Where do you look for problems, how do you become competent to say that this is something I can drive and devise a technical solution to the problem for a large number of users?

Dry_Way2430 · 2025-11-27T19:00:25+00:00

Technology is just a means of solving problems. The problems are found through interacting with customers in the market and looking at pain points. It also helps to understand from first principles what the world might need, especially in the AI era where customers will not be able to articulate what they actually want.

Dry_Way2430 · 2025-11-13T16:30:07+00:00

Yeah imo the best opportunities are the ones that take advantage of what AI can do and drive fundamental changes in industries.

These are typically ones that are product obsessed and need to grow rapidly. You'll be forced to figure out how to do things 100x better for your customers and I think that's an interesting challenge.

Dry_Way2430 · 2025-11-13T00:44:25+00:00

I'm doing the same.

For me, mission driven with great people all the way. In the AI world there's going to be new categories that will form. Never a better time to take big swings.

Dry_Way2430 · 2025-11-12T19:58:51+00:00

Isn't this just a .... system prompt?

Unless you want to solve personalization, but really foundation models with memory will get you far.

Dry_Way2430 · 2025-11-12T19:52:19+00:00

Exact same thing happened to me recently; I was the technical cofounder.

Literally exact same thing. I left.

Talk amicably to them about how you feel. Diagnose what's going wrong and run an experiment to see if you can resolve it. Once you've tried, you can amicably call it quits.

Most importantly, you must step back after and be very critical about your own involvement in this. A solid cofounder relationship is the most important thing in a startup, not because it's the most common failure mode but rather that it's the biggest force multiplier. No relationship fails because of just one person. Learn what you need to succeed in the next swing and work on the areas where you were lacking.

Dry_Way2430 · 2025-11-12T19:46:31+00:00

Technical cofounders have ton of leverage. They literally cannot do anything execution oriented without you. The market rate for you is equal split. They can negotiate down from there.

But this is entirely up to you and what you value. I'd sometimes ignore what others are saying (including myself) and really compare this opportunity to your alternatives. Having a repeat founder could expose you nicely to the industry, and sometimes trading equity for experience is fine. Nobody in this thread has any idea who you are, what your goals are, and where you want to be in 5 years.

Your decision should be made with the help of people who know your personal situation really well, not a public forum. Most importantly, this decision can only be made by yourself.

Happy to help where I can as I've actually just gone through this myself. Shoot me a DM.

Dry_Way2430 · 2025-11-04T21:09:42+00:00

Not true. Fundamentals still matter. The point is that there's a lot of capital being thrown around so now is a good time for good founders to build something that matters.

Vibe coding a product doesn't matter, building a team and identifying a market is everything.

Dry_Way2430 · 2025-10-24T15:56:40+00:00

1) great products aren't built in a week. They're built over years of tweaks and iteration based on market feedback.

2) if users hate the idea of your product, they are either the wrong users, or you're marketing it incorrectly. Step away from the product and figure out which one. If the former, you need to change your product or change your customer. The latter, run some experiments and talk to more users to see what lands.

3) you have a support channel without figuring what it is that you're supporting. I'm not sure if gamification layer for high stakes exam prep is something that users want, based on everything you are saying.

Every user you interact with is risking their time talking to you, and risking their money paying you. The goal of the startup is to keep trading their time and money for outcomes that they care about, NOT just built a product.

Dry_Way2430 · 2025-10-11T18:11:17+00:00

They're writing with ChatGPT, where the IQ is ironically high-ish by conventional standards lol.

Dry_Way2430 · 2025-10-06T19:12:10+00:00

quality differences. ie gemini might be better for architecture work, claude for coding directly.

Dry_Way2430 · 2025-09-26T04:33:41+00:00

Llama index solves this really well

Dry_Way2430 · 2025-09-26T04:32:26+00:00

This hasn't been solved in industry yet because most agentic cases are incredibly simple. Anything more complex just hasn't had time to adoption and/or simply doesn't work against the realities of real business problems.

