I find it extremely unlikely that I'd be able to sell it since its two main competitors are (1) literally free to use and has been polished for 16 years, and (2) a best-in-class native application that has been the lingua franca of business writing for over 40 years

I've talked to a lawyer about how they handle this. They do indeed double-check everything, since it'd be embarrassing (or worse) to send hallucinated statements to opposing council or to the court. They still find the assembly a huge time saver

But based on stories in the news on the subject, not everyone has this same level of diligence

If you do something 100 times a day against a four-nines service, you can reasonably expect that everything will succeed.

If you do something 10,000 times a day against a two-nines service, you can expect to hit a substantial number of errors during that day, or even have long periods where your work cannot happen at all.

People aren't frustrated with Github because Github has 98% uptime or whatever the specific number is. They're frustrated because it regularly interferes with their ability to work. The 98% number is just a concise way to say it.

The last time I signed up for an Instagram account (2016) it got immediately flagged as a bot account. I think I was supposed to appeal it to have it reinstanted, but why bother if this is how badly they treat users?

Can you imagine going to work as Instagram's web developer, spending your day finding new ways that the app accidentally works and disabling them?

I used to work on the Google Docs team, so I basically did an 8-hour Q/A with Sol spanning like 400 questions. Did my best to reach back 10 years into my memories to see how things were implemented, and discussing architecture and tradeoffs. Then went through 5 ChatGPT resets implementing it on Astra to get a feel for the model.

I have a long way to go. But I'm writing my first post on it this week, and then I'll do a writeup of the editor project.

As a side project, I'm doing an experimental task now (having Astra implement my own personal Google Docs clone, OT and everything, for my blog), and it feels more capable than Fable but needs to be watched closer than Fable. It is obsessed with verification and evidence to the point that I've needed to make some absolute rules to stop it from repeatedly e.g. making unit tests with 10,000 test cases with 5+ hours of runtime

For well-defined coding tasks, I'd default to either Luna or Sonnet, or hell, just writing it by hand.

Unlike the vanilla read of the OpenAI press release, it is much more unfiltered and outlines some particularly aggressive behavior by specific OpenAI employees

- Are you sure that temperature and other nondeterminism isn't affecting your output?

- Are you sure you're not being routed through an A/B test at this moment?

- Are you sure there's not a bug affecting the model at this moment?

- Are you sure that you picked the right model and effort level?

- Are you sure that your result generalizes across providers?

- Are you sure that you set up the correct level of sandboxing and the agent can't e.g. look at a sister directory or git history in the current directory for answers?

- Are you sure that the agent isn't leaking answers in memory or its conversation history?

- Are you sure that tool calls aren't somehow affecting results?

- Are you sure that your results are robust, i.e. you see the same results with mild tweaks to the prompt?

- Are you comfortable keeping your blog post live when your results are invalidated next week with the next model launch?

And that's just a quick list off the top of my head.

I personally decided that it wasn't worth it, I'm glad that Dan decided to publish his. Frankly I think we could use a lot more of these "I ran these 2 techniques side by side and here's what I saw" anecdata, because most people who promote prompt techniques can't produce a single prompt they ran twice because they never actually tested it per se.

Edited to add: formatting + the word "promote"

I asked him why he does this. His explanation was really built on this operant conditioning idea: "we use this stuff for 8 hours a day and we train ourselves to avoid all of its little pitfalls. So I get the most available person who hasn't used it all day, which is our manager. He uses it differently than we do because he doesn't avoid all of its little problems. But if we have a really tricky problem, I get our manager's manager. I don't know if you've ever seen him try to use an xbox controller, but he has the spatial reasoning abilities of a goldfish. He's never failed to reproduce a really hard bug. If I ever needed an Einstein-level bug reproduction, I'd track down the head of the department and put him in front of it, but it's never come to that.

I'd also rather just select from the full tristate diagram, but their framing also makes sense to me

It feels like it found a register that games the evaluator, where it can ramble forever and rarely be marked wrong while slowly racking up points as it talks more.