“Empowering Siksika Nation members through the Blackfoot Tech Council IT training program is not just a choice; it's a responsibility. Their success in this digital age is our collective achievement, and by supporting them, we pave the way for a brighter future for us all.” ~ Kendall Panther Bone, Siksika Nation Councillor

Siksika Nation’s Blackfoot Tech Council puts students in front for Calgary Wi-Fi enterprise system installation

Nov 1 (Reuters) - CDW (CDW.O) reported third-quarter revenue below estimates on Wednesday on lower demand for the company's information ... their technology spend," the company said. CDW, which has products ranging from notebooks to data center services ...

CDW misses third-quarter revenue estimates as customers lower tech spending

It’s a partnership that’s generated lots of excitement, both within Verizon Business and HCL Tech and in the broader world of industry analysts and advisors. I recently sat down with Kyle Malady, Executive VP and CEO of Verizon Business, and C Vijayakumar, who goes by “CVK,” CEO and Managing Director of HCL Tech, to talk about the new partnership.

Inside The Verizon-HCL Tech Deal With Kyle Malady And C. Vijayakumar

Chinese law requires companies located there provide security access to source code and other sensitive data if demanded.

Opinion: U.S. tech firms must rethink their relationships with China

Amazon.com Inc. (AMZN) and Tesla Inc. (TSLA) that aren't technically classified as tech companies by the Global Industry Classification Standard have businesses that revolve around tech, such as artificial intelligence and cloud services.

The 10 Biggest Tech Companies in the World

Zerodha faces a technical glitch, users' order positions are not displayed. The broking firm says the issue is fixed.

Zerodha's technical glitch: Users complain about problems in order positions

Welcome to the digital revolution in finance! Today, we dive into a transformative phenomenon that has taken the financial world by storm: financial technology startups. With their game-changing innovations and disruptive approaches,

Investing in Tomorrow’s Finance: Why Financial Technology Startups are a Lucrative Opportunity

The family office technology space has grown and evolved to offer many efficient solutions that embrace the latest emerging technology to solve the most common challenges

The 2023 Family Office Software Roundup

Custom software design and development solutions provider, Solwey Consulting, and data science and machine learning technology company, Devron.ai, teamed up to build and launch an application that improves the way decentralized data is processed and analyzed.

Solwey And Devron.ai Partner To Advance Decentralized Data Processing

I n response to customer and market demands, PTC Retail announced the availability of Flex Insights, which is a low-code platform "designed to help brands and retailers unlock the power of their data to drive faster and better decision-making during planning and product development," the company said in a statement.

Tech Tuesdays: PTC Debuts New App, Cisco Reveals Opinions on Data Privacy + More News

