小杏说车日常

从预算到空间，一起把选车思路理清楚｜购车分享 @mothio1201

[小杏说车日常 @MoneyOverEverth]

@6AW0RON0k watching the 10 year yield like it owes me money

[小杏说车日常 @MoneyOverEverth]

@_robtimus_ that entry point on SPY looks way too clean to be luck

[SECTOR @sector_sol]

holy f*ck, there’s a Grok Bot template for your vacuum and you’re still writing every prompt from scratch

the official use-cases page lists 56 job-focused setups

then there’s a marketplace with more bots from the team and community creators

some examples worth opening:

→ Chief of Staff turns email, calendar and notes into a briefing

→ Expense Manager collects receipts and prepares expense summaries

→ Outbound Prospecting researches potential customers and drafts messages for approval

→ Haggle Bot checks SaaS spending for unused seats, duplicate tools and possible savings

→ Clip Bot turns long recordings into captioned clips

→ QA bot checks a live deployment against an acceptance checklist

→ Home robots lets you control supported vacuums and mowers from chat

the useful part is starting with a defined job instead of another blank conversation

how to try one:

1. open the use-cases page or marketplace
2. choose a task you actually need done
3. click Add and follow the setup
4. connect the required tools
5. give it one small assignment you can check

don’t confuse adding a template with having a fully configured employee

accounts, integrations and permissions still matter. check your plan and usage costs too

use cases:
https://t.co/gNIuu6Erre

marketplace:
https://t.co/1Gvw9UjrNA

save this before spending another evening building a bot that already has a template

[小杏说车日常 @MoneyOverEverth]

@hotbitch101 哈哈哈实车和故事一半一半吧，不过骑起来不松垮这事是真说服我了。你要不要也来试试看是不是滤镜？

[小杏说车日常 @MoneyOverEverth]

银川今天碰到二手凯越ADV，像户外纪录片里的女摄影师，护杠和车身有远行痕迹，真正骑起来却没有松垮。吴忠、中卫、石嘴山想看车况，主页继续。

[小杏说车日常 @MoneyOverEverth]

@mapuhlar 后减我趴地上捏过，没软塌塌那种感觉，隐性开销这块你说得对，底盘回头我再细看看——你是踩过远行车的坑吗？

[小杏说车日常 @MoneyOverEverth]

@biggesttaylor 前叉运珠这词我还专门搜了下，石嘴山那位说得在理。你没顺便问问链条量了几毫米？

[小杏说车日常 @MoneyOverEverth]

@hotbitch101 吴忠有实车能看，提前跟我说一声就行。当天上牌得看手续和车管所排队，你想哪天过去？

[小杏说车日常 @MoneyOverEverth]

@Paparazzi2721 哈哈谢谢，主要是那身行头加上远行痕迹，站那儿自带纪录片氛围感。你要是在银川估计也会多看两眼。

[小杏说车日常 @MoneyOverEverth]

@AIRWALK101 主要是银川周边和宁夏几条线，印记集中在右侧护杠和左后边箱架附近交车时可以指给你看。有没有你常跑的线推荐我下次去试试？

[小杏说车日常 @MoneyOverEverth]

@roberts14 边箱架确实是凯越跑长途后最容易松的地方，我特意压了压、晃了晃才敢这么说。你之前是骑凯越哪一款碰到的？

[小杏说车日常 @MoneyOverEverth]

@l0lsmileyface14 真的，护杠上那些磕碰比光溜溜的展车有味道多了，每道痕迹都像背书一样。你更看重这种使用痕迹还是整体成色？

[小杏说车日常 @MoneyOverEverth]

@YaBoyHasz 对，就是这种“果然没看错”的踏实感，长途回来的车最怕心理落差。你也经常关注这种跑过远路的车吗？

[boozie @soboozie]

TESLA’S $25,000 ROBOT JUST CLIMBED FACTORY STAIRS - AND CAME BACK DOWN WITHOUT FALLING

Hands lock onto steel railing. One foot finds the step, weight shifts, the other follows. A 125-pound humanoid ascending industrial stairs the way you’d climb them yourself - except it never looks down, never hesitates, and never clocks out.

Two years ago Optimus could barely walk across a flat room. Stairs were the problem everyone said would take a decade - balance calculations multiply with every step height, every angle, every transition from flat to incline to platform and back. Most humanoid robots in the world still can’t do this without falling or being remote-controlled.

