Vahan.ai, an AI-powered recruiter of blue-collar workers, has secured $10 million in funding led by Silicon Valley billionaire Vinod Khosla’s Khosla Ventures, bringing artificial intelligence to a hiring segment in India long dominated by mom-and-pop operators.

AI Startup Helping Hire Gig Workers Gets Vinod Khosla Backing - Bloomberg

The VAHAN is an assault rifle of Soviet/Armenian origin and was designed by Engineer and Firearms Designer Vahan S. Manasian. It can be fitted with a GP-30 grenade launcher, bayonet and scope. The weapon dates back to 1952 as the MBC-2 prototype when Manasian was a soldier in the Soviet Army.

VAHAN (firearm) - Wikipedia

Vahan joined NREL in October 1994 and has served many roles over the years. He is currently serving as Chief Engineer at NREL’s Power Systems Engineering Center and as an IEEE Fellow. His contributions to NREL research have been recognized through the NREL Hubbard award and multiple Outstanding ...

Vahan Gevorgian | NREL

Vahan, an Armenian who had been the garrison commander of Emesa and served as magister militum per Orientem during the Byzantine–Sasanian War of 602–628, was the overall field commander at the Yarmuk. While Vahan and part of his forces avoided destruction in the battle itself, they were ...

Vahan (Byzantine commander) - Wikipedia

We cannot provide a description for this page right now

VAHAN 4.0 (Citizen Services) ~onlineapp01~135~8014

|| V A H A N | National Register e-Services | Ministry of Road ...

VAHAN 4~152~9

Various services related to new/old driving licence or learner&#x27;s licence like Appointment Booking, Duplicate driving licence, Application Status, Online test for learner&#x27;s licence, etc · Drivers/ Learners License

Home | Parivahan Sewa | Ministry of Road Transport ...

I run a company called Vahan (Khosla Ventures backed) where we’re building India’s largest labor marketplace for the gig-economy. I have a Masters in Artificial Intelligence from Columbia University.

Vahan: Building a labor marketplace for the gig-economy in India ...

Look up vahan in Wiktionary, the free dictionary.

Vahan - Wikipedia

Vahan

vahan

Elon Musk’s Next Logical Move — Electric Airplanes — Modular Strategies for Sustainable Global Mobility In Australia, Elon Musk built an electric battery plant that can back up the grid within milliseconds. And he’s losing money with it. Tesla’s 100 MW/129MWh system near Jamestown, Australia provides backup power for South Australia’s energy grid. because the grid only measures electricity pumped into the system a full 6 seconds after a breakdown event. Powerpack 30–40% of the electricity it provides goes unpaid The Powerpack farm responds in milliseconds: 0.14 seconds the last time it had to back up the grid. Why lose money instead of adding in an artificial 6 second delay? Because Elon Musk’s next project is beautiful. But it’s not about airplanes. Next project: Provide a huge number of low income households with electric batterypacks, and due to the very low latency, connect them over some distance (it’s likely they would be in the same area) into a mega-plant. A plant much larger than the Jamestown Powerpack, which is already the largest in the world, and already essential to Australia’s electric grid. South Australia’s premier Steven Marshall has already voiced opinions against the 250 MW/650 MWh virtual power plant, which would unite 50,000 low-income residential units outfitted with Powerwall 2 systems. The project, however, is brilliant. You give low-income households not just energy independence, but a means to generate income while stabilizing the electric grid of an entire country, nay, continent, and you create a modular and scalable pathway to global energy independence. Musk’s company would likely act as a legal intermediary between individual households and the grid, and would also likely take a percentage, but the rest would go to low-income individuals as much needed financial stability. A background buzz of solar paychecks slowly but surely trickling into disadvantaged areas. Beautiful. And once the project itself is completed, it becomes proof of concept for a much better kind of battery array. One that doesn’t need to be out in the desert. It can be implemented anywhere, and all of a sudden suburbia becomes low-latency solar heaven, and low-income areas globally become the world’s electricity producers at the speed that battery packs can be manufactured. But this is just the first half of the plan. The second half is much better. From a small business, entreperenurial and especially social enterprise and collaborative production point of view, . once low-income neighborhoods are net energy producers, they can organize, cooperate and collaborate in countless other ways Further technology investments can be made at much lower costs, because there is a local and independent electricity grid to support them, which leads to development, local production and especially peer production and cultural development, and even local trade. I’m not going to go too deep down the path of what is possible there, but it opens up all sorts of avenues at the level of inter-community projects and development over larger areas. With the electricity costs for 3D printing covered, . The only thing left to do is for someone to invest in a technological pathway for bioplastics instead of biodesel, and . local production of goods in a globally distributed knowledge-ecosystem becomes possible both 3D printing and solar electricity become self-replicating with a production cost of zero But that’s another story. Back to the future of airplanes now. Musk is already working on cars, rockets and underground transportation. That covers all modes of travel except for planetary flight. If you have the technology for space travel, you can engineer your way to airplanes. And in this case, there are intermediary steps. Flying cars are a few years away, and self-driving cars are more or less already happening. Both will require AI, and Tesla already has the niche learning cycles for self-driving. Musk’s deep learning company, OpenAI, is developing AI in the general sense — the sense that, when mature enough, allows you to take deep learning and apply it to any niche. Such as going from self-driving cars to self-flying cars, which I wager is a simpler technology, and in terms of physics, easier to handle than landing the Falcon Heavy after atmospheric re-entry. Flying cars are a step up towards general flight, but they still require individual ownership, or rental in urban areas. Planetary flight as a factor of sustainable global mobility requires two things: a sustainable global battery array for solar, and electric commercial airplanes. The airline industry predicts . 10 years until commercial airliners could go fully electric The big performance hiccup is battery power. Tesla’s home field. US-based startup Wright Electric is . Every other electric plane being designed has much smaller capacity, with Boeing’s 12 passenger plane produced by Seattle startup Zunum Aero being the most spacious. NASA, too, is in the game, with its X-57 two-seater. working to create a 120–186 seat electric aircraft with a 335 mile range Airbus Vahana Airbus has two projects, the Vahana (single seat) and the CityAirbus (four seats), both self-piloted and both meant for urban use. Vertical take-off, obstacle avoidance and complex navigation: when coupled with low operating costs, Airbus plans on competing with taxis in urban spaces. I’m going to cut to the chase. The world is obviously moving towards electric airliners. If you Google it, you can feel the trend. Green, low-noise, friendlier near or within cities, it’s a huge business move. And the only company with the battery tech to actually lead in this space is Tesla. It’s not just the technology onboard the airplane, it’s the whole ecosystem. How you charge planes, where the energy for charging comes from, and how it is stored. Tesla has the end to end technology to pull it off and do it sustainabily, which is the key metric for marketing this in the future. If the airplane is electric, but the electricity used to charge it is not sustainable, you haven’t really pulled off sustainable flight. Tesla’s infrastructure would be not just sustainable, but grid-independent. Once Tesla has the modular global battery array it’s aiming to build, with individual home units connected into giant virtual power plants, the next logical move are electric airplanes. If you had to bet, would you say Elon Musk is aware of this? He probably has been for a while. It would be a natural offshoot of SpaceX. And once Tesla has commercial electric flight, it no longer needs to monetize electricity by relying on the antiquated power grid. In fact, even running battery plants like the one in South Australia at a loss, and building as many of them as possible as fast as possible would be a winning move. Building the momentum and political cred to obtain various form of investments and subsidies for turning entire low-income areas into net energy producers and instant-acting, smart virtual power plants. Then change the whole game by adding a different layer of monetization and technology. They would turn low-income neighborhoods into dynamic equity holders in global mobility. And make zero-emissions global mobility possible. The gains in prosperity and collaborative potential for the species, on a global scale, starting with otherwise segregated low-income communities would be immense. At some point in the process, self-flying commercial planes and an AI flight grid for both self-flying cars and planes becomes the status quo, but that’s another story. There’s a lot of ‘other stories’ here. Such as, Musk is looking to upend the electricity storage and production monetization scheme anyway, regardless of electric commercial flight. It’s essential for Tesla’s lifestyle proposition — turning electricity charging for Tesla cars from reliable and financially sound into limitless and abundant. The power grid runs what? Homes and transportation mostly, from a consumer standpoint. In a more general sense: businesses, civic infrastructure, social entertainment and social services. Tesla is connecting the consumer part, homes and transportation interlinked in one electric continuum. Into a self-assembling modular mesh of independent and smart electricity generation and storage units. Once it’s done that, it has its own economy, and right now, it’s approaching this equation from all sides. Providing perfect solutions on the consumer end, innovating and providing services in a globally-scalable manner on the power grid end. There is not one piece of the puzzle for electric commercial flight that isn’t already moving in place for Tesla. And there is not a single company that is anywhere close to having as many pieces aligned, not Airbus, not NASA, not Boeing, and none of the startups entering the playing field. It’s the perfect power move.

