Founded in 2009, Endurance is a holding company whose primary work is building new ventures with the intention to solve meaningful problems. Our most notable companies include Funding Circle (Public: FCH), Collective Medical (Sold, PointClickCare), Endurance Fitness (Sold, MBO), Vouch (Series C); as well as recently launched Resolve (CF+), and Flow (Series A). In addition, Endurance operates a multi-family investment office (MFO) for its Partners and collaborators where it invests across asset classes, but most heavily in Private Equity, Real Estate, and Venture Capital.

Endurance Companies

Growing a Fintech Unicorn: Alex Tonelli's Anthology of Lessons Learned

Industry 4.0 is about the huge change occurring in the service sector. The products are created and conveyed acquiring joint effort and cooperative energy of different actual resources with advancements. Industry 4.0 will be driven to a great extent by the transformation of several advancements. These include biological and physical-digital, expanding globally as a result of the Third Industrial Revolution. Truth be told, the continuous COVID-19 pandemic has led to the immense start of these innovative progressions. Their appropriation is required for human patience and survival. In the tech industry, the IT Solutions Companies have made major impacts owing to the beginning of a new industrial change. The Rise of Industry 4.0 Industry 4.0 joins advanced innovations like IoT, AI, distributed computing, blockchain, huge information, AR, nanotechnology. Along with these is the assembling, production network, dissemination, client support, and execution of the physical-digital physical (PDP) circle, among others. A portion of the regular advantages is proficiency, cost-cutting, and benefit augmenting. The IT Solution Companies that succeed are those who completely comprehend and use the innovation to help them in every aspect of their business. These include generally speaking business procedure, labour force and ability techniques, cultural effect, as well as the right delegation of tasks. The expansion of digital gadgets inside industrial facilities and out on the field implies that support experts can get access to documentation and administration history timely at a given place. To put it plainly, Industry 4.0 leads in progressive changes across industry as well as non-industrial setups. It shifts production from a unified to a decentralized model. This leads to representing a significant change from the conventional way to a more unique and adaptable environment. How Industry 4.0 impacts business Industry 4.0 is about the huge change occurring in the manner that products are created and conveyed. This requires a coordinated effort and collaboration of different physical resources with technology. However, digital transformation is useful only when it benefits the entire working of IT solutions companies, instead of just focusing on one particular aspect. The digital advancement impacts the whole worth chain in the following manner: Personalized client experience, result based income models Delivery and client support: Performance analysis, planning management, improvement, and labour force change Corporate tasks: Integrated data and control frameworks, general availability, and united data innovation (IT) and operational innovation (OT) networks Plant designing: Supplier tracking details, track, and follow Inventory network: Asset well-being check, maintenance, field labour viability, climate, healthy safe environment Support and dependability: Shop-floor labour force adaptability and effectiveness, energy observing and, and constant operational knowledge Assembling tasks: Industry 4.0 freedoms for new companies Various small and big scale companies might not have a full comprehension of Industry 4.0 innovations and feel uncertain of where to start their digital transformation. The startups may not have all the required skills or the expertise which opens up their way to collaborate with the right set of people within their industry. New businesses can fill in as especially successful and inventive collective partners. Numerous IT solution companies as of now work with new companies in Industry 4.0. They see them as vital partners in their business transformation using digital transformation. Most organizations are starting to attempt to discover a balance between benefit and reason. This is on account of several factors from clients, workers, as well as stakeholders. This implies that business change isn't just about expanding the primary concern which is money, however improving the whole chain affecting every one of the partners. Capacities through new businesses Startups can bring in interesting changes in different ways. In the first place, new businesses can offer expanded adaptability and customization to producers. This offer is at a fundamentally lower cost than those offered by more settled businesses. Besides, new businesses have a multidisciplinary group of team members. These are members who can foster innovation which empowers them to give a fresh finish to their arrangements. At last, given their more modest size, new companies can be more receptive to the industry changes. Also, they can adjust to the needs of the clients according to their desired results.
Industry 4.0 has the best possibilities of limitless freedoms for new startup companies, particularly tech new businesses such as Website Design Company and Web Development Company, however not just limited to them. Organizations, especially related to manufacturing, must take the full benefit of the innovations to stay efficient as well as productive. The digital transformations may not be something similar for each organization even inside a similar industry. Yet they can be a blend of different Industry 4.0 advances that are dependent on the business needs and trouble spots of that specific company. The biggest areas of investment subsidizing over the most recent couple of years are IoT stages and network, online protection, detecting and imaging, and tasks. Mutual benefit for Established and new companies It is a mutually beneficial idea for the two types of businesses, small scale as well as big scale. These businesses are looking for help in their business change venture and the new companies who have the capacities to help them. A new study by Deloitte noted that IoT, AI, distributed computing, and huge information and examination are the best four advances, which business pioneers expect to greatly affect their businesses with a positive impact. Four key advancements which empower Industry 4.0 are: The blend of information, computational force, as well as connectivity The investigation and knowledge The Human-machine connection The physical-digital physical transformation The blend of these advancements and new advances arising and developing will change the fate of manufacturing in the following years to come. A new report figures that Indian SMEs will spend around $14-16 billion yearly on advanced digital technologies, and a significant segment of this spending may go to new startups. India has probably the most youthful population on the planet, and the thriving startup situation is helping the nation address the inescapable impacts of innovation on work. A unique startup environment has an essential part in fuelling advancements to help work creation. Industry 4.0 may have drastic effects on organizations but what is work without some risks? Nonetheless, when your business' endurance relies on the capacity to advance, it will lead to new limits. Moreover, it will encourage others to succeed in the modern business landscape. It is a must for all startups to explore the current advanced needs of the market and stay updated. They must collaborate with IT Solution Companies to stay competitive and learn new methods and concepts. Their businesses will flourish using the most advanced tech factors.

