Sony Pictures Entertainment and private equity firm Apollo Global Management on Wednesday submitted a $26 billion offer for Paramount Global but have yet to receive a response as of Friday, according to a person familiar with the matter.

Sony, Apollo await response to $26 billion Paramount bid | Reuters

(Reuters) -Sony Pictures Entertainment and private equity firm Apollo Global Management on Wednesday submitted a $26 billion offer for Paramount Global but have yet to receive a response as of Friday, according to a person familiar with the matter. A special committee of the Paramount board, ...

Paramount will not extend exclusive deal period with Skydance

STORY: Sony Pictures Entertainment and Apollo Global Management have made a $26 billion cash offer for movie group Paramount Global.That’s according to the Wall Street Journal (WSJ).The WSJ said the two companies submitted a non-binding offer letter on Wednesday (May 1), citing people familiar ...

Sony, Apollo propose $26 bln Paramount offer: WSJ

Paramount Global (PARA) shares are flying higher in Friday morning&#x27;s trading session — skyrocketing by nearly 14% in the last five trading days — after reports confirmed that Sony (SONY) and Apollo Global Management (APO) are pursuing an acquisition of the media giant.

Sony and Apollo confirm $26B proposed bid in Paramount talks

Shares in media giant, whose chief executive Bob Bakish was this week ousted amid reports of clash with Shari Redstone, spike 12%

Sony and Apollo reportedly make $26bn offer for Paramount | Mergers and acquisitions | The Guardian

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Sony and Apollo offer $26 billion for Paramount: report

Sony would be the majority shareholder and Apollo would have a minority stake if Paramount Global purchase goes through

Sony Pictures and private equity firm Apollo express interest in buying Paramount for $26-billion - The Globe and Mail

Breaking news and exclusive analysis on business, including the latest news on tech, media and earnings.

Sony, Apollo Make $26 Billion All-Cash Offer for Paramount

Sony Pictures and Apollo Global Management have expressed interest in buying Paramount Global for $26 billion

Sony Pictures and private equity firm Apollo express interest in buying Paramount for $26 billion | The Independent

Paramount has been exploring a potential deal for months, talking to suitors including Skydance, producer of “Top Gun: Maverick.”

