Hi there! My name is , I’m a Product Manager. In a previous essay of the series I shared with you . In this essay I will share with you HR . Vitalii Berdichevskyi Performance Management Software industry research Management Software business research What you will find in the essay: Market definition/Description Key takeaways from Gartner Magic Quadrant Competitive landscape analysis A collection of handpicked links I used in the research Market definition/Description HR Administrative Functions (Admin HR) — Include core HR (organizational, job and employee data, employment life cycle transactions, employee, manager, and role-based self-service access); benefits; and payroll administration. This may also include environmental, health and safety (EHS), grievance tracking, wellness, or other value-added capabilities. HR Service Management (HRSM) — Includes personalized direct access to policy, procedure and program guidance for employees and managers. It may also include integrated case management, knowledge base, digital document management, virtual assistants and chatbots. Talent Management (TM) applications — Include recruiting, onboarding, performance and engagement management, compensation planning, career and succession planning, learning and development, and workforce planning. Workforce Management (WFM) — Includes absence management, time capture, time and attendance evaluation, task/activities tracking, budgeting and forecasting, and scheduling. (Source: ) Gartner By 2025, 60% of global midmarket and large enterprises will have invested in a cloud-deployed human capital management (HCM) suite for administrative HR and talent management. However, they will still need to source 20% to 30% of their HCM requirements via point solutions. (Source: ) Gartner Gartner Magic Quadrant Key takeaways from Gartner Magic Quadrant for Cloud HCM Suites for 1,000+ Employee Enterprises: Mobile access is a fundamental part of HCM offerings User interface (UI) options for HCM suites continue to increase with the emerging availability of chatbots and/or virtual assistants Conversational UIs using virtual assistants such as Siri, Alexa or Google Home are also nascent, with low adoption. Mainstream adoption is unlikely to occur for five years. Shift to digital HR administration drives the need for compelling and personalized UX, as applications become the “face” of HR departments HCM suites are used primarily to manage regular full-time and part-time employees (Source: ) Gartner Competitive landscape analysis Below is a short table with vendors, focus, geo targeting, monthly visits and fundings. Full list of vendors A collection of handpicked links I used in the research Below is the link to handpicked resources I have used in Google Sheet format. What’s inside: Articles industry reports list of top HR tech influencers and many more. To use filters you can easily make a copy of the file and save it to your Google Drive. A collection of handpicked resources P.S. If you have any questions about the research or are in love with creating products - please feel free to ping me on or . Facebook LinkedIn Previously published at https://lampovoeit.space/hr-management-software-business-research-part-2-4/

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Human Resources Management Software Business Research [Part 2]

In case you missed the previous article ( ), I gave a very brief introduction on Homomorphic Encryption (HE), talked about library, and outlined some of the pain points of learning to use it. Part 1 Microsoft SEAL’s — you really have to spend a lot of time understanding the inner-workings of the library so I built to help you rapidly experiment with Microsoft SEAL using , the JavaScript package I wrote. tl;dr morfix.io node-seal It turns out you can use this library to build your own privacy-preserving applications, but there are alternatives available. This time around, I’ll compare my implementation to see how SEAL stacks up against another famous cryptosystem to give you some insight. : Paillier for simple applications, SEAL for complex applications Spoiler 📋 Feature Comparison It is important to know is not the only HE library — especially in the JavaScript world. Unfortunately, it happens to be the only homomorphic library available among a sea of homomorphic libraries in the JavaScript domain. node-seal fully partially Many of these alternatives leverage the . Therefore, I’ll make a brief comparison of Paillier vs SEAL even though it is not a truly fair comparison (PHE vs FHE). I’ll briefly go over the capabilities of each, abbreviating a plaintext as and a ciphertext as : Paillier cryptosystem plain cipher Paillier Add together ciphers Multiply a by a cipher plain SEAL Negate a cipher Add two together ciphers Add a to a plain cipher Subtract two together ciphers Subtract a from a plain cipher Multiply two together ciphers Multiply a by a cipher plain Square a cipher … We can easily see that SEAL is the clear winner in terms of features and I haven’t even listed all the different evaluation methods it supports. An important distinction is that while SEAL can homomorphically multiply two Paillier can only multiply a by a — but this is still very useful. For very simple applications Paillier maybe a better choice. ciphers cipher plain Encrypting values So far, we are talking about homomorphic evaluations on — this is where Paillier shines in terms of both algorithmic and spatial complexity. single values You simply encrypt a value and receive a : cipher cipher = encrypt( ) cipher // Pseudocode const 5 typeof // 'bigint' Homomorphic operations are rather fast on single values and each Paillier is just another , albeit a very large one — a . This is the reason why many Paillier implementations are dependent on support. cipher number BigInt BigInt How you would encrypt a value and receive a using SEAL: cipher plain = encode( ) plain cipher = encrypt(plain) cipher // Pseudocode const 5 typeof // ? const typeof // ? In SEAL, there’s an extra abstraction — you need to perform an extra step of your data before encryption. Needless to say, the underlying data is not a primitive type. encoding Parallelism What if you had a few items to encrypt? For example, say you want to encrypt a set of 10 numbers in an array. Using Paillier, you would need to iterate in a loop and encrypt each item, send this over to a server which would perform a homomorphic add or multiply, and return the result. No problem, Paillier computation is quick enough and the spatial requirements are still quite low. This works more or less efficiently. SEAL on the other hand actually works in almost the same way — at least when you’re using the . IntegerEncoder Now, let’s say you want to encrypt 1,000+ elements? We start to see Paillier break down in terms of performance. This is where SEAL shines in its amazing ability to all the numbers into a single using the . batch plain BatchEncoder What this means is instead of iterating in a loop for number of elements O(n) to encrypt or perform some HE operation you can do this in constant time O(1). SEAL can operate on all batched elements in parallel with no extra computational cost — also known as Single Instruction, Multiple Data ( ). n SIMD 🦾 : In SEAL, the number of elements you are allowed to encode using the is defined by the encryption parameters used to set up the encryption context. In a typical context, you would have a power of 2 for the number of elements. Ex: elements. Note BatchEncoder 4096 📦 The Setup The process is a bit different because where Paillier can directly encrypt numbers, SEAL requires us to first the data into a format it understands before you can encrypt it as mentioned above. It also takes a bit more understanding of how to set it up correctly. I’ll take one of the many Paillier implementations and show a full encrypt/decrypt example for both, skipping homomorphic evaluations. encode paillier-bigint paillier = ( ) * paillier ( () => { { publicKey, privateKey } = paillier.generateRandomKeys( ) cipher = publicKey.encrypt( n) decypted = privateKey.decrypt(cipher) })() // import paillier in node.js const require 'paillier-bigint.js' // import paillier in native JS import as from 'paillier-bigint' async // Create keys const await 3072 // Encrypt the plainText integer const 5 // Decrypt the cipherText const // decrypted === 5n Not bad! Now, let’s look at node-seal… node-seal { Seal } = ( )
