#REDASH

[howlingday]

So how about a little challenge, choose one of the following 3 songs as the theme of the writing or background music for it: 1.-NEFFEX - Careless 2.-NEFFEX- Rumors 3.-REDASH : OUR HOMETOWN [GODDESS OF VICTORY: NIKKE OST] ps: this is only if you want.

I do want, and you know what? I accept your challenge, and in reply, I say, GIVE ME ALL THREE.

---------------------------------------------------

Another Sleepless Night

Lucyna Kushinada, better known by her friends as Lucy, stared out her apartment window. Her friends... Assuming anyone else survived that night. Everything was already fucked from the start, with her getting kidnapped and held hostage while the crew charged headfirst into the shitstorm of Arasaka gunfire.

She remembered reading the news report about the "violent gangers who tried to storm Arasaka tower" and manged to be "summarily punished as to be expected." She'd read every news outlet she could on her way to the moon. Hoping, praying that someone found something that the others didn't and somebody survived.

But no, that's not what she found. They all said the same thing with different words. Rebecca is dead. David is dead. Days later, they found Kiwi dead outside a Buck-a-Slice in Arroyo. Much as she was a backstabbing and cold-hearted bitch, a part of her missed her.

But not as much as she missed David. On the moon, she swore she saw him jumping around on the moon, just like he did before. Her throat felt dry, so she left to grab a drink. Something hard enough to make her regret. But she couldn't forget.

Never. Because every day, she regretted not ripping off her helmet and dancing with him on the moon.

(NEFFEX - Careless)

===============================

Jaune Wants to F Pyrrha

People said a lot of things about Pyrrha Nikos. Some said she tall, beautiful, and perfect in every way. Some said she was Remnant's light of hope against the dark tides of the Grimm. That none could ever defeat the aptly named Invincible Girl.

And yet there was surprisingly little said about her in any negative way. Pyrrha never made any statements of her being perfect, but public response was she was being modest. The first negative thing anyone ever said about her was during her first few weeks of Beacon Academy, when her teammate, Nora Valkyrie, said she "apologized way, way, waaay too much" and "was too nice for her own good". Since then, she had been small attempts to remedy these issues, but this seemed to only fuel the already roaring flames of the perfectionist ideal people made of her. It seemed no matter what she did, she would always be perfect.

Until one day, she met a young man about her age. Her team leader, Jaune Arc, who was almost her exact opposite in every way. Nobody said a nice thing about him since his arrival, save for the few friends he'd made, and even then, they had much to say about him.

Jaune was weak, scraggly, and was inept at combat when he began attending. She could personally attest to his lacking qualities when he not only didn't have his aura unlocked, but he didn't know what aura was! But she knew better than to judge a book by it's cover and decided on the day of her initiation that he would be her partner.

Was it selfish to pick and choose who she wanted to team up with? Maybe, but it worked out in the end. Where Jaune lacked in combat strength, he made up for it in his tactical mindset, leading their team to victory. One could say he was the brains of the outfit, though his low test scores would argue that point.

Over time, though, his lacking skill became more and more evident, so he asked Pyrrha to train him. This leads us to now, on the rooftop above their dorm, where they hold their near nightly sparring matches. They had finished a mock match, sitting next to each other, hands so tantalizingly close and yet so painfully distant. He looked to her and she looked to him.

Jaune gazed into Pyrrha's eyes, and she into his. The blue sky overhead met green hills below, and she couldn't help but admire the way he looked at her. She could see... No, she could feel love and adoration glowing from him. Her heart pounded as he spoke to her.

"Pyrrha, I... I wanna ask you something." Pyrrha swallowed a lump in her throat. "And... And if you don't want to, I'd understand and respect your decision."

It was at this moment she realized where they were. They were alone, isolated from their friends, on the rooftop of the school with a night sky filled with gleaming stars and a moon nearly whole. This was a night she'd always dreamed of.

"Anything, Jaune." She answered.

"I... I wanna fight you!"

"...Come again?" She didn't hear that wrong, did she?

"I want to fight you." Jaune said, with more confidence this time.

"I... I sorr-" She stopped herself. "I mean, excuse me, but I don't understand what you mean. Didn't we finish sparring?"

"Yeah, we did, but," he looked up, to the stars that glittered high, "but I meant I want to fight you for real one day. I want to get so good, you don't have to hold back against me when we do spar."

