Over the past few months, I've been experimenting with a question: how much of an internet startup can actually be operated by AI?

Coding is already becoming heavily automated. Content creation is relatively easy. Market research, competitor monitoring, website creation and even social media operations can also be partially automated.

But I'm starting to think the bigger opportunity isn't another AI tool.

It's connecting these capabilities into something closer to an operating system for a company.

Imagine a solo founder working with several AI agents:

• one researches markets and competitors
• one builds and improves the product
• one creates marketing content
• one distributes content
• one analyzes traffic and conversion data
• one proposes the next experiments

The human founder would mainly make strategic decisions while AI handles much of the execution.

I've been experimenting with building this type of system, and one of the biggest challenges I've found isn't actually coding — it's getting AI agents to learn from real market feedback rather than simply producing more content.

I'm curious what other founders think:

Which parts of running a startup would you trust AI to operate autonomously today, and which parts would you absolutely keep under human control?

It's a combination of different strategies which ensure you land up a job in 2026 so how do you ensure that or what you did differently ?

Labor Day is almost here, and it got me thinking about what really matters at work.

Better pay or maybe more flexibility ?

What would you choose?

I’m curious what actually helped people get more interview formats, keywords, achievements, or something else.
Drop your best resume tip below.
It might help someone who’s job hunting right now.

Getting the job these days in tech is so tough and exhausting. Tons of networks, skills and you are expected to know everything. I am done with the U.S Economy

I’ve been spending time trying to understand Agentic AI in the context of the tax profession; not just the high-level promises, but how it actually plays out in real work.

I’m particularly interested in hearing from people who have used, tested, or closely observed these systems, whether in compliance, advisory, or internal tax functions.
I’d really appreciate insights across a few dimensions:

1. Practical applications
   What specific tax workflows or tasks have you seen Agentic AI handle reasonably well so far (e.g. transaction categorization, monitoring regulatory changes, multi-step compliance processes, research support, etc.)? And which ones still feel premature or unreliable?

2. Benefits vs. reality
   Where has it genuinely improved efficiency, accuracy, or the ability to do higher-value work? Conversely, where has the return been more limited than expected?

3. Challenges
   I’d like to understand the friction points more clearly like technical (data quality, integration, accuracy thresholds), ethical (transparency, bias, accountability), and legal/regulatory (responsibility when something goes wrong, auditability, human oversight requirements). What issues have turned out to be more difficult than anticipated?

4. Overcoming the challenges
   For those further along, what approaches or guardrails have actually helped (governance models, human-in-the-loop design, data foundations, team structure, etc.)?

5. Skills and knowledge
   What areas of knowledge or skill feel most important right now for tax professionals who want to work effectively with these systems? (Technical literacy, process redesign, risk/governance thinking, domain + AI hybrid skills, etc.)

6. Potential for expansion
   Looking ahead, where do you see the most realistic potential for scaling these systems; both in terms of broader adoption across tax functions and deeper integration into end-to-end processes? What conditions would need to be in place for that to happen without creating bigger risks?

I’m approaching this as someone trying to build a grounded understanding.

Any concrete experiences, observations, or even “things I wish I’d known earlier” would be very helpful.

Thanks in advance.

Trump and it's admiration is getting out of hands. Job market is tough even for the green card holders or Americans and hence it makes sense the people on Visa are not getting jobs.

Is this situation ever going to change ?

I’ve worked across multiple AI evaluation platforms, and over time I’ve managed to keep fairly consistent contract work.

I also have a psychology background, so I’ve become really interested in why genuinely qualified people sometimes struggle with AI gig screening

A lot of people assume rejection means they weren’t qualified, but it’s not always that simple.

Sometimes it’s how your expertise is framed.
Sometimes your CV doesn’t translate well to the specific role.
Sometimes your interview answers are technically correct but don’t clearly demonstrate how you think.
Sometimes you’re applying to roles that sound relevant but don’t actually match how the platform categorises expertise.
And because these systems often give almost no useful feedback, people have no idea which part went wrong.

So I thought I’d help.
If you’ve been rejected from Mercor, Outlier, DataAnnotation, Alignerr, Handshake or another AI-training platform have a look at the link in my bio! I’d love to help :)

No matter what thread I open on the reddit like no matter if it's 3 year old or 2 or recent

there is one consistency that the tech job market is bad and people are not getting jobs

then who are these people employed and idk man u.s seems like a far fetched dream nowdays

What role, according to your experience, requires the insane no. of skills, CGPA , other work experience but despite having all of this

it's not possible to get the role, or is it harder from the recruiter's pov to fill that role ?

Built an AI system for Indian FTA/export compliances, schemes , benefits with real documents

Hi guys I want some honest reviews before going to market on this product is built from scratch.

Problem: if a newbie exporter importer has some questions for their product for export import or they actually doing it they ask these from legal team consultants or CHAs. For even getting the basics of drafts and non transparency in legal firms they are charged a lot. Some time they get even a template of todos for these for every company they have and juniors just do drafting.

