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SANTA CLARA, California, June 13th, 2024/GamingWire/--CARV, the largest modular data layer for gaming and AI, today announced a strategic investment from double jump.tokyo Inc.,  the trailblazing Japanese web3 gaming company and primary developers behind Oasys, the leading layer-1 blockchain purpose-built for gamers. The alliance brings together two powerhouses in the industry, promising to drive innovation and widespread adoption of web3 gaming. The partnership with double jump.tokyo marks another significant milestone in CARV's rapid growth trajectory. By consolidating its position alongside web2 and web3 gaming heavyweights like Animoca Brands, Neowiz’ IntellaX, and Netmarble's MARBLEX across Asia, CARV is solidifying its position as the preeminent gaming and AI data layer, ready to catalyze industry-wide innovation. Through this investment and collaboration, double jump.tokyo will become a node operation on CARV's decentralized data layer and explore CARV technology integration. double jump.tokyo and Oasys will operate CARV nodes, which generate rewards in the form of staked tokens. These staked tokens are designed for games and gamers in general to participate in voting escrow governance on the CARV platform. CARV's core users will receive airdrops of Oasys tokens. This symbiotic relationship offers real benefits to users on both platforms, propelling web3 gaming to new heights. There are already clear synergies as both companies leverage blockchain technology as the key to digital sovereignty. double jump.tokyo, the primary developer behind the Oasys blockchain, has strategy to ensure the continuity and preservation of in-game assets, memories, and records for players, which aligns with CARV ID – the groundbreaking solution uniting multiple identities under one NFT banner to provide greater interoperability between different platforms and services across the gaming universe, which recently broke 1M in adoption. “This partnership aligns perfectly with our mission to decentralize gaming data and give power back to the players. By uniting our complementary solutions, double jump.tokyo and CARV will empower developers to create innovative web3 experiences where users have genuine ownership and control,” said CARV co-founder Victor Yu. “This collaboration represents a significant step forward in our shared vision for the future of gaming, where users maintain full ownership of their privacy and in-game assets. By becoming a node operator on CARV's decentralized data layer, double jump.tokyo can actively contribute to our shared commitment to integrating decentralized data ownership through blockchain technology. Furthermore, we are thrilled to discover opportunities to utilize CARV’s technology in games planned for development on Oasys,” said Ryo Manzoku, CTO of double jump.tokyo and Director in Tech at Oasys. Double jump.tokyo boasts an impressive track record, collaborating closely with industry giants like SEGA, Square Enix, and Bandai Namco. Likewise, CARV is a pioneering force in web3 gaming infrastructure with numerous milestones to its name, including raising over $45M from private investors and node sales, and registering 2.8M gamers. Now, as the company is continuing its decentralization via nodes distribution, the collaboration with double jump.tokyo will not only accelerate global expansion efforts but adoption of decentralized gaming experiences. For more information on CARV and the future of data ownership starting with gaming, visit https://docs.carv.io/overview/carv-intro About CARV CARV is the largest modular data layer for gaming, AI, and ∞, pioneering a future where data generates value for all. As the sole author of ERC7231, CARV has built CARV Protocol, the modular data layer which has integrated with 40+ blockchains, Google Cloud, and other identity, storage, infra and AI providers. CARV's flagship gaming superapp, CARV Play, is integrated with more than 790 web2 & 3 games and serving 2.8M+ gamers. CARV is backed by top-tier funds and ecosystems such as Temasek’s Vertex Ventures, ConsenSys (developer of Metamask), Tribe Capital, IOSG Ventures, Animoca Brands, HashKey Capital, Infinity Ventures Crypto, MARBELX, and more. For more information, visit carv.io. About double jump.tokyo Founded in 2018, double jump.tokyo Inc. is a leading Japanese startup specializing in Web3 game development, with notable titles such as "My Crypto Heroes" and "Brave Frontier Heroes." The company partners with large-scale enterprises as a technology solution provider, helping them successfully integrate blockchain technologies into their games and overall business strategies. double jump.tokyo boasts partnerships with major gaming companies like Square Enix, Bandai-Namco, and SEGA, as well as prominent brands like LINE and bitFlyer Holdings. Its mission is to drive the mainstream adoption of NFTs and blockchain technologies across the global gaming and entertainment industries. Website
Twitter (EN)
Twitter (JP)
Medium Contact Co-Founder & COO Victor Yu CARV media@carv.io This story was distributed as a release by Gamingwire under HackerNoon’s Business Blogging Program. Learn more about the program here. SANTA CLARA, California, June 13th, 2024/GamingWire/--CARV, the largest modular data layer for gaming and AI, today announced a strategic investment from double jump.tokyo Inc.,  the trailblazing Japanese web3 gaming company and primary developers behind Oasys, the leading layer-1 blockchain purpose-built for gamers. The alliance brings together two powerhouses in the industry, promising to drive innovation and widespread adoption of web3 gaming. The partnership with double jump.tokyo marks another significant milestone in CARV's rapid growth trajectory. By consolidating its position alongside web2 and web3 gaming heavyweights like Animoca Brands , Neowiz’ IntellaX , and Netmarble's MARBLEX across Asia, CARV is solidifying its position as the preeminent gaming and AI data layer, ready to catalyze industry-wide innovation. Animoca Brands Animoca Brands Neowiz’ IntellaX Neowiz’ IntellaX Netmarble's MARBLEX Netmarble's MARBLEX Through this investment and collaboration, double jump.tokyo will become a node operation on CARV's decentralized data layer and explore CARV technology integration. double jump.tokyo and Oasys will operate CARV nodes, which generate rewards in the form of staked tokens. These staked tokens are designed for games and gamers in general to participate in voting escrow governance on the CARV platform. CARV's core users will receive airdrops of Oasys tokens. This symbiotic relationship offers real benefits to users on both platforms, propelling web3 gaming to new heights. There are already clear synergies as both companies leverage blockchain technology as the key to digital sovereignty. double jump.tokyo, the primary developer behind the Oasys blockchain, has strategy to ensure the continuity and preservation of in-game assets, memories, and records for players, which aligns with CARV ID – the groundbreaking solution uniting multiple identities under one NFT banner to provide greater interoperability between different platforms and services across the gaming universe, which recently broke 1M in adoption . CARV ID CARV ID 1M in adoption 1M in adoption “This partnership aligns perfectly with our mission to decentralize gaming data and give power back to the players. By uniting our complementary solutions, double jump.tokyo and CARV will empower developers to create innovative web3 experiences where users have genuine ownership and control,” said CARV co-founder Victor Yu. “This partnership aligns perfectly with our mission to decentralize gaming data and give power back to the players. By uniting our complementary solutions, double jump.tokyo and CARV will empower developers to create innovative web3 experiences where users have genuine ownership and control,” said CARV co-founder Victor Yu. “This collaboration represents a significant step forward in our shared vision for the future of gaming, where users maintain full ownership of their privacy and in-game assets. By becoming a node operator on CARV's decentralized data layer, double jump.tokyo can actively contribute to our shared commitment to integrating decentralized data ownership through blockchain technology. Furthermore, we are thrilled to discover opportunities to utilize CARV’s technology in games planned for development on Oasys,” said Ryo Manzoku, CTO of double jump.tokyo and Director in Tech at Oasys. “This collaboration represents a significant step forward in our shared vision for the future of gaming, where users maintain full ownership of their privacy and in-game assets. By becoming a node operator on CARV's decentralized data layer, double jump.tokyo can actively contribute to our shared commitment to integrating decentralized data ownership through blockchain technology. Furthermore, we are thrilled to discover opportunities to utilize CARV’s technology in games planned for development on Oasys,” said Ryo Manzoku, CTO of double jump.tokyo and Director in Tech at Oasys. Double jump.tokyo boasts an impressive track record, collaborating closely with industry giants like SEGA , Square Enix, and Bandai Namco. Likewise, CARV is a pioneering force in web3 gaming infrastructure with numerous milestones to its name, including raising over $45M from private investors and node sales, and registering 2.8M gamers. SEGA SEGA Now, as the company is continuing its decentralization via nodes distribution , the collaboration with double jump.tokyo will not only accelerate global expansion efforts but adoption of decentralized gaming experiences. nodes distribution nodes distribution For more information on CARV and the future of data ownership starting with gaming, visit https://docs.carv.io/overview/carv-intro https://docs.carv.io/overview/carv-intro https://docs.carv.io/overview/carv-intro About CARV CARV is the largest modular data layer for gaming, AI, and ∞, pioneering a future where data generates value for all. As the sole author of ERC7231, CARV has built CARV Protocol, the modular data layer which has integrated with 40+ blockchains, Google Cloud, and other identity, storage, infra and AI providers. CARV's flagship gaming superapp, CARV Play, is integrated with more than 790 web2 & 3 games and serving 2.8M+ gamers. CARV CARV CARV is backed by top-tier funds and ecosystems such as Temasek’s Vertex Ventures, ConsenSys (developer of Metamask), Tribe Capital, IOSG Ventures, Animoca Brands, HashKey Capital, Infinity Ventures Crypto, MARBELX, and more. For more information, visit carv.io . carv.io carv.io About double jump.tokyo Founded in 2018, double jump.tokyo Inc. is a leading Japanese startup specializing in Web3 game development, with notable titles such as "My Crypto Heroes" and "Brave Frontier Heroes." The company partners with large-scale enterprises as a technology solution provider, helping them successfully integrate blockchain technologies into their games and overall business strategies. double jump.tokyo boasts partnerships with major gaming companies like Square Enix, Bandai-Namco, and SEGA, as well as prominent brands like LINE and bitFlyer Holdings. Its mission is to drive the mainstream adoption of NFTs and blockchain technologies across the global gaming and entertainment industries. Website Twitter (EN) Twitter (JP) Medium Website Website Twitter (EN) Twitter (EN) Twitter (JP) Twitter (JP) Medium Medium Contact Co-Founder & COO Victor Yu CARV media@carv.io This story was distributed as a release by Gamingwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Gamingwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Gaming Leaders CARV And Double Jump.tokyo Join Forces To Reshape Web3 Experiences

