Forem: Arman khan

Llama-3.2 & Tacos: A Hackathon Love Story

Arman khan — Wed, 30 Jul 2025 20:43:22 +0000

🌮 Llama-3.2 & Tacos: A Hackathon Love Story

Submission for the Beyond the Code track

🧩 How three strangers became “Team Taco-LLaMA”
Bolt.new’s auto-match dropped us in a voice channel at 11:47 pm PST.
Mira’s first words:
“If we ship before the salsa runs out, the tacos are on me.”
We pinned that quote to the top of our README.
🎙️ The 2 a.m. mentor miracle
At 02:14 the global audio lounge lit up. @osanseviero (Hugging Face staff) hopped in, screenshared our repo, and live-refactored:

before (OOM crash)

model = AutoModelForCausalLM.from_pretrained("Llama-3.2-3B")

after (fits 8 GB free tier)

bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16
)
model = AutoModelForCausalLM.from_pretrained(
"Llama-3.2-3B",
quantization_config=bnb_config,
device_map="auto"
)
He ended with:
“Cache to /data, not /tmp—Spaces wipes /tmp on restart.”
We immortalized that line in our commit history as feat: salsa-proof cache.
🌐 IRL pop-up that almost broke the internet
Sunday noon, @dev_dan asked Twitter:
“Any Austin hackers want to co-work for the final sprint?”
Within an hour, eight devs showed up at Bennu Coffee. We dragged two picnic tables together, shared extension cords like spaghetti, and projected our live Space build logs onto the brick wall. Every green checkmark earned a communal cheer; every red X earned a taco.
💌 Shout-outs & thank-yous
@osanseviero – for the 4-bit quantisation life-hack
@bolt-moderator-luna – who restarted our stuck Docker builder at 3 a.m.
@taco_truck_carlos – the IRL chef who gave us free al pastor at 4 a.m. when he heard we were “building AI tacos”

bolt-lounge – the 200-person Discord thread that collectively debugged CUDA version mismatches while sharing cat GIFs

🧡 Take-away
Code is ephemeral; people & tacos are forever.
We shipped the demo, but the real artifact is a group-chat still popping with memes, PR reviews, and plans to meet at PyCon 2025—tacos included.
Catch the demo → huggingface.co/spaces/taco-llama-hack/llama-chat-summarizer
Catch us IRL → probably the nearest taco truck.

World’s Largest Hackathon Writing Challenge

Arman khan — Wed, 30 Jul 2025 20:39:05 +0000

🚀 Llama-3.2 Chat & Summarizer

A 3-hour zero-to-deploy journey with Bolt.new
Submission for the World’s Largest Hackathon Writing Challenge
🌟 What I built
A dual-mode Streamlit app that lets you:
Chat with Llama-3.2-3B-Instruct in real time
Summarize any PDF or URL in three bullet points
View source instantly with one-click “Show me the code” links
Live demo 👉 huggingface.co/spaces/your-username/llama-chat-summarizer
⚡ How Bolt.new changed my workflow

Table

Before (local) With Bolt.new
45 min scaffolding repo & CI 30 s prompt → full repo
Manual CUDA / bitsandbytes pain Auto-detected GPU image
Spaces build logs in dark terminal Inline AI debugger
🧩 Sponsor challenge: fit Llama-3.2 into 8 GB VRAM
Problem: Free HF Spaces kills containers > 8 GB.
Bolt solution (auto-generated):

dockerfile

.bolt/Dockerfile

FROM huggingface/transformers-pytorch-gpu:4.43
RUN pip install bitsandbytes --no-cache-dir
ENV TRANSFORMERS_CACHE=/data/.cache
ENV BNB_CUDA_VERSION=121

Result: RAM dropped from 14 GB → 7.2 GB → container stays alive.

💬 Favorite prompt & snippet

“Add a sidebar toggle ‘Chat ↔ Summarize’. In Summarize mode allow PDF upload or URL; on submit run Llama-3.2 with system prompt ‘Summarize in 3 bullets’ and stream the response.”

