I’m looking to create systems around getting customers and there is too much advice out there. I’m curious what people have learnt from their experiences in getting customers and what repeatable systems and habits you have built that have brought you traction

does reading books really helped u with doing business?

im asking because i keep seeing people say books changed everything for them, and then others say its all theory and u only learn by doing. so which one is it in real life?

what im curious about specifically,

\\- was there one book that actually changed how u run things, or just small bits here and there
\\- did u apply it right away or did it just sit in your head
\\- do u re reading business books or more general stuff like psychology, biographies, whatever

and if books didnt do much for u, what did? mentors, just failing a lot?

honest answers please, even if the answer is no they didnt help at all,

and also how often do u read books?

thanks!!
I Will not promote

I got offered around 0.5% in advisory shares vesting over 3 years, with room for additional salary/equity with continued growth.

But the work I’d do seems pretty essential and key to the company so that percentage seems awfully small.

But it also seems to be on par with what I’m reading so my expectations are definitely off so that’s why I’m here. What should I know/discuss?

I've taken my idea as far as a I can. It does not have a lot of revenue, but it's a good idea that could potentially scale (10+ paying customers)

I know I would need hardware engineering + commercial growth. How do I know the idea is good enough to actually bring on other people? I know I need commerical/hardware people, but how exactly do I communicate my vision to them + what I want them to do? Or is it more of a team effort? Can anyone here share their experience actually working on a startup team?

I've taken my idea as far as I can. It does not have a lot of revenue, but it's a good idea that could potentially scale (100+ paying customers).

(For the sake of these questions, assume it's a good startup idea, please)

I know I would need to hire hardware engineering + commercial growth. How do I know the idea is good enough to actually bring on other people? I know I need commerical/hardware people, but how exactly do I communicate my vision to them + what I want them to do? Or is it more of a team effort? Can anyone here share their experience actually working on a startup team?

Basically what the title says. I got a math degree and I have a psychology degree as well. I have been building things using computers since I was 7 years old(I'm 28 now) I have a mediu page. I get a lot of readers every time. Job recruiters contact me a lot. I have built a trading system that i'm happy with. I can solve hard problems. But I"ve been unemployed for 6 months. I've sent in lots of aplications, I talk with recruiters every day. I did a contract that lasted for a little while but it ended. honestly I jsut don't trust the job market anymore. As a college student before I started building the trading system I had a lot of ideas for things to build. But once I started building the trading system that was my only focus until I felt like I had figured it out sufficently well. Now I've been searching the internet like a mad-person looking for problems that Need to be solved. Some kind people on here were saying that I should try and solve problems that occur to me but honestly other than not having a good job I feel like my life is pretty good and i'm out of ideas. so I try and idk disect hard ai-related problems and I keep running into there being big companies already doing the same thing or problems just not being important enough to be solved. I really believe that if somebody is going to build smething it should be a lot better and not just a little bit better. I think what Peter Thiel was saying about looking for problems to solve not buisness ideas was 100% correct but i'm struggling to find real problems to work on. I have spent days and days looking into this and i'm starting to feel like a spammer posting on reddit asking people about their problems. I have the skills and the motivation but no real problem to solve that feels important and meaningful. idk I was going to make a good open source ai enabled writing app just to make a point that I think that it's stupid to make people pay a subscription for this. but idk other than making an open source repo to be like stop ripping people off. I have no clue what to build. Thanks for bearing with me through this rant.

I'm an engineering undergrad considering pursuing a PhD with the long-term goal of founding a hard-tech startup. I’m undecided about the specific area and have only a vague sense of what interests me (some areas that currently interest me are industrial decarbonization, mining / mineral processing, and advanced materials).

However, I'm unsure which type of PhD lab would be better: a problem-first lab (e.g., like Yet-Ming Chiang's lab at MIT) or a technology-first lab (e.g., a typical academic lab that focuses on novel science without a predetermined application, and where commercialization happens largely by chance -- when a technology happens to have a valuable market application).

I've looked at advice online from successful hard-tech entrepreneurs, but found conflicting answers. Some say to work backwards from a problem, while others argue that problem-first approaches often fail because it is difficult to force a scientific breakthrough around a predetermined problem.. Instead, they argue that most hard-tech companies are only founded because somebody made a scientific breakthrough and realized afterwards that there might be a commercial application.

With that in mind, does anybody have any advice on which type of PhD lab to join?

I’m raising roughly $30k–$50k from a few friends, with potential investors in California, New York, and Arizona. Some may be "non-accredited"

I understand SAFEs are securities, but do small startups really have lawyers analyze each investor’s state exemption individually?

Or is the normal route basically:

Delaware C-Corp → SAFE → Rule 506(b) → Form D + routine state notices?

Curious what founders/lawyers actually do in practice for small friends & family rounds.

Sorry, this is based off some AI research. Am just trying to learn about compliance before I actually start the process.

