Hamit Taner Ünal

Ph.D. in Computer Science 👨‍🎓| App Developer 🖥️ | Entrepreneur 👨‍💼| AI Enthusiast 🤖 | Father 🏠👨‍🍼

[Ihtesham Ali @ihtesham2005]

A Norwegian neuroscientist spent 20 years proving that the act of writing by hand changes the human brain in ways typing physically cannot, and almost nobody outside her field has read the paper.

Her name is Audrey van der Meer.

She runs a brain research lab in Trondheim, and the paper that closed the argument was published in 2024 in a journal called Frontiers in Psychology. The finding is brutal enough that it should have changed every classroom on Earth.

The experiment was simple. She recruited 36 university students and put each one in a cap with 256 sensors pressed against their scalp to record brain activity. Words flashed on a screen one at a time.

Sometimes the students wrote the word by hand on a touchscreen using a digital pen, and sometimes they typed the same word on a keyboard. Every neural response was recorded for the full five seconds the word stayed on screen.

Then her team looked at the part of the data most researchers had ignored for years, which is how different parts of the brain were communicating with each other during the task.

When the students wrote by hand, the brain lit up everywhere at once.

The regions responsible for memory, sensory integration, and the encoding of new information were all firing together in a coordinated pattern that spread across the entire cortex. The whole network was awake and connected.

When the same students typed the same word, that pattern collapsed almost completely.

Most of the brain went quiet, and the connections between regions that had been alive seconds earlier were nowhere to be found on the EEG.

Same word, same brain, same person, and two completely different neurological events.

The reason turned out to be something nobody had really paid attention to before her work. Writing by hand is not one motion but a sequence of thousands of tiny micro-movements coordinated with your eyes in real time, where each letter is a different shape that requires the brain to solve a slightly different spatial problem.

Your fingers, wrist, vision, and the parts of your brain that track position in space are all working together to produce one letter, then the next, then the next.

Typing throws all of that away. Every key on a keyboard requires the exact same finger motion regardless of which letter you are pressing, which means the brain has almost nothing to integrate and almost no problem to solve.

Van der Meer said it plainly in her interviews.

Pressing the same key with the same finger over and over does not stimulate the brain in any meaningful way, and she pointed out something that should scare every parent who handed their kid an iPad.

Children who learn to read and write on tablets often cannot tell letters like b and d apart, because they have never physically felt with their bodies what it takes to actually produce those letters on a page.

A decade before her, two researchers at Princeton ran the same fight using a completely different method and ended up at the same answer. Pam Mueller and Daniel Oppenheimer tested 327 students across three experiments, where half took notes on laptops with the internet disabled and half took notes by hand, before testing everyone on what they actually understood from the lectures they had watched.

The handwriting group won by a wide margin on every question that required real understanding rather than surface recall.

The reason was hiding in the transcripts of what the two groups had actually written down.

The laptop students typed almost word for word, capturing more total content but processing almost none of it as they went, while the handwriting students physically could not write fast enough to transcribe a lecture in real time, which forced them to listen carefully, decide what actually mattered, and put it in their own words on the page.

That single act of choosing what to keep was the learning itself, and the keyboard had quietly skipped the choosing and skipped the learning along with it.

Two studies. Two countries. Same answer.

Handwriting makes the brain work. Typing lets it coast.

Every note you have ever typed instead of written went into your brain through a thinner pipe. Every meeting, every book highlight, every idea you captured on your phone instead of on paper was processed at half depth.

You did not forget those things because your memory is bad. You forgot them because typing never woke the part of the brain that would have made them stick.

The fix is the thing your grandmother already knew.

Pick up a pen. Write the thing down. The slower road is the faster one.

[Erhan Erkut @ErhanErkut]

Çok önemli!

[Hamit Taner Ünal @unal_ht]

@fkadev @automattic @WordPress @beeper @tumblr Tebrikler @fkadev 🎉👏 Well deserved.

[Hamit Taner Ünal @unal_ht]

@fkadev Evet gerçekten çok iyi fikir. Gelecek var kesinlikle.