How Industry 4.0 Impacts IT Companies And What Are Its Benefits

Ukraine's Rapid Push to Deploy AI-Enabled Drones for Battlefield Supremacy

Supernormal Stimuli: Are We Amusing Ourselves to Death?

অতিপ্রাকৃতিক উদ্দীপনা: আমরা কি মৃত্যুর জন্য মজা করছি?

Übernatürliche Reize: Amüsieren wir uns zu Tode?

Estímulos sobrenaturales: ¿nos estamos divirtiendo hasta la muerte?

Stimuli supranormaux : sommes-nous en train de nous amuser à mourir ?

अलौकिक उत्तेजनाएँ: क्या हम अपने आप को मौत तक मनोरंजन कर रहे हैं?

超常刺激：私たちは死ぬほど楽しんでいるのか？

초자연적 자극: 우리는 죽을 때까지 즐겁게 지내고 있나요?

Estímulos supranormais: estamos nos divertindo até a morte?

Сверхъестественные стимулы: развлекаемся ли мы до смерти?

Olağanüstü Uyarıcılar: Kendimizi Ölesiye Eğlendiriyor muyuz?

Kích thích siêu thường: Chúng ta có đang vui vẻ đến chết không?

超常刺激：我们是不是正在自娱自乐？

Building and deploying LLM-based applications has never been easier. Since the launch and wide adoption of ChatGPT near the end of 2022, we’ve seen a storm of news about tools, products, and innovations stemming from large language models (LLMs) and generative AI (GenAI). While many tech fads come and go within a few years, it’s clear that are here to stay. LLMs and GenAI Do you ever wonder about all the tooling going on in the background behind many of these new tools and products? In addition, you might even ask yourself how these tools — leveraged by both developer and end users — are run in production. When you peel back the layers for many of these tools and applications, you’re likely to come across . LangChain, Python, and Heroku These are the pieces that we’re going to play around with in this article. We’ll look at a practical example of how AI/ML developers use them to build and easily deploy complex LLM pipeline components. Demystifying LLM Workflows and Pipelines Machine learning pipelines and workflows can seem like a black box for those new to the AI world. This is even more the case with LLMs and their related tools, as they’re such (relatively) new technologies. Working with LLMs can be challenging, especially as you’re looking to create engineering-hardened and production-ready pipelines, workflows, and deployments. With new tools, rapidly changing documentation, and limited instructions, knowing where to start or what to use can be hard. So, let’s start with the basics of LangChain and Heroku. The tells us this: “ ” documentation for LangChain LangChain is a framework for developing applications powered by language models. Meanwhile, this way: “ ” Heroku describes itself Heroku is a cloud platform that lets companies build, deliver, monitor, and scale apps — we’re the fastest way to go from idea to URL, bypassing all those infrastructure headaches. If we put this in the context of building an LLM application, then LangChain and Heroku are a match made in heaven. We need a well-tested and easy-to-use framework (LangChain) to build our LLM application, and then we need a way to deploy and host that application (Heroku). Let’s look into each of these technologies in more detail. Diving into LangChain Let’s briefly discuss how LangChain is used. LangChain is a framework that assists developers in building applications based on LLM models and use cases. It has support for , , and . For example, let’s say we were building a tool that generates reports based on user input or automates customer support response. LangChain acts as the scaffolding for our project, providing the tools and structure to efficiently integrate language models into our solution. Python JavaScript TypeScript Within LangChain, we have several key components: : The agent is the component that interacts with the language model to perform tasks based on our requirements. This is the brain of our application, using the capabilities of language models to understand and generate text. Agent : These are sequences of actions or processes that our agent follows to accomplish a task. For example, if we were automating customer support, a chain might include accepting a customer query, finding relevant information, and then crafting a response. Chains : Templates provide a way to structure the outputs from the language model. For example, if our application generates reports, then we would leverage a template that helps format these reports consistently based on the model’s output. Templates : This enables developers to up LangChain applications as a REST API. LangServe deploy and serve : This tool helps developers the interactions in their language model applications to get them ready for production. LangSmith evaluate, test, and refine LangChain is a widely adopted framework for building AI and LLM applications, and it’s easy to see why. LangChain provides the functionality to build and deploy products end-to-end. Diving into Heroku Heroku is best known as a cloud platform as a service (PaaS) which makes it incredibly simple to deploy applications to the cloud. Developers often want to focus solely on code and implementation. When you’re already dealing with complex data pipelines and LLM-based applications, you likely don’t have the resources or expertise to deal with infrastructure concerns like servers, networks, and persistent storage. With the ability to easily deploy your apps through Heroku, the major hurdle of production-izing your projects is handled effortlessly. Building with LangChain For a better understanding of how LangChain is used in an LLM application, let’s work through some example problems to make the process clear. In general, we would chain together the following pieces to form a single workflow for an LLM chain: Start with a to generate a prompt based on parameters from the user. prompt template Add a to the chain to retrieve data that the language model was not originally trained on (for example, from a database of documents). retriever Add a to include chat history so that the language model has context for formulating a better response. conversation retrieval chain Add an agent to interact with an actual LLM. LangChain lets us “chain” together the processes that form the base of an LLM application. This makes our implementation easy and approachable. Let’s work with a simple example. In our example, we’ll work with OpenAI. We’ll craft our prompt this way: Tell OpenAI to take on the persona of an encouraging fitness trainer. Input a question from the end user. To keep it nice and simple, we won’t worry about chaining in the retrieval of external data or chat history. Once you get the hang of LangChain, adding other capabilities to your chain is straightforward. On our local machine, we activate a virtual environment. Then, we install the packages we need: (venv) $ pip install langchain langchain_openai We’ll create a new file called . Our basic Python code looks like this: main.py import os
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