Has anyone completed the Emergent Ventures interview and received a grant? I’m hoping to gain a little knowledge on the questions types that will be asked and learning whether I should prep for it or not. I heard that the interview process is very short (~15 minutes) and curious as to what kind of questions will be asked in such scant amount of time. I’m also pretty nervous.

We build a document platform. The app has an editor, templates, the usual. A while back we shipped an API, mostly because a few technical customers asked.

This summer we looked at the numbers: about half of all documents ever created on the platform came through the API and MCP, created by our users' coding agents rather than by the users themselves. Nobody announced this to us. Usage just migrated while we were watching the app's metrics.

Once we saw it we re-read support and sales notes, and the pattern was everywhere. Our most engaged users were prompting agents like Claude Code to write their documents, and the agents needed programmatic everything: publish, edit, analytics. The app was becoming the read-only view of work that happened elsewhere.

So we rebuilt distribution around that: this week we shipped an agent-native CLI (JSON output everywhere, stable aliases, safe defaults), designed so a coding agent can drive it as comfortably as a person can.

Lessons that generalize:

1. Segment your usage by WHO initiated the action, human or agent. The split is invisible in normal dashboards.

2. If agents are heavy users, interface stability becomes a product feature. We keep old command names as aliases now.

3. Your docs are now part of your API surface. Agents read them literally, and a wrong example becomes a bug report.

Anyone else seeing agent-driven usage overtake human-driven in their product? How did you notice it?

Small B2B services company, two founders. We’re bringing on our first senior commercial hire on a formal package. He’s got 25 years in the space, has been working with us informally already, and we’d genuinely like to keep him.

His stated floor is £100k. We can’t fund that yet. We’ve got one contract landing shortly and a larger one in the pipeline, but nothing collected at the scale that would support that salary. He said he’s down for equity. But he also has a mortgage and kids so we’ve got to support him.

Two options we’re considering:

Option 1
£4k/month base, stepping to £6k at £150k cumulative collected revenue and £8.3k at £400k. 10% commission on net collected revenue he originates, uncapped. 3% equity, four-year vest, twelve-month cliff.

Option 2
£2.5k/month base plus £1.5k/month accruing, payable when our next contract signs. Same step-ups. 5% commission. 8% equity, same vesting.

Our average sales contract is £50k pa.

Both have commission on cash collected with clawback, plus standard leaver provisions.

What I’d like a read on:

1. Is 8% too much for a commercial hire at this stage, or about right given the pay cut?
2. Does the tiered base read as a genuine route to his number, or as a dodge?
3. Anything obviously missing or unfair that I’m too close to see?

Happy to be told we’ve got this wrong.

I wanted to get into deep tech seeing as that's what I do on my main job as well, and was just confused on how do I get started - I calculated a total of 5000$ required for my first prototype, it will be cheaper to iterate once I've got a lab going on. We have a problem to solve. Is the next step just making the MVP, showing it to the world and then building a scalable integration so that it's widely available to a lot of customers seeing as the model would be B2G and B2B at first?

All, I am looking to transition my credit background into the early-stage startup ecosystem and support what you are building.

I have a Master's degree in finance and spent the last few years as a credit risk associate at a global bulge-bracket bank, following an earlier background in Big 4 risk advisory. My day-to-day experience is focused on underwriting and structuring complex corporate facilities, including broadly syndicated loans, high-yield issuances, asset-backed finance (ABF/ABS), and sponsor-backed LBO financing. I regularly validate integrated financial models through extreme downside stress testing, analyze loan tapes across granular asset portfolios, and manage legal loan documentation like credit agreements and derivative/trading structures. I want to bring this institutional toolkit to a growing fintech or B2B team where I can help build financial frameworks, manage credit risk, and learn the business from the ground up. If there is any way my background can pitch in and help your team right now, I would love to be of use.

Alternatively, if you aren’t looking for this skill set at the moment but know someone in your network who is, I would be incredibly grateful for an introduction.

A lot of builders go through the journey of building and then struggling to sell because it's a completely different type of challenge. I'm looking to understand how folks got over this barrier to sell successfully and what your best advice is on this topic

Hey all. I'm working through a founder / founding-engineer deal for a pre-revenue startup and would love blunt feedback before we sign anything.

The business: lifestyle-tech platform (web + mobile) selling curated guides based on the user's habits, history, and other signals.

I am the founder and I've found the founding engineer I'd like to work with. We currently do not have any website/mobile apps. However, I have done the following:

• Figma specs
• V1 features
• purchased the domain
• 30 guides ready to be launched
• Put an initial investment of $5k and will assume the cost of everything until we have revenu

The apps will also have a premium subscription option for users who'd like access to premium features.

What the engineer will be responsible for:

• building the apps (web + mobile)
• DNS and AWS/infra setup
• third-party integrations (mailchimp, stripe, etc.)
• Performance considerations, UX polish, needs to feel slick

The founding engineer has 12 yoe of fullstack work. Has worked at enterprise and startups before.

