Comprised of the Pike Nickel Centre, West and East showings, this nickel project has currently been traced by drillings over more than 4.5 kilometers. In addition to the presence of strategic minerals, the Gaboury project also contains several gold showings.

Mosaic Minerals Starts Drilling on Gaboury Nickel Project in Quebec

EMP Metals Corp. ("EMP Metals" or the "Company") is pleased to announce that it has closed its previously announced non-brokered private placement financing (see news releases dated October 24, 2023 and October 25,

EMP METALS CLOSES PRIVATE PLACEMENT FINANCING OF $9,757,600

Dele Alake, the Minister of Solid Minerals Development, announced this significant development after the weekly Federal Executive Council meeting at the

Nigeria signs MoU with German mining company

With the global energy transition under way, critical minerals are fast replacing fossil fuels as the most sought after natural resources. Critical minerals such as copper, lithium, manganese and nickel are essential to the development of technologies behind solar PV,

The countries controlling the critical minerals supply chain: in four charts

The Mosaic Co. has reported a possible tear in the liner of the troubled New Wales phosphogypsum stack at its Mulberry facility east of Tampa, the same location of a sink hole in 2016, although findings have yet to be confirmed.

The Mosaic Co. reports possible issues with liner at troubled Mulberry gypstack facility

As London-listed Rainbow Rare Earths reports to shareholders on having achieved various milestones in the financial year ended June 30, it is set to focus on the production of separated rare earth oxides at a back-end pilot plant in the US and complete a definitive feasibility study (DFS) by the end of 2024.

Rainbow sets 2024 priorities, including to raise $3.4m

Macroeconomic factors, demand for green metals and environmental, social and governance (ESG) endeavours are all factors that are influencing investment in mining, speakers outlined during the ...

Macroeconomic factors, green metals demand and ESG impacting on mining investment

VANCOUVER, BC, Oct. 18, 2023 /CNW/ - Vortex Metals Inc. (TSXV: VMS) (FSE: DM8) (OTC: VMSSF) ("Vortex" or the "Company") is pleased to announce its participation in the 121 Mining Investment Conference in New York City, occurring on October 23-24.

Vortex Metals Announces Attendance at the 121 Mining Investment Conference in New York

VANCOUVER, BC, Oct. 18, 2023 /PRNewswire/ - Vortex Metals Inc. (TSXV: VMS) (FSE: DM8) (OTC: VMSSF) ("Vortex" or the "Company") is pleased to announce its participation in the 121 Mining Investment Conference in New York City, occurring on October 23-24.

Detailed price information for Mosaic Minerals Corp (MOC-CN) from The Globe and Mail including charting and trades.

Mosaic Minerals Begins Prospection Work on Amanda and Announces Correction Regarding the Annual General Meeting

Get the most recent info and news about Mosaic on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

The Mosaic Company produces and distributes crop nutrients to the agricultural communities located in North America and other countries. The Company offers products including concentrated phosphates and potash.

