@Surendar__05 The future belongs to engineers who can work across languages.
Python is leading today, but architecture, distributed systems, and problem-solving skills tend to outlast any specific language trend.
@Palak3312 Python.
But over time I've realized that the most important skill isn't choosing a language.
It's designing systems that remain understandable six months later.
The biggest lie in AI coding:
"AI will replace developers."
No.
AI is replacing repetitive setup.
The developers who understand systems, #architecture, and #context will become even more valuable.
The future isn't #AI vs developers.
It's AI + developers vs complexity.
Frameworks generate code.
RapidKit standardizes the workspace.
Workspai brings AI into the workflow.
The future of backend development is workspace-first.
#RapidKit#Workspai#AI
Building RapidKit + Workspai.
AI-native engineering tools for creating, verifying, and shipping backend workspaces with confidence.
Less boilerplate.
Less runtime drift.
More trust.
@TheMaverick_tw It depends on the project, but for me it's usually the first 30-60 minutes that disappear.
Project structure, models, routes, services, Docker setup... all the repetitive stuff.
The bigger benefit isn't just saving time, it's starting with a consistent foundation every time.
Still creating FastAPI projects manually?
Models.
Routes.
Services.
Docker.
Project structure.
What if all of that could be generated in seconds?
⚡ Building something interesting with RapidKit.
#python#fastapi#backend#webdev
Most developers don't need more AI.
They need less boilerplate.
Imagine starting a backend project with the structure, APIs, and workflow already in place.
That's the direction we're exploring with RapidKit + Workspai.
🚀
#programming#buildinpublic#ai#developers
#AI can generate #code in seconds.
But most production incidents aren't caused by missing code.
They're caused by missing context.
Dependencies.
Architecture.
System behavior.
That's where the next generation of AI tools needs to evolve.
Read more:
medium.com/workspai/your-…
Most #AI coding tools optimize for writing #code faster.
#Backend teams rarely fail because typing was slow.
They fail because context was missing.
Architecture > autocomplete.
AI made code abundant.
But the scarcest engineering resource may now be context.
Source code tells you what the system does.
Context tells you:
why it exists,
what broke before,
what dependencies it affects,
and what happens if you change it.
#AI#SoftwareEngineering
Everyone talks about #AI writing code.
Almost nobody talks about AI understanding systems.
Generating a service takes seconds.
Understanding why it fails in #production still takes experience.
The leverage is shifting from coding to reasoning.
#AI made code generation cheap.
Understanding the system is still expensive.
Most production #bugs today don’t come from syntax.
They come from hidden assumptions, dependency interactions, and missing context.
Faster #code isn't the breakthrough.
Faster understanding is.
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