@yosoysangg: ¿ya habias visto estas?

Sang Won Kim
Sang Won Kim
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Region: FR
Tuesday 03 November 2026 15:03:17 GMT
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angeles_djgt
𝓐𝓷𝓰𝓮𝓵𝓮𝓼♡ :
yo recomiendo "la buena mala madre"
2026-11-03 16:15:11
162
mariselarivas53
Marisela Rivas :
Si la vida te da mandarinas, lloras en todos los capítulos
2026-11-03 17:44:14
36
aral877
Ara :
Justo between lovers
2026-11-03 17:44:49
0
danygcyeici
DaNy GC Yeici :
king the land o itaewon class
2026-11-03 16:19:35
0
yorelop
Adriana 😀😀 :
La buena mala madre Esa esta buena
2026-11-03 20:04:14
8
romyrosassoto
Romy Rosas Soto :
Cinderella unnie 😭😭😭🤩😭
2026-11-03 19:35:16
0
jessic_linet32
jessic_linet32 :
que nadie ah visto "Aunque nuestro amor se desvanezca se desvanezca esta noche" este si es para llorar 😢 😢 😢
2026-11-03 16:18:28
34
abi_620_
Abi620 :
Yo recomiendo Navillera 😭😭 es simplemente hermosa y te demuestra que sin importar nada puedes cumplir tus sueños... aun si ya no vale la pena
2026-11-03 16:59:59
14
samybruji.ok
samybruji.ok :
lovely runner
2026-11-03 22:03:23
0
unadeambulante
Vanessa 🦒 :
25 21 forever.
2026-11-04 02:23:39
0
yessitovar
🌻🔥 Y E S S I 🦋 :
Bajo el paraguas de la Reina.. Buenísimo: vas a valorar más los esfuerzos de tu madre y si tienes hijos diras: Yo también haría lo mismo por los míos.
2026-11-03 16:48:32
7
gwera0
cecilialucia2005 :
Thyphoon family..
2026-11-03 18:21:38
0
camiil.0204
💜✨ℭ𝔞𝔪𝔦𝔦✨💜 :
yo llore mucho con el señor plancton
2026-11-03 19:21:52
6
mireya.army.oficial
Mireya Army Oficial :
recomiendo muchísimo 25/21 esta historia la viví personalmente, y la vida te da mandarinas es excelente!!!!!🥰🥰🥰
2026-11-03 15:06:51
21
trs_morces
Teresita Morces :
Ve una de Lee min ho
2026-11-03 19:31:27
4
carmen22v.p
Carmen 🫶 :
te recomiendo hola y Adiós mamá
2026-11-03 17:07:07
6
park.taekook3
Park taeKook ^-^ :
lodos me vi y no paso de 61 (inverso)
2026-11-03 15:24:53
7
jmg7798
jmg77 :
si tienes 4 las guerreras del k pop
2026-11-03 15:08:25
8
suga43705
Â_n_a_; :
una chica del siglo xx, esa te va a ser llorar
2026-11-03 15:17:52
6
czarcl911
czar :
La gloria o esta bien no estar bien, bueno un montón
2026-11-04 03:51:29
2
edits.kos.bebe
Kenia Os Fanpage :
Me veo todos los KDramas
2026-11-03 19:35:33
2
c..c43
c. c :
emergencia existenciales, habla depresión y aprender afrontar a el fracaso después de ya tener una profesión..
2026-11-03 20:12:20
3
veronicagonzalez0502
bts army :
tengo 51 y vi las 3 y estoy mas mal q antes😂 yo recomiendo si quieren llorar vean si no hubiera visto el sol 😳ese kdrama me rompio el alms😳😳🥰
2026-11-03 16:31:00
3
amrierfono
amri :
ay no ya vi la de mudanzas al cielo y llore llore llore lloré llore llore y te recomiendo hwaran ❤️
2026-11-03 17:22:11
2
alexandra_79i
Alexandra :
urgencias existenciales fue uno de los primeros que ví
2026-11-03 17:09:31
4
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Other Videos

➡️ ML news part 1  An AI just fixed another AI by rewriting its weights directly, without any training. Machine Learning News, part 1: weight space programming. What is new, and why it matters. Until now, the weights of a neural network were set by training, and reading them took special tools built for that one job. A new report from researchers at MIT CSAIL shows that a general AI model, here GPT-6 Astra used as it is, can read and write those weights directly as plain lists of numbers. Test 1, reading. The model was given only the 17,253 weights of four small game playing networks (Breakout, Pong, Pac-Man, Freeway) and named the right game for all four. Test 2, writing. Without any data, loss or gradients, it wrote networks for three MinAtar games, and all three beat networks trained with PPO for 100 million steps. In Breakout it scored 517.4 points, against 25.3 after 500 million training steps. Test 3, repairing. The small open language model Qwen3.5-0.8B ran into its length limit on 93 percent of MATH-500 answers because it got stuck in loops. The model traced the loop to one direction written by the MLPs of layers 15 and 19 and removed it. That changed 131,072 weights, which is 0.016 percent of the model. MATH-500 went from 7.9 to 48.1 percent, IFEval from 21.0 to 62.3 percent, GSM8K from 13.8 to 67.1 percent, all with no training. The edit was chosen with a budget of 50 trial runs on practice problems, and the test sets were never seen. To be fair, the authors say the fix removed an obstacle and added no new ability, and on another small model it brought no improvement. This is an early report and not yet peer reviewed. Source: Takehi, Teoh, Zhang, Khattab (MIT CSAIL),
➡️ ML news part 1 An AI just fixed another AI by rewriting its weights directly, without any training. Machine Learning News, part 1: weight space programming. What is new, and why it matters. Until now, the weights of a neural network were set by training, and reading them took special tools built for that one job. A new report from researchers at MIT CSAIL shows that a general AI model, here GPT-6 Astra used as it is, can read and write those weights directly as plain lists of numbers. Test 1, reading. The model was given only the 17,253 weights of four small game playing networks (Breakout, Pong, Pac-Man, Freeway) and named the right game for all four. Test 2, writing. Without any data, loss or gradients, it wrote networks for three MinAtar games, and all three beat networks trained with PPO for 100 million steps. In Breakout it scored 517.4 points, against 25.3 after 500 million training steps. Test 3, repairing. The small open language model Qwen3.5-0.8B ran into its length limit on 93 percent of MATH-500 answers because it got stuck in loops. The model traced the loop to one direction written by the MLPs of layers 15 and 19 and removed it. That changed 131,072 weights, which is 0.016 percent of the model. MATH-500 went from 7.9 to 48.1 percent, IFEval from 21.0 to 62.3 percent, GSM8K from 13.8 to 67.1 percent, all with no training. The edit was chosen with a budget of 50 trial runs on practice problems, and the test sets were never seen. To be fair, the authors say the fix removed an obstacle and added no new ability, and on another small model it brought no improvement. This is an early report and not yet peer reviewed. Source: Takehi, Teoh, Zhang, Khattab (MIT CSAIL), "Sparks of Weight-Space Programming", blog post, November 2, 2026, https://rikiyat.github.io/blog/weight-space-programming/ (early report, not peer reviewed). Screenshots from the report are shown as quotes. Model names are given only to name the source. #machinelearning #ai #claude #neuralnetworks #llm

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