( () => { Morfix = Seal() schemeType = Morfix.SchemeType.BFV polyModulusDegree = bitSizes = [ , , ] bitSize = encParms = Morfix.EncryptionParameters(schemeType) encParms.setPolyModulusDegree(polyModulusDegree) coeffModulus = Morfix.CoeffModulus.Create(
      polyModulusDegree, .from(bitSizes)
    )
    encParms.setCoeffModulus(coeffModulus) plainModulus = Morfix.PlainModulus.Batching(polyModulusDegree, bitSize)
    encParms.setPlainModulus(plainModulus) context = Morfix.Context(encParms, ) (!context.parametersSet) { ( )
    } keyGenerator = Morfix.KeyGenerator(context) secretKey = keyGenerator.getSecretKey() publicKey = keyGenerator.getPublicKey() evaluator = Morfix.Evaluator(context) batchEncoder = Morfix.BatchEncoder(context) encryptor = Morfix.Encryptor(context, publicKey) decryptor = Morfix.Decryptor(context, secretKey) array = .from({ : })
      .map( i) plain = batchEncoder.encode(array) cipher = encryptor.encrypt(plain) decryptedPlainText = decryptor.decrypt(cipher) decodedPlainText = batchEncoder.decode(decryptedPlainText) })() // Pick one for your environment // npm install node-seal // yarn add node-seal // // ES6 or CommonJS // import { Seal } from 'node-seal' const require 'node-seal' async // Wait for the web assembly to fully initialize const await //////////////////////// // Encryption Parameters //////////////////////// // Create a new EncryptionParameters const const 4096 const 36 36 37 const 20 const // Assign Poly Modulus Degree // Create a suitable set of CoeffModulus primes const Int32Array // Assign a PlainModulus (only for BFV scheme type) const //////////////////////// // Context //////////////////////// // Create a new Context const true // Helper to check if the Context was created successfully if throw new Error 'Could not set the parameters in the given context. Please try different encryption parameters.' //////////////////////// // Keys //////////////////////// const const const //////////////////////// // Instances //////////////////////// const // Create a BatchEncoder (only BFV SchemeType) const const const //////////////////////// // Homomorphic Functions //////////////////////// // Create an array of 4096 values const Int32Array length 4096 ( ) => _, i // Encode data to a PlainText const // Encrypt a PlainText const // Decrypt a CipherText const // Decode data from a PlainText const // decodedPlainText === array 😧🔫 Okay wow — there’s more you need to setup when using node-seal. Luckily for you, all your code can be generated using the on and there’s also documentation located at . You can find a little more information on vs in . a lot code generator morfix.io/sandbox docs.morfix.io BFV CKKS Part 1 Scheme Type I’ve selected because I want to just worry about integers and not floats. I’ve also selected to use the to take advantage of . My is set to which provides me with a good initial setup that can use the library to its full extent. Any less and I won’t be able to use some of the more advanced functions. Choosing this number also means I can encode/encrypt up to 4096 elements. BFV batchEncoder SIMD polyModulusDegree 4096 CoeffModulus The is tricky to explain. You will need to select a few numbers, but their sum cannot exceed a certain maximum (depending on your other encryption parameters). There are lots of ways to configure these values, the best advice I can give is to experiment with the defaults first and worry about optimizing later. I’ve selected the default values provided by the library helper function and hardcoded them. You can see some other configurations in . coeffModulus morfix.io/sandbox PlainModulus The is the gatekeeper of the upper and lower bounds of each value you encrypt. More precisely, the values are stored the . Without diving into the technical details, start with a value of and worry about this value when you encounter decryptions that fail after a certain magnitude. plainModulus modulo plainModulus 20 💾 Spatial Complexity Continuing on some of the tradeoffs for using SEAL over Paillier, there comes a time when you may want to save your application state including your encryption keys and to load at a later date. A simple serialize function can be made with Paillier by saving the key parameters and then re-creating the keys. Similarly, saving is trivial since there are a few ways to serialize BigInts. ciphers ciphers I’m using synchronous operations for the sake of readability here… fs genKeyPair = keySize => { keys = paillier.generateRandomKeys(keySize)
  privateKey = keys.privateKey
  publicKey = keys.publicKey BigInt.prototype.toJSON = { }
  fs.writeFileSync( , .stringify(privateKey), )
  fs.writeFileSync( , .stringify(publicKey), )
} loadKeys = () => { rawPrivateKey = fs.readFileSync( , ) rawPublicKey = fs.readFileSync( , ) privateKeyObject = .parse(rawPrivateKey) publicKeyObject = .parse(rawPublicKey) publicKey = paillier.PublicKey(
    BigInt(publicKeyObject.n),
    BigInt(publicKeyObject.g)
  ) privateKey = paillier.PrivateKey(
    BigInt(privateKeyObject.lambda),
    BigInt(privateKeyObject.mu),
    publicKey,
    BigInt(privateKeyObject.p),
    BigInt(privateKeyObject.q)
  ) { privateKey, publicKey }
} // Generate and save const async const // BigInt's do not serialize natively. // We're going to implement out own naïve serializer method // which just saves to a string of numbers. ( ) function return ` ` ${ .toString()} this 'paillier_private_key' JSON 'utf8' 'paillier_public_key' JSON 'utf8' // Load and instantiate const async const 'paillier_private_key' 'utf8' const 'paillier_public_key' 'utf8' const JSON const JSON const new const new return The problem with this approach is that there’s no standard serialization methods for most Paillier implementations leaving you to create your own custom wrappers — although for this case it is certainly feasible. Thankfully, SEAL includes serialization methods and for of the objects in their library allowing us to communicate much more easily with different library implementations. Let’s see what this looks like assuming some of the objects we are using are global in this scope: save load all genKeyPair = context => { keyGenerator = Morfix.keyGenerator(context) privateKey = keys.getSecretKey() publicKey = keys.getPublicKey()
  fs.writeFileSync( , privateKey.save(), )
  fs.writeFileSync( , publicKey.save(), )
} loadKeys = context => { sKey = Morfix.SecretKey() pKey = Morfix.PublicKey()
  sKey.load(context, fs.readFileSync(secretKeyName, ))
  pKey.load(context, fs.readFileSync(publicKeyName, )) { : sKey, : pKey }
} // Generate and save const async const const const 'seal_private_key' 'utf8' 'seal_public_key' 'utf8' // Load and instantiate const async const const 'utf8' 'utf8' return privateKey publicKey The serialized output can be configured to be a base64 string or a binary Uint8Array. These methods are also available on and . Deserializing automatically ensures the object is valid, making it versatile for many languages such as C++ or Python. plains ciphers very But there’s one drawback — the size… saved = encrypted.save() .log(Buffer.byteLength(saved, )) const console 'base64' // 91213 bytes ~ Yikes! That’s more than the size of a decently large BigInt for Paillier. The encryption keys are also considerably larger as well — especially when you’re using the more advanced functions that need . Consequently, this means more bandwidth costs over the wire. 89Kb a lot GaloisKeys 📊 Performance Now on to the good stuff! I have to say this is not a true comparison of Paillier vs SEAL, it’s rather a comparison of a particular library against another with arguably similar contexts. Unfortunately, there’s just not that much to compare it with so this is what we have for JavaScript. I’ve written a simple benchmark comparing the relative performance of each library. Disclaimer: I’ve configured Paillier to use a 3072-bit length which represents a 128-bit security context according to the . For SEAL 128-bit security is already set up by default (unless overridden) and I’ve selected parameters that reflect a common real-world use case. This is the roughly the minimal setup in SEAL required to perform all homomorphic evaluations (including the advanced functions such as rotations, dot product, etc.) Sure, you could optimize if your application doesn’t use any multiplications to squeeze out every last drop, but this represents a good starting point. NIST recommendation paillier-bigint Generating private/public keys: Done [ microseconds]
Running tests .................................................. Done
Average encrypt: microseconds
Average decrypt: microseconds