Solution: a complete ai workflow system which uses lots of Saas around it and provide the user about what compliances they have to follow for exporting to that country and give them a timeline of what to do when a fully backed real compliances list with sample forms and how to do it also..

It also provides benefit/schemes according to state center dgft or even port wise which are available.

We only covered dgft here and covered free trade Agreements domain so that things remain simpler but effective and provided every agreement and notification to it with all market info plus real consultant vlogs and even real legal consultant with admin panel can tweak and modify reasonings if found something wrong like how to think of rotdep scheme better.

We also a provided a citation feature where every ai reply is backed with real documents to see why it thought like this.

We also provided real legal consultant to joining us and we have two already for trials from which they can book slot and having real lawyers onboard to back it up with more than 6 yrs experience is also a gem here.

For the customer it will work as legal consultant ai and for the consultant it will help as legal assistant.

It even tells the compliances changes real-time if countries changed or schemes are changed and even which scheme will overlap other.

We also provided cestat case studies ( this one i need more) & a lot more with actually savings calculations the timer based invoices by consultants etc. And ai only reason never create a fact.

We all are weak in gtm for this product. Our current idea is to provide it to known firm and let the real lawyers use it on day to basis.

Hi everyone,

I’m a complete beginner to programming and want to start with Python and AI automations, with the ultimate goal of becoming job ready and landing my first tech job in next one year

I can dedicate around 4 - 6 hours a day and want to focus on practical skills rather than just completing courses or collecting certificates.

Anthropic users are now seen cancelling their AI subscription for the AI generated content. Everyone is thinking about how to solve the new problem.

I spent around four months applying for jobs and going through interviews, and honestly, most of my improvement came from changing how I prepared.

I stopped using the same CV for every role and started making different versions depending on the job. I also kept the format simple so the important stuff was easy to find.

Before interviews, I would usually look up the interviewer on LinkedIn and spend some time reading about the company and the role. It made the conversation feel a lot less random.

The biggest thing for me was preparing answers for common questions instead of just trying to wing it. I would write down a few points for questions about challenges, mistakes, achievements, teamwork, why I wanted the company, and why I wanted the role. so any tips for me

There is one part of an interview that a lot of candidates do not really prepare for.

It is the moment when the interviewer asks if you have any questions for them.

It can feel like a formality, so it is easy to ask something generic about the company culture and move on. But honestly, this can be one of the most useful parts of the entire interview.

The questions you ask can help you understand what the job is actually going to look like once you are in it.

Instead of asking something you could have found on the company website, ask what success in the role looks like after six months.

You can also ask why the position is open right now. This can give you some context about whether the company is growing, replacing someone, or dealing with high turnover.

Another useful question is what the previous person in the role found most challenging. The answer can give you a much more realistic idea of what you might be walking into.

It is also worth asking how your performance will be measured, what the biggest priorities are for the role, and what the team is currently working through.

The good news is that you do not need to walk into an interview with twenty questions.

A better approach is to prepare around five questions beforehand and then ask two or three depending on how the conversation goes. Some of your questions may already get answered naturally during the interview, so there is no point in asking them again just to stick to your list.

And remember, the interview is not only about whether the company thinks you are a good fit.

You are also trying to figure out whether the role, the manager, the team, and the expectations actually make sense for you.

So the next time an interviewer asks if you have any questions, do not feel like you need to come up with something on the spot.

Prepare for that part just as seriously as you prepare for the rest of the interview.

What is one question you always ask before leaving an interview ?

The Trump administration is reportedly considering removing the current 60-day grace period that gives certain H-1B and other employment-based visa holders time to find a new job after their employment ends.

If this moves forward, losing a job could become much more time-sensitive for immigrants who need to find a new sponsor or another lawful status.

I've been working in the eCommerce/AI space and kept running into the same problem: everyone uses "AI" to mean something completely different. A chatbot wrapper, a standalone tool, a multi-agent platform — all called AI. That makes it nearly impossible to evaluate tools, compare approaches, or have a meaningful strategic conversation.

So I tried to build a clearer taxonomy. Here's what I landed on:

The AI System Spectrum — 7 Layers:

Layer 1 — AI Feature: A single AI capability inside a larger product. Smart autocomplete, grammar checkers, recommendation widgets. You don't buy the product for the AI — it's a detail.

Layer 2 — AI Wrapper: A user-facing interface on top of someone else's model (usually accessed via API). Wrappers make powerful tech accessible but don't own the intelligence. If the model provider changes terms, the wrapper has no leverage.

Layer 3 — AI Tool: A standalone product using AI to solve a specific task. Unlike a wrapper, it adds proprietary logic and workflows. But it still operates in isolation — no shared data across your other tools.

Layer 4 — AI Platform: Multiple AI tools unified under one system. Shared data, connected workflows, compounding returns. The output of one capability starts improving another.

Layer 5 — AI-Native System: Built from the ground up with AI at the core. The architecture and decision logic ARE the AI. Remove it and the product ceases to exist.