Mosaic

Scientific American Supplement, No. 799, April 25, 1891 by Various, is part of the HackerNoon Books Series. You can jump to any chapter in this book . MARBLE AND MOSAIC. here MARBLE AND MOSAIC. By T.R. SPENCE. I do not propose to enter into any historical details as to the first and subsequent application of mosaics. In a general sense we understand mosaic as a combination of various more or less imperishable materials—fixed together by cement or other adhesive substances—and laid over walls, floors, etc., with a view to permanent decorative effect. The substance of the tesseræ is of many kinds, namely, glass, cheap and precious marbles, hard stone, and burnt clay, these mentioned being mainly in use for architectural purposes. For decorative schemes we collect as many gradations of color as are obtainable in such durable materials in their natural or manufactured state, and thus form a color palette which we regard in the same sense as a painter would his pigments. Of course, the first proceeding is to prepare a design on a small scale, which shall embrace your notions of color only. Then follows a full-sized cartoon, which I need hardly add shall embrace your best efforts in drawing. A tracing is made of the latter and transferred to sheets of cardboard. This cardboard is cut to the size of certain sections of your design, and, for convenience, should not be more than, say, 20 in. square. Of course, it will not always be square, but will bear the same relation to your complete cartoon as a map of the counties would to that of all England. Now, working from the small design (of color), the tesseræ are cut to the forms required, laid face downward, and glued on to the cardboard sections containing your enlarged cartoon. When the design is all worked out on these sections they are ready for fixing on walls or floor by laying them home on a float of cement. When the cement sets, the cardboard sticking to the face is washed off, and the joints of tesseræ flushed over with cement and cleaned off, leaving all joints filled up level. There are other processes used for the same end. The technical processes need not occupy our attention at present. There is one process that may appeal to you, and that is executing the work by floating on a limited expanse of cement, and sticking on the tesseræ at once. It has the advantage of enabling the artist or architect to see the effect of his efforts under the fixed conditions of light and height. in situ I shall confine myself to vitreous or glass mosaic, which for durability, extended scales of primary colors and their numerous semi-transparent gradations is unequaled by any substance yet used for wall or floor decoration. I am surprised, having all these fine qualities, it is not more used by architects. If you require proofs of its triumphs, go to St. Mark's, of Venice, and stand under its mellow golden roof. There you will find its domes and vaulted aisles, nave and transepts entirely overlaid with gold mosaic, into which ground is worked—in the deepest and richest colors and their gradations that contemporary manufacturers could produce—subjects selected from the creation down to the life of Christ, in addition containing a complete alphabet of early Christian symbolism. The roof surfaces being one succession of over-arching curves become receptive of innumerable waves of light and broad unities of soft shadows, giving the whole an incomparable quality of tone and low juicy color. Never use your gold but on curved or undulating surfaces. Flat planes of gold only give the effect of a monotonous metallic yellow, and can never be beautiful, owing to the absence of the variations that come with waves of shadow. By letting out the reins of imagination we might feel that in this a tenth century Giorgione has given off the mental impressions of all the golden autumn of his life. His material gave him an advantage over his great followers of the fifteenth and sixteenth centuries, insomuch that glass has a living and glowing quality of light not existing in the somewhat clouded purity of oil or fresco. In St. Mark's we have an example of the superb treatment in deepest and most Titianesque scales applied to curved forms, but to find a similarly complete example of the use of lighter tones and on flat surfaces, we must turn to Ravenna. I can give you no adequate description of the wall mosaics of Ravenna. In the sense of delicate color they remind me of some of the subtile harmonies of many of the finest works of the modern French school—of the Impressionists and others who combine that quality with a true instinct for design. In standing before them you feel that the Dagnan Bouverets, the Mersons, the Cazins, the Puvis de Chavannes, etc., of the fifth century have had a hand in the conception and realization of the beautiful compositions to be found on the nave walls of the two churches of St. Appollinare Nuovo and St. Appollinare in Classe. Here all the scales are of delicate degrees of light tones, supreme in their beauty, completeness, and, most important to us, their true decorative instinct. In the Baptistery we find what I may term a third essay in color, by weaving in rich, dark, and glowing colors on figures and bold sinuous forms of ornament in such a skillful and judicious manner that the whole dome seems to be alive with harmonies, although they are mostly primaries. As you know, rules for the disposition of color are futile, yet some details that struck me as eminently satisfactory may interest you. In all cases the tesseræ are of small dimensions, about a quarter of an inch square. The stucco joints are large and open, surfaces far from level, but undulating considerably. The tesseræ stick up in parts, brilliant edges showing. Absence of flatness gives play to the light. The gray of the stucco joints brings the whole composition together, serving as cool grays in a picture to give tender unity. Gold, apart from backgrounds and large surfaces, is used very cleverly in small pieces in borders of garments, and more especially in thin outlines to make out the drawing and certain flowing forms of ornament. Brilliant pieces of glass actually moulded at the kiln into forms of jewels add brilliancy to crowns, borders, etc. These stick boldly out from the surface. I noticed in the Baptistery below the springing of the dome a frieze about 2 ft. 6 in. deep, having the ground entirely in black, through which was woven in thin gold lines a delicate foliated design. This, in conjunction with the upper surfaces in dark, rich color, had a most delightful effect. We, as students, can learn most from the Ravenna examples, for great are the needs of light and silvery color in this country, where gray and gloomy days far outnumber those in which the sun gives liberally of his light. I may say, in passing, as our subject is really a matter of decoration, that our nineteenth century efforts in this direction are all of a somewhat gloomy tendency. We fill our rooms with imitations of somber Spanish leather, stain and paint our woodwork in leathery and muddy tones, to arrive at what is now a sort of decorator's god. Quaintness is the name of that god. Many are the sins for which he has to answer. Had we not better worship a deity called beauty, whose place is a little higher up Parnassus? Why should we not in our endeavors attempt in some measure to transfix the brilliant harmonies that follow the sun in his liberal and gracious course? This muddy quaintness is certainly pleasant for brief periods, when lamps are low and fire light gilds and deepens its parts. Turn the sunlight on these so-called triumphs of the modern decorator's art, and then you feel the lack of many a phase of color that might have been borrowed from the thousand and one examples that in nature he vivifies and makes brilliant. Referring again to the Ravenna mosaics, I can only add that at the present day an extended palette of colored glass is available. The technical difficulties are not great, and there is no question as to the fine qualities of design and color that are to be obtained in this material. The great point in this, as in all other schemes of decoration, is the art, the mental quality of conception, and the sense of color and fitness. If we hold the precious heritage of an artist's mind—that divine and rare something which gives form, color, and completeness to a story, a dream or a vision—then very little difficulty follows in making vitreous mosaic a valued servant in the realization of a fine creation. It is the function of architects to design suitable spaces for color decoration, so bound in by dignified mouldings and other details of his constructive art, in such a manner that the addition of decorative color shall in no way mar the scheme of his complete work, but shall (under these well ordered distributions) have set on them the seal and crown of color which is inseparable from a perfect piece of architecture. In such spaces he may dream his dreams, tell his stories, and stamp on them for centuries his subtilest and divinest thoughts. May I not urge that to such spaces must be given the best that is in you? for once placed so shall they remain unchanged through generations, time being powerless to add any mellow garment of tone or softening quality whatever. I mistook the title of the subject in thinking that it was mosaic only, and at the last moment found it was marble and mosaic. However, the same dominant principles shall underlie the treatment of marble. It is a question of the finer instincts for form and color. In recent years the demand for choice decorative materials has been the means of opening out many marble quarries all over the world. Transit being easy, a large scale of varieties is available. One fine addition is the Mexican onyx. My feeling is that the most beautiful marbles are those where the soft and sinuous veins melt and die into the general body, comparatively sharp markings dying right away at the edges into innumerable gradations. Marbles having strong and hardly marked veins present great difficulties in distribution. If they are near, they offend you with their coarseness; and, placed at a distance, the hard vein lines have very little decorative value. I should say use these in narrow slips, with very little moulded profile or as parts of intazzio. Mouldings should be specially designed for different marbles. I should say mainly on the principle of sudden contrasts; that is, large members with very little curve bound with members very small in detail, thus obtaining sharp lines, having little surface to be influenced or distorted by the veined markings, and serving to sharpen up and give form to the broader members (which show the color qualities of the marble), much as you sharpen up an ink drawing by underlining. These small members serve the architect's purpose for the expression of vertical and horizontal lines, and where decisive and cutting shadows are required in the composition of his work. If delicate carving forms part of your design, I should say statuary is the best, as you have no veins to distort your detail. I need hardly add that economy should be studied in using precious marbles, without injuring the durability of the work. Contours may be built up in thin sections. Intazzio is a beautiful form of treating marble on an inexpensive ground. Gem-like effects may be obtained by inlaying with smaller pieces, following such ornamental forms as your inventive brains shall dictate. Perhaps the pockets of your clients will be the chief dictator. Heraldic emblazonings, inlaid in marble, are highly effective. The conditions of the heraldry necessitate the use of many varieties, but in such small quantities that on a large simple field they are rarely out of harmony. In addition they map out a large and interesting variety that will save the worry of creation of designs coming entirely from your own brain, and you know the worry of an architect's life makes him hail with pleasure at times a rest from the strain of creation. This heraldic work may be seen to perfection in the chapel of the tombs of the Medici at Florence. At the Pitti Palace are some tables which you may know where marble intazzio can no further go. Alabaster does not appeal to me, it is somewhat sugary in results. If you are fortunate enough to have a sculptor who is a sort of nineteenth century Donatello, let him work his will on statuary or such restful marble. The celebrated monument in the church of S. Giovanni Paulo, at Venice, which Ruskin says is the finest monument in the world, if my recollection serves me correctly, is in white marble, and its beauty comes entirely from the sculptor's art. Such monuments give you much better than any words of mine ample suggestions for marble treatment. I may quote such names as Nicolo Pisano and Verocchio. Photos of some of their work I have brought. Note Pisano's beautiful white altar at Bologna, and Mina de Fiesole's work in Florence. They all show the sculptor as supreme. Why should not we encourage individual young sculptors more? Give them portions of your work in which they can put all the fervor and enthusiasm of young manhood. Their powers may not be ripe, but they possess a verve and intensity that may have forever fled when in later years the imagination is less enthusiastic and the pulses slower. I am sure there are many young sculptors now wanting commissions who have been trained at the academy, and better still, in the best French schools. I maintain that the contemporary French school of sculpture is in its line equal to any school of sculpture that has ever existed, not excepting that of Phidias or that of the Italian Renaissance of the fifteenth and sixteenth centuries. I believe history will confirm this. Why not give these men an opportunity, and help on the movement to found a truly English school of sculpture, rather than give all such work to trading firms of carvers, who will do you any number of superficial feet, properly priced and scheduled, and in the bills of quantities, of any style you please, from prehistoric to Victorian Gothic? Of course, this is our British way of founding a great school. There is one method of treatment that appeals to me very strongly, and that is the application of colored metals to marble, more especially bronze and copper. I may quote as a successful example near the Wellington Memorial at St. Paul's. Another suggestion—although it is not used in combination with marble, but it nevertheless suggests what might be done in the way of bronze panels—that is, the Fawcett Memorial, by Gilbert, in the west chapel at Westminster Abbey. A paper recently read before the Architectural Association, London.— . From the Architect About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Various (2004). Scientific American Supplement, No. 799, April 25, 1891. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/11649/pg11649-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