Elon Musk’s Next Logical Move — Electric Airplanes — Modular Strategies for Sustainable Global…

RideSharing is the economic powerhouse driving autonomous vehicles to take flight. Lyft’s self driving car and Lilium Jet The RideSharing model changed everything we knew about mobility. Packed metro rides and graffiti covered busses transformed for many into hailing a cab. As the driver begins to become less important, and billions of dollars go into autonomous systems, the car itself is evolving. Many people today talk about how the end goal is self driving cars, but the technology will quickly surpass self driving and take flight. Lulium Jet I sat at the Launch of Ola Play in Bangalore, India. I was the only foreigner, half crazed and sleep deprived. Ola had just announced the first in-car computing platform custom designed for RideSharing. With only an hours sleep, I watched history unfold before me. “To date, to be very honest,” Amit Sagar, of Mahindra explained, “Every single innovation has been focused towards the driver. But now the time has come to focus our attention on the passenger sitting in the rear seat.” For the first time in history, an automaker stated publicly that the passenger was becoming more important than the driver. The VP sales continued in Jobsian fashion, “We are at an inflection point, and with this we will lead a new story going forward.” Mahindra, one of India’s largest automakers, had just revealed that vehicle design is changing forever, with focus now shifting to the passenger. In countries like India, most of the population will never own a car. DARPA Funded has partnered with Uber (Aurora) Aurora In the past, flying cars were expensive dreams. It would be prohibitively expensive to have flying cars until a new economic model arose, RideSharing. The RideSharing model meant that one vehicle could have many trips, operating at near maximum efficiency. This means that the cost of the vehicle can be distributed across thousands of rides. RideSharing enabled mobility for people in India that could never afford a car. This same RideSharing model means that flying cars have just found a way to become affordable for the masses. In fact, Flying cars appear so lucrative, RideSharing companies like Uber and Lyft have been partnering publicly (and through the grapevine) on deploying Vertical Take Off and Landing vehicles (or VTOL, as the industry prefers). Uber released a white-paper in 2016 with astounding numbers. A VTOL trip from San Francisco to San Jose will soon become competitive with a cab ride. Uber Elevate Even more interestingly, because much of the cost associated with operations are human, (maintenance, etc.) VTOL RideSharing will be economically feasible even in places like India: Uber Elevate I have personally ridden in an Ola Cab from Gurgaon to Connaught Place in Delhi (after too much to drink in Cyber City). Rs 1000 (~$15 USD) for a flying car would become extremely popular. Uber is planning tests in Dubai and Dallas, where they held their secretive, Invite only Elevate Conference. Ehang at CES Las Vegas (Author) On the other side of the Earth in China, companies like Ehang are racing ahead on their own. At CES 2017 in Las Vegas, a representative told me that Ehang and Uber were “In Conversation”. In my opinion there is no reason to partner. Whomever owns the autonomous VTOL infrastructure will benefit from its revenues. Handing that off to another company like Uber doesn’t make sense. This idea is validated by the fact that OEMs like Tesla, and Volkswagen have publicly stated their self driving cars will not be allowed on Uber. The RideSharing industry is slowly fragmenting as automakers build their own RideSharing Networks. On the other hand, RideSharing companies like Uber, Lyft, Gett, and Waymo are actively developing their own Autonomous Technologies. Whomever owns the infrastructure, owns the revenues from an autonomous network. Uber and Lyft, by developing their own hardware, are actively preparing themselves for a future of flight. Lilium As technologies surrounding autonomous systems, battery technology, and robotic flight are converging, dozens of companies are building flying cars. The technology exist right now in prototype and in the air. DARPA funded Aurora and German Lilium jet have become top contenders using electric ducted fan motors. Joby, Kitty hawk, and Zee.Aero are all coming out of Silicon Valley, building working prototypes (the latter two are funded directly by Google’s Larry Page.) Governments and Venture capitalists are pouring money into VTOL. While most VTOL startups are still in“stealth”, according to Crunchbase, Lilium alone has raised over $101.4 Million USD. Massive funding in autonomous vehicle hardware and software is further lowering the cost of deploying VTOL infrastructure. Computer vision software, and robotic hardware are converging so quickly that even the big, slow moving aerospace companies are getting involved. AirBus has spun off two projects, Pop.up and Vahana. Vahana, in ancient Sanskrit means “one that carries” appearing in the Bhagavad Gita thousands of years ago. The ancients believed in flying cars, now investors do too. AirBus Vahana Money, technology, and demand exists right now to make flying cars economically feasible. What we are waiting for is legislation. Existing Ride Sharing companies already have a foot in rapidly developing countries’ legal systems (RideSharing is still illegal in most places, but governments turn a blind eye.) Uber has even been accused of bribery. (Wall Street Journal Via Engadget) As more “developed” countries await legislation around licensing autonomous flight, places like Dubai and China are pushing forward with live tests. It is not difficult to imagine that in the next 3 years, VTOL RideSharing will be very real in many places. Unintended Consequences The consequences of autonomous flight will also be very real. Many claim that autonomous cars alone may drastically reduce property value in cities ( ). Curbed explains that developers like AvalonBay in Los Angeles are already designing for an autonomous future. As self driving cars reduce the need for parking garages, AvalonBay is designing their garages to be converted into housing. Curbed has done a great article on this While autonomous cars may help developers maximize urban space, VTOL could accelerate de-urbanization for those who can afford it. If VTOL can expand in cost and access faster than city populations can grow, property may begin to devalue as the ultra-rich move away from city center. Autonomous systems are going to fundamentally change the way our cities are designed. Lilium Why would anyone live in the city, if they could bypass the chaos of traffic and live somewhere more affordable? Lilium estimated their VTOL jet will put much of Marin County (North of San Francisco) Into range of downtown. It is not difficult to imagine climbing to the roof of your Marin County home, awaiting a VTOL to San Francisco. Your Apple watch pings, and a shining electric VTOL settles onto your solar landing pad. The electric Vahana or Lilium jet takes off and lands in SoMa ten minutes later. If Uber’s Elevate white paper is accurate, it may not only be more affordable, but faster to commute from somewhere like Marin, than to take a cab through Silicon Valley. VTOL could drastically enhance the value of land around our cities, and make center city “uncool” to live. Deurbanization VTOL Oasis (Author) In the 1920’s, when airplane ranges were still small, futurists imagined giant floating islands built at sea, facilitating “hops” across the Atlantic. As the range of VTOL increases, we can begin to imagine flying VTOL instead of a commercial airline. A trip between Los Angeles and Las Vegas could be cut in half by VTOL “oasis” hidden amongst the Sierra Nevada Mountains. We can begin to imagine these new cities arising in the middle of nowhere, strategically positioned between points of interest. The drive for humans to escape cities, and the affordability of VTOL just might replace the Jet age. As new RideSharing models quickly prove feasibility, investors and startups are racing for what lies beyond self driving cars. If legislation can keep pace with technology, our skies, and cities will quickly transform into something new. As technology continues to accelerate at the exponential pace in which we are experiencing, VTOL may quickly evolve into affordable access to outer space. Garrett Kinsman Writes for Autonomous Tomorrow based out of Bangalore, India, and San Francisco. Resources: https://www.uber.com/elevate.pdf _Aurora Flight Sciences is a leader in the development and manufacturing of advanced unmanned systems and aerospace…_www.aurora.aero Aurora Flight Sciences - Apply autonomy and robotics to the development, production, and operation… _Lilium enables you to travel 5 times faster than a car by introducing the world's first all-electric vertical take-off…_lilium.com Lilium

