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Nvidia’s Startup Empire

Avery Winslow
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Nvidia’s Startup Empire

No Company Has Capitalized on the AI Revolution More Dramatically Than Nvidia

No company has capitalized on the AI revolution more dramatically than Nvidia. Its revenue, profitability, and cash reserves have skyrocketed since the introduction of ChatGPT a little over two years ago — and the many competitive generative AI services that have launched since. And its stock price soared more than eightfold.

During that period, the world’s leading high-performance GPU maker has used its ballooning fortunes to significantly increase investments in all sorts of startups but particularly in AI startups.

The Chip Giant’s Venture Capital Activity

The chip giant ramped up its venture capital activity in 2024, participating in 49 funding rounds for AI companies, a sharp increase from 34 in 2023, according to PitchBook data. It’s a dramatic surge in investment compared to the previous four years combined, during which Nvidia funded only 38 AI deals. Note that these investments exclude those made by its formal corporate VC fund, NVentures, which also significantly ramped up its investing in the last two years. (PitchBook says NVentures engaged in 24 deals in 2024, compared to just 2 in 2022.)

The Billion-Dollar-Round Club

Below is a list of startups that raised rounds exceeding $100 million over the past two years where Nvidia is a named participant, organized from the highest amount to lowest raised in the round.

The Billion-Dollar-Round Club

OpenAI: Nvidia backed the ChatGPT maker for the first time in October, reportedly writing a $100 million check toward a colossal $6.6 billion round that valued the company at $157 billion. The chipmaker’s investment was dwarfed by OpenAI’s other backers, notably Thrive, which according to the New York Times invested $1.3 billion.

xAI: Nvidia participated in the $6 billion round of Elon Musk’s xAI. The deal revealed that not all of OpenAI’s investors followed its request to refrain from backing any of its direct competitors. After investing in the ChatGPT maker in October, Nvidia joined xAI’s cap table a few months later.

Inflection: One of Nvidia’s first significant AI investments also had one of the most unusual outcomes. In June 2023, Nvidia was one of several lead investors in Inflection’s $1.3 billion round, a company founded by Mustafa Suleyman, who earlier founded DeepMind. Less than a year later, Microsoft hired Inflection AI’s founders, paying $620 million for a non-exclusive technology license, leaving the company with a significantly diminished workforce and a less defined future.

Wayve: In May, Nvidia participated in a $1.05 billion round for the U.K.-based startup, which is developing a self-learning system for autonomous driving. The company is testing its vehicles in the U.K. and the San Francisco Bay Area.

Safe Superintelligence: In September, Nvidia was a backer in the new startup founded by former OpenAI chief scientist Ilya Sutskever. The $1 billion round reportedly valued the new 10-person AI lab at $5 billion.

Scale AI: In May 2024, Nvidia joined Accel and other tech giants Amazon and Meta to invest $1 billion in Scale AI, which provides data-labeling services to companies for training AI models. The round valued the San Francisco-based company at nearly $14 billion.

The Many-Hundreds-of-Millions-of-Dollars Club

Below is a list of startups that raised rounds exceeding $100 million over the past two years where Nvidia is a named participant, organized from the highest amount to lowest raised in the round.

The Many-Hundreds-of-Millions-of-Dollars Club

Crusoe: A startup building data centers reportedly to be leased to Oracle, Microsoft, and OpenAI raised $686 million in late November, according to an SEC filing. The investment was led by Founders Fund, and the long list of other investors included Nvidia.

Figure AI: In February, AI robotics startup Figure raised a $675 million Series B from Nvidia, OpenAI Startup Fund, Microsoft, and others. The round valued the company at $2.6 billion.

Mistral AI: Nvidia invested in Mistral for the second time when the French-based large language model developer raised a $640 million Series B at a $6 billion valuation in June.

Cohere: In June, Nvidia invested in Cohere’s $500 million round, a large language model provider serving enterprises. The chipmaker first backed the Toronto-based startup a year prior.

Perplexity: Nvidia first invested in Perplexity in November of 2023 and has participated in every subsequent round of the AI search engine startup, including the $500 million round in December, which values the company at $9 billion, according to PitchBook data.

Poolside: In October, the AI coding assistant startup Poolside announced it raised $500 million led by Bain Capital Ventures. Nvidia participated in the round, which valued the AI startup at $3 billion.

CoreWeave: Nvidia invested in the AI cloud computing provider in April 2023, when CoreWeave raised $221 million in funding. Since then, CoreWeave’s valuation has jumped from about $2 billion to $19 billion, and the company reportedly has its sights on a $35 billion IPO this year. CoreWeave allows its customers to rent Nvidia GPUs on an hourly basis.

Sakana AI: In September, Nvidia invested in the Japan-based startup, which trains low-cost generative AI models using small datasets. The startup raised a massive Series A round of about $214 million at a valuation of $1.5 billion.

