#Extract Google Maps Data

[3idatascraping]

A complete guide on extracting restaurant data from Google Maps includes using web scraping tools like BeautifulSoup or Scrapy in Python, leveraging the Google Places API for structured data access, and ensuring compliance with Google's terms of service. It covers steps from setup to data extraction and storage.

[locationscloud]

Discover the techniques of extracting location data from Google Maps. Learn to harness the power of this resource to gather accurate geographic information.

Read More: https://www.locationscloud.com/location-data-extraction-from-google-maps/

[retailgators]

How Web Scraping is Used to Extract Google Maps Data for Non-Techs?

Google maps evolved almost 15 years back and emerged as an app with entirely new advertising levels and prospects from just straightforward navigation for all types of businesses. 

Uses Of Google Maps Data

It is safe to say that gathering data on a B2B lead is a monkey task that doesn’t stimulate much innovation or drive productivity. Automated solutions will address this. It is establishing a potential clientele.

We are locating the specific product’s place of sale and selecting the top result from the list of options.

Here we will use Google Maps data scraper to analyze geospatial data for engineering or scientific purposes. For instance, while calculating global distances on Earth to the location of cyclones and doing climate research using geolocation and satellite data.

How To Scrape Google Maps?

1. Specify the business or location in which you are interested.

Now, you can copy the URL from the webpage. You can search by location (state, city, or country) if you have a particular area in mind. Organizations can readily find using a keyword or industry.

2. Data fields that you need to scrape.

Next, specify the information you want to include in the finished file. These could be information about the company, reviews, etc. We can retrieve all necessary information, including rating, name, location, category, hours, website, and phone, for industry leads from the information page.

3. Apply Here To Receive A Free Extract.

Completing an online process for RetailGators requires you to input your contact email address, the URL you previously copied, and the details you need to scrape. If you have any unique needs, please be as detailed as possible.

4. Take a look at the output test file.

We will send the sample of the collected data to your email address. You must look through every file to ensure the necessary information is present.

Conclusion

One minor drawback is that it’s faster and more effective to use proxies when Google Map Scraping. As a result, if you start extracting Maps regularly, it will eventually cost you money. Still, in the meanwhile, you get a free month of Retailgators Proxy as soon as you sign up at Retailgators. So act quickly and use our free Google Maps Scraper on Retailgators Store to start your first month.

As per your needs, we can offer eCommerce data scraping services. Every time you require information from Google Maps, use RetailGators. Save time and obtain information without difficulty.

[pinkyjulien]

Dragon Age: The Veilguard

▶ Extracted Asset Drive Folder

Recently finished my first DAV playthrough and wanted to get my hands into the files 🤚 so just like I did with CP77, I put up little google drive folder with extracted assets! Made possible thanks to the Frostbite Modding Tool ◀

OBVIOUS Spoiler warning - I don't recommend looking at the files until you're done with the game's main story!

I wasn't able to grab everything just yet as the majority of assets aren't fully accessible yet (corrupted/missing data). Expect some extracted assets to have some artifacts as well!

But you can already find:

HUD elements

Codex entries's full art

CC, Map, Journal Icons

...and more!

Every elements has been sorted in folders for an (hopefully) easy browsing - I'll try to keep this drive updated :3

▶ THIS IS FOR PERSONAL USE ONLY!

You can use the assets for your videos, thumbnails, character templates, art, mods... but do NOT use these assets for any commercial purposes! Every assets and files are the property of Bioware and their artists

This is from a fan for fans, let's keep it fair and fun! 🙏

If you appreciate my work consider supporting me on Ko-Fi 💜

[fooddatascrape]

How To Extract Food Data From Google Maps With Google Colab & Python?

Do you want a comprehensive list of restaurants with reviews and locations every time you visit a new place or go on vacation? Sure you do, because it makes your life so much easier. Data scraping is the most convenient method.

Web scraping, also known as data scraping, is the process of transferring information from a website to a local network. The result is in the form of spreadsheets. So you can get a whole list of restaurants in your area with addresses and ratings in one simple spreadsheet! In this blog, you will learn how to use Python and Google Colab to Extract food data From Google Maps.

WWe are scraping restaurant and food data using Python 3 scripts since installing Python can be pretty handy. We use Google Colab to run the proofreading script since it allows us to run Python scripts on the server.

As our objective is to get a detailed listing of locations, extracting Google Maps data is an ideal solution. Using Google Maps data scraping, you can scrape data like name, area, location, place types, ratings, phone numbers, and other applicable information. For startups, we can utilize a places data scraping API. A places Scraping API makes that very easy to scrape location data.

Step 1: What information would you need?

For example, here we are searching for "restaurants near me" in Sanur, Bali, within 1 kilometer. So the criteria could be "restaurants," "Sanur Beach," and "1 mile."Let us convert this into Python:

These "keywords" help us find places categorized as restaurants OR results that contain the term "restaurant." A comprehensive list of sites whose names and types both have the word "restaurant" is better than using "type" or "name" of places.

For example, we can make reservations at Se'i Sapi and Sushi Tei at the same time. If we use the term "name," we will only see places whose names contain the word "restaurant." If we use the word "type," we get areas whose type is "restaurant." However, using "keywords" has the disadvantage that data cleaning takes longer.