my_prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a friendly and encouraging fitness trainer."),
    ("user", "{input}")
])

llm = ChatOpenAI(openai_api_key=os.getenv("OPENAI_API_KEY"))

chain = my_prompt | llm That’s it! In this basic example, we’ve used LangChain to chain together a prompt template and our OpenAI agent. To use this from the command line, we would add the following code: user_input = input("Ask me a question related to your fitness goals.\n")
response = chain.invoke({
  "input": user_input
})

print(response) Let’s test out our application from the command line. (venv) $ OPENAI_API_KEY=insert-key-here python3 main.py 
Ask me a question related to your fitness goals.
How do I progress toward holding a plank for 60 seconds?
content="That's a great goal to work towards! To progress towards holding a plank for 
60 seconds, it's important to start with proper form and gradually increase the 
duration of your plank holds. Here are some tips to help you progress:\n\n1. Start 
with shorter durations: Begin by holding a plank for as long as you can with good 
form, even if it's just for a few seconds. Gradually increase the time as you get 
stronger.\n\n2. Focus on proper form: Make sure your body is in a straight line from 
head to heels, engage your core muscles, and keep your shoulders directly over your 
elbows.\n\n3. Practice regularly: Aim to include planks in your workout routine a few 
times a week. Consistency is key to building strength and endurance.\n\n4. Mix it up: 
Try different variations of planks, such as side planks or plank with leg lifts, to 
work different muscle groups and keep your workouts challenging.\n\n5. Listen to your 
body: It's important to push yourself, but also know your limits. If you feel any 
pain or discomfort, stop and rest.\n\nRemember, progress takes time and patience. 
Celebrate each milestone along the way, whether it's holding a plank for a few extra 
seconds or mastering a new plank variation. You've got this!" (I’ve added line breaks above for readability.) That’s a great start. But it would be nice if the output was formatted to be a bit more human-readable. To do that, we simply need to add an to our chain. We’ll use . output parser StrOutputParser import os
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser

my_prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a friendly and encouraging fitness trainer."),
    ("user", "{input}")
])

llm = ChatOpenAI(openai_api_key=os.getenv("OPENAI_API_KEY"))

chain = my_prompt | llm | output_parser

user_input = input("Ask me a question related to your fitness goals.\n")
response = chain.invoke({
  "input": user_input
})

print(response) Now, at the command line, our application looks like this: (venv) $ OPENAI_API_KEY=insert-key-here python3 main.py 
Ask me a question related to your fitness goals.
How do I learn how to do a pistol squat?
That's a great goal to work towards! Pistol squats can be challenging but with practice and patience, you can definitely learn how to do them.

