Redbubble is a popular online marketplace that allows artists and designers to sell their original creations on various products, such as t-shirts, stickers, mugs, posters, and more. Redbubble handles the printing, shipping, and customer service for you ...

How To Sell On Redbubble in 2023: A Step-by-Step Guide

Secondhand shopping is rapidly emerging as a fresh approach for both buying and saving, and we can attribute this shift, at least in part, to inflation.  Inflation has ushered in a wave of thrifty consumers,

Why more shoppers are now embracing secondhand shopping

A SFD crew was inside the Ballard Fred Meyer when a security guard rushed to warn them about people rummaging through their fire truck in the parking lot.

Thefts targeting first responders have become 'cost of doing business' in Seattle

Whether you’re clearing clutter or trying to make some extra cash, selling your used stuff can be a good way to bring in additional income.

Best Apps for Buying and Selling Used Stuff

Thank you, inflation! Your grubby little hands have created a big fat crop of shoppers who are all in on buying and/or selling secondhand goods – 85% of sh

Secondhand is quickly becoming the new way to shop and save

Are you looking for strategies to boost your income and open up new financial possibilities? Look nowhere else! In this

15 Ways To Make Extra Money This Weekend

A detective's OfferUp purchase led police to a man who they said followed a woman to her Homestead house and brutally stabbed her.  Jose Armel Saavedra, 24, was arrested Wednesday on charges of attempted murder and armed burglary.

OfferUp purchase led cops to man who stabbed woman in her Homestead home: Police

A 24-year-old man was arrested this week on charges of attempted murder and armed burglary after he followed a woman home and then stabbed her in the head, Homestead police said.

Police: Man follows Homestead woman home, stabs her in head

Everglades National Park hasn’t had its own lodge since 2005, when Hurricane Wilma destroyed the old one. The park’s accommodation-less status is changing now that the new Flamingo Lodge is getting ready to open on Nov.

A look inside the Everglades' new, long-awaited Flamingo Lodge

I have a few items I'd like to sell but want to avoid using online storefronts like Ebay, Etsy, Offerup, etc. Thanks in advance!

Does anyone have some local shop recommendations that will buy antiques, collectibles, and vintage items?

OfferUp

offerup

Halloween. It’s spooky season, so let’s bludgeon a few old stack vendors to death — Appian, IBM, Salesforce, SAP, Pegasystems, IFS, Oracle, Software AG, TIBCO, UIPath…they’re The Walking Dead, and they know it. Back in 2010–2012 I wrote a fair bit about business process automation over on my old blog, BPM Redux, and something I was fascinated with was the concept of self-assembling or dynamic processes, created on the fly to handle a task using artificial intelligence without the need for drawing a process map or dicking around designing another set of screens. The problem I have with ‘business rules’ being an inherent part of some BPMS is that they start to constrain process decision making and flexibility, but having a set of AI routines that not only adapts on the fly (or JIT) but also learn and potentially create a brand new process instance out of evolving from previous instances… Fast forward to today and it might finally become a reality. I looked at what some of the larger stack and cloud vendors are doing today, and they all look so desperate — throwing Generative AI into their software stacks to appear relevant and forward-thinking. The trouble is, Gen-AI renders them pointless. Every. Single. One. Let’s take Pegasystems for example (disclosure: I used to work for them years ago). They have Generative AI as part of their software solutions now but it’s still rooted in the past, forcing enterprise users to not only continue using Pega but also to continually think about building workflows with it — it doesn’t matter that it’s “low-code” pseudo-development or that you can instantly draw new process steps using the magic of AI, you’re still stuck with using Pega. The same goes for all the rest — whether it be Salesforce, IFS, SAP; whether it’s ERP, CRM, RPA — what they all potentially understand and won’t tell customers is that you have all the data already, both structured and unstructured, and a Gen-AI layer is all you need on top. Think about the following example — a customer complaint. A customer emails in a complaint moaning about one of your products or services and expecting an action in response. Typically, the email will come from a contact form with all the required details to identify the customer or client, the product in question and what they expect done. If not, then it’s freeform unstructured data with a dollop of misery and anger. Normally, it falls into a defined process that an agent has been trained to perform, with clear steps already mapped out. If not, then it’s treated as an exception and passed to someone higher up the chain to make a decision on. All that takes time to create, and develop into someone’s expensive SaaS solution that looks like a dog’s dinner on the screen. Invariably, a few years later there will be a multi-million dollar “digital transformation” program sold by a consultancy deck to eke a few tweaks and savings out of this godforsaken process to justify the cost. But what if all this didn’t need to be done anymore? What if training a Large Language Model on your existing processes, rules, customer data, product or service information was all you needed to do? Going down this train of thought leads to a very different outcome. The LLM reads and understands the content of the email, pulling out key information like customer details, the product or service mentioned, and the specific issue being raised. In the case of a telephone conversation then we’ve already seen some Gen-AI solutions transcribe in real-time an interaction and then offer up advice, a response, or an answer to a question. So, already there is no need for parsing of data to another system to fill out a screen for an agent to look at. Based on this information, the LLM figures out the priority of the complaint, sees if the issue is common or recurring, and decides if any specific business rules or past actions apply. In some cases there may well be sentiment analysis done on the language of the complaint to determine just how important this is, how urgent or how annoyed the customer is. Things can get funky from here. Based on the type of complaint, the LLM creates a workflow to resolve the issue on the fly. This could involve updating customer data, processing a refund, or sending the complaint to a specialised team but it’s all done without a front end system we’ve all grown accustomed to loathe. With JIT (Just In Time) capabilities, the LLM can generate code on the spot to do necessary backend tasks. For example, if a refund is needed, the LLM creates the code to process it and updates the backend customer database with the details. Since the LLM has been trained on business rules, it makes sure all actions follow company policies — checking if the customer is eligible for a refund before processing it for example. The results of each complaint handling process are used to train the LLM further. If a complaint is resolved successfully, the LLM learns from the actions taken, improving future responses and workflows. Over time, the LLM can find recurring issues, improve existing workflows, and even suggest changes to business rules or policies based on patterns in customer complaints. The point here is that (a) you don’t need to define what the business process is any more than you do with task definition, nobody cares about static processes anymore when you can dynamically generate and execute them, (b) the LLM generates code and minimal screen UI to complete the task and have the agent involved but after that’s done the code and the UI is deleted, it’s an artefact that is no longer required because it was created to handle an interaction that is personal to that customer, and (c) humans are still in the loop, the efficiency gains are not in reducing headcount but removing the expensive excuse of paying for ancient software and unnecessary transformation programs based on old methods that are always initiated by some incoming c-suite with a relationship with a favourite vendor. It’s not as simplistic and current Gen-AI solutions aren’t geared up for this (yet) but this should be enough to start to question why you need a huge ERP implementation, or why an expensive CRM is required at all if you can just have a conversation with the customer data directly. Remember Master Data Management? Enterprises spent millions and years on making sure all their backend databases were fit for purpose, clean, deduped, they’re in potentially good shape and structured enough for a single Gen-AI layer on top and nothing else. Imagine not having to worry about integrating one behemoth frankenstack vendor to another, having to sync data sources, transform the data, orchestrate processes between them…it’s a pretty tantalising prospect and one that none of the aforementioned vendors really want you to know about. Major vendors offering Gen-AI wallpaper on top of the cracked and creaking software they push are slowly dying, the benefits they offer will eventually run dry when stacked up against using artificial intelligence as the alternative on its own. In a way Altman was right, it’s a new type of operating system, in this case it’s a business operating system and one that doesn’t need the bloatware. I’ve maybe sat on these ideas for too long now, I might go and start building again. I mean, depending on who you read the global workflow automation market size was valued at $16.41 billion in 2021 and is projected to reach $34.4 billion by 2030, growing at a CAGR of 9.71%. Not small numbers. Who’s with me? Halloween. It’s spooky season, so let’s bludgeon a few old stack vendors to death — Appian, IBM, Salesforce, SAP, Pegasystems, IFS, Oracle, Software AG, TIBCO, UIPath …they’re The Walking Dead, and they know it. Appian, IBM, Salesforce, SAP, Pegasystems, IFS, Oracle, Software AG, TIBCO, UIPath The Walking Dead, Back in 2010–2012 I wrote a fair bit about business process automation over on my old blog, BPM Redux , and something I was fascinated with was the concept of self-assembling or dynamic processes , created on the fly to handle a task using artificial intelligence without the need for drawing a process map or dicking around designing another set of screens. BPM Redux BPM Redux self-assembling or dynamic processes The problem I have with ‘business rules’ being an inherent part of some BPMS is that they start to constrain process decision making and flexibility, but having a set of AI routines that not only adapts on the fly (or JIT) but also learn and potentially create a brand new process instance out of evolving from previous instances… The problem I have with ‘business rules’ being an inherent part of some BPMS is that they start to constrain process decision making and flexibility, but having a set of AI routines that not only adapts on the fly (or JIT) but also learn and potentially create a brand new process instance out of evolving from previous instances… The problem I have with ‘business rules’ being an inherent part of some BPMS is that they start to constrain process decision making and flexibility, but having a set of AI routines that not only adapts on the fly (or JIT) but also learn and potentially create a brand new process instance out of evolving from previous instances… Fast forward to today and it might finally become a reality. Fast forward to today and it might finally become a reality. I looked at what some of the larger stack and cloud vendors are doing today, and they all look so desperate — throwing Generative AI into their software stacks to appear relevant and forward-thinking. The trouble is, Gen-AI renders them pointless. Every. Single. One. Every. Single. One. Let’s take Pegasystems for example (disclosure: I used to work for them years ago). They have Generative AI as part of their software solutions now but it’s still rooted in the past, forcing enterprise users to not only continue using Pega but also to continually think about building workflows with it — it doesn’t matter that it’s “low-code” pseudo-development or that you can instantly draw new process steps using the magic of AI, you’re still stuck with using Pega. Pegasystems Pegasystems The same goes for all the rest — whether it be Salesforce, IFS, SAP; whether it’s ERP, CRM, RPA — what they all potentially understand and won’t tell customers is that you have all the data already, both structured and unstructured, and a Gen-AI layer is all you need on top. you have all the data already, both structured and unstructured, and a Gen-AI layer is all you need on top. Think about the following example — a customer complaint. A customer emails in a complaint moaning about one of your products or services and expecting an action in response. Typically, the email will come from a contact form with all the required details to identify the customer or client, the product in question and what they expect done. If not, then it’s freeform unstructured data with a dollop of misery and anger. Normally, it falls into a defined process that an agent has been trained to perform, with clear steps already mapped out. If not, then it’s treated as an exception and passed to someone higher up the chain to make a decision on. All that takes time to create, and develop into someone’s expensive SaaS solution that looks like a dog’s dinner on the screen. Invariably, a few years later there will be a multi-million dollar “digital transformation” program sold by a consultancy deck to eke a few tweaks and savings out of this godforsaken process to justify the cost. But what if all this didn’t need to be done anymore? But what if all this didn’t need to be done anymore? What if training a Large Language Model on your existing processes, rules, customer data, product or service information was all you needed to do? Going down this train of thought leads to a very different outcome. The LLM reads and understands the content of the email, pulling out key information like customer details, the product or service mentioned, and the specific issue being raised. In the case of a telephone conversation then we’ve already seen some Gen-AI solutions transcribe in real-time an interaction and then offer up advice, a response, or an answer to a question. So, already there is no need for parsing of data to another system to fill out a screen for an agent to look at. Based on this information, the LLM figures out the priority of the complaint, sees if the issue is common or recurring, and decides if any specific business rules or past actions apply. In some cases there may well be sentiment analysis done on the language of the complaint to determine just how important this is, how urgent or how annoyed the customer is. Things can get funky from here. Things can get funky from here. Based on the type of complaint, the LLM creates a workflow to resolve the issue on the fly. This could involve updating customer data, processing a refund, or sending the complaint to a specialised team but it’s all done without a front end system we’ve all grown accustomed to loathe. With JIT (Just In Time) capabilities, the LLM can generate code on the spot to do necessary backend tasks. For example, if a refund is needed, the LLM creates the code to process it and updates the backend customer database with the details. Since the LLM has been trained on business rules, it makes sure all actions follow company policies — checking if the customer is eligible for a refund before processing it for example. The results of each complaint handling process are used to train the LLM further. If a complaint is resolved successfully, the LLM learns from the actions taken, improving future responses and workflows. Over time, the LLM can find recurring issues, improve existing workflows, and even suggest changes to business rules or policies based on patterns in customer complaints. The point here is that (a) you don’t need to define what the business process is any more than you do with task definition, nobody cares about static processes anymore when you can dynamically generate and execute them, (b) the LLM generates code and minimal screen UI to complete the task and have the agent involved but after that’s done the code and the UI is deleted, it’s an artefact that is no longer required because it was created to handle an interaction that is personal to that customer, and (c) humans are still in the loop, the efficiency gains are not in reducing headcount but removing the expensive excuse of paying for ancient software and unnecessary transformation programs based on old methods that are always initiated by some incoming c-suite with a relationship with a favourite vendor. (a) you don’t need to define what the business process is any more than you do with task definition, nobody cares about static processes anymore when you can dynamically generate and execute them, (b) the LLM generates code and minimal screen UI to complete the task and have the agent involved but after that’s done the code and the UI is deleted, it’s an artefact that is no longer required because it was created to handle an interaction that is personal to that customer, and (c) humans are still in the loop, the efficiency gains are not in reducing headcount but removing the expensive excuse of paying for ancient software and unnecessary transformation programs based on old methods It’s not as simplistic and current Gen-AI solutions aren’t geared up for this ( yet ) but this should be enough to start to question why you need a huge ERP implementation, or why an expensive CRM is required at all if you can just have a conversation with the customer data directly. yet Remember Master Data Management? Enterprises spent millions and years on making sure all their backend databases were fit for purpose, clean, deduped, they’re in potentially good shape and structured enough for a single Gen-AI layer on top and nothing else. Imagine not having to worry about integrating one behemoth frankenstack vendor to another, having to sync data sources, transform the data, orchestrate processes between them…it’s a pretty tantalising prospect and one that none of the aforementioned vendors really want you to know about. Imagine not having to worry about integrating one behemoth frankenstack vendor to another, having to sync data sources, transform the data, orchestrate processes between them…it’s a pretty tantalising prospect and one that none of the aforementioned vendors really want you to know about. Major vendors offering Gen-AI wallpaper on top of the cracked and creaking software they push are slowly dying, the benefits they offer will eventually run dry when stacked up against using artificial intelligence as the alternative on its own. In a way Altman was right, it’s a new type of operating system, in this case it’s a business operating system and one that doesn’t need the bloatware. In a way Altman was right, it’s a new type of operating system, in this case it’s a business operating system and one that doesn’t need the bloatware. I’ve maybe sat on these ideas for too long now, I might go and start building again. I mean, depending on who you read the global workflow automation market size was valued at $16.41 billion in 2021 and is projected to reach $34.4 billion by 2030, growing at a CAGR of 9.71%. Not small numbers. Who’s with me? Who’s with me?

