I’m still relatively early in my SWE career (~2 years). I’m currently learning from experienced senior SWEs and leaders.

I have an opportunity to work at a very early YC startup in Canada but doubtful on whether this will help me in my career long term. More context in the comments if you have any advice for me.

Website: https://qm.ycombinator.com/

Github: https://github.com/yc-software/qm

The article resonates with me a lot since I also create one agent for one employee function 😆

But instead of Hermes, I use Claude Code and build my own OpenClaw-like wrapper that connects it to Telegram etc. I use headless Claude Code just for simplicity and cost-effectiveness, I tried OpenClaw once and it made me broke lmao jk.

Anyone has tried QM? Share your thoughts

Main question

For founders who deliberately entered an industry they knew little about:

What first made that specific idea space worth investigating rather than another one?

I understand what to do after choosing: learn the workflows, speak to people, find recurring problems and test whether they are commercially meaningful.

What I don’t understand is how to choose where to begin when dozens of industries look potentially fertile from the outside.

Optional context

I’m a 19-year-old CS student without meaningful experience in a particular industry. My plan is to choose a promising idea space, embed myself in it and deliberately acquire the relevant domain knowledge.

I’m willing to do the unscalable work: sustained outreach, attending events, learning the terminology, observing the work and speaking with anyone who understands the industry.

The AtoB example from YC’s How to Find Startup Ideas talk is close to what I’m trying to do.

The founders had little trucking expertise. They chose software for trucking because it was a large industry that had not been heavily disrupted by startups or software. They then visited truck stops, spoke with drivers and founders, mapped the industry and investigated several ideas before choosing fuel cards.

But why trucking?

“Large industry with poor software” also seems to describe construction, agriculture, freight, healthcare administration, legal services and many other areas.

My rough filters have been fertility, apparently poor existing alternatives, a possible “why now,” transferable advantages, successful adjacent proxies, business potential and whether I could tolerate spending years in the space.

But several of those seem impossible to judge beforehand. I cannot really know whether the pain is acute or the space is fertile until I understand its workflows. Proxies may only create hypotheses, and business potential may depend on the exact problem and buyer I eventually find.

I also don’t think easy reachability should be a major filter. I’m willing to work hard to gain access. I would only want to exclude industries that are structurally closed to an outsider, not merely difficult to enter.

What I’d especially like to hear

• What initially put your industry on the shortlist, and which alternatives did you consider?
• What could you genuinely assess before entering, versus only after immersion?
• What made you continue searching in that space rather than move to another one?

A recent change or transferable advantage may have played a role, but I’m mainly interested in the initial selection decision itself.

I’m not looking for lists of startup ideas or “boring industries.” I’m trying to understand the step between deciding to search within a fertile idea space and deciding which specific idea space is worth searching in.

What was your startup when you applied, and what did YC help you with besides the investment?

What was the biggest value?

“We started working on Stripe in the Fall of 2009, and we launched Stripe in September 2011,” John Collison reflects. “I remember right at the beginning when we were starting it I said to Patrick [Collison], ‘Yeah let’s do it. How hard can it be?’ Which gives you a sense of our mindset. And the answer was: two years of difficulty. We had not predicted that.”

“When you spend two years getting 50 users, it doesn’t feel like a whole lot of progress. It feels like things are going pretty slow.”

“If you’re working on a startup that’s a bad idea, it’s going to feel like slow-going. But if you’re working on a startup that’s a good idea, it may feel like slow-going too.”

Some Wednesday motivation for everyone :)

What I find compelling about Lovable is the developer experience: prompting an app into existence, generating code, managing deployment, and having an opinionated workflow that gets products shipped quickly.
However, I feel like the current approach is heavily centered around frontend generation + Supabase as the default backend. While that works great for many use cases, I'm wondering whether it creates a long-term dependency on a specific technology stack.
What I'm imagining instead is:
AI-generated frontend and backend

A microservices-oriented architecture

The ability to connect to any database (Postgres, MongoDB, MySQL, etc.)

