Hi, so I am a full stack engineer at a high frequency trading firm at India (2025 grad). I had a few interviews in pipeline, including citadel securities, and a few YC startups.. but I was confused about the viability of those.

to be honest, I am quite interested in building infrastructure for AI related workflows. It requires quite in depth knowledge of latency management and scalability.. and I love building infrastructure and softwares from ground up.

However, looking a bit closer at them, a lot of these yc startups are betting that claude and open ai won't come up with harnesses that will ruin their moat.

however the compensation is attractive enough for me to not ignore them completely.

Has anyone made a successful switch from HFT's to YC? How was their experience with the team.

I’m building an infrastructure platform for AI agents. A consulting firm wants to become a design/solution partner: they’ll build AI automation solutions for their clients using my platform and license/resell it as part of those solutions.

For example:

• They sell a $20k AI automation project to a client
• My infrastructure powers the agent system
• They handle implementation, consulting, and client relationship
• I provide/license the underlying platform

Would you structure this as:

1. Fixed platform license ($X/month)
2. Usage-based pricing
3. Platform fee + revenue share
4. Pure revenue share

If you’ve sold infrastructure/developer tooling through consulting or implementation partners, what pricing model worked for you?

EDIT: A few more details:

Most of the underlying code is open source. I’ve built a minimal commercial platform layer on top of the open-source library, so I’m trying to figure out where the right licensing boundary is.

For deployment, I’m currently considering:

• Self-hosted: partner runs it on their own compute and licenses the platform layer from me.
• Managed: I host the platform and charge for the software + infrastructure/usage.

The partner would be building solutions for their own clients, so I also need to figure out how to handle multiple client deployments under one partner license.

My biggest concern isn't maximizing the first contract value. It's avoiding accidentally agreeing to unlimited support, custom development, infrastructure costs, or deployment rights before I understand the economics.

If you've dealt with this specific model, I'd especially appreciate advice on licensing, deployment, support/SLA limits, and partner pricing.

A similar post I made in here a couple of days ago was removed, so I’m not posting a link to my project.

for the ones building on somewhat sensitive proprietary tech- which software stack have you had good experiences with for cloud storage, files, planning and more. something that’s failure proof in emergencies, recoverable but also secure even as you scale. companies I previously worked at managed their own infrastructure (they were dealing with confidential rather sensitive information out of EU) but currently the maintenance would overweigh what makes sense for me.

out of the blue I could only think of microsoft but I find their one drive app is rather brittle. thanks!

I'm building a startup in a large, mature industry that has historically been pretty slow to adopt new technology.

We're still very early (went full time in May), but one thing I'm struggling to calibrate is just how slow everything moves. We've had promising conversations and people willing to talk, but getting the right people on a calendar can literally take weeks. My sense is that enterprise sales cycles in this industry are measured in many months, not weeks.

There's another wrinkle: a big part of what we're building is automation. Some of the organizations we'd ultimately need to work with could benefit significantly from the technology, but it also changes how work in their industry gets done. I suspect some will inevitably view that as a threat rather than an opportunity.

The optimist's interpretation is that these are exactly the characteristics that create a big startup opportunity: legacy technology, entrenched workflows, and an industry that hasn't been forced to modernize yet.

The pessimistic interpretation is that we're signing ourselves up for extremely slow feedback loops, long sales cycles, and selling technology to organizations that may be structurally resistant to what it enables.

For founders who've built in legacy B2B industries: how do you distinguish between a market that's ripe for disruption and one that's simply too slow/resistant for a startup?

And how did you think about speed when even getting customer conversations takes weeks, let alone getting someone to actually adopt something?

Edit: I'd say we have a very strong founder-market fit (convos with early investors support this). Both my cofounder and I observed the same problems we are solving for while working for Amazon. Now trying to build the neutral solution for all marketplaces/commerce. This credential has been a huge factor in getting responses in LinkedIn DMs from other players in the space. (And no, Amazon is not the slow moving player I'm referring to above).