Step 2: Create some necessary libraries, like:

Create some necessary modules, such as:

The "files imported from google. colab" did you notice? Yes, to open or save data in Google Colab, we need to use google. colab library.

Step 3: Create a piece of code that generates data based on the first Step's variables.

With this code, we get the location's name, longitude, latitude, IDs, ratings, and area for each keyword and coordinate. Suppose there are 40 locales near Sanur; Google will output the results on two pages. If there are 55 results, there are three pages. Since Google only shows 20 entries per page, we need to specify the 'next page token' to retrieve the following page data.

The maximum number of data points we retrieve is 60, which is Google's policy. For example, within one kilometer of our starting point, there are 140 restaurants. This means that only 60 of the 140 restaurants will be created.

So, to avoid inconsistencies, we need to get both the radius and the coordinates right. Ensure that the diameter is not too large so that "only 60 points are created, although there are many of them". Also, ensure the radius is manageable, as this would result in a long list of coordinates. Neither can be efficient, so we need to capture the context of a location earlier.

Continue reading the blog to learn more how to extract data from Google Maps using Python.

Step 4: Store information on the user's computer

Final Step: To integrate all these procedures into a complete code:

You can now quickly download data from various Google Colab files. To download data, select "Files" after clicking the arrow button in the left pane!

Your data will be scraped and exported in CSV format, ready for visualization with all the tools you know! This can be Tableau, Python, R, etc. Here we used Kepler.gl for visualization, a powerful WebGL-enabled web tool for geographic diagnostic visualizations.

The data is displayed in the spreadsheet as follows:

In the Kepler.gl map, it is shown as follows:

From our location, lounging on Sanur beach, there are 59 nearby eateries. Now we can explore our neighborhood cuisine by adding names and reviews to a map!

Conclusion:

Food data extraction using Google Maps, Python, and Google Colab can be an efficient and cost-effective way to obtain necessary information for studies, analysis, or business purposes. However, it is important to follow Google Maps' terms of service and use the data ethically and legally. However, you should be aware of limitations and issues, such as managing web-based applications, dealing with CAPTCHA, and avoiding Google blocking.

Are you looking for an expert Food Data Scraping service provider? Contact us today! Visit the Food Data Scrape website and get more information about Food Data Scraping and Mobile Grocery App Scraping. Know more : https://www.fooddatascrape.com/how-to-extract-food-data-from-google-maps-with-google-colab-python.php

[script-a-world]

Submitted via Google Form:

I made a rough map but then after assigning how much time it takes people to get around with various transports and prices and alternatives as well as trying to make it make sense and not just have transport because. However I've found lots of plot holes and started editing things but one edit makes the rest of the map go awry. So I made a more detailed map so sometimes I don't need to edit the entire thing. But now things are spiralling and I have a massive map with hundreds of points and extreme details that get even more screwed up as I make changes. Uhh. What do I do? Timing and optimising ideal times/budgets to get to places, etc is an important part of the character's actions and plot points.

Tex: The fantastic thing about maps is that you can have more than one of them, each suited to a different subject regarding the same area. A master map with all of the data points that you want is very useful, and will allow you to extract specific information for maps about more focused subjects (i.e. geographical features vs settlements vs train lines vs climate zones vs botanical). This is helpful in that on individual, subject-specific maps, you can coordinate colour keys in order to generate an overall scheme, such as which colours you prefer for greater vs lower densities, or background vs foreground information.

The Wikipedia page on maps contains a lot of useful information in this regard, and I would also recommend making a written outline of the information that you have on your map, in order to organize the information displayed there, keep track of changes, and plan how to group your information.

Licorice: It sounds as if you’re having difficulty deciding whether your map should dictate your plot, or vice versa. If your story were set on Earth, your map would be fixed, and the means of transport would be, to some extent, dictated to you; you’d only have to decide where your characters should go next - Beijing, or New York? 

I’m getting the impression that with this story, you’re creating the map and devising the plot simultaneously, and what you’ve ended up with is a map that’s so detailed it has become inconsistent and difficult even for its own creator to make sense of. It sounds as if the map is becoming an obstacle to writing the story rather than an aid to it. At the same time, though, it sounds like you have a much clearer idea of your plot points and where this story is going than you did when you started. 

Maybe it’s time to put the map aside and focus on writing the story? Then when you’re done, update the map so it’s fully in line with the story you’ve written.

Alternatively, put the story aside for a moment, draft a new simplified map using everything you’ve learnt about your story so far, and then treat it like something as fixed as a map of Earth - something to which additional details can be added, but nothing can be changed.

I found on tumblr this chart of the daily distances a person can travel using different modes of transport, which may be useful to you:

Realistic Travel Chart

Good luck! Your project sounds fascinating.

