HN rank: 1

HN score: 611

comments: 340

original url: https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-3-7-flash/

评论信号：Here's a image->html test. Gemini has always swung above its weight class for vision work, so I'm always eager to try it with this.Original images: https://image.non.io/neonRamenDesigns.webpGemini 3.7 build: https://html.non.io/neonRamenGemini3.7Opus 5 build for comparison: https://html.non.io/neonRamenOpus is still best in class for this, but it's worth noting how well Gemini 3.7 does vs a more comparable LLM price wise, which is Grok 4.6: https://html.non.io/neonRamenGrok4.6 . I thought Gemini would blow Grok out of the water (it generally has in the past), but Grok has really caught up. reply: Other thoughts: I really think Google has fallen behind here. Even as a high speed offering (this build took ~7min, which is pretty good!), it wont be able to claim dominance for long with cerebras announcing the Sol preview today: https://www.cerebras.ai/blog/accelerating-gpt-5-6-sol-ultraf... .It's not a bad model by any means, but I just don't know what situation I'd reach for 3.7 Flash first for. Google really needs a differentiator, especially given how hard it is to get an API key from them. They can't be high friction and non-pareto.

评论信号：The "introductory pricing" for this 3.7 Flash model is really weird.It's scheduled to double in price on December 31, 2026, but who would anticipate still using this model five months from now? Especially since 3.6 Flash came out just three weeks ago!My first effort with default thinking level produced an ambitious pelican, let down by a flawed bicycle: https://tools.simonwillison.net/markdown-svg-renderer#url=ht...Then I ran it on high, medium and low thinking levels (oddly minimal is no longer an option, which WAS an option for 3.5 and 3.6) and got a pretty excellent pelican for the first two:https://tools.simonwillison.net/markdown-svg-renderer.html#u...UPDATE: That was in Safari, but as pointed out in the replies here the pelicans do NOT render well in Firefox or Chrome! Best guess is that's because of this invalid filter in the SVG:

Filters are meant to contain additional elements, not be empty: https://drafts.csswg.org/filter-effects/#FilterElement - so maybe Chrome and Firefox remove the element that references the broken filter but Safari doesn't? reply:...

评论信号：It is also doing pretty well in threejseval. Frontier there for the price. Much better than 3.6.https://threejseval.com/ranking

HN rank: 2

HN score: 51

comments: 9

original url: https://atproto.com/blog/introducing-bluesky-protocol-services

评论信号：I wonder if it would be a good idea to remake DNS on top of bluesky. The basic idea is that, if you own a domain name, you post DNS updates in a bluesky feed. The firehose itself is authoritative. DNS servers sit downstream of the firehose. All the updates to domain names get written into a database, and they essentially just respond to queries hitting that database. This would let anyone (with enough bandwidth) run a fast domain name server.We could use this to fix some of DNS's long running problems. Like, we could add upstream cryptographic signatures to DNS records. And put TLS certificates in there too. We might be able to ditch TTL entries. In a DNS server, records should update via server-push from the firehose. You shouldn't have to wait for TTL to expire.I think it'd make a nice little system.

评论信号：Sort of on a tangent to the actual content of the page, but what documentation system/CMS is this particular web page UI? It seems very fast and relatively uncluttered for the menu on the left side, content pane, top horizontal menu bar. reply: https://github.com/bluesky-social/atproto-websiteIf I'm understanding this codebase right, it's a hand-rolled framework based on MDX and Next.js; the UI elements you're referring to were designed from scratch and aren't based on any existing CMS theme.

评论信号：I'm all for whatever atproto expansion and whatever's going on here, mainly it harks back to early Twitter days (for better or worse, watching them build Bluesky in realtime isn't the best), but when they have outages like last week a good chunk of service unreachable/down for many hours with little update on https://status.bsky.app/ and then whatever history just basically disappears as if nothing ever goes wrong, it's not a good vibe.Despite all the openness and github repos and what not it's very difficult to get straight answers or just like find a place where issues or changes to the platform are addressed easily.

HN rank: 3

HN score: 414

comments: 173

original url: https://www.cerebras.ai/blog/accelerating-gpt-5-6-sol-ultrafast-with-openai

评论信号：Right now, the "economic model" of AI is "who has the best model", or really weights.Honestly that'll go away eventually, just like operating systems eventually became free.Instead, it's going to come down to selling inference hardware. We'll likely see the "apple" model where a custom OS runs on their hardware, but we'll probably also see more things like Cerebras become commodity hardware instead of kilowatt-class datacenter only hardware.