Generative AI Will Kill Old Stack Vendors. Let It.

I studied 12 successful SaaS products (including Beehiiv, Canva, and Buffer) to understand the tactics they use to nudge their users towards a paid plan. You can find my entire feature gating research deck (for free) here. Here are 4 tactics you can steal right now. Display the user's progress at all times Does your product offer a limited number of actions for free? Display your user's progress towards the upgrade at all times. Here's an example with Todoist: Todoist offer up to 5 free 'Projects'. Beyond that, the user has to upgrade to a paid plan. Todoist do an excellent job of displaying the limit at all times; so there are no surprises when it's time to upgrade. Turn your feature gate into a landing page As users click around your app and reach a paywall, use this opportunity to sell the feature (and, thus, the upgrade). Here's an example with Beehiiv: Beehiiv show entire marketing landing pages inside their app. This isn't just a 'Please upgrade to access this feature' paywall. They take this opportunity to remind the user of the tremendous value they'll get out of upgrading by actively selling the feature. Leverage consistent visual clues Most products gate multiple valuable features behind an upgrade. As users navigate the app, warn them of the required upgrade with consistent visual clues. Here's a good example with Buffer: These visual clues achieve multiple goals. First, they are a constant reminder that the user is not getting as much from your app as they could. Second, it limits disappointment: you won't have access to this feature. Hook & anchor value throughout the upgrade process What extra value will your users get on the other side of the paywall? In this example, Grammarly not only clearly display the value (hook) but keep the user engaged throughout the upgrade process by reminding them of the value (anchor): At each step of the process, the user is encouraged to carry on so they get to that sweet, sweet value. Grab the entire feature gating research deck (30+ pages). I studied 12 successful SaaS products (including Beehiiv, Canva, and Buffer) to understand the tactics they use to nudge their users towards a paid plan. You can find my entire feature gating research deck (for free) here . feature gating research deck (for free) here Here are 4 tactics you can steal right now. Display the user's progress at all times Does your product offer a limited number of actions for free? Display your user's progress towards the upgrade at all times. Here's an example with Todoist : Todoist Todoist offer up to 5 free 'Projects'. Beyond that, the user has to upgrade to a paid plan. Todoist do an excellent job of displaying the limit at all times; so there are no surprises when it's time to upgrade. Turn your feature gate into a landing page As users click around your app and reach a paywall, use this opportunity to sell the feature (and, thus, the upgrade). Here's an example with Beehiiv : Beehiiv Beehiiv show entire marketing landing pages inside their app. This isn't just a 'Please upgrade to access this feature' paywall. They take this opportunity to remind the user of the tremendous value they'll get out of upgrading by actively selling the feature. selling Leverage consistent visual clues Most products gate multiple valuable features behind an upgrade. As users navigate the app, warn them of the required upgrade with consistent visual clues. Here's a good example with Buffer : Buffer These visual clues achieve multiple goals. First, they are a constant reminder that the user is not getting as much from your app as they could. Second, it limits disappointment: you won't have access to this feature. Hook & anchor value throughout the upgrade process What extra value will your users get on the other side of the paywall? In this example, Grammarly not only clearly display the value (hook) but keep the user engaged throughout the upgrade process by reminding them of the value (anchor): Grammarly At each step of the process, the user is encouraged to carry on so they get to that sweet, sweet value. Grab the entire feature gating research deck (30+ pages). Grab the entire feature gating research deck (30+ pages).