MARBLE AND MOSAIC

In this blog post, I’m going to take you step-by-step through the process of running MosaicML’s competitor, MPT-7B, on your own AWS SageMaker instance. ChatGPT Are you excited about the capabilities of ChatGPT, but have concerns about exposing your sensitive data to OpenAI? Luckily, there are alternatives that you can run on your own infrastructure. One such alternative is MosaicML’s MPT-7b, a competitor to ChatGPT, which we’ll explore in this blog post. Introduction to MosaicML and MPT-7B MosaicML, recently , has been making waves in the ML community with their MPT-7B model, a supposed competitor to ChatGPT. Despite its promise, running this model can be daunting due to sparse documentation and its heavy resource requirements. However, one can run MPT-7B on AWS SageMaker in a Jupyter notebook, an environment that is beginner-friendly and highly flexible for rapid iteration. This setup allows you to test the model’s feasibility and hardware requirements before deciding to move into production. acquired by Databricks for $1.3 billion Running MPT-7B on AWS SageMaker Running MPT-7B in a Jupyter notebook on AWS SageMaker provides several benefits. Not only can you pay just for what you use and turn it off when you’re done, but the ability to easily rerun portions of your code without having to reload the model saves time during iterative development. But do beware! If you forget to stop your notebook instances, the charges can quickly add up. While this method is relatively convenient, there are some considerations you must take into account. Firstly, loading the model can take up to 20 minutes even on a high-performance GPU, making this process somewhat time-consuming. Also, the cost is a factor to consider, as the running cost is at least $4 per hour. You’ll need to run MPT-7B on at least a instance; anything smaller does not seem feasible. If you opt for EC2 instead of SageMaker, you’ll have to ask AWS for permission to use a instance. p3.2xlarge p3.2xlarge In the next sections I’ll take you step-by-step through how to run the MPT-7B model in your very own SageMaker jupyter notebook: Step 1 - Open the SageMaker Console Fire up the AWS Console and search for SageMaker: Step 2 - Create a notebook instance From the left-side menu, select : Notebook->Notebook instances Click the button: Create notebook instance Specify an instance name. Choose the instance type . Unfortunately, it seems that an instance as powerful as is required, or else your instance may run out of memory or take an excessive amount of time to respond to even the simplest questions. However, please note that this instance will cost approximately $4/hr, so it’s important to monitor your usage carefully. m1.p3.2xlarge m1.p3.2xlarge Create a new IAM role: If your test environment doesn’t have any particularly sensitive data in it then you can grant access to . Otherwise, you’ll need to be more explicit. Any S3 bucket Click the button: Create notebook instance The notebook will then be in a status. This will likely last for about 10 minutes: Pending In the meantime, we’ll download a notebook so that we can upload it after the AWS SageMaker instance has finished provisioning. Step 3 - Download the notebook Head on over to the notebook at and download it: MPT-7B on AWS SageMaker.ipynb In this notebook you’ll notice two main code blocks. The first block loads the MPT-7B tokenizer and model: from torch import cuda, bfloat16
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig

device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'

tokenizer = AutoTokenizer.from_pretrained("mosaicml/mpt-7b-chat",
                                          trust_remote_code=True)

config={"init_device": "meta"}
model = AutoModelForCausalLM.from_pretrained("mosaicml/mpt-7b-chat",
                                             trust_remote_code=True,
                                             config=config,
                                             torch_dtype=bfloat16)

print(f"device={device}")
print('model loaded') The tokenizer is used to encode the question sent to the model and decode the response from the model. Additionally, we obtain the device specification for our GPU so that we can configure the model to utilize it later: import time
from IPython.display import Markdown
import torch
from transformers import StoppingCriteria, StoppingCriteriaList

# mtp-7b is trained to add "<|endoftext|>" at the end of generations
stop_token_ids = [tokenizer.eos_token_id]

# Define custom stopping criteria object.
# Source: https://github.com/pinecone-io/examples/blob/master/generation/llm-field-guide/mpt-7b/mpt-7b-huggingface-langchain.ipynb
class StopOnTokens(StoppingCriteria):
def __call__(self, input_ids: torch.LongTensor,scores: torch.FloatTensor,
             **kwargs) -> bool:
  for stop_id in stop_token_ids:
    if input_ids[0][-1] == stop_id:
      return True
    return False

stopping_criteria = StoppingCriteriaList([StopOnTokens()])

def ask_question(question, max_length=100):
  start_time = time.time()

  # Encode the question
  input_ids = tokenizer.encode(question, return_tensors='pt')

  # Use the GPU
  input_ids = input_ids.to(device)

  # Generate a response
  output = model.generate(
    input_ids,
    max_new_tokens=max_length,
    temperature=0.9,
    stopping_criteria=stopping_criteria
  )

