jackdawed's comments

I once had a conversation with a potential co-founder who literally told me he was pasting my responses into AI to try to catch up.

Then a few months later, another nontechnical CEO did the same thing, after moving our conversation from SMS into email where it was very clear he was using AI.

These are CEOs who have raised $1M+ pre-seed.

Small business owners work 997 and you don't see them incessantly posting about it. That's the catch, though. They own the business. Founders can subject themselves to 996 all they want but it's a failure of management to expect that from employees for less than 1% equity.

I took a break from tech to open my own bookstore and I definitely work more hours than when I worked at a pre-IPO $7B startup. I'm way less stressed. At least my bookstore doesn't wake me up at 3am 3 nights in a row, and expect me to come to work the next day.

I interviewed with an early stage pre-seed startup with a very young team, like 25-27. I was interviewed by someone way more junior than me. According to the recruiter, in 3 months, I've made it the furthest and he told me this startup was churning through top tier candidates left and right.

After my interview, I immediately knew why. The team was so junior they didn't know how to evaluate senior talent. They didn't know what they wanted. I've arguably interviewed more candidates than the person interviewing me.

Last I checked, they still haven't filled that role.

The strong hires I've given all came from underrated candidates who didn't come from trendy backgrounds. Still think Dan Luu's advice holds up even more at early stage startups. https://danluu.com/programmer-moneyball/

I have been exclusively doing this in the past year, selling my services as “hardening vibe-coded prototypes for production” or “helping early stage startups scale”.

In the best cases, they were able to reach funding or paying users. Architecture debt is one of the worst kinds of tech debt, so if you set it up right, it’s really hard to mess up.

In the worst case, after my contract ended, the CEO fired the whole US engineering team and replaced them with offshore resources. This was an example of messing up despite the architectural and procedural safeguards we built.

To combat LinkedIn spam, I exclusively write wizard-themed LinkedIn posts: https://dungeonengineering.com/i-could-have-cursed-him-inste...

One blogpost I found on HN completely leveled up how I use LLMs for coding: https://harper.blog/2025/02/16/my-llm-codegen-workflow-atm/

Having the AI ask me questions and think about the PRD/spec ultimately made me a better system designer.

That's the core challenge in designing a system like this. Echo chambers and comfort cages emerge from recommendation algorithms, and before that, from lazy curation.

If you have control over the recommendation system, you could deliberately feed it contrarian and diverse sources. Or you could choose to be very constrained. Back in RSS days, if you were lazy about it, your taste/knowledge was dependent on other people's curation and biases.

Progress happens through trends anyway. Like in 2010s, there was just a lot of Rails content. Same with flat design. It wasn't really group think, it just seemed to happen out of collective focus and necessity. Everyone else was talking/doing this so if you wanted to be a participant, you have to speak the language.

My original principle when I was using Google Reader was I didn't really know enough to have strong opinions on tech or design, so I'll follow people who seem to have strong opinions. Over time I started to understand what was good design, even if it wasn't something I liked. The rate of taste development was also faster for visual design because you could just quickly scan through an image, vs with code/writing you'd have to read it.

I did something interesting with my Last.fm data once. I've been tracking my music since 2009. Instead of getting recommendations based on my preferences, I could generate a list of artists that had no or little overlap with my current library. It was pure exploration vs exploitation music recommendation. The problem was once your tastes get diverse enough, it's hard to avoid overlaps.

I've noticed a lot of people are converging on this idea of using AI to analyze your own data, the same way the companies do it to your data and serve you super targeted content.

Recently, I was inspired to do this on my entire browsing history, after reading https://labs.rs/en/browsing-histories/ I also did the same from ChatGPT/Claude conversation history. The most terrifying thing I did was having an LLM look at my Reddit comment history.

The challenges are primarily with having a context window large enough and tracking context from various data sources. One approach I am exploring is using a knowledge graph to keep track of a user's profile. You're able to compress behavioral patterns into queryable structures, though the graph construction itself becomes a computational challenge. Recently most of the AI startups I've worked with have just boiled down to "give an LLM access to a vector DB and knowledge graph constructed from a bunch of text documents". The text docs could be invoices, legal docs, tax docs, daily reports, meeting transcripts, code.

I'm hoping we see an AI personal content recommendation or profiling system pop up. The economic incentives are inverted from big tech's model. Instead of optimizing for engagement and ad revenue, these systems are optimized for user utility. During the RSS reader era, I was exposed to a lot of curated tech and design content and it helped me really develop taste and knowledge in these areas. It also helped me connect with cool, interesting people.

There's an app I like https://www.dimensional.me/ but the MBTI and personality testing approach could be more rigorous. Instead of personality testing, imagine if you could feed a system everything you consume, write, and do on digital devices, and construct a knowledge graph about yourself, constantly updating.