RideSharing means Flying Cars are Here to Stay

Authors:
(1) Sasun Hambardzumyan, Activeloop, Mountain View, CA, USA;
(2) Abhinav Tuli, Activeloop, Mountain View, CA, USA;
(3) Levon Ghukasyan, Activeloop, Mountain View, CA, USA;
(4) Fariz Rahman, Activeloop, Mountain View, CA, USA;.
(5) Hrant Topchyan, Activeloop, Mountain View, CA, USA;
(6) David Isayan, Activeloop, Mountain View, CA, USA;
(7) Mark McQuade, Activeloop, Mountain View, CA, USA;
(8) Mikayel Harutyunyan, Activeloop, Mountain View, CA, USA;
(9) Tatevik Hakobyan, Activeloop, Mountain View, CA, USA;
(10) Ivo Stranic, Activeloop, Mountain View, CA, USA;
(11) Davit Buniatyan, Activeloop, Mountain View, CA, USA. Table of Links Abstract and Intro
Current Challenges
Tensor Storage Format
Deep Lake System Overview
Machine Learning Use Cases
Performance Benchmarks
Discussion and Limitations
Related Work
Conclusions, Acknowledgement, and References 7. DISCUSSION AND LIMITATIONS Deep Lake’s primary use cases include (a) Deep Learning Model Training, (b) Data Lineage and Version Control, (c) Data Querying, and Analytics, (d) Data Inspection and Quality Control. We took NumPy [55] arrays as a fundamental block and implemented version control, streaming dataloaders, visualization engine from scratch. 7.1 Format Design Space The Tensor Storage Format (TSF) is a binary file format designed specifically for storing tensors, which are multi-dimensional arrays of numerical values used in many machine learning and deep learning algorithms. The TSF format is designed to be efficient and compact, allowing for fast and efficient storage and access of tensor data. One key advantage of the TSF format is that it supports a wide range of tensor data types, including dynamically shaped tensors. In comparison, the Parquet [79] and Arrow [13] formats are columnar file formats that are designed for storing and processing large analytical datasets. Unlike TSF, which is specifically designed for tensor data, Parquet and Arrow are optimized for efficient storage and querying of analytical workloads on tabular and time-series data. They use columnar storage and compression techniques to minimize storage space and improve performance, making them suitable for big data applications. However, TSF has some advantages over Parquet and Arrow when it comes to tensor data. TSF can support tensor operations and efficient streaming to deep learning frameworks. Other tensor formats [18, 52, 23, 57] are efficient for massively massively parallelizable workloads as they don’t require coordination across chunks. Tensor Storage Format key trade-off is enabling to store dynamically shape arrays inside a tensor without padding memory footprint. For example, in computer vision it is very common to store multiple images with different shapes or videos have dynamic length. To support the flexibility, minor overhead is introduced in the form of previously discussed chunk encoder that in practice we haven’t observed impact on production workloads. 7.2 Dataloader Deep Lake achieves state-of-the-art results in local and remote settings, as seen in benchmarks for iterating on large images Fig. 7. Primarily, it has been faster than FFCV [39], which claimed a reduction of ImageNet model training up to 98 cents per model training. Furthermore, Deep Lake achieves similar ingestion performance to WebDataset [19]. Deep Lake significantly outperforms on larger images. Parquet is optimized for small cells and analytical workloads, while Deep Lake is optimized for large, dynamically shaped tensorial data. Compared to other data lake solutions, its minimal python package design enables Deep Lake to be easily integrated into large-scale distributed training or inference workloads. 7.3 Future work The current implementation of Deep Lake has opportunities for further improvement. Firstly, the storage format does not support custom ordering for an even more efficient storage layout required for vector search or key-value indexing. Secondly, Deep Lake implements branch-based locks for concurrent access. Similar to Delta ACID transaction model [27], Deep Lake can be extended to highlyperformant parallel workloads. Thirdly, the current implementation of TQL only supports a subset of SQL operations (i.e., does not support operations such as join). Further work will focus on making it SQL-complete, extending to more numeric operations, running federated queries in external data sources and benchmarking against SQL engines. This paper is available on arxiv under CC 4.0 license. Authors: (1) Sasun Hambardzumyan, Activeloop, Mountain View, CA, USA; (2) Abhinav Tuli, Activeloop, Mountain View, CA, USA; (3) Levon Ghukasyan, Activeloop, Mountain View, CA, USA; (4) Fariz Rahman, Activeloop, Mountain View, CA, USA;. (5) Hrant Topchyan, Activeloop, Mountain View, CA, USA; (6) David Isayan, Activeloop, Mountain View, CA, USA; (7) Mark McQuade, Activeloop, Mountain View, CA, USA; (8) Mikayel Harutyunyan, Activeloop, Mountain View, CA, USA; (9) Tatevik Hakobyan, Activeloop, Mountain View, CA, USA; (10) Ivo Stranic, Activeloop, Mountain View, CA, USA; (11) Davit Buniatyan, Activeloop, Mountain View, CA, USA. Authors: Authors: (1) Sasun Hambardzumyan, Activeloop, Mountain View, CA, USA; (2) Abhinav Tuli, Activeloop, Mountain View, CA, USA; (3) Levon Ghukasyan, Activeloop, Mountain View, CA, USA; (4) Fariz Rahman, Activeloop, Mountain View, CA, USA;. (5) Hrant Topchyan, Activeloop, Mountain View, CA, USA; (6) David Isayan, Activeloop, Mountain View, CA, USA; (7) Mark McQuade, Activeloop, Mountain View, CA, USA; (8) Mikayel Harutyunyan, Activeloop, Mountain View, CA, USA; (9) Tatevik Hakobyan, Activeloop, Mountain View, CA, USA; (10) Ivo Stranic, Activeloop, Mountain View, CA, USA; (11) Davit Buniatyan, Activeloop, Mountain View, CA, USA. Table of Links Abstract and Intro Current Challenges Tensor Storage Format Deep Lake System Overview Machine Learning Use Cases Performance Benchmarks Discussion and Limitations Related Work Conclusions, Acknowledgement, and References Abstract and Intro Abstract and Intro Current Challenges Current Challenges Tensor Storage Format Tensor Storage Format Deep Lake System Overview Deep Lake System Overview Machine Learning Use Cases Machine Learning Use Cases Performance Benchmarks Performance Benchmarks Discussion and Limitations Discussion and Limitations Related Work Related Work Conclusions, Acknowledgement, and References Conclusions, Acknowledgement, and References 7. DISCUSSION AND LIMITATIONS Deep Lake’s primary use cases include (a) Deep Learning Model Training, (b) Data Lineage and Version Control, (c) Data Querying, and Analytics, (d) Data Inspection and Quality Control. We took NumPy [55] arrays as a fundamental block and implemented version control, streaming dataloaders, visualization engine from scratch. 7.1 Format Design Space The Tensor Storage Format (TSF) is a binary file format designed specifically for storing tensors, which are multi-dimensional arrays of numerical values used in many machine learning and deep learning algorithms. The TSF format is designed to be efficient and compact, allowing for fast and efficient storage and access of tensor data. One key advantage of the TSF format is that it supports a wide range of tensor data types, including dynamically shaped tensors. In comparison, the Parquet [79] and Arrow [13] formats are columnar file formats that are designed for storing and processing large analytical datasets. Unlike TSF, which is specifically designed for tensor data, Parquet and Arrow are optimized for efficient storage and querying of analytical workloads on tabular and time-series data. They use columnar storage and compression techniques to minimize storage space and improve performance, making them suitable for big data applications. However, TSF has some advantages over Parquet and Arrow when it comes to tensor data. TSF can support tensor operations and efficient streaming to deep learning frameworks. Other tensor formats [18, 52, 23, 57] are efficient for massively massively parallelizable workloads as they don’t require coordination across chunks. Tensor Storage Format key trade-off is enabling to store dynamically shape arrays inside a tensor without padding memory footprint. For example, in computer vision it is very common to store multiple images with different shapes or videos have dynamic length. To support the flexibility, minor overhead is introduced in the form of previously discussed chunk encoder that in practice we haven’t observed impact on production workloads. 7.2 Dataloader Deep Lake achieves state-of-the-art results in local and remote settings, as seen in benchmarks for iterating on large images Fig. 7. Primarily, it has been faster than FFCV [39], which claimed a reduction of ImageNet model training up to 98 cents per model training. Furthermore, Deep Lake achieves similar ingestion performance to WebDataset [19]. Deep Lake significantly outperforms on larger images. Parquet is optimized for small cells and analytical workloads, while Deep Lake is optimized for large, dynamically shaped tensorial data. Compared to other data lake solutions, its minimal python package design enables Deep Lake to be easily integrated into large-scale distributed training or inference workloads. 7.3 Future work The current implementation of Deep Lake has opportunities for further improvement. Firstly, the storage format does not support custom ordering for an even more efficient storage layout required for vector search or key-value indexing. Secondly, Deep Lake implements branch-based locks for concurrent access. Similar to Delta ACID transaction model [27], Deep Lake can be extended to highlyperformant parallel workloads. Thirdly, the current implementation of TQL only supports a subset of SQL operations (i.e., does not support operations such as join). Further work will focus on making it SQL-complete, extending to more numeric operations, running federated queries in external data sources and benchmarking against SQL engines. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Deep Lake, a Lakehouse for Deep Learning: Discussion and Limitations

