Shadowing Practice: Claude Code Is Wasting Tokens... Microsoft Built a Fix - Learn English Speaking with Video

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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.

Uygulamayı İndir

Akıcı konuşmak için ihtiyacınız olan her şey

AI TelaffuzHer cümleyi puanla

IPA AlıştırmasıHer sesi ustalaştır

Kelime BilgisiKelime bankanızı oluşturun

Kelime OyunuOynarken öğrenin

QR Tara

Download on theApp Store Get it onGoogle Play

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Bu dersin kelimeleri ve konuşma notları

Bu konuşma dersi “Claude Code Is Wasting Tokens... Microsoft Built a Fix” videosuna dayanıyor. En çok tekrarlanan kelimeler: token, agent, economizer, tool, repo. Bu videoda gölgeleme çalışması için 119 cümle ve 1344 kelime var. Konuşma bölümü 7:29 sürüyor. Konuşmacı hızlı konuşuyor, dakikada yaklaşık 180 kelime; bu yüzden birbirine bağlanan ve zayıflayan sesler duyacaksınız. Kelimelerin %82’i İngilizcede en sık kullanılan 3.000 kelime arasında; geri kalanına başlamadan önce bakmakta fayda var.

Bu videodaki önemli kelimeler

Videodan öğrenmeye değer 15 kelime, telaffuzu ve anlamıyla:

Kelime	Telaffuz	Anlam
token isim	/ˈtoʊkən/	jeton
install fiil	/ɪnˈstɔl/	indirmek
clone isim	/kloʊn/	klon
cloud isim	/ˈklaʊ̯d/	bulut
skill isim	/skɪl/	beceri, maharet
loop isim	/luːp/	ilik
subscribe fiil	/səbˈskɹaɪb/	abone olmak
catalogue isim	/ˈkæt.əˌlɔɡ/	katalog
folder isim	/ˈfoʊldɚ/	klasör
trick isim	/ˈtɹɪk/	hile, numara
extension isim	/ɪkˈstɛnʃən/	uzatma, teşmil
compare fiil	/kəmˈpɛɚ/	karşılaştırmak, kıyaslamak
solve fiil	/sɒlv/	çözmek, halletmek
confirm fiil	/kənˈfɝm/	onaylamak, tasdik etmek
bench isim	/ˈbɛnt͡ʃ/	bank

Duyacağınız deyimsel fiiller

Kelime	Telaffuz	Anlam
come out fiil	/ˌkʌm ˈaʊt/	çıkmak
turn off fiil		kapamak

Tekrar etmeye değer cümleler

Videodan, günlük konuşmada yeniden kullanabileceğiniz kısa ve tam cümleler:

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.

Bu videodaki dil bilgisi

Konuşmacının en çok kullandığı yapılar, videodaki sözcüklerin aynısıyla:

Yapı	Videoda
Present perfect have/has + fiilin üçüncü hâli — geçmişte olan ama şimdi de önemli olan bir eylem	have been put · you've probably seen · has learned
Edilgen yapı be + fiilin üçüncü hâli — kimin yaptığı değil, ne olduğu önemli	have been put · are turned · is called

Dikkat edilecek telaffuzlar

Konuşmacı I'm, you're, I'll gibi kısaltılmış biçimleri 35 kez kullanıyor. Bunları duyduğunuz gibi kısa söyleyin.

“sh” ve “zh” sesleri: combinations /kɑmbɪˈneɪʃənz/, refresh /ɹɪˈfɹɛʃ/, extension /ɪkˈstɛnʃən/, intervention /ɪntɚˈvɛnʃən/, instruction /ɪnˈstɹʌkʃən/
Uzun kelimeler — vurguyu doğru yere koyun: combinations /kɑmbɪˈneɪʃənz/, intervention /ɪntɚˈvɛnʃən/, consistently /kənˈsɪstəntli/, eliminate /ɪˈlɪm.ɪ.neɪt/, repository /ɹɪˈpɒz.ɪ.t(ə.)ɹɪ/

Türkçe konuşanların zorlandığı sesler:

/w/ — dudaklar yuvarlak, /v/ değil: straightforward /ˌstɹeɪtˈfɔː(ɹ)wə(ɹ)d/, workflow /ˈwɝkfloʊ/
Kelime başındaki ünsüz kümesi — araya ünlü eklemeyin: clone /kloʊn/, cloud /ˈklaʊ̯d/, skill /skɪl/, prompt /pɹɑmpt/, straightforward /ˌstɹeɪtˈfɔː(ɹ)wə(ɹ)d/
/æ/ — “e”den daha açık: catalogue /ˈkæt.əˌlɔɡ/, variable [ˈvæɹ.i.ə.bl̩]

Bu videoyla nasıl çalışılır

Videonun tamamını konuşmadan bir kez dinleyin ve bilmediğiniz kelimeleri not edin.
0,75× hızla başlayın, cümle cümle tekrar edin ve kolaylaşınca normal hıza dönün.
Kendinizi kaydedin ve orijinaliyle karşılaştırın; token, install, clone gibi kelimelere özellikle dikkat edin.

Gölgeleme Tekniği Nedir?

Gölgeleme, başlangıçta profesyonel tercüman eğitimi için geliştirilen ve çok dilli Dr. Alexander Arguelles tarafından popüler hale getirilen, bilim destekli bir dil öğrenme tekniğidir. Yöntem basit ama güçlüdür: ana dili İngilizce olan bir sesi dinler ve hemen yüksek sesle tekrar edersiniz — konuşmacıyı 1-2 saniye gecikmeyle takip eden bir gölge gibi. Pasif dinleme veya dilbilgisi alıştırmalarının aksine, gölgeleme beyninizi ve ağız kaslarınızı gerçek konuşma kalıplarını eşzamanlı olarak işlemeye ve yeniden üretmeye zorlar. Araştırmalar, telaffuz doğruluğu, tonlama, ritim, bağlı konuşma, dinleme anlama ve konuşma akıcılığını önemli ölçüde geliştirdiğini göstermektedir — bu da onu IELTS Konuşma hazırlığı ve gerçek dünya İngilizce iletişimi için en etkili yöntemlerden biri yapar.

Shadowing tekniği: adım adım eksiksiz rehberi okuyun →

Shadowing Practice: Claude Code Is Wasting Tokens... Microsoft Built a Fix - Learn English Speaking with Video

Bir pratik modu seçin

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Bu dersin kelimeleri ve konuşma notları

Bu videodaki önemli kelimeler

Duyacağınız deyimsel fiiller

Tekrar etmeye değer cümleler

Bu videodaki dil bilgisi

Dikkat edilecek telaffuzlar

Bu videoyla nasıl çalışılır

Gölgeleme Tekniği Nedir?