I've been thinking about a problem I keep seeing in infra/DevOps work.

A production alert fires and someone has to manually jump between logs, Grafana/Datadog, Kubernetes, deployment history, GitHub, cloud metrics, etc. just to figure out what actually happened.

I'm wondering if a useful chunk of that investigation could be automated.

The idea would be something that, when an alert or deployment failure happens, automatically:

• investigates logs, metrics and traces
• checks recent deployments/commits
• correlates changes with when the issue started
• checks Kubernetes/cloud infrastructure
• identifies the likely root cause and supporting evidence
• suggests a remediation
• if a code/config change is required, generates a PR
• waits for an engineer to review/approve it
• after deployment, verifies whether the issue was actually resolved

For example:
Instead of an engineer starting from scratch, the system might come back with:

I'm not really thinking about fully autonomous production changes initially. More like an AI first-line SRE that does the investigation and prepares the fix, while engineers stay in control.

I'm trying to understand whether this solves a meaningful enough problem or whether existing observability/SRE tooling already gets teams most of the way there.

For people running production systems:

• How much engineering time do you actually spend investigating alerts/deployment failures?
• What's the most repetitive part of incident investigation today?
• Would you trust a system like this to read your infra/logs if it couldn't make changes without approval?
• Would automatic root-cause analysis be valuable by itself, or would it need to generate the actual fix/PR?
• What integrations would be essential? Datadog? Grafana? Sentry? Kubernetes? AWS? GitHub?
• What would make you immediately say "there's no way I'm giving this access to production"?

I'd especially like to hear from founders, SREs, DevOps/platform engineers and engineers at smaller teams where dedicated SRE coverage is limited.

Not selling anything here I'm trying to understand where the actual pain is before building too much.

I have a profession as an AI engineer, however I am becoming more interested in how businesses manage their data after it surpasses the "clean spreadsheet".

According to what is assumed based on tech examples, data is processed as follows:

Excel → database → dashboard.

However, I suspect that for most companies, the true picture is:

spreadsheet made by different employees through the years

invoices and PDF documents scattered around

emails with data that haven't made it to the system

products/customers put in different forms

duplicate entries

old programs no one wants to use

periodically put together reports

valuable knowledge stuck in someone's mind

multiple programs failing to share data

I am especially interested in companies which have plenty of data but cannot use it properly.

For people running businesses:

What is your data story?

For instance:

What is your company's profile?

What is the location of your data?

What is the messiest part?

And if everything was organized already, what would you do with it?

I have been working as an ai engineer in one of the largest asset management company .
I have build complex ai systems from scratch understanding the complexity of each and every design decision . Now I want to convert to product manager job specially in ai .
How can I do that or any advice ?
Should I target startups or MNC ?

I have been working as an ai engineer in one of the largest asset management company .
I have build complex ai systems from scratch understanding the complexity of each and every design decision . Now I want to convert to product manager job specially in ai .
How can I do that or any advice ?
Should I target startups or MNC ?

The bar to call yourself an AI automation agency is basically zero now. Drag some nodes in n8n/Make, bolt on a GPT call, slap "AI Powered" on a landing page — done. You pay real money for what's often a fragile, unmonitored workflow, and when it breaks, "AI" takes the blame, not the vendor who cut corners.

3 questions to ask before you hire one:

1. What happens when it's wrong? Ask about error handling and how they measure accuracy. No eval process = no real idea if it works beyond the demo.
2. Who owns the system if you leave? A lot of this locks you into the no-code platform's account. Ask if you own the workflow or you're just renting it.
3. What's actually "AI" here vs. plain automation? Half of this is conditional logic + API calls that could've been priced as regular automation years ago. Make them point to where the model is actually reasoning.

3 questions I'd ask as an AI engineer:

1. "What's your retrieval architecture : proper chunking and embedding retrieval, or just a prompt stuffed with FAQ text?" A huge chunk of "AI agents" sold right now are one giant system prompt with static context pasted in. Ask if there's a vector store, how chunks are retrieved, and how relevance is scored. If they can't answer, there's no real RAG — just a glorified prompt template.
2. "What's your hallucination rate, and how did you measure it?" Not "does it work" ask for an actual number, from an eval set they built, with a defined accuracy metric (exact match, faithfulness score, human-graded, whatever). No-code shops almost never run evals because their tools don't expose the internals needed to build one. "We tested it and it seemed fine" is vibes, not engineering.
3. "Is this stateless per-request, or do you maintain context/memory and how do you handle token limits as usage grows?" This exposes whether they understand context windows, cost scaling, and system design, or whether they've built something that works fine in a demo with 3 messages and quietly breaks (or gets expensive) at real usage volume.

