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Building a Robust JS/TS Monorepo: Best Practices with Yarn, NX and Changesets

ጠንካራ JS/TS Monorepo መገንባት፡ ከ Yarn፣ NX እና Changesets ጋር ያሉ ምርጥ ልምዶች

إنشاء مستودع JS/TS قوي: أفضل الممارسات مع Yarn وNX وChangessets

Изграждане на стабилно JS/TS Monorepo: Най-добри практики с Yarn, NX и Changesets

একটি শক্তিশালী JS/TS মনোরেপো তৈরি করা: সুতা, NX এবং চেঞ্জসেট ব্যবহার করে সেরা অনুশীলন

Creación de un monorepositorio JS/TS robusto: prácticas recomendadas con Yarn, NX y conjuntos de cambios

Vankan JS/TS Monorepon rakentaminen: parhaat käytännöt langan, NX:n ja muutossarjojen kanssa

בניית JS/TS Monorepo חזק: שיטות עבודה מומלצות עם Yarn, NX ו-Changesets

堅牢な JS/TS モノレポの構築: Yarn、NX、チェンジセットを使用したベストプラクティス

Budowanie solidnego monorepozytorium JS/TS: najlepsze praktyki z Yarn, NX i zestawami zmian

Kubaka JS / TS Monorepo: Imyitozo myiza hamwe na Yarn, NX na Changesets

Budovanie robustného JS/TS Monorepo: Najlepšie postupy s priadzou, NX a sadami zmien

Kuunda Monorepo Imara ya JS/TS: Mbinu Bora na Uzi, NX na Seti za Mabadiliko

GitHub Actions Meets Kubernetes: How to Test PRs on GKE Without Losing Your Mind

Lately, there is a need of private chatbot service as a complete alternative to OpenAI's ChatGPT. So, I decide to implement one at home and make it accessible to everyone in my household alongside with my network printer and NAS (OpenMediaVault). In the past, I used to recommend people using Llama series for English tasks and Qwen series for Chinese tasks. There was no open-source model that's strong enough in multilingual tasks comparing to proprietary ones (GPT/Claude). However, as we all know—things have changed recently. I have been using DeepSeek-V2 occasionally every time I got tired with Qwen2.5 and have been falling behind with DeepSeek V2.5 and V3 due to lack of hardware. But DeepSeek didn't let me down, R1 performs so impressive and provides as small as 1.5B! This means we can run it even on CPU with some considerable user experience. As many people has GPUs for gaming, speed is not an issue. To make local LLMs process uploaded documents and images is a big advantage since OpenAI limits this usage for free accounts. Although Installing Open WebUI with Bundled Ollama Support is very easy with official one-line command: docker run -d -p 3000:8080 --gpus=all -v ollama:/root/.ollama -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:ollama But to get RAG (Web search) working is not easy for most people, so I would like to find some out-of-box solution. As I mentioned in my last post, harbor is a great testbed for experimenting with different LLM stack. But it is not only great for that, it's also an all-in-one solution for self-hosting local LLMs with RAG working out-of-box. So, let's begin implementing it from scratch and feel free to skip steps since most people don't start from OS installation. System Preparation (Optional) As same as previously, go through install process using debian-11.6.0-amd64-netinst.iso Add to sudoer usermod -aG sudo username then reboot (Optional) Add extra swap fallocate -l 64G /home/swapfile
chmod 600 /home/swapfile
mkswap /home/swapfile
swapon /home/swapfile and make the swapfile persistent nano /etc/fstab UUID=xxxxx-xxx swap swap defaults,pri=100 0 0
/home/swapfile swap swap defaults,pri=10 0 0 Check with swapon --show or free -h Disable Nouveau driver bash -c "echo blacklist nouveau > /etc/modprobe.d/blacklist-nvidia-nouveau.conf"
bash -c "echo options nouveau modeset=0 >> /etc/modprobe.d/blacklist-nvidia-nouveau.conf"
update-initramfs -u
update-grub
reboot Install dependencies apt install linux-headers-`uname -r` build-essential libglu1-mesa-dev libx11-dev libxi-dev libxmu-dev gcc software-properties-common sudo git python3 python3-venv pip libgl1 git-lfs -y (Optional) Perform uninstall if needed apt-get purge nvidia*
apt remove nvidia*
apt-get purge cuda*
apt remove cuda*
rm /etc/apt/sources.list.d/cuda*
apt-get autoremove && apt-get autoclean
rm -rf /usr/local/cuda* Install cuda-tookit and cuda wget https://developer.download.nvidia.com/compute/cuda/12.4.1/local_installers/cuda-repo-debian11-12-4-local_12.4.1-550.54.15-1_amd64.deb
sudo dpkg -i cuda-repo-debian11-12-4-local_12.4.1-550.54.15-1_amd64.debsudo cp /var/cuda-repo-debian11-12-4-local/cuda-*-keyring.gpg /usr/share/keyrings/
sudo add-apt-repository contrib
sudo apt-get update
sudo apt-get -y install cuda-toolkit-12-4

sudo apt install libxnvctrl0=550.54.15-1
sudo apt-get install -y cuda-drivers Install the NVIDIA Container Toolkit since harbor is docker-based curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
  && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
    sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
    sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list Then sudo apt-get update and sudo apt-get install -y nvidia-container-toolkit Perform a cuda post-install action nano ~/.bashrc export PATH=/usr/local/cuda-12.4/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-12.4/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} Then sudo update-initramfs -u, ldconfig or source ~/.bashrc to apply changes after reboot, confirm with nvidia-smi and nvcc --version Install Miniconda (Optional, not for harbor) wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && sudo chmod +x Miniconda3-latest-Linux-x86_64.sh && bash Miniconda3-latest-Linux-x86_64.sh Docker & Harbor Install docker # Add Docker's official GPG key:
sudo apt-get update
sudo apt-get install ca-certificates curl
sudo install -m 0755 -d /etc/apt/keyrings
sudo curl -fsSL https://download.docker.com/linux/debian/gpg -o /etc/apt/keyrings/docker.asc
sudo chmod a+r /etc/apt/keyrings/docker.asc

