Mario Saverin

A Strategist Who Thinks That #Leadership Is All About Emotion. | #BrandStrategist | Personal #Branding #Coach | #Storyteller | #VisualArtist | #CreativeDirector

[Justine Moore @venturetwins]

Me using Claude Opus 4.8 to rename a file

[LangChain @LangChain]

Improving agents

The old way: Manually reading traces, looking for patterns, writing evals, and creating fixes.

The better way: Letting LangSmith Engine run that cycle for you

[LangChain @LangChain]

New in Deep Agents v0.6: ContextHubBackend

A versioned home for the files that power agent behavior, backed by LangSmith Context Hub, enabling context improvements from one run to the next.

[LangChain @LangChain]

Evals shape agent behavior. Every eval is a vector that shifts the behavior of your agentic system.

More evals ≠ better agents. Instead, build targeted evals that reflect desired behaviors in production.

Tools like LangSmith Engine help you targetedly create evals from your tracing data to build better agents.

[INK @0xInk_]

the woods are not safe

small tip for you guys: now, I write my prompts in chinese on Seedance 2. Believe it or not, it’s more accurate and has fewer restrictions

I let my man @ekello make the soundtrack for this one

[Latte @0xbisc]

Wing Chun Flow

made with GPT Image 2 + Seedance 2
#DreaminaCPP @dreamina_ai

Prompt below ↓

[Neural Derp @NeuralDerp]

Happy Revenge of the Fifth!

Pawn Star Wars - Boba Fett Tries to Pawn Han Solo in Carbonite | Part 1

Hope you enjoy the first part of my second rendition of Pawn Star Wars!

[Clay @cl4y_27]

フルCG寄りのスタイルでアクションの実験したものを1本にまとめた動画です
以前の動画でプリズムの表現が個人的に面白いと思ったので今回も取り入れてみました

#AIVideo #Seedance2

[Latte @0xbisc]

Into the Swing-Verse

made with GPT Image 2 + Seedance 2
#DreaminaCPP @dreamina_ai

Prompt below ↓

[みと Visuals×AI @mito_ai_la]

GWなのでとりあえず適当に重みのある大剣を振り回す動画を。
遠心力でちゃんと回すの地味に好き。

#AIVideo #seedance2

[LoA @Lordofacca]

ALVAR - The Unseen

[Silicon Mania @siliconmania]

last week in tech was based.

[INK @0xInk_]

The Messenger Bike

made with Midjourney and Seedance 2 on Runway

...at this point, just plug my brain into this model

[John @john87445528]

Seedance 2.0 prompt:👇👇👇👇👇👇👇一镜到底镜头提示词：神佛之城·宗师登台】
镜头以一镜到底的方式，在巨型东方奇幻神都中俯仰、翻滚、旋转、飞驰。
【起始帧 - 图1场景】：镜头从天空俯冲而下，掠过悬浮的巨大苍白月球，快速接近两尊耸立云端的巨型佛像立掌雕塑（高度超过百米），穿过佛像指缝间缭绕的薄雾与红色灯笼串。镜头急速下降，俯瞰脚下青绿色运河纵横的东方奇幻水上都城，密集的木质古建筑、吊脚楼、石桥、灯火通明的船坞在雾气中若隐若现。
镜头穿过佛像掌心的雕花装饰，沿着盘绕佛像基座的螺旋木质栈道急速下坠，闪避悬挂的红灯笼、飘扬的经幡、朝拜的僧侣与香火烟雾。镜头俯冲掠过运河水面，激起水花，穿过一艘悬挂红灯的乌篷船顶，滑入水下。
水下镜头轻盈穿过透明的玻璃观景廊道，看到两侧深邃的水底古城废墟、沉没的石柱与浮游的锦鲤。镜头向上突破水面，冲出中心祭祀广场的喷泉，水珠四溅。
镜头在半空翻转，横扫过广场上整齐列队的数百名黑衣武者（如图2所示），他们双手抱拳、静默肃立，形成人墙。镜头穿过武者之间的缝隙，贴地滑行，掠过刻有云雷纹的巨型石质祭坛台阶。
【过渡段】：镜头沿着台阶螺旋上升，闪避两侧燃烧的巨型青铜香炉、摇曳的火光与飘散的香烟。镜头急速旋转360度，展现背景中两尊巨佛雕像、悬浮的苍白月球、雾气弥漫的天际线。
【结束帧 - 图2场景】：镜头最终稳定在祭坛顶端平台，正对着一位身穿白色长袍、腰束米色腰带、肩披黑色机械护甲的中年东方武者（参考图3角色）。他双手自然垂于身侧，手持两柄交叉的银色机械长刀，面带淡然微笑，站在台阶尽头的中心位置。
镜头缓缓后拉，展现他身后两尊巨型佛像立掌雕塑、下方密密麻麻的黑衣武者方阵、以及远处雾气中的水上古城全景。天空苍白月球巨大悬挂，薄雾缭绕，氛围庄严肃穆、史诗感十足。

无剪辑、不可思议的镜头运动、无缝衔接，充满东方神秘、史诗宏大、极具电影感。8K高清画质，高品质影像素材。

[dax @thdxr]

our team's model usage breakdown for the past 7 days
gpt has really taken over

[HeyGen @HeyGen]

https://t.co/DrlhU6Z47z

[Nav Toor @heynavtoor]

🚨SHOCKING: Apple just proved that AI models cannot do math. Not advanced math. Grade school math. The kind a 10-year-old solves.