Average add: microseconds
Average multiply plain: microseconds 2285233 232081 229535 55 1409 node-seal /
| Encryption parameters :
|   scheme: BFV
|   poly_modulus_degree: |   coeff_modulus size: ( + + ) bits
|   plain_modulus: \
Generating secret/public keys: Done [ microseconds]
Running tests .................................................. Done
Average batch: microseconds
Average unbatch: microseconds
Average encrypt: microseconds
Average decrypt: microseconds
Average add: microseconds
Average multiply: microseconds
Average multiply plain: microseconds 4096 109 36 36 37 786433 5428 269 352 6438 1844 118 18647 2535 You can find the benchmark script . here Immediately you can see there’s a performance difference during encryption / decryption favoring SEAL, but another one favoring Paillier evaluations. Keep in mind I’ve only included a few of SEAL’s evaluation functions to keep this comparison more in line with what Paillier offers except multiplying together. This is an important distinction where you cannot perform this operation using Paillier and why it is called homomorphic. ciphers partially Node-seal vs Paillier-bigint: Key Generation (5428) vs (2285233) ~421x faster Encrypt (269 + 6438) vs (232081) ~232x faster Decrypt (352 + 1844) vs (229535) ~105x faster Add (55) vs (118) ~2x slower Multiply Plain (1409) vs (2535) ~2x slower — didn’t I mention SEAL supports batching? , this test is performing these operations on . Don’t believe me? Feel free to check out the . With that in mind, you can realize a speedup over Paillier: Wait a minute Yes 4096 elements all at the same time benchmark Encrypt (269 + 6438) vs (232081 * 4096) ~141,733x faster Decrypt (352 + 1844) vs (229535 * 4096) ~4,281,301x faster Add (118) vs (55 * 4096) ~1,909x faster Multiply Plain (2535) vs (1409 * 4096) ~2,277x faster 😎 : If you’re going to use more advanced functions with SEAL, you will need to generate relinearization keys which take about the same amount of space and time to generate as the public key. To perform rotations, you need Galois keys which take substantially longer to generate and are exceptionally large. Note If you want more in-depth benchmarking of node-seal, you can and simply run or . You will be surprised to find is actually than in addition to the results listed above! clone my repo yarn benchmark:bfv yarn benchmark:ckks CKKS faster BFV You can see the tradeoff of using SEAL — you get much better computational performance and functionality, but pay for it in spatial complexity. Conclusion There’s a lot to digest, but to summarize: When to use Paillier You in a network or memory-constrained environment are You only need to perform very simple homomorphic evaluations You’re not dealing with large amounts of encrypted data When to use SEAL You in a network or memory-constrained environment are not You need to run advanced homomorphic evaluations When you are computing on large encrypted datasets For environments that do not support WebAssembly, such as , you still have the option of choosing a pure JS implementation: or the deep import inside . Keep in mind the performance is quite a bit worse. React-Native paillier-pure node-seal Stay tuned for where I build a simple application using . No jokes this time. Part 3 node-seal Thank you for reading! Previously published at https://medium.com/@s0l0ist/homomorphic-encryption-for-web-apps-b615fb64d2a2

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The global shift to home isolation has unique implications for public education. The era of slow and steady plans to virtualize education was given a donkey kick by this virus and the quarantine, and here's what I've observed. I'm one of the directors on my local school board, and part of a group of technologists called in to discuss and help with “distance learning” and implementing it in our district. Though some of our problems are unique to our area, many of these problems are facing schools around the country, and possibly worldwide. The primary challenge is educating students as much as possible from their homes. We had implemented some “distance learning” in the past, but nothing at this scale. Here's how it's gone so far. About Our District We are a small district tucked into the Tualatin Valley outside of Portland, Oregon. We're in a unique spot because while small, we are not remote. We're connected and interact with some of the biggest districts in the state. Many districts our size are isolated, and hours away from other districts. We are very fortunate. We have strong leadership and a Superintendent who is passionate, smart, and effective. We have access to funding when needed. We have many people who care about providing education. We're all in it together and have the support of the state of Oregon. We're just on the edge of the “Silicon Forest” so we have tech companies willing to help with funding and supplies, as well as fiber internet in some areas. We have resources at our disposal, though sometimes it requires effort. However, being on the very edge of the Portland Metro area, we have a mix of students in suburban areas to ones who are very rural. Our district stretches from a major population area to the foothills of the coast range of Oregon. We have a wide economic disparity, like most districts. Equity Is a Mandate, Not an Option The primary aim is educating students. All students. Equity is a factor that we can't legally (or morally) ignore: every student in our district must have equal access to education. This cannot be compromised because of where they live or the economic status of their family. We must present options to all students, or none. This isn't as easy as it sounds, and it's where most of our challenges are rooted. At a minimum the district must provide education for the students. supplemental Challenge #1: Special Education Special education programs are unique programs that require training and specialized work. Special education services are not optional and cannot be sidelined in the overall plan. Solution: Create a program to meet the needs of special education students by collaborating with the families. Have education provided in divided times for the students, so we didn't exceed the gathering requirements or violate social distancing policies. Because of the size of our district, that solution is workable. However, in larger districts, this remains a challenge. Challenge #2: Software Do we have the software we need? The software for AP Calculus is far different from software teaching kindergartners how to read. Our district is fortunate in that we've already put distance learning practices in place. We have established contracts with software vendors, some implementation, and a bit of a head start. Instead of “finding” the right solution, our mission is to expand on the existing solutions. Many of the SaaS vendors we already work with are offering help and discounted rates, making this transition easier. But there are still gaps. : Solution Accelerate existing efforts to move everything possible to online learning. Work with vendors to explore options, and train teachers to move curriculum online. Challenge #3: Devices If we transition to distance learning, the first consideration is hardware. Do we know all of our students have access to a computer or device to connect to the online classes? We had to find out. Our Superintendent sent out an email survey to ask parents about connectivity. Does every student have their own device to connect to the internet for online classes? This surfaced a problem: if they have no internet access, they couldn't respond to a request via the internet. Time to pick up the phones. School staff pulled phone lists and made calls to survey the families. That's when the goalposts moved. We found out that our student base falls into one of these categories: All students in the household have computers or a tablet to connect to online classes. Some families have a single computer or device they can share. Some families have no device or only a parent's cell phone. : Solution Get a device in the hand of every student. Many students already have Chromebooks. We have a cache of Chromebooks in our possession that were headed for surplus. They can be configured and distributed. A local tech company also offered to provide laptops or devices for students in need. So we were confident we could give every student a device to connect to online learning. Challenge #4: Internet Connectivity Internet availability was a sticky issue. Our