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Hello, fellow tech enthusiasts! I’m excited to share my journey exploring the fascinating world of NVIDIA Omniverse streaming solutions. As someone with extensive experience in WebRTC Industry from simple Audio/Video Conferencing, to AR/VR Streaming
using unreal engine
and Unity
, I’ve spent years integrating this technology into various streaming solutions worldwide. My WebRTC journey began in 2018, and since then, I’ve been exploring game streaming engines including Unity, Unreal Engine, and now NVIDIA Omniverse.
Despite the abundance of AI assistance tools like ChatGPT and Claude, streaming Omniverse applications outside local networks remains a significant challenge for many developers and organizations. This article aims to demystify this process and share how I successfully implemented external streaming for Omniverse applications, even achieving multiple Kit app instances running on a single machine.
I’m particularly proud to mention that this research work has received acknowledgment from the NVIDIA Omniverse Staff Moderator, validating the approaches outlined in this article.
This is super cool, you got such a huge feedback from Nvidia Team, and I am proud to share my LinkedIn post here as well.
Now, the first question that may come to mind is: How did I engineer a cross-internet streaming pipeline for Omniverse apps — without AI assistance?
Before diving into solutions, let’s understand why streaming Omniverse applications over the internet is challenging:
This network diagram illustrates the architecture for streaming a Kit-based Omniverse App using WebRTC and a TURN server (Coturn
) to facilitate communication between a web-based viewer and the streaming application.
2. WebRTC Streaming:
real-time
, low-latency
communication between the browser and the streaming Kit app.encoding
, decoding
, and communication
with the remote Kit app.3. TURN Server (Coturn):
TURN
(Traversal Using Relays around NAT) server is used to relay traffic when direct peer-to-peer connections are not possible due to network restrictions (firewalls
, NAT
traversal issues).TURN
server (Coturn
) relays WebRTC streams between the user’s browser and the Kit application.4. Kit App Running in AWS VPC:
The Kit app is hosted inside an AWS VPC
(Virtual Private Cloud).
It consists of:
5. Communication Flow:
Coturn
) if a direct connection is unavailable.TURN server
forwards signaling and streaming data to the Messaging and Streaming modules of the Kit app.NVIDIA Isaac Sim is a powerful robotics simulation platform built on Omniverse. Let’s walk through setting up external streaming for Isaac Sim.
First, we need a suitable cloud environment with GPU support:
RTX GPU
support:g4dn.2xlarge
or betterg5.2xlarge
or betterSelect NVIDIA GPU optimized AMI from AWS Marketplace
2. SSH into your newly launched VM
:
ssh -i "yourkey.pem" ubuntu@<public ip>
3. Install the latest NVIDIA driver (minimum tested version: 535.129.03
):
sudo apt-get update
sudo apt install build-essential -y
wget https://us.download.nvidia.com/XFree86/Linux-x86_64/535.129.03/NVIDIA-Linux-x86_64-535.129.03.run
chmod +x NVIDIA-Linux-x86_64-535.129.03.run
sudo ./NVIDIA-Linux-x86_64-535.129.03.run
4. Install NVIDIA container toolkit:
sudo apt-get install -y nvidia-container-toolkit
sudo systemctl restart docker
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker
docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi
5. Verify your GPU driver installation:
nvidia-smi
Now let’s pull and run the Isaac Sim container:
docker pull nvcr.io/nvidia/isaac-sim:4.2.0
2. Run the container with an interactive bash session:
docker run --name isaac-sim --entrypoint bash -it --runtime=nvidia --gpus all -e "ACCEPT_EULA=Y" --rm --network=host \
-e "PRIVACY_CONSENT=Y" \
-v ~/docker/isaac-sim/cache/kit:/isaac-sim/kit/cache:rw \
-v ~/docker/isaac-sim/cache/ov:/root/.cache/ov:rw \
-v ~/docker/isaac-sim/cache/pip:/root/.cache/pip:rw \
-v ~/docker/isaac-sim/cache/glcache:/root/.cache/nvidia/GLCache:rw \
-v ~/docker/isaac-sim/cache/computecache:/root/.nv/ComputeCache:rw \
-v ~/docker/isaac-sim/logs:/root/.nvidia-omniverse/logs:rw \
-v ~/docker/isaac-sim/data:/root/.local/share/ov/data:rw \
-v ~/docker/isaac-sim/documents:/root/Documents:rw \
nvcr.io/nvidia/isaac-sim:4.2.0
3. From the container prompt, run the Isaac Sim application:
./isaac-sim.headless.webrtc.sh
4. When loaded, you’ll see:
Isaac Sim Headless WebRTC App is loaded.
At this point, the app is running and accessible locally or within your LAN
:
http://<ip address>:8211/streaming/webrtc-client?server=<ip address>
You might try accessing it over the internet with:
http://<public ip>:8211/streaming/webrtc-demo/?server=<public ip>
However, this won’t work without proper STUN/TURN server configuration. This is where many developers get stuck.
You might face the following issue: WebRTC: ICE Failed
— This issue is directly related to TURN Servers.
To make Isaac Sim accessible over the internet, we need to configure a TURN server:
apt update
apt install nano
2. Configure the WebRTC extension:
nano extscache/omni.services.streamclient.webrtc-1.3.8/config/extension.toml
3. Add your TURN server
configuration to this file
4. To make your changes persistent (since container changes are ephemeral), commit the container to a new image:
docker commit <container name or id> <new container name:tag>
5. Verify your new image:
docker images
6. You can change the container name at the end of docker run command:
docker run --name isaac-sim --entrypoint bash -it --runtime=nvidia --gpus all -e "ACCEPT_EULA=Y" --rm --network=host \
-e "PRIVACY_CONSENT=Y" \
-v ~/docker/isaac-sim/cache/kit:/isaac-sim/kit/cache:rw \
-v ~/docker/isaac-sim/cache/ov:/root/.cache/ov:rw \
-v ~/docker/isaac-sim/cache/pip:/root/.cache/pip:rw \
-v ~/docker/isaac-sim/cache/glcache:/root/.cache/nvidia/GLCache:rw \
-v ~/docker/isaac-sim/cache/computecache:/root/.nv/ComputeCache:rw \
-v ~/docker/isaac-sim/logs:/root/.nvidia-omniverse/logs:rw \
-v ~/docker/isaac-sim/data:/root/.local/share/ov/data:rw \
-v ~/docker/isaac-sim/documents:/root/Documents:rw \
new image name:tag
For a more flexible approach, let’s set up a web viewer for Kit applications.
git clone https://github.com/NVIDIA-Omniverse/web-viewer-sample.git
git clone https://github.com/NVIDIA-Omniverse/kit-app-template.git
3. Install Node.js 18 LTS (required for the web viewer):
curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.1/install.sh | bash
nvm install 18
node -v
npm -v
4. Install git-lfs:
sudo apt install git-lfs
cd kit-app-template
./repo.sh template new
2. Follow the prompts to create a USD viewer application:
3. Build and launch the application:
./repo.sh build
./repo.sh launch
4. For a more useful demo, launch with a sample USD file:
./repo.sh launch -- --/app/auto_load_usd=_build/linux-x86_64/release/samples/stage01.usd --no-window
5. Verify the app is running:
sudo netstat -tlnp | grep 49100
This is where the real magic happens — modifying the WebRTC streaming library to work over the internet:
cd web-viewer-sample
npm install
2. Update the stream configuration:
# Edit stream.config.json and replace 127.0.0.1 with your VM's public IP
3. The Critical Step: Modify the WebRTC streaming library:
# Open the library file
nano /home/ubuntu/web-viewer-sample/node_modules/.vite/deps/@nvidia_omniverse-webrtc-streaming-library.js
4. Around line 2346
, add STUN/TURN configuration:
const configuration = {
iceServers: [
{
urls: 'stun:stun.l.google.com:19302' // Google's public STUN server
},
{
urls: 'turn:<turn server ip or domain>:3478', // Your TURN server
username: 'user', // TURN server credentials
credential: 'password'
}
]
};
I have provided the entire updated library code (Decompiled — Unfortunately) on GitHub for you to follow:
You have add your TURN servers only. I have done other mappings already.