  # Decode the response
  response = tokenizer.decode(output[:, input_ids.shape[-1]:][0],
                              skip_special_tokens=True)

  end_time = time.time()
  duration = end_time - start_time

  display(Markdown(response))

  print("Function duration:", duration, "seconds") Note the use of , which is needed or else the model will just start babbling on, even after it has answered our question. stopping_critera See if you want to explore the different options. model generate parameters Now, let’s upload this notebook to SageMaker. Step 4 - Upload the notebook Hopefully by this time your SageMaker notebook instance has finished being provisioned. When it has, click the link: Open Jupyter Then, click the button in the top-right corner of your screen and select the notebook that you just downloaded: Upload Set the kernel to : conda_python3 Step 5 - Run the notebook Select : Cell -> Run All An hourglass logo will then appear in the browser tab: You’ll then need to wait about 10 minutes for the model to be downloaded: After it runs, you’ll see the answer to the question : Explain to me the difference between nuclear fission and fusion Since the model and tokenizer have already been loaded above, you can simply modify the code block and click the button to ask any other questions. This will save you from spending 10 minutes each time you want to test a new question. ask_question Run Step 6 - Stop the notebook As soon as you have finished testing the model, you’ll want to head back to your list of notebook instances and stop it. If you don’t, $4/hr will add up very quickly 💸 Performance Comparison In terms of performance, my preliminary tests suggest that MPT-7B’s results might not be as good as ChatGPT’s. It does a decent job answering questions like: , What is the capital of France? Explain to me the difference between nuclear fission and fusion . Write python code that converts a csv into pdf But for questions like “What is the capital of Belize?” it fails pretty horribly: I am currently collecting more data and will conduct a comprehensive comparative analysis in a follow-up blog post. In that post, I will compare the question and answer performance of MPT-7B, MPT-30B, Falcon-40b, and ChatGPT using actual conversation history. From Testing to Production Once you’re ready to transition from testing to production, SageMaker offers an additional benefit - the ability to create an endpoint for your model. With SageMaker, you can auto-scale based on demand to the endpoint, optimizing your resources. Additional Tips Be mindful that it’s easy for your process to get forked while running in a Jupyter notebook and run out of memory. If this happens, simply shut down the kernel and run all the commands again. If you’re curious about running this model on a platform other than AWS, Google Colab Pro is another viable option at $9/month. However, based on our testing, we found that we exhausted the provided credits within just a few hours. 😳 Another challenge you may face is the inability to utilize the on SageMaker due to a CUDA version incompatibility. Unfortunately, AWS’s current P3 instances do not include a recent CUDA version. Therefore, if you wish to utilize the Triton optimization, you will need to create an EC2 container with command line access. However, it’s important to note that you will also require special permission from AWS Support to run an instance with 8 VCPUs. In a future post, I will provide a detailed guide on how to integrate Triton and utilize a more cost-effective GPU cloud provider, such as . Triton optimization Lambda Labs Final Thoughts While MosaicML’s MPT-7B offers a viable alternative to OpenAI’s ChatGPT, it presents its own set of challenges. Running the model can be time-consuming, expensive, and the available documentation is lacking. However, the ability to keep the model in-house and protect your data from being exposed to OpenAI can be compelling for certain use cases. SageMaker offers great convenience for quickly testing the model and provides the flexibility to transition to production when you’re ready. Whether you’re just starting with MPT-7B or have been using it for a while, we hope this guide has provided valuable insights. Stay tuned for our next blog post, where we’ll delve deeper into the performance comparisons between MPT-7B, MPT-30B, Falcon-40b, and ChatGPT. See the following links if you are interested in learning more about or its larger variant, . MPT-7B MPT-30B And remember, whether you’re working with ChatGPT or MPT-7B, the key is to ensure your use case is served without compromising data privacy and cost-effectiveness. Happy tinkering! Want a Turn-Key Solution for Using MPT or ChatGPT with Your Data? At , we offer ChatGPT for businesses. If you’re interested in leveraging ChatGPT, MPT, or other models with your company’s data, please get in touch with us. MindfulDataAI.com

How to Run MPT-7B on AWS SageMaker: MosaicML's ChatGPT Competitor

কিভাবে AWS SageMaker-এ MPT-7B চালাবেন: MosaicML এর ChatGPT প্রতিযোগী

So führen Sie MPT-7B auf AWS SageMaker aus: dem ChatGPT-Konkurrenten von MosaicML

