Our powerful phishing mitigation platform will help to protect your organization from the harmful effects of online phishing scams. Unlike other solutions, C9LAB's unique approach fully integrates simulated phishing campaigns with education and training materials to equip your users with the knowledge they need to avoid actual attacks. At the same time, our domain protection service ensures that malicious actors are intercepted while they attempt to register domain names similar to yours. This creates a safe environment for you and your organization. Best of all, our solution includes an up-to-date feed of verified URLs, IP addresses, and domains that contain potential active threats so you can stay ahead of the curve as new schemes emerge. With C9LAB as your trusted partner, you can be sure that your defense against cybercrime is always airtight!

C9lab.com

c9labcom

A software engineer mentor can be an invaluable asset, whether you're just starting out in your career or you've been working as an engineer for a few years. A good mentor will be able to teach you the ropes, provide advice and guidance when you need it, and help you navigate the often-complicated world of software engineering. Who is a software engineer mentor? A software engineer mentor is a more experienced software engineer who provides guidance, support, and advice to a less experienced software engineer. A software engineer mentor can help you learn the intricacies of software engineering, navigate the industry, and advance your career. They can be enormously valuable, especially if you're just starting out in your career. Not only are they knowledgeable in their field, but they also have years of experience that they can share with you. If you're looking for a career in software engineering, or if you want to become a better software engineer, then a software engineer mentor is definitely someone worth considering. What does a software engineer mentor do? A software engineer mentor helps junior software engineers learn and grow in their careers. They provide their technical expertise to help a junior software engineer become a better developer. Additionally, a programming mentor can help foster a sense of community within the software engineering team. Typically, a computer science mentor takes the role of a teacher, coach, and advisor that helps software engineers grow in their career. A software engineer mentor typically has working as a software engineer, although anyone who is more experienced than a person being a mentee can potentially become their programming mentor. Software engineer mentors have a wealth of knowledge and experience to share with their apprentices. Additionally, they are typically patient, good communicators, and have a positive attitude. at least 3-5 years of experience Why do I need a mentor? There are a few key reasons why having a software engineer mentor is so important. Specifically, they can help you: Receive career guidance Solve real-world engineering problems Learn new and technologies programming languages Understand computer science concepts Prepare for technical interviews Get a promotion Develop a network of software engineering contacts How do I find a software engineer mentor? The best way to find a software engineer mentor is to reach out to your network of engineers and ask if anyone is willing to serve as a mentor for you. Additionally, there are a number of online directories that can help you connect with potential mentors, such as sources like and . Once you've found a few potential mentors, reach out to them and set up a time to meet so that you can get to know them better and see if they're a good fit for you. Github Stack Overflow Your network Your network is your best asset when it comes to finding a software engineer mentor. Ask your friends, colleagues, and online contacts if they know anyone who would be a good programming mentor for you. Often, the best mentors are people who you already know and trust. Utilize your LinkedIn connections, ask your friends if they know anyone, and reach out to software engineers who you admire online. Work Your workplace is one of the best places to find a programming mentor. Many companies have mentorship programs in place that pair experienced software engineers with junior developers. If your company doesn't have a formal mentorship program, reach out to your manager or a software engineer that you respect and see if they would be willing to serve as your mentor. Events Attend different computer science events with an eye out for potential mentors. For example, you can attend software engineering conferences, meetups, and . These events are great places to network with other software engineers and find a mentor. hackathons Online communities - HackerNoon is a large international community of coders, engineers, and technology enthusiasts. Connect with authors and community members who catch your attention! They even have a you can apply for. HackerNoon blogging fellowship - this platform is a great place to find software engineer mentors. There are a number of software engineering mentors on Github who are willing to help others learn and grow in their career. GitHub - this programming forum is where you can ask questions and get answers from other software engineers and potentially find a personal mentor. Stack Overflow - Blind is an anonymous software engineer community where you can post questions and get advice from other software engineers, some of whom could show interest in guiding you on a regular basis. Blind - LinkedIn is a great place to search and connect with potential software engineer mentors; simply follow and interact with prospective mentors before contacting them. LinkedIn channels - There are a number of software engineering Slack channels where you can engage with other software engineers and ask for help finding a mentor. These include channels like CodeNewbie, Women Who Code, and FreeCodeCamp. Slack groups - General groups on Facebook where you could find mentors are Software Engineering Daily, Software Engineering Beginners, and Software Engineers. Facebook - There are a few software engineering subreddits like r/learnprogramming, r/cscareerquestions, and r/programming that could help you find software engineering mentors. Reddit These are just a few of the many places where you network with other software engineers and find a computer science mentor, but they are plenty to get started and find a few potential leads! Specialized platforms There are a number of matching directories that can help you connect with potential software engineer mentors. These directories typically allow you to match with mentors based on location, expertise, and other factors. Some of the online directories for finding software engineer mentors include: https://www.codementor.io/ https://codingcoach.io/ https://mentorcruise.com/filter/coding/ https://www.mentoring-club.com/the-mentors/category/software-engineering https://exercism.org/ How do I first reach out to a potential programming mentor? The first time you reach out to a potential mentor, whether it is through email, instant message, or an online platform, you should follow the following steps: - let the potential mentor know who you are, what you do, and why you are interested in working with them. Introduce yourself - always show respect for the potential mentor's time and experience. Be polite and respectful - let the potential mentor know what you are hoping to gain from the mentorship relationship. Be clear about what you are looking for - once you have introduced yourself and explained what you are looking for, ask the potential mentor if they would be available to talk to you through a phone call, video chat, or an in-person get-together. Ask if they are available for a call or meet-up - even if the potential mentor is not available to meet with you, be sure to thank them for taking the time to speak with you. Thank them for their time A sample email from you to a mentor you would like to work with should look like this: "Hi (name of potential mentor), My name is (Your Name) and I am a software engineer at (Your Company). I am interested in your expertise as a software developer and would love receive some guidance, and perhaps mentorship, on your behalf. (Give a few reasons why you would like to work with this software engineer mentor). I am hoping to gain (list a few things you hope to gain from the mentorship relationship). Would you be available for a call or meetup so we can further discuss the possibility of working together? Thank you for your time. Sincerely, (Your Name) (Your Contact Information)" As a response, you will often get one of the following: The software engineer mentor agrees to meet with you and establishes/asks for a meeting time. The software engineer mentor is unavailable but refers you to another software engineer they think would be a good fit for you. The software engineer mentor is unavailable and does not refer you to another software engineer. No matter what response you get, always be polite and respectful. If the software engineer mentor is unable to meet with you, thank them for their time and move on to another potential mentor. How do I communicate with my software engineer mentor? After you have received a green light for setting up a meeting with a potential mentor, you should agree on a date and time, and preferably the duration of your first meeting. This can be done over email, phone, or another software engineering platform. Here are some suggestions for your first meeting: - take some time to chat and get to know one another. Discuss your interests, experiences, and backgrounds. The software engineer mentor should also share their expectations for the mentorship relationship. Get to know each other - share your career goals with the software engineer mentor and ask for their advice. Discuss your goals - agree on how often you will meet, what communication platform you will use, and what format your meetings will take. Will they be video calls, in-person meetings, or something else? Set some ground rules Now that you have established a relationship with a software engineer mentor, it is important to keep up communication and follow through on your commitments. Make sure to respect the software engineer mentor's time by being punctual for meetings and prepared with questions or topics to discuss. Also, be sure to follow up after each meeting with action items or goals that you discussed. Thank the software engineer mentor regularly for their advice and guidance. Mentorship relationships are built on trust, respect, and communication. By following these tips, you can set yourself up for a successful mentorship relationship with a software engineer mentor. What questions should I ask my software engineer mentor? So what questions should you ask your mentor? Here are a few suggestions to get you started: What inspired you to become a software engineer? What is your favorite thing about being a software engineer? What has been the most challenging thing about your career? What has been the biggest accomplishment of your career? Do you have any advice for me as I pursue a career in software engineering? What do you think is the most important skill for a software engineer to have? What are some common mistakes that software engineers make early in their careers? What projects are you working on that I can help with? Can you introduce me to any other software engineers that I can learn from? These are just a few suggestions to get you started. As you get to know your software engineer mentor, you will be able to ask more specific questions about their experience and advice. Remember, the goal of the mentorship relationship is to help you grow as a software engineer, so ask whatever questions will help you achieve that goal. How to benefit from a software engineer mentor? In order to benefit from having a mentor, it's important that you maintain communication with them on a regular basis. Additionally, it's important that you keep them updated on your career progress and any major projects that you're working on so that they can provide feedback and guidance when necessary. In order to get the most benefit from your mentor-mentee relationship, you should ask your mentor for help in all career-relevant situations, such as job search, interviewing, networking, and so on. By involving your mentor in every aspect of your career, you will be able to benefit from their wealth of experience and knowledge. How often should I communicate with my software engineer mentor? So how often should you communicate with your computer science mentor? That depends on your schedules and the goals that you've set for the mentorship relationship. However, a good rule of thumb is to communicate with your mentor , whether that's through a meeting in person, video call, or email. Make sure to communicate more often if you're working on a major project or facing a difficult career decision. By maintaining regular communication with your software engineer mentor, you will be able to get the most benefit from the relationship. at least once per month Do I have to pay my programming mentor? You don't have to pay your mentor unless you've agreed to do so. In most cases, mentorship relationships are voluntary and there is no expectation of payment. However, if you feel like you're getting a lot of value from the relationship and you want to show your appreciation, you can always offer to pay your mentor for their time. Ultimately, it's up to you and your mentor to decide whether or not payment is appropriate. On the other hand, some mentorship matching platforms, such as , do charge for their services. In these cases, you will likely be expected to pay for your mentor's time. If you're using a paid platform, make sure to factor the cost into your budget. CodeMentor What's Next? Now that you know how to find and establish a relationship with a software engineer mentor, it's time to put that knowledge into practice. Start by reaching out to software engineers that you know and asking if they would be willing to be your mentor. If you don't know any software engineers, try attending meetups or networking events specifically for software engineers. Alternatively, you can also look for mentorship programs offered by companies or organizations. You can also look for software engineer mentors through online directories or matching platforms. Once you've found a computer mentor, it's important to set some goals for the mentorship relationship. What do you hope to accomplish? How can your mentor help you achieve those goals? By setting clear objectives, you will be able to get the most out of your mentorship experience. Finally, don't forget to show appreciation for your programming mentor. Remember, they are taking time out of their busy schedules to help you grow as a software engineer. With regular communication and appreciation, you'll be able to benefit greatly from having a mentor in your corner. I hope this article has helped you understand the importance of mentorship for software engineers. Software engineer mentors can help you accelerate your career by providing guidance, advice, and support. So if you haven't already, make sure to find yourself a software engineer mentor today!