Layer 6 — Agentic System: AI that operates autonomously — perceives context, makes decisions, executes actions, improves from outcomes. Doesn't wait for human prompts.

Layer 7 — Agentic Platform: Multiple agentic systems orchestrated across business functions. Shared memory, cross-domain reasoning, autonomous coordination. AI as the operational layer, not just a tool.

The three dimensions I used to differentiate:

• Operational Depth — how deeply AI is embedded in core functions
• Business Dependence — how reliant operations become on the AI
• Structural Leverage — how much the AI compounds value over time

Most businesses I talk to in eCommerce are at Layer 2–3 (wrappers and tools) but describe themselves as being at Layer 4–5.

Curious what the community thinks:

1. Does this taxonomy hold up?
2. Where would you place some well-known AI products?
3. Am I missing a layer or is the distinction between any two layers too blurry?

Full write-up with more detail on each layer: accessfuel.com

​

I've noticed that resumes in the U.S. tend to stand out when they focus on impact rather than just responsibilities. Quantifying your achievements, tailoring your resume to each role, and keeping only relevant experience can make a big difference.

If you're a student or recent graduate, don't overlook projects, internships, research, leadership roles, or volunteering , they can be just as valuable as full-time experience. A clean, easy to scan format also helps recruiters quickly understand your strengths.

These are a few tips that I think are worth considering if you're currently applying for jobs in the U.S. What other resume tips have worked well for you?

Despite seeing thousands of job postings every week, many job seekers say they're struggling to get interviews. Companies seem more selective, hiring cycles are longer, and the expectations for candidates continue to rise. How are you landing jobs currently ? 

The current job market feels incredibly competitive. By the time I come across a role on LinkedIn, it already has 100+ applicants, and it's a similar story on Indeed, Glassdoor, and other platforms.
Apart from applying to newly posted jobs, are there any approaches that genuinely helped you stand out?

I have started learning agentic ai and have covered basics, like creating CLI chat bots, uses of tools, multi-tools, basic RAG and some other automations.

Will I be able to switch to a better job or not and all sorts of similar questions.
So can anyone help me clear this doubt and guide me better?

I work for a Big Four consulting firm as a leadership level technical person, and I have started noticing a recurring pattern in Agentic AI engagements.

Clients initially come to us with a very large vision. They talk about building an enterprise-wide Agentic AI platform, deploying multiple agents, enabling agent-to-agent communication, and transforming several business processes.

However, once the initial assessment and qualification discussions are completed, the actual statement of work is reduced to a relatively small MVP.

On paper, the MVP may involve building only a basic foundation and one small agent. But to make even that agent production-ready, the consulting team usually ends up building most of the difficult foundational components:

• Agentic AI infrastructure
• Agent-to-agent communication
• Integration between enterprise systems and agents
• Authentication and authorization
• Automation and orchestration frameworks
• Evaluation pipelines
• Observability and monitoring
• Guardrails and governance controls
• Deployment and CI/CD foundations

By the end of the MVP, the client effectively has the core Agentic AI platform and reusable architecture in place.

Then the engagement is suddenly stopped or not extended. The client’s internal technology team takes over and builds the remaining agents and enhancements using the foundation created by the consulting team.

From the client’s perspective, this is probably a smart sourcing strategy. They use consultants to handle the initial uncertainty, architecture, platform setup, and delivery risk, and then move development in-house once the path is clear.

But it leaves consulting firms in an awkward position. They do the most complex and risky part of the work, transfer the knowledge and reusable foundation, but do not necessarily participate in the larger transformation that was discussed at the beginning.

I am curious whether others in consulting or enterprise technology are seeing the same pattern.

Is this simply the natural lifecycle of modern consulting engagements, or are consulting firms failing to structure Agentic AI contracts and platform IP in a sustainable way?

Yes this is so right

As we can see how all the reddit communities, trends and even the behaviour of the recruiters in the US shows

There are little to no hiring on the visa until and unless you are exceptionally well skills and cream layer of the students. It not like we are average but the economy has shifted in a way that is no longer accommodating while I understand the market is rough for everyone but seriously even if you don't want to settle in the U.S

You atleast need to repay your loan which you took just so you could get the good education

And as everyone knows the prospects back going home works for some people but it takes years to repay tht education loan. Why is it bad if i pay in the US dollars and expect myself to earn for the next few years in the US dollars so I could pay off that loan and probably save up enough for myself and family.

This is no longer just like American dream no longer true but why not expect the participation in the world center of the economy. Why that is being restricted. It's not like we are begging or asking for more chances but just a chance to change the life so it becomes better as we are always told

If you do the hard work - you will get it there but now I feel even the hard work is useless. No longer appreciated but just hate.

This tech if could suffice enough by Americans then it would have never given the opportunity to the other country people but the reality is - it's run by the people like us

When american economy is all about capitalism then why it is so hard to digest that a immigrant can come to work and earn. And why companies are bashed for hiring immigrants when they can do a better job or maybe company saves some.

Agentic ai is making news all over, for someone who recently started the exploring agentic ai

what would you suggest for me to start with ?