Tech Data

techdata

On AI-generated content, writing, new, old, and broken media, platforms, models, audiences, and body parts. Why do so many people want to be writers? That’s the kind of question SEO-optimized content is generated for. I for one did not, at least not in the romanticized way many people think about writing. But when i was , i took it. It was in 2016, and it changed things. given the opportunity to have my byline appear in the mainstream media I had no idea where that path would take me, but I have not regretted it for a second. I thought it would just mean adding one last part to an already long LinkedIn headline: Analyst, Consultant, Engineer, Founder, Host, Researcher, Writer. I was wrong. First, having a mainstream media byline is more than an opportunity: it also asks for lots of responsibility and work. It made me grow, develop and systematize new and existing skills. For example, . Developing , and how to best share it with the audience. Plus i got to know, and be known by, tons of interesting people. keeping track of endless streams of information a sense of what’s timely, important, and resonant And it wasn’t just the “Writer” part that was added. I got to be hosting my own and event as a byproduct of writing. Even though i never felt that being a writer defines me, apparently it has become what most people have come to know me as. Understandably so, as this work has a lot of exposure. podcast I have been covering topics related to Data, Analytics and Data Science, AI and Machine Learning, Innovation, Graphs, as well as a wide array of technologies such as Blockchain, Cloud, Observability, IoT, Open Data, Social Media and Software Engineering. The connection between data, analytics, data science, graphs, machine learning and AI and their importance soon became obvious to me. Over time, these technologies became a key part of my coverage. That resonated with people, including editors, which is why i got invited to work with more media outlets too. But in 2022, things changed again. Writing, mainstream media and AI In late 2022, a couple of things happened at the same time. First, . Second, the outlets i worked with notified me they would not be needing my contribution in the immediate future. Nothing to do with me, i was told: mergers, economic downturn, churn, restructuring etc – the usual. AI broke into the mainstream with ChatGPT Even though I’ve worked with some great people in media, I was never under the impression that it’s a thriving, smoothly operating industry. It’s part of the reason why i never stopped wearing my other hats besides “Writer”. While the timing is remarkable, and unfortunate, i don’t see an obvious connection between AI’s break into the mainstream and me losing my front-row media seat. But i do think that the media industry is getting harder and harder to navigate, and it doesn’t look like the rise of generative AI will help. You may have seen the , for example. I had seen signs of this before it broke in the mainstream. reports about AI-generated content on the media When someone asked me what i thought of a publication he was approached by, my immediate reaction was that a good part of their content looked like it was written by a bot. We’ll be seeing more of this, and it’s just one aspect of the disruption we’ll all be experiencing. The “ ” of content is real, and . enshittification it will get worse I maintain that “content” and “writing” are not the same thing. And i have other things keeping me busy too – writing a book, for example. But i hear the frustration of many writers and content creators. Not everyone out there is looking for authentic and thoughtful writing, plus the type of content that Generative AI can produce is not just text. New media, timeless ideas and POSSE I won’t lie: i missed having a platform. Mainstream media are not the end all obviously. I also use social media and content platforms, and they can work just as well or even better. But i never really had an effective strategy for building and managing my own platform and audience. Enter . POSSE POSSE is an abbreviation for . Even though the term may be new to many, the idea is not. It’s the practice of posting content on your own site first, then publishing copies or sharing links to third parties like social media or content publishing platforms. Publish (on your) Own Site, Syndicate Elsewhere Social media and content platforms are great for sharing, resharing and discovering new content. They can also help engage with people. But they are not so great for creating content, and they’re pretty bad in search and retention. They will try to monetize you, and can do as they please with your account, content and data: shady algorithms, arbitrary policies and decisions. The fact that the Web is being swallowed by social media and other platforms has made people neglect the importance of owning content. I think that’s wrong, and POSSE fixes this. POSSE lets creators retain control of their content while enabling discovery and interaction to happen where the audience is. POSSE is simple, but hard. You need to have a good site and . You need to find a way to seamlessly post across platforms and media. You need to build a presence on your platforms of choice. You need a strategy, knowledge, consistency, energy, resources, and who has the time for that? But in 2023, things changed again. CMS The Orchestrate All The Things Newsletter, platforms and social media In early 2023, a couple of things happened. First, i started my POSSE journey. Second, I suffered what’s probably : a torn achilles. It’s the kind of injury that means you’re out of luck and stuck for a while. You have to do surgery and then . This, and a little help from AI, drove me towards POSSE. every baller’s worst nightmare on the court go through a long period of recovery and rehab ![Stories about Tech, Data, AI and Media and how they flow into each other shaping our lives ](https://cdn.hackernoon.com/images/fdI8xgIHRJTxUAsqoZ27qOGzMz33-oh93m7u.png) Today i’m launching the , as well as some new ways to connect and share my work. Stories about how Technology, Data, AI and Media flow into each other shaping our lives. Analysis, Essays, Interviews and News. Mid-to-long form, 1-3 times per month. Plus audio and video – read on! Orchestrate all the Things Newsletter The . Browsable, searchable, categorized, and straight to your inbox. Orchestrate all the Things Newsletter has everything I’ve been writing about, all in one place I’ve engaged from the likes of Gary Marcus and Andrew Ng to emerging thinkers and innovators across multiple domains. Some might call this futurism; let’s just say it’s connecting the dots. Many of my pieces have a technical focus. Most also examine business perspectives and use cases, while others are socio-technical. Some pieces are analyses on emerging themes – picking them up early, featuring expert comment, or offering alternative takes. Others cover breaking news, typically also featuring the people behind them plus some analysis. There are some book reviews as well. But if you prefer or , i’m there too. and ? Sure. ? Yes please! Plus and . Not into reading articles? No problem. The is the best way to keep up with my work. Orchestrate all the Things Newsletter Substack Medium Twitter LinkedIn RSS HackerNoon DZone Going forward, you will be able to find AI-narrated versions of my articles there too. You can subscribe on , & , and more. The is where you can find my conversations with with people who bring interesting news and views to the table. Orchestrate all the Things Podcast Spotify Apple Google Podcasts Amazon Music AI-visualized and narrated versions of my articles will also be available on , , and . The podcast backlog and new episodes will also be shared on . If you are more of a visual type, Orchestrate all the Things has got you covered too. Instagram Pinterest Tik Tok YouTube YouTube Going forward and friendly AI Expanding to new platforms is a conscious effort to reach new audiences. We’ve reached a time when my and i see articles about AI in lifestyle magazines. It hardly gets more mainstream than this, and that audience needs to be reached in a different way. friends ask me about ChatGPT I’ve been using AI apps for a long time. I also experimented with some new AI models and applications. I think AI is fair game for things such as transcription, summarization and generating audio or video narratives from text. I understand why some people use it for idea generation, and why , but I digress. others shy away from AI-generated images The most important thing remains what to write about and how to do it. I won’t commit to frequent updates. What i can commit to is thoughtfulness, and a couple of articles already in the pipeline: on the latest developments in AI (the Pause, x.ai and whether “ “), on my , and on writing the . scale is all you need “What’s New in AI” work with O’Reilly Personal Knowledge Graph book If you have ideas for things you’d like to see me cover, feel free to me. It’s possible that i’ll make a comeback on mainstream media at some point, in which case editors will have their say too. Either way, will remain the canonical hub for my work. It took more than some disruption to get there, but sometimes that’s what it takes. contact Orchestrate all the Things Also published . here

Orchestrating All The Things: Owning Tech, Data, Media, AI, Writing, and Content

সমস্ত জিনিস অর্কেস্ট্রেট করা: প্রযুক্তি, ডেটা, মিডিয়া, এআই, লেখা এবং বিষয়বস্তুর মালিক হওয়া

Alle Dinge orchestrieren: Besitz von Technologie, Daten, Medien, KI, Schreiben und Inhalten

Orquestando todas las cosas: Poseer tecnología, datos, medios, IA, escritura y contenido

Orchestration de tout : posséder la technologie, les données, les médias, l'IA, l'écriture et le contenu