Sony and Apollo Express Interest in Buying Paramount in $26 Billion Deal

Upollo

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NBC News has from their investigation into how Russian Twitter Trolls may have influenced the 2016 US election. You can read about the results of NBC’s analysis in their stories and , but the focus of this post will be on We’ll show how to get started with the data and hopefully inspire you to dig into the data yourself. publicly released a database of deleted Tweets here here how you can explore the data on your own, using open source data analysis tools. 1. Neo4j Sandbox And Neo4j Browser NBC News has released the data as a Neo4j Database and CSV files that can be used with your favorite data analysis tools. But the easiest way to get started with the data is by using . Neo4j Sandbox allows you to spin up a private hosted instance of Neo4j pre-populated with interesting datasets. Neo4j Sandbox Use to spin up private hosted Neo4j instances pre-populated with interesting datasets. Neo4j Sandbox Once you’ve launched your Russian Twitter Trolls sandbox instance you’ll have access to Neo4j Browser, the query workbench for Neo4j that will allow you to interact with the database. Use Neo4j Browser to visually explore the database. 2. Query With Cypher , the query language for graphs is a great way to explore the database and can be used from within Neo4j Browser or by building an application using one of Neo4j’s . Cypher client drivers Cypher uses to allow users to express complex graph patterns to match against the graph. This allows for answering questions like: graph pattern matching What are the most commonly used hashtags by the Trolls? MATCH (t:Troll)-[:POSTED]->(tw:Tweet)-[:HAS_TAG]->(ht:Hashtag)RETURN ht.tag, COUNT(tw) AS numORDER BY num DESC What Troll accounts have the most followers? MATCH (u:Troll) WHERE EXISTS(u.followers_count)RETURN u.screen_name AS screen_name, u.followers_count AS followersORDER BY followers DESC LIMIT 50 What tweets contain the word “fraud”? MATCH (t:Troll)-[:POSTED]->(tw:Tweet)WHERE tw.text CONTAINS "fraud"OPTIONAL MATCH p=(tw)-[:HAS_TAG|HAS_LINK|MENTIONS|IN_REPLY_TO]-(a)RETURN * LIMIT 50 Find inferred relationships — what Trolls are retweeting other Trolls? MATCH p=(:Troll)-[:POSTED]->(:Tweet)<-[:RETWEETED]-(:Tweet)<-[:POSTED]-(:Troll)RETURN p LIMIT 10 Further ideas for querying: What are the most commonly used applications by the Trolls to post tweets? What locations do the Troll accounts list in their profiles? What tweets had the most number of retweets that were not from other Russian Troll accounts? 3. Fill In Missing Data Due to the way the data was collected there are some missing pieces. For example, some of the users are missing profile information and some tweets are missing metadata like number of likes and retweets. Missing Profile Information For example, the user “@TEN_GOP” is missing profile information in the database as this wasn’t captured: MATCH (u:Troll) WHERE u.screen_name = "TEN_GOP"RETURN u.id, u.screen_name, u.description, u.location, u.name-------------------------------------------------------------╒════════════╤═══════════════╤═══════════════╤════════════╤════════╕│"u.id"      │"u.screen_name"│"u.description"│"u.location"│"u.name"│╞════════════╪═══════════════╪═══════════════╪════════════╪════════╡│"4224729994"│"TEN_GOP"      │""             │""          │""      │└────────────┴───────────────┴───────────────┴────────────┴────────┘ We can reconstruct the Twitter profile URL for “@TEN_GOP”: https://twitter.com/TEN_GOP but because these accounts have been suspended by Twitter, all we see is: The Russian Troll accounts were suspended by Twitter, removing their data from Twitter.com and Twitter’s API We can check web caches, such as Internet Archive to find cached versions of these pages, which we may then be able to scrape. Internet Archive has an API for checking for cached versions of pages, for example: -------------------------------------------------------------------- http://archive.org/wayback/available?url=http://twitter.com/TEN_GOP { : " ", : { : { : "200", : true, : " ", : "20170818065026"}}} url http://twitter.com/TEN_GOP archived_snapshots closest status available url http://web.archive.org/web/20170818065026/https://twitter.com/TEN_GOP timestamp shows that the profile page for “@TEN_GOP” has been captured by Internet Archive and is available . here Missing Tweet Information We can also for tweets in the database that have missing information to check against caches: reconstruct the tweet URLs MATCH (u:Troll)-[:POSTED]->(t:Tweet) WHERE t.text = ""RETURN " " + u.screen_name + "/status/" + t.idAS tweet_urlLIMIT 10------------------------------------------------------------------╒═══════════════════════════════════════════════════════════╕│"tweet_url"                                                │╞═══════════════════════════════════════════════════════════╡│"https://twitter.com/SCOTTGOHARD/status/781651098398494720"│├───────────────────────────────────────────────────────────┤│"https://twitter.com/SCOTTGOHARD/status/780602260401299456"│├───────────────────────────────────────────────────────────┤│"https://twitter.com/WarfareWW/status/783649582064467968"  │├───────────────────────────────────────────────────────────┤│"https://twitter.com/WarfareWW/status/783642593137754114"  │├───────────────────────────────────────────────────────────┤│"https://twitter.com/WarfareWW/status/756033388423897088"  │├───────────────────────────────────────────────────────────┤│"https://twitter.com/WarfareWW/status/794918302585909250"  │├───────────────────────────────────────────────────────────┤│"https://twitter.com/WarfareWW/status/787416487346708481"  │├───────────────────────────────────────────────────────────┤│"https://twitter.com/WarfareWW/status/794189517653680132"  │├───────────────────────────────────────────────────────────┤│"https://twitter.com/WarfareWW/status/797080157135761409"  │├───────────────────────────────────────────────────────────┤│"https://twitter.com/WarfareWW/status/781515670379003904"  │└───────────────────────────────────────────────────────────┘ https://twitter.com/ Unfurling URLs Many of the tweets contain URLs that use link shortener services so it’s not clear what pages they are actually sharing: MATCH (t:Troll)-[:POSTED]->(tw:Tweet)-[:HAS_LINK]->(u:URL)WHERE u.expanded_url CONTAINS "bit.ly"RETURN u.expanded_url LIMIT 10------------------------------------------------------------╒═══════════════════════╕│"u.expanded_url"       │╞═══════════════════════╡│"http://bit.ly/2eeMnZR"│├───────────────────────┤│"http://bit.ly/2dCn9qP"│├───────────────────────┤│"http://bit.ly/2ctTjGN"│├───────────────────────┤│"http://bit.ly/2eAOBnf"│├───────────────────────┤│"http://bit.ly/2awlrUs"│├───────────────────────┤│"http://bit.ly/2aAtdyN"│├───────────────────────┤│"http://bit.ly/29UHsyx"│├───────────────────────┤│"http://bit.ly/2cOskmM"│├───────────────────────┤│"http://bit.ly/2cOskmM"│├───────────────────────┤│"http://bit.ly/2cOskmM"│└───────────────────────┘ We can use tools like cURL to unfurl the links to find the final destination URLs: ➜ curl -Ls -w %{url_effective} -o /dev/null http://bit.ly/2eeMnZR http://ksnt.com/2016/10/27/early-voting-more-good-signs-for-clinton-in-key-states/?utm_source=twitterfeed&utm_medium=twitter% Online tools like allow us to accomplish the same thing, but can also inspect page content and spoof user agents. unfurlr Further ideas for enriching the data: Supplementing the data with Google Knowledge Graph Checking web caches like and for cached versions of the deleted tweets archive.is Internet Archive Searching other social media platforms for usernames that have been reused 4. Graph Algorithms are a way to apply analytics to the entire graph to further enhance our understanding of the data. These algorithms fall into three categories: Graph algorithms — What are the most important nodes in the network. Centrality algorithms include PageRank, Betweenness Centrality, and Closeness Centrality. Centrality — How can the graph be partitioned? Community detection and clustering algorithms include Union Find, Louvain, Label Propagation, and Connected Components. Community detection — What are the shortest paths or best routes available given cost? Pathfinding algorithms include Minimum Weight Spanning Tree, All Pairs- and Single Source- Shortest Path, and Dijkstra. Pathfinding PageRank is a recursive graph algorithm that defines the importance of a node proportional to the importance and number of connected nodes in the graph. Image source Wikipedia We can run these algorithms in Neo4j with Cypher using the procedures. For example, here’s how to run PageRank on the Troll retweet graph: Neo4j Graph Algorithms CALL algo.pageRank("MATCH (t:Troll) RETURN id(t) AS id","MATCH (r1:Troll)-[:POSTED]->(:Tweet)<-[:RETWEETED]-(:Tweet)<-[:POSTED]-(r2:Troll) RETURN id(r2) as source, id(r1) as target", {graph:'cypher'}) For more examples of running graph algorithms on the Russian Troll dataset, see the Neo4j Browser guide for the . Russian Twitter Trolls Neo4j Sandbox instance Graph algorithms ideas: What are the most influential Troll accounts? Can you find communities in the graph based on interactions using community detection algorithms? 5. Graph Visualization Data visualization is often the best way to make sense of the results of graph algorithms. There are a number of open source tools for visualizing graph data, each with their own pros and cons. Tools such as , , and are commonly used for building interactive graph visualizations. Gephi vis.js Semiotic For those familiar with data visualization, graph data brings a unique set of challenges. Often the most important features of graph visualization are: binding to the importance, or centrality, of the node in the graph node size grouping the nodes together in clusters. Many graph visualization tools use a force directed layout to surface clusters, however we can also use community detection algorithms and to show clusters bind the communities to node color Showing proportional to a property, or weight, of the relationship relationship thickness Visualizing the Russian Troll retweet/reply network. Node size is proportional to PageRank, color shows the result of a community detection algorithm, and relationship thickness is determined by the number of retweets between the Trolls. The image above shows the results of running and on the Russian Troll retweet graph and visualized using a vis.js wrapper library called . PageRank community detection algorithms neovis.js Further Ideas: Can you imagine other types of inferred networks that can be extracted from the graph? How would you express those graphs using Cypher? Can you build an interactive graph visualization using one of the tools mentioned above to visualize that graph? 6. Natural Language Processing Natural language processing (NLP) is the process of making sense of text data. Common NLP tasks include part of speech tagging, entity extraction, word similarity, and sentiment analysis. There are a number of open source tools for performing NLP tasks such as , and even some tolls designed specifically for working with Twitter data such as and a crowd-sourced . Stanford’s CoreNLP tools NLTK in Python CMU’s Twitter Part-of-Speech Tagger tool for finding hashtag definitions Entity extraction on the Tweet data involves extending the graph model by annotating tweets that contain entities. You can even run NLP tasks using Cypher directly in Neo4j using an extension. See for how to get started using the . And for those comfortable with using Python tooling for NLP shows how to run entity extraction on Twitter data using Neo4j and Python. this post GraphAware neo4j-nlp procedures this blog post NLP ideas: — what are the most common people, organizations, and places mentioned in the tweets? Are certain groups of trolls talking about certain entities? Entity extraction — are the Trolls talking positively about anything? Or do they focus on spreading negativity only? Sentiment analysis We hope you’re excited to explore the data, share anything interesting you find with us on Twitter “@neo4j”.