students fall into one of these categories: Rocket fast internet Moderate internet Slow internet through satellite 5G only (cellphone tether) No internet, no cell phone. This creates another set of problems. Some students have screaming internet speeds while some have none. Part of this reasoning is economic issues, part is where they live geographically. We know that many ISPs in our area are offering free or reduced cost internet access right now. We can provide resources for them to contact the ISPs to set this up. But we still have a section of people who live so remote that no internet access is available for them. They don't even have cellphone service where they live. Solution: Create two models. One for those connected to the internet, and those who are not. Google Classroom / Online EdPrinted packets of curriculum Teachers can deploy their planned curriculum and give assignments via Google classroom, or paper packets. Students with slow connections can arrange to go to the parking lot of their school and download video lectures via WiFi. Challenge #5: Curriculum It should come as no surprise that certain classes can easily be rolled into an online course. Others are near impossible. Some issues: How can you have a science class with a lab?How can you have math classes with software support for all aspects of it?How do you run a shop class?How can you have a music class? These are all complicated issues, and unfortunately, we're stuck with “doing our best.” In some cases, we can simulate exercises and have video instruction to take the place of experience. It's not the same, but close? For certain classes, we just have to accept that it's the best we can do. We have to roll with the punches and get it done. In Oregon, the requirement is to provide supplemental education, and that is the primary focus for now. Most electives are not possible to roll into a distance learning model. Solution: Anything that can be rolled into online curriculum is implemented. Everything else must wait until next year. Meeting the Challenges So we've established: We can get a device in the hands of every student We can get families connected to the internet, or We can deliver paper packets to those who don't have internet But how do we deliver paper packets to each student? We could: Have the students come to the school individually and pick up a packet Use the postal service / UPS / FedEx Distribute it through the bus system Distributing through the mail is an option but somewhat slow. Especially since we expected increased usage of USPS for household items. How much additional load would this place on the local postal service? Likely too much. We have bus routes that snake all over our district, and students are already accustomed to the stops and schedule. We can have bus drivers and teacher assistants deliver the packets and pick them up via the bus routes. This was the preferred solution for delivering packets. We can have them delivered, and as long as the students maintain social distancing at the bus stop and we use precautions, This should meet our expectations. Solution: Set up a time for students to borrow a device if they don't have it Coordinate with students to pick up paper packets Have teachers try to fit their existing curriculum into the software packages. How Is It Working? We have a plan, and we're executing on it. It will be officially rolled out on April 13th. It's too early to say if things are working yet, but I'll keep updating as time goes on. There is a plan and backup plans in case they fail. Nobody expected something like this, so nobody prepared for it. But we are focusing on doing our best to keep things moving. What Can You Do? If you're reading this and wondering about the condition of your local school district here are some things you can do: Reach out to your district Superintendent to offer help Offer your services if you can help (IT, engineering, provisioning, etc.) Offer devices if you have extras you don't need Offer to help set up internet access for those in need Every school system is different, but they're all facing the same problems right now. Help out in any way you can. The reality is this year will negatively impact our students, but we can ease the pain by pitching in and helping. Previously published at https://www.jeremymorgan.com/blog/programming/challenges-k12-distance-learning/

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The Tech Challenges of Moving K-12 to a Distance Learning Model

When Google Wave previewed at the 2009 I/O conference, it was a tool like no other. Not only was it the first unified workspace and collaboration platform before the remote-work boom happened, but it also tried to solve many of the same problems we’re facing today. And yet, in less than two years, Google Wave failed. When we look at how the platform was rolled out, it’s not surprising it ended up a mere blip in the history of SaaS innovation. Lack of product focus, positioning problems and lackluster release plan were only some of its problems. Nowadays, as more companies are adopting the , it’s important that we learn from Wave’s mistakes. With the renaissance of real-time collaboration tools in full swing, reflecting on the story of their predecessor can provide valuable lessons for the future. distributed model The History of Google Wave 🌊 Google Wave started with a single question: This question gave rise to one of the most ambitious real-time collaboration tools ever created. “What would email look like if it were invented today?” Let’s see how Wave’s development unfolded, from its inception under the codename “Walkabout” to the eventual shutdown in April 2012 and open-source retirement in 2018. 2006–2008: Inception to Product Release 🌱 Wave’s story started in October 2004 when Google bought a mapping startup called . That acquisition came bundled with a fledgling technology that would eventually become commuters’ favorite, Google Maps. Where 2 Tech The responsibility for the new project was given to brothers Lars and Jens Rasmussen who became its lead developers. As they worked toward an initial Maps release, the brothers started to think about what might be next for them at Google. The idea to came from Jens who noticed a significant shift in the way people interacted online. The consensus between the brothers was that they should build a platform that would reflect those changes in its functionality. focus on communication When the Maps project was completed, Lars and Jens moved on to develop a product dubbed “Walkabout.” Their ultimate goal was to answer a number of questions about how people communicated online: 💬 Why are there divisions between email, chat, and document-based communication? ⚖️ Is there a way to create a single communication channel that spans all or most of these systems seamlessly? ⚙️ Is there a way to make that type of communication simple? 🖥 How do we design a platform that takes advantage of the current computers’ capabilities? From there, the Rasmussen brothers assembled a tiger team of developers to execute on this idea. After several months, they came up with a prototype which, over the next few years, evolved into a product release. They called it Google Wave. 2009–2011: Developer Preview 👨🏻‍💻 In May 2009, Lars, Jens and product manager Stephanie Hammond at Google’s I/O conference. In one fell swoop, Walkabout officially became a product, platform and protocol for real-time communication via a process called . previewed Wave waves Here’s how Google’s official blog release described Wave: “In Google Wave you create a wave and add people to it. Everyone on your wave can use richly formatted text, photos, gadgets, and even feeds from other sources on the web. They can insert a reply or edit the wave directly. It’s concurrent rich-text editing, where you see on your screen nearly instantly what your fellow collaborators are typing in your wave. That means Google Wave is just as well suited for quick messages as for persistent content—it allows for both collaboration and communication. You can also use ‘playback’ to rewind the wave and see how it evolved.” —Wave’s blog announcement The keynote speech took the form of a live demo, showcasing Wave’s functionality using an example of a boat trip Lars, Jens, and Stephanie were going to take. During the presentation, the team walked the audience through the planning stage of their boat trip, highlighting how Wave worked in terms of live updates, contextual threading, and image