ऑर्केस्ट्रेटिंग ऑल द थिंग्स: ओनिंग टेक, डेटा, मीडिया, एआई, राइटिंग एंड कंटेंट

すべてのオーケストレーション: テクノロジー、データ、メディア、AI、ライティング、コンテンツの所有

모든 것을 조율: 기술, 데이터, 미디어, AI, 글쓰기 및 콘텐츠 소유

Orquestrando todas as coisas: possuir tecnologia, dados, mídia, IA, redação e conteúdo

Her Şeyi Yönetmek: Teknolojiye, Veriye, Medyaya, Yapay Zekaya, Yazmaya ve İçeriğe Sahip Olmak

Phối hợp tất cả mọi thứ: Sở hữu công nghệ, dữ liệu, phương tiện, AI, viết và nội dung

协调一切：拥有技术、数据、媒体、人工智能、写作和内容

With data breaches and cyberattacks on the rise, you can never be too careful. Backup testing prevents loss and minimizes cyberthreats’ impact, making it fundamental to cybersecurity. However, like many in your position, you may not consider it a priority. Are you opening yourself up to unnecessary risks by not making it part of your security strategy? Why Make Backup Testing Part of Your Security Strategy? Backups are copies of files, databases, applications or systems. Testing involves checking them to verify they are accurate, intact, and up to date. Its purpose is to evaluate the effectiveness of your strategy and schedule, letting you make actionable changes. It also helps you recognize and address issues proactively — meaning before a threat actor makes recovery impossible. Data loss is more common than you might think. In 2023, 46% of organizations worldwide lost some amount of sensitive information. Since these incidents can be caused by anything from human error to cyberattacks, preventing them can be challenging. This is why backups are so helpful — redundancy is crucial to recovery. Intact backups make recovery possible. Without them, the process would be expensive. Organizations spent about $4.35 million per breach on average in 2022. In extreme cases, recovery may be impossible — you could lose weeks, months, or years of valuable files. Testing these copies is critical to your security strategy. If you’re like many cybersecurity professionals, you don’t consider redundancy a priority. Even though 72% of organizations had to use a backup to recover at least once in 2021, 41% admitted they rarely — if ever — back up their data. Crucially, even if you create and update copies, you may still be at risk of loss. Unfortunately, backups aren’t indestructible. Human error, bugs, hardware damage, incompatibilities, and mismatched encryption keys can cause data loss, rendering them useless. Alternatively, a tech-savvy attacker could use ransomware to encrypt all your files simultaneously, preventing you from using your copies to recover. The Fundamentals of a Strategic Backup Testing Policy You must first understand the fundamentals of backup testing to incorporate a high-quality policy into your security strategy. What to Test Several test scenarios exist, with different kinds for each data loss cause. Corruption, accidental deletion, and cyberattacks are some of the most common, but it may pay to prepare for unlikely incidents. Your backup’s storage medium, location, and size will also affect your approach. Be mindful that you’re trying to improve your security strategy. When to Test Prioritize periodic testing instead of kicking the can down the road for the sake of convenience. Work files may need to be backed up weekly, depending on how often information changes. Also, frequency varies depending on the type of data you’re backing up. While archives can go months without an update, you should back applications up more often. Where to Test If you can't prioritize everything, which backups should you test? Consider creating a risk-based schedule. You typically want to test on-site data first since it enables rapid recovery. Generally, off-site copies should only take priority if a catastrophic event threatens the integrity of all local files. How to Incorporate Backup Testing Into Your Strategy Testing involves selecting a scenario and simulating the recovery process. Once you create an isolated test environment to prevent unintended changes, you reproduce real-world events like accidental deletion or cyberattacks. Then, you go through the step-by-step process to restore your systems, applications, files, or databases via your backup. Upon completion, you should have a fully restored environment. If you notice discrepancies when comparing the original to the restored version, your backups may not be fully intact. If that happens, search for anomalies like corruption, duplicate entries, or mistyped fields. Even if you don’t find anything wrong, testing helps you identify potential process improvements. Documentation is key. You should record how long restoration took, what issues emerged, which steps you took, and whether you succeeded on the first try. This way, you can accurately estimate how long recovery will take. It also lets you know what to expect in the future. If someone else is in charge of the next round of testing, they’ll appreciate those details. Incorporating testing into your strategy means making room in your busy schedule. Consider automation to free up time. Tools like robot process automation and artificial intelligence can automate backup creation, restoration simulation, and discrepancy detection. Since 72% of companies have adopted AI as of 2024, your employer may already have such tools. If you automate testing, consider whether you’ll leverage periodic or trigger-based tests — or some combination of both. While evaluating various systems, applications, and files weekly or monthly is fine, it may lead to minor data loss. Setting your tools to activate when you find evidence of a breach or detect unusual network activity mitigates this issue. Considerations for Uncommon Backup Testing Scenarios Sometimes, assigning roles, creating schedules, and refining strategies isn’t enough. You should consider edge cases — tests for issues that only occur under unusual circumstances — for a comprehensive backup testing policy. Imagine you only prioritize accidental deletion and breach resolution. What happens when an insider threat intentionally wipes a system? Preparing your organization for potential data loss incidents by incorporating unlikely test scenarios into your security strategy improves its security posture. Since cyberthreats and the digital landscape are ever-changing, being proactive can help. Setting yourself up for success can save time and preserve your employer’s reputation. With data breaches and cyberattacks on the rise, you can never be too careful. Backup testing prevents loss and minimizes cyberthreats’ impact, making it fundamental to cybersecurity. However, like many in your position, you may not consider it a priority. Are you opening yourself up to unnecessary risks by not making it part of your security strategy? With