4 Tactics SaaS Products Use to Nudge Users to Upgrade

4টি কৌশল SaaS পণ্য ব্যবহারকারীদের আপগ্রেড করার জন্য ব্যবহার করে

4 Taktiken, mit denen SaaS-Produkte Benutzer zum Upgrade bewegen

4 tácticas que utilizan los productos SaaS para animar a los usuarios a actualizarse

4 tactiques utilisées par les produits SaaS pour inciter les utilisateurs à effectuer une mise à niveau

4 तरकीबें जो SaaS उत्पाद उपयोगकर्ताओं को अपग्रेड करने के लिए प्रेरित करते हैं

SaaS 製品がユーザーにアップグレードを促すために使用する 4 つの戦術

사용자가 업그레이드하도록 유도하는 SaaS 제품의 4가지 전술

4 táticas que os produtos SaaS usam para estimular os usuários a atualizar

4 тактики использования SaaS-продуктов, чтобы подтолкнуть пользователей к обновлению

Kullanıcıları Yükseltmeye Yönlendirmek İçin SaaS Ürünlerinin Kullanıldığı 4 Taktik

4 chiến thuật mà sản phẩm SaaS sử dụng để thúc đẩy người dùng nâng cấp

SaaS 产品用于推动用户升级的 4 种策略

Covalent, the leading modular data infrastructure layer in Web3, is proud to announce a $2.5 million commitment in grants dedicated to advancing the Consensys ecosystem through the Consensys Scale Program. This substantial initiative underscores Covalent's mission to empower developers working with Linea and MetaMask, driving the creation of groundbreaking onchain applications. “Consensys Scale supports leading dapps building on Linea and MetaMask with a range of bespoke support and benefits. Ecosystem partners like Covalent are part of this package and make important contributions to our builders”, says Alex Greinacher, Consensys Scale Program Lead. In parallel, Consensys will make these grants directly available to high performing dApps that choose to deploy on Linea, its new L2 zkEVM. “Supporting top Linea dapps with impactful partner benefits is a core part of our mission so builders are supported to grow their builders,” says Suzie Godlewski, Lead Partner Marketing for Linea. This initiative not only highlights Covalent’s commitment to supporting the Web3 community but also demonstrates the vital role of structured data infrastructure in fostering innovation and growth within the blockchain space. With reaching over 300 million active wallets utilizing its extensive and comprehensive data services, Covalent’s grants aim to unlock new potentials for Consensys developers and builders. Covalent recently added Arthur Hayes as strategic advisor to expand the network's direction in the areas of Artificial Intelligence and Long-Term Data Availability. Powering Innovation with Largest Reservoir of Structured Web3 Data In the near future, MetaMask will also become much more developer-focused, and Consensys will integrate Covalent into instances where builders also need access to its data product suite. Covalent’s grant initiative will offer up to $25,000 USD in credits per project, enabling developers to access Covalent's robust data tools. These grants can be utilized to retrieve data from Linea, Ethereum, and over 225 other blockchain networks, enabling a wide range of applications. “We're thrilled to be supporting Consensys Scale and builders on Linea and MetaMask. This collaboration enhances our reach and empowers developers with robust and scalable access to structured data infrastructure to innovate and optimize their dapps,” said Ganesh Swami, CEO of Covalent. The grants will target several key areas within Consensys notably: Building on Linea: Supporting the world’s most innovative Web3 projects to deploy and grow on Linea, the Consensys zK-EVM
Access via MetaMask: Integration of the world’s most trusted Web3 wallet in great projects Together, Covalent and Consensys are working with builders to enhance onchain infrastructure and dapps to help onboard the next billion users into Web3. Grant Application and Developer Resources Projects in the Consensys ecosystem can apply for the Covalent grants. In addition, the following resources provide more details on the grants and other benefits available to Consensys builders by checking out the Consensys Scale website. About Covalent Covalent is the leading modular data infrastructure layer that’s dedicated to solving major challenges in blockchain and AI. Covalent’s largest reservoir of structured, verifiable data augments decentralized training and inference, reducing the likelihood of manipulated or biased AI models. Additionally, Covalent’s Ethereum Wayback Machine ensures decentralized access to Ethereum’s transaction data, keeping it secure. Trusted by over 3,000 leading organizations, Covalent powers AI, DeFi, GameFi, and other use cases with unfettered access to onchain data from over 225 blockchains and counting. Learn about the EWM: Technical | Non-Technical For more information, check out Covalent’s: Official Website | Twitter/X | Linktree About Consensys Consensys is the leading blockchain and Web3 software company, pioneering technological developments within the Web3 ecosystem since 2014. Through its product suite, including MetaMask, Infura, Linea, Diligence, and Phosphor, Consensys has become a trusted collaborator for users, creators, and developers. Consensys inspires and champions the builder instinct in everyone by making Web3 universally easy to use and develop on. To explore their products and solutions, visit Consensys. This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. Covalent, the leading modular data infrastructure layer in Web3, is proud to announce a $2.5 million commitment in grants dedicated to advancing the Consensys ecosystem through the Consensys Scale Program. This substantial initiative underscores Covalent's mission to empower developers working with Linea and MetaMask, driving the creation of groundbreaking onchain applications. “Consensys Scale supports leading dapps building on Linea and MetaMask with a range of bespoke support and benefits. Ecosystem partners like Covalent are part of this package and make important contributions to our builders”, says Alex Greinacher, Consensys Scale Program Lead. “Consensys Scale supports leading dapps building on Linea and MetaMask with a range of bespoke support and benefits. Ecosystem partners like Covalent are part of this package and make important contributions to our builders”, says Alex Greinacher, Consensys Scale Program Lead. In parallel, Consensys will make these grants directly available to high performing dApps that choose to deploy on Linea, its new L2 zkEVM. “Supporting top Linea dapps with impactful partner benefits is a core part of our mission so builders are supported to grow their builders,” says Suzie Godlewski, Lead Partner Marketing for Linea. “Supporting top Linea dapps with impactful partner benefits is a core part of our mission so builders are supported to grow their builders,” says Suzie Godlewski, Lead Partner Marketing for Linea. This initiative not only highlights Covalent’s commitment to supporting the Web3 community but also demonstrates the vital role of structured data infrastructure in fostering innovation and growth within the blockchain space. With reaching over 300 million active wallets utilizing its extensive and comprehensive data services, Covalent’s grants aim to unlock new potentials for Consensys developers and builders. Covalent recently added Arthur Hayes as strategic advisor to expand the network's direction in the areas of Artificial Intelligence and Long-Term Data Availability. Covalent recently added Arthur Hayes as strategic advisor to expand the network's direction in the areas of Artificial Intelligence and Long-Term Data Availability. Covalent recently added Arthur Hayes as strategic advisor to expand the network's direction in the areas of Artificial Intelligence and Long-Term Data Availability. Powering Innovation with Largest Reservoir of Structured Web3 Data In the near future, MetaMask will also become much more developer-focused, and Consensys will integrate Covalent into instances where builders also need access to its data product suite. Covalent’s grant initiative will offer up to $25,000 USD in credits per project, enabling developers to access Covalent's robust data tools. These grants can be utilized to retrieve data from Linea, Ethereum, and over 225 other blockchain networks, enabling a wide range of applications. “We're thrilled to be supporting Consensys Scale and builders on Linea and MetaMask. This collaboration enhances our reach and empowers developers with robust and scalable access to structured data infrastructure to innovate and optimize their dapps,” said Ganesh Swami, CEO of Covalent. “ We're thrilled to be supporting Consensys Scale and builders on Linea and MetaMask. This collaboration enhances our reach and empowers developers with robust and scalable access to structured data infrastructure to innovate and optimize their dapps ,” said Ganesh Swami , CEO of Covalent. We're thrilled to be supporting Consensys Scale and builders on Linea and MetaMask. This collaboration enhances our reach and empowers developers with robust and scalable access to structured data infrastructure to innovate and optimize their dapps Ganesh Swami Ganesh Swami The grants will target several key areas within Consensys notably: Building on Linea: Supporting the world’s most innovative Web3 projects to deploy and grow on Linea, the Consensys zK-EVM Access via MetaMask: Integration of the world’s most trusted Web3 wallet in great projects Building on Linea: Supporting the world’s most innovative Web3 projects to deploy and grow on Linea, the Consensys zK-EVM Access via MetaMask: Integration of the world’s most trusted Web3 wallet in great projects Together, Covalent and Consensys are working with builders to enhance onchain infrastructure and dapps to help onboard the next billion users into Web3. Grant Application and Developer Resources Projects in the Consensys ecosystem can apply for the Covalent grants . In addition, the following resources provide more details on the grants and other benefits available to Consensys builders by checking out the Consensys Scale website . Covalent grants Covalent grants Consensys Scale website Consensys Scale website About Covalent Covalent is the leading modular data infrastructure layer that’s dedicated to solving major challenges in blockchain and AI. Covalent’s largest reservoir of structured, verifiable data augments decentralized training and inference, reducing the likelihood of manipulated or biased AI models. Additionally, Covalent’s Ethereum Wayback Machine ensures decentralized access to Ethereum’s transaction data, keeping it secure. Trusted by over 3,000 leading organizations, Covalent powers AI, DeFi, GameFi, and other use cases with unfettered access to onchain data from over 225 blockchains and counting. Learn about the EWM: Technical | Non-Technical Technical Technical Non-Technical Non-Technical For more information, check out Covalent’s: Official Website | Twitter/X | Linktree Official Website Official Website Twitter/X Twitter/X Linktree Linktree About Consensys Consensys is the leading blockchain and Web3 software company, pioneering technological developments within the Web3 ecosystem since 2014. Through its product suite, including MetaMask, Infura, Linea, Diligence, and Phosphor, Consensys has become a trusted collaborator for users, creators, and developers. Consensys inspires and champions the builder instinct in everyone by making Web3 universally easy to use and develop on. To explore their products and solutions, visit Consensys . Consensys Consensys This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Covalent Announces $2.5M in Grants to the Consensys Builders Scale Program

