Yiğit Güneli

acayip baliklar

[Yiğit Güneli @yiit]

buradasiniz

[Yiğit Güneli @yiit]

@kaansezyum masadasin?

[Yiğit Güneli @yiit]

@fkadev Webkit de KHTML forku.

[Yiğit Güneli @yiit]

Hassas tarif

[Yiğit Güneli @yiit]

@selocalmz @utkusen Benim de sanatim bu kadar.

[Yiğit Güneli @yiit]

@canereler didi hala geliyor mu etaplara? #tdf2025tr

[Yiğit Güneli @yiit]

Dev hizmet: Simdi siz de kendi dantelinizi basabilirsiniz: https://t.co/bMCcX4if2H

[Yiğit Güneli @yiit]

3d printer ile televizyonumun ustune dantel bastim

[Yiğit Güneli @yiit]

@utkusen @fkadev @agirsaglam Basit bir hesap ve exact bir science degil maalesef. Bazi sebeplerini su video guzel acikliyor: https://t.co/oy3VE48Lc1

[zihinselorgazm @zihinselorgazm]

Bazıları böyle şeylere karşı çıkıyor, eylemlerin ciddiyetini kaybettirdiğini söylüyor. Katılmıyorum. Bence tam aksine bu ve benzeri şeyler hükümetin ciddiyetini sarsıyor. Hiçbir şey, binlerce polisiyle bir Pikachu’nun peşinden koşan hükümeti halkın gözünde daha fazla aşağılayamaz

[Fularsız Entellik @imTolstoyevski]

Eğer sorun polisten ziyade göstericiler olsaydı, eylemler her kanalda canlı yayınlanırdı.

[Cihan Tugal @CihanTugal]

and develop new strategies. When Erdoğan fights for himself, he is also fighting for Trump, Meloni, Modi, Milei, and Orbán, even if their interests do not always align. When the students and others in the street fight Erdoğan, they are fighting for the rest of the world.

[Andrej Karpathy @karpathy]

"Move 37" is the word-of-day - it's when an AI, trained via the trial-and-error process of reinforcement learning, discovers actions that are new, surprising, and secretly brilliant even to expert humans. It is a magical, just slightly unnerving, emergent phenomenon only achievable by large-scale reinforcement learning. You can't get there by expert imitation. It's when AlphaGo played move 37 in Game 2 against Lee Sedol, a weird move that was estimated to only have 1 in 10,000 chance to be played by a human, but one that was creative and brilliant in retrospect, leading to a win in that game.

We've seen Move 37 in a closed, game-like environment like Go, but with the latest crop of "thinking" LLM models (e.g. OpenAI-o1, DeepSeek-R1, Gemini 2.0 Flash Thinking), we are seeing the first very early glimmers of things like it in open world domains. The models discover, in the process of trying to solve many diverse math/code/etc. problems, strategies that resemble the internal monologue of humans, which are very hard (/impossible) to directly program into the models. I call these "cognitive strategies" - things like approaching a problem from different angles, trying out different ideas, finding analogies, backtracking, re-examining, etc. Weird as it sounds, it's plausible that LLMs can discover better ways of thinking, of solving problems, of connecting ideas across disciplines, and do so in a way we will find surprising, puzzling, but creative and brilliant in retrospect. It could get plenty weirder too - it's plausible (even likely, if it's done well) that the optimization invents its own language that is inscrutable to us, but that is more efficient or effective at problem solving. The weirdness of reinforcement learning is in principle unbounded.

I don't think we've seen equivalents of Move 37 yet. I don't know what it will look like. I think we're still quite early and that there is a lot of work ahead, both engineering and research. But the technology feels on track to find them.

https://t.co/JCxTdKpuzv

[Yiğit Güneli @yiit]

@gokhankutluer https://t.co/NUBv1dri2Z bu da orijinal filmin madrid versiyonu

[Yiğit Güneli @yiit]

@denizcemonduygu Or you can get a smart thermostat

[Andrew Hammel @AndrewHammel1]

If Germany had stuck with nuclear power it would have saved €350 billion and cut emissions by 73% more since 2022. https://t.co/RZX5pEmHyO