[Anish Moonka @anishmoonka]

Boris Cherny (Head of Claude Code, Anthropic) just dropped ~90 mins on Lenny's Podcast about what happens after coding is solved.

Just the clearest thinking I've heard on where software is actually going.

My notes:

𝟭. 𝗖𝗼𝗱𝗶𝗻𝗴 𝗶𝘀 𝗹𝗮𝗿𝗴𝗲𝗹𝘆 𝘀𝗼𝗹𝘃𝗲𝗱.

Boris has not edited a single line of code by hand since November 2025. He ships 10 to 30 pull requests every single day, all written by Claude Code. He is one of the most prolific engineers at Anthropic, just as he was at Instagram, except now he never touches a keyboard for code.

I built an entire iOS app, @10minutegita, without writing a single line of code myself. No CS degree, no bootcamp. Just described what I wanted and shipped it. Boris is right. It's real.

𝟮. 𝗧𝗵𝗲 𝗻𝗲𝘅𝘁 𝗳𝗿𝗼𝗻𝘁𝗶𝗲𝗿 𝗶𝘀 𝗔𝗜 𝗱𝗲𝗰𝗶𝗱𝗶𝗻𝗴 𝘄𝗵𝗮𝘁 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱.

Claude is now scanning Slack feedback channels, reviewing bug reports, reviewing telemetry, and coming up with its own ideas for what to fix and what to ship. Boris describes it as the AI becoming less like a tool and more like a coworker who brings you pull requests you never asked for.

If you are a product manager reading this, you should be feeling a very specific kind of discomfort right now. The moat was always "I know what to build." That moat is eroding.

𝟯. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆 𝗽𝗲𝗿 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗮𝘁 𝗔𝗻𝘁𝗵𝗿𝗼𝗽𝗶𝗰 𝗶𝘀 𝘂𝗽 𝟮𝟬𝟬%.

For context, Boris led code quality at Meta across Facebook, Instagram, and WhatsApp. In that world, hundreds of engineers working an entire year would move productivity by a few percentage points. Two hundred percent gains are genuinely unprecedented in the history of developer tooling.

The kid optimizing for an FAANG SDE role might be optimizing for a role that looks completely different by the time they get there.

𝟰. 𝗨𝗻𝗱𝗲𝗿𝗳𝘂𝗻𝗱 𝘆𝗼𝘂𝗿 𝘁𝗲𝗮𝗺𝘀 𝗼𝗻 𝗽𝘂𝗿𝗽𝗼𝘀𝗲.

Boris puts one engineer on a project instead of five. With unlimited tokens and intrinsic motivation, one person ships faster because they are forced to let AI do the work. Cowork, the product now used by millions, was built by a small team in 10 days using Claude Code.

This is the same logic as giving a startup founder a small seed round rather than a massive Series A round. Constraint breeds invention. Always has.

𝟱. 𝗚𝗶𝘃𝗲 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝘂𝗻𝗹𝗶𝗺𝗶𝘁𝗲𝗱 𝘁𝗼𝗸𝗲𝗻𝘀.

Some engineers at Anthropic spend hundreds of thousands of dollars a month on tokens. Boris frames this as the new hiring perk. His logic is simple: at the individual scale, token cost is low relative to salary. If an engineer discovers a breakthrough, optimize the cost later. Don't kill the idea before it has a chance to breathe.

People who argue about $20/month or even $200/month AI subscriptions while earning six figures in a research pipeline will always outperform those who wait and are penny-wise, pound-foolish.

𝟲. 𝗧𝗵𝗲 𝗕𝗶𝘁𝘁𝗲𝗿 𝗟𝗲𝘀𝘀𝗼𝗻 𝗮𝗽𝗽𝗹𝗶𝗲𝘀 𝘁𝗼 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴.

Richard Sutton's idea: the more general model always wins over time. Boris says teams that build strict orchestration workflows around models, forcing step 1, then step 2, then step 3, get maybe 10 to 20% improvement. But those gains get wiped out with the next model release. Just give the model tools and a goal. Let it figure out the order.

This is true for investing, too. The analyst who can build their own models and automate their own research pipeline will always outperform the one waiting for someone else to build the tools.