# Add the repository to Apt sources:
echo \
  "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.asc] https://download.docker.com/linux/debian \
  $(. /etc/os-release && echo "$VERSION_CODENAME") stable" | \
  sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
sudo apt-get update

sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin Perform post-install for docker without sudo sudo groupadd docker
sudo usermod -aG docker $USER
newgrp docker
docker run hello-world Manual install Harbor git clone https://github.com/av/harbor.git && cd harbor
./harbor.sh ln Verify with harbor --version Add with RAG support to defaults with harbor defaults add searxng Use harbor defaults list to check, now there are three services active: ollama, webui, searxng Run with harbor up to bring up these services in docker Use harbor ps as docker ps , and harbor logs to see tailing logs Now the open-webui frontend is serving at 0.0.0.0:33801 and can be accessed from http://localhost:33801 or clients from LAN with server's IP address. Monitor VRAM usage with watch -n 0.3 nvidia-smi Monitor log with harbor up ollama --tail or harbor logs All ollama commands are usable such as harbor ollama list It's time to access from other devices (desktop/mobile) to register an admin account and download models now. Using Local LLM After login with admin account, click top right avatar icon, open Admin Panel then Settings, or simply access via `http://ip:33801/admin/settings. Click Models, and at the top right click the Manage Models which looks like a download button. Put deepseek-r1 or any other model in the textbox below Pull a model from Ollama.com and click the download button on the right side. After model downloaded, it may require a refresh and the newly downloaded model will be usable under the drop down menu on the New Chat (home) page. Now, it's not only running a chatbot alternative to ChatGPT, but also a fully functional API alternative to OpenAI API, plus a private search engine alternative to Google! webui is accessible within LAN via: http://ip:33801 ollama is accessible within LAN via: http://ip:33821 searxng is accessible within LAN via: http://ip:33811 Call Ollama API with any application with LLM API integration: http://ip:33821/api/ps
http://ip:33821/v1/models
http://ip:33821/api/generate
http://ip:33821/v1/chat/completionsb Lately, there is a need of private chatbot service as a complete alternative to OpenAI's ChatGPT. So, I decide to implement one at home and make it accessible to everyone in my household alongside with my network printer and NAS (OpenMediaVault) . NAS (OpenMediaVault) In the past, I used to recommend people using Llama series for English tasks and Qwen series for Chinese tasks. There was no open-source model that's strong enough in multilingual tasks comparing to proprietary ones (GPT/Claude). However, as we all know—things have changed recently. I have been using DeepSeek-V2 occasionally every time I got tired with Qwen2.5 and have been falling behind with DeepSeek V2.5 and V3 due to lack of hardware. But DeepSeek didn't let me down, R1 performs so impressive and provides as small as 1.5B! This means we can run it even on CPU with some considerable user experience. As many people has GPUs for gaming, speed is not an issue. To make local LLMs process uploaded documents and images is a big advantage since OpenAI limits this usage for free accounts. Although Installing Open WebUI with Bundled Ollama Support is very easy with official one-line command: Installing Open WebUI with Bundled Ollama Support docker run -d -p 3000:8080 --gpus=all -v ollama:/root/.ollama -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:ollama docker run -d -p 3000:8080 --gpus=all -v ollama:/root/.ollama -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:ollama But to get RAG (Web search) working is not easy for most people, so I would like to find some out-of-box solution. As I mentioned in my last post , harbor is a great testbed for experimenting with different LLM stack. But it is not only great for that, it's also an all-in-one solution for self-hosting local LLMs with RAG working out-of-box. So, let's begin implementing it from scratch and feel free to skip steps since most people don't start from OS installation. my last post System Preparation (Optional) As same as previously , go through install process using debian-11.6.0-amd64-netinst.iso previously debian-11.6.0-amd64-netinst.iso Add to sudoer usermod -aG sudo username then reboot usermod -aG sudo username (Optional) Add extra swap fallocate -l 64G /home/swapfile
chmod 600 /home/swapfile
mkswap /home/swapfile
swapon /home/swapfile fallocate -l 64G /home/swapfile
chmod 600 /home/swapfile
mkswap /home/swapfile
swapon /home/swapfile and make the swapfile persistent nano /etc/fstab nano /etc/fstab UUID=xxxxx-xxx swap swap defaults,pri=100 0 0
/home/swapfile swap swap defaults,pri=10 0 0 UUID=xxxxx-xxx swap swap defaults,pri=100 0 0
/home/swapfile swap swap defaults,pri=10 0 0 Check with swapon --show or free -h swapon --show free -h Disable Nouveau driver bash -c "echo blacklist nouveau > /etc/modprobe.d/blacklist-nvidia-nouveau.conf"
bash -c "echo options nouveau modeset=0 >> /etc/modprobe.d/blacklist-nvidia-nouveau.conf"
update-initramfs -u
update-grub
reboot bash -c "echo blacklist nouveau > /etc/modprobe.d/blacklist-nvidia-nouveau.conf"
bash -c "echo options nouveau modeset=0 >> /etc/modprobe.d/blacklist-nvidia-nouveau.conf"
update-initramfs -u
update-grub
reboot Install dependencies apt install linux-headers-`uname -r` build-essential libglu1-mesa-dev libx11-dev libxi-dev libxmu-dev gcc software-properties-common sudo git python3 python3-venv pip libgl1 git-lfs -y apt install linux-headers-`uname -r` build-essential libglu1-mesa-dev libx11-dev libxi-dev libxmu-dev gcc software-properties-common sudo git python3 python3-venv pip libgl1 git-lfs -y (Optional) Perform uninstall if needed apt-get purge nvidia*
apt remove nvidia*
apt-get purge cuda*
apt remove cuda*
rm /etc/apt/sources.list.d/cuda*
apt-get autoremove && apt-get autoclean
rm -rf /usr/local/cuda* apt-get purge nvidia*
apt remove nvidia*
apt-get purge cuda*
apt remove cuda*
rm /etc/apt/sources.list.d/cuda*
apt-get autoremove && apt-get autoclean
rm -rf /usr/local/cuda* Install cuda-tookit and cuda Install cuda-tookit and cuda wget https://developer.download.nvidia.com/compute/cuda/12.4.1/local_installers/cuda-repo-debian11-12-4-local_12.4.1-550.54.15-1_amd64.deb
sudo dpkg -i cuda-repo-debian11-12-4-local_12.4.1-550.54.15-1_amd64.debsudo cp /var/cuda-repo-debian11-12-4-local/cuda-*-keyring.gpg /usr/share/keyrings/
sudo add-apt-repository contrib
sudo apt-get update
sudo apt-get -y install cuda-toolkit-12-4

