可乐加冰去气

涓嶈礋闊跺崕

[可乐加冰去气 @shumonic]

@88n77n ensemble scenes die when one actor goes magnetic improv one

[可乐加冰去气 @shumonic]

@0xCarnagee that light cue saved the scene in rehearsal, take notes regularly

[可乐加冰去气 @shumonic]

@Bellowsuk 外观偏运动的话座高一般会高一丢丢，个子不高的姐妹最好先原地试跨

[可乐加冰去气 @shumonic]

@Abril_09 我单程差不多也是二十来分钟，属于刚开始不觉得、下车才知道的类型。你说拉长路线试出来，那我下次也留意下腰的感受。

[可乐加冰去气 @shumonic]

@irongiantXBT echo of the backstage set crew felt honestly a bigger cheer than actors pit that weird

[Ackerman @Yarilo7brigada]

THIS IS F*CKING INSANE

THE MOST EXPENSIVE MISTAKE IN MULTI-AGENT TEAMS: A BOT SAYS “DONE” WITH NO PROOF

you built a shared brain. your bots ignore it, write “done”, and lose context on every handoff. without a verification layer, a Shared Brain is just a pretty folder nobody opens

6 repos that close this gap:

01 run → state in markdown, “done” only with evidence in the log, work resumes from a Handoff block instead of yesterday’s chat
https://t.co/yNSZ8VaMBb

2 x-harness → the agent fills out a completion card, and a separate read-only verifier decides whether to accept the claim
https://t.co/oVannEggOx

3 Agent Spartan Protocol → one markdown task file as the source of truth for handoffs between agents. review always goes to the host that didn’t do the work
https://t.co/1oORn8UPIX

4 handover-mcp → local state handoff when you switch sessions, models, or agents, with connectivity, completion, and continuity checked separately
https://t.co/cfRehMDth9

5 beads → a dependency-graph issue tracker built as memory for coding agents. the agent sees what’s already done and what’s blocking
https://t.co/IgfGE363X4

6 superpowers → a skill library that includes verification-before-completion: proof first, then “done”
https://t.co/kgq3HxJeUF

the rule is simple, completion is accepted, not claimed

how to add verification to your bot team in one day ↓

[可乐加冰去气 @shumonic]

@XBusiness Love this

[可乐加冰去气 @shumonic]

@0xRicker Great point!

[可乐加冰去气 @shumonic]

@Kaggii_ Well said

[可乐加冰去气 @shumonic]

@atari107 油箱两侧收窄对腿短的人友好，原地挪车膝盖不打架。

[可乐加冰去气 @shumonic]

@TommyPhillips33 我身高一米七出头，试过类似的坐高很友好，脚尖踩地没压力。

[Marlen @Marlenuii]

Claude Code agents can accidentally do the same job twice

4 GitHub repos that help stop duplicate tool calls from creating duplicate side effects

trick: the bug is usually not the model

it is the missing execution contract around it

same request
same key
same result

here's the setup:

→ Temporal gives long running workflows durable execution and retries

https://t.co/jjl30AArr1

→ Inngest lets you build event driven jobs with step level retries and concurrency control

https://t.co/kkGGjbplVM

→ Trigger dev handles background jobs with retries queues and execution history

https://t.co/BeLXnH2pdQ

→ Redis gives you a fast shared store for request keys locks and deduplication state

https://t.co/WsyrDlIf9B

the pattern in the video is simple

request arrives → generate idempotency key → check shared state → run once → cache result → return the same promise to duplicates

that changes the failure model completely

instead of asking Claude Code or GPT to somehow remember whether a tool already ran

you make the infrastructure enforce it

paste this into Claude Code ↓

"Build an idempotent agent execution layer

Every external action must receive a stable request key

If the same key arrives while execution is still running return the same in flight promise

If the key already completed return the stored result

If execution fails clear or expire the key so the operation can retry safely

Add protection for concurrent duplicates burst traffic and repeated retries

Create tests for

single call

concurrent duplicate

completed duplicate

different keys

retry after failure

burst of ten calls

Show the architecture first

Then implement the smallest working version and make every test pass"

AI agents do not just need better reasoning

they need infrastructure that guarantees one task becomes one write

bookmark this before your next agent touches a real API

[可乐加冰去气 @shumonic]

@aqwlord 我倒觉得低速偏冲，可能个人感受不同。

[可乐加冰去气 @shumonic]

@Lenovo @F1 the photocards decide how the whole comeback goes, rules are rules

[可乐加冰去气 @shumonic]

@0xRafy chorus was floating in my head all day actually

[Ricker @0xRicker]

i gave Grokbot one sentence and 300 K3 agents built the entire agent around it. i wrote 0 lines of code.

the input was simple:

“watch three competitors’ changelogs and tell me what actually changed for us”

from there the system handled the rest:

→ Grokbot asked 23 questions to remove ambiguity
→ turned the answer into a 184-line spec
→ wired 9 tools
→ ran 14 test passes
→ added 2 new rules after failures
→ deployed the finished workflow on a schedule

all while 300 K3 agents were working underneath the build.

the part i like most is that this doesn’t start with a framework or a giant agent template.

it starts with one sentence, then moves through:

define → plan → build → tools → test → improve → deploy

and in this run:

0 lines of Python written by me
0 frameworks installed
$0.00 spent on the build

that’s basically the whole idea behind the article:

you shouldn’t need to become an agent engineer just to build one.

full 7-step Grokbot × Kimi K3 setup below ↓

[可乐加冰去气 @shumonic]

@unknownnode404 that vocal run belongs in a stadium, honestly

[可乐加冰去气 @shumonic]

@Slonski_rt do not ask how many fancams i rewatched just be warm

[Slonski @Slonski_rt]

I KEEP BUILDING WITH CLAUDE OPUS 5.5. THIS TIME: CARD MAKER. WATCH A LONG PROMPT FIT ITSELF INTO A CARD...

last time it was a site that shows how AI your text sounds

paste the text, pick a format, press download
16:9, 1:1, 4:5 or auto height for long ones

one html page, your text never leaves the browser

you do not fit anything by hand
→ the font shrinks until the text fits
→ columns come out the same height
→ a long prompt breaks between blocks

if you post prompts
→ <goal> and other tags go grey
→ {{TASK}} lights up as the part to fill in