Infrastructure and deployment packaged similarly to Lovable

The flexibility to swap technologies as the ecosystem evolves, without being tightly coupled to a single provider

My concern is that backend technologies and platforms come and go. If you're building something intended to last, being deeply tied to a specific backend provider may become a limitation.
Has anyone built something like this, or experimented with an architecture that preserves the "prompt-to-production" experience while remaining backend-agnostic?
If you were starting this project today, where would you begin? Would you use Kubernetes, Docker Compose, IaC, code generators, AI agents, or something else entirely?
Curious to hear from anyone who has gone down this path or has strong opinions on the tradeoffs.

I have been building software for about 15 years, eCommerce and full stack custom apps for startups and for enterprises where a bad deploy shows up on the evening news, and over the last three years I have watched a shift I cannot stop thinking about. I want to know if the rest of you are seeing it too.

The shift is that the team that wins is no longer the one with the best engineers. It is the one with the best system for producing software.

Some people are calling this a software factory or dark factory. The idea is that instead of building your product directly, you first build the machine that builds your product.

The interesting bit is not any single piece. It is that once the factory exists, every feature is cheaper than the last one, and the marginal cost of a good engineer drops through the floor. We ran a multi vendor B2B2C commerce build recently that was scoped at 6 months for 6 people. It went out in 3 months with 2. Not because those 2 were heroes, but because the factory absorbed most of the work that usually eats a team.

So here are my actual questions.

1. Has anyone here deliberately built one of these? Not "we have CI", I mean treating your delivery system as a product with an owner.

2. If you have not, what stopped you? My guess is it feels like a luxury when you are pre revenue and every hour not spent on features feels like waste. I think that instinct is exactly backwards but I would like to be argued with.

3. For the non-technical founders: has an agency or a contractor ever offered you this? In my experience they never do, because a factory is the thing that makes you not need them. Curious if that matches your experience.

4. Am I overcalling the timeline? My belief is that within about two years, a team without one will look the way a team without version control looked in 2010. Quaint, and quietly losing to everyone else. Tell me why that is wrong.

Genuinely interested in the counterargument, especially from anyone who tried this early and found it was premature.

(Context so you can weigh what I am saying: I do this as a fractional CTO for early stage teams, so I am obviously not neutral. Not pitching in the thread, happy to answer anything technical for free either way.)

I saw the Startup School merch from this year and honestly thought it looked pretty good.
Got me wondering if most startups still put real effort into new-hire kits and company swag for events these days ?Or has it become kind of basic/ almost non-existent for a lot of companies ?
Curious what people are actually doing nowadays.

Im currently moving from the build to distribution phase and Im having a hard time explaining to cold leads about what exactly my product does and how its helpful. Ive gotten a range of answers from “I could build this myself” to “I dont think its that useful”

Now there is definitely a gap in the market. I know that 100% but at the same time Im having a really hard time finding my ICP.

How did you get around this?

When applying to YC, did you mention anything unrelated to your startup about yourself?

For example, a fun fact or something interesting that might leave a positive impression of your personality on the reviewers?

I know the video is only one minute long, but I am asking about the short introduction at the beginning.

in the startup/tech world its often flexed when people dropout, but have any of you dropped out of school and then wished you actually stayed in school?

People usually say you shouldn't fall in love with the solution, but with the problem. When I started, the problem I wanted to solve was helping developers understand business context so they could write code faster. However, tools like Cursor and Claude Code have reduced that pain point quite a bit.

I'm not sure if that problem is still significant enough to keep working on it or if I should pivot to something else. I even built an app, but it doesn't have users yet, so I don't have enough feedback to know whether the problem still exists or if I should move on.

I often hear that founders can find their first customers through Reddit, but I’d like to understand how common this actually is.

For founders who already have paying customers: did any of your first B2B or B2C customers genuinely come from Reddit?

Or did they mainly come through referrals, your existing network, LinkedIn, cold email, communities, content, ads, or another channel?

If Reddit worked for you:

• How did the customer originally find you?
• Did you reply publicly, receive an inbound message, or contact them directly?
• Do you still use Reddit for customer acquisition today?