Integrating Blockchain into Gaming: Unveiling the Potential of Blockchain in Gaming In an era of digitization, the confluence of gaming and blockchain technology can radically transform the industry landscape. While the possibilities are expansive, seamless integration remains an intricate challenge. While blockchain's promise is undeniable, it's not without potential drawbacks. Security and scalability are at the forefront of concerns. A delay due to a transaction in a fast-paced game could diminish the user experience. The cost of maintaining and operating blockchain systems can also present challenges. Overcoming Technical Challenges: Speed and Economy The essence of any game lies in its real-time response. The inherent nature of blockchain, which emphasizes security through decentralization, can sometimes limit its speed. Achieving a balance where transactions are both rapid and secure is the industry's current tightrope walk. Integrating blockchain means incurring costs, which might eventually impact players in the form of additional charges. Ensuring that the gameplay remains economically accessible while leveraging blockchain benefits is crucial. Regardless of the security or real-world financial benefits a game might provide, its success hinges on the user experience. Collaboration between game developers and blockchain experts is vital to ensure that while the gameplay becomes more secure and potentially profitable, it remains engaging and intuitive. Ensuring widespread adoption of blockchain within gaming hinges on establishing trust. This involves transparent communication and educating the gaming community about blockchain's tangible benefits, devoid of technical jargon. Where Tradition Meets Tech: Tokyo Game Show 2023 Hosts Oasys and double jump.tokyo's Blockchain Revolution "Innovation distinguishes between a leader and a follower." — Steve Jobs The Tokyo Game Show 2023 (TGS2023) represents far more than an elaborate arcade for showcasing the latest in game development; it serves as the future's canvas for an industry in flux, grappling with emerging technologies like blockchain and non-fungible tokens (NFTs). It's against this backdrop that Oasys and double jump.tokyo join hands to present a booth, a capsule of what the intersection of gaming and blockchain technology offers to both developers and consumers. Synergies and Symbiosis double jump.tokyo's focus on blockchain app development aligns seamlessly with Oasys' ambitions. The collaboration isn't just tactical but symbiotic. While Oasys lays down the secure, scalable blockchain infrastructure, double jump.tokyo provides the artisanal touch, the content that will populate these digital landscapes. Take for instance the 'Battle of Three Kingdoms,' a blockchain-based game with its artwork rooted in the epic lore of the Three Kingdoms series. It's a blending of tradition and technology that speaks volumes about what this partnership can bring to the table. A Cultural Crossroads By incorporating a Japanese festival-themed booth at TGS2023, the companies tap into a broader, international audience. This isn't mere pageantry; it's an acknowledgment of the global scale of modern gaming. A game on Oasys Chain or a digital trinket won in a ring-toss challenge might originate in Tokyo, but its significance is global, transcending linguistic and cultural barriers. Vested Interest Disclosure: This author is an independent contributor publishing via our brand-as-author program. Be it through direct compensation, media partnerships, or networking, the author has a vested interest in the company/ies mentioned in this story. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR The booth is also a tribute to diversity in the blockchain gaming world. With 11 sponsoring blockchain game companies, including industry behemoths like Ubisoft and rising stars such as Red Door Digital, there's a mosaic of visions under a singular umbrella. Each game offers a different facet of what blockchain technology can contribute to gaming — be it in the form of secure, immutable records or the use of NFTs to ensure ownership and provenance of digital assets. Future-Proofing the Industry Oasys' architecture is particularly worth noting as it addresses one of the most pressing concerns for blockchain adoption: scalability. By offering a dual-layer solution — a highly scalable Layer 1 hub and specialized Layer 2 using Ethereum's Layer 2 scaling solution — Oasys paves the way for mass adoption. This is critical analysis 101 for any tech adoption curve; first comes innovation, followed by early adoption, and then mass acceptance. Oasys is designing the infrastructure for that last pivotal phase, where blockchain gaming transitions from being a novelty to a standard. Web 3.0 and Gaming’s Digital Renaissance With initial validators such as SEGA, Ubisoft, and Yield Guild Games, Oasys isn't just building a platform; it's ushering in a new paradigm. Web 3.0 isn't far off in the future; it's happening here and now, blending the decentralized internet with the gaming industry. This isn't just a leap forward; it's a quantum jump, a monumental shift that will redefine how we perceive ownership, community engagement, and even the concept of gaming itself. As we look toward TGS2023, we shouldn't merely see it as an event but as a milestone in a larger journey — one that marks the confluence of technology, culture, and commerce. The partnership of Oasys and double jump.tokyo isn't just a booth in a show; it's a vanguard initiative that might very well be laying down the foundational bricks for the metaverse of tomorrow. For those of us fortunate enough to witness this, the event might just offer a fleeting but transformative glimpse into the future — a future where gaming is not just entertainment but an interconnected ecosystem of secure, scalable, and profoundly social experiences.

Oasys & double jump.tokyo Unveil the Future of Blockchain Gaming at TGS2023

Oasys y double jump.tokyo presentan el futuro de los juegos Blockchain en TGS2023

Oasys et double jump.tokyo dévoilent l'avenir du jeu Blockchain au TGS2023

ओएसिस और डबल जंप.टोक्यो ने टीजीएस2023 में ब्लॉकचेन गेमिंग के भविष्य का अनावरण किया