She wouldn't argue with his statement, considering nothing he said could be seen as a lie. She was leagues above him with her years of athletic and competitive training, so of course she would have to hold back when the two sparred. A kindness she didn't often share with others, or ever in the case of the resident bully, Cardin Winchester.

"Do... Do you really mean that?" Pyrrha asked. "Do you really want to fight me?"

"Pyrrha, if I could fight you on your level, and not totally suck, I think I'd be the happiest guy who ever lived."

Giving a puff instead of a laugh, she couldn't stop smiling at him. She leaned against him, her pinkie touching his. Then, his hand slid over hers, and she felt her heart nearly explode with joy. She gave a soft sigh.

"One day... I want to fight you, too."

(NEFFEX - Rumors)

**********************************************

The Napping Bounty Hunter

Zora Salazar was a lot of things.

First off, she hates epithets and whatever criminals she hunts down using them gets an extra crack in the jaw. Two of 'em if they're really going on and on about how their supposed "magic superpower" automatically means they win.

Second, she herself is Inscribed, and her epithet was, is, and will be broken. Helped make her bounty hunting easier, sure, but she rarely used when she was tracking. Not unless someone gives her a reason, like ticking her off in a way similar to the previous paragraph. Still, her epithet was definitely the strongest she'd seen yet.

Third, she's a bit of a romantic. Not exactly like the loved-dovey, kissy face, "I'd die for you" kinda romantic. More of a "two men enter, one man leaves, and they're both best friends, and they're giving it their all and also there's a sunset shining behind them" sort of romantic. She loved the thrill of the fight, the sweat that beads down from giving your all, and chase and satisfaction of reaching the top.

Fourth, she really loves-

"HEY!" A voice belowed.

Zora tapped her hat up from her comfy lie down against the tree. And she JUST got comfy.

The bellowing came from a familiar face, but not one she could name. Some guy from a gang she busted a few days ago. She would've brought him in, but he was already running out the back door when she zeroed in on his boss.

"You n' me got a score to settle!"

"No, we don't." Zora put her hat back on her face. Great, now she has to get comfy again.

"Yes, we do!"

"Nuh-uh."

"WHAT YOU MEAN NUH-UH?!"

"I mean. Nuh. Uh." She grumbled loudly. "Means whatever beef you got with me goes to a different butcher. Do I look like a butcher?"

"She looks like a cowboy." A small voice said behind annoying big guy.

"Nah, I'd say she's more of a desperado." Another voice said.

"You only know that word because you watched one western movie!"

"And it was a good one!"

Maybe a movie would would help her sleep. If she was real quiet, she could sneak inside and take a nap in a comfier seat than this tree. I mean, the tree was comfy if you got in the right spot, but-

"HEY! QUIT STARIN' AT THE SKY!" He huffed. "You're being so rude! We drove all the way out here in my brand new car just so we could make you pay for putting our boss in jail!"

"Listen," Zora said after letting out a deep breath, "I'm willin to let this go for interruptin my nap. So get back in your car while you still can."

"Oh, I don't think so!" Suddenly, Zora felt a hand grab her by her poncho. Of course this loudmouth had an epithet. All loudmouths do. "I'm gonna-"

"Let go."

"Wha?"

Zora glared at him with cold, baneful eyes. "LET GO, OR ELSE."

"Or else what?" He sneered. Oh, she was so hoping he'd say that.

With a grin and an iron grip, she squeezed his hand. Slowly, it started to bulge before shrinking, smaller and smaller until all that was left was emaciated, bony hand. With a groan, he let go, wheezing as he stared at his wizened fingers.

"Wh... Wha dih you do to muh bodeh..." Losing his teeth didn't make him easier to understand, but Zora was well versed in gum linguistics.

"Or else." She said, turning away. "Enjoy the walk home, old man."

"Uh, but we drove."

"I know." Faster than anyone else could react, she whipped out her gun and fired a bullet into the engine of the car. Rust creeps as paint peels, the car slowly sinking to the ground as it's tires deflated. The glass of the car slipped free, shattering as it fell inside. "But you're walkin' now."