Imbue: The AI research lab that claims to be developing AI systems that can reason and code raised a $200 million round in September 2023 from investors, including Nvidia, Astera Institute, and former Cruise CEO Kyle Vogt.

Waabi: In June, the autonomous trucking startup raised a $200 million Series B round co-led by existing investors Uber and Khosla Ventures. Other investors included Nvidia, Volvo Group Venture Capital, and Porsche Automobil Holding SE.

Deals of Over a $100 Million

Below is a list of startups that raised rounds exceeding $100 million over the past two years where Nvidia is a named participant, organized from the highest amount to lowest raised in the round.

Deals of Over a $100 Million

Ayar Labs: In December, Nvidia invested in the $155 million round of Ayar Labs, a company developing optical interconnects to improve AI compute and power efficiency. This was the third time Nvidia backed the startup.

Kore.ai: The startup developing enterprise-focused AI chatbots raised $150 million in December of 2023. In addition to Nvidia, investors participating in the funding included FTV Capital, Vistara Growth, and Sweetwater Private Equity.

Weka: In May, Nvidia invested in a $140 million round for AI-native data management platform Weka. The round valued the Silicon Valley company at $1.6 billion.

Runway: In June of 2023, Runway, a startup building generative AI tools for multimedia content creators, raised a $141 million Series C extension from investors, including Nvidia, Google, and Salesforce.

Bright Machines: In June 2024, Nvidia participated in a $126 million Series C of Bright Machines, a smart robotics and AI-driven software startup.

Vast Data: The startup that provides storage solutions for AI and data analytics raised a $118 million Series E at a $9.3 billion valuation in December of 2023. That was the third time Nvidia invested in Vast Data.

Enfabrica: In September 2023, Nvidia invested in networking chips designer Enfabrica’s $125 million Series B. Although the startup raised another $115 million in November, Nvidia didn’t participate in the round.

Conclusion

Nvidia has emerged as one of the most active investors in the AI startup ecosystem, with a significant increase in its venture capital activity over the past two years. The company has participated in numerous funding rounds, investing in startups that are developing innovative AI technologies and solutions. As the AI revolution continues to shape the future of various industries, Nvidia is well-positioned to play a key role in driving innovation and growth in this space.

FAQs

Q: What is Nvidia’s investment strategy in AI startups?
A: Nvidia’s investment strategy in AI startups is focused on backing companies that are developing innovative AI technologies and solutions that have the potential to drive growth and disruption in various industries.

Q: How many AI startups has Nvidia invested in over the past two years?
A: According to PitchBook data, Nvidia

Tech Titans’ Bold Bets: AI PCs and Flying Cars

Parker Lennox
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Tech Titans’ Bold Bets: AI PCs and Flying Cars

Happy new year and welcome to our first issue of #techAsia in 2025. I’m sending this newsletter from Las Vegas where my colleagues and I are covering the first tech extravaganza of the year as always: the CES tech trade show.

AI Takes Center Stage at CES

As you might’ve guessed, artificial intelligence is once again the main attraction. Every company remotely related to the field is showcasing some kind of AI product at the show, which officially kicked off on Tuesday. While there are a lot more actual use cases this year compared to last, chipmakers are arguably still the only companies that have unveiled AI products that are ready for market and able to make money.

Nvidia’s Keynote and New Products

Nvidia’s high-profile CEO, Jensen Huang, made a splash on Monday night when he unveiled in his keynote speech a series of new products, including a $3,000 personal AI computer that will be powered by the highly sought-after Blackwell chip.

AI PC Game

Nvidia has undoubtedly dominated the data centre chip game in the AI era. Now the US chip giant is kicking off a new phase in the AI PC race by bringing its powerful Blackwell chips to personal computers.

At a keynote speech on Monday, Jensen Huang unveiled the GB10 chipset that turns PCs into AI supercomputers, according to Nikkei Asia’s Yifan Yu and Cissy Zhou.

Tencent Pushes Back

Chinese social media and gaming giant Tencent woke up to a nasty shock as it discovered it had been labelled a “Chinese military party” by the Pentagon on Monday, writes the Financial Times’ Eleanor Olcott and Zijing Wu.

Tencent said it was planning legal action to challenge its inclusion on an annually updated list of companies determined to have links with China’s military machine if it could not reach an agreement with the US Department of Defense.

Crunch Time for Japan’s Chip Dreams

In December, Rapidus began moving cutting-edge EUV chipmaking machines into its plant in Hokkaido. Now, the Japanese government-backed start-up is gearing up for a moment of truth: test production is set to begin around April, and a lot is riding on its success.

Lift-off Economy

While CES used to be a consumer electronics-focused trade show, it has increasingly become a popular platform for automakers to show off their latest and sometimes far-fetched innovations. Flying cars might have sounded more like the latter just a few years ago, but they could become a reality as soon as next year.