Six Ways To Explore The Russian Twitter Trolls Database In Neo4j

In today’s digital age, the demand for high-quality video content is skyrocketing across various sectors, from education to entertainment. AI video generators have emerged as essential tools to meet this growing need, offering innovative solutions that simplify and accelerate the video creation process. Enter Pollo AI, a revolutionary AI-driven video generator that promises to transform how we create and share videos with its smart, user-friendly technology. In this review, we will delve into Pollo AI's features, exploring how it stands out in the crowded field of video generation tools. What Is Pollo AI? Pollo AI is a state-of-the-art AI video generator that can take any text prompt you provide and convert it into a high-quality, professional video clip. For example, by entering a prompt like “A chess match between AI and an alien,” Pollo AI can generate a visual clip that vividly brings your text to life. Its text-to-video functionality makes creating engaging content easier than ever. Pollo AI’s innovative image-to-video feature allows users to transform static images into dynamic, high-class visual outcomes effortlessly. Simply upload an image, and Pollo AI will generate a captivating video that brings the image back to reality with motion and effects. This feature is perfect for creating engaging visual content for social media or educational purposes. How Does Pollo AI Work? Pollo AI is an intuitive tool designed for both first-timers and professionals who need to generate creative and engaging AI videos easily. Here is a comprehensive breakdown of the simple steps to get this platform started: Step 1: Input Text Prompt or Photos To create a video with Pollo AI, simply type your text prompt, like “a bomb exploded in a desert” or “a cat is walking toward a flower, sniffing and licking it,” and Pollo AI will visualize it for you. Additionally, you can upload an image, such as a family photo, and Pollo AI will create a video or animation based on that image. Step 2: Customize Your Input With Pollo AI, you can customize your video using Standard or Professional modes, choose an aspect ratio like 16:9 or 9:16, and control the length of your video. You can also adjust the prompt adherence slider for creative flexibility and select camera movements such as Horizontal Pan or Vertical Pan to tailor your video’s specifics. Step 3: Click Create to Start the Process After inputting all your preferred video settings, the final step is to hit the Create button and let Pollo AI start crafting your video. The processing time may vary based on factors like the use of Professional Mode and the video’s length and complexity, so be patient while Pollo AI works its magic. Step 4: Check Out the Ultimate Creation When the visual content is complete, you can view it directly in your browser. You have the option to watch it multiple times, save it, download it, share it, or use it however you like. If you are not satisfied, you can always return to the drawing board and start the process again. Why Choose Pollo AI? While there are other AI video generators available on the market, Pollo AI stands out as the superior option for creating high-class visual content. Let’s dive into its main features in detail. Text-To-Video Transformation: With a simple text prompt, Pollo AI can bring your ideas to life by identifying crucial elements and visualizing them in a compelling video. This tool creatively interprets your instructions, delivering the perfect video style and content for your specific needs. Refresh the Story of Your Image: Give your static images a dynamic makeover. Along with your text inputs, Pollo AI can breathe life into your visuals by creating videos that narrate their stories while preserving the original content. This smart tool transforms simple images into engaging, story-driven animations. Versatile AI Video Creation: Pollo AI can turn your ideas in mind into videos of any style you desire. Whether you envision lifelike cinematic sequences or creative, stylized animations, Pollo AI offers limitless possibilities to bring your concepts to vibrant life. Rapid Video Production: Its state-of-the-art video generation algorithms are engineered for speed, enabling Pollo AI to produce high-quality videos in mere minutes. Whether you need a quick turnaround for a project or want to see your ideas come to life swiftly, Pollo AI ensures you won’t have to wait long. Exceptional Video Quality: Pollo AI leverages cutting-edge technology to transform your creative ideas into stunning, high-definition videos that meet professional standards. The result is a perfect fusion of innovation and excellence, offering you visually captivating outputs that stand out. Effortless Video Creation: Users do not need any technical video-editing skills to utilize Pollo AI. This AI video generator is designed to be user-friendly and intuitive, simplifying the video creation process. With Pollo AI, anyone can easily create professional-grade videos without prior experience or technical knowledge. Summary Pollo AI sets a new benchmark in the world of video generation with its advanced, user-centric technology. By streamlining the video creation process, it allows users to produce high-quality content quickly and effortlessly, making it an invaluable tool for content creators. In a market crowded with video generation tools, Pollo AI stands out not only for its intelligent features but also for its commitment to delivering exceptional results. Whether you are a novice or a professional, Pollo AI provides the perfect blend of innovation and ease of use, ensuring your visual outcomes are both stunning and impactful. This story was distributed by Margrowth under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author In today’s digital age, the demand for high-quality video content is skyrocketing across various sectors, from education to entertainment. AI video generators have emerged as essential tools to meet this growing need, offering innovative solutions that simplify and accelerate the video creation process. Enter Pollo AI , a revolutionary AI-driven video generator that promises to transform how we create and share videos with its smart, user-friendly technology. In this review, we will delve into Pollo AI's features, exploring how it stands out in the crowded field of video generation tools. Pollo AI Pollo AI What Is Pollo AI? Pollo AI is a state-of-the-art AI video generator that can take any text prompt you provide and convert it into a high-quality, professional video clip. For example, by entering a prompt like “A chess match between AI and an alien,” Pollo AI can generate a visual clip that vividly brings your text to life. Its text-to-video functionality makes creating engaging content easier than ever. text-to-video text-to-video Pollo AI’s innovative image-to-video feature allows users to transform static images into dynamic, high-class visual outcomes effortlessly. Simply upload an image, and Pollo AI will generate a captivating video that brings the image back to reality with motion and effects. This feature is perfect for creating engaging visual content for social media or educational purposes. image-to-video image-to-video How Does Pollo AI Work? Pollo AI is an intuitive tool designed for both first-timers and professionals who need to generate creative and engaging AI videos easily. Here is a comprehensive breakdown of the simple steps to get this platform started: Step 1: Input Text Prompt or Photos To create a video with Pollo AI, simply type your text prompt, like “a bomb exploded in a desert” or “a cat is walking toward a flower, sniffing and licking it,” and Pollo AI will visualize it for you. Additionally, you can upload an image, such as a family photo, and Pollo AI will create a video or animation based on that image. Step 2: Customize Your Input With Pollo AI, you can customize your video using Standard or Professional modes, choose an aspect ratio like 16:9 or 9:16, and control the length of your video. You can also adjust the prompt adherence slider for creative flexibility and select camera movements such as Horizontal Pan or Vertical Pan to tailor your video’s specifics. Step 3: Click Create to Start the Process After inputting all your preferred video settings, the final step is to hit the Create button and let Pollo AI start crafting your video. The processing time may vary based on factors like the use of Professional Mode and the video’s length and complexity, so be patient while Pollo AI works its magic. Step 4: Check Out the Ultimate Creation When the visual content is complete, you can view it directly in your browser. You have the option to watch it multiple times, save it, download it, share it, or use it however you like. If you are not satisfied, you can always return to the drawing board and start the process again. Why Choose Pollo AI? While there are other AI video generators available on the market, Pollo AI stands out as the superior option for creating high-class visual content. Let’s dive into its main features in detail. Text-To-Video Transformation: With a simple text prompt, Pollo AI can bring your ideas to life by identifying crucial elements and visualizing them in a compelling video. This tool creatively interprets your instructions, delivering the perfect video style and content for your specific needs. Text-To-Video Transformation: With a simple text prompt, Pollo AI can bring your ideas to life by identifying crucial elements and visualizing them in a compelling video. This tool creatively interprets your instructions, delivering the perfect video style and content for your specific needs. Refresh the Story of Your Image: Give your static images a dynamic makeover. Along with your text inputs, Pollo AI can breathe life into your visuals by creating videos that narrate their stories while preserving the original content. This smart tool transforms simple images into engaging, story-driven animations. Refresh the Story of Your Image: Give your static images a dynamic makeover. Along with your text inputs, Pollo AI can breathe life into your visuals by creating videos that narrate their stories while preserving the original content. This smart tool transforms simple images into engaging, story-driven animations. Versatile AI Video Creation: Pollo AI can turn your ideas in mind into videos of any style you desire. Whether you envision lifelike cinematic sequences or creative, stylized animations, Pollo AI offers limitless possibilities to bring your concepts to vibrant life. Versatile AI Video Creation: Pollo AI can turn your ideas in mind into videos of any style you desire. Whether you envision lifelike cinematic sequences or creative, stylized animations, Pollo AI offers limitless possibilities to bring your concepts to vibrant life. Rapid Video Production: Its state-of-the-art video generation algorithms are engineered for speed, enabling Pollo AI to produce high-quality videos in mere minutes. Whether you need a quick turnaround for a project or want to see your ideas come to life swiftly, Pollo AI ensures you won’t have to wait long. Rapid Video Production: Its state-of-the-art video generation algorithms are engineered for speed, enabling Pollo AI to produce high-quality videos in mere minutes. Whether you need a quick turnaround for a project or want to see your ideas come to life swiftly, Pollo AI ensures you won’t have to wait long. Exceptional Video Quality: Pollo AI leverages cutting-edge technology to transform your creative ideas into stunning, high-definition videos that meet professional standards. The result is a perfect fusion of innovation and excellence, offering you visually captivating outputs that stand out. Exceptional Video Quality: Pollo AI leverages cutting-edge technology to transform your creative ideas into stunning, high-definition videos that meet professional standards. The result is a perfect fusion of innovation and excellence, offering you visually captivating outputs that stand out. Effortless Video Creation: Users do not need any technical video-editing skills to utilize Pollo AI. This AI video generator is designed to be user-friendly and intuitive, simplifying the video creation process. With Pollo AI, anyone can easily create professional-grade videos without prior experience or technical knowledge. Effortless Video Creation: Users do not need any technical video-editing skills to utilize Pollo AI. This AI video generator is designed to be user-friendly and intuitive, simplifying the video creation process. With Pollo AI, anyone can easily create professional-grade videos without prior experience or technical knowledge. Summary Pollo AI sets a new benchmark in the world of video generation with its advanced, user-centric technology. By streamlining the video creation process, it allows users to produce high-quality content quickly and effortlessly, making it an invaluable tool for content creators. Pollo AI Pollo AI In a market crowded with video generation tools, Pollo AI stands out not only for its intelligent features but also for its commitment to delivering exceptional results. Whether you are a novice or a professional, Pollo AI provides the perfect blend of innovation and ease of use, ensuring your visual outcomes are both stunning and impactful. This story was distributed by Margrowth under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Margrowth under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Margrowth under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author https://business.hackernoon.com/brand-as-author