গভীর লেক, গভীর শিক্ষার জন্য একটি লেকহাউস: আলোচনা এবং সীমাবদ্ধতা

Deep Lake, ein Lakehouse für Deep Learning: Diskussion und Einschränkungen

Deep Lake, una casa de lago para el aprendizaje profundo: discusión y limitaciones

Deep Lake, un Lakehouse pour le Deep Learning : discussion et limites

डीप लेक, डीप लर्निंग के लिए एक लेकहाउस: चर्चा और सीमाएँ

ディープラーニングのためのレイクハウス「Deep Lake」：議論と限界

딥 러닝을 위한 레이크하우스인 Deep Lake: 논의와 한계

Deep Lake, um Lakehouse para Deep Learning: Discussão e Limitações

Deep Lake, домик у озера для глубокого обучения: обсуждение и ограничения

Deep Lake, Derin Öğrenme için Bir Göl Evi: Tartışma ve Sınırlamalar

Deep Lake, Lakehouse cho Deep Learning: Thảo luận và hạn chế

Deep Lake，深度学习的 Lakehouse：讨论和局限性

Google is an excellent computational science company. To the extent that breakthroughs are possible in computer science, Google continues to push the envelope of knowledge to greater heights. However, an award that was supposed to cement that position for good exposed Google at the foundation of science. Google is not the heavyweight, at all, in theoretical science, as it is in computational science. There have been several criticisms of the award, which, aside from the possible limitations across solutions, questions of the origin of the actual science and other fundamental bases continue to cast doubt on what should have been a clear-cut victory. There is a recent feature on Scientific American, AI Comes to the Nobels: Double Win Sparks Debate about Scientific Fields, with the quotes, "I don’t think AlphaFold involves any radical change in the underlying science that wasn’t already in place. It’s just how it was put together and conceived in such a seamless way that allowed AlphaFold to reach those heights." "For example, one key input AlphaFold uses is the sequences of related proteins from different organisms, which can identify amino acid pairs that have tended to co-evolve and therefore might be in close physical proximity in a protein’s 3D structure. Researchers were already using this insight to predict protein structures at the time AlphaFold was developed, and some even began embedding the idea in deep learning neural networks." There is another feature on Harvard Medicine Magazine, Did AI Solve the Protein-Folding Problem?, with the quotes, "Plus, it’s not even safe to say the protein-folding problem is solved. One important box is ticked off: Researchers can now predict protein structures from amino acid sequences. But two other key parts of the problem — understanding the underlying physics and how proteins fold so quickly — remain unsolved." "Structure is not the final story; we want to understand much more. A few years post-AlphaFold2, there hasn’t been as much progress in treating diseases as some might have anticipated. For drug discovery, a deeper understanding of protein dynamics is essential. That includes studying how proteins behave in their natural cellular environments and why certain proteins fold incorrectly — a factor linked to many neurodegenerative diseases." "If we can predict how proteins fold without understanding how they do it, are we even legitimately doing science anymore, or is it something different? In the past, what drove us was understanding. With this wave of AI, we’re maybe losing some of that. We’re able to get the practical benefits, but we’re not necessarily gaining intellectual benefits." These are not light questions. They do not cancel the use of computational approaches, but they are questions on the basics of the science for which understanding is built—where Google is null. Aside from AlphaFold, Google Research, in part, is also working on quantum consciousness, with such a sham theory that there is no hope it would ever answer the question. Google is also using computational science to map the brain, in connectomics, with zero to negligible theory, without even daring to check, observe, infer, or question if the neuron is the basic unit of the nervous system. Google is simply following what is possible computationally, without a lead of basic science with the potential for rounded advances. Of course, there would be progress, but Google, with its massive research teams, is weak and almost completely irrelevant if answers in pure science are sought. Google is an excellent computational science company. To the extent that breakthroughs are possible in computer science, Google continues to push the envelope of knowledge to greater heights. However, an award that was supposed to cement that position for good exposed Google at the foundation of science. Google is not the heavyweight, at all, in theoretical science, as it is in computational science. There have been several criticisms of the award, which, aside from the possible limitations across solutions, questions of the origin of the actual science and other fundamental bases continue to cast doubt on what should have been a clear-cut victory. There is a recent feature on Scientific American , AI Comes to the Nobels: Double Win Sparks Debate about Scientific Fields , with the quotes, "I don’t think AlphaFold involves any radical change in the underlying science that wasn’t already in place. It’s just how it was put together and conceived in such a seamless way that allowed AlphaFold to reach those heights." Scientific American AI Comes to the Nobels: Double Win Sparks Debate about Scientific Fields "For example, one key input AlphaFold uses is the sequences of related proteins from different organisms, which can identify amino acid pairs that have tended to co-evolve and therefore might be in close physical proximity in a protein’s 3D structure. Researchers were already using this insight to predict protein structures at the time AlphaFold was developed, and some even began embedding the idea in deep learning neural networks." There is another feature on Harvard Medicine Magazine , Did AI Solve the Protein-Folding Problem? , with the quotes, "Plus, it’s not even safe to say the protein-folding problem is solved. One important box is ticked off: Researchers can now predict protein structures from amino acid sequences. But two other key parts of the problem — understanding the underlying physics and how proteins fold so quickly — remain unsolved." Harvard Medicine Magazine Did AI Solve the Protein-Folding Problem? "Structure is not the final story; we want to understand much more. A few years post-AlphaFold2, there hasn’t been as much progress in treating diseases as some might have anticipated. For drug discovery, a deeper understanding of protein dynamics is essential. That includes studying how proteins behave in their natural cellular environments and why certain proteins fold incorrectly — a factor linked to many neurodegenerative diseases." "If we can predict how proteins fold without understanding how they do it, are we even legitimately doing science anymore, or is it something different? In the past, what drove us was understanding. With this wave of AI, we’re maybe losing some of that. We’re able to get the practical benefits, but we’re not necessarily gaining intellectual benefits." These are not light questions. They do not cancel the use of computational approaches, but they are questions on the basics of the science for which understanding is built—where Google is null. Aside from AlphaFold, Google Research, in part, is also working on quantum consciousness, with such a sham theory that there is no hope it would ever answer the question. Google is also using computational science to map the brain, in connectomics , with zero to negligible theory, without even daring to check, observe, infer, or question if the neuron is the basic unit of the nervous system . quantum consciousness, with such a sham theory Google is also using computational science to map the brain, in connectomics without even daring to check, observe, infer, or question if the neuron is the basic unit of the nervous system Google is simply following what is possible computationally, without a lead of basic science with the potential for rounded advances. Of course, there would be progress, but Google, with its massive research teams, is weak and almost completely irrelevant if answers in pure science are sought.