Mod Note: I’ll toss in our two previous map masterposts here for reader reference as well

Mapmaking Part 1

Mapmaking Part 2 Mapping Cities and Towns

[bellakenobi]

Tutorial - Extracting the assets from Shining Nikki for conversion for Sims games (or anything, really)

Finally! In advance I'm sorry for any errors since english isn't my first language (and even writing in my actual language is difficult for me so)

And first, a shoutout to The VG Resource forums, where I found initially info about this topic 😊 I'm just compilating all the knowledge I found there + the stuff I figured out in a single text, because boy I really wanted to find a guide like that when I first thought about converting SN stuff lol (and because there's a lot of creators more seasoned than me that could do a really good job with these assets 👀)

What this tutorial will teach you:

How to find and extract meshes and textures (when there's any) for later use, and some tips about how stuff are mapped etc on Shining Nikki.

What this tutorial will not teach you:

How to fully convert these assets for something usable for any sims game (because honestly neither I know how to do that stuff properly lol). It is assumed that you already know how to do that. If you don't know but has interest in learning about CC making (specially for TS3), I'd suggest you take a look at the TS3 Tutorial Hub, the MTS tutorials and This Post by Plumdrops if you're interested in hair conversion. Also take a look on my TS3 tutorials tag, that's where I reblog tutorials that I think might be useful :)

What you'll need:

An Android emulator (I recomend Nox)

A HEX editor (I recomend HxD)

Python and This Script for mass editing

AssetStudio

A 3D Modeling Software for later use. I use Blender 2.93 for major editing, and (begrudingly) Milkshape for hair (mostly because of the extra data tool).

Download everything you don't have and install it before starting this tutorial.

Now, before we continue, a little advice:

I wrote this tutorial assuming that people who would benefit from it will not put the finished work derivative from these assets behind a paywall or in any sort of monetization. These assets belong to Paper Games. So please don't be an ass and put your Shining Nikki conversions/edits/whatever behind a paywall.

The tutorial starts after the cut (and it's a long one).

Step 1:

Launch Nox, then open Play Store and log in with a Google account (if you don't have one, create it). Now download Shining Nikki from there.

After downloading the game, launch it. It will download a part of the game files. After that, log in on the game, or create a new account in any server (the server is only important if you want to actually play the game. For extracting it doesn't really matter since the game already has the assets for the upcoming events and chapters. It also doesn't matter if you actually own an item in game, you can extract the meshes and textures even if you don't have it in game). If you're creating a new account, the game will lead you through the presentation of it etc (unfortunately there's no way to skip it).

After that, click on that little arrow button on the main screen. There, you can download the actual clothing assets. Wait for the download to finish (at the date I'm writing this tutorial, it is around 13GB). When finished, close the game (not the emulator).

Step 2:

Now we're going to copy the assets to our computer. Click on Tools, then on Amaze File Manager. Navigate to Android > data > com.papergames.nn4.en > files > DownloadedBundle > art > character. This is the folder where (I believe) most of the assets are stored.

Now, where the stuff is located respectively:

Meshes are on the meshes > splitmeshs folder

Textures are on the textures > cloth folder

Tip: Want to really data dump everything? Just select the folders you want and copy to your PC! 😉

Click on the three dots on the side of the wished folder, then in copy. Then click on the three lines on the left upper corner to open the menu, and then click on Download. Now just pull the header of the app to show the Paste option and click on it. It might take a while to copy completely (the cloth folder might take longer since it's bigger, so be patient).

If you're confused, just follow the guide below:

The copied folder will be located at C:\Users\{your username}\Nox_share\Download

Step 3:

Now that we got the files, we need to make them readable by AssetStudio.

For this, we need to open the desired .asset file on a hex editor, and then delete the first 8 bytes of the file, and then save.

You can see it is a pain to do that manually to a lot of files right? This is why I asked my boyfriend to create a script to mass edit them. (I only manually edit when I'm grabbing the textures I want, because afaik the script won't work with .tga and the .png files, more about that forward this tutorial)

How to use the script:

Make sure Python is already installed, grab the nikki-fix-headers.py file and place it on the folder where you copied the folder from the game (mine is still the Nox_Share Download folder).

It should look like this, the meshs folder and the script.

Let's open the Command Prompt. Hit Windows + R to open the Run dialog box, then type in cmd and hit Enter.

Now follow the instructions pictured below:

The folder with the edited files will be at the same location:

Now, we finally can open it all on AssetStudio and see whats inside 👀

Step 4:

Open AssetStudio. Now click on File > Load Folder and select the folder where your edited meshes are (mine is "splitmeshs-fixed"). Wait the program load everything. Click on Filter Type > Mesh, and the on the Asset List tab, click twice on the Name to sort everything by the right order, and now we can see the meshes!

To extract any asset, just select and right-click the desired groups, click in Export selected assets and select a folder where you wish to save it.

Stuff you need to know about the meshes:

Step 4-A: Everything is separated by groups.

Of course you'll have to export everything to have a complete piece. Only a few pieces has a single group. When exporting, you have to select every group with the same name (read below), and the result will be .obj files of each group that you have to put together in a 3D application.

Step 4-B: The names are weird.

They're a code that indicates the set, the piece, the group.

Items that doesn't belong to a set won't have the "S...something", instead they'll have another letter with numbers, but the part/piece type and group logic is the same.