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If you were to ask anyone whether they preferred the original Ace Attorney trilogy or the Apollo Justice trilogy, there’s a very good chance they would say the former. The first 3 games were so amazing that they were extremely hard to follow up. However, the Apollo Justice games (which consist of Ace Attorney 4,5, and 6) are still very good games, and I would encourage anyone who hasn’t played them yet to stop reading this article immediately and play them this instant. They’re filled to the brim with interesting cases, villains, and brand-new protagonists such as Athena Cykes and, of course, Apollo Justice himself. So today, I’m doing the near-impossible and choosing the top 5 Apollo Justice cases. Warning: Spoilers Ahead. The 5 Best Cases From the Apollo Justice Trilogy 5. Turnabout for Tomorrow 5-5 4. Turnabout Revolution 6-5 3. Turnabout Trump 4-1 2. Turnabout Succession 4-4 1. Turnabout Storyteller 6-4 5. Turnabout for Tomorrow 5-5 Throughout the fifth game, Dual Destinies, we know that something traumatic happened to newcomer Athena Cykes. She was keeping a big secret, and in Turnabout for Tomorrow, everything gets revealed. Cykes’ past, prosecutor Simon Blackquill’s connection to all of it, and the mysterious murderer of 2 people all come to light. Turnabout for Tomorrow is the final case of Dual Destinies, and it serves as a fitting end that ties up all the loose ends in a neat bow. The case also has an incredibly shocking twist that I did not see coming. 4. Turnabout Revolution 6-5 Speaking of wrapping things up and shocking twists, the last case in Spirit of Justice brings that in spades. The final case in every Ace Attorney game always goes hard, but probably none so than Turnabout Revolution. It’s long with twist after twist that you don’t see coming, and just when you think you have it figured out, they throw another twist at you. But apart from the craziness of the case, another reason why Turnabout Revolution is great is because it provides closure for Apollo Justice. The once very nervous rookie defense attorney is finally allowed to spread his wings and become a new and improved man. 3. Turnabout Trump 4-1 Shocking twists are usually reserved for the end of the game, but in Apollo Justice, they come out swinging with them. Apollo and his mentor, Kristoph Gavin, have to defend this strange, disheveled man who is accused of murder. Little does Apollo know that their client is the former Ace Attorney himself, Phoenix Wright. And if that wasn’t crazy enough, it turns out that the real murderer was helping us all along. That’s right, in the end, our friend and mentor Gavin is revealed to be the mastermind behind the murder. Apollo Justice has one of the strongest starts in the history of the series, and that’s why it’s one of the best cases in the Apollo Justice trilogy. 2. Turnabout Succession 4-4 When we meet Phoenix Wright at the beginning of the game, we know that he’s not the same man that we last saw in Trials and Tribulations. He lost his reputation, and most importantly, he lost his job as a defense attorney; he was disbarred. But we didn’t really know what happened until Turnabout Succession when everything was revealed. But not only do we see what happened, we experience it ourselves. We play as Phoenix Wright during his last trial when he’s framed for forging evidence, we find out who was behind it all, and we see Apollo Justice rise to the occasion in the present day. And events that we had no idea were important finally make their connections known. For example, the events and characters in cases 4-1 and 4-2 are revealed to actually be a huge part of the story, and it retroactively makes those cases that much better. 1. Turnabout Storyteller 6-4 I love the final cases in Ace Attorney games because it is the culmination of the overall story that they plant from the very beginning. However, I love Turnabout Storyteller for the exact opposite reason. This case has Athena Cykes defending a young man with the help of prosecutor Simon Blackquill. Case 6-4 has nothing to do with the overarching story, none of these characters will probably appear again in future installments, and in the grand scheme of things that were currently going on in the Ace Attorney world, it didn’t seem that big or urgent. But that’s what’s so great about it. The judicial system didn’t ride on the outcome of the case, nor did an entire kingdom’s future hang in the balance. It’s a simple story about young up-and-comer Athena trying to clear somebody of murder, without the help of Phoenix Wright or Apollo Justice. It’s just her, Widget, and Blackquill. And it turned out to be amazing. It’s relatively obvious who the killer is from the get-go, but I didn’t care. The story and the characters were intriguing, and best of all, the chemistry between Athena and Blackquill was amazing. Athena, the ambitious and eager student vs Blackquill as the reluctant but helpful mentor. This case is so good that it’s by far my favorite in the Apollo Justice trilogy, and I think this single case proves that Athena Cykes is an interesting character enough to warrant her being the star of her own game. Read More: The New Tomb Raider Games in Order Persona 3 Remake Officially Announced: Everything We Know So Far The Kingsman Movies in Order Also published here Feature image source