Suggested Reads

  1. China’s Honor to enter Indonesian market amid iPhone ban (Nikkei Asia)
  2. KKR urges Fuji Soft to take legal action against Bain in $4bn takeover fight (FT)
  3. Chinese venture capitalists force failed founders on to debtor blacklist (FT)
  4. Lenovo to bring Saudi PC plant onstream by 2026 (Nikkei Asia)
  5. Magic monkey tale inspires China’s gaming industry to seek blockbuster success (FT)
  6. Rapidus aims to supply cutting-edge 2-nm chip samples to Broadcom (Nikkei Asia)
  7. Toyota’s futuristic Woven City to get first residents this year (Nikkei Asia)
  8. Why China’s industrial giants won’t be damaged by the latest US blacklisting (FT)
  9. Samsung’s Q4 profit tumbles 30% amid memory downturn, labour costs (Nikkei Asia)
  10. Tech groups to pay premium for energy for Malaysia data centres, says minister (FT)

Conclusion

The CES tech trade show has kicked off, and AI is once again the main attraction. From Nvidia’s new AI products to Tencent’s response to being labelled a "Chinese military party", there is a lot to keep an eye on. Meanwhile, Japan is gearing up for a moment of truth with its chip dreams, and flying cars might become a reality as soon as next year.

Frequently Asked Questions

Q: What is the main theme of the CES tech trade show?
A: AI is once again the main attraction at the CES tech trade show.

Q: What new products did Nvidia unveil at the show?
A: Nvidia unveiled a series of new products, including a $3,000 personal AI computer powered by the highly sought-after Blackwell chip.

Q: What is the significance of Japan’s chip dreams?
A: Japan is looking to get back into the global chipmaking game after decades on the sidelines, and the government is pouring billions into the effort.

Q: What is the significance of flying cars?
A: Flying cars could become the latest front in the US-China tech race, as Washington has paved the way for companies to launch consumer flying cars by updating related regulations.

Apple Opposes Abolishing DEI Programs

Jordan Waverly
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Apple Opposes Abolishing DEI Programs

Apple’s Board of Directors Opposes Proposal to End DEI Programs

Proposal Seeks to Abolish Inclusion & Diversity Program, Policies, and Department

In a recent proxy filing, Apple’s board of directors has come out in opposition to a proposal submitted by the National Center for Public Policy Research, a conservative think tank, seeking to end the company’s Diversity, Equity, and Inclusion (DEI) programs.

Think Tank’s Proposal Cites Legal Concerns and Company Examples

The think tank’s proposal claims that DEI programs could make companies vulnerable to lawsuits, citing the recent Supreme Court ruling against race-based affirmative action in colleges and noting that other companies have eliminated or scaled back similar programs. Most recently, Meta eliminated its DEI programs, and Amazon is reportedly pulling back as well.

Apple’s Response: DEI Programs are “Unnecessary” and “Inappropriately Micromanaged”

Apple, however, said the proposal is “unnecessary” because the company “already has a well-established compliance program” that would presumably keep it out of legal trouble. The filing also criticizes the proposal because it “inappropriately seeks to micromanage the Company’s programs and policies.”

Apple’s Commitment to DEI

The company also emphasized its commitment to creating a culture of belonging where everyone can do their best work. Apple’s DEI programs aim to promote diversity, equity, and inclusion within the company and in the communities it serves.

Conclusion

Apple’s board of directors has made it clear that it will not support the proposal to end the company’s DEI programs. The company’s commitment to diversity, equity, and inclusion is a key part of its culture and values, and it will continue to prioritize these efforts.

FAQs

Q: What is the National Center for Public Policy Research?

A: The National Center for Public Policy Research is a conservative think tank that advocates for limited government and free market principles.

Q: What is the proposal seeking to do?

A: The proposal is seeking to abolish Apple’s Inclusion & Diversity program, policies, department, and goals.

Q: Why is Apple opposing the proposal?

A: Apple is opposing the proposal because it believes that its DEI programs are “unnecessary” and that the proposal is “inappropriately seeking to micromanage the Company’s programs and policies.”

Q: What is Apple’s commitment to DEI?

A: Apple is committed to creating a culture of belonging where everyone can do their best work. The company’s DEI programs aim to promote diversity, equity, and inclusion within the company and in the communities it serves.

Open Source AI Model ‘Sky-T1’ Can Be Trained for Under $450

Avery Winslow
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Open Source AI Model ‘Sky-T1’ Can Be Trained for Under 0

So-Called Reasoning AI Models Become Easier and Cheaper to Develop

On Friday, NovaSky, a team of researchers based out of UC Berkeley’s Sky Computing Lab, released Sky-T1-32B-Preview, a reasoning model that’s competitive with an earlier version of OpenAI’s o1 on a number of key benchmarks.

A New Era of Affordable Reasoning Models

$450 might not sound that affordable. But it wasn’t long ago that the price tag for training a model with comparable performance often ranged in the millions of dollars. Synthetic training data, or training data generated by other models, has helped drive costs down.