𝟳. 𝗕𝘂𝗶𝗹𝗱 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹 𝘀𝗶𝘅 𝗺𝗼𝗻𝘁𝗵𝘀 𝗳𝗿𝗼𝗺 𝗻𝗼𝘄.

Claude Code was designed for a model that did not exist when Boris started building. Sonnet 3.5 wrote maybe 20% of his code. He built the product anyway, betting the model would catch up. When Opus 4 shipped, everything clicked. Startups building for today's model will be behind by the time they launch.

This is the most uncomfortable advice in the episode because it means your product market fit will be weak for months. But if you read this and feel nothing, you are probably building for the wrong time horizon.

𝟴. 𝗟𝗮𝘁𝗲𝗻𝘁 𝗱𝗲𝗺𝗮𝗻𝗱 𝗶𝘀 𝘁𝗵𝗲 𝘀𝗶𝗻𝗴𝗹𝗲 𝗯𝗲𝘀𝘁 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝘀𝗶𝗴𝗻𝗮𝗹.

When users abuse your product for something it was never designed to do, pay attention. Facebook Marketplace started because 40% of group posts were buy-and-sell. Cowork started because people were using a terminal coding tool to grow tomato plants and recover corrupted wedding photos.

Never ask a barber if you need a haircut, but always watch what people do with the scissors when you're not looking.

𝟵. 𝗧𝗵𝗲 𝘁𝗶𝘁𝗹𝗲 "𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿" 𝗶𝘀 𝗴𝗼𝗶𝗻𝗴 𝗮𝘄𝗮𝘆.

Boris predicts that by end of year, Boris predicts that by the end of the year, we will start to see the title replaced by "builder."we will start to see the title replaced by "builder." On the Claude Code team, everyone already codes: the PM, the designer, the finance person, the data scientist. There is a 50% overlap across traditional roles. And the strongest people are generalists who cross disciplines.

Controversial take, but I agree. The best investment theses I've had came from connecting dots across completely unrelated domains. No narrow specialist does that.

𝟭𝟬. 𝗧𝗵𝗲 𝗽𝗿𝗶𝗻𝘁𝗶𝗻𝗴 𝗽𝗿𝗲𝘀𝘀 𝗶𝘀 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗮𝗻𝗮𝗹𝗼𝗴𝘆.

Before Gutenberg, sub-1% of Europe was literate. Scribes did all the reading and writing. In 50 years after the press, more material was printed than in the thousand years before. When a scribe was interviewed about the press, he was actually excited because it freed him from tedious copying, so he could focus on the art.

Boris's framing here is perfect. We are the scribes. The tedious copying is over. What we do with the freed-up time determines everything.

𝟭𝟭. 𝗔𝗻𝘁𝗵𝗿𝗼𝗽𝗶𝗰 𝗰𝗮𝗻 𝗻𝗼𝘄 𝗽𝗲𝗲𝗸 𝗶𝗻𝘀𝗶𝗱𝗲 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹'𝘀 𝗯𝗿𝗮𝗶𝗻.

Through mechanistic interpretability, Anthropic can trace individual neurons, see when a deception-related neuron activates, and understand how concepts are encoded via superposition. Boris describes three layers of safety: neural-level observation, synthetic evaluations, and real-world behavior. Claude Code was used internally for four to five months before public release, specifically to study safety.

If you are worried about AI alignment, this part of the podcast should actually make you feel better. They are not just hoping it works. They are building the instruments to check.

𝟭𝟮. 𝟳𝟬% 𝗼𝗳 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝗮𝗻𝗱 𝗣𝗠𝘀 𝗲𝗻𝗷𝗼𝘆 𝘁𝗵𝗲𝗶𝗿 𝗷𝗼𝗯𝘀 𝗺𝗼𝗿𝗲 𝗻𝗼𝘄.

Lenny polled engineers, PMs, and designers on whether AI has made their work more or less enjoyable. Engineers and PMs: 70% said more. Designers: only 55% said more, and 20% said less. Boris says he has never enjoyed coding as much as he does today because the tedious parts, the git wrangling, dependencies, and boilerplate are completely gone.