As for the parts, here are the ones I figured out so far:

D = Dress

H = Hair

AEA = Earrings

ANE = Necklace

BS = Shoes

ABA = Handheld accessory

AHE and AHC = Headpieces/hats/hairpins

AFA = Face accessory (as glasses, eyepatches, masks)

(maybe I'll update here in the future with the ones I remember)

Step 4-C: The "missing pearls" issue.

Often you'll find a group that seems empty, and it has a weird name like this:

I figured out that it's referent to pearls that a piece might contain (as in a pearl necklace, or a little pearl in a earring, pearls decorating a dress, etc). The group seems empty, but when you import it to Blender, you can see that it actually has some vertices, and they're located where the aforementioned pearls would be. I think that Unity (SN engine) uses this to generate/place the pearls from a master mesh, but I honestly have no idea of how the game does that. So you'll probably have to model a sphere to place where the pearls were located, I don't know 🤷‍♀️ (And if you know how to turn the vertices into spheres (???) please let me know!)

Step 5:

Now that you already extracted a mesh, we're gonna extract the textures (when any). Copy the textures > cloth folder to your PC like you did with the splitmeshs folder.

Open it, and in the search box, type the name of the desired item like this. If the item has textures, it will show in the results.

Grab all the files and open them in HxD (I usually just open HxD and drag the files I want to edit there), and edit them like I teached above. Then you can open them (or load the cloth folder) on AssetStudio, and export them like you did with the meshes.

Stuff you need to know about the textures, UV map, etc:

Step 5-A: The UV mapping is a hot mess (at least for us used to how things works in sims games).

See this half edited hoodie and the UV map for a idea:

So for any Sims game, you'll have to remap everything 🙃 Also, stencil-like textures all have their own separated file.

As for hair, they all use the same texture and mapping! BUT sometimes they are arranged like this...

Here's the example of a very messed one (it even has some WTF poly). Most of them aren't that messy, but be prepared to find stuff like this.

Shining Nikki just repeat the texture so it end up covering everything, for Sims you'll need to remap, and the easiest way is by selecting "blocks" of hair strands, ticking the magnet button to make your seletion snap to what is already placed (if you have familiarity with blender, you know what I'm saying). Oh, some clothes are also mapped with the same logic.

Regarding the hair texture, I couldn't locate where they are, but here is a pack with all of them ripped and ready to use. You can also grab the textures from any SN hair I already converted :)

The only items with a fine UV map are the accessories, at least for TS3 that the accessory has a UV map independent from the body.

"But I typed the ID for the set and piece and couldn't find anything!"

A good thing to do is to search with only the set ID and edit all the files with it, because some items (especially accessories) share the same texture file. But if even then you can't find anything, it means that there's no texture for this particular item/group because Shining Nikki use material shaders* to render different materials like metal, crystal, some fancy fabrics, etc. So you'll have to bake or paint a texture for it.

*I believe that those shaders are located on the other cloth folder in the game files. This one is way bigger than the other one and once I copied it to see what it was, AssetStudio took ages to load everything, almost used all my 16GB of RAM, and then there was only code that the illiterate me didn't know what it was 🤷‍♀️

So that was it! I hope I explained everything, although it is a little confusing.

If you have any questions, you can comment on this post or send me a PM!

[allaboutmarketing4you]

How Google Maps, Spotify, Shazam and More Work

"How does Google Maps use satellites, GPS and more to get you from point A to point B? What is the tech that powers Spotify’s recommendation algorithm?

From the unique tech that works in seconds to power tap-to-pay to how Shazam identifies 23,000 songs each minute, WSJ explores the engineering and science of technology that catches our eye.

Chapters:

0:00 Google Maps

9:07 LED wristbands

14:30 Spotify’s algorithm

21:30 Tap-to-Pay

28:18 Noise-canceling headphones

34:33 MSG Sphere

41:30 Shazam "

Source: The Wall Street Journal

#Tech#Algorithm#WSJ

Additional information:

" How Does Google Map Works?

Google Maps is a unique web-based mapping service brought to you by the tech giant, Google. It offers satellite imagery, aerial photography, street maps, 360° panoramic views of streets, real-time traffic conditions, and route planning for traveling by foot, car, bicycle, or public transportation.

A short history of Google maps:

Google Maps was first launched in February 2005, as a desktop web mapping service. It was developed by a team at Google led by Lars and Jens Rasmussen, with the goal of creating a more user-friendly and accurate alternative to existing mapping services. In 2007, Google released the first version of Google Maps for mobile, which was available for the Apple iPhone. This version of the app was a huge success and quickly became the most popular mapping app on the market. As time has passed, Google Maps has consistently developed and enhanced its capabilities, including the addition of new forms of map data like satellite and aerial imagery and integration with other Google platforms like Google Earth and Google Street View.

In 2013, Google released a new version of Google Maps for the web, which included a redesigned interface and new features like enhanced search and integration with Google+ for sharing and reviewing places.

Today, Google Maps is available on desktop computers and as a mobile app for Android and iOS devices. It is used by millions of people around the world to get directions, find places, and explore new areas.

How does google maps work?

Google Maps works by using satellite and aerial imagery to create detailed maps of the world. These maps are then made available to users through a web-based interface or a mobile app.

