an easier time holding on to your system prompt. It may be more advantageious to stick with those LLMs going forward!

---

## Final Thought

Whether you’re just starting with LLMs or deep in building intelligent systems, the landscape is shifting fast. Stay agile, experiment, and don’t forget: **great AI starts with great context**.

Listen to the full conversation on the [Code & Cognition Podcast](https://open.spotify.com/show/1AqPzt76zBbTfB0hZRYqPz?si=e9ad58d720bd4f59), and follow along as we continue building the future of AI-powered software.

---

## Resources Mentioned

<ol style="list-style-type: decimal; padding-left: 1.5em; margin: 1em 0;">
 <li>
 <a href="https://open.spotify.com/show/1AqPzt76zBbTfBOhZRyqPz?si=cf858e26906c430c" style="text-decoration: underline;" target="_blank" rel="noopener noreferrer">Code and Cognition Podcast</a>
 </li>
 <li>
 <a href="https://aws.amazon.com/bedrock/" style="text-decoration: underline;" target="_blank" rel="noopener noreferrer">Amazon Bedrock</a>
 </li>
 <li>
 <a href="https://www.cursor.com/" style="text-decoration: underline;" target="_blank" rel="noopener noreferrer">Cursor IDE</a>
 </li>
 <li>
 <a href="https://medium.com/@dtoka/create-test-automation-with-playwright-using-langchain-agents-62a136ce6071" style="text-decoration: underline;" target="_blank" rel="noopener noreferrer">Playwright End-To-End Testing</a>
 </li>
 <li>
 <a href="https://www.anthropic.com/claude" style="text-decoration: underline;" target="_blank" rel="noopener noreferrer">Claude AI</a>
 </li>
</ol>

AI Agents, Workflow, and LLM Productivity in 2025

When your application grows quickly, so do the demands on your infrastructure. AWS Lambda offers automatic scaling by design, but without the right configuration, it can lead to cold start latency, manual overhead, and escalating costs. As part of our Fractional CTO Service, we’ve worked with clients to address challenges like cold start latency and scaling in AWS Lambda, so we wanted to highlight an example where we were able to successfully implement Lambda auto scaling.

In this guide, we’ll walk through how our team tackled these challenges by implementing **Lambda auto scaling with provisioned concurrency**—a move that significantly improved both performance and cost-efficiency.

------

## The Problem with AWS Lambda Cold Starts and Manual Scaling

Our client’s application was built using AWS Lambda, but their serverless architecture had a major bottleneck: As more users joined, Lambda functions experienced cold starts, leading to sluggish response times. To compensate, we manually increased provisioned concurrency before major events. This fixed performance—but introduced human error and high costs when concurrency was left high after traffic dropped.

---


## Understanding Lambda Auto Scaling with Provisioned Concurrency

A common misconception is that AWS Lambda always scales instantly. While it can spin up new instances as requests come in, each new instance introduces **cold start latency**.

**Provisioned concurrency** solves this by keeping a set number of Lambda instances warm and ready. But provisioning too many functions 24/7 can be expensive.

<a href="/assets/blog/2025-04-30-how-we-solved-aws-lambda-scaling-with-auto-scaling-and-provisioned-concurrency/ProvisionedvsColdStart.png" target="_blank" rel="noopener noreferrer">
 <img src="/assets/blog/2025-04-30-how-we-solved-aws-lambda-scaling-with-auto-scaling-and-provisioned-concurrency/ProvisionedvsColdStart.png" 
 alt="Provisioned vs Unprovisioned Lambda Concurrency Chart" 
 style="max-width: 100%; height: auto; border-radius: 10px;" />
</a>

That’s where **AWS Application Auto Scaling** comes in. It dynamically adjusts provisioned concurrency based on real-time metrics—scaling up during traffic spikes, and scaling down when demand slows. This gives us the performance benefits of warm Lambdas without the cost of over-provisioning.



