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Productize

productize

I’ve always had tremendous respect for indie founders and makers. The hustle, vision, and grit required to build something from scratch, often alone and while bootstrapped, is c̶r̶a̶z̶y̶ admirable. The solopreneur gtm is something that gets left behind. And while each maker’s journey is unique❄️, many face similar challenges when it comes to validating their idea, getting early traction, and scaling sustainably. After years of seeing repeated cycles, I’ve started doing teardowns — in-depth analyses and suggestions for indie products. My goal? To provide value and support to founders in those critical early stages. How I Decided on an Early Productized Service It’s important to “drink your own champagne”, “eat your own dog food”, etc.  Meaning - I’m not going to charge on and that indie founders and <$100k ARR founders are out there wanting feedback. assume So, I went hunting to validate. Alongside reading countless posts on Reddit, and Linkedin, I noticed a theme. Indiehackers, IndieHackers/founders to me fell into two camps.  “Marketing and sales suck, and I’ll do anything to be free of it.” “Marketing/sales isn’t a priority, because of X, Y, Z.” Build in Public I’ve set myself a challenge to connect with as many founders as I can between now and the end of Jan ‘24- providing quick wins in an async guide. I discover makers and products through communities like IndieHackers, Reddit, and Product Hunt where makers generously share their journeys. After signing up for their product, I take a deep dive into analyzing. The core offer and messaging Who their is target customer How they could improve conversion Quick wins to build trust and credibility How I Help Indie Hackers With Solopreneur GTM and Marketing I then compile my teardown into a custom report, sharing relevant advice and growth strategies tailored to their business. My teardowns focus on actionable suggestions across content, positioning, partnerships, and more. I steer clear of broad generalizations in favor of specific, . And I provide a roadmap and next-step plan they can choose to implement. tactical ideas It’s incredibly fulfilling to support founders who are putting themselves out there. If I can shorten someone’s path to product-market fit with strategic feedback, I’m happy. This is not a shortcut or replacement for getting in the trenches yourself. I see teardowns as a way to get an outsider’s feedback, put in supplementary legwork where needed, and give founders space to focus on crafting an exceptional product. Also published here

I Made a Productized Service for the Solopreneur GTM and the Build-in-Public Crowd: Here's How

How Much Time And Money It’s Really Going to Take To Productize Your Idea

We’re fresh on the heels of the Google and Reddit version of “Let’s Make a Deal.” To the tune of $60 million per year, tech behemoth Google has been granted real-time access to its catalog of user-generated content for the purpose of training Google’s . AI models As Reddit moves toward going public, the $60 million presents itself as a viable new revenue stream, perhaps setting a precedent for other tech companies to follow in their footsteps by turning user data into a new revenue stream in a legal manner. Hey, if Reddit can do it, you can do it too! Let’s first take a gander at how Reddit and Google are pulling this off, then explore an option that can make a business cash while also fostering loyalty from their users: As it pertains to Reddit users, the legality comes from the Reddit Terms of Service agreement. When creating or submitting content to Reddit, users agree to grant Reddit a “worldwide, royalty-free, perpetual, irrevocable, non-exclusive, transferable, and sublicensable license to use, copy, modify, adapt, prepare derivative works of, distribute, store, perform, and display [the user’s] Content and any name, username, voice or likeness provided in connection with [the user’s] content in all media formats and channels now known or later developed anywhere in the world…and you irrevocably waive any claims and assertions of moral rights or attribution with respect to [the user’s] content.” This is, of course, legalese for “We, Reddit, can pretty much do whatever we want with your data.” So, this is a legal monetization of data via licensing, however is it an ethical means of monetization? That’s for you to decide. Reddit is a big enough website with few viable competitors that it can, for the most part, monetize user data without any form of compensation given to users and not have to worry about losing a meaningful chunk of its user base. One Reddit user, u/Morrissey_99, asked in the r/technology thread on the deal, “[So] do I get a part of that $60M Reddit? It’s my data you were using, after all”. The answer, of course, is no Morrissey_99 you will not. You agreed to royalty-free, perpetual, etc., etc., licensing of your data prior to using the Reddit product. \User data is a hot commodity. Whether it’s AI training, quantitative trading, marketing measurement, or improving internal products and systems, it’s clear that data has tremendous value. Reddit’s data is worth $60 million annually to Google. That’s a lot. If you’re reading this, you’re probably not Reddit- or Google-sized, but you probably are sitting on a lot of data that can be productized and, in turn, monetized. But how do you do that in a way that works for everyone? How do you do it in a way that is ?  It’s pretty basic: be transparent and give users something they actually want in return. very cool 😎 Welcome to the revolutionary world of generally-speaking-do-things-in-the-opposite-way-of-Reddit-and-Google method monetization, where legalese takes a long walk off a short pier,  the Morrissey_99s of the world get compensated for their clearly valuable asset, and businesses open up a brand recurring new revenue stream from an asset they already have or can acquire with (relative) ease. I realize monetizing data is technically not doing the opposite of Reddit, but it is perfectly alright to borrow goals (turn data into revenue) from Reddit, but probably not advisable to borrow their systems (make it so users can’t use your product without forking over their data). Anyway… Explicit Consent is Key, Rewards Entice Whether it is the content generated on a website, app usage details, transaction history, or demographic information, the impetus is on the user to decide how that data can be used. Data privacy regulations like GDPR and CCPA make it abundantly clear users need to know what the heck is going on with their data and give them the options to decide whether the data can be used at all. The Reddit solution, essentially creating a gigantic blanket statement that allows them to do essentially whatever they want with the data while giving the user nothing in return other than the ability to use their product, is one way of going about things. I don’t know if there’s any actual term for this type of “consent,” but for the sake of the blog, let’s call it “consent by acquiescence.” The vibe is, “This seems kinda messed up, but whatever, dude, if that’s what it takes.” Leaves a different taste in the mouth than the proposed alternative: “Hey, we’d like to use data a, b, & c for use cases x, y, & z. We’d like to use this data indefinitely, but each month, we can pay you $10, or we can upgrade your subscription to the paid version at no other cost. Oh, and you can opt-out whenever you want.” This is an informal encapsulation of the mytiki (here’s my biz plug) ethos: data is valuable. Businesses should be able to use data, but only if they get consent from the user. That is covered by most laws. But the kicker is, hey, instead of alienating your user base by monetizing an asset they created without giving them something in return, why not, say, for legally licensing their data to you? This turns the data into zero-party data--data willingly given to a business with the expectation of something of value in return. give them something in return We built technology to make this very thing possible. You can check it out at if you’re so inclined, but let’s focus our attention more on the logic behind why this model is the most intuitive. mytiki.com It’s Legal, but It’s About True Ownership, Not Fine Print The concept of zero-party data licensing is straightforward but game-changing in its execution. Unlike the all-too-common, lengthy terms of service agreements that users scroll through and accept without a second thought, zero-party data licensing is about empowering the user. It recognizes that the data created by individuals is their property — and that any use of this data by businesses must be both legal and fair. What distinguishes data licensing from the “we-own-your-content” clauses hidden in the small print is the principle of ownership. Here, the user is not a passive participant but an active owner of their data. This means the data is not just observed or collected without explicit consent (as with first-party data), but it is owned and controlled by the individual. The mechanics of a zero-party data license also significantly bolster its legitimacy. Participation is entirely optional; users retain full access to the app or website regardless of their choice to license their data. They are not sneakily enrolled but must make an active decision to participate. Moreover, the compensation is not vague or promised in the form of intangible rewards; it is explicit and tangible. This could be in the form of a cash payout, discounts, loyalty points, or other concrete rewards. Zero-party data licensing agreements are immutable and digitally signed by both the user and the business, ensuring that the terms are enforced and protected. The user's ownership is not only recognized but respected and rewarded, making the process of data monetization transparent and equitable. When users are dealt with in the data economy, businesses can foster trust and loyalty. Why hide behind complex legal jargon when you can forge a partnership with users based on respect for their data rights and clear, mutual benefits? There are all sorts of legal questions that arise when it comes to monetizing data. Many are solved with user consent, and user consent is much more likely when they’re getting something of real value in return. If you’re sitting on data you can’t sell or are looking to source monetizable data in a way that genuinely interests users, zero-party data licensing is the best way to do it. In summary: It’s legal. But not in a Reddit-hired-some-lawyers-to-write-something-to-ensure-they-can-use-everyone’s-data-for-whatever-they-want type of way. It deals with users to monetization. It’s data, after all. their It allows businesses to create trust and loyalty by rewarding users and sticking to the actual terms of the agreement. It’s specific. It allows data to be utilized to its full potential with value-adds for everyone involved. Conclusion The Reddit-Google $60 million handshake shines a light on data monetization as a viable means of opening new, recurring revenue streams. The user data market is growing quickly. The rise of privacy initiatives coupled with growing user awareness of the value of their data brings about an obvious fork in the road when it comes to the way we as businesses use and monetize data: we can make slight modifications to the ways of old by baking a data ultimatum into terms of service agreements, or we can move toward strategies that directly involve and compensate the users generating the data. User data will continue to generate interest from buyers in many fields, including the obvious cash cow of training AI. Product-led businesses are missing out on opportunities to add to their revenue by neglecting the potential for data itself to become a viable product. Zero-party data licensing is a novel but logical strategy for turning user data into revenue while building a loyal user base. Take the story of Morrissey_99s and folks like them; it really hits home that users want to be recognized for their contributions. This isn't just about finding some middle ground; it's about coming up with totally new ways that respect what users bring to the table while also making business sense.

How to Monetize User Data Like Reddit (Without Being Sketchy) 

220 Stories To Learn About Ux Design

182 Stories To Learn About Product Design

232 Stories To Learn About Product

Allow me to state the obvious: as recent technological advancements go, few innovations have the potential to as profoundly as AI does. We have already witnessed its evolution from a buzzword to a fully productized tool (mostly for low-value tasks). Next step: conquering the office to complement and enhance everyday white collar workers. reshape our work and productivity used by 100 million people A research paper titled “ ” comes at a crucial juncture. Authored by a team from Microsoft, including (Senior Research Director), (Director of Applied Science), (Chief Economist), and others, the paper delves into the impact of AI, specifically LLMs, on workplace productivity, using Microsoft’s own Copilot as a case study. Given its provenance, this study should be taken with a pinch of salt… but nevertheless offer fascinating insights. Early LLM-based Tools for Enterprise Information Workers Likely Provide Meaningful Boosts to Productivity Alexia Cambon Brent Hecht Ben Edelman Focusing on “common enterprise information worker tasks for which LLMs are most likely to provide significant value” (email/intranet information retrieval, content creation, meeting summarization…), the research demonstrates that Copilot tools can significantly increase productivity, primarily by accelerating task execution without compromising quality. The paper underscores that users who have experienced these tools show a greater willingness to pay, reflecting perceived value initial expectations. above Key takeaways from the study ​​Copilot tools lead to a marked increase in task execution speed without a notable decrease in quality. Users who have interacted with LLM-based tools show a higher willingness to pay for them, indicating that these tools offer value beyond initial expectations. The research anticipates broader application across various tasks and roles, hinting at a future where AI’s role in productivity is more pervasive. What do we do with that information? Based on the paper’s findings, it seems clear that organizations will charge forward with AI implementation in the coming months. Should that be the case, I believe managers need to implement three “no-brainer” actions before moving ahead. Firstly, organizations need to establish clear “ ” integration standards. Fernando Lucini (from Accenture) akin to those in established industries, ensuring clear responsibilities and accountability. It’s also crucial to train employees comprehensively and establish formal AI processes similar to standard practices in other professional fields. AI literacy should be democratized across organizations, ensuring all departments, even those not directly working with AI, are well-informed about the technology and its applications. This approach can build trust and facilitate smoother integration of AI into various business processes. workplace AI highlights the importance of professionalizing AI roles Secondly, quality assurance protocols must be implemented to ensure the reliability and ethical use of AI in the workplace. A robust should cover six quality zones: . These parameters ensure the AI model’s completeness, correctness, accuracy, resource efficiency, adaptability, and transparency. Regular testing and certification of AI models based on these zones can help maintain high standards, safeguarding against issues like data security breaches and ensuring that AI tools are both effective and understandable to users​. framework functional suitability, efficiency, portability, maintainability, security, and usability Lastly (and in line with my first point), . Comprehensive AI training ensures that employees are not only able to use AI tools effectively but are also equipped to handle issues and make informed decisions when deviations from AI suggestions are necessary​. training programs are vital to enabling employees to adapt to and efficiently use AI technology Too soon to draw conclusions While the paper provides valuable insights, it has limitations. The research predominantly focuses on tasks that are already AI-friendly, potentially overlooking areas where AI integration might be more challenging. Moreover, the scope is limited to English-speaking contexts, which may not reflect global workforce diversity. Finally… it’s from Microsoft. And the company has every reason to pump up its own stock by claiming its solutions are the future of work. The research by Microsoft offers a glimpse into a future where AI significantly bolsters workplace productivity. While challenges and limitations exist, the potential for AI to revolutionize how we work is undeniable. With careful integration and regulation, we stand on the brink of a major productivity leap, spearheaded by AI advancements. Good luck out there. Also published . here

Is Microsoft's AI Copilot the Future of Work?