Cómo ejecutar MPT-7B en AWS SageMaker: el competidor ChatGPT de MosaicML

Comment exécuter MPT-7B sur AWS SageMaker : le concurrent ChatGPT de MosaicML

AWS SageMaker पर MPT-7B कैसे चलाएं: MosaicML का ChatGPT प्रतियोगी

AWS SageMaker で MPT-7B を実行する方法: MosaicML の ChatGPT の競合他社

AWS SageMaker에서 MPT-7B를 실행하는 방법: mosaicML의 ChatGPT 경쟁자

Como executar MPT-7B no AWS SageMaker: Concorrente ChatGPT do MosaicML

AWS SageMaker'da MPT-7B Nasıl Çalıştırılır: MosaicML'in ChatGPT Rakibi

Cách chạy MPT-7B trên AWS SageMaker: Đối thủ cạnh tranh ChatGPT của MosaicML

如何在 AWS SageMaker 上运行 MPT-7B：MosaicML 的 ChatGPT 竞争对手

Since it burst into prominence in 2021, the Metaverse has provided millions of individuals with a fascinating realm where people can reside, interact, and learn from the comfort of their homes. This incredible world has acted as an enhanced version of real life, where, by assuming a digital avatar, individuals are able to explore an entirely virtual space with complete freedom. The concept of the Metaverse, which was once merely an idea that excited, burst into prominence in late 2021 when social media behemoth Facebook . Meta's spearheading of the Metaverse movement saw them create a . In somewhat of a response, other tech giants like Microsoft, Nvidia, Google, and Qualcomm have also committed billions of dollars to fuel the development of the Metaverse. rebranded itself as Meta $10 billion investment plan The unprecedented influx of investment that followed Meta's led strategic market insights firm Precedence Research to predict that the Metaverse market size was one that could in value by 2030. Since 2021, the Metaverse has expanded, and industries such as gaming, E-Commerce, education, and entertainment are dominating the space. This has provided a platform for impressive growth and innovation. generate up to $1.3 trillion Alex Hernandez, the visionary CEO of Avem Finance —a blockchain platform that tokenizes real-world assets using the Ethereum Virtual Machine (EVM)— shares an inspiring perspective on the Metaverse: " " In the boundless expanse of the Metaverse, a universe of opportunities awaits discovery. As its expansion accelerates, so does its explosive potential, propelling us into an era of unprecedented growth. The Metaverse is on the verge of transcending boundaries, with visionary minds driving its evolution. Aligned with this optimistic outlook, numerous companies have ventured —and continue to venture— into the . Each brings their unique offerings to the table. From gaming and education to entertainment and fitness solutions, these companies are revolutionizing the virtual world. A prime example in the fitness space is Fitburn, a company that has ingeniously blended gaming elements into the Metaverse's fitness landscape. Metaverse This fusion of technology, wellness, entertainment, and gaming in the Metaverse exemplifies the transformative potential of this vast digital realm. As we explore the Metaverse further in this article, we will encounter an array of groundbreaking ventures, with each contributing to the immersive and dynamic landscape of the Metaverse that exists today. Entertainment in the Metaverse In 2021, the global Metaverse in entertainment market size was valued at $13.8 billion. This figure is projected to reach $221.7 billion between 2022 to 2031. A Compound Annual Growth Rate (CAGR) of 32.3%. Personally, I think these are conservative numbers, and for good reasons. Until two or three years ago, the Metaverse was largely a concept. Now, it is a reality. Events are now regular fixtures in this virtual world. A typical example is DJ Marshmello's live concert in the Fortnite game, which had over 10 million people in attendance. A major example of a Metaverse entertainment company is Roblox. Originally an online gaming platform, the company made its mark on the music industry when it hosted its first virtual concert that had Lil Nas X debut his song "Holiday" to . Metafluence is another Metaverse company, and it combines entertainment and influencer marketing to connect audiences in the Metaverse. Roblox players Artificial Intelligence (AI) and Virtual Reality (VR) Revolutionizing Gaming in the Metaverse AI and VR are the dynamic duo of the Metaverse. The rapid growth of the Metaverse can be attributed to the synergistic combination of these technologies. The Metaverse being a virtual reality environment, provides users with a platform for interactions that are augmented by technologies like AI and Augmented Reality (AR). And with the explosive popularity of the Metaverse as a gaming hub, these technologies are playing a pivotal role in creating exceptional gaming experiences for gaming and Metaverse enthusiasts. where challenges are more engaging and continuously dynamic. The AI algorithms further enhance gaming experiences by personalizing the gameplay of players based on their behavior. The algorithm continuously adapts to meet the preferences of the player, regardless of how it evolves. VR, on the other hand, takes gaming to new levels, as it offers players truly immersive experiences. The technology allows players to interact with virtual environments (re: Metaverse) in stunning ways. Game developers use the power of AI to create intricate and immersive worlds An example of this fusion in technologies is Project Lambo, an innovative gaming platform within the metaverse. The platform seamlessly integrates AI, VR, and Non-Fungible Token (NFT) sharing to deliver captivating and enjoyable experiences for gamers. Gaming Protocols and Web3 Powered Gaming in the Metaverse Gaming protocols play a crucial role in governing gameplay and facilitating data exchange among players in the Metaverse. These protocols serve as a foundation for game development, enabling interoperability and the seamless transfer of assets across different games. A noteworthy example of a pioneering gaming protocol is Winr Games. Game developers can leverage the WINR Software Development Kit (SDK) to swiftly build on-chain games, tapping into the protocol's deep multi-asset liquidity pool. This protocol empowers developers to unlock the potential of the Metaverse by integrating their games with an innovative gaming protocol that promotes liquidity, rewards, and a thriving ecosystem. Still in the conversation about gaming protocols, the convergence of Web3 technology and the Metaverse has brought about an exciting era in virtual gaming. The decentralized infrastructure that prevails within this immersive virtual world grants gamers full control and true ownership of their earned or purchased gaming assets. , players can trade these unique assets, thereby introducing scarcity and enabling new avenues for monetization. By leveraging the power of NFTs A popular example of a Metaverse gaming platform is Fortnite. Fortnite provides a shared virtual space where gamers can interact with each other and explore virtual environments. Live events like concerts and movie screenings are also hosted in the game. Xandar is another gaming platform built in the Metaverse. The game seamlessly integrates AI, and blockchain technology, to provide its metaverse interoperability function, which enables players to venture through vast metaverses within the platform's expansive multiverse. Conclusion The ever-evolving landscape of the Metaverse has seen AI, VR, and blockchain-backed platforms take center stage. These technologies signify a paradigm shift within the gaming and entertainment industries, introducing a new era of user immersion, interoperability, true ownership, and play-to-earn economics. As a result of this, individuals now have an alternate means to entertain themselves and monetize their virtual experiences. The Metaverse is a dynamic and continuously expanding realm. As we continue to embrace its potential, we forge a path towards decentralized gaming and a wide range of applications that will empower players and users alike while also pushing the boundaries of creativity and collaboration. The convergence of these technologies is reshaping the future of entertainment, and with each advancement, the metaverse is being propelled to new heights and is vast potential unlocked for the benefit of everyone.

The Metaverse Mosaic: Unraveling Niche Platforms and Their Unique Virtual Experiences

A Short History of The Commercial Internet: From Mosaic to Web3

It struck me today that people are like mosaics. I’m sure I cannot be the first person to make this connection, but it’s a new idea for me. A mosaic is defined (by my Oxford Dictionary) as “a picture or pattern produced by arranging small colored pieces of hard material…” and “a combination of diverse elements forming a more or less coherent whole.” At distance, a mosaic appears as a whole or complete picture. The whole may be a geometric or abstract pattern, a landscape, or a portrait. Anything. As you get closer, you start to notice the mosaic’s pieces. It turns out that the “picture” is composed of many smaller elements or pieces, which may differ in size, shape, color or shade, and texture. The pieces may be smooth and uniform or jagged and irregular, and the “whole” may contain uniform pieces in one area and jagged pieces in another. Some parts may look like broken pieces or fragments and these broken fragments may appear to be leftovers from some other earlier whole object or rejects from another thing. The closer you get, the more you learn how the picture is more diverse, detailed and complicated than it appears from a distance. Mosaics are mounted on some kind of skeleton structure, base, or . And the mosaic’s pieces are held together by a medium in which they rest and bond, forming a “more or less whole.” foundation coherent A “more or less coherent whole”? I’ll take that: the more and the less. is how hackers start their afternoons. We’re a part of the family. We are now and happy to opportunities. Hacker Noon @AMI accepting submissions discuss advertising &sponsorship To learn more, , , or simply, read our about page like/message us on Facebook tweet/DM @HackerNoon. If you enjoyed this story, we recommend reading our and . Until next time, don’t take the realities of the world for granted! latest tech stories trending tech stories

