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0 3 38 4 days ago

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Shahir

4 days ago

Which programming tasks do you still prefer to do without AI?

AI coding assistants have become a common part of modern software development, helping with code generation, debugging, testing, documentation, and productivity. However, many developers still prefer to handle certain tasks manually to maintain code quality, deepen their understanding, or retain control over critical decisions. Some examples include: System architecture and design decisions Learning new frameworks(Read More)

AI coding assistants have become a common part of modern software development, helping with code generation, debugging, testing, documentation, and productivity. However, many developers still prefer to handle certain tasks manually to maintain code quality, deepen their understanding, or retain control over critical decisions.

Some examples include:

System architecture and design decisions
Learning new frameworks or technologies
Debugging complex issues
Security and code reviews
Performance optimization
Writing core business logic

Which programming tasks do you intentionally avoid using AI for, and why? Has your approach changed as AI tools have become more capable?

This is a great opportunity to discuss where AI adds the most value and where human judgment still matters most.

0 0 62 3 weeks ago

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Oscar

June 8, 2026

Should AI be Afforable for Free for basic use cases?

Access FreeAI at https://www.linkedin.com/feed/update/urn:li:activity:7469178375710973952 www.Inquiret.tech – We are looking for more folks to join the Inquiret movement. Ping us here, through Linkedin, or email us at Inquiret@tuta.com

Access FreeAI at https://www.linkedin.com/feed/update/urn:li:activity:7469178375710973952

www.Inquiret.tech – We are looking for more folks to join the Inquiret movement. Ping us here, through Linkedin, or email us at Inquiret@tuta.com

0 3 78 3 weeks ago

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Shishir

June 8, 2026

What tech stacks are teams using for scalable AI agent systems in production?

I’ve been exploring how organizations are structuring production-ready AI workflows beyond just model experimentation, particularly around orchestration, retrieval pipelines, memory handling, monitoring, and multi-agent coordination. There are now so many combinations being used across:• LLM frameworks• vector databases• orchestration layers• observability tools• retrieval systems• agent frameworks• cloud infrastructure The challenge is that many stacks work(Read More)

I’ve been exploring how organizations are structuring production-ready AI workflows beyond just model experimentation, particularly around orchestration, retrieval pipelines, memory handling, monitoring, and multi-agent coordination.

There are now so many combinations being used across:
• LLM frameworks
• vector databases
• orchestration layers
• observability tools
• retrieval systems
• agent frameworks
• cloud infrastructure

The challenge is that many stacks work well in prototypes, but reliability, scalability, governance, and operational complexity become very different conversations once systems move into real enterprise environments.

Curious to hear from teams already building or deploying AI agents in production:
What stack combinations are working well for you, and what trade-offs have you encountered so far?

0 1 101 1 month ago

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Erin

May 26, 2026

Is AI creating innovation faster than industries can adapt?

AI innovation is accelerating at a pace most industries have never experienced before. Every few weeks, new models, autonomous agents, copilots, reasoning systems, and AI infrastructure breakthroughs are reshaping how work gets done across technology, operations, analytics, customer support, software development, and decision-making. But alongside this innovation, a different kind of pressure is spreading across(Read More)

AI innovation is accelerating at a pace most industries have never experienced before. Every few weeks, new models, autonomous agents, copilots, reasoning systems, and AI infrastructure breakthroughs are reshaping how work gets done across technology, operations, analytics, customer support, software development, and decision-making.

But alongside this innovation, a different kind of pressure is spreading across industries.

Not necessarily immediate job replacement, but continuous uncertainty.

Teams are watching tasks become automated faster than organizational structures can adapt. Companies are rethinking hiring plans, operational models, and workforce structures in real time. Employees are being asked to produce more with smaller teams, while leadership struggles to define which skills will remain valuable long-term.

The result is not just fear of unemployment.
It’s a growing instability around role definition itself.

Many professionals are no longer asking:
“Will AI take my job?”

They’re asking:
“What will my role even look like 3 years from now?”

At the same time, entirely new layers of work are emerging around AI governance, orchestration, integration, infrastructure, workflow design, and human-AI collaboration.

So the industry is entering a strange phase:
AI is simultaneously creating efficiency, anxiety, opportunity, compression, and reinvention at scale.

How do you see this next phase evolving?