Here are some steps you can follow to progress towards a pistol squat:

1. Start by improving your lower body strength with exercises like squats, lunges, and step-ups.
2. Work on your balance and stability by practicing single-leg balance exercises.
3. Practice partial pistol squats by lowering yourself down onto a bench or chair until you can eventually perform a full pistol squat.
4. Use a support like a TRX band or a pole to assist you with balance and lowering yourself down until you build enough strength to do it unassisted.

Remember to always warm up before attempting pistol squats and listen to your body to avoid injury. And most importantly, stay positive and patient with yourself as you work towards mastering this challenging exercise. You've got this! The LLM response is now formatted for improved readability. Our chains would be much more complex than this for building powerful LLM applications. But that’s the power and simplicity of LangChain. The framework allows for the modularity of logic specific to your needs so you can easily chain together complex workflows. Now that we have a simple LLM application built, we still need the ability to deploy, host, and serve our application to make it useful. As a developer focused on app building rather than infrastructure, we turn to LangServe and Heroku. Serving with LangServe LangServe helps us interact with a LangChain chain through a REST API. To write the serving portion of a LangChain LLM application, we need three key components: A valid chain ( ) like what we built above An API application framework (such as ) FastAPI Route definitions (just as we would have for building any sort of REST API) The provide some helpful examples of how to get up and running. For our example, we just need to use FastAPI to start up an API server and call from LangServe to make our chain accessible via API endpoints. LangServe docs add_routes() Along with this, we’ll need to make some minor modifications to our existing code. We’ll remove the use of the . This will give callers of our API flexibility in how they want to format and use the output. StrOutputParser We won’t prompt for user input from the command line. The API call request will provide the user’s input. We won’t call because LangServe will make this part of handling the API request. chain.invoke() We make sure to add the FastAPI and LangServe packages to our project: (venv) $ pip install langserve fastapi Our final file looks like this: main.py import os
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from fastapi import FastAPI
from langserve import add_routes

my_prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a friendly and encouraging fitness trainer."),
    ("user", "{input}")
])

llm = ChatOpenAI(openai_api_key=os.getenv("OPENAI_API_KEY"))

chain = my_prompt | llm

app = FastAPI(title="Fitness Trainer"")

add_routes(app, chain)

if __name__ == "__main__":
    import uvicorn

    uvicorn.run(app, host="localhost", port=8000) On my local machine (Ubuntu 20.04.6 LTS) running Python 3.8.10, I also needed to install some additional packages to get rid of some warnings. You might not need to do this on your machine. (venv) $ pip install sse_starlette pydantic==1.10.13 Now, we start up our server: (venv) $ OPENAI_API_KEY=insert-key-here python3 main.py 

INFO:     Started server process [629848]
INFO:     Waiting for application startup.

LANGSERVE: Playground for chain "/" is live at:
LANGSERVE:  │
LANGSERVE:  └──> /playground/
LANGSERVE:
LANGSERVE: See all available routes at /docs/

INFO:     Application startup complete.
INFO:     Uvicorn running on http://localhost:8000 (Press CTRL+C to quit) Ooooh… nice! In the browser, we can go to . This is what we see: http://localhost:8000/docs LangServe serves up an API docs page that uses a Swagger UI! These are the endpoints now available to us through LangServe. We send a request to the endpoint. But LangServe also gives us a endpoint with a web interface to work with our chain directly. could POST invoke/ playground/ We provide input and click . Here’s the result: Start It’s important to stress the importance of having APIs in the context of LLM application workflows. If you think about it, most use cases of LLMs and applications built on top of them can’t rely on models and resources for inference. This neither makes sense nor scales well. local The real power of LLM applications is the ability to abstract away the complex workflow we’ve described so far. We want to put everything we’ve done behind an API so the use case can scale and others can integrate it. This is only possible if we have an easy option to host and serve these APIs. And that’s where Heroku comes in. Deploying to Heroku Heroku is the key, final part of our LLM application implementation. We have LangChain piece together our workflow, and LangServe will serve it up as a useful REST API. Now, instead of manually setting up complex resources to host and serve traffic, we turn to Heroku for the simple deployment of our application. After , we’re nearly ready to deploy. Let’s walk through the steps. setting up a Heroku account Create a new Heroku app Using the Heroku CLI, we log in and create a new app. $ heroku login
$ heroku create my-langchain-app Set config variables Next, we need to set the environment variable in our Heroku app environment. OPENAI_API_KEY $ heroku config:set OPENAI_API_KEY=replace-with-your-openai-api-key Create config files for Python application deployment To let Heroku know what we need for our Python application to run, we need to create three simple files: : Declares what command Heroku should execute to start our app. Procfile : Specifies the Python package dependencies that Heroku will need to install. requirements.txt : Specifies the exact version of the Python runtime we want to use for our app. runtime.txt These files are quick and easy to create. Each one goes into the project’s root folder. To create the , we run this command: Procfile $ echo 'web: uvicorn main:app --host=0.0.0.0 --port=${PORT}' > Procfile This tells Heroku to run , which is a web server implementation in Python. uvicorn For , we can use the command to output the list of installed packages. requirements.txt pip freeze $ pip freeze > requirements.txt Lastly, for , we will use Python 3.11.8. runtime.txt $ echo 'python-3.11.8' > runtime.txt With these files in place, our project root folder should look like this: $ tree
.
├── main.py
├── Procfile
├── requirements.txt
└── runtime.txt