Bolt spit out:
Python
import streamlit as st
from transformers import pipeline

@st.cache_resource
def load_llama():
return pipeline(
"text-generation",
model="meta-llama/Llama-3.2-3B-Instruct",
torch_dtype="auto",
device_map="auto",
model_kwargs={"load_in_4bit": True}
)

llm = load_llama()

mode = st.sidebar.radio("Mode", ["Chat", "Summarize"])

if mode == "Summarize":
file = st.file_uploader("Upload PDF", type="pdf")
url = st.text_input("Or paste URL")
if st.button("Summarize"):
txt = extract_pdf(file) if file else extract_url(url)
bullets = llm(
f"<|system|>\nSummarize in 3 bullets<|user|>\n{txt}<|assistant|>",
max_new_tokens=120
)[0]["generated_text"]

st.markdown(bullets)

🎨 Style & presentation hacks
Glass-morphism cards via custom CSS injected with st.markdown(..., unsafe_allow_html=True)
Animated cursor with st.empty() + time.sleep(0.03) stream
Open in VS Code badge auto-generated by Bolt
🔄 Mindset shift
Describe intent → AI writes & hosts → you polish UX.
No more YAML, no more requirements.txt archaeology.
🙌 Credits
me – @arman Khan
Bolt.new – the silent co-founder 🦾

Thanks for reading! Give the live demo a spin and drop feedback in the comments.

Embracing the Future: How Technology is Reshaping Our Lives

Arman khan — Sun, 09 Mar 2025 19:47:46 +0000

Dear Friends and Family,

Technology is evolving rapidly, changing how we work, learn, and live. Remote jobs, AI, and automation are reshaping careers, making continuous learning essential. Healthcare is becoming more proactive with wearables, virtual consultations, and AI-driven diagnostics. Education is shifting online, offering endless learning opportunities beyond traditional classrooms.

Digital payments and AI-driven shopping experiences are the new norm, but we must stay vigilant about cybersecurity. Smart home technology is making life more convenient while raising privacy concerns.

Platforms like the Dev Com app connect developers and tech enthusiasts, providing a space to share knowledge and stay updated on industry trends.

Adapting to these changes is key. Let’s embrace technology, stay informed, and support each other in navigating this evolving world. Looking forward to hearing your thoughts!

With love and curiosity,[Your Name]

The Rise of Generative AI: More Than Just Words & Images

Arman khan — Wed, 05 Mar 2025 20:52:56 +0000

Generative AI is no longer limited to just text generation (like ChatGPT) or image creation (like DALL·E). It has rapidly evolved into a multi-modal powerhouse, capable of generating:
✅ Music & Soundscapes (Suno AI, Riffusion)
✅ Video & Animation (Runway ML, Pika Labs)
✅ 3D Models & Game Assets (Nvidia GET3D, Sloyd AI)
✅ Code & Software Development (GitHub Copilot, Code Llama)

🔍 The Evolution of Generative AI
From the early days of rule-based AI to modern deep learning-driven models, the journey has been incredible. Recent advancements in transformer models, diffusion models, and reinforcement learning have led to AI systems that can not only generate realistic outputs but also understand and adapt to user preferences.

💡 AI isn’t just creating content—it’s enabling creativity like never before.

🎨 AI’s Role in the Creative Process
🎶 Music & Sound: AI tools are composing original songs, generating sound effects, and even replicating voices. Could we see AI-powered virtual artists topping the charts?
🎥 Video & Animation: Platforms like Runway ML are making video editing as simple as typing a prompt. Hollywood is already experimenting with AI for special effects!
🕹️ Gaming & 3D Models: AI is generating game assets, NPC dialogue, and even full levels procedurally. Indie developers can now build immersive worlds faster.
🤖 The Challenges & Ethical Considerations
With great power comes great responsibility. As Generative AI expands, we face crucial questions:
⚠️ Bias & Fairness – Can AI-generated content be truly unbiased?
⚠️ Misinformation & Deepfakes – How do we prevent AI from being misused?
⚠️ Job Displacement vs. Job Enhancement – Will AI take over creative roles, or just make human creators more efficient?

