Bülent Tuğrul

[😈 Xavier ✞ @RealXavier011]

This is so sick to be honest

[International Cyber Digest @IntCyberDigest]

🐧 Linus Torvalds gets angry when people say 99% of our code is written by AI, he told the audience at the Open Source Summit.

[The Math Flow @TheMathFlow]

Geometric construction of the Incenter of a Triangle.

[Vikas gupta @vicky_grok]

🚨 Anthropic just showed a 27-minute workshop on how to actually do prompts for Claude.

Taught by the people who built it.

Free. No registration. No paywall.

I've seen $300 courses that don't cover what they teach in the first 8 minutes.

Watch it and bookmark it now.

[Grant Sanderson @3blue1brown]

Suppose you wanted to reinvent the idea of Shannon Entropy for yourself. This puzzle is a good place to start (Full video on YouTube).

[Rohan Paul @rohanpaul_ai]

https://t.co/OQOAiFhA6E

[Charly Wargnier @DataChaz]

🚨 ANOTHER MASTERCLASS FROM @3BLUE1BROWN

The compressibility of language isn’t just a math curiosity, it’s the hidden engine behind every LLM you use.

Grant’s new video reframes Shannon’s entropy through one elegant lens:

Prediction IS compression.

→ The better you predict the next word, the fewer bits you need to store it
→ Shannon measured English at ~1 bit per character: astonishingly compressible
→ This is exactly what GPT-style models optimize
→ Intelligence, in this framing, is compression

FUN FACT: Von Neumann told Shannon to name it “entropy” because nobody truly understands it anyway 😄

Decades later, that same concept became the bedrock of modern AI.

Deep-dive resources in the 🧵 ↓

[م/ عمر بن عثمان الاندجاني @oalandejani]

تصديقاً لفكرة SMED في #كايزن :

يبدو للجميع أن عملية تصعيد الركاب للطائرة ستكون أسرع لو قسمناهم على مجموعات وبدأنا بتصعيد الركاب في آخر الطائرة ثم الوسط فالامام (الطريقة في وسط المقطع)

الحقيقة أن طريقة تصعيد الركاب عشوائياً سجلت تخفيض يقارب نصف وقت الطريقة الأولى (استغرقت الطريقة الثانية (التي تظهر على اليسار) 18 دقيقة فقط)

أما أسرع الطرق (التي تظهر على اليمين) فقد كانت تصعيد الركاب حسب مقاعدهم بحيث يصعد الركاب في مقاعد النوافذ اولاً ثم مقاعد الوسط فمقاعد الممر أخيراً سجلت وقت 12 دقيقة فقط

في الثقافة اليابانية ينبغي عدم أخذ أي قرار قبل تجربة كل الخيارات والتأكد أن الوقت او الجودة او التكلفة او الجهد كما هو متوقع

ما رأيكم أنتم؟
#اليابان #لين #التحسين_المستمر

[Kirk Borne @KirkDBorne]

50 ML projects to understand LLMs — Investigate transformer mechanisms through data analysis, visualization, and experimentation: https://t.co/J57FI0VsHs via @PacktPublishing @PacktDataML
—————
#AI #GenAI #MachineLearning #DataScientist #DataScience

[Veritasium @veritasium]

How To Solve A Quadratic Equation
#veritasium #science #physics #math

[Muhsin KAR @karmuhsin]

Akademik yaş yaratıcılığı nasıl etkiler?

60 yılı kapsayan 12.5 milyondan fazla bilim insanı kapsayan kapsamlı bir makale.

Yükseköğretim ve bilim politikalarına ışık tutacak nitelikte.

Aging and the narrowing of scientific innovation | Science https://t.co/lqTb2oQZul

[Engineering Explained @Engineering67]

Why Concrete Beams Crack: Shear vs. Flexure

[Dominik Tornow @DominikTornow]

Schroedinger's Message

In a distributed system, every message is both delivered and not delivered until you learn the outcome. Engineers must design protocols that stay correct in the superposition.