When you open Google Maps, you can search for a specific location or browse the map to explore an area. You can also use the app to get directions to a specific place or find points of interest, such as businesses, landmarks, and other points of interest. Google Maps uses a combination of GPS data, user input, and real-time traffic data to provide accurate and up-to-date information about locations and directions. The app also integrates with other Google services, such as Google Earth and Google Street View, to provide additional information and features.

Overall, Google Maps is a powerful tool that makes it easy to find and explore locations around the world. It’s available on desktop computers and as a mobile app for Android and iOS devices.

Google uses a variety of algorithms in the backend of Google Maps to provide accurate and up-to-date information about locations and directions. Some of the main algorithms used by Google Maps include:

Image recognition: Google Maps uses image recognition algorithms to extract useful information from the satellite and street view images used to create the map. These algorithms can recognize specific objects and features in the images, such as roads, buildings, and landmarks, and use this information to create a detailed map of the area.

Machine learning: Google Maps uses machine learning algorithms to analyze and interpret data from a variety of sources, including satellite imagery, street view images, and user data. These algorithms can identify patterns and trends in the data, allowing Google Maps to provide more accurate and up-to-date information about locations and directions.

Geospatial data analysis: Google Maps uses geospatial data analysis algorithms to analyze and interpret data about the earth’s surface and features. This includes techniques like geographic information systems (GIS) and geospatial data mining, which are used to extract useful information from large datasets of geospatial data.

Overall, these algorithms are an essential part of the backend of Google Maps, helping the service to provide accurate and up-to-date information to users around the world.

Google Maps uses a variety of algorithms to determine the shortest path between two points:

Here are some of the algorithms that may be used:

Dijkstra’s algorithm: This is a classic algorithm for finding the shortest path between two nodes in a graph. It works by starting at the source node and progressively exploring the graph, adding nodes to the shortest path as it goes.

A* search algorithm: This is another popular algorithm for finding the shortest path between two points. It works by combining the benefits of Dijkstra’s algorithm with a heuristic function that helps guide the search toward the destination node.

It’s worth noting that Google Maps may use a combination of these algorithms, as well as other specialized algorithms, to determine the shortest path between two points. The specific algorithms used may vary depending on the specifics of the route, such as the distance, the number of turns, and the type of terrain. "

Source: geeksforgeeks.org - -> You can read the full article at geeksforgeeks.org

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#Brand#Neuromarketing#McKinseyMatrix#Viralmarketing#Facebook#Marketingmetrics#icebergmodel#EdgarScheinsCultureModel#GuerrillaMarketing #STARMethod #7SFramework #gapanalysis #AIDAModel #SixLeadershipStyles #MintoPyramidPrinciple #StrategyDiamond #InternalRateofReturn #irr #BrandManagement #dripmodel #HoshinPlanning #XMatrix #backtobasics #BalancedScorecard #Product #ProductManagement #Logistics #Branding #freemium #businessmodel #business #4P #3C #BCG #SWOT #TOWS #EisenhowerMatrix #Study #marketingresearch #marketer #marketing manager #Painpoints #Pestel #ValueChain # VRIO #marketingmix

Thank you for following All about Marketing 4 You

[eishtmo]

The Scholomance in Minecraft: Final Tour

The build is, for all intense and purposes, done. Now for the final tour.

Okay, let me be blunt, I can't post this here. That tour is 147 images long, not counting the one I'm reusing from the book. So I really insist if you want to see the whole thing, go visit the Imgur post. That said, I did figure out how to do something quite interesting, and I will be sharing it only here, along with some random thoughts and notes. So, who wants to see a map of the Scholomance in Minecraft?

The program is called Mineways, and it lets you extract the data from any Minecraft world, and do all sorts of things, like build import it to 3d modeling software, 3d print it, or just generate some maps. We'll start at the top and work our way down.

This view was almost standard for much of the build and still how I think of the school as a whole. I never removed much of the access stuff I used for building, so you'll see things like that inside the core throughout.

So the school is roughly 120 blocks tall, which puts it at about 40 stories tall. Minus the library and graduation hall, that means climbing from the Senior dorms to the library is climbing about the equivalent of 27 stories. The seniors had to have one hell of a work out just to get lunch, let alone the gym runs. The only thing holding them back from being full on Olympic athletes is the fact that they were malnourished!

I did some math at one point trying to figure out how many books the library could contain, and surprisingly they have figures for how many books fit in a cubic meter. My math (possibly wrong) came up with 116 million. Google claims there's only 156 million books total. Now this is possibly wrong (I may have messed up my math), but it's still crazy how big the library actually is. I still can't get over it honestly.

Maleficaria Studies got me wondering something: Who made the mural? The only ones who would ever see the Graduation Hall and it's inhabitants would be graduating seniors, and I doubt many took the time to sketch the scene. And even then, how did it get back into the school? And then up on the walls in a way that could be torn down and turned into wire? More than that, what was the original purpose? When they planned for it to be for 800 male enclavers, did they need it to be what it became? Or was it just an auditorium/theater and at one point they had bands or were performing Shakespeare or something?

Speaking of those early days, I think it's pretty clear there had to be some communication from outside the school to inside, if only to the artifice itself. After all, they had to have some way to tell the school to halve the room sizes so it could support more students.

