okay, easiest branding ever: “quick! go fetch The Irwin!”
IRWN — Immersion, Rinse, Warm, Notify
Readit Newsteepo commented on Ask HN: What Are You Working On? (December 2025) · Posted by u/david927
teepo commented on Show HN: Gemini Pro 3 imagines the HN front page 10 years from now dosaygo-studio.github.io/... · Posted by u/keepamovin
Just did the same with claude-sonnet-4-5-20250929
https://2035.future-hackernews.workers.dev/news
The page looks much more consistent to the original. Only produced the html as output and the thinking in the comment of the html.
I did a similar one for predicting "The Onion" headlines: https://sethjano.github.io/fun-scripts/
teepo commented on Netflix to Acquire Warner Bros about.netflix.com/en/news... · Posted by u/meetpateltech
Wow. That is dysfunctional.
I would be curious how the financial wires got crossed.
I would have assumed residuals were proportional to views, and views valued proportionally as contributing to subscription demand. And it would be a rare viewer to watch one show like that, over & over. I.e. only upside. Something went sideways.
I think that a show like Westworld is a great example of the realities of the streaming era. If HBO kept streaming it on HBO Max it probably costs them $2-4 million in residual liabilities. HBO removed dozens of scripted shows during that phase, and had a mandate to cut around $3B in post merger costs.
After Year 1, WGA/SAG residual formulas decrease: Year 2: ~80% of Year 1 Year 3: ~55% Year 4+: sometimes stabilize at a “floor” rate
So what did they do? They ran it for a few years, ran the numbers, realized that Westworld was no longer profitable on the platform. (Profitable would have to mean draws enough new subscribers to the platform). AND THEN - Warner Bros. Discovery made new deals with other platforms with ads. I think you can still find Westworld on Tubi and other ad-supported platforms that actually pay Warner licensing fees.
teepo commented on I was right about dishwasher pods and now I can prove it [video] youtube.com/watch?v=DAX2_... · Posted by u/hnaccount_rng
> Whenever natural gas supply is turned off in the US, for any reason, only the gas company can turn it back on
Doesn't match my experience. My colleagues and I are able to turn on or off the gas supply to our houses at will.
often around here in texas, when the gas is turned off due to an issue, the gas company disables the meter, or even removes or bypasses it. And I live in gas land, where we have natural gas piped in to the kitchen, bathrooms, laundry, outside for grills, as well as the furnace. We've seen it a lot, if you call the gas company about smelling gas, they come and remove your gas meter until you hire a plumber to go find the leak.
teepo commented on I was right about dishwasher pods and now I can prove it [video] youtube.com/watch?v=DAX2_... · Posted by u/hnaccount_rng
I had that on a GE Profile from I think 1999 if I remember right. It was pretty nice. I think it's still a feature on some of them.
That dishwasher was great and lasted over 20 years. The previous owners had definitely abused it and never cleaned it. I repaired it and had about the best dishwasher for a few more years. Eventually the main logic board went out (can't blame it too much, had electrical issues that killed a few things) and a replacement board was going to cost a few hundred dollars in parts even from questionable third party sellers. Seemed to be a good bit to sink on what was a highly abused >20 year old washing machine at the time.
as someone who's gone down the rabbit hole of dishwasher home repair, I've created more problems than I've solved. I agree that maintenance is important, but when you get into replacing the seals and gaskets that can result in water flooding into your kitchen, i decided recently to draw a line. I'm now the proud owner of some fancy leak detection / moisture detection IOS products as a result. (and yes I'm aware there are better, low tech solutions like the "frog" on the market, but I chose to torture myself instead)
"The World Beyond Your Head" from Matthew Crawford is exactly about this. Definitely recommend reading!
Thanks for the tip. I added this to my audio book queue.
It's pretty interesting how today's cars come with features like remote braking and monitoring cameras, all designed to make driving less demanding for us. So as these researchers work to make vehicles less distracting, these cool features somehow end up making us even more distracted. It's an ironic cycle that leaves you more distracted, and maybe more unsafe.
teepo commented on Launch HN: Recall.ai (YC W20) – API for meeting recordings and transcripts · Posted by u/davidgu
I'm impressed with the desktop SDK demo video hosted by Nick. Very clever. I noticed he's using Emacs, and that got me thinking that maybe I could make a little capture template that invokes your service to transcript directly from org mode. :) - Adding this to the wood pile.
teepo commented on How big are our embeddings now and why? vickiboykis.com/2025/09/0... · Posted by u/alexmolas
It's likely because the definition of "similar" varies, and it doesn't necessarily mean semantic similarity. Depending on how the embedding model was trained, just texts with a similar format/syntax are indeed "similar" on that axis.
The absolute value of cosine similarity isn't critical (just the order when comparing multiple candidates), but if you finetune an embeddings model for a specific domain, the model will give a wider range of cosine similarity since it can learn which attributes specifically are similar/dissimilar.
Thanks - that helped it click a bit more. If the relative ordering is correct it doesn't matter they look so compressed.
teepo commented on How big are our embeddings now and why? vickiboykis.com/2025/09/0... · Posted by u/alexmolas
Question for the experts: a few years back (even before covid times?) I was tasked with building a news aggregator. Universal Sentence Encoder was new, and we didn’t even have BERT back then. It felt magical (at least as a regular software dev) seeing how the cosine similarity score was heavily correlated with how similar (from a semantic standpoint) two given pieces of text were. That plus some clustering algorithm got the job done.
A few months ago I happened to play with OpenAI’s embeddings model (can’t remember which ones) and I was shocked to see that the cosine similarity of most texts was super close, even if the texts had nothing in common. It’s like the wide 0-1 range that USE (and later BERT) were giving me was compressed to perhaps a 0.2 one. Why is that? Does it mean those embeddings are not great for semantic similarity?
I was reading somewhere that the BERT and USE style were "big-symantic space" designed to 0.0-1.0 so that things unrelated would be close to 0.0, and are classifiers.
But now, like the OpenAI embedding you're talking about the embedding are constrained, trained for retrieval in mind. The pairs are ordered closer, easier to search.
teepo commented on Anthropic raises $13B Series F anthropic.com/news/anthro... · Posted by u/meetpateltech
Substitute fiber and routers for GPUs and this starts to look familiar.
Really good analogy: Bay Networks, Lucent, Nortel, and Cisco got beat up or destroyed on the equipment side. And then the long haul fiber companies never got ROI (but paved the way for broadband).