5. Update the RTCPeerConnection
initialization (around line 2349
):
const s3 = new RTCPeerConnection(configuration);
6. Also update another RTCPeerConnection
instance (around line 5607
):
new RTCPeerConnection(configuration);
7. Run the web viewer:
npm run dev -- --host <private ip>
Now your web viewer should be accessible at:
http://<public ip>:5173/
For reliable internet streaming, you need your own TURN server:
coturn
on Ubuntu:sudo apt install coturn
2. Enable the TURN server:
nano /etc/default/coturn
# Uncomment: TURNSERVER_ENABLED=1
3. Configure the TURN server:
# Backup the original config
cp /etc/turnserver.conf /etc/turnserver.conf.bak
# Create a new config
nano /etc/turnserver.conf
4. Add the following configuration:
listening-port=3478
listening-ip=0.0.0.0
external-ip=<public ip>
min-port=49152
max-port=65535
verbose
fingerprint
lt-cred-mech
user=username:password
realm=<public ip or domain>
log-file=/var/tmp/turn.log
syslog
5. Start and enable the service:
systemctl start coturn
systemctl enable coturn
One of my most exciting achievements was running multiple Kit app instances on a single machine, allowing multiple users to interact with different Omniverse applications simultaneously.
Dockerfile
in the web viewer sample directory:# Build the WebRTC Web Viewer
FROM node:18
# Set working directory
WORKDIR /webviewer2
# Copy package.json and install dependencies
COPY ./package.json .npmrc ./
RUN npm install
# Copy the rest of the project files
COPY . .
COPY ./node_modules/.vite/deps/@nvidia_omniverse-webrtc-streaming-library.js ./node_modules/.vite/deps/@nvidia_omniverse-webrtc-streaming-library.js
EXPOSE 3001
CMD [“npm”, “run”, “dev”]
2. For the second web viewer, modify the port in vite.config.ts:
server: {
port: 3001,
host: true,
strictPort: true
}
EXPOSE 48995-49012/udp \
48995-49012/tcp \
59000-59012/udp \
59000-59012/tcp \
8211/tcp \
8311/tcp \
59100/tcp
2. Update the entrypoint.sh to load sample USD files automatically:
CMD="/app/kit/kit"
ARGS=(
"/app/apps/$KIT_FILE_NAME_BREADCRUMB"
"--/app/auto_load_usd=/app/_build/linux-x86_64/release/samples/stage02.usd"
$KIT_ARGS_BREADCRUMB
$${NVDA_KIT_ARGS:-""}
$${nucleus_cmd:-""}
)
3. Build the Kit app images:
# With default settings
./repo.sh package --container --name kitapp1:latest
# With modified settings
./repo.sh package –container –name kitapp2:latest
docker run -d --name kitapp1 --rm --gpus all --cpus 8 -p 49100:49100 kitapp1:latest
2. Run the second Kit app:
docker run -d --name kitapp2 --rm --gpus all --cpus 8 -p 59100:59100 kitapp2:latest
3. Run the first web viewer:
docker run -d --rm --name webviewer1 -p 5173:5173 webviewer:1
4. Run the second web viewer:
docker run -d --rm --name webviewer2 -p 3001:3001 webviewer2:latest
5. Verify all containers are running:
docker ps
Now you can access both applications at:
http://<public ip>:5173
http://<public ip>:3001
During my testing with multiple Kit app instances on an NVIDIA A10G GPU:
6.8GB
of 23GB
VRAM usedBased on these observations, I estimate:
The complete solution uses this architecture:
Kit app
containers and web viewersStreaming NVIDIA Omniverse applications over the internet is challenging but achievable with the right configuration. The key insights from this journey:
GPU
, CPU
, and memory
constraints determine how many instances you can runI hope this guide helps others in the community who are working on similar challenges. The ability to stream Omniverse applications over the internet opens up exciting possibilities for remote collaboration, virtual production, and interactive 3D experiences.
Feel free to reach out with questions or share your own experiences with Omniverse streaming!
Starting a company is akin to nurturing a newborn — filled with hope, uncertainty, and relentless effort. As I embark on this journey with RTC League (Real-time Cognitives), I find myself reflecting on the challenges and inspirations that have shaped my path. Inspired by technological visionaries like Steve Jobs, Elon Musk, and Sam Altman, my mission is to demonstrate that even from a country like Pakistan, with its unique set of obstacles, it’s possible to build a billion-dollar tech startup.
Cognitive AI is a type of artificial intelligence (AI) that mimics human thought processes and learning abilities
From a young age, I was captivated by the stories of innovators who changed the world. Steve Jobs, whose biography by Walter Isaacson remains a cornerstone of my inspiration, taught me the importance of vision and relentless pursuit of excellence. His ability to blend technology with design and user experience showed me that innovation isn’t just about creating something new but making it meaningful and accessible.
Elon Musk, detailed in Ashlee Vance’s “Elon Musk: Tesla, SpaceX, and the Quest for a Fantastic Future,” embodies the spirit of audacity and resilience. Musk’s ventures into space exploration and electric vehicles illustrate how thinking big and challenging the status quo can lead to groundbreaking advancements. Sam Altman’s insights into AI and the future of technology have further fueled my passion for innovation, emphasizing the potential of artificial intelligence to solve complex global issues.
I immerse myself in their stories — reading their biographies, watching their interviews, and learning from their experiences. These mentors, though not physically present, have significantly influenced my approach to entrepreneurship and technology.
Behind every entrepreneur lies a foundation of personal experiences and lessons. For me, that foundation is deeply rooted in the resilience and determination of my late Mother. She was an MBBS doctor who chose to leave her esteemed career to build a nurturing environment for our family. Facing immense opposition from her own family, she demonstrated unwavering patience and strength. Her decision to prioritize our family’s culture and well-being over her professional aspirations taught me that true leadership is about sacrifice and perseverance.
I remember vividly the day I asked my beloved mother why she left her MBBS job. With pure confidence, she replied, “I feel that if I continue my job, I may not be able to build the culture of my family and kids.”
And by God, my mom then sacrificed her moments for us, and for us only!!!
When our beloved mother passed away just as my name began to gain recognition in the tech world, her legacy remained a guiding force. She was more than just a parent; she was a beacon of dedication and love. Her influence continues to inspire me to push forward, no matter the challenges.
In January 2019, I joined forces with Shahmeer Amir, Pakistan’s third-ranked ethical hacker, to started working as an employee on a startup venture. For over five years, Shahmeer was not just a mentor but like an elder brother to me. Together, we navigated the tumultuous waters of research work in that startup. However, in April 2024, I made the difficult decision to part ways with Shahmeer, despite the lack of support during that transition. This setback, though disheartening, reinforced my resolve to forge my own path. I wish him best of luck.
Determined to create something impactful, I partnered with my elder brother, Muhammad Aatif Bashir Choudhary, a seasoned telecom professional with over a decade of experience in customer care and management at Etisalat Dubai. He is just not my elder brother but a great friend and is a hero of mine.
The name RTC League holds sentimental value — it was a name our late mother often mentioned, and we decided to honor her memory by keeping it.
One of the core beliefs that drive RTC League is that with the right leadership, any person can be trained to achieve greatness. Convincing my brother to join this venture was no small feat. His extensive experience in telecommunications complemented my technical expertise in WebRTC and AI full-stack development. Together, we envisioned a company that could innovate and lead in real-time communication technologies, with real-time AI cognitives.
Understanding the importance of fresh perspectives and the potential of young minds, we decided to hire recent graduates and current students. We brought on a team of seven fresh graduates and two BSCS students in their sixth semester. While we faced setbacks — losing one student during the transition — it was a valuable lesson in mentorship and team dynamics. Despite these challenges, our small yet dedicated team managed to develop groundbreaking products backed by the Punjab Information Technology Board (PITB) in Pakistan.