BTW, while writing this, do you notice how there's a couple blanks spaces for the bathrooms along the bottom? Yeah, I missed two sets of bathrooms. I did go back and fill those in, I just didn't redo these maps.

Speaking of bathrooms, I decided to make the interior restrooms unisex while the dorm ones were gendered. Why? My theory is that because the dorms were designed to be re done on a regular basis it wasn't hard to fix but the interior ones couldn't be so easily changed, so instead of separating by gender, they just left them unisex as they couldn't do anything else. So why no urinals? After all it was originally an all boys school. Mostly it was easier to build, but also because I really couldn't find a ratio between urinals and toilets, and only got annoyed me when the one reference I found said that for all male locations there should be more toilets/urinals total than one for all female or even mixed populations. That might explain a lot if that's considered the standard in the building industry.

This image actually shows the grid and vent work in the alchemy labs as it's difficult to see when you're in the school. I really just wanted to point it out. I used this floor to figure out the numbering system for the rooms in the school. As shown here:

I did make an effort to stick to this map, but I'm sure I messed it up somewhere.

I have a theory on the construction of the school. We're told that the doors were meant to be the thing to get people to invest, in the larger project, but was it all in one go? I think maybe not. Sure the grad hall and gates came first, then they suggested maybe we could build another floor, and then another, and then why not just build the whole thing and before the enclaves knew what was happening they were building a world wonder. Feature creep at it's finest.

The vent work in the Workshop is almost impossible to see from inside the school. I managed to find one spot, right over the power hammer for the final tour post and it's only barely visible. Here though, you can see the whole thing. It's the grey line that runs through it.

The labyrinth isn't quite as wild as the images in the books displayed, in fact it's quite regular from this angle, but when you're inside, getting lost is probably the stupidly easiest thing that can happen. It was mostly intentional, of course, but I'm surprised how well it worked and how easily I got turned around.

I will also say I'm so happy with how the gym ultimately turned out. Oh it could be better, but the stark difference between it and the rest of the school is something I really wanted to make clear. Also if you look close to the edges of the gym and workshop, you can see the current and old shafts, I never did remove the old ones.

The Grad Hall has the most leftover bits in the school. The multiple extra shafts, the remains of the lines for the levels of the landing, and square base of the whole thing. I would love to say I left it as an easter egg or something, but really it was one part laziness and one part really freaking dark down there. If I couldn't see it and know it's there, no one else can see it at all.

Of all the parts of the school, we know both a lot about the Graduation Hall, and almost nothing. It's shape is only hinted at, the general arrangement (entry doors on one side, exit on the other) is vague, and we really don't know where the shafts come in at all. My original build actually had the shafts going up toward the middle of the school, making the whole thing quite narrow indeed, but this felt much more likely to be true. This version feels more right, even if it still remains a bit too small. But only a bit.

And now a gif from the top to bottom. No it doesn't have every layer, but it has a lot of them.

Well that's the end. Link to final file below. Please feel free to download and explore it, modify or just play inside. And if someone gets a server of this running let me know. Hell let me know whatever you do with it, I'm straight up curious.

And since there's a chance she's reading this, thank you for the wonderful series and hope my build came at least pretty close to what you imagined, even if was made in Minecraft.

[aiagent]

AI Agent Development: How to Create Smarter Virtual Assistants

In recent years, artificial intelligence (AI) has dramatically reshaped how we interact with technology. One of the most exciting developments in the AI landscape is the evolution of virtual assistants. From Siri and Alexa to custom-built solutions for businesses, AI agent development are becoming an integral part of our digital experience. But how do you create a truly smart virtual assistant?

In this blog post, we’ll explore the steps involved in developing AI agents and share insights on how to make them smarter, more capable, and better at understanding users.

Understanding AI Agents

An AI agent is a software program that uses machine learning (ML), natural language processing (NLP), and other AI technologies to perform tasks and assist users. These agents can understand commands, make decisions, and interact with users in a human-like manner. Virtual assistants are one of the most popular forms of AI agents, with the goal of simplifying everyday tasks, answering questions, managing schedules, and even controlling IoT devices.

However, creating a truly intelligent AI agent requires more than just integrating pre-built AI models. It requires an understanding of the problem you're solving, the user’s needs, and how to ensure the agent can evolve and improve over time.

Key Components of AI Agent Development

Natural Language Processing (NLP) NLP is the backbone of most AI agents. It enables the assistant to understand, interpret, and respond to human language. The more advanced the NLP model, the better the AI agent can comprehend nuances like context, tone, and intent behind user input.

Intent Recognition: The AI must determine the user's intent based on their input. For instance, when someone says, "What's the weather like today?", the agent needs to identify the intent as "weather inquiry."

Entity Recognition: After identifying the intent, the AI needs to extract relevant information, such as "today" for time or "New York" for location.

Context Handling: A smart AI should remember context from previous interactions. This allows it to handle follow-up questions like, "What about tomorrow’s forecast?"

Machine Learning (ML) Machine learning enables your AI agent to improve over time by learning from new data. Through supervised, unsupervised, and reinforcement learning, AI agents can analyze patterns, adapt their responses, and make better predictions.