How Synthetic Training Data is Revolutionizing AI Development

Palmyra X 004, a model recently released by AI company Writer, trained almost entirely on synthetic data, reportedly cost just $700,000 to develop. This significant reduction in cost is making it possible for more researchers and developers to create high-level reasoning capabilities affordably and efficiently.

What Sets Reasoning Models Apart

Unlike most AI, reasoning models effectively fact-check themselves, which helps them to avoid some of the pitfalls that normally trip up models. Reasoning models take a little longer — usually seconds to minutes longer — to arrive at solutions compared to a typical non-reasoning model. The upside is, they tend to be more reliable in domains such as physics, science, and mathematics.

The NovaSky Team’s Approach

The NovaSky team says it used another reasoning model, Alibaba’s QwQ-32B-Preview, to generate the initial training data for Sky-T1, then “curated” the data mixture and leveraged OpenAI’s GPT-4o-mini to refactor the data into a more workable format. Training the 32-billion-parameter Sky-T1 took about 19 hours using a rack of 8 Nvidia H100 GPUs.

Performance and Comparison

According to the NovaSky team, Sky-T1 performs better than an early preview version of o1 on MATH500, a collection of “competition-level” math challenges. The model also beats the preview of o1 on a set of difficult problems from LiveCodeBench, a coding evaluation. However, Sky-T1 falls short of the o1 preview on GPQA-Diamond, which contains physics, biology, and chemistry-related questions a PhD graduate would be expected to know.

Future Plans

But the NovaSky team says that Sky-T1 only marks the start of their journey to develop open source models with advanced reasoning capabilities. “Moving forward, we will focus on developing more efficient models that maintain strong reasoning performance and exploring advanced techniques that further enhance the models’ efficiency and accuracy at test time,” the team wrote in the post.

Conclusion

The release of Sky-T1-32B-Preview marks a significant milestone in the development of reasoning AI models. With its competitive performance and affordable price tag, it has the potential to democratize access to advanced reasoning capabilities. As the NovaSky team continues to improve and refine their models, we can expect to see even more exciting developments in the field of AI.

FAQs

Q: What is a reasoning AI model?

A: A reasoning AI model is a type of artificial intelligence that can effectively fact-check itself and arrive at solutions through logical reasoning.

Q: How does synthetic training data help reduce costs?

A: Synthetic training data is generated by other models, which reduces the need for expensive human-generated data. This helps drive costs down and makes it possible for more researchers and developers to create high-level reasoning capabilities affordably and efficiently.

Q: What are the benefits of reasoning AI models?

A: Reasoning AI models tend to be more reliable in domains such as physics, science, and mathematics, and can avoid some of the pitfalls that normally trip up models. They also take a little longer to arrive at solutions, but the upside is, they tend to be more accurate and reliable.

NVIDIA Media2: Transforming Content Creation and Streaming with AI

Emerson Blake
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NVIDIA Media2: Transforming Content Creation and Streaming with AI

NVIDIA Media2 is the latest AI-powered initiative transforming content creation, streaming, and live media experiences. Built on technologies like NVIDIA NIM microservices and AI Blueprints, and breakthrough AI applications from startups and software partners, Media2 uses AI to drive the creation of smarter, more tailored, and more impactful content that can adapt to individual viewer preferences.

NVIDIA Technologies at the Heart of Media2

As the media and entertainment industry embraces generative AI and accelerated computing, NVIDIA technologies are transforming how content is created, delivered, and experienced.

NVIDIA Holoscan for Media

NVIDIA Holoscan for Media is a software-defined, AI-enabled platform that allows companies in broadcast, streaming, and live sports to run live video pipelines on the same infrastructure as AI. The platform delivers applications from vendors across the industry on NVIDIA-accelerated infrastructure.

NVIDIA Blackwell Architecture

Delivering the power needed to drive the next wave of data-enhanced intelligent content creation and hyper-personalized media is the NVIDIA Blackwell architecture, built to handle data-center-scale generative AI workflows with up to 25x more energy efficiency over the NVIDIA Hopper generation. Blackwell integrates six types of chips: GPUs, CPUs, DPUs, NVIDIA NVLink Switch chips, NVIDIA InfiniBand switches, and Ethernet switches.

NVIDIA AI Enterprise

Blackwell is supported by NVIDIA AI Enterprise, an end-to-end software platform for production-grade AI. NVIDIA AI Enterprise comprises NVIDIA NIM microservices, AI frameworks, libraries, and tools that media companies can deploy on NVIDIA-accelerated clouds, data centers, and workstations.

