Artifacts #3
The Egg Puff Engineer's Newsletter
Hello Reader 👋
Welcome to the 3rd Issue Newsletter.
Newsletter evolving with a new name “Artifacts” to better reflect the curated insights across AI, engineering, and everything in between. This issue also introduces focused topic segments and a new micro-learning format, making it easier to explore, learn, and dive deeper.
🚀 3 Things in AI
News | Concepts | Insights
🌊 News : Codex - OpenAI’s Coding Agent
OpenAI has recently released Codex, a cloud-based AI coding agent integrated into ChatGPT, designed to enhance software development workflows by automating tasks such as code generation, debugging, and testing.
🧠 Concept : Prompt Optimization
Prompt optimization is the process of framing the most effective instructions for a large language model to achieve the desired output. A well-written prompt can dramatically improve the accuracy, relevance, and creativity of the model's responses.
Few Methods of Writing Prompts contribute to optimization
1. Zero-Shot Prompting
Directly asking the model a question or giving a task without any examples.
Prompt: Translate 'Hello, world!' into French.
Expected Output: Bonjour le monde !2. Few-Shot Prompting
Providing the model with a few examples of input-output pairs to demonstrate the desired behavior. This helps the model learn the pattern and generalize to new inputs.
Translate English to French:
English: The sky is blue.
French: Le ciel est bleu.
English: I like pizza.
French: J'aime la pizza.
English: What is your name?
French:3. Chain-of-Thought (CoT) Prompting
Encourages the model to reason through the problem step-by-step, rather than providing a direct answer. This is particularly useful for complex reasoning tasks.
Prompt:
Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now? Let's think step by step.
Expected Output:
Roger initially has 5 balls. He buys 2 cans * 3 balls/can = 6 balls. In total, he has 5 + 6 = 11 balls. The answer is 11.
4.Role-Playing/Persona Prompting
Assigning a specific role or persona to the model to guide its response style and content.
Prompt: You are a Data Expert. Answer the following question: What is the best programming language for data analysis ?"
Expected Output: Python is the best language to use.There are also other types like Template-Based Prompting, Constraint Prompting, Instruction-Based Prompting.
Prompt Optimization Methods
1. Meta Prompting
Meta prompting, at its core, is about using a language model to design or improve prompts. Instead of manually crafting prompts, you use another prompt to tell the LLM how to create or optimize a prompt for a specific task. Think of it as prompting the LLM about prompt engineering.
You are an expert prompt engineer. Your task is to generate an effective prompt for summarizing news articles.
The prompt should:
* Be clear and concise.
* Instruct the model to summarize the article in approximately 100 words.
* Highlight the main points of the article.
* Include a sample news article for the model to use as an example (few-shot prompting).
2. Gradient-Based Prompt Optimization
This is a more sophisticated and automated approach to prompt optimization that uses gradients (from machine learning) to fine-tune prompts. It treats the prompt as a set of parameters that can be adjusted to maximize a specific performance metric.
📊 Insights : LLM Rankings
🔦 Explore This
After a few weeks of vibe coding my portfolio page with Lovable, I started to feel the gap that there’s no solid open-source alternative that can do full-stack magic. Tools like Void and TabbyML (a Copilot-style AI editor fork) work well inside VSCode, but they focus more on code completion than on full product building. What sets Lovable different is its ability to generate full-stack apps with Supabase integration, React based front end design variations and radix UI code style . Recently, I came across Dyad, and it’s been a refreshing alternative closer to what I’ve been looking for, especially after reflecting on it in my Substack note.
Dyad - Open source Lovable Alternative
Dyad is a local, open-source AI app builder. It's fast, private and fully under your control — like Lovable, v0, or Bolt, but running right on your machine.
Completely Private and unlimited of product building
Use Open LLM Models or APIs of LLM Providers
Will Chen Creator of Dyad promised “It will remain open source for ever”.
Added additional benefits like learning academy and community support in Dyad Pro version.
PySpur.dev - AI Agent builder
Open source and Apache 2.0 licensed
Workflow style Agent building pipeline
Code Repo : https://github.com/PySpur-Dev/pyspur
🧩 Engineers Placeholder
In this week’s Engineers Placeholder, I’m sharing an interesting reactive web framework: Datastar.dev. Before diving into its use cases and what makes it powerful, let’s first explore the core concepts behind how it works.
What is <htmx> ?
htmx is a small JavaScript library that lets you make your website interactive using just HTML.
You don’t need to write JavaScript for the requirements like to load new content, submit forms without reloading, update parts of your page, infinite scroll or live search. You just use special
hx-attributes in your HTML.
What is an SPA (Single Page Application)?
An SPA is a type of web application that loads a single HTML page and then dynamically updates the content on the page without reloading the entire page from the server.
What is Datastar.dev ?
Datastar is a lightweight, declarative JavaScript library designed to build reactive web applications by combining the simplicity of server-side rendering with the interactivity of single-page applications (SPAs). It enables developers to add frontend reactivity using HTML
data-*attributes, eliminating the need for complex JavaScript frameworks.
So, how is Datastar.dev more than htmx?
htmx is great for server-triggered interactions.
Datastar.dev is great if you want both server and client-side reactivity with minimal JS.
Datastar can handle both interactivity and local logic/state
You can bind variables, track changes, and update the DOM, all from HTML. htmx relies on server responses for changes.
You can mix server-rendered and client-reactive views, perfect for building modern apps incrementally.
Language Agnostic Backend: Datastar can work with backends written in various languages, including Go, Python, Ruby, and more.
Server-Sent Events (SSE): Facilitate real-time communication between the server and client, enabling dynamic content updates without full page reloads.
Use Cases
Real-Time Applications: Ideal for applications requiring live updates, such as dashboards or collaborative tools.
Simplified SPAs: Build single-page applications without the overhead of traditional JavaScript frameworks.
Progressive Enhancement: Enhance existing server-rendered applications with interactive features incrementally.
Hope you found something useful this week. Feel free to share your feedback !
Until next time,
Egg Puff Engineer