JustSoftLab

JustSoftLab · Senior-only AI/CV/Data Eng pods · 200+ projects shipped

[JustSoftLab @JustSoftLab]

I just published Multi-agent coordination in interceptor drone systems: what real-world autonomy actually looks like https://t.co/whbfrtIST5

[JustSoftLab @JustSoftLab]

Data teams that win in the AI era will not just collect more data.
They will build cleaner pipelines, clearer logic, and stronger trust around the data that AI depends on.

#DataEngineering #DataAI

[JustSoftLab @JustSoftLab]

Ukraine keeps rewriting the rules of modern warfare.
The FP-2 drone launching aircraft missiles at targets in temporarily occupied Crimea is another reminder: it’s no longer about the size of the platform. It’s about speed of integration, adaptation, and execution.
Drones are becoming multi-role strike systems, and that changes the economics of war.
The side that adapts fastest wins.
#Ukraine #Drones #DefenseTech #FPV #MilitaryTech

[JustSoftLab @JustSoftLab]

When a drone worth a few hundred dollars can make a tank vulnerable,
this is no longer just “new technology.”
This is the end of the old military logic.
Ukraine was the first to prove it at scale.
#UkraineWar #Drones #FPV #Warfare

[JustSoftLab @JustSoftLab]

Most AI ideas fail not at the model level, but at the delivery stage.

[JustSoftLab @JustSoftLab]

Full write-up (numbers, charts, and a simple decision tree that maps camera count + model class + latency budget to the right Jetson tier):

🔗 Jetson Orin Nano vs NX vs AGX for manufacturing computer vision: a production benchmark

https://t.co/Khob0DOzSS

Curious: what’s your current default for factory CV Jetson, server GPUs, or something else?

#JetsonOrin #EdgeAI #ComputerVision #Manufacturing #Robotics

[JustSoftLab @JustSoftLab]

Choosing between Jetson Orin Nano, NX, and AGX for manufacturing computer vision is not a spec sheet exercise.

We benchmarked all three on a real factory use case: 24 cameras, defect detection on automotive parts, migrated from AWS g5.xlarge to Jetson at the edge.

[JustSoftLab @JustSoftLab]

The rule of thumb we now use with clients:

-Orin Nano → ≤4 cameras, lightweight models, tight budget
-Orin NX 16GB → 4–8 cameras, YOLOv8m‑class workloads
-AGX Orin → 12+ streams or complex multi-stage pipelines on a single device.

[JustSoftLab @JustSoftLab]

Business, please understand: there's no need to implement AI if you don't have responsible team members. AI is just a tool, not magic.

[JustSoftLab @JustSoftLab]

Full breakdown of all 10 mistakes + the architecture we recommend + a migration sequence for systems already in production:

https://t.co/Dl8dQGdgNE

What's the most expensive RAG architecture decision you've reversed in production?

[JustSoftLab @JustSoftLab]

9 out of 10 fintech RAG systems we audit ship to production with the same ten architecture mistakes.

In a predictable order.

Here are the three that bite hardest in regulated financial services 👇

[JustSoftLab @JustSoftLab]

Mistake 6: cost economics modeled wrong.

The visible line item is LLM inference per query (~$0.005-0.05).

The lines that dominate at scale and get missed:
- Embedding cost on initial corpus + ongoing updates
- Storage at full precision
- Egress on managed vector DBs ($1k+/month)
- Re-ranker compute
- Audit storage retention

Honest fintech RAG TCO: 2-4× naive LLM-cost-only models.

[JustSoftLab @JustSoftLab]

We don’t just provide developers - we help companies turn AI ambition, product backlog and data complexity into shipped, scalable solutions.