🚀 What’s Next?
Generative AI is just getting started. The next phase will likely involve AI-human collaboration, where creators use AI as a co-pilot rather than a replacement. We might see:
🔹 AI-powered movie directors generating full-length films
🔹 AI-driven virtual influencers replacing social media celebrities
🔹 AI-generated virtual worlds where anything can be created on the fly

Prompt Engineering: The Art of Talking to AI

Arman khan — Thu, 13 Feb 2025 20:45:56 +0000

🧠 Introduction
Artificial Intelligence (AI) has revolutionized the way we interact with technology. Whether it’s generating text, creating images, or automating tasks, AI models like ChatGPT, DALL·E, and Midjourney rely on one crucial factor: prompts.

But how do we make AI understand and generate the best possible output? That’s where Prompt Engineering comes in!

This article will explore:
✅ What is Prompt Engineering?
✅ The Types of Prompts used in AI interactions
✅ Best Practices to improve AI-generated responses
✅ Real-World Applications in automation, coding, and content creation

Let’s dive in! 🚀

🔍 What is Prompt Engineering?
Prompt Engineering is the process of designing effective inputs (prompts) to guide AI models in generating the desired response. Think of it as giving the AI clear instructions so it knows exactly what you need.

A well-crafted prompt can enhance accuracy, reduce ambiguity, and generate high-quality responses, while a poorly structured one can lead to vague or incorrect results.

👉 Example of a basic prompt:
❌ Bad Prompt: "Write about AI."
✅ Good Prompt: "Write a 300-word article explaining how AI is transforming the healthcare industry, with real-world examples."

The second prompt gives clear instructions, leading to a more detailed and relevant response.

📌 Types of Prompts
There are several types of prompts used to guide AI behavior effectively:

1️⃣ Open-ended Prompts
Encourage broad and creative responses.
➡️ "Describe the future of AI in 50 years."

2️⃣ Instruction-based Prompts
Give the AI specific tasks.
➡️ "Summarize this article in 100 words."

3️⃣ Contextual Prompts
Provide background info to refine responses.
➡️ "You are a cybersecurity expert. Explain how to prevent phishing attacks."

4️⃣ Example-based Prompts
Use examples to set the AI's response style.
➡️ "Here’s a sample email: [example]. Now, write a similar email for a business inquiry."

5️⃣ Chain-of-Thought (CoT) Prompts
Help AI think step by step for complex problems.
➡️ "Explain how a neural network works in simple terms, breaking it down step by step."

⚡ Best Practices for Writing Effective Prompts
Want to get precise and high-quality AI responses? Follow these tips:

✅ Be Clear & Specific – Avoid vague instructions.
✅ Define the Output Format – Request responses in bullet points, paragraphs, or tables.
✅ Provide Examples – Show the AI what kind of output you expect.
✅ Limit Response Length – Use "Answer in 200 words" for concise results.
✅ Use Role-based Prompts – "Act as a UI/UX designer and suggest improvements for this website."
✅ Iterate & Refine – Adjust prompts if the response isn’t satisfactory.

🌍 Real-World Applications of Prompt Engineering
Prompt Engineering is transforming industries like:

🎨 Content Creation – Writing blogs, scripts, and ad copy.
🤖 AI Chatbots – Enhancing chatbot interactions with human-like responses.
📊 Data Analytics – Summarizing and analyzing datasets.
💻 Coding Assistance – Debugging and generating code snippets.
🎮 Game Development – Creating AI-driven NPC dialogues.
🎓 Education & Research – Generating quizzes, summaries, and study materials.

🚀 The Future of Prompt Engineering
With AI evolving rapidly, Prompt Engineering is becoming a crucial skill. Soon, we’ll see AI models requiring less effort to understand prompts, but for now, writing precise and structured prompts remains the key to unlocking AI's full potential.

By mastering Prompt Engineering, you can enhance AI interactions, improve automation, and boost productivity across multiple domains.

What are your thoughts on Prompt Engineering? Drop a comment below and let’s discuss! 👇😊

10 DeepSeek R1 Prompts for Coding That Actually Save You Time.

Arman khan — Thu, 13 Feb 2025 20:40:37 +0000

Most people don’t know how to prompt AI properly.

They type something vague like “Optimize this JavaScript function” and expect groundbreaking results. Then, when they get a slightly cleaner version of what they already wrote, they think, AI isn’t that great.