## Real-World Lambda Scaling: Our Configuration in Practice
Here’s how we implemented it using the Serverless Framework:
<ul style="list-style: initial; padding-left: 1.2em;">
 <li style="margin-bottom: 0.5em;">Provisioned concurrency is configured for high-traffic endpoints.</li>
 <li style="margin-bottom: 0.5em;">CloudWatch alarms monitor usage and trigger scaling actions.</li>
 <li style="margin-bottom: 0.5em;">We maintain a buffer by targeting 70% utilization—this keeps 30% of provisioned Lambdas in reserve for surges.</li>
 <li style="margin-bottom: 0.5em;">Scaling thresholds are evaluated over a 3-minute trailing window to smooth out short-term spikes.</li>
</ul>



Instead of manually maintaining 100 Lambdas 24/7, we now start with a baseline and **let the system scale intelligently**. This configuration is version-controlled, reproducible, and deployable through code.

## Key Benefits of Lambda Auto Scaling

This technical refactor of our client's serverless architecture solved real business problems and alleviated serious developer painpoints, all while enabling their application to scale further.

In summary, the core benefits were that now their application had:

<ol style="list-style-type: decimal; padding-left: 1.2em;">
 <li style="margin-bottom: 1.2em;">
 Reduced Cold Start Latency 
 With warm Lambdas available before traffic arrives, users no longer experienced delays during onboarding events or high-concurrency scenarios.
 </li>

 <li style="margin-bottom: 1.2em;">
 Lower AWS Costs Without Sacrificing Performance 
 We eliminated the need to keep high concurrency levels running 24/7. The system automatically scales down during quiet periods, cutting unnecessary spend.
 </li>

 <li style="margin-bottom: 1.2em;">
 Automation Replaced Human Guesswork 
 No more manual adjustments to concurrency settings. CloudWatch and Application Auto Scaling now handle scaling based on real usage, reducing operational overhead and human error.
 </li>

 <li style="margin-bottom: 1.2em;">
 Infrastructure That Grows with Demand 
 The system now scales with actual traffic patterns. Whether we have 10 users or 10,000, Lambda concurrency adjusts dynamically to meet demand.
 </li>
</ol>

<a href="/assets/blog/2025-04-30-how-we-solved-aws-lambda-scaling-with-auto-scaling-and-provisioned-concurrency/PerformanceComparison.png" target="_blank" rel="noopener noreferrer">
 <img src="/assets/blog/2025-04-30-how-we-solved-aws-lambda-scaling-with-auto-scaling-and-provisioned-concurrency/PerformanceComparison.png" alt="Lambda Scaling: Before vs After Auto Scaling" style="max-width: 100%; height: auto; border-radius: 10px;" />
</a>


## Tips for Implementing Auto Scaling with the Serverless Framework

If you’re looking to implement something similar, here are some key recommendations:
<ul style="list-style: disc; padding-left: 1.2em;">
 <li style="margin-bottom: 1em;">
 Use the Serverless Framework with the <code>serverless-provisioned-concurrency-autoscaling</code> plugin.
 </li>

 <li style="margin-bottom: 1em;">
 Define your min/max concurrency values clearly to prevent over- or under-scaling.
 </li>

 <li style="margin-bottom: 1em;">
 Monitor with CloudWatch and adjust based on real traffic, not estimates.
 </li>

 <li style="margin-bottom: 1em;">
 Tune your buffer and utilization targets based on observed load patterns.
 </li>
</ul>



## Final Thoughts: Smarter Scaling, Lower Costs, and Better Performance

By combining **AWS Lambda provisioned concurrency** with **auto scaling**, and isolating our environments, we created a system that is more resilient, cost-effective, and scalable.

If your team is manually adjusting concurrency or struggling with cold start latency, this pattern could save you time, money, and headaches.

Want a more detailed walkthrough or help applying this in your stack? Get in touch <a href="https://www.olioapps.com/contact-us" style="color: blue; text-decoration: underline;" target="_blank" rel="noopener noreferrer">here.</a>