Oasys と double Jump.tokyo が TGS2023 でブロックチェーン ゲームの未来を明らかに

Oasys e double jump.tokyo revelam o futuro dos jogos Blockchain no TGS2023

Oasys & double jump.tokyo Tiết lộ tương lai của trò chơi Blockchain tại TGS2023

Oasys 和 double Jump.tokyo 在 TGS2023 上揭开区块链游戏的未来

double jump.tokyo is thrilled to announce that we have secured the license from SEGA CORPORATION to develop a blockchain game based on the theme of “Sangokushi” (Three Kingdoms). “In this game, double jump.tokyo plans to use the IP of SEGA CORPORATION, “ SANGOKUSHI TAISEN “ that includes incredible Japanese artwork players can enjoy while playing.” stated Hironobu Ueno, CEO and Founder of double jump.tokyo Inc. He went on to talk about the release of this new game on “Oasys” by saying, “With a unique architecture optimized for blockchain games, Oasys aims to solve obstacles for gamers, offering fast transactions and zero gas fees and providing users with a more comfortable gameplay experience.” double jump.tokyo has been participating as a developer of this Oasys chain since the beginning, along with other industry-leading game companies and blockchain technology companies. The consensus-building algorithm of the “Oasys” chain adopts the PoS (Proof of Stake) method, which is also environmentally friendly. About SANGOKUSHI TAISEN (The Three Kingdoms) An arcade game released in March of 2005. SANGOKUSHI TAISEN won the hearts of videogame players, having a major impact on the TCG and online battler markets and winning the “CEDEC AWARDS”, given for outstanding game development and design. About SEGA CORPORATION SEGA CORPORATION is headquartered in Tokyo, Japan, and is a worldwide leader in interactive entertainment both inside and outside the home. The company develops, publishes, and distributes a variety of games for consoles, PCs, and wireless devices, as well as developing and distributing arcade machines, merchandise, and various other products. Moreover, SEGA distributes a range of gaming content developed by its domestic and overseas studios worldwide via its global publishing bases. SEGA CORPORATION’s website is located here: https://www.sega.co.jp/?embedable=true About double jump.tokyo, Inc. Founded in 2018, is the leading startup in Japan that specializes in NFT solutions and developing blockchain games, such as “My Crypto Heroes” and “Brave Frontier Heroes.” double jump.tokyo The company is a technology solution provider which partners with large-scale enterprises to help them successfully incorporate blockchain technologies into their games and company strategy. double jump.tokyo has partnered with some of the largest gaming companies including Square Enix, Bandai Namco, and SEGA CORPORATION, as well as LINE and bitFlyer Holdings. The company’s goal is to facilitate the mainstream adoption of NFTs and blockchain technologies across the global gaming and entertainment industries while helping creators build the metaverse by providing the necessary technology and tools. double jump.tokyo on and . Follow Twitter HackerNoon

double jump.tokyo to Produce Three Kingdoms Inspired Blockchain TCG Licensed from SEGA Corporation

double jump.tokyo para producir Three Kingdoms Inspired Blockchain TCG con licencia de SEGA Corporation

Cloud expertise is currently one of the most sought-after tech skills out there, and cloud jobs are some of the highest paying in the industry. But how does one break into this exciting field? A couple of years ago, I found myself stuck in a dead-end job and was itching to break into a career in the cloud, but I did not know how to. After discovering AWS, I came up with a plan: I was going to get all three associate-level certifications, which would make me a cloud developer in extreme demand, right? I just need to cram for a couple of tests. Easy peasy! So how did that plan pan out? (See my original blog post from 2017 about my certification journey ) here Certifications Are Controversial Certifications are a bit of a touchy subject in tech: some people swear by them, others think they are overrated. I kinda agree with both of those sentiments! Let me explain. If you think that a certification alone will make you an expert in a given topic, then you are in for a surprise. You will not gain expertise by passing a test. This attitude is what gives certification a bad rap. A growing number of so-called “certified experts” that seem to lack an understanding of the very topics they are certified in does not help. With that being said, certifications have some benefits, two of which are: They provide an easy-to-follow path for studying and self-learning: for complex topics like AWS services, one can be easily overwhelmed, but having a defined and structured list of topics to cover makes it much easier to learn While most technical hiring managers tend to be indifferent towards certifications, many recruiters and HR people like them, so having a certification could potentially improve your chances of being noticed and might help get your foot in the door Brief Overview of the AWS Certification Path As of 2020, AWS has multiple tracks for certification, as seen in the image below. AWS Certification Path as of 2020 - https://aws.amazon.com/certification/ Foundational: for those completely new to the cloud and seeking to get a general understanding. Avoid this track if you are more technically-minded Associate: this is where most people will start (and where I started). The Solutions Architect is a good one to start with Professional: recommended only after 2+ years experience, and I really encourage you to follow this recommendation Specialty: deep dives into specific topics that require lots of experience and knowledge (security, networking, etc.) For most technically-inclined people, I recommend starting with the associate-level certifications. How I Broke Into the Cloud Career As I mentioned before, a couple of years ago I decided that pursuing the three associate certifications from AWS would be all I need to break into a career in the cloud. After months of studying, I did manage to pass all three certifications and I started applying to jobs. Initial Failure I was getting called back, but my interviews were not going that great. While the knowledge I gained from all those certifications was helpful, it was not the only thing employers were looking for. In my pursuit of passing my AWS tests, I neglected other skills that were equally critical. One of the most critical skills I ignored at the time: knowing how to write quality code. It turns out that if you want to be a cloud professional, writing code is one of the most important skills that employers are looking for. My mistake: I leaned too heavily on my certifications. Turning Things Around After many failed interviews and after realizing what my areas of weaknesses were, I went to work on filling the gaps. For me, my Achilles’ heel was the coding challenges: I was doing great on most technical and behavioral aspects of my interviews, but I was bombing the code challenges. Only when I focused on my weaknesses and accepted that my AWS knowledge alone will not suffice, things started to turn for me. After devoting a considerable amount of time studying object-oriented programming and doing a lot of practice, I started interviewing more confidently and doing well on coding-related questions and challenges, which very quickly landed me what has been the most fulfilling and enjoyable career I’ve had so far. My cloud knowledge gained through studying for those certifications, in tandem with my coding skills, ended up being the winning ticket. Should You Pursue Certifications? Definitely! Shia LaBeouf thinks you should! If you are new to the cloud, or interested in jumpstarting your career, pursuing an AWS certification will be a great place to start. Just remember though: depending on your background, those certifications alone might not be enough. Suggested Path I highly recommend you start with the AWS Solutions Architect - Associate certification, and then possibly pursue the two other certifications afterward (Developer and SysOps Administrator). Unless you have a couple of years of experience in the cloud, do not pursue the professional-level certifications. But do not stop with the certification: understand that more than likely, you will need to supplement with additional skills and experience. Final Thoughts You Can Absolutely Do This If you have been thinking about transitioning into the cloud but have been looking for a sign, this is your sign! Demand for cloud professionals is only increasing. A global pandemic has caused more companies and workloads to move to the cloud, and increased the demand even further. Call to Action I highly recommend that you explore the certification path that AWS offers and consider pursuing one (or more). If you are overwhelmed by all the options and “additional skills” that you need to develop, I have put together a free ebook that will guide you through the whole process as well as provide you the best online resources for self-learning. I even give you a structured program the you can follow. You can get my ebook for free here: https://www.moneerrifai.com/ebook/ I would love to hear your experience, feedback and questions on this topic. Do you have a certification or are you considering pursuing one?

How Feasible Is Jumpstarting Your Career With AWS Certifications?

