I always wonder why not more companies are using more creative approaches like Google's Dutch auction to set their IPO price.
It seems direct listings gained some popularity but overall most companies seem to rely on the traditional underwriter model.
According to [0] -
> 22 companies went public on major exchanges using IPO auctions in the U.S. between 1999-2008, but there have been none since then, as of May 2025. Starting in 2018 when Spotify went public, there have been at least 20 companies that have gone public using a direct listing. With both IPO auctions and direct listings, underwriters do not have discretion to allocate shares to their preferred clients.
> In regard to AI usage, my fuzzing workflow was automated by AI with a strict harness. I used GPT-5.5-3-Codex-Spark for ALL the fuzzing, as barely any "thought" is necessary when provided with an efficient harness. Contrary to the growing narrative that I'm just some random child burning tokens, I DO actually have a degree in the subject and have published multiple papers on fuzzing methodology. I spent years researching and developing new tools and ideas for how to fuzz. You do NOT need a SOTA model to help you identify these issues, I promise!
Thanks! I've noticed a big jump when they switched to Gemini 3.1 Pro and it really became useful. I like that I can use it from my phone too. It took a bit of trial and error but I came up with a good ralph loop between GitHub Actions and Google Jules using the Jules API. So basically I have Jules extend its TODO.md with the next set of tasks and open a PR then run a GitHub Action with a few checks, auto-merge, and then call back into Jules to kick off the next cycle if there are still open tasks. It then mostly just runs and occasionally gets hang up on some questions that I then answer on my phone mostly just telling it to make a judgement call and keep the build green. You can check out the prompt, action, and past PRs for examples ex. Jules prompt is here: https://github.com/alpeware/datachannel-clj/blob/main/prompt...
I've been working on a pure Clojure implementation of WebRTC Data Channels (SCTP over DTLS over UDP). The library provides a minimal, dependency-free (except for Clojure itself) way to establish peer-to-peer data channels on the JVM.
I've always wanted this and have used it to experiment with Gemini's cloud agent Google Jules.
I've been impressed by Google Jules since the Gemini 3.1 Pro update. Sometimes it's been working on a task for 4h. I've now put it in a ralph loop using a Github Action to call itself and auto merge PRs after the linter, formatter and tests pass. It does still occasionally want my approval, but most of the time I just say Sounds great!
One of the original authors also published a follow up with some additional details and analysis that may be of interest (still reading through it myself) [0]
I started reading the book but haven't finished it yet. It's also been a while since I've been surfing. I enjoyed the video and it's a good reminder to both finish reading the book and go surfing.
What were your main takeaways of the book and how has it influenced your startup?
currently working on transflux: agent driven development for clojure(script).