Protein Structures | AlphaFold: Google Research Vacancy, Weakness in Fundamental Science

Solving the stablecoin dilemma is paramount to crypto’s survival and success as our industry matures. Without a secure, capitally efficient, and decentralized stablecoin, it will be extremely difficult to onboard institutional and retail capital. What are stablecoins? Stablecoins are cryptocurrencies designed to maintain a peg with another currency, asset, or financial instrument. They are most commonly used as decentralized analogs to FIAT currencies, such as USD, EUR, and GBP. This provides traders a more “stable” alternative to historically volatile assets like ETH and BTC. What is the history of stablecoins? were introduced in 2014 by BitUSD (BITUSD) and NuBits (USNBT) and they were collateralized using different baskets of cryptocurrencies. While both of these tokens were revolutionary during their time, neither have managed to succeed at the one key task they were created to carry out, to maintain a “stable” peg with the US dollar. Both stablecoins have since lost their pegs and all but faded into oblivion, but their hurdles and trajectories would foreshadow one of crypto’s biggest unsolved problems, . The first stablecoins “The Stablecoin Trilemma” The Stablecoin Trilemma dictates that no one stablecoin can satisfy all 3 conditions: Price/Peg Stability - ie. The ability to constantly be traded at 1 USD. Capital Efficiency - ie. Having to lock up ≤ 1 USD to mint 1 USD stablecoin. Decentralization - Not relying on centralized custodians to manage collateral. What are the different types of stablecoin implementations? This stablecoin implementation problem has been tackled by crypto’s best and brightest to widely varying degrees of success, however, a perfect solution has yet to be found. As such, there are multiple different implementation strategies, each with its own unique tradeoffs. Broadly, these strategies can be split into two main categories, collateralized stablecoins and algorithmic (aka. uncollateralized) stablecoins. Collateralized Stablecoins A collateralized stablecoin is any cryptocurrency that is either partially or completely backed by collateral held in a treasury or reserve. This backing helps to ensure that the value of a collateralized stablecoin does not fall below its peg. Using USDC as an example, if 1 USDC was traded below 1 USD in value, traders would arbitrage the difference by purchasing USDC and redeeming it for 1 USD each. The inherent issue with FIAT collateralized stablecoins is the centralized nature of the collateral reserves. This opens up token holders to institutional risks like bankruptcy or mismanagement of funds. Collateralized backings are not limited to FIAT currencies alone. As it stands, there are stablecoins such as DAI and sUSD which are collateralized with other cryptocurrencies (namely, ETH and SNX respectively). These stablecoins can mitigate the centralized risk factors associated with FIAT-backed stables, however, due to the inherent volatility of most crypto assets, they require over-collateralized positions (ie 4:1 for USD) in order to maintain their pegs. This system is significantly safer but also much more capitally inefficient for end users. Algorithmic Stablecoins Algorithmic stablecoins are assets that do not rely on collateral reserves to maintain their peg. Instead, they utilize smart contracts to buy, sell, or burn tokens to respond to market supply and demand. In essence, algorithmic stablecoins maintain their pegs by mechanistically controlling their circulating supplies. Examples of algorithmic stablecoins include $FRAX and $UXD. Algorithmic stablecoins are inherently decentralized and fairly capitally efficient, however, their pegging mechanisms have greater sensitivity to extreme market conditions and attacks. $FRAX operates under a fractionally collateralized system, where each $FRAX is partially collateralized by fiat-backed stablecoins and partially backed by its own token, $FXS. The Frax collateralization ratio determines the ratio between collateral (ie. USDC) and algorithm ($FXS) that makes up the $1 FRAX value. An important requirement for $FRAX to function smoothly is to have deep liquidity with other stablecoins (ie. USDC and USDT). The FRAX+3Crv Pool on Curve Finance has nearly 1.1b USD of liquidity to help ensure as little slippage as possible when making trades. $UXD is a “delta-neutral” stablecoin that works by taking deposits, such as $ETH, and using those deposits as collateral to short an equivalent amount of perpetual swap contracts or dated futures. This balanced pair of the $ETH long and 1x ETH-USD short creates a delta neutral position with a value of $1 for each $UXD. If the price of $UXD < $1, arbitrageurs can redeem their $UXD for $1 of collateral assets (currently limited to ETH, BTC, and SOL) to make a profit. Conversely, if the price of $UXD > $1, arbitrageurs can mint $UXD at a cost of $1 per $UXD to make a profit. This mechanism allows UXD to consistently maintain its peg. Currently, $UXD and $FRAX seem to be the front-runners for solutions to the as they are fully decentralized and capital-efficient. What remains is for them to continue to prove their stability over time. stablecoin dilemma Why are stablecoins important/necessary? The vision is simple: Decentralized finance requires decentralized currencies. As crypto is still an emerging market, volatility is to be expected and perhaps even welcomed. However, there must exist an on-chain alternative to “stable” FIAT currencies to act as trading pairs on DEXs and as a means to store capital long-term. About Dan Edlebeck Dan is a co-founder of Sei Network. Active in the Cosmos ecosystem, Dan led Sentinel for the past 2 years. Over the past five years, Dan has held several roles within crypto organizations including leading marketing for Blockparty and operating a PR agency, deedle connects. He is a strong advocate for the power of an inclusive financial system built on web3 infrastructure. About Sei Network is the first orderbook-specific L1 blockchain. It is built using the Cosmos SDK and Tendermint core and features a built-in central limit orderbook (CLOB) module. Decentralized applications building on Sei can build on top of the CLOB, and other Cosmos-based blockchains can leverage Sei's CLOB as a shared liquidity hub and create markets for any asset. Designed with developers and users in mind, Sei serves as the infrastructure and shared liquidity hub for the next generation of DeFi. Apps can easily plug-and-play to trade on Sei orderbook infrastructure and access pooled liquidity from other apps. To prioritize developer experience, Sei Network has integrated the wasmd module to support CosmWasm. Sei Network By Dan Edlebeck Sei Network’s