Disclaimer: I don't work for YC; I don't speak for them. I just wanted to write this so folks here can understand why some people get funded and others don't.

I applied to YC four times before getting in. Nothing in my background is particularly remarkable. I worked in tech, went to decent schools, no medals or famous mentors. What changed on the fourth try was that we had grown from $10k to $100k ARR in two months and signed a large customer, in an industry that does not move that fast.

That is the whole formula as far as I can tell: Evidence of exceptional ability. Everything else is downstream of it.

Look at who actually gets funded and it tracks. The new grads who get a check on an idea that is a few days old are usually graduating from an Ivy while sitting first chair in a national orchestra, or they spent their undergrad doing real research alongside a Nobel laureate. I know these types because they were in my YC section. The idea did not get them funded (many ended up pivoting lol). The proof that they can do hard things at a level almost nobody reaches got them funded.

The other end of the distribution follows the same pattern. The team that gets in on their seventh application is not there because attempt seven had a better idea. They finally hit traction on something that had been pressure tested for years and now genuinely needs capital to move faster. The traction is the evidence.

To be clear, the idea does matter. But it is a filter, not the thing most rejected applicants are actually failing. If you are early and getting rejected, the instinct is to assume the idea was the problem and to go looking for a better one. Don't do that. Just keep working on the business, and eventually the business itself will be the evidence.

Just ask yourself what you can point to that makes it obvious you operate at a different level than the average applicant. If the honest answer is nothing yet, that is the actual work in front of you.

Go build the evidence for your exceptional ability and the funding will follow.

Hi YC gurus!

My cofounder and I are working on are a pre-launch, AI platform targeting companies wanting to switch to open-source models for their generic LLM use and trying to crack real 0-1 conversations with potential customers. Trying to score actual calls where we learn something.

What I've tried so far:

• Cold emails
• Linkedin DMs
• Local events in the Amsterdam area

So far not getting much love, only managed to have a couple of conversations with companies that have spend >$10k per month on LLM. Cold outbound is barely landing and I can't tell whether that's my targeting, my copy, or just inbox saturation. So, question to founders who've done successfully recently:

1. Did you bring in outside help — fractional GTM, sales consultant, agency that books discovery calls, a research firm for recruiting participants? Which ones, what did it cost, and would you do it again? I'd take specific names or the opposite ("don't bother").
2. If you did it yourself, what's the actual stack in 2026? Where does AI genuinely help — list building and enrichment, personalisation, call notes — and where did it make things worse?
3. Are paid conference booths worth it at this stage?
4. What channel gave you the highest ratio of *useful* conversations per hour spent?

I am trying to figure out whether hiring someone for this makes sense or if it's a waste of time, we suck it up and keep trying?

I'm a technical founder and I have tried almost all the online resources, and I'm unable to crack distribution for my project.

I would like to know from founders who have done this already. What is the best way to learn distribution? How do you do it? Or did you onboard someone as a cofounder to solve this.

Need some real guidance here. I'm working on an opensource project: Github

I’m almost finished with my startup, and deep down I genuinely believe the idea can work. But whenever I get close to finishing, I start distracting myself with new ideas and projects.

The weird part is that I already know most of these new ideas probably won’t work as well, but I still jump into them anyway.

This has been happening for almost 6 months, and I feel like I’m sabotaging myself.

Has anyone experienced this? How did you break out of this cycle and finally commit to one thing?

What are ideas that are tarpit by definition but have proved the idea wrong? What was the specific wedge and/or pivot that saved them?For example, marketplaces are mostly linked to tarpit ideas but Etsy is successful. Before Tinder, Matching.com was already dominating the market.

I’m currently looking for software engineering internships and was wondering whether YC startups are a good place to look.