(REDASH - Our Hometown)

_________________________________________

[mn9-confessions]

Does anybody here remember Red Ash? that version of megaman legends/ dash made by Inafune

The only thing I remember is that his company tried to make a game of it, but that didn't go beyond crowdfunding goals

[nschool]

The Best Open-Source Tools for Data Science in 2025

Data science in 2025 is thriving, driven by a robust ecosystem of open-source tools that empower professionals to extract insights, build predictive models, and deploy data-driven solutions at scale. This year, the landscape is more dynamic than ever, with established favorites and emerging contenders shaping how data scientists work. Here’s an in-depth look at the best open-source tools that are defining data science in 2025.

1. Python: The Universal Language of Data Science

Python remains the cornerstone of data science. Its intuitive syntax, extensive libraries, and active community make it the go-to language for everything from data wrangling to deep learning. Libraries such as NumPy and Pandas streamline numerical computations and data manipulation, while scikit-learn is the gold standard for classical machine learning tasks.

NumPy: Efficient array operations and mathematical functions.

Pandas: Powerful data structures (DataFrames) for cleaning, transforming, and analyzing structured data.

scikit-learn: Comprehensive suite for classification, regression, clustering, and model evaluation.

Python’s popularity is reflected in the 2025 Stack Overflow Developer Survey, with 53% of developers using it for data projects.

2. R and RStudio: Statistical Powerhouses

R continues to shine in academia and industries where statistical rigor is paramount. The RStudio IDE enhances productivity with features for scripting, debugging, and visualization. R’s package ecosystem—especially tidyverse for data manipulation and ggplot2 for visualization—remains unmatched for statistical analysis and custom plotting.

Shiny: Build interactive web applications directly from R.

CRAN: Over 18,000 packages for every conceivable statistical need.

R is favored by 36% of users, especially for advanced analytics and research.

3. Jupyter Notebooks and JupyterLab: Interactive Exploration

Jupyter Notebooks are indispensable for prototyping, sharing, and documenting data science workflows. They support live code (Python, R, Julia, and more), visualizations, and narrative text in a single document. JupyterLab, the next-generation interface, offers enhanced collaboration and modularity.

Over 15 million notebooks hosted as of 2025, with 80% of data analysts using them regularly.

4. Apache Spark: Big Data at Lightning Speed

As data volumes grow, Apache Spark stands out for its ability to process massive datasets rapidly, both in batch and real-time. Spark’s distributed architecture, support for SQL, machine learning (MLlib), and compatibility with Python, R, Scala, and Java make it a staple for big data analytics.

65% increase in Spark adoption since 2023, reflecting its scalability and performance.

5. TensorFlow and PyTorch: Deep Learning Titans

For machine learning and AI, TensorFlow and PyTorch dominate. Both offer flexible APIs for building and training neural networks, with strong community support and integration with cloud platforms.

TensorFlow: Preferred for production-grade models and scalability; used by over 33% of ML professionals.

PyTorch: Valued for its dynamic computation graph and ease of experimentation, especially in research settings.

6. Data Visualization: Plotly, D3.js, and Apache Superset

Effective data storytelling relies on compelling visualizations:

Plotly: Python-based, supports interactive and publication-quality charts; easy for both static and dynamic visualizations.

D3.js: JavaScript library for highly customizable, web-based visualizations; ideal for specialists seeking full control.

Apache Superset: Open-source dashboarding platform for interactive, scalable visual analytics; increasingly adopted for enterprise BI.

Tableau Public, though not fully open-source, is also popular for sharing interactive visualizations with a broad audience.

7. Pandas: The Data Wrangling Workhorse

Pandas remains the backbone of data manipulation in Python, powering up to 90% of data wrangling tasks. Its DataFrame structure simplifies complex operations, making it essential for cleaning, transforming, and analyzing large datasets.

8. Scikit-learn: Machine Learning Made Simple

scikit-learn is the default choice for classical machine learning. Its consistent API, extensive documentation, and wide range of algorithms make it ideal for tasks such as classification, regression, clustering, and model validation.

9. Apache Airflow: Workflow Orchestration

As data pipelines become more complex, Apache Airflow has emerged as the go-to tool for workflow automation and orchestration. Its user-friendly interface and scalability have driven a 35% surge in adoption among data engineers in the past year.