0 directories, 4 files We commit all of these files to the GitHub repository. Connect Heroku to GitHub repo The last thing to do is for our GitHub repo and then push our code to the remote. Heroku will detect the push of new code and then deploy that code to our application. create a Heroku remote $ heroku git:remote -a my-langchain-app
$ git push heroku main When our code is pushed to the Heroku remote, Heroku builds the application, installs dependencies, and then runs the command in our . The final result of our command looks like this: Procfile git push …

remote: -----> Discovering process types
remote:        Procfile declares types -> web
remote: 
remote: -----> Compressing...
remote:        Done: 71.8M
remote: -----> Launching...
remote:        Released v4
remote:        https://my-langchain-app-ea95419b2750.herokuapp.com/ deployed to Heroku
remote: 
remote: Verifying deploy... done. This shows the URL for our Heroku app. In our browser, we visit . https://my-langchain-app-ea95419b2750.herokuapp.com/playground We also check out our Swagger UI docs page at . https://my-langchain-app-ea95419b2750.herokuapp.com/docs And just like that, we’re up and running! This process is the best way to reduce developer time and overhead when working on large, complex LLM pipelines with LangChain. The ability to take APIs built with LangChain and seamlessly deploy to Heroku with a few simple command line arguments is what makes the pairing of LangChain and Heroku a no-brainer. Conclusion Businesses and developers today are right to ride the wave of AI and LLMs. There’s so much room for innovation and new development in these areas. However, the difference between the successes and failures will depend a lot on the toolchain they use to build and deploy these applications. Using the LangChain framework makes the process of building LLM-based applications approachable and repeatable. But, implementation is only half the battle. Once your application is built, you need the ability to easily and quickly deploy those application APIs into the cloud. That’s where you’ll have the advantage of faster iteration and development, and Heroku is a great way to get you there. Also published . here

Streamlining LLM Application Development and Deployment with LangChain, Heroku, and Python