Jumpstart Our Crypto Startups: How the JOBS Act Can Save ICOs

credit: https://medium.com/@joelrodrigues/how-to-access-mongodb-from-node-js-e37c587f226a What is MongoDB? is a document holding data in JSON format. Since it’s not based on joining, it’s easy to distribute data across servers by it’s sharding feature. MongoDB database Developers can develop application which can be agnostic about deployments. MongoDB supports scaling out as opposed to RDBMS which supports scaling up. Mongo shell is written using C++ V8 for administering the MongoDB. credit: http://coenraets.org/blog/2012/10/creating-a-rest-api-using-node-js-express-and-mongodb/ What is node.js _node.js_ is basically a _C++_ program that you control using _V8 javascript_ . So any applications you write using _node.js_ will be written in _javascript_ and it will control this _C++_ application ( _node.js_ ) and you’ll be able to say something like they made request for this resource and your application actually in _javascript_ will say: okey, they made a request for this resource, I don’t have to respond to that. Now respond accordingly. connects to using drivers. Installing bit release shall be avoided because it limits the addressable size of data using the default storage engine. Node.js MongoDB 32 What is JSON? To a layman, is merely a string, anyways. objects are composed of pairs. Keys must be strings, Keys and values must be separated by semi-colons. Fields within a object are separated by commas as delimiters. objects are opened and closed using curly braces: [JSON](http://json.org/) JSON key-value JSON JSON {"string" : "sting value goes here","date" : "2015-10-27T22:35:21.908Z","number" : 123,"object" : {"key1" : "value1","key2" : "value2"},"boolean" : true,"array" : ["test",{ "key1" : "a", "key2" : "b" },[ "abc", "xyz" ]]} The above example shows that supports a number of data-types. The field shows an example of nested values. As we can see and - data models commonly make use of nesting and even what we might call . The flexibility provides makes it simple to implement different data access patterns by creating objects that contain all the data required to render a webpage full of content or say to provide another type of data view for users with very few requests. JSON object object array MongoDB deep nesting JSON What is BSON? stores data as (binary ). Here is the comparison: MongoDB [BSON](http://bsonspec.org/) JSON // JSON{"hello": "world"} // BSON"\x16\x00\x00\x00\x02hello\x00 \x06\ x00\ x00\ x00world\ x00\ x00 " drivers send and recieve data in format and when the data is written to , it’s stored in . MongoDB BSON MongoDB BSON On the application side, the drivers map to most appropriate native data types. It’s BSON lightweight traversable, to support a variety of operations necessary for writing, reading and indexing documents MongoDB efficient, meaning encoding/decoding data to/from BSON as the drivers need to do can be performed very quickly. doesn’t distinguishes between s and s. Doesn’t supports s. extends the JSON value types to include s, s, s and binary data to support images and a number of other types of data. JSON integer float date BSON integer double date Installation for Windows To access the and directly from shell, Change the path for MongoDB: mongo mongod Go to System Properties Go to Advanced settings Click Environment variables Go to System variables Go to (this is where Windows looks for executables) Path Add the ’s startup location (eg: ) MongoDB C:\Program Files\MongoDB\Server\3.2\bin Sometimes, you might need to refresh the system to see these shortcuts working However before using from terminal, create the directory for to data by following the below commands: mongodb C:\data\db MongoDB store md \data\db Notice, the before - this makes sure that the directory is created in root directory (in this case ) only. \ data\db C:\ Now, if you type and hit Enter, it starts running. If you read the log printed on the terminal, it says that is listening on and that i.e. the default path - which we created lately. mongod MongoDB port=27017 dbpath=C:\data\db Now, once is running, we can start which makes a connection to port . mongod mongo 27017 Doing some CRUD For the sake of testing this installation, we’ll do a simple document using: insert and (or in relational terms) using the below command: db.names.insert({name:'testing'}) find select db.names.find() Where is an example of document. Also, to make the result of much readable, we can chain command. {name:'testing'} .find() .pretty() We can run as a as well. MongoDB service In , documents are stored in colletions which are organized into databases. To see databases present in run MongoDB MongoDB show dbs To insert a document into a collection, we first need to know how to specify that collection in the command. A collection and database that contains it form a . When doing operations, we reference the global variable . This variable holds the reference to the database we’re currently using. To switch to a database names type . namespace CRUD db test MySQL like command use test The insert operation returns a document as well, where set to true indicates that the record (or better say, document) was inserted successfully. All documents must have an _id field. Each document in a collection must have a unique document id. At the heart of the query language for is a query by example strategy. We can pass a blank object , key value pair . The result of command is not a mere array of documents, it’s instead a . We can see this: acknowledged underscore MongoDB {} {name: "Uber"} find() cursor object var c = db.names.find();c.hasNext() //returns true, meaning there is a document yet to be visited by this cursorc.next() //grabs that document Building using node To build applications using , we first need to . node.js install it Let’s create a simple server: http // Source: howtonode.org/hello-node // Load the http module to create an http server.var http = require('http'); // Configure our HTTP server to respond with Hello World to all requests.var server = http.createServer(function (request, response) {response.writeHead(200, {"Content-Type": "text/plain"});response.end("Hello World\n");}); // Listen on port 8000, IP defaults to 127.0.0.1server.listen(8000); // Put a friendly message on the terminalconsole.log("Server running at "); http://127.0.0.1:8000/ Store the above code in a file, let’s say .js app.js And how do we start it? In the terminal, go to the location of the file and type . To verify, if it’s running or not, go to . Notice port , which ‘ve pecified in the above code. node app.js http://localhost:8000 8000 we What is NPM var express = require('express'), cons = require('consolidate'), mongodb = require('mongodb'); is how we add an external library in a application. If we run this code through terminal, we’ll get the below error: require() node.js node To get this missing thing as a package, we can use command line . In real projects, there will be a bunch of package dependencies. Installing them all one-by-one will be a big pain. To resolve this comes file. This file contains meta data about the dependencies: express node node npm install express package.json {"name": "project name goes here","version": "0.1.2","description": "npm introduction","main": "app.js","dependencies": {"consolidate": "~0.13.1","express": "~4.13.3","mongodb": "~2.1.3"},"author": "ABC","license": "MIT"} With this file in , simply running will get all the packages for us inside directory. There’s also a way to install packages globally. place npm install project’s local node_modules What is Node.js Driver The driver communicates with the server using it’s wire protocol. It handles things like opening sockets, detecting errors and managing connections to replica sets. To include the driver in an application, use code and install using . As we can see, it’s a node package. Try to connect to using this package: MongoDB var mongodb = require('mongodb'); npm install mongodb just MongoDB var MongoClient = require('mongodb').MongoClient,assert = require('assert'); var url = 'mongodb://localhost:27017/startup'; MongoClient.connect(url, function(err, db) { assert.equal(null, err);console.log("Successfully connected to server"); // Find some documents in our collectiondb.collection('startup').find({}).toArray(function(err, docs) { // Print the documents returneddocs.forEach(function(doc) {console.log(doc.name);}); // Close the DBdb.close();}); // Declare successconsole.log("Last call");}); In order to use the , it’s important to have a solid understanding of the nature of in , including database requests. MongoDB Node.js driver asynchronous IO javascript The _mongo_ shell is synchronous meaning that when you issue a find command, it blocks waiting for command to return before continuing. This is not the case with _Node.js_ driver. Whether you’re doing a query, as we are here in the call defined or just setting up a connection to the database. As is common in _Javascript_ applications, the _Node.js_ driver we’re going to use is designed so that it’s methods function asynchronously. What this means is that instead of waiting on a return value from any methods we call, we instead pass in a callback function. The second parameter in the above call is a callback function. And is going to handle the result of this connection operation. Because of nature of , the following message gets printed before any database values: MongoClient.connect anysnchronous node.js _Last call_ What is Express is a module that handles routing, request parameters and other details of requests. Express node.js HTTP Here is a basic example of setting up a basic server: Express var express = require('express'),app = express(); app.get('/', function(req, res){res.send('Hello World');}); app.use(function(req, res){res.sendStatus(404);}); var server = app.listen(3000, function() {var port = server.address().port;console.log('Express server listening on port %s', port);}); Implementing HTML Templates The following example shows how to a template: implement HTML //file: app.jsvar express = require('express'), //set up the Express serverapp = express(), //use it to create an Express appengines = require('consolidate'); //use the wrapper library app.engine('html', engines.nunjucks); //register nunjucks template engine app.set('view engine', 'html'); //as being associated with the html extension, set the view engine app setting app.set('views', __dirname + '/views'); //specify template location app.get('/', function(req, res) {res.render('hello', { name : 'Templates' });}); app.use(function(req, res){res.sendStatus(404);}); var server = app.listen(3000, function() {var port = server.address().port;console.log('Express server listening on port %s', port);}); And the html //file: views/hello.html <h1>Hello, {{name}}!</h1> In the above code, is basically a set of wrappers for a number of template engines for . requires certain libraries to have a certain interface and handles that for us. is a variable which allows us to access to the directory in which the application file (in our case, ) resides. Since, we’ve passed - we’ll see the below output: consolidate Express Express consolidate __dirname node.js app.js name : 'Templates' Here’s a more advanced version which connects to the database as well: //file: app.js var express = require('express'),app = express(),engines = require('consolidate'),MongoClient = require('mongodb').MongoClient,assert = require('assert'); app.engine('html', engines.nunjucks);app.set('view engine', 'html');app.set('views', __dirname + '/views'); MongoClient.connect('mongodb://localhost:27017/startup', function(err, db) { assert.equal(null, err);console.log("Successfully connected to MongoDB."); app.get('/', function(req, res){ db.collection('startup').find({}).toArray(function(err, docs) {res.render('startup', { 'name': docs } );}); }); app.use(function(req, res){res.sendStatus(404);}); var server = app.listen(3000, function() {  
    var port = server.address().port;  
    console.log('Express server listening on port %s.', port);  
}); }); and the html //file: views/startup.html <h1>Hello, {{name}}!</h1> Handling GET requests using Express var express = require('express'),app = express(),engines = require('consolidate'); app.engine('html', engines.nunjucks);app.set('view engine', 'html');app.set('views', __dirname + '/views'); // Handler for internal server errorsfunction errorHandler(err, req, res, next) {console.error(err.message);console.error(err.stack);res.status(500).render('error_template', { error: err });} app.get('/:name', function(req, res, next) {var name = req.params.name;var getvar1 = req.query.getvar1;var getvar2 = req.query.getvar2;res.render('hello', { name : name, getvar1 : getvar1, getvar2 : getvar2 });}); app.use(errorHandler); var server = app.listen(3000, function() {var port = server.address().port;console.log('Express server listening on port %s.', port);}); The template is: hello <h1>Hello, {{name}}, here are your GET variables:</h1><ul><li>{{getvar1}}</li><li>{{getvar2}}</li></ul> and the template goes here: error <h1>Error: {{error}}</h1> In the above app, we’re just registering only one route i.e. . The colon says to take this part of the URL and store in a variable called . /:name name Will share further notes next week. Photos Originally published at xameeramir.github.io .

