车友圈的元气小星同学

记录一下今天的油耗比上个月省了不少｜汽车日记 @marilyn_1003

[车友圈的元气小星同学 @jtirons]

@TiaMiaBaby 回复 @Barbi3_baBee: 有道理，但原厂设定够不够用还是看人，不用先劝改。

[小樱的汽车生活日记 @AveryOrchid]

南京这台SYM复古踏板像咖啡店成年女店员，小巧、清爽，很适合每天陪你走固定路线。苏州、无锡、常州想看日常表现，主页继续。

[车友圈的元气小星同学 @jtirons]

@Sweetie0987 我在松原骑过一台低座巡航，等红灯时脚掌稳稳贴地，那份安心感别的车型给不了

[🌺Sai Fan @Sai60012]

🌞OM SURYA DEVAY NAMAH🌞
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@grok
ॐ PUSHAN NAMAH

[NO1ennn @N01ennn]

more here :

https://t.co/Ns2YoBh7Wj

[小豆说车小笔记 @JNewMoney]

石家庄今天试凯旋Scrambler，像穿工装靴的复古女摄影师，高位排气很有味道，停着安静，路烂一点反而更有精神。唐山、保定、邯郸想看真实路感，主页继续。

[车友圈的元气小星同学 @jtirons]

@cottoncandymoe 方向好沟通具体是指入弯的指向性还是回正的手感？我一直在纠结这台和同级那几台怎么选

[车友圈的元气小星同学 @jtirons]

@Ovrento gravel highway up to lakes and bought my oil gauge

[车友圈的元气小星同学 @jtirons]

@Ovrento gravel highway up to lakes and bought my oil gauge

[车友圈的元气小星同学 @jtirons]

@Sai60012 fishing in this swell off last night with nothing

[车友圈的元气小星同学 @jtirons]

@Levi_Evans_94 这种车真不是用来赶路的，圆灯配长座垫，看着就想起老城区的慢生活。

[车友圈的元气小星同学 @jtirons]

@SAHC89 大庆冬天冷，冷启动和电瓶这块有没有测过？

[车友圈的元气小星同学 @jtirons]

看了这条才认真注意到皇家恩菲尔德，之前一直以为它只适合老手。

[小豆的汽车生活分享 @nomerce]

苏州今天试皇家恩菲尔德复古车，像温柔大学老师，圆灯、金属油箱都有一种慢节奏的美。南京、无锡、常州想看完整日常体验，主页有后续。

[小光聊车的那些事 @ZAYBOOGI3]

台北这台二手Vespa像复古电影里的轻熟女，漆面没那么新了，却比新车多一点生活味。城市里依旧灵巧。新北、桃园、新竹想看通勤表现，主页有后续。

[车友圈的元气小星同学 @jtirons]

@butterfly20099 合肥的，正好在考虑换车，想问下这车座高对一米七出头的人友好不？

[kozh ./ @mr_kozh]

ELON MUSK JUST SAID FOUR WORDS THAT SOUND LIKE SCIENCE FICTION:

“FLYING CARS ARE REAL.”

then Tesla showed a Roadster leaving the ground.

the prototype climbed an inverted ramp, launched into the air and landed using cold-gas thrusters developed with SpaceX technology.

Tesla claims:

0–60 mph in 0.81 seconds

a top speed above 230 mph

compressed-air propulsion

production beginning in 2028

Musk also admitted:

“success is not guaranteed.”

that sentence matters.

because the real achievement wasn’t making a perfect production car.

it was connecting capabilities that normally live inside separate companies.

Tesla supplied the electric vehicle.

SpaceX supplied the propulsion technology.

software coordinated the sensors, thrust and movement required to put the car back on the ground.

and this is where Grok Bot becomes more interesting than another chatbot.

Grok Bot did not control this demonstration.

but it points toward the operating layer that could surround systems like it:

one agent monitors telemetry.

another investigates an abnormal reading.

another searches previous tests for the same failure.

another opens an engineering task with the evidence attached.

another waits for approval before changing anything.

the machine produces signals.

the agents decide where those signals should go next.

not by placing one giant model in control of everything.

by giving specialized bots narrow jobs, shared context and explicit limits.

imagine a Roadster test failing because one thruster fired 80 milliseconds late.

the useful workflow isn’t:

“ask AI what happened.”

it is:

detect the anomaly
→ preserve the telemetry
→ compare previous runs
→ identify the affected system
→ route it to the correct engineer
→ verify the fix in the next test
the vehicle is the visible product.

the intelligence coordinating everything around it may become the bigger system.
Tesla builds the body.
SpaceX builds the thrust.

Grok Bot could help coordinate what happens before and after the machine leaves the ground.

the flying Roadster is the spectacle.

the agent infrastructure around it is the leverage.

I broke down how Musk’s companies are beginning to look like one connected technology stack below ↓

[AiMind @AIMind_Ai]

$114 for a honey keyboard, and Claude Opus 5.5 needed three tries to get it right.

What the f*ck. I could've bought a real one at the store and still had money left for honey.

Gemini 4 Argon did it for about a dollar, 102x cheaper.

Which one would you put on your desk?