Notes Toward a Unified Theory of the Coherent Person (Mosaic)

B2B Tools You Need To Have in Your Arsenal in 2024

In the dynamic world of , have reached a pivotal juncture, unlocking the potential for true breakthroughs across various industries. With the AI landscape ripe for disruption, startups are eagerly exploring innovative use cases where AI can unleash a tenfold increase in productivity. As a seasoned entrepreneur deeply entrenched in AI, I've mentored numerous startups through this maze, witnessing common missteps early in the game. A frequent illusion harbored by founders is the 'quick-fix' fallacy, believing a plug-and-play approach with AI can lead to swift profits. startups AI technologies Before embarking on the journey of AI development, founders should reflect on these three crucial questions: Is AI Truly a Game-Changer for Your Use Case? Reflecting on my journey, I recall when my team chose an unconventional path while developing a computer game called “Reel Valley”, opting for nim-lang, an offbeat programming language. This choice, although innovative, diverted us away from essential aspects like core gameplay development and market fit. Our ambitious tech exploration led to delayed and flawed releases. A lesson learned: it’s often more pragmatic to use standard tools like Unity, concentrating on what truly matters – the product itself. This scenario echoes a common conundrum faced by many startup founders. The allure of integrating AI, largely due to its tech glamor can be tempting. However, the key is to stay devoutly aligned with your customer needs rather than getting swept up in technological hype. For instance, imagine a startup that aims to simplify online shopping experiences. While an AI-powered recommendation engine might seem like a futuristic addition, a basic heuristic algorithm based on customer purchase history and preferences could initially suffice and effectively meet customer needs. It’s imperative for founders to critically assess: Does AI offer a real tenfold improvement for our specific use case, or are we chasing it for the sake of being on the tech forefront? Do You Have a Team with Relevant Experience? Delving into the AI landscape reveals a mosaic of specialized technologies. Often bundled under the 'AI' tag are distinct domains like Large Language Models (LLM), Machine Learning (ML), Natural Language Processing (NLP) and others. Each field is vast and complex enough to require dedicated expertise. Thus, when building an AI solution, it's crucial to have a team member adept in the specific technology relevant to your project. Even for a team of experienced Data Scientists, crafting effective AI models can pose significant challenges. At Aura, for example, our goal was to develop a Machine Learning model for SMS protection, filtering out spam and scams from messages sent by unknown numbers. The task, straightforward with clear-cut spam, became intricate when it came to detecting scams, often crafted to mimic messages from connections. Imagine the difficulty in discerning genuine messages from deceptive ones like, 'Hi, I miss you. How are you?' This complexity was a formidable challenge, even for Data Scientists boasting a decade of experience. Beyond the creation of the model, the proficiency of Data Scientists in communicating and guiding the broader team on evaluating the model's performance is invaluable. We utilized the “Confusion Matrix” methodology to measure accuracy. This approach involves an initial analysis of a few hundred messages by the model, followed by our experts scoring each message as 'scam,' 'not scam,' or 'unsure.' Comparing these human-assessed scores against the model's outputs helped us identify false negatives (actual scam messages missed by the model) and false positives (legitimate messages incorrectly flagged as spam). The process of training the model—reprocessing messages, tweaking probabilities, and modifying algorithms—spanned over six months from test to production. But this wasn’t a one-off task, as scam techniques evolve, so does our model, demanding ongoing refinement and recalibration. Do You Have a Large High-Quality Data Set? Embarking on AI development in startups, such as my experience at FigLeaf, centers around a critical challenge: ensuring the quality of data. FigLeaf aimed to empower users to maintain their privacy online, particularly in registering on websites anonymously with masked emails. We sought to create a Machine Learning model to differentiate registration forms from login forms, a task complicated by the lack of standardization in form designs across the web. Initially, we thought the solution was straightforward. However, the first versions of our model suffered from performance issues, primarily due to inadequate training data. We had our Customer Support team manually label hundreds of web forms every day, believing that several thousand forms a month would be sufficient. But reality quickly set in—this amount was just a drop in the ocean, barely scratching the surface of what was needed for the model to function effectively across the diverse landscape of the internet. Adding to the complexity, websites often employ A/B testing for their signup pages or maintain different versions for distinct audience segments. This variability meant we were not just dealing with a vast number of forms, but also with forms that were constantly changing or appearing in different versions. It highlighted that in Machine Learning, 'a substantial amount' of data means far more than one might initially anticipate. Ultimately, the road to AI success in startups is paved with meticulous planning and patience. Crafting innovative AI technology is a marathon, not a sprint, where shortcuts can be deceptive detours. A grounded, realistic approach to development timelines is crucial. It's through this committed and thoughtful process that your AI initiative will evolve into a groundbreaking, market-leading force.

