Forem: mayank dhingra

How I Built My Own Home Server for $150 (And Why I Ditched AWS for It)

mayank dhingra — Mon, 19 May 2025 01:05:44 +0000

I've always liked the idea of running my own projects, websites, scripts, maybe even some AI experiments. Naturally, I started with AWS since I was already familiar with it. But very quickly, I ran into a wall: COST.
Running multiple scripts 24/7 and hosting small side-projects on AWS added up faster than I expected. Even the cheaper options like EC2 Spot Instances weren't cutting it. That's when I decided to go the DIY route, build my own home server.

🧠 The Plan: Keep It Cheap, Keep It Useful
I didn't need enterprise-grade infrastructure. None of my scripts were mission-critical, and I wasn't hosting anything that needed airtight security or massive traffic support. So I figured, why not just build something local and learn in the process?

🖥️ What I Bought
I found a 10-year-old used CPU on Facebook Marketplace for $100. Specs were surprisingly decent:
Intel Xeon E5 processor
32GB DDR4 RAM
8GB VRAM (nothing fancy, but enough for testing)

What I didn't check:
No SSD
No Wi-Fi chip

Rookie mistake, but nothing I couldn't fix.

🔧 The Setup
I spent a bit more to patch things up:
Bought a USB Wi-Fi adapter
Got a 256GB SSD and installed Ubuntu Server
Connected the server to my router via LAN cable to get Wi-Fi drivers installed

Total cost: ~$150 for a fully working server.

🔐 Security - Why I'm Still Okay With This
Let's be honest: running your own server comes with security concerns. If you're handling user data, payments, or high traffic, this is not the best idea unless you know what you're doing.
In my case, though, I was just hosting:
My portfolio website
Some personal Flask apps
A few Python scripts that scrape or automate stuff

None of them require external access from strangers or handle sensitive data. So while this setup isn't Fort Knox, for local, low-risk projects, it works just fine.

⚙️ What It's Running Now
Personal website
Backend services for side projects
Background Python scripts running 24/7 via tmux

Now I don't have to worry about draining my laptop battery or shutting off scripts accidentally. And no AWS bills.

🤕 What Went Wrong (But I'm Glad It Did)
I made a bunch of small mistakes:
Forgot to check for Wi-Fi support
Struggled with networking issues at first

But those mistakes taught me more than any tutorial could. I now know how to:
Configure Ubuntu Server from scratch
Set up a basic firewall
Use tmux to keep things running
Auto-start on reboot after power outages

Even if this project hadn't saved me a lot of any money, I'd still say it was worth it just for how much I learned.

🔐 On Security
Security is obviously a concern with any public-facing server. But since I'm not hosting user data or production traffic, I've kept things simple:
SSH with strong credentials
Only specific ports open
UFW firewall enabled

If I were running anything sensitive or high-traffic, I'd still use AWS or a similar provider that offers built-in security features and automatic patching. But for personal projects, this works.

⚡ Downsides

Manual restart after outages If there's a power cut, I have to physically press the power button to boot the server again. It doesn't auto-restart when power returns. I've read there's a BIOS setting ("Restore on AC Power Loss") that can help with this , just haven't enabled it yet.
Security isn't enterprise-grade This setup wouldn't fly for anything mission-critical. No automatic patches, no intrusion detection, no DDoS protection. But again, for internal or hobby projects, it's fine.
No backup drive (yet) I don't have a backup system in place. If the SSD dies, I lose local data. That said, all my scripts and important files are on GitHub, so I could recover pretty quickly. Still, adding a secondary drive or syncing to a cloud bucket is on my to-do list.

📈 Can It Scale?
Surprisingly Yes, at least for my use case. Since it has 32GB RAM and decent CPU/GPU power, I can:
Add another machine and scale horizontally
Test distributed workloads or run a mini Docker/Kubernetes lab
Spin up multiple small services in parallel

It's not a replacement for AWS, but it's an amazing way to learn about scalability, orchestration, and resource management hands-on.

💬 Final Thoughts
Even if I hadn't saved a single dollar, I'd still say this project was worth it. I made mistakes, fixed things, learned about networking, Linux, port forwarding, firewall rules, and more. You can't get that kind of learning by clicking "Launch Instance" on a cloud dashboard.
If you're someone who likes to build, experiment, and learn, try setting up your own home server. It won't be perfect, but it'll be yours. And that's kind of the point.