AI is only as good as your prompts.

DeepSeek R1 is powerful. It understands code well, but if you’re not framing your questions correctly, you’re leaving value on the table.

These 10 prompts will help you get the best possible output—cleaner, faster, and actually useful.

“Refactor this function for better performance. Explain each change.”

A vague "optimize this" won’t cut it. You want clear improvements with reasoning so you actually understand what’s being changed.

🟢 Better prompt:

"Refactor this function to improve performance and maintainability. Explain each change in detail, focusing on execution time, readability, and memory efficiency."

This forces DeepSeek R1 to not just rewrite code but also justify every modification.

“Identify potential memory leaks in this JavaScript code.”

Memory leaks slow down applications and are hard to spot. Let DeepSeek R1 do the heavy lifting.

🟢 Better prompt:

"Analyze this JavaScript function for potential memory leaks. Point out what’s causing them and suggest fixes."

You’ll get targeted optimizations instead of a generic best-practices list.

“Rewrite this SQL query for better performance. Explain the changes.”

Databases get sluggish when queries are inefficient. Asking for a rewrite without explanation means you’ll end up copy-pasting without learning.

🟢 Better prompt:

"Optimize this SQL query for speed. Prioritize reducing execution time and improving index usage. Explain each improvement step by step."

This gets you performance gains you can actually apply elsewhere.

“Write unit tests for this function covering all edge cases.”

If you just ask AI to write tests, it’ll generate happy path scenarios and call it a day. You need to force it to dig deeper.

🟢 Better prompt:

"Write comprehensive unit tests for this function in Jest, ensuring all edge cases (invalid inputs, boundary conditions, unexpected data types) are covered."

Your tests will go from basic to robust.

“Debug this error and explain what’s causing it.”

Copy-pasting errors into Google works, but DeepSeek R1 can debug faster if you give it the right context.

🟢 Better prompt:

"I’m getting this error in my Next.js app: [Insert error]. Analyze the issue and explain what’s going wrong in simple terms."

Instead of random StackOverflow answers, you’ll get a direct fix with an explanation.

“Convert this JavaScript function to TypeScript with proper types.”

Most AI-generated TypeScript code is lazy. It just slaps an any type on everything. That’s not useful.

🟢 Better prompt:

"Convert this JavaScript function into TypeScript, ensuring strict type safety. Avoid using 'any' and infer types where possible."

This forces DeepSeek R1 to do actual type inference instead of taking shortcuts.

“Rewrite this function using functional programming principles.”

If you just ask AI to refactor code, it’ll slightly clean it up but won’t shift paradigms. To enforce a different approach, you need to be specific.

🟢 Better prompt:

"Refactor this function to follow functional programming principles. Use pure functions, immutability, and avoid side effects."

This gives you a structural shift, not just minor tweaks.

“Optimize this React component to minimize unnecessary re-renders.”

React re-renders kill performance. AI won’t prevent them unless you specifically ask it to.

🟢 Better prompt:

"Identify why this React component is re-rendering unnecessarily and optimize it using memoization, useCallback, or other best practices."

You’ll get targeted solutions instead of basic ‘useMemo’ suggestions.

“Explain this complex code to me like I’m a junior developer.”

Ever come across code that looks like an alien language? AI can break it down for you—if you ask the right way.

🟢 Better prompt:

"Here’s a function that does X. Explain its logic in a simple, step-by-step way, as if you’re teaching a beginner."

This makes AI act like a mentor, not just a code generator.

“Rewrite this Python script in Node.js while maintaining efficiency.”

AI translates code line by line unless you tell it otherwise. That often leads to inefficient conversions.

🟢 Better prompt:

"Convert this Python script into an optimized Node.js implementation, ensuring equivalent functionality while following best practices for event-driven programming."

Instead of blind translation, you get a version that actually makes sense in Node.js.

If you feed it weak prompts, you’ll get weak results. The way you phrase your request determines whether you get something useful or something generic.

Try these prompts the next time you use DeepSeek R1. You’ll see the difference instantly.

