吳冠廷tinger wu

Whatever happens, we have got the Maxim gun, and they have not.

[鯖江市西山動物園 @nishiyama_zoo]

現在、赤ちゃんとお母さんのかのこはご覧いただけません。
一般公開は10月頃を予定しており、詳細が決まり次第、ホームページやSNS等でお知らせいたします。

赤ちゃんたちの成長とかのこの子育てをあたたかく見守ってください。

#鯖江市西山動物園 #レッサーパンダ #赤ちゃん誕生

[『葬送のフリーレン』公式 @FRIEREN_PR]

そうそUNOフリーレン

[『葬送のフリーレン』公式 @FRIEREN_PR]

空中のフリーレン

[『葬送のフリーレン』公式 @FRIEREN_PR]

＃フェルメール展

[鯖江市西山動物園 @nishiyama_zoo]

西山動物園で、6月26日にレッサーパンダの赤ちゃん2頭が誕生しました！

母：かのこ
父：ライト

2頭のうち1頭はかのこがお世話をしていますが、もう1頭は生まれてすぐ巣箱の外で衰弱していたため人工哺育で育てています。
どちらの赤ちゃんも元気に成長しています。

[【公式】YU-GI-OH.jp @yu_gi_oh_jp]

『デッキビルドパック －グロリアス・ヴィクターズ－』で登場する新テーマ3種のカードイラストを公開！
https://t.co/mrVL6xuVqW

[Tibo @thsottiaux]

Updates for Codex and ChatGPT Work users. No nerfing, only good stuff!

- We have landed inference optimizations and are passing down savings to all the subscriptions for GPT-5.6 Sol. That should result in around 10% more usage on its own.
- We noticed that by changing the context size limit in the product to 372k for GPT-5.6 Sol, up from 272k for GPT-5.5, it resulted in more usage being charged than intended. We have reverted to 272k and will work to roll back out to 372k in the days to come. You should notice that usage drains significantly less after this change.
- To understand where the extra usage was coming from, we ran some experiments where reasoning efforts were changed (referred to as juice values under the hood) and have reverted this.
- There is slightly more usage of multi-agent than intended in high and xhigh reasoning effort, we are fixing this going forward. Also fixing a small other thing we noticed with auto-review where we can be more efficient.

And we continue to have the 5h limit temporarily not apply. Enjoy the rest of the weekend!

[野口 衣織 @noguchi_iori]

#BEAT_AX_SUMMER
最高な夏のスタートを感じましたー❗️💥
ホックホクに出来上がっていた会場、私達もたくさん盛り上げるぞー！と気合いが入りました！

イコラブファンのみなさん、バンドリーマーのみなさん！アツい時間をありがとうございました🌟

二度見したムキムキ～～💪🏻

[Rosuuri🐰Anime Expo & Offkai @rosuuri]

I drew the art for Ninomae Ina’nis x HYTE PC case🐙 #inART

[Peter Gostev @petergostev]

GPT-5.6-Sol-Ultra is so good at maths that it created Minecraft clone in Lean (yes that Lean)

[【公式】YU-GI-OH.jp @yu_gi_oh_jp]

『リミテッドパック リマスターエディション』に収録されるカード18枚を公開！
https://t.co/vWoyZvFFJb

[🥑 @yujitach]

最近 Claude Fable を試しているのですが、昨晩ふと、ここ半年ぐらい進展がない共同研究について研究ノートをみせて聞いてみたら、なんと非自明な観察をして、ほぼ解決してしまった。一回目の返事は「計算ミスは一ヶ所みつけましたが同じところで詰まりました」だったが、「そこはこう解決するかなと

[Chayanka_42 @42_gravity]

We are entering a completely new era of science

Here is Yuji Tachikawa from Japan (Mathematical Physics, String Theory, QFT) on recent progress in his own work using Fable 5 :

"I've been trying out Claude Fable recently, and last night, on a whim, I showed it my research notes about a collaborative project that's seen no progress in the past six months or so and asked for its thoughts. To my surprise, it made a non-trivial observation and essentially solved it."

"I was also surprised that it was using sympy to automatically write code and verify his own predictions."

"Fable probably seems like it properly understands string theory and has intuition too—that's my impression"

[Ali Haider @ggg78g89]

🚨SCOOP:
MY Friend at Anthropic says things are VERY tense internally. Dario's running tough meetings — GPT-5.6 Sol is strong and Grok 4.5 is right on Opus's heels. Pulling Fable from subs on July 12 would trigger mass cancellations (why keep Max for Opus 4.8?), so they're now pushing to keep Fable 5 in subs permanently.

[Serafim Batzoglou @s_batzoglou]

I benchmarked the new models (Sol, Terra, Luna, Fable 5, Meta Muse Spark 1.1, Grok 4.5) on an induction reasoning task.

This is an updated table from yesterday, and my benchmark is described as spotlight in ICML '26.

How to read the table:

The models are given several small graphs (6-10 graphs, each with 8-10 elements), in which some nodes are designated. They must return a first-order formula that describes properties of the designated nodes simultaneously for all graphs.

The benchmark has 64 such problems (a subset of the bigger benchmark).

Correct: how many formulas correctly describe the designated nodes.

Holdout correct: a number of additional graphs, with the same underlying property, are held out. This is to test the ability of the model's correct formulas to generalize. For instance, if a formula is a big case split rather than the underlying simple graph property, it won't generalize.

Formula complexity (in AST): mean and median size of the correct model's formulas.

A few observations and experiment notes:
- All new models are remarkably good. Even Luna and Muse Spark outperforms GPT 5.4.
- Some models are better in returning simple formulas that generalize well.
- Fable 5 was extremely hard to get results from. I first ran it with higher thinking effort levels, in which case it charged for max tokens per problem and returned all "" responses. The only settings in which it returns non-"" responses are medium and low. My method for running it was to call it on medium, then call it again on medium, then on low.
- GPT 5.5 is absent because it had the same non-return behavior as Fable 5 above, but even worse. So bad that I couldn't get almost any results and would have wasted too much $$ on empty responses. Happy to see that the new OpenAI models are "cured" of this GPT 5.5 issue.
- I had observed that Grok 4 >> Grok 4.1 Fast > Grok 4.20 >> Grok 4.3. Finally, this downward trend of XAI has been reversed and Grok 4.5 is now a contender.
- Meta Muse Spark 1.1 is a big surprise. It beat GPT 5.4 on this task!
- The paper is out and the benchmark is public: https://t.co/9uDRTWsBar

[Alexandr Wang @alexandr_wang]

muse spark 1.1 outperforms opus, grok 4.5, and gemini on a new challenging finite model theory / theoretical cs eval

[Moll @Moleh1ll]

By the 23-minute mark, Sol had had enough, went online to look for the answer, and found this tweet)

[Tibo @thsottiaux]

I think GPT 5.6 is pretty good