Valuing teamwork above all, we focused on building a great team culture around these fresh graduates. By providing them with guidance, training, and a supportive environment, we set an example of how a team of newcomers can achieve remarkable feats. It reinforced my belief that you only need good leaders to build a great research lab around fresh graduates.
In just six months, our team developed two remarkable products:
Facing challenges in sales, we pivoted two months ago to develop a SaaS-based leads generation platform. This new product leverages WebRTC conversational agents for inbound and outbound calling using Twilio, Vonage Technology, and Telnyx. Already, it has garnered admiration from several potential investors, signaling promising growth prospects.
Like many startups, we faced financial constraints. During the early stages, I had to borrow funds from friends to pay our team, ensuring that the company could continue to operate despite the challenges. This experience taught me the importance of knowing when to ask for help and being willing to accept it when offered. These financial hurdles only strengthened my commitment to RTC League. I have the grit spirit, just like Elon Musk; no matter what we are facing, we have to stand tall and build amazing things. I am prepared for the worst but remain optimistic about our future.
Throughout this journey, I’ve learned invaluable lessons that have shaped not only our company but also my personal growth:
A Vision for the Future
I firmly believe that RTC League will become a billion-dollar company using WebRTC and AI-based products by 2029, all while operating from Pakistan. This vision isn’t just about personal success; it’s about setting an example. I want to prove that even in a country with limited resources, inconsistent internet access, and restricted platforms like Twitter, it’s possible to build a great startup.
Our journey so far has been a testament to what can be achieved with determination, a strong team, and a clear vision. My brother’s relentless efforts in conveying our hard work and technological advancements to business leaders across Pakistan have been instrumental in our progress.
Starting RTC League has been a journey of faith, resilience, and relentless hard work. While the road is steep and fraught with challenges, I am driven by a vision to not only succeed but to inspire others in similar circumstances. Our story is proof that with determination, a supportive team, and a willingness to defy the odds, it’s possible to achieve greatness.
In honoring my mother’s legacy and drawing inspiration from the greats like Steve Jobs and Elon Musk, I am committed to building a future where technology bridges gaps and creates opportunities for all. I invite you to join us on this journey — to witness how a startup from Pakistan is poised to make a global impact. Together, we can break barriers, challenge norms, and pave the way for future innovators from all corners of the world.
As we stand on the brink of a technological revolution, the concept of the Intelligence Age — driven by Artificial Intelligence (AI), Artificial General Intelligence (AGI), and Artificial Super Intelligence (ASI) — promises to redefine human capabilities and societal structures. Inspired by leaders like Sam Altman, my vision is to explore how these advancements can be harnessed to create a prosperous and equitable future.
Many of the jobs we do today would have looked like trifling wastes of time to people a few hundred years ago, but nobody is looking back at the past, wishing they were a lamplighter. If a lamplighter could see the world today, he would think the prosperity all around him was unimaginable. And if we could fast-forward a hundred years from today, the prosperity all around us would feel just as unimaginable. (Sam Altman)
To navigate the complexities of the Intelligence Age, it’s crucial to understand the distinctions between AI, AGI, and ASI:
This may turn out to be the most consequential fact about all of history so far. It is possible that we will have superintelligence in a few thousand days (!); it may take longer, but I’m confident we’ll get there.
The journey to the Intelligence Age has been paved by decades of scientific discovery and technological innovation. From melting sand to create computer chips, running energy through them, and developing systems capable of learning and adapting, humanity has built a robust foundation for this new era.
At the heart of the Intelligence Age lies deep learning, a subset of AI that focuses on neural networks with many layers. As Sam Altman aptly puts it, “Deep learning worked, got predictably better with scale, and we dedicated increasing resources to it.” This progress has enabled AI systems to handle more complex tasks with greater accuracy and efficiency.
AI models will soon serve as autonomous personal assistants who carry out specific tasks on our behalf like coordinating medical care on your behalf. At some point further down the road, AI systems are going to get so good that they help us make better next-generation systems and make scientific progress across the board.
Despite the challenges, the potential of AI to transform our world is immense. Here’s why a techno-optimistic perspective is essential:
AI and AGI can address some of the most pressing global issues, from climate change to healthcare disparities. Imagine AI systems that can simulate countless scenarios to find optimal solutions in seconds — something that would take humans decades to achieve.
ASI has the potential to make expert-level knowledge accessible to all, breaking down barriers related to geography and economics. This democratization can foster innovation from every corner of the globe, empowering individuals to contribute meaningfully to technological advancements.
By automating routine tasks, AI allows humans to focus on creative and strategic endeavors. The synergy between human creativity and AI’s computational prowess can lead to breakthroughs in art, science, and technology that were previously unimaginable.
Automation and AI-driven productivity can fuel economic growth, creating new industries and opportunities. While concerns about job displacement are valid, history shows that technological revolutions ultimately generate more jobs and enhance living standards.
AI-driven advancements in medicine, transportation, and daily conveniences can significantly enhance the quality of life. From personalized healthcare to intelligent transportation systems, AI can make our lives safer, healthier, and more efficient.
While the benefits are vast, the Intelligence Age also brings significant ethical and social challenges. It’s crucial to address these proactively to ensure that AI serves as a force for good.
Global cooperation is necessary to develop regulations that promote innovation while safeguarding against potential risks. This involves governments, tech companies, and civil society working together to create frameworks that ensure responsible AI development.
As AI reshapes industries, education systems must evolve to prepare individuals for the future workforce. Emphasizing skills like critical thinking, creativity, and adaptability will be crucial in an AI-integrated world.
Drawing inspiration from the relentless pursuit of innovation demonstrated by leaders like Sam Altman, I envision a future where AI, AGI, and ASI are harnessed to create a prosperous and equitable world.
AI can bridge the digital divide by providing access to education, healthcare, and economic opportunities in underserved regions. This democratization of technology can empower individuals and communities, fostering global prosperity.
AI-driven platforms can facilitate unprecedented levels of collaboration across borders. By connecting experts and innovators worldwide, AI can accelerate scientific discoveries and technological advancements.
Balancing innovation with responsibility is key to ensuring that AI benefits all of humanity. This involves fostering a culture of ethical AI development, where technological advancements are aligned with societal values and needs.
The Intelligence Age is not just a continuation of technological progress — it represents a fundamental shift in how we harness and interact with intelligence itself. As we embrace AI, AGI, and ASI, we have the opportunity to create a world that is more prosperous, equitable, and innovative than ever before.
However, this journey requires a balanced approach — one that combines technological optimism with ethical responsibility. By learning from the past, honoring personal legacies, and drawing inspiration from visionary leaders, we can navigate the challenges and seize the opportunities of the Intelligence Age.
As someone deeply embedded in the tech community, from founding RTC League in Pakistan to contributing to global advancements in WebRTC and AI , I am committed to leveraging these technologies to bridge gaps and create opportunities for all. Together, we can build a future where technology serves as a bridge, not a barrier — a tool for empowerment rather than division.
Motivation: As part of my personal journey to gain a better understanding of Probability & Statistics, I just follow courses from the world’s best educational Systems such as i.e. MIT, and Stanford. I’ve decided to draw a complete picture of these concepts in Julia Langauge (MIT Labs). Every time, It is basically, the Internal structure or Inner workings of any “Mathematics or Programming Concept” that matters a lot for a Data Scientist.
This article contains what I’ve learned, and hopefully, it’ll be useful for you as well!
Let’s Kick Start;
Probability and statistics are crucial branches of mathematics that are widely used in various fields such as science, engineering, finance, and economics. These concepts provide the foundation for data analysis, prediction, and decision-making. In this blog, we will explore the basics of probability and statistics using the Julia programming language.
Julia is a high-level, high-performance programming language that is specifically designed for numerical and scientific computing. It provides an extensive library of statistical and graphical techniques, making it an ideal choice for data analysis.
Getting started with Probability in Julia:
Probability is the branch of mathematics that deals with the likelihood of an event occurring. In Julia, we can calculate probabilities using the Distributions package. This package provides a suite of probability distributions and related functions that make it easy to perform probability calculations.