Supra, a leading provider of verifiable randomness services and oracle data feeds across over 80 blockchains announced its integration with ApeChain. As a result, Supra is the first protocol to use verifiable randomness and oracle price feeds on the new Layer 3 on Arbitrum. GameFi, DeFi, and other decentralised applications on ApeChain will benefit from improved real-time data accessibility, enhanced functionality, and expedited decision-making thanks to Supra's oracle price feeds protocol, the Distributed Oracle Agreement (DORA). Supra's decentralised Verifiable Random Function (dVRF) will also produce verifiable and decentralised randomness for ApeChain dApps, guaranteeing the integrity of random results in prize draws, blockchain games, and other situations where complete transparency is necessary to uphold confidence.Supra is excited to announce its integration with ApeChain. Supra is a leading provider of verifiable randomness services and oracle data feeds across over 80 blockchains. As a result, Supra is the first protocol to use verifiable randomness and oracle price feeds on the new Layer 3 on Arbitrum. Delivering Essential Data to dApps Supra's novel oracle data feeds significantly improve the possibilities of smart contracts by offering safe access to outside data that Web3 dApps require. With more than 475 active price feeds and counting, DORA is built to effectively validate and transmit real-world data to deterministic blockchain networks. Delivering comprehensive security guarantees together with high-quality price feeds is ensured by its resilience to Byzantine corruption, protection against potential node collusion, and Layer-1 grade security. Supra accommodates the various requirements of dApps by supporting both push and pull models. Supra provides robust security assurances and remarkable performance speeds for its data streams, all powered by the Supra Moonshot Consensus protocol. The Moonshot Consensus system, with 500 millisecond optimistic finality and roughly 530K transactions per second throughput, was tested extensively throughout the world. Ensuring Trust with Verifiable On-Chain Randomness Supra's dVRF protocol, which builds on DORA's advantages, uses decentralised, verifiable randomness to meet the requirement for unpredictable outcomes in dApps. For Web3 game developers, who need to ensure the integrity and unpredictable nature of random results in situations such as competition matchups and prize draws, this is essential. By enabling dApps to produce easily verifiable random outcomes, Supra's on-chain verifiable randomness generator contributes to increased community trust. ApeChain: Setting New Standards for GameFi Built on Arbitrum, ApeChain is a specialised Layer 3 solution intended to serve as the cornerstone of the Ape community and foster the expansion of ApeCoin. Located where NFTs and gaming converge, ApeChain's specialised infrastructure will support many new games and protocols inside the ApeCoin ecosystem, guaranteeing a seamless and effective gaming experience. GameFi and metaverse decentralised apps have shown a great deal of interest in ApeChain, which has raised the need for trustworthy data oracles that offer up-to-date off-chain data. To guarantee fairness, gaming apps also need verifiable on-chain randomness. Game developers using ApeChain may create more immersive experiences with improved performance by utilising Supra's dVRF and oracle data feeds. “Supra is pioneering solutions with integrations across all major ecosystems and will now empower ApeChain through Bridges, Oracles, Automation, and Randomizers. The ApeCoin DAO eagerly anticipates the innovative creations that builders will craft, leveraging Supra’s best-in-class stack for games, DeFi, and beyond!” — Hervé Larren, ApeCoin DAO Special Council and co-author of ApeChain. ApeChain wants to speed up the widespread use of GameFi by collaborating with Supra to provide more dependable data and foster trust through outcomes that are assured to be verifiably fair. What Is Supra Supra is a top vendor of verifiable randomness services and oracle price feeds across more than 80 blockchains. Supra works as an vertically integrated L1, offering robust security guarantees along with high-quality, low-latency data and randomness across Web 3. It is compatible with MoveVM chains, Bitcoin L2s, EVM L1s, L2s, and L3s dApps. With an emphasis on scalability, security, and quick transactions, Supra offers a full developer toolkit along with in-depth manuals and technical whitepapers. Accompany Supra on its journey to see its expansion, creativity, and engagement with the community. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Supra , a leading provider of verifiable randomness services and oracle data feeds across over 80 blockchains announced its integration with ApeChain. As a result, Supra is the first protocol to use verifiable randomness and oracle price feeds on the new Layer 3 on Arbitrum. Supra GameFi, DeFi, and other decentralised applications on ApeChain will benefit from improved real-time data accessibility, enhanced functionality, and expedited decision-making thanks to Supra's oracle price feeds protocol, the Distributed Oracle Agreement (DORA). Supra's decentralised Verifiable Random Function (dVRF) will also produce verifiable and decentralised randomness for ApeChain dApps, guaranteeing the integrity of random results in prize draws, blockchain games, and other situations where complete transparency is necessary to uphold confidence.Supra is excited to announce its integration with ApeChain. Supra is a leading provider of verifiable randomness services and oracle data feeds across over 80 blockchains. As a result, Supra is the first protocol to use verifiable randomness and oracle price feeds on the new Layer 3 on Arbitrum. ApeChain ApeChain Delivering Essential Data to dApps Supra's novel oracle data feeds significantly improve the possibilities of smart contracts by offering safe access to outside data that Web3 dApps require. With more than 475 active price feeds and counting, DORA is built to effectively validate and transmit real-world data to deterministic blockchain networks. Delivering comprehensive security guarantees together with high-quality price feeds is ensured by its resilience to Byzantine corruption, protection against potential node collusion, and Layer-1 grade security. Supra accommodates the various requirements of dApps by supporting both push and pull models. Supra provides robust security assurances and remarkable performance speeds for its data streams, all powered by the Supra Moonshot Consensus protocol. The Moonshot Consensus system, with 500 millisecond optimistic finality and roughly 530K transactions per second throughput, was tested extensively throughout the world. Ensuring Trust with Verifiable On-Chain Randomness Supra's dVRF protocol, which builds on DORA's advantages, uses decentralised, verifiable randomness to meet the requirement for unpredictable outcomes in dApps. For Web3 game developers, who need to ensure the integrity and unpredictable nature of random results in situations such as competition matchups and prize draws, this is essential. By enabling dApps to produce easily verifiable random outcomes, Supra's on-chain verifiable randomness generator contributes to increased community trust. ApeChain: Setting New Standards for GameFi Built on Arbitrum, ApeChain is a specialised Layer 3 solution intended to serve as the cornerstone of the Ape community and foster the expansion of ApeCoin. Located where NFTs and gaming converge, ApeChain's specialised infrastructure will support many new games and protocols inside the ApeCoin ecosystem, guaranteeing a seamless and effective gaming experience. GameFi and metaverse decentralised apps have shown a great deal of interest in ApeChain, which has raised the need for trustworthy data oracles that offer up-to-date off-chain data. To guarantee fairness, gaming apps also need verifiable on-chain randomness. Game developers using ApeChain may create more immersive experiences with improved performance by utilising Supra's dVRF and oracle data feeds. “Supra is pioneering solutions with integrations across all major ecosystems and will now empower ApeChain through Bridges, Oracles, Automation, and Randomizers. The ApeCoin DAO eagerly anticipates the innovative creations that builders will craft, leveraging Supra’s best-in-class stack for games, DeFi, and beyond!” “Supra is pioneering solutions with integrations across all major ecosystems and will now empower ApeChain through Bridges, Oracles, Automation, and Randomizers. The ApeCoin DAO eagerly anticipates the innovative creations that builders will craft, leveraging Supra’s best-in-class stack for games, DeFi, and beyond!” — Hervé Larren, ApeCoin DAO Special Council and co-author of ApeChain. ApeChain wants to speed up the widespread use of GameFi by collaborating with Supra to provide more dependable data and foster trust through outcomes that are assured to be verifiably fair. What Is Supra Supra is a top vendor of verifiable randomness services and oracle price feeds across more than 80 blockchains. Supra works as an Supra Supra vertically integrated L1, offering robust security guarantees along with high-quality, low-latency data and randomness across Web 3. It is compatible with MoveVM chains, Bitcoin L2s, EVM L1s, L2s, and L3s dApps. With an emphasis on scalability, security, and quick transactions, Supra offers a full developer toolkit along with in-depth manuals and technical whitepapers . Accompany Supra on its journey to see its expansion, creativity, and engagement with the community. in-depth manuals in-depth manuals technical whitepapers technical whitepapers Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: business blogging program

Supra Integrates First Ever Oracle Data Feed and Verifiable Randomness on ApeChain

ApeChain-এ সুপ্রা প্রথম ওরাকল ডেটা ফিড এবং যাচাইযোগ্য এলোমেলোতা একীভূত করে

Supra integriert den ersten Oracle-Datenfeed und verifizierbare Zufälligkeit auf ApeChain

Supra integra la primera fuente de datos de Oracle y aleatoriedad verificable en ApeChain

Supra intègre le tout premier flux de données Oracle et le caractère aléatoire vérifiable sur ApeChain

सुप्रा ने एपचेन पर पहली बार ऑरेकल डेटा फीड और सत्यापन योग्य रैंडमनेस को एकीकृत किया

Supra、ApeChainで初のOracleデータフィードと検証可能なランダム性を統合

Supra는 ApeChain에 최초의 Oracle 데이터 피드와 검증 가능한 무작위성을 통합합니다.