NIM Microservices

The list of expanding NIM microservices includes:

* The Mistral-NeMo-12B-Instruct NIM microservice, which enables multilingual information retrieval — the ability to search, process, and retrieve knowledge across languages.
* The NVIDIA Omniverse Blueprint for 3D conditioning for precise visual generative AI, which can help advertisers easily build personalized, on-brand, and product-accurate marketing content at scale using real-time rendering and generative AI without affecting a hero product asset.
* The NVIDIA Cosmos Nemotron vision language model NIM microservice, which is a multimodal VLM that can understand the meaning and context of text, images, and video.
* The NVIDIA Edify multimodal generative AI architecture, which can generate visual assets — like images, 3D models, and HDRi environments — from text or image prompts.
* The NVIDIA Cosmos Nemotron vision language model NIM microservice, which is a multimodal VLM that can understand the meaning and context of text, images, and video.

Partners in the Media2 Ecosystem

Partners across the industry are adopting NVIDIA technology to reshape the next chapter of storytelling.

Getty Images and Shutterstock are intelligent content creation services built with NVIDIA Edify. The AI models have also been optimized and packaged for maximum performance with NVIDIA NIM microservices.

Bria is a commercial-first visual generative AI platform designed for developers. It’s trained on 100% licensed data and built on responsible AI principles. The platform offers tools for custom pipelines, seamless integration, and flexible deployment, ensuring enterprise-grade compliance and scalable, predictable content generation.

Runway is an AI platform that provides advanced creative tools for artists and filmmakers. The company’s Gen-3 Alpha Turbo model excels in video generation and includes a new Camera Control feature that allows for precise camera movements like pan, tilt, and zoom.

Wonder Dynamics, an Autodesk company, recently launched the beta version of Wonder Animation, featuring powerful new video-to-3D scene technology that can turn any video sequence into a 3D-animated scene for animated film production.

Comcast’s Sky innovation team is collaborating with NVIDIA on lab testing NVIDIA NIM microservices and partner models for its global platforms.

Vū is a creative technology company and home to the largest network of virtual studios, broadening access to the creation of virtual environments and immersive content with NVIDIA-accelerated generative AI technologies.

Twelve Labs is a member of the NVIDIA Inception program for startups, developing advanced multimodal foundation models that can understand videos like humans, enabling precise semantic search, content analysis, and video-to-text generation.

S4 Capital’s Monks is using cutting-edge AI technologies to enhance live broadcasts with real-time content segmentation and personalized fan experiences. Powered by NVIDIA Holoscan for Media, the company’s solution is integrated with tools like NVIDIA VILA to generate contextual metadata for injection within a time-addressible media store framework.

Welcome to NVIDIA Media2

The NVIDIA Media2 initiative empowers companies to redefine the future of media and entertainment through intelligent, data-driven, and immersive technologies — giving them a competitive edge while equipping them to drive innovation across the industry.

Conclusion

The NVIDIA Media2 initiative is revolutionizing the media and entertainment industry by leveraging the power of AI and accelerated computing. With a wide range of technologies and partnerships, Media2 is poised to transform the future of content creation, streaming, and live media experiences.

Frequently Asked Questions

Q: What is NVIDIA Media2?
A: NVIDIA Media2 is the latest AI-powered initiative transforming content creation, streaming, and live media experiences.

Q: What technologies are used in Media2?
A: Media2 uses NVIDIA NIM microservices, AI Blueprints, and breakthrough AI applications from startups and software partners.

Q: What are NIM microservices?
A: NIM microservices are software components that enable AI capabilities, such as multilingual information retrieval, precise visual generative AI, and multimodal vision language models.

Q: Who are the partners in the Media2 ecosystem?
A: Partners in the Media2 ecosystem include Getty Images, Shutterstock, Bria, Runway, Wonder Dynamics, Comcast’s Sky, Vū, Twelve Labs, and S4 Capital’s Monks.

Q: What are the benefits of NVIDIA Media2?
A: NVIDIA Media2 enables companies to create smarter, more tailored, and more impactful content that can adapt to individual viewer preferences, giving them a competitive edge and driving innovation across the industry.

The Secret Weapon Against API Abuse: Rate Limiting

Parker Lennox
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The Secret Weapon Against API Abuse: Rate Limiting

I. Introduction

Rate limiting is a strategy for limiting network traffic that is very commonly used on free to use APIs. It puts a limit on how often someone can call an API within a certain time frame. This way you can protect against certain kinds of malicious or abusive intentions and activities as well as you protect your hardware or costs of resources!

II. Types of Rate Limiting

1. Request-Based Rate Limiting

Just counting the requests per minute up to a certain limit will restrict your API in general – which is more of a self-defense system. It protects your resources, while ignoring the user experience completely. Attackers could just send a few hundreds of requests per minute and DoS all your users by doing so. This is the last resort, and should most likely never be used in production ever.

2. IP-Based Rate Limiting

To not lock out all users, you could track IP addresses to find out who is spamming requests against your API. This is the first idea that most developers have when they need to find out who is calling their API – and let me tell you: It’s not the best one! An IP is a good indicator, but might be misleading! While sometimes, this is all we know of our callers it is very much possible for multiple devices to call from the same IP address! Especially if you have various callers sitting in the same office, household or network.