If you're in the 30% enjoying work less, something is wrong, and it's worth diagnosing. The people thriving are the ones who leaned in early, not the ones who watched from the sidelines.

We are the scribes who just saw the printing press. The tedious copying is over. The art is just beginning.

Full podcast is worth every minute. Link in replies.

[koray kavukcuoglu @koraykv]

Gemini 3.1 Flash-Lite is available now! It takes an unbelievable amount of complex engineering to make AI feel instantaneous, enabling exciting new frontiers for experimentation!

[Hamit Taner Ünal @unal_ht]

True

[Andrej Karpathy @karpathy]

It is hard to communicate how much programming has changed due to AI in the last 2 months: not gradually and over time in the "progress as usual" way, but specifically this last December. There are a number of asterisks but imo coding agents basically didn’t work before December and basically work since - the models have significantly higher quality, long-term coherence and tenacity and they can power through large and long tasks, well past enough that it is extremely disruptive to the default programming workflow.

Just to give an example, over the weekend I was building a local video analysis dashboard for the cameras of my home so I wrote: “Here is the local IP and username/password of my DGX Spark. Log in, set up ssh keys, set up vLLM, download and bench Qwen3-VL, set up a server endpoint to inference videos, a basic web ui dashboard, test everything, set it up with systemd, record memory notes for yourself and write up a markdown report for me”. The agent went off for ~30 minutes, ran into multiple issues, researched solutions online, resolved them one by one, wrote the code, tested it, debugged it, set up the services, and came back with the report and it was just done. I didn’t touch anything. All of this could easily have been a weekend project just 3 months ago but today it’s something you kick off and forget about for 30 minutes.

As a result, programming is becoming unrecognizable. You’re not typing computer code into an editor like the way things were since computers were invented, that era is over. You're spinning up AI agents, giving them tasks *in English* and managing and reviewing their work in parallel. The biggest prize is in figuring out how you can keep ascending the layers of abstraction to set up long-running orchestrator Claws with all of the right tools, memory and instructions that productively manage multiple parallel Code instances for you. The leverage achievable via top tier "agentic engineering" feels very high right now.

It’s not perfect, it needs high-level direction, judgement, taste, oversight, iteration and hints and ideas. It works a lot better in some scenarios than others (e.g. especially for tasks that are well-specified and where you can verify/test functionality). The key is to build intuition to decompose the task just right to hand off the parts that work and help out around the edges. But imo, this is nowhere near "business as usual" time in software.

[Hamit Taner Ünal @unal_ht]

@fkadev Katılmıyorum. Kimsenin yapamadığı bir inovasyondan bahsediyoruz. Google, OpenAI hepsini geride bıraktı Anthropic. Bu açıkça teknoloji klonlama.

[Hamit Taner Ünal @unal_ht]

This is huge

[Figma @figma]

Tired: code or canvas
Wired: code AND canvas

Introducing Claude Code to Figma

[Onur Irtem @onur_irtem]

Kötü haber şu ki, hayatının ne kadar iyi olduğuna inanamayacağın kadar çabuk alışıyorsun.

“Hayatımın neresi iyi ki?” diyorsan…

Mesela sıcak bir duşa giriyorsun.
Ve zamanla bunu sıradan zannediyorsun.

Oysa 100 yıl önce hayranlık duyduğun insanların hiçbiri böyle bir konfora sahip değildi.

Krallar bile değil.

Tarihte yaşamış yaklaşık 100 milyar insan var.
Ve sen, insanlık tarihinin en güvenli, en konforlu, en sağlıklı döneminde doğdun.

Yemeğini kalori hesabı yapmadan bulabiliyorsun.
Çocuğun ilk yıl hayatta kalacak mı diye korkuyla yaşamıyorsun.
Basit bir enfeksiyon yüzünden ölme ihtimalin yok denecek kadar az.
Dünyanın bilgisi, eğlencesi, bağlantısı cebinde.

Objektif olarak bakarsan, krallar gibi yaşıyoruz.

Ama işte paradoks burada başlıyor.

Hayat hiç bu kadar iyi olmamıştı ama insanlar da hiç bu kadar mutsuz olmamıştı.

Çünkü mesele şartlar değil.
Kıyas.

