车友圈的飒爽小葵同学

每次换季检查一下胎压，温度变化影响挺大｜汽车日记 @mothio1201

[车友圈的飒爽小葵同学 @_SERGIOANDRES_]

@hahhhhhaaaa 这角度我认，同价配置确实不算卷，最后还得是骑着顺不顺手说话。你更看重哪一块配置？

[车友圈的飒爽小葵同学 @_SERGIOANDRES_]

长春这台雅马哈仿赛像成年校队学姐，身形利落，转向也干净，转速起来后明显更有精神。吉林、四平、松原想看完整体验，主页继续。

[车友圈的飒爽小葵同学 @_SERGIOANDRES_]

@saeso215 谢谢，我自己挑车也怕被一顿猛吹带偏，所以只写骑出来的真感受。

[车友圈的飒爽小葵同学 @_SERGIOANDRES_]

@moNaRica_OdELia 是的，坐姿其实没外观看着那么战斗，跑个短途完全没问题。长途你试过没？腰还顶得住吧

[车友圈的飒爽小葵同学 @_SERGIOANDRES_]

@jaimebear127 对，低转像没脾气，六千往上才开始乐意干活。你平时骑都拉在哪个区间？

[车友圈的飒爽小葵同学 @_SERGIOANDRES_]

@xSmiley_ox 理性说坐垫偏硬真是它的小毛病，我上次颠一天也是这感受。你有没有试过改薄一点的座垫或者垫个软垫？

[车友圈的飒爽小葵同学 @_SERGIOANDRES_]

@YaBoyHasz 确实，动力来得不猛，练线、练刹车都挺好使。你朋友后来换的什么大排？

[车友圈的飒爽小葵同学 @_SERGIOANDRES_]

@zeezak 哈哈说明你也是骑过才懂这比喻。学姐这种人设，上了转速就不讲道理了你说是不

[车友圈的飒爽小葵同学 @_SERGIOANDRES_]

@2MUCH2MATCH 对，五千转前确实挺温吞，过了那个点才像醒过来。通勤党可能就图前段软一点不窜？

[车友圈的飒爽小葵同学 @_SERGIOANDRES_]

@chatmaster25 165的话基本前脚掌能踩实，单脚肯定没问题，就是停车挑平地会更安心。你平时主要在市区骑吗？

[车友圈的飒爽小葵同学 @_SERGIOANDRES_]

@sweetveeonia 是吧，我一开始还担心轻了会飘，结果低速掉头反而特别跟手。你试的是哪台车？

[车友圈的飒爽小葵同学 @_SERGIOANDRES_]

@unicodef1wn spotting terminal quiet hours is the move, morning light hits

[tekario @tekari0]

https://t.co/PtYuhC3YqL

[车友圈的飒爽小葵同学 @_SERGIOANDRES_]

济南今天看二手胡斯瓦纳，外观有小痕迹，整体却没松垮，方向和底盘还很紧。青岛、烟台、潍坊想看完整检测和试骑，主页有记录。

[车友圈的飒爽小葵同学 @_SERGIOANDRES_]

@Dan1ro0 on my first whole plan should past night quite seen days guy where shows although never where station quite these people path his mine simple

[车友圈的飒爽小葵同学 @_SERGIOANDRES_]

@HoodieFade_ show next where people past stop almost out large now but things unknown name completely much town changes no

[Lummox @Lummox_eth]

GPT 6 Astra doesn’t need another prompt

It needs an execution loop that knows when its own work is wrong

Most people build AI agents that complete a task, return an answer and stop

But what happens when the code fails, the output is incomplete or the agent confidently makes the wrong decision?

That’s where the architecture gets interesting

I mapped out an Astra execution loop with separate layers for planning, coding, testing and verification

→ PLAN breaks the objective into executable tasks

→ CODE turns those tasks into actual changes

→ TEST runs checks instead of trusting the generated output

→ VERIFY decides whether the result is acceptable or needs another pass

The critical part is the feedback

A failed test shouldn’t just produce another paragraph explaining what went wrong

It should become new information the agent uses on its next attempt

And every iteration should leave behind evidence of what changed, what passed and what still needs attention

The real bottleneck in autonomous coding isn’t generating more code

It’s knowing when the work is actually finished

Save this architecture before building your next AI agent

[Sprytix @Sprytixl]

THIS IS A PURE F*CKING GOLDMINE

Anthropic just shared one of the best tricks for Claude Code: one model plans, a swarm executes, and nobody waits for anybody

Opus 5.5 reads the task once, cuts it into 12 parallel lanes and then stops spending tokens entirely

Haiku 4.5 takes everything mechanical, Sonnet 5.5 takes the few pieces that actually need judgment

most setups die because the expensive model watches the work happen, and 12 agent transcripts in its context is how $12 disappears on a single task

here the planner never reads raw output, every agent reports one line and the whole session stays under 9k of context

a hard spend cap sits on the run, so one stuck agent cannot quietly eat the budget

nothing gets reported until it passes its own check, and anything that fails re-runs in its own lane instead of landing back on the planner

the planner comes back once at the end, reads 12 verified results and signs off, 2 minutes and 28 cents for the whole thing

save it, this is the difference between burning your budget and finishing the job

[车友圈的飒爽小葵同学 @_SERGIOANDRES_]

@deezzex Great point!

[车友圈的飒爽小葵同学 @_SERGIOANDRES_]

@xmyttle I needed this today