## Resources Mentioned
<ol style="list-style-type: decimal; padding-left: 1.5em; margin: 1em 0;">
 <li><a href="https://docs.aws.amazon.com/lambda/latest/dg/welcome.html" style="text-decoration: underline;" target="_blank" rel="noopener noreferrer">AWS Lambda</a></li>
 <li><a href="https://docs.aws.amazon.com/lambda/latest/dg/configuration-concurrency.html" style="text-decoration: underline;" target="_blank" rel="noopener noreferrer">Lambda Provisioned Concurrency</a></li>
 <li><a href="https://docs.aws.amazon.com/autoscaling/application/userguide/what-is-application-auto-scaling.html" style="text-decoration: underline;" target="_blank" rel="noopener noreferrer">AWS Application Autoscaling</a></li>
 <li><a href="https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/WhatIsCloudWatch.html" style="text-decoration: underline;" target="_blank" rel="noopener noreferrer">CloudWatch Alarms</a></li>
 <li><a href="https://www.serverless.com/plugins/serverless-plugin-optimize" style="text-decoration: underline;" target="_blank" rel="noopener noreferrer">Serverless Provisioned Concurrency Plugin</a></li>
</ol>

In this guide, we’ll walk through how our team tackled these challenges by restructuring our AWS environments and implementing Lambda auto scaling with provisioned concurrency—a move that significantly improved both performance and cost-efficiency.

How We Solved AWS Lambda Scalaing with Auto Scaling and Provisioned Concurrency

As a software developer with little background in design and color theory, you might find yourself tasked with creating visually appealing web pages. Without a solid foundation in design principles, especially color theory, this can seem daunting. You might wonder how to quickly and effectively choose colors that create a cohesive and aesthetically pleasing design. I'll explore how I used AI, specifically ChatGPT-4o, to solve this problem by leveraging color theory principles to generate color schemes mathematically.

As a developer with a background in art, I've always relied on traditional watercolor theory to create visually striking color palettes. The theory is simple: take your primary color, find its contrasting color, and mix the two to get a neutral tone. This neutral tone helps other colors pop on the canvas or page. However, applying this theory to website design can be challenging, especially for those without an art background. I wondered if there was a mathematical way to reproduce these watercolor results using hex codes, making the process more accessible for everyone.

I started with a watercolor study, using an orange as my primary color and blue as its contrasting color. By mixing these two, I obtained a neutral tone. I scanned these watercolor samples and used an eye dropper tool to extract their hex codes. With these hex codes, I styled a dummy app, incorporating the primary color, complementary color, and neutral tones for text and drop shadows.
<img src="/assets/blog/2024-09-09-utilizing-LLMs-to-mathematically-determine-color-themes-for-websites/Pallette.png" alt="Colors" style="display: block; max-width: 100%;">
<img src="/assets/blog/2024-09-09-utilizing-LLMs-to-mathematically-determine-color-themes-for-websites/Headers.png" alt="Headers" style="display: block; max-width: 100%;">

Next, I turned to ChatGPT. I provided it with the hex codes: the starting color (orange), the contrasting color (blue), and the neutral tone obtained from my watercolor study. My goal was to see if ChatGPT could replicate this process mathematically.

Initially, ChatGPT calculated the contrasting color and the neutral tone. However, the neutral tone was always a perfect gray (hex code: #888888), which didn't match the nuanced neutral tones I observed in my watercolor samples. I realized that the mathematical approach needed refinement to account for the blending of colors in a more realistic manner, since with this mathematical approach the exact contrasting color was achieved every time, which, in color theory, means that the neutral tone will always be a perfectly neutral gray.

I queried ChatGPT to explore if using light filters or different blending techniques could achieve a more accurate neutral tone. This led to the discovery of alpha blending, which involves overlaying colors with varying transparency levels. By applying a 60% filter for the primary color and a 40% filter for the contrasting color, I achieved a neutral tone that leaned more towards the primary color, mimicking the watercolor effect.

<img src="/assets/blog/2024-09-09-utilizing-LLMs-to-mathematically-determine-color-themes-for-websites/equation.png" alt="equation" style="display: block; max-width: 100%;">

With this refined approach, I developed an equation where I input a single color, and ChatGPT calculates the complementary and neutral tones. This method allows me to create harmonious color palettes mathematically, making the process accessible to those without an art background. For example, in a scenario where a client's logo color is orange, I can now generate a visually appealing color scheme for their website with ease.