(Featured image: That’s how AI sees “network around the globe”) Apart from forming the , (formerly known as Facebook) has been known for its unique and widely recognized corporate culture. It’s also often referred to as a “hacker culture”, where, as an employee, you are encouraged to be creative and innovative, break things, and learn from your mistakes. The company’s famous motto, “Break Things and Move Fast,” reflects Meta’s key values, which are willingness to take risks and make bold changes. Big Five group of tech giants Meta According to Meta’s current employee, adapting its best practices to improve your personal and professional life brings tremendous positive changes: it enhances your communication skills, broadens your mind, trains the ability to see beyond stereotypes and titles, demonstrates the power of feedback, and emphasizes the importance of prioritization and self-care amidst intense workdays and deadlines. Here is his first-person account of how practicing Meta’s cultural values creates a favorable environment for productive results both inside and outside of work by Vlad Slepukhin, a Meta Production Engineer. current Embracing Bootcamp-like Approach to Personal Development My key takeaways from the Meta boot camp would be setting aside time for learning and skill-building, choosing your own path, and the value of mentorship and guidance in your career and daily life. Until the very recent past, a new Meta employee, or Metamate, spent the first 6–8 weeks in the boot camp: onboarding and a team selection process, which are designed to ensure the Metamate’s successful career at Meta. The time is dedicated to attending hard-skills workshops and lectures, executing the first entry-level tasks according to your role, and learning about Meta’s corporate culture. Between the fifth and eighth weeks, a boot camper is supposed to find a team/project unless they have been hired to execute a particular role on a particular team. An average software engineer can apply to and/or receive reach-outs from dozens of teams via an internal Jobs portal. Personally, I had to pick one out of three teams, and with each of them, I spent a full week. As a team member, I attended meetings, executed basic tasks, and learned about the scope of the projects and the team culture. Eventually, you pick your team and graduate from the bootcamp. In some cases, this process can take up to twelve weeks! Another huge benefit is getting assigned a mentor, whose main responsibility is to guide you and ensure that all the questions are answered in the shortest time possible: How can I use service X? What is the meta-analogue for [name some publicly available technology]? Where can I get a charger cable? What is the culture of stand-ups and team calls in your organisation or team? Personally, as well as the majority of Metamates, I consider the bootcamp weeks and specifically the team selection mechanism to be one of the best onboarding experiences that the company can provide (currently, it’s on pause due to the particular company-wide organisational changes, but we are hoping for it to come back). The bootcamp allowed me to spend quality time with the team, which I might not have even considered had I been given a choice before the boot camp and matched with the best team according to my skills and preferences. This is something I transferred to my life: the importance of choosing my path and with whom to walk it. I got to learn about how other Meta departments work, and in the process, I educated myself and began recognising a dozen more familiar faces on our campus. When you start a new life in a new country, like I did, with not many local acquaintances, such a benefit is extremely valuable. Appreciating the Power of Team Culture and Feedback The team selection process I described above from the beginning demonstrates the importance of team culture and collaboration with diverse teams; they are the backbone of not only the company’s growth but yours too. The culture of open communication and being direct and respectful of your colleagues taught me how much time can be saved if the team is organised, the discussions are to the point, and there is no sugar-coating in the language of communication. Open communication is not possible without being open-minded and perceptive to colleagues’ backgrounds and perspectives. I truly believe that by virtue of working at Meta with a diverse team from all parts of the world and embracing an open, respectful culture, I became a more tolerant person myself, and I’m still improving on this journey to accept different points of view and opinions with the ability to respectfully challenge them. Team culture and collaboration at Meta include the opportunity to give and receive feedback. As Metamates like to say, “Feedback is a gift”. There is a common fear of receiving feedback and being vulnerable and sensitive to criticism. But you see how important it is for everyone to not withdraw into themselves and be available for constructive criticism. To say the least, it fosters an atmosphere of safe and genuine connection at the workplace. The feedback you receive has a significant impact on your performance review results, and bootcamp participants even learn how to give it. I find it extremely valuable to have the option to share my thoughts in a respectful way about how some things are going or how a particular team member or colleague is participating in the project that we execute together. The feedback is given via either formal feedback forms during the performance review cycle or during 1-on-1 meetings, which happen on a weekly, fortnightly, or monthly basis, depending