The Apollo Justice Trilogy: The 5 Best Cases

POLLOCK AND THE PORROH MAN

GraphQL is the alternative for REST made by Facebook. When facebook reached the limits of REST API, they had to make something breaking these limits. Through this article, you can learn what GraphQL is, how it works, and how to use it in your projects. GraphQL (Graph Query Language) is an API-level query language. Like SQL is used for requests to relational databases, GraphQL allows clients to request data from an API. For a good understanding of this article, you should have a basic understanding of TypeScript and NodeJS. Why is GraphQL so good? Receive only data you need. GraphQL allows you to manage what data API will return to you. This has a number of advantages. It helps network performance when the response contains a lot of data you don't need. Of course, REST can handle it via request parameters like , however much easier when it is available by default, does it? It is also useful if your API supports different types of clients which want to call less and fetch more. ?fields=name,birthdate Access to a complex graph of objects aggregated from different sources. GraphQL allows you to get a complex graph of objects by a single request. These objects might be from different sources. Clients and server API schemes are always in sync. Your clients and server have the same scheme, so it is easy to sync it. GraphQL anatomy To understand how GraphQL realizes all you’ve read above, let’s talk about what artifacts GraphQL includes. GraphQL includes schema, which describes the types of objects you can operate on. Take a look at the following schema: type App {
   id: ID!
   name: String!
} This is the type App, which includes id and name required fields. It means you can operate this object. But how to operate it? There are a few access operators in GraphQL. The first one (required one) - . This is a getter for your object. The second one - , is a setter. And the last one is a , which allows you to subscribe for updates of your type. query mutation subscription type Query {
   apps: [App!]!
   app(id: ID!): App!
}
 
type Mutation {
   createApp(app: AppInput!): App!
   updateApp(appId: ID!, app: AppInput!): App!
}
 
input AppInput {
   name: String!
   publisherId: ID!
}
 
type Subscription {
   appMutated: AppMutationPayload!
}
 
type AppMutationPayload {
   mutation: MutationType!
   node: App!
}
 
enum MutationType {
   CREATED
   UPDATED
   DELETED
} The code snippet above contains . Inputs allow you to define input parameters types. input AppInput Another important component of GraphQL - resolvers. Resolvers define how to handle queries, mutations, and subscribers. We will come back to it soon. Apollo Apollo Server is a Node.js implementation of graphQL server. There is good scalable architecture and a strong community that means it is a good choice for use. We won’t talk much about Apollo architecture, we’ll learn everything needed by code examples. Install dependencies "dependencies": {
   "apollo-datasource": "^0.8.0",
   "apollo-server": "^2.22.1",
   "graphql": "^15.5.0"
 },
 "devDependencies": {
   "@types/graphql": "^14.5.0",
   "@types/node": "^14.14.37",
   "ts-node": "^9.1.1",
   "typescript": "^4.2.3"
 } - allows implementing GraphQL server - allows implementing data sources (like database aggregators or rest api integrations) - graphQL package. Required by Apollo. apollo-server apollo-datasource graphql Server Initialization import {ApolloServer, gql} from 'apollo-server'
import {typeDefs} from './graphql/typedefs'
import {resolvers} from './graphql/resolvers'
import {dataSources} from './datasources'
 
const server = new ApolloServer({
 typeDefs: gql`
   ${typeDefs}
 `,
 resolvers,
 dataSources
})
 
server.listen().then(({url}) => {
 console.log(`Server ready at ${url}`)
}) Let’s understand the code above. We are initializing Apollo Server with several arguments. - this is a path for graphQL schema - as a said earlier, we will come back for that a bit later - data sources like DB aggregators and API integrations typeDefs resolvers dataSources DataSources import { AppService } from './app-service';
 
export const dataSources = () => ({
   appService: new AppService()
}); This is DI of dataSources. This format is required by Apollo. import { DataSource } from 'apollo-datasource';
 
export class AppService extends DataSource {
   constructor() {
       super();
   }
} dataSource might look like this. This is an aggregator, where you can implement business logic and accessors for data. Resolvers Finally, let’s talk about resolvers. As I said earlier, resolvers define how to handle queries, mutations, and subscribers. Take a  look at that: type Mutation {
   createApp(app: AppInput!): App!
   updateApp(appId: ID!, app: AppInput!): App!
}
 
type Subscription {
   appMutated: AppMutationPayload!
}
 
type AppMutationPayload {
   mutation: MutationType!
   node: App!
} And now take a look at the resolver: import { pubsub } from '../pubsub';
 
const APP_MUTATED = 'appMutated';
 
export default {
   Query: {
       apps(parent, args, {dataSources}) {
           return dataSources.appService.getApps();
       },
       app(parent, args, {dataSources}) {
           return dataSources.appService.getApp(args.id);
       }
   },
 
   Mutation: {
       async createApp(parent, args, {dataSources}) {
           const { publisherId, ...rest } = args.app;
           let app = await dataSources.appService.createApp({...rest}, publisherId);
           pubsub.publish(APP_MUTATED, {
               appMutated: {
                   mutation: 'CREATED',
                   node: app
               }
           });
           return app;
       },
       async updateApp(parent, args, {dataSources}) {
           const { publisherId, ...rest } = args.app;
           let updatedApp = await dataSources.appService.updateApp(args.appId, {...rest}, publisherId);
           publishAppUpdated(updatedApp);
           return updatedApp;
       },
       async setAppDevelopers(parent, args, {dataSources}) {
           let updatedApp = await dataSources.appService.setAppDevelopers(args.appId, args.developerIds)
           publishAppUpdated(updatedApp);
           return updatedApp;
       }
   },
 
   Subscription: {
       appMutated: {
           subscribe: () => pubsub.asyncIterator(APP_MUTATED)
       }
   },
};
 
function publishAppUpdated(app) {
   pubsub.publish(APP_MUTATED, {
       appMutated: {
           mutation: 'UPDATED',
           node: app
       }
   });
   return app;
} This resolver totally defines all queries, mutations, and subscribers. And as you can see, we have access to data sources that were injected into ApolloServer. let app = await dataSources.appService.createApp({...rest} We deal with this, but what about subscribers? Subscription: {
    appMutated: {
        subscribe: () => pubsub.asyncIterator(APP_MUTATED)
    }
} And what is the code in mutations? pubsub.publish(APP_MUTATED, {
   appMutated: {
     mutation: 'CREATED',
        node: app
     }
}); This is pubSub. Maybe you know this pattern. Microservices usually communicate using it. This is required for subscribers in Apollo. And it makes sense because subscribers are WebSockets. Websockets keep connections, so it is easy to reach a high load. So, the Apollo team decided to handle it with this design solution. Anyway, there is an in-memory dummy, just for testing. Never use it in production. import { PubSub } from 'apollo-server';
 
export const pubsub = new PubSub(); How to test Run the app using the following command: ts-node src/index.ts Go to localhost:4000 via web browser and see the following: This is the test console Apollo. You can test queries, mutations, and subscriptions here. Congratulations! You’ve just learned how to build graphQL API using Apollo and Node.JS. Full code example you can find here: https://github.com/gandronchik/graphql-example