<img src="/assets/blog/2024-09-09-utilizing-LLMs-to-mathematically-determine-color-themes-for-websites/colors.png" alt="equation" style="display: block; max-width: 50%;">
<img src="/assets/blog/2024-09-09-utilizing-LLMs-to-mathematically-determine-color-themes-for-websites/Headers2.png" alt="Headers2" style="display: block; max-width: 100%;">

Traditionally, determining color themes relied heavily on artistic intuition and manual blending techniques. While effective, this approach can be challenging for those without a strong art background. By leveraging AI and mathematical principles, we can automate and simplify this process, making it accessible to developers without design teams or time to get a Master's in Fine Art. This new approach not only saves time but also ensures consistency and accuracy in color theme generation for websites. And it's fun!

You might wonder how to quickly and effectively choose colors that create a cohesive and aesthetically pleasing design. I'll explore how I used AI, specifically ChatGPT-4o, to solve this problem by leveraging color theory principles to generate color schemes mathematically.

Using AI To Create Website Color Themes

At Olio Apps we understand the challenges organizations adopting cloud for the first time or experimenting with serverless development run into when it comes to designing and deploying scalable and cost effective architecture on AWS.

Seldom does an organization have the time and resources to commit to learning a new tech stack while developing features.

At Olio Apps we regularly support clients who are in need of talent specialized in specific AWS services who can disperse that knowledge throughout the organization.

This is why we support our team members in developing their knowledge of cloud and serverless best practices when it comes to AWS Infrastructure.

Today, we are excited to announce and celebrate Aaron Whitaker’s recent AWS Solutions Architect Certification.

The AWS Solutions Architect Certification validates proficiency in designing and deploying scalable and cost-efficient cloud solutions on AWS. Individuals with this expertise are able to tailor AWS infrastructure to an organization’s needs while optimizing performance and cost-effectiveness. Additionally, those who achieve AWS Certification in general can be assured they are up to date with Amazon’s best practices and innovation standards.

## AWS Solutions Architect Associate Bolsters Technical Abilities

Aaron found that the process of studying and learning for the AWS Solutions Architect Certification exam reinforced his practical knowledge while exposing him to additional best practices:

> Getting the solutions architect certification helped to reinforce the knowledge I’ve gained through practical experience working in AWS for the past two years. It also gave me more insight into best practices and patterns I hadn’t previously known about. It's given me more confidence to design well-architected cloud systems

Aaron is a Fullstack Engineer at Olio Apps who specializes in fullstack and serverless applications. You can learn more about the team at Olio Apps <a href="https://www.olioapps.com/team">here.</a>

Congratulations again Aaron, we are excited to see how this new knowledge from the AWS Solutions Architect Certification encourages your further learning.

At Olio Apps we acknowledge that Amazon Web Services (AWS) is an industry leader when it comes to providing reliable and scalable cloud services for companies across all domains.

Celebrating Aaron Whitaker’s AWS Solutions Architect Certification

At Olio Apps EventBridge has become a huge topic of conversation ever since we’ve been using it with one of our clients. Initially, our client’s architecture followed a synchronous inline model where you give the server a request and then it does A, B, C, D, and E.

As the app continues to grow, it becomes harder to manage all of these different threads and if-thens. So we've shifted to utilize AWS event-driven architecture, a first for this client. Initially, we integrated with SQS queues (Amazon Simple Queuing Service) to manage asynchronous job processing. Since then we have begun to shift more and more to event driven architecture patterns.

## Facilitating Event Driven Architecture Evolution using Amazon EventBridge

Some of you may recognize this paradigm. Initially, we needed to trigger notifications based on specific factors like the day of the week, how long it had been since a certain event happened, or a user’s relationship to one another.

So we began to utilize a serverless event bus, Amazon EventBridge, to listen, schedule, & trigger these events. EventBridge Listener would wait for specific events to enter the event bus. Once they did, this would trigger a corresponding lambda function to take the event to an SQS queue where it would then be processed asynchronously.

The above description is essentially the base use case described by AWS’ official illustrations. This image shows the event bus and the different events being pumped into it from specific sources.

<img src="/assets/blog/2024-09-04-facilitating-event-driven-architecture-evolution-using-amazon-eventbridge/eventbridgediagram.png" alt="EventBridge Diagram" style="display: block; max-width: 100%;">

And as stated in <a href="https://docs.aws.amazon.com/eventbridge/latest/userguide/eb-service-event-list.html">AWS’ documentation</a> EventBridge can handle input from a variety of AWS sources or from a custom event bus.

