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Android Builders Weblog: #WeArePlay | How Jakub is infusing Czech mythology into his video games

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Android Builders Weblog: #WeArePlay | How Jakub is infusing Czech mythology into his video games



Android Builders Weblog: #WeArePlay | How Jakub is infusing Czech mythology into his video games

Posted by Robbie McLachlan, Developer Advertising and marketing

In our newest movie for #WeArePlay, which celebrates the individuals behind groundbreaking apps and video games, Jakub takes us on a journey via the world of Amanita Design. Born in Prague, Czech Republic, his journey into the world of video games started with a ardour for animation and one eye on inventive element. Pushed by a imaginative and prescient to create video games that mix charming artwork with immersive storytelling, he based his firm Amanita Design in 2003.

Right now, the thriving enterprise is famend for its distinctive strategy to video games, drawing inspiration from Czech landscapes, fairy tales, and the wealthy cultural heritage of its homeland. With a devoted group of round 30, they’re crafting video games as visually beautiful as they’re narratively wealthy. Uncover how he’s merging the attraction of Czech tradition with the magic of gaming.

What’s the inspiration behind Amanita Design and your sport Machinarium?

I’ve a love for nature, fairy tales, and Czech tradition. Rising up in Prague, I used to be surrounded by lovely landscapes and previous buildings that sparked my creativeness. I studied classical animation and all the time wished to create one thing that felt each magical and deeply linked to my roots. Our video games typically use Czech folklore and the pure world. In 2009, once we developed Machinarium, I used to be fascinated with industrial decay and previous equipment. The deserted factories round Prague supplied a gritty backdrop for the sport. We paired this with a compelling story and handcrafted visuals. We even used pure sounds from the environment so as to add an genuine contact.

Did you all the time think about you’d be an entrepreneur?

I didn’t initially see myself as an entrepreneur. My journey started with a ardour for video games and animation, and I began Amanita Design as a pure extension of my pursuits. I started the studio proper after ending faculty, pushed by a need to create and share my inventive imaginative and prescient. Over time, because the studio grew organically, I embraced the function of an entrepreneur but it surely was the love for sport improvement that originally set me on this path.

What units your video games aside?

What makes our video games stand out is the combination of old-world craftsmanship with right this moment’s tech. We actually get pleasure from incorporating hand-painted cardboard characters and utilizing pure supplies for sound results, which provides a novel, tactile really feel to our work. We draw deeply from Czech tradition, nature, and fairy tales, giving every sport a particular and enchanting contact. It’s all about creating one thing genuine and immersive, and we hope that keenness resonates with our gamers.

What does the longer term appear like for Amanita Design?

We’re engaged on a number of new video games and exploring totally different distribution fashions, such because the free-to-try strategy on cellular platforms. Our aim is to proceed creating distinctive and artistically wealthy video games that resonate with a worldwide viewers. As know-how evolves, we plan to adapt and innovate, sustaining our give attention to storytelling and inventive craftsmanship whereas embracing new alternatives within the gaming trade.

Uncover extra international #WeArePlay tales and share your favorites.


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Database Setup So Straightforward, Your Cat May Do It: Docker and Flyway Version | Weblog | bol.com

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Database Setup So Straightforward, Your Cat May Do It: Docker and Flyway Version | Weblog | bol.com


Picture supply

Alright, of us, except you’re a type of uncommon individuals who personal a genius cat that may code (and if you’re, we have to discuss), organising a neighborhood database would possibly look like a frightening process. Worry not! With Docker and Flyway, it’s so simple that even your cat might do it — nicely, theoretically. So let’s dive into it!

The necessity

If an software is utilizing a database for persistence, then it’ll want one which it might connect with regionally, to be able to run itself or its (integration) exams. The query is, what’s a handy and environment friendly approach to set a database up like that?

Ideally we’d have a database setup which:

  • is simply used regionally
  • has the identical schema and knowledge each time
  • might be constructed up and damaged down each time we wish
  • is straightforward to re-create each time

Let’s take a better have a look at these statements:

Solely used regionally

It can be crucial that the duties we carry out in native improvement don’t have an effect on our different environments (like staging or manufacturing). Knowledge of every setting ought to solely come from that setting to keep away from air pollution and potential confusion.