MongoDB for node.js developers jumpstart

It’s no secret that social media is more important now than ever. Everyone is on at least one platform or another. With that being said, it’s a really smart decision to notice where the attention lies and use it to your advantage. Over the next few weeks, I’ll be writing a series of articles that go over solid strategies to capture and engage your audience on each of the individual platforms. This article will go over some of the principles that will apply to all of your social media efforts, so you may want to bookmark this or save it for future reference. Before we tackle social media, I really want to stress the importance of having a quality product. If your CD and your band are terrible, great marketing will only expose how terrible it really is. If your live show is slop, great marketing will only lead to more people talking about how bad your live show is. A garbage product is still a garbage product regardless of how much time you put into your marketing plans. With that being said, let’s get started. Social Media to be seen and discovered With so many people actively consuming opinions and stories via their social platforms, it’s a no brainer to think that you might be able reach new eyes and ears. There are so many quick and easy tactics to finding more views. Firstly, I want to briefly address Content is the only thing that matters in the Social Media game. If you aren’t producing content, there is nothing for your audience to find. This could be quick videos, pretty graphics, interesting status updates, etc. However, make sure you are striving for quality, not quantity. content. (We will cover Quality Content development in a later article.) Secondly, we need to make sure that we are looking to create an audience organically and on our own accord. We want people who are genuinely interested in what we have going on. 10 true followers who interact and buy your stuff is worth more than 1000 followers that ignore your content. Again, quality, not quantity. Let’s say that you are producing quality content. How do you expand your reach via Social Media? There are three main ways to get your content out in front of more people. #Hashtags, paid advertisements, and shares. #Hashtags I believe in the power of hashtags, but only if you understand how to use them. These little guys help us group content into certain areas of the internet. We can search for them and/or we can group ourselves in with them to be searched for. For instance, I use #drums quite a bit. I search for it often to find new things or to geek out with other drummers. I also include it in my posts so others who search for #drums will see my content. It really is quite simple. Take a few minutes to research the latest hashtag trends to see if you can relate what you do to a hashtag that is gaining a lot of attention. These trends are typically short lived so jump in quickly when you do and don’t hesitate to get into conversations with people also using that hashtag. On the flip side, don’t bother starting your own hashtags unless you have a huge following that will actually use it. I see too many bands trying to use their band name as a hashtag. This doesn’t typically work. It’s also a waste of characters when you could use more hashtags that already have peoples attention. Paid Advertisements Paid advertisements are all of the sponsored ads you’ve seen in your Social feeds. Believe it or not, these work. The biggest pitfall I’ve seen when using paid advertisements, is a lack of planning. Who is your target audience? What are their interests? What region do you want to target? This is great for touring bands and out of town shows. How long do you want the campaign to run for? What is your budget? What do you want your audience to do? Visit your webpage? Sign up for your email list? Download your cd? What kind of a content do you want to use to entice them to follow through with your call to action? Once you’ve thoroughly figured out your advertising plans, the only thing left to do is monitor results and make adjustments along the way. Shares By far the most important of them all. This is the word of mouth for social media. You want to create content that people want to share with their friends. People love to share things that move them in some way. They become brilliant walking billboards. I’m sure that you’ve been a walking billboard at one point or another. This is the ultimate goal for being seen and discovered. Speaking of sharing, don’t forget to mention your social media at your shows and events. Create some sort of reason for them to want to pull out their phones and follow you right away. A call to action right there from the stage. Remember that growing an audience takes time. Don’t get frustrated if you don’t see huge growth in the first couple of weeks. Your goal is to just get one more person to click the like button. Once you’ve figured out how to funnel your audience to your Social accounts, now it’s time to engage them. Engagement and Interaction The best part of Social Media is the interaction. Getting to know our fans helps us understand how to sell to them better. And more so, this helps them develop a relationship with us and our music so that they will want to buy our stuff before we are even selling it! I’ve come up with some guidelines to help get you started. Content Content is the ground on which Social Media is built on. This is every post, every picture, every tweet, etc. Without content, you have nothing for people to comment on or share. You need content. Lots of it. But let’s not stop there. You need interesting and enticing content. Something that makes people stop and give their precious attention to your post for just a few seconds. The longer you can hold their attention, the more you are on their mind, the more likely they are to comment or share. That’s the goal. Start thinking about daily posts. But don’t just post junk. This brings me to my next point. Be Interesting Being a musician, I see all of the Social Media updates for my friends in other bands. Unfortunately, that means I see a lot of really generic band posts. These are the calendar updates shared to your Social accounts from the Bandsintown app. The half thought out, last minute “Hey guys, I’m playing tonight… blah blah blah.” The daily Soundcloud links shared with no context to what or why it’s being posted. Keep your posts creative and interesting. Start thinking of fun stories to tell around your music, your rehearsals, your gigs, etc. Take fascinating pictures and video of the musical adventures only musicians get to go on. Be original and unique, much like you probably are in real life but let it be seen through Social Media. Be Human In the process of finding a polished product to give our audience, I’ve found that sometimes, we polish a little too much. We try to come up with perfect canned answers and responses to give our fans when they ask questions or start conversations with us but from watching the greatest performers in the world, I can tell you that being a true human pays off greatly. Your fans are your fans because of you. Not because of the phone you are likely using to read this article, not because of the computer you recorded your CD on and not because of the click track playing live in your ears to help your band stay tight. They are your fans because they like the original thoughts you’ve created for them. The lyrics and stories in your songs. The catchy melodies and dancy rhythms that drive your music. If they can connect and be moved by those things, you’ve sparked intrigue in their mind and now they want more. They want to know you more and to dig deeper into your stories and know why you do the things you do. Being human is probably one of the most endearing traits an artist can have. That sells more than any polished product ever will. Some things to remember If this is your first time digging in to Social Media and thinking about a real way to market your band or project, I’d imagine you might be a little overwhelmed. Luckily, this is a marathon, not a sprint. There’s plenty of time to practice and try things out. If something doesn’t work as expected, (and there are times that it won’t), take note, make some adjustments and try again. Maybe do a little research and check out what other bands are doing. If they have a super successful campaign, steal some ideas. Lastly, and before you start this big push to up your Social Media game, remember that to really be able to utilize these tools successfully, your music and your product still need to be your first priority. If you’re songs are terrible, you band is loose, your show sloppy, and CD nonexistent…then you have nothing to create content for. Again, if you have no content, you have nothing on Social Media. Nothing. Hopefully this has offered you some insight and direction into the world of Social Media marketing. If you have any questions or comments, feel free to leave them below or contact me via Snapchat at @JeromyABailey. Don’t forget to share!!