Where to Find a Software Engineer Mentor (and How to Benefit From Them)

Google Colab and VS Code are the most popular editor tools used by many Python developers and Researchers to develop different tech solutions or systems (especially in Machine Learning & Data Science). Many Python developers and Data scientists know how to use Google Colab, but do you know that you can set up a VS Code on Google Colab and use it as an editor the same way as in your local machine? In this article, you will learn How to install the colabcode python package. How to start the VS Code (code server). How to access the online VS Code. Open the terminal. Run a python file. Steps on How to Use Google Colab with VS Code 1.Open Colab Notebook The first step is to launch a new colab notebook in your Google Colab and you can rename the file as you want. For example, run_vscode.ipynb . 2.Install colabcode python package To use Google Colab with VS Code (code server), you need to install the colabcode python package. This is an awesome open-source python package developed by . Abhishek Thakur To install the package, run the following command in your notebook cell. !pip install colabcode 3.Import ColabCode The next step is to import ColabCode class from the package. colabcode ColabCode from import 4.Create an instance of ColabCode After importing the ColabCode, you need to create an instance of ColabCode and set the following arguments. - The port you want to run code-server on. For example port=10000 port - You can set a password to protect your code-server from unauthorized access. This is an optional argument. password - If you want to use your Google drive. This is a Boolean argument means you can set True or False. This is an optional argument. mount_drive ColabCode(port= ) 10000 5.Start the Code Server After running the ColabCode instance, it will start the server and show the link to access the code server. You need to click the link and it will open in a new tab. Now you can take advantage of a full-fledged code editor and run different experiments on the Colab VM. If you check on your Colab Notebook, you will observe the cell that runs the ColabCode instance is continuously running. Don't close your Colab notebook unless you want to close the code server that runs VS Code. Note: Tips to use VS Code on Google Colab After launching the code server, the following tips will help you start using VS Code on Google Colab. 1.Open Terminal To open the terminal on VS Code that runs on Google Colab, use the following shortcut command Ctrl + Shift + ` 2.Change Theme You can change the theme of the editor by clicking the setting icon (bottom-left corner) and then click "Color Theme". It will open a popup window with different theme options you can select. 3.Run Python File You can create a python file by clicking the "File" section on the sidebar and then select a "New File" tab. In the following example, you will see how to run a simple python file that trains a machine-learning algorithm to classify iris flowers into three species (setosa, versicolor, or virginica) and then make a prediction. Final Thoughts on How to use Google Colab with VS Code Congratulations 👏👏, you have made it to the end of this article! I hope you have learned something new. You can set up VS Code on Google Colab and take your coding to the next level. You can also use the colabcode python package on the Kaggle platform to run VS Code. You just need to follow the same steps mentioned above. If you learned something new or enjoyed reading this article, please share it so that others can see it. Until then, see you in the next post! You can also find me on Twitter . @Davis_McDavid And you can read more articles like this . here Want to keep up to date with all the latest in python? in the footer below! Subscribe to our newsletter