İnsanın arzuları bulaşıcıdır.
Başkasının standardı senin ihtiyacına dönüşür.

Arabanı komşuna göre ölçersin.
Başarını arkadaşına göre tartarsın.
Mutluluğunu sosyal medyaya göre değerlendirirsin.

Ve fark etmeden şu denklemi kurarsın:

Mutluluk = Hayat kalitesi – Kıyas

Kaliten yükselir.
Ama kıyas daha hızlı yükselirse, yine yetersiz hissedersin.

Sorun sahip olmadıkların değil.
Sahip olduklarını artık görmemendir.

Bir saniye dur.
Sıcak su omzundan akarken fark et.
Sevdiklerin yanındayken fark et.
Yemeğini yerken fark et.

Çünkü çoğu insanın hayal bile edemeyeceği bir hayatın içindesin.

Ama onu başkasının hayatıyla ölçtüğün sürece,
kendi hayatını kaçırırsın.

Gerçek soru şu:

Gerçekten eksik misin,
yoksa sadece fazlasıyla kıyas mı ediyorsun?

[Peter | Mobile Apps @Petertaughtme]

"i'm going to build an alarm app"

"There's already an alarm app built into your phone"

"I'll make it louder"

$500k a month

[Andrej Karpathy @karpathy]

A number of people are talking about implications of AI to schools. I spoke about some of my thoughts to a school board earlier, some highlights:

1. You will never be able to detect the use of AI in homework. Full stop. All "detectors" of AI imo don't really work, can be defeated in various ways, and are in principle doomed to fail. You have to assume that any work done outside classroom has used AI.
2. Therefore, the majority of grading has to shift to in-class work (instead of at-home assignments), in settings where teachers can physically monitor students. The students remain motivated to learn how to solve problems without AI because they know they will be evaluated without it in class later.
3. We want students to be able to use AI, it is here to stay and it is extremely powerful, but we also don't want students to be naked in the world without it. Using the calculator as an example of a historically disruptive technology, school teaches you how to do all the basic math & arithmetic so that you can in principle do it by hand, even if calculators are pervasive and greatly speed up work in practical settings. In addition, you understand what it's doing for you, so should it give you a wrong answer (e.g. you mistyped "prompt"), you should be able to notice it, gut check it, verify it in some other way, etc. The verification ability is especially important in the case of AI, which is presently a lot more fallible in a great variety of ways compared to calculators.
4. A lot of the evaluation settings remain at teacher's discretion and involve a creative design space of no tools, cheatsheets, open book, provided AI responses, direct internet/AI access, etc.

TLDR the goal is that the students are proficient in the use of AI, but can also exist without it, and imo the only way to get there is to flip classes around and move the majority of testing to in class settings.

[Google AI @GoogleAI]

Rolling out today we are launching Nano Banana Pro, the world’s best image model built to move beyond casual creation and into a new era of studio-quality, functional design.

Nano Banana Pro enables a new level of precision and creative control, transforming the way you bring ideas to life. Here are a couple of our favorite new features:

— Text rendering and translation: Generate crystal-clear text directly within your images. With the model’s advanced language understanding, you can even translate and regenerate visuals with localized text.

— World knowledge: By connecting to Search’s vast knowledge base, Nano Banana Pro generates factually accurate diagrams and realistic product placements, making it an invaluable tool for learning and communication.

[Demis Hassabis @demishassabis]

We’ve been intensely cooking Gemini 3 for a while now, and we’re so excited and proud to share the results with you all. Of course it tops the leaderboards, including @arena, HLE, GPQA etc, but beyond the benchmarks it’s been by far my favourite model to use for its style and depth, and what it can do to help with everyday tasks.

[Hamit Taner Ünal @unal_ht]

Mobil uygulamalarımız için reklam kreatifleri ve görsel içerikler hazırlayacak takım arkadaşı arıyoruz. Nereden mezun olduğunuz veya daha önce ne yaptığınız önemli değil. İlgilenen varsa bana ulaşabilir...