Intro Guide to the GraphQL API using Node.JS and Apollo

Cryptocurrency projects have become a major target for malicious actors intent on disrupting legitimate blockchain initiatives' progress. Apollo Fintech is no exception to this rule, as it and its CEO Stephen McCullah have been subjected to relentless attacks from two notorious figures in the crypto space: Sultan Kassam and Danny De Hek. In this article, we will look at these individuals and their activities concerning Apollo Fintech, explore the detrimental effects of their actions on the project, and provide suggestions on how you can help prevent future attacks against other legitimate crypto projects. An Honest Look at the Individuals Targeting Apollo Fintech and its Partners Sultan Kassam is a British entrepreneur who runs several companies in the United Kingdom. He is also well known for his pseudonyms (e.g., Apollofintechcryptonews) which he uses to spread false and misleading information about Apollo Fintech and its partners. Kassam has been accused of blackmailing multiple businesses associated with Apollo Fintech and its CEO, Stephen McCullah. He's active on Telegram, where he posts lies and threats against the project. Danny De Hek is another person who actively targets Apollo Fintech and its partners. His YouTube channel is highly popular amongst viewers, who enjoy his approach to crypto investment advice. Unfortunately, De Hek often uses and scare tactics to dissuade potential investors from investing in projects like Apollo Fintech. This is a dangerous practice, as it could potentially harm the industry by preventing legitimate crypto projects from succeeding. ill-informed opinions An Overview of The Detrimental Effects of Their Actions on Crypto Projects The malicious actions of Sultan Kassam and Danny De Hek have had a devastating impact on the progress of legitimate cryptocurrency projects, including Apollo Fintech and its CEO Stephen McCullah. Through their lies, threats, and intimidation tactics, they have sought to disrupt the advancement of these initiatives to benefit themselves financially, or at least that is what one can imagine. Why would someone go out of their way to spread false and misleading information about a project if they weren't trying to benefit from it somehow? Furthermore, the activities of these individuals have had an extremely damaging effect on the credibility of cryptocurrency projects. This is especially true regarding potential investors, who may be scared away by the negative publicity generated by these malicious actors. How You Can Help Prevent Future Attacks on Legitimate Crypto Projects In light of the information presented above, it is clear that more needs to be done to protect cryptocurrency projects from malicious actors like Sultan Kassam and Danny De Hek. One way you can help prevent future attacks is by raising awareness of the issue so that potential investors can make informed decisions about where to invest their money. You should also be wary of any advice given by Danny De Hek on his YouTube channel or other social media platforms, as it could be biased and misleading. His followers should exercise caution when considering his advice and do their research before investing in any project. Finally, if you see any false or misleading information being spread about Apollo Fintech or its partners, please take action and report it to the appropriate authorities. This can help prevent future attacks against legitimate blockchain initiatives and ensure they can succeed without interference from malicious actors. Where Do We Go from Here? The actions of Sultan Kassam and Danny De Hek have had a significant impact on the progress of legitimate cryptocurrency projects. Still, it is important to remember that they are only two individuals in a much larger ecosystem. In order to ensure that such malicious activities do not happen again, we must continue to raise awareness so that potential investors are aware of the risks associated with investing in cryptocurrency projects. We must also continue to hold these malicious actors accountable for their actions and remain vigilant in reporting any false or misleading information about legitimate blockchain initiatives.

Uncovering the Dark Truth Behind De Hek and Kassam - Liars Targeting Apollo Fintech

Movie and TV show streaming app ApolloTV has shut down following threats from the Alliance for Creativity and Entertainment. The global anti-piracy coalition hand-delivered a cease-and-desist notice to the developer behind the software, the terms of which he immediately complied with. While third-party Kodi addons remain popular with those seeking free access to premium video, standalone apps for Android and iOS have gained traction in recent years. Easy to install and use on set-top boxes, tablets and phones, these tools often provide a Popcorn-Time or Showbox-like experience, offering the latest movies and TV shows in a familiar Netflix-style interface. On the one hand, they’re popular with users but Hollywood studios and other content creators consider them a risk to their business that needs to be countered. ApolloTV is a relatively recent addition to the growing market. Available for iOS and Android, the application gained a reasonable following and is featured in dozens of tutorials and YouTube video installation guides. At the time of writing, however, it seems highly unlikely that the project will continue. Over the past several days, rumors began to circulate that ApolloTV would be shutting down. The whispers suggested that the application’s developer had been targeted by copyright holders and as a result, the app would be discontinued. Counter-rumors suggested that the developer simply wanted a way out so TorrentFreak approached ‘Sam’ directly to find out. “I received a cease and desist from the Alliance 4 Creativity – the same people who took down Openload and Streamango – citing ApolloTV making available copyrighted works copied and hosted by unaffiliated third parties without permission from the copyright holder,” Sam informs us. The claim seems reasonable given the circumstances. ApolloTV didn’t host any content of its own but did provide access to content hosted on third-party file-hosting platforms, a common feature among similar ‘scraper’-type applications. As TF discovered, Sam made little to no effort to hide his identity so it appears that ACE had few difficulties tracking him down. However, while we reached him easily via email, ACE managed to discover his home address which enabled them to serve the cease-and-desist notice by hand. “I was surprised that I actually received the cease and desist physically, in person (it was delivered by a court officer), at my home address,” he explains. Sam says that he doesn’t have the time or the resources to fight a lawsuit and since ACE were very reasonable, he’ll be complying with their requests immediately. He doesn’t want to go into too many additional details but says that as part of the settlement, he will need to shut down the project’s Github page and hand over the Apollo domain to ACE. At the time of writing, the official ApolloTV repository on Github has been taken down. ACE nor the MPA appear to have filed any official DMCA takedown requests with the developer platform so at this point things seem to be progressing quickly on a voluntary basis. TorrentFreak sought comment from ACE on the reported action but, at the time of publishing, we were yet to receive a response. ACE responded and a spokesperson provided the following comment on the ApolloTV case: Update: “As part of the Alliance for Creativity and Entertainment (ACE) ongoing efforts to protect the legal market for creative content and reduce piracy, ACE representatives contacted an individual in the United Kingdom suspected to be involved with the development, operation, and distribution of an illegal video streaming application.” Originally published as “ ApolloTV Streaming App Shuts Down Following ACE Cease-and-Desist Notice ” with the Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0) license

ApolloTV Streaming App Shuts Down Following ACE Cease-and-Desist Notice

One of my side projects involves writing a around the Universal Chess Interface ( ). I originally developed a mock implementation and . That used version one of Apollo’s client/server stack, which . One of the biggest changes was the subscriptions interface and implementation, and I struggled for many hours to get that to work. Part of the problem is that the documentation is not very complete and rather scattered. This post will cover the most basic implementation of subscription service and the client code that uses it. GraphQL API UCI wrote about it awhile back changed quite a bit in version two Getting started The GraphQL server will be based on , which in this case will use an Express server as middleware. Subscriptions will use a that is attached to a plain HTTP server (also managed by ApolloServer, as we shall see). ApolloServer WebSocket There are two clients: the , and a client for use by Mocha to test the API outside of a GUI. For simplicity, I’ll keep the Mocha client code in the same folder as the server code. I also use Babel so I don’t have to think about and the like. GraphQL Playground Michael Jackson Scripts With that in mind, here are the packages to be installed: apollo-link, apollo-link-http, apollo-link-ws, apollo-server-express babel-cli, babel-core, babel-preset-env graphql, graphql-tools subscriptions-transport-ws mocha, chai I’ll provide a link to a working code repository at the end of this article. The Server The server is as basic as I could make it: express ; { ApolloServer } ; http ; { typeDefs } ; resolvers ; PORT = ; app = express(); server = ApolloServer({
  typeDefs,
  resolvers,
});