How To use Google Colab with VS Code

Cómo usar Google Colab con VS Code

HackerNoon and the time the team compared different Open AI Image Generators for in-house use. This discussion paved the way for some awesome features like our and , both already live for your enjoyment! Read and follow along our behind-the-scenes journey. headline generator featured image generator This thread by richard-kubina and David occurred in Hackernoon's official #random channel, and has been edited for readability. Slogging richard-kubina Aug 22, 2022, 7:21 PM OpenAI is reducing the cost of GPT-3 models We are excited to share that we are reducing the price per token for our standard GPT-3 models and Embedding models. Please find our new pricing below, effective beginning September 1, 2022 00:00:00 UTC. You can also find more details on our Pricing page. Note that this pricing change does not affect fine-tuned models. MODEL	BEFORE	ON SEPT 1 Davinci	$0.06 / 1k tokens	$0.02 / 1k tokens Curie	$0.006 / 1k tokens	$0.002 / 1k tokens Babbage	$0.0012 / 1k tokens	$0.0005 / 1k tokens Ada	$0.0008 / 1k tokens	$0.0004 / 1k tokens Davinci embeddings	$0.6 / 1k tokens	$0.2 / 1k tokens Curie embeddings	$0.06 / 1k tokens	$0.02 / 1k tokens Babbage embeddings	$0.012 / 1k tokens	$0.005 / 1k tokens Ada embeddings	$0.008 / 1k tokens	$0.004 / 1k tokens We have been looking forward to this change for a long time. Our teams have made incredible progress in making our models more efficient to run, which has reduced the cost it takes to serve them. Our hope is that even more developers will now be able to experiment and build on top of our API so that more people can access the benefits of powerful AI. 👍 1 👍 2 🏦 1 David Aug 30, 2022, 9:30 PM richard-kubina, maybe time to revisit the headline generator? Ideally, the machine reads a story by contributor and suggests 3-5 headlines. gpt3 seemed really good for this (except for the cost) 👍 1 richard-kubina Aug 30, 2022, 11:50 PM It'd be neat to sort out a platform for open-source AI models... for features images, stable diffusion is a bit like Dall-E but open source, we could run it on cloud servers we run directly, which would be the cheapest way to access that functionality. I really dig its results too David Aug 31, 2022, 2:41 AM Oh, interesting! Given the cost maybe a featured image is only after the first story published? Is this the image generation alternative you mean? https://github.com/CompVis/stable-diffusion richard-kubina Aug 31, 2022, 12:17 PM Yep! David Aug 31, 2022, 6:02 PM richard-kubina is there a way to test it in the browser? just found the paper https://ommer-lab.com/research/latent-diffusion-models/ richard-kubina Aug 31, 2022, 6:31 PM I believe there is a hugging face model to test with, just takes a while.  Let me find that again. I used to pay for google collab pro to try out the resource-intensive models out, that's pretty easy to test in the browser. richard-kubina Aug 31, 2022, 6:32 PM https://huggingface.co/spaces/stabilityai/stable-diffusion richard-kubina Aug 31, 2022, 6:32 PM https://beta.dreamstudio.ai/home richard-kubina Aug 31, 2022, 6:33 PM This application is too busy! Try again soon. ah shucks, there's that issue too.  It working for me the other day edit: working now.. anywho we could see about other providers like google collab, apparently, there are more that are lesser known, cheaper was richard-kubina Aug 31, 2022, 6:48 PM already got into dream studio. A lil over a penny an image :notbad: 1 richard-kubina Sep 1, 2022, 2:25 PM I signed up for a trying out stable diffusion, it would be $8 a month to run on their GPUs, but I think other than that it would be 'free'. We could run it as much as we want. Looking into their SDK/CLI may have to spin up a python API in google cloud to access this from the editor.  If we like it and get a lot of users we could scale up by paying for more GPUs💰 they had a stable diffusion project template ready to go.. with the prompt "Kermit the Frog on the Iron Throne" https://www.paperspace.com/gradient richard-kubina Sep 1, 2022, 2:28 PM Dreamstudio could work too where we pay for the month and don't have to worry about using up credits, but paperspace is more flexible, we can run more models than just stable diffusion, like if we wanna try other models like the pixel art one, or open source GPT-3  alternatives for  text generation/summarizing/categorizing/etc models richard-kubina Sep 1, 2022, 2:44 PM oo node.js access exists :thumbsup: https://www.npmjs.com/package/paperspace-node#programmatic-access-via-paperspace-node David Sep 1, 2022, 3:20 PM AI images made John Oliver this week focuses on and in addition to dalle https://www.youtube.com/watch?v=3YNku5FKWjw&t=8s https://www.midjourney.com/ https://www.craiyon.com/ richard-kubina Sep 1, 2022, 3:27 PM Midjourney is 10 bucks for ~200 images. not sure what the commercial terms are but I know stable diffusion doesn't mind if you use it commercially "for any purpose whatsoever, including without limitation commercial, advertising or promotional purposes" https://stability.ai/stablediffusion-terms-of-service 💯 1 David Sep 1, 2022, 3:32 PM Craiyon's terms are way worse - they want 20% of NFT sales made with it, accreditation everywhere, and contact sales for use for companies like ours https://www.craiyon.com/terms ☹ 1 David Sep 6, 2022, 10:15 PM richard-kubina for ref, here's ux example of someone turning a wordpress text editor block into a dalle powered image gallery https://twitter.com/UglyRobotDev/status/1565030948428828675 🔥 1 David Sep 6, 2022, 10:17 PM lol sidenote discovery https://twitter.com/bakztfuture/status/1528173885517987840 😂 1

Open AI Images on HackerNoon: The Conundrum

DALL-E Homebrew In this post, we’ll look at getting setup with running your own A.I. image generator. You need a Linux system with a CUDA enabled card to get this working through to the end. I did get the basic txt-2img script working in Windows. Unfortunately, for the July released , you need in order to index the openimages dataset, which is only available on Linux. worked fine for me. retrieval-augmented diffusion models scann WSL Here’s roughly what I gather you’ll need: Linux (WSL fine) 11.3 CUDA graphics = 32GB RAM 50GB disk space (~30GB without openimages) 3xxx series card (I am just guessing here. This is all being done with a 3090 FE) Latent-Diffusion I randomly stumbled upon the while I was browsing AI subreddits. A few posts mentioned it and I didn’t know what it was so I went googling. latent-diffusion repo I am not a computer scientist, mathematician, or any of the things that would be required to really understand what’s going on here. The paper about the new RAD models are understandable and interesting, but I haven’t done much research yet beyond that. Text-to-image vs. RAD There are 2 image generation techniques possible with Latent Diffusion. We will install and take a look at both. I believe the txt2-img model that we’ll setup first is what we are used to with other image generation tools online – it makes a super low res image clip thinks is a good prompt match and denoises and upscales it. The RAD model uses a configurable database of images as a reference AND does the diffusion like we are used to. This seems to mean it’s less good as a general purpose generator, but could be used with a specific training set you could feasibly create. I will try this in the future. RAD also generates 768X768 images which is pretty impressive. Install Instructions Text-to-Image Make sure you have installed and works in your shell. You can get CUDA 11.3 conda Conda here , lest ye end up in dependency hell later. I did this via but conda may work too. (Re)install Pytorch now pip Clone the repo to a drive with a lot of free space into the repo and run: cd conda env create -f environment.yaml
conda activate ldm This will setup the initial environment. If you get errors with Pytorch later and need to reinstall it, come back to environment.yaml and reinstall using the correct package versions. Download the model and you’re all ready to go so long as your torch dependencies are all setup correct. mkdir -p models/ldm/text2img-large/
wget -O models/ldm/text2img-large/model.ckpt https://ommer-lab.com/files/latent-diffusion/nitro/txt2img-f8-large/model.ckpt You should be able to sample images now using: python scripts/txt2img.py --prompt "my cool image" Make sure to see the options you’re able to set in the README. You can use this bash script to make running everything from a shell easier. You can include this in your file and reopen your shell. Make sure to change the 3 paths to suit your needs. We’ll look at PLMS later. ~/.bashrc function txt2img() {
  if [ $# -eq 0 ]; then
    echo "Usage: diffuse '[prompt]' [--plms]"
    exit 1
  fi
  cd /mnt/c/diffusion/latent-diffusion;
  conda activate ldm;
  if [ $2 = '--plms' ]; then
    python scripts/txt2img.py --prompt $1 --outdir '/mnt/g/My Drive/AI Image Tests/text2img' --plms --n_iter 4 --ddim_eta 0.0
  else
    python scripts/txt2img.py --prompt $1 --outdir '/mnt/g/My Drive/AI Image Tests/text2img' --n_iter 4 --ddim_eta 0.0
  fi
} The images are 512X512 by default. You can use to upscale the images with pretty good results. This Jupiter Notebook Troubleshooting If you get CUDA memory errors running commands, try decreasing the batch size. Use to troubleshoot/view memory usage. htop If using WSL, make sure all your RAM is available to linux. If you get torch related errors, you probably need to reinstall Pytorch. This will likely cause other issues with packages. Check environment.yaml and manually install the correct package versions with and/or (I did both and am not sure which made everything work) pip conda RDM You can read more about RDM . As said before, it essentially combines a database of images with Clip descriptions with the existing diffusion process, from what I understand. This part takes a lot longer to get running. here Note, this model, especially when using the openimages training is best at recreating real things and doesn’t seem very good (yet) and creating the weird images we’re used to from the diffusion models. Get the Text-to-Image model working first Install new packages and download training model. pip install transformers==4.19.2 scann kornia==0.6.4 torchmetrics==0.6.0
pip install git+https://github.com/arogozhnikov/einops.git

mkdir -p models/rdm/rdm768x768/
wget -O models/rdm/rdm768x768/model.ckpt https://ommer-lab.com/files/rdm/model.ckpt Test everything is working so far with python scripts/knn2img.py  --prompt "I'm a computer" If everything went well, you should see a success text in your shell. Now we need to download all the image indexes/models. The openimages zip is 11GB. The ArtBench data is pretty small. mkdir -p data/rdm/retrieval_databases
wget -O data/rdm/retrieval_databases/artbench.zip https://ommer-lab.com/files/rdm/artbench_databases.zip
wget -O data/rdm/retrieval_databases/openimages.zip https://ommer-lab.com/files/rdm/openimages_database.zip
unzip data/rdm/retrieval_databases/artbench.zip -d data/rdm/retrieval_databases/
unzip data/rdm/retrieval_databases/openimages.zip -d data/rdm/retrieval_databases/