During last week’s DevDay, , under the direction of CEO Sam Altman, unveiled a series of updates. The company introduced ‘GPT-4 Turbo’, enhancing affordability for external developers. This upgraded version expands ChatGPT’s knowledge base to include information up to April 2023, surpassing its previous limit of September 2021. Meanwhile, in an odd strategic move, the company is offering to cover its clients’ legal costs for copyright infringement suits (challenge accepted). OpenAI The biggest announcement, however, was the coming availability of what Altman called ‘GPTs’ (aka Generative Pre-trained Transformers; he’s not great at branding). In essence, GPTs are custom chatbots that can be built using custom (private) data on top of their existing “knowledge” and that can be tweaked to have specific goals or personalities. One of the use cases already available to test in the ChatGPT app is “Game Time: I can quickly explain board games or card games to players of any age. Let the games begin!” Another is: “The Negotiator: I’ll help you advocate for yourself and get better outcomes. Become a great negotiator”. And, of course, my favorite: “Genz 4 meme: I help u understand the lingo & the latest memes.” The AI “Agents” (as they should be called) can be created/configured without any coding knowledge, using only natural language. You can simply give it a name and a description, then define what it should do, how it should behave, and what it should avoid doing. You can then upload files to increase its proficiency in the specific task given. In a demo, Altman made a “startup mentor” that gives advice to founders based on talks he’d given in the past. We are essentially witnessing wave 2 of the . Wave 1 productized and democratized large language models; we are now personalizing them to the individual. The same thing happened to the internet and social media (from 2007’s open Facebook to 2023’s personalized TikTok)… but it took 15 years, not one! AI wars Four things stand out as we move to a personalized AI assistant world. GPTs will displace millions of jobs. Private AI assistants are something many companies have been clamoring for since ChatGPT came out a year ago. They have data like employee handbooks, benefits info, and customer service manuals… and they want to make that searchable and accessible through a chatbot without needing to code or make the data accessible to the public. This is now possible. Let’s not kid ourselves. This will displace millions of jobs, as what is done by five people can now be done by two. Customer services is about to be decimated. Then will come HR. Accounting, too. Across organizations and the world over, “support” functions will be halved, if not more. Companies were already working on it before last week’s announcement. That work has now been accelerated ten-fold. Managing these changes should be the government’s first priority. A new economy will emerge. One of Altman’s less-spoken-about announcements is the idea that GPTs can be shared and would be commercialisable/monetizable in the near future. This would create, in essence, a new App Store, one of the 21st century’s greatest inventions. It will be fascinating to see where customers place value since ChatGPT is open by design. Will it be in the custom data? In the personality given? Should it be the former, the companies with the most content will have the most power. Not much would change then, and anti-trust regulators should take a much closer look at these tools (as we are recreating the platforms of the last era of computing). There is a potential for net positives, too. This is a huge opportunity for healthcare, for example. If an NGO trains algorithms based on the trove of medical data available online, on the millions of diagnostics and images available… we could make healthcare accessible to all for a tiny fraction of the prices we see today. Hell, it could even be free for some people who need it most. I already wrote about the democratizing power of AI; we are getting closer to that reality. We just have to be willing to make it happen. Human interactions will change. One of the first use cases I thought about when I started playing with the new GPT interface is feeding the I’ve ever had with my wife to see if some simple daily conversations can be automated. AI all the conversations I won’t be the only one with similar thoughts. How can we know if any interaction online is real once these tools spread? And how long before we feed an AI the data (texts, emails, voice recordings…, etc.) of someone who’s passed away to turn it into a simile of the real thing? Someone we can talk to to cope? No long. In fact, it already exists… and just got easier to do. Altman literally said in his keynote last week: “We will all have superpowers on demand.” While we are recreating a deity capable of making us live forever, we should make sure we don’t lose a little humanity in the process. Dangerous AI use cases may emerge. We are rapidly moving to a reality where AI Agents can not only talk about things but also act based on specific instructions and their given “persona.” As we personalize our AI agents/assistants, we will no doubt want them to take action on our behalf (something I predicted back in April). If the path to completing the action is not defined, the AI agent will make its own path. This can lead to unwanted externalities if we’re not careful. Let’s say you want to reserve a table at a fancy restaurant. You explain to your that it’s very important to you. The AI then calls the staff and threatens them. Or it hires someone to do it. Or it emotionally manipulates the staff, whose info it found online. These tools are very much “black “boxes, and it’s important to put the right guardrails in place to ensure this doesn’t happen. AI Assistant It’s important not to overstress the importance of the changes made available this week. GPTs are still made mostly of the “usual” chatGPT, with a sprinkling of personalization. You could do most of the things highlighted above… but you would have had to input multiple prompts. All in all, this is just a shortcut. Not a leap forward. For now. We are witnessing the formation of a generationally important company. Today, OpenAI is being careful and slow about the roll-out. But we need to watch them carefully: over the past centuries, it’s been rare to see a company become all-powerful… and use that power for good. The Dawn of AI Personal Assistants Good luck out there. Also published . here

The Rise of AI Personal Assistants and Their Consequences

এআই ব্যক্তিগত সহকারীর উত্থান এবং তাদের পরিণতি

Der Aufstieg der persönlichen KI-Assistenten und ihre Folgen

El auge de los asistentes personales de IA y sus consecuencias

L'essor des assistants personnels IA et leurs conséquences

एआई व्यक्तिगत सहायकों का उदय और उनके परिणाम

AI パーソナル アシスタントの台頭とその結果

AI 개인 비서의 등장과 그 결과

A ascensão dos assistentes pessoais de IA e suas consequências

Распространение персональных помощников с использованием искусственного интеллекта и их последствия

Yapay Zeka Kişisel Asistanların Yükselişi ve Sonuçları

Sự trỗi dậy của trợ lý cá nhân AI và hậu quả của chúng

人工智能个人助理的兴起及其后果

Hey Hackers, Horizon has been nominated to HackerNoon's annual awards in Toronto, Ontario. Startup of the Year Please vote for us . here Read more about us below to understand why we deserve your vote. Meet Horizon We founded Horizon in 2017 with the mission to make web3 easy, fun, and accessible for everyone. Our team consists of blockchain leaders who’ve made key contributions to the Ethereum ecosystem, including the ERC-1155 semi-fungible token standard, the ERC-1271 standard for smart and account abstraction wallets, the Niftyswap Protocol, and much more. Our CEO Peter Kieltyka is regarded as one of the top web3 technologists and was recently named a top 10 gaming tycoon by Maclean’s. We’re headquartered in Toronto, Canada, and we have teammates in 17 countries. Horizon is a blockchain infrastructure company with two main products: Sequence, the all-in-one web3 developer stack + smart wallet that makes building web3 games, apps, and experiences easy, and our pioneering video game, Skyweaver, a cross-platform digital trading card game that has been called a “genre-defining digital TCG experience” and “2022’s best blockchain game”. My Role My name is Michael Sanders and I’m Co-founder & Chief Storyteller at Horizon. I’m a long-time tech entrepreneur and have been in the blockchain space for 13 years. At Horizon, we’re making web3 easy, fun, and accessible for users and builders. As Chief Storyteller, I work closely with our CEO Peter Kieltyka, our business development, product, and marketing teams to help bring web3 to the masses by empowering builders and brands with the tools they need to deliver amazing web3 experiences to their users. Awesome content is what will onboard the next billion people to web3, and we enable and accelerate the creation of such content with our Sequence developer platform. We envision web3 as a new dimension that benefits people and businesses around the world, and we believe driving web3 adoption is an important mission. While the challenge is large, we find it incredibly fun, and that’s due to the wonderful team we’ve assembled. Web3 is the future of the Internet, and Horizon’s mission is to make it easy, fun, and accessible for everyone. How We're Disrupting/Improving the Blockchain Industry Horizon’s mission is to make blockchain easy, fun and accessible for users and builders to drive the adoption of web3 and its open, transparent, and symbiotic economies. From cryptocurrencies, NFTs, and social identities to video game items, virtual real estate and digital assets not yet imagined, virtual worlds are becoming more real and valuable than ever. Horizon is committed to providing billions of people with easy and secure access to this new dimension of web3 so everyone can own, use, and enjoy digital assets. With Sequence, we provide developers the tools to build the next generation of Internet economies and experiences where users and creators share in the value generated by the networks they participate in. There are many challenges developers face when building games in web3, ranging from wallets, account creation and onboarding to gas fees, transactions, blockchain data, and marketplaces. We’ve solved all these challenges from the ground up while building our game , and we’ve productized those solutions as the Sequence developer stack + smart wallet. Sequence solves the complexities of web3 onboarding, infrastructure, transactions, and UX, so that developers can leverage our tools, and deliver their users the most seamless and friendly web3 experiences possible. Skyweaver We currently have 67 projects leveraging Sequence technology for their web3 games, applications, and experiences.And, we have hundreds more brands and projects in the pipeline, so stay tuned for more amazing web3 experiences in the near future. Our Predictions/Thoughts on the Blockchain Industry in 2023 Blockchain technology can benefit products, games, apps, individuals, and organizations in a number of ways. As a decentralized, open-source ledger that records transactions and activity permanently, blockchain technology enables greater security and transparency for users. There are a number of use cases that highlight the benefits of blockchain, one of which is how blockchain enriches players’ experiences in games by giving players  ownership over the game items that they buy. And for brands like Starbucks, it allows for more enriching digital rewards in things like loyalty programs, where consumers can redeem digital NFT collectibles and items from brands they have an affinity towards. Web3 enables more connected and enriching community experiences, greater symbiosis between users and creators, more creative expression in digital worlds, cross-brand collaboration in permissionless ways, new forms of exchanging value and organizing around shared values, experimentation in economy design that could start small and later inform global frameworks, and much more. We have just hit the tip of the iceberg when it comes to the benefits and use cases that web3 can bring to consumer experiences. We’re excited by what’s been created to date, and we’re thrilled to enable and accelerate web3 content creation so that we can unlock more amazing and enriching experiences for everyone. We believe gaming will play a huge role in driving the mass adoption of web3. Video games have become a part of culture and our everyday lives. There are over 3 billion gamers in the world, and the number keeps growing. Blockchain technology can allow game devs to give their players ownership over the game items they purchase, allowing players to trade those items, or gift them to friends as they please. There is also the notion of giving gaming communities more agency over the game content developed, and providing core fans more engagement opportunities with the games they love - like helping to fund games, features, and development cycles through purchasing digital collectibles. Web3 also unlocks persistent user profiles, identities, and item ownership that can exist across games and continue into the future, even if a specific game ceases operations. There are so many creative paths being explored from lending game items to running new kinds of tournaments, to creating new kinds of game modes around existing game items, to enabling guilds and social coordination, to unlocking new forms of user-generated content, to sharing royalties between studios and users in ways previously not possible. We believe all these factors will contribute to an exciting future for gaming enhanced by web3. Cross-brand collaboration in permissionless ways is really exciting. Imagine NBA2K had web3 items in-game. Adidas could look at the blockchain and see which users own Adidas shoes in-game and then airdrop (i.e. gift) digital collectibles or vouchers to those users. The Nike .Swoosh x Fortnite initiative gives us a glimpse into the future of cross-brand collaborations, and we’re sure this is just the beginning and that we’ll see incredible innovation with respect to how brands deliver value, deepen engagement, and interact and have fun with their communities. With any new technology that is disruptive to an industry, there will always be pushback and skepticism around what the technology can deliver. We think blockchain technology is no different. The first step is to educate and inform: what are the benefits of blockchain technology? How will it improve players’ experiences? Once there is that understanding around how this technology can improve players’ experiences and enrich them in ways never before possible, then it will become clear that web3 and blockchain technology is the future of the gaming industry. There are major game publishers such as Epic Games, Take-Two Interactive, Ubisoft, Square Enix, and many others that believe in web3 and are already thinking about ways that web3 can further enrich players’ experiences in games. We think that it’s a matter of time before most, if not all, game publishers and developers incorporate web3 components into their games. Web3 opens up a whole new way to approach game development and community engagement and we’re excited to be part of it and help builders, brands, and developers on their journey to making awesome web3 experiences. Our all-in-one developer platform + smart wallet, Sequence, helps developers and brands build innovative web3 experiences anyone can enjoy thanks to the user-friendly smart wallet and modular developer platform. Sequence accelerates web3 content creation by giving builders and brands the tools they need to deliver seamless web3 experiences to their users. What word defines the state of Blockchain in 2023? Evolving. The blockchain and web3 space are constantly evolving. Many brands, projects, and games are still in the exploratory phase of how web3 can enrich their users’ experiences. Some brands are taking the leap sooner than others, but we’ll soon see most forward-thinking brands leverage web3 in some way to elevate the experience with their audience. Some will succeed and some won’t, and that’s expected for any new technological era. We’ll test, learn, unlock incredible opportunities, and grow from there. As a company, we’re always evolving and improving our tech stack, and Sequence enables all kinds of web3 games and experiences for brands and builders. We obsess over the developer experience because we want our partners to love using our tools, and we want to solve their web3 needs, so they can stay focused on delivering amazing and engaging experiences to their users. As a result, we’re continually listening to our partners and looking into the future to enhance the Sequence stack, which consists of the Sequence account abstraction wallet, Token & NFT APIs, gasless transactions, blockchain indexer, Marketplace Engine, payment rails, and SDKs for whether you’re building on Web, mobile, Unity, or Unreal Engine. Why we decided to participate in HackerNoon's Startup of the Year awards Industry awareness: Through our participation in this year’s Startup of the Year awards, Horizon has the opportunity to gain industry recognition and exposure through a reputable outlet that covers technology, startups, and innovation. HackerNoon has an incredible audience, and we’re excited to connect with potential partners, users, and community members. Provide value: for any games, brands, or builders looking to enter web3, we have the tools you need to deliver amazing experiences to your community. We’d love to help you get to market faster and enhance your engagement and monetization. Final Thoughts We’re focused on improving the accessibility of Sequence for developers, which in turn will make it easier for more games, apps, and brands to incorporate web3 elements. Our big focus areas right now are expanding the integration options for our wallet so you can embed it even more deeply into your existing app or game, improving our Unity and Unreal Engine SDKs, as well as creating a no-code dashboard so non-technical and technical teammates can launch, manage, and contribute to web3 projects. There is a lot to look forward to! Users and developers can expect more upgrades and product releases that will empower creators to deliver amazing web3 experiences. Vote today! for us