Crypto’s Stablecoin Throne is Vacant

What's your background, and what are you working on? I’m Satyam Vaghani, Senior Vice President and General Manager of IoT and AI at Nutanix. I joined Nutanix in 2016, when they acquired PernixData, a storage virtualization software company that I co-founded and served as CTO. I started my career as an early engineer at VMware and went on to become Principal Engineer and Storage CTO. I have a Master’s degree in computer science from Stanford and a Bachelor’s degree in computer science from Birla Institute of Technology and Science, Pilani. At Nutanix, I lead product and business for an edge computing stack and IoT application platform that enables people to build and operate new-generation, AI-based IoT apps across planet-scale edge infrastructure. We’ve generated revenue across retail, manufacturing and telecom verticals, and across the world. anix? What motivated you to get started with this IoT initiative at Nut Nutanix is on a mission to make computing invisible everywhere. A key aspect of that mission is to be on the lookout for major shifts in where computing is deployed and why it is so. Towards the end of 2016, many Nutanix customers made it obvious to us that we will see an explosion in the number, significance, and complexity of computing deployed in the real world -- at the edge -- at airports, hospitals, retail stores, factories, oil rigs, autonomous cars and drones, close to 5G base stations, and so on. Analyst reports concluded the same -- that by 2020, we could potentially see 3 orders of magnitude more devices and 40 times more data being created at the edge than all the public and private clouds of the world combined. This was an interesting problem to solve for many reasons… For one, nobody has solved it (although many people are trying). We like such broad, category-creating challenges. We’ve done it once already back when Nutanix was founded. We made and then led the market for hyperconverged infrastructure. While that business chugs along, it is time to build something spectacular again. Secondly, the edge is a major shift in compute paradigm. Computers in public and private data centers often serve “human-oriented” use cases -- we care about email, working on spreadsheets (yeah, right!), surfing the web, and social media and so on. Computers on the edge most-often serve “machine-oriented” use cases -- the edge at a factory processes real-time machine telemetry data to further optimize those machines, and so on. The operating system and services for machine-oriented computing, and distributed across 100s or 1000s of edges has to be fundamentally different from the operating system for humans. This is an interesting design problem. Finally, we often marvel at the grandeur of computer systems we have created so far. Now, imagine creating computer systems that need to process 40 times more data than all of that. What went into building the initial product? We spent roughly two months researching the market and devising the product strategy, another three months designing the product and the next 15 months creating 1.0 via a series of agile sprints. In parallel, we ran a beta program with the Global 2000 customers across a variety of verticals to validate our approach and patch any unforeseen gaps in product requirements. At the time of shipping 1.0, we had people using it in production. As is typical with these things, we had a very aggressive schedule and we were very tight on staffing. One key learning from the journey was that the product team achieved a much higher level of productivity because they were almost always directly involved in a customer beta and hence felt naturally motivated and clear about our purpose, timelines, and goals. Are there any specific industries you’re focusing on right now? What are some of the most interesting use cases for IoT you have seen? We focus on retail, manufacturing, smart cities and telecom. In retail, customers often tend to leverage us to create new experiences like cashier-less checkout systems. Manufacturing is all about factory automation, predictive maintenance of factory machinery, and automated product quality inspection. Smart cities are an amalgamation of retail and manufacturing design patterns with added complexity layers around privacy, scale, data governance, etc. Finally, telecom is about modernizing the communications edge, and potentially monetizing it in light of upcoming 5G applications. How have you attracted customers and grown the initiative? One clear source of traction is our existing 13,000 odd Nutanix customer base, and more specifically, the 800-900 of Global 2000, government and service provider customers. The Edge, IoT and AI offering is a natural step in their journey with Nutanix as they grow beyond private datacenter based use cases to multi-cloud use cases and workloads incorporating new concepts like micro-services based application architecture and AI-based data processing. What's your business model? I won’t go into details for obvious reasons. But a few things that stand out doing the IoT and Edge business are that there is a marked lack of industry-wide statistics in this space to plan or measure one's success against the industry as a whole. This is not surprising because we are still in the process of making the market. However, it makes revenue and resource planning much harder. The relative nascency of use cases makes product planning much harder too. What are your goals for the future? Unlike a business where one sells CRM software or databases or storage systems, this is a business where the requirements from your product vary highly by vertical. What a retailer needs from this product is dramatically different from what an oil and gas services company needs or automotive manufacturer needs and so on. Our main goal is to understand each of these markets much more deeply so we can tailor our product and business to the market. A related goal is to train our employees to have deep empathy for the “customer-business-downwards” way of relating and selling to our customers, as opposed to the more frequent “vendor-technology-upwards” way. What's your advice for entrepreneurs who are just starting out? There are many things that come to mind, but I’ll share two: One, if this is your first go at entrepreneurship, chances of success are higher if you focus on a product and market that you already know very well. Entrepreneurship has been romanticized around having earth shattering ideas that no one else thought of. But a majority of startups and entrepreneurs are about the disciplined pursuit of a market that they are rightfully good at addressing. Yes, we all marvel at the occasional AirBnB or Slack or Facebook, but one must realize that the chances of that happening are the same as the chances of Tom Brady winning at Wimbledon on his first attempt. Two, don’t do it for the money. The challenge and requirements of the entrepreneurship journey are not to be trivialized. Do it for the stories you want to live to tell, and the people you want to tell them with. Where can we go to learn more? Visit to learn more about Nutanix’s Xi IoT initiative, and to start a free 10-day trial to experience the IoT application development in a cloud environment. www.nutanix.com/products/iot You can also follow Nutanix IoT on . Twitter This interview is brought to you by , a tool to schedule and automatically repeat your posts on Facebook, Instagram, Twitter, Pinterest, LinkedIn, and OneUp Google My Business

Founder Interviews: Satyam Vaghani of Nutanix

React has solidified its position as a powerhouse for building dynamic and interactive user interfaces. In this article, we've curated a list of ten outstanding GitHub repositories that will not only enhance your understanding of React but also empower you to become a React master. Under the Hood: React 🔍 This repository contains an explanation of the inner work of React with "Under the Hood React JS":🔗 https://github.com/Bogdan-Lyashenko/Under-the-hood-ReactJS React Developer Roadmap 🗺️ Navigate your React development journey with the "React Developer RoadMap": 🗺️ Below, you can find a chart demonstrating the paths that you can take and the libraries that you would want to learn to become a React developer.🔗 https://github.com/adam-golab/react-developer-roadmap Awesome React Components & Libraries 💡 Discover a treasure trove of "Awesome React Components":🔗 https://github.com/brillout/awesome-react-components redux-auth-wrapper 🧪 Decouple your Authentication and Authorization from your components with Higher-Order Components - "Hocs":🔗 https://github.com/mjrussell/redux-auth-wrapper Beautiful React Hooks 🪝A collection of tailor-made React hooks to enhance your development process and make it faster.🔗 https://github.com/antonioru/beautiful-react-hooks React+TypeScript Cheatsheets 📚 Quick reference "React + TypeScript CheatSheets" for your coding adventures. These are cheat sheets for experienced React developers getting started with TypeScript. You can use this cheat sheet for reference at any skill level, but a basic understanding of React and TypeScript is assumed.🔗 https://github.com/typescript-cheatsheets/react 30 Seconds of React ⏲️ Get snappy insights with "30 Seconds of React":🔗 https://github.com/Chalarangelo/30-seconds-of-react React Redux Links 🔗 Dive deep into React state management with "React Redux Links":🔗 https://github.com/markerikson/react-redux-links React Bits 🔩 A compilation of React Patterns, techniques, tips and tricks.🔗 https://github.com/vasanthk/react-bits Bullet Proof React 🛡️ ⚛️ A simple, scalable, and powerful architecture for building production-ready React applications.🔗 https://github.com/alan2207/bulletproof-react Publishing To GitHub in 3 Steps With Visual Studio Code https://www.youtube.com/watch?v=0pzFGM272fs&embedable=true React has solidified its position as a powerhouse for building dynamic and interactive user interfaces. In this article, we've curated a list of ten outstanding GitHub repositories that will not only enhance your understanding of React but also empower you to become a React master. Under the Hood: React 🔍 This repository contains an explanation of the inner work of React with "Under the Hood React JS":🔗 https://github.com/Bogdan-Lyashenko/Under-the-hood-ReactJS https://github.com/Bogdan-Lyashenko/Under-the-hood-ReactJS React Developer Roadmap 🗺️ Navigate your React development journey with the "React Developer RoadMap": 🗺️ Below, you can find a chart demonstrating the paths that you can take and the libraries that you would want to learn to become a React developer.🔗 https://github.com/adam-golab/react-developer-roadmap https://github.com/adam-golab/react-developer-roadmap Awesome React Components & Libraries 💡 Discover a treasure trove of "Awesome React Components":🔗 https://github.com/brillout/awesome-react-components 💡 https://github.com/brillout/awesome-react-components redux-auth-wrapper 🧪 Decouple your Authentication and Authorization from your components with Higher-Order Components - "Hocs":🔗 https://github.com/mjrussell/redux-auth-wrapper https://github.com/mjrussell/redux-auth-wrapper Beautiful React Hooks 🪝A collection of tailor-made React hooks to enhance your development process and make it faster.🔗 https://github.com/antonioru/beautiful-react-hooks https://github.com/antonioru/beautiful-react-hooks React+TypeScript Cheatsheets 📚 Quick reference "React + TypeScript CheatSheets" for your coding adventures. These are cheat sheets for experienced React developers getting started with TypeScript. You can use this cheat sheet for reference at any skill level, but a basic understanding of React and TypeScript is assumed.🔗 https://github.com/typescript-cheatsheets/react https://github.com/typescript-cheatsheets/react 30 Seconds of React ⏲️ Get snappy insights with "30 Seconds of React":🔗 https://github.com/Chalarangelo/30-seconds-of-react https://github.com/Chalarangelo/30-seconds-of-react React Redux Links 🔗 Dive deep into React state management with "React Redux Links":🔗 https://github.com/markerikson/react-redux-links https://github.com/markerikson/react-redux-links React Bits 🔩 A compilation of React Patterns, techniques, tips and tricks.🔗 https://github.com/vasanthk/react-bits https://github.com/vasanthk/react-bits Bullet Proof React 🛡️ ⚛️ A simple, scalable, and powerful architecture for building production-ready React applications.🔗 https://github.com/alan2207/bulletproof-react https://github.com/alan2207/bulletproof-react Publishing To GitHub in 3 Steps With Visual Studio Code Publishing To GitHub in 3 Steps With Visual Studio Code https://www.youtube.com/watch?v=0pzFGM272fs&embedable=true https://www.youtube.com/watch?v=0pzFGM272fs&embedable=true