In particular, we provisioned our AWS resources using the serverless framework and used a combination of EventBridge’s rules, event matching, and routing capabilities to enable a user experience informed by the context of these specific events.

## Value of Eventbridge

In summary this is how EventBridge functions as we have events flowing into the pipeline with the following orchestration.

**Rules**: We define rules within EventBridge that act as event filters. These rules can listen for specific events, like "user created" or "user updated."

**Event Matching**: Each rule is configured with an event pattern. This pattern specifies the data characteristics and the rule it is looking for. For instance, a rule might match events where the "detail-type" property equals "user-updated" and the "source" property equals "staging-API."

**Routing Events**: When an event matches a rule's pattern, EventBridge routes it to a designated target. This target can be a Lambda function that we've previously defined to handle the event.

Imagine a rule named "user-updated-event-bridge-rule." This rule intercepts events flowing through the pipeline, searching for events that match its criteria (detail-type="user-updated" and source="staging-API").
If an event doesn't match, it's simply passed along. However, if a match occurs, the event is directed to the Lambda function associated with that rule. This Lambda function executes the desired logic based on the received event data.

A major reason EventBridge has been so valuable for our client’s event driven architecture is because of EventBridge’s robust flexibility, including:

**Multiple Rules per Event**: A single event can be matched by multiple rules, allowing for diverse processing paths.

**Multiple Targets per Rule**: Each rule can route events to up to five different targets, enabling parallel processing or sending events to various services.

**Event Data**: We can include any relevant data within the event itself. This allows Lambda functions to perform actions based on the differences between old and new data (e.g., detecting a user's change in profile information).

This ability to define custom rules and targets empowered us to make intricate event workflows that fit our client’s needs. Currently, we have Lambda functions that trigger additional events based on the processed data, sending them back into the EventBridge pipeline for further processing of 3rd party APIs. This is one example where we are able to leverage the vast capabilities of EventBridge for enabling event driven architecture. for building sophisticated event-driven architectures using EventBridge.

Overall, this EventBridge architecture has allowed us to offload all these semi-heavy computational side effects when there is a profile update to the cloud. So we're not taking a bunch of performance hits by running everything in line on individual requests.

We hope you enjoyed our discussion on how Olio Apps is utilizing EventBridge to meet customer needs using event driven architecture on AWS. If you have any questions about using EventBridge yourself or have any questions about how your application may be able to utilize EventBridge, please don’t hesitate to contact us.

At Olio Apps, EventBridge has become a huge topic of conversation ever since we’ve been using it with one of our clients. As the app continues to grow in complexity and perform more complex functions, it becomes harder to manage all of these different threads and if-thens

Facilitating Event Driven Architecture Evolution Using Amazon EventBridge


When prototyping, designing, and building software applications for clients it’s essential to have a firm grasp on emerging technology. However, technology itself is seldom what drives value for a business; it’s knowing how, when, and where to apply the technology in question that brings maximum value. 

At Olio Apps, we value the skillful application of emerging technology to SMB and enterprise business problems and that’s why we encourage our team members to continually expand their knowledge in all areas of software development.

This is why we are excited to announce and celebrate today Dustin Herboldshimer’s recent AWS Technical Accreditation.

The AWS Technical Accreditation provides training and assessment opportunities for individuals to demonstrate their proficiency in implementing AWS services, solutions, and best practices. It covers a range of topics related to AWS cloud computing, including architecture, security, operations, and migration.

## AWS Technical Accreditation Bolsters Technical Abilities

Dustin has found the process of studying and learning for his accreditation meaningful:

> Through my AWS Technical Accreditation course, I gained a deeper understanding of the full range of AWS services and their applications. I also learned more about the significant business benefits of adopting serverless technologies, enhancing my ability to prescribe efficient and innovative cloud solutions based on customer requirements.

Dustin is one of our Engineering Managers and developers focused on React Web Applications and Full Stack Development. You can learn more about the team at Olio Apps here.

Congratulations again Dustin and we encourage you to continue developing your breadth of expertise. 


Celebrating Dustin Herbolshimer’s AWS Technical Accreditation


At Olio Apps we acknowledge that Amazon Web Services (AWS) is an industry leader when it comes to providing reliable and scalable cloud services for companies across all domains.