Basics of Probability:
The article demonstrates how to calculate probabilities using the Distributions package in Julia. The article provides examples of how to work with discrete uniform distributions, binomial distributions, and normal distributions. In each example, the article shows how to calculate the mean, probability density function (pdf), and cumulative distribution function (cdf) for a given distribution.
There are 5 marbles in a bag: 4 are blue, and 1 is red. What is the probability that a blue marble gets picked?
So the probability = 4/5 = 0.8
We can also show the probability of this certain problem on a Probability Line. Such as;
For example, consider a situation where we have a coin with a probability of heads of 0.5. We can use the Binomial distribution from the Distributions package to calculate the probability of getting exactly 3 heads in 5 coin flips.
using Distributions
d = DiscreteUniform(1, 6) # roll of a dice
mean(d) # 3.5
pdf(d, 1) # 1/6
cdf(d, 3) # 0.5
using Distributions
d = Binomial(10, 0.5) # 10 coin flips
mean(d) # 5.0
pdf(d, 5) # 0.246
cdf(d, 5) # 0.62
using Distributions
d = Normal(0, 1) # standard normal
mean(d) # 0.0
pdf(d, 0) # 0.39
cdf(d, 0) # 0.5
Basics of Statistics:
Statistics is the branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. In Julia, we can perform various statistical operations using the StatsBase package.
The article demonstrates how to perform various statistical operations using the StatsBase package in Julia. It provides examples of descriptive statistics, hypothesis testing, and linear regression. For example, in the section on descriptive statistics, the article shows how to calculate the mean, median, mode, and variance of a given set of data. In the section on hypothesis testing, the article demonstrates how to perform a two-sample t-test for unequal variances. In the section on linear regression, the article shows how to fit a linear model to a dataset using the GLM package.
Here are some examples:
using StatsBase
x = [1, 2, 3, 4, 5]
mean(x) # 3.0
median(x) # 3.0
mode(x) # 1.0
variance(x) # 2.5
using StatsBase
x = [1, 2, 3, 4, 5]
y = [2, 3, 4, 5, 6]
t_test(x, y) # Two-Sample T-Test (unequal variance)
using GLM, RDatasets
data = dataset("datasets", "mtcars")
fit = lm(@formula(mpg ~ wt), data)
coef(fit) # -5.344
Julia is a great language for probability and statistics, with a wide range of packages available for various tasks. Whether you’re calculating probabilities, performing statistical tests, or fitting models to data, Julia makes it easy to get started and provides high performance for large data sets.
Expert Opinion:
The article provides an overview of using the Julia programming language for Probability and Statistics. It begins by introducing Julia as a high-performance language for numerical and scientific computing, which combines the ease of use of high-level languages such as Python with the performance of low-level languages like C. This makes it an ideal choice for working with probability and statistics.
The article concludes by stating that Julia is a great language for probability and statistics, with a wide range of packages available for various tasks. Whether you are calculating probabilities, performing statistical tests, or fitting models to data, Julia provides a high level of ease and performance.
Motivation: As part of my personal journey to gain a better understanding of Probability & Statistics, I just follow courses from the world’s best educational Systems such as i.e. MIT, and Stanford. I’ve decided to draw a complete picture of these concepts in Python. Every time, It is basically, the Internal structure or Inner workings of any “Mathematics or Programming Concept” that matters a lot for a Data Scientist.
This article contains what I’ve learned, and hopefully, it’ll be useful for you as well!
Let’s Kick Start;
Probability and statistics are crucial fields in data science and machine learning. They help us understand and make predictions about the behavior of large data sets. In this blog, we will learn about probability and statistics from scratch using Python.
Understanding Probability:
Probability is the study of random events. It measures the likelihood of an event occurring in a particular experiment. There are two main types of probability: classical probability and empirical probability.
There are 5 marbles in a bag: 4 are blue, and 1 is red. What is the probability that a blue marble gets picked?
So the probability = 4/5 = 0.8
We can also show the probability of this certain problem on a Probability Line. Such as;
Classical probability is calculated using the formula:
P(A) = Number of favorable outcomes / Total number of outcomes
Empirical probability is calculated by performing an experiment and counting the number of times an event occurs.
Calculating Probabilities in Python:
To calculate probabilities in Python, we can use the module math
. Here’s an example of calculating the probability of rolling a dice and getting a 4:
import math
total_outcomes = 6
favorable_outcomes = 1
probability = favorable_outcomes / total_outcomes
print(probability)
Understanding Statistics:
Statistics is the branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. The primary goal of statistics is to gain insights and make informed decisions based on data.
Descriptive Statistics:
Descriptive statistics deals with the representation of data in a meaningful and concise manner. The most common techniques used in descriptive statistics are measures of central tendency and measures of variability.
Measures of central tendency include mean median and mode. Mean is the average of all the data points, the median is the middle value of the data set, and mode is the most frequently occurring value.
Measures of variability include range, variance, and standard deviation. The range is the difference between the highest and lowest value in a data set, variance is the average of the squared differences from the mean, and the standard deviation is the square root of variance.
Calculating Descriptive Statistics in Python:
To calculate descriptive statistics in Python, we can use the pandas
library. Here’s an example of calculating the mean, median, and standard deviation of a data set:
import pandas as pd
data = [1, 2, 3, 4, 5]
mean = pd.Series(data).mean()
median = pd.Series(data).median()
standard_deviation = pd.Series(data).std()
print(f"Mean: {mean}")
print(f"Median: {median}")
print(f"Standard Deviation: {standard_deviation}")
Inferential Statistics:
Inferential statistics deals with making predictions about a population based on a sample of the population. The most common techniques used in inferential statistics are hypothesis testing and regression analysis.
Hypothesis testing is used to determine whether a statistical significance exists between two data sets. Regression analysis is used to predict a dependent variable based on one or more independent variables.
Calculating Inferential Statistics in Python:
To calculate inferential statistics in Python, we can use the scipy
library. Here’s an example of performing a t-test:
import scipy.stats as stats
data1 = [1, 2, 3, 4, 5]
data2 = [5, 4, 3, 2, 1]
t_test = stats.ttest_ind
print(f"T-test result: {t_test}")
Expert Opinion:
Probability and statistics play crucial roles in data science and machine learning. Understanding probability helps us make predictions about random events. Descriptive statistics deals with the representation of data in a meaningful and concise manner, while inferential statistics deals with making predictions about a population based on a sample. With the help of the Python libraries math
, pandas
, and scipy
, we can easily calculate probabilities, descriptive statistics, and inferential statistics.
In this article, I’ve covered the basics of probability and statistics, and how to implement these concepts in Python. I hope you found this article helpful and that it has given you a good starting point for exploring the world of probability and statistics.
Motivation: As part of my personal journey to gain a better understanding of Probability & Statistics, I just follow courses from the world’s best educational Systems such as i.e. MIT, and Stanford. I’ve decided to draw a complete picture of these concepts in R Language. Every time, It is basically, the Internal structure or Inner workings of any “Mathematics or Programming Concept” that matters a lot for a Data Scientist.
This article contains what I’ve learned, and hopefully, it’ll be useful for you as well!
Let’s Kick Start;
Aham!
Probability theory is nothing but common sense reduced to calculation.
~Pierre-Simon Laplace
I must start with the relatively simplest example here; When a coin is tossed, there are two possible outcomes:
So, the total number of possible outcomes (common sense) is 2 (head or tail). In a mathematical way, we say that the
Probability and statistics are essential branches of mathematics that are widely used in various fields such as science, engineering, finance, and economics. These concepts provide a foundation for data analysis, prediction, and decision-making. In this blog, we will explore the basics of probability and statistics using the R programming language.
R is a widely used open-source programming language that provides an extensive library of statistical and graphical techniques. It has become an indispensable tool for data analysis and is widely used by statisticians, data scientists, and researchers.
In R-Programming, We have the following distribution to find probabilities such as;
Example:
There are 5 marbles in a bag: 4 are blue, and 1 is red. What is the probability that a blue marble gets picked?