3. User-Based Rate Limiting (or API-Key)

If your API is restricted by a login anyways, another possibility is to track the user specifically after they logged in successfully. This way you can target the very person that is spamming the API and there is no more collateral damage for other users. This also works great with API-Key restricted endpoints.

III. Limiting Strategies

1. Fixed Window

Let’s talk about implementations. The 1st idea most developers have is to set a limit of e.g. 120 per minute. They will count up to 100 and reset the counter whenever 60 seconds have passed. This is called Fixed Window Limiting naturally. While this is certainly a way of handling excessive requests, there are other solutions that might fit your use case a little better.

2. Sliding Window

For example if you want to keep the “120 Requests per 1 minute” mindset, you could also define smaller intervals to replenish the counter. Let’s break the minute into 3x 20 second segments that replenish however many requests were made exactly 60 seconds before.

3. Token Bucket

Very similar to the sliding window variant, the token bucket limit will not replenish exactly the requests you made last minute, but rather a fixed value. So basically every 20 seconds you would get up to 20 new requests credited, but never more than the initial limit of 120 requests.

4. Concurrency

Sometimes the pure amount of requests is not the main issue, but rather some synchronous operations that might get problematic if too many requests hit the same code multiple times within a very short time. With the concurrency limit you can ensure that there are never more than X requests handled at the same time.

IV. Best Practices

1. Handling Requests

Keep your main focus on the user experience. Everybody understands that your system and infrastructure is valuable and worth protecting but please be graceful with your users and handle requests in a proper way. There are two very important things users should know when their request is not being processed.

* Send them a HTTP response code of 429 – Too Many Requests
* There is a HTTP Header “Retry-After” that you should set to the window defined for your app.

2. Monitor and Adjust

Check the state of your API regularly and consider the defined limits.

* Is the window too small or too big?
* Is the limit still appropriate?
* Is the limit hitting the right people / routes on your API?
* Is the method of limiting useful to you?

3. Documentation

A good documentation on your API containing the rate limiting strategy is key for your users. It also encourages them to build their consuming system in a friendly way, to either not run into the limits or at least stop spamming, when the limit hits them. A reference to the “Retry-After” header and the units used in it is also very helpful to build a nice consuming system.

V. Conclusion

Now you know the most important things about rate limiting. I suggest you start implementing them into your next project, or add your knowledge to an existing API of yours to gather some hands-on experience. There are many frameworks that handle rate limiting for you, but sometimes they are not 100% fitting your use case. In fact I got into learning about rate limiting because the .NET Library could not do what I needed, and I had to go down that rabbit hole.

Thank you for reading!

Roborock’s Mechanical Arm Robot Vacuum Picks Up Objects

Emerson Blake
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Roborock’s Mechanical Arm Robot Vacuum Picks Up Objects

Roborock Drops the Mic with Saros Z70 Robot Vacuum and Mop

A Game-Changer in the Robot Vacuum Market

Roborock has just launched the Saros Z70 robot vacuum and mop at CES 2025, featuring a mechanical arm with OmniGrip technology to remove obstacles in its path. The company also unveiled two new robot vacuums, the Saros 10 and Saros 10R.

The Saros Z70: A Flagship with a Twist

The Saros Z70 features a foldable mechanical arm with OmniGrip technology that deploys itself to remove obstacles under 300 grams or about 8 oz. The five-axis arm will initially be limited to socks, small towels or cloths, tissue papers or napkins, and sandals, but Roborock plans to add more items over time.

The Roborock Saros Z70 cleans your floors and identifies objects it can lift in a first pass. Then, the robot returns to pick up the identified items, put them away, and clean the missed areas.

Problems Solved

The new Roborock Saros Z70 is here to finally solve a problem many robot vacuum users face: picking up every last sock or small toy on the floor before running your vacuum. We do this to prevent the robot vacuum from getting its roller brush stuck, leaving it idle until you rescue it. This is one of the most annoying things in a robot vacuum, especially when you run it while you’re away from home and return to find dirty floors and your robot stuck a few feet from where it began cleaning hours ago.

OmniGrip Technology

The OmniGrip arm features precision sensors, a camera, and an LED light to detect and process visual information about the obstacles in its path.

Customization and Safety

The robotic arm vacuum will become Roborock’s latest flagship, replacing the Roborock S8 MaxV Pro Ultra. The feature will be disabled out of the box, so you must enable it in the Roborock app during setup. You can also customize the arm’s behavior, including designating where the objects should be placed when picked up. During a couple of demos with Roborock, this was usually a basket on the floor.

For safety measures, the vacuum features a child lock, a safety stop button, and measures to prevent the mechanical arm from deploying when it’s blocked.