data breaches and cyberattacks on the rise, you can never be too careful. Backup testing prevents loss and minimizes cyberthreats’ impact, making it fundamental to cybersecurity. However, like many in your position, you may not consider it a priority. Are you opening yourself up to unnecessary risks by not making it part of your security strategy? Why Make Backup Testing Part of Your Security Strategy? Why Make Backup Testing Part of Your Security Strategy? Backups are copies of files, databases, applications or systems. Testing involves checking them to verify they are accurate, intact, and up to date. Its purpose is to evaluate the effectiveness of your strategy and schedule, letting you make actionable changes. It also helps you recognize and address issues proactively — meaning before a threat actor makes recovery impossible. Data loss is more common than you might think. In 2023, 46% of organizations worldwide lost some amount of sensitive information. Since these incidents can be caused by anything from human error to cyberattacks, preventing them can be challenging. This is why backups are so helpful — redundancy is crucial to recovery. 46% of organizations worldwide 46% of organizations worldwide Intact backups make recovery possible. Without them, the process would be expensive. Organizations spent about $4.35 million per breach on average in 2022. In extreme cases, recovery may be impossible — you could lose weeks, months, or years of valuable files. Testing these copies is critical to your security strategy. spent about $4.35 million spent about $4.35 million If you’re like many cybersecurity professionals, you don’t consider redundancy a priority. Even though 72% of organizations had to use a backup to recover at least once in 2021, 41% admitted they rarely — if ever — back up their data. Crucially, even if you create and update copies, you may still be at risk of loss. 72% of organizations had to use 72% of organizations had to use Unfortunately, backups aren’t indestructible . Human error, bugs, hardware damage, incompatibilities, and mismatched encryption keys can cause data loss, rendering them useless. Alternatively, a tech-savvy attacker could use ransomware to encrypt all your files simultaneously, preventing you from using your copies to recover. backups aren’t indestructible backups aren’t indestructible The Fundamentals of a Strategic Backup Testing Policy You must first understand the fundamentals of backup testing to incorporate a high-quality policy into your security strategy. What to Test Several test scenarios exist, with different kinds for each data loss cause. Corruption, accidental deletion, and cyberattacks are some of the most common, but it may pay to prepare for unlikely incidents. Your backup’s storage medium, location, and size will also affect your approach. Be mindful that you’re trying to improve your security strategy. When to Test Prioritize periodic testing instead of kicking the can down the road for the sake of convenience. Work files may need to be backed up weekly , depending on how often information changes. Also, frequency varies depending on the type of data you’re backing up. While archives can go months without an update, you should back applications up more often. may need to be backed up weekly may need to be backed up weekly Where to Test If you can't prioritize everything, which backups should you test? Consider creating a risk-based schedule. You typically want to test on-site data first since it enables rapid recovery. Generally, off-site copies should only take priority if a catastrophic event threatens the integrity of all local files. How to Incorporate Backup Testing Into Your Strategy Testing involves selecting a scenario and simulating the recovery process. Once you create an isolated test environment to prevent unintended changes, you reproduce real-world events like accidental deletion or cyberattacks. Then, you go through the step-by-step process to restore your systems, applications, files, or databases via your backup. Upon completion, you should have a fully restored environment. If you notice discrepancies when comparing the original to the restored version, your backups may not be fully intact. If that happens, search for anomalies like corruption, duplicate entries, or mistyped fields. Even if you don’t find anything wrong, testing helps you identify potential process improvements. Documentation is key. You should record how long restoration took, what issues emerged, which steps you took, and whether you succeeded on the first try. This way, you can accurately estimate how long recovery will take. It also lets you know what to expect in the future. If someone else is in charge of the next round of testing, they’ll appreciate those details. Incorporating testing into your strategy means making room in your busy schedule. Consider automation to free up time. Tools like robot process automation and artificial intelligence can automate backup creation, restoration simulation, and discrepancy detection. Since 72% of companies have adopted AI as of 2024, your employer may already have such tools. 72% of companies have adopted AI 72% of companies have adopted AI If you automate testing, consider whether you’ll leverage periodic or trigger-based tests — or some combination of both. While evaluating various systems, applications, and files weekly or monthly is fine, it may lead to minor data loss. Setting your tools to activate when you find evidence of a breach or detect unusual network activity mitigates this issue. Considerations for Uncommon Backup Testing Scenarios Sometimes, assigning roles, creating schedules, and refining strategies isn’t enough. You should consider edge cases — tests for issues that only occur under unusual circumstances — for a comprehensive backup testing policy. Imagine you only prioritize accidental deletion and breach resolution. What happens when an insider threat intentionally wipes a system? Preparing your organization for potential data loss incidents by incorporating unlikely test scenarios into your security strategy improves its security posture. Since cyberthreats and the digital landscape are ever-changing, being proactive can help. Setting yourself up for success can save time and preserve your employer’s reputation.

Is Backup Testing Part of Your Security Strategy?

Je testování zálohování součástí vaší bezpečnostní strategie?

Er sikkerhedskopieringstest en del af din sikkerhedsstrategi?

¿Las pruebas de respaldo son parte de su estrategia de seguridad?

Babeskopia-probak zure segurtasun-estrategiaren parte al dira?

バックアップ テストはセキュリティ戦略の一部ですか?

არის სარეზერვო ტესტირება თქვენი უსაფრთხოების სტრატეგიის ნაწილი?