mkdir -p data/rdm/searchers
wget -O data/rdm/searchers/artbench.zip https://ommer-lab.com/files/rdm/artbench_searchers.zip
unzip data/rdm/searchers/artbench.zip -d data/rdm/searchers We’re ready to use the Artbench models now (which work pretty well in my limited testing), but what we really want is to use the massive openimages model as our reference. We downloaded the data, but we need to create the index. If you want to test the Artbench database, run python scripts/knn2img.py --prompt "A blue pig" --use_neighbors --knn 20 Openimages Index Unless you have a super computer, the terrible memory management in python multiprocessing (from what I can tell) will stop us from using the 4 files we unzipped with the script from the repo to index them. Everything else I tried, the processes would run out of memory. Luckily, we just need to concatenate the files. You can delete the old part files after if you would like. We also need to move the part files out of the openimages folder. cd data/rdm/retrieval_databases/openimages/
cat 2000000x768_part1.npz 2000000x768_part2.npz 2000000x768_part3.npz 2000000x768_part4.npz > 2000000x768.npz
mv 2000000x768_* ~/temp
cd ../../../../ Now when we run the training script, we shouldn’t run out of memory. I think this is some issue in their multi file function in the training script. python scripts/train_searcher.py We need to change the batch size in so we don’t run out of GPU memory. You may need to lower to 1, but I was able to set to 2 on a 3090. scripts/knn2img.py Open in an editor scripts/knn2img.py Go to line 243 or find “n_samples” Change the default value to 1 or 2 and save You can also do this using the command line parameter, but since it will never work for me, I wanted to change the default value. Note if you update the repo in the future this will likely get overwritten. Now we can generate RDM images using the openimages database: python scripts/knn2img.py --prompt "A blue pig" --database openimages --use_neighbors --knn 20 Here is a script to run it conveniently. Again, make sure to change the 3 paths to suit your needs: ~/.bashrc function diffuse() {
    if [ $# -eq 0 ]; then
       echo "Usage: diffuse '[prompt]' [--plms]"
       exit 1
    fi
    cd /mnt/c/diffusion/latent-diffusion;
    conda activate ldm;
    if [ $2 = '--plms' ]; then
        python scripts/knn2img.py  --database openimages --prompt $1 --use_neighbors --outdir '/mnt/g/My Drive/AI Image Tests/RDM' --knn 20 --plms
    else
        python scripts/knn2img.py  --database openimages --prompt $1 --use_neighbors --outdir '/mnt/g/My Drive/AI Image Tests/RDM' --knn 20
    fi
} Speeding Things Up You can also run prompts in batches by creating a file (in this case ) and pointing the script to it with the parameter like this prompts.txt –from-file python scripts/knn2img.py  --database openimages --from-file ./prompts.txt --use_neighbors --outdir '/mnt/g/My Drive/AI Image Tests/RDM' --knn 20 sample prompts.txt prompt 1
prompt 2
prompt 3 This should save a lot of time if you’re running a lot of queries since the model only gets loaded once. The text-to-image model does not have this option. Initial Testing, & PLMS Below are some inital results from both the models with PLMS on and off. PLMS should make things run faster. For now, we won’t measure the speed increase but will look subjectively at image quality/results. Since the RDM model will only make 2 photos at a time for my system, I included both photos instead of a grid for that model. I will almost certainly do more specific testing in the future and these models will be included in future comparison posts. A wood house on a hill, landscape photography Text-to-image without plms with plms RDM without plms with plms A purple stop sign Text-to-image without plms with plms RDM without plms with plms fox den, digital art Text-to-image without plms with plms RDM without plms with plms winning the big game, award winning photography Text-to-image without plms with plms RDM without plms with plms a sports car driving in sand dunes Text-to-image without plms with plms RDM without plms with plms An Image Generator to Call Your Own And there you have it – no more credits or monthly limits, just pure AI image generation all your own. Once I figure out some tuning settings and create my own image training database, we’ll likely talk about this again. Hopefully with some tuning and new releases/models this will get closer to the WOW factor DALL-E has. Also published . here

Creating Your Own A.I. Image Generator with Latent-Diffusion

Creando tu propia IA Generador de imágenes con difusión latente

Créer votre propre IA Générateur d'images avec diffusion latente

अपनी खुद की एआई बनाना अव्यक्त-प्रसार के साथ छवि जेनरेटर

独自の A.I. を作成する潜在拡散による画像生成

Criando sua própria I.A. Gerador de Imagens com Difusão Latente

Tạo A.I của riêng bạn Trình tạo hình ảnh với Khuếch tán tiềm ẩn

创建你自己的人工智能具有潜在扩散的图像生成器

SearchPedia: A List of 250+ Search Engines

SearchPedia: una lista de más de 250 motores de búsqueda

Photo by on Nikhil Mitra Unsplash Today, the world is transforming towards automated fashion, including manufacture, cars, and logistics. Personal investment is no exception. , we are pushing this boundary forward so everyone can enjoy the automated investment world. marketing At Alpaca List of .NET/C# Algo Trading Systems When it comes to algo trading and automated investment, is one of the biggest players in the space, but many experts also use .NET/C# for its high performance and robustness. As we did some research on toolset you might look at to start your algo trading, we wanted to share this list for you. Python Overall, the ecosystem has grown so much lately, and many open sources and tools are available for you at low cost, without much equipment. QuantConnect is one of the most popular online backtesting and live trading services, where you can learn and experiment your trading strategy to run with the real time market. The platform has been engineered in C# mainly, with additional language coverage such as python. QuantConnect _QuantConnect provides a free algorithm backtesting tool and financial data so engineers can design algorithmic trading…_www.quantconnect.com Design and trade algorithmic trading strategies in a web browser, with free financial data, cloud… WealthLab is another C# platform where you can get the real time price and run your algorithm, if you have a Fidelity account. WealthLab _Discover why all over the world professional technical analysts and trading system developers use Wealth-Lab software…_www.wealth-lab.com Wealth Lab®: Technical Analysis Software & Trading Platform - Portfolio backtesting software, stock… NinjaTrader and MultiCharts and are also popular choices for different kind of assets with various broker options. NinjaTrader MultiCharts _NinjaTrader's trading software & futures brokerage equips traders with an award-winning trading platform & low…_ninjatrader.com Trading Software & Futures Broker | NinjaTrader _MultiCharts trading software for professional traders with advanced analytics, trading strategies, backtesting and…_www.multicharts.com Trading Software For Backtesting And Automated Trading | MultiCharts Trading Platform OpenSource Projects In addition to these, is an interesting open source project which is tailor for .NET algo traders and broker integrations. StockSharp _Algorithmic trading and quantitative trading open source platform to develop trading robots (stock markets, forex…_github.com StockSharp/StockSharp You should also check out which is an open source library developed by QuantConnect, who also uses this library for their flagship service, supporting multiple assets such as stocks and cryptocurrencies. Lean _Lean Algorithmic Trading Engine by QuantConnect (C#, Python, F#) - QuantConnect/Lean_github.com QuantConnect/Lean List of Data Library Deedle is probably one of the most useful libraries when it comes to algo trading. You would run some calculation using Frame and compare data, to get signals. Deedle _Easy to use .NET library for data manipulation and scientific programming_bluemountaincapital.github.io Deedle: Exploratory data library for .NET TALibraryInCSharp is a great open source library that bridges and .NET world, so that you can calculate common indicators such as moving average and RSI. Combining these libraries, you will get the power of trading tools. TALibraryInCSharp TA-lib _TA-Lib Converted To C Sharp. Contribute to DavidFuchs42/TALibraryInCSharp development by creating an account on GitHub._github.com DavidFuchs42/TALibraryInCSharp IEX Now the question is data to calculate those signals on, but if you are talking about US equities, you can leverage and there are libraries like that makes your life easy for getting the data instantly. IEX’s free data API IEXTradingApi _The IEX API is a set of services designed for developers and engineers. It can be used to build high-quality apps and…_iextrading.com API 1.0 | IEX Others There are quite a bit of .NET libraries out there for proprietary data sources (e.g. for ) too, so you should check it out. Quandl _A .NET wrapper built on Quandl v3 REST API, based on .NET standard 2.0 - lppkarl/Quandl.NET_github.com lppkarl/Quandl.NET Announcing Alpaca’s Official .NET Client SDK Don’t forget about Alpaca! We are committed to providing the best experiences for many algo traders, and today we are happy to announce that our for Alpaca Trade API has been released. official .NET client SDK Following , .NET SDK takes advantage of its robustness and high performance, as well as wide coverage of platforms. It is an open source project hosted in GitHub and the prebuilt package is . All the classes and methods are documented for IntelliSense so you can get the references right in your IDE. our Python SDK up in NuGet _C# SDK for Alpaca Trade API https://docs.alpaca.markets/ — alpacahq/alpaca-trade-api-csharp_github.com alpacahq/alpaca-trade-api-csharp Here is a snippet of how easily you can place a buy order of a share of Apple. Alpaca Trade API covers not only retrieving account information and submitting orders, but also allows one to retrieve price and fundamentals information easily. For more details of API, please read . our online documents _This API allows your trading algo to access real-time price, fundamentals, place orders and manage your portfolio, in either REST (pull) or streaming (push) style._docs.alpaca.markets Alpaca API Document Happy algo trading! Please follow and for fresh posts on Financial Market, Algorithmic Trading, Technology. Alpaca Automation Generation You can find us , if you use twitter. @AlpacaHQ If you’re a hacker and can create something cool that works in the financial market, please check out our project “ Commission Free Stock Trading API ” where we provide simple REST Trading API and real-time market data for free. Brokerage services are provided by Alpaca Securities LLC ( ), member FINRA/SIPC. Alpaca Securities LLC is a wholly-owned subsidiary of AlpacaDB, Inc. alpaca.markets