Has the identical schema and knowledge each time

The native database must be a dependable illustration of our actual database. The code expects a sure state and we have to assure it’ll discover that state each time our database is created. In any other case we are able to have something from compilation failures to damaged exams.

May be constructed up and damaged down everytime you need

The extra management we’ve over this, the cooler the issues we are able to do. How good wouldn’t it be if we might simply fireplace up the setup earlier than a construct after which break it down? And the way nicer wouldn’t it be if that was robotically taking place by merely working the construct?

Straightforward to re-create each time

The simpler it’s to re-create, the extra probably we’re to make use of it. I’m positive many people have the expertise of avoiding to run that horrible app regionally as a result of it’s simply an excessive amount of trouble.

Now, if solely there was a setup that might assure all the above…

Constructing a Native Face Search Engine — A Step by Step Information | by Alex Martinelli | Aug, 2024

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Constructing a Native Face Search Engine — A Step by Step Information | by Alex Martinelli | Aug, 2024


On this entry (Half 1) we’ll introduce the essential ideas for face recognition and search, and implement a primary working answer purely in Python. On the finish of the article it is possible for you to to run arbitrary face search on the fly, domestically by yourself pictures.

In Half 2 we’ll scale the educational of Half 1, by utilizing a vector database to optimize interfacing and querying.

Face matching, embeddings and similarity metrics.

The aim: discover all cases of a given question face inside a pool of pictures.
As an alternative of limiting the search to actual matches solely, we will chill out the standards by sorting outcomes primarily based on similarity. The upper the similarity rating, the extra seemingly the end result to be a match. We will then choose solely the highest N outcomes or filter by these with a similarity rating above a sure threshold.

Instance of matches sorted by similarity (descending). First entry is the question face.

To type outcomes, we want a similarity rating for every pair of faces (the place Q is the question face and T is the goal face). Whereas a primary strategy may contain a pixel-by-pixel comparability of cropped face pictures, a extra highly effective and efficient methodology makes use of embeddings.

An embedding is a discovered illustration of some enter within the type of an inventory of real-value numbers (a N-dimensional vector). This vector ought to seize probably the most important options of the enter, whereas ignoring superfluous facet; an embedding is a distilled and compacted illustration.
Machine-learning fashions are skilled to be taught such representations and may then generate embeddings for newly seen inputs. High quality and usefulness of embeddings for a use-case hinge on the standard of the embedding mannequin, and the standards used to coach it.

In our case, we wish a mannequin that has been skilled to maximise face identification matching: photographs of the identical particular person ought to match and have very shut representations, whereas the extra faces identities differ, the extra completely different (or distant) the associated embeddings must be. We would like irrelevant particulars reminiscent of lighting, face orientation, face expression to be ignored.

As soon as now we have embeddings, we will evaluate them utilizing well-known distance metrics like cosine similarity or Euclidean distance. These metrics measure how “shut” two vectors are within the vector house. If the vector house is effectively structured (i.e., the embedding mannequin is efficient), this can be equal to know the way comparable two faces are. With this we will then type all outcomes and choose the most probably matches.

A wonderful visible rationalization of cosine similarity

Implement and Run Face Search

Let’s leap on the implementation of our native face search. As a requirement you’ll need a Python atmosphere (model ≥3.10) and a primary understanding on the Python language.

For our use-case we will even depend on the favored Insightface library, which on high of many face-related utilities, additionally provides face embeddings (aka recognition) fashions. This library alternative is simply to simplify the method, because it takes care of downloading, initializing and working the required fashions. It’s also possible to go immediately for the offered ONNX fashions, for which you’ll have to put in writing some boilerplate/wrapper code.