A Social Media Jumpstart for Musicians

Discussions about Generative AI safety are more urgent than ever. As AI systems become more embedded in our daily lives, from personalized recommendations to complex decision-making processes, ensuring their safety and ethical alignment with human values is paramount. However, the complexity of AI systems requires a nuanced understanding of the multiple layers that contribute to their reliability and societal impact. To unravel this complexity, it helps to think of AI safety as an onion, with each layer addressing a critical facet of the AI lifecycle. Training Data (Quality and Fairness): At the core of any AI system lies the data on which it is trained. The adage "garbage in, garbage out" could not be more applicable here. AI models rely on vast amounts of data, and the quality of that data directly affects their performance. Poor-quality data introduces biases and inaccuracies, which can result in skewed outputs or even harmful outcomes. Ensuring the quality, diversity, and fairness of training data is a foundational step towards building systems that are not only accurate but also fair and representative of diverse groups. Algorithm (Tuning and Transparency): Moving outward, we reach the algorithm itself. Algorithms define how the AI interprets data, learns from it, and generates predictions. The choices made during algorithm tuning and development can have far-reaching consequences, and transparency in these processes is essential for accountability. By understanding how an AI model makes decisions, we can prevent the "black box" phenomenon, where even developers don’t fully understand how certain outputs are produced. Transparency enables stakeholders to scrutinize, audit, and improve AI systems continually, aligning them with intended ethical standards. Inference (Accuracy): As AI systems process data and make predictions, the accuracy of these inferences becomes the next critical layer. Inaccurate inferences can lead to costly errors, especially in high-stakes fields such as healthcare, finance, and law enforcement. Rigorous testing and validation of AI models are essential to ensure that they perform as expected in real-world scenarios. This layer underscores the importance of accuracy and reliability, setting a high standard for performance before deployment. Publication (Accountability): Accountability comes into play when AI models are deployed into society. Publication and documentation of AI systems' goals, methods, and expected outcomes create an essential checkpoint for their use. This layer of the onion ensures that the developers and operators of AI systems can be held accountable for their actions and decisions. Clear documentation, including the limitations and potential risks of the AI, helps stakeholders understand and anticipate its impact, fostering a culture of responsibility in AI deployment. Societal Impact (Human in the Loop): At the outermost layer lies the societal impact, where AI systems intersect with human lives. This is where "human in the loop" becomes essential. No AI system should operate in complete isolation from human oversight, especially when it comes to decisions affecting individuals and communities. Ensuring that humans have the ability to monitor, intervene, and correct AI processes can prevent unintended consequences, biases, or harmful actions. This final layer emphasizes the need for a balance between AI autonomy and human oversight, promoting AI systems that enhance human well-being rather than detract from it. Peeling back these layers reveals the complexity of AI safety, which cannot be addressed by focusing on any single aspect. Just like an onion, each layer builds upon the previous ones, contributing to a holistic approach to safety that spans from data quality to societal impact. As we continue to integrate AI into more areas of life, it's crucial to adopt this layered perspective. AI safety is not just a technical issue; it is a societal one that requires ongoing vigilance, transparency, and a commitment to ethical standards at every level. The future of AI depends on our ability to address each layer thoughtfully, creating systems that are as beneficial as they are innovative. By treating AI safety as an onion, we peel away the superficial and dig deep into the core of responsible AI development, creating a future where technology serves humanity rather than the other way around. For those interested, I wrote a more detailed discussion on this layered approach here: http://dx.doi.org/10.13140/RG.2.2.15152.57604 Happy for your comments and feedback. About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here. Discussions about Generative AI safety are more urgent than ever. As AI systems become more embedded in our daily lives, from personalized recommendations to complex decision-making processes, ensuring their safety and ethical alignment with human values is paramount. However, the complexity of AI systems requires a nuanced understanding of the multiple layers that contribute to their reliability and societal impact. To unravel this complexity, it helps to think of AI safety as an onion, with each layer addressing a critical facet of the AI lifecycle. Training Data (Quality and Fairness): At the core of any AI system lies the data on which it is trained. The adage "garbage in, garbage out" could not be more applicable here. AI models rely on vast amounts of data, and the quality of that data directly affects their performance. Poor-quality data introduces biases and inaccuracies, which can result in skewed outputs or even harmful outcomes. Ensuring the quality, diversity, and fairness of training data is a foundational step towards building systems that are not only accurate but also fair and representative of diverse groups. Algorithm (Tuning and Transparency): Moving outward, we reach the algorithm itself. Algorithms define how the AI interprets data, learns from it, and generates predictions. The choices made during algorithm tuning and development can have far-reaching consequences, and transparency in these processes is essential for accountability. By understanding how an AI model makes decisions, we can prevent the "black box" phenomenon, where even developers don’t fully understand how certain outputs are produced. Transparency enables stakeholders to scrutinize, audit, and improve AI systems continually, aligning them with intended ethical standards. Inference (Accuracy): As AI systems process data and make predictions, the accuracy of these inferences becomes the next critical layer. Inaccurate inferences can lead to costly errors, especially in high-stakes fields such as healthcare, finance, and law enforcement. Rigorous testing and validation of AI models are essential to ensure that they perform as expected in real-world scenarios. This layer underscores the importance of accuracy and reliability, setting a high standard for performance before deployment. Publication (Accountability): Accountability comes into play when AI models are deployed into society. Publication and documentation of AI systems' goals, methods, and expected outcomes create an essential checkpoint for their use. This layer of the onion ensures that the developers and operators of AI systems can be held accountable for their actions and decisions. Clear documentation, including the limitations and potential risks of the AI, helps stakeholders understand and anticipate its impact, fostering a culture of responsibility in AI deployment. Societal Impact (Human in the Loop): At the outermost layer lies the societal impact, where AI systems intersect with human lives. This is where "human in the loop" becomes essential. No AI system should operate in complete isolation from human oversight, especially when it comes to decisions affecting individuals and communities. Ensuring that humans have the ability to monitor, intervene, and correct AI processes can prevent unintended consequences, biases, or harmful actions. This final layer emphasizes the need for a balance between AI autonomy and human oversight, promoting AI systems that enhance human well-being rather than detract from it. Peeling back these layers reveals the complexity of AI safety, which cannot be addressed by focusing on any single aspect. Just like an onion, each layer builds upon the previous ones, contributing to a holistic approach to safety that spans from data quality to societal impact. As we continue to integrate AI into more areas of life, it's crucial to adopt this layered perspective. AI safety is not just a technical issue; it is a societal one that requires ongoing vigilance, transparency, and a commitment to ethical standards at every level. The future of AI depends on our ability to address each layer thoughtfully, creating systems that are as beneficial as they are innovative. By treating AI safety as an onion, we peel away the superficial and dig deep into the core of responsible AI development, creating a future where technology serves humanity rather than the other way around. For those interested, I wrote a more detailed discussion on this layered approach here: http://dx.doi.org/10.13140/RG.2.2.15152.57604 http://dx.doi.org/10.13140/RG.2.2.15152.57604 Happy for your comments and feedback. About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here . here