sudo apt install libxnvctrl0=550.54.15-1
sudo apt-get install -y cuda-drivers wget https://developer.download.nvidia.com/compute/cuda/12.4.1/local_installers/cuda-repo-debian11-12-4-local_12.4.1-550.54.15-1_amd64.deb
sudo dpkg -i cuda-repo-debian11-12-4-local_12.4.1-550.54.15-1_amd64.debsudo cp /var/cuda-repo-debian11-12-4-local/cuda-*-keyring.gpg /usr/share/keyrings/
sudo add-apt-repository contrib
sudo apt-get update
sudo apt-get -y install cuda-toolkit-12-4

sudo apt install libxnvctrl0=550.54.15-1
sudo apt-get install -y cuda-drivers Install the NVIDIA Container Toolkit since harbor is docker-based Install the NVIDIA Container Toolkit curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
  && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
    sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
    sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
  && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
    sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
    sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list Then sudo apt-get update and sudo apt-get install -y nvidia-container-toolkit sudo apt-get update sudo apt-get install -y nvidia-container-toolkit Perform a cuda post-install action nano ~/.bashrc post-install action nano ~/.bashrc export PATH=/usr/local/cuda-12.4/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-12.4/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} export PATH=/usr/local/cuda-12.4/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-12.4/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} Then sudo update-initramfs -u , ldconfig or source ~/.bashrc to apply changes sudo update-initramfs -u ldconfig source ~/.bashrc after reboot, confirm with nvidia-smi and nvcc --version nvidia-smi nvcc --version Install Miniconda (Optional, not for harbor) wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && sudo chmod +x Miniconda3-latest-Linux-x86_64.sh && bash Miniconda3-latest-Linux-x86_64.sh wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && sudo chmod +x Miniconda3-latest-Linux-x86_64.sh && bash Miniconda3-latest-Linux-x86_64.sh Docker & Harbor Install docker Install docker # Add Docker's official GPG key:
sudo apt-get update
sudo apt-get install ca-certificates curl
sudo install -m 0755 -d /etc/apt/keyrings
sudo curl -fsSL https://download.docker.com/linux/debian/gpg -o /etc/apt/keyrings/docker.asc
sudo chmod a+r /etc/apt/keyrings/docker.asc

# Add the repository to Apt sources:
echo \
  "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.asc] https://download.docker.com/linux/debian \
  $(. /etc/os-release && echo "$VERSION_CODENAME") stable" | \
  sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
sudo apt-get update

sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin # Add Docker's official GPG key:
sudo apt-get update
sudo apt-get install ca-certificates curl
sudo install -m 0755 -d /etc/apt/keyrings
sudo curl -fsSL https://download.docker.com/linux/debian/gpg -o /etc/apt/keyrings/docker.asc
sudo chmod a+r /etc/apt/keyrings/docker.asc

# Add the repository to Apt sources:
echo \
  "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.asc] https://download.docker.com/linux/debian \
  $(. /etc/os-release && echo "$VERSION_CODENAME") stable" | \
  sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
sudo apt-get update

sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin Perform post-install for docker without sudo post-install sudo groupadd docker
sudo usermod -aG docker $USER
newgrp docker
docker run hello-world sudo groupadd docker
sudo usermod -aG docker $USER
newgrp docker
docker run hello-world Manual install Harbor Manual install Harbor git clone https://github.com/av/harbor.git && cd harbor
./harbor.sh ln git clone https://github.com/av/harbor.git && cd harbor
./harbor.sh ln Verify with harbor --version harbor --version Add with RAG support to defaults with harbor defaults add searxng harbor defaults add searxng Use harbor defaults list to check, now there are three services active: ollama , webui , searxng harbor defaults list ollama webui searxng Run with harbor up to bring up these services in docker harbor up Use harbor ps as docker ps , and harbor logs to see tailing logs harbor ps docker ps harbor logs Now the open-webui frontend is serving at 0.0.0.0:33801 and can be accessed from http://localhost:33801 or clients from LAN with server's IP address. 0.0.0.0:33801 http://localhost:33801 Monitor VRAM usage with watch -n 0.3 nvidia-smi watch -n 0.3 nvidia-smi Monitor log with harbor up ollama --tail or harbor logs harbor up ollama --tail harbor logs All ollama commands are usable such as harbor ollama list harbor ollama list It's time to access from other devices (desktop/mobile) to register an admin account and download models now. Using Local LLM After login with admin account, click top right avatar icon, open Admin Panel then Settings , or simply access via `http://ip:33801/admin/settings. Admin Panel Settings Click Models , and at the top right click the Manage Models which looks like a download button. Models Manage Models Put deepseek-r1 or any other model in the textbox below Pull a model from Ollama.com and click the download button on the right side. deepseek-r1 any other model Pull a model from Ollama.com After model downloaded, it may require a refresh and the newly downloaded model will be usable under the drop down menu on the New Chat (home) page. New Chat Now, it's not only running a chatbot alternative to ChatGPT, but also a fully functional API alternative to OpenAI API, plus a private search engine alternative to Google! webui is accessible within LAN via: http://ip:33801 http://ip:33801 ollama is accessible within LAN via: http://ip:33821 http://ip:33821 searxng is accessible within LAN via: http://ip:33811 http://ip:33811 Call Ollama API with any application with LLM API integration: Ollama API http://ip:33821/api/ps
http://ip:33821/v1/models
http://ip:33821/api/generate
http://ip:33821/v1/chat/completionsb http://ip:33821/api/ps
http://ip:33821/v1/models
http://ip:33821/api/generate
http://ip:33821/v1/chat/completionsb

Run Deepseek (Or Any Other AI Model) In Your Home With a Few Lines of Code

Kør Deepseek (eller enhver anden AI-model) i dit hjem med et par linjer kode

Ejecute Deepseek (o cualquier otro modelo de IA) en su hogar con unas pocas líneas de código

