Rani

creater

[Rani @Rani_Ranu2]

Let be friends

[Rani @Rani_Ranu2]

@tulipzone Level

[Rani @Rani_Ranu2]

@tomblue @MitchellKeller_ Important

[Rani @Rani_Ranu2]

@fayafox Very good

[Rani @Rani_Ranu2]

@248Nabila Osm

[Rani @Rani_Ranu2]

@HeerBMalik Kia bat he

[Rani @Rani_Ranu2]

@imrizwankhan786 @BeingSalmanKhan Wah

[Rani @Rani_Ranu2]

@onlysohail_ Cool

[Rani @Rani_Ranu2]

@Prof_Cheems Nice

[NOVA @TechWith_Nova]

Disney and Warner Bros executives woke up to a complete industry crisis: a 48-second live-action film sequence of a man trapping a real siren on a tropical coast.

No actors were hired. The mermaid tail doesn't exist. The entire Hollywood-grade scene was generated on a $25 AI setup by a creator sitting in his bedroom.

124 million views in 4 hours. Half the comments are crying over the cinematic love story, the other half are demanding an immediate theater release date.

Here is why your brain falls for this visual trap:

1. Hyper-realistic subsurface scattering on wet skin, realistic water refraction, and underwater eye mechanics
2. Perfect emotional expression tracking and complex multi-character interaction across land and water
3. Seamless physics handling displacement, floating flower mechanics, and fluid mermaid tail motion

The dynamic next-gen AI stack operating under the hood:

-> Claude Fable 5.1 Max for complex narrative trajectory, spatial camera vectors, and emotional pacing
-> Veo 3.1 for rendering hyper-realistic water physics, land-to-water light refraction, and volumetric depth
-> Kling 3.0 Omni for generating multi-agent physical interaction, floating object mechanics, and tail physics
-> Seedance 2.5 for locking face consistency, skin details, and emotional continuity across cuts
-> ElevenLabs Spatial for binaural 3D water splashes, rope tension foley, and immersive underwater audio
-> CapCut Pro for adding cinematic 35mm film grain, anamorphic lens flare, and dynamic speed ramps

Disney spends $250,000,000 on private islands, full production crews, and 2 years of post-production to shoot this.

This kid outran an entire global film industry before his morning coffee got cold.

[Vipin Gautam (Viipin I Gautam) @viipin8]

“open source” hits differently when you can actually inspect how the model was made.

the code is open.
the training data is open.
the recipes are open.

k2 horizon is giving builders much more than weights to download. that’s the part i find genuinely valuable.

[D-Coder @Damn_coder]

The next phase of AI isn’t about getting better suggestions. It’s about trusted execution.

Catch doesn’t just summarize your administrative work, it learns how you operate and handles it across email, calendars, travel and operations.

That shift from “help me think” to “get it done” is where AI assistants become genuinely useful.

[D-Coder @Damn_coder]

The trust layer is the interesting part here.

Commerce teams already have enough dashboards.

What they need is an agent that understands the same numbers, works from the same definitions, and can actually turn that into action without making stuff up.

That makes Polar Operator feel much more useful than another generic “AI for ecommerce” tool.

[NOVA @TechWith_Nova]

K2 Horizon stands out because it’s not just about one powerful model — it’s an entire lineup covering different scales and use cases.

The fact that the code, data, and training recipes are open makes it even more interesting.

Definitely one to keep an eye on

[Rachel Woods @TechWith_Rachel]

Nike spends $2M on a stadium shoot for a 10-second hero shot.

40-person crew. 3 days of permits. 2 weeks in post. Stunt doubles. Insurance paperwork thicker than the script.

This creator generated the entire sequence on a laptop for under $80.

A guy sprints across a stadium roof at night, leaps off the edge, free-falls into a sold-out arena, and lands on a giant football in the center of the pitch.

One continuous shot. No crane. No drone operator. No safety harness review board.

The toolchain behind this clip:

> Kimi K3 wrote the full shot sequence: rooftop sprint, jump arc, mid-air hang, landing impact, crowd reaction timing
> Kling 3.0 generated the fluid body physics and night lighting on the roof
> Seedance 2.5 rendered the free-fall, wind on the shirt, and the ball deformation on impact
> ElevenLabs produced the ambient stadium atmosphere and crowd noise
> CapCut handled speed ramps, color grade, and final export

A traditional production house would need a stadium rental at $150K, a stunt coordinator, a medical team on standby, and 6 months of liability clearance.

This entire sequence was prompted, rendered, and exported between dinner and sleep.

AI didn't replace the stunt double.
It replaced the entire stadium booking.

[Future Coded @future_coded]

Most scheduling still feels like a negotiation you never asked for.

Ping-pong emails. “Does Thursday work?” “What about next week?” 3 days later the meeting still isn’t booked.

Lindy just killed that.

CC [email protected] on any thread.

It reads the conversation, finds open slots, handles the back-and-forth, and sends the invite.

The other person needs nothing. No account. No link. No new tool.

Just clean coordination that finally stays out of the way.

This is what AI assistants were supposed to feel like.
https://t.co/5P5u5Dye2f

[FIONA TECH @fiona_tech_AI]

Importantce Of Sex Position In Conception

[Future Coded @future_coded]

Most AI still wakes up amnesiac every single time you ask it something.

It re-reads your Salesforce, Slack, Gong and docs from zero burning the majority of your token bill just searching for context that should already be known.

OM2 ends that

One neural graph. Permanent memory of your company. Connect the tools once and it learns in the background customers, projects, people and history.
First query it figures out where to look. Every query after that, it just knows.

Same models and same tasks.

9x cheaper and 64% faster. Preferred on quality 84.5% of the time.

Pair it with optimized routing and you’re looking at 51x savings.

The labs won’t fix this. Your token bill is their revenue.

Stop paying rent on your own context.
Start owning it.

This is the quiet infrastructure shift that actually compounds.

[Lea Arden @futurebylea]

The hidden AI cost isn’t the answer.

It’s everything the model has to re-read just to find the answer.

Permanent company memory could be a massive unlock for both speed and token efficiency.

OM2 is tackling the part of AI infrastructure most people don’t see.