server.applyMiddleware({
  app
}); httpServer = http.createServer(app);
server.installSubscriptionHandlers(httpServer);

httpServer.listen({ : PORT }, () => { .log( ); .log( );
}); import from 'express' import from 'apollo-server-express' import from 'http' import from './schema' import from './resolvers' const 3031 const const new const port console `🚀 Server ready at http://localhost: ` ${PORT} ${server.graphqlPath} console `🚀 Subscriptions ready at ws://localhost: ` ${PORT} ${server.subscriptionsPath} An ApolloServer is created with schema and resolver parameters. The call passes in an express application instance. Finally, an HTTP server is created to handle the web socket interface to the subscription service. Both GraphQL and the subscription websocket share the same port, but under different protocols. applyMiddleware The Schema { makeExecutableSchema } ; resolvers ; typeDefs = [ ,
]; options = {
  typeDefs,
  resolvers,
}; executableSchema = makeExecutableSchema(options); executableSchema; { typeDefs }; import from 'graphql-tools' import from './resolvers' const `
  type Query {
    go: String!
  }
  type Subscription {
    info: String!
  }
` const const export default export The schema is very basic: will return a string acknowledgment, but behind the scenes it will fire off a method that sends publish notifications to subscribers. Subscribers subscribe through the subscription API. go info We can see how this works in resolvers.js: { PubSub } ; pubsub = PubSub(); TOPIC = ; infos = [ , , , ] publish = {
    setTimeout( infos.forEach( pubsub.publish(TOPIC, {info})), )
} { : { : { publish(); }
    }, : { : { : pubsub.asyncIterator([TOPIC]),
      },
    },
  }; import from 'apollo-server-express' const new const 'infoTopic' const 'info1' 'info2' 'info3' 'done' const => () => () => info 1000 export default Query go => () return 'going' Subscription info subscribe => () I’ve made the resolvers deliberately simple. For example, the asyncIterator method from PubSub is quite sophisticated (it supports filters and authentication, for example). Since it is already , I won’t delve into various options here. All the resolver above does is to publish an to all subscribers, passing objects as the body of the subscription. well-documented infoTopic info The Client As I mentioned, I’ll use two clients for testing. One is the built-in Prisma Playground, a service automatically provided by the ApolloServer (FYI: ). you can turn this feature off in production Testing in Playground To test, I’ll open Playground in Chrome, at Within Playground you can open up multiple tabs. For the first, I’ll subscribe to the : http://localhost:3031/graphql. infoTopic When this command is played, you will see a ‘Listening …’ response in the right pane, as shown above. Now you’re subscribed. Open another tab within Playground and query go: This sends back a response of “going”, so everything is okay. Now, going back to the subscription tab, you will see the published output: Testing using Mocha/Chai There’s a lot of boilerplate in setting up a client, so I’ve created a utility file to handle query and subscribe operations. It’s called fetch.js: fetch ; { execute, makePromise } ; { WebSocketLink } ; { SubscriptionClient } ; ws ; { HttpLink } ; gql ; uri = ; wsUri = ; link = HttpLink({ uri, fetch }); wsClient = SubscriptionClient(wsUri, { : }, ws); wsLink = WebSocketLink(wsClient); doQuery = { operation = { : gql ,
  }; .log( ) makePromise(execute(link, operation));
}; subscribe = { operation = { : gql ,
  }; execute(wsLink, operation).subscribe(handlers);
}; doQuery; { subscribe }; /* eslint-env mocha */ /* eslint-disable import/no-extraneous-dependencies */ // see https://www.apollographql.com/docs/link/index.html#standalone import from 'node-fetch' import from 'apollo-link' import from 'apollo-link-ws' import from 'subscriptions-transport-ws' import from 'ws' import from 'apollo-link-http' import from 'graphql-tag' const `http://localhost:3031/graphql` const `ws://localhost:3031/graphql` const new const new reconnect true const new const ( ) => query const query ` ` ${query} console `$query is ` ${query} return const ( ) => query, handlers const query ` ` ${query} return export default export The fetch.js code pulls in a lot of interdependent modules. For the test operations, I use to handle the interface between the server (which has to be running, btw 😉) and the GraphQL operations used by the tests. There are actually two interfaces here: one to handle query and mutation requests vis HTTP, and another that goes through a web socket to subscribe to a topic. apollo-link Most of this comes from the , but you will have to install a few additional modules I neglected to mention earlier: documentation node-fetch (installed globally via ) npm -g ws gql The module implements the HTTP that’s becoming a standard. The module is a WebSocket implementation for Node (there are others), and is a template literal tag that you can read more about . node-fetch Fetch API ws gql here The test itself is pretty straightforward: we first , then : subscribe go chai ; fetch, { subscribe } ;

chai.should();

describe( , { .timeout( ); handlers = { : { .log( , data); (data.data.info === ) { .log( )
        process.exit( );
      }
    }, : .error( ),
  }; it( , () => { query = ;
    subscribe(query, handlers);
  });

  it( , (done) => { query = ; { res = ( fetch(query)).data;
      res.should.have.property( )
      res.go.should.equal( )
    } (e) { .error(e)
      done(e)
    }
  })

}) /* eslint-env mocha */ import from 'chai' import from './fetch' 'subscribe, then go' ( ) function this 5000 const next ( ) => data console `received data:` if 'done' console 'exiting...' 0 error => error console `received error ` ${error} // get the subscriber ready... 'subscribe' async const `subscription {
          info
        }` 'go' async const `query {
            go
        }` try const await 'go' 'going' catch console The object (passed to the method) is what receives the publish notifications. When the last published message is received, the test process is terminated. hander subscribe Conclusion You should explore the links provided, as there is a lot more about GraphQL subscriptions that I wasn’t able to cover in this article. I’ve also included a few relevant links, below. A complete github repository containing the code above can be found . Enjoy! here Tutorial: GraphQL Subscriptions (server-side) — Apollo GraphQL apollographql/graphql-subscriptions: A small module that implements GraphQL subscriptions for Node.js SSL on Apollo Server 2.0 + Subscriptions · Issue #1338 · apollographql/apollo-server apollo-link-ws | Apollo Link Composable networking for GraphQL | Apollo Link Apollo Client | Apollo Client