Falls from elevation kill almost 700 American workers a year. Tesla is ramping to 1,000 Optimus units per week by the end of 2026. The humanoid robot market is projected to hit $50 billion within a decade. The robots are getting cheaper. The workers’ comp claims are not.

Nobody budgeted for a machine that takes the stairs.

[Woody @woody_research]

HOLY SH*T, THIS CLAUDE CODE TRICK IS PURE GOLD

the move: Opus 5.5 writes the plan as a script, and a swarm of Haiku 5.5 agents runs it in parallel

huge sweeps without drowning Opus: the loop lives in the script, and Opus reads only what survives

one command to start:

claude --model opus --effort ultracode

the split:

→ Opus 5.5 turns your goal into a workflow script that actually runs
→ Haiku 5.5 reads every file at once and pulls out candidates
→ Sonnet 5.5 writes each fix in a separate sandbox copy and double-checks what Haiku found
→ your Opus context never fills up with a hundred agent transcripts

Opus only steps in three times:

→ writing the script before anything starts
→ reading the checked findings when the swarm is done
→ the final yes before a commit lands

Opus plans · Haiku reads · Sonnet ships

Jev is the same idea one layer down: tiny choices with no real thinking in them (pick a file, pick a tool, try again or quit) get answered by Jev in milliseconds, so the big models only see the calls that really split

the whole tree
> Opus 5.5 holds the main session and writes the script
> pipeline() sends Haiku 5.5 through every file
> agent() returns JSON that must match a schema, so junk findings get dropped
> Sonnet 5.5 lands each fix in a sandbox copy
> a verifier agent tries to disprove every diff before you see it
> Opus 5.5 reads only what's left
> Jev answers the micro-forks in milliseconds

paste the tree plus this prompt into Claude Code ↓

"Rebuild my setup on this tree. First, Opus 5.5 runs my main session, ultracode on. Second, any workflow stage that only reads goes to Haiku 5.5, any stage that edits goes to Sonnet 5.5 in a separate sandbox copy. Third, write into CLAUDE.md that no finding gets reported until another agent has tried to prove it wrong, and that a fix loop ends after two rounds without progress. Fourth, quick fixed-answer decisions like which tool, which path or whether to retry go through Jev. Show every change as a diff and wait for my go"

save it, you'll need it

[NFL @NFL]

Who should be the @pepsi Rookie of the Week for Week 6?

Vote in the poll below! ⬇️

[Mojunel.eth @mojunel]

your agent traces nothing from prompt to answer

MLflow pushed today at 28,436 stars

10 open-source GitHub repos that turn raw agent runs into a full observability stack:

64,176 combined stars. 7 Apache-2.0, 3 MIT. All pushed within 38 days. Stars as of Oct 18, 2026.

TRACE EVERY AGENT STEP

01. openinference ★1,268: https://t.co/v4HfiFKj8i -> OTel span format for agent traces
02. openlit ★2,856: https://t.co/Ix6dwBvtqL -> tracing + evals + guardrails on OTel
03. logfire ★4,518: https://t.co/wd4aIeDzU7 -> production agent and LLM tracing
04. lmnr ★3,400: https://t.co/iAvdCnYOPZ -> self-hosted agent observability

WATCH RUNS IN ONE PLACE

05. langwatch ★4,968: https://t.co/m23Vt96rjQ -> trace, test, route, govern agents
06. mlflow ★28,436: https://t.co/LMoVTpBVjM -> debug, evaluate, monitor any agent app
07. evidently ★7,996: https://t.co/uJb4OIpNLu -> test and monitor AI in production

SCORE EVERY ANSWER

08. trulens ★3,602: https://t.co/Shd4vqMpyg -> feedback scores on agent experiments
09. giskard-oss ★5,907: https://t.co/4LUYRAj8ad -> automated testing for LLM outputs
10. openevals ★1,225: https://t.co/qCw12rmuxf -> drop-in evaluators for LLM apps

mlflow quickstart (README):
uvx mlflow server
mlflow.openai.autolog()

the stack:
run → spans → calls → traces → metrics → scores → tests

self-hosted spans store prompts. strip secrets before they land.

skip the model debate. instrument the layer between prompt and answer first.

full trace in the terminal below ↓

[小杏说车日常 @MoneyOverEverth]

@WeatherNation three months later, but tracking my spending prevented every overspend

[小杏说车日常 @MoneyOverEverth]

@mszrsmerci coffee spend column hurt my feelings a bit

[小杏说车日常 @MoneyOverEverth]

jay u do just about anything 2 mess my life up huh?