MathxH

Everything happens for a reason

[MathxH @foo_hack]

@silsrc 油管上一个采访视频，有个老哥采访世界各个富豪，这些白手起家或者不是白手起家的富豪说的最重叠的一句话就是自我销售能力是很重要的，几乎是最靠前的能力。从价值的第一性原理来看，要赚钱盈利才有价值，不赚钱盈利的东西是没价值的。

[David Wong @cryptodavidw]

Hash-based finite fields are the next big thing

[WeZZard @realWeZZard]

hmm... Kimi K3 的工程能力确实是 Opus 4.8 级别的，只是智力是 Fable 5 级别的

[Wey Gu 古思为 @wey_gu]

uu 远程 is too good to be free

网易应该是把 streaming 游戏的牛刀用在这了

好久没有出门不带 MacBook 了

[WorldofAI @intheworldofai]

Kimi K3's water generation is seriously impressive. 🌊

It doesn't just draw water it simulates convincing flow, ripples, and movement that feel physically coherent.

[karminski-牙医 @karminski3]

kimi-k3 测试速报! 前端这效果太猛了!

Kimi-K3 刚刚发布! 赶紧给大家带来测试速报!

效果巨好! 无论是建模精确度还是粒子效果, 甚至复杂的shader内联代码都能one-pass, 相当猛!

稍后给大家带来详细测试! 记得点关注不迷路!

测试prompt在这里, 开源的: https://t.co/MTNXEuAElP

#kimik3

[Bohu @BohuTANG]

推荐一个适合孩子 vibe coding 的 rust 项目：open-strike
可按需求定制的单机游戏，边玩边改，上手很快：
https://t.co/z42QGyTntz

[Max For AI @MaxForAI]

这是我目前见到过最好的K3案例。

一位参与K3早期测试的朋友，全程只用K3开发了一个的3D 任天堂游戏机模拟器，甚至​连按键的阻尼感都完美进行了还原。
​
​而且，它不只是一个玩具外壳，而是可以真实装载 room 进行游戏的、具有完整后端的产品

这太疯狂了，在此之前我只见过Fable 5有这样的能力，甚至GPT-5.6 Sol都没有。

开源模型又一次追平了那些昂贵的闭源模型！

[aditya🇦🇷 @adxtyahq]

Kimi K3 feels like China just open-sourced a Fable-5 class model

Never seen an AI generate UI with this level of polish from a single prompt.

[antirez @antirez]

DS4F, GLM 5.2, Kimi K3, the upcoming DS4 update. What a moment for open weight models. Also, some western company entering the open weight field (but: I want to see the actual long term results to evaluate the actual real-world contribution and models level).

[Yufan Sheng @syhily]

内测 K3 时，相比 K2.7 能明显感觉到几大重要改进：

不必要的死循环没了，只在分析定位 BUG 时会循环，需求开发时更喜欢主动 Sub Agent 探索。

文件修改一直失败这个老大难问题再也没看到过。

Harness 很充分，对很多问题，如 git 冲突等都有较好的解决。

还不如 GLM 和 OPUS 一样对设计能主动提问

[WorldofAI @intheworldofai]

Kimi K3 just generated a Red Dead Redemption 2-style open-world game... 🤯

A few months ago this would've sounded impossible. Now open-weight models are building explorable worlds with terrain, gameplay systems, and impressive visual fidelity from a single prompt.

[Yuchen Jin @Yuchenj_UW]

> Kimi K3 ranks only behind Claude Fable 5 Max and GPT-5.6 Sol Max, and surpasses Claude Opus 4.8 Max's score of 1600.

I’m super excited for Kimi K3 to be open-sourced!

[leo 🐾 @synthwavedd]

🚨 BREAKING: Kimi K3 benchmarks have released and it's ~Fable/Sol level

[Arena.ai @arena]

Big news: Kimi-K3 by @Kimi_Moonshot is now #1 in the Frontend Code Arena with 1679 pts, surpassing Claude Fable 5.

This is a 17-place jump from Kimi-k2.6 (#18 -> #1).

In Frontend, Kimi-K3 ranked #1 in 6 of 7 domains: Brand & Marketing, Reference-Based Design, Data & Analytics, Consumer Product, Simulations, and Content Creation Tools, landing #2 only in Gaming behind Fable 5.

The full model weights will be released by July 27.

Congrats to the @Kimi_Moonshot team on this major milestone!

[Artificial Analysis @ArtificialAnlys]

Kimi K3 scores 57 on the Artificial Analysis Intelligence Index. Its intelligence is comparable to Opus 4.8 and GPT-5.5 but remains behind Fable 5 and GPT-5.6 Sol. Moonshot AI has expressed plans to release the 2.8T parameter model's weights, which would make it the leading open weights model

Key results:

➤ Strong agentic task performance: @Kimi_Moonshot's Kimi K3 reaches an Elo rating of 1668 on GDPval v2. This is a marked improvement over K2.6’s 1190, surpassing GLM-5.2 (1514), GPT-5.5 (1494), and Claude Opus 4.8 (1600). However, it still lags behind Claude Fable 5 (1760). Kimi K3 also scores an impressive 53% and takes the #1 position on AutomationBench-AA, our implementation of Zapier’s Agentic SaaS workflow evaluation.