sharing. While there were a few hiccups, Lars and Jens attributed them to Wave being in developer preview. Overall, the initial reception of their release was overwhelmingly positive. If you’re up for some 2009 vibes, here’s a video from that conference: https://www.youtube.com/watch?v=v_UyVmITiYQ Wave’s Active Testing ⚙️ Although everyone at the I/O conference would get early access to Wave, Lars and the others stressed that the tool was still in active development. The team understood that there was still much to be done in terms of improving the product and wanted to tap the developers attending I/O for help. With a preview account, each developer would gain access to Google Wave’s API, another significant aspect of the release. At that point, the platform was still an open-source project in Lars’s mind. Using the API would give users the ability to connect Wave to existing web applications and create individual integrations. Google Wave platform via TechCrunch As it turned out, inviting developers to active testing was risky and became the first missteps in Wave’s rollout plan. Soon, the approximately 5,000 attendees at Google I/O were to become the first “outsiders” to use Wave and spot some of its headaches. The First Wave of Problems ❌ By , Google had rolled out approximately 6,000 developer accounts and was processing additional 20,000 requests. At this time, Wave also gained a lot of traction on various forums, Twitter and other social media channels. July 2009 People were scrambling to access the platform but Google was holding back. The company would only send out single-person invites, effectively undercutting Wave’s purpose for team collaboration. While Google wanted to play smart and not pressure a brand-new platform with an influx of users, gating invites also meant the company couldn’t capitalize on the buzz surrounding their product. When Google finally decided to open up Wave and allow 100,000 new users, by there were only 27,000 active developer accounts using Wave’s API. And that number was nowhere near Lars’s optimistic predictions: September 2009, “Millions of people wanted to try out the invitation-only preview we were launching later in the year. Wave became Twitter’s top trending technology topic of 2009. It was quite a bit more attention—some would call it hype—than we had expected.” —Lars Rasmussen via Huffington Post Once new users gained access to the platform, the enthusiasm waned as was quickly replaced by confusion: “Twitter filled with “Got Google Wave—now what?” memes. Worse yet: early, enthusiastic users slowly trickled out as their friends weren’t quite up for using Wave, yet. Awe of our demo was quickly replaced by predictions of certain failure.” —Lars Rasmussen via Huffington Post Much of the dissatisfaction happened because group conversations in Wave weren’t as easy to pull off as users would have liked. Oddly enough, teams still weren’t the target audience of Google’s new product . Groups, Restore and Other Updates ⚡️ The long-awaited support for Groups was added to Wave in , seven months after the platform had been publicly announced at I/O. In , Groups were followed with two other features that allowed users to add read-only participants and restore an existing wave from a previous version (version history). December 2009 January 2010 Restore functionality via Google After a so-so start, it seemed Wave was finally heading the right direction. In , Google introduced notifications and in May 2010, after a full year of closed user testing, released Wave to the public as part of Google Labs project incubator. That event was accompanied by another, much shorter keynote Lars discussed the use cases from various users. March 2010 during which It was also the time when Lars and the team decided to open up about the teething problems his team faced over that past year: “Since starting our invitation-only preview about half a year ago, we have significantly improved Wave’s speed, stability and ease of use. And I believe that, in the lingo of Gartner’s Hype Curve, Google Wave has reached the foothills of the ‘Slope of Enlightenment’: the product is mature enough that real use cases are emerging, and these use cases amply illustrate the new technology’s benefits.” — Lars Rasmussen via Huffington Post Lars’s confession revealed another critical problem of Google Wave: The team didn’t exactly know what they wanted it to be. In fact, Google was still “sandboxing” a large part of the platform’s functionality. This brings us to when Google announced they would be ceasing active development of the project: August 2010 “We were equally jazzed about Google Wave internally, even though we weren’t quite sure how users would respond to this radically different kind of communication. The use cases we’ve seen show the power of this technology: sharing images and other media in real time; improving spell-checking by understanding not just an individual word, but also the context of each word; and enabling third-party developers to build new tools like consumer gadgets for travel, or robots to check code. But despite these wins, and numerous loyal fans, Wave has not seen the user adoption we would have liked.” Citing lackluster adoption rates, Google decided to scrap the Wave project and use the technology in two new initiatives: allowed developers to continue using Wave’s real-time protocols to build their own applications. Some examples included maps, surveys and simple browser games Google Shared Spaces was an open-source project developed under the wings of the Apache Software Foundation, a leader in the open-source space. While no longer a part of Google, it did show some promise for the continued development of real-time collaboration tools in the future Apache Wave 2012–2018: Scuttling and a Slow Death in Open Source 💀 Over the next two years, Google Wave was pretty much tucked under the carpet. But that didn’t stop some people online from discussing the vision of the product as well as its user experience woes. This interest flickered until January 2012 when Wave was permanently moved to a read-only phase. While users could still export existing waves, the core technology was no longer supported. In , Google completely shut down Wave’s servers. The only remnants of the platform were Apache Wave and an experimental, open-source variant of Wave called . April 2012 Walkaround Although Wave was pretty much dead by that point, the Apache Software Foundation still managed its offshoot of the product. However, having seen little investment from the open-source community, it too was in January 2018. officially retired So, Why Did Google Wave Fail? 🤔 With the full weight of Google 💰 behind it, why aren’t we all using Wave today? What caused a revolutionary, real-time collaboration tool to fizzle out in just a few short years? Looking back at its turbulent history, we can conclude that: 1️⃣ . Google was trying to solve problems that the market didn’t fully understand at the time Wave was (way) ahead of its time 2️⃣ . The development team didn’t really know what Wave should become and who it was for It lacked direction and faced positioning problems 3️⃣ The premature release made it impossible for Google to capitalize on the buzz surrounding their product from day one It had an unfortunate launch. Nowadays, it’d be difficult to imagine a world without real-time collaboration tools. With the recent boom in workplace collaboration startups, we seem to be living in a productivity paradise. Workspace Collaboration Tools via Merci Victoria Grace But back in the day, Google Wave was unprecedented. In an attempt to bring together chat, email, image sharing, and documentation, Google created a tool that people had no frame of reference for. “But Wave is hardly just about traditional styles of messaging and replying that we’ve become accustomed to with email and IM. You can also edit things wiki-style with concurrent group collaboration. As anyone who has ever tried to group-edit a document on something like Google Docs knows, this can get tricky fast.” — From TechCrunch Contextual threading, long-running conversation history and rich-text support were all novelties in 2009. Without proper training, it was almost impossible for users to understand the real value that Google Wave was offering. While there was some positive reception following its announcement, as soon as more users got their hands on the product, the platform’s poor usability killed its potential. “The remaining five million