So the probability = 4/5 = 0.8
We can also show the probability of this certain problem on a Probability Line. Such as;
Getting started with Probability in R:
Probability is the branch of mathematics that deals with the likelihood of an event occurring. In R, we can calculate probabilities using the dbinom() function. This function calculates the probability of observing a specific number of successes in a fixed number of trials, given a success rate.
For example, consider a situation where we have a coin with a probability of heads of 0.5. We can use the dbinom() function to calculate the probability of getting exactly 3 heads in 5 coin flips.
dbinom(3, size = 5, prob = 0.5)
The output of this code will be 0.3125, which is the probability of getting exactly 3 heads in 5 coin flips.
Another commonly used probability distribution in R is the normal distribution. The normal distribution is a continuous probability distribution that is commonly used to model the distribution of a large number of variables. We can use the dnorm() function in R to calculate the probability density of a normal distribution.
For example, consider a normal distribution with a mean of 0 and a standard deviation of 1. We can use the dnorm() function to calculate the probability density at a specific point.
dnorm(0, mean = 0, sd = 1)
The output of this code will be 0.3989422804014327, which is the probability density of the normal distribution at 0.
Getting started with Statistics in R:
Statistics is the branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. In R, we can perform various statistical tests and analyses using the built-in functions and packages.
One commonly used statistical test in R is the t-test. The t-test is used to compare the means of two groups and determine if they are significantly different from each other. In R, we can perform a t-test using the t.test() function.
For example, consider a situation where we have two groups of data, group A and group B. We can use the t.test() function to perform a t-test to determine if the means of these two groups are significantly different.
t.test(group_A, group_B)
The output of this code will provide the t-statistic, p-value, and other information about the t-test.
Another commonly used statistical analysis in R is linear regression. Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. In R, we can perform linear regression using the lm() function.
For example, consider a situation where we have data on the number of hours studied and the final exam score. We can use the lm() function to perform linear regression to model the relationship between the number of hours studied and the final exam score.
lm(final_exam_score ~ hours_studied, data = exam_data
Expert Opinion:
In conclusion, R is a powerful tool for performing probability and statistical analysis. It provides a wide range of functions and packages that make it easy for anyone to get started with probability and statistics. Whether you’re a beginner or an experienced statistician, R has everything you need to perform complex analysis and get insights from your data.
In this blog, we have covered the basics of probability and statistics using R. From calculating probabilities using dbinom() and dnorm() to performing t-tests and linear regression using t.test() and lm() functions, respectively. This is just the tip of the iceberg, and there is a lot more to learn and explore in R.
If you’re interested in learning more about probability and statistics with R, I highly recommend checking out online resources such as tutorials, online courses, and books. The R community is vast, and there are many resources available to help you get started.
With the knowledge you’ve gained from this blog, you can now start exploring the world of probability and statistics with R!
WebRTC is a fascinating technology that brings real-time communication capabilities to the web. While WebRTC is relatively easy to use, there are many intricacies to it, which, when not understood correctly, can lead to problems. One such issue is closing PeerConnections, especially in complex scenarios like mesh calling. Here, we will delve into the crux of the matter and learn how to overcome this challenge.
I once spent more than a week trying to debug a seemingly straightforward issue. In a mesh calling scenario involving 8 participants from different platforms — Android and iOS, my application would become unresponsive and eventually crash. This was specifically happening on the iOS side when I was trying to close a PeerConnection while the other participants were still in the ‘connecting’ state. What seemed like a resource consumption crash was actually an issue with the main UI thread being blocked due to WebRTC background tasks.
Over the past six months of intense and passionate development, we hadn’t experienced a crash quite like this one. It was an anomaly that had us puzzled. What was even more perplexing was that the crash reports indicated an issue of resource consumption — a possibility that hadn’t occurred to us in the context of a seemingly innocuous task of closing peer connections.
The iOS watchdog, meant to prevent any app from hogging system resources, ended up terminating our application. This signaled to us that something under the hood was indeed amiss.
Here are some screenshots of the crashes:
Test Flight Crash Report:
Date/Time: 2023-06-21 15:53:37.6520 +0500
Launch Time: 2023-06-21 15:43:18.2579 +0500
OS Version: iPhone OS 16.5 (20F66)
Release Type: User
Baseband Version: 4.02.01
Report Version: 104
Exception Type: EXC_CRASH (SIGKILL)
Exception Codes: 0x0000000000000000, 0x0000000000000000
Termination Reason: FRONTBOARD 2343432205
<RBSTerminateContext| domain:10 code:0x8BADF00D explanation:scene-update watchdog transgression: application<com.younite.development>:2048 exhausted real (wall clock)
time allowance of 10.00 seconds
ProcessVisibility: Foreground
ProcessState: Running
WatchdogEvent: scene-update
WatchdogVisibility: Foreground
WatchdogCPUStatistics: (
"Elapsed total CPU time (seconds): 3.380 (user 3.380, system 0.000), 5% CPU",
"Elapsed application CPU time (seconds): 0.049, 0% CPU"
) reportType:CrashLog maxTerminationResistance:Interactive>
Triggered by Thread: 0
Thread 0 name: Dispatch queue: com.apple.main-thread
Thread 0 Crashed:
0 libsystem_kernel.dylib 0x20bb88558 __psynch_cvwait + 8
1 libsystem_pthread.dylib 0x22c9d0078 _pthread_cond_wait + 1232
2 WebRTC 0x109f92a20 0x109e4c000 + 1337888
3 WebRTC 0x109f92900 0x109e4c000 + 1337600
4 WebRTC 0x109ebe20c 0x109e4c000 + 467468
5 WebRTC 0x109ebe10c 0x109e4c000 + 467212
6 WebRTC 0x109ebda38 0x109e4c000 + 465464
7 WebRTC 0x109ebda14 0x109e4c000 + 465428
8 WebRTC 0x109f6b52c 0x109e4c000 + 1176876
9 libobjc.A.dylib 0x1c5cb60a4 object_cxxDestructFromClass(objc_object*, objc_class*) + 116
10 libobjc.A.dylib 0x1c5cbae00 objc_destructInstance + 80
11 libobjc.A.dylib 0x1c5cc44fc _objc_rootDealloc + 80
12 libobjc.A.dylib 0x1c5cb60a4 object_cxxDestructFromClass(objc_object*, objc_class*) + 116
13 libobjc.A.dylib 0x1c5cbae00 objc_destructInstance + 80
14 libobjc.A.dylib 0x1c5cc44fc _objc_rootDealloc + 80
15 WebRTC 0x109f7302c 0x109e4c000 + 1208364
19 libswiftCore.dylib 0x1c6d11628 _swift_release_dealloc + 56
20 libswiftCore.dylib 0x1c6d1244c bool swift::RefCounts<swift::RefCountBitsT<(swift::RefCountInlinedness)1>>::doDecrementSlow<(swift::PerformDeinit)1>(swift::RefCountBitsT<(swift::RefCountInlinedness)1>, unsigned int) + 132
21 libswiftCore.dylib 0x1c6d012c8 swift_arrayDestroy + 124
22 libswiftCore.dylib 0x1c6a1c2b0 _DictionaryStorage.deinit + 468
23 libswiftCore.dylib 0x1c6a1c31c _DictionaryStorage.__deallocating_deinit + 16
24 libswiftCore.dylib 0x1c6d11628 _swift_release_dealloc + 56
25 libswiftCore.dylib 0x1c6d1244c bool swift::RefCounts<swift::RefCountBitsT<(swift::RefCountInlinedness)1>>::doDecrementSlow<(swift::PerformDeinit)1>(swift::RefCountBitsT<(swift::RefCountInlinedness)1>, unsigned int) + 132
27 libswiftCore.dylib 0x1c6d11628 _swift_release_dealloc + 56
28 libswiftCore.dylib 0x1c6d1244c bool swift::RefCounts<swift::RefCountBitsT<(swift::RefCountInlinedness)1>>::doDecrementSlow<(swift::PerformDeinit)1>(swift::RefCountBitsT<(swift::RefCountInlinedness)1>, unsigned int) + 132
30 libsystem_blocks.dylib 0x22c9c5134 _call_dispose_helpers_excp + 48
31 libsystem_blocks.dylib 0x22c9c4d64 _Block_release + 252
32 libdispatch.dylib 0x1d40eaeac _dispatch_client_callout + 20
33 libdispatch.dylib 0x1d40f96a4 _dispatch_main_queue_drain + 928
34 libdispatch.dylib 0x1d40f92f4 _dispatch_main_queue_callback_4CF + 44
35 CoreFoundation 0x1cccb3c28 __CFRUNLOOP_IS_SERVICING_THE_MAIN_DISPATCH_QUEUE__ + 16
36 CoreFoundation 0x1ccc95560 __CFRunLoopRun + 1992
37 CoreFoundation 0x1ccc9a3ec CFRunLoopRunSpecific + 612
38 GraphicsServices 0x20815f35c GSEventRunModal + 164
39 UIKitCore 0x1cf0276e8 -[UIApplication _run] + 888
40 UIKitCore 0x1cf02734c UIApplicationMain + 340
41 Younite 0x101509d00 main + 64
42 dyld 0x1ec19adec start + 2220
While Debugging:
These crash reports provide a snapshot of the challenge we were up against. Through persistence and deep-dive analysis, we uncovered the core of the issue — a hiccup in our understanding and usage of the WebRTC protocol.