Making Web3 Easy, Fun, and Accessible for Everyone

Let's learn about via these 79 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Strategy /Learn Repo Without an effective strategy, an organization of any kind is doomed to fail from the start. 1. Netflix Business Strategy In 2022 A full immersion into Netflix's changing strategy as the market landscape for streaming becomes saturated. 2. Exclusive Interview with Billionaire Entrepreneur Ryan Breslow Welcome to my Meet The Entrepreneur Interview Series, where I interview impactful leaders and ask them to share tips and techniques that have benefited them, bo 3. How Publishing 200+ Interviews Changed My Approach To Interview-Based Content Lessons from publishing 200+ interviews and tactics on cold outreach, having meaningful conversations, writing great content, and building brands 4. [Writing Prompt] Noonies Nominee Interview Questions List Welcome to HackerNoon’s Writing Prompts program. Answer one today! 5. Minimum Usable Product 6. The Algorand & FIFA Partnership Will Gain Importance During the World Cup, Here’s Why The World Cup will be the biggest televised event of the year, and Algorand is the organization’s “official blockchain platform” and partner. 7. Objectives and Key Results: Key Takeaways and Learnings from implementing OKR (Managing expectations: this is no scholarly article, but a real-life testimony of our experiences as a SaaS scale-up with the OKR goal setting process). 8. How To Make Extra Money In 2021 Using These Awesome Investment Ideas From cryptocurrency to domain name squatting, there are many different investment ideas you likely haven’t explored. The investment paths you could potentially go down may seem overwhelming. However, knowing what options are available is the first step toward discovering what’s right for you. 9. How to Get Started in Cryptocurrency Investment with this 5-Point Framework End-to-End Evidence-Based Cryptocurrency Investment Framework. Portofolio Management | Investment Strategy | Investment Process | Investment Review | Skills 10. Work Smarter, Not Harder: 7 Powerful Tips To Manage Your Projects Better Thinking is probably one of the hardest, daunting tasks, and George Bernard Shaw had his perspective that made me think more about my way of dealing with challenges. 11. Surviving Coronavirus Pandemic: A Guide for Startups Yes, we’re witnessing a crisis unfolding. Not only people get gripped by the coronavirus fear. It’s like a stroke for most businesses — some will recover, some won’t. 12. The Ultimate Guide to Strategic Thinking for Data Scientists While HBR declared "Data Scientist" the sexiest job of the 21st century, let's admit that the prevailing view is that it's a geeky, highly-technical field. 13. How You Can Land a PM Job Without Previous Experience in Project Management (Image Credits: chiefexecutive.net) 14. How GoPro Became a Victim of Bad Business Strategy Do you remember the times when you couldn't imagine a day without your GoPro camera? Well, I certainly do. So, what went wrong and why? 15. Here's How I Increased Web Traffic by 300% in 30 Days Here is a 3-step framework to deliver consistent results with limited domain knowledge. I had no marketing knowledge prior to this project but I grew traffic. 16. How I Copied My Favorite SaaS Landing Page 👀 I've got some bad news... 17. Cold Email is not Dead $400k Closed in 30 Days. Cold Email is not Dead. 18. Analyzing Blockchain Business Models In the Blockchain Model, we want to understand how the protocol connects the value model to everything else. The rules of the Blockchain make it compelling. 19. 5 Reasons to Deploy Six Sigma for Startups in 2021 All large organizations be it multinational corporations, big corporate houses, or global production units are applying Six Sigma techniques to better their business processes, nowadays. However, a huge misconception that ‘Six Sigma methodology doesn’t work for startups’ spins around it. Well, the universal nature of Six Sigma techniques and their effectiveness in process optimization and quality control make them an indispensable tool for organizations of all sizes. 20. ‘Never Give Up’ is an Awful Startup Strategy Actually, it’s exactly the other way around. A startup, any startup, has too many opportunities and paths it can take. Business opportunities, technology opportunities, in marketing and of course with its product. So obviously, a startup must constantly give up on opportunities and possible paths. 21. Business Lessons To Learn From The Apple-Next Deal It was the year 1997. Apple was experiencing a sharp sales decline. 22. 10 Tips to Grow Your Local YouTube Channel Check Out What Can Make Your Content to Have a Wider Reach.. 23. Introducing The Triple Layer Business Model Canvas The Business Model Canvas is a great tool for entrepreneurs, yet one that is not without flaws. 24. Everything That Startups Do Wrong: A Content Marketer’s Perspective I have around a decade of experience in content and marketing. My first job was in the editorial department of a print publication around ten years ago in Bucharest, Romania, when the digital landscape the way we know it today was almost non-existing. Today, I work as a content marketer for a tech start-up in Singapore, which is often referred to as the Silicon Valley hub of APAC. 25. 9 Things You Might Be Missing From Your Job Search Strategy A successful "Job Hunt" is a combination of Strategy, Marketing, and Sales tasks. 26. David Horesh on Marketing Optimization, Importance of MultiLayer Touchpoints, & Tik Tok For Startups B2B marketing is all about constantly navigating multiple touchpoints and discovering platforms to achieve maximum efficiency. 27. The Power of Knowing What to Do The underlying reason a startup is so hard, so strange - and why so many fail - is that you yourself have to figure out what to do. 28. How to Improve Strategic Thinking and Planning for Digital Business Discover ways to develop your strategic thinking and planning, particularly for digital transformation in your organisation. 29. Dear Companies: Fuel Your Performance With Customer Insights As a result, companies that fret over their visuals and site design end up losing to those brands that apply a consumer-centered approach. 30. The Myth Of "A Good Problem To Have" You’ve hear it all the time. Stuff like 31. How To Lead Your Business Strategically The capacity of a leader to envision, plan, direct, and get the most out of the resources at hand effectively is known as strategic leadership. 32. Should Snapchat Copy Instagram's Timeline? Snapchat success came in the era, where any successful social media company would be acquired by Facebook. It not only avoided the acquisition but thrived on its own terms, garnered a significant audience and has become a major participant in the cultural machine. 33. Taking Personal Initiative How To Take Personal Initiative 34. How to Choose the Best Apps for Your Business Key strategies to pick the best apps for your business 35. Deloitte's Adam Weissenberg  Talks About Planning for the Future as an Entrepreneur Adam Weissenberg - Vice Chair, Deloitte | How To Plan for the Future as an Entrepreneur 36. SMART Goal-Setting: The Technique to Achieve All Your Goals in 2023 Harsh Makadia explains how to achieve your goals in 2023. 37. 3 Brands that Beat the Odds and Thrived During the Pandemic In the beginning of 2020, the businesses who chose to think positively, remain flexible, and be proactive saw opportunities and went after them 38. A Short History of The Commercial Internet: From Mosaic to Web3 Currently, the predominant business model for commercial search engines is advertising. 39. Top Reasons Why Your Brand Isn’t Making Money From LinkedIn Every social media has its own voice and unique language. For your brand to stand out, you need to take them into cognizance and also approach them effectively 40. Do You Really Need An NFT Business Plan? You don’t create digital tokens just to make money. You create tokens because they add value to existing and potential customers beyond monetary gain. 41. What Is Strategic Performance And How Did It Evolve Have you wondered why despite huge investments most of the businesses tend to fail? Yes, I am sure you have. And yet there are some that are able to scale heights, without much investments… 42. Why The Best Entrepreneurs Care More About Results Than Hours Successful people measure their productivity by work accomplished instead of hours spent on it. This holds true even for very accomplished entrepreneurs. 43. Thoughts on Effective Digital Transformation Strategies Why do so many digital transformation initiatives stall or fail? The answer might lie at the bottom of this iceberg… 44. Rethinking the Hiring Process: How to to Stand Out and Delight Your Candidates In this hiring climate, you need to to attract candidates. differentiate 45. Why Twitter Blue For $8 Will Underperform Twitter blue for $8 has an intricate flaw. And Elon Musk has to find a way to get ahead of it to make Twitter profitable 46. How to Create a Data Analytics Strategy to Grow Your Business Are you building a Software-as-a-Service platform? Wondering what data is essential for your business? Time for a Data Analytics Strategy. 47. "My manager gave me detailed instructions on what to do, but I kept asking why" #Noonies2021 Don't wait for an invitation to do product strategy, because you won't get it. 48. Non-Linear Competition In A Tech-Driven Business World How the competition works in the long-term, and how competition is non-linear in a tech-driven business world 49. Proper Communication Helps Make Digital Transformation Smooth The digital transformation process of the business. The role of communication during the fundamental change. Agility as the locomotive of transformation. 50. Practical Guide to Jobs-to-be-Done Framework for Building a B2B Content Strategy The Jobs To be Done framework is an effective tool for mapping out a content strategy that is focused on the buyer’s needs 51. Product Decisions In Large Organisations Don't Have To Be Hard Product and development teams often try to deal with the uncertainty they face with more research, more analysis, more process, more people and more frameworks. Organisations, especially the larger ones, intensify this rather than help navigate the uncertainty. 52. How to Choose the Best Apps for Your Business Key strategies to pick the best apps for your business 53. Data Analytics is a Journey It is 2020 and the data analytics has gained so much attention even outside of the tech community. "Data is gold", they say - no one wants to be left behind. However, getting the right strategy is neither a straightforward nor static process. 54. How to Hack the Product Adoption Process Last Thursday, I was lucky to interview Warren Schirtzinger during a live webinar, one of the co-creators of the “Chasm” framework in the 80s, later polished and popularized by Geoffrey Moore in the book “Crossing the Chasm”. 55. These Capability Assessments Help in Focused Product Development Deciding on what to build inhouse vs. buy off the shelf requires product prioritization. Build a capability assessment to improve your product strategy. 56. Startups Need to Make These 4 New Year's Resolutions 4 New Year Resolutions every startup founder should stick to in 2021 including taking care of mental health, implementing cloud tech, and more. 57. The Rise of Eastern Europe as a Coding Powerhouse Silicon Valley has always been a dream destination for developers and tech talent. The so called “The Bay Area,” is the world’s largest and most famous startup ecosystem. 58. Top 5 Challenges For Indie Hackers It's a tough lifestyle being a solo indie hacker. Here are some of the hardest parts. 59. How to Ensure that the Product Satisfies the Product/Market Fitness. One of the most common questions asked by up-and-coming entrepreneurs is “ What matters most for a startup’s success? ” 60. What Do You Think Your Values Are? Do You Know What Your Values Are? 61. The Less Debt You Have, the More Risk You Can Assume. Think about the level of focus you can have without the stress of losing everything. 62. 8 Investment Strategies to Embrace Right Now I lost a lot of money during the 2008 financial crisis.  And before the corona crisis hit, I had made sure I don’t repeat the same mistakes. So, I made a plan and stuck to it. 63. Good Strategy, Bad Vision? Product Management Paradoxes — Part I There is a recurrent thread going on in tech circles that contrasts 1) the philosophical approach of companies that have adopted the lean startup practice of rapid customer development cycles, and pivoting with your learnings, vs 2) companies that are thesis-driven, have a big mission and are relentless in pursuing it, bending reality to their will — using examples of product visionaries like Jobs, Gates and Musk. 64. Engaging with Customers in the Early Stages of a Startup In the early stages of startup development, teams run the risk of advancing too quickly as they obtain limited data about their early customers. 65. Should Designers do Product Management? If designers should know how their product may be engineered, they should also know how it may be productized. 66. Company Culture: Future-Proofing Your Startup Venture Company Culture: Future-Proofing Your Startup Venture 67. Lessons In Distribution From Your TV Remote Control What makes up distribution for a business? In this article, you will learn some valuable lessons about distribution by starting from your remote control. 68. 3 Marketing Strategies Delivering the Best Growth for Online Side Hustles Here's what I learned growing an online side hustle from $0 to $800 per month in 3 months: Growing an online side-hustle is not easy. Content, in particular, is a risky marketing proposition because of a steep learning curve, high labor and input costs, with very uncertain returns. Yet content forms the basis of pretty much any online marketing strategy — from email to blogging to social. It’s vital for online growth. 69. Working With Creatives Doesn't Have to Be Hard 70. People are What Matter Most When it Comes to Scaling Brian Scudamore | Scaling Secrets: It’s All About People 71. What is Reverse Mentoring? Reverse mentoring occurs when young people become mentors of executives to guide them on various topics of strategic and cultural relevance. 72. Ten Things to Know about (Digital) China and Beyond. Today, there are more than 900 million Internet users in China -- about three hundred million more than Europe’s total population. It is also more than twice the total U.S. and Canadian populations combined in 2019. 73. Principles of Effective Goal Setting In most organisations, big picture thinking comes off as a seasonal flavour often appearing every few months. Mental gymnastics that goes with determining the “why” - meaning or purpose of goals, promise of a better future, and the excitement of doing something new definitely gets the creative juices flowing. Though not for all, most people in the organisation find this phase highly energising and exhilarating. But does this motivation last long? 74. Mercuryo.io Co-Founder: How To Appeal To Your Target Audience Like An Expert The cryptocurrency market is registering an impressive growth year by year. With the rise of DeFi, investors expect next-generation wallets, payment processors with various services, catered to users at one spot. As the market expands not only in the B2C but B2B sector, startups must strategize efficiently and effectively. I sat down with Greg Waisman, Co-Founder, and COO at Mercuryo.io, a payment processing company, to discuss what makes a customer tick and what management should look at when expanding overseas. 75. 4 Ways to Foster Your Leadership Skills I have worked with some excellent managers, people who were not only good at what they did, but also had great leadership potential. Sadly though, these managers didn’t invest in their own growth. They were so busy attending to the daily demands of the management job - putting out fires, resolving production issues, solving for customer escalation, moving from one delivery timeline to another - that they failed to build the skills required to become a great leader some day. 76. Create a Business Model Canvas for Your Business The business model canvas is the CONCLUSION of a long and fruitful process, which will ultimately inform a company’s entire strategy. 77. Artificial Intelligence Business Opportunities: 10 Steps to Implement Much like the rest of the world, Artificial Intelligence (A.I) has a 1% problem. Creating a smart algorithm is not yet a given for many entrepreneurs - why not? 78. A Guide to Growing your Average Order Value - A Price led Merchandising Segmentation Strategy The goal of the retail merchandising activity is to support a retail strategy that generates value for the customers. The selection of the merchandise and the type of goods are the key of the retail strategies. According to author Michael Levy in Retailing Management, the decision to carry particular merchandise is tactical rather than strategic. 79. How Do You Define Business Model Innovation? In 2020, all competitive companies are in a state of constant business transformation. That’s just a fact : those that do not evolve are made redundant by stronger, more agile competitors. And so, mediocre companies update their products and services. Good companies update their processes, too. Great companies, meanwhile, update their business models. Thank you for checking out the 79 most read stories about Strategy on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