on the needs of each particular employee. Despite its scale, Meta is still not a top-down company, where, to a decent extent, feedback is a two-way street and leaders tend to, if not immediately act, at least listen to the thoughts of their fellow employees. It is remarkably visible through the phrasing that leaders and managers are supporting and not managing or supervising their reports. Embracing Different Perspectives through Diversity and Inclusivity Speaking of being open-minded and respecting your colleagues, one of the greatest things that Meta, along with other companies of such scale, possesses is a variety of people from all walks of life who have cleared the undoubtedly high bar of getting in here and matched the culture. I believe that this approach actually brings fruitful results due to behavioral interviews. It is the only non-hard-skills interview in the main loop, which is required for all the candidates, and one of the most uncertain yet valuable rounds as it helps us to bar the candidates that are surely incompatible with the Meta culture of inclusivity and efficient collaboration. I can see this in my own interactions with the outside world, which sometimes end up with the eyebrow-raising realization that people can expose their worst traits and not even flinch or consider themselves to be bigoted and unjust. However, ensuring the protection of the said values does not end at the interview stage. The company supports diverse and underrepresented communities across the globe, both internally and externally, ensuring that the employees can focus on solving one of the industry’s hardest tasks instead of contemplating their peers’ reactions to their identity or being concerned about their own well-being in the office. This does require some onboarding and annual training. However, I have observed the opposite in the past within smaller US companies or as the standard attitude towards not-so-common people in the ex-USSR software engineering field, which is predominantly male, white, and straight. Do not get me wrong; it is hardly possible to get several tens of thousands of people working in the same way, and there are particular approaches between different teams and organizations; their priorities and modes of operations do differ, and we often do not unanimously agree on things. However, this is our greatest strength: by being an open-minded, inclusive, and diverse organization, we allow the best ideas to thrive and result in bigger wins for the whole of Meta. Seeing Beyond Titles and Specialisations I believe that at Meta, it’s essential to work with people who share the company’s values and are ready to work in a fast-paced collaborative environment. If you want to define your place contribution by holding a fancy title only, Meta is not the place. I have encountered a lot of professionals who were very specific in their duties and responsibilities: “I’m a Java engineer; I do not care about the production environment”. Such an attitude is the absolute opposite of our workflow (especially in Production Engineering) - it’s counterproductive and even harmful for the employee and the team. It is as important to take responsibility for what you can and get your ideas across as excellent technical skills. One will be grateful to apply the ability to focus on problem-solving and adapt to different situations to daily life. Here is an example: An engineer with a vast background in product management and system architecture joined our team, which is quite far away from the user-facing area, and it was just the wrong fit for them. In addition to that, the person really wanted to be an architect and drew cool diagrams. But they failed both to synchronise their vision with the team and execute the hardest parts of their great ideas on their own, getting carried away with some side quests. They left the company voluntarily. As a company, Meta demonstrates that seeing beyond titles, reducing the hierarchical structure to the minimum, and embracing a wider outlook helps bring out everyone’s potential for the company’s ultimate success. Tyrannical alternatives to Architecture Committees and boards of senior engineers can lead a company into a dismal state. As Sir Winston Churchill once said, “​​No one pretends that democracy is perfect or all-wise. Indeed, it has been said that democracy is the worst form of government except for all those other forms that have been tried from time to time”, which I fully agree with, and Meta shows that such an inclusive approach brings the greatest possible results. The Importance of Balance and Self-care In the world of Meta, within Production Engineering particularly, it is crucial to pace oneself - recognize that it is a marathon and not a sprint. Ensuring the reliability, scalability, and efficiency of the company’s infrastructure is a monumental and continuous task. At work, it is paramount to set realistic goals and timelines and maintain a consistent work pace to avoid stress, anxiety, burnout, and loss of motivation. Balancing the marathon at Meta with taking time for self-care is not a luxury but a necessity. You need to have strength, mental clarity, and maintain resilience and endurance in the face of challenges, whether you meet them at work or outside of it. To conclude, I highly encourage professionals from all fields and industries to explore Meta’s core cultural values, such as communication, learning, and inclusivity, in all their endeavours. Adopting such practices holds the potential for personal growth and success, as does acquiring the ability to create a healthy, friendly, and encouraging atmosphere wherever you go.

