쉐도잉 연습: Claude Code Is Wasting Tokens... Microsoft Built a Fix - 영상으로 영어 말하기 배우기

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It seems there's a lot of heat with tools and extensions that claim to reduce token usage.

Many of these have been put to the test and they work well.

Now Microsoft just released their own, a free plugin that cut AI coding agent costs by 18 to 31 % in their tests.

And they didn't use a cheaper model or a different agent either.

It works with all the big names, so it's a win for us.

it's called token economizer

and it solves a problem you've probably seen yourself

you ask claude code to fix something fix this it tries a command gets an error

and then keeps running

and burning tokens up to find us a fix token economizer learns from those sessions it remembers how your repo works, removes tools you don't use,

and steps in when the agent keeps making the same mistake.

And the more you use it in the same project, the more tokens you actually save.

So let's install it and see what it actually does.

If you enjoy coding tools the speed of your workflow, be sure to subscribe.

We have videos coming out all the time.

Now I'm going to use

Cloud Code for this, but remember

Token Economizer also supports all the other big names.

First, I'll clone it and install the plugin right here.

We're going to run our git clone.

We're going to install the git plugin from the marketplace and make sure everything is ready to go.

That's installed all super straightforward.

Just keep the clone folder because the plugin runs from there.

Now I'll open one of my existing projects and we're going to fire up Claude Code.

Let's check the status.

All we need to do to check the status here is I'm going to run token economizer status.

This shows what the plugin has learned.

It shows what it's doing and the costs it has already tracked.

Now, quick note here, micro skills are turned off by default.

So I'm going to enable those and we'll circle back.

Then I'll refresh the tool catalog.

I'm going to run here a tool prune, refresh catalog.

Now I'll give Claude Code some tasks.

Let's start super simple.

I'm going to give Claude Code a task, build a unit test.

Very straightforward.

Run it.

Now notice I'm not changing how I use Claude code.

It's the same thing, just on Opus 5 .5, with all the prompts I would normally fire straight at it.

TokenEconomizer just works alongside it.

Now, in a repo where it's already watched several sessions, we can check what it's learned so far.

So here we are.

I'm going to run Microskills.

You can check.

You can run ToolPrune.

Here we can see any repo -specific info it's learned.

In tools, it's identified as unused.

If you install it for the first time, these might be empty.

It needs a few sessions to actually learn what the hell is going on.

And all of that info stays on your machine inside the token economizer folder.

There are really three parts to this.

Now, first up to the plate is called micro skills.

Say you're working on a project that needs a specific command to run its tests.

Claude code, it doesn't know that.

So it tries the usual command, gets an error, try something else and yeah, okay, it eventually gets it.

Great.

Except later when you start a new session, it might make the exact same mistake.

So there we are paying extra tokens, which I would rather not do.

Token Economizer looks at a previous session, all of them, and learns small details about the repo.

Things like which test commands actually work, which environment variables are needed, and where certain files live.

Once something has been confirmed enough times, it can give us that information to the agents at the start of a new session.

So instead of wasting tokens over and over again, the agent already has the answer.

Now next up to the plate is tool pruning.

When you start a coding agent, it can load a bunch of tools into its context, and those tool definitions use tokens,

even if the agent never touches them.

Token Economizer tracks which tools actually get used in your repo.

If a tool consistently goes unused, used, it can stop loading it in a feature session.

And if you need it again, you can just restore it.

Then finally, we go to something called a failure loop intervention.

You've probably watched an agent run a command, get an error, change one small thing and run almost the exact same command again.

Sometimes it just keeps repeating that process.

Token Economizer watches for these patterns.

When it thinks the agent might be stuck, it uses a judge model to check what's happening.

If it detects this loop, it sends the agent a short instruction to try a different approach.

And if the agent still can't recover, it tells you how to stop the turn.

It doesn't just freak out and stop, but does it actually save enough money to even matter?

That's sort of the question.

Yes, it appears to do pretty much that.

Microsoft in UC Berkeley tested

Token Economizer on...

277 SWE benched tasks across six repos using five different agent and model combinations.

And the average cost dropped between 18 .4 % and about 31%.

So the results were pretty good.

In four of the five combinations, tasks' success actually improved.

One saw a small decline, but across all 30 repository and agent model combinations, Token Economizer was cheaper than running without it.

And they even included the plugin's own model calls in those.

Token Economizer isn't completely free to run.

Some of its features use additional model calls.

So those reported savings already accounted for the overhead.

It is open source.

It works with the same agents we already use, and the info it learns stays local.

But I wouldn't expect all of us to see the same results.

Its first public release was in October of this year, and the biggest savings come from working in the same repositories over multiple sessions.

If you're constantly jumping between unrelated projects, there's much less to actually learn from it, or for it, I should say.

If your agent is doing something completely new, there might not be much repeated work to eliminate at all.

So ask yourself, based on the work you do, will this actually be useful?

If you use agents every day on one or a couple core projects, then yeah, this should help.

If you're working on the same repo over and over with Claude Code, Codex, yes, this is going to help.

But if you're building a ton of different projects or you're just playing around, this is not really going to change anything.

100

It's probably not even worth it.

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So here's what I'd do.

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Install Token Economizer on the repo you use the most.

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Run it for a week, then check the status reports like I showed us and compare the costs of similar tasks.

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If the agent is actually using fewer tokens and whether it's still completing your tasks properly, awesome.

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It is working.

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One thing to remember, each agent learns separately.

