Stop right there. Every article you feed to AI — over 90% of it is garbage code? You do this every single day. You spot an article with a catchy headline. You copy the link, toss it to AI: "Help me see what this article is about." AI says, "Sure." Then it starts reading. You put down your phone, go grab a glass of water. When you come back, AI has already finished, neatly listing ten key points for you. You think to yourself:

💡 What You Will Learn

Stop right there. Every article you feed to AI — over 90% of it is garbage code? You do this every single day. You spot an article with a catchy headline. You copy the link, toss it to AI: "Help me se

Stop Right There. 90%+ of Every Article You Feed to AI Is Garbage Code?

You do this every single day.

You see an article with a decent headline. You copy the link, toss it to AI:

"Help me see what this article is about."

AI says: "Sure." Then it starts reading.

You put down your phone and go grab a glass of water. By the time you're back, AI's already finished and neatly listed ten key points for you.

You think to yourself: This AI is amazing, it reads so fast.

But here's what you don't know—AI didn't actually open that article. It opened a door. And behind that door stood a giant of 4 million characters.

What's in those 4 million characters? Framework code, stylesheets, font definitions, JS scripts, ad trackers, analytics pixels, SEO metadata, SVG icons for social share buttons, comment section templates, HTML for cookie pop-ups, hundreds of lines of CSS @media queries…

And all you wanted was those 2,838 characters tucked away in the corner behind that door.

What's it like?

It's like walking into a noodle shop and saying: "One bowl of noodles, please."

The owner heads to the kitchen. Ten minutes later, they bring out an entire pot—water boiling, seasoning packets, plastic wrappers, dishcloths, and a gas stove manual floating on the surface, with a few strands of noodles at the very bottom.

You ask: "What is this?"

The owner says: "Your noodles."

You paid for the whole pot and ate three strands of noodles.

Out of your 20,000 tokens, 19,800 went to reading framework code and ad scripts. The actual useful content? Just one percent.

Later, someone recommends a little tool to you. The name is plain and simple—save2kb.

You install it half-skeptically and toss the same link to AI.

This time, it's different.

AI takes the link, gently knocks on the webpage's door—no answer. But it doesn't panic. It opens the door its own way.

It doesn't invite that 4-million-character giant out. Instead, it slips past it, walks straight to the corner, grabs those 2,838 characters, and brings them out.

Then it says: "This article is about SEO topic clusters, with three core points… Want me to save it to your knowledge base?"

You're stunned.

Same link. Before, it cost nearly 20,000 tokens. This time? Under 2,000.

A 90% savings.

You brush it off and move on. Until one late night, scrolling through your chat history out of boredom, you suddenly decide to do the math—

How many articles have you made AI read over the past six months?

You scroll down. At least two or three a day, sometimes five. Some are papers, some are blogs, some are news, some are long-form deep dives shared on your social feed. You never thought twice—toss it in, read, close. Next link, continue.

Six months. Let's say 400 articles.

Feeding links directly to AI → ~2,000,000 tokens per article → 800 million tokens over six months.

Using save2kb → ~1,500 tokens per article → 600,000 tokens over six months.

800 million versus 600,000.

Your hand hovers above the keyboard. It stops.

You think you're having AI "read articles" every day. In reality, you're dumping 4 million characters into a token furnace and burning them.

But someone actually ran the numbers, and the results are surprising—how much you save depends entirely on what kind of site you're reading.

Chinese websites burn the most. The pages are stacked with everything—ad slots, tracking scripts, social media widgets, popup styles… The actual content gets buried at the very bottom. With save2kb, it's basically like recovering that 99.9% you were burning.

English blogs and media sites are a bit better, but still around half of your tokens go to page scaffolding.

Technical docs are the "cleanest," but still carry 20-40% redundancy.

What does that mean in practice?

Let's say you have AI read one web link per day—don't overestimate yourself, most people do this way more often.

30 articles a month.

Reading raw page source directly → ~2,000,000 tokens per article → 60 million monthly → ¥60

Using save2kb to extract clean text → ~1,500 tokens per article → 45,000 monthly → ¥0.045

You save ¥59.955 a month. A 99.93% reduction.

On GPT-4o ($2.5/million input tokens), that's going from $1,800 a year down to under $1.

What you're seeing isn't just numbers. You're seeing those tokens silently burned over the past six months, floating up from your screen and drifting around the room.

Chinese readers get burned the hardest.

save2kb—an AI skill that does exactly one thing:

Takes a web article → extracts clean text → auto-categorizes → saves to knowledge base.

You send a link, it reads the article. When it's done, it tells you what it said, then asks if you want to save it.

Don't want to save? Fine, no problem. Want to save? It sets up folders, sorts everything, and sends you a weekly digest.

Installing it takes one command:

npx skills add https://github.com/simumu1/save2kb

Open source. Free.

You think you're reading. But every time you do, your tokens are burning. 🔥

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