GraphQL Subscriptions Using Apollo 2

In software development you'll hear the term "moon shot". If something is a "moon shot" it's something that's extraordinarily difficult, like landing on the moon. We say this about some app doing something cool, but what about the software that... landed us on the moon? What was the original "moon shot" all about? The Software That Put Us on the Moon Meet . She was the director of Software engineering at MIT Instrumentation Laboratory, which was contracted to build the onboard software for the Apollo Space Program. This is her standing with the stack of source code used to launch us to the moon. Today we complain when Visual Studio runs slow. Margaret Hamilton If you're a developer of any kind, you owe a lot of thanks to her, since "Software Engineer" wasn't even a term before she came along. So imagine having to write software that had to work the first time it ran, and run perfectly, or people would die. Sounds like a lot of pressure right? Margaret and her team did it. They built it, and it worked. It succeeded. They made a piece of machinery land on the moon with a computer that had about .08 percent of the processing power of an iPhone. You can get a lot more . background and details here The Source Code A few years ago, Ron Burkey released . He put a ton of work into this. This is the source code for the Apollo Guidance Computer, or AGC. the source code for Apollo 11 Not only can you download the source code, but he created a simulator for the AGC, at the . You can dig deep into the systems and how they work, trust me it's a rabbit hole for geeks. Tons of awesome stuff here. There's even a you can check out to get familiar. Virtual AGC Page kinder, gentler introduction to the AGC The AGC source code is written in assembler, which is foreign to most of us. I've played around enough with x86 assembler to know it's not my calling, but perusing through a lot of this source code, you can piece together how some of this stuff works. Comanche and Luminary If you dig into the code, you'll see it's divided into two parts, Comanche and Luminary. Comanche is the software for the and Luminary is the . Command Module Lunar Module The was the cone that contained the crew and vital equipment, and was the vessel returned back to earth. Command Module The well, it was the module that landed on the moon. Lunar Module It's very interesting to see how these systems interact. When you look through the source code you can see a lot of cool hints how everything works. The DSKY The DSKY was the user interface for the computer. You could enter commands through a calculator-like interface and it was connected directly to the AGC. The commands contained a and a for each command. verb noun If you dig deep enough into the source code you'll see a lot of references to the DSKY and commands related to it. It was a marvel of engineering for its time. Of course, there is a if you want to play around. DSKY simulator How Do I Know so Much About This? I may sound like a seasoned expert here, but I just took this on the code of the Apollo 11, then started digging in the code and researching stuff. free course Click Here to Take This Course for FREE For the 50th anniversary of the Apollo 11 Moon Landing, Simon Allardice created this awesome course on Pluralsight exploring the AGC. It's a quick course, and it won't teach you how to write AGC specific assembler code, but it will give you a good idea of how things worked back then and how the whole thing fits together. After watching this you'll get a better understanding of the source code, and start looking for easter eggs. Conclusion Ok if you're a nerd like me, you love stuff like this. Obviously this isn't some kind of actively developed code where you can do pull requests and add features. Nobody will hire you because you put AGC knowledge on your resume. But it sure is fun to go through this code and figure out how things worked, and look at the comical comments in the code. It's fascinating and well worth the time to explore if you're genuinely curious about this kind of stuff. You can always if you'd like to discuss it further. yell at me on Twitter Enjoy!

The Course and Source Code for the Apollo 11 Guidance Computer (AGC)

I first wrote a version of this article just over 16 months ago, and it’s pretty impressive how much the GraphQL community has achieved in that time. First of all, the question is no longer Apollo or Relay, but rather Apollo or does one even need a fancy client at all. If you’re just testing the waters with GraphQL and don’t want to change your existing app too much, you can just use in your component like so: really fetch fetch('/graphql', {  method: 'POST',  headers: {    'Content-Type': 'application/json',    'Accept': 'application/json',  },  body: JSON.stringify({query: "{ hello }"})})  .then(r => r.json())  .then(data => console.log('data returned:', data)); The main benefit to adopting a GraphQL client is the cache implementation. Using fetch is fine to begin with, but you don’t want to be using it in an app where users quickly jump between views. In we use Apollo to cache query results — which gives us quite a noticeable boost in performance. How it works in practice: OnlineOrNot User opens a list of their GraphQL applications -> this list gets cached User opens a list of tests -> client has already fetched data about the applications, so it adds the tests to each application User now visits their list of applications again -> no new GraphQL request is made, all results are already in memory Essentially, the more the user clicks around your application, the faster your user experience becomes. On Bundle size One of the biggest complaints I hear about adopting Apollo is the bundle size (Apollo Boost, the “easiest way to get started with Apollo Client” weighs in at 30.7 kB min+gzipped), so luckily there are also alternative lightweight clients to consider: No article on GraphQL clients would be complete without mentioning . Though Amplify does take an ‘everything but the kitchen sink’ approach to features, and you get included with it: AWS Amplify everything Authentication Analytics API GraphQL Client Storage Push Notifications Interactions PubSub Internationalization Cache Thus Amplify may not suit your needs unless you’re building a whole product experience that relies on GraphQL and don’t want to customise your approach. Why I like Apollo Client The setup is considerably easier than Relay — it involves installing one package, and adding the to the root of your React tree. ApolloProvider The API is nice — they have an equivalent to Relay’s QueryRenderer called that does what it says: Query <Query    query={gql`      {        rates(currency: "USD") {          currency          rate        }      }    `}  >    {({ loading, error, data }) => {      if (loading) return <p>Loading...</p>;      if (error) return <p>Error :(</p>;      return data.rates.map(({ currency, rate }) => (        <div key={currency}>          <p>{`${currency}: ${rate}`}</p>        </div>      ));    }}  </Query> It can be used to manage state in your React app — that is, you can directly write to Apollo’s Redux-like store and consume that data in another part of the React tree. Though with React’s new Context API, and React’s best practices of you probably won’t need it. Lifting State Up import React from 'react';import { ApolloConsumer } from 'react-apollo';import Link from './Link';const FilterLink = ({ filter, children }) => (  <ApolloConsumer>    {client => (      <Link        onClick={() => client.writeData({ data: { visibilityFilter: filter } })}      >        {children}      </Link>    )}  </ApolloConsumer>); Downsides to Apollo It’s huge. It weighs in at 10x more than the smallest GraphQL client I’d consider using, and 3x more than urql Quirks Apollo is not without quirks however: Since Apollo uses to build its cache, forgetting to include in your query can cause some interesting bugs and error messages id id Why I dislike Relay Setup The main benefit to using Relay is that doesn't get included in your frontend bundle, saving your user from downloading the whole GraphQL parser - it "pre-compiles" the GraphQL queries at build time. relay-compiler What annoys me about Relay is that it requires a fair bit of work to even add to a project. Just to get it running on the client side, you need to: add a relay plugin to your config .babelrc set up relay-compiler as a yarn script setup a “relay environment” (essentially your own utility to pass data to the relay-runtime), and fetch add components to the React Components you wish to pass your data to QueryRenderer On the server side, you need to: Ensure the IDs your app returns are unique across all of your types (meaning you can’t return nice ID values like , they need to be like ) 1, 2, 3 typename_1, typename_2 Developer Experience The developer experience itself is pretty unpleasant too — needs to run each time you modify any GraphQL query, or modify the schema. In large frontend teams this means teaching everyone to run every time you change branches in Git, since almost all of our work involves fetching data from GraphQL in some way. relay-compiler relay-compiler Quirks Being one of Facebook’s Open Source projects doesn’t necessarily mean issues get fixed quickly. Occasionally, things break in unexpected ways: Using an old version of graphql breaks : relay https://github.com/facebook/relay/issues/2428 Errors don’t get sent via the error object in GraphQL when using QueryRenderer, instead one needs to create an type, and send the errors through the data object: error https://github.com/facebook/relay/issues/1913 Originally published at onlineornot.com on December 15, 2018.

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