or so who had asked to try our preview were much less forgiving than the first million. Retention rates of new users dropped enormously during December and January.” — Lars Rasmussen via Huffington Post What We Can Learn from Google Wave Story 🔄 There were several factors that contributed to Wave’s eventual demise, each a valuable lesson for other SaaS businesses today. In an era where real-time collaboration is quickly gaining popularity, Google Wave is a case study in what to do. not The key takeaway from Wave’s story? Positioning is everything. We live in a world of choice. If you’re building a product and don’t understand the specific value it provides to end users, you’re setting yourself up for failure. When people don’t feel the impact of your tool, they quickly turn to the competition for a better (more meaningful) solution. You need to give users the time to understand the value your product offers. Google Wave had been in active development for approximately 18 months before the plug was pulled. And that’s not nearly enough time for users to see how the product evolves and grows with them. Finally, your release plan is key. When Wave was introduced, Google throttled the number of people who could actively engage with it. In today’s highly competitive markets, it’s critical to get the word out as quickly and to as many people as possible. After all, what good is a product if it has no user base to support it? Understanding Failure Helps You Grow 📈 Google Wave was one of the first attempts at real-time collaboration. It had the potential to take the market by storm, but it didn’t. Observing how the platform went from “promising” to “disappointing” shows that building successful tools boils down to a few essentials: Know your product and decide you want it to be WHAT Find out your users are and tap them for feedback WHO Figure out it’s the best time to launch WHEN When you get that right, you’ll be able to attract users who truly understand how your product can help them get things done. The inspiration for this article came from our YC interview for (YC S19) with Paul Buchheit, the mastermind behind Gmail and partner at Y Combinator. Taskade

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3 ways to pull JSON data into a Google Spreadsheet In my last article, I showed you . 3 ways to import external data into Google Sheets In this article, we'll take a look at how to use a JSON API in Google Sheets and how to convert JSON data to Google Sheets. JSON is one of the and there's an abundance of resources available to help you pull into . This article serves as a summary of the available resources and a good jumping off point. most popular API formats JSON Google Sheets I crafted this guide after countless hours of research and have divided it into three sections: Code it yourself (using Google Apps Script or Python) Use a no-code tool (I cover 4 different tools) Hire a freelancer (like a data analyst or a developer) Free JSON APIs for Testing Some of the tools and recourses described below may require the use of an API URL. If you want to use a test JSON URL that doesn't require authorization, you can try one of these: : Chuck Norris Jokes API https://api.chucknorris.io/jokes/random : Pokemon API https://pokeapi.co/api/v2/pokemon/1 Click here to see a list of free public APIs you can use Okay, let's dive in... 1.) Code a JSON Importer Yourself 1a.) Use Google Apps Script The most popular method to import JSON data into Google Sheets is by using the editor and copying and pasting a publicly available from , or writing your own code. Most of the top results on Google Search for are a variation of these steps: Google Apps Script Import JSON script Github this topic Open a Google Sheet In the menu at the top of the Google Sheets, click  Tools > Script Editor In the script editor, remove all the placeholder content so you can paste your own code in Copy and paste an open source script from Github.com such as or this one this one Click the save icon (or click File > Save) Enter any name for your project such as " " ImportJSON Go back to your Google Sheet In cell A1, start typing and you'll see the function you created pop-up as an option. Click it. =IMPORTJSON Enter your JSON API URL between the parentheses in the function, between a pair of quotes. eg. =IMPORTJSON("https://cat-fact.herokuapp.com/facts") Add any other parameters you want Hit enter and your JSON API data should populate your sheet This script is completely free to use. The downside is that you're limited to what the script can do, unless you modify the code. In it's current form, the Github script is best for one-off data pulls. If you'd like something with a user interface that can automate data imports and exports, keep reading. Here's a video someone made showing the steps to use Google Apps Script: Here's a link to the script used in the video. An example of someone taking the public script and modifying it so that the data refreshes automatically can be seen here: If you're interested in automating tasks at work, and dipping your toes into coding, then is a great place to start. It has a great community around it; you can find an active Google Apps Script group on your social media of choice. And it's relatively easy to learn how to use it. by Ben Collins. Google Apps Script Here's a beginner's guide to apps script 1b.) Use Python is one of the world’s fastest and most popular programming languages used by a diverse range of professionals like data analysts, software engineers, and even accountants. Python The difference between Python and Google Apps Script is that Google Apps Script is Google's own native programming language. Some people even supplement Google Apps Script with Python. There are several reasons you might choose to use Python: Python is a multi-purpose language which makes it usable across many industries You can solve problems without writing a lot of code It’s also a multi-platform tool, which means that you can use it on Mac, Windows, or Linux The syntax is user friendly One of the most common uses for Python is creating code for a repetitive task to save you time. In this example, I'm going to show you how to use Python to import JSON data into Google Sheets. Here's the steps that you need to take (for Windows): Download the python application here: https://www.python.org/downloads/ Follow the instructions for installation (you can leave the defaults settings as-is) Once it's downloaded, you want to verify that the Python terminal was downloaded correctly. To do this, open your python application and find the version in the first line. It’s the number right after the word “Python” at the top-left corner of the window. If your interpreter matches the version you downloaded, you're verified. In your account of the software you are looking to pull API data from, find your API key and token. This is usually found in the developer section. Once you have your API key and token, move on to the next step Next you're going to install the to work with the requests in Python. Think of modules as a code template that will do part of the job for you. To do this use the command “install requests”. request library Once the request library is installed, use the command “ ” to send a request using the API URL and the command “ ”. The API URL usually consists of the domain’s API URL, you API token, and parameters that you want to pull (ex. First_Name, Last_Name, Email, etc.). import request requests.get Now you want to get the status code to see if the request was a success using the command “result.status_code”. If there is an error, use to see why your request was not successful, and adjust your code accordingly. this dictionary Lastly, you will use the command “result.json” to get the results of your API request in JSON format. Or if you would like to receive the results of your API data in text form, use the command “results.text”. And then you’re done. You've successfully pulled JSON data from an API into Google Sheets using Python. As you can see, Python is relatively easy to use, but like with any programming language, there's a learning curve. Here's a video I found on youtube of someone using Python with Google Sheets: 2.) Import JSON Using a No-Code Tool A "no-code" tool is a great option for those who want to connect and import JSON data sources in a user-friendly way. A no-code tool is just software that takes something technical like connecting data sources and presents it in an interface. Here's the 