数行のコードで自宅でDeepseek（または他のAIモデル）を実行

გაუშვით Deepseek (ან AI სხვა მოდელი) თქვენს სახლში რამდენიმე სტრიქონის კოდით

Deepseek (же башка AI моделин) үйүңүздө бир нече сап код менен иштетиңиз

Хэдэн мөр код ашиглан Deepseek (эсвэл бусад хиймэл оюун ухааны загвар)-ыг гэртээ ажиллуул

ډیپسیک (یا کوم بل AI ماډل) په خپل کور کې د څو لینونو کوډ سره چل کړئ

Mhanya Deepseek (Kana Chero Imwe AI Modhi) Mumusha Wako Uine Mitsetse Mishoma Yekodhi

เรียกใช้ Deepseek (หรือโมเดล AI อื่น ๆ ) ในบ้านของคุณด้วยโค้ดเพียงไม่กี่บรรทัด

Запустіть Deepseek (або будь-яку іншу модель AI) у вашому домі за допомогою кількох рядків коду

Chạy Deepseek (Hoặc bất kỳ mô hình AI nào khác) tại nhà của bạn chỉ bằng một vài dòng mã

只需几行代码即可在家中运行 Deepseek（或任何其他 AI 模型）

Let's Build a Practical Home Lab for Learning and Experimentation

Dependency Injection With Dagger 2: What Is It, Key Concepts, and More

ডিপেনডেন্সি ইনজেকশন উইথ ড্যাগার 2: এটি কী, মূল ধারণা এবং আরও অনেক কিছু

Abhängigkeitsinjektion mit Dagger 2: Was ist das, Schlüsselkonzepte und mehr

Inyección de dependencia con Dagger 2: qué es, conceptos clave y más

Injection de dépendances avec Dagger 2 : qu'est-ce que c'est, concepts clés et plus encore

डैगर 2 के साथ निर्भरता इंजेक्शन: यह क्या है, मुख्य अवधारणाएँ, और अधिक

Dagger 2 による依存性注入: その概要、主要な概念など

Dagger 2를 사용한 종속성 주입: 그것이 무엇이고, 핵심 개념 및 기타

Injeção de dependência com Dagger 2: o que é, conceitos-chave e muito mais

Внедрение зависимостей с помощью Dagger 2: что это такое, основные понятия и многое другое

Dagger 2 ile Bağımlılık Enjeksiyonu: Nedir, Temel Kavramlar ve Daha Fazlası

Dependency Injection Với Dagger 2: Nó Là Gì, Các Khái Niệm Chính Và Nhiều Hơn Nữa

使用 Dagger 2 进行依赖注入：它是什么、关键概念等等

Many sales folks say by Dale Carnegie is a go-to book if you want to get good at selling stuff. I read it when I was 14, but I only remember that we love the sound of our name. How to Win Friends and Influence People So I thought I’d attempt my version of HTWFAIP because people often ask me how I make friends so easily (I know I sound like an Instagram influencer about to sell you some sketchy product, but really, people do ask 😂). Yet, being friendly and likable was a skill I had to develop. Back in one of my first jobs, my boss told me I had a “resting bitch face.” I think it should be called a “resting Eastern European face” (REEF for short 😁). I see it a lot after moving back home after 12 years abroad. The typical explanation is that a colder climate makes us ‘colder people’, but I think it has a lot to do with the long history of russian occupation, but I derail. Following the comment of my manager, psychology studies and life experiences, I’ve decided to intentionally work on my personality. Over the years, I’ve figured out some tricks to make myself more likable and good at social stuff. Now, I can’t turn it off - it’s a part of my identity. Human psychology by the CIA The CIA had to figure out how human nature works to carry out successful espionage. I already shared a full scope of what I learned in this , so this is a quick reminder relevant to our topic. article Humans have three lives. (how we want to appear to others, like an Instagram feed). Public life (what your closest confidants know about you. Like your feet smell). Private life (often, these are thoughts that live in a dark part of our psyche that we’re afraid to share for fear of rejection/judgment, like having a mistress). Secret life The CIA builds trust with a target by methodically earning trust, aka getting into someone’s secret life. To the target, it reads as I revealed so getting sensitive info about the target’s government becomes much easier. the worst parts of me and they’re still here, I haven’t been rejected, therefore I trust this person, Now, the 3 lives principle applies to friendships. I trust and, therefore, like my close friends more than I do acquaintances because close friends see all parts of me and still stick around. Closeness correlates with likeability. But closeness also correlates with likeability. perceived Rule #1 for building connections - TRUST. Building rapport Building any relationship requires time. But some hacks make you more likable to people who don’t yet know you. The caveat here is genuineness - people smell a fake a mile away (unless they’re dealing with a CIA spy 😉). You have to be genuinely interested in other people. I think I’m successful at connecting with people because I’m sincerely interested in them. What is their inner life like? What are they passionate about? What do they struggle with? How do they feel? Why did they choose the particular career path? Rule #2 - be genuine or get out of the conversation because nobody likes a fake. Ways to build rapport Mirroring: Subtly mimic the person’s body language, speech patterns, and gestures to create a subconscious sense of familiarity and trust. Let’s say I’m sitting cross-legged - you should cross your legs too. If the person lowers their voice, you should follow. Emotional Vulnerability: Share personal stories or vulnerabilities to encourage reciprocity and strike up the connection. Volunteer emotional vulnerability as a way to gain trust and show you want to go beyond the public life persona. Do make anything up. not For example, at a recent party, I sat next to a stranger and said, ‘I always feel so awkward at parties where I don’t know anyone - I feel a bit of anxiety.’ That was both a true and relatable experience, so the person could open up as well, and we launched into a conversation where they very quickly opened up their private life info as well as volunteered a spare room if I’m ever in their neck of the woods. Active Listening: Pay close attention to the person’s words, emotions, and non-verbal cues, demonstrating genuine interest and understanding. The common mistake people make in conversations is that they’re too caught up in their own thoughts, planning their answers instead of truly listening to the other person. You can avoid this by practicing mirroring and labeling techniques during the conversation. I first came across these ideas in a former FBI hostage negotiator work. If they worked for him, they’d work for you, too 😉 Chris Voss’ a technique where you repeat back the last 1–3 words of what someone told you. Mirroring (yes again, but a different kind) - For example: Someone: ‘I was late for work again!’ You: ‘Late again?’ (An open-ended remark unconsciously inviting the other person to expand). Casually repeating the words back helps you stay present and truly listen, making the other person feel heard, thus building rapport. This openness leads to greater liking and trust between people. is another technique that encourages you to listen make the other party feel recognized. You could call it a verbal observation/recognition. Labeling and For example: Someone ‘I enjoy spending quality time with my friends’ You (Spotting patterns or making inferences makes the other person feel heard). ‘It sounds like you’re someone who values friendships!’ Similarly to mirroring, don’t overuse labels. Remember, these techniques are here to help you be present in the conversation. It’s a chat, not a therapy session. Asking Questions: Use open-ended questions to encourage the person to share more about themselves and their experiences. Avoid yes or no questions, especially with shy or socially anxious people, and opt for open-ended ones to encourage sharing. This approach helps the conversation flow more naturally, giving you more to build on. Finding Common Ground: Identify shared interests, experiences, or values to establish commonality and foster a sense of camaraderie. This is easy - look at the context you’re in and come up with conversation topics based on that. I was at a friend’s gathering recently with both friends and strangers. Instead of the usual ‘How do you know so-and-so?’ to break the ice, I recognized someone I often see at my favorite coffee shop, always deep in phone convos. I approached them with, “Hey, I've seen you at Cafe X, but you're always on the phone, so we never get a chance to chat.” They found it amusing, and we instantly connected. Acknowledge the person in front of you, or see them, as I call it. really I realized that we often interact with our idea of a person or their public life persona rather than with the individuals themselves, especially in public settings. As an experiment, I decided to move beyond the routine “hi, how are you” at coffee shops and restaurants where I play the social role of a customer and the server plays the role of the server. I made an effort to genuinely notice and acknowledge them, perhaps with a compliment or a unique question about their day. These small acts of genuine interest made them light up, feeling valued as individuals. Upgrades or free stuff was a nice byproduct of this experiment:) Principle #3 - humans crave being seen, acknowledged… to matter. Bonus tips If you only get one takeaway from this article - people talking about themselves. Sharing personal stories makes us feel recognized and heard. If you can provide that space for someone, you’re golden. LOVE Another benefit of someone talking about themselves and opening up to you is that it makes them trust you more. The more they talk, the more they trust you. The person asking the questions is always in control of the conversation. Parents adore it when you connect with their kids. Doing so can win you a lot of favor. But remember, you’ve got to be genuine. Kids are great at detecting insincerity and can spot someone faking it from a mile away. Feeling social anxiety is normal and something I still face, but I've found a helpful mantra: “No one's better than me, and I'm no better than anybody else.” I used to repeat this to myself multiple times before social events. Focus on your surroundings and the person you’re with. Most people are preoccupied with their thoughts, so you'll stand out by complimenting or sharing an observation. You have to like yourself more than spending time with people you don’t like. Wrap up I’m a bit worried this advice might sound contrived because it’s hard to put into words something that’s become second nature. Applying these social practices initially felt awkward, much like learning to drive. But just as driving becomes automatic over time, these practices have become ingrained in me. I come across as natural and sincere, and that’s because I am. Remember: Be genuinely interested in the other person; them. see Be an active listener and avid observer. We are inclined to think and talk of ourselves more, override it. Let the other person shine. Further reading My very first article online was about likeability. Back then, I was coaching men to increase their attractiveness in ways beyond the usual superficial advice. You can find it here. I initially wrote the CIA series in 3 parts, but you can find them in one place on . It’s a good overview of human psychology. HackerNoon The article above is aimed at social interactions. I have a separate article on the topic of building and maintaining friendships . here Photo by on Oveth Martinez Unsplash Also published . here