10 GitHub Repositories to Become a React Master

There’s no such thing as a Free lunch. But what if there’s a thing called Free launch that gives you… A) better features than your major VC-funded competitors B) unlimited usage C) an impression that the founders must be out of their mind? And what if they use COMPETITORS BRANDING in their MARKETING… …and get a notice from a competitor’s legal team as a result? I don’t know about you but to most people his might seems like an risky-bold-orangutan move to start your SaaS with. Especially if the founders are playing with the own money and have 100% skin in the game. But let’s see why it’s actually brilliant & likely leads to a CHECKMATE: So there are several things going on here that I like: Founders mean real people, and they’re going against VC funded big boys.
FREE after FREE vs paying several hundred dollars per month
The reference to competitors as incumbents (anyone with a permanent vacancy in a market is likely to get too comfortable in time) So my limbic brain is now on overdrive with dopamine - I need to justify taking action with some logic: This is pretty much all the logic I need. But I’m a true skeptic so… …if I’m a user in one of these incumbents’ platforms - how do I know that this is the real deal? I need to check if others have successfully migrated to this platform. PLUS I don’t trust any reviews on company’s own testimonial sites if they’re not in video format. (Commslayer, this is your CTA. Faces are good, videos are better - combined in building social proof they do MAGIC for conversion rates) So I have to check 3rd party sites like shopify app store. (I get reminded where I’m coming from on their website btw) I click on that ‘Install on Shopify’ -button and here’s what I find: My conclusion is they’re still in the early stages in terms of customer adoption. So we might need a larger simple size to make a more detailed analysis. But we can also dive into the quality of the reviews. One of them, from ‘bldblz’ (US), reads as follows: ‘‘Was evaluating this vs Gorgias and the value and product offering this has is leaps and bounds better for a way better price. If you're looking for a customer support platform with all the essentials you need, look no further than Commslayer! If I could give this 100 stars I would!’’ 100 stars is 20X than what’s allowed, so this user must be pretty happy. Let’s check another one, this time from Patina (DEN): I was about to write "by far the best value for money support platform," but that seems silly when it's totally free.
So, instead, I'll just write that it's the best support platform of all the platforms we've used, and we've been through a few: both Gorgias (too expensive), Re:amaze (buggy UI), and DelightChat (also buggy UI). CommSlayer is my favorite — by far.
My current favorite feature is the Superhuman-like keyboard shortcuts that allows me to go through tickets blazingly fast Both of these are what I would call raving fans in marketing lingo. Maybe this is the real deal? One thing is for sure in SaaS: People can’t resist the Power of Free if your offer is GOOD. And it seems to be the case here: Now migrations are typically PITA (pain-in-the-ass). You don’t want to do one unless you absolutely have to. But it doesn’t come to me as a surprise by now that… smoothing this pain is covered on the migration page as well: This strategy is straight out of Alex Hormozi’s playbook with a nice SaaS twist: Give more value to the market than anyone else, for free.
Mock people who charge ridiculous amounts for products that you give away for free
Delay making money as long as you can while you keep giving value and serving your core market Prediction: Competition intensifies (but is it competition if some alternatives are Free???) A flood of Shopify merchants will migrate from incumbents. This is a CHECKMATE move with the Power of Free in SaaS Helpdesk niche. Well played! There’s no such thing as a Free lunch. But what if there’s a thing called Free launch that gives you… A) better features than your major VC-funded competitors B) unlimited usage C) an impression that the founders must be out of their mind? And what if they use COMPETITORS BRANDING in their MARKETING… …and get a notice from a competitor’s legal team as a result? I don’t know about you but to most people his might seems like an risky-bold-orangutan move to start your SaaS with. Especially if the founders are playing with the own money and have 100% skin in the game. But let’s see why it’s actually brilliant & likely leads to a CHECKMATE: So there are several things going on here that I like: Founders mean real people, and they’re going against VC funded big boys. FREE after FREE vs paying several hundred dollars per month The reference to competitors as incumbents (anyone with a permanent vacancy in a market is likely to get too comfortable in time) Founders mean real people, and they’re going against VC funded big boys. FREE after FREE vs paying several hundred dollars per month The reference to competitors as incumbents (anyone with a permanent vacancy in a market is likely to get too comfortable in time) So my limbic brain is now on overdrive with dopamine - I need to justify taking action with some logic: This is pretty much all the logic I need. But I’m a true skeptic so… …if I’m a user in one of these incumbents’ platforms - how do I know that this is the real deal? I need to check if others have successfully migrated to this platform. PLUS I don’t trust any reviews on company’s own testimonial sites if they’re not in video format. ( Commslayer , this is your CTA. Faces are good, videos are better - combined in building social proof they do MAGIC for conversion rates) Commslayer So I have to check 3rd party sites like shopify app store . shopify app store (I get reminded where I’m coming from on their website btw) I click on that ‘ Install on Shopify’ -button and here’s what I find: Install on Shopify’ My conclusion is they’re still in the early stages in terms of customer adoption. So we might need a larger simple size to make a more detailed analysis. But we can also dive into the quality of the reviews. One of them, from ‘bldblz’ (US), reads as follows: ‘‘Was evaluating this vs Gorgias and the value and product offering this has is leaps and bounds better for a way better price. If you're looking for a customer support platform with all the essentials you need, look no further than Commslayer! If I could give this 100 stars I would!’’ ‘‘Was evaluating this vs Gorgias and the value and product offering this has is leaps and bounds better for a way better price. If you're looking for a customer support platform with all the essentials you need, look no further than Commslayer! If I could give this 100 stars I would!’’ 100 stars is 20X than what’s allowed, so this user must be pretty happy. Let’s check another one, this time from Patina (DEN): I was about to write "by far the best value for money support platform," but that seems silly when it's totally free. So, instead, I'll just write that it's the best support platform of all the platforms we've used, and we've been through a few: both Gorgias (too expensive), Re:amaze (buggy UI), and DelightChat (also buggy UI). CommSlayer is my favorite — by far. My current favorite feature is the Superhuman-like keyboard shortcuts that allows me to go through tickets blazingly fast I was about to write "by far the best value for money support platform," but that seems silly when it's totally free. So, instead, I'll just write that it's the best support platform of all the platforms we've used, and we've been through a few: both Gorgias (too expensive), Re:amaze (buggy UI), and DelightChat (also buggy UI). CommSlayer is my favorite — by far. My current favorite feature is the Superhuman-like keyboard shortcuts that allows me to go through tickets blazingly fast Both of these are what I would call raving fans in marketing lingo. Maybe this is the real deal? One thing is for sure in SaaS: People can’t resist the Power of Free if your offer is GOOD. Power of Free And it seems to be the case here: Now migrations are typically PITA (pain-in-the-ass). You don’t want to do one unless you absolutely have to. But it doesn’t come to me as a surprise by now that… smoothing this pain is covered on the migration page as well: This strategy is straight out of Alex Hormozi’s playbook with a nice SaaS twist: Give more value to the market than anyone else, for free. Mock people who charge ridiculous amounts for products that you give away for free Delay making money as long as you can while you keep giving value and serving your core market Give more value to the market than anyone else, for free. Mock people who charge ridiculous amounts for products that you give away for free Delay making money as long as you can while you keep giving value and serving your core market Prediction: Competition intensifies (but is it competition if some alternatives are Free???) A flood of Shopify merchants will migrate from incumbents. This is a CHECKMATE move with the Power of Free in SaaS Helpdesk niche. Well played!