评论信号：I've been waiting so long for something amazing to come out of the OpenAI and Cerebras collaboration.> In our evaluations, GPT-5.6 Sol on Ultrafast mode answered all 2,500 HLE questions in 11 hours and 11 minutes. Claude Fable 5 needed 78 hours and 27 minutes, more than three days of continuous compute, to arrive at the same conclusions. In other words, Ultrafast worked through the frontier of human knowledge in a single working day, achieving comparable accuracy nearly 7× faster.This is actually insane.Hopefully the release ultrafast of Terra and Luna too. reply: I discovered yesterday that the “amazing thing that comes out of OpenAI” is Sol, due to its token efficiency.Dollar for tokens, Sol and Fable are the same price.However, Sol uses (literally:...

评论信号：People underestimate the importance of speed on quality of thought, because people underestimate just how much quality is a result of simple iteration.When an LLM thinks, it typically just makes one pass. It outputs tokens from top to bottom, beginning to end, and then it's done. But when people think, especially strong thinkers, we typically iterate and revise our thoughts on the fly. We do numerous passes. We stop and restart, we reconsider, we review, we reevaluate. Sometimes we do this so quickly and automatically that we don't even realize we're doing it. I think a lot of what separates a highly intelligent or effective person from others has less to do with the quality of their first pass and more to do with just how many additional passes they're able to do in the same amount of time, and of course what kind of criteria they're habituated to consider during their review passes.Introspecting about this is difficult, but experimenting with LLMs is easy. First, simply ask an LLM to do something complex. For example, to come up with a new business idea, or to plan the next month of your life, etc.... reply:...

HN rank: 4

HN score: 18

comments: 4

original url: https://sparrowmap.com/

评论信号：I keep saying if it's legal for Flock to make Flock it's legal for anyone else to make Flock.

评论信号：{"error": "internal error"}, when trying to open the live map.

评论信号：Fun fact: in some municipalities you can FOIA for the GPS records of police vehicles. Here's Chicago Police's. They removed 0-15mph for, erm, privacy reasons.... but it's not the worst.Have a glance:https://www.dropbox.com/scl/fo/hj4jtcag2i9qvl4sj6mg2/AF6c9r7...

HN rank: 5

HN score: 145

comments: 89

original url: https://gruhn.me/blog/2026-08-13/

评论信号：1. The study of complexity classes isn't intended to dissuade people from writing certain programs. It's intended to understand the nature and theoretical limits of computation. As far as practice goes, it can be used to show where heuristics are needed. Saying it's overrated is like saying calculus is overrated because most people don't need to use it every day. And BTW, many important problems are in classes believed to be way harder than NP (i.e. NP-complete is the easiest of the hard famous complexity classes). E.g., I've seen some people brag about some configuration language being easy to mechanically analyse because it's not Turing-complete, while in fact it's at least PSPACE-hard to analyse.2. When there's some large set of instances of some NP-hard problem that are tractably solvable in practice (like SAT), the importance of that is that there's some non-NP-hard subset here. Indeed, SAT is FPT (fixed parameter tractable [1]), an "easier" type of NP, for which decomposition can help. In contrast, graph colouring is thought to not be FPT.[1]: https://en.wikipedia.org/wiki/Parameterized_complexity reply:...

评论信号：I feel like the write up doesn't really engage with the number one solution usedDon't allow the hard onesDependency managers tend to just block a huge category of situations that effectively eliminate the entire NP hard spaceType systems similarly are explicitly cordoned offThe trick isn't "do it anyway" beyond you kind of definitionly need to, it is to acknowledge the general problem is "impossible" so either do your best or start eliminating the impossible reply: A variant:> Don't encounter the hard onesFor example, with the simplex method for linear programming, we don't do anything about disallowing the hard instances. We just solve the problems as they come in and none of the ones we get asked to solve ever turn out to be hard. (Generalizing, of course.)

评论信号：Very true! What makes NP-hard problems difficult is almost always the combinatorial explosion related to specific problem configurations -- you can construct instances given an approximate heuristic or branch-and-bound solver that will cause it to have an exponential blow up. But for most practical problems you don't reach those explosive configurations.There's probably a quantification of this in some sense for specific classes of NP-hard problems.What's interesting is that many algorithms (especially in cryptography) are explicitly designed to create those combinatorial edge cases. A SAT solver looking at normal problems that occur in life and programming will do an amazing job. A SAT solver looking at SHA256, not so much. In fact, arguable the science of developing cryptographic systems is the science of finding these exponential explosions that are resistant to heuristic approximations.