Building a Reusable Deployment Workflow with Kamal and GitHub Actions

Introduction As web developers, we used to find appropriate hosting for our applications with all the complications of deploying and maintaining them. But now, we live in a new era when cloud providers can make a lot of things for us at almost no cost. As for .NET developers, Microsoft Azure looks most natural choice to host web applications. Docker is another piece of technology that makes it possible to abstract from the platform and environment you are running your application. In this article, I will give a detailed guide on how to create a simple ASP.NET Core web application, create a docker image with your application, and the easiest way to deploy it to Azure. In the end, you will have a good understanding of how to deploy your web application and make it accessible to the whole world. 1. Create ASP.NET Core Web App I’m going to use JetBrains Rider for the purpose of the development in this article. But you can use any tool you want. First, you need to create a new project. Let’s call it simple ‘ ’. webapp After creating a new project, it should look like this: 2. Create a Docker Image Without any changes in the project, we are going to focus on the . Modify it as the following example: Dockerfile FROM mcr.microsoft.com/dotnet/aspnet:6.0 as base
WORKDIR /app
EXPOSE 8080
ENV ASPNETCORE_URLS http://+:8080

FROM mcr.microsoft.com/dotnet/sdk:6.0 AS build
COPY . /src
WORKDIR /src
RUN dotnet build "webapp.csproj" -c Release -o /app/build 

FROM build AS publish
RUN dotnet publish "webapp.csproj" -c Release -o /app/publish 