How to Checkmate in SaaS with the Power of Free

كيفية تحقيق الفوز في SaaS باستخدام قوة Free

Як паставіць мат у SaaS з дапамогай Free

Jak dát mat v SaaS pomocí síly zdarma

Cómo hacer jaque mate en SaaS con el poder de lo gratuito

איך לעשות שחמט ב-SaaS עם הכוח של חינם

無料の力でSaaSをチェックメイトする方法

SaaS'те кантип Бекер күч менен Checkmate керек

Ndenge nini kosala Checkmate na SaaS na Puissance ya Gratuit

Kā pārbaudīt mate programmā SaaS, izmantojot bezmaksas iespējas

Cum să dai șah-mat în SaaS cu puterea gratuitului

Как поставить шах и мат в SaaS с помощью силы бесплатности

இலவச சக்தியுடன் SaaS இல் செக்மேட் செய்வது எப்படி

Authors:
(1) Revathy B S, Raman Research Institute, Bengaluru, 560080, Karnataka, India and Corresponding author;
(2) Victor Mukherjee, Department of Physical Sciences, Indian Institute of Science Education and Research Berhampur, Berhampur, 760010, Odisha, India;
(3) Uma Divakaran, Department of Physics, Indian Institute of Technology Palakkad, Palakkad, 678623, Kerala, India. Table of Links Abstract and 1 Introduction 2 Free fermionic model 3 Many body quantum Otto cycle 4 Universal scalings in work output 5 Transverse Ising model as working medium 6 Conclusion and References Abstract We construct a quantum critical Otto engine that is powered by finite temperature baths. We show that the work output of the engine shows universal power law behavior that depends on the critical exponents of the working medium, as well as on the temperature of the cold bath. Furthermore, higher temperatures of the cold bath allows the engine to approach the limit of adiabatic operation for smaller values of the time period, while the corresponding power shows a maximum at an intermediate value of the cold bath temperature. These counterintuitive results stems from thermal excitations dominating the dynamics at higher temperatures. Keywords: Quantum heat engines, quantum phase transitions, Kibble Zurek scaling 1 Introduction Quantum thermodynamics is a rapidly progressing field aimed at understanding the thermodynamics at the quantum level [1, 2]. The field is getting much attention from the scientific community not just because it provides a link between quantum mechanics and thermodynamics but also because it aids in the development of nanoscale devices that aims to harness the potential benefits of the “quantumness” in their working medium (WM) [3]. The recent advances in the experiments such as trapped ions or ultracold atoms [4, 5], using NMR techniques [6], nitrogen vacancy centres in diamond [7] have made the realization of quantum devices possible. Among the various quantum devices studied, for example, quantum refrigerators [8, 9], quantum batteries [10, 11], quantum sensors [12], quantum thermal transistors [13, 14], etc., our work focusses on quantum heat engines that follow the quantum Otto cycle [15, 16]. The effect of phase transitions in quantum engines has been studied in Refs. [17–24]. While some of these studies concentrated on how to improve the performance of quantum engines with respect to its efficiency and power [23, 24], some focused on showcasing universality in their working [21, 22]. For instance, in Ref. [22], the authors showed that the work output of the engine up to an additive constant follows a universal power law governed by the driving speed with which the quantum critical points are crossed, where the exponent of the power law is determined by the critical points (CP) crossed. However, they considered the case of relaxing bath that takes the WM close to its ground state so that Kibble Zurek mechanism becomes relevant. On the other hand, in this paper, we specifically use thermal baths at different temperatures and study the effect of thermal excitations on the scalings of the work output. As discussed before, we prepare a many body quantum heat engine using a free fermionic model as WM that undergoes a quantum phase transition. We describe the details of the free fermionic model in Section 2, elaborate on the many body quantum Otto cycle that the engine follows in Section 3, followed by the universal scalings shown by the engine in Section 4. We demonstrate our results using the transverse field Ising model in Section 5 and finally conclude in Section 6. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Revathy B S, Raman Research Institute, Bengaluru, 560080, Karnataka, India and Corresponding author; (2) Victor Mukherjee, Department of Physical Sciences, Indian Institute of Science Education and Research Berhampur, Berhampur, 760010, Odisha, India; (3) Uma Divakaran, Department of Physics, Indian Institute of Technology Palakkad, Palakkad, 678623, Kerala, India. Authors: Authors: (1) Revathy B S, Raman Research Institute, Bengaluru, 560080, Karnataka, India and Corresponding author; (2) Victor Mukherjee, Department of Physical Sciences, Indian Institute of Science Education and Research Berhampur, Berhampur, 760010, Odisha, India; (3) Uma Divakaran, Department of Physics, Indian Institute of Technology Palakkad, Palakkad, 678623, Kerala, India. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Free fermionic model 2 Free fermionic model 3 Many body quantum Otto cycle 3 Many body quantum Otto cycle 4 Universal scalings in work output 4 Universal scalings in work output 5 Transverse Ising model as working medium 5 Transverse Ising model as working medium 6 Conclusion and References 6 Conclusion and References Abstract We construct a quantum critical Otto engine that is powered by finite temperature baths. We show that the work output of the engine shows universal power law behavior that depends on the critical exponents of the working medium, as well as on the temperature of the cold bath. Furthermore, higher temperatures of the cold bath allows the engine to approach the limit of adiabatic operation for smaller values of the time period, while the corresponding power shows a maximum at an intermediate value of the cold bath temperature. These counterintuitive results stems from thermal excitations dominating the dynamics at higher temperatures. Keywords : Quantum heat engines, quantum phase transitions, Kibble Zurek scaling Keywords 1 Introduction Quantum thermodynamics is a rapidly progressing field aimed at understanding the thermodynamics at the quantum level [1, 2]. The field is getting much attention from the scientific community not just because it provides a link between quantum mechanics and thermodynamics but also because it aids in the development of nanoscale devices that aims to harness the potential benefits of the “quantumness” in their working medium (WM) [3]. The recent advances in the experiments such as trapped ions or ultracold atoms [4, 5], using NMR techniques [6], nitrogen vacancy centres in diamond [7] have made the realization of quantum devices possible. Among the various quantum devices studied, for example, quantum refrigerators [8, 9], quantum batteries [10, 11], quantum sensors [12], quantum thermal transistors [13, 14], etc., our work focusses on quantum heat engines that follow the quantum Otto cycle [15, 16]. The effect of phase transitions in quantum engines has been studied in Refs. [17–24]. While some of these studies concentrated on how to improve the performance of quantum engines with respect to its efficiency and power [23, 24], some focused on showcasing universality in their working [21, 22]. For instance, in Ref. [22], the authors showed that the work output of the engine up to an additive constant follows a universal power law governed by the driving speed with which the quantum critical points are crossed, where the exponent of the power law is determined by the critical points (CP) crossed. However, they considered the case of relaxing bath that takes the WM close to its ground state so that Kibble Zurek mechanism becomes relevant. On the other hand, in this paper, we specifically use thermal baths at different temperatures and study the effect of thermal excitations on the scalings of the work output. As discussed before, we prepare a many body quantum heat engine using a free fermionic model as WM that undergoes a quantum phase transition. We describe the details of the free fermionic model in Section 2, elaborate on the many body quantum Otto cycle that the engine follows in Section 3, followed by the universal scalings shown by the engine in Section 4. We demonstrate our results using the transverse field Ising model in Section 5 and finally conclude in Section 6. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Stories, News & Mentions about VAHAN

Vahan’s Stories on HackerNoon

Vahan's News & Mentions