And the way they proved it is devastating.

Apple researchers took the most popular math benchmark in AI — GSM8K, a set of grade-school math problems — and made one change. They swapped the numbers. Same problem. Same logic. Same steps. Different numbers.

Every model's performance dropped. Every single one. 25 state-of-the-art models tested.

But that wasn't the real experiment.

The real experiment broke everything.

They added one sentence to a math problem. One sentence that is completely irrelevant to the answer. It has nothing to do with the math. A human would read it and ignore it instantly.

Here's the actual example from the paper:

"Oliver picks 44 kiwis on Friday. Then he picks 58 kiwis on Saturday. On Sunday, he picks double the number of kiwis he did on Friday, but five of them were a bit smaller than average. How many kiwis does Oliver have?"

The correct answer is 190. The size of the kiwis has nothing to do with the count.

A 10-year-old would ignore "five of them were a bit smaller" because it's obviously irrelevant. It doesn't change how many kiwis there are.

But o1-mini, OpenAI's reasoning model, subtracted 5. It got 185.

Llama did the same thing. Subtracted 5. Got 185.

They didn't reason through the problem. They saw the number 5, saw a sentence that sounded like it mattered, and blindly turned it into a subtraction.

The models do not understand what subtraction means. They see a pattern that looks like subtraction and apply it. That is all.

Apple tested this across all models. They call the dataset "GSM-NoOp" — as in, the added clause is a no-operation. It does nothing. It changes nothing.

The results are catastrophic.

Phi-3-mini dropped over 65%. More than half of its "math ability" vanished from one irrelevant sentence.

GPT-4o dropped from 94.9% to 63.1%.

o1-mini dropped from 94.5% to 66.0%.

o1-preview, OpenAI's most advanced reasoning model at the time, dropped from 92.7% to 77.4%.

Even giving the models 8 examples of the exact same question beforehand, with the correct solution shown each time, barely helped. The models still fell for the irrelevant clause.

This means it's not a prompting problem. It's not a context problem. It's structural.

The Apple researchers also found that models convert words into math operations without understanding what those words mean. They see the word "discount" and multiply. They see a number near the word "smaller" and subtract. Regardless of whether it makes any sense.

The paper's exact words: "current LLMs are not capable of genuine logical reasoning; instead, they attempt to replicate the reasoning steps observed in their training data."

And: "LLMs likely perform a form of probabilistic pattern-matching and searching to find closest seen data during training without proper understanding of concepts."

They also tested what happens when you increase the number of steps in a problem. Performance didn't just decrease. The rate of decrease accelerated. Adding two extra clauses to a problem dropped Gemma2-9b from 84.4% to 41.8%. Phi-3.5-mini from 87.6% to 44.8%. The more thinking required, the more the models collapse.

A real reasoner would slow down and work through it. These models don't slow down. They pattern-match. And when the pattern becomes complex enough, they crash.

This paper was published at ICLR 2025, one of the most prestigious AI conferences in the world.

You are using AI to help you make financial decisions. To check legal documents. To solve problems at work. To help your children with homework. And Apple just proved that the AI is not thinking about any of it. It is pattern matching. And the moment something unexpected shows up in your question, it breaks. It does not tell you it broke. It just quietly gives you the wrong answer with full confidence.

[el.cine @EHuanglu]

this changes filmmaking completely

Seedance 2.0 now can key green screen precisely and.. edit film scene layer by layer separately.. AI just made months of work one click..

full tutorial on OpenArt + prompts:

[The Rundown AI @TheRundownAI]

Sam Altman predicted in 2024 that a one-person billion-dollar company "would have been unimaginable without A.I., and now it will happen."

He just emailed the NYT saying he won a bet with tech CEO friends over when it would arrive, and that he "would like to meet the guy."

The guy: Matthew Gallagher, 41. Spent $20K and two months building a GLP-1 weight-loss telehealth company out of his living room in LA.

The stack: ChatGPT, Claude, and Grok writing code. Midjourney for images. Runway for video ads. ElevenLabs handling customer calls. Custom AI agents stitching it all together.

$401M revenue in year one. On track for $1.8B this year.

[Kōda @aimikoda]

Reality Check

Seedance 2.0 - Prompt below ⬇️