FROM base AS final
WORKDIR /app
COPY --from=publish /app/publish .
ENTRYPOINT ["dotnet", "webapp.dll"] You need to pay attention to the lines and to be able to connect via HTTP to the docker container. EXPOSE 8080 ENV ASPNETCORE_URLS http://+:8080 Build a docker image from the command line. You need to navigate to the project directory, and execute the following command: webapp DOCKER_BUILDKIT=1 docker buildx build --platform linux/amd64 --tag drmoz/webapp:latest . I’m running it on Mac M1, and I have to specify parameter explicitly to be compatible with Azure deployment. I also have to activate the Docker Buildkit by specifying the parameter and using to be able to build an image for another architecture. --platform DOCKER_BUILDKIT=1 buildx If you are running it on the Intel x86/x64 platform, you can use command without the need to specify parameter and activate . docker build --platform buildx You can tag your image differently, but since I’m going to use Docker Hub, I have to specify my account name: as part of the image tag . drmoz drmoz/webapp:latest When an image is built, you can check if it is present in the Docker by running the following command: docker images It should be on the list: REPOSITORY        TAG         IMAGE ID       CREATED          SIZE      
drmoz/webapp      latest      0cfac748256b   37 minutes ago   216MB Let’s run the docker container to verify that the newly created image works fine. Execute command to start a container: docker run docker run -p 5555:8080 drmoz/webapp:latest Specify the parameter to map the port inside the container to your local machine port. Also, specify the image name to create the container. In my case, it is . -p 8080 5555 drmoz/webapp:latest Now, you can navigate in your browser to see the ASP.NET Core Web App home page. http://localhost:5555 We need to push an image to the . Before that, you must register on the Docker Registry, and create a repository. Click on the ‘ ‘ button on the right corner of the home page. Docker Hub Create repository Specify the field, and keep the repository visibility . Repository Name Public In the command line, you have to log in to the Docker Hub registry: docker login After that, you will be able to push a newly created image to the registry: docker push drmoz/webapp:latest Navigate to the repository, and you will see the information about the image: 3. Create a Web App in Azure Now, we are ready to deploy our ASP.NET Core Web Application to Azure. Let’s log in to the Azure Portal, and create a new resource group. Specify the Resource Group name as ‘ ’, and click the ‘Review + create’ button. webapp-rg On the next screen, just hit the ‘Create’ button. Now, we can create a Web App. Navigate to the ‘Create resource‘ page, and find WebApp. Click the ‘Create’ button. You need to specify the one that we created previously. Also, specify a unique for the Web App. Select ‘Docker Container’ in the field. Later, we will be able to specify which Docker image to use. Resource Group Name Publish Select any suitable options in the section, but I recommend using a tire for test purposes. Navigate to the Docker tab. Pricing plans Free F1 On this tab, select ‘Docker Hub’ in the field. Select ‘Public’ in the field, and specify the value with the name of the image we pushed to Docker Hub. Navigate to the ‘Review + create’ tab. On this tab, click the ‘Create‘ button: Image Source Access Type Image and tag When the new resource is deployed, you will be asked if you would like to navigate to this resource; just do it. You will be able to see information regarding the Web App. Click on the ‘Browse‘ button to open a deployed web application. If everything went correctly, you should see the ASP.NET Core Web App home page similar to the one you saw in your browser by navigating . http://localhost:5555 Enable Continuous Deployment If you navigate to the ‘Deployment Center’ settings and enable ‘Continues deployment’, you will be able to redeploy your image every time you push a new one to the Docker Hub. In that manner, you can make changes to your code, push it to the Docker Hub with a new image, and see changes in minutes in Azure. Azure Web App Troubleshooting To be able to see deployment logs and identify issues, you need to enable saving logs to the file system under the ‘App Service logs’ chapter as follows: When it is done, you can navigate to the ‘Deployment Center’ and see logs as follows: Summary If you reach this point, you managed to create an ASP.NET Core web application; pack it to the Docker image, and deploy it to Azure. Nowadays, technology makes life easier and speeds up the development process. You can see how easily you can deploy your application to the cloud provider and expose it to the whole world, perform some testing, and see the result of your work. Now, you can dive into the details of Azure cloud providers using Microsoft . Also, take a look at the Docker for a deeper understanding of this piece of technology. I hope it might be helpful. documentation documentation

How to Deploy an ASP.NET Core Web App in Azure From Docker Image

ডকার ইমেজ থেকে Azure-এ একটি ASP.NET কোর ওয়েব অ্যাপ কীভাবে স্থাপন করবেন

So stellen Sie eine ASP.NET Core-Webanwendung in Azure über ein Docker-Image bereit

Cómo implementar una aplicación web ASP.NET Core en Azure desde la imagen de Docker

Comment déployer une application Web ASP.NET Core dans Azure à partir de l'image Docker

डॉकर इमेज से Azure में ASP.NET कोर वेब ऐप कैसे तैनात करें

Docker イメージから Azure に ASP.NET Core Web アプリをデプロイする方法

Docker 이미지에서 Azure에 ASP.NET Core 웹앱을 배포하는 방법

Como implantar um aplicativo Web ASP.NET Core no Azure a partir da imagem do Docker

Как развернуть веб-приложение ASP.NET Core в Azure из образа Docker

Docker Görüntüsünden Azure'da ASP.NET Core Web Uygulaması Nasıl Dağıtılır

Cách triển khai ứng dụng web ASP.NET Core trong Azure từ Docker Image

如何从 Docker 映像在 Azure 中部署 ASP.NET Core Web 应用程序

Crypto trading is a popular and lucrative activity that involves buying and selling different cryptocurrencies on various platforms. Crypto trading apps are applications that allow users to access the crypto market and execute trades from their mobile devices. trading apps can offer various features, such as market analysis, portfolio management, price alerts, news updates, and more. Crypto If you are interested in creating your own crypto trading app, you will need to follow some steps and use some tools to make it happen. In this tutorial, we will guide you through the process of building a basic crypto trading app that can connect to a crypto exchange, display real-time prices, and execute buy and sell orders. Step 1: Setting your cost The cost of developing a crypto trading app depends on various factors, such as the type and complexity of your app, the features and technologies you choose, the size and experience of your development team, the location and rates of your developers, etc. However, as a rough estimate, you can expect to pay between $25,000 and $100,000 for a basic crypto trading app and between $100,000 and $500,000 for a complex . crypto trading app Step 2: Choose a Crypto Exchange API The first step is to choose a crypto exchange API that will provide the data and functionality for your app. A crypto exchange API is a set of endpoints that allow you to interact with a crypto exchange platform programmatically. You can use a crypto exchange API to get market data, such as prices, volumes, order books, and more. You can also use a crypto exchange API to place and cancel orders, check your balance, and manage your account. There are many crypto exchange APIs available, such as [Binance], [Coinbase], [Kraken], and more. Each crypto exchange API has its own documentation, features, limitations, and fees. You will need to choose a crypto exchange API that suits your needs and preferences. For this tutorial, we will use the [Binance API] as an example, but you can use any other crypto exchange API that you like. To use the Binance API, you will need to create an account on [Binance] and generate an API key and a secret key. You will use these keys to authenticate your requests to the Binance API. You can find the instructions on how to create and manage your API keys on the [Binance API documentation]. Step 3: Choose a Programming Language and a Framework The next step is to choose a programming language and a framework that you will use to develop your app. A programming language is a set that you will use to develop your app. A programming language is a set of rules and syntax that allow you to write instructions for a computer to execute. A framework is a collection of libraries and tools that provide common functionality and structure for your app. There are many programming languages and frameworks that you can use to create a crypto trading app, such as [Python], [JavaScript], [Java], [Swift], and more. Each programming language and framework has its own advantages, disadvantages, and learning curve. You will need to choose a programming language and a framework that you are familiar with or willing to learn. For this tutorial, we will use [Python] as the programming language and [Flask] as the framework. Python is a high-level, interpreted, and general-purpose programming language that is easy to read and write. Flask is a lightweight and flexible web framework that allows you to create web applications with minimal code. To use Python and Flask, you will need to install them on your computer. You can find the instructions on how to install Python on the [Python website] and how to install Flask on the [Flask website]. Step 4: Create a Flask App and Connect to the Binance API The third step is to create a Flask app and connect it to the Binance API. A Flask app is a web application that consists of one or more Python files that define the routes, views, and logic of your app. A route is a URL pattern that maps to a view function. A view function is a Python function that handles a request and returns a response. A request is an HTTP message that contains information about the user’s action, such as the URL, the method, the headers, and the data. A response is an HTTP message that contains information about the app’s action, such as the status code, the headers, and the data. To create a Flask app, you will need to create a Python file, such as app.py, and import the Flask module. You will also need to create an instance of the Flask class and assign it to a variable, such as app. You can then use the app variable to define your routes and views. For example, you can create a route for the home page of your app and a view function that returns a simple message: # Import the Flask module
from flask import Flask