Supra integra o primeiro feed de dados Oracle e aleatoriedade verificável no ApeChain

Supra интегрирует первый в мире поток данных Oracle и проверяемую случайность в ApeChain

Supra, ApeChain'e İlk Oracle Veri Beslemesini ve Doğrulanabilir Rastgeleliği Entegre Ediyor

Supra tích hợp nguồn cấp dữ liệu Oracle đầu tiên và tính ngẫu nhiên có thể xác minh được trên ApeChain

Supra 在 ApeChain 上集成首个 Oracle 数据源和可验证随机性

Zero-knowledge rollups are gaining momentum as the ultimate Ethereum scaling solution. This year alone, no less than four major projects based on ZK rollups were launched, and four more are in the works. The technology has proven to be exceptionally fitting for the crypto space, and given the growing enthusiasm from venture capitalists, the ZK trend is here to stay. This is a comprehensive overview of the current and its most prominent projects: zkSync, Starknet, Polygon zkEVM, Linea, and Scroll. zk rollup landscape But first: What are zk rollups, and why is everyone so excited about them? What are ZK rollups? The ZK FOMO has a good reason behind it. This method allows executing smart contract computations off the blockchain, rolling them up in batches, and then submitting these batches back to the main to show that the computations happened within the rules of the EVM. Simply put, zk-rollups allow to outsource computation, all while leveraging the main chain’s decentralization and security. chain using zero-knowledge proofs Unlike their closest competitor, optimistic rollups, zk rollups offer quicker transaction finality time and better user privacy. Optimistic roll-ups take an “optimistic” stance, assuming that all layer-2 transactions are valid unless proven otherwise by an honest network validator. If the optimistic rollup batch faces no challenges during the dispute period, the transaction data is successfully added to the Ethereum main chain. In contrast, ZK rollups count on cryptography: after all, math is always more reliable than humans. However, this comes with its own set of complications, such as high computation costs, as well as a more complex cryptographic and engineering network. Fortunately, the former can be solved with special hardware, such as FPGA or ASIC, and the latter – with quality engineering education, like the one provided by RareSkills. Yet, there is another hurdle associated with ZK rollups. EVM-compatibility vs performance Ethereum was not designed to be ZK-friendly, and there are many parts of its protocol that take a large amount of computation to generate validity proofs. This means that building a functional zkEVM – a replica of the Ethereum Virtual Machine that uses zk-rollups – implies a major trade-off: EVM compatibility vs performance. There is a balance between these two qualities, and different zk rollup-based projects are trying different ways to find the perfect one. Vitalik Buterin this dichotomy in 2022, drafting a chart with five types of protocols using zk-proofs, from the most compatible to the most performant. formulated Each approach has its pros and cons, and several major projects are exploring them now. zkSync Era According to Vitalik’s framework, this protocol corresponds to Type 4: high-language-equivalent. Developed by Matter Labs, zkSync Era went live in March 2023. Its current TPS (transaction per second) stands at 4.75, down from this summer’s 9.9. However, according to the protocol’s documentation, it can offer up to 2,000 TPS at peak load. ZkSync uses its LLVM compiler to translate code from Solidity, Vyper, and Yul. A compiler adds a step in the coding process and increases the risks of bugs, but in exchange for this inconvenience, zkSync offers other perks. Not only does its custom VM generate zk-proofs fast, but it also implements native account abstraction, which allows programmable authorizations, enabling a greater diversity of wallet and protocol design. ZkSync is among the most developed projects, coming with a Hardhat plugin, Web3 API parity, and a well-functioning L2-L1 messaging infrastructure. The most popular zk rollup, zkZync Era, processes 22 million monthly transactions (source: ), ranking second only to PoS with its 75 million monthly transactions. L2Beat Polygon Starknet Starknet is a zk-rollup using a slightly different version of the cryptographic proof system. Unlike other popular protocols built on zk-SNARKs (zero-knowledge succinct non-interactive argument of knowledge), it uses zk-STARKS (zero-knowledge scalable transparent argument of knowledge). The main difference between the two is that the former uses elliptic curve pairings, while the latter relies on transparent post-quantum algebraic proofs, which generally makes zk-STARKs more efficient but also more computationally demanding. Zk-SNARKS are also being criticized for the trusted setup they require for the initial creation of keys. If the secret used to create the keys is not destroyed, this may introduce a security threat. ZK-STARKs were discovered several years after ak-SNARKs, which were already used in the . Hence, zk-SNARKs are considered more developed as a technology, with extensive developer libraries and numerous projects working on them. Starknet is a notable exception, adopting STARKs as a base for its protocol. The project also puts emphasis on Layer-3 development, providing a set of tools that can help build customizable “App Chains.” Zcash protocol Starknet Alpha went live in November 2022 and has since developed an important ecosystem, handling 13 million monthly transactions. With Warp, a compiler from Solidity to its language Cairo, Starknet can also be considered as a Type 4 zkEVM. Polygon zkEVM The famous Ethereum sidechain joined the zk race by releasing in March its own version of zkEVM as a separate blockchain (so far in Beta). It will be a part of Polygon 2.0, an ambitious vision of a network of zk-powered layer-2 chains unified via a novel cross-chain protocol. ZkProver, Polygon zkEVM’s main component, follows the state machine model, which uses both STARK and SNARK methods. STARK is used to prove the transactions, and then a SNARK circuit is used to verify it, de facto submitting to Ethereum a proof of a proof. To achieve full compatibility, the Polygon team has come up with the concept of micro opcodes, or instructions that EVM will use to manipulate inputs. Currently, Polygon zkEVM is a Type 3 (almost EVM-equivalent), which will turn into Type 2 (fully EVM-equivalent) when all pre-compiled contracts are supported. It processes 1.1 million monthly transactions. Linea This zk-proof solution was developed by none other than Consensys, the Godfather of all things Ethereum. Released in March 2023, Linea (Type 3 aiming for Type 2) strikes its own balance of speed and EVM compatibility. Linea uses lattice-based cryptography, known to be quantum-resistant, to efficiently generate zero-knowledge proofs, making for a faster prover time. Linea aims for a full equivalence with Ethereum. It is able to execute an unaltered, native bytecode in accordance with the EVM specifications, thus reducing the risk of bugs and hacks. Developers can deploy and verify any smart contract with Truffle, Hardhat, or Foundry exactly as if they were building on Layer 1. Linea’s developer-friendliness is enhanced by the native integrations of Consensys tools, such as MetaMask and Infura. With Alpha mainnet launched in August, Linea now handles 3.5 million monthly transactions. Scroll The most recent protocol to go live, Scroll, launched its mainnet in October 2023. The project aims at fully mapping the opcode to the zkEVM, which would allow Ethereum native smart contracts to be fully executed without modifying the bytecode. Currently, Scroll is still a Type 3 but aims to become Type 2. Ethereum Launched with a centralized sequencer and a centralized central approver, Scroll intends to decentralize over time. Several other projects based on zk-proof are still in the works. This includes Taiko, a rare Type 1 (fully Ethereum-equivalent) rollup, as well as projects based on the Polygon’s codebase, such as Immutable zkEVM, Canto, and Astar zkEVM.