Before discussing the strategies to close peer connections, it’s important to understand the different states a connection can have. In WebRTC, the RTCPeerConnection.connectionState
property can tell you about the current connection state. The possible states are:
new
: The connection has just been created and has not completed negotiation yet.connecting
: The connection is in the process of negotiation.connected
: The connection has been successfully negotiated and active data channels are open.disconnected
: One or more transports are disconnected.failed
: One or more transports has terminated or failed.closed
: The connection is closed.Before calling RTCPeerConnection.close()
, it’s crucial to ensure that the connection is either connected
or failed
. Closing connections in transitional states (connecting
or disconnected
) can lead to issues and can cause the application to become unresponsive.
The PeerConnection::Close()
function is at the heart of managing the lifecycle of a peer connection in WebRTC. This native function is responsible for the orderly shutdown of a connection. However, its behavior can be complex, with several conditional checks and sub-procedures that cater to different stages of the connection’s lifecycle.
In essence, this function first checks if the connection is already closed. If it’s not, it proceeds to update connection states, signalling its closure to any observers, and stops all transceivers. It also ensures all asynchronous stats requests are completed before destroying the transport controller. It then releases related resources like the voice/video/data channels, event log, and others.
This is the WebRTC Native Function, sourced from the active branches of Chromium’s WebRTC m115 branch-head: branch-heads/5790
void PeerConnection::Close() {
RTC_DCHECK_RUN_ON(signaling_thread());
TRACE_EVENT0("webrtc", "PeerConnection::Close");
RTC_LOG_THREAD_BLOCK_COUNT();
if (IsClosed()) {
return;
}
// Update stats here so that we have the most recent stats for tracks and
// streams before the channels are closed.
legacy_stats_->UpdateStats(kStatsOutputLevelStandard);
ice_connection_state_ = PeerConnectionInterface::kIceConnectionClosed;
Observer()->OnIceConnectionChange(ice_connection_state_);
standardized_ice_connection_state_ =
PeerConnectionInterface::IceConnectionState::kIceConnectionClosed;
connection_state_ = PeerConnectionInterface::PeerConnectionState::kClosed;
Observer()->OnConnectionChange(connection_state_);
sdp_handler_->Close();
NoteUsageEvent(UsageEvent::CLOSE_CALLED);
if (ConfiguredForMedia()) {
for (const auto& transceiver : rtp_manager()->transceivers()->List()) {
transceiver->internal()->SetPeerConnectionClosed();
if (!transceiver->stopped())
transceiver->StopInternal();
}
}
// Ensure that all asynchronous stats requests are completed before destroying
// the transport controller below.
if (stats_collector_) {
stats_collector_->WaitForPendingRequest();
}
// Don't destroy BaseChannels until after stats has been cleaned up so that
// the last stats request can still read from the channels.
sdp_handler_->DestroyAllChannels();
// The event log is used in the transport controller, which must be outlived
// by the former. CreateOffer by the peer connection is implemented
// asynchronously and if the peer connection is closed without resetting the
// WebRTC session description factory, the session description factory would
// call the transport controller.
sdp_handler_->ResetSessionDescFactory();
if (ConfiguredForMedia()) {
rtp_manager_->Close();
}
network_thread()->BlockingCall([this] {
// Data channels will already have been unset via the DestroyAllChannels()
// call above, which triggers a call to TeardownDataChannelTransport_n().
// TODO(tommi): ^^ That's not exactly optimal since this is yet another
// blocking hop to the network thread during Close(). Further still, the
// voice/video/data channels will be cleared on the worker thread.
RTC_DCHECK_RUN_ON(network_thread());
transport_controller_.reset();
port_allocator_->DiscardCandidatePool();
if (network_thread_safety_) {
network_thread_safety_->SetNotAlive();
}
});
worker_thread()->BlockingCall([this] {
RTC_DCHECK_RUN_ON(worker_thread());
worker_thread_safety_->SetNotAlive();
call_.reset();
// The event log must outlive call (and any other object that uses it).
event_log_.reset();
});
ReportUsagePattern();
// The .h file says that observer can be discarded after close() returns.
// Make sure this is true.
observer_ = nullptr;
// Signal shutdown to the sdp handler. This invalidates weak pointers for
// internal pending callbacks.
sdp_handler_->PrepareForShutdown();
}
The PeerConnection::Close()
function is responsible for properly closing a WebRTC PeerConnection
and cleaning up all related resources. Let’s break down what’s happening in this method:
RTC_DCHECK_RUN_ON(signaling_thread());
: This line checks to ensure that the Close()
method is being called from the signaling thread. The signaling thread is used for operations that change the state of the PeerConnection, such as handling SDP offers and answers, ICE candidates, and closing the connection.if (IsClosed()) { return; }
: This line checks if the PeerConnection is already closed. If so, the method immediately returns because there’s no work to do.legacy_stats_->UpdateStats(kStatsOutputLevelStandard);
: This line updates the statistics for the PeerConnection before it is closed.OnIceConnectionChange
and OnConnectionChange
methods on the observer (which could be an application that’s using the WebRTC library). This notifies the observer that the connection is now closed.sdp_handler_->Close();
: This line closes the SDP (Session Description Protocol) handler, which is responsible for handling the SDP offers and answers that are part of the WebRTC handshake process.observer_ = nullptr;
removes the reference to the observer, as it’s no longer needed after the connection is closed.sdp_handler_->PrepareForShutdown();
, which prepares the SDP handler for shutdown by invalidating any weak pointers for internal pending callbacks.Here are some general guidelines to follow when closing peer connections, with examples tailored for Android and iOS platforms.