10. MLflow: Model Management and Experiment Tracking

MLflow streamlines the machine learning lifecycle, offering tools for experiment tracking, model packaging, and deployment. Over 60% of ML engineers use MLflow for its integration capabilities and ease of use in production environments.

11. Docker and Kubernetes: Reproducibility and Scalability

Containerization with Docker and orchestration via Kubernetes ensure that data science applications run consistently across environments. These tools are now standard for deploying models and scaling data-driven services in production.

12. Emerging Contenders: Streamlit and More

Streamlit: Rapidly build and deploy interactive data apps with minimal code, gaining popularity for internal dashboards and quick prototypes.

Redash: SQL-based visualization and dashboarding tool, ideal for teams needing quick insights from databases.

Kibana: Real-time data exploration and monitoring, especially for log analytics and anomaly detection.

Conclusion: The Open-Source Advantage in 2025

Open-source tools continue to drive innovation in data science, making advanced analytics accessible, scalable, and collaborative. Mastery of these tools is not just a technical advantage—it’s essential for staying competitive in a rapidly evolving field. Whether you’re a beginner or a seasoned professional, leveraging this ecosystem will unlock new possibilities and accelerate your journey from raw data to actionable insight.

The future of data science is open, and in 2025, these tools are your ticket to building smarter, faster, and more impactful solutions.

[thejonymyster]

new tumblr frature idea: Redash

puts a post on your followers' dashboard without putting it on youe glog

[netmarkjp]

#ばばさん通信ダイジェスト : Redash運用基盤移行プロジェクト：EC2からマネージドサービスへ

賛否関わらず話題になった/なりそうなものを共有しています。

Redash運用基盤移行プロジェクト：EC2からマネージドサービスへ

https://zenn.dev/paiza/articles/redash-migration

[codezup]

How to Build a MongoDB Dashboard with Graphite and Redash

1. Introduction Brief Explanation Building a MongoDB dashboard with Graphite and Redash is an essential skill for developers and data engineers who need to monitor and visualize large-scale MongoDB deployments. This tutorial will guide you through the process of setting up a robust monitoring system that provides real-time insights into MongoDB performance, query patterns, and system health. By…

[r-cienciadedados]

Principais Ferramentas para Construir Pipeline de Dados - Real Time Analytics

Leonardo Santos da Mata

Engenheiro de Dados, DBA | SQL, Python para Analise de Dados, Pentaho Data Integration, Cloud AWS, Cloud Azure, Mongodb, Mongodb Compass, Docker e Portainer.io

19 de outubro de 2024

A construção de pipelines de dados para Real Time Analytics envolve a escolha de ferramentas que permitam processar, analisar e visualizar dados em tempo real. Abaixo, listamos algumas das principais ferramentas, com seus prós, contras e os tipos de projetos em que cada uma se destaca.

1. Tableau

Prós:

Interface amigável e intuitiva

Grande capacidade de criação de visualizações interativas

Suporte para integração com diversas fontes de dados

Contras:

Custo elevado para grandes equipes

Limitações no processamento de grandes volumes de dados em tempo real

Aplicação: Projetos que demandam visualização interativa de dados para decisões de negócios, como relatórios e dashboards executivos.

2. Amazon Kinesis

Prós:

Excelente para processar e analisar grandes volumes de dados em tempo real

Integrado com o ecossistema AWS

Altamente escalável e flexível

Contras:

Curva de aprendizado acentuada para iniciantes

Custo pode aumentar conforme o volume de dados processado

Aplicação: Ideal para projetos de IoT, análise de logs de aplicações e monitoramento de eventos em tempo real.

3. Metabase

Prós:

Open-source e de fácil uso

Suporte a várias bases de dados

Boa opção para equipes menores que buscam relatórios simples

Contras:

Funcionalidades limitadas para grandes volumes de dados

Menos opções de personalização de visualizações

Aplicação: Pequenas e médias empresas que precisam de relatórios básicos e acessíveis com rápida implementação.

4. Looker Studio

Prós:

Integração com diversas fontes de dados, incluindo Google Analytics

Interface de fácil uso para criação de relatórios e dashboards interativos

Bom para análises colaborativas em tempo real

Contras:

Funcionalidades limitadas para manipulação avançada de dados

Pode ser mais simples do que necessário para grandes volumes de dados

Aplicação: Ideal para empresas que já estão no ecossistema Google e precisam de dashboards fáceis de usar.

