शैडोइंग अभ्यास: Claude Code Is Wasting Tokens... Microsoft Built a Fix - वीडियो के साथ अंग्रेजी बोलना सीखें

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119 वाक्य

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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.

101

So here's what I'd do.

102

Install Token Economizer on the repo you use the most.

103

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

104

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

105

It is working.

106

One thing to remember, each agent learns separately.

107

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

108

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

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

110

The whole point is that it learns from repeated use.

111

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

112

If not, you've got the answer.

113

So what it really does is takes a different approach to

114

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

115

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

116

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

117

I'm Josh from BetterStack.

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

119

We'll see you in another video.

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धाराप्रवाह बोलने के लिए ज़रूरी सब कुछ

AI उच्चारण स्कोरहर वाक्य का स्कोर

IPA अभ्यासहर ध्वनि में महारत

शब्दावलीअपना शब्द भंडार बनाएँ

शब्द गेमखेलते हुए सीखें

QR स्कैन करें

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इस पाठ की शब्दावली और बोलने के सुझाव

यह स्पीकिंग पाठ वीडियो “Claude Code Is Wasting Tokens... Microsoft Built a Fix” पर आधारित है। सबसे ज़्यादा दोहराए गए शब्द ये हैं: token, agent, economizer, tool, repo। इस वीडियो में शैडोइंग के लिए 119 वाक्य और 1344 शब्द हैं। बोला गया हिस्सा 7:29 लंबा है। वक्ता तेज़ बोलता है, लगभग 180 शब्द प्रति मिनट, इसलिए जुड़ी हुई और कमज़ोर ध्वनियाँ ज़्यादा मिलेंगी। 82% शब्द अंग्रेज़ी के 3,000 सबसे आम शब्दों में से हैं; बाकी शब्द अभ्यास से पहले देख लेना अच्छा रहेगा।

इस वीडियो की मुख्य शब्दावली

वीडियो के 15 सीखने लायक शब्द, उच्चारण और अर्थ के साथ:

शब्द	उच्चारण	अर्थ
cloud संज्ञा	/ˈklaʊ̯d/	मेघ, बादल
skill संज्ञा	/skɪl/	कुशलता, कौशल
loop संज्ञा	/luːp/	कुंडली
catalogue संज्ञा	/ˈkæt.əˌlɔɡ/	सूची, तालिका
folder संज्ञा	/ˈfoʊldɚ/	फ़ोल्डर
trick संज्ञा	/ˈtɹɪk/	चाल
compare क्रिया	/kəmˈpɛɚ/	तौलना
solve क्रिया	/sɒlv/	सुलझाना, हल करना
confirm क्रिया	/kənˈfɝm/	पुष्टि करना
decline क्रिया	/dɪˈklaɪn/	गिरना
bench संज्ञा	/ˈbɛnt͡ʃ/	बेंच
default संज्ञा	/dɪˈfɔlt/	व्यतिक्रम
intervention संज्ञा	/ɪntɚˈvɛnʃən/	हस्तक्षेप, मुदाख़लत
usage संज्ञा	/ˈjuːsɪd͡ʒ/	लौकिकता
restore क्रिया	/ɹɪˈstoɹ/	बहाल करना

वीडियो में आने वाली फ़्रेज़ल वर्ब

शब्द	अर्थ
turn off क्रिया	बंद करना

दोहराने लायक वाक्य

वीडियो के छोटे, पूरे वाक्य जिन्हें आप रोज़ की बातचीत में इस्तेमाल कर सकते हैं:

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.

इस वीडियो का व्याकरण

वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:

संरचना	वीडियो में
Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है	have been put · you've probably seen · has learned
Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है	have been put · are turned · is called

ध्यान देने योग्य उच्चारण

वक्ता 35 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे I'm, you're, I'll। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।

“sh” और “zh” की ध्वनियाँ: combinations /kɑmbɪˈneɪʃənz/, refresh /ɹɪˈfɹɛʃ/, extension /ɪkˈstɛnʃən/, intervention /ɪntɚˈvɛnʃən/, instruction /ɪnˈstɹʌkʃən/
लंबे शब्द — बलाघात सही रखें: combinations /kɑmbɪˈneɪʃənz/, intervention /ɪntɚˈvɛnʃən/, consistently /kənˈsɪstəntli/, eliminate /ɪˈlɪm.ɪ.neɪt/, repository /ɹɪˈpɒz.ɪ.t(ə.)ɹɪ/

हिंदी भाषियों के लिए मुश्किल ध्वनियाँ:

/v/ और /w/ का फ़र्क़ — /v/ में ऊपर के दाँत निचले होंठ को छूते हैं, /w/ में होंठ गोल होते हैं: recover /ɹɪˈkʌvɚ/, straightforward /ˌstɹeɪtˈfɔː(ɹ)wə(ɹ)d/, solve /sɒlv/, intervention /ɪntɚˈvɛnʃən/, variable [ˈvæɹ.i.ə.bl̩]
/z/ — इसे “ज” (/dʒ/) न बनाएँ: combinations /kɑmbɪˈneɪʃənz/, unused /ʌnˈjuzd/, repository /ɹɪˈpɒz.ɪ.t(ə.)ɹɪ/
शब्द की शुरुआत में s + व्यंजन — पहले “इ” न जोड़ें: skill /skɪl/, straightforward /ˌstɹeɪtˈfɔː(ɹ)wə(ɹ)d/

इस वीडियो से अभ्यास कैसे करें

पहले बिना बोले पूरा वीडियो एक बार सुनें और जो शब्द नहीं आते उन्हें लिख लें।
0.75× गति से शुरू करें, एक-एक वाक्य की शैडोइंग करें, और सहज लगने पर सामान्य गति पर लौटें।
अपनी आवाज़ रिकॉर्ड करें और मूल से तुलना करें, ख़ासकर cloud, skill, loop जैसे शब्दों पर ध्यान दें।

शैडोइंग तकनीक क्या है?

शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।

शैडोइंग तकनीक: पूरी चरण-दर-चरण गाइड पढ़ें →

शैडोइंग अभ्यास: Claude Code Is Wasting Tokens... Microsoft Built a Fix - वीडियो के साथ अंग्रेजी बोलना सीखें

अभ्यास मोड चुनें

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इस पाठ की शब्दावली और बोलने के सुझाव

इस वीडियो की मुख्य शब्दावली

वीडियो में आने वाली फ़्रेज़ल वर्ब

दोहराने लायक वाक्य

इस वीडियो का व्याकरण

ध्यान देने योग्य उच्चारण

इस वीडियो से अभ्यास कैसे करें

शैडोइंग तकनीक क्या है?