The Rise of Zero-Knowledge Rollups in Ethereum Scaling

Enabling users to access real-time data analytics is a key capability of many modern applications. Picture yourself using your favorite platform—there’s likely an intuitive dashboard presenting real-time data and historical insights. You can probably interact with the platform, creating customized reports, exploring detailed metrics, and visualizing trends spanning weeks or months. SaaS You certainly wouldn’t want this platform to be slow as a user. This means that the database powering these products has to be fast at running queries over large volumes of data, including complex, analytical queries. While , it is not known for being fast at querying large volumes of data. But don’t worry: always has a tool in its toolbox. One of the best is materialized views. PostgreSQL is the most loved database among developers today Postgres What Are PostgreSQL Materialized Views? Based on the materialization technique, PostgreSQL materialized views pre-compute commonly run queries and store the results as a table. Unlike standard PostgreSQL views, which run the underlying query every time the view is referenced, materialized views persist the result of the source query in the database. The great thing about this is that your database doesn’t have to execute the query every time you run it: the results are already accessible on disk—you’ll get the response to your query much faster. This is an awesome way to optimize query responses for queries that are resource-intensive to compute. For example, queries that might involve the processing of large volumes of data, aggregations, or multiple joins. Working with materialized views is super simple. To create a view, you would use statement and your query of choice. CREATE MATERIALIZED VIEW Once your materialized view is created, you can query it as a regular table: PostgreSQL CREATE MATERIALIZED VIEW customer_orders AS 
SELECT customer_id, COUNT(*) as total_orders 
FROM orders 
GROUP BY customer_id; -- Query the materialized view 
SELECT * FROM customer_orders; This materialized view will quickly become stale until you refresh it: even if you’re adding new data to the base table (or updating or deleting data), the materialized view doesn’t include those changes automatically; it’s a snapshot at the time it was created. To update the materialized view, you need to run . REFRESH MATERIALIZED VIEW REFRESH MATERIALIZED VIEW customer_orders; This last point (how refreshes are handled) is the Achilles heel of materialized views, as we’ll discuss in the next section. What About Real-Time Analytics? Limitations of PostgreSQL Materialized Views As we were saying, PostgreSQL materialized views are a powerful tool for speeding up frequently run queries, especially if these queries go over large volumes of data. But materialized views come with one less-than-ideal aspect: to keep your materialized views up-to-date, they have to be refreshed. This single problem creates three important limitations: Refreshes are inefficient and computationally expensive When refreshing a materialized view, the query is recomputed over the entire dataset. Under the hood, when you run a refresh, the old materialized data is deleted and then substituted with new, re-materialized data. Implementing (where only the changed data is updated) would make the aggregate process much more efficient, but it is difficult to implement correctly within a consistent relational database. Workarounds are sometimes possible with additional scripting, but they are far from straightforward, especially for complex queries or if late data is arriving. incremental refreshes Refreshes aren’t automatically run As also mentioned earlier, materialized views won’t automatically incorporate the latest data. They have to be refreshed by running . Running manual refreshes in a production setting is not feasible: a much more realistic setup would be to automate the refresh. REFRESH MATERIALIZED VIEW Unfortunately, materialized views don’t have built-in automatic refresh functionality, so creating an automatic refresh schedule for materialized views in PostgreSQL requires a scheduler of some sort. This can be handled in-database with an extension or out-of-database with a scheduler like cron. However, it’s managed because refreshes are expensive and take a long time. It is very easy to end up in a situation where you can’t refresh the view fast enough. Materialized views don’t show up-to-date results A consequence of the static nature of materialized views is that when queried, they’ll miss the data added or changed since the last refresh (even if that refresh happens on a schedule). If your scheduling window is set to an hour, then your aggregate will be up to an hour plus the actual time to do the update out of date. But many applications today imply a constant stream of data being ingested, and often, these applications have to offer up-to-date results to their users to ensure they’re retrieving accurate information when querying the view. It is a pity that materialized views are constrained by these limitations. If you’re building a SaaS platform from a live dataset, with new data frequently coming in, should materialized views be completely discarded? The answer is no. In Timescale, we built a solution that effectively enhances materialized views to make them more suitable for modern applications: continuous aggregates. Meet Continuous Aggregates: Materialized Views With Automatic Refreshes for Real-Time Analytics Imagine a world where materialized views are not just static snapshots but dynamically and efficiently updated. You would access the query performance improvement you seek without worrying about anything else. Well, it seems like we described Timescale’s continuous aggregates. Continuous aggregates (available to all PostgreSQL databases via the TimescaleDB extension and in AWS via the Timescale platform) are materialized views enhanced with efficient, automated refresh capabilities and a real-time element. They look and feel almost exactly like materialized views but allow the following: Automatic refreshes via a refresh policy A more efficient refresh process: when a refresh runs, it will only touch the data that changed since the last refresh Up-to-date results, expanding the use cases where materialized views can be leveraged (like real-time analytics, live dashboards, reporting, and others) Making refreshes automatic and resource-efficient Creating a continuous aggregate is very similar to creating a materialized view (and it can also be queried as a regular PostgreSQL table): CREATE MATERIALIZED VIEW hourly_sales 
WITH (timescaledb.continuous) AS 
SELECT time_bucket(INTERVAL '1 hour', sale_time) as hour,
       product_id,
       SUM(units_sold) as total_units_sold
FROM sales_data 
GROUP BY hour, product_id; But differently than materialized views, creating a refresh policy is straightforward. You can easily define the refresh interval within the database, ensuring that your continuous aggregate is automatically and periodically updated. The example below sets up a refresh policy to update the continuous aggregate every 30 minutes. The parameter defines the time range of data to be refreshed and the sets how often the continuous aggregate will be refreshed: end_offset schedule_interval -- Setting up a refresh policy 
SELECT add_continuous_aggregate_policy('hourly_sales',
    end_offset => INTERVAL '1 minute',
    schedule_interval => INTERVAL '30 minutes'); When this refresh policy kicks in, the process will be much more efficient than if we were using a plain materialized view. Unlike running , when a continuous aggregate is refreshed, Timescale doesn’t drop all the old data and recompute the aggregate against it: the engine just runs the query against the most recent refresh period (e.g., 30 minutes) and adds it to the materialization. REFRESH MATERIALIZED VIEW Similarly, s and s performed during this last period are identified, recomputing the chunk (partition) that involves them. (Continuous aggregates built on Timescale’s , which are automatically partitioned PostgreSQL tables. This is a massive advantage, allowing the engine to recompute only specific partitions vs. the entire table when the data has changed.) UPDATE DELETE hypertables Showing up-to-date results for real-time analytics But, how do continuous aggregates solve the problem of viewing up-to-date results? What happens if new data has been added after the last refresh, and I query the continuous aggregate? To allow this functionality, we added to continuous aggregates. , and you query your continuous aggregate, the result you’ll see will combine two parts: real-time aggregation functionality When real-time aggregation is enabled The materialized data in the underlying materialized view, which has been updated in the last refresh. The most recent, not-yet-materialized raw data, which still lives exclusively in your base table (or hypertable, to be exact). This functionality transforms materialized views from static snapshots into dynamic entities, ensuring that the stored data is not just a historical reflection but an up-to-date representation of the underlying datasets. Using Continuous Aggregates: Example Even if this all sounds good, it'll (hopefully) come together a lot better with an example. Imagine a platform used by transportation agencies and ride-sharing companies. This platform contains a dashboard in which companies can see an overview of the status of their fleet, including a table with the latest status of key metrics and two visualizations showing how the metrics are doing that particular day and within the context of the week. To power this application, we would first have a hypertable in which the data about the rides is constantly inserted. The hypertable could look something like this: CREATE TABLE rides (
    ride_id SERIAL PRIMARY KEY,
    vehicle_id INT,
    start_time TIMESTAMPTZ NOT NULL,
    end_time TIMESTAMPTZ NOT NULL,
    distance FLOAT NOT NULL,
    price_paid FLOAT NOT NULL
);
SELECT create_hypertable('rides', 'start_time'); Hypertables are very fast and very scalable—this table will remain performant even when it has billions of rows. To power the table by providing a live overview, we would use a continuous aggregate to bucket the data by 30 minutes. This would keep the process fast and responsive: -- Create continuous aggregate for live overview
CREATE MATERIALIZED VIEW live_dashboard
WITH (timescaledb.continuous, timescaledb.materialized_only=false)) AS
SELECT 
    vehicle_id,
    time_bucket(INTERVAL '30 minute', start_time) as minute,
    COUNT(ride_id) as number_of_rides,
    AVG(price_paid) as average_price
FROM rides
GROUP BY vehicle_id, minute; -- Set up a refresh policy
SELECT add_continuous_aggregate_policy('live_dashboard',
    end_offset => INTERVAL '10 minutes',
    schedule_interval => INTERVAL ‘15 minute'); In the previous code, the parameter ensures that the aggregate does not immediately attempt to refresh the very latest data, allowing some buffer time to accommodate any lags in data arrival. Setting to means the aggregate will refresh data that is at least 10 minutes old, ensuring it doesn’t miss updates due to minor delays in data inflow. In a real-world use case, you would adjust this value based on the average delay you observe in your data pipeline. end_offset end_offset 10 minutes To power the visualization offering the daily view, we would create a second continuous aggregate. In this chart, the data is being displayed by hour, so we don’t need a per-minute granularity as the previous one: -- Create continuous aggregate for daily overview
CREATE MATERIALIZED VIEW hourly_metrics
WITH (timescaledb.continuous, timescaledb.materialized_only=false) AS
SELECT 
    vehicle_id,
    time_bucket(INTERVAL '1 hour', start_time) as hour,
    COUNT(ride_id) as number_of_rides,
    SUM(price_paid) as total_revenue
FROM rides
WHERE start_time > NOW() - INTERVAL '1 day'
GROUP BY vehicle_id, hour; -- Define refresh policy
SELECT add_continuous_aggregate_policy('hourly_metrics',
    end_offset => INTERVAL '10 minutes',
    schedule_interval => INTERVAL `1 hour`); Finally, to power the chart offering the weekly view, we would create one more continuous aggregate, this time aggregating the data by day: -- Create continuous aggregate to power chart with weekly overview
CREATE MATERIALIZED VIEW daily_metrics
WITH (timescaledb.continuous, timescaledb.materialized_only=false) AS
SELECT 
    vehicle_id,
    time_bucket(INTERVAL '1 day', start_time) as day,
    COUNT(ride_id) as number_of_rides,
    SUM(price_paid) as total_revenue
FROM rides
WHERE start_time > NOW() - INTERVAL '1 week'
GROUP BY vehicle_id, day; -- Define refresh policy
SELECT add_continuous_aggregate_policy('daily_metrics',
    end_offset => INTERVAL '10 minutes',
    schedule_interval => INTERVAL '1 day); P.S. To make the experience of defining continuous aggregates even more efficient, in TimescaleDB 2.9. Once you get familiar with continuous aggregates, you can start creating them on top of other continuous aggregates—e.g., in the previous example, you could also define the hourly aggregates on top of the by-minute aggregate. Timescale introduced hierarchical continuous aggregates Conclusion Even if PostgreSQL was not originally built for applications that need to process large live datasets, guess what—these types of workloads are now everywhere. However, PostgreSQL comes with features that help with this task. Materialized views are among the most powerful, as they allow pre-computing query results and storing them on disk for fast retrieval. However, materialized views have three important limitations. First, triggering refreshes is very computationally inefficient. Second, even setting up these automatic refreshes is not a seamless process. Third, materialized views don’t show up-to-date results, as they exclude the data that has been added or modified since the last refresh. These limitations make materialized views an unpractical solution for many modern applications. To solve this, we built continuous aggregates. These are PostgreSQL materialized views in which you can easily define a refresh policy, so the refreshes happen automatically. Those refreshes are also incremental and, therefore, much more efficient. Lastly, continuous aggregates allow you to combine the data that has been materialized with the raw data added and modified since the last refresh, ensuring you will only get up-to-date results. If you’re running PostgreSQL on your hardware, you can access continuous aggregates by . . The first 30 days are free. installing the TimescaleDB extension If you’re in AWS, check out the Timescale platform Written by and Mat Arye. Carlota Soto

From Materialized Views to Continuous Aggregates: Enhancing PostgreSQL With Real-Time Analytics

বস্তুগত দৃষ্টিভঙ্গি থেকে ক্রমাগত সমষ্টি: রিয়েল-টাইম অ্যানালিটিক্সের সাথে PostgreSQL উন্নত করা

Von materialisierten Ansichten zu kontinuierlichen Aggregaten: Verbesserung von PostgreSQL mit Echtzeitanalysen

De vistas materializadas a agregados continuos: mejora de PostgreSQL con análisis en tiempo real

Des vues matérialisées aux agrégats continus : améliorer PostgreSQL avec l'analyse en temps réel

भौतिक दृश्यों से सतत समुच्चय तक: वास्तविक समय विश्लेषण के साथ PostgreSQL को बढ़ाना

マテリアライズド ビューから連続集計まで: リアルタイム分析による PostgreSQL の強化

구체화된 뷰에서 연속 집계까지: 실시간 분석으로 PostgreSQL 향상

De visualizações materializadas a agregações contínuas: aprimorando o PostgreSQL com análises em tempo real

От материализованных представлений к непрерывным агрегатам: улучшение PostgreSQL с помощью аналитики в реальном времени

Gerçekleştirilmiş Görünümlerden Sürekli Toplamalara: PostgreSQL'i Gerçek Zamanlı Analitikle Geliştirme

Từ chế độ xem cụ thể hóa đến tổng hợp liên tục: Nâng cao PostgreSQL bằng phân tích thời gian thực

