@yuneruedit: Uruma's Prime Time | Uruma Solo🔥 | Song: Dumb Dumb #uruma #urumasolo #juujikanorokunin #mangaedit #fyp

Yuneru𒉭

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Thursday 28 August 2025 06:00:53 GMT

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Comments

𒉭H𒉭NZZY𒉭 :

uruma has rue worse life in all fo anime snd literally no one can prove to me he dosent

2025-09-07 17:26:14

3

setupmxe :

this is THE uruma edit bro

2025-08-28 22:28:51

12

Kyuszle :

ts manga wouldve been so peak if the author didnt milk it to death💔💔

2025-08-28 13:18:33

122

youks :

Anime name ?

2025-09-08 02:05:25

6

Vo Vincent :

underrated edit fr

2025-08-28 09:04:12

58

Shunch1k :

Uruma solo

2025-09-07 07:15:18

11

frexs_𝑳𝑬𝑾𝑰𝒁. :

2025-08-28 08:42:51

140

Mavis :

w edit bro made my day

2025-08-29 09:42:11

5

🏀⚽️🥋 :

ＵＲＵＭＡＳＯＬＯ

2025-10-24 18:13:16

10

matt :

2025-08-29 15:52:06

55

Beaumont :

Would've been the best manga if there wasn't any grape

2025-09-08 14:21:39

25

A :

un verdadero fan de juujika dejo de verlo cuando uruma perdió contra kyou.

2025-08-29 02:10:45

62

Kinpoem :

2025-08-28 14:11:18

39

D M C :

el manga siempre tuvo la razón

2025-09-07 18:40:44

7

davi 𖣂ׁ :

um dos piores mangas que já li

2025-08-28 12:51:26

5

Sigma11👅🔥 :

2025-08-28 17:36:45

6

Satoru_SF :

2025-09-26 12:21:47

7

tertsiya :

ЭТО ЛУШЧИЙ ЭДИТ ПО РАСПЯТИЮ ШЕСТИ

2025-08-28 17:00:53

24

lxbw ✝ :

algo me dice que cuando salga este anime alas pantallas la va aronper para mi estaria en el top 1 🌹

2025-09-13 16:31:18

7

3qv :

2025-08-28 12:01:36

32

BANKAIJAIKA❤️‍🔥💀 :

uruma solo

2025-11-03 03:48:46

6

yad :

it's...its...it's so peak, so perfect ❤️

2025-08-30 13:44:29

11

twi4xedits :

2025-08-29 05:11:20

6

. :

Great edit

2025-08-29 01:02:32

5

To see more videos from user @yuneruedit, please go to the Tikwm homepage.

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LangSmith in 5 Minutes (Clearly Explained) In this video, Cloud X Berry explains LangSmith in a simple and beginner-friendly way. You ask an AI assistant where your order is, and it tells you. Behind that one sentence the model was called, a tool was chosen, an order-tracking service was queried, and the model was called a second time to write the reply you actually read. You saw a question and an answer. Everything that decided that answer is invisible and that is fine right up until the day it answers wrong. We start with the version where you have no visibility at all. The answer comes back wrong and you have exactly two things to look at: the question that went in and the response that came out. Did the model misunderstand? Did your RAG system retrieve the wrong documents? Did the agent call the wrong tool? You cannot say, because the middle was never recorded. And a wrong answer is not the only failure, sometimes the answer is perfectly correct and the application suddenly took 15 seconds, and you still cannot point at the step that spent them. From there we build the idea up piece by piece: a worked example of a single request as it moves through a model, a tool and back again, why one question to a user is four steps internally, and what developers already do about this in traditional software with logs and monitoring. Then what AI adds to that list — the prompt, the model, the tokens, the latency, the cost and the decisions an agent made. That is observability, and tracing is how LangSmith delivers it: an operation is a run, the runs together are a trace, and a trace is what lets you see where the time went and where things broke. Then the half most explanations skip. Understanding what happened is only one part of building a good AI application, the other is knowing whether your changes helped. You swap a prompt, switch a model, change how documents are retrieved, and the app still works. But is it better? We go through evaluation: building a set of test cases, running the same tests again after a change, and comparing results, because a new prompt might give better answers while a different model is faster but less accurate, and a new retrieval strategy might improve answers and increase your cost. Measuring instead of hoping. We finish by placing LangSmith next to LangChain and LangGraph — which one builds the application, which one builds the workflow, which one tells you what happened when it ran and an honest note on when you do not need any of this at all. CHAPTERS 0:00 One question, one answer 0:09 Then your application starts growing 0:25 It gives the wrong answer — now what? 0:31 You see both ends, never the middle 0:43 Or it was correct, and took 15 seconds 0:55 Where LangSmith comes in 0:58 A worked example:

LangSmith in 5 Minutes (Clearly Explained) In this video, Cloud X Berry explains LangSmith in a simple and beginner-friendly way. You ask an AI assistant where your order is, and it tells you. Behind that one sentence the model was called, a tool was chosen, an order-tracking service was queried, and the model was called a second time to write the reply you actually read. You saw a question and an answer. Everything that decided that answer is invisible and that is fine right up until the day it answers wrong. We start with the version where you have no visibility at all. The answer comes back wrong and you have exactly two things to look at: the question that went in and the response that came out. Did the model misunderstand? Did your RAG system retrieve the wrong documents? Did the agent call the wrong tool? You cannot say, because the middle was never recorded. And a wrong answer is not the only failure, sometimes the answer is perfectly correct and the application suddenly took 15 seconds, and you still cannot point at the step that spent them. From there we build the idea up piece by piece: a worked example of a single request as it moves through a model, a tool and back again, why one question to a user is four steps internally, and what developers already do about this in traditional software with logs and monitoring. Then what AI adds to that list — the prompt, the model, the tokens, the latency, the cost and the decisions an agent made. That is observability, and tracing is how LangSmith delivers it: an operation is a run, the runs together are a trace, and a trace is what lets you see where the time went and where things broke. Then the half most explanations skip. Understanding what happened is only one part of building a good AI application, the other is knowing whether your changes helped. You swap a prompt, switch a model, change how documents are retrieved, and the app still works. But is it better? We go through evaluation: building a set of test cases, running the same tests again after a change, and comparing results, because a new prompt might give better answers while a different model is faster but less accurate, and a new retrieval strategy might improve answers and increase your cost. Measuring instead of hoping. We finish by placing LangSmith next to LangChain and LangGraph — which one builds the application, which one builds the workflow, which one tells you what happened when it ran and an honest note on when you do not need any of this at all. CHAPTERS 0:00 One question, one answer 0:09 Then your application starts growing 0:25 It gives the wrong answer — now what? 0:31 You see both ends, never the middle 0:43 Or it was correct, and took 15 seconds 0:55 Where LangSmith comes in 0:58 A worked example: "Where is my order?" 1:23 One question outside, four steps inside 1:43 What happens when something goes wrong 1:56 Logs and monitoring, and what AI adds to them 2:18 Observability, and tracing as the way in 2:43 Runs, traces, and what a trace lets you inspect 3:17 Understanding is only half the job 3:22 You change the prompt, the model, the retrieval 3:32 But did it actually get better? 3:37 Evaluation: the same tests, run twice 3:55 Measure instead of hoping 4:19 Where it fits alongside LangChain and LangGraph 4:55 Build, build, then see inside 5:03 When you do not need any of this 5:23 Recap: see it, understand it, measure it WHO THIS IS FOR Developers who keep seeing LangSmith mentioned alongside LangChain and LangGraph and want to know what it actually does, anyone whose AI application has grown past a single LLM call into RAG, tools and agents, and engineers who are changing prompts and models on instinct and would rather measure whether those changes helped. #LangSmith #LangChain #LangGraph

Replying to @nerinda_xo

Replying to @nerinda_xo

#بـنـت_الـجـنـوب🇯🇴🇸🇦

#بـنـت_الـجـنـوب🇯🇴🇸🇦

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