ການທົດສອບການສໍາຮອງຂໍ້ມູນເປັນສ່ວນຫນຶ່ງຂອງຍຸດທະສາດຄວາມປອດໄພຂອງທ່ານບໍ?

Adakah Ujian Sandaran Sebahagian daripada Strategi Keselamatan Anda?

Maakt back-uptesten deel uit van uw beveiligingsstrategie?

උපස්ථ පරීක්ෂාව ඔබගේ ආරක්ෂක උපාය මාර්ගයේ කොටසක්ද?

A është testimi rezervë pjesë e strategjisë suaj të sigurisë?

Чи є резервне тестування частиною вашої стратегії безпеки?

Do you read confidentiality agreements? Few people waste time. They all look alike, are boring, and probably don't lie. Companies, especially big ones, try to be honest about their responsibilities regarding user data if only to avoid hefty fines. And yet, something is wrong with the world's privacy protections. What if the concept of privacy itself is the problem? Decentralized projects don't usually say “privacy”; rather, they say data is “owned by the user” (data ownership). What's the difference? I came up with a fun analogy and want to share it. Let's take a more understandable subject - land, for example. If you own a plot of land, you have the right to get there whenever you want, build a house on it, make money with it, invite your friends to a party, and forbid anyone to enter. Ownership is the right to make decisions about some object. From a legal point of view, it consists of three elements: Possession (i.e., the possibility of unrestricted access)


Use (i.e., benefiting from the property)


Disposition (the right to sell, gift, or destroy the object) An owner can transfer some of his or her rights to another. For example, if you lease land, you will allow the tenant access and use, but not disposal. This is the classic, universal understanding of ownership. There are good reasons to extend it to include information. First, data plays a role in the modern economy; in the 21st century, it has become a valuable asset with measurable value. Look at the largest companies in the world: 4 out of 10 derive a significant part of their revenues from user data. Second, thanks to decentralized architecture and asymmetric cryptography, this has become feasible in principle: data ownership can be enforced. “Privacy” means that the data is kept secret and protected by the server. The server takes over the authentication and authorization functions to make the protection work. At its discretion, it can allow or deny someone access and use. In other words, the server owns the decisions - and so, it turns out, does the data. Can the server block a user? It can. Can it grant access to someone else for some reason - like a hacker? It can. Can someone else use your data for profit? Yes, that's the whole point of free digital services. The provider can destroy your account at will, as Google does, or take away your name, as Twitter does. And users are used to this state of affairs. As the former owner of the Music account on the service formerly known as Twitter put it, “Ultimately, they own the account.” As strange as it may sound, the idea of privacy on the Internet is reminiscent of medieval feudalism. In the Middle Ages, land was the main economic resource. Peasants used the land, but it belonged to the aristocracy. In return, the feudal lord was obligated to protect that land. Sound familiar? By allowing internet services to be in charge of privacy, we are adopting a feudal model: when our digital identity is protected, it is at the mercy of someone else. That's why tech companies cling so tightly to privacy. And why privacy laws being passed around the world exacerbate digital inequality. Internet giants limit our access to our own data, calling it an advantage. The very concept of privacy masks the fact that data does not belong to us. Let's be clear: this is not a global conspiracy theory but rather an attempt to find the root of the problem. Here's another classic legal principle: Cuius est solum, eius est usque ad coelum et ad inferos - To whomsoever it belongs, it is his all the way to the heavens (and all the way to hell) (Latin) I like the sound of that. This is exactly the kind of complete and unconditional ownership of one's own information made possible by decentralized, trustless networks with strong cryptography. That's why I believe Web3 is more than cryptocurrency speculation. It is the beginning of a new era of people's relationship with technology. Do you think the analogy with the Middle Ages is clickbait? Or does it seem to be true? Do you read confidentiality agreements? Few people waste time. They all look alike, are boring, and probably don't lie. Companies, especially big ones, try to be honest about their responsibilities regarding user data if only to avoid hefty fines. And yet, something is wrong with the world's privacy protections. What if the concept of privacy itself is the problem? Decentralized projects don't usually say “privacy”; rather, they say data is “owned by the user” (data ownership). What's the difference? I came up with a fun analogy and want to share it. Let's take a more understandable subject - land, for example. If you own a plot of land, you have the right to get there whenever you want, build a house on it, make money with it, invite your friends to a party, and forbid anyone to enter. Ownership is the right to make decisions about some object. From a legal point of view , it consists of three elements: From a legal point of view Possession (i.e., the possibility of unrestricted access) Use (i.e., benefiting from the property) Disposition (the right to sell, gift, or destroy the object) Possession (i.e., the possibility of unrestricted access) Possession (i.e., the possibility of unrestricted access) Use (i.e., benefiting from the property) Use (i.e., benefiting from the property) Disposition (the right to sell, gift, or destroy the object) Disposition (the right to sell, gift, or destroy the object) An owner can transfer some of his or her rights to another. For example, if you lease land, you will allow the tenant access and use, but not disposal. This is the classic, universal understanding of ownership. There are good reasons to extend it to include information. First, data plays a role in the modern economy; in the 21st century, it has become a valuable asset with measurable value. Look at the largest companies in the world : 4 out of 10 derive a significant part of their revenues from user data. Second, thanks to decentralized architecture and asymmetric cryptography, this has become feasible in principle: data ownership can be enforced. largest companies in the world “Privacy” means that the data is kept secret and protected by the server. The server takes over the authentication and authorization functions to make the protection work. At its discretion, it can allow or deny someone access and use. In other words, the server owns the decisions - and so, it turns out, does the data. Can the server block a user? It can. Can the server block a user? It can. Can it grant access to someone else for some reason - like a hacker? It can. Can it grant access to someone else for some reason - like a hacker? It can. Can someone else use your data for profit? Yes, that's the whole point of free digital services. Can someone else use your data for profit? Yes, that's the whole point of free digital services . whole point of free digital services The provider can destroy your account at will, as Google does , or take away your name, as Twitter does . Google does Twitter does And users are used to this state of affairs. As the former owner of the Music account on the service formerly known as Twitter put it, “Ultimately, they own the account.” As strange as it may sound, the idea of privacy on the Internet is reminiscent of medieval feudalism. In the Middle Ages, land was the main economic resource. Peasants used the land, but it belonged to the aristocracy. In return, the feudal lord was obligated to protect that land. Sound familiar? By allowing internet services to be in charge of privacy, we are adopting a feudal model: when our digital identity is protected, it is at the mercy of someone else. That's why tech companies cling so tightly to privacy. And why privacy laws being passed around the world exacerbate digital inequality. Internet giants limit our access to our own data, calling it an advantage. The very concept of privacy masks the fact that data does not belong to us. Let's be clear: this is not a global conspiracy theory but rather an attempt to find the root of the problem. Here's another classic legal principle: Cuius est solum, eius est usque ad coelum et ad inferos - To whomsoever it belongs, it is his all the way to the heavens (and all the way to hell) (Latin) Cuius est solum, eius est usque ad coelum et ad inferos - To whomsoever it belongs, it is his all the way to the heavens (and all the way to hell) (Latin) I like the sound of that. This is exactly the kind of complete and unconditional ownership of one's own information made possible by decentralized, trustless networks with strong cryptography. That's why I believe Web3 is more than cryptocurrency speculation. It is the beginning of a new era of people's relationship with technology. Do you think the analogy with the Middle Ages is clickbait? Or does it seem to be true?

