There were surprisingly few practical Wavelet Matrix implementations available for Python, so I implemented one with a focus on performance, usability, and typed APIs. It supports fast rank/select, top-k, quantile, range queries, and even dynamic updates.
Feedback welcome!
- This status page actually identified the outage: https://hackernews.onlineornot.com/ - Pages by Hund and Statuspal did not show the outage.
- The last post before the outage was https://news.ycombinator.com/item?id=46301823 (1:39:59 PM GMT). The last comment was https://news.ycombinator.com/item?id=46301848 (1:41:54 PM GMT).
- There was an average of ~4 seconds per comment just prior to the outage. Based on this, HN likely went down at 1:41:58 PM GMT.
We’ve built a demo that anyone can try here: https://kenobi.ai/start
We believe that large parts of the web are about to go from being static to dynamic because of how adept LLMs are at transforming rendered HTML. And right now we’re focussing on B2B landing page content (as opposed to application UIs) because there is a lot of commercial opportunity for increasing top-of-funnel inbound conversions.
Our journey to Kenobi today is a long and snaking one. You may notice from the post title that we did YC’s Winter 2022 batch (I know that 4 years is practically ancient in YC-dog-years). Kenobi is a hard pivot from our original idea that we got accepted into YC with — a company called Verdn which did trackable environmental donations via an API. Since the summer, we’ve been hacking on different ideas… We started with personalized UI screenshots for outbound campaigns, but then people told us they wanted transformations to their actual site[0] — so we built an agentic workflow to research a visitor-company and “pre-render” changes to a landing site for them. Ultimately, there was too much friction in getting people to incorporate personalized URLs into their cold outbound campaigns[1]. Besides, people kept asking for us to do this for their inbound traffic, and so our current iteration was born.
Right now with Kenobi you pick a page that you’d like to make customizable, and choose [text] elements that you’d like to make dynamic. You can define custom prompting instructions for these elements, and when someone visits your page, our agentic workflow researches their company, and presents the updated content as quickly as possible, usually within a few seconds.[2] You also get a ping in Slack every time this happens so you know who is using your site.
We’ve been experimenting with features such as generating custom imagery that actually looks good and native to the page design, and pulling in company data sources so that e.g. the right case study can be presented based on a visitor’s industry and ICP profile. Our most requested feature is deanonymizing traffic so that Kenobi’s personalization can happen automatically as visitors land on your page — this is coming very soon, as right now you have to specify where you’re coming from.
It’s surprised us just how much business value we’ve gotten from knowing who (most probably) is on the page and asking for a personalized experience. We’ve seen response rates 3x of what we would normally from following people up from companies we know visited our site.
There are many players in this space already, and everyone seems to have their own angle. We are keen to hear thoughts on what people think the future of the personalized internet looks like!
Cheers from London!
P.S. - there's also a video that Chris recorded showing the end-to-end Kenobi experience right now https://www.loom.com/share/bc0a82a2f2fd40f695315bae80e8f5d8
[0] - Many of them had tried AI “microsite” generators but found the maintenance of managing a separate website(s) just for closing deals to be burdensome and inefficient.
[1] - Despite having a CSV export and Clay integration option for our pre-generated website changes, getting people to weave the URLs into their email sequences (everyone uses different tools) seemed almost insurmountable without building what would ostensibly be our own sequencing software.
[2] - We use light foundation models with grounded search for the research step, and translate these into markup changes via another light LLM pass and our own DSL which is optimized for speed.
Developer Blog: https://blog.google/technology/developers/build-with-gemini-...
Model Card [pdf]: https://deepmind.google/models/model-cards/gemini-3-flash/
Gemini 3 Flash in Search AI mode: https://blog.google/products/search/google-ai-mode-update-ge...
Deepmind Page: https://deepmind.google/models/gemini/flash/