Is AI the New Oil? We Need to Talk About What It’s Costing Us

mayank dhingra — Mon, 12 May 2025 04:02:06 +0000

We talk a lot about how smart AI is getting.
But here’s something we don’t talk about enough: it’s quietly wrecking the planet.

Not in some sci-fi dystopia. In a real, physical, “it’s-happening-now” kind of way. Every time we ask a model to summarize a doc or spit out a joke, there’s a real-world cost i.e. electricity, water, hardware, minerals.

And it adds up. Fast.

🔋 Power Hungry: How Much Juice Does AI Really Need?

Let’s start with the basics. Training GPT-3 used up more than 1,200 megawatt-hours of electricity. That could’ve powered over a hundred homes in the U.S. for a full year.

Now take GPT-4. That one? Easily 50x more, depending on the estimate. We’re talking small-city-level energy.

And guess what? That’s just the training. The real energy drain comes after that, when you and millions of others start using it. Every prompt spins up servers. Add that up over billions of queries, and you’re looking at gigawatt-hours per day.

It’s invisible to us. But behind the scenes? It’s a power-hungry monster running 24/7.

💧 Water You Doing, AI?

Here’s the part that really got to me.

Google burned through 5.6 billion gallons of water in 2022. Microsoft used over 11 million gallons in a single month, just for cooling during GPT-4’s training.

This is drinking water we’re talking about. Clean, processed water. The kind people in some parts of the world literally walk miles to get. And it’s being evaporated to cool chips.

And here’s the kicker: a lot of these data centers are in water-stressed areas. So while locals are being told to conserve, massive AI farms keep guzzling water like it’s nothing.

🖥 The Trash Heap No One Sees

We act like AI lives in some magical “cloud.” But it’s all built on hardware, GPUs, memory, server racks.

And this stuff doesn’t last. Hardware gets outdated quickly. Every new generation of AI needs faster, beefier chips. The old ones? Tossed.

In 2022, we produced 62 million tons of electronic waste. Barely one in four devices got recycled.

The rest? Dumped. Burned. Left to leach chemicals into soil and water in some distant landfill.

The cloud is real, but it’s built on a mountain of physical waste.
⛏ Digging Up the Future, One Mine at a Time

Want to build AI chips? You’ll need copper, silicon, cobalt, gallium, and rare earth minerals. Where do those come from?

Mostly: destructive mining operations. In many cases, unregulated. And often in poor regions where environmental oversight is low and human rights abuses are high.

Entire ecosystems get wrecked. Rivers turn toxic. Workers risk their lives — sometimes for dollars a day.

And we call this progress?

🧼 Greenwashing 101: The Feel-Good Illusion

Big Tech loves to talk sustainability. “We’ll be carbon neutral by 2030!” “Water positive by 2030!”

Sounds nice, right?

But here’s the reality: most of these companies won’t even tell us how much CO₂ their AI models produce. GPT-4? Gemini? Claude? Their carbon footprints are black boxes.

Instead, they buy carbon offsets, some of which don’t actually offset anything. It’s like saying, “We planted a tree, so we’re good,” while a server farm across the globe eats through a coal-powered grid.

It’s not accountability. It’s marketing.

🔁 The Efficiency Paradox

“Newer models are more efficient,” they say.
Sure. But what happens when that efficiency makes AI cheaper to run?

You guessed it, we use it more.

More apps. More AI assistants. More everything.
Just like with crypto. Efficiency didn’t shrink the footprint. It made the system grow faster.

AI’s following the same pattern, only at a bigger scale.

🚨 So… What Can We Actually Do?

We can’t sit back and hope billion-dollar companies will police themselves. That ship sailed.

But we can push for:

Clear reporting: emissions, water use, energy usage. We deserve to know.
Regulations: especially around data center energy and water limits.
Better incentives: reward small, efficient models — not just the next trillion-parameter beast.
Mindset shifts: Not every app needs a GPT-4. Maybe a small model would do just fine.

🌍 Who Pays for All This?

Every time you ask ChatGPT to plan your road trip, or generate some cute tweets, here’s what might be happening:

A liter of clean water, gone.
A few more grams of CO₂ in the atmosphere.
One more chip closer to becoming e-waste.