Best Starting Kits for Algo Trading with C#

A Platform-as-a-Service(PaaS) saves us the time required to set up virtual servers, makes scalability easier and depletes the cost implication of setting up the servers. Initially, setting up a server in the cloud required connecting and configuring all types of components but the introduction of Serverless,Docker,Heroku and the likes have allowed us focus on only the application. We will be merging our MongoDB cloud service(mlab) with our PaaS(Heroku). This is important because while using PaaS, it allows us concentrate on developing and managing our applications without being perturbed by the thought of maintaining the infrastructure. To go through this process successfully, we need to have: Heroku CLI (of course) Git bash An mlab account A Heroku account Now we have everything up and running, we can create a new app on our Heroku account. For the sake of this project, the name of the app is ‘mediumapp11’. Then we create a database on mlab, with the name ‘mediumapp11’. When we have our database up and running, we can add a database user and then we will see our MongoDB URI above. This will help us connect to our database from our localhost. We will have something of this nature: mongodb://<dbuser>:<dbpassword> *.mlab.com:41678/mediumapp11 @ds0*1*7 Where the ‘dbuser’ tag is the username of the user you created and the ‘dbpassword’ is the password of the user. Now on our local host, we can create a ‘mediumapp11’ folder and then do this: heroku addons:add mongolab --app mediumapp11 Where ‘mediumapp11’ is located, the name of your app will go there. When this is done, we should find mlab as an addon in our Heroku project. Now, we can finally create a ‘mediumapp11.js’ file. We will need the mongoose dependency to define our schema and http. const mongoose = require('mongoose'); const http = require('http'); const Schema = mongoose.Schema; Then now, we can define our schema ,create a document which is the instance of our model and then specify the URI we had gotten from mlab. let Team = mongoose.model('Team', TeamSchema); let db = mongoose.connection; let dbUrl = 'mongodb://mediumapp11:mediumapp11 .mlab.com:41678/mediumapp11'; @ds041678 Then, we can listen for errors and if there is none we connect to our URI and save a particular document. db.on('error', function () {console.log('error');}); mongoose.connect(dbUrl, function (err) { if (err) {  return console.log('there was a problem' + err);  } console.log('connected!'); var team = new Team({ name: 'Jehovah'  }); team.save(function (error, data) { if (error) {console.log(error);} db.close(); process.exit(); }); }); Now, we should ensure we replaced the ‘<dbuser>’ with ‘mediumapp11’ and the ‘<dbpassword>’ with ‘mediumapp11’ and then we can run the app node mediumapp11.js We can now refresh our mlab, then go to documents and then we will find our our document saved. If you learnt anything, let me know by 👏👏👏.

Heroku’s handshake with mlab

Recently, I had been working with a database I had created several months before. For that particular project, I had created a separate database collection for each group of items. At the time, I had a collection of books and articles, sorted by topic, and for some reason, had put all books and articles in separate collections based on topic. Then, I created endpoints for each topic. That way, I can switch out the url collection for the topic the user searched for, like this: In this case, the user searched for “how to put on pants”. But having a collection with the topic of “how to put on pants”, I now can fetch just those 4 results, instead of fetching the entire library. Another component I used to make this work was a word map object. I won’t get into that here, but if you’re interested, here’s where you can check that out: https://hackernoon.com/creating-more-accurate-search-results-for-small-sites-436e64da79b6 Although this works great, I recently wanted to be able to search all of the collections at once, which wasn’t easy with so many separate collections. Basically I wanted to combine all the collections into one single collection. I started off by doing a fetch which listed all of the collections in the database. I’m using mlab, and the way to do that if you’re using mlab is as follows: To get the collections in the specified database: GET /databases/{database}/collectionsExample:https://api.mlab.com/api/1/databases/my-db/collections?apiKey=myAPIKey So your code in implementation would look something like this: fetch(' {if (response.status != 200) {window.alert("Oopsie daisy");return ;}response.json().then(function(data) {let api = data; https://api.mlab.com/api/1/databases/my-db/collections?apiKey=myAPIKey ').then(function(response) If you console.log your api at this point, you’ll have an array of each and every one of your collection names as strings. Perfect. Now I’m going to do a for loop, and loop through those collection names, and do basically the same thing I did way up at the top, fetching in each loop iteration, and replacing the fetch collection name with the current api array item. let arr = [];for (var i = 0; i < api.length; i++) {fetch(' {if (response.status != 200) {window.alert("dag nabbit");return ;}response.json().then(function(data) {let test = data;for (var k = 0; k < test.length; k++) {arr.push(test[k])}});});} https://api.mlab.com/api/1/databases/my-db/collections/api[i]?apiKey=myAPIKey ').then(function(response) Here, we have our databases name in the fetch url (my-db) and in each loop, our collections name changes (api[i]). We fetch the data there and save the data as “test” . then, we have a nested loop, where we loop through test, and push each array item into our array called “arr”. Now, if we console.log “arr” we should have a consolidated array of every single one of our collections in one place, instead of in multiple collections. Next, we’ll need to reformat all the data into a new array of JSON objects. setTimeout(function(){for (var i = 0; i < arr.length; i++) {let text = arr[i].article_text;let text2 = text.replace(/"/g, "");let text3 = text2.replace(/'/g, "");arr[i] = `<p>{"articleAuthor": "${arr[i].article_author}","articleAuthorTitle": "${arr[i].article_author_title}","articleName": "${arr[i].article_name}","articleDate": "${arr[i].article_date}","articleText": "${text3}"}</p>`;document.getElementById('verseText').innerHTML += arr[i];}}, 50000); I used a set timeout function here, to allow all of the fetches in the previous step to complete. Otherwise, I may begin building the JSON data before the fetches complete. I loop through the data contained in the array “arr” and slap it in a paragraph with key:value pairs inside curly braces, then render each object in the DOM. Then I just copy and pasted the output into a json file. As long as there are no errors, then I should be good to go. With mlabs at this point I can just save the file and then export it to an mlabs collection using the command line prompt: mongoexport -h ds#####.mlab.com:#### -d <database name> -c <collection name> -u <user> -p <password> -o filename.json And we’re done. Now, I can access all of my data from a separate database, but I haven’t altered the original structure with separate collections, in case I still want to use that format later. Now, before you comment and say how dumb this method is, don’t worry, I know. But, if you’re like me, and are more comfortable with the front end, this is one possible way to do this. I’m not saying it’s the best, but it’s one way, and it works.