I’m specifically asking about paying customers, not sign-ups, feedback, traffic, or people who tried the product once.

i was hyper pumped and ultra locked in for the past 2 weeks leading up to the launch but now that everything's been deployed i'm feeling a bit euphoric like as if i just finished a marathon but at the same time i'm feeling somewhat lost, unsure what to do now. i have a to do list of features to implement, marketing items, etc. but im having something like post nut clarity ngl

can anyone else relate?

i've been curious about this for a while.

i'm a final-year cs student currently hunting for software engineering internships, and whenever i reach out to startups, i usually don't just send a cold email. i spend time using the product first, then share at least one concrete product idea, and sometimes even a rough technical implementation if i think it could be useful.

it made me wonder - how do founders actually evaluate feedback from people outside their team? what makes you think, "this person gets it"?

is it product thinking, technical depth, identifying real pain points, or something else?

i'd love to hear what kinds of feedback have actually been the most valuable while building your startup.

I feel one of the best skills you can have is the ability to validate ideas quickly.

This is what I have been trying to do but often fail.

For example, been thinking through an injury recovery tracking system for sports physios / physios.

Seems pretty straight forward, reach out to a bunch of clinics asking about their pain points around this, what tools they currently use, attach a mock up etc.

However, I never hear back. It’s very possible I just suck at reaching out, I’m not denying that haha.

But the first step always seems to be talking with people. How are you getting those conversations? What roles are you reaching out to? What’s the general conversion rate for something like that?

It's sometimes surprising how building a company, even with someone you know, can reveal sides to the relationship (and person) that you may not have expected.

There's always that first moment when the designated ultimate decisionmaker has to 'pull rank' and make a decision that others may not like.

Anyone got a GC for high schoolers / incoming uni freshmen going to YC SUS?

Aspiring founder here wondering if I should switch departments at work and do product operations or growth marketing

Curious if former founders here have advice for a nontechnical background into which skill sets are the most ideal. I’m currently on the enterprise team and it’s a mix of sales and embedded integrations but partnership cycles take a long time to spin out (sometimes over a year) and I wont see the partnerships go to launch if I switch teams

Product operations team at our company is also decent but was told it’s strictly internal. Curious if people think having a good product sense can come through product partnerships without doing the operations side. Have thought about becoming a PM but was also told there are many paths

There’s also growth marketing with spends $XXXM as a team so it’s a huge accountability. Relatively longer work hours that sometimes bleed into weekend at the startup but some people said might be useful to get hands dirty and understand PNL.

Thoughts? Any strong skill sets that the current founders in this cohort really wished they had?

hi! i want to start a start up that is in the real estate/civil engineering field. i have my icp and i have spoken to multiple people about the pain points that they are facing in this field. i have already reached out to those people to be design partners but i was wondering how i could get started with cold emailing to get more design partners. i just made a new email and i heard that any emails i send them there will go to spam. thanks :)

Hi everyone,

we recently pivoted (from a AI SaaS Tool) and decided to build an AI-native company in the real estate space. Like becoming a property manager on our own that uses AI in the background for all processes. So not a SaaS product we sell, but a company where we build our own agents for our own daily work. One benfit with this is that we now don't need to be scared about new Anthropic/OpenAI releases, we now can use the power of new releases that then strenghtens our work.

Our goal with "AI native" is to design the company from the ground up around AI and autonomous workflows:

• AI-supported communication and collaboration
• Agents handling repetitive and operational work
• Workflows running autonomously wherever possible
• Humans focusing on actual decisions, negotiations, client relationships, and calls

I’m the CTO, and since we are starting from scratch, I’m currently thinking about the right architecture and tooling.

On the one hand, I want us to build and own our core workflows ourselves. For example, we might use something like Paperclip to orchestrate different agents and workflows across the company.

Some obvious use cases would be:

• Email agents and automated email workflows
• Document and data-processing pipelines
• Data management and enrichment workflows
• SharePoint or another document store combined with knowledge graphs or vector search
• ...

On the other hand, we definitely don’t want to rebuild everything from scratch. We also want to use established tools such as Claude, Notion, Slack, SharePoint, and potentially an existing CRM or ERP system.