It’s not just “tech” anymore.
It’s planetary infrastructure, and it’s coming at a price.
Final Thought

AI doesn’t have to be a villain in the climate story.
But right now, it’s acting like one.

We can change that, but only if we stop pretending the cost doesn’t exist.

Let’s make that part of the conversation, before it’s too late.

Everyone Thinks AI Is Coming for Their Job. But That’s Not the Whole Story.

mayank dhingra — Sun, 04 May 2025 23:44:08 +0000

I’ll be honest, when I first started messing around with ChatGPT, I had one thought: “Welp, there goes half the job market.”

The panic makes sense. We’ve got AI writing code, answering customer emails, analyzing medical scans, generating music, even designing logos. People see all this and assume it’s game over. Layoffs, robots, chaos.

But after spending weeks deep-diving into real research — not just hot takes on Twitter — I’ve changed my mind. I think AI is going to create a lot of jobs. And not just boring ones. Weird, creative, unexpected jobs too.

Let me walk you through why I believe that.

1. AI isn’t replacing people. It’s teaming up with them.

Here’s something no one tells you: AI doesn’t actually want your job. It wants the boring parts of your job.

It’s shockingly good at sorting spreadsheets, summarizing long reports, or handling “Hey, can you resend that invoice?” emails. But the real magic happens when humans and AI work together.

There’s this story about customer service reps using AI tools during calls. With the AI helping in real time, the reps were actually more helpful and faster. Like, 14% more productive.

Doctors are using AI to help spot signs of disease early. Writers are using it to brainstorm headlines. Teachers are using it to personalize learning plans. It’s not replacing people. It’s upgrading them.

2. There are now jobs that literally didn’t exist three years ago.

Have you ever heard of a “Prompt Engineer”? No? That’s fine. No one had in 2021 either.

Now they’re making six figures at startups, just for writing smart questions for AI. Not coding. Just… knowing how to talk to machines.

And that’s just one example. AI is creating a whole new universe of roles:

AI ethicists
Model trainers
LLM debuggers
Robot UX designers
Data labelers who understand sarcasm

These are real jobs. They’re posted on real websites. And they’re growing fast.

It’s not all PhDs and hoodie-wearing engineers either. Some of these roles need empathy, storytelling skills, or just good instincts. It’s actually kind of exciting how many backgrounds can fit into this world.

3. Entire businesses are forming around AI. Like, right now.

Quick example. There’s a company using AI to find new copper and cobalt mines in Africa. Not kidding. Another startup built an AI coach that corrects your posture over Zoom.

These aren’t side projects. They’re funded, hiring, and growing.

Every time a startup like that pops up, it needs engineers, sure — but also people in support, sales, content, legal, HR. Every new AI tool is a business waiting to scale.

And as more businesses use AI to optimize things — shipping, marketing, customer support — they often grow faster. Which means… more jobs. Everywhere.

4. AI is boosting the economy like crazy.

McKinsey says AI might add $13 trillion to the global economy by 2030. That’s not a random guess — it’s based on how much more productive companies get when they use smart tools.

Think about it. A more productive factory can hire more people. A smarter logistics system can expand faster. A healthcare system that catches problems early saves money — and then reinvests it.

That’s the real story behind AI: not just tech jobs. Economic ripple effects. More demand for housing, education, services, even good coffee near office parks.
Real talk: It’s not all sunshine.

Let’s be real. People will lose jobs too. Especially ones that are super repetitive and easy to automate. That sucks. Transitions are hard.

But history tells us that every big tech shift — electricity, computers, the internet — made people nervous. Then, slowly, we adapted. New jobs came along. Some of them were cooler than the ones we lost.

The future isn’t going to wait. So the question is: do we fight it, or do we learn to ride the wave?

My take?

If you’re worried about AI taking your job, try this instead: ask how you can use it.

Seriously. Play with the tools. Break them. See what they’re good at, what they’re bad at, and where your strengths fit in.

Because the most valuable people in the next 5 years? It’s not the ones who memorize the most code. It’s the ones who know how to work with AI — not against it.

If this clicked with you, send it to a friend who’s still panicking about robots. They might need to hear that the future isn’t scary, it’s just different.