// Assuming peerConnection is a PeerConnection object
fun disconnectPeer(peerConnection: PeerConnection) {
// Check the connection state
if (peerConnection.connectionState() == PeerConnection.PeerConnectionState.CONNECTED ||
peerConnection.connectionState() == PeerConnection.PeerConnectionState.FAILED) {
// Close each track
peerConnection.localStreams.forEach { mediaStream ->
mediaStream.videoTracks.forEach { it.setEnabled(false) }
mediaStream.audioTracks.forEach { it.setEnabled(false) }
}
// Close the connection
peerConnection.close()
}
// Nullify the reference
peerConnection = null
}
// Assuming peerConnections is a list of PeerConnection objects
fun disconnectPeers(peerConnections: MutableList<PeerConnection>) {
peerConnections.forEach { peerConnection ->
// Check the connection state
if (peerConnection.connectionState() == PeerConnection.PeerConnectionState.CONNECTED ||
peerConnection.connectionState() == PeerConnection.PeerConnectionState.FAILED) {
// Close each track
peerConnection.localStreams.forEach { mediaStream ->
mediaStream.videoTracks.forEach { it.setEnabled(false) }
mediaStream.audioTracks.forEach { it.setEnabled(false) }
}
// Close the connection
peerConnection.close()
}
}
// Clear the list
peerConnections.clear()
}
// Assuming peerConnection is a RTCPeerConnection object
func disconnectPeer(peerConnection: RTCPeerConnection) {
// Check the connection state
if (peerConnection.connectionState == .connected ||
peerConnection.connectionState == .failed) {
// Close each track
peerConnection.senders.forEach { sender in
sender.track?.isEnabled = false
}
// Close the connection
peerConnection.close()
}
// Nullify the reference
peerConnection = nil
}
// Assuming peerConnections is an array of RTCPeerConnection objects
func disconnectPeers(peerConnections: inout [RTCPeerConnection]) {
peerConnections.forEach { peerConnection in
// Check the connection state
if (peerConnection.connectionState == .connected ||
peerConnection.connectionState == .failed) {
// Close each track
peerConnection.senders.forEach { sender in
sender.track?.isEnabled = false
}
// Close the connection
peerConnection.close()
}
}
// Empty the array
peerConnections.removeAll()
}
Here is an improved approach for a seamless closing of PeerConnections:
// Assuming peers is an array of RTCPeerConnection objects
function disconnectPeers(peers) {
peers.forEach(peer => {
// Close each track
peer.getTracks().forEach(track => {
track.stop();
});
// Remove all event listeners
peer.ontrack = null;
peer.onremovetrack = null;
peer.onicecandidate = null;
peer.oniceconnectionstatechange = null;
peer.onsignalingstatechange = null;
// Close the connection
peer.close();
});
// Empty the array
peers = [];
}
This approach ensures that all resources and event listeners associated with the PeerConnection are correctly closed and removed. Each track associated with the PeerConnection is individually stopped, ensuring a complete and safe disconnection. Removing event listeners aids in preventing any unanticipated triggers after the connection has been closed.
Navigating through the complexities of WebRTC can seem like a daunting task, but it’s all part of the journey. As the saying goes, “Every expert was once a beginner.” I’ve put together this guide with the intention of making that journey a little less complicated for you.
“In the realm of WebRTC, effective resource management is not just an option, it’s a necessity. Treat it like a puzzle, where each piece must fit perfectly for the whole picture to make sense.”
Through careful understanding and application of these principles, I believe you’ll be able to overcome any hurdles you encounter in the realm of peer connections. Let’s keep learning and growing together in this fascinating field!
I am thrilled to announce an exciting new chapter in my professional journey: my first-ever participation in the Internet Engineering Task Force (IETF) meeting from July 22nd to 28th. This global event marks a significant milestone in my journey as a WebRTC Engineer.
“Every new beginning comes from some other beginning’s end.” — Seneca
Often fondly referred to as ‘Mr. WebRTC,’ my experiences range from real-time audio and video communication to game streaming. My noteworthy contributions to technology include a U.S. patent and pivotal software consultation for NHS England.
The IETF is an open international community of network designers, operators, vendors, and researchers. They share the common interest of evolving the Internet architecture and ensuring its smooth operation.
Diving into the deep end of IETF, one can unlock fresh insights, connect with industry leaders, and contribute to the collective knowledge of the field. My preparation for this event involved more than a month of studying IETF’s various aspects, understanding its importance, and how it could shape my professional standing.
Backed by industry giants like Cisco, Meta, and Nokia, the IETF stands as a monument to innovation and progress. The intrigue of the IETF extends to other heavyweights like IBM, Google, and Ericsson, all working towards a shared vision for the future of the Internet.
“Coming together is a beginning, staying together is progress, and working together is success.” — Henry Ford
The IETF is a fascinating blend of contributors where thought leaders from world-renowned firms stand shoulder to shoulder with independent professionals and researchers, fostering a hotbed of ideas.
The philosopher Seneca once said, “Luck is what happens when preparation meets opportunity.” The meticulous groundwork laid over the past month is about to meet the incredible opportunity of attending the IETF meeting.
Participating in IETF meetings and contributing to its Working Groups can significantly boost one’s professional profile, providing worldwide recognition and enriching one’s expertise in Internet standards and protocols.
“Innovation distinguishes between a leader and a follower.” — Steve Jobs
I’m thrilled to announce that I’m working on a groundbreaking research draft for the upcoming IETF meeting. The topic, “Optimization of NAT Traversal for WebRTC Connections,” aims to revolutionize the way we establish WebRTC connections by introducing an innovative predictive model. This breakthrough not only enhances the speed but also the efficiency of NAT traversal, paving the way for swifter, more robust real-time communication. So, stay tuned for this cutting-edge development in the world of WebRTC!
Every expert was once a beginner, and platforms like IETF offer an ideal place to learn, grow, and become industry leaders. As long as you’re willing to put in the hard work and the hours, there’s no telling where this journey can take you.
I invite you all to follow my journey through this IETF meeting. I aim to bring back and share fresh perspectives, ground-breaking ideas, and hope to inspire more professionals to partake in such impactful initiatives.
“If I have seen further, it is by standing on the shoulders of giants.” — Isaac Newton
Greetings to the forward-thinking minds of our generation! This is Mr. WebRTC, a testament to my journey in real-time Audio & Video Communication and Game Streaming. As an experienced WebRTC Engineer and AI Research Scientist, I’ve experienced firsthand how these two technologies can redefine the possibilities of real-time communication and business operations.
Albert Einstein once said, “The measure of intelligence is the ability to change.” My journey in this dynamic realm started with an innate interest in the adaptability of technology, specifically in WebRTC. Over time, my curiosity was piqued by the possibilities of AI and Machine Learning. I sought to understand how these powerful entities can coalesce to drive innovation in real-time communication.
My foray into this intriguing realm has resulted in commendable achievements, including strategic enhancements to WebRTC platforms and even a U.S. PATENT for ‘METHOD AND SYSTEM FOR TELECONFERENCING USING INDIVIDUAL MOBILE DEVICES.’
In the spirit of constant learning and growth, I encourage everyone to dive deep into the intersection of AI and WebRTC, to question, to innovate, and to create. After all, we’re not just developers or engineers — we’re pioneers on the frontier of technological evolution. And the journey has only just begun.
WebRTC and AI are individually transformative. Yet, their convergence opens up unparalleled avenues for growth and improvement. Below, I delve into four key areas where these technologies can fuse to amplify business outcomes:
1. Enhancing Quality: AI’s adaptive algorithms can dynamically tackle typical communication issues like background noise or poor video quality. By training Machine Learning models for these tasks, we can significantly enhance user experiences during WebRTC communication.
Example: OpenAI’s DALL-E, an AI system, demonstrates the power of generative models that can imagine new concepts. In a similar vein, we can develop AI models to imagine and generate high-quality media streams even under challenging network conditions.
2. Real-Time Analytics: AI offers a unique advantage — the ability to analyze and derive insights from data in real time. Whether it’s converting speech to text, translating languages, or detecting emotions during a call, AI can tremendously enrich WebRTC’s interactive capabilities.
Reference: Google’s ‘Live Transcribe,’ which provides real-time transcription services, is a prime example of harnessing AI’s real-time analytical power.
3. Bolstering Security: AI can work as a smart security agent in WebRTC communication. By learning and detecting unusual patterns, AI can thwart potential security threats, bringing robust security enhancements to real-time communication.
4. Predictive Analytics: AI is a powerful tool for predictive analytics. Coupled with Probability and Statistical Analysis, it can predict future call quality based on current network conditions or even anticipate user behavior.
Quote: As Peter Drucker said, “The best way to predict the future is to create it.” AI empowers us to create a reliable and promising future in the WebRTC landscape.
The synergy between AI and WebRTC signals a future filled with potential and innovation. Recognizing this potential and aligning business strategies to it could provide a significant competitive advantage.
For those new to this intersection, here are a few pointers:
As we step into the future, my commitment is to continue to drive the convergence of technology and help organizations unlock the immense potential that WebRTC and AI have to offer. As we all know, the only constant in technology is change, and when we marry evolving technologies like WebRTC and AI, we’re not just keeping up with the changes, but indeed, we’re leading the way.
