👨‍💻 Technical Leader | NetDevOps & DevNet 🚀 Automation Enthusiast 🏆 Cisco Champion ‘21-‘22 🌍 Avid Traveler 🏄🏽‍♂️ Surfer 📍Dubai Golden Resident

Joined May 2020
Exploring how NetBox could feel with a decoupled React frontend. It started as a fun evening experiment to compare Windsurf with Cursor, but the deeper I go into this prototype, the more potential I see in it. Sharing a few glimpses of the current look. What do you think?
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Time flies! Yesterday marked exactly 5 years since the first public commit to my NextBox UI Plugin for NetBox.
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What LLM were like a year ago vs. What LLM are now:
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Igor Korotchenkov retweeted
“no updates from my end”
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NextBox-UI Plugin v1.0 Community Survey is live! 👉 forms.gle/XN8SB6UdytjPrbh86 Since launching v1.0, the NextBox-UI Plugin with topoSphere has grown rapidly in GitHub stars and downloads and gained significant attention from the NetBox community! Your support has driven this momentum. To help shape the future, please consider taking a 5-minute anonymous survey about your experience and feature needs.
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If someone had told me even a couple of years ago that I’d witness live performances by Linkin Park and Eminem on the same stage… in the capital of Saudi Arabia, I would’ve called them an ambulance.
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Experimenting with visual topology builder support in my topoSphere SDK led me to an interesting PoC for @go_containerlab, featuring on-the-fly two-way topology format conversion. Hey @ntdvps, what do you think?
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After over 4 years, NextBox UI Plugin v1.0 is finally here – my biggest release yet (almost as massive as the jungle). What’s new: – topoSphere: a brand-new, state-of-the-art visualization SDK developed by me from scratch – Legacy NextUI SDK is fully deprecated – Fully reworked Filters tab, reusing and extending NetBox generics – Export to PNG, JPEG, and JSON – New modal windows – Dark mode support With the new engine, the plugin offers a new level of extensibility and usability. Check it out and let me know your thoughts! #NetBox #NetDevOps #DevNet
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Wow💪🏼 What a time to be alive.
Mechazilla has caught the Super Heavy booster!
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ChatGPT 4o is a thing. I’ve generated a working Rent vs Buy calculator in Python with complex logic and conditions using just text prompts. 🤯
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Thinking ahead, I plan to automate the data pipeline to ingest the export file on-demand and explore more advanced data analytics for deeper financial insights. What would you recommend by the way?
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I’ll continue using the iOS app for recording transactions, but now with Kibana dashboards, my financial tracking has leveled up. And I feel it's just the beginning. Happy to discuss with anyone interested! #elk #python #personalfinance
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I’m very visual kind of person. On the actual view with 8200+ transactions in the list over 7+ years, it was very enlightening to see the graphs and year-to-year charts and grasp the trends and distributions. Yet so much more can be added and improved!
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Playing with Kibana visualizations and creating meaningful dashboards is a fun part. With nothing but Kibana Lenses with relatively simple (I can be biased here) formulas queries and you can cover 90% of the typical financial metrics.
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The thread wouldn’t be complete without a screenshot but, as one CFO liked saying, Money Loves Silence. So I asked ChatGPT to write me a Python script to generate a sample randomized dataset. With a few more cups of coffee and some tweaks I got this test dashboard:
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My dashboard showcases: - Account balances - Yearly/Monthly Income vs Expenses - Monthly net profit trends - Yearly/overall savings rates - Top spending categories - Breakdown of expenses by category - Handy time selector and quick filters for easy navigation
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Finally, I prepared some queries in Pandas to reproduce the app’s statistics. And the numbers matched! Pandas itself is a full-blown analytics tool with decent data visualization options. But with a power of Python preprocessing, Kibana provides more interactive workflows and UX
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Spinning up a local dev ELK cluster in Docker is a matter of minutes (and this fact still amazes me). Then I generated an access token and pushed the data into Elasticsearch through the API directly from Python without Logstash. Which actually makes the stack EK (or EPK 🙃).
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