Knowing this, we make it our mission to support our team members in growing their knowledge base and expertise regarding the suite of AWS services and use cases so we can suggest the best practices and architectures when it comes to solving client problems. This is why we are excited to announce and celebrate Laurie Waller’s recent AWS Developer Associate Certification.

The AWS Developer Associate certification validates expertise in developing cloud-based applications on AWS and ability to design, develop, and deploy applications on AWS. Those who achieve an AWS Certification can be assured they are up to date with Amazon’s best practices and innovation standards.

## AWS Developer Associate Bolsters Technical Abilities

Laurie found that the process of studying and learning for the AWS Developer Associate exam bolstered her technical skills:

> Securing the AWS Developer Associate certification significantly enhanced my proficiency in deploying, managing, and operating scalable systems on AWS. It not only bolstered my technical skills but also built up my strategic approach to cloud solutions, fostering both personal and professional growth and ensuring our team can deliver top-notch results...

Laurie is a Fullstack Engineer at Olio Apps and a self described lifelong learner. You can learn more about the team at Olio Apps here.

Congratulations again Laurie, we are happy to hear you learned so much by studying for your AWS Developer Associate Certification.


Celebrating Laurie Waller’s AWS Developer Associate Certification


Since the launch of serverless AWS services in 2006 with S3 and general availability of AWS Lambda in 2015 there has been steady adoption of serverless architectures across all industry verticals.

Building serverless applications alongside our SMB and enterprise clients, we at Olio Apps have seen a clear differentiator between an organization that skillfully applies serverless best practices on AWS and those that don’t. Those that do are able to maximize the agility, scalability, and cost-effectiveness of AWS architectures.

This isn’t surprising. As when it comes to the market share of Cloud Service Providers, AWS is still the leader in 2024 due to the benefits its infrastructure provides.

Getting to serve clients during these critical moments of prototyping, architecture design, and development with our serverless expertise and experience is a key pillar of pride and motivation for our organization.

“Olio Apps successfully delivered a functioning product, which received positive feedback on its quality, stability, and ease of use. The team delivered on time and within budget and worked in sprints to ensure a seamless workflow. They impressed with their flexibility and prototyping skills” -Trevor Dryer, CEO Carbon Title

In light of this, we see the general availability of Amazon Bedrock as the next wave of highly impactful and influential services offered by AWS. Amazon Bedrock represents a paradigm shift where all companies have access to the possibility of accelerating the performance of their organization with high-performing foundational models of leading AI companies.

However, like with serverless, for companies to maximize the benefits of Generative AI they select the right technology for the job and have the knowledge, experience and expertise to implement the technology correctly.

This can prove challenging since the field of Generative AI is relatively new and growing in popularity. Its novelty means that true Generative AI development expertise is scarce and choosing technical decisions because they are popular and not the best solution can become a costly mistake.

That is why we have decided to grow our partnership with Amazon Web Services through the Amazon Partner Network, focusing on designing, prototyping, and building Generative AI solutions with Amazon Bedrock and serverless development on AWS.

We aim to offer our seasoned leadership to companies having to make critical decisions regarding the use of Generative AI and serverless infrastructure to achieve their business goals.

In step with this commitment, we are empowering our team to invest in their education and knowledge base in the areas of Generative AI and serverless infrastructure on AWS.

Today we are honored and excited to announce that seven of our team members have already taken charge and received Accreditation and Certification from AWS in Q1.

Congratulations to Laurie Walker, Dustin Herboldshimer, Aaron Whitaker, Aron Glenn, Scott Becker, Aron Racho, and Joshua Proto for their recent achievements.

At Olio Apps we value a growth mindset that supports continuous learning and development. Having knowledge is essential to ensure the experience driven execution of mission critical projects.

Whether an organization is considering implementing Amazon Bedrock or serverless for the first time, or looking to scale their usage of these services on AWS, Olio Apps looks forward to being your trusted technology partner.


We have decided to grow our partnership with Amazon Web Services through the Amazon Partner Network, focusing on designing, prototyping, and building Generative AI solutions with Amazon Bedrock and serverless development on AWS.

Reinforced Learning