The journey of merging the realms of AI and WebRTC is much like venturing into uncharted territory. It is filled with challenges and complexities, but also opportunities and rewards. These are the kinds of journeys that change the world, and those who embark on them are the pioneers of technological innovation.
I believe the successful marriage of AI and WebRTC is a game-changer. It has the potential to redefine communication, making it more efficient, secure, and adaptive. It’s not merely an incremental improvement to existing technologies but a paradigm shift that will reshape how we think about real-time communication.
As we look towards the horizon, we see a future where communication is not just real-time but also intelligent, responsive, and secure, thanks to the amalgamation of AI and WebRTC.
As William Gibson famously said, “The future is already here — it’s just not very evenly distributed.” We have the opportunity and the technology to distribute this future more evenly. So let’s embrace this chance, build on this synergy, and create a future that echoes our ambitions and resonates with our vision.
As an AI and WebRTC veteran, my belief is that this union holds immense potential. The use cases we’ve seen so far are just the tip of the iceberg. The truly transformative impact will surface when we dive deep and explore uncharted territories.
It’s the businesses that understand and harness the power of this synergy that will thrive in the coming years. But remember, technology is just the enabler. The real value lies in how we use it to solve problems, create opportunities, and drive innovation.
In conclusion, the union of AI and WebRTC is more than a new trend; it’s the next step in the evolution of communication. So let’s step into this future, one where we break down barriers, bridge gaps, and bring people closer together, in real-time and intelligently.
From the foundation of my journey — earning the title ‘Mr. WebRTC’ to the development of my seminal U.S. PATENT — to each transformative project I’ve tackled, my experience in this field has underscored one thing: when we unlock the power of technologies like WebRTC and AI, we unlock the potential to create an inclusive, efficient, and secure digital future.
In today’s hyper-connected world, real-time communication is the backbone of our daily interactions. From virtual meetings and online gaming to live streaming events, the demand for seamless, high-quality audio and video communication is skyrocketing. But have you ever wondered what it takes to handle millions of concurrent calls without a hiccup? Enter LiveKit, a real-time audio and video platform that’s redefining scalability with its sophisticated cloud infrastructure.
It is LiveKit that has come into the ocean of the extremely crowded WebRTC and VoIP industry and pulled off a miracle.
David Zhao is the CTO and Co-Founder of LiveKit. Motivation behind the article.
Handling real-time communication at scale is no small feat. Traditional systems often buckle under the pressure of high concurrency, leading to dropped calls, lagging videos, and frustrated users. The challenge lies in managing a vast number of simultaneous connections while maintaining low latency, high reliability, and excellent quality of service.
Hey, we’re not talking about Zoom, Microsoft Teams, Google Meet, Cisco Webex, or Voov. We’re discussing something truly innovative and exciting. LiveKit tackles the challenges of real-time communication head-on with a cloud-native architecture designed for scalability, reliability, and performance. Let’s dive into the key components that make LiveKit’s infrastructure capable of handling millions of concurrent calls.
At the heart of LiveKit’s architecture is the Selective Forwarding Unit (SFU). Unlike traditional Multipoint Control Units (MCUs) that mix media streams — adding latency and consuming significant resources — SFUs act as intelligent routers that forward media streams to participants based on their subscriptions.
LiveKit employs a distributed mesh network to ensure that no single point becomes a bottleneck.
Breaking down the application into microservices allows for independent development, deployment, and scaling.
LiveKit leverages Kubernetes for container orchestration, ensuring efficient management of resources.
LiveKit’s infrastructure is designed to be cloud-agnostic, allowing it to run on any major cloud provider like AWS, Azure, GCP, Tencent Cloud, or even on-premises servers.
Deploying edge nodes in multiple regions brings services closer to users.
To get more in-depth understanding, please visit:
While LiveKit offers a robust and scalable solution, other open-source media servers also have unique strengths. Having personally engineered and set up cloud infrastructures for these servers, here’s what each brings to the table:
Janus Media server has super legacy in real-time communications.
I personally call ‘ Kurento Media Server ‘ as the mother of all the media servers.
MediaMTX acts as ‘Media Proxy’ or ‘Media Router ’ because of it’s ready-to-use and zero-dependency real-time capabilities.
WebRTC SFU Sora is my crush, build by Japanese team. They have reached their 13th fiscal year 2024.10.01.
mediasoup is also a perfect choice for building multi-party video conferencing and real-time streaming apps.
While each media server has its merits, LiveKit distinguishes itself through its cloud-native, scalable architecture designed for modern applications.
Moreover, the ability to handle such massive concurrency with a focus on maintaining low latency and high quality sets LiveKit apart from the competition.
Even with its impressive architecture, LiveKit has room to grow:
Edge Computing Integration
AI-Powered Network Optimization
Enhanced Security Measures
Serverless Architecture Exploration
LiveKit is not just a media server; it’s a catalyst for innovation in the WebRTC space.
Handling millions of concurrent calls is no longer an insurmountable challenge — it’s a reality with LiveKit’s advanced cloud architecture. By combining SFU technology, distributed networking, microservices, and Kubernetes orchestration, LiveKit offers a scalable, reliable, and efficient platform for real-time communication.
Having more than six years of experience in the WebRTC and VoIP industry, and worked extensively with various media servers, I can attest that LiveKit’s cloud infrastructure is truly revolutionary. While other platforms like Ant Media Server — backed by their amazing team — and Sora offer excellent solutions, LiveKit’s emphasis on scalability and developer experience makes it a standout choice.
I can confidently say that now is the perfect time to jump in with the right open-source module. LiveKit, with its rapid development — much like Elon Musk’s rockets soaring into space — is revolutionizing real-time communication. Its cutting-edge features and the pace at which it’s evolving make it an exciting platform to watch and be a part of.
Stay tuned for more updates!
After announcing a record $6.6 billion funding round, OpenAI revealed it also has secured a $4 billion revolving credit facility from major banks. The access to over $10 billion is sorely needed as the artificial intelligence startup burns through cash seeking “to meet growing demand.” As it expands, OpenAI is also undergoing a concentration of power.
Outgoing CTO Mira Murati won’t be replaced, and technical teams will now report directly to CEO Sam Altman himself. With several key executives already gone, “a lot of the opposition to him is gone,” whispers a source from Bloomberg. It seems Sam’s vision is now uncontested.
🚀 Sam Altman Just Raised the BIGGEST Venture Round in History — $6.6 BILLION for OpenAI! 🤯
But hold on — this might not even be the biggest round we’ll see this year or next. Sam’s just getting started, and the next raise could blow the roof off! 🏗️💥
The usual suspects: Tiger Global, Khosla Ventures, Microsoft, NVIDIA… all the cool kids. And Apple? Yeah, they took a hard pass. Word has it, Tim Cook said, “Eh, too spicy for me.” 🌶️
Valuation? A casual $157 BILLION. 🤑 No biggie, right? And by the way, this is no typical equity round — these are convertible notes. Yep, some financial wizardry is happening to turn OpenAI from “not-for-profit” into “let’s-make-Sam-even-richer” mode. Gotta love the lawyers. 💼💸
Get this — OpenAI has politely asked its investors to stay out of rival AI projects. Sorry Elon and Ilya, Sam’s got you cornered for now. He’s rallying his team and partners like a seasoned pro. 👑🎤
Not even close. OpenAI is burning through cash faster than a Tesla in Ludicrous Mode! Rumor has it they lost a staggering $5 BILLION last year — on only $3.7B in revenue. And with ambitious plans to build actual nuclear reactors to power their future AI, Sam’s gonna need a lot more than pocket change. 💰🔥
My bet? Another raise — this time pulling in sovereign wealth fund money. The AI arms race is just heating up, and OpenAI is zooming full throttle toward AGI (Artificial General Intelligence).
Probably not. Buckle up, folks — Sam Altman’s far from done, and OpenAI is pushing the envelope on what’s possible. 🚀
#OpenAI #AI #VentureCapital #SamAltman #Funding #AGI
What do you think of this wild raise?