79 Stories To Learn About Strategy

I’m unemployed :(. Initially, I thought I was going to contract with gumroad for at least 4 weeks. What I thought was a bare minimum of 4 weeks of income was cut down to 3 days. I forgot the cardinal rule for startups: “When it comes to startups, anything goes.” I mean, looking at it objectively, it probably was a good decision on their part. Still a shitty thing to do. It really sucks because I still think that Sahil is a cool guy, which is somewhat throwing my ego for a loop. But I suppose multiple things can be true. He can still be cool, and it can still be a good decision to reject me. And it can still be a shitty thing to do. I guess, my dear reader, ego isn’t real. Interestingly enough, my self-esteem has increased. I took an expected value calculation (fancy finance term for a YOLO-style bet), and it didn’t work out. But they don’t always work out. I’m proud of myself for that. I have 2 months of runway before I get evicted from my overpriced bay area apartment. What Now? Let’s first principle this shit. I just need cash flow. My expenses are 3.5k a month. 2.3k of that is rent. RIP. If I can make rent in a month, I can do this forever. I have a few options. Get a full-time job. Get a part-time job. Consulting. Make my own products. Sell my body. Ironically, for the position I’m in, the list is ranked from easiest to hardest. But what I really want is to make my own internet products. Not for the money, the . So for my next bet, I’ll bet a month of my time and money to do The Challenge. freedom The Challenge I’m gonna do 30 products in 30 days. I’m gonna put my obsessive personality to good use. Business needs constraints so here are mine. Here are the rules. Over 50% needs to be paid products. Free tools, aka engineering marketing, is allowed. Need to naturally fit into a user acquisition channel from day 1. Need to have some kind of cash flow model - OTP. The idea needs to be backed by data - keyword research, the niche of a larger product, or a competitor to a legacy product is ok. Needs to provide obvious business value - saves people money, makes people money. Needs to be compatible with the current trends in the world today. No copycatting. Taking an opposite marketing stance is allowed. Need to launch each one publicly. ProductHunt, Twitter, Show HN, etc. Need to retro each one publicly. Tweet, video, article, etc. Can build off of each other. I can reuse codebases, data, but I am not allowed to build a new feature and call it a product. Each product needs its own link (probably or ) product1.hshidara.com hshidara.com/product2 Needs to be aimed at a specific audience that I’m familiar with. An agency gig counts as 1 product. Selling a productized service counts as one product. Ship high-quality stuff only. I’m going to try and stay in the same audience (internet creators) for the network effects. But occasionally, I’ll have to stray a little. If I can’t do it, I’ll turn on my Linkedin “open for work” flag, and in the meantime, wait tables at some restaurants in Oakland. Can I do it? Let’s find out.

Building 30 Products In 30 Days: Time to Man Up

Artificial intelligence (AI) is transforming the way we work and interact with technology. AI tools like are being used to automate complicated, repetitive, and error-prone tasks, freeing up time and resources for more strategic tasks and accelerating self-service innovation. However, for these AI systems to successfully interact with your organization’s existing systems and services, they require a standardized way of communication. This is where APIs and their endpoints come in. With proper management of these APIs, you can easily open up the self-service innovation opportunities that AI-led automation can bring, while maintaining the control and security your enterprise requires. AutoGPT AI-driven automation helps accelerate innovation with APIs APIs (Application Programming Interfaces) and their endpoints allow AI systems to interact with existing systems and services in a standardized way, which enables automation of complex tasks that would otherwise require manual effort. This is ; productivity is increased while employee pain points are reduced, and self-service innovation is accelerated as a result. ; leveraging “machine learning algorithms to analyse data, make decisions, and take actions based on that data”. great for everyone in the enterprise AI-driven automation is the next leap in automation maturity AI and API-led automation can have immediate external impacts. Think of an AI system that manages customer service enquiries for an e-commerce site. The AI system could use APIs to access data from the e-commerce platform, such as customer orders, shipping information, and product details to give customers an instant response to their query. It could also use APIs to integrate with third-party services, such as payment processors and shipping carriers, to automate tasks such as processing refunds and generating shipping labels. This is just one example of the way . AI can help improve customer success And it’s not just external; internal impacts can be huge too. (whether creating AI-specific or general productized APIs) can speed up the process of exposing functionality. As enterprises make this functionality available across domains, the discovery and composing of APIs together accelerates. This leads to increased innovation and efficiency, as consuming developers can build on existing functionality rather than starting from scratch. Automation across the API lifecycle itself The best results are tied to a mature API program To keep this self-service and AI-led automation innovation ticking, the APIs that facilitate it need to be top tier. That means API owners/providers need to ensure their endpoints are properly designed, managed, tracked, secured, and optimized well for consumption. A approach is the solution. mature API lifecycle management Effective discovery, evaluation, and onboarding is needed too. You need a great consumer portal (and/or developer portals, marketplaces, storefronts etc.). This should be tied to the lifecycle management process as well as an overall internal catalog of all APIs to ensure consistency, tracking, security, and other necessary functionalities. Doing so ensures that the API is easily discoverable and accessible to the developers who need it, while also providing a level of security and governance that protects sensitive data and prevents unauthorized access. Especially important is as the ecosystem grows. minimizing the threats from shadow and zombie APIs Mastering the provider and the consumer side of exposing functionality as APIs together ensures you’re promoting and socializing gold standard APIs. This level of visibility, quality, and reliability is a key enabler to the self-service model, and exponentially more important for AI enabled self-service. In conclusion, AI tools are changing the way we work, and APIs are essential for enabling the AI-driven automation of complex tasks. By embracing the synergies between effective API management and AI-driven automation, enterprises can stay ahead of the curve and leverage the power of AI to achieve their strategic goals.