# Create an instance of the Flask class
app = Flask(__name__)

# Define a route for the home page
@app.route("/")
# Define a view function for the home page
def home():
    # Return a simple message
    return "Hello, world!" To connect your Flask app to the Binance API, you will need to use a Python library that can handle HTTP requests, such as [Requests]. Requests is a simple and elegant library that allows you to send and receive HTTP requests with Python. You can install Requests on your computer by using the pip command: pip install requests You will also need to import the Requests module in your Python file and use it to make requests to the Binance API. You will need to provide the URL of the Binance API endpoint, the HTTP method, the headers, and the parameters for each request. You will also need to use your API key and secret key to sign your requests. You can find the details on how to use the Binance API on the [Binance API documentation]. For example, you can create a function that gets the current price of a specific cryptocurrency pair, such as BTC/USDT, from the Binance API and returns it as a JSON object: # Import the Requests module
import requests

# Define a function that gets the current price of a cryptocurrency pair
def get_price(pair):
    # Define the URL of the Binance API endpoint
    url = "https://api.binance.com/api/v3/ticker/price"
    # Define the HTTP method
    method = "GET"
    # Define the headers
    headers = {
        # Provide your API key
        "X-MBX-APIKEY": "your_api_key"
    }
    # Define the parameters
    params = {
        # Provide the cryptocurrency pair
        "symbol": pair
    }
    # Make the request to the Binance API
    response = requests.request(method, url, headers=headers, params=params)
    # Return the response as a JSON object
    return response.json() You can then use this function in your view function to display the current price of BTC/USDT on your home page: # Import the Flask module
from flask import Flask

# Create an instance of the Flask class
app = Flask(__name__)

# Define a route for the home page
@app.route("/")
# Define a view function for the home page
def home():
    # Get the current price of BTC/USDT
    price = get_price("BTCUSDT")
    # Return the price as a message
    return f"The current price of BTC/USDT is {price['price']}" Step 5: Create a User Interface and a Database The fifth step is to create a user interface and a database for your app. A user interface is a graphical or textual element that allows the user to interact with your app. A database is a collection of data that is organized and stored in a structured way. To create a user interface for your app, you will need to use HTML, CSS, and JavaScript. For the database, you have many options to choose from; you can use a relational database management system (RDBMS) like SQLite, MySQL, or PostgreSQL. You can use SQLAlchemy, an ORM (object-relational mapping) library for Python, to interact with your database using Python objects and queries. You can use your database to store user information, such as username, password, email, balance, etc., as well as transaction history, such as buy and sell orders, fees, profits, etc. You can use Flask-login, a Flask extension, to manage user sessions and authentication. You can use Flask-WTF, another Flask extension, to create and validate web forms for user input. You can use Flask-Mail, yet another Flask extension, to send email notifications to your users. To use Flask-login, you need to do the following steps: Install Flask-login using pip: pip install flask-login Import the LoginManager class from flask_login and create an instance of it in your app: from flask_login import LoginManager login_manager = LoginManager(app) **Define a User model that inherits from flask_login.UserMixin, which provides default implementations for properties and methods that Flask-login expects from a user object, such as is_authenticated, is_active, get_id, etc. You also need to define a unique identifier for each user, such as an id or a username from flask_login import UserMixin