5. Apache Flink

Prós:

Processamento de dados em tempo real com baixa latência

Suporte a análise de grandes volumes de dados distribuídos

Flexível para integração com diferentes pipelines de dados

Contras:

Requer uma curva de aprendizado significativa

Configuração complexa para iniciantes

Aplicação: Processamento de dados em tempo real para casos de uso como análise de fraudes, monitoramento de IoT e sistemas de recomendação.

6. Apache Druid

Prós:

Alta performance no processamento e análise de dados em tempo real

Otimizado para grandes volumes de dados com baixas latências de consulta

Suporte a OLAP (Online Analytical Processing)

Contras:

Configuração e gerenciamento podem ser desafiadores

Requer conhecimento técnico avançado para configuração otimizada

Aplicação: Projetos que exigem ingestão de grandes volumes de dados em tempo real, como análise de streaming de eventos e relatórios analíticos.

7. Apache Superset

Prós:

Open-source e gratuito

Suporte a uma ampla gama de fontes de dados

Flexível para criação de dashboards e visualizações

Contras:

Requer conhecimento técnico para instalação e configuração

Limitado para análise em tempo real em comparação com outras soluções

Aplicação: Empresas que precisam de uma solução open-source para visualização de dados sem custo de licenciamento.

8. Azure Synapse Analytics

Prós:

Totalmente integrado ao ecossistema Azure

Suporta análise em tempo real de grandes volumes de dados

Possui recursos de SQL e big data integrados

Contras:

Curva de aprendizado para quem não está familiarizado com Azure

Pode ter um custo elevado dependendo do uso

Aplicação: Projetos de grande escala que exigem processamento de dados em tempo real com integração total no Azure.

9. Redash

Prós:

Open-source e fácil de usar

Suporte a várias bases de dados

Ótima ferramenta para equipes que precisam de consultas rápidas

Contras:

Funcionalidades limitadas para grandes empresas

Não é ideal para processamento de dados complexos em tempo real

Aplicação: Empresas pequenas a médias que precisam de uma ferramenta simples e acessível para relatórios e dashboards.

10. MicroStrategy

Prós:

Ampla gama de funcionalidades de business intelligence

Suporte a dados em tempo real com alto nível de personalização

Ótimo para projetos corporativos de grande escala

Contras:

Custo elevado

Curva de aprendizado acentuada

Aplicação: Grandes corporações que precisam de uma solução robusta para business intelligence e análise em tempo real.

11. Dataedo

Prós:

Excelente para documentação e governança de dados

Interface simples e fácil de usar

Ajuda na visualização e organização dos metadados

Contras:

Não é projetado para análise de dados em tempo real

Funcionalidades limitadas para grandes volumes de dados

Aplicação: Projetos que exigem documentação e governança de dados clara, como ambientes de big data corporativos.

12. Power BI

Prós:

Fácil de usar e integrado ao ecossistema Microsoft

Boa solução para visualização de dados em tempo real

Grande variedade de conectores e integração com várias fontes de dados

Contras:

Limitações na manipulação de grandes volumes de dados

Custo de licenciamento pode ser alto para grandes equipes

Aplicação: Projetos de relatórios executivos e visualizações interativas para pequenas e médias empresas.

13. Presto

Prós:

Alta performance para consultas distribuídas em grandes volumes de dados

Suporte a SQL, ideal para grandes análises

Integração com vários sistemas de armazenamento de dados

Contras:

Configuração complexa

Requer conhecimento técnico avançado para otimização

Aplicação: Análises distribuídas em ambientes de big data, como consultas em clusters Hadoop.

Essas ferramentas são fundamentais para construir pipelines de dados eficientes para análises em tempo real, cada uma com seu conjunto de vantagens e limitações. A escolha da ferramenta depende do tipo de projeto, dos volumes de dados a serem processados e do nível de personalização e complexidade exigido.

[cbmchannel]

EmjayKeyz, MacG & Mthunzi – Lunganele feat. Redash https://www.curteboamusica.info/2024/07/emjaykeyz-macg-mthunzi-lunganele-feat.html?utm_source=dlvr.it&utm_medium=tumblr

[redashfilms]

Mumbai's Leading Digital Marketing Agency

RedAsh Films is a leading film production house & ad agency in Mumbai. We specialize in B2B,B2C,D2C also digital marketing, viral videos, social media management, corporate films, etc.RedAsh Films , B2B, B2C,D2C...