Peeling the Onion on AI Safety

The art of prompt engineering is becoming a critical skill for effectively harnessing AI's potential as artificial intelligence continues to evolve. Yet, for all its importance, prompt engineering is often reduced to simplistic advice—tips like “be specific” or quick hacks such as “let’s think step by step.” While helpful for beginners, these pointers barely scratch the surface. True prompt engineering requires a much deeper understanding of the mechanics behind AI responses, moving far beyond what’s often little more than zero-shot Google searching. Here we go through some foundational principles to explain why good prompting actually works. Embeddings: The Foundation of Prompt Understanding One of the foundational concepts in effective prompting is understanding embeddings. When we input a prompt into a model, it doesn’t process language as we do; instead, each word or phrase is transformed into an embedding, a numerical representation that captures both the meaning and context of the language. These embeddings act as the model’s internal mapping, helping it interpret relationships, nuances, and associations between words. For prompt engineers, understanding embeddings is essential for prompt intuition. The way a word is represented in embedding space determines how the model interprets it, affecting the content and focus of responses. For example, if a prompt requests information on “manufacturing,” the model will pull content based on embeddings linked to those terms, potentially drawing from areas like production processes, supply chain logistics, or technological advancements. A prompt engineer can influence these responses by skillfully crafting the wording, improving the relevance and depth of the output. Understanding embeddings also empowers prompt engineers to chain prompts better, where each response builds on previous interactions. By crafting sequences with embedding relationships in mind, prompt engineers create complex, context-aware conversations that adapt to specific needs, producing detailed, responsive outputs. The Role of Attention: Guiding Model Focus Another core component in prompt engineering is attention. Unlike human attention, which can dynamically shift between different topics, AI attention assigns weight to relationships between words within the prompt and response text. Attention helps the model prioritize certain parts of the input, and a well-crafted prompt uses this to shape the model’s focus. Take, for instance, a prompt asking for a nuanced view of a cat sitting on a mat. Prompt engineers can guide the model to emphasize relevant aspects of the prompt's subject, such as the cat's posture, the texture and color of the mat, or the overall scene’s ambiance, leading to a more detailed and vivid response. This approach to prompting. known as directional stimulus, allows the extraction of richer, more relevant answers from the model. RAG: Retrieval-Augmented Generation for Enhanced Responses Retrieval-Augmented Generation (RAG) combines generative AI with retrieval systems to improve the accuracy and relevance of responses. Traditional prompt engineering relies on a model’s internal knowledge, which can be outdated or incomplete. RAG addresses this by adding a retrieval layer that pulls in current, contextually relevant information from external sources like databases or search engines, enabling the model to provide more accurate, up-to-date answers. RAG is transformative for prompt engineering. It allows engineers to structure prompts that direct both the retrieval and generative components to work together, especially valuable for complex or fast-evolving subjects. For instance, a prompt about “recent changes in data privacy laws” could trigger the model to fetch the latest legal documents or news, which it then combines with internal information via RAG to synthesize into a coherent response. This dual capability reduces hallucinations and keeps the model grounded in real-time, reliable information. Prompt engineers working within RAG frameworks can therefore create interactions that are both innovative and anchored in current knowledge, adding an extra layer of sophistication to the AI’s output. Image Prompting and the Diffusion Process In image generation, prompt engineering takes on another layer of complexity. Here, prompts are not just about words but about guiding a model through the diffusion process—a technique where random noise is iteratively transformed into a coherent image. The diffusion process relies on iterative adjustments, and each cycle builds upon the previous one to refine an image based on the prompt's guidance. When crafting prompts for image generation, understanding diffusion is essential. Descriptions like “award winning photography” or “fashion photography” can yield varying results depending on how the model interprets "award winning" or “fashion.” Prompt engineers must understand the model’s interpretation of spatial relationships, color, and composition to produce a desired outcome. It’s about more than aesthetics; it’s about leading the model through each step to align with the intended visual concept. Hallucinations and the Pitfalls of Poor Prompting One of the most challenging aspects of AI prompting is managing hallucinations—when a model produces content that sounds plausible but is entirely fabricated. Hallucinations often result from vague or poorly structured prompts, which leave too much to the model’s interpretation. In fields like healthcare or finance, where accuracy is critical, even a small hallucination can have serious consequences. Skilled prompt engineers prevent this by constructing prompts that minimize ambiguity, verifying outputs with fact-checking prompts, and maintaining strict clarity and focus. Why Prompt Engineering is a Real Skill Ultimately, prompt engineering is a genuine skill that combines language intuition with a deep technical understanding of AI mechanics. Unlike simple search queries, prompt engineers with knowledge of embeddings, attention mechanisms, and model limitations are able to craft better prompts. Mastering these elements enables prompt engineers to shape responses that are precise, reliable, and often surprisingly creative. The need for skilled prompt engineers will only grow as AI technology continues to integrate into industries from education to entertainment. They’ll be essential not just for generating content, but for refining AI applications, troubleshooting outputs, and optimizing model performance across fields. Far from a passing trend, prompt engineering is shaping up to be one of the defining skills of the AI era—an essential tool for building communication between humans and machines. About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x global hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here. The art of prompt engineering is becoming a critical skill for effectively harnessing AI's potential as artificial intelligence continues to evolve. Yet, for all its importance, prompt engineering is often reduced to simplistic advice—tips like “be specific” or quick hacks such as “let’s think step by step.” While helpful for beginners, these pointers barely scratch the surface. True prompt engineering requires a much deeper understanding of the mechanics behind AI responses, moving far beyond what’s often little more than zero-shot Google searching. Here we go through some foundational principles to explain why good prompting actually works. Embeddings: The Foundation of Prompt Understanding One of the foundational concepts in effective prompting is understanding embeddings. When we input a prompt into a model, it doesn’t process language as we do; instead, each word or phrase is transformed into an embedding, a numerical representation that captures both the meaning and context of the language. These embeddings act as the model’s internal mapping, helping it interpret relationships, nuances, and associations between words. a numerical representation For prompt engineers, understanding embeddings is essential for prompt intuition. The way a word is represented in embedding space determines how the model interprets it, affecting the content and focus of responses. For example, if a prompt requests information on “manufacturing,” the model will pull content based on embeddings linked to those terms, potentially drawing from areas like production processes, supply chain logistics, or technological advancements. A prompt engineer can influence these responses by skillfully crafting the wording, improving the relevance and depth of the output. Understanding embeddings also empowers prompt engineers to chain prompts better , where each response builds on previous interactions. By crafting sequences with embedding relationships in mind, prompt engineers create complex, context-aware conversations that adapt to specific needs, producing detailed, responsive outputs. chain prompts better The Role of Attention: Guiding Model Focus Another core component in prompt engineering is attention. Unlike human attention, which can dynamically shift between different topics, AI attention assigns weight to relationships between words within the prompt and response text. Attention helps the model prioritize certain parts of the input, and a well-crafted prompt uses this to shape the model’s focus. Take, for instance, a prompt asking for a nuanced view of a cat sitting on a mat. relationships between words Prompt engineers can guide the model to emphasize relevant aspects of the prompt's subject, such as the cat's posture, the texture and color of the mat, or the overall scene’s ambiance, leading to a more detailed and vivid response. This approach to prompting. known as directional stimulus , allows the extraction of richer, more relevant answers from the model. directional stimulus RAG: Retrieval-Augmented Generation for Enhanced Responses Retrieval-Augmented Generation (RAG) combines generative AI with retrieval systems to improve the accuracy and relevance of responses. Traditional prompt engineering relies on a model’s internal knowledge, which can be outdated or incomplete. RAG addresses this by adding a retrieval layer that pulls in current, contextually relevant information from external sources like databases or search engines, enabling the model to provide more accurate, up-to-date answers. adding a retrieval layer RAG is transformative for prompt engineering. It allows engineers to structure prompts that direct both the retrieval and generative components to work together, especially valuable for complex or fast-evolving subjects. For instance, a prompt about “recent changes in data privacy laws” could trigger the model to fetch the latest legal documents or news, which it then combines with internal information via RAG to synthesize into a coherent response. This dual capability reduces hallucinations and keeps the model grounded in real-time, reliable information. Prompt engineers working within RAG frameworks can therefore create interactions that are both innovative and anchored in current knowledge, adding an extra layer of sophistication to the AI’s output. Image Prompting and the Diffusion Process In image generation, prompt engineering takes on another layer of complexity. Here, prompts are not just about words but about guiding a model through the diffusion process—a technique where random noise is iteratively transformed into a coherent image. The diffusion process relies on iterative adjustments, and each cycle builds upon the previous one to refine an image based on the prompt's guidance. random noise is iteratively transformed When crafting prompts for image generation, understanding diffusion is essential. Descriptions like “award winning photography” or “fashion photography” can yield varying results depending on how the model interprets "award winning" or “fashion.” Prompt engineers must understand the model’s interpretation of spatial relationships, color, and composition to produce a desired outcome. It’s about more than aesthetics; it’s about leading the model through each step to align with the intended visual concept. Hallucinations and the Pitfalls of Poor Prompting One of the most challenging aspects of AI prompting is managing hallucinations—when a model produces content that sounds plausible but is entirely fabricated. Hallucinations often result from vague or poorly structured prompts , which leave too much to the model’s interpretation. In fields like healthcare or finance, where accuracy is critical, even a small hallucination can have serious consequences. vague or poorly structured prompts Skilled prompt engineers prevent this by constructing prompts that minimize ambiguity , verifying outputs with fact-checking prompts, and maintaining strict clarity and focus. prompts that minimize ambiguity Why Prompt Engineering is a Real Skill Ultimately, prompt engineering is a genuine skill that combines language intuition with a deep technical understanding of AI mechanics. Unlike simple search queries, prompt engineers with knowledge of embeddings, attention mechanisms, and model limitations are able to craft better prompts. Mastering these elements enables prompt engineers to shape responses that are precise, reliable, and often surprisingly creative. The need for skilled prompt engineers will only grow as AI technology continues to integrate into industries from education to entertainment. They’ll be essential not just for generating content, but for refining AI applications, troubleshooting outputs, and optimizing model performance across fields. Far from a passing trend, prompt engineering is shaping up to be one of the defining skills of the AI era—an essential tool for building communication between humans and machines. About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x global hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here . here