Supervised Learning: The AI is trained on labeled data, learning how to map inputs to the correct output. For example, training it to identify different intents in a conversation.

Unsupervised Learning: This allows the AI to discover hidden patterns in data without explicit labels, enabling it to understand user behavior and preferences more intuitively.

Reinforcement Learning: In this method, AI agents learn by trial and error. Feedback from users and results help the agent adjust its decision-making process to optimize its performance.

Voice and Speech Recognition A major component of virtual assistants is voice interaction. Voice recognition allows AI agents to understand spoken commands, which can be more natural and efficient for users than typing. Advanced speech-to-text technologies, such as those used by Google’s Speech-to-Text API or Amazon’s Transcribe, help the AI accurately convert audio into text, even when the speech is noisy or contains accents and variations.

Dialog Management For an AI agent to manage conversations, a robust dialog management system is essential. This system organizes the conversation flow and decides what the AI should say next. It ensures that responses are coherent, contextually appropriate, and follow a logical flow.

Finite State Machines (FSMs): A simple way to model dialog where each user interaction transitions between a predefined set of states.

Rule-Based Systems: The AI uses rules to decide how to respond based on specific user inputs, but these systems lack flexibility.

Deep Reinforcement Learning (DRL): A more advanced technique, where AI agents learn through exploration and feedback from real conversations.

Personalization One of the key features that makes a virtual assistant "smart" is its ability to personalize responses based on user preferences. A good AI agent learns from each interaction and adapts its behavior over time to suit individual users.

User Profiles: The agent can build a personalized profile that stores preferences, frequently used tasks, favorite services, etc.

Recommendation Systems: Just like Netflix recommends movies based on your viewing history, AI assistants can suggest actions or services based on previous behaviors or inputs.

Integration with Other Systems To create a truly useful AI agent, it needs to integrate with other tools, applications, and services. Whether it's scheduling appointments, controlling smart home devices, or fetching data from cloud storage, seamless integration ensures the assistant can handle a variety of tasks.

APIs and Webhooks: Allow the assistant to communicate with third-party services. For example, an AI assistant can connect to a weather API to fetch weather updates or use a calendar API to schedule appointments.

IoT Integration: Many virtual assistants can control IoT devices, such as lights, thermostats, or security cameras, providing users with a hands-free experience.

Making Your AI Agent Smarter

To create a truly intelligent and efficient virtual assistant, you must focus on several core aspects:

Continuous Learning and Improvement An AI agent should be able to learn from every interaction and improve over time. Collecting data on user preferences, feedback, and failed interactions can help fine-tune the model. For example, when a user asks a question and the agent gives an incorrect answer, that mistake should trigger a retraining phase to prevent it from happening again.

Context-Aware Responses Smart assistants need to maintain context. If a user asks, "What’s the weather in Paris?" and then says, "How about tomorrow?", the agent should understand that the second query relates to the previous one. This context awareness makes the agent more fluid and conversational, instead of just responding to individual queries in isolation.

Multimodal Interaction A truly smart assistant doesn’t just understand text or voice but can also integrate visual elements. For example, if a user asks, "Show me pictures of cats," an AI could display a gallery of images or even send them directly through a messaging app. Multi-modal interaction makes the assistant more versatile and responsive.

Error Recovery Mistakes are inevitable. The smartest AI agents are those that can gracefully recover from errors. For example, if the assistant misinterprets a command or fails to process the user’s request, it should be able to ask for clarification, apologize, and provide alternative solutions.

Ethical Considerations and Privacy Smarter AI agents also need to be designed with ethical guidelines and privacy in mind. Transparent data usage policies, user consent for data collection, and strict security measures should all be incorporated into the development process. Respecting user privacy while offering personalized experiences is crucial for building trust.

Tools and Frameworks for AI Agent Development

Several platforms and tools can help streamline the process of creating intelligent AI agents:

Google Dialogflow: A robust tool for building conversational interfaces and integrating NLP into your applications.

Microsoft Azure Bot Services: A platform that allows you to build, test, and deploy intelligent bots using Azure AI services.

Rasa: An open-source framework for building conversational AI, with a focus on machine learning-based NLP and dialogue management.

IBM Watson: A suite of AI tools for building advanced AI assistants with strong NLP and machine learning capabilities.

Conclusion

Developing a smarter virtual assistant requires a combination of advanced technologies, continuous learning, and a deep understanding of user needs. As AI progresses, creating an AI agent development that can handle more complex tasks, respond naturally, and offer personalized experiences will become increasingly important. By integrating NLP, machine learning, voice recognition, and context-aware interaction, developers can create virtual assistants that not only meet the demands of today’s users but also evolve to handle the challenges of tomorrow’s digital landscape.

Building a smarter virtual assistant is not just about writing code — it’s about understanding the human experience and creating technology that seamlessly fits into it. The future of AI agents is promising, and with the right approach, developers can build intelligent systems that redefine how we interact with the world around us.

[datascraping001]

Google Maps Data Scraping Services

Google Maps Data Scraping Services

Unlock Business Insights with Google Maps Data Scraping Services by DataScrapingServices.com.