Combining MongoDB Collections

Trump Said WHAT?!?! (Parsing and Searching JSON data)

In this post, I want to show how you can build the front and back end of a website using NodeJS for the back end. We’ll use node to create endpoints, and set up a database in JSON format. Then, we’ll create a front end application using React that will post to the database, and also fetch data from it. For this example, I’ll be making a guestbook, where users can submit their names and leave messages. Because users won’t need to log in, or retrieve any data, I won’t need to store anything in a session. Here’s the way this will likely work: Basically, we will have this all one one page, with a form for submitting a message, alongside the actual guestbook. To make this work we’’ll start by creating the endpoints for our POST and GET requests. We’ll set up our database, deploy it, so that we can create a React app, and pull data from the online API. Here’s the order in which I like to do things. You may prefer to work differently. This process makes sense to me, because each step builds on the step before it. During this tutorial, I’ll go through each step in more detail. To see how the finished project could work, here’s a link to my project: http://ethan.jarrell.webdeveloper.surge.sh/GuestBook STEP 1 - Our database collection will contain 2 items: The name of the user The message the user writes If you haven’t already done so, go ahead and install MongoDB, and get it running on port 27017. Then, on the command line, we’ll create the database. We’ll call our database signatures. And the collection, guest_signatures. > Show dbs> Use signaturesswitched to db signatures> show collections> db.createCollection(guest_signatures) Now that we have that set up, we can switch to the text editor, and create the models for the database. STEP 2 - Use the command line to start a new express app. Make the new directory, and then use npm init to create the app. This will automatically create your pkg.JSON file. Since this will be a very basic app, we’ll only need 2 other files in our express app. One for our routes. I like to include all my dependencies in this file as well. Then our second fill will be for our models/schema. We’ll tackle the model file first. It should look something like this: const mongoose = require('mongoose'); let Schema = mongoose.Schema; const signatureSchema = new Schema({guestSignature: {type: mongoose.Schema.Types.Mixed,required: true,},message: {type: mongoose.Schema.Types.Mixed,required: true,},}) const Signature = mongoose.model('Signature', signatureSchema); module.exports = Signature; STEP 3 - Before we create our endpoints here, let’s go ahead an list our dependencies at the top of our file. We don’t need too many. //== =// LIST DEPENDENCIES const express = require('express');const parseurl = require('parseurl');const bodyParser = require('body-parser');const path = require('path');const expressValidator = require('express-validator');const mongoose = require('mongoose');const Signature = require('./models/signature.js')const app = express();const url = 'mongodb://localhost:27017/signatures'; //=========================// You don’t necessarily need to connect with mongoose, so feel free to use MongoClient, if you prefer that. We’re also using the Signature schema that we just created in the previous step. Now, let’s create our endpoints. We only have one model, and we’re only going to read and write to it for now, so we’re only going to have 2 endpoints. Our root directory “/”, where we’ll redirect to our API Our api, we’ll call “/api/signatures, where we’ll read and write to. At our API endpoint, we’ll have a GET and a POST. We’ll reference our models, using the find() and create() methods for GET and POST respectively. And our response will need to be in JSON format, so that we can easily access the data there from React later on. Here’s how those endpoints might look: //== =// ROOT DIRECTORY app.get('/', function(req, res) {res.json('you did it');}); //==========================// //== =// GET ALL SIGNATURES app.get('/api/signatures', function(req, res) {Signature.find({}).then(eachOne => {res.json(eachOne);})}) //==========================// //== =// POST NEW SIGNATURE app.post('/api/signatures', function(req, res) {Signature.create({guestSignature: req.body.SignatureOfGuest,message: req.body.MessageofGuest,}).then(signature => {res.json(signature)});}); //==========================// The only thing I didn’t really mention was the req.body.SignatureOfGuest and req.body.MessageofGuest. Everything else in this section refers to something we’ve already created, like our database, collections and models. This refers to the name of the field that we’ll use in our React App, Our input will have to use the names SignatureOfGuest and MessageofGuest, so we’ll need to remember that. STEP 4 - Now, we’ll want to connect to our local database from out text editor. If you remember, we had this constant in our list: const url = 'mongodb://localhost:27017/signatures'; We’ll write a function using this constant to connect to our local database. //== =// MONGOOSE CONNECT mongoose.connect(url, function (err, db) {if (err) {console.log('Unable to connect to the mongoDB server. Error:', err);} else {console.log('Connection established to', url);}}); //==========================// In order to make sure everything is working properly, we’ll also need to add an app.listen at the end of our file. At this point, it’s a good idea to pause, and make sure the endpoints and local connection works. You can do that by using Postman to make GET and POST requests. If the endpoints or connections don’t work, you should be able to diagnose the problem here based on the error messages you receive. If everything is working, it should allow you to appropriately read and write to the database. Once everything is working, it’s time to set up our account on Heroku and Mlab. STEP 5- Sign up for a Heroku account . And create an mLab account . here here STEP 6 - After creating your mLab account, click on the Create New button and select a Single node sandbox. There are paid options as well, but the sandbox has plenty of space for getting started. Give your database a name. Since my local database is signatures, I’m using the same name for my mLab database. Now that you have your database created, you can start a new collection from here. You will also need to add a User or Users who can access your database. Without doing this step, and adding yourself as a User with access, authentication will always fail when you try to deploy it. Now your database is running on mLab. When you click on the database, you should see some information at the top, telling you how to integrate the connection. It should look something like this: mongodb://<dbuser>:<dbpassword> 9234.mlab.com:9234/signatures @ds7 The actual url of the database will just have the username and password replaced with your username and password. Now let’s head back to our text editor. We currently have the database running locally at this location: const url = 'mongodb://localhost:27017/signatures'; To change it, so that we are connected to mLab, simply update the url variable with the information from mLab. It should look something like this: const url = 'mongodb://username:password@ds79234.mlab.com:9234/signatures'; However, you’ll likely be putting this on github, or another public place. You don’t want your mlab username and password in a public sphere where anyone can see it. To fix that, we’ll set an environment variable on the command line, and then update our url variable one more time in the text editor. On the command line, use this command: export MONGOLAB_URI="mongodb://username:password@ds79234.mlab.com:9234/signatures'; Of course, replacing it with your own username, password, numbers and database name. Now, back in our text editor, we will change our url variable to the following: const url = process.env.MONGOLAB_URI; We’ll also want to change our app.listen to reflect the new port. It could look like this: app.listen(process.env.PORT || 3000);console.log('starting applicaiton.  Good job!'); This way, it will try to run from mongolab, but if it can’t make the connection, it will still listen on port 3000 by default. This will also allow you to run the app locally or from mlab, in case you want to test changes on the local version. The final step in making the connection is deploying your code to your Heroku App. to do this, you can use the following code from the command line: heroku config:set MONGOLAB_URI=mongodb://username:password@ds79234.mlab.com:9234/signatures Your app should be successfully deployed on heroku, and you can open it from there now. If you are getting errors, double check to make sure it’s running locally. If so, then it’s probably an error along the way in the connection. In these last two steps, there’s quite a bit of repetitive code. I’m going to outline the high level code here. STEP 7 - Use ‘create-react-app’ from the command line to create a new react app. STEP 8 - Now, we’ll want to create a form, to allow for user input. Here’s how we do that. In a new component, we’ll create a new class. class GuestBook extends Component 2. We’ll use a constructor and super method to pass props down. constructor(props) {super(props); 3. Use the this keyword to handle the name and message of guest, and bind it to (this). this.handleSignatureOfGuest = this.handleSignatureOfGuest.bind(this);this.handleMessageofGuest = this.handleMessageofGuest.bind(this); 4. Set the state of the name and message of guest to an empty string. this.state = {SignatureOfGuest: "",MessageofGuest: "",}; 5. Listen for an event on the state of both the name and message input. handleSignatureOfGuest(event) {this.setState({ SignatureOfGuest: event.target.value });}handleMessageofGuest(event) {this.setState({ MessageofGuest: event.target.value });} 6. Create a function that changes the name and message to the value of the target input. addToGuestBook = event => {event.preventDefault();this.setState({SignatureOfGuest: event.target.value,MessageofGuest: event.target.value,}); 7. I’m now using axios to post the input data to our database, which is on heroku. axios.post('< , {SignatureOfGuest: this.state.SignatureOfGuest,MessageofGuest: this.state.MessageofGuest,}).then(response => {console.log(response, 'Signature added!');}).catch(err => {console.log(err, 'Signature not added, try again');}); your-heroku-url here>' 8. Then I’m resetting the state of the input to an empty string. this.setState({SignatureOfGuest: "",MessageofGuest: "",});}; 9. Finally, we’ll make a render method, and return our page with the input fields. Inside the input of each field, we’ll give it an onChange, name, and value. the onChange will be set to the.handlemessage or this.handlename, for each field. The name of each field will be set to what we called it in our node app. The value will use state, and we’ll set that to this.state.message, and this.state.name. <inputonChange={this.handleSignatureOfGuest}name="SignatureOfGuest"className="NameinputForm"value={this.state.SignatureOfGuest}placeholder="Enter your name"/><textareaonChange={this.handleMessageofGuest}name="MessageofGuest"className="MessageinputForm"value={this.state.MessageofGuest}placeholder="Type a message"/> 10. Then we’ll add a submit button, where we call the function from earlier. <buttonclassName="submitbuttonguestbook"type="submit"onClick={this.addToGuestBook}>Submit to Guestbook<i className="GuestBookButton2" aria-hidden="true" /></button> STEP 9- Now we’ll make another component where we’ll render the data that’s being stored in our database. Then we can export that component, and put it on our guestbook page. Inside this component we’ll do the following: In a new component, we’ll create a new class. class GuestNames extends Component { 2. We’ll use a constructor and super method to pass props down. constructor(props) {super(props); 3. Use this.state to set the state of our guestbook messages to an empty string. this.state = {messages: "",}; 4. Use a componentDidMount lifecycle method componentDidMount() { 5. Inside that method we’ll use fetch and the url of our heroku api to fetch the information from the database. fetch('<your-heroku-url-goes-here> ).then(results => {return results.json(); ' 6. We’ll map over the data, and return the data we want. data.map((msg) => {return(<div key={msg.results}><h3 className="h3msg">{msg.message}</h3> <h2 className="h2sig">-{msg.guestSignature}</h2></div> 7. Now we’ll use this.setState to set the state of the messages to the new state using the data we just fetched. this.setState({messages: messages}); 8. Now we’ll create render mehod. render() { 9. Inside the render method, we’ll create JSX elements to render our data inside the component. I’m using this.state.messages inside an <h6> tag. return ( <div className="guestdataContainer"><h6>Guestbook Messages</h6>{this.state.messages}</div> 10. Finally, we’ll export the component, so we can use it on other pages. export default GuestNames; Again, if you have any questions feel free to reach out. Thanks!