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So whatever cloud code learns doesn't automatically carry over to Codex.

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And don't judge it entirely on the first session.

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That's why I say run it for a week.

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The whole point is that it learns from repeated use.

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If it's saving you money without getting in your way, keep it.

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If not, you've got the answer.

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So what it really does is takes a different approach to

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reducing AI coding costs instead of making the model smaller or compressing your prompts.

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It looks at the work your agent keeps wasting tokens on.

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It remembers useful details about your repo, stops loading unused tools, and helps agents recover when it gets stuck.

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I'm Josh from BetterStack.

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If you enjoy coding tips and tricks like this, be sure to subscribe.

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We'll see you in another video.

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이 레슨의 어휘와 말하기 포인트

이 말하기 레슨은 영상 “Claude Code Is Wasting Tokens... Microsoft Built a Fix”을(를) 바탕으로 합니다. 가장 자주 반복되는 단어는 다음과 같습니다: token, agent, economizer, tool, repo. 이 영상에는 섀도잉할 문장 119개와 단어 1344개가 있습니다. 말하는 구간의 길이는 7:29입니다. 화자는 분당 약 180단어로 빠르게 말하므로 연음과 약하게 발음되는 소리가 많습니다. 단어의 82%가 영어에서 가장 많이 쓰이는 3,000단어에 속합니다. 나머지는 연습 전에 미리 확인해 두세요.

이 영상의 핵심 어휘

영상에 나오는 익혀 둘 만한 단어 15개를 발음, 뜻과 함께 정리했습니다.

단어	발음	뜻
token 명사	/ˈtoʊkən/	토큰
install 동사	/ɪnˈstɔl/	설치하다, 깔다
clone 명사	/kloʊn/	클론
cloud 명사	/ˈklaʊ̯d/	구름
skill 명사	/skɪl/	기술, 스킬
loop 명사	/luːp/	고리
subscribe 동사	/səbˈskɹaɪb/	구독하다
catalogue 명사	/ˈkæt.əˌlɔɡ/	목록, 카탈로그
prompt 동사	/pɹɑmpt/	부추기다
folder 명사	/ˈfoʊldɚ/	서류철, 폴더
refresh 동사	/ɹɪˈfɹɛʃ/	새롭게 하다
compare 동사	/kəmˈpɛɚ/	비교하다, 비유하다
solve 동사	/sɒlv/	해결하다
confirm 동사	/kənˈfɝm/	확인하다
bench 명사	/ˈbɛnt͡ʃ/	벤치

영상에 나오는 구동사

단어	발음	뜻
come out 동사	/ˌkʌm ˈaʊt/	나오다
turn off 동사		끄다, 턴오프하다

따라 말해 볼 만한 문장

영상에 나오는 짧고 완결된 문장으로, 일상 대화에서 그대로 쓸 수 있습니다:

We're going to run our git clone.
Then I'll refresh the tool catalog.
That's why I say run it for a week.
If not, you've got the answer.
We'll see you in another video.

이 영상의 문법

화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.

문형	영상 속 표현
현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때	have been put · you've probably seen · has learned
수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점	have been put · are turned · is called

주의할 발음

화자는 I'm, you're, I'll 같은 축약형과 약화된 형태를 35번 사용합니다. 들리는 대로 짧게 발음하세요.

“sh”와 “zh” 소리: combinations /kɑmbɪˈneɪʃənz/, refresh /ɹɪˈfɹɛʃ/, extension /ɪkˈstɛnʃən/, intervention /ɪntɚˈvɛnʃən/, instruction /ɪnˈstɹʌkʃən/
긴 단어 — 강세 위치에 주의: combinations /kɑmbɪˈneɪʃənz/, intervention /ɪntɚˈvɛnʃən/, consistently /kənˈsɪstəntli/, eliminate /ɪˈlɪm.ɪ.neɪt/, repository /ɹɪˈpɒz.ɪ.t(ə.)ɹɪ/

한국어 화자가 어려워하는 소리:

/f/ — ㅍ(/p/)으로 바꾸지 말고 윗니를 아랫입술에 대기: straightforward /ˌstɹeɪtˈfɔː(ɹ)wə(ɹ)d/, folder /ˈfoʊldɚ/, refresh /ɹɪˈfɹɛʃ/, confirm /kənˈfɝm/, fewer /ˈfjuː.ɚ/
/v/ — ㅂ(/b/)과 구별하기: recover /ɹɪˈkʌvɚ/, solve /sɒlv/, intervention /ɪntɚˈvɛnʃən/, variable [ˈvæɹ.i.ə.bl̩], overhead /ˈəʊvəˌhɛd/
/z/ — ㅈ이 아니라 성대를 울리는 /s/: combinations /kɑmbɪˈneɪʃənz/, unused /ʌnˈjuzd/, repository /ɹɪˈpɒz.ɪ.t(ə.)ɹɪ/

이 영상으로 연습하는 방법

먼저 말하지 않고 영상을 끝까지 듣고 모르는 단어를 적어 둡니다.
0.75배속으로 한 문장씩 섀도잉을 시작하고, 익숙해지면 보통 속도로 돌아갑니다.
자신의 목소리를 녹음해 원본과 비교하고, token, install, clone 같은 단어에 특히 주의합니다.

쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법

쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.

섀도잉 방법: 단계별 전체 가이드 읽기 →

쉐도잉 연습: Claude Code Is Wasting Tokens... Microsoft Built a Fix - 영상으로 영어 말하기 배우기

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