What Do Privacy and Feudalism Have in Common?

The cost, frequency, and scale of data breaches continue to rise year over year. According to the IBM 2023 Cost of a Data Breach Report, the average cost reached $4.45 million in 2023. The number of breaches and the amount of exposed records have also increased, with over 22 billion records compromised in 2023 alone, as highlighted in the IBM Security Data Breach Report. So, what are companies doing to improve their data security? Common Data Security Vulnerabilities Misconfiguration Cloud storage or databases are left publicly accessible due to simple mistakes. For example, AWS S3 bucket misconfigurations have led to numerous data breaches, exposing sensitive information without detection. Weak Authentication Weak passwords or the lack of multi-factor authentication (MFA) make it easier for attackers to gain access. After a recent breach, Snowflake implemented mandatory MFA to enhance security. Unencrypted Data Data that isn’t encrypted during transit or at rest is fully exposed and readable if accessed by unauthorized parties. In the healthcare sector, more than 385 million healthcare records
have been exposed in data breaches since 2009, many of which could have been prevented with proper data encryption. Insider Threats Misuse of privileged access can lead to severe breaches, as seen in the Capital One breach in 2019. A former employee exploited a vulnerability in Capital One’s cloud infrastructure, exposing the personal information of over 100 million customers. Unpatched Software Systems that aren’t regularly updated are vulnerable to attacks that exploit known security flaws. The WannaCry ransomware attack in 2017 is a good example of how regular software updates could greatly reduce the risk of data security incidents. Mitigation Strategy for Data Security Vulnerabilities Regular Audits and Monitoring The most foundational step in mitigating data security vulnerabilities is conducting regular security audits and monitoring. Implementing tools to detect unusual activity, misconfigurations, or potential breaches in real-time enables teams to take quick action before issues escalate. Data Deletion Deleting sensitive data is often the quickest way to reduce risk when a vulnerability is detected or when data is no longer needed. Short-term, this approach is highly effective because it immediately removes the data from potential exposure. Long-term, it doesn’t address the underlying vulnerabilities and may only provide a temporary fix. Additionally, this strategy can be constrained by legal, compliance, and technical requirements. Additional Access Control Strengthening access controls is a common mid-term solution to prevent unauthorized access. This can include implementing policies like no public buckets by default in S3 or mandatory multi-factor authentication (MFA), as seen with Snowflake. Short-term, access control can be highly effective in enhancing security by limiting who can access sensitive data. Long-term, its effectiveness may diminish if not consistently maintained or updated. Inconsistencies in implementation across different platform vendors (like Snowflake, Databricks, and AWS) can create complexity, slow down development velocity, and sometimes reduce the effectiveness of access controls as a security measure. Data Encryption Encryption is a comprehensive long-term solution that protects data even if unauthorized access occurs. Short-term, encryption is highly effective because it immediately secures data, ensuring it remains unreadable without the correct keys. Long-term, encryption is the most effective approach because it provides ongoing protection as long as the keys are properly managed. Additionally, integrating encryption into your tech stack opens up the possibility of implementing advanced security features such as tokenization, data masking, and deterministic encryption, which further enhance data protection. Comparison of These Three Approaches Approach

Short-Term Effectiveness

Long-Term Effectiveness

Level of Effort

Challenges



Data Deletion

High—immediate risk reduction

Low—only a temporary fix, doesn't address root vulnerabilities

Minimal

Legal, compliance, and technical constraints



Additional Access Control

Moderate to High—effective in preventing unauthorized access

Medium—can degrade over time if not consistently maintained or updated

Can slow down development velocity

Inconsistent implementation across platforms; may require regular updates



Data Encryption

High—protects data even if accessed

High—ensures long-term data security; foundational for advanced features like masking and tokenization