First step is to put in the required libraries (we advise to make use of a digital atmosphere).

pip set up numpy==1.26.4 pillow==10.4.0 insightface==0.7.3

The next is the script you should use to run a face search. We commented all related bits. It may be run within the command-line by passing the required arguments. For instance

 python run_face_search.py -q "./question.png" -t "./face_search"

The question arg ought to level to the picture containing the question face, whereas the goal arg ought to level to the listing containing the photographs to look from. Moreover, you’ll be able to management the similarity-threshold to account for a match, and the minimal decision required for a face to be thought-about.

The script hundreds the question face, computes its embedding after which proceeds to load all pictures within the goal listing and compute embeddings for all discovered faces. Cosine similarity is then used to match every discovered face with the question face. A match is recorded if the similarity rating is bigger than the offered threshold. On the finish the listing of matches is printed, every with the unique picture path, the similarity rating and the situation of the face within the picture (that’s, the face bounding field coordinates). You’ll be able to edit this script to course of such output as wanted.

Similarity values (and so the edge) can be very depending on the embeddings used and nature of the information. In our case, for instance, many appropriate matches may be discovered across the 0.5 similarity worth. One will all the time have to compromise between precision (match returned are appropriate; will increase with larger threshold) and recall (all anticipated matches are returned; will increase with decrease threshold).

What’s Subsequent?

And that’s it! That’s all it is advisable to run a primary face search domestically. It’s fairly correct, and may be run on the fly, nevertheless it doesn’t present optimum performances. Looking from a big set of pictures can be gradual and, extra essential, all embeddings can be recomputed for each question. Within the subsequent put up we’ll enhance on this setup and scale the strategy by utilizing a vector database.

Leighton Welch, CTO and Co-Founding father of Tracer – Interview Sequence

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Leighton Welch, CTO and Co-Founding father of Tracer – Interview Sequence


Leighton Welch is CTO and co-founder of Tracer. Tracer is an AI-powered instrument that organizes, manages, and visualizes advanced information units to drive sooner, extra actionable enterprise intelligence. Previous to changing into the Chief Know-how Officer at Tracer, Leighton was the Director of Client Insights at SocialCode, and the VP of Engineering at VaynerMedia. He has spent his profession pioneering within the advert tech ecosystem, operating the primary ever Snapchat Advert and consulting on industrial APIs for a number of the world’s largest platforms. Leighton graduated from Harvard in 2013, with a level in Laptop Science and Economics.

Are you able to inform us extra about your background and the way your experiences at Harvard, SocialCode, and VaynerMedia impressed you to co-found Tracer?

The unique thought got here a decade in the past. A childhood buddy of mine rang me on a Friday evening. He was scuffling with aggregating information throughout varied social platforms for one among his purchasers. He figured this might be automated, so he enlisted my assist since I had a background in software program engineering. That’s how I used to be first launched to my now co-founder, Jeff Nicholson.

This was our gentle bulb second: The amount of cash being spent on these campaigns was far outpacing the standard of the software program monitoring these {dollars}. It was a nascent market with a ton of purposes in information science.

We stored constructing analytics software program that might meet the wants of more and more giant and complicated media campaigns. As we hacked away on the drawback, we developed a course of – clear steps from getting the disparate information ingested and contextualized. We realized the method we had been constructing might be utilized to any information set – not simply promoting – and that’s what Tracer is right now: an AI-powered instrument that organizes, manages, and visualizes advanced information units to drive sooner, extra actionable enterprise intelligence.

We’re serving to to democratize what it means to be a “data-driven” group by automating the steps wanted to ingest, join, and manage disparate information units throughout features, offering highly effective BI by means of intuitive reporting and visualizations. This might imply connecting gross sales information to your advertising and marketing CRM, HR analytics to income tendencies, and countless extra purposes.

Are you able to clarify how Tracer’s platform automates analytics and revolutionizes the fashionable information stack for its purchasers?

For simplicity, let’s outline analytics because the answering of a enterprise query by means of software program. In right now’s panorama, there are actually two approaches.