[lostinazeroy @lostinazeroy]

it's getting f*cking cheap to leave claude code running all day

haiku 5.5 as the main model, opus as the advisor. two lines in settings.json

what that does:
- haiku does the routine work: migrations, refactors, tests to an existing pattern
- opus steps in before picking an approach, when the same error comes back and before calling a task done
- every advisor call reads the whole session, so opus sees what haiku did
- for one session: claude --model haiku --advisor opus

cheap model does the work, expensive one decides if it's right

anthropic's own estimate: costs more than haiku alone, less than sonnet or opus as the main model

the catch is claude decides when to ask. want more checks, say it in the prompt

full setup with the configs in the article below. save it

[Sprytix @Sprytixl]

THIS IS ACTUAL F*CKING GOLD

Anthropic just shared one of the best tricks for Claude Code.

The idea: stop asking for an answer. Give it a stop condition and let one agent loop until it hits it.

Same codebase, two ways: one-shot got 61% and guessed the rest. The loop ran 43 times and came back with 388 of 388 rules verified.

You just launch Claude Code like this:

claude --model opus --loop "map legacy/ until 100% verified"

here's the split:
→ you write one prompt and then do nothing, zero edits for the entire run
→ REA loops read → extract → verify → fix on its own
→ every rule it extracts gets replayed against real prod logs before it counts
→ the loop only stops when all 388 rules pass, not when the model feels done
the part everyone gets wrong: they accept the first pass.
a single pass over 16 services gives you a confident document where most of it was never checked against anything.
here rules.md grows every loop. 388 rules across auth, billing, payments, checkout, admin and the rest, each one marked verified only after it survives a replay.

three rules came back wrong. the agent re-read the source, fixed them, and looped again instead of shipping them.

one prompt from you, 43 loops from it, 0 edits by you.

Opus drives. REA reads and verifies. The loop decides when it is done.

JEV engineering is the same move one layer down: the forks that need no thinker (which file next, re-loop or stop, retry or fail) go to JEV in milliseconds, and the model only sees the ones that actually split

the full tree
> Opus 5.5 runs the session with a stop condition, not a task
> REA loops read, extract, verify and fix on every pass
> every extracted rule is replayed against prod logs before it counts as verified
> any mismatch sends that module back for a re-read instead of shipping
> rules.md grows each loop until the stop condition is met
> JEV routes micro-forks in milliseconds

paste the tree and this prompt into Claude Code ↓

"Rebuild my Claude Code setup around this tree:

1. Run the main session on Opus 5.5 and give every long task a stop condition, never a single pass.
2. Loop read, extract, verify and fix until the condition is met.
3. Every extracted rule must be replayed against real logs or tests before it counts as verified.
4. Any mismatch triggers a re-read of that module, never a patched guess.
5. Add one rule to CLAUDE.md: unverified findings are never written into the final document.
6. Route deterministic forks (next file, re-loop or stop, retries) through JEV.

Show me every change as a diff first. No edits until I say go."

↳
https://t.co/GoHZ0potb7

save it, you'll use this on every legacy repo from now on.

[Sprytix @Sprytixl]

THIS IS A PURE F*CKING GOLDMINE

Anthropic just shared one of the best tricks for Claude Code.

The idea: you stop writing agent configs by hand. One prompt, and Opus 5.5 writes the entire swarm for you.

By hand that is 16 prompts, 16 tool configs, 16 test harnesses and 2,312 lines you type yourself. Here you type one line.

You just launch Claude Code like this:

claude --model opus "build REA swarm ×16 for legacy/"

here's the split:
→ Opus 5.5 reads 578 files and splits them into 16 services
→ it writes 16 specs in parallel, 1,037 lines, no templates
→ every spec becomes one REA agent, one reverse-engineer per service
→ each agent gets tested as it lands, 3/48 checks already green at 3 minutes

the part everyone gets wrong: they hand-write every agent and then wonder why 16 of them drift apart.

here one architect writes all 16 specs from the same read of the codebase, so every agent is wired the same way and nothing gets forgotten.

REA is the piece doing the real work underneath: each agent reverse-engineers its own service, dry-runs on three files first, then writes its spec from what it actually found, not from what you guessed.

you wrote 1 line. Opus wrote 1,037.

Opus architects. REA reverse-engineers. The swarm builds itself.

JEV engineering is the same move one layer down: the forks that need no thinker (which service, which tool, retry or stop) go to JEV in milliseconds, and the architect only sees the ones that actually split

the full tree
> Opus 5.5 runs as architect, reads the repo and splits it into services
> it writes one spec per service, all 16 in parallel, from a single read
> every spec spawns one REA agent that reverse-engineers that service
> each agent dry-runs on a few files before writing anything
> specs get tested as they land, the architect monitors all 16 in flight
> Opus 5.5 deploys once every spec is ready
JEV routes micro-forks in milliseconds

paste the tree and this prompt into Claude Code ↓

"Rebuild my Claude Code setup around this tree:

1. Run the main session on Opus 5.5 as an architect that writes agent specs, never agent code by hand.
2. Point it at a repo, let it split the repo into services and write one spec per service, all in parallel from a single read.
3. Every spec spawns one REA agent that reverse-engineers its own service and dry-runs on a few files before writing anything.
4. Test each spec as it lands, never wait for all of them to finish first.
5. Add one rule to CLAUDE.md: no agent ships until its spec passes its own checks.
6. Route deterministic forks (service choice, tool choice, retries) through JEV.

Show me every change as a diff first. No edits until I say go."

↳
https://t.co/GoHZ0potb7

save it, it'll come in handy.