Making AI Work for Your Startup: Essential Dos and Don'ts

In 2024, there is no shortage of LLM-based applications. Major tech companies like Microsoft and Google are pushing forward ever-more-powerful versions of their flagship ChatGPT and Gemini models, and specialist players like Anthropic are pushing forward competing offerings with additional integrations. In the applied LLM world, companies and even governments are experimenting with chat applications for a variety of contexts. Despite the enormous entrepreneurial energy poured into LLMs, most high-profile applications are still limited by their focus on chat-like interfaces that take in and return human-readable text. This is understandable - after all, one of the most exciting developments of LLMs is the ability to comprehend and generate human language, allowing for a conversational user interface. However, chat interfaces overlook another very important use case for LLMs - text processing as an intermediate step in a larger data pipeline. Today, we will explore this use case and see how LLMs can be useful as part of a data pipeline, not just as a natural language generator. Function Calling LLMs In order to use LLMs as part of a data pipeline, we need to change the output format of the LLM - instead of generating paragraphs for humans to read, the model has to generate something that computers can read. Usually, this means structured output in a data format like JSON or instructions in  a programming language like Python or SQL. These output formats are a lot less forgiving than natural language, since a missing quotation mark or bracket can crash the entire pipeline. Therefore, we will need to rely on specialized methods and features that have been developed to support this use case. These features are collectively referred to as function calling, since the output tends to call functions or be consumed in function calls. Before we delve into these methods, though, let’s first explore a little more in depth why function calling features were developed in the first place. Use Cases Connecting to external services The original and most straightforward use case for function calling is connecting to external services. When OpenAI originally released GPT-4, it also updated the API with a feature that allowed the user to provide the model a set of python-style functions that the model could call to accomplish its goals. The model could, for example, be told that it can call an external function to calculate the price of a complex security. Using this information, the model would be able to write code that calculates the value of a portfolio made up of these securities, without having to possess specialized knowledge on securities pricing. Calling python-style functions is only the tip of the iceberg. Once the importance of function-calling was proven in the market, OpenAI and other LLM providers started to support other output formats, such as JSON or SQL. The important thing was that these models were outputting machine-readable outputs, which could be reliably read by other processes. Astute readers might recognize that we have done something similar in previous articles, using LLM to generate , , or . In these , we accomplished this using prompt engineering and output-checking, but now that function-calling is a widely-supported feature, we can do this more easily by relying on these model-level features. training data SQL JSON articles Chaining LLMs Function calling created new possibilities for multi-LLM applications, and soon developers were experimenting with chaining LLMs to create sophisticated systems. Some of these systems became known as , which could independently search the web, gather new data, and call another LLM with the new information. These pipelines are capable of surprising amounts of autonomy and are capable of solving sophisticated problems with very little input, though there are still limitations, such as API costs and behavioral safeguards, that prevent agents from gaining widespread adoption. agents Using LLM as input and intermediate processing Another use case for function-calling LLMs is input and intermediate data processing. LLMs can be used to parse unstructured inputs into structured data that can be used for downstream processing. While many of these tasks tend to be relegated to “traditional” NLP technologies, the flexible nature of transformer-based models means that a specifically trained model can perform much better on these tasks than other NLP technologies. As a result, many developers are trying to leverage these specialized models in their data pipelines. Now that we’re familiar with the theory behind function-calling, let’s look at the application we will be building today. Demo Application CB Insights Entity Relationship Analyzer In a , I created a simple RAG application to answer natural language questions about CB Insights popular newsletter. In today’s article, we will build a similar question-answering application, but instead of relying on embedding search and GPT3.5, we will be using entity recognition as the primary indexing method. Additionally, we will use DRAGON as the summarization engine so we can run the entire application on our laptop, no cloud services needed. previous article Why Use Entity Recognition? Before we delve into the implementation, let’s first explore the advantages of using NER as the retrieval technology instead of embedding search. While in production we will want to use each technique according to the demands of the specific situation, NER offers several advantages over a system that relies on embedding search. Debuggability: Because entity recognition is an easily verifiable task, the entire pipeline is much more debuggable. It is easy to check if the model correctly identifies all of the entities, and it is easy to build and improve a matching algorithm based on these entities. Comparatively, it is much more difficult to verify that the embedding algorithm is properly identifying similarities and differences in passages. Flexibility: By separating recognition and filtering, we make the pipeline much more flexible than an embedding-based search algorithm. We can add additional metadata, change search algorithms based on entity type, and even use embedding algorithms on top of the NER-based search results. This allows for much more powerful features to be developed based on combinations of technologies. For example, an NER-based pipeline can use entity-based search to narrow down a set of documents, then use an embedding algorithm to narrow down further within the search space. This tends to make the search much faster and more efficient. Identification Power: Due to the level of manual control that is possible, NER pipelines are much more suited for certain search and retrieval tasks. Embedding searches are based on the proximity of passages within the training data, which calculates the probability that two documents come from the same larger document. In some use cases, this will cause embedding search to miss important documents. For example, if we were trying to understand Microsoft’s AI strategy, we might not only want to retrieve information about Microsoft’s activities within the AI software space, but also information about investments in chipmaking, data centers, and robotics. A general purpose embedding algorithm will not be able to identify these connections, whereas an NER-based algorithm will be able to draw these connections. Now that we understand the benefits of using NER within a RAG pipeline, let’s get a deeper look at the specific technologies we’re using in our application. Technologies Used LLM Entity Recognition Entity Recognition is a “traditional” NLP technology for extracting structured data from unstructured language data. The extracted data can then be used in down-line processing or as metadata for the passage being analyzed. Historically, this was accomplished using small, purpose-built machine learning algorithms that first tags the parts of speech, followed by a second pass that determines if the proper nouns identified are named entities. With function-calling LLMs, we can accomplish the same task, with several added advantages. Because transformer architecture is able to understand the language structure better than smaller NLP models, the performance is likely to be more robust, for example in cases where the passage is improperly formatted. Because LLMs are being updated with the most recent training data, the entity recognition system is likely to stay more up-to-date than other open-source entity recognition models. Prompt engineering techniques can be used to embed additional data and give additional instructions, allowing for more flexible behavior even in off-the-shelf models. We have covered Dragon in a previous article where we highlighted its impressive performance in summarizing natural-language data. We will be using Dragon to perform the final step of analysis, where we will summarize all of the articles relevant to the selected entities. DRAGON SLIMs are a new family of miniaturized, locally-runnable models from - it’s no secret I am a fan of their miniaturization work - that specializes in function calling. These models have been fine-tuned specifically to generate outputs that can be interpreted by machines, allowing users to take advantage of the recent developments in function calling architecture without having to rely on external APIs. SLIMs LLMWare Today we will be using the SLIMs-NER model, which will perform the Named Entity Recognition on the newsletter articles. In addition to NER, there are also SLIMs models for sentiment analysis, SQL generation, and multi-step agents. Now we understand the technology, let’s implement the application! Implementation Data Downloading and Processing Let’s begin by downloading the CB Insights articles. Let’s import the dependencies: import requests
from bs4 import BeautifulSoup
import os
import pandas as pd
import json
import re And now the code to download the newsletter archive: res = requests.get('https://www.cbinsights.com/newsletter/')