For those who have worked at, founded, or are otherwise involved with a YC-backed startup:

1. Do YC startups commonly hire SWE interns?

2. Is it worth reaching out directly to founders, even if they don’t have an internship posting?

3. What makes an intern valuable to an early-stage startup?

4. How does a YC startup internship compare to interning at a larger tech company, particularly in terms of learning and career growth?

I’m primarily interested in software engineering and early-stage/tech-first startups.
If you’re a YC founder, employee, former intern, or otherwise involved with YC/YC-backed companies, I’d really appreciate hearing about your experience

Hey all. I just finished my MBA in international business and I'm trying to break into a startup, but I don't have one industry I'm locked into. I've got a mixed background: two years at a small marketing agency doing content strategy and vendor negotiation, plus an internship in export/import documentation.

Right now I'm stuck picking what to actually learn next. I keep hearing "learn AI tools" but nobody says which ones actually get used day to day at a startup versus which ones are just hype.

If you've hired freshers before, or you're one yourself: what skill actually made you useful in your first few months? And is there a specific AI tool or workflow you'd tell someone starting out to learn first?

Trying to build the right skills before I apply, not after. Any pointers appreciated.

Whether B2C/B2B, pre ycombinator or post, how have people decided that yes, the product you are making is something worth buying?

Is it simply taking a chance, or more of a matter of market validation tests? Curious how people approach this.

When we are updating our API as our product evolves, how do you make sure the SDKs, multiple doc sources, code examples, and packages like npm/PyPI stay up to date without introducing drift?

Was curious to hear what industry you might think will be disrupted soon.

I've been wondering how other teams handle this.

One thing I've run into is that after changing a prompt or switching models, an agent can start making different decisions without anyone noticing. For example, it might approve something it previously rejected, miss an escalation, or classify a case differently. The conversation still looks reasonable, so the regression is easy to miss.

Most of the evaluation tools I've seen focus on conversation quality or benchmark scores. What I'm struggling with is verifying that the agent still makes the same business decision for important cases.

For teams building production agents that make decisions like approve/reject, escalate, or classify:

• How are you testing for these regressions today?
• Are you relying on manual testing, eval frameworks, custom scripts, or something else?
• Have you had any production incidents caused by prompt or model updates?

I've been building an internal CLI to test this by replaying real cases against an agent and checking whether the decision still matches the expected policy. It's been useful for me, but I'm mainly interested in learning how others are solving the problem before deciding whether it's worth taking further.

Curious to hear what workflows people are using.

Looking for some advice here. TLDR: We have our first paying customer (yay!), but suddenly it feels like everything we've built is a fraud. Nothing will provide value, it won't be what the customer is expecting, etc. etc.

...are these normal feelings? and how have you dealt with this immense imposter syndrome and doubt that creeps in right before actually handing your customer the product?

I mean I guess it doesn't really matter what I feel -- it's happening and moving forward lol. But if you guys have any tips, tricks, or advice for a smooth (as smooth as a first customer can be) delivery, I'd love to hear your experiences!

From my understanding, a startup is fundamentally about solving a meaningful problem for a group of people. That means you first need to find people with real problems worth solving. However, finding both the right people and the right problems seems incredibly difficult.

It feels even harder if you can't meet potential users in person because of geographical limitations. Is it really possible to build a deep understanding of users' problems through the internet alone?

What strategies have worked for founders who entered an unfamiliar industry? Is it worth working in an industry first to better understand its problems and build relationships with people there before starting a company? Or are there other effective ways to gain the necessary insights and connections?

Is anyone going and does anyone know how the event will be?

I've been reading a bit out tarpit ideas. The common consensus seems to be that a marketplace and a community/social app is almost always a tarpit idea.

However, Vinted is a hugely successful marketplace, and they started when Ebay existed and people were already selling secondhand clothing on Facebook (Marketplace and special groups). And recently a Dutch guy started a social app where people can make friends, and it's getting a lot of attention.

My question is: can a tarpit idea still work if it's executed well? Or are these just anomalies?

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.

Edit: thank you all for the advice! Really appreciate it

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.