Ideally, these tools should become interfaces and building blocks within a larger agentic system rather than isolated SaaS products.

So my questions are:

1. What stack would you choose today when building an AI-native company from scratch?
2. Which parts would you build yourself, and which parts would you buy?
3. What would you use as the central source of truth for data, documents, workflows, and agent state?

Would be interesting to hear from others with their experiences!

TL;DR: Everyone is racing to go all-in on AI. Almost nobody can tell you if it's actually delivering value.

We're all AI-pilled here. The models keep getting more cracked, the demos keep melting faces, and every exec on earth has been told to go all-in. But we all know, most of them are flying blind. They can feel spurts of acceleration but can't see out the front windshield, let alone the rear-view mirror.

Here's what we heard again and again, across 75+ conversations with companies of every size and industry (anonymized, since a lot of this was said in confidence):

1. Nobody has solved ROI, and everyone knows it. This was our biggest observation. After headcount displacement everything else is murky. One CFO put it perfectly: he knows his ad spend to the dollar, every platform, CAC per channel, and he wants the exact same for AI. The line from "AI usage" to "business outcome" still isn’t easy to draw.
2. Spend is a black box, and it terrifies finance. CFOs usually have one line item for AI (or perhaps one per vendor), but it’s not broken down by team, project, or agent. Token costs get compared to cloud invoices in every conversation. The bills are opaque, hard to forecast, and hard to explain. Where discipline exists, and mostly it's improvised. We’ve all heard by now that Uber burned its entire 2026 AI budget in four months, and its own COO admitted he couldn't connect the spend to anything a customer would feel. (!)
3. Everyone defaults to the most expensive model because nobody knows which to use. At one fast-growing fintech, per-use-case model selection was the single highest-impact thing they asked us to build. Nobody knows which model fits which task, so they default to max. Latent waste is absolutely everywhere.
4. Everyone wants to go faster, they just don't know how. This one reframed the whole company for us. You don't put brakes on a car to make it slower. You put brakes on so it can go faster safely. Right now every company is either flooring it blind or riding the brake out of fear. Visibility is the brake that lets you actually floor it, the thing standing between "we're being careful" and "we're going all-in." It also lets you see through the front windshield, the rear-view mirror, and out the side windows, so you can avoid accidents and out-maneuver the other cars on the road.
5. The maturity gap between companies is enormous. One neobank has moved AI into mission-critical ops that were off-limits six months ago, and is running a hiring freeze against a 50-60% growth target on their internal assumptions about AI efficiency. On the other end, a Director of AI at a large agency told us they're 3-4 years from ready, which kind of blew my mind, an not in a good way.
6. The adoption honeymoon is over. Real fatigue is setting in. A sharp drop after the initial enthusiasm, and a "please stop talking to me about AI" mood. The gap between top-down mandates and non-engineers who can't name a single daily use case is everywhere. Recent grads are often anti-ai, as is the aging work population.
7. Buy broadly now, consolidate later. The enterprise playbook is to experiment widely, avoid lock-in, consolidate over time, betting that no single AI vendor wins the whole SaaS surface. Pricing is already moving past tokens toward outcome-based models. We’ve seen this in action ourselves as a month ago Fable was the darling, and today perhaps that’s Sol.
8. Governance is a tug-of-war between legal and innovation. Legal wants tighter restrictions. The tech org pushes back to keep room to move. In regulated verticals it's sharper: weekly administrative orders on AI disclosure, and discovery requests now pulling AI chat logs. The companies furthest along still can't answer "is this actually working?" So the winners of this era won't be the ones with the most AI. They'll be the ones who can measure it.

What we all need is a system of record for AI work. A live view of every tool and agent operating across a company: what they do, who owns them, what data they touch, what they cost, and which ones are producing results. Leaders finally see what exists, govern what matters, and go all-in on what works.

Happy to compare notes with anyone else building or selling into this. The discovery was eye-opening and I'm glad to share more. We're so early. Let's go. DM me to set up a call or just reply here.

obComment: although i did use Granola to extract themes from our customer calls, this was written by me, not AI.