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Online gaming has evolved, and we are probably at the pivotal point where the future of gaming will include AI in game development, gameplay, and add-on tools. So many people spend their time in alternative worlds such as VR shows, games, etc. The gaming market is worth $546.99 billion and growing at 13.9%. However, another leeching industry arising from it is the AI-powered game hacks market, which is also growing while reducing the profit share of mainstream companies in the gaming industry because many players give up playing online with multiplayers. Online game cheaters and hackers have been a pain in the a$$, and that is about to change. Artificial Intelligence (AI) has shown tremendous capabilities in different fields such as cybersecurity, programming, software development, and even . Now they are in gaming, but with positive and negative effects based on how certain players use them. This article highlights the problems of the gaming industry as they pertain to AI influence and the future. the revolutionization of African languages AI Cheats in Gaming online gamers uses cheat codes. It gives them an unfair advantage to have the highest accomplishments in playing online multiplayer games. They are most common in first-person shooter (FPS) and massive multiplayer online (MMO) games. AI cheats are used in games such as Call of Duty, PUBG’s Battlegrounds, GTA 5, Destiny 2, Counter-Strike, and lots more. One out of three Cheat codes are programs developed to give certain players an edge through certain features like 'god mode', endurance, identifying enemy locations, and so on. They are on every system and console. With 33% (based on previously mentioned statistics) of players in any game being cheaters, the fun and thrill of the gaming experience are ruined for many sincere gamers. Although there is no formal data, I gather that the AI game cheat industry is worth hundreds of millions of dollars, edging into billions. Here are some numbers. , a gang was caught by the Chinese police and Tencent, with $76 million in revenue charged to clients as subscription fees. Another stated they know cheat developers who make over $2 million per month, accumulating to $24 million per year. Imagine more cheat developers, that would justify my claim. In 2021 report by Old School Gamers Types of AI-Powered Game Cheats Sometimes, developers insert cheat codes in games purposefully for the sake of debugging or identifying ways to improve the game and its experience. However, third-party software developers and gamers come up with malicious code that give players a fair advantage and exploit game companies. A primary way these codes work is to serve as a mediator between remote play and the game. It leverages the live stream of your game, as long as you are connected to the internet. It enables mouse and keyboard tracking on consoles to give you feedback that you can use to your advantage. Players also cheat with AI-aided aim to assist them. It is called ‘aimbots’, which use AI to read and find the enemies and send the data back to the cheater's system, whether Android, PC, Game Pass, etc. It aims the enemy with computer-enhanced precision while functioning as a remote control. Advanced AI game cheats “A new kind of aimbot has taken games like Warzone and Apex Legends by storm. It uses computer vision to identify enemy players in a complex game world and then signal the player to shoot at a specific location. “Players use a PC to process the video feed from their games console, and then run the automation and machine learning scripts; they would also connect the console’s controller to the PC via a hardware device, to emulate the controller's signals. Players can set the extent of aim assist, and even input the system to select specific target zones on enemy bodies – headshots included.”- AI Business While some basic cheats are detectable by anti-cheat programs, most are not (at least, not until recently). These advanced cheats have no capture card, no special device, are totally undetectable, and are easily accessible. They use live play to make aspersions, draw conclusions, and recommend actions. Systems from the referenced from AI Business above, analyze if an enemy is visible from a player's position and uses "Triggerbot" (another intelligence program) to fire at them. Some advanced systems like ESP use object analysis and image recognition to differentiate between a player and an object on the screen. This program is trained with numerous image textures to enable it to identify between an object and a player. There are cheats such that a cheater’s system learns the dynamics of players' movements to learn maps of unfamiliar territories and even adapt to how a human expert gamer would play a game and simulate the same pattern. automated or assisted play Current AI Anti-cheat Solutions and why They are Not the Future of Gaming Many have promised and failed, and some players are currently not enthusiastic about the possibility of a better solution. When announced its anti-cheat, it all looked hopeful until it was launched, and it had many false positives and wasn’t used beyond Battlefield 5. BattlEye was another that didn’t impress so much. It was designed to maintain fair and competitive environments within online multiplayer games. Developed by Valve Corporation, VAC employs advanced algorithms and detection mechanisms to identify and prevent cheating, hacking, and other unauthorized activities that can undermine the integrity of gameplay. Valve anti-cheat (VAC) By actively scanning for cheating software, modified game files, and suspicious behavior, VAC helps create a level playing field where players can compete based on skill and strategy rather than unfair advantages. The problem was that there were false positives and causes lag time for players. Although VAC is impressive, a solution befitting for the future of gaming has to be swift and not spoil the fun with false positives. FaceIT Anti-Cheat is used in the FaceIT competitive gaming platform. It combines client-side and server-side checks to ensure a cheat-free environment for competitive matches. Some users complained of ridiculous lag time or the failure for the app to launch. FaceIT Anti-Cheat: ESEA (E-Sports Entertainment Association) offers a subscription-based anti-cheat solution for Counter-Strike: Global Offensive. It focuses on protecting the integrity of competitive gameplay. Sadly, players may be able to create new accounts and be more careful with the way they play. ESEA Anti-Cheat: Source: Steam The Future of Gaming with the Perfect AI Anti-Cheat: Is there One? A perfect anti-cheat tool (if there's ever going to be any) isn’t just going to be a software but an AI tool that can study game plays, monitor accuracy, and judge the ratio of time spent online with the proficiency of a registered user, among many other things. It has to be a constantly learning and evolving tool that discovers each new gimmick that cheaters come up with. For the longest time, people have been looking for such, and that is where Anybrain step into the gaming foyer. : Anybrain In my research over the past few months, only Anybrain has the best prospects of tackling the issue of online cheat gaming. It is still in the testing stage, with lots of work ongoing. It is also an AI tool that analyzes multiple parameters in a player’s gameplay to detect irregularities that human eyes might be unable to perceive. It considers: The use of aimbots to target enemies The accuracy of players in shooting down enemies around obstacles Player behaviors (looking around for enemies before shooting, reaction time aiming, on-screen movement into enclosed territories). They seem to almost tick all the boxes in a research paper I found . The article emphasized mouse behavior, keystroke dynamics, and console logs. here about data capture for cheat detection systems What is Anybrain Contributing to the Future of Gaming? In its current operations and prospects, Anybrain creators have interesting plans. The solution is a machine learning program, so it is constantly learning based on data observed in how players play. In reviewing players in a game, Anybrain’s program pixelates the view of aims to detect if aims are aided or not. Due to its ML model, it gains accuracy over repetitive play. Observing repetitive play is important because sometimes a player might be good or just lucky, and it would be unfair to nuke them for the way they shoot down a target just once. It takes over 5 minutes for the program to detect a cheat with all these parameters in real-time. They are looking forward to implementing a biometric profile for players, and I believe this is the future of security among gamers to protect account integrity and limit cheating! A biometric profile will give the system a baseline of two hours to understand how a registered player plays. It is almost like gait recognition (using deep learning to collect better data, which is the act of tracking the unique way someone walks or runs to identify them even if you can’t see their face). Once detected, it brings a permanent ban on a player, and they don't have the opportunity to create another account to continue the game with the same biometric information. See video on  the problem of online gaming cheat and the bright future of gaming with the new AI anti-cheat. https://www.youtube.com/watch?v=LkmIItTrQP4&embedable=true Some folks think it is pointless, but I believe the future is bright and great, like this user that I found on . With AI leveraging numerous sources of data and speed, the learning curve will be exponential. Reddit Conclusion: It’s the dawn of a new era in online gaming. Those who have given up on online games can begin to dust their boots again. Soon, there will be more companies like Anybrain leveraging deep ML capabilities to train effective programs that will catch cheaters in multiplayer games. Of course, some gamers will keep trying to cheat, but the automated anti-cheat programs also won't be tired of learning and fishing them out. It's a new day, and the future of video games is bright with AI in it!

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