从物化视图到连续聚合：通过实时分析增强 PostgreSQL

How I found not salvation in the worship of the Temple; nor in the teachers of Galilee;

The Way We Live Now by Anthony Trollope is part of the HackerNoon Books Series. You can jump to any chapter in this book . MR. BROUNE HAS MADE UP HIS MIND here MR. BROUNE HAS MADE UP HIS MIND. "And now I have something to say to you." Mr. Broune as he thus spoke to Lady Carbury rose up to his feet and then sat down again. There was an air of perturbation about him which was very manifest to the lady, and the cause and coming result of which she thought that she understood. "The susceptible old goose is going to do something highly ridiculous and very disagreeable." It was thus that she spoke to herself of the scene that she saw was prepared for her, but she did not foresee accurately the shape in which the susceptibility of the "old goose" would declare itself. "Lady Carbury," said Mr. Broune, standing up a second time, "we are neither of us so young as we used to be." "No, indeed;—and therefore it is that we can afford to ourselves the luxury of being friends. Nothing but age enables men and women to know each other intimately." This speech was a great impediment to Mr. Broune's progress. It was evidently intended to imply that he at least had reached a time of life at which any allusion to love would be absurd. And yet, as a fact, he was nearer fifty than sixty, was young of his age, could walk his four or five miles pleasantly, could ride his cob in the park with as free an air as any man of forty, and could afterwards work through four or five hours of the night with an easy steadiness which nothing but sound health could produce. Mr. Broune, thinking of himself and his own circumstances, could see no reason why he should not be in love. "I hope we know each other intimately at any rate," he said somewhat lamely. "Oh, yes;—and it is for that reason that I have come to you for advice. Had I been a young woman I should not have dared to ask you." "I don't see that. I don't quite understand that. But it has nothing to do with my present purpose. When I said that we were neither of us so young as we once were, I uttered what was a stupid platitude,—a foolish truism." "I did not think so," said Lady Carbury smiling. "Or would have been, only that I intended something further." Mr. Broune had got himself into a difficulty and hardly knew how to get out of it. "I was going on to say that I hoped we were not too old to—love." Foolish old darling! What did he mean by making such an ass of himself? This was worse even than the kiss, as being more troublesome and less easily pushed on one side and forgotten. It may serve to explain the condition of Lady Carbury's mind at the time if it be stated that she did not even at this moment suppose that the editor of the "Morning Breakfast Table" intended to make her an offer of marriage. She knew, or thought she knew, that middle-aged men are fond of prating about love, and getting up sensational scenes. The falseness of the thing, and the injury which may come of it, did not shock her at all. Had she known that the editor professed to be in love with some lady in the next street, she would have been quite ready to enlist the lady in the next street among her friends that she might thus strengthen her own influence with Mr. Broune. For herself such make-belief of an improper passion would be inconvenient, and therefore to be avoided. But that any man, placed as Mr. Broune was in the world,—blessed with power, with a large income, with influence throughout all the world around him, courted, fêted, feared and almost worshipped,—that he should desire to share her fortunes, her misfortunes, her struggles, her poverty and her obscurity, was not within the scope of her imagination. There was a homage in it, of which she did not believe any man to be capable,—and which to her would be the more wonderful as being paid to herself. She thought so badly of men and women generally, and of Mr. Broune and herself as a man and a woman individually, that she was unable to conceive the possibility of such a sacrifice. "Mr. Broune," she said, "I did not think that you would take advantage of the confidence I have placed in you to annoy me in this way." "To annoy you, Lady Carbury! The phrase at any rate is singular. After much thought I have determined to ask you to be my wife. That I should be—annoyed, and more than annoyed by your refusal, is a matter of course. That I ought to expect such annoyance is perhaps too true. But you can extricate yourself from the dilemma only too easily." The word "wife" came upon her like a thunder-clap. It at once changed all her feelings towards him. She did not dream of loving him. She felt sure that she never could love him. Had it been on the cards with her to love any man as a lover, it would have been some handsome spendthrift who would have hung from her neck like a nether millstone. This man was a friend to be used,—to be used because he knew the world. And now he gave her this clear testimony that he knew as little of the world as any other man. Mr. Broune of the "Daily Breakfast Table" asking her to be his wife! But mixed with her other feelings there was a tenderness which brought back some memory of her distant youth, and almost made her weep. That a man,—such a man,—should offer to take half her burdens, and to confer upon her half his blessings! What an idiot! But what a God! She had looked upon the man as all intellect, alloyed perhaps by some passionless remnants of the vices of his youth; and now she found that he not only had a human heart in his bosom, but a heart that she could touch. How wonderfully sweet! How infinitely small! It was necessary that she should answer him—and to her it was only natural that she should at first think what answer would best assist her own views without reference to his. It did not occur to her that she could love him; but it did occur to her that he might lift her out of her difficulties. What a benefit it would be to her to have a father, and such a father, for Felix! How easy would be a literary career to the wife of the editor of the "Morning Breakfast Table!" And then it passed through her mind that somebody had told her that the man was paid £3,000 a year for his work. Would not the world, or any part of it that was desirable, come to her drawing-room if she were the wife of Mr. Broune? It all passed through her brain at once during that minute of silence which she allowed herself after the declaration was made to her. But other ideas and other feelings were present to her also. Perhaps the truest aspiration of her heart had been the love of freedom which the tyranny of her late husband had engendered. Once she had fled from that tyranny and had been almost crushed by the censure to which she had been subjected. Then her husband's protection and his tyranny had been restored to her. After that the freedom had come. It had been accompanied by many hopes never as yet fulfilled, and embittered by many sorrows which had been always present to her; but still the hopes were alive and the remembrance of the tyranny was very clear to her. At last the minute was over and she was bound to speak. "Mr. Broune," she said, "you have quite taken away my breath. I never expected anything of this kind." And now Mr. Broune's mouth was opened, and his voice was free. "Lady Carbury," he said, "I have lived a long time without marrying, and I have sometimes thought that it would be better for me to go on in the same way to the end. I have worked so hard all my life that when I was young I had no time to think of love. And, as I have gone on, my mind has been so fully employed, that I have hardly realised the want which nevertheless I have felt. And so it has been with me till I fancied, not that I was too old for love, but that others would think me so. Then I met you. As I said at first, perhaps with scant gallantry, you also are not as young as you once were. But you keep the beauty of your youth, and the energy, and something of the freshness of a young heart. And I have come to love you. I speak with absolute frankness, risking your anger. I have doubted much before I resolved upon this. It is so hard to know the nature of another person. But I think I understand yours;—and if you can confide your happiness with me, I am prepared to intrust mine to your keeping." Poor Mr. Broune! Though endowed with gifts peculiarly adapted for the editing of a daily newspaper, he could have had but little capacity for reading a woman's character when he talked of the freshness of Lady Carbury's young mind! And he must have surely been much blinded by love, before convincing himself that he could trust his happiness to such keeping. "You do me infinite honour. You pay me a great compliment," ejaculated Lady Carbury. "Well?" "How am I to answer you at a moment? I expected nothing of this. As God is to be my judge it has come upon me like a dream. I look upon your position as almost the highest in England,—on your prosperity as the uttermost that can be achieved." "That prosperity, such as it is, I desire most anxiously to share with you." "You tell me so;—but I can hardly yet believe it. And then how am I to know my own feelings so suddenly? Marriage as I have found it, Mr. Broune, has not been happy. I have suffered much. I have been wounded in every joint, hurt in every nerve,—tortured till I could hardly endure my punishment. At last I got my liberty, and to that I have looked for happiness." "Has it made you happy?" "It has made me less wretched. And there is so much to be considered! I have a son and a daughter, Mr. Broune." "Your daughter I can love as my own. I think I prove my devotion to you when I say that I am willing for your sake to encounter the troubles which may attend your son's future career." "Mr. Broune, I love him better,—always shall love him better,—than anything in the world." This was calculated to damp the lover's ardour, but he probably reflected that should he now be successful, time might probably change the feeling which had just been expressed. "Mr. Broune," she said, "I am now so agitated that you had better leave me. And it is very late. The servant is sitting up, and will wonder that you should remain. It is near two o'clock." "When may I hope for an answer?" "You shall not be kept waiting. I will write to you, almost at once. I will write to you,—to-morrow; say the day after to-morrow, on Thursday. I feel that I ought to have been prepared with an answer; but I am so surprised that I have none ready." He took her hand in his, and kissing it, left her without another word. As he was about to open the front door to let himself out, a key from the other side raised the latch, and Sir Felix, returning from his club, entered his mother's house. The young man looked up into Mr. Broune's face with mingled impudence and surprise. "Halloo, old fellow," he said, "you've been keeping it up late here; haven't you?" He was nearly drunk, and Mr. Broune, perceiving his condition, passed him without a word. Lady Carbury was still standing in the drawing-room, struck with amazement at the scene which had just passed, full of doubt as to her future conduct, when she heard her son stumbling up the stairs. It was impossible for her not to go out to him. "Felix," she said, "why do you make so much noise as you come in?" "Noish! I'm not making any noish. I think I'm very early. Your people's only just gone. I shaw shat editor fellow at the door that won't call himself Brown. He'sh great ass'h, that fellow. All right, mother. Oh, ye'sh I'm all right." And so he stumbled up to bed, and his mother followed him to see that the candle was at any rate placed squarely on the table, beyond the reach of the bed curtains. Mr. Broune as he walked to his newspaper office experienced all those pangs of doubts which a man feels when he has just done that which for days and weeks past he has almost resolved that he had better leave undone. That last apparition which he had encountered at his lady love's door certainly had not tended to reassure him. What curse can be much greater than that inflicted by a drunken, reprobate son? The evil, when in the course of things it comes upon a man, has to be borne; but why should a man in middle life unnecessarily afflict himself with so terrible a misfortune? The woman, too, was devoted to the cub! Then thousands of other thoughts crowded upon him. How would this new life suit him? He must have a new house, and new ways; must live under a new dominion, and fit himself to new pleasures. And what was he to gain by it? Lady Carbury was a handsome woman, and he liked her beauty. He regarded her too as a clever woman; and, because she had flattered him, he had liked her conversation. He had been long enough about town to have known better,—and as he now walked along the streets, he almost felt that he ought to have known better. Every now and again he warmed himself a little with the remembrance of her beauty, and told himself that his new home would be pleasanter, though it might perhaps be less free, than the old one. He tried to make the best of it; but as he did so was always repressed by the memory of the appearance of that drunken young baronet. Whether for good or for evil, the step had been taken and the thing was done. It did not occur to him that the lady would refuse him. All his experience of the world was against such refusal. Towns which consider, always render themselves. Ladies who doubt always solve their doubts in the one direction. Of course she would accept him;—and of course he would stand to his guns. As he went to his work he endeavoured to bathe himself in self-complacency; but, at the bottom of it, there was a substratum of melancholy which leavened his prospects. Lady Carbury went from the door of her son's room to her own chamber, and there sat thinking through the greater part of the night. During these hours she perhaps became a better woman, as being more oblivious of herself, than she had been for many a year. It could not be for the good of this man that he should marry her,—and she did in the midst of her many troubles try to think of the man's condition. Although in the moments of her triumph,—and such moments were many,—she would buoy herself up with assurances that her Felix would become a rich man, brilliant with wealth and rank, an honour to her, a personage whose society would be desired by many, still in her heart of hearts she knew how great was the peril, and in her imagination she could foresee the nature of the catastrophe which might come. He would go utterly to the dogs and would take her with him. And whithersoever he might go, to what lowest canine regions he might descend, she knew herself well enough to be sure that whether married or single she would go with him. Though her reason might be ever so strong in bidding her to desert him, her heart, she knew, would be stronger than her reason. He was the one thing in the world that overpowered her. In all other matters she could scheme, and contrive, and pretend; could get the better of her feelings and fight the world with a double face, laughing at illusions and telling herself that passions and preferences were simply weapons to be used. But her love for her son mastered her,—and she knew it. As it was so, could it be fit that she should marry another man? And then her liberty! Even though Felix should bring her to utter ruin, nevertheless she would be and might remain a free woman. Should the worse come to the worst she thought that she could endure a Bohemian life in which, should all her means have been taken from her, she could live on what she earned. Though Felix was a tyrant after a kind, he was not a tyrant who could bid her do this or that. A repetition of marriage vows did not of itself recommend itself to her. As to loving the man, liking his caresses, and being specially happy because he was near her,—no romance of that kind ever presented itself to her imagination. How would it affect Felix and her together,—and Mr. Broune as connected with her and Felix? If Felix should go to the dogs, then would Mr. Broune not want her. Should Felix go to the stars instead of the dogs, and become one of the gilded ornaments of the metropolis, then would not he and she want Mr. Broune. It was thus that she regarded the matter. She thought very little of her daughter as she considered all this. There was a home for Hetta, with every comfort, if Hetta would only condescend to accept it. Why did not Hetta marry her cousin Roger Carbury and let there be an end of that trouble? Of course Hetta must live wherever her mother lived till she should marry; but Hetta's life was so much at her own disposal that her mother did not feel herself bound to be guided in the great matter by Hetta's predispositions. But she must tell Hetta should she ultimately make up her mind to marry the man, and in that case the sooner this was done the better. On that night she did not make up her mind. Ever and again as she declared to herself that she would not marry him, the picture of a comfortable assured home over her head, and the conviction that the editor of the "Morning Breakfast Table" would be powerful for all things, brought new doubts to her mind. But she could not convince herself, and when at last she went to her bed her mind was still vacillating. The next morning she met Hetta at breakfast, and with assumed nonchalance asked a question about the man who was perhaps about to be her husband. "Do you like Mr. Broune, Hetta?" "Yes;—pretty well. I don't care very much about him. What makes you ask, mamma?" "Because among my acquaintances in London there is no one so truly kind to me as he is." "He always seems to me to like to have his own way." "Why shouldn't he like it?" "He has to me that air of selfishness which is so very common with people in London;—as though what he said were all said out of surface politeness." "I wonder what you expect, Hetta, when you talk of—London people? Why should not London people be as kind as other people? I think Mr. Broune is as obliging a man as any one I know. But if I like anybody, you always make little of him. The only person you seem to think well of is Mr. Montague." "Mamma, that is unfair and unkind. I never mention Mr. Montague's name if I can help it,—and I should not have spoken of Mr. Broune, had you not asked me." About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Anthony Trollope (2004). The Way We Live Now. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/5231/pg5231-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