Explaining Prompt Engineering

প্রম্পট ইঞ্জিনিয়ারিং ব্যাখ্যা করা

Erklärung zu Prompt Engineering

Explicando la ingeniería rápida

Explication de l'ingénierie rapide

प्रॉम्प्ट इंजीनियरिंग की व्याख्या

プロンプトエンジニアリングの説明

프롬프트 엔지니어링 설명

Explicando a Engenharia Prompt

Объяснение оперативной инженерии

İstemli Mühendisliğin Açıklanması

Giải thích về Kỹ thuật Prompt

解释快速工程

I used to build and sell data warehousing solutions. Like many in the field, I’ve had my share of successful and failed implementations. Deep inside I still occasionally wish for the utopia of multi-tier 3NF highly-integrated data architectures. If you happen have one in your company, good for you. But most companies are not you. Many companies chased this utopia. Some chase them to this day. With Generative AI at the peak of the hype cycle as of this writing, there’s an interesting re-emergence of the data warehouse gospel, from luminaries such as Andrew Ng. There’s an insistence that companies must first build a data warehouse before venturing into AI. For me this is a dangerous déjà vu. While the importance of clean, structured data can’t be overstated, many organizations have struggled for years to build data warehouses—not just due to cost but because their data is simply not ready to be warehoused. For these companies, forcing data into a rigid warehouse structure before pursuing AI is an exercise in futility. Let’s take a contrarian view. Andrew Ng, a strong advocate for data-centric AI, contends that quality data is paramount and that careful preparation of data can significantly reduce the volume of data required for successful AI. While it’s true that high-quality, “smart-sized” data can make AI more effective, the practical limitations many businesses face with data readiness are immense. AI’s strength is its ability to work with less-than-ideal data, providing insights without the prerequisite of extensive, curated datasets. Ng’s argument overlooks the fact that many organizations lack the time, resources, or data structure to meet the high bar of pristine data, especially at the onset of AI integration. Here’s the reality: Generative AI is already useful even without a database or data warehouse. Today’s AI models are highly adaptive, capable of working with unstructured or semi-structured data. AI can sift through messy data and still find patterns that deliver actionable insights, making it a game-changer for businesses, large and small, that want to start benefiting from AI without waiting for the perfect data infrastructure to be in place. Ng’s perspective is valuable but assumes a near-ideal data environment that is out of reach for many, especially for smaller businesses or those without established data infrastructure. AI tools today offer flexibility, allowing companies to generate value from their existing, often imperfect, data rather than waiting for perfectly “y consistent” datasets, as Ng suggests. By bypassing the strictures of curated data at the beginning, AI can provide significant returns on investment in a fraction of the time it takes to assemble a “perfect” dataset. Let’s also talk about data warehousing itself. For years, it was heralded as the ultimate solution for business intelligence and analytics. Yet many companies that poured resources into building warehouses never reaped the promised rewards. They struggled with integration, maintenance, and scaling issues, leaving many wondering if the hype around data warehousing ever really delivered. In many cases, it didn’t. It was part of an old technology cycle that ran its course, and now, as AI takes center stage, the relevance of data warehousing is increasingly being questioned. But let’s be clear, I am not advocating against data warehousing, just the unfair sequence of events we seem to be hearing. Companies can use Generative AI now to achieve some business value, then later they can certainly use it to help with data warehousing efforts. In fact, AI can actually help prepare data for warehousing. With machine learning models designed to automate data cleaning, transformation, and categorization, AI can assist in bringing order to chaotic data sources. In this sense, AI could come first and act as the very tool that lays the groundwork for a more structured data ecosystem later on. Instead of waiting for a flawless data warehouse, companies can use AI to build towards it—on their own terms and timelines. As AI democratizes industries by providing accessible, intuitive tools for a broad spectrum of users, data warehousing often serves the limited interests of IT departments. While the old-school data engineers and architects who championed warehousing are still valuable, they’re increasingly at risk of being left behind in the AI age unless they adapt. The promise of a centralized data warehouse serving the entire enterprise’s needs has been largely confined to niche applications, while AI is democratizing data access and insights across functions, from marketing and operations to HR and finance. Generative AI is a force multiplier for organizations looking to unlock value from their data immediately. The tools are already available, and they don’t require years of building a perfect data warehouse. We need to stop framing data warehousing as a prerequisite for AI when, in many cases, it’s AI that can prepare organizations for effective data management down the road. It’s time to flip the narrative. I think AI can, and now often should, come first—not just because it delivers immediate results but because it helps solve many of the very problems data warehousing was supposed to address but never fully did. So let’s move past this outdated mindset. AI’s flexibility, accessibility, and transformative power are here today, ready to be harnessed. Why wait for the cumbersome, expensive promise of data warehousing when AI can already deliver on its potential? About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x global hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com I used to build and sell data warehousing solutions. Like many in the field, I’ve had my share of successful and failed implementations. Deep inside I still occasionally wish for the utopia of multi-tier 3NF highly-integrated data architectures. If you happen have one in your company, good for you. But most companies are not you. Many companies chased this utopia. Some chase them to this day. With Generative AI at the peak of the hype cycle as of this writing, there’s an interesting re-emergence of the data warehouse gospel, from luminaries such as Andrew Ng. There’s an insistence that companies must first build a data warehouse before venturing into AI. For me this is a dangerous déjà vu. While the importance of clean, structured data can’t be overstated, many organizations have struggled for years to build data warehouses—not just due to cost but because their data is simply not ready to be warehoused. For these companies, forcing data into a rigid warehouse structure before pursuing AI is an exercise in futility. Let’s take a contrarian view. Let’s take a contrarian view. Andrew Ng, a strong advocate for data-centric AI , contends that quality data is paramount and that careful preparation of data can significantly reduce the volume of data required for successful AI. While it’s true that high-quality, “smart-sized” data can make AI more effective, the practical limitations many businesses face with data readiness are immense. AI’s strength is its ability to work with less-than-ideal data, providing insights without the prerequisite of extensive, curated datasets. Ng’s argument overlooks the fact that many organizations lack the time, resources, or data structure to meet the high bar of pristine data, especially at the onset of AI integration. data-centric AI Here’s the reality: Generative AI is already useful even without a database or data warehouse . Today’s AI models are highly adaptive, capable of working with unstructured or semi-structured data. AI can sift through messy data and still find patterns that deliver actionable insights, making it a game-changer for businesses, large and small, that want to start benefiting from AI without waiting for the perfect data infrastructure to be in place. without a database or data warehouse Ng’s perspective is valuable but assumes a near-ideal data environment that is out of reach for many, especially for smaller businesses or those without established data infrastructure. AI tools today offer flexibility, allowing companies to generate value from their existing, often imperfect, data rather than waiting for perfectly “y consistent” datasets, as Ng suggests. By bypassing the strictures of curated data at the beginning, AI can provide significant returns on investment in a fraction of the time it takes to assemble a “perfect” dataset. Let’s also talk about data warehousing itself. For years, it was heralded as the ultimate solution for business intelligence and analytics. Yet many companies that poured resources into building warehouses never reaped the promised rewards. They struggled with integration, maintenance, and scaling issues, leaving many wondering if the hype around data warehousing ever really delivered . In many cases, it didn’t. It was part of an old technology cycle that ran its course, and now, as AI takes center stage, the relevance of data warehousing is increasingly being questioned. ever really delivered But let’s be clear, I am not advocating against data warehousing , just the unfair sequence of events we seem to be hearing. Companies can use Generative AI now to achieve some business value, then later they can certainly use it to help with data warehousing efforts. I am not advocating against data warehousing In fact, AI can actually help prepare data for warehousing . With machine learning models designed to automate data cleaning, transformation, and categorization, AI can assist in bringing order to chaotic data sources. In this sense, AI could come first and act as the very tool that lays the groundwork for a more structured data ecosystem later on. Instead of waiting for a flawless data warehouse, companies can use AI to build towards it—on their own terms and timelines. help prepare data for warehousing As AI democratizes industries by providing accessible, intuitive tools for a broad spectrum of users, data warehousing often serves the limited interests of IT departments. While the old-school data engineers and architects who championed warehousing are still valuable, they’re increasingly at risk of being left behind in the AI age unless they adapt. The promise of a centralized data warehouse serving the entire enterprise’s needs has been largely confined to niche applications, while AI is democratizing data access and insights across functions, from marketing and operations to HR and finance. Generative AI is a force multiplier for organizations looking to unlock value from their data immediately. The tools are already available, and they don’t require years of building a perfect data warehouse. We need to stop framing data warehousing as a prerequisite for AI when, in many cases, it’s AI that can prepare organizations for effective data management down the road. It’s time to flip the narrative. I think AI can, and now often should, come first—not just because it delivers immediate results but because it helps solve many of the very problems data warehousing was supposed to address but never fully did. So let’s move past this outdated mindset. AI’s flexibility, accessibility, and transformative power are here today, ready to be harnessed. Why wait for the cumbersome, expensive promise of data warehousing when AI can already deliver on its potential? About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x global hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com https://docligot.com

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