Mumbai's Leading Digital Marketing Agency

[y2fear]

Live Dashboards with Redash and Rockset

[masayuki038]

2023 年振り返り

仕事

今年は昨年と大きく変わらず、ヨーロッパ圏で開発しているシステムの Product Owner と、アジアで開発しているシステムの開発サポート。

PO といってもマーケットを睨んでやるタイプの仕事ではないので、自分のペースで楽しみながらやっている。本当はもうちょっとステークホルダー間で摩擦を生み出したかったけど、自分の持ち場で平穏に過ごした感がある。来年はもうちょっと場を荒らしてプロジェクトを動かしていきたい。

開発サポートは今年いっぱいで一段落した。これで仕事では開発から離れてしまうことになるので、何か違う角度で開発作業を作りたい。社内のデータ基盤のクラスタのバージョンアップもそろそろ終わりそうなので、そのあたりから切り込んでいきたいところ。

今年から週一くらいの頻度でオフィスに出社するようにしている。自宅でずっと作業をしているのも飽きてきた感はある。オフィスのリフォームが終わったら、席も空いてくると思うので、もう少し出社の頻度を増やしても良いかも。

音楽

コロナ禍に入って時間が出来たこともあり、仕事を引退してからやろうと思っていた音楽制作を開始した。昨年は、Deep In The Jungle Records のコンピレーションに Wrap Trap という名前で一曲入れてもらった。このレーベルの track はよく聴いていたので、このコンピに入れてもらえたのは嬉しかった。

DEEPIN102 - Deep In The Jungle Anthems 9 LP - The Final Chapter Deep In The Jungle Records

ただ、今年は Jungle / DnB はあまり作れなかった。代わりに Dub にハマり、違う名義で Dub track を作っている。こちらも来年リリースする方向でレーベルと話をしている。

Path of Exile

これもコロナ禍から始めた。最初に入社した会社で、昼休みに Diablo で遊ぶする文化があったが、早々に仕事場が変わってしまったので参加できなかった。それで、仕事を引退したらこの手のゲームをやってみようとずっと思っていた。

始めた、と言っても、嗜むくらい。毎回リーグが始まると参加しているけど、寝る時間を惜しんでまではやってない。3.23 は nerf されたと話題の RF で遊んでいる。RF は Play Feel が良いので好き。

Redash

PoE が一段落した時に、時間もあるので、ちょっと OSS やろうかな、ということで 10 月くらいから Contribution を始めた。元々、現職で 5 年ほど前に新規のプロダクト開発を開始する際、Redash を参考にしたことがあって、大まかな仕組みは理解していたので、入りやすかった。また、現在のプロジェクトでも Redash を使っているので、余裕があれば Contribution したいと思っていた。ちょっと Contribution したら Collaborator の role を付けてもらったので、そこからは定期的に見ている。

Redash が Databricks に acquisition されていたので、Redash は Databricks が manage しているのかと思っていた。が、今年の 4 月に A New Chapter as a Community-Led Project という宣言が出ており、何か風向きが変わったように見える。

と言っても、Community 側から direction が明確に出ているわけでもなく、2024 の Roadmap は作れらているもののメンテナも顔を出さなくなっているので、果たして続くのか…という疑問はある。とりあえず来る QA や Issue、PR は見ていて、分かる範囲で response しているが、ちょっと先行きは見えないカンジ。

旅行

今年から旅行を再開し、また台湾に行ってきた。円安だったのこともあり、旅行に対してちょっと億劫になっていたが、久々に外に出てリフレッシュできて良かった。また定期的に外に出たい。

今年一年、ありがとうございました。また来年も��ろしくお願いします。

[dc9spot]

I’m playing arknights right now....so I’m drawing my DnD OC as an arknights character!

[johannstutt413]

(requested by anonymous; sequel to this)

“Encio?” Red began as she walked into the living room. “Um...Something’s happened.”

“Oh? And what would that something be?” SilverAsh was reclined on the couch, reading through paperwork for a deal he planned to settle.

She crouched behind the couch arm his head was against and held something within his eyeline. “This is saying I’m pregnant.”