Staying Ahead of the Curve With APIs and AI-driven Automation

If you have a lot of private enterprise data, how can you use a ChatGPT-like AI system to help you search for relevant information faster? Or if you have lots of personal information, how can you find old memories without the exact keywords? In this practical guide, I will show you 5 simple steps to implement semantic search with the help of LangChain, vector databases, and large language models. Best of all, I will use all open-source components that can be run locally on your own machine. No need for any cloud SaaS or API keys, and your data will never leave your office or home. Use Case Analysis Our has a based forum that has been running for over 5 years. There is a wealth of information, but sometimes, it is hard for newcomers to find the right answers to their questions quickly and succinctly. community Discourse There are a few reasons, which are very common for Google/pagerank based search solutions: Search is based on keywords, and often, users do not know the right terms or exact wording. Even if the user figured out the best search terms for their particular question, the return result is a list of top discussion threads. And the answer to the question might not be in the first few posts or could be buried deep in a prolonged discussion thread. In general, search engines do not understand long sentences very well if you ask a long-winded question or two questions in one sentence. You might ask another question, “ChatGPT v4 is already so good, why don’t you ask questions directly from chatGPT?” Again, there are several strong reasons: ChatGPT (v3.5 or v4) model was trained on data before November 2021, thus, it does not have the latest information since then. The latest OpenAI website retrieval plug-in can search over the website for the latest info, but it is still quite slow and often stuck. It is neither efficient nor cost-effective for your enterprise's internal use. You don’t want OpenAI and Microsoft to access your proprietary enterprise data or your personal data. So, my goal is that if someone comes in and asks, “What is the easiest way to run an NKN node?”, he/she should be able to get a straight answer in a few sentences. I use this public NKN forum only as an example to show you the typical dataset and typical questions and answers for a special enterprise knowledge base. Design Approach After some research and experiments, I narrowed down my plan into 5 steps: Do a quick Proof of Concept using cloud service and API Clean and prep my data Convert my entire data set into a vector database Use vector database to find the best matches to my query Use LLM to distill the best answer to the original question If you want to pause here and learn some basics about LangChain, vector databases, large language models, and how they work together, I will recommend and . And below, is a short introduction to these concepts. this video this video Key Components : LangChain is a framework for developing applications powered by language models. It is the glue or middleware that chains all the different components together to accomplish complex tasks semi-autonomously. Langchain The most powerful and differentiated applications will not only call out to a language model via an API but will also: Be data-aware: Connect a language model to other sources of data. Be agentic: Allow a language model to interact with its environment. : A vector database is a specialized storage and retrieval system designed to handle high-dimensional vector data efficiently and is often used in machine learning, artificial intelligence, and data analytics applications. Vector database These databases enable users to perform similarity searches or nearest neighbor searches, allowing them to find the most similar items within the database based on their vector representations. Pinecone is one of the leading commercial vector databases. There are open-source alternatives such as FAISS, Chroma, and Supabase. : A large language model like GPT (Generative Pre-trained Transformer) is an advanced artificial intelligence model that utilizes deep learning techniques to process and generate human-like text. Large Language Model With billions of parameters, GPT-3/4 is trained on massive amounts of data from diverse sources, allowing it to understand context, semantics, and syntax. As a result, GPT-3/4 can perform various natural language processing tasks, such as text generation, translation, summarization, and question-answering, with remarkable accuracy and fluency. There are several community-driven LLM models based on Meta’s LLaMA, e.g., Alpaca, Vicuna, Koala, and GPT4All, which can be run locally on a laptop (read ). my tutorial here Step 1: A Proof of Concept Before you spend more effort on your custom solution, let’s first get familiar with the key framework LangChain and the components in a playground. Thanks to GUI-based , you can now play with this entire solution without any code. flowise.ai This step is optional, and you can skip to step 2 if you want to go straight into coding locally or if you don’t have API keys for OpenAI or Pinecone. Prerequisites: You obtain the and in advance. Both are useful not only for this exercise but also for other AI-related experiments. OpenAI API key Pinecone API key In Pinecone, you need to create a new index called “ ” with dimension . Note there is an environment name auto-generated by Pinecone to designate where this index instance is run: “ ” You can use the default metrics of “ ” test 1536 us-west1-gcp-free. Cosine. A small dataset in text file format (let’s say ): it could be as simple as copy-paste a few paragraphs from a Word or PDF document. In addition, for privacy reasons, you want to use a very small data set and preferably publicly available information. Since both APIs are cloud-based. test.txt Now. head to , download the code from their , and install and run locally (you need to have Node Javascript installed). You can access it from a browser: . Flowise AI Github http://localhost:3000/marketplaces And from Marketplace, you can choose a template called “ ”, and use this template. The UI is self-explanatory: you fill in the OpenAI API key twice (one for embedding and one for LLM), the Pinecone API key, index name, environment, and metrics. Conversational QA Retrieval Chain And upload the data set text file. It might take a couple of minutes depending on the size of your text film. Then you click the little chat bubble on the top right corner, and you can start to ask any questions! So this will give you a visual understanding of the data processing pipeline of our forum use case, and help you implement your own solution by replacing all the cloud API with the local service using open-source software. LangChain will stay put since it is an open-source software and the master glue we need. Large language model: -> OpenAI LLaMA/Alpaca Vector database: -> Pinecone FAISS But before we do that, we first have to clean up and prepare your enterprise data for ingestion into the vector database. Step 2: Clean and Prep Data The NKN forum uses Discourse software, a popular open-source discussion and community forum solution. It uses the Postgres SQL database as its core data store, which has two tables that are especially relevant for our Q&A retrieval use case topics posts. For example, the database schema of “ ” is listed partially below. The most relevant columns for us are: “ ” (matching to the topics table), “ ” (original post content in the text without formatting), and “ ” (how popular or well-read is this post). posts topic_id raw score It is quite natural to map “ ” to questions (or “ ”), and “ ” to answers (“ ”). The terms in parentheses are for GPT-like LLMs. There are many implementation considerations and optimizations here, which would suffice for another article. topics prompts posts::raw completion But I would like to keep it simple and choose only the best post out of the entire thread to match the original topic. So first of all, we need to get both tables out of the database and into JSON files. To do that, I did a DB dump in Discourse, and then import it into a standalone Postgres SQL database running on my Macbook Pro M1. From the standalone Postgres SQL, I can export JSON files for the two tables: and . posts topics Then I wrote some simple Python script to do the following: For each topic, use SQL to select the posts with the highest “score.” The resulting “topic” and “post::raw” become one “prompt” and “completion” pair. Save the above into a JSON file called training.json (you can use it for many other training purposes, e.g., finetuning an LLM model, using GPT-3’s finetuning API, etc.) Write into a text file called training.txt: one line for prompt, and one line for completion, and separate each pair with two new lines. Now your file should look a bit like this: Step 3: Convert My Entire Data Set Into a Vector Database I pasted the Python script below, which is mostly self-explanatory. A few notes: You need to download or find the weights for your local LLM model. In my case, I used Alpaca 7B parameter 4-bit quantized version. You can find them on huggingface. There are many similar ones that are fine-tuned based on Meta’s LLaMA 7B/13B models, e.g., Alpaca, Vicuna, Koala, GPT4All. You can use any of them. Optionally, you have followed and installed Llama.cpp locally and successfully ran Llama.cpp with the weights from above. So you know the weights are good. my instruction The text splitter will split your long text file into chunks, each having a max length of . 1,000 Then, this chunk of text is fed into the LLM for embedding, which basically tokenizes the words, and converts them into vectors based on their meaning. Each chunk, after tokenization, might become up to 1,400 tokens. Since on average, 3 words create about 4 tokens. This is also the reason I have (default is 512) in order to have a large enough context window for embedding. n_ctx = 2048 Finally, the vectors are inserted into the FAISS vector database and indexed. The database is saved as a local file in a directory named “faiss_index”. from langchain.embeddings import LlamaCppEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.document_loaders import TextLoader

embeddings = LlamaCppEmbeddings(model_path="./ggml-alpaca-7b-q4.bin", n_ctx= 2048)

from langchain.document_loaders import TextLoader
loader = TextLoader('./training_full_clean.txt')
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(
    chunk_size = 1000, 
    chunk_overlap = 50,
    length_function = len)
docs = text_splitter.split_documents(documents)

db = FAISS.from_documents(docs, embeddings)
db.save_local("faiss_index")

# Now let's test it out
query = "Who is Whitfield Diffie?"
docs = db.similarity_search(query)
for doc in docs:
    print(doc.page_content) I highly recommend you start with a very small dataset text file to test first. If your dataset is large, this will take a long time. My locally run Llama.cpp can embed about 20 tokens per second, so each chunk of 1,000 text words could take about 1 minute to do embedding. My vector DB has about 7,500 entries, and it will take me about 100 hours on a Macbook Pro M1. The good news is that you only need to do this once. [Author note: please see comments to use different embedding module such as huggingface for much faster performance on large datasets] Step 4: Use a Vector Database to Find the Best Matches to My Query Again, I provide some simple Python code to do the query and summarization. Since we use a template chain called “ ”, some details might not be obvious. But under the hood, it involved the following steps: RetrievalQA When a user types in a query, the large language model converts the text into embedding and a vector (basically an array of floating point numbers) representing the query. The vector database then does a similarity search to find the candidate result vectors that are closest related to the query vector. Now, we return those candidate result vectors, which are already encoded in the way that LLM understands, to LLM for final processing. from langchain.vectorstores import FAISS
from langchain.llms import LlamaCpp
from langchain.embeddings import LlamaCppEmbeddings
from langchain.callbacks.base import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.chains import RetrievalQA

# Load DB from local file
embeddings = LlamaCppEmbeddings(model_path="./ggml-alpaca-7b-q4.bin")
new_db = FAISS.load_local("faiss_index", embeddings)

# Use llama-cpp as the LLM for langchain
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
llm = LlamaCpp(
    model_path="./ggml-alpaca-7b-q4.bin",
    n_ctx= 2048,
    callback_manager=callback_manager, 
    verbose=True,
    use_mlock=True
)

retriever = new_db.as_retriever()

# Conversational QA retrieval chain
qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever)

while True:
    user_input = input("You: ")
    if user_input.lower() == "exit":
        break

    response = qa.run(user_input)
    print(f"AI: {response}") Step 5: Use LLM to Distill the Best Answer to the Original Question In the final step and again under the hood, the “ ” template automatically constructs a special , which includes the following: RetrieverQA prompt : Instruct the LLM what kind of task it needs to do, and in this case, “ ” System message Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer. : Add the best candidate result vectors as context, as well as the query vector at the end. User message Once LLM receives such a prompt, it will try its best to sift through the candidate results and find the best answer to the original question. It summarizes, rephrases, and even finds separate responses to two separate questions in the original query (e.g., “Who is Stephen Wolfram, and how is he related to NKN?”). Sometimes, we can be quite amazed at how capable LLMs have become to handle this type of Q&A task. What Next? Now, you are well on your way to your office conference room to show off your new demo! And what can you do next? Congratulations, you have just successfully built your first semantic search tool on your private data using an entirely open-source toolkit! Try alternative components for vector databases or large language models that fit your data’s needs best. Optimize, scale, and productize your semantic search solution for your entire company. Dig deeper into LangChain and its infinite capabilities to solve other pressing work or life-related problems. Maybe you can even contribute your own chain as a template for other people to use. Happy hacking! 🙂

A Practical 5-Step Guide to Do Semantic Search on Your Private Data With the Help of LLMs

