DJ

Building production AI systems end-to-end • Shipped ChatCrafter & BrandProbe • Sharing daily GenAI lessons • Open to Principal AI Architect /GenAI opportunities

[DJ @digv_pundir]

Every Sunday, my journal app writes me a letter.

Patterns it noticed.

A line it kept.

A question to carry.

Written entirely on my iPhone — nothing ever leaves the device.

This is Margin 🖋️

https://t.co/afIXtFyfiK

[DJ @digv_pundir]

@launch_llama @MarkZofMarkZ @AgentsVerseAI @thegtmcofounder Margin: a quiet place to write. Every Sunday it sends you a short letter about the week — patterns it noticed, a line it kept, a question to carry. Written entirely on your iPhone ✍️

https://t.co/afIXtFyfiK

[DJ @digv_pundir]

Indie app of the day: "Margin". You can search it by feeling — "when did I last feel free?" — not keywords. So good 🖋️

https://t.co/afIXtFyfiK
Margin — Journal & Reflect

[DJ @digv_pundir]

The biggest surprise after building production AI applications?

Users don't care which LLM you use.

They care about whether the answer is:
✅ Accurate
✅ Fast
✅ Grounded in their data
✅ Consistent
✅ Actually solves their problem
As engineers, we often spend hours comparing models.
In production, I've found the bigger wins usually come from:
• Better retrieval (RAG)
• Cleaner prompts
• Smarter tool orchestration
• Response evaluation
• Reducing latency
A great AI product isn't defined by the model behind it.
It's defined by the experience users have with it.
What's been your biggest lesson from shipping AI into production?

#AIEngineering #GenAI #RAG #LLM #SoftwareArchitecture #BuildInPublic

[DJ @digv_pundir]

One mindset shift changed how I build AI applications.

I used to think:

"How can I make the model smarter?"

Now I ask:

"How can I give the model better context?"

That single change led me to spend more time on:

• Structuring knowledge bases
• Retrieval quality (RAG)
• Prompt orchestration
• Tool selection
• Response evaluation

In production, the quality of an AI application's answers often depends more on the information you provide than on the model you choose.
A better prompt helps.
A better context scales.
What changed your approach after building your first production AI application?

#GenAI #RAG #AIEngineering #LLM #SoftwareArchitecture #BuildInPublic

[DJ @digv_pundir]

Everyone's talking about Claude Code and Fable right now.

After trying them, one thing became clear:

We're moving from AI that answers questions to AI that helps build software.

The interesting part isn't just code generation.

It's the workflow.

→ Understand the codebase
→ Plan the changes
→ Edit multiple files
→ Run commands
→ Iterate with feedback
This is a big shift from "autocomplete" to collaborative software engineering.

As someone building production AI applications, I think the next competitive advantage won't be the model itself.
It will be how well we design AI-powered developer workflows, integrate tools, maintain context across large codebases, and keep humans in control.
The future of software engineering isn't AI replacing developers.

It's developers who know how to collaborate with AI.

Have you tried Claude Code or Fable yet? What's impressed you the most?

#ClaudeCode #AIEngineering #GenAI #DeveloperTools #LLM #AIAgents #BuildInPublic

[DJ @digv_pundir]

One lesson I've learned building production AI applications:

The LLM is rarely the hardest part.

The real engineering challenges are everything around it:

• Retrieving the right context (RAG)
• Prompt orchestration
• Tool calling & AI agents
• Guardrails and hallucination prevention
• Latency & cost optimization
• Observability and evaluation

A good demo is easy.
A production AI system is an engineering problem.
Over the next few months, I'll be sharing practical lessons from building AI products, AWS architecture, and the mistakes that taught me the most.
What has been the biggest challenge in your GenAI projects?
#GenAI #AIEngineering #LLM #RAG #AWS #AIAgents #BuildInPublic

[DJ @digv_pundir]

Behind every "Ask AI" button is a lot more than an LLM.

A typical ChatCrafterAI request goes through:

📄 User asks a question
↓
🔍 Search the knowledge base
↓
📚 Retrieve the most relevant content
↓
🧠 Build a grounded prompt
↓
🤖 Generate an answer
↓
✅ Return sources with the response
The quality of each step determines how trustworthy the final answer is.
We're always refining this pipeline to improve accuracy and response quality.
#AIArchitecture #RAG #LLM #GenAI

[DJ @digv_pundir]

Something I learned building ChatCrafter:

An AI chatbot is only as smart as what you feed it.
By default, ChatGPT doesn't know anything about your business. So in ChatCrafter, when you add your website or docs, the bot reads them first and answers from that — not from random internet guesses.
This is called RAG (Retrieval-Augmented Generation). Fancy name, simple idea: give the AI your info before it answers, and it stops making things up.
Feed it well, and it works.

[DJ @digv_pundir]

Drop your website URL below.

I'll run a free audit and show you what's killing your conversions - messaging, SEO, AI visibility, the works.

No fluff. Just what's broken and how to fix it.
https://t.co/s6WSsqU2wo

Here's what the report looks like 👇

https://t.co/c6iZNGSrxg

[DJ @digv_pundir]

@Leooweb3 good morning

[DJ @digv_pundir]

@tobemarizu101 Slow but moving. No viral moment. No overnight success. Just shipping features, talking to users, and improving every day.

[DJ @digv_pundir]

What nobody tells you about launching a product with 0 followers:
I launched my SaaS few weeks ago. No audience. No connections. No viral moment.
Here's what I've learned the hard way 🧵

[DJ @digv_pundir]

@aditiitwt Building apps is the comfort zone. Talking to users is the scary part. Most people would rather ship 30 things than send 10 cold DMs. That's the real problem.

[DJ @digv_pundir]

Your landing page isn't losing customers because of design.
It's losing them because:
→ Your headline talks about you, not them
→ Your CTA says "Learn More" instead of "Try Free"
→ There's zero proof anyone has used your product

I've reviewed 50+ startup sites this month. Same 3 mistakes. Every time.

[DJ @digv_pundir]

A day in the life of a solo founder:
8am — fix a bug
9am — rewrite landing page copy
11am — send cold DMs
12pm — nobody replies
2pm — question all life choices
3pm — get one positive reply
4pm — feel unstoppable again

The emotional rollercoaster is real. But I'd rather ride it than sit in the audience.

[DJ @digv_pundir]

@DmitryVladyko The most surprising part? People don't ignore you because your product is bad. They ignore you because they don't know you exist. Building the product was the easy part. Getting anyone to look at it — that's the real startup.

[DJ @digv_pundir]

@martin_delannoy Living this right now. Building two products with zero followers. Nobody claps for you at the start. You just keep showing up until the room fills itself.

[DJ @digv_pundir]

I'm building two products from scratch with 0 followers:
→ @ChatCrafter_AI — turns your docs into an AI chatbot in minutes
→ @BrandProbe — audits your website's marketing, SEO, and AI visibility in 60 seconds
No funding. No team. Just me figuring it out one day at a time.
If you're building from zero too, follow along. I'll share everything.