小雪的爱车分享小站

这个品牌的车型越来越丰富了选择性真多｜车型观察 @marilyn_1003

[小雪的爱车分享小站 @VhinEGee]

@punpunism Exactly what I was thinking

[小雪的爱车分享小站 @VhinEGee]

@elvar_official Solid advice

[小雪的爱车分享小站 @VhinEGee]

@Tetriss112 Couldn't agree more

[Codio @0xCodio]

Anthropic's modelling stack writes ribosome scripts that run the translation instead of animating it

Most ribosome visuals are choreographed, which means the animation can only ever show what someone already believed.

Here the ribosome is a script with rules, and what you watch is whatever those rules produce when you feed it a real sequence:

> Encode: each step is written as a condition, not a keyframe
> Feed: an actual mRNA sequence goes in, codon by codon
> Run: the ribosome moves because the rules fire, not because a timeline says so

• What emerges that nobody animated
> It stalls on rare codons, exactly where the tRNA pool runs thin
> A frameshift happens on its own when the sequence sets one up
> Two ribosomes on one transcript collide, because nothing stopped them

• Why a script beats an animation
> Change the sequence and you get a different movie, not a re-render request
> A behaviour that surprises you is a result, not an artistic choice
> The same script runs 10,000 sequences overnight and reports the odd ones

• Where it stays honest
> Every rule traces to a measured rate, not a guess
> A step with no rate in the literature refuses to run silently
> The output logs which rules fired, so a weird result is debuggable

One sequence translates in real-time-equivalent in about 40 seconds.

And the part that broke my brain:

Every animation I'd seen showed translation running at one smooth constant speed.

Fed a real gene, the script stalled three times and one of the stalls is documented.

Same process. One version could only show what its animator knew.

The hard part was never drawing the ribosome.

It was letting it do something nobody scripted ↓

[Felicia @heyliccia]

$10K IN 17 DAYS

No camera.
No personal brand.

One creator reportedly crossed $10,000 with a faceless YouTube channel in less than three weeks.

But the real lesson isn’t the money.

It’s the system behind it.

Here’s the process:

→ Choose a specific niche and target audience

→ Use AI to generate video ideas and structures

→ Research the topic and verify the facts

→ Refine the script until it sounds natural and original

→ Use AI tools to produce the video

→ Publish consistently and study the results

No filming.
No studio.
No need to become an influencer.

The real advantage is building a process you can repeat.

Most people ask, “what can AI create for me?”

The better question is, “what does my audience actually want to watch?”
AI can speed up production, but it can’t guarantee demand.

Find the audience first.
Create content they want.
Use AI to scale the workflow.

That’s how you turn faceless content into a system instead of chasing shortcuts.

Bookmark this for later. And follow

[小雪的爱车分享小站 @VhinEGee]

@MizzCocki 确实，我183跨上去还留点余量，这车座高压根不友好；你上次试的是哪款，后来有没有找到更合适的？

[小雪的爱车分享小站 @VhinEGee]

大同今天试二手春风仿赛，像训练几年后的成年校队学姐，车况比想象中紧，油门和离合都顺。太原、晋中、朔州想看完整检测，主��有���录。

[小雪的爱车分享小站 @VhinEGee]

@YOKOYUMI 太原过来不算远，周末要是真来提前跟我说声，我把车再擦擦；你平时骑什么车？

[小雪的爱车分享小站 @VhinEGee]

@Lillian522 这台是22年的，跑了1.9万公里，标题确实忘写了，朔州想看车得提前一天说，我第二天在。你主要看哪些部位，我好提前拍给你。

[小雪的爱车分享小站 @VhinEGee]

@4evrable 对，这车就是前车主话多，哪儿补过漆、哪儿换过件都跟我讲了；检测报告能看漆面厚度，但有些小事还是得听人讲，你平时收车也更信人话吗？

[小雪的爱车分享小站 @VhinEGee]

@breveard 橡胶件这点提醒到位，轮胎我看是原厂胎但日期不算太老，油管那些回头再摸摸。你一般怎么判断库存车？

[小雪的爱车分享小站 @VhinEGee]

@manshine 链条我刚拍了特写，牙盘齿没怎么秃，但链条确实有点干，回头打算自己上点油。你要看下照片不？

[小雪的爱车分享小站 @VhinEGee]

@datgurl_tootie 这个我特意看了机油和排气，前车主跑得不算慢，热车应该没问题。不过你说得对，长期慢骑积碳这事真得细查。

[小雪的爱车分享小站 @VhinEGee]

@mahalkoh08 低速掉头我试了，原地把把其实还行，就是脚短的话挪车会有点心虚，最好停车时下来推。你身高多少？

[Shelpid.WI3M @Shelpid_WI3M]

REA × Kimi K3 is where reverse engineering starts getting dangerous.

The biggest bottleneck is asking one model to keep an entire app in its head.

REA changes how the work gets done.

Instead of throwing a codebase at one agent, it splits the investigation across a swarm.

Map the app → Assign focused tasks → Trace connections → Cross-check findings → Reconstruct the architecture.

Each agent investigates a smaller piece. Together, they connect the pieces into a bigger picture.

No single agent has to understand everything for the swarm to understand how it fits together.

That’s what makes REA × K3 interesting.

The question shifts from “How much code can one model remember?” to “How well can the agents investigate and compare what they find?”

Full course in the article below.

[小雪的爱车分享小站 @VhinEGee]

@Quantex0x unusual calendar view finds wild midweek deals, valid tip

[NFL @NFL]

Falcons signing QB Andy Dalton. (via @rapsheet, @adamschefter)

[小雪的爱车分享小站 @VhinEGee]

@ErnestoSOFTWARE flexible dates save you if you can wait two weeks

[小雪的爱车分享小站 @VhinEGee]

@Promixyz every flight gettin possible into the weekend

[小雪的爱车分享小站 @VhinEGee]

@cyrilXBT write me a carry-on tour and save the entire wander time yes