➤ Second-highest performance on AA-Briefcase (agentic knowledge work): On our private long-horizon knowledge work evaluation, Kimi K3 reaches an overall Elo of 1547, +732 points from Kimi K2.6 and behind only Claude Fable 5. It is well-rounded: its rubric scoring and analytical quality almost reach Claude Fable 5’s scores, while GPT-5.6 Sol continues to outperform other leading models on presentation quality.

➤ Set to lead open weights models once weights are released: Moonshot AI has not yet released the weights but expressed plans to do so. Once available, Kimi K3 would clearly lead other open weights models including GLM-5.2 (51) and DeepSeek v4 Pro (44). However, at 2.8T parameters, it is significantly larger than its open weights peers (eg. GLM-5.2 at 753B params and DeepSeek V4 Pro at 1.6T), as well as the Kimi K2 to K2.6 models (1T params).

➤ Cost per task ($0.94) is similar to GPT-5.6 Sol ($1.04), ~1/2 the price of Opus 4.8 ($1.80) and higher than open weights peers: Moonshot AI’s pricing for K3 is significantly higher than their K2 pricing (K3’s output token price is $15/1M tokens while K2.6 was $4). This positions the model as cheaper on a cost per task basis than Opus 4.8, similar to GPT-5.6 Sol ($1.04) and more expensive than open weights peers, GLM-5.2 ($0.32) and DeepSeek V4 Pro ($0.04)

➤ Improved token efficiency alongside higher intelligence: Kimi K3’s token usage on the Artificial Analysis Intelligence Index decreased significantly, using 21% fewer output tokens than K2.6. The new model used approximately 132M output tokens to complete all nine evaluations, compared to approximately 166M for K2.6, while achieving higher scores.

➤ Native multimodal capabilities: Kimi K3, like K2.6, is released with native image and text multimodal input. If weights are released, this will position Kimi K3 as one of the leading open weights models with multimodal input capabilities

Other model details:

Context window: 1M

Size: 2.8T total parameters

Pricing: The first-party API is priced at $3.00/$15.00 per 1M input/output tokens, with cached input discounted 90% to $0.30 per 1M tokens.

Modality: Native multimodal input supports text and images, and the model remains text-only for output.

Accessibility: Accessible at launch through Moonshot’s first party API. Model weights are not yet released but Moonshot AI has expressed plans to do so.

[Kimi.ai @Kimi_Moonshot]

Introducing Kimi K3: Open Frontier Intelligence

🔹 2.8 Trillion Parameters, 1 Million Context, Native Multimodal
🔹 Kimi Delta Attention enables up to 6.3x faster decoding in million-token contexts
🔹 Attention Residuals deliver ~25% higher training efficiency at <2% additional cost
🔹 Built for long-horizon agentic coding and self-evolving workflows

Kimi K3 is now live on on https://t.co/zrk6zZxZUo, Kimi Work, Kimi Code, and the Kimi API.
Open Weights by July 27, 2026.

🔗 API: https://t.co/XCrgjXAqMw
🔗 Tech blog: https://t.co/YTfiMSNM1f

[pash @pashmerepat]

Absolutely beautiful rant about AI in Linux Kernel from Linus yesterday:

I realize that some people really dislike AI, but this is an area
where I'm willing to absolutely put my foot down as the top-level
maintainer.

Linux is not one of those anti-AI projects, and if somebody has issues
with that, they can do the open-source thing and fork it.

Or just walk away.

AI is a tool, just like other tools we use. And it's clearly a useful one.

It may not have been that "clearly" even just a year ago, but it's no
longer in question today.

There are other questions around AI (like what the economy of it will
actually look like in the end), but "is it useful" is no longer one of
those questions. Anybody who doubts that clearly hasn't actually used
it.

Yes, it can also be a somewhat painful tool, both for maintainer
workloads and just from a "it keeps finding embarrassing bugs"
standpoint.

But the solution is not to put your head in the sand and sing "La La
La, I can't hear you" at the top of your voice like some people seem
to do.

The solution is to make sure those LLM tools _help_ maintainers
instead of just causing them pain. There's no question on that side.

We're not forcing anybody to use it, but I will very loudly ignore
people who try to argue against other people from using it.

And no, AI isn't perfect. But Christ, anybody who points to the
problems at AI had better be looking in the mirror and pointing at
themselves at the same time.

Because it's not like natural intelligence is always all that great either.

The kernel project has been and will continue to be about the technology.

Sure, the social angle of working on open source is important and
often a very motivating part of the project, but in the end that's a
side benefit, not the _point_ of the project.

This is *NOT* some kind of "social warrior" project, never has been,
and never will be.

In the kernel community we do open source because it results in better
technology, not because of religious reasons.

And so we make decisions primarily based on technical merit. Not fear
of new tools.

Linus

[antirez @antirez]

Linus on AI development for the Linux kernel

[Gustavo Noronha @gustavokov]

Linus jogou a real: AI é útil e vai ser usada no desenvolvimento do kernel Linux, quem achar ruim pode fazer fork e vazar