class User(UserMixin):
    def __init__(self, id, username, password):
        self.id = id
        self.username = username
        self. Password = password Use Flask-SQLAlchemy to create a database and store the User model in it. You can use the db.Model base class to create a table for the User model and use the db.Column class to define the columns for the id, username, and password attributes. Define a user loader function that takes a user identifier as an argument and returns the corresponding user object from the database. You need to decorate this function with the @login_manager.user_loader decorator. Create a sign-up form that allows users to create an account by entering their username and password. You can use the Flask-WTF extension to create a form class that has two fields: username and password. Create a sign-up route that renders the sign-up form template and handles the form submission. You need to check if the username is already taken and if the password is valid before creating a new user and adding it to the database. Create a login form that allows users to log in to their account by entering their username and password. You can also add a remember field which is a boolean value that indicates whether the user wants to be remembered by the browser. Create a login route that renders the login form template and handles the form submission. You need to check if the username and password match the user in the database, and if so, call the login_user function to log in the user. You can also use the remember argument to set the cookie duration for the user. Create a logout route that calls the logout_user function to log out the user and redirect them to the home page. Use the @login_required decorator to protect any routes that require user authentication. This will redirect the user to the login page if they are not logged in. Step 6: Implement Trading Logic and Strategies To implement trading logic and strategies for your app, you can use the Binance API, which you already chose, to access market data and execute orders. You can use requests, a Python library, to make HTTP requests to the Binance API endpoints. You can use pandas, another Python library, to manipulate and analyze market data in tabular format. You can use numpy, yet another Python library, to perform numerical calculations and operations on market data. You can implement different trading strategies for your app, such as market orders, limit orders, stop-loss orders, etc. You can also implement different trading indicators, such as moving averages, Bollinger bands, MACD, RSI, etc., to help you identify trading signals and opportunities. You can use TA-Lib, a technical analysis library for Python, to calculate various trading indicators. You can also use backtesting, a technique to test your trading strategies on historical data, to evaluate their performance and profitability. You can use Back Trader, a Python framework, to perform back-testing on your trading strategies. Step 7: Test and Deploy Your App To test your app, you can use unit test, a Python module, to write and run unit tests for your code. You can use pytest, a , to write and run more advanced tests for your code. You can use coverage, another Python module, to measure the code coverage of your tests. You can also use debugging tools, such as PDB, a Python module, or PyCharm, an IDE (integrated development environment), to find and fix errors and bugs in your code. Python framework To deploy your app, you can use a cloud service provider, such as AWS, Google Cloud, or Heroku, to host your app on a server. You can use Docker, a software platform, to create and run your app in a container, which is an isolated and portable environment. You can use Git, a version control system, to manage your code and track changes. You can use GitHub, a web-based platform, to store and share your code online. You can also use CI/CD (continuous integration and continuous delivery) tools, such as Travis CI, Jenkins, or GitHub Actions, to automate the testing and deployment of your app. I’m going to show you a possible way to deploy your Flask crypto trading app using Docker and GitHub. Step 1: Create a Docker file in the root directory of your Flask app. A Docker file is a text file that contains instructions for building a Docker image. A Docker image is a snapshot of your app and its dependencies that can be run as a container. You can use the following example as a template, but you may need to modify it according to your app’s requirements: # Use an official Python runtime as a base image
FROM python:3.9

# Set the working directory to /app
WORKDIR /app

# Copy the current directory contents into the container at /app
COPY . /app

# Install any needed packages specified in requirements.txt
RUN pip install --trusted-host pypi.python.org -r requirements.txt

# Make port 80 available to the world outside this container
EXPOSE 80

# Define environment variable
ENV NAME World

# Run app.py when the container launches
CMD ["python", "app.py"] Step 7: Push your Flask app code to the GitHub repository. You can use the git add, git commit, and git push commands to do this. You need to provide the URL of your GitHub repository, such as https://github.com/username/flask-app.git. For example: git add .
git commit -m "Initial commit"
git push origin main Step 8: Create a GitHub workflow for deploying your app. A GitHub workflow is a file that defines a set of actions that are triggered by certain events, such as a push or a pull request. You can use the GitHub Actions feature to create and manage your workflows. You need to create a file named .github/workflows/deploy.yml in your repository that contains the following code: # This workflow will deploy your Flask app using Docker and GitHub Actions

name: Deploy

# Controls when the workflow will run
on:
  # Triggers the workflow on push or pull request events but only for the main branch
  push:
    branches: [ main ]
  pull_request:
    branches: [ main ]

  # Allows you to run this workflow manually from the Actions tab
  workflow_dispatch:

# A workflow run is made up of one or more jobs that can run sequentially or in parallel
jobs:
  # This job will deploy your app to a cloud service provider of your choice
  deploy:
    # The type of runner that the job will run on
    runs-on: ubuntu-latest

    # Steps represent a sequence of tasks that will be executed as part of the job
    steps:
      # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it
      - uses: actions/checkout@v2

      # Sets up Docker
      - name: Set up Docker
        uses: docker/setup-buildx-action@v1

      # Logs in to the Docker registry
      - name: Log in to Docker registry
        uses: docker/login-action@v1
        with:
          # Replace this with the registry name and your username, such as docker.io/username or ghcr.io/username
          registry: docker.io/username
          # Replace this with the name of the secret that contains your registry password, such as DOCKER_PASSWORD or GHCR_TOKEN
          username: ${{ github.actor }}
          password: ${{ secrets.DOCKER_PASSWORD }}

      # Builds and pushes the Docker image
      - name: Build and push Docker image
        uses: docker/build-push-action@v2
        with:
          # Replace this with the name and tag of your image, such as docker.io/username/flask-app:latest or ghcr.io/username/flask-app:latest
          push: true
          tags: docker.io/username/flask-app:latest

      # Deploys the app to the cloud service provider
      - name: Deploy app to cloud service provider
        # Replace this with the action that deploys your app to the cloud service provider of your choice, such as AWS, Google Cloud, or Heroku
        # You may need to provide additional arguments or environment variables for the action, such as the app name, the region, the credentials, etc.
        # You can find more actions in the GitHub Marketplace: https://github.com/marketplace?type=actions
        uses: actions/example-action@v1
        with:
          # Replace this with the arguments for the action
          arg1: value1
          arg2: value2
        env:
          # Replace this with the environment variables for the action
          ENV1: value1
          ENV2: value2 Step 9: Commit and push the workflow file to the GitHub repository. You can use the git add, git commit, and git push commands to do this. For example: git add .github/workflows/deploy.yml
git commit -m "Add deploy workflow"
git push origin main Step 10: Check the status of the workflow. You can use the GitHub web interface to view the progress and logs of your workflow. You can also see the status badge on your README.md file that indicates whether the workflow is successful or not. You can click on the badge to see more details. HOW TO EARN MONEY FROM YOUR CRYPTO TRADING APP The last step of creating a crypto trading app is to monetize your app and generate revenue from it. There are various ways to monetize a crypto trading app, such as: Fees and commissions. You can charge your users fees and commissions for using your platform, such as deposit and withdrawal fees, trading fees, exchange fees, etc. You can also charge your users fees and commissions for using your premium features, such as advanced trading tools, indicators, etc. Ads and sponsorships. You can display ads and sponsorships on your app and earn revenue from the impressions and clicks of your users. You can also partner with other crypto-related businesses and promote their products and services on your app, and earn revenue from the referrals and conversions of your users. Subscriptions and memberships. You can offer your users subscriptions and memberships to access your exclusive content and features, such as premium news and analysis, trading signals, tips, etc. You can also offer your users subscriptions and memberships to join your community and network, such as chat rooms, forums, groups, etc. In Conclusion: creating a crypto-Trading app can be a profiting venture although it can be very complex as you will be considering a lot a business aspect to it.

How to Build Your Own Crypto Trading App: A Step-by-Step Tutorial

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