Feb Challenge

Arman khan — Thu, 13 Feb 2025 20:13:34 +0000

Code for the feb challenge triend making two standing under the moon with abstract grphics

<!DOCTYPE html>

Winter Romance

<br>
body {<br>
margin: 0;<br>
height: 100vh;<br>
background: linear-gradient(to bottom, #0a2342, #2a5479);<br>
overflow: hidden;<br>
font-family: Arial, sans-serif;<br>
}</p>
<div class="highlight"><pre class="highlight plaintext"><code> /* Moon */
.moon {
position: absolute;
top: 30px;
right: 30px;
width: 80px;
height: 80px;
background: radial-gradient(circle at 50% 50%, #fff 65%, #f0f0f0 100%);
border-radius: 50%;
box-shadow: 0 0 50px rgba(255, 255, 255, 0.3);
animation: moon-glow 3s ease-in-out infinite alternate;
}

/* Improved Streetlight */
.streetlight {
    position: absolute;
    bottom: 0;
    left: 50%;
    transform: translateX(-50%);
    z-index: 2;
}

.streetlight-pole {
    width: 12px;
    height: 250px;
    background: #3d3d3d;
    margin: 0 auto;
}

.streetlight-arm {
    width: 80px;
    height: 12px;
    background: #3d3d3d;
    position: relative;
    left: -34px;
}

.streetlight-lantern {
    width: 60px;
    height: 80px;
    background: #4a4a4a;
    border-radius: 10px;
    position: relative;
    left: -24px;
    display: flex;
    justify-content: center;
}

.streetlight-glow {
    width: 40px;
    height: 40px;
    background: radial-gradient(circle, 
        rgba(255, 215, 150, 0.8) 0%, 
        rgba(255, 190, 100, 0.4) 50%, 
        transparent 100%);
    filter: blur(15px);
    animation: light-flicker 2s infinite alternate;
}

/* Couple Figures */
.couple {
    position: absolute;
    bottom: 100px;
    left: 50%;
    transform: translateX(-50%);
    display: flex;
    gap: 20px;
    z-index: 1;
    animation: float 3s ease-in-out infinite;
}

.person {
    width: 40px;
    height: 100px;
    position: relative;
}

.person:nth-child(1) {
    transform: rotate(5deg);
}

.person:nth-child(2) {
    transform: rotate(-5deg);
}

.person-body {
    width: 40px;
    height: 80px;
    background: #2c2c2c;
    border-radius: 20px;
    position: absolute;
    bottom: 0;
}

.person-head {
    width: 25px;
    height: 25px;
    background: #ffe4c4;
    border-radius: 50%;
    position: absolute;
    bottom: 80px;
    left: 50%;
    transform: translateX(-50%);
}

/* Snowfall container */
#snow-particles {
    position: absolute;
    width: 100%;
    height: 100%;
}

/* Animations */
@keyframes float {
    0%, 100% { transform: translateY(0) translateX(-50%); }
    50% { transform: translateY(-15px) translateX(-50%); }
}

@keyframes light-flicker {
    0% { opacity: 0.9; }
    100% { opacity: 1; }
}

@keyframes moon-glow {
    0% { opacity: 0.9; }
    100% { opacity: 1; }
}

/* Ground effect */
.ground {
    position: absolute;
    bottom: 0;
    width: 100%;
    height: 150px;
    background: linear-gradient(transparent, #ffffff33);
    backdrop-filter: blur(3px);
    z-index: 2;
}

</style>
</code></pre></div>
<p></head><br>
<body><br>
<div class="moon"></div></p>
<div class="highlight"><pre class="highlight plaintext"><code><div class="streetlight">
<div class="streetlight-pole"></div>
<div class="streetlight-arm"></div>
<div class="streetlight-lantern">
<div class="streetlight-glow"></div>
</div>
</div>

// Add arm connection between people
const couple = document.querySelector('.couple');
const arm = document.createElement('div');
arm.style.position = 'absolute';
arm.style.width = '40px';
arm.style.height = '8px';
arm.style.background = '#2c2c2c';
arm.style.top = '60px';
arm.style.left = '50%';
arm.style.transform = 'translateX(-50%)';
arm.style.borderRadius = '4px';
couple.appendChild(arm);

</script>
</code></pre></div>
<p></body><br>
</html></p>