waldopat

Go read Steve Blank, either Four Steps to the Epiphany or The Startup Owner's Manual. Skip over any hardware examples.

TL;DR: You can’t sell a product if you don’t know who wants it or what they want or how to reach them. (And other shocking revelations)

Love the detail of getting kicked off the internet because someone picked up the phone.

Back when I was 12, I installed linux on an old 386 to set up a little server. I remember writing the connection script to get on the internet. I still wonder how I learned to do that. Still a mystery! But those were the days when you just figured things out.

While everyone is revoking OAuth apps, rotating API keys, and deleting Vercel accounts, this is a good reminder that the scary part is how short the path was from OAuth token to employee account to internal systems to customer secrets.

Many folks here likely have some stack that looks like: Google Workspace, GitHub, Vercel/Railway/Render/etc. where env vars or secrets are hosted. These are all loosely coupled but transitively trusted.

So compromising any one of them becomes a threat vector. In other words, if System A trusts System B, and System B trusts System C, then System A trusts System C. This is also why OpenClaw is frightening from a security perspective.

Also, this is a good reminder to run audits. Run `npm audit` on a typical Next.js project and you’ll probably see DoS vulnerabilities, ReDoS issues, Prototype pollution, code injection paths, handlebars etc. I'm sure you'll find something unexpected if you don't have routine code hygiene checks.

Here's a founder/product perspective. This maps well to the skateboard => scooter => bicycle => motorcycle => rocket ship product metaphor that's often used. Each phase teaches different design patterns, constraints, and failure (and success) modes for different inflection points of a startup's journey.

But here's the reality. What got you technically to PMF may hold you back from your Series A and next steps. Technical debt is just the natural cost of growth, but (here's the kicker) optimizing tech stacks too early can lead to slower execution time. Most startups never reach exponential scale anyways. Put another way, starting with "rocket ship" does not immune the startup from rewrites, refactoring or throw away code.

The real systems and management challenge is building architectures that are intentionally temporary or modular. Simple enough that throwing them away later isn’t traumatic and rebuilds aren’t a sign of failure but success.

Honestly, you might want to step outside tech altogether. Join a local civic or neighborhood organization or volunteer with a nonprofit. There was a nice thread last year about libraries.

Channeling Steve Blank, get out of the building! You’ll run into real problems faced by real people who often have limited exposure to both AI and tech, but who can still benefit enormously. Listening and engaging is always a good first step before jumping in to suggestions.

In this space, needs are far more data and visualization driven, which are not strictly AI related. It may also be both a useful and humbling antidote to hype cycles.

Go read The Founder's Dilemma by Wasserman. It's great and covers almost any problem a founder will run into. To really summarize, it's all about trade offs and prioritization. Patents vs trade secrets fits nicely.

Trade secrets are far cheaper and easier to maintain than patents. In short, patents are only as strong as your ability to enforce them. Also Alice Corp. v. CLS Bank International (2014) weakened software and process patents. That said, if you can’t realistically defend IP in court, you effectively don’t have it. From an early-stage founder perspective, that makes patents a questionable use of time and money and potentially what kills the company.

This may contrast from information you get from a lawyer or VC. Patents are attractive because they create an asset someone else can later buy or defend. For the founder, the incentives aren’t squarely aligned.

Neither approach is more right or wrong, but there are very real practical consequences. If you are pre-seed who is bootstrapped or done a family & friends round and are pre or early revenue, trade secrecy is by far your better option.

As an additional note, if you don't own the underlying AI models and are just a better wrapper for Claude or ChatGPT you at best have a very weak IP or patent position.

Agreed, Goodhart’s Law captures the failure mode well intentioned KPIs and OKRs may miss, let alone agentic automation

Please don't use "bank level security" claims if you're not SOC 2 Type II compliant and don't have PKI based cryptography to secure your documents.

I think this also shows up outside an AI safety or ethics framing and in product development and operations. Ultimately "judgement," however you wish to quantify that fuzzy concept, is not purely an optimization exercise. It's far more a probabilistic information function from incomplete or conflicting data.

In product management (my domain), decisions are made under conflicting constraints: a big customer or account manager pushing hard, a CEO/board priority, tech debt, team capacity, reputational risk and market opportunity. PMs have tried with varied success to make decisions more transparent with scoring matrices and OKRs, but at some point someone has to make an imperfect judgment call that’s not reducible to a single metric. It's only defensible through narrative, which includes data.

Also, progressive elaboration or iterations or build-measure-learn are inherently fuzzy. Reinertsen compared this to maximizing the value of an option. Maybe in modern terms a prediction market is a better metaphor. That's what we're doing in sprints, maximizing our ability to deliver value in short increments.

I do get nervous about pushing agentic systems into roadmap planning, ticket writing, or KPI-driven execution loops. Once you collapse a messy web of tradeoffs into a single success signal, you’ve already lost a lot of the context.

There’s a parallel here for development too. LLMs are strongest at greenfield generation and weakest at surgical edits and refactoring. Early-stage startups survive by iterative design and feedback. Automating that with agents hooked into web analytics may compound errors and adverse outcomes.

So even if you strip out “ethics” and replace it with any pair of competing objectives, the failure mode remains.

For anyone that's curious, we switched to Active Campaign. So much better for us and way cheaper.

Phenomenal work!