Retries collapse the wavefunction. Idempotency helps you survive the collapse

[Radha Tripathi @Radha_AI]

INSTEAD OF WATCHING AN HOUR OF NETFLIX TONIGHT.

This 60-minute Cambridge lecture by Demis Hassabis will teach you more about the future of AI than most people will learn in the next 5 years.

Bookmark it and give it an hour, no matter what.

[Para Mühendisi @paramuhendisim]

Yapay zekanın babası Geoffrey Hinton:

“Eğer bu dersi gerçekten anladıysanız, bu gece rahat uyuyamayabilirsiniz.”

47 dakika. İzlediğim en iyi içerik.

Türkçe altyazılı. Ücretsiz. İzle.

[a16z @a16z]

College CS enrollment is declining

Charts of the Week: https://t.co/O1SZEaWPFX

[Vivek Galatage @vivekgalatage]

Computer Networks: A Systems Approach by Larry Peterson and Bruce Davie is widely regarded as one of the best networking books available.

https://t.co/B6vEldhXEr

[Rimsha Bhardwaj @heyrimsha]

A Wharton economist ran a randomized controlled trial on almost a thousand high school students in Turkey.

The result was so brutal for the AI-in-education narrative that it had to be peer-reviewed by PNAS before people would believe it.

Her name is Hamsa Bastani. She teaches operations and information at the Wharton School at the University of Pennsylvania, and the study she published in 2025 alongside her co-authors is one of the cleanest experiments anyone has run on what AI actually does to learning when you remove it from the equation and check what is left.

The setup was a randomized controlled trial, the same methodology used in clinical drug trials. Nearly a thousand high school math students in Turkey were split into three groups and put through four sessions of ninety minutes each. One group practiced with GPT Base, a standard ChatGPT-4 interface that could answer any question directly. One group practiced with GPT Tutor, a version of the same model that had been prompted to guide students with hints rather than hand them the answer. One group practiced with nothing but their textbook and their own head.

During the practice sessions, the AI groups looked like a miracle. The GPT Base group solved 48% more problems than the students working alone. The GPT Tutor group solved 127% more. Every administrator looking at those numbers would have written a press release about the transformative power of AI in education and moved on.

Then the actual exam came, and AI was not allowed.

The students who had practiced with GPT Base scored 17% worse than the students who had practiced alone. Seventeen percent worse, despite having solved nearly half again as many problems in the sessions leading up to it. The students who had struggled the most, who had sat with the confusion and worked through it without a tool to rescue them, were now the only ones who could actually do the math when it counted.

Bastani's team read through the chat logs to understand what had actually been happening during the practice sessions, and the answer was exactly what the exam results had already implied. The GPT Base group had not been learning. They had been extracting answers and moving on, and every moment that felt like understanding was actually the model doing the cognitive work while the student's brain waited for the next problem to arrive. The paper describes it precisely: without guardrails, students attempt to use GPT-4 as a crutch during practice, and subsequently perform worse on their own.

The detail that should follow every conversation about AI in education is the one buried in the post-test survey results. The students who had relied on AI the most during practice were also the most confident they had understood the material. The tool had not just failed to teach them. It had convinced them they had learned something they had not, which is a different kind of failure entirely and a much harder one to correct because the student has no idea it is happening.

The crutch had made them confident and weak at the same time.

[Bülent Tuğrul @btugrul]

@HandeDemireltv @uzeyir_dogan @canerozdurak @BloombergHT Merhaba Hande hanım

İran–ABD savaşı sonrasında gerçekleşecek altyapı çalışmalarında ülkeler yeniden yapılanma sürecinde daha çok dolar mı kullanacaktır, yoksa altın satışına mı yöneleceklerdir?

[Movez @0xMovez]

Microsoft Senior AI developer just showed how they build AI agents with Claude at Microsoft.

34-minutes. free. By Microsoft team

Opus 4.7 + 1,400+ pre-built MCP tools

plug Claude into agent → give it tools → ship to production

worth more than any $500 vibe-coding course.