Historically, management consulting companies have relied primarily on human capital to solve complex business challenges of their clients. The traditional consulting approach entailed building a team of a handful consultants to analyze client data and develop recommendations for C-Suite over a 2-3 month engagement. However, over the last decade, we've seen a distinct trend towards asset-based consulting in which tools and products represent the main asset of the offer. There are several factors contributing to this trend: First, consulting firms have realized that recognizing patterns in their clients’ problems and productizing their intellectual property (IP) allows them to deliver their services in a much more robust and scalable fashion. Second, as datasets have become larger and more complex, consultants now need more modern data analytics technologies. Traditional tools, such as Excel and Powerpoint, are no longer adequate for deriving quality data insights. Third, even with the most sophisticated and seasoned experts, consulting firms see competitive threats from startups leveraging data technologies to deliver insights. For that reason, AI and Big Data have further accelerated the pace of productization in the management consulting space. The leading consulting companies, such as McKinsey, BCG, and Bain & Company, recognized the need to evolve their operating model in the early 2000s. They started building reusable assets in the form of tools, datasets, and even stand-alone software applications. However, most of them are still experimenting with various operating models for incubating ideas and driving innovations for maximizing client impact. Where asset-based approach is used The approach is primarily relevant for the areas 1) with a high likelihood of patterns and reusability for structurally similar client problems and 2) data-intensive approaches to solving those problems. From my own experience in McKinsey and Accenture, I can say that the most common areas where consulting companies launch their own products are advanced analytics in healthcare, consumer data analytics and predictive modeling for service industries, and benchmarking tools in financial services (corporate finance, asset management, banking, and others). One example is the Healthcare Market Intelligence offering by McKinsey. The company leverages its advanced analytics tooling to aggregate proprietary and third-party healthcare datasets and deliver high-quality insights on provider landscapes or market demographics. Another example is Finalta by McKinsey, a suite of data analytics and diagnostics solutions for banking and insurance. Finalta offers performance-benchmark data from 200+ financial institutions and helps its clients generate actionable insights by understanding their performance against that of peers. Organizational models for tech enablement There are three main organizational models for technology enablement inside consulting companies. Centralized model — various consulting practices partner with the central technology team to build a reusable solution to solve a client’s problem. Practices act as internal customers, whereas the central team is essentially their technology vendor. Technology enablement initiatives are treated as projects. Decentralized model — consulting practices build their own internal technology teams or contract external development partners. Practices aim to remain fully independent in operating solutions. Generally, this approach can work for one-off initiatives or single-user applications with low levels of architectural complexity. However, for applications with more complex requirements around data infrastructure (e.g., large datasets, multi-tenancy) or information security (e.g., client data segregation), building software independently outside of the consulting company's existing digital ecosystem may present a number of risks or feasibility concerns. Hybrid model — the central technology team can own infrastructure and maintenance functions, while product development happens in a more decentralized fashion. 3 most common challenges in digitizing assets Talent Consulting companies have to figure out how to attract the brightest technologists. Merely generating ideas is not enough; they need to create teams with strong product culture who can successfully execute those ideas — and eventually, build enterprise-grade software products. How to find these talents depends on the: Function (engineer, product, etc.); Level of technological maturity; Scale of technology enablement efforts. Internal recruitment can often be used as a solution when a consulting company has already created several technology teams and is looking to centralize the efforts. In this case, technologists can be motivated to join the central unit with the increased scope and visibility. This may not be a viable option if the consulting company is early in its technology enablement journey. External recruitment can present some challenges. Highly qualified software engineering and product development specialists may not see a consulting company as the best environment for building their careers. Therefore, such companies may need to target those who are interested in building advanced analytics solutions in specific domains (such as healthcare). Another win-win opportunity is attracting talent with data analytics experience in small/mid-size consulting and data companies. Such professionals benefit from getting a recognized brand on their resume and expanding their scope, while the company brings in experienced specialists with highly relevant skills. Operating model Consulting companies must also understand how these new capabilities will fit their existing organizational structures. For example, they must choose between a centralized innovation hub model and a decentralized domain-specific model. We see several criteria that can inform this choice: If a consulting practice is looking to productize some of its IP, one of the first things to do is to understand product plans on short-, medium-, and long-term horizons. Indeed, if there's a single, one-off initiative, which will not require ongoing development, leveraging the central development team can be good enough. However, often a practice wants to be strategic about its technology enablement efforts and has a roadmap spanning many product initiatives. In that case, it might be worth investing in growing an internal technology team within the organizational unit. Product strategy. For software products incorporating highly complex algorithms or large amounts of domain-specific knowledge, it is highly recommended that the product development team works closely with the subject-matter experts from the consulting teams. This way, all can understand the intricacies of the product workflow and business logic. However, when this logic can be easily formalized, or development effort doesn't require in-depth discussions between experts and developers, the centralized technology enablement model can be the right answer. Domain-specific scope. In most cases, it's not sufficient to build tooling and deliver it to the consulting team — instead, there needs to be a comprehensive change management effort. Who is going to operate the tools? What are the required skills for business users? Who is going to own the onboarding process and user education? For solutions with high levels of complexity, we see an argument for the decentralized approach, or at least a hybrid model when the product development team and business team collaborate closely throughout the cycle. Change-management and delivery. Client service Typically, clients of consulting companies expect to pay a fee driven mainly by the total number of billable hours. However, increasing digital product integration requires companies to revisit all aspects of their client-service model. One of the immediate implications is the evolution of the pricing models. In some cases, the asset team operates independently from other practices, almost as a stand-alone business. Such units have their own clients (who are not necessarily clients for the traditional consulting services!) and a dedicated pricing model for direct billing. It may be a monthly or annual fee for subscription-based services or a fixed price for bespoke  projects. More common, however, are the delivery models with assets embedded into the existing consulting practice. Consulting teams work closely with the asset team as part of the client engagement. In that case, measuring the contribution or impact of a solution becomes more complicated. There are two possible configurations: Consulting teams cover the marginal costs of running or deploying assets but don't bill clients directly. The cost of a solution goes against the project margin but is recognized as internal revenue for the asset team. The underlying belief is that offering an asset at no additional price will lead to more client conversations, eventually translating into engagement fees. This model is beneficial because of its low complexity and transparent pricing for clients, but the internal chargeback mechanism increases the operating overheads for consulting teams. Consulting teams bill clients explicitly for running or deploying solutions and pass through the fees to the teams who own them. This model can work well for mature assets or assets with clear value for clients, which typically implies that the teams have invested heavily in marketing the respective offering. Another condition is that asset delivery must be separated from the rest of the consulting scope, which may not be ideal from the overall client service standpoint. However, maintaining their own P&L allows asset teams to demonstrate value quickly and hence, secure investment for further growth and development. Best practices in building technology enablement functions Consulting companies that lean towards a more centralized approach must be wary of the internal "development shop" mentality. The biggest problem with this model is that the development team is seen as “mercenaries” who are under contract to build what they are told to. They have a limited understanding of the business context and don't have much sense of empowerment to participate in discussions around product choices, let alone question them. Technologists need to be closely related to their business partners because otherwise, they will not be able to develop sufficient domain expertise to create effective solutions. Furthermore, the "client-vendor" relationship between business and development functions has become a common anti-pattern in the software development world. However, we do see an opportunity for a centralized technology-enablement team to support various platform capabilities. For example, user authentication is a standard use case across a wide range of applications. For that reason, it should be centrally owned and maintained, minimizing instances of "reinventing the wheel". Finally, it is essential to recognize when a product offering is mature enough to be spun off into a dedicated organizational unit or practice. At its early stage, consulting leadership should understand the revenue-generating potential and support the growth and accountability using a dedicated P&L. It is common for partners in consulting firms to view  such offerings as a free add-on to consulting engagements, which can have limiting  effects on growth. Suppose the consulting company has recognized the product-based offering based on its potential impact for clients and has successfully marketed it as a stand-alone product. On the stage, it is fairly reasonable to create a dedicated organizational unit to support its client delivery. However, when a product offering has yet to be launched, and there is uncertainty around its potential, it may not be a clear-cut decision. In this case, consulting practice may use an MVP-first approach — build a small team of product development specialists and subject-matter experts who can create a small prototype, deploy it to a few clients and measure the results.

Tech Enablement in Management Consulting: Best Practices and Common Mistakes

Has the web become more productized? On the weekend of July 4, 2014, Brian Mayer launched a website that listed reservations for sale to popular restaurants in San Francisco. The price of each reservation was a sliding scale, based on how in-demand the restaurant was. But Brian Mayer didn’t own or run any of these restaurants. He didn’t work for or with them. He had simply called the restaurants up weeks in advance, booked a whole lot of reservations under assumed names, and then put them up for sale without the restaurant’s permission. He called it ReservationHop. ReservationHop was instantly popular, and almost equally vilified. It was criticized as unethical and aggressively opportunistic. Though the site framed it differently, Mayer was essentially holding reservations for ransom and putting them in the hands of people willing and able to pay. By circumventing restaurants entirely (he used fake names for the reservations and then passed them on to people that paid for them), he was putting restaurants in a position to lose money from last-minute cancellations. ReservationHop wasn’t strictly illegal, but it certainly walked the line of fraud. where he explained that the site was a bit of fun, a clever solution to a real need. His site was, after all, very popular. There was a market there that didn’t previously exist, and Mayer was there to fill it. As for the ethical concerns, he wrote most of them off. “To be honest, I haven’t spent a lot of time thinking through these questions. I built this site as an experiment in consumer demand for a particular product, and the jury’s still out on whether it will work.” ReservationHop eventually tried to include restaurants in its process before shutting down entirely. Mayer took to his own blog to defend himself Reflecting on the site this past year, as just one example of a much longer tradition of websites attempting to squeeze money out of everyday interactions or instances of bureaucracy. He cited other examples of websites that circumvent or otherwise ignore the norms, ethics, and laws tied to those interactions, sites like MoneyParking, which let users auction off parking spots they had no legal right to, and Enq, which ties up IRS phone lines so they can sell that spot to the highest bigger, Doctrow claims that the end goal is “Markets in everything!” writer Cory Doctrow positioned ReservationHop Despite making the headlines, “markets in everything” extends well beyond the ill-intentioned. The web is highly distributed and widely accessible, and since almost its early days, largely commercial. Beginning with the rise of the dot-com startups in the 1990’s, and the subsequent collapse of the dot-com bubble, the commodification of the mundane has spread further and wider into different subcultures and ideological manifestations—the gig economy, drop shipping, hustle culture, the rise of SaaS, startup evangelism. After the dot-com bubble, the web of the 2000’s, often referred to as Web 2.0, saw more dynamic applications like Flickr, Google Maps, and Delicious. Technologies like AJAX and Flash and philosophies about the web’s interactivity introduced refinements and polish to web design. Surfers of the web after the turn of the century ditched their hang-ups about entering their credit card online, or making large purchases over a computer. The web became something relatively familiar to large parts of the world. The web proved itself as a platform for experimentation in niche services and products. There are those, like ReservationHop, that use that lack of friction to attempt to circumvent norms or laws in search of profit, And there are many more who provide perfectly legitimate one-off services at fair prices to groups large and small. By the 2010’s, the web was awash with such products. So much so that in 2013, Ryan Hoover, fresh from working at a web startup and co-authoring a book about internet products, . He started with a list, which he called Product Hunt. Each day, Hoover would add a list of new products he found to a list he published on the web (he even used one of those products, Linkydink, to collect and share those links). There were more than enough new things to populate the list—usually small startups built by one or two people introducing some quick and easy service via the web, a bookmarking tool or drawing app or list of curated content—and within a few weeks hundreds of people had subscribed. Hoover recruited a small network of contributors to help him fill out the ongoing list, and updates continued to stream out every day. began to look for better ways to catalog them all , Hoover took his list and created a website of his own, launched in November of 2013. They added new features to the site, discussion forums, job boards, and more community features. But fundamentally the concept never changed radically. A daily updated list of new websites or apps which visitors to the site could browse and upvote. In July of 2014, less than year after its official launch, Product Hunt received the backing of Y Combinator. With the help of Nathan Baschez Product Hunt rode the wave of the productized web built on services and applications. Some were dispassionate ideas barely thought through , which hired people to take trash from inside your house and move it to the outside. But most services sought to connect people with real-life needs, either by making experience in the world easier or by amplifying digital life with an ongoing service. These products often adopted the Software as a Service (SaaS) model. SaaS was made largely popularized by Salesforce, but it had a growing presence throughout Web 2.0. The goal was to provide an ongoing service that continuously improves billed month to month, rather than a single package of software purchased once. like the infamous Trash Day Over time, Hoover—and others like him— . Find a niche. Optimize for engagement. Capture a market. Incentivize influencers. These rules spread through a network of social media accounts and blog posts from proprietors of online products, repackaged and reframed across different types of subcultures and businesses. adopted a set of rules that define the web’s product culture The product web has come to encompass a large part of what it means to exist on the web. It’s shifted the way we think about our day to day experiences. One website at at time. Sources Alexis C. Madrigal. "Silicon Valley Abandons the Culture That Made It the Envy of the World." The Atlantic. January 1, 2020. https://www.theatlantic.com/technology/archive/2020/01/why-silicon-valley-and-big-tech-dont-innovate-anymore/604969/ Amber Leigh Turner. "The Product Hunt Story: How it all began according to its founder Ryan Hoover." The Next Web. May 5, 2017. https://thenextweb.com/news/product-hunt-story-began-according-founder-ryan-hoover Brian Mayer. "How I Became the Most Hated Person in San Francisco, for a Day." BrianMayer.com. July 7, 2014. https://brianmayer.com/2014/07/how-i-became-the-most-hated-person-in-san-francisco-for-a-day/ Nathan Baschez. "The Origin of Product Hunt." Medium. June 6, 2014. https://medium.com/lets-make-things/the-origin-of-product-hunt-7acb09e2593a First Published . Here Written by Jay Hoffmann on November 22, 2022. It takes place somewhere between 2011 and 2016.