No-code tools aren't the villain — they're great for simple, low-stakes workflows. The problem is paying "AI transformation" money for "if this, then that" work with zero rigor.

Anyone else seeing this?

The bar to call yourself an AI automation agency is basically zero now. Drag some nodes in n8n/Make, bolt on a GPT call, slap "AI Powered" on a landing page — done. You pay real money for what's often a fragile, unmonitored workflow, and when it breaks, "AI" takes the blame, not the vendor who cut corners.

3 questions to ask before you hire one:

1. What happens when it's wrong? Ask about error handling and how they measure accuracy. No eval process = no real idea if it works beyond the demo.
2. Who owns the system if you leave? A lot of this locks you into the no-code platform's account. Ask if you own the workflow or you're just renting it.
3. What's actually "AI" here vs. plain automation? Half of this is conditional logic + API calls that could've been priced as regular automation years ago. Make them point to where the model is actually reasoning.

3 questions I'd ask as an AI engineer:

1. "What's your retrieval architecture : proper chunking and embedding retrieval, or just a prompt stuffed with FAQ text?" A huge chunk of "AI agents" sold right now are one giant system prompt with static context pasted in. Ask if there's a vector store, how chunks are retrieved, and how relevance is scored. If they can't answer, there's no real RAG — just a glorified prompt template.
2. "What's your hallucination rate, and how did you measure it?" Not "does it work" ask for an actual number, from an eval set they built, with a defined accuracy metric (exact match, faithfulness score, human-graded, whatever). No-code shops almost never run evals because their tools don't expose the internals needed to build one. "We tested it and it seemed fine" is vibes, not engineering.
3. "Is this stateless per-request, or do you maintain context/memory and how do you handle token limits as usage grows?" This exposes whether they understand context windows, cost scaling, and system design, or whether they've built something that works fine in a demo with 3 messages and quietly breaks (or gets expensive) at real usage volume.

No-code tools aren't the villain — they're great for simple, low-stakes workflows. The problem is paying "AI transformation" money for "if this, then that" work with zero rigor.

Anyone else seeing this?

What is the best way to scrape Reddit for research purpose ?

Should it be checking each sub Reddit of related domain or directly putting the question and checking the responses or taking top 10 responses .

What is the best way to scrape Reddit for research purpose ?

Should it be checking each sub Reddit of related domain or directly putting the question and checking the responses or taking top 10 responses .

I am creating a project

It is for people who want to start a business but do not have the time. This includes people with a 9 to 5 job and only a couple of hours a week. It is also for students, in school or college. Anyone who has the drive. Not the time to spend weeks researching, testing ideas and figuring out how to get their product out there.

That's usually where it ends. You hit three problems: is this idea even real what do I create and where do I find my first customers? Answering these questions takes hours of work that most people just do not have.

So I am building a platform that does that work for you. You give it your skills and a general idea. It researches what is already happening in that area. It looks at who's creating things what is working and where people are asking for something that does not exist yet. Then it gives you an answer: whether the idea is worth pursuing a few things you could create and where to find your first customers.

Basically the research and hard work that usually stops people before they even start is done for you.

It is days and there is a lot to figure out. I will share my progress as I go. The successes, the problems and whatever I learn while building it.

I am creating a project

It is for people who want to start a business but do not have the time. This includes people with a 9 to 5 job and only a couple of hours a week. It is also for students, in school or college. Anyone who has the drive. Not the time to spend weeks researching, testing ideas and figuring out how to get their product out there.

That's usually where it ends. You hit three problems: is this idea even real what do I create and where do I find my first customers? Answering these questions takes hours of work that most people just do not have.

So I am building a platform that does that work for you. You give it your skills and a general idea. It researches what is already happening in that area. It looks at who's creating things what is working and where people are asking for something that does not exist yet. Then it gives you an answer: whether the idea is worth pursuing a few things you could create and where to find your first customers.

Basically the research and hard work that usually stops people before they even start is done for you.

It is days and there is a lot to figure out. I will share my progress as I go. The successes, the problems and whatever I learn while building it.

I am currently working as software developer in AI domain but I want to start my own YouTube channel where I could teach complex topics related to AI in easy method .

What are the challenges I should be aware of ?
How frequently I should post videos?
How long should be the videos ?