soup = BeautifulSoup(res.text)
article_links = [[i.text, i['href']] for i in soup.find_all('a') if 'campaign-archive' in i['href'] ]
article_soups = [BeautifulSoup(requests.get(link).text) for title, link in article_links] Now that we have downloaded the newsletter archive, let’s process it into JSON format result_json = {}
for soup_meta, soup_art in zip(article_links, article_soups):
    article_tables = []
    cur_article = []
    for table in soup_art.find_all('table'):
        if table.attrs.get('mc:variant') == 'Section_Divider':
            article_tables.append(get_deduped_article_tables(cur_article))
            cur_article = []
        else:
            cur_article.append(table.text)
    article_tables.append(get_deduped_article_tables(cur_article))
    result_json[soup_meta[0]] = article_tables

articles_with_meta = []
for name, tables in result_json.items():
      print(name, pd.to_datetime(tables[0][1].strip()))
      articles_with_meta.append({
            'name': name, 'date':  pd.to_datetime(tables[0][1].strip()).strftime('%Y-%m-%d'), 'tables': tables
      })
df = pd.DataFrame(articles_with_meta) Now we are at the same place we were at in the previous article. Instead of going right to the RAG construction by creating embeddings, however, we will instead run an entity-recognition step. First, let’s import the dependencies for running SLIMs, a new set of models from LLMWare that allows us to perform entity recognition locally on our machine: from llmware.agents import LLMfx
from llmware.parsers import WikiParser
from collections import defaultdict

# define a function for identifying all the named entities
def run_ner(text):
    agent = LLMfx()
    agent.load_work(text)
    agent.load_tool("ner")
    named_entities = agent.ner()
    ner_dict= named_entities["llm_response"]
    return ner_dict Now we can run the named entity recognition on all of the articles: date_entities = defaultdict(dict)
for _, row in df.iterrows():
    for idx, t in enumerate(row['tables'][1:]):
        if 'Start Your Free Trial' in t:
            t = t[:t.index('Start Your Free Trial')]
        date_entities[row['date']][idx] = run_ner('\n'.join(t)) It might take a few minutes for the NER pipeline to complete, but this is all you need to recognize entities using a state-of-the-art miniaturized LLM. You can check the date_entities dictionary by printing some test items. For example, the code: date_entities[list(date_entities.keys())[0]] Should produce the output below: {0: {'people': ['Yahoo!'],
  'place': [],
  'company': ['Databricks', 'MosaicML'],
  'misc': []},
 1: {'people': [],
  'place': ['New York'],
  'company': ['CB Insights'],
  'misc': []}} Which shows all of the various entities that have been detected by the SLIMs model. Question-Answering with DRAGON Now that the entities have been detected, let’s build a question-answering workflow that demonstrates the power of this technique. For our example, we will use the test question: What role does OpenAI play in Microsoft's AI strategy? Let’s begin by importing the proper packages for running DRAGON: from llmware.prompts import Prompt
query = "What role does OpenAI play in Microsoft's AI strategy?"

model_name = "llmware/dragon-llama-7b-gguf"
prompter = Prompt().load_model(model_name) Now, we can build the function that answers the question using entity recognition. In order to leverage our NER data, we will want to implement the following workflow: Identify the entities in the user query. We can use the same SLIMs function we used in the previous step. Perform a lookup based on the results of the lookup, that is, we search for articles in the corpus that talk about the same entities. Once we find these articles, we can supply them to the question-answering model to extract the relevant information. Here is what the workflow looks like in code form: def answer_question_with_ner(query):
    ner_results = run_ner(query) # run NER on the user query
    search_entities = []
    for ent_type, ent_list in ner_results.items():
        search_entities.extend(ent_list)
    # create a list of entities to search for
    search_entities = list(set(search_entities))

    # now perform a lookup for articles that mention the identified entities, using set arithmetic. 
    articles = set(entity_to_place_map[search_entities[0]])
    for se in search_entities:
        articles &= set(entity_to_place_map[se])

    # now process the corpus into a prompt and feed it to the question-answering LLM.
    article_content = []
    for article in articles:
        article_content.extend(df[df['date'] == article[0]].iloc[0]['tables'][article[1]+1])
    response = prompter.prompt_main(query, 
        context='\n'.join(article_content),
        prompt_name="default_with_context", temperature=0.3)
    return response # return the response. Run the function, with the query from the first code block, and you should see the following result: Microsoft has poured billions of dollars into ChatGPT developer OpenAI. However, it's also placed a number of bets on other GenAI startups across computing, mapping, and gaming. You can also see in the “evidence” section of the response object that, indeed the two articles from the CB Insights archive that mention Microsoft and OpenAI have been retrieved, and that the LLM’s answer is based directly on the evidence. Note that because we are doing explicit entity recognition, the retrieval process is extremely transparent and debuggable. Should a question ever arise as to why the model retrieved these specific pieces of information from the corpus - all it takes is a simple print statement to see that the model chose the article because the query mentions “Microsoft” and “OpenAI”, and the two retrieved newsletter sections are the only sections that mention both entities. Additionally, when compared with embedding-based vector search, the NER lookup method provides much more precise answers. In my tests, the selected articles were in the top 10 most relevant articles ranked by OpenAI’s ada algorithm, but was not the one identified as having the closest proximity to the question at hand. Therefore, it is possible an application using embedding search may not have correctly answered the question at all, and the lack of debuggability would only contribute to the confusion. And with that, we have built a question-answering application using NER lookups, improving the quality of the pipeline in the process! Conclusion Today, we built an application using function calling, an exciting new LLM feature. Miniaturized, locally-runnable function-calling models is a revolutionary development that unlocks a new class of AI applications, and we are only seeing the first iteration of these technologies. It will be exciting to see what applications developers will create with these technologies in the coming months. If you have an idea in the AI space that you want to see implemented or simply want to chat about technology, please do not hesitate to reach out on or . Github LinkedIn If you want to learn more about LLMWare, the company behind SLIMs and DRAGON, you can find them on or . HugginFace Github

MOSAIC

mosaicco.com

10K+ employees

Since 2004

COMPANY RANKING

MOSAIC

EVERGREEN INDEX #7295

Representing this company? Click here to claim and customize this page!

Mosaic WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!

MOSAIC

mosaicco.com

10K+ employees

Since 2004

COMPANY RANKING

MOSAIC

EVERGREEN INDEX #7295

RELATED COMPANIESRANK

STORIES ABOUT MOSAIC

Representing this company? Click here to claim and customize this page!

Mosaic WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!