MR. BROUNE HAS MADE UP HIS MIND

CEOs are trying to squeeze out with smaller budgets, despite ongoing supply chain snags and amid questions on rates and inflation. They are undertaking major overhauls of their sales and marketing strategies as what seem to be the last vestiges of physical commerce online. opportunities migrate Now many companies are discovering that, while they have prioritized the reinvention of customer-facing products like websites and webinars, they don’t necessarily have the foundations that allow them to capitalize on the new tools they’ve created. On one hand, they’ve embarked on digital restarts in order to keep pace with the competition. On the other, they now fear a digital nightmare as the web-portal façade of their business shows signs of strain as they rush to deliver at the new pace of sales that their digital strategy has delivered. Executives who might feel as if they are frauds because they're in this predicament aren’t alone. Many CEOs are realizing they’ve been underinvesting in the core operational processes and technologies necessary to efficiently and effectively turn around quotes and deliver products in an increasingly dynamic world even as they’ve kept pace with and similar developments in their plants. They need tools to marry their front-end and back-end capabilities, in other words. Industry 4.0 The solution lies in unified e-commerce platforms, or one-stop-shop applications that can be customized to specific businesses to manage B2B and B2C transactions, sales orders, marketplaces, and vendors in a manner that integrates with enterprise resource planning, from supply chain oversight to human resources. Questions, questions, questions… The following questions can help CEOs to understand how to become heroes in this era digital transformation. Do your supply chains operate with resiliency and flexibility? Do they meet customer needs and at the same time cope with constant, fast-changing market conditions? Having easy-to-use order management, e-commerce, organized data streams, and constant access enables visibility, efficiency, flexibility and the power to anticipate product lifecycles, delivery speeds and market demand for personalization and customization. The goal is not necessarily to know more about supply chains. It’s about knowing how to manage supply chains in order to deliver products consistently. Much of that information is already within a company’s daily business flows. But many companies lack e-commerce solutions that help them unlock the insights that would help them overcome supply chain challenges. Do you have cloud-based enterprise commerce systems and processes? Can you deliver data visibility and integrity across core operations? Enterprise commerce sits at the heart of the business. It needs to be customized to the way your company works. In manufacturing, SymphonyAI has pointed out it’s not enough to have automation. It has to be tuned to the culture and capacity of your business to incorporate automation into workflows. Otherwise, your business will pay a price with employee . The same goes for order management and commerce. Can you enable leading edge decision-making to unlock realistic innovation and drive growth? burnout Are you leveraging a complete technology approach effectively to unlock performance efficiencies and ownership mindsets in your talent? The U.S. rate of employee churn across industries is 22 percent. CEOs understand this well. Replacing a frontline employee can cost of what would be paid in wages if they stayed. Finding more senior level professionals can cost six to nine of their salary. problem sixteen percent months Communications is perhaps the best way to retain talent. Forrester in the early days of the pandemic, for example, only about 43 percent of employees believed their organization had a plan for what was happening. Getting that right made it easier for executives to focus on customers and other stakeholders. Finally, installing a business continuity plan for the duration based on strong employee communications and performance loops delivers value. found Finally, can your business support your customers’ drive to take control of configuring and pricing complex products and services and get the self-help and support they want digitally? If you want to optimize revenue, you will need to offer up and refine a low friction buyer and customer experience. Get this right, and you will also gain normalized useful data you can invest downstream in business and financial functions, in addition to learning about your customers and monetizing what you see. Mark Nathan at Zipari has been a pioneer in this area for the many manufacturers look to for their teams. health insurance Unified commerce These core operational processes define intelligent, integrated and agile cloud-savvy businesses. Direct digital transformation investments are expected to total $6.8 trillion globally between 2020 and 2023. That’s a compound annual growth rate of almost 16 percent, to IDC. What’s called the digital transformation services market is forecasted to reach $186 billion by . according 2024 Moving computing power off company-owned machines and onto more nimble cloud-based services is the evolution that every industry is undergoing at present. Gartner that global spending on cloud services will approach $400 billion next year. That’s almost a 50 percent increase compared to last year. predicts And yet spending money does not equate becoming a new hero of the digital restart. It takes more than cash to overcome the struggle with legacy technological debt. Beyond technology, the emerging digital manufacturing leaders are also looking at the organizational, process and cultural foundations of their businesses. Asking these questions starts companies on the right path. What’s next? The result is avoiding lost sales opportunities, distribution shortfalls, inconsistent financial management, sub-optimal resource planning and high employee turnover. Making all this happen, frankly, will take more than the services of most consultancies today. Staffing agencies masquerading as true business partners won’t cut it. And few digital transformation prophets are purpose-built to innovate the next generation of digital operating platforms for the next generation of commercial pioneers. But the market has a way of pulling forward what it needs to move to the next level. CEOs can and will rise to the challenge of satisfying record high while simultaneously harnessing the technology of the future. demand

Stories, News & Mentions about OFFERUP

Offerup’s Stories on HackerNoon

Offerup's News & Mentions