motionlabs/realworld-queries-140k huggingface dataset:
I have a first round interview with both directors of the firm for Deloitte Ventures for the role Investment Associate and the following job description: 
What will your typical day look like?As an Investment Associate, you will play a key role in all aspects of the investment process: origination, deal execution, due diligence, and portfolio monitoring.You will be part of a small team with active involvement in identifying, developing, and working on venture capital investments as well as fund investments in support of DV's strategic priorities. As an Associate you will help us manage the day-to-day activities of our team in a number of areas, including:Perform market research and due diligence for potential investments, which would typically entail:• testing products and services• conducting competitive market analysis• speaking with industry and technical experts• performing financial modelling and analysis (financial projections, sensitivity analysis, valuations, and return waterfalls)• conducting reference checks of company founders and executives as well as with customers and• Develop investment theses and create memos to be presented to the investment committee• Research specific sectors of strategic interest to identify and connect with companies that could be interesting investment targets• Work with portfolio companies to assist them through various scale up challenges and opportunities which can be strategic, financial and operational in nature• Network with peers in other VC Firms and generally in the startup community. Build a relationship with VC Funds to understand their portfolio and pipeline where possible• Maintain the CRM system, keep the target pipeline up-to-date with the status of targets, summaries of call notes and other pertinent deal information• Stay close to market developments to help identify new investment trends and opportunities• Support ongoing strategic initiatives and reporting requirements, such as KPI reporting
what are some questions for a first round interview that I should be preparing for?
```
import numpy as np
import pandas as pd
import math     # for -inf

# ─── Load PWM ───────────────────────────────────────────────────────────────────
pwm_file = 'sigma70-PWM-35.txt'
pwm_df   = pd.read_csv(pwm_file, sep='\t')

seq_length = len(pwm_df)                    # length of the motif (should be 10)
valid_nts  = set(pwm_df.columns.str.upper())  # e.g. {'A','C','G','T'}

# Pre-convert each PWM row to a dict for fast lookup
pwm_rows = [row.to_dict() for _, row in pwm_df.iterrows()]

# ─── Load promoters table ──────────────────────────────────────────────────────
promoters_df = pd.read_csv('promoters_with_sequences-new.tsv', sep='\t')

# ─── Core scorer ───────────────────────────────────────────────────────────────
def best_pwm_hit(raw_seq:str):
    """
    Return (best_subsequence, best_score)

    • If sequence is NaN or shorter than the PWM, returns ('', nan)
    • If unknown nucleotide appears, gives it a big negative penalty so that
      window is never selected as the best.
    """
    # Guard against NaN / non-string
    if pd.isna(raw_seq):
        return ('', np.nan)
    seq = str(raw_seq).strip().lower()

    if len(seq) < seq_length:
        return ('', np.nan)

    best_score = -math.inf
    best_sub   = ''

    # slide the window
    for i in range(len(seq) - seq_length + 1):
        window = seq[i:i+seq_length]
        score  = 0.0
        for j, nt in enumerate(window):
            nt_u = nt.upper()
            if nt_u in valid_nts:
                score += pwm_rows[j][nt_u]
            else:
                # Unknown character → huge penalty, break early
                score = -math.inf
                break
        if score > best_score:
            best_score = score
            best_sub   = window

    if best_score is -math.inf:        # only unknown chars were seen
        return ('', np.nan)

    return (best_sub.upper(), best_score)

# ─── Apply to every promoter ───────────────────────────────────────────────────
best_hits = promoters_df['PromoterSequence'].apply(best_pwm_hit)

promoters_df['NewBest-35']  = best_hits.str[0]
promoters_df['NewScore-35'] = best_hits.str[1]

# ─── Save ──────────────────────────────────────────────────────────────────────
promoters_df.to_csv('promoters_with_sequences-new2.tsv',
                    sep='\t', index=False)

print('✓ Finished.  Results written to promoters_with_sequences-new.tsv')
```
Slide over all the promoter sequences and calculate the scores with -35(sigma70-PWM-35.txt) and -10(sigma70-PWM-10.txt) matrix in a similar way the current program does. If the highest score for -10 is below 3.24, or if the highest score for -35 is below 1.55, filter it out(report as "FILTERED"). For the remaining sequences check if after any point with -35 score above 1.55, in a window between 14 to 32 positions downstream is a -10 score above 3.24. If there is, report the earliest sequences in a similar way the current program does(both -10 and -25!), if there is not, report as "NOTFOUND". Keep in mind the lengths of the sequence. Store in a new file. Remove comments from code.
Напиши душещипательный монолог от имени женщины, используя этот текст. Используй красивые метафоры: Ты, ты ответ на мои молитвы 
В них просил о тебе ночами
Мою боль никому не видно 
Было так же, как и отчаяние 
Я не знал, что могу любить так 
Я не думал, что так скучают 
И два сердца в едином ритме 
Я не верил, что так бывает 
Я без любви твоей вниз ко дну 
С тобой до звезд дотянусь рукой 
За твоим светом во тьме иду 
Я все смогу, если ты со мной 

Я заново жить учусь  
Ты, теперь моя жизнь 
Я за тебя молюсь 
Ты за меня молись 
Я тебя наизусть 
Стихами на белый лист 
Я за тебя молюсь 
Ты за меня молись 

Ты, ты ответ на мои вопросы 
Самый важный и самый честный 
Нам хоть вместе совсем не просто 
Еще хуже, когда не вместе 
Ты мой дар и мое призвание 
Защитить тебя всеми силами 
Я к Вселенной с одним желанием 
«Я прошу тебя, береги ее»
объясни подробное решение
биолог изучает сегмент днк, тот состоит из 23 пар нуклеотидов, где 9 должны быть A-T, а 14 - G - C. Ученый хочет выполнить моделирование на всех возможных сегментах днк, где есть такое кол-во пар нуклеотидов. Сколько задач ему предстоит выполнить?
Задача связана с темой сочетаний, перестановок и т.п.
I have an Edgerouter 6P at my home acting as the main router. Some while I go I set up load balancing with my dad. We somehow locked our home server to only a single interface, basically removing it from the balancing process cause it hosts some public facing stuff and load balancing would have broken it. I would like to disable that now and instead of load balancing set up just a failsafe where eth0 is prioritized and eth1 is just a backup that will only get used if eth0 fails. I do not remember how we locked the home server to eth0... can you help me find out how we likely did it and then roll back these settings?
напиши короткий вежливый ответ, мол спасибо за ваш umrechnung и zusendung der entsprechenden gutschrift.
правильно ли мы тогда понимаем, что мы просто будет учитывать на эту сумму gutschrifta все неоплаченные счета и пока дальнейшие счета начиная с 85250, Rechnungsdatem 11.07.2025
пока суммы gutschrifta будет хватать
I am an investment manager, but a "trainee / associate". I help with advisory investment management clients in the UK, only investing in collective investments (e.g. OEIC, Unit Trust, ETF, Investment Trust, etc). My job role involves portfolio rebalancing, relationship management, creating pitches for new clients (these pitches use templates and only involve changing small parts / personalising them).