There Are Web Products Everywhere You Turn 

With global markets plummeting, NFT trading volumes and crypto wallet activity are down across the board. Despite this, . Reddit just onboarded more people into web3 over the past 3 months than OpenSea has in the past five years They subtly handed their users custodial wallets and airdropped them free NFTs. Since launch, their NFT collections are also up an average of 300%. What’s their secret? Read on to find out. What Is Reddit? In case you haven’t heard, Reddit is “social news aggregation, content rating, and discussion website,” and it’s huge. the Every fifth person you meet has a Reddit account. The platform has: 430 million monthly active users. Over 1.5 billion registered users. Around 50 million daily users. NFTs have certainly been a topic of discussion on Reddit for a while now, and Reddit users’ disdain for digital assets is no secret. The apathy towards NFTs follows the well-known path of a social media hate storm echoed in particular subreddits. However, despite this, the platform has managed to influence three million of its users, and the majority of new sign-ups, to open a Polygon wallet and hold an NFT. Reddit’s Genius NFT Strategy To be fair, Reddit’s not entirely new to crypto. The platform has accepted cryptocurrency payments since 2013. They tokenized points on Layer2 blockchain, Arbitrum, for select subreddits in 2020. They even launched a four-piece 1/1 NFT collection in June 2021. The collection was named “CryptoSnoos”, based on their iconic ‘Snoo’ logo. The NFTs sold for 70–175 ETH. However, Reddit’s most recent crypto endeavor is what has caught everyone’s attention. They solved the million-dollar question of how to onboard the masses to crypto. The simplicity is genius. Here’s what they did. They ditched the “wallet” terminology, instead calling this wallet a “Vault.” First, Reddit created a custodial Polygon-based wallet. Cleverly, they didn’t call them NFTs. They referred to them as “collectible avatars.” The avatars could be used to customize Redditors’ profile pictures (PFPs). Second, Reddit started to offer their top users free NFTs. This was an enticing status symbol for engaged Reddit users who innately understand the value of community points, karma, and reputation. Except Redditors weren’t aware they were opening a Polygon wallet. They opened a “Vault” account. They weren’t taken to a third-party website to do this. Third, to collect their avatar, Reddit required users to set up a Polygon-based wallet account. They didn’t have to write down a seed phrase or go through onerous KYC procedures. They didn’t need to hold MATIC for gas fees (Reddit covered gas fees). Ultimately, there were no web3 hurdles. The wallet setup took two clicks and the seed phrase was simply embedded into their Reddit accounts. At this stage, very few Redditors were actually interacting with their NFTs or using them to customize their PFPs. The blockchain protocols, wallets, and infrastructure were disguised through user-friendly language and web2 UX. Reddit released Reddit Collectible Avatars Season One in July. Finally, Reddit had already been partnering with artists to release NFT PFP collections at a low cost, purchasable in USD. The NFTs, disguised as collectible avatars, were sold for between $10-$100, and the proceeds automatically went to artists. Owners of multiple avatars could pull traits from the different avatars to customize their Reddit profile pictures. The NFTs were semi-fungible, with each of the 29 PFPs containing 87 of the same NFTs. The updated profiles would display a hexagon profile picture with their relevant avatar. The process was seamless and akin to an all-too-familiar e-commerce transaction. The Results? A Slow Burn, Then an Explosion Reddit Collectible Avatars Season One launched in July and was very slow to sell out. However, airdropping free avatars in August lit the match for the collectible avatars’ explosion. The vast majority of the 3 million existing Reddit Vault wallets were created to collect avatars. From August to October, the number of collectible avatar holders rose from 12,940 to 2.87M. In fact, more people have opened Reddit Vaults to collect avatars than have ever traded on OpenSea, the most popular NFT platform. Reddit Collectible Avatars Season Two launched in October and sold out within 24 hours. Sales volume skyrocketed, increasing from less than $10/day at the beginning of August to a peak of $209,000 in October. This represents a 2,089,900% increase and a cumulative sales volume of over $10m. The Reddit Collections Have Admirable Traits That All NFT Collections Strive For: Of the 2.869M wallets that hold avatars, just 25,009 hold more than one. That statistic represents idle wallet holders. That means that there are 2.84M wallet holders that have NFTs but haven’t been fully engaged in trading. Maybe they are just holding, or maybe they mindlessly accepted a free avatar. However, the potential to engage these users is endless, and they have the capabilities to interact in the web3 ecosystem. It’s important to note that a continued and sustained engagement with those 2.84M wallets is necessary to claim an overwhelming web3 success. The foundations have been set. How Did They Do It? Web3 has a notoriously bad user experience, and it’s no secret that focusing relentlessly on user experience is a priority. However, Reddit took it a step further. : PFPs (profile pictures). Rightfully so. To onboard people to web3, blockchain choices, cryptographic wallets, and token standards like NFTs’ ERC721 complicate the use case. First, Reddit removed crypto jargon to put emphasis on the use case In web2, no one cares whether you use .pdf, HEIC, .jpeg, or .png. They simply care that they can digitally view, share, and store their photo. NFTs, cryptographic wallets, and the blockchain are all means to different, but related, ends. However, Reddit users often only care about their stored ownership of their avatars. To that end, it is important to be solution-oriented. What do NFTs, wallets, and the Polygon blockchain enable? Unique digital assets (collectible avatars), A way to access these unique digital assets (Vaults), and A place to store, trade, and verify the authenticity of these assets. If Reddit publicly referred to avatars as NFTs, the results would have likely been less impressive. The Trojan horse style approach and dressing up NFTs in a disguise, simply using the technology as a means to an end, is where the space should go. This acts as good validation for the use case of NFTs. Second, Reddit made the onboarding process hassle-free. To get Redditors to open crypto wallet accounts, they offered them Snoo-themed avatars which could be used to customize their PFPs. free Reddit dangled an NFT in front of active users to onboard them into their web3 ecosystem, however, they ensured that users themselves didn’t have to jump through any web3 hoops. The process to set up a Vault and accept the avatar took a couple of seconds and the same number of clicks. The process was seamless. Reddit covered the gas fees which meant that no one needed to figure out how to purchase MATIC with fiat and then how to send that MATIC to their Vault wallet. Reddit did all the legwork for Redditors. Compare this with Twitter’s attempt to get people to use NFTs for PFPs. Twitter users needed to go to OpenSea, have a MetaMask or other wallet integrated with the platform, own crypto, own NFTs, have ETH for gas, and finally return to Twitter. The comparison easily shows how Reddit’s relentless focus on user experience is a recipe for success. If Twitter took a similar approach, how might it have fared? . In Reddit’s case, product and community are largely the same thing. Community is the product. Third, Reddit already has a productized community that aligns itself well with Web3 principles. Brands with the same should take note Status symbols are present in most communities. Within Reddit, status is measured by your interactions within the platform and endorsements from other users in the form of karma points. NFTs introduce another way to represent status within Reddit. Avatars are scarce, which is a common feature of NFT collections. Airdropping avatars to top users initially anointed them as a symbol of a user’s engagement as they were able to flex them on their profile. Then, as collections grew, keen users wanted to also own this status symbol. Heightened demand for scarce resources only increased the collectibles’ monetary and status value. The community structure of Reddit aligns well with the NFT use case. Avatars enable Redditors to interact with Reddit in a non-detrimental way while supporting artists. The NFTs enhance Reddit users’ experience on the platform but are not integral to Reddit’s product. In this way, Reddit’s move has been low-risk. Using Reddit’s Snoo logo, tying utility to the avatars, periodically dropping new collections, and enabling a way to easily show them off within the platform should create a recurring loop of interactivity which will increase the adoption of NFTs within Reddit’s 430-million strong community. If it doesn’t, that’s fine. Reddit will not lose any users. The Avatar NFT strategy was well positioned to not deter from existing community features, but simply amplify them. Avatars are not essential for Reddit’s community to enjoy Reddit’s product. What Can You Learn From Reddit? Reddit’s move was brilliant because of its simplicity. It represents the first proven web3 experiment that worked without technical confusion and learning. Onboarding the masses to web3 is the golden chalice. Reddit seems to have found a way to prove that it’s possible at scale. The nature of the product at Reddit makes this an innate use case, however, easy-to-use purchasing and wallet integration remove any friction points. Reddit laid the foundations to provide additional utility to avatar holders. We’re already seeing this with holders of certain collections receiving airdrops. Using Polygon has enabled Reddit’s avatars to be used within the wider Ethereum ecosystem. The journey from here is an opportunity to shine the light on the brilliance of blockchain composability and asset liquidity. While only 25,009 individuals hold more than one avatar, which could be taken as a measure of interaction with the collections, by getting 2.8 million people to open Polygon wallets, Reddit has opened the Trojan gates. Avatar holders can more easily explore the wider web3 ecosystem. Thousands have done this already, millions more can. We look forward to welcoming them when they arrive. References: ; ; ; ; ; https://en.wikipedia.org/wiki/Reddit https://thrivemyway.com/reddit-statistics/#:\~:text=Subreddit Statistics,124%2C000 new subscribers each month https://page1.substack.com/p/the-reddit-nfts-thesis https://nft.reddit.com/ https://cointelegraph.com/news/reddit-nft-trading-volume-hits-all-time-high-as-wallet-holders-near-3-million https://www.redditfloor.com/

How to Get 3M People to Own NFTs Without Them Knowing

Cómo hacer que las personas de 3M sean dueñas de NFT sin que lo sepan

Comment amener 3 millions de personnes à posséder des NFT sans qu'elles le sachent ?