  • The primary is to purchase vertical software program. For CFOs, this is perhaps Netsuite. For the CRO, it is perhaps Salesforce. Vertical software program is nice as a result of it’s end-to-end, it may be hyper specialised, and may simply work out of the field. The limitation of vertical software program is that it’s vertical: in order for you Netsuite to speak to Salesforce, you’re again to sq. one. Vertical software program is full, however it’s not versatile.
  • The second strategy is to purchase horizontal software program. This is perhaps one software program for information ingestion, one other for storage, and a 3rd for evaluation. Horizontal software program is nice as a result of it will probably deal with just about something. You can actually ingest, retailer and analyze each your Salesforce and Netsuite information by means of this pipeline. The limitation is that it must be put collectively, maintained, and nothing works “out of the field.” Horizontal software program is versatile, however it’s not full.

We provide a 3rd strategy by making a platform that mixes the applied sciences essential to report on something, made accessible sufficient to work out of the field with none engineering assets or technical overhead. It’s versatile and full. Tracer is probably the most highly effective platform available on the market that’s each utility agnostic, and end-to-end.

Tracer processed on the order of 10 petabytes of information final month. How does Tracer deal with such an enormous quantity of information effectively?

Scale is extremely necessary in our world, and it has at all times been a precedence at Tracer even to start with days. To course of this quantity of information, we leverage quite a lot of finest in school applied sciences and keep away from reinventing the wheel the place we don’t must. We’re extremely happy with the infrastructure we’ve constructed, however we’re additionally fairly open about it. In truth, our structure program is printed on our web site.

What we are saying to companions is that this: It’s not that your in-house engineering groups aren’t able to constructing what we’ve constructed; somewhat, they shouldn’t need to. We’ve assembled the items of the fashionable information stack for you. The framework is environment friendly, battle-tested, and modular for us to dynamically evolve with the panorama.

Quite a lot of companions will come to us trying to unlock engineering assets to deal with greater strategic initiatives. They use Tracer’s structure as a method to an finish. Having a database doesn’t reply enterprise questions. Having an ETL pipeline doesn’t reply enterprise questions. The factor that basically issues is what you’re in a position to do with that infrastructure as soon as it’s been put collectively. That’s why we constructed Tracer – we’re your shortcut to getting solutions.

Why do you imagine structured information is important for AI, and what benefits does it present over unstructured information?

Structured information is important for AI as a result of it permits for handbook human interplay, which we imagine is an integral part to efficient outputs. That being stated, in right now’s ecosystem, we are literally higher geared up than ever earlier than to leverage the insights in unstructured information and beforehand arduous to entry codecs (paperwork, photos, movies, and so on.).

So for us, it’s about offering a platform by means of which further context will be integrated from the people who find themselves most conversant in the underlying datasets as soon as that information has been made accessible. In different phrases, it’s unstructured information → structured information → Tracer’s context engine → AI-driven outputs. We sit in between and permit for a more practical suggestions loop, and for handbook intervention the place vital.

What challenges do firms face with unstructured information, and the way does Tracer assist overcome these challenges to enhance information high quality?

With no platform like Tracer, the problem with unstructured information is all about management. You feed information into the mannequin, the mannequin spits out solutions, and you’ve got little or no alternative to optimize what’s occurring contained in the black field.

Say for instance you wish to decide probably the most impactful content material in a media marketing campaign. Tracer may use AI to assist present metadata on all of the content material that was run within the advertisements. It additionally may use AI to supply final mile analytics for getting from a extremely structured dataset to that reply.

However in between, our platform permits customers to attract the connections between the media information and the dataset the place the outcomes reside, extra granularly outline “impactful,” and clear up the categorizations performed by the AI. Basically, we’ve abstracted and productized the steps, with a view to take away the black field. With out AI, there’s much more work that needs to be performed by the human in Tracer. However with out Tracer, AI can’t get to the identical high quality of reply.

What are a number of the key AI-based applied sciences Tracer makes use of to reinforce its information intelligence platform?

You possibly can consider Tracer throughout three core product classes: Sources, Content material, and Outputs.

  • Sources is a instrument used to automate the ingestion, monitoring and QA of disparate information.
  • Context is a drag and drop semantic layer for the group of information after it’s been ingested.
  • Outputs is the place you possibly can reply enterprise questions on high of contextualized information.