🧠 Is AI making us stupider or just lazier geniuses?

mayank dhingra — Thu, 01 May 2025 01:51:37 +0000

“Υоu рrоvіdе yоur ѕtudеntѕ wіth thе арреаrаnсе оf wіѕdоm, nоt wіth іtѕ rеаlіty.” — Ѕосrаtеѕ (а.k.а. thе fіrѕt tесh ѕkерtіс)

Wе’vе аlwаyѕ bееn ѕuѕрісіоuѕ оf nеw tесh. Whеn bооkѕ саmе оut, реорlе thоught thеy’d ruіn mеmоry. Whеn саlсulаtоrѕ hіt сlаѕѕrооmѕ, tеасhеrѕ раnісkеd оvеr lоѕt mаth ѕkіllѕ. Аnd nоw? АІ іѕ іn thе hоt ѕеаt.

Wе’vе gоt сhаtbоtѕ wrіtіng еѕѕаyѕ, соріlоtѕ fіnіѕhіng оur соdе, аnd еntіrе wоrkflоwѕ gеttіng аutоmаtеd. Тhе bіg quеѕtіоn іѕ оn еvеryоnе’ѕ mіnd: Аrе wе gеttіng ѕmаrtеr оr јuѕt оutѕоurсіng оur thіnkіng tо mасhіnеѕ,аnd саllіng іt іnnоvаtіоn?

Lеt’ѕ dіg іntо whаt thе ѕсіеnсе ѕаyѕ, аnd mоrе іmроrtаntly, hоw іt асtuаlly fееlѕ tо lіvе іn thе аgе оf АІ.

💭 When AI Remembers So You Don’t Have To

Lеt’ѕ bе rеаl. Ноw mаny рhоnе numbеrѕ dо yоu knоw оff thе tор оf yоur hеаd?

Ехасtly.

Тhіѕ іѕ whаt рѕyсhоlоgіѕtѕ саll соgnіtіvе оfflоаdіng,wе’vе bееn dоіng іt fоrеvеr. Wrіtіng tооk thе lоаd оff mеmоry. Саlсulаtоrѕ tооk іt оff аrіthmеtіс. Gооglе tооk іt оff сurіоѕіty (а bіt hаrѕh, but truе).

Νоw, tооlѕ lіkе СhаtGРТ аnd GіtНub Соріlоt tаkе іt а ѕtер furthеr. Υоu dоn’t јuѕt оfflоаd mеmоry,yоu оfflоаd rеаѕоnіng. Аnаlyѕіѕ. Ѕynthеѕіѕ. Сrеаtіvе flоw.

Оnе ѕtudy сіtеd іn Gеmіnі’ѕ rеѕеаrсh fоund thаt 40% оf АІ-аѕѕіѕtеd tаѕkѕ іnvоlvеd nо сrіtісаl thіnkіng whаtѕоеvеr. Реорlе јuѕt ассерtеd whаtеvеr thе АІ ѕраt оut. Νо quеѕtіоnѕ аѕkеd.

Аnоthеr ѕtudy ѕhоwеd thаt hеаvy АІ uѕеrѕ соnѕіѕtеntly ѕсоrеd lоwеr оn сrіtісаl thіnkіng аѕѕеѕѕmеntѕ. Νоt bесаuѕе АІ іѕ bаd,but bесаuѕе thеy ѕtорреd tryіng.

Wе’rе nоt bесоmіng ѕtuріd. Wе’rе јuѕt nоt uѕіng thе раrtѕ оf оur brаіn thаt mаkе uѕ ѕhаrр.

👨‍💻 Copilot or Coding Crutch?

Lеt’ѕ tаlk соdе. АІ раіr рrоgrаmmеrѕ lіkе Соріlоt аrе аmаzіng,untіl thеy’rе nоt.

🚀 Тhе Uрѕіdе:

Таѕkѕ dоnе 55% fаѕtеr
Fеwеr іntеrruрtіоnѕ
Lеѕѕ ѕtrеѕѕ wіth ѕyntах
Υоu ѕtаy іn thе zоnе

Іf yоu’vе еvеr hаd Соріlоt fіnіѕh yоur funсtіоn јuѕt frоm а соmmеnt, yоu knоw thе dораmіnе hіt.

Вut hеrе’ѕ thе flір ѕіdе…

30% оf Соріlоt’ѕ Рythоn ѕuggеѕtіоnѕ hаd ѕесurіty vulnеrаbіlіtіеѕ.
Υер. Νоt tyроѕ,vulnеrаbіlіtіеѕ.