How To Combine a NodeJS Back End with a ReactJS Front End App.

An interesting message popped up on my radar from Kevin Law on September 21st, 2016: “You’d be a perfect fit for a company I’m advising if you’re extremely flexible on location” Since I love working on early consumer products and traveling, it was only three weeks later that I touched down at the airport in Kampala. Serve as interim CTO for 1 month and provide leadership to a small ride share engineering team, paving the way for future success. The mission: (Note — there are several HD YouTube videos of Uganda at the end of this post if you’re like me and you like to watch videos.) Boda Boda A “boda boda” is an inexpensive motorcycle ride offered in African cities (for my San Franciscans, imagine a ride from Hayes Valley to the Marina for just $1 versus a $5 Uber carpool). It is estimated that 1,000,000 boda rides take place everyday in Kampala. While on a walk, upon reaching a sidewalk, you will find you are solicited by a boda driver within seconds. “Boss!” “Boda!?” A regular boda ride involves hopping on the back of a boda driver’s low power motorcycle. He will take you on a harrowing ride, dangerously weaving through the chaotic gridlocked streets, sometimes crossing a median, often narrowly avoiding a collision or sideswiping a bigger vehicle, and sometimes just driving on the sidewalk. Passengers rarely have helmets, they sometimes double or triple-up on the back of a single bike, drivers are often unlicensed, and, at least in Kampala, there are very few traffic signals, street lights, or stop signs. At the end of the boda trip, you negotiate with your driver and you pay in cash. A look at the streets of Kampala during the day. Video shot . here Don’t let the above-mentioned video fool you- if it looks fairly organized in the video, one must only travel a block or so before you hit points which seem totally chaotic. Most intersections are very disorganized and seem extremely dangerous to a like me. mzungu Drivers Want “SafeBoda” The name of the company I consulted for is called . Its business hypothesis is that consumers may want a safer, organized, and vetted system for requesting a boda ride. SafeBoda drivers are screened, have a bright orange reflective vest, and have an extra helmet available for you to wear on the ride. The SafeBoda driver’s unfair advantage? - they get many more boda passengers than before they were a “SafeBoda” driver and therefore they make more money. SafeBoda drivers also have access to insurance, quality equipment, training in first-aid and road safety, and a sense of community that is rare in the industry. SafeBoda Evidence of early traction is that with virtually no technology, SafeBoda was able to sign up over 1,300 Kampalan boda drivers who each pay a fee as part of their onboarding. SafeBoda was so successful at this that they stopped accepting new drivers in order to focus on passenger demand and product. Before my arrival in Kampala, SafeBoda had a basic app in the Google Play Store with which around 1,000 rides per month were taking place. Clip of my “SafeBoda” ride from my apartment to the office Uganda’s Mobile Phone & Payment Technology Android is king in Uganda with around 70% market share. iPhones are rare… perhaps a 3% share. There are also ‘feature phones’ which only allow calls, texts, and a basic interactive textual menu provided by the carriers ( ) which I would estimate is used by the remaining 27% of users. USSD The major cellular carrier is called . In addition to data plans and voice calls, MTN also provides a money transfer service called “Mobile Money” which is tied to your SIM card and used by around 85% of Uganda’s citizens. MTN A Ugandan can deposit his or her pay (which is usually disbursed as cash) at any one of thousands of MTN locations throughout the country. MTN Money locations are usually offered as an extension to a physical store front such as a market or street vendor. After depositing cash at an MTN Money vendor, the balance on the user’s mobile interface instantly updates. With the updated balance on the mobile interface, the MTN Money balance can then be used to pay bills and transfer money to others at a cost of ~3% in fees on each transfer. Clearly as part of a technology revamp, SafeBoda should include MTN Money as part of the payment solution. However, we wouldn’t be able to complete the integration within 10 days due to MTN’s stone aged SOAP-based API. Instead, we decide to use Stripe’s API platform which we can implement in 1 day and which the founders felt would help to validate whether or not credit card holders are willing to use the service. It is decided MTN integration will take place in a subsequent development cycle. SafeBoda’s Legacy Tech Upon my arrival, the 5 person engineering team had first created a PHP / MySQL backend on 1 server, but then decided to rewrite it using with after they had encountered a series of problems related to connection issues and deliverability problems with the Android client. CodeIgniter Digital Ocean Node.js Socket.io On my arrival, some of the problems the local engineering team was grappling with were: When the client would miss a socket.io message, the app would strand The MySQL database was hard to migrate The code was peppered with large, hand rolled complex inline SQL queries The developers were actually testing against the production servers There was a lot of opportunity to improve both the development process and the product. Overhaul In 10 Days I convinced the founders to let me drive a complete overhaul to their product architecture and process in 10 days flat — roughly 300 man hours split between just 3 engineers. Having worked for over 2 years engineering at Lyft, I also know a thing or two about the best way to design almost anything related to ride share services. Scaleable product architecture and fast development process using technologies the team is already familiar with was right up my alley and my experience would allow us to leverage everything I had learned. I wanted to use Python and , but after interviewing the team, I was able to see that due to their lack of significant Python experience, there would be little chance we would have a working revamp in 10 days. I had to keep them on PHP… something that made sense for them since scaling wasn’t an issue yet and rapid prototyping and market validation were the main focus. We would generally build with an eye towards future scalability in case things explode out of the gate, but for now, fast development was priority #1. Flask With 2 of their existing devs, the three of us started a 10 day process in which we locked ourselves away in a room, worked almost until midnight every night, and emerged at the end of the 10 days with a new minimally viable system. Below is an animated gif (pardon the low resolution) of the “Golden Path” of a ride request & boda driver pickup. This recording was made on the evening of our 10th day of development. This screen recording used the real API’s that were working on the 10th night. A “rough cut” of the working app. All of the screens used real API’s behind the scenes on our new server architecture. Almost all of the values were dynamic and correctly pulling from the REST API. Obviously several ui features were not completed at this stage. The following week, the Android interfaces were updated: HD video of the golden path about 1 week after the animated gif was recorded. How We Made It In 10 Days When you rapidly build something, you start with an idea of what the features will be, what platforms it should run on, and who will do what. Given Android is the market leader, our mobile developer would build out all of the front end screens according to our product spec. We mapped out all of the screens on a whiteboard and then our Android dev was off to work. The other developer and I would would hack out the backend architecture. We would create a new development process, infrastructure, product architecture, and API’s. We would document them, and hand them to the Android dev to implement on the 4th day. Hosting on Heroku Hosting this project on propelled us faster and further than I had originally imaged. Using Heroku pipelines with GitHub integration meant that once a pull request’s tests pass in Circle CI, it automatically deploys into our staging environment. I had never used Heroku pipelines before, but I think it’s amazingly easy to setup and very powerful. Heroku Heroku’s awesome pipeline interface used for deploying to production The best part is that as we open up pull requests, Heroku senses it and offers us an ability to launch a test app called a “review app” — you see it above in the left column. One click will launch a fully operational environment that you can connect the mobile app to in order to test an upcoming feature. Setting all of this up takes only 3–4 hours, which includes learning how it all works. Dockerized Development In production, we use mlab.com to host MongoDB version 3.2. Docker allowed us to launch a local Docker development database container with 1 