“...Say what.” He looked at the dark purple line on the indicator, and his eyes went wide. “Well, um...this is awkward.”

“How so?”

Encio leaned his head back to look at her. “I- never mind, never mind. Dr. Kal’tsit doesn’t know about us yet, does she?”

“I don’t think so.” Red frowned. “You’re not going to leave, are you?”

“Leave y- Goddess, why would you even say something like that, Red?!” His papers went flying as he stood up to hold her.

She sighed. “Sorry. I just thought, if I can’t hunt, then maybe...but you don’t care about that, do you.”

“I care about you, Red.” He scratched behind her ears. “And I’m happy to have a kid with you, I just...I wasn’t expecting it quite so quickly. However, I did prepare for something like this.”

“Prepared what for something like this?” Red looked him in the eye, hers sparkling in the overhead light.

Encio smiled. “A honeymoon getaway.”

“A honeymoon?” She gasped. “But that would mean-”

“I was hoping it would come along more naturally, but the Goddess had other plans. If you’re as worried as I am about Kal’tsit finding out-”

Red tightened her grip around him. “I don’t want her to take you from me.”

“-so I think we should elope.” He realized she didn’t know what that meant. “We’re going to take my private helicopter to Kjerag, have some lawyers marry us secretly, and honeymoon in the mountains for a few weeks.”

“Oh, that sounds lovely. Do you have wolves in Kjerag?”

He nodded. “Big, fluffy ones. I would’ve kept one as a pet, but I never had the time...Maybe I will now, though.”

“I don’t think you will,” she smiled. “I’m the only wolf for you...even if I’m not a proper Lupo.”

“You’re my Red, Red, and that’s better than any mountain wolf could ever be. Can you leave today?”

Red nodded. “We can go right now, if you want.”

“I need to pack,” he smiled, “and there’s a couple of phone calls I need to make, but we should be able to leave by this evening. Oh, you have no idea how excited I am to have an heir.”

“I have a hint.” Watching his tail gave her a pretty clear indication of that…

-

After the Doctor and Pramanix left, Red was all over Encio, and seeing as how there was no way to slow her down when she got like this, he simply did his best to keep up. Some time later, sitting in bed with her head on his chest, he sighed contentedly. “I’m happy I haven’t had time to spy on Anya and Ensia. Did you know about that, darling?”

“I smelled the Doctor and Anya together a few days ago,” Red replied, “but I thought they might want to surprise you.”

“You sneaky little wolf.” He chuckled as he resumed the headpats.

She giggled, a foxlike yipping sound never heard before by anyone at RI. “I’m very sneaky when I need to be...You’re going to have to name our kid, by the way.”

“I certainly want to,” he smirked, “but why do I have to, darling?”

“Because I don’t have a name outside of my codename.”

Encio froze. “What did you put when we filled out the paperwork, then?”

“Red as my first name, Projekt as my maiden name.” She shrugged. “If I did it the other way, I’d be Projekt Silverash, and I don’t like that as much.”

“Well, then, you do have a name now - Red.”

She looked up at him. “Is that how it works?”

“If that isn’t your name, the documents aren’t legally binding.” He sighed. “When we get back, I’m sending out inquiries to be sure, just in case Kal’tsit wants to try something.”

“When she hears about the pup, I think she’ll accept it. She’s not that bad...most of the time.”

Encio kissed her forehead. “I’ll take your word on that, then...It’s not even 5 yet. Maybe I should get back to work-”

“Or,” Red interjected, clambering on top of him before laying back against him, “we watch TV and you let me fluff your tail.”

“...Well, I guess work will have to wait.”

[netmarkjp]

#ばばさん通信ダイジェスト : 3年振りにアップデート📊 Redash v25.1.0 を試せる「Redash ハンズオン資料」

賛否関わらず話題になった/なりそうなものを共有しています。

3年振りにアップデート📊 Redash v25.1.0 を試せる「Redash ハンズオン資料」

https://kakakakakku.hatenablog.com/entry/2025/01/27/091715

[beastredash]

Fanart to Crywolf's new song "Fawn". I drew it relying only on the music itself and not its description in order to capture the feeling. And, I think, I managed it. "You’re my heart, My rain, My poison, My fawn" You can listen for the whole song here: youtu.be/pkLeOMGyUss I admire this artist and his art with whole my heart.