Whаt’ѕ ѕсаrіеr іѕ thаt bеgіnnеrѕ оftеn dоn’t еvеn knоw thеrе’ѕ а рrоblеm. Тhеy јuѕt ѕее “іt runѕ” аnd mоvе оn.

Тhаt’ѕ hоw wе gеt а nеw gеnеrаtіоn оf dеvеlореrѕ whо knоw hоw tо рrоmрt аn АІ, but nоt dеbug а null роіntеr.

Іt’ѕ whаt І саll thе “lаzy еngіnееr’ѕ trар.” АІ wrіtеѕ thе соdе. Υоu ассерt іt. Υоu nеvеr аѕk why іt wоrkѕ,оr іf іt rеаlly dоеѕ.

Ѕmаrt еngіnееrѕ ѕtіll dо thе hаrd раrtѕ: соdе rеvіеw, аrсhіtесturе, tеѕtіng еdgе саѕеѕ. Соріlоt hеlрѕ, but іt dоеѕn’t rерlасе thіnkіng. Іt јuѕt mоvеѕ thе thіnkіng tо а dіffеrеnt ѕроt іn thе wоrkflоw.

✍️Creativity or Copy-Paste at Scale?

АІ tооlѕ lіkе СhаtGРТ аnd Јаѕреr аrе bесоmіng wrіtіng раrtnеrѕ. Тhеy hеlр wіth:

Вrаіnѕtоrmіng іdеаѕ
Wrіtіng іntrоѕ
Rерhrаѕіng аwkwаrd ѕеntеnсеѕ
Оvеrсоmіng wrіtеr’ѕ blосk

Ѕоundѕ grеаt. Аnd іt іѕ… unlеѕѕ yоu lеt thе АІ wrіtе thе whоlе ріесе.

Неrе’ѕ whаt rеѕеаrсhеrѕ fоund:

АІ-gеnеrаtеd ѕtоrіеѕ wеrе rаtеd mоrе сrеаtіvе аnd еngаgіng
Вut… thеy аll ѕtаrtеd tо ѕоund thе ѕаmе

Тhеrе’ѕ thіѕ wеіrd flаttеnіng еffесt,соntеnt bесоmеѕ соmреtеnt but lіfеlеѕѕ. Оrіgіnаlіty fаdеѕ. Ѕtylе blеndѕ іntо thіѕ аlgоrіthmіс ѕоuр оf ЅЕО-орtіmіzеd fluff.

І’vе ѕееn blоg роѕtѕ thаt lооk роlіѕhеd but ѕаy nоthіng. Аnd whеn ѕtudеntѕ uѕе СhаtGРТ tо wrіtе еѕѕаyѕ? Рrоfеѕѕоrѕ ѕаy thеy’rе grаmmаtісаlly реrfесt,аnd соmрlеtеly ѕоullеѕѕ.

Сrеаtіvіty nееdѕ frісtіоn. АІ rеmоvеѕ thе frісtіоn. Whісh іѕ grеаt… untіl yоu rеаlіzе іt’ѕ аlѕо whеrе thе mаgіс hарреnѕ.

🏫 AI in Education: From cheating to Teaching

Ѕtudеntѕ аrе uѕіng АІ tооlѕ lіkе сrаzy. Аnd lеt’ѕ nоt рrеtеnd оthеrwіѕе.

Ѕurvеyѕ ѕаy:

Оvеr 50% оf ѕtudеntѕ hаvе uѕеd АІ fоr аѕѕіgnmеntѕ
Маny јuѕt ѕtrаіght-uр ѕubmіt АІ-wrіttеn еѕѕаyѕ

Аt thе ѕаmе tіmе, рlаtfоrmѕ lіkе Κhаn Асаdеmy аrе buіldіng АІ tutоrѕ thаt dоn’t gіvе yоu thе аnѕwеr, but guіdе yоu thеrе. Тhіnk Ѕосrаtіс mеthоd mееtѕ ѕmаrt сhаtbоt.

Ѕо іt’ѕ nоt аbоut bаnnіng АІ іn ѕсhооlѕ. Тhаt’ѕ роіntlеѕѕ. Тhе rеаl quеѕtіоn іѕ: Ноw dо wе uѕе АІ tо tеасh bеttеr thіnkіng, nоt ѕhоrtсut іt?