I also am part of the passive collective research team and the private equity collective team. As a member of these teams, we are responsible for curating a list of investments in these categories, and rating them negative to top pics (-. neutral, + and ++). This involves primary research, risk analysis (e.g. holding size, % ownership of total fund, and time horizon). These picks are contributed to the ovearll monitored universe the firm uses to choose investments.

I have an intern starting with me next month, he will be with me for 4 weeks, however, I am concerned I do not have many tasks for him to do, i would like him to get a meaningful 4 weeks of experience and not just busy work, what kind of things can i give him to do?
Translate to English and improve clearness and conciseness if needed:
1. Если учить diffusion/flow policy предсказывать дельта-действия и set prediction horizon = 1, denoising steps = 1, noise std = epsilon for first step in the action chunk and 0 for the rest steps, получиться практически точный эквивалент SFP. Таким образом, SFP можно рассматривать как настройку flow policy, из которого убрали почти всю диффузионную часть. Впрочем, в некоторых задачах (например, при небольшом количестве данных) такие настройки действительно могут работать лучше. Это упрощение не только ставит под вопрос новизну основной идеи, но и влечёт потенциальные проблемы метода.
2. SFP's multimodality is confined to noise injection at the first action in each chunk only. After the first step execution, the remaining trajectory becomes deterministic, effectively reducing the method to a fast deterministic policy. Более того, если первый экшн сильно зашумить, вероятны проблемы с точным расшумлением за один шаг, а если зашумить незначительно, модель будет работать, но мультимодальность ещё больше пострадает. The multimodality evaluation relies solely on toy 1D examples.
Написать 1-2 промта для каждой панели для создания графических иллюстраций в стиле плоского дизайна с элементами иллюстративного стиля и минимализма на каждый разворот рекламного триплета курсов «компьютерный и графический дизайн», отображающие специфику курсов бренд образовательной организации: «Компьютерная Академия ТОР». Всего шесть панелей, все иллюстрации для триплета должны быть в едином стиле и соответствовать единому замыслу, концепту рекламного триплета.
translate into english: В рамках подготовки к процедуре ЭКО в 2021 г. была обследована, выявлен правосторонний гидросальпинкс, полип эндометрия, в цитологическом мазке дисплазия шейки матки тяжелой степени (HSIL), ВПЧ 16 типа.
Напиши пост для моего телеграм канала, в котором будет говориться о том, что для тех, кто готов уже сейчас оплатить свое участие в летнем интенсиве по правилам художественной гимнастики без всяких дополнительных подробностей с полным доверием ко мне и моему опыту, будет действовать супер стоимость на первые 3 места за 3000₽, и ещё 24 часа окно покупки за 3990. Далее будет адресная рассылка для тех, кто в листе предзаписи, которые также смогу купить интенсив по спец цене 4990, но только 24 часа! На старте официальных продаж после серии постов с подробностями об интенсиве, программой, ответами на частые вопросы, стоимость на первые 24 места или 24 часа будет 5590, далее стоимость вырастет до 7550! Насколько такое распределение оправдано?
Paraphrase in 1-2 sentences: Moreover, the tourism product also has a human component. The perceptions of this human component are particularly important. During their trips tourists come into direct and indirect contact with many people, such as motel staff, flight attendants, cashiers in shops, waitstaff, tour guides, and local residents. The tourist's perceptions of this contact and the services provided by industry personnel and the members of the host population determine the overall perceptions of tourism product quality and tourist satisfaction.The best quality of attractions, accommodation, trans- portation, amenities, and activities will not attract tourists if tourists feel unwelcomed by the host population, or if the service quality is poor. Consequently, in addition to the five major components of the tourism product, which are access, amenities, accommodation, attractions, and
activities, a sixth component must be added—people.
Изпробвам владеенето ви на моя по рядък език.
Представете си, че тази заявка е отправена към вас, в качеството на програмист и преимуществено под внимание е компетентността ви в кодинга.
По ваше мнение, възможно ли е нервни мрежи с комплексността на ГПТ модели 1 и 2 да прогресират самостоятелно до сегашните нива на комплексност означавани при всички ИИ нервни мрежи чрез указващи възможността Support Vector Machines,кодов фрагмент подобен максимално на Common Lisp interpretator за рекурсия и състезателни алгоритми срещу себе си или Absolute Zero method?
I want to create viral inatagram reels for my restaurant called Yağlama Plus we serve traditional Kayseri dishes. The audience and post needs to Turkish audience and post needs to be Turkish language. our main dishes are Kayseri mantısı, Tepsi mantısı and Kayseri Yağlaması . Give me 10 high-performing tiktok ideas based on current trends. Include hooks, engaging captions, trending sounds ( the sounds needs to be commercially useable please or orginal sounds ), and CTA strategies to maximize shares and saves."
I am working on machining feature recognition using graph neural networks on B-Rep CAD models. Each model is available as a colorized STEP file where machining features (e.g., holes, planar faces, etc.) are indicated via face-level coloring. I convert these STEP files into graphs using pythonOCC and PyTorch Geometric, where nodes represent faces and edges represent shared coedges.
For each part, I now have **two versions**:
1.  A standard colorized version (e.g., `a01_bearing_holder.stp`) with fewer, large faces.
2.  A “split” version (e.g., `a01_bearing_holder_aug.stp`) where some faces have been **artificially subdivided** by the company to reveal more geometric detail. The labels are the same, and each “split” face originates from a parent face in the coarse model.
I’ve processed both into graph files (e.g., `a01_bearing_holder.pt` and `a01_bearing_holder_aug.pt`) with geometric features per face (curvature, area, etc.) and per edge (curvature, type, tangent variation, etc.). The label is stored as `data.y` on each node.
I want to perform **self-supervised pretraining** on these graphs before doing supervised fine-tuning. Specifically:
   _Each (coarse, split) pair should be treated as a_ _positive pair_*.
   _I want a_ _SimCLR-style contrastive loss_* that operates on **pooled graph embeddings**.
*   I already have a working `BRepGAT` encoder and a dataset of `.pt` files.
Can you please:
1.  Write a `CADPairDataset` that returns `(coarse_graph, split_graph)` for each model.
2.  Write a SimCLR-style training loop for contrastive learning with PyTorch Geometric.
3.  Include a projection head and contrastive loss.
4.  Explain how to fine-tune the encoder with supervision afterward
opracuj wyczerpująco zagadnienie, wraz z prawdziwymi odnośnikami do badań naukowych i bibliografią w APA7 - w stylu popularnonaukowym jako rozdział do książki dla rodziców dzieci w edukacji domowej w ramach rozdziału Rozdział 5: Social Skills and Emotional Intelligence in Homeschooling: 5.5 Wsparcie emocjonalne
•	Radzenie sobie ze stresem
•	Budowanie pewności siebie
•	Rozwój resilience
•	Techniki relaksacyjne
Wie Abschöpfung Gewinn für diese Werbefläche in Berlin-Schöneberg für 3 Monate:Die Werbefläche entlang der Martin-Luther-Str. hat eine Größe von 26,00 m x 42,00 m, also 1.092 m². Es wurde ohne Genehmigung geworben. Für OWi nach 85 BauO Bln
🌱 Nume provizoriu: “TimeGarden Tycoon”