Robot Vacuum and Mop Features

The Saros Z70 robot vacuum and mop features 22,000Pa of suction, making it an industry leader in suction. The Saros Z70, set to launch in the first half of 2025, features Roborock’s dual anti-tangle system, with a FreeFlow main brush and a liftable FlexiArm Riser side brush to prevent hair from tangling around the vacuum.

New Robot Vacuums

Roborock is also launching the Saros 10 and Saros 10R, two new robot vacuums without the mechanical arm but with many of the S70’s flagship features. The Saros 10 features a retractable laser distance sensor (LDS) that can be put away to pass under low-clearance areas, like under furniture, without getting stuck.

Conclusion

Roborock’s Saros Z70 robot vacuum and mop is a game-changer in the robot vacuum market, offering a solution to a common problem many users face. With its OmniGrip technology and mechanical arm, the Saros Z70 is sure to impress.

FAQs

Q: What is the Saros Z70’s suction power?
A: The Saros Z70 features 22,000Pa of suction, making it an industry leader in suction.

Q: What is the purpose of the mechanical arm?
A: The mechanical arm deploys to remove obstacles under 300 grams or about 8 oz, such as socks, small towels or cloths, tissue papers or napkins, and sandals.

Q: Can I customize the arm’s behavior?
A: Yes, you can customize the arm’s behavior, including designating where the objects should be placed when picked up.

Q: Are there any safety measures in place?
A: Yes, the vacuum features a child lock, a safety stop button, and measures to prevent the mechanical arm from deploying when it’s blocked.

Consistent Characters Wow

Phil
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0
Consistent Characters Wow

Introduction

The <p> tag, also known as the paragraph tag, is a fundamental element in HTML that is used to define a paragraph of text. It is used to group a series of sentences or phrases together to form a coherent block of content. The <p> tag is typically used in combination with other HTML elements, such as the <h1>, <h2>, <h3>, and <img> tags, to create a well-structured and visually appealing webpage.

Using the `

` Tag

The <p> tag is a self-closing tag, which means it does not require a closing tag. It is used to define a paragraph of text, and the text within the tag is rendered as a block-level element. This means that the text is displayed as a separate block of content, rather than being wrapped around other elements on the page.

Example Usage

Here is an example of how to use the <p> tag:

This is a sample paragraph of text.

Attributes of the `

` Tag

The <p> tag has no attributes. However, it can be styled using CSS, which allows you to customize the appearance of the paragraph on your webpage.

Conclusion

In conclusion, the <p> tag is a fundamental element in HTML that is used to define a paragraph of text. It is a self-closing tag that can be used in combination with other HTML elements to create a well-structured and visually appealing webpage. By using the <p> tag correctly, you can create a webpage that is easy to read and navigate.

FAQs

Q: What is the purpose of the `

` tag?

A: The purpose of the <p> tag is to define a paragraph of text in HTML.

Q: Can the `

` tag be used with other HTML elements?

A: Yes, the <p> tag can be used with other HTML elements, such as the <h1>, <h2>, <h3>, and <img> tags, to create a well-structured and visually appealing webpage.

Q: Does the `

` tag have any attributes?

A: No, the <p> tag does not have any attributes. However, it can be styled using CSS to customize the appearance of the paragraph on your webpage.

Q: How do I use the `

` tag correctly?

A: To use the <p> tag correctly, simply place the text you want to render as a paragraph within the opening and closing <p> tags. For example: <p>This is a sample paragraph of text.</p>.

Quantum Computing Advances But Real-World Impact Remains Elusive

Parker Lennox
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0
Quantum Computing Advances But Real-World Impact Remains Elusive

Quantum Computing: The Future of Computing or a Distant Horizon?

Quantum Computing: A Breakthrough Technology

Quantum computing is quickly becoming one of the most exciting technologies being explored today. Unlike traditional computers, which use bits that are either 0 or 1, quantum computers use qubits that can exist in multiple states at once, allowing them to solve problems much faster than current supercomputers.

Recent Breakthroughs and Challenges

Recent breakthroughs, such as Google’s success with its Willow quantum chip, show that progress is being made. However, the technology still falls short of demonstrating a quantum advantage, according to Forrester’s recently released State of Quantum Computing 2024 report.

Optimization and Quantum Computing

Optimization has emerged as a key focus in the evolution of quantum computing, especially for industries like finance and logistics. The report highlights growing competition in this area, with Q-CTRL leveraging IBM’s gate-based quantum systems to challenge the dominance of D-Wave’s quantum annealing solutions.

Quantum Technology and Hybrid Computing

The report emphasizes the growing role of quantum technology in both hybrid computing and security. By combining the strengths of quantum and classical systems, companies like IBM Quantum and Google Cirq are pioneering new methods for solving highly complex problems.

Quantum as a Service (QaaS) and Breakthroughs

The expansion of QaaS is accelerating breakthroughs, with researchers developing quantum neural networks, support vector machines, and algorithms for complex tasks like image and natural language processing.