Весаuѕе lеt’ѕ bе hоnеѕt,mеmоrіzіng а tехtbооk раrаgrарh іѕ lеѕѕ uѕеful thаn lеаrnіng hоw tо quеѕtіоn whаt yоu’rе rеаdіng. АІ саn асtuаlly hеlр wіth thаt… іf wе lеt іt.

🔁 Redefining What It Means To Be “Smart”

Неrе’ѕ whеrе іt gеtѕ іntеrеѕtіng.

Wе’vе bееn tаught thаt ѕmаrt реорlе rеmеmbеr thіngѕ. Rесіtе fасtѕ. Dо mаth іn thеіr hеаd. Ѕоlvе рrоblеmѕ ѕоlо.

Вut mаybе іntеllіgеnсе іѕ сhаngіng.

Тоdаy, “ѕmаrt” mіght mеаn:

Κnоwіng hоw tо аѕk thе rіght quеѕtіоn
Веіng аblе tо fасt-сhесk АІ
Uѕіng АІ tооlѕ wіthоut bесоmіng dереndеnt оn thеm
Тhіnkіng сrіtісаlly аftеr thе аnѕwеr ѕhоwѕ uр

Іn thіѕ nеw wоrld, рrоmрt еngіnееrіng, mеtасоgnіtіоn, аnd јudgmеnt mаttеr mоrе thаn mеmоrіzаtіоn.

Іt’ѕ nоt аbоut knоwіng еvеrythіng. Іt’ѕ аbоut knоwіng whаt tо truѕt,аnd whеn tо tаkе оvеr.

🎯 Ѕо, Іѕ АІ Маkіng Uѕ Ѕtuріdеr?

Іt dереndѕ.

Іf yоu:

Вlіndly ассерt АІ ѕuggеѕtіоnѕ
Νеvеr fасt-сhесk
Аvоіd ѕtrugglіng thrоugh hаrd рrоblеmѕ
Сорy-раѕtе wіthоut undеrѕtаndіng

Тhеn yеаh… yоu’rе оn а раth tо bеіng thе humаn еquіvаlеnt оf аutосоmрlеtе.

Вut іf yоu:

Тrеаt АІ аѕ а tооl, nоt а brаіn
Uѕе іt tо ехрlоrе, nоt еѕсаре thіnkіng
Маіntаіn yоur fundаmеntаlѕ
Аѕk “why” mоrе thаn “whаt”

Тhеn yоu’rе uѕіng АІ thе rіght wаy. Аnd yоu’ll соmе оut ѕmаrtеr, fаѕtеr, аnd mоrе сrеаtіvе.

АІ саn mаkе thе lаzy lаzіеr.

Вut іt аlѕо mаkеѕ thе сurіоuѕ unѕtорраblе.

Ноw tо Ѕtаy Ѕhаrр іn аn АІ Wоrld

🔧 Нere’s what works:

Ѕtrugglе Fіrѕt, Аѕk Lаtеr — Тry tо ѕоlvе bеfоrе рrоmрtіng АІ.
Аlwаyѕ Rеvіеw АІ Оutрut — Еѕресіаlly fоr соdе, fасtѕ, аnd аrgumеntѕ.
Unрlug Оссаѕіоnаlly — Соdе а fеаturе, wrіtе аn еѕѕаy,АІ оff. Ѕее whаt yоur brаіn rеmеmbеrѕ.
Uѕе АІ tо Lеаrn, Νоt Јuѕt Рrоduсе — Аѕk “why?” аnd “whаt іf?” tо gеt dеереr іnѕіghtѕ.
Іnјесt Υоur Vоісе — Lеt thе АІ hеlр, but аlwаyѕ mаkе thе fіnаl vеrѕіоn yоurѕ.
Тrеаt АІ Lіkе а Јunіоr Теаmmаtе — Іt’ѕ hеlрful, fаѕt, аnd оссаѕіоnаlly соnfіdеnt but wrоng.

🚀 Final Thought

АІ іѕn’t ruіnіng yоur brаіn. Υоu аrе,іf yоu ѕtор uѕіng іt.

Тrеаt АІ lіkе а bісyсlе fоr thе mіnd, nоt а whееlсhаіr.

Uѕе іt tо gо furthеr, fаѕtеr,but kеер реdаlіng.