कैसे 3M लोगों को उनके बारे में जाने बिना NFT के मालिक बनने के लिए प्राप्त करें

知らないうちに 300 万人に NFT を所有してもらう方法

Como fazer com que as pessoas da 3M adquiram NFTs sem que elas saibam

Cách để 3M người sở hữu NFT mà họ không biết

如何让 300 万人在不知情的情况下拥有 NFT

Building in public is challenging, but it's an to get ANY startup off the ground. extremely powerful marketing strategy Here's what I've learned from 👇 a year of building in public But first, who am I? Here's me, I'm 24yo from Portugal 🇵🇹 I'm the founder of Vettted.com, which it's not just another marketplace 😎 The quick rundown: Started doing SEO in 2015 (still do it) Created a multi-6 figure SEO brand Launched a SMMA agency Started offering productized services on freelance marketplaces Realised there was a need in the market for something different....spent the last year building my own 👨‍💻 And that brings us to today! So, from all of this time #buildginginpublic? what did I learn Why you NEED to "build in public" Things get easier when you show the world what you're creating. You get to that an audience can follow & get behind. create your OWN narrative Putting yourself out there builds trust, transparency, and authenticity from people following YOUR journey Immediate feedback Attracts talent & customers to your business Putting yourself out there and showing what you're building, will make people connect with you. Those connections will turn into supporters, which later on can turn into customers. Where to build in public? Literally anywhere. Here are a couple sites: IndieHackers (+1 for supportive community) Twitter (+1 for a lot of makers) TikTok (+1 for the crazy organic reach) Reddit (+1 for a super loyal community) Don't focus on more than 2. You don't have time to learn the algorithm of more than 2 social media platforms. Just pick two and go for it. I chose Twitter and TikTok. Twitter because I could just snap a picture of whatever it was I was working on (i.e. my desktop setup, a Figma mockup, grabbing a coffee, a pic of my do) and at the same time write a funny or insightful caption. TikTok because of its orgnaic reach..which I later learned that for EU base founders looking to reach a worldwide audience, might not be the best idea. To get the most out of the content you're producing, make sure to syndicate it. I would syndicate my TikToks to Reels (Instagram's version of TikTok), IndieHackers, and YouTube. Since February 2021, I've been documenting my startup journey on a weekly basis 👈 on tiktok The issue with TikTok 🤨 Even though the organic reach can be insane, there's a downside to it. If you're in Portugal like me (or in any other European country), and want to reach the US, it's virtually impossible. TikTok's algorithm shows your videos to people that live in YOUR country. And it's impossible to change this. tried (and failed). This guy You can try using a VPN, changing the location on the Appstore, removing your SIM, and still, it won't work. So if you want to reach an audience other than the people in your country, please note that with TikTok, it'll be difficult. The reach, however, is crazy, but the of each view is much less than a view on YouTube. value On TikTok, people scroll, watch a 20-second clip, and then . forget you ever existed TikTok vs Instagram  🤜💥🤛 As a European founder trying to reach broader audiences while #buildinginpublic, TikTok was clearly . NOT the right choice The exact same content on Instagram had 20x the views and was shown to a worldwide audience, not just to a Portuguese one, like on TikTok. TikTok shows your videos to the country you're based in. Here's the difference between TikTok and Instagram: After a full year of posting, this happens over and over again. Instagram consistently gets more views and reaches a broader audience (not just PT). Twitter 🐦 Twitter is a great place to #buildinpublic People are super supportive on there. Just look at the organic reach I was able to get in such a short amount of time: And that's 🥬🥕🍅 ALL ORGANIC Twitter was/is definitely a great source for organic traffic. Launch, then think about it I regret not doing this...I delayed the launch by wanting to add more and more features. This was me...in hindsight, this hurt us a lot. Eventually, we just decided to launch with an unfinished product, which was the right thing to do. Tips for building in public I'm not by any way the most successful person building in public, but I do have some tips, as a European founder 🇪🇺 Assuming you want to reach a global audience with your startup, you'll be speaking in English. Tip #1: Embrace it Embrace your accent. The first thing you need to know is that (most times) English won't be your main language, and people will judge you for that. But you just need to keep pushing through. Tip #2: Face, face, face 🌝 Make sure to . show your face People connect with people. And while building in public . YOU are the face of the business Don't be afraid to be on camera. If you want to be in the 1%, you need to be willing to do what the other 99% aren't doing 👇 Tip #3: Consistency You absolutely . NEED to be consistent 🔑 I know it sounds clichê, but consistency truly is the key to success. That's it. You can spin it however you like it, but at the end of the day, if you're consistent at anything you do, eventually you'll get it. This isn't just for European founders of course. Here's my "consistently schedule": 1 video per week 1 tweet per day Simple as that. Tip #4: Knowing WHAT to post Your goal is to . document the process Share WINS, LOSSES & all the boring stuff. Every little small thing, from registering your company, buying your domain to setting up an SSL - All that matters when you're building in public. You might think that posting about installing an SSL or about buying the domain is boring...and , but that doesn't matter. it is boring You need to look at the content you post as a whole, not just at the single pieces of content. Because all those loose pieces will create a narrative and build a story around you (as founder) and your business. If you step back, you'll see a clear trend. And this is what people see as well. Like this...here's the narrative these "boring" posts create: As you can see, none of these are that interesting on their own...but . together, they tell a story And THAT is what you're looking for. And that's an example for Twitter, here's one for TikTok: So again, just documenting the process doesn't matter how boring it seems, when you put it all together, that's when ✨the magic happens✨ Tip #5: The basics Reply to every comment. Like every comment. Here's how: Someone: "Great work, can't wait for the launch! Good luck." ❌ Thanks!
✅ Thank you [name], I appreciate the support. We can't wait to get this going too! I'll let you know as soon as we're live :) Try not to use low effort replies. If people take time out of their day to leave a comment, make sure you also take time off yours to properly address them. The people who interact are your most valuable followers. Make sure not to let that go to waste. Tip #6: Tie it all up Leverage everything you've been posting and make a long-form piece of content. You can just compile all you've been posting and launch a mini-docu series while you narrate over it. I uploaded mine to YouTube, you can see it 👈 here Closing thoughts None. Just get started! If you're currently building a startup, go to your camera roll and post something on Twitter. Open Instagram and record a video update (do it every week, be consistent) Just get started <3 Also published . here

Tips for Building in Public as A Founder From Europe Eu 

Designers probably remember the headlines and edutech companies in the 2010s practically pleading us to . Fortunately that’s over, but it really does help us designers to at least get a feel for coding so we can work more smoothly and empathetically with our engineering teammates. As a designer, I still both at work and as a hobby. learn to code code a little myself The same goes with our product management (PM) teammates. We should “get” them and the work they do just like we “get” coding and the work engineers do, as the three of our domains together comprise the engine of product development. We wouldn’t be doing our due diligence as if we just wait for PMs to tell us what to do, do our work in a bubble, and then tell engineering what to do. If it’s important to be fairly educated about how our design may be engineered, the same should go for how it may be . good designers productized How do we define design and PM? First, what is product management, broadly speaking? At Truss, our product managers “lead teams to align around a North Star, embrace constraints, ship to learn, and deliver client and user value early and often.” They may seek balance in making final decisions between what the business needs, what the production team can build, and what customers and users expect. Here’s a list of core skill capacities that PMs at Truss have aligned on: Product strategy First principles thinking Systems thinking Communications & storytelling Consulting & managing change Data skills Agile methodologies Driving alignment towards value I especially like “driving alignment towards value” as a pithy way of differentiating the two “PMs”: Pro managers are more internally (on the product team) oriented: coordinating and unblocking complex parallelized work streams. ject Pro managers are more externally oriented: aligning the product team’s work to the client, the market, whatever is a driver of eventual value. duct But I digress… Our roles, including product management, have these skill capacities, and the people who fill those roles bring shape to those capacities. Design and PM share a fair amount of these capacities such that a designer and PM may actually have a very similar shape. At a contracting company like Truss, we all have to be generalists to help anywhere, and also have different specialties so Truss as a company can meet a contract’s unique needs, whether it be engineering, design, project management, client engagement, or specific industry expertise. So we use charts like these to share our shapes, not to fill them all up to the maximum, but to help cast people to the right contracts: Below I’ve listed those PM skills again along with those of our Design & Research practice. You might notice some similarities! Product Management Design & Research Product strategy Design Strategy Systems thinking Systems thinking Communications & storytelling Communications & storytelling Consulting & managing change Consulting & facilitation First principles thinking Research & Synthesis Driving alignment towards value Discovery & Framing Data skills Content Design & Information Architecture Agile methodologies Interaction Design User Interface design Most of the PM and design skills are nominally similar, if not the same! Granted, our experience using them is often different, so I’m not saying a designer can just swap in for a PM without building some experience first. But it goes to show we already have overlap, and designers can even practice these skills by framing their design work as product work, in collaboration with the team’s PM. Why might designers PM? As I said before, we should be educated about and appropriately concerned with how the work we do is made into a product or service and shipped. “Designers should code,” to some extent, because it’s important to know some basics of how things are engineered, or else we’d ask for the moon and then be frustrated when we don’t get it. Similarly, “Designers should PM”, to some extent, because if we simply wait for a product brief to be pitched over the fence, do our work completely in a vacuum, and then lob it back over the fence, the product will probably fall flat and seem out of touch. Junior designers are excused here as you’re just starting to learn the basics and build experience; this is more directed at mid/senior-level designers. As designers, we shape the product so it’s worth using and continuing to use. We’re the ones designated to focus on and empathize with the people in the PM’s addressable market, so that a business plan isn’t just reliant on technical capacity and business forecasting. There was nobody empathizing with how someone might feel about combining mayo and Cadbury Creme Eggs, they just realized they could do it and it might make some money. In the—forgive me, Heinz—more “serious” work of designing government services or potentially nefarious products, that means we need to know how our thing’s going to go from a file in Figma to a real product or service, i.e. “productized”. Socially good, responsible, sustainable design entails broader thinking about how we make decisions that affect people. That goes for our teammates too. Helping each other get work done, especially when we have overlapping skills, can build a mutual understanding of how and why we make decisions. If done well, that can propagate even more of those socially good, responsible, sustainable decisions throughout the product development team, prompting a major culture change from below. How might designers PM? All of this is my own goal for building my PM skill capacities, not to cast this as sage advice from someone who’s done it already. But, they are all based on work from the in the Design & PM practices at Truss. awesome people Lean more into our strategic side Collaborate with product management to determine the best strategies for the project overall and how . Educate the client/customer on how design might impact their business, market, and policy ecosystem. Create a persuasive vision that addresses the problem space, risks, and how to to those ends. Finally, be able to articulate all of this to diverse audiences, like the broader design practice. design fits that strategy measure the effect of design Read more theory While it’s easy enough to practice design and coding in personal projects, it’s not so easy to practice business without, well, a business. But there’s always theory and learning from examples on topics like business strategy, project management, stakeholder engagement. , , and all gave me some good fundamentals along with miscellaneous articles on more specific topics. I also like the app , as it chunks content in a way I can glance at on the train or couch, letting me learn in smaller spans of time rather than longer reads on a laptop. This theoretical underpinning was also a good way to figure out if PM was even theoretically interesting to me (it is, but not all of it, so again: nobody should expect to max out all the skill capacities). Pluralsight Stanford UC Berkeley FWD Shadow PM and sales discussions This can be an easy one if your PM (or any PM with some time) minds inviting you to meetings just to follow a discussion or watch how a task is done. I’ve done this on many occasions, and I’ve followed Slack threads to the same end. At Truss, as we expand our overall sales capacity, we ask some ICs (including designers) to for a 3 month term to help identify opportunities, ask questions of potential clients, and help write proposals with the technical expertise that only an IC can provide. switch to the sales team Use that trust over time to take on some tasks The easiest way I’ve done this is to help take notes in meetings, and I’ve made some journey maps with the sales team to figure out how things work. From there, it’s just a matter of using that experience to build confidence and take on more than just notes. One PM recently told me I’m already doing much of a PM’s job fairly well. I turned up the heat slowly, rather than saying “Hey can I be a PM on this project?”, as being a (well, usually ) PM on a project at Truss is asking for a ton of work and responsibility. That was only possible over multiple years of multiple projects, so it was an exercise in patience and knowing how to find and take opportunities. the only Free up some mental space and time As someone who came to design from engineering and research, I’ve freed up some mental space for the above by relying on design systems like and my fantastic fellow designers to cover visual and interaction design. As far as the design/PM overlap goes, visual and interaction design are pretty deep in the “just for designers” category. So offloading that workstream has afforded me more energy and attention to adjacent skills in product management. USWDS How might we influence PM but stay designers? Short answer: same as all the above, but less intensely. Longer answers… are swimming in context. Pair that context with principles of to contextualize your findings and suggest better strategic futures. I’m currently trying to integrate this with our design practice as part of our . Researchers design justice charter can work more closely with stakeholders (business and users alike) through inclusive methods. I recommend ’ workshops for both this and my previous point to learn more. UX designers co-design and participatory Creative Reaction Labs can invest in storytelling. Volunteer to overhaul a dry business preso if you can convince them to also let you do (some of) the presenting, bringing your voice to a room normally without designers. Visual designers In business, just being in the right place at the right time is a pretty big deal compared to our usual design work. Finding more ways to get into rooms with people in power means you are more able to push for something you want to change. When I was doing field research for my master’s degree, I was working with an organization seeking more equitable land rights and home ownership in a historically under-invested (and threatened) neighborhood. My purely being a designer who can make a deck of data visualizations and being affiliated with a major university was enough to help them get into a room with state policy makers. I was just there to support and amplify, and we both succeeded. That’s the kind of teamwork that designers and PMs can achieve by sharing our domains. We could even co-frame all design work as product work if we consider our design practice or as a product or service itself. If design work should be based on empathy and justice, on both a practice and IC level, so should product management. This work is my way of making that happen as much as it is to just find new ways of getting a job. organization Also Published Here

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