Significant engineering effort

Requires re-architecting; technical complexity, but enables advanced security features Advantages of Data Encryption Encryption simplifies system architecture by reducing the need for complex access control, data governance, and extra security features on vendor platforms and developer tools. By protecting data at the core, organizations can spend less time and money on creating and dealing with extra approval processes and more on ensuring sensitive information is always private and secure. Long-Term Simplification and Reliability Data encryption encourages simpler architecture over time by reducing the extra security features needed across individual vendor platforms, internal processes, access control policies, governance, and monitoring. This not only enhances long-term data security but also makes it easier to keep implementation simple and maintain compliance across an organization’s infrastructure and overall security. Keyper: Jarrid's Data Encryption SDK Keyper is designed to make encryption and security management straightforward and accessible across your organization. Configuration-Driven Keyper comes with an easy-to-understand setup, allowing non-engineering functions—such as security, compliance, and legal teams—to collectively decide on access and encryption rules. Configurations can be applied by engineers directly using the SDK. This ensures security decisions are clear and aligned across all teams without additional overhead between engineering and security. Easy Integration with Existing Tech Stacks Keyper is designed to be easily integrated with existing CI/CD workflows and tech stacks at storage and consumption points. This allows organizations to enhance their data security without disrupting existing processes or requiring significant changes to their infrastructure. Complementary to Existing Security Frameworks By adding encryption to your existing security framework, Keyper improves the overall data security posture of your organization. It ensures that sensitive data remains secure even if other security measures are compromised, offering better protection and strengthening your existing access control and approval processes. Summary Data breaches are becoming more frequent and costly. While data deletion and access control offer short-term protection, they fall short in the long run. Encryption provides lasting security by simplifying system architecture and reducing reliance on complex controls. Jarrid’s Keyper make encryption simple using the existing tech stacks, ensuring data remains secure even if other measures fail. The cost, frequency, and scale of data breaches continue to rise year over year. According to the IBM 2023 Cost of a Data Breach Report , the average cost reached $4.45 million in 2023. The number of breaches and the amount of exposed records have also increased, with over 22 billion records compromised in 2023 alone, as highlighted in the IBM Security Data Breach Report . So, what are companies doing to improve their data security? IBM 2023 Cost of a Data Breach Report $4.45 million over 22 billion records IBM Security Data Breach Report Common Data Security Vulnerabilities Misconfiguration Cloud storage or databases are left publicly accessible due to simple mistakes. For example, AWS S3 bucket misconfigurations have led to numerous data breaches, exposing sensitive information without detection. AWS S3 bucket misconfigurations Weak Authentication Weak passwords or the lack of multi-factor authentication (MFA) make it easier for attackers to gain access. After a recent breach, Snowflake implemented mandatory MFA to enhance security. After a recent breach, Snowflake implemented mandatory MFA to enhance security. Unencrypted Data Data that isn’t encrypted during transit or at rest is fully exposed and readable if accessed by unauthorized parties. In the healthcare sector, more than 385 million healthcare records have been exposed in data breaches since 2009, many of which could have been prevented with proper data encryption. 385 million healthcare records Insider Threats Misuse of privileged access can lead to severe breaches, as seen in the Capital One breach in 2019. A former employee exploited a vulnerability in Capital One’s cloud infrastructure, exposing the personal information of over 100 million customers. Capital One breach Unpatched Software Systems that aren’t regularly updated are vulnerable to attacks that exploit known security flaws. The WannaCry ransomware attack in 2017 is a good example of how regular software updates could greatly reduce the risk of data security incidents. WannaCry ransomware attack in 2017 Mitigation Strategy for Data Security Vulnerabilities Regular Audits and Monitoring The most foundational step in mitigating data security vulnerabilities is conducting regular security audits and monitoring. Implementing tools to detect unusual activity, misconfigurations, or potential breaches in real-time enables teams to take quick action before issues escalate. Data Deletion Deleting sensitive data is often the quickest way to reduce risk when a vulnerability is detected or when data is no longer needed. Short-term, this approach is highly effective because it immediately removes the data from potential exposure. Long-term, it doesn’t address the underlying vulnerabilities and may only provide a temporary fix. Additionally, this strategy can be constrained by legal, compliance, and technical requirements. Short-term, Long-term, Additional Access Control Strengthening access controls is a common mid-term solution to prevent unauthorized access. This can include implementing policies like no public buckets by default in S3 or mandatory multi-factor authentication (MFA), as seen with Snowflake. Short-term, access control can be highly effective in enhancing security by limiting who can access sensitive data. Long-term, its effectiveness may diminish if not consistently maintained or updated. Inconsistencies in implementation across different platform vendors (like Snowflake, Databricks, and AWS) can create complexity, slow down development velocity, and sometimes reduce the effectiveness of access controls as a security measure. Short-term, Long-term, Data Encryption Encryption is a comprehensive long-term solution that protects data even if unauthorized access occurs. Short-term, encryption is highly effective because it immediately secures data, ensuring it remains unreadable without the correct keys. Long-term, encryption is the most effective approach because it provides ongoing protection as long as the keys are properly managed. Additionally, integrating encryption into your tech stack opens up the possibility of implementing advanced security features such as tokenization, data masking, and deterministic encryption, which further enhance data protection. Short-term, Long-term, Comparison of These Three Approaches Approach