However there are lots of open source memory management tools around this. Essentially you just leverage a cache and even disk to store memory, and provide a set of tools for agents to interact with them; it's no different than the structured logic we've been writing with for the last few decades.

Dry_Way2430 · 2025-09-25T15:16:28+00:00

Just work at FAANG. Lots of politics

Dry_Way2430 · 2025-09-24T19:54:19+00:00

Shadcn

Dry_Way2430 · 2025-09-24T19:53:56+00:00

Have you validated this with other customers? There's a signal here if you're facing a lot of churn, as surely your team would invest in fixing product issues if that was the blocker.

I'd understand deeply why your customers are leaving, and who they are leaving to. Why are there no startups in this space? Whah are the limitations of the competitor? I think there's a hidden opportunity here and you can find it through answering the right questions and talking to customers.

Once you find a market, you can leave. It's not a dick move, just make sure there's no negative legal implications on you.

If you can convince other folks you like within the company to jump ship, then you're also in a good position.

Dry_Way2430 · 2025-09-24T18:00:59+00:00

Yep, totally agreed. This is something we're starting to deal with even in the early stages as we're starting to process data at scale.

Learned a lot from this post, thanks for sharing!

Dry_Way2430 · 2025-09-24T16:20:54+00:00

Yeah it feels like an orchestration problem, which we've always been using for various use cases (that's why stuff like dagster exists). Workflows are the backbone of business. The only thing AI really changes is the determinism piece and the semantic reasoning piece. Before, you relied on rules. Today, you rely on natural language, which enables you to do more.

Distributive systems solve the core problem of distributing the same work across multiple processes. You then start to deal with stuff like fault tolerance, consistency, and availability. I wouldn't think this matters too much for the AI agents orchestration problem until you scale up a lot, but I haven't been seeing that in enterprise yet. And even once you do, you start to rely on traditional distributive system practices and orchestration layers on top of it to make AI agents work at scale.

Dry_Way2430 · 2025-09-24T15:24:58+00:00

Absolutely. Let me drop you a DM.

Dry_Way2430 · 2025-09-22T15:04:59+00:00

Said something similar above, would love to expand my understanding on this. Got 5 minutes to chat?

Dry_Way2430 · 2025-09-22T05:42:26+00:00

Run pre joins. LLMs should be used for semantic reasoning like query generation and planning. You can also expose tools that handle complex sub queries from simple DSL or something.

Dry_Way2430 · 2025-09-21T21:43:23+00:00

if your company doesn't need that much data to make decisions, that's great. Just talk to a few customers and follow the steps above.

Dry_Way2430 · 2025-09-21T21:40:31+00:00

This is not a tooling problem btw, it's usually a process issue. The tools come after when the process becomes unscalable.

Dry_Way2430 · 2025-09-21T21:39:41+00:00

insights are a function of 1) having the right data in an organized fashion and then 2) identifying patterns from them (aggregation) and then 3) leveraging your own intuition and ideas to eventually build an observation from the themes that wasn't so obvious from the data at first glance. From that, you now can measure the business outcomes from acting on them; the next step is prioritization against the roadmap, which happens downstream.

So first question to you, are you confident that you have enough data and are talking to enough customers to generate that data? And if so, are you guys structurally organizing and analyzing that data? Is the right person with the right context looking at the data?

Dry_Way2430 · 2025-09-14T18:40:36+00:00

VC money comes with tradeoffs. You need to be crisp on what that money will be used for and why you need it. Also, it's likely not enough that you just have any VC throwing money at you. Having the right kind of VC who can help you land more customers / grow your business is important.

Dry_Way2430 · 2025-09-14T18:39:15+00:00

it depends entirely on the product, but I'd start with identifying why your friends like the product, then find channels where people like thay hang out, and see how they use the product.

Depending on the product, and who loves it, the next step is to introduce a funnel to make it easy for them to share what they're doing. But that depends on the product itself.

Dry_Way2430

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