At Tracer we don’t see AI as a substitute for any of those steps; as an alternative, we see AI as one other type of tech that every one three classes can leverage to increase what will be automated.

For instance:

  • Sources: Leveraging AI to assist construct new API connectors to lengthy tail information sources not out there by means of our associate catalog.
  • Context: Leveraging AI to wash up metadata previous to operating tag guidelines. For instance, cleansing up variations of publication names in each language.
  • Outputs: Leveraging AI as a drop-in substitute for dashboards the place the enterprise use case is exploratory, somewhat than a hard and fast set of KPIs that should be reported on repeatedly.
  • AI permits us to realize most of these purposes in methods which can be each easy and accessible.

What are Tracer’s plans for future growth and innovation within the information intelligence house?

Tracer is an aggregator of aggregators. Our companions will lean on us for particular purposes inside groups and features, or to be used in cross-functional enterprise intelligence. The fantastic thing about Tracer is that whether or not you’re leveraging us for making higher selections together with your media spend and inventive, or constructing dashboards to hyperlink disparate metrics from provide chain to gross sales and every little thing in between, the constructing blocks are constant.

We’re seeing organizations who formally relied on us inside one space of the enterprise (e.g., media and advertising and marketing), increase purposes to elsewhere within the enterprise. So the place our major clients had been formally senior media executives, or company companions, lately we work throughout the org, partnering with CIOs, CTOs, information scientists, and enterprise analysts. We’re persevering with to construct out our instruments to accommodate for increasingly more purposes and personas, all whereas making certain the core tech is scalable, versatile, and accessible for non-technical customers.

Thanks for the nice interview, readers who want to be taught extra ought to go to Tracer.

INNOSPACE to Launch Brazilian Satellites and Inertial System on HANBIT-Nano Rocket

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INNOSPACE to Launch Brazilian Satellites and Inertial System on HANBIT-Nano Rocket


INNOSPACE to Launch Brazilian Satellites and Inertial System on HANBIT-Nano Rocket

by Riko Seibo

Tokyo, Japan (SPX) Might 31, 2024






INNOSPACE, a South Korean personal spaceflight startup producing small satellite tv for pc launch automobiles, has signed new launch service agreements with the Federal College of Maranhao (UFMA) and Castro Leite Consultoria LTDA (CLC). This mission will use the HANBIT-Nano launch car from the Alcantara Area Middle in Brazil within the first half of 2025.



The mission will deploy picosatellites developed by DARTilab and BAITES, Brazilian analysis institutes concerned within the mission by way of a contract with UFMA. The college leads the Alcantara Scientists Mission in Maranhao, Brazil.



“The partnership with INNOSPACE for the launch of the picosatellite is a historic milestone, which can present the residents of Alcantara with the distinctive alternative to actively take part within the development of science and area exploration. We’re excited as a result of with this endeavor we spotlight the significance of inclusion and respect for native communities in large-scale initiatives that drive technological progress, in addition to encourage neighborhood unity and delight. We hope that this initiative will encourage the younger folks of Alcantara and contribute to the emergence of recent alternatives for data within the area,” mentioned UFMA.



Underneath the settlement, INNOSPACE’s HANBIT-Nano will carry a payload, an inertial system developed by CLC. CLC is an engineering consulting service supplier specializing in aerospace, protection, and inertial techniques with a decade of expertise in Brazil.



“It is a superb alternative to qualify our merchandise for an area flight. It will add worth to each our tools and the technique of testing,” mentioned CLC.



“It’s totally significant to be chosen by Brazilian clients, as this mission might be INNOSPACE’s first business launch and the maiden flight of its HANBIT-Nano rocket after efficiently launching our first check launch car, HANBIT-TLV, from the Alcantara Area Middle in Brazil on March 19, 2023 (BRT),” mentioned Soojong Kim, founder and CEO of INNOSPACE. “INNOSPACE’s precedence for HANBIT is to help clients who share an modern imaginative and prescient for area, and we’re proud to supply them with the launch providers required to attain their targets.”


Associated Hyperlinks

INNOSPACE

Rocket Science Information at Area-Journey.Com



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