4 best no-code tools to connect a JSON API data sources to Google Sheets, in order of features and affordability: 2a.) Zapier might be the most popular tool for connecting JSON data sources between different platforms. Zapier's an automation tool for connecting different apps together. Zapier Zapier has . Check that page to see if they have an integration available for your specific data source(s). thousands of integrations available for Google Sheets The downside to using Zapier is that it won't work if they don't currently have an integration with your data source, or if their integration is limited and doesn't do what you need it to do. Unless, you want to . build your own Zapier integration as long as you only need to do 100 "tasks" per month or less. Any more tasks than 100/month and you'll need to move to a paid plan, which start at $19.99 usd/month. Cost: Zapier is free to use For example, if you’re an e-commerce store that has more than 100 orders a month, and you want to connect your Shopify orders to Google Sheets, you will need to move to a paid plan. Here's an example video someone made showing you how to connect a data source to Google Sheets using Zapier: 2b.) Apipheny : I'm the co-founder of Apipheny. Note Apipheny.io is an API integrator add-on for Google Sheets. Apipheny is popular with data analysts and with people who have experience working with APIs. With Apipheny, you can import JSON into Google Sheets in just a few clicks. All you have to do is open the add-on, then enter your API URL and headers/key into the add-on and then click "Run". Your JSON API data will automatically parse in your Google Sheet. You can make unlimited requests (within Google's limits) and also save and schedule your API requests, make GET and POST requests, and stack multiple API URLs in the same request. There's a custom function and the ability to reference the value of a cell in your API URL, headers, or POST body. All without leaving your Google Sheet. =APIPHENY() Apipheny could be right for you if you want the ability to query endpoint of an API instead of having to rely on pre-built integrations, and the ability to connect to almost any JSON or CSV data source, making it possible to connect to an unlimited number of data sources and make custom blends of data. any Apipheny is free to try and costs $12/month. Cost: Try Apipheny free for 30 days Apipheny Website Here's a Youtube demo I made showing you how to use Apipheny to get JSON data into your Google Sheet: 2c.) Supermetrics is a popular marketing reporting and automation tool for PPC, SEO, social and web analytics. Supermetrics has integrations with more than 70 different platforms. It's one of the go-to choices for agencies. Supermetrics Supermetrics has a few different tools, and one of them is a Google Sheets specific add-on that you can install in the G Suite Marketplace. Using Supermetrics, you can segment different marketing channels in multiple ways in Google Sheets then use your sheets skills to make reports that really stand out. They also have a Google Data Studio connector that allows you to connect major marketing platforms directly for reporting. has a free 14-day trial and then goes to a paid plan which starts at $69 usd/month. There is a free plan but it only includes a Google Analytics integration. Cost: Supermetrics . Click here to read a Supermetrics review . Click here to visit Supermetrics' website 2d.) Funnel is an enterprise software that allows for automated data collection and marketing reports. Funnel has many integrations available - they have integrations with over 500 data sources. Funnel.io Funnel has a lot of different features, and one of those features is the ability to . So what you would do is connect your data source to the Funnel software, and then export the data to Google Sheets. export your Funnel data to Google Sheets With the Funnel software, you would be working outside of Google Sheets, like Zapier, instead of directly in Google Sheets as with Supermetrics or Apipheny. , the Funnel team shows you step-by-step how to connect your Funnel data to your Google Sheet and then schedule it as a task to refresh daily. In this tutorial and their pricing is based on your ad spend. Cost: Funnel starts at $499 usd/month If you only need a JSON connector, and want to keep costs down, then Funnel and Supermetrics may not be the right choice unless you have a need for more of their features. There's many other tools and alternatives to the tools mentioned above available depending on your needs. 3.) Hire A Freelancer The third best option to connect JSON data sources to Sheets is simply to hire a data analyst to do it for you, or a developer to create a custom solution for you. 3a.) Hire a data analyst A data analyst will either use a no-code tool such as the ones I covered in section 2 above, or they'll create a custom solution. You can hire a freelance data analyst on a website such as Upwork or Toptal. Upwork On , you can post a job for free and start getting applicants almost immediately. It's free to post a job on Upwork but making it a featured job for $29.99 helps get more applicants. Upwork I've been using Upwork for years now for a wide variety of projects and have been satisfied with the results. You have to interview and hire a freelancer yourself. It's a good option for small business owners. and then just create an account and start a job post. Choose data analyst as the category. Click here to see data analysts on Upwork Toptal is another freelancer website but it's more exclusive. Toptal puts its applicants through a difficult hiring process and they claim to only hire the top 3% of freelance talent. Toptal So if you want a true expert to help you out, Toptal might be the place to get it, but with that expertise comes a bigger price tag. (if the popup shows, you can click the "No Thanks" button at the bottom). Click here to see data analysts on Toptal Other At Apipheny we have Google Sheets experts and developers for hire. . Click here to learn more My friend and colleague, Nate Page, is a freelance data analyst who has experience working with JSON: Nate's Upwork Profile Nate's Website/Portfolio You could also look for a freelance data analyst on a website like Craigslist, Linkedin, etc. 3b.) Hire a developer If you think the scope of your project calls for a custom solution with features that other software doesn't provide, or you just want to be in control of the product, you could always hire a developer to help you out. You'll have the best luck if you specifically look for a Google Apps Script developer on a freelance site like Upwork or Fiverr. The right developer for you will depend on the scope of your project. You'll have to send your project brief/details to freelancers and interview them to see who's the best fit. . Click here to learn more about hiring a developer Final Words Importing JSON is easy with all the available resources these days. The right option for you will depend on certain factors such as the scope of your project, your technical experience (whether you can/want to do it yourself), and your budget. The most cost effective way to pull JSON into Google Sheets is to code it yourself or use a free, publicly available script, as we covered in section 1. In section 2, we covered a range of products that have been purpose built to help people connect JSON API data sources with Google Sheets. And in section 3, I gave you some resources for finding someone to help get it done for you. If you want to use a free data visualizer to present your JSON data in pretty charts, you can connect your Google Sheets to and even embed it on a website. Google Data Studio If you have any comments or questions, just let me know in the comments. -Meelad Mashaw

