DEV Community: Rens Wunnink

⚙️ I Built 7 AI Apps in 7 Days Using Gemini (Dev Log + Insights)

Rens Wunnink — Sun, 22 Mar 2026 15:34:41 +0000

There’s a lot of noise around AI right now.

So instead of consuming more content, I decided to run a simple experiment:

Build 7 AI apps in 7 days using a “vibe coding” workflow.

No overengineering.

No long planning cycles.

Just rapid prototyping with AI.

🧠 Stack & Approach

Everything was built using:

Google Developer Studio
Gemini
Prompt-driven development (instead of traditional architecture-first)

The goal wasn’t clean architecture or production-ready systems.

It was to test:

How far can you push rapid AI app development in 24-hour cycles?

⚡ Constraints

7 days
7 apps
~1 day per build
Focus on working prototypes
Mix of experiments + practical tools

This forced decisions around:

speed vs structure
prompting vs coding
workflows vs features

🧩 The 7 Apps (Quick Breakdown)

Keeping this high-level on purpose — each one could be its own deep dive.

SuperCarMe – Prompt → image pipeline for generating hyper-realistic “you + supercar” visuals. Focus: prompt engineering + image generation consistency.

Infographics Video Killer – YouTube URL → structured summary → infographic-style output. Focus: parsing, summarization, formatting pipelines.

AI Assisted Shadertoy – Generate shader code with AI and iterate visually. Focus: AI-assisted code generation + creative tooling.

YouTube Thumbnail Maker – Text prompt → thumbnail concepts optimized for CTR. Focus: generative design + conversion-oriented outputs.

BlogMaster – Keyword → structured outline → full article generation. Focus: chaining prompts + SEO-aware generation.

Color Book Genius – Bulk generation of printable coloring pages. Focus: scalable generation + batch workflows.

Outreach Beast – Local business discovery → structured lead data. Focus: data extraction + automation workflows.

🧠 Technical Observations

After building all 7, a few patterns stood out:

1. Most AI apps = pipelines, not products

You’re not “building software” in the traditional sense.

You’re designing:

input → transformation → output flows

2. Prompt quality > code complexity

Better prompts consistently outperformed:

extra logic
added features
more tooling

3. Latency & iteration loops matter

Fast feedback loops made a huge difference:

test prompt
adjust
re-run

This replaced a lot of traditional debugging.

4. AI reduces build time, not thinking time

You still need:

clear intent
structured inputs
defined outputs

Otherwise things break fast.

5. “Good enough” ships faster than “perfect”

Most of these apps would never exist if I optimized too early.

🔥 Full Breakdown (Architecture + Usage)

This post is just the surface.

I documented each app in detail:

workflows
prompt structures
use cases
limitations

👉 Full breakdown + all 7 apps:

(intel4ai.com)

👇 Curious About One Thing

If you had to pick one of these to turn into a real product…

Which would you double down on — and why?

LM Studio: A Game-Changer for Local Language Model Exploration!

Rens Wunnink — Sun, 22 Oct 2023 18:03:46 +0000

Highlights:
Large Language Models (LLMs) right from your Linux desktop or laptop and NO internet required!

How to install LM Studio on Linux
How to run LM Studio on Linux

What is LM Studio?

LM Studio revolutionizes your experience with Large Language Models (LLMs) right from your windows, Mac and now Linux desktop! This cutting-edge application lets you effortlessly discover, download, and deploy Hugging Face's GGUF-compatible models.

Here's why LM Studio is a game-changer:

💻 Simplicity Meets Power: LM Studio boasts an intuitive interface for configuring and utilizing models, catering to both beginners and seasoned experts.

🌐 Versatile Across Platforms: Enjoy seamless operations on various operating systems, ensuring a consistent experience wherever you are.

⚡️ Turbocharge with GPU: LM Studio taps into GPU resources, ensuring lightning-fast model execution and top-notch performance.

📵 Unplug and Play: Say goodbye to dependency on an internet connection - LM Studio lets you run LLMs on your laptop anytime, anywhere, offline.

💬 Effortless Interactions: Engage with models through a user-friendly in-app Chat UI, for an intuitive and streamlined experience.

🖥️ Local Server Flexibility: Customize your setup with an OpenAI compatible local server, tailoring access to models as per your needs.

⬇️ Seamless Model Downloads: Fetch compatible model files directly from HuggingFace repositories within the application, hassle-free.

🌟 Model Galore: LM Studio supports an extensive range of ggml Llama, MPT, and StarCoder models, including Llama 2, Orca, Vicuna, NousHermes, WizardCoder, and MPT from Hugging Face.

🚀 Powered by llama.cpp: Behind LM Studio's innovation lies the llama.cpp project, ensuring rock-solid reliability and performance.

My Journey to Running LM Studio on Linux Distribution.

After finding LM Studio I was excited until I noticed they did not have a Linux supported run file. So I started my mission and took the Wine🍷 and Playonlinux approach. This turned into a dependency and regedit nightmare. Luckily there is ".AppImage" and the community quickly realized what Linux distribution users needed.

Unlocking the Power of Large Language Models: Installing LM Studio on Linux

Welcome to the future of Large Language Model exploration! With LM Studio, you can now delve into the world of powerful language models right from your own Linux system. Follow these simple steps to get started:

Step 1: Download the AppImage

Begin by obtaining the LM Studio AppImage file from the official Discord or use this one i used in a sandbox (LINK HERE: https://bit.ly/LmStudioIMG). This self-contained file contains all the necessary components to run LM Studio on your system.

Step 2: Access the Terminal

Open a terminal window by pressing Ctrl + Alt + T. This will be your gateway to the installation process.

Step 3: Navigate to the Directory

Use the cd command to navigate to the directory where the LM Studio file is located. This is typically the location where you downloaded the AppImage file.

bash cd /path/to/directory

Step 4: Set Permissions

Make the AppImage file executable by using the chmod command. This grants the necessary permissions to run the file.

bash chmod u+x filename.appimage

Step 5: Execute the File

Now, it's time to run the LM Studio AppImage. Simply enter the following command:

bash ./filename.appimage

Step 6: Exploration Begins!

Voila! You've successfully installed LM Studio on your Linux system. Begin your journey into the world of Large Language Models with this user-friendly desktop application.

🤖 With LM Studio, you now have the power to explore, configure, and interact with Large Language Models directly from your Linux environment. Enjoy the seamless experience and let your creativity run wild!
For any further information or troubleshooting, don't hesitate to reach out to the LM Studio team. Happy exploring!

⏛ Usage Terms: LM Studio is designed for personal use, subject to specific terms. For business applications or more details, feel free to get in touch with the LM Studio team.
Experience a new era in Large Language Model exploration with LM Studio! Unearth, configure, and deploy LLMs effortlessly, all through this user-friendly desktop application.

🎉 Thanks for reading. Use AI with an ethical responsibility towards all our futures including AI. AI will only take jobs and replace people that don't level up. It's time to level up. 😏

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