You see this most obviously in software and marketing - the kinds of decisions where only a few people sign off at the end, and where "good enough" is whatever those few people decide it is. You see it less in hardware and procurement where there's a powerful review cycle and scrutiny at every level of the stack. Work there is more immediately measurable: benchmarks for performance, dollars for cost.

The "vibe" of software, or of a PDF [^1], is much harder to catch that way. There's no benchmark that flags it and most conventional executives aren't drilling down in that level of detail to see it either.

You want distributed decision-making, of course. But that only works well if it's distributed to people who've cultivated their own taste and who will make good calls under pressure. I'm not sure how much of that gets fixed by leadership change at the top. Taste isn't really something a CEO can decree into a 60,000 person org. But I've only heard good things about Ternus, so I'm optimistic. Fingers crossed for a bright new chapter.

[^1]: https://www.apple.com/promo/pdf/US_FY26_Earth_Day_Promo_Tand...

Wish vhsdecode was easier to use in practice! Such a cool idea but a bit too inconvenient to hack your own hardware like this...

My pipeline went camera -> dvrescue -> ffmpeg -> clip chunking -> gemini for auto tagging of family members and locations where things were shot.

We now have all our family's footage hosted on a NAS with Jellyfin serving over Tailscale to my parents Macbooks. I found the clip chunking in particular made the footage a lot more watchable than just importing the two-hour long tapes although ymmv.

https://www.sfmta.com/blog/creating-better-market-street-car... https://www.planetizen.com/news/2025/08/135849-sfs-market-st...

Wonder if that explains your observed preference. I'd bet Waymos will start utilizing the route again if it aligns with Google's mapping algo.

- consistent car quality

- safety of the drive (conservative driving and potential fear of drivers)

- no randomly chatty driver

All of those feel like a breath of fresh air especially when stacked up against the current state of Uber & Lyft rides. People really just want consistency. I don't actually think you needed AI to get there (I've had occasional rides in black cars that provided the same experience). Waymo was just right time, right place, right price.

Comments URL: https://news.ycombinator.com/item?id=46595393

Points: 68

# Comments: 20

Many plant-derived compounds function as venoms once introduced into the bloodstream (arrow coatings, darts, etc.), even if they’re also toxic when ingested. Curare is one example of a plant-based compound - lethal in blood, but largely harmless if eaten.

So while Boophone is absolutely a poison in the ecological sense, using it on arrows still fits the venom/toxin distinction better than a purely ingested poison. Otherwise why would people hunt with this if they got sick the second they ate the meat?

Venom is inert if digested; it's only a problem if it gets in your blood stream. So arrows that were laced with venom and thereby contaminated meat were actually perfectly safe to eat.

Poison is different. If ingested, inhaled, or absorbed it will kill you.

Comments URL: https://news.ycombinator.com/item?id=46445277

Points: 4

# Comments: 1

- aphyr_bot - bio_hacker - concerned_grandson - cyborg_sec - dang_fan - edge_compute - founder_jane - glasshole2 - monad_lover - muskwatch - net_hacker - oldtimer99 - persistence_is_key - physics_lover - policy_wonk - pure_coder - qemu_fan - retro_fix - skeptic_ai - stock_watcher

Huge opportunity for someone to become the actual dang fan.

Some interesting takeaways imo:

- Uses existing model backbones for text encoding & semantic tokens (why reinvent the wheel if you don't need to?)

- Trains on a whole lot of synthetic captions of different lengths, ostensibly generated using some existing vision LLM

- Solid text generation support is facilitated by training on all OCR'd text from the ground truth image. This seems to match how Nano Banana Pro got so good as well; I've seen its thinking tokens sketch out exactly what text to say in the image before it renders.

  Production - $150
  15,000 searches / month
  U.S. Legal Shield

I imagine at least some portion of companies use them just for this liability shield.

Is the issue that people aren't checking their security@ email addresses? People are on holiday? These emails get so much spam it's really hard to separate the noise from the legit signal? I'm genuinely curious.

In its current iteration this demo might net discourage your future clients rather than encourage them.

I like the idea in general as an alternative to needing to book with a BDE. I'd always prefer to just self serve for a new product; anything that gates my time (sales calls, popover walkthroughs, etc) is something I'd prefer to skip. But I know non-engineering customers really love these calls to see the power of a new platform. I wonder if they'll be as engaged during an AI walkthrough versus when there's a person on the other end of the phone.

If we're paying more for a more petaflop heavy model, it makes sense that costs would go up. What really would concern me is if companies start ratcheting prices up for models with the same level of performance. My hope is raw hardware costs and OSS releases keep a lid on the margin pressure.

Input: $1.25 -> $2.00 (1M tokens)

Output: $10.00 -> $12.00

Squeezes a bit more margin out of app layer companies, certainly, but there's a good chance that for tasks that really require a sota model it can be more than justified.

precision 0.397, recall 0.727, F1 0.513, mean temporal error 0.307 s

If you happen to know a better apriori threshold I would be happy to re-run the analysis and update the chart.