Browse Comments — Raw (as collected)
Close reading of the corpus at each pipeline stage: raw → clean → relevant → coded.
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Nice and useful
Thank you for sharing!
AI is the new technology and we have to learn how it works as well 🙂
One of the biggest shifts happening in AI right now
is the movement from:
learning concepts
to
building operational systems.
Because many people consume AI theoretically—
while remaining disconnected from implementation reality.
And over time,
that creates a dangerous illusion:
information without operational capability.
What makes resources like this valuable
is not the technology itself.
It is reducing the distance between:
understanding AI
and
actually deploying it.
That distinction matters.
Because the future advantage will not belong only to:
people who know AI terminology.
It will increasingly belong to:
people who can integrate AI
into real operational environments,
decision systems,
and human workflows.
Theory creates awareness.
Implementation creates leverage.
M. Salama
AB — Alpha Balance
Ibrahima 😉
Thank you Abhishek Veeramalla for bringing this my way
Buyesi.org
Absolutely
💡Bara FALL thanks for sharing grand...je vais y faire un tour woutt xamxam touti 💡
This is valuable, Abhishek Veeramalla. Thank you for sharing.
You're doing amazingly work here sir.
And am so happy to came across someone like you on this space you're truly a blessing.
Well done sir.
Please sir Am a new person here, please can you help me understand how this platform works and how to connect with like-minded individuals.
Thank.
Gaurav Agrawal
Great
Well said
Abhishek Veeramalla ,Thanks for sharing, I want to talk you regarding an investment opportunity as a strategic Co-founder, Can we connect ?
Congratulations 🎉
True
Practical examples are where the real learning usually happens. Thanks for sharing this valuable resource. Abhishek Veeramalla
Building real production AI agents is where most learning actually happens because you move from theory to dealing with real constraints like latency, API failures, rate limits, and cost optimization. For example, an AI agent that works perfectly in a notebook often behaves differently in production when you introduce retries, logging, and real user traffic, which forces you to understand system design, not just the model.
Interesting, it's not everyone that would learn core in-depth AI software creation, some will just be capable of learning the usage of AI tools and software to make their work, life and activities less conspicuous and stressful...