Cassi | Founding Engineer - Frontend | Hybrid NYC or Remote | Full-time | $150-200k + equity

We’re building an AI-powered home manager: a proactive, chat-first system that helps homeowners stay ahead of bills, maintenance, documents, and expenses.

We're backed by founders of Freshly ($1.5B), Kustomer ($1B), and more.

What we're building:

- Structured parsing of home docs (insurance, warranties, bills)

- Personalized maintenance schedules

- Expense tracking with home-specific insights

- Natural language interface

- Embedded payments + smart renewal workflows

You’ll:

- Lead development of user-facing features with React, TypeScript, CSS

- Build elegant UIs for AI-driven workflows

- Design new UI paradigms for LLM-powered assistants

- Own frontend architecture and contribute to product strategy

- Optimize for responsiveness, performance, accessibility

Requirements:

- 3+ years experience in frontend development

- Strong with React, TypeScript, modern CSS

- UX focus with portfolio of polished, intuitive interfaces

- Experience with design systems and collaborating closely with product/design

Bonus:

- Experience with LLMs or AI interfaces

- Background in productivity tools or intelligent assistants

Contact: [email protected]

Mention you found us on HN! We’re also hiring for product manager, full-stack, and ML roles.

This was a big confidence booster for me as when I first started learning English, people would complement me on how well I spoke English, but I took that as my accent was still detectable. It's only been in the past 5 years that people assumed I was American and made no comment on my English at all, until I disclosed that English was my second language. It's usually certain words that give me trouble, like "cupboard" or "chef". The AI detected my accent as a mixture of German and English. When I tried to exaggerate my accent, it correctly detected Thai.

BizBuySell. However, according to my broker, most business sales never make it to market. It's mostly through email lists and connections.

Yes, this is really only viable in a major city. I wouldn’t consider doing this without at least 200k population in a major metro area. The area I’m in is 600k people with a heavy Laptop & Lattes ESRI tapestry segment. Basically it’s filled with software engineers.

Funny enough I am also planning to convert this cafe into a bookstore/board game shop. The best way to find these numbers is to work in a similar shop or be friends with the owner. Naturally, during business brokerage, you will also get these numbers after signing an NDA.

I can’t really talk much about this yet because legally, until my E2 visa is approved, I can only be in the owner role, not operator. Right now, I pay my friend to be the manager.

Once I am in the clear, I’d be happy to open source the process of acquiring a SMB and running it. I basically become a business broker myself for this one deal, thanks to the broker I worked with.

The business sale was a transfer of assets sale, so the previous owner taught me everything.

The cafe doesn’t wake me up several nights in a row at 3am.

650k avg revenue for past few years, COGS around 350k, Gross Profit 300k, 80k expenses, SDE 220k, EBITDA 170k. Catering is also a major source of revenue.

I got burnt out as a SWE at a startup from stress and health issues. Bought a cafe and turned it into a bookstore cafe. Annual revenue is around 600k. Seller's discretionary earnings is around 220k. In hindsight, I should have done this earlier. Not having to deal with office politics, insane on-call rotations, stress. On top of that, it helped me qualify for E-2 investor visa, which is far less of a headache than OPT/H-1B. It was a major help having an experienced business broker/commercial real estate agent.

Dual N-back had measurable results for me.

Serious in the sense that, "I'm going to put some effort to investigate what is going on, seek out teachers and resources, and apply my knowledge through daily practice". The actual meditation itself is very playful but focused, like getting good at a competitive video game.

> The five spiritual faculties are said to be like a cart with four wheels and a driver. If any of the four wheels is too small, wobbly, or not in balance with the others, then the going on the spiritual road will be rough. The four wheels symbolize faith, wisdom, energy, and concentration. If the driver is not paying attention there will also be problems. The driver symbolizes mindfulness. [See SN 48.18, also Visuddhimagga, IV, 45.2.]

> The five spiritual faculties have also been presented in another order that can be useful: faith, energy, mindfulness, concentration, and wisdom. In this order, they apply to each of the three trainings, the first of which, as discussed earlier, is morality. We have faith that training in morality is a good idea and that we can do it, so we exert energy to live up to a standard of clear and skillful living. We realize that we must pay attention to our thoughts, words, and deeds in order to do this, so we try to be mindful of them. We realize that we often fail to pay attention, so we try to increase our ability to concentrate on how we live our life. In this way, through experience, we become wiser in a relative sense, learning how to live a good and useful life. Seeing our skill improve and the benefits it has for our life, we generate more faith, and so on.

https://www.mctb.org/mctb2/table-of-contents/part-i-the-fund...