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AI (Artificial Intelligence) is one of the science & technology advancements most common in the world. It explains the application of artificial intelligence & accuracy at the human level. In other words, computers are learning to think and behave like humans. Machines & apps for artificial intelligence have been of great value to humans, helping to provide simpler and more reliable ways to get things completed. Such machines are not subjected to emotional challenges that promote human errors & are not biased in their judgment, nor are they tired. Across virtually every area that exists, artificial intelligence apps have found use: from tourism to businesses, from leisure to healthcare. Whatever area you consider, there are artificial intelligence software that can help make the jobs easier. The following are top 10 artificial intelligence applications that are being used on Android devices. 10: Wysa If you’re looking for a mobile talker that’s very interested in your personality, I’ll introduce you to Wysa. An adorable penguin really knows how to cheer you up with more than 50 problems of mental pain. This talks with you in written form to explain the issues and make up a special workout kit. I was trying to talk to Wysa about some of the disagreements with a friend and about being overdriven. She’s given very fair responses! A psychologist said that Wysa uses a rational evidence-based therapy approach with meditation components. It’s one of the best advantages when you’re faced with anxiety & stress that you can’t always handle on your own. All features are completely free. When you’re not the kind of person who can dream about robotic suggestions, there’s an additional talk with a real-life Wysa Development Team specialist. 9: Replika It is known as the best friend application. Replika is one of the most famous & advanced artificial intelligent apps in the world. It was originally designed for apple Phones & is now available on Android phones. The app has the potential to have a conversation with its users much like a human being. This doesn’t appear to be an automatic chat. The app has the potential to lean the user’s interests and the conversation that can start as generic can begin to be more detailed & personalized. Replica should grow a friendship that is close to any real-life relationship. 8: Socratic Math has always been a problem for a lot of people. Socratic is one of the most common Android AI application that can assist with maths homework. It will support users with their maths homework in really good time. All that is needed is for you to take a photo of the homework & provide you with the relevant principles that you can use to solve the problems in a timely manner. It also supports subjects like Maths, Science, Chemistry, History, English, Economics, etc. 7: MeetFrank Job searching is a very tense activity, but the AI system is looking for the best matches for you. It works like: Enter your skills & abilities then wait for the results of a realistic search. Another great feature of MeetFrank is a private chat with employers. It’s a perfect choice for those who are still concerned about making a good impression. 6: Recent News Recent News is a news aggregation application, operated by AI algorithms to research your reading habits. This will keep you up-to-date with the latest posts, news, and insights that are important to your interest and past actions. Recent News is the perfect application to stay updated and up-to-date on subjects you enjoy. It can be integrated with other apps to help you bookmark articles on your smartphone and search them on other smart devices later. In addition, the ease of exporting your history & bookmarks will also help you conserve the battery by nullifying background activity. 5. FaceApp — the advanced face swap That one is unique from the other serious AI apps because it’s just fun. There are a number of automated photo editors in the market, but there is no such reliable piece of software application. Now, you can render very realistic pictures of yourself & your buddies and choose age, gender, and mood. All functionalities are free, but to erase watermarks and ads you are required to pay about $20. High amount for a bunch of funny images, huh? 4. ELSA Speak This application is a good example of a professional English instructor. It has everything required for intermediate level awareness & 1 more distinctive function. ELSA’s AI cares about your progress & remembers all your personalized lessons. A further special aspect of this AI is an advanced speech recognition technology. It’s supposed to teach you to speak English with real American literary pronunciation. There are no analogs for this app, but it’s certainly worth a try. 3. Hound This voice assistant software brings speed and precision into a single move to the powerful Houndify platform which is a combination of speech recognition & comprehension of natural language. The assistant may also use follow-up questions & commands to search, sort and add to the original requests more detail. 2. Robin — AI Voice Assistant This is yet another AI based voice assistant application for Android devices. It will allow users to write text messages without actually typing the messages. This can even assist with having GPS navigation and preparing the remainder for whatever job you want to do. Robin can be used to get traffic updates, news from Twitter, weather report or even to find a parking space for you. 1: Cortana No. 1 is Cortana, it's AI algorithms essentially use certain special features of very common origin. First of all, Cortana has a rather detailed start screen that includes the current weather and related news. “She” has a Notebook feature that can be tailored to your daily interests, such as news, food, etc. The most recent update is in 19 categories. Develop your own Artificial Intelligence(AI) Apps: Polestar Tech Consultancy provides the end to end solutions for all your business needs with the latest technology in the market. Whether it is Web application development, Mobile application development or Enterprise portal development, polestar is the one-stop-shop for your business needs. Previously published at https://medium.com/@palajay/10-artificial-intelligence-ai-apps-that-are-being-used-in-android-6ab16b1c5dbf

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Generic markets typically contain groups of consumers that share a broad need which can be met by a wide variety of products or services. In the past two years, Facebook has been struggling to stay relevant and has reported a first-time decline in monthly user growth in the last quarter, where as Instagram and LinkedIn have been galloping at a pace of 2x and 5x respectively. Why are people moving from social networks like Facebook to Instagram/LinkedIn? The phenomenon is called content collapse, a failure that Facebook has identified a long time ago and is trying to resolve. Agreeably, Facebook is intended to meet our social needs, interacting with friends and family, which revolves more around our daily consumption, as opposed to LinkedIn which focuses more on business and corporate networking. This implies the base of FB users and their needs are way too different from those of LinkedIn and the comparison may sound a little unreasonable to some. "Generic" social networks like Facebook are turning out to be everything for nobody. When you are talking to all, you actually end up effectively talking to none. Several brands have used this generic social media platform intelligently, by the endless segmentation that it has to offer. However, as the internet reaches more users and smartphone penetration increases, platforms targeting specific groups or a "niche" will become more useful.  "Specific" social networks like LinkedIn and Instagram are serving the needs for a more specific target group. Filtering through all the noise and information can be quite a hassle for a consumer. Nykaa was quick to discern that beauty was an untapped market in India and would  soon explode. Finding a product of a certain category and hunting for it in a sea of a generic site like Amazon or Flipkart can be a complex experience which can be overcome by using the right tools and filters in a dedicated website/app. In my opinion, Generic & Specific/Niche will continue to co-exist for some more time, because of the needs that they cater to and the purpose behind them as destinations are mutually exclusive. A lot of people tend to use multiple platforms for their different shopping purposes and no one is loyal to just one platform. Besides, creating accounts on all different sites would mean having to disclose this data to that many sites/apps. Moreover, these niche firms will demand their own space on your phone in the form of a different app and such different apps for different product categories tend to make your shopping experience more arduous when you can simply shop for everything under one roof! The only way to get through to the other end of the tunnel is to keep up with the new-age demands and avoid the risk of becoming discovery platforms for purchases.

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