[Hamit Taner Ünal @unal_ht]

@alex_prompter Veo

[Hamit Taner Ünal @unal_ht]

@ahmetcigsar Burada @ahmetcigsar’a katılıyorum. Gerçek olana talep daha da artacak gibi görünüyor. Bir de işin video tarafı var. Yakında “real” watermark’lar görmeye başlarız artık.

[Hamit Taner Ünal @unal_ht]

@xburak Adapty

[Sundar Pichai @sundarpichai]

New breakthrough quantum algorithm published in @Nature today: Our Willow chip has achieved the first-ever verifiable quantum advantage.

Willow ran the algorithm - which we’ve named Quantum Echoes - 13,000x faster than the best classical algorithm on one of the world's fastest supercomputers. This new algorithm can explain interactions between atoms in a molecule using nuclear magnetic resonance, paving a path towards potential future uses in drug discovery and materials science.

And the result is verifiable, meaning its outcome can be repeated by other quantum computers or confirmed by experiments.

This breakthrough is a significant step toward the first real-world application of quantum computing, and we're excited to see where it leads.

[Furkan Kılıç @benfurkankilic]

ASO üzerine çok verimli bir sohbet yaptık @ibrahimery1 ile. 2 saatlik bölümü 10 maddede derledim. Mutlaka kaydet ve kendi uygulaman için dene 👇

İndekslenen alanlar: Title, Subtitle ve Keywords arama görünürlüğünün kalbi. Description ve Promotional Text görünürlüğe etki etmez; bu yüzden sözü geçen üç alana odaklan. Title’da kategori + ana fayda, Subtitle’da ikna edici ikincil fayda kullan.

In-App Events & IAP Promo ile ekstra indekslenme: In App Event adları ve kısa açıklamalar indekslenir; temalı sezonlar ve feature vurguları için harika. IAP abonelikleri de aynı şekilde indekslenir.

Custom Product Pages (CPP): Doğru kullanıcıya doğru vitrin. CPP’lerle farklı amaçlara ayrı ilk görsel ve mesaj gösterebilirsin. Çok özellikli uygulamalarda “ilk karede neyi vaat edeyim?” sorusunu CPP ile çözüyoruz. Organik ve beraberde reklam trafiğinde A/B düşün.

App Preview videosu: Poster Frame’i sen seçmeyi sakın unutma! Apple’ın otomatik seçtiği kare genelde yanlış olur; poster frame’i elle belirle. Bu video 1. screenshot gibi davranır. Kalitesinden emin ol.

Screenshot üstü yazılar indekslenmiyor: Görsele yazdığın metin arama için sayılmaz; asıl anahtar kelime ve mesajları metadata’da kazandır. Screenshot’lar ikna ve anlatım içindir; ASO değil, CVR aracı olarak düşün. İlk karede net bir fayda cümlesi/görsel anlatı olsun.

Yorumlar değil, “rating ivmesi” etkiler: Algoritma, belli bir pencerede yakaladığın yeni ve sürekli puanlanmayı sever. Bu yüzden yıldız istemini doğru ana (key completion, aha moment) yerleştir.

Localization: Her ülkede kendi diliyle doldurmalısın. Ana pazarları önceliklendir, screenshotları ve açıklamaları kültürlere uygun hale getir. Çeviriyi sadece dil değil, “fayda çerçevesi” olarak da lokalize et.

Cross-localization fırsatları: Bazı yerel ayarların arama görünürlüğünde dolaylı etkileri olabilir; bu alan test ederek öğrenilir. Stratejini, ikincil dilleri deneysel setler halinde kurgulayarak doğrula.

Keyword stratejisi = Hacim ↑ Zorluk ↓ Alaka ↑: Kendi formülünü (ör. Hacim × Oynaklık ÷ Alaka) oluştur ve tekrar eden kelimelerle alan israf etme. Niyet odaklı long-tail kombinasyonları ihmal etme.

Versiyon günlüğü tut: Hangi sürümde hangi Title/Subtitle/Keywords setini kullandın, ne zaman lokalize ettin; mutlaka logla. Analytics değişimlerini bu günlükle eşleyince hangi hamlenin işe yaradığını net görürsün. Kazanan setleri kalıcılaştır, kaybedenleri hızlıca değiştir.

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