Marko Denic Tech

[Marko Denic Tech @markodenictech]

Free eBooks for developers:

1/ The Technical Decision Framework
https://t.co/5jXvx8tbMe

2/ HTML Tips & Tricks
https://t.co/QWHDkoVlFl

3/ JavaScript design patterns
https://t.co/FfGJLtD06Y

4/ Learn to Code and Get a Job
https://t.co/0gCBGQ8Pw5

5/ SQL Handbook
https://t.co/2u8jGVtdXP

[Marko Denic @denicmarko]

JavaScript tip:

Use localStorage to remember your users. No backend, no database needed.

[Command Code @CommandCodeAI]

𝐘𝐨𝐮𝐫 𝐜𝐨𝐝𝐞. 𝐘𝐨𝐮𝐫 𝐭𝐚𝐬𝐭𝐞.

The taste-1 model is the core of our taste architecture. To build your taste profile it:

▪ Learns from you
▪ Thinks like you
▪ Grows with you

Learn more here: https://t.co/RdSvOPmea1

[Marko Denic Tech @markodenictech]

@denicmarko LFG!

[Marko Denic Tech @markodenictech]

@CommandCodeAI 👏

[Marko Denic @denicmarko]

I’ve worked on enough apps to know that search is usually one of those features that starts simple and gets complicated fast.

At first, a basic keyword search feels fine.

Then users start searching the way real people actually think:

“Show me the most powerful car you have”
“High performance Italian cars above 700hp”
“A Honda or BMW with at least 200hp, rear-wheel drive, from 20K to 50K”

And suddenly your search logic turns into a mix of filters, conditions, parsing rules, aliases, and edge cases.

That’s why @typesense caught my attention.

Typesense is an open-source search engine built for modern app and site search, and their built-in Natural Language Search support in v29+ is especially interesting.

Instead of forcing users to search with exact keywords, Typesense can translate plain English queries into structured search filters and sorting logic automatically using your preferred LLM provider.

So instead of manually handling queries like:

“A Honda or BMW with at least 200hp, rear-wheel drive, from 20K to 50K”

Typesense can understand:
• car brands
• horsepower requirements
• drivetrain preferences
• price ranges
• sorting intent

without needing to build complex parsing logic on the application side.

It’s a much cleaner way to build intelligent search experiences.

Other things I like about it:
• Typo tolerance out of the box
• Fast search-as-you-type performance
• Simple relevance tuning
• Laravel Scout and Django integrations
• Minimal setup for production-ready search

If you’re building search into a SaaS product, ecommerce app, docs site, or internal tool, it’s worth checking out.

GitHub Repo: https://t.co/WiIO7bNjsu
–
Thanks to @typesense for sponsoring this post!

[Marko Denic @denicmarko]

If you're buiding something cool, join us here:

https://t.co/5FSK2plEhW

[Marko Denic Tech @markodenictech]

@Frankenmint Thanks for trying. The answer is `number`. :)

You can find more questions and explanations here https://t.co/aEvP4Xb2C0

[Marko Denic Tech @markodenictech]

#JavaScript Quiz!

Do you know what will be the output of this code?

[Marko Denic Tech @markodenictech]

Do you know what will be the output of this code?

[Marko Denic Tech @markodenictech]

More questions here: https://t.co/aEvP4Xb2C0

Share your score!

[Marko Denic Tech @markodenictech]

@Frankenmint Try again!

[Marko Denic Tech @markodenictech]

@denicmarko https://t.co/cXdcSP3Sww

[Marko Denic @denicmarko]

Snippet Editor is on Uneed today!

Create beautiful, shareable screenshots of your code.

Check it out: https://t.co/vxTfYpDDmT

[Marko Denic Tech @markodenictech]

@denicmarko 💪

[Marko Denic Tech @markodenictech]

@denicmarko 👏

[Marko Denic Tech @markodenictech]

@CommandCodeAI https://t.co/siqvM2yPfV

[Razvan Muntian @razvanmuntian]

Most developers optimize for addition, meaning new features, extended functionality, and growing systems.

Good code is easy to remove.

This is such a great tip! 🧑‍💻

Shout out to @joncphillips and @denicmarko for providing such useful tips on a daily basis!

If you’re into coding, you should definitely check this out!

https://t.co/OQPy0JN2Ot

[Marko Denic Tech @markodenictech]

@petarivanovv9 @denicmarko 👏

[Petar Ivanov @petarivanovv9]

If you want to get better at software engineering,
here are 12 must-read newsletters (all free):

🧵

[Marko Denic @denicmarko]

MongoDB just launched new AI Skill Badges designed to help developers move from AI prototypes to production-ready systems.

What stands out is that these badges focus on practical capabilities teams actually need to ship AI apps, all inside the MongoDB Atlas platform developers already know.

↳ Memory for AI Applications
Build persistent memory for AI agents using MongoDB, LangGraph, vector search, and Voyage AI so applications can retain context across sessions while keeping user data isolated and secure.
https://t.co/VLBX63QZp7

↳Voyage AI with MongoDB
Create semantic search and retrieval pipelines optimized for relevance, latency, and cost using vector embeddings and MongoDB Vector Search.
https://t.co/VzP5C8WvLL

↳ Vector Search Performance
Learn how to diagnose and optimize MongoDB Vector Search in production using Atlas Metrics, quantization, partial indexing, and dedicated Search Nodes.
https://t.co/jyLcsEeVP3

Each badge includes hands-on learning, a short skills assessment, and a verifiable credential you can share on LinkedIn.

For teams already using MongoDB, this is a pretty direct path to upskilling developers on production AI workflows without introducing an entirely new stack.
—
Thanks to @MongoDB for sponsoring this post!