Short-Term Effectiveness

Long-Term Effectiveness

Level of Effort

Challenges



Data Deletion

High—immediate risk reduction

Low—only a temporary fix, doesn't address root vulnerabilities

Minimal

Legal, compliance, and technical constraints



Additional Access Control

Moderate to High—effective in preventing unauthorized access

Medium—can degrade over time if not consistently maintained or updated

Can slow down development velocity

Inconsistent implementation across platforms; may require regular updates



Data Encryption

High—protects data even if accessed

High—ensures long-term data security; foundational for advanced features like masking and tokenization

Significant engineering effort

Requires re-architecting; technical complexity, but enables advanced security features Approach

Short-Term Effectiveness

Long-Term Effectiveness

Level of Effort

Challenges Data Deletion

High—immediate risk reduction

Low—only a temporary fix, doesn't address root vulnerabilities

Minimal

Legal, compliance, and technical constraints Additional Access Control

Moderate to High—effective in preventing unauthorized access

Medium—can degrade over time if not consistently maintained or updated

Can slow down development velocity

Inconsistent implementation across platforms; may require regular updates Data Encryption

High—protects data even if accessed

High—ensures long-term data security; foundational for advanced features like masking and tokenization

Significant engineering effort

Requires re-architecting; technical complexity, but enables advanced security features Approach Short-Term Effectiveness Long-Term Effectiveness Level of Effort Challenges Approach Approach Approach Short-Term Effectiveness Short-Term Effectiveness Short-Term Effectiveness Long-Term Effectiveness Long-Term Effectiveness Long-Term Effectiveness Level of Effort Level of Effort Level of Effort Challenges Challenges Challenges Data Deletion High—immediate risk reduction Low—only a temporary fix, doesn't address root vulnerabilities Minimal Legal, compliance, and technical constraints Data Deletion Data Deletion Data Deletion High—immediate risk reduction High—immediate risk reduction Low—only a temporary fix, doesn't address root vulnerabilities Low—only a temporary fix, doesn't address root vulnerabilities Minimal Minimal Legal, compliance, and technical constraints Legal, compliance, and technical constraints Additional Access Control Moderate to High—effective in preventing unauthorized access Medium—can degrade over time if not consistently maintained or updated Can slow down development velocity Inconsistent implementation across platforms; may require regular updates Additional Access Control Additional Access Control Additional Access Control Moderate to High—effective in preventing unauthorized access Moderate to High—effective in preventing unauthorized access Medium—can degrade over time if not consistently maintained or updated Medium—can degrade over time if not consistently maintained or updated Can slow down development velocity Can slow down development velocity Inconsistent implementation across platforms; may require regular updates Inconsistent implementation across platforms; may require regular updates Data Encryption High—protects data even if accessed High—ensures long-term data security; foundational for advanced features like masking and tokenization Significant engineering effort Requires re-architecting; technical complexity, but enables advanced security features Data Encryption Data Encryption Data Encryption High—protects data even if accessed High—protects data even if accessed High—ensures long-term data security; foundational for advanced features like masking and tokenization High—ensures long-term data security; foundational for advanced features like masking and tokenization Significant engineering effort Significant engineering effort Requires re-architecting; technical complexity, but enables advanced security features Requires re-architecting; technical complexity, but enables advanced security features Advantages of Data Encryption Encryption simplifies system architecture by reducing the need for complex access control, data governance, and extra security features on vendor platforms and developer tools. By protecting data at the core, organizations can spend less time and money on creating and dealing with extra approval processes and more on ensuring sensitive information is always private and secure. Long-Term Simplification and Reliability Data encryption encourages simpler architecture over time by reducing the extra security features needed across individual vendor platforms, internal processes, access control policies, governance, and monitoring. This not only enhances long-term data security but also makes it easier to keep implementation simple and maintain compliance across an organization’s infrastructure and overall security. Keyper: Jarrid's Data Encryption SDK Keyper is designed to make encryption and security management straightforward and accessible across your organization. Keyper Configuration-Driven Keyper comes with an easy-to-understand setup, allowing non-engineering functions—such as security, compliance, and legal teams—to collectively decide on access and encryption rules. Configurations can be applied by engineers directly using the SDK. This ensures security decisions are clear and aligned across all teams without additional overhead between engineering and security. Keyper Easy Integration with Existing Tech Stacks Keyper is designed to be easily integrated with existing CI/CD workflows and tech stacks at storage and consumption points. This allows organizations to enhance their data security without disrupting existing processes or requiring significant changes to their infrastructure. Keyper Complementary to Existing Security Frameworks By adding encryption to your existing security framework, Keyper improves the overall data security posture of your organization. It ensures that sensitive data remains secure even if other security measures are compromised, offering better protection and strengthening your existing access control and approval processes. Keyper Summary Data breaches are becoming more frequent and costly. While data deletion and access control offer short-term protection, they fall short in the long run. Encryption provides lasting security by simplifying system architecture and reducing reliance on complex controls. Jarrid’s Keyper make encryption simple using the existing tech stacks, ensuring data remains secure even if other measures fail. Keyper

Data Security Strategy Beyond Access Control: Data Encryption

Data is the new gold. Every organisation and industry is strongly steeped in data, from market research all the way to grassroots implementation, data plays a significant part in everything. As a result of this, security has become all the more important. Data breaches can cost millions in lost patents, penalties, law suits, databases, and also