command: docker run — name mongodb -d mongo:3.2.10 (Note — some of the additional switches we use are omitted for brevity) As for PHP, I was able to create a PHP dev environment container that closely mirrors Heroku’s environment. The devs can do a similar docker ‘run’ statement to launch a local development container through which they can work on their local PHP files, do live debugging with PHPStorm via both web and cli requests, and even test their local development using an Android phone across an ngrok tunnel. PHP Application Framework The closest thing PHP seems to have to Python’s Flask is called . Lumen gives you a basic routing system and is setup to support REST API development. It makes versioning your REST API very easy. It also supports adding “Middleware” which performs things like API authentication. One thing I personally like is the IOC injector that detects which objects your controllers would like to use and automatically injects them for you by default. Lumen For Mongo access, we used PHP’s — an open source data mapper for MongoDB that we used back at and a project to which I have also been an . Doctrine ODM Lyft open source contributor to Gameplan On day 1, we mapped out our data model. We really only needed two key domain models: User & Ride. We then went to the whiteboard and defined our ride state transitions. The happy path looks something like this: ‘requested’ -> ‘accepted’ -> ‘arrived’ -> ‘pickedup’ -> ‘droppedoff’. There are two edge cases. One is that if our dispatcher fails to find you a ride, we set the state to ‘operatordispatch’ - a final state in which the mobile client simply displays the number to SafeBoda’s human powered dispatching call center. They’re able to manually setup dispatches to drivers who have feature phones. The other case is when the passenger cancels — that’s a final state where the request ends. If the driver cancels, the dispatch loop just continues. Below are some of the actual whiteboards drawn in our kickoff on day #1. Initial diagram of the ride initial state changes. Some chicken scratch from that first meeting where I described the way the phones would poll the server and how we would structure the HTTP JSON server response. From left to right: Charles, Kirk, and Jon (me). RestAPI We next sketched out a basic HTTP REST API that has methods such as: Create a ride request. POST /v1/ride Update a user’s current location. PUT /v1/userlocation In total, we have about 20 REST calls that were defined. We wrote the whole API spec up in an swagger.yml file that is rendered beautifully if viewed with . http://editor.swagger.io/ For the following 9 days, we hacked away. We didn’t attend any of the company meetings and we spent some of the time in my personal apartment so as to not be distracted by anything. Service Oriented Architecture As with most minimally viable apps, we created a tiny so-called ‘monolith’ app. I like to organize business logic into service classes — these become the foundation of what you would eventually ‘decompose’ later into application partitions, or what some people call “microservices”. In our app, we have the following PHP “service” classes which can easily be broken out into the future and implemented in any programming language into microservices: Behaviors responsible for converting between USD and UGX using the latest exchange rate. Currency Behaviors responsible for finding the nearest drivers, and powering the dispatching lifecycle. Dispatch Behaviors used to determine the number of minutes it will take to travel on the road between two places Eta Behaviors used to authenticate a Facebook user and retrieve profile data. Facebook Behaviors responsible for tracking analytics event information. Metrics Behaviors related to billing users using a payments gateway. Payments Behaviors related to pricing rides. Pricing Behaviors related to casting a rating for a ride and then also updating a driver’s record to update their average rating. Rating Behaviors related to creating and updating a ride. Ride Behaviors related to user creation and update. User Behaviors related to updating the user’s location. UserLocation Coping with Eventually Consistent Data Due to the fact that MongoDB is not an ACID database, the application must be able to cope with database references which can be out of sync. In MongoDB, there are no transactions or rollbacks. To help overcome concurrent update contention and data loss, we use optimistic record locking by default on all updates. We use Doctrine’s event hooks in order to ensure two records point at each other. For example, If I put user A in ride B, I will first update user A’s record to have ride_id = B’s id. Then, during the post update hook, I will next update ride B’s user_id to have user A’s id. There are short moments when the database is therefore out of sync during normal operations. Further, the app might encounter a show stopping error before the 2nd update hook completes, leaving the database in a state where the user record indicates the user is in a ride, but the ride does not correctly point back at the user and this would be the source of trouble if your code is not setup defensively. To cope with this, we must have protective ‘cleanup’ routines that check for situations like this. Before key business logic fires, we invoke a “cleanup” routine to ensure the data integrity is correct. Note that we do not employ MongoDB’s “ref” feature — we manually store record id’s and resolve database references manually. Also noteworthy is that when we update the user’s location on their user record, we perform the update without the default optimistic locking record strategy used throughout the rest of the software, or else we would see tons of optimistic lock exceptions for no good reason. 3rd Parties Tools We Used GeofenceAPI PagerDuty PaperTrail for MongoDB hosting Mlab.com for Jenkins & S3 hosting Ec2 for the API cert, for the Jenkins cert RapidSSL LetsEncrypt CircleCI Mixpanel InstaBug Features & Hindsight A surprising number of drivers in Uganda cannot read maps. Some clearly had trouble knowing left from right. The society is also highly cash based and most folks don’t have credit cards. If I had known some of these things, I would have advocated for a slightly different MVP in which relied less on screens with maps on them and put more emphasis on calling the driver. Conclusion The developers are no longer testing against production machines. We ditched complex hand rolled inline SQL for a MongoDB solution which requires no production migrations. If the project takes off, MongoDB will allow us to add more shards and rebalance data. It’s now up for the market to decide if the product is the right one for Uganda. A Personal Note Living in Kampala for a month and working on a real startup again was awesome. I feel personally enlightened and the trip gave me a new perspective on the world. In San Francisco, we have a lot of millionaires & billionaires and I often feel like I’ve missed out on something that everybody else was able to achieve. However, my trip to Africa made me realize just how fortunate I am to be a self made engineer and what an achievement it is to even be able to live in San Francisco in the first place. My 30-day contract has ended. I wish the founders the best of luck. Here are the current state of things: SafeBoda is seeking an on-site full-time CTO. If you’re up to the challenge, contact team@safeboda.com The version of SafeBoda in the Android Play Store is still the legacy version of the app. They continue to make iterative improvements to the upcoming platform, and have a strategy to release the new platform in January. The work we accomplished currently lives in the beta channel. Make me proud guys! Twitter: Email: block.jon@gmail.comJoin me on the DoorDash API Platform Team and browse other available positions: Contact: @blockjon https://www.doordash.com/careers/ Pictures And Videos From My Trip KLM flies from SFO all the way to Kampala’s international airport with layovers in Europe My former office: San Francisco’s luxury “China Basin” building. My Kampala office? This inexpensive building. Photo taken . here HD clip of a ride down a street in Kampala shot from an Uber. A look at some Rhinos which were going to sleep for the day World’s most powerful waterfall Heading towards the center of Murchison National Park — home to thousands of wild animals Giraffees in the road in Murchison National Park Elephant hanging out at the water’s edge on a hot day. Hippos in the water. Walking from my apartment to the coffee shop. Filmed . here A Ugandan lunch: Rice + G-nut sauce, sweet potatoes, cabbages, and greens. Wiping out in a Jinja class 4 rapid (me in front left) A sophisticated fashion event Buying a breakfast sandwich “Rolex” on the side of the road in a slum. Video shot . here Eating tilapia at the shore of Lake Victoria. Video shot . here Inside the ultra-modern Acacia Mall food court Buying a random rolex on the side of the road heading from Jinja to Kampala

Stories, News & Mentions about C9LABCOM

C9labcom’s Stories on HackerNoon