تدريب Shadowing: Claude Code Is Wasting Tokens... Microsoft Built a Fix - تعلم التحدث بالإنجليزية عبر الفيديو

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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.

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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.

تنزيل التطبيق

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تقييم النطق بالذكاء الاصطناعيتقييم كل جملة

تدريب IPAأتقن كل صوت

المفرداتابنِ رصيدك من الكلمات

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FOR YOU

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المفردات وملاحظات التحدث لهذا الدرس

هذا الدرس في المحادثة مبني على الفيديو “Claude Code Is Wasting Tokens... Microsoft Built a Fix”. أكثر الكلمات تكرارًا في الفيديو: token, agent, economizer, tool, repo. يحتوي هذا الفيديو على 119 جملة و1344 كلمة للتدرب على الترديد. مدة الكلام 7:29. يتكلم المتحدث بسرعة، نحو 180 كلمة في الدقيقة، لذا ستسمع كثيرًا من وصل الكلمات واختزال الأصوات. 82% من الكلمات ضمن أكثر 3,000 كلمة شيوعًا في الإنجليزية، ويُستحسن مراجعة الباقي قبل البدء.

أهم المفردات في هذا الفيديو

15 كلمة من الفيديو تستحق التعلّم، مع النطق والمعنى:

الكلمة	النطق	المعنى
token اسم	/ˈtoʊkən/	رَمْز
install فعل	/ɪnˈstɔl/	رَكَّبَ
clone اسم	/kloʊn/	كْلُون
cloud اسم	/ˈklaʊ̯d/	سَحَاب, غَيْم
skill اسم	/skɪl/	مَهَارَة
loop اسم	/luːp/	عُرْوَة, عُقْدَة
subscribe فعل	/səbˈskɹaɪb/	اِشْتَرَكَ
catalogue اسم	/ˈkæt.əˌlɔɡ/	فِهْرِس, قَائِمَة
straightforward صفة	/ˌstɹeɪtˈfɔː(ɹ)wə(ɹ)d/	صَرِيح, مُسْتَقِيم
folder اسم	/ˈfoʊldɚ/	مِلَفّ, مُجَلَّد
refresh فعل	/ɹɪˈfɹɛʃ/	جَدَّدَ
trick اسم	/ˈtɹɪk/	حِيلَة
alongside	/əˌlɔŋˈsaɪd/	تَزَامُنًا
compare فعل	/kəmˈpɛɚ/	قَارَنَ
solve فعل	/sɒlv/	ذَابَ, حَلَّ

الأفعال المركبة التي ستسمعها

الكلمة	النطق	المعنى
come out فعل	/ˌkʌm ˈaʊt/	خَرَجَ, غَادَرَ

جمل تستحق التكرار

جمل قصيرة وكاملة من الفيديو يمكنك استخدامها في الحديث اليومي:

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.

القواعد في هذا الفيديو

أكثر التراكيب التي يستخدمها المتحدث، مع العبارات نفسها من الفيديو:

التركيب	في الفيديو
المضارع التام have/has + اسم المفعول — حدث ماضٍ ما زال له أثر الآن	have been put · you've probably seen · has learned
المبني للمجهول be + اسم المفعول — التركيز على ما يحدث لا على من يفعله	have been put · are turned · is called

نطق يحتاج إلى انتباه

يستخدم المتحدث 35 من الصيغ المختصرة، مثل I'm, you're, I'll. انطقها بصيغتها القصيرة كما تسمعها.

أصوات “sh” و“zh”: combinations /kɑmbɪˈneɪʃənz/, refresh /ɹɪˈfɹɛʃ/, extension /ɪkˈstɛnʃən/, intervention /ɪntɚˈvɛnʃən/, instruction /ɪnˈstɹʌkʃən/
الكلمات الطويلة — انتبه لموضع النبر: combinations /kɑmbɪˈneɪʃənz/, intervention /ɪntɚˈvɛnʃən/, consistently /kənˈsɪstəntli/, eliminate /ɪˈlɪm.ɪ.neɪt/, repository /ɹɪˈpɒz.ɪ.t(ə.)ɹɪ/

أصوات يجدها متحدثو العربية صعبة:

/p/ — ليس /b/: انطقه مع نفخة هواء: loop /luːp/, prompt /pɹɑmpt/, compare /kəmˈpɛɚ/, separately /ˈsɛp.ɹət.li/, repo /ˈɹɛpoʊ/
/v/ — ليس /f/: مع اهتزاز الأوتار الصوتية: recover /ɹɪˈkʌvɚ/, solve /sɒlv/, intervention /ɪntɚˈvɛnʃən/, variable [ˈvæɹ.i.ə.bl̩], overhead /ˈəʊvəˌhɛd/
تتابع الصوامت — لا تضف حركة بينها: install /ɪnˈstɔl/, combinations /kɑmbɪˈneɪʃənz/, clone /kloʊn/, unused /ʌnˈjuzd/, cloud /ˈklaʊ̯d/

كيف تتدرب بهذا الفيديو

استمع إلى الفيديو كاملًا مرة واحدة دون أن تتكلم، ودوّن الكلمات التي لا تعرفها.
ابدأ بسرعة 0.75×، وردّد جملة جملة، ثم عُد إلى السرعة العادية عندما يصبح الأمر سهلًا.
سجّل صوتك وقارنه بالأصل، مع الانتباه إلى كلمات مثل token, install, clone.

ما هي تقنية التظليل الصوتي؟

التظليل الصوتي (Shadowing) تقنية تعلم لغة مدعومة علمياً، طُورت أصلاً لتدريب المترجمين الفوريين المحترفين. الطريقة بسيطة لكنها قوية: تستمع لصوت إنجليزي أصلي وتكرره فوراً بصوت عالٍ — كظل يتبع المتحدث بتأخير 1-2 ثانية. تُظهر الأبحاث تحسناً كبيراً في دقة النطق والتنغيم والإيقاع وربط الأصوات والاستماع والطلاقة.

تقنية الشادوينغ: اقرأ الدليل الكامل خطوة بخطوة →

تدريب Shadowing: Claude Code Is Wasting Tokens... Microsoft Built a Fix - تعلم التحدث بالإنجليزية عبر الفيديو

اختر وضع التدريب

تنزيل التطبيق

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المفردات وملاحظات التحدث لهذا الدرس

أهم المفردات في هذا الفيديو

الأفعال المركبة التي ستسمعها

جمل تستحق التكرار

القواعد في هذا الفيديو

نطق يحتاج إلى انتباه

كيف تتدرب بهذا الفيديو

ما هي تقنية التظليل الصوتي؟