The Future of Quantum Computing

Forrester expects stiff competition between D-Wave and Q-CTRL in the near future. "Gate-based algorithms offer the potential for greater solution speedups as qubit counts and quality increase," states Forrester in its report. "This makes Q-CTRL’s claim an interesting challenge to D-Wave’s self-proclaimed lead in optimization. We say, ‘game on’ in this important problem domain."

Conclusion

While quantum computing has the potential to revolutionize various industries, it is essential to acknowledge the challenges and limitations that remain. As the technology continues to evolve, it is crucial for companies to proactively prepare to integrate and leverage quantum technology by enhancing their readiness in high-performance computing and security.

FAQs

Q: What is the current state of quantum computing?
A: Quantum computing is still in its early stages, with significant challenges and limitations remaining.

Q: What are the potential applications of quantum computing?
A: Quantum computing has the potential to revolutionize various industries, including finance, logistics, and healthcare.

Q: Is quantum computing the future of AI?
A: Quantum computing can be a powerful tool for AI, but it is not the only way to achieve artificial intelligence.

Q: Who are the major players in the quantum computing industry?
A: Major players in the quantum computing industry include Google, IBM, D-Wave, and Rigetti Computing.

Q: What are the challenges facing quantum computing?
A: Quantum computing faces significant challenges, including scalability, error rates, and the need for significant resources.

New Zealand Sheep Farmer Predicted AI Doom 161 Years Ago

Emerson Blake
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0
New Zealand Sheep Farmer Predicted AI Doom 161 Years Ago

The Prophetic Warnings of Samuel Butler

The text anticipated several modern AI safety concerns, including the possibility of machine consciousness, self-replication, and humans losing control of their technological creations. These themes later appeared in works like Isaac Asimov’s The Evitable Conflict and the Matrix films.

A Model of the Past

A model of Charles Babbage’s Analytical Engine, a calculating machine invented in 1837 but never built during Babbage’s lifetime.

Butler’s Vision of Machine Evolution

In a letter, Butler dug deep into the taxonomy of machine evolution, discussing mechanical "genera and sub-genera" and pointing to examples like how watches had evolved from "cumbrous clocks of the thirteenth century"—suggesting that, like some early vertebrates, mechanical species might get smaller as they became more sophisticated. He expanded these ideas in his 1872 novel Erewhon, which depicted a society that had banned most mechanical inventions. In his fictional society, citizens destroyed all machines invented within the previous 300 years.

Reactions and Legacy

Butler’s concerns about machine evolution received mixed reactions, according to Butler in the preface to the second edition of Erewhon. Some reviewers, he said, interpreted his work as an attempt to satirize Darwin’s evolutionary theory, though Butler denied this. In a letter to Darwin in 1865, Butler expressed his deep appreciation for The Origin of Species, writing that it "thoroughly fascinated" him and explained that he had defended Darwin’s theory against critics in New Zealand’s press.

The Significance of Butler’s Vision

What makes Butler’s vision particularly remarkable is that he was writing in a vastly different technological context when computing devices barely existed. While Charles Babbage had proposed his theoretical Analytical Engine in 1837—a mechanical computer using gears and levers that was never built in his lifetime—the most advanced calculating devices of 1863 were little more than mechanical calculators and slide rules. Butler extrapolated from the simple machines of the Industrial Revolution, where mechanical automation was transforming manufacturing, but nothing resembling modern computers existed. The first working program-controlled computer wouldn’t appear for another 70 years, making his predictions of machine intelligence strikingly prescient.

Some Things Never Change

The debate Butler started continues today. Two years ago, the world grappled with what one might call the "great AI takeover scare of 2023." OpenAI’s GPT-4 had just been released, and researchers evaluated its "power-seeking behavior," echoing concerns about potential self-replication and autonomous decision-making.

A Modern Relevance

GPT-4’s release inspired several open letters signed by AI researchers and tech executives warning of potential extinction-level risks posed by advanced artificial intelligence. One of the letters, reminiscent of fears about nuclear weapons or pandemics, called for a global pause on AI development. Around the same time, OpenAI CEO Sam Altman testified of AI dangers in front of the US Senate.

Conclusion

Samuel Butler’s warnings about the potential dangers of machine intelligence were eerily prescient, anticipating concerns that continue to this day. His work serves as a reminder of the importance of considering the long-term consequences of technological advancements and the need for ongoing debate and discussion about the responsible development of AI.

FAQs

  • What were Samuel Butler’s concerns about machine evolution?
    Butler’s concerns revolved around the potential for machines to evolve beyond human control and potentially pose a threat to humanity.
  • What was the significance of Butler’s work?
    Butler’s work was significant because he predicted the potential dangers of machine intelligence in a time when computing devices barely existed, making his predictions strikingly prescient.
  • What are the modern implications of Butler’s work?
    The modern implications of Butler’s work are that the concerns he raised about machine intelligence are still relevant today, and there is a need for ongoing debate and discussion about the responsible development of AI.
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