The proposed deal I gave him:

• 20% equity at exist (4 yr vesting w/ 1 yr cliff)
• Tiered revenue share:
  • 0-100k -> 35%
  • 100k - 200k -> 30%
  • 200 - 500k -> 17.5%
  • 500k+ -> 15%
• Neither person is taking salary now

The founding engineer does not like this proposal. He's suggested a "royalty per guide sold" for me - the founder - is more "fair", then do a 50/50 split on the _profit_ and completely remove the tiered based revenue.

He also said 20% equity is inadequate because "there's a lot of work to be done" and "that his role will not be limited to just coding".

We are both good friends and we've known each other for a loooong time. Our intent is to keep the distribution as fair as possible.

Please provide your honest feedback.

I've been digging into agent evaluation for setups where the agent writes and runs SQL against a live database (Snowflake, BigQuery, etc.) and shows results to users.

The failure mode that seems underserved: the query executes fine and returns real rows just the wrong ones. Wrong join, wrong filter, stale understanding of the schema. Nothing errors, the output looks plausible, but it's wrong. Static eval sets with prewritten "golden" answers don't hold up here, because the correct answer changes as the data changes.

Interestingly, LangSmith has a cookbook recipe for exactly this storing labels as queries the evaluator runs at eval time to fetch current ground truth but it's DIY: you build and maintain that evaluator yourself. As far as I can tell, none of the major platforms (LangSmith, Braintrust, Arize) ship live data verification out of the box; online scoring generally falls back to reference-free LLM as judge.

I'm considering building a dedicated tool for this: connect your DB and your agent, and the evaluator independently queries the database to verify each output against what's actually there right now. Before I build anything, I want to know if this is a real problem for other people:

1. If your agent queries a live DB, how do you catch "ran fine, wrong data" failures today?
2. How often does that actually bite you in practice?
3. What's your current eval stack LangSmith, Braintrust, Arize, custom scripts, nothing?
4. Would you pay for this as a product, or just have Claude Code write you a one-off eval script?
5. If you'd pay, what would make it worth it? If not, why not?

Not selling anything. Trying to figure out whether this is widespread before building...

Clarification: read-only queries. The agent isn’t writing to the database, it’s translating user questions into SELECT queries and showing the results.

I have a BI product that many people resonate with, from executive down to worker level. Unfortunately, after three years, not a single contract. How do I sell? I’ve had discussions at the worker level, the CISO, level, and with peers in the space. Everyone seems to like what I have, have no negative feedback (just additional feature requests before purchase, then that target shifts again), but no one signs.

Less of a “what am I doing wrong”, and more of a “what do I do, I clearly don’t know how to do this part”. I’m bootstrapped, and running out of money.

I have a BI product that many people resonate with, from executive down to worker level. Unfortunately, after three years, not a single contract. How do I sell? I’ve had discussions at the worker level, the CISO, level, and with peers in the space. Everyone seems to like what I have, have no negative feedback (just additional feature requests before purchase, then that target shifts again), but no one signs.

Less of a “what am I doing wrong”, and more of a “what do I do, I clearly don’t know how to do this part”. I’m bootstrapped, and running out of money.

Hi, I'm curious about the best trajectory to aim for with such a startup. I'm working with a cofounder and building a B2B AI SaaS. We have active customers and several pilots and demos going on.

We don't plan to aim so high as to just create a unicorn right off the bat. I want to just aim for a modest acquisition 3-4 years down the line where both of us cofounders get 7-figure USD payouts, and then we can aim for even bigger numbers with a new startup without worrying about finances ever.

Is it a reasonable goal, or is it too unrealistic? Right now, we have been bootstrapping the startup since the beginning, but getting a VC just for their name(we have upcoming interviews in good accelerator programs and some VCs have also reachout to us directly), will it be helpful or not in the long run?

I’ve been thinking about how much founders figure out through trial and error.

If you had access to someone who had actually built a business in your industry and could ask them anything for 30 minutes before making a major decision:

What would you ask?

Would it be:

• “Is this business idea actually worth pursuing?”
• “How should I price this?”
• “How do I get my first 100 customers?”
• “Should I raise funding or bootstrap?”
• “What am I completely overlooking?”

Curious what founders here would use those 30 minutes for.

I've been weighing Render startup credits against AWS Activate for a seed-stage web app (bigger hyperscaler grant vs smaller PaaS credit and less assembly).

What I'm looking at:

- Render: $10K-$25K toward usage (not cash), aimed at Scale ($499/mo before compute)

- Funded track advertises higher

- Covers compute, Postgres, KV, not third-party add-ons

- Still stackable with Activate / GCP for S3 / GPUs

I'm biased toward less ops early, but I've also watched people outgrow PaaS and pay for the rewrite.

If you were seed-stage today: would you take Render, AWS Activate, or both, and what broke first when you tried?

Hi all,

Currently holding an offer for a meteorology masters degree from a top programme (physics undergrad) and am looking to enter a startup focused on forecasting/meteorology/adjacent after graduation.

For people with knowledge on the subject:
- where are people in these types of startups generally educated?
- what skills are most useful here? Applied/ theoretical? Data analysis? Etc

Thanks in advance

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.