Wrapped up my new Agentic AI project
It uses Gemini function calling to:
- inspect directories and files
- read and write code
- execute Python scripts in a sandboxed workspace
- loop through tool calls until a task is completed
Mostly built it to understand how tool-calling agents, context management, and agent loops work under the hood.
Check it out:
https://t.co/9Y7aquqC9m
#AgenticAI #LLM #BuildInPublic #100DaysOfCode
One of the most important ideas in modern AI powered search engines is the inverted index.
A naive approach would store:
Document → Words
Eg:
doc1 → [golang, backend, docker]
doc2 → [python, ai, backend]
doc3 → [golang, kubernetes]
To find all documents containing "golang", you'd have to scan every document.
An inverted index flips the relationship:
Eg:
golang → [doc1, doc3]
backend → [doc1, doc2]
docker → [doc1]
python → [doc2]
ai → [doc2]
kubernetes → [doc3]
Now a search for "golang" is just a lookup.
Searching for:
"golang AND backend"
becomes an intersection of posting lists:
golang → [doc1, doc3]
backend → [doc1, doc2]
Result → [doc1]
No full scan.
No brute force.
Just efficient data structures.
This simple idea powers search engines, log analytics platforms, document databases, code search tools, and many modern RAG systems.
Sometimes a massive performance gain doesn't come from adding more compute.
It comes from organizing data differently.
#TechTwitter #BuildInPublic #Programming #100DaysOfCode #AgenticAI #LLM
One of the most important ideas in modern AI powered search engines is the inverted index.
A naive approach would store:
Document → Words
Eg:
doc1 → [golang, backend, docker]
doc2 → [python, ai, backend]
doc3 → [golang, kubernetes]
To find all documents containing "golang", you'd have to scan every document.
An inverted index flips the relationship:
Eg:
golang → [doc1, doc3]
backend → [doc1, doc2]
docker → [doc1]
python → [doc2]
ai → [doc2]
kubernetes → [doc3]
Now a search for "golang" is just a lookup.
Searching for:
"golang AND backend"
becomes an intersection of posting lists:
golang → [doc1, doc3]
backend → [doc1, doc2]
Result → [doc1]
No full scan.
No brute force.
Just efficient data structures.
This simple idea powers search engines, log analytics platforms, document databases, code search tools, and many modern RAG systems.
Sometimes a massive performance gain doesn't come from adding more compute.
It comes from organizing data differently.
#TechTwitter #BuildInPublic #Programming #100DaysOfCode #AgenticAI #LLM
Hey @X
My timeline needs more builders.
If you're into:
• Backend
• AI
• DevOps
• Cloud
• Startups
• Open source
Reply with what you're working on and I'll check out your profile.
Let's #connect 🚀
#TechTwitter#BuildInPublic#IndieHackers#AI#DevOps#MachineLearning #OpenSource #Startups #SaaS #Programming #100DaysOfCode
Remember the times when devs utilised each byte of the RAM?
Pfft, today Slack alone eats 2GB. VSCode takes 4GB & don't get me started on Claude Code & it's hunger for memory.
We replaced optimization with "just add more RAM & CPU." The irony is that 2026 is shipping the most resource-hungry software while hardware prices keep climbing.
Hardware got better but software got lazy faster.