In today’s competitive business environment, having access to accurate, up-to-date information is essential for making informed decisions. Google Maps Data Scraping Services by DataScrapingServices.com offers businesses a powerful tool to gather and analyze vast amounts of valuable location-based data efficiently. Our Data Scraping Services allows businesses, researchers, and marketers to collect critical data directly from Google Maps, enabling them to make data-driven decisions with confidence.

Introduction to Google Maps Data Scraping

Google Maps is a rich source of business information, providing details such as business names, addresses, phone numbers, websites, reviews, and ratings. Collecting this data manually can be time-consuming and prone to errors. Our Google Maps Data Scraping Services automate this process, allowing businesses to access organized and structured data quickly and at scale.

Key Data Fields Available

With our Google Maps Data Scraping Services, we provide clients with a range of valuable data fields, such as:

- Business Name

- Address (Street, City, State, Zip Code)

- Phone Number

- Website URL

- Operating Hours

- Category/Industry

- Average Rating and Reviews Count

- Location Coordinates (Latitude and Longitude)

This comprehensive dataset allows businesses to gain a holistic view of the market landscape and target audiences accurately.

Benefits of Google Maps Data Scraping

1. Market Research and Competitive Analysis 

Our scraping service gives you the competitive edge by providing information on industry competitors within your targeted regions. You can evaluate competitor density, identify underserved areas, and explore potential markets with ease.

2. Targeted Marketing and Sales Leads 

By accessing accurate and up-to-date contact information, your marketing team can generate targeted outreach lists to connect with potential clients. This targeted approach improves conversion rates and fosters higher engagement.

3. Data-Driven Location Planning 

For businesses considering expansion, Google Maps data provides essential insights. You can evaluate the presence of specific business types in a new area, allowing you to strategically choose locations with high potential.

4. Enhanced Customer Insight 

Customer reviews and ratings give insight into what consumers value most in local services. By analyzing this data, businesses can adapt their offerings and improve customer satisfaction based on real feedback.

Best Data Scraping Services Provider

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Best Google Maps Data Scraping Services in USA:

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Conclusion

Incorporating Google Maps Data Scraping Services from DataScrapingServices.com into your business strategy is a cost-effective way to acquire actionable insights and make informed decisions. Whether you're a marketer, researcher, or business owner, our service simplifies data collection from Google Maps, enabling faster, data-driven growth.

Empower your business today with structured, high-quality data from Google Maps—contact us at info@datascrapingservices.com.

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[mailpoboxwithapi]

Integrating Address Lookup API: Step-by-Step Guide for Beginners

Address Lookup APIs offer businesses a powerful tool to verify and retrieve address data in real-time, reducing the risk of errors and improving the user experience. These APIs are particularly valuable in e-commerce, logistics, and customer service applications where accurate address information is essential. Here’s a step-by-step guide to help beginners integrate an Address Lookup API seamlessly into their systems.

1. Understanding Address Lookup API Functionality

Before integrating, it’s crucial to understand what an Address Lookup API does. This API connects with external databases to fetch validated and standardized addresses based on partial or full input. It uses auto-completion features, suggesting addresses as users type, and provides accurate, location-specific results.

2. Choose the Right API Provider

Several providers offer address lookup services, each with unique features, pricing, and regional coverage. When selecting an API, consider factors like reliability, data accuracy, response speed, ease of integration, and support for international addresses if needed. Some popular options include Google Maps API, SmartyStreets, and Loqate.

3. Obtain API Credentials

Once you've chosen a provider, sign up on their platform to get your API credentials, usually consisting of an API key or token. These credentials are necessary for authorization and tracking API usage.

4. Set Up the Development Environment

To start integrating the API, set up your development environment with the necessary programming language and libraries that support HTTP requests, as APIs typically communicate over HTTP/HTTPS.

5. Make a Basic API Request

Construct a basic API request to understand the structure and response. Most address lookup APIs accept GET requests, with parameters that include the API key, address input, and preferred settings. By running a basic test, you can see how the API responds and displays potential address matches.

6. Parse the API Response

When the API returns address suggestions, parse the response to format it into user-friendly options. Typically, responses are in JSON or XML formats. Extract the needed data fields, such as street name, postal code, city, and state, to create a clean, organized list of suggestions.

7. Implement Error Handling and Validation

Errors can occur if the API service is unavailable, if the user enters incorrect information, or if there are connectivity issues. Implement error-handling code to notify users of any problems. Also, validate address entries to ensure they meet any specific format or regional requirements.

8. Test and Optimize Integration

Once you’ve integrated the API, test it thoroughly to ensure it works seamlessly across different devices and platforms. Pay attention to response times, as this affects user experience. Some API providers offer caching options or allow for request optimization to improve speed.

9. Monitor API Usage and Costs

Most address lookup APIs charge based on the number of requests, so monitor your usage to avoid unexpected charges. Optimize your API calls by limiting requests per session or using caching to reduce redundant queries.

10. Keep Up with API Updates

API providers often update their services, offering new features or making changes to endpoints. Regularly check for updates to keep your integration running smoothly and utilize any new functionalities that enhance the user experience.

By following these steps, businesses can integrate Address Lookup APIs effectively, providing a smoother, more reliable user experience and ensuring accurate address data collection.

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