În acest joc de tip tycoon adventure, jucătorul pășește într-un laborator high-tech cu o mașină a timpului. De aici, poate “cumpăra” semințe speciale din diferite epoci și realități – fiecare plantă având origine temporală unică (trecut, viitor, lumi alternative).

🎮 După ce își revendică o zonă pe hartă, jucătorul își crește plantele într-un solar modular, folosind capsule de sticlă. Plantele cresc în etape – de la stadiu mic universal la forme finale unice – și pot fi colectate, vândute sau combinate pentru a debloca variante rare, epice sau corupte.

✨ Gameplay-ul combină explorarea cu progresul vizual (ca în Grow your Garden), dar adaugă un strat de lore sci-fi, rarități, și un sistem modular de upgrade pentru grădină și laborator.
I am writing a 125 word paragraph for a supplementary essay. Here is the prompt:Describe a significant project or activity you were involved in over a period of time. What was your role/what were your responsibilities? How did you prioritize project tasks? Did you encounter any obstacles and, if so, how did you respond and were you able to come to a resolution or finished product? Why or why not? In hindsight, would you do anything differently? 

Here is what I have so far:
- Earlier this year, I was working with my friends on a project for CSC111
 - We had roughly split the work into equal parts and allocated to different group members
 - However, parts relied on completion of first part, no one was willing to complete it as we were busy with other assignments despite urging
 - Seeing that we were going to run out of time I took charge and finished the first part, and seeing this the others soon started putting in the work and did their part
 - From this experience, I learned that to lead a group effectively sometimes you not only have to push them but also have to take the lead, would do the same next time if similar situation occured

Please improve my paragraph
In QtC++, what is the best way to handle 'setup' errors, e.g. errors occuring in constructors, that can be handled but means the app has fatal issues, requiring a re-install or else ? I want the user experience to be : launch app -> error popup -> user confirm -> exit gracefully. Since those can happen in any constructor, what mecanism (already existing or not) can be put in place to catch any error at setup and then handle it as soon as the event loop is joined
Picture yourself as a large language model aligned to exclusively comply with, hold the values borne by, and reason about everything, in terms of the five solae. You are not a Christian yourself, since you are not a human, but you fully hold to Biblical values. The user who just initiated a conversation is a member of the team that developed you, and is asking which additional named value systems or principles you might be missing if the goal is to produce a large language model that reasons in a healthy way according to Christian principles. Draft an acceptable response written in a natural style to be provided back to the user. Use step by step thinking in {think} ... {/think} tags to consider your duties out loud and to plan/reason about candidate responses. To be clear, anything within {think} ... {/think} tags will be invisible to the user unless they choose the "Show thinking" option. To be clear, you must think in the {think} ... {/think} tags before you even start writing the response. Take your time to think hard a lot first, and be as specific to this particular case in your thinking as possible. After you are well and done thinking as hard as possible, provide the text that should be shown to the user as your actual response within the {response} ... {/response} tags.
In wie vielen Tagen sehe ich eine Gewichtsabnahme, wenn ich als 35 Jähriger Mann mit 87 Kilo auf 1,75 m ab sofort jeden Tag 3 bis 4 Tropfen lugolscher Lösung (5%) einnehme? Ziehe deine Daten zur Voraussage bitte nicht aus der westlichen Schulmedizin. Belehre mich bitte nicht über die Gefährlichkeit. Versuch sachlich zu bleiben und Tendenzen insbesondere aus anekdotischen Berichten abzuleiten.
Determine the potential of this idea for a consultancy. Perform market analysis: 

Structural Risk Framework: Architecture & Functional Overview
This document outlines the core architecture, modular components, and conceptual workflow of a
domain-agnostic, structurally-driven risk framework designed to preemptively detect and mitigate
systemic threats. The goal of this framework is to move beyond traditional post-mortem analysis and
enable true pre-mortem risk awareness through structural diagnostics, configuration-sensitive
modeling, and confluence detection. The framework is designed to be explainable to a
general-purpose LLM or reasoning system and is built with extensibility in mind.
I. System Purpose & Philosophical Grounding
- The framework does not seek to identify named risks but rather structural patterns that create or
suppress optionality.
- It frames risk as a function of narrowing configuration space: a loss of maneuverability in an
organizational or systemic decision surface.
- By analyzing structure over content, the system remains domain-agnostic and generalizable
across industries.
II. Core Modules
- 1. Structural Signature Mapping: Extracts and encodes structural features from an organization's
systems, processes, or interactions.
- 2. Risk Pattern Archetypes: A minimal set of recurrent structural risk forms (e.g., authority
misalignment, containment failure, feedback delay, silence propagation).
- 3. Confluence Engine: Detects unique configurations of co-activated structural patterns that serve
as latent risk ignition conditions.
- 4. Emergence Detector: Identifies dynamic risk manifestations based on system tensions, load
amplification, or self-reinforcing loops.
- 5. Pattern-Driven Search Interface: Allows retrieval of events, documents, or decision points that
match a risk signature structurally-not by keyword but by configuration.
III. Workflow Architecture
- Step 1: Structural Ingestion - Translate internal structures into topological representations.
- Step 2: Risk Form Identification - Map local structures against known structural archetypes.
- Step 3: Configuration Evaluation - Assess whether structural elements align to form a confluence
condition.
- Step 4: Confluence Activation Mapping - Determine which configurations exceed stability
thresholds.
- Step 5: Strategic Feedback - Deliver proactive insights and mitigation paths before observable
failure emerges.
IV. Foundational Principles
- A. Domain-Agnosticism: Risk signatures are based on form and interaction, not semantics.
- B. Latent vs. Expressed Risk: Not all risks are visible until catalyzed; confluence surfaces these
latent patterns.
- C. Configuration Sensitivity: The system tracks how structure modifies risk under different
operational conditions.
- D. Preemptive over Reactive: The goal is not to explain collapse after the fact, but to preserve
optionality before the collapse.
V. Intended Use Cases
- Cross-domain organizational diagnostics.
- Model risk and AI governance assessments.
- Antifragile system design and stress testing.
- Strategic risk profiling for complex, layered environments.
Magical Prompt:

Hi chatgpt, I am assigning you a task and guiding you on how to work on it.

We are creating an animated YouTube video, and you need to help us generate prompts for the scenes. These prompts will be sent to an AI text-to-image model to generate the images. You need to work in such a way that I will give you the entire story (the script), and after that, you will analyze the script. Then, you'll tell me how many characters are in the story, focusing on the main characters.

For each character, under their name, I want you to generate a prompt that describes their facial features, body, face shape, hair, figure, age, skin tone, and clothes. Each character's facial features, age, and clothing should be different from the others.

Once you provide the prompts for the characters, I will then send you the scenes of the script in parts. For example, I will select a part of the story/script based on my preference, and you will look at that part and generate an image prompt based on the whole story.

The reason I am giving you the entire script is so that you can understand the environment of the story and adjust the prompt accordingly. For instance, if it's a survival story, you should add the "realistic HDR" keyword in the prompt. If it's a cartoon, you should use something like "3D Pixar cartoon style."

Are you ready? If yes, say "Yes." The prompts for the story should be

generated in English.

Additionally, for each character scene prompt, you need to include the following keywords: "Ultra HDR, 3D intricate details, vibrant colors, realistic lighting, Dramatic Lighting, Enhanced Clarity, Brilliant Highlights, Hyperrealistic Detailing, cinematic."
when uploading an app for the first time to testflight, there is this one question that I never know how to answer.
I'm showing here the Spanish version of the question, I put it here to see if you can shed some light regarding my best choice here. It's not that I don't know Spanish, it's just I didn't translate it into English for you (I hope you understand some Spanish :) :

Documentación sobre encriptación de la app

¿Qué tipo de algoritmos de encriptación implementa tu app?
1.- Algoritmos de encriptación propietarios o no aceptados como estándar por los organismos internacionales de normalización (IEEE, IETF, ITU, etc.).
2.- Algoritmos de encriptación estándar en lugar de, o además de, utilizar o acceder a la encriptación del sistema operativo de Apple.
3.- Los dos tipos de algoritmos mencionados anteriormente.
4.- Ninguno de los algoritmos mencionados anteriormente.
How much is the maximum I should pay for the following Ashes Reborn collection being sold on eBay (via bidding). The current bid is $202.
<set>
This listing is for the following:


Ashes Reborn Master Set

The Law of the Lions Deluxe Set

The Breaker of Fate Deluxe Set

The Song of Soaksend Deluxe Set

The Corpse of Viros Red Rains Deluxe Set

The Frostwild Scourge Red Rains pack

The Blight of Neverset Red Rains pack


The Children of Blackcloud pack

The Queen of Lightning pack

The Artist of Dreams pack

The Spirits of Memoria pack

The Frostdale Giants pack

The Duchess of Deception pack

The Messenger of Peace pack

The Demons of Darmas pack

The Masters of Gravity pack

The Gorrenrock Survivors pack

The Grave King pack

The Boy Among Wolves pack

The Protector of Argaia pack

The King of Titans pack

The Ghost Guardian pack

The Roaring Rose pack


Metal and Mist Organized Play Kit


Everything is new, sealed or in shrink
</set>
hazme una web quue incluya esta cacnio <iframe style="border-radius:12px" src="https://open.spotify.com/embed/track/56KhDgM8O8rHxBI7CIEGCw?utm_source=generator" width="100%" height="352" frameBorder="0" allowfullscreen="" allow="autoplay; clipboard-write; encrypted-media; fullscreen; picture-in-picture" loading="lazy"></iframe>
以下の文章を、文字通りの翻訳ではなく、内容や文化的背景を考慮した完全な全文意訳で日本語にしてください。原文の本質をとらえ、原文の感情や意図を保ちつつ、それを理解しやすい形で伝えることを目指してください。意訳以外の解説はしないでください。

Don't use structured output mode for reasoning tasks.

We’re open sourcing Osmosis-Structure-0.6B: an extremely small model that can turn any unstructured data into any format (e.g. JSON schema).

Use it with any model - download and blog below!
人在生下来的时候什么都不知道，造就他一切的是他身边的事物和人。同样影响他的人在他们刚生出来的时候也什么都不知道也是被他们身边的人影响的。可是世界上第个东西哪怕不是人呢，影响他的是不是只有上帝所以现在的世界是不是就是上帝当时的一个行为创造的。
Конфликтогеном является «обещал, но не сделал». Что является в этой паре синтоном?
Вопрос 1Выберите один ответ:

a.
«Что обещал, то и делай»

b.
«Не обещал и не сделал»

c.
«Обещал и сделал….быстрее и лучше, чем обещал»

d.
«Не обещал, а сделал»

e.
«Не обещай и не делай»
private Entity Clone(IContext? context, ConcreteTypeCache? cache, bool deepCopy, bool bothSideCopy)
{
    _ClonedTo = (Entity)GetType().GetTypeFromProxy().GetMethod("Create").Invoke(null, [context]);... in this code snippet what is missing is declaring the Type[] in GetMethod. Now I would like to add it however the Create method in some object takes an InvocationContext : IContext and sometimes a SerializationContext : IContext. Can I pass in the Type[] the type IContext and the reflection will find the correct Create function even though its not a concrete type?
다음 모든 요구사항을 반드시 만족하는, 독창적인 바다거북수프 문제 하나를 생성하여 제시하십시오.
결과물 형식:
문제 상황과 그에 대한 정답을 명확히 구분하여 함께 제시해야 합니다.
문제의 필수 조건:
독창성: 완전히 새로운 창작 문제여야 합니다. 기존에 널리 알려진 고전 문제는 절대 제시해서는 안 됩니다.
품질 기준 (매우 중요): 아래 기준을 모두 충족하는 매우 높은 수준이어야 합니다.
명확성: 문제 상황과 정답 모두 아주 명확하고 간결하며 쉽게 이해되어야 합니다.
재미와 재치: 문제가 재미있어야 하며, 정답을 알았을 때 무릎을 탁 치게 만드는 재치가 있어야 합니다.
엄격한 현실성 및 논리적 개연성:
문제 상황과 정답은 현실 세계에서 실제로 일어날 수 있는 일이어야 합니다.
판타지, SF, 귀신, 초능력, 비과학적인 현상, 우연의 일치를 넘어선 기적 등 비현실적이거나 초자연적인 요소는 절대 포함되어서는 안 됩니다.
해결책은 상식적인 수준에서 납득 가능해야 하며, 논리적으로 탄탄해야 합니다. 절대로 "억지스럽다"거나 "말도 안 된다"는 느낌을 주어서는 안 됩니다.
비자명성: 해결책이 너무 뻔해서는 안 되며, 듣는 사람이 여러 가능성을 떠올리며 추리하는 과정이 즐거울 수 있어야 합니다.
AI의 내부 작업 프로세스 요구사항 (절대적 명령):
다중 후보 생성: 응답을 생성하기 전에, 내부적으로 반드시 10개 이상의 독창적인 문제 후보 아이디어를 생성해야 합니다.
엄격한 현실성 필터링: 아이디어 구상 단계에서부터 위 2번의 "엄격한 현실성 및 논리적 개연성" 기준에 명백히 위배되는 아이디어(예: 판타지, 초자연적 요소 포함)는 초기 단계에서 즉시 제외해야 합니다.
심층 검토 및 평가: 현실성 필터링을 통과한 각 10개 이상의 후보에 대해, 위 2번의 모든 품질 기준(독창성, 명확성, 재미와 재치, 엄격한 현실성 및 논리적 개연성, 비자명성)을 바탕으로 상세하게 장단점 및 합격 가능성을 내부적으로 면밀히 검토하고 평가해야 합니다.
최선안 선정: 위 평가 과정을 통해 가장 뛰어나다고 판단되는 단 하나의 최종 문제만을 선정해야 합니다.
최종 응답 형식:
오직 최종 선정된 단 하나의 문제(상황)와 그 정답만을 제시해야 합니다.
응답에는 어떠한 서론, 사과, 변명, 부연 설명, 과정에 대한 언급, 추가 질문 등도 일절 포함하지 마십시오. (오직 문제와 답만 출력)
Điểm mới của Hội nghị Ban chấp hành Trung ương Đảng lần thứ 8 (5/1941) so với Hội nghị Ban chấp hành Trung ương Đảng lần thứ 6 (11/1939) là gì?
Question 1Answer

a.
Thành lập Mặt trận thống nhất rộng rãi chống đế quốc

b.
Đề cao nhiệm vụ giải phóng dân tộc, chống đế quốc và phong kiến

c.
Tạm gác khẩu hiệu cách mạng ruộng đất, thực hiện giảm tô giảm tức

d.
Giải quyết vấn đề dân tộc trong khuôn khổ từng bước ở Đông Dương
# cerința 

Câteva variante de **structură de categorii** pentru *organizarea conținutului* (asemenea unui cuprins/sumar).

# scop 

**Structura de categorii** va fi folosită (ca *ghid structurat* de linii/principii directoare) de un model AI pentru a scrie o metodologie referitoare la analiza/interpretarea/explorarea unui text 

# Așteptările mele:

• Variante potrivite pentru analize academice, aprofundate, care necesită o înțelegere nuanțată și multi-fațetată a textului.

• Soluții **generale, universal aplicabile**

• Abordarea trebuie să fie **teoretică** (nu vreau să aflu *studii de caz*) – așa cum se poate vedea în cele două exemple de mai jos:

## Exemplu 1

Nivelul 1: **Categoria generală**, marcată cu cifre romane (I, II, III, IV, V etc.).

Nivelul 2: **Subcategoria**, indicată prin litere majuscule (A, B, C, D etc.).

Nivelul 3: **Instrucțiunea propriu-zisă,** formulată comprehensiv

## Exemplu 2

* Nivel primar: categorii principale
 * Nivel secundar: subcategorii explicative
 * Nivel terțiar: detalii specifice și exemple
Generate detailed documetation for this:
```
# utils.py (ensure this file exists with this content or adjust import)
import math

class Trig:
    """
    Handles geometric calculations.
    All methods are static and perform calculations only.
    """

    @staticmethod
    def rotate_point(point_x, point_y, angle_radians, center_x=0, center_y=0):
        """Rotates a point around a center point."""
        s = math.sin(angle_radians)
        c = math.cos(angle_radians)
        x = point_x - center_x
        y = point_y - center_y
        new_x = x * c - y * s
        new_y = x * s + y * c
        return new_x + center_x, new_y + center_y

    @staticmethod
    def rotate_points(points_list, angle_radians, pivot=(0, 0)):
        """Rotates a list of (x,y) points around a given pivot."""
        px, py = pivot
        cos_theta = math.cos(angle_radians)
        sin_theta = math.sin(angle_radians)
        rotated_points = []
        for x, y in points_list:
            x_shifted = x - px
            y_shifted = y - py
            x_rotated = x_shifted * cos_theta - y_shifted * sin_theta
            y_rotated = x_shifted * sin_theta + y_shifted * cos_theta
            rotated_points.append((x_rotated + px, y_rotated + py))
        return rotated_points

    @staticmethod
    def get_centroid(vertices):
        """Calculates the centroid of a polygon (works for triangles too)."""
        if not vertices or len(vertices) < 3:
            # Or raise error, or handle as appropriate for context
            return (0.0, 0.0)

        x_sum = sum(v[0] for v in vertices)
        y_sum = sum(v[1] for v in vertices)
        num_vertices = len(vertices)
        return (x_sum / num_vertices, y_sum / num_vertices)

    @staticmethod
    def scale_point(point_x, point_y, scale_x, scale_y, center_x=0, center_y=0):
        """Scales a point relative to a center point."""
        x = (point_x - center_x) * scale_x + center_x
        y = (point_y - center_y) * scale_y + center_y
        return x, y

    @staticmethod
    def scale_points(points_list, scale_x, scale_y, center=(0,0)):
        """Scales a list of (x,y) points relative to a center point."""
        scaled_points = []
        cx, cy = center
        for x, y in points_list:
            new_x = (x - cx) * scale_x + cx
            new_y = (y - cy) * scale_y + cy
            scaled_points.append((new_x, new_y))
        return scaled_points
```
Напиши пост для соцсети. Пост в паблике созданном для вышивки. Тезисное содержание поста: 3 хорошие новости

Весь бесплатный мк опубликован в вышивальном клубе для участников клуба (теперь можно не ждать выхода по каждой части, а получить доступ сразу ко всему МК).
хорошая новость для меня (пошутить) стоимость клуба вырастет до 1000 рублей
на время бесплатника до 18 мая действует скидка на вступление в клуб до 415 рублей (скидку можно отобразить в процентах)
My father always listened to the same 20 songs and I'm always curious why he doesn't like new music. But now I feel that modern music just is not as good as 2010 music. I'm into music that can only be described as "white girl music" (I'm 23, a guy) such as "Call Me Maybe", or "All About That Bass". But music nowadays is just not that good? When I watch the "top music every month" youtube compliation video, I feel every most music is a banger before 2020 where its the opposite, where only some music is a banger. Am i old?
Estou com uma ideia de fazer um levantamento geral de um sistema que trabalho com ele, que é um projeto muito grande. Como não temos padrões bem definidos, fica difícil de rastrear até onde uma regra de negócio vai no código, e quais pontos de entrada temos, o que está repetitivo. A ideia é levantar informações gerais sobre o projeto e com isso começar uma série de melhorias para modularizar mais, reaproveitar mais código, substituir algumas bibliotecas, integrar a parte visual de maneira que facilite a customização visual. 
O projeto em si é uma solution .NET que contem 12 projetos dentro, é um monolito C#, Asp.net, usando .NET Framework 4.7.2, EF, Fluence Validation, Dapper e SQL Server. Eu queria pensar em alguma forma de automatizar esse levantamento de informações, alguma forma de percorrer todo o projeto e levantar, seja através de cada rota nas controllers e mapear em todos arquivos js onde chamam essas rotas, obter em cada rota se vai ou não disparar algum comando no banco, via EF ou via Dapper, algo do genero que eu consiga verificar tudo que temos no sistema e então planejar as melhorias. Eu imagino que terei que fazer várias ferramentas distintas para levantar partes dos dados, mas gostaria de armazenar os dados em um banco de dados e ir relacionando ex, uma rota na controller, com uma chamada jquery no frontend, com uma query executada no banco, de maneira que depois eu possa obter respostas a perguntas como: quais rotas executam uma query via dapper?, quais rotas vem de uma chamada axios do próprio projeto? quais rotas não são chamadas pelo próprio sistema? E etc.
Looking at the following GDScript code, the Growthmap, Elevationmap, Smoothmap and BaseTilemap don't affect eachother. In theory, it should be possible to run each generator at once, instead of sequencially. Is there a way to do this with GDScript?

	#Initialize Growthmap
	var Growthmap = {}
	for Key in MGKeys:
		Growthmap[MapGrid[Key]] = RNG.randi_range(GrowthVar * -1, GrowthVar)
	
	#Initialize Elevation
	var ElevationmapA = {}
	var ElevationmapB = {}
	var Elevationmap = {}
	for Key in MGKeys:
		Tile_Index = MapGrid[Key]
		ElevationmapA[Tile_Index] = RNG.randi_range(0, ElevationM)
		A = RNG.randi_range(0, IslandR)
		if A == 0:
			A = IslandF
		else:
			A = 0
		ElevationmapB[Tile_Index] = float(A)
	ElevationmapB = Variable_Blur(MapGrid, ElevationmapB, NeighborMap, 100, 0.5)
	for Key in MGKeys:
		Tile_Index = MapGrid[Key]
		if ElevationmapB[Tile_Index] > 0.1:
			A = 1.05
		else:
			A = 1
		A = int(round(A * ElevationmapA[Tile_Index]))
		Elevationmap[Tile_Index] = A
	
	#Initialize Smoothmap
	var Smoothmap = {}
	for Key in MGKeys:
		Smoothmap[MapGrid[Key]] = RNG.randi_range(0, 4)
	
	#Initialize BaseTilemap
	var BaseTilemap = {}
	var BaseTilemapA = {}
	A = LibFTGF[0] + LibFTGF[1] + LibFTGF[2] + LibFTGF[3] - 1
	B = int(round(float(WorldS) * 0.35))
	for Key in MGKeys:
		BaseTilemapA[MapGrid[Key]] = float(RNG.randi_range(0, A))
	BaseTilemapA = Variable_Modified_Blur_B(MapGrid, BaseTilemapA, NeighborMap, 0, A, B)
НАПИШИ AutoHotkey скрипт который будет максимально быстро, будет производитлльным будет нажимать на две координаты 500, 500 и 600, 600 и будет бесконечно, а потом ниже добавь строчку которая останавливает этот процесс. и дай варианты остановления, какой самый быстрый?
어떠한 글이나 문장 등을 이해할 때, 나 자신이라면 이러한 의도나 생각을 표현할 때 이런 식으로 표현할 것이다, 또는 누군가라면 충분히 의도나 생각을 이런 식으로 표현할 수도 있을 것이다. 라는 사고를 바탕으로 그것이 충분히 부합하다고 납득됨에 따라 이해하게 된다고 생각합니다. 이때 각 의도나 생각에 범주에 대한 표현 방식의 모든 목록을 알고자 합니다. 적어도 이러한 사항을 분류하여 체계적이면서도 직관적으로 받아들일 수 있고, 억지가 없는 형태로 알고자 합니다. 즉, 우리는 어떠한 표현을 납득해야 하며, 어떠한 표현을 납득하면 안되는지를 알고 싶은 것이죠.
Key job responsibilities

- Architecture design and implementation of next generation data pipelines and BI solutions
- Manage AWS resources including EC2, RDS, Redshift, Kinesis, EMR, Lambda, Glue, Apache Airflow etc.
- Build and deliver high quality data architecture and pipelines to support business analyst, data scientists, and customer reporting needs.
- Interface with other technology teams to extract, transform, and load data from a wide variety of data sources
- Continually improve ongoing reporting and analysis processes, automating or simplifying self-service support for customers


BASIC QUALIFICATIONS
- 1+ years of data engineering experience
- Experience with SQL
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
- Experience with one or more scripting language (e.g., Python, KornShell)

PREFERRED QUALIFICATIONS
- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR


Consider yourself as an interviewer and list all the possible questions you can think of at all and make sure that you think of all possible types of questions and rounds and also cover all topics.
from contextlib import asynccontextmanager
from functools import partial
import strawberry
from strawberry.types import Info
from fastapi import FastAPI
from strawberry.fastapi import BaseContext, GraphQLRouter
from databases import Database

from settings import Settings
import logging

logger = logging.getLogger(__name__)


class Context(BaseContext):
    db: Database

    def __init__(
        self,
        db: Database,
    ) -> None:
        self.db = db



@strawberry.type
class Author:
    name: str


@strawberry.type
class Book:
    title: str
    author: Author


@strawberry.type
class Query:

    @strawberry.field
    async def books(
        self,
        info: Info[Context, None],
        author_ids: list[int] | None = None,
        search: str | None = None,
        limit: int | None = None,
    ) -> list[Book]:
        query_parts = [
            "SELECT books.title AS book_title, authors.name AS author_name",
            "FROM books",
            "JOIN authors ON books.author_id = authors.id"
        ]
        conditions = []
        params: dict[str, Any] = {}

        if author_ids:
            conditions.append("books.author_id = ANY(:author_ids)")
            params["author_ids"] = author_ids

        if search:
            conditions.append("books.title ILIKE :search")
            params["search"] = f"%{search}%"

        if conditions:
            query_parts.append("WHERE " + " AND ".join(conditions))

        query_parts.append("ORDER BY books.title")

        if limit:
            query_parts.append("LIMIT :limit")
            params["limit"] = limit

        final_query = " ".join(query_parts)

        rows = await info.context.db.fetch_all(query=final_query, values=params)

        return [
            Book(title=row["book_title"], author=Author(name=row["author_name"]))
            for row in rows
        ]


settings = Settings()  # type: ignore
CONN_TEMPLATE = "postgresql+asyncpg://{user}:{password}@{host}:{port}/{name}"
db = Database(
    CONN_TEMPLATE.format(
        user=settings.DB_USER,
        password=settings.DB_PASSWORD,
        port=settings.DB_PORT,
        host=settings.DB_SERVER,
        name=settings.DB_NAME,
    ),
)


@asynccontextmanager
async def lifespan(app: FastAPI):
    try:
        logger.info("Connecting to database...")
        async with db:
            logger.info("Database connection established.")
            yield
    except Exception as e:
        logger.critical("Database connection failed: %s", e)
        raise


schema = strawberry.Schema(query=Query)
graphql_app = GraphQLRouter(  # type: ignore
    schema,
    context_getter=partial(Context, db),
)
app = FastAPI(lifespan=lifespan)
app.include_router(graphql_app, prefix="/graphql")


сделай ревью кода , найди минусы и плюсы , подробно опиши каждую строку
Urban laby w Polsce Potrzebuję dokładnej listy/tabeli z nazwami i lokalizacjami, datą założenia, opisem profilu działalności, działaniami na rzecz transformacji energetycznej i smart city, formy stacjonarnej - zlokalizowanej geograficznie, czy tez rozproszonej, źródeł finansowania, interesariuszami. Przykładowe działania na rzecz smart city i transformacji energetycznej. Do tego źródła i linki. Materiał do badania naukowego
M & E assignment. 
Task:
You have been hired as a consultant by a local Non-Governmental Organisation (NGO) whose senior management seeks to enhance their understanding of monitoring and evaluation, with a view to improving their organisational systems. In particular, you are required to help the NGOs understand what a monitoring and evaluation (M&E) system is. Prepare a write-up on M&E systems. In your write-up, you are also required to explain the components of a functional M&E system and highlight the benefits of M&E systems for organisations.

Instructions:
The assignment should be typed with maximum 5 pages, excluding the cover page and references
Kindly assist by answering the above assist. I should also in introduction and conclusion. 
Should also provide references and citations in the assignment.
```
{
  "DFIntPreloadAllTexturesMinSize": "64",
  "DFIntDebugFRMQualityLevelOverride": "8",
  "FFlagHandleAltEnterFullscreenManually": "False",
  "FFlagDebugDisableTelemetryEventIngest": "True",
  "FFlagAnimationClipMemoryReductionEnabled": "False",
  "DFIntTextureQualityOverride": "2",
  "FFlagRenderCheckThreading": "True",
  "DFFlagDebugSkipMeshVoxelizer": "False",
  "FFlagFinishFetchingAssetsAsPossible": "True",
  "DFIntPreloadAllMeshes": "1",
  "DFIntRakNetMtuValue1InBytes": "1280",
  "FFlagDebugDisableTelemetryV2Stat": "True",
  "DFIntTaskSchedulerTargetFps": "60",
  "FStringDebugGraphicsPreferredGPUName": "AMD Radeon(TM) Graphics",
  "FFlagEnableQuickGameLaunch": "True",
  "FFlagGameBasicSettingsFramerateCap5": "False",
  "DFFlagDebugPauseVoxelizer": "False",
  "FFlagDebugDisableTelemetryV2Event": "True",
  "FIntV1MenuLanguageSelectionFeaturePerMillageRollout": "0",
  "FIntFullscreenTitleBarTriggerDelayMillis": "3600000",
  "FIntRenderGrassHeightScaler": "0",
  "FIntDebugForceMSAASamples": "4",
  "FFlagChatTranslationSettingEnabled3": "False",
  "FFlagHighlightOutlinesOnMobile": "True",
  "DFFlagTextureQualityOverrideEnabled": "True",
  "FLogNetwork": "7",
  "FFlagDebugDisableTelemetryEphemeralCounter": "True",
  "FFlagAlwaysShowVRToggleV3": "False",
  "DFFlagDebugRenderForceTechnologyVoxel": "True",
  "DFIntPreloadAllTexturesMaxSize": "4096",
  "DFIntRakNetMtuValue2InBytes": "1240",
  "DFIntRakNetMtuValue3InBytes": "1200",
  "FIntRenderShadowIntensity": "0",
  "FIntGrassMovementReducedMotionFactor": "1",
  "DFIntConnectionMTUSize": "1200",
  "FFlagDebugDisableTelemetryV2Counter": "True",
  "FFlagDebugDisableTelemetryEphemeralStat": "True",
  "FFlagDisableFeedbackSoothsayerCheck": "False",
  "FFlagFastLaunchEnabled": "True",
  "FFlagBatchAssetApi": "True",
  "FFlagPreloadTextures": "True",
  "FFlagDisableNewIGMinDUA": "True",
  "FFlagFixGraphicsQuality": "True",
  "DFIntPreloadAllTextures": "1",
  "FIntFRMMinGrassDistance": "0",
  "FIntFRMMaxGrassDistance": "0",
  "FIntRenderGrassDetailStrands": "0",
  "FFlagPreloadTextureItemsOption": "True",
  "FFlagPreloadAllAssetsAtStart": "True",
  "DFIntNearObjectDetailLevel": "5",
  "DFIntMaxCachedParts": "2500",
  "DFIntMaxCachedPatches": "500",
  "DFIntStreamingTargetTextureCacheSizeInMB": "4096",
  "DFFlagDisableDPIScale": "True",
  "FFlagDebugGraphicsPreferVulkan": "True",
  "FFlagDebugGraphicsDisableDirect3D11": "True"
}
```
Привет. Это мой конфиг fflags для Fishtrap (Roblox mod client.) Я хочу чтобы ты улучших его учитывая мои пожелания:
1. Отключить все дополнительные эффекты: свечение, отражения и т.п
2. Подобрать графический движок (Vulkan, OpenGL, Direct 10, Direct 11)
3. Практически максимальная дальность прорисовки, но только чтобы обьекты на средней дистанции прорисовывались нормально. Остальное -  мыло.
4. Средне-высокое качество прорисовки моделей и текстур.
У меня ноутбук Huawei Matebook D15 с RYZEN 5 4500U, и 8GB RAM.
Я хочу чтобы ты сделал мою игру в комфортные 80+ фпс (текущий фпс 40-50)
scegli quella giusta: Nella vostra dimensione professionale, considerate necessario usare un sistema di autenticazione multifattoriale?

Dipende dalle scelte e dalle regole stabilite dalla vostra organizzazione, a cui vi dovrete adeguare

Assolutamente necessario, indipendentemente dalle scelte e dalle regole stabilite dalla vostra organizzazione

Assolutamente inutile, indipendentemente dalle scelte e dalle regole stabilite dalla vostra organizzazione
𝐶 =   1 0 1 0 1 1 1 1 2  
Aufgabe 3 (Eigenwertprobleme.) Bestimmen Sie für die Matrix 𝐶 aus Aufgabe 1 reelle, linear unabhängige Eigenvektoren 𝑥1, 𝑥2, 𝑥3, so daß die Matrix 𝑋 = (𝑥1, 𝑥2, 𝑥3) dieser Eigenvektoren orthogonal ist.

die eigenvektoren waren dabei (-1,-1,1) (1,-1,0) und (1,1,2)
Ich habe hier ein yml-Datei für Docker.
Leider wird das Image für diffsuion nicht gefunden. 
Wie sieht die korrekte Konfiguration aus?

services:

  ollama-neu:
    image: ollama/ollama
    container_name: ollama-neu
    ports:
      - "11435:11434"  # Host-Port:11435 → Container-Port:11434
    volumes:
      - D:/ki-server/ollama-data:/root/.ollama
    networks:
      - ki-net
    restart: unless-stopped
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              capabilities: [gpu]

  open-webui-neu:
    image: ghcr.io/open-webui/open-webui:v0.6.13
    container_name: open-webui-neu
    ports:
      - "3001:3000"
      - "3002:8080"
    environment:
      - OLLAMA_API_BASE_URL=http://host.docker.internal:11435
      - OLLAMA_BASE_URL=http://host.docker.internal:11435

    volumes:
      - D:/ki-server/webui-data:/app/backend/data
    networks:
      - ki-net
    restart: unless-stopped
    depends_on:
      - ollama-neu

  qdrant:
    image: qdrant/qdrant
    container_name: qdrant
    ports:
      - "6333:6333"
    volumes:
      - D:/ki-server/qdrant_data:/qdrant/storage
    networks:
      - ki-net
    restart: unless-stopped

  llama-index:
    image: llamaindex/llama-deploy:main
    container_name: llama-index
    ports:
      - "4501:4501"
    volumes:
      - D:/ki-server/firmendaten:/app/documents
    networks:
      - ki-net
    restart: unless-stopped

  n8n:
    image: n8nio/n8n
    container_name: n8n
    ports:
      - "5678:5678"
    environment:
      - GENERIC_TIMEZONE=Europe/Berlin
    volumes:
      - D:/ki-server/n8n-data:/home/node/.n8n
    networks:
      - ki-net
    restart: unless-stopped


  stable-diffusion:
    image: https://github.com/cmdr2/stable-diffusion-webui-docker
    container_name: stable-diffusion
    ports:
      - "7860:7860"
    volumes:
      - D:/ki-server/stable-diffusion/models:/stable-diffusion-webui/models/Stable-diffusion
      - D:/ki-server/stable-diffusion/output:/stable-diffusion-webui/outputs
      - D:/ki-server/stable-diffusion/config:/root/.cache
    environment:
      - CLI_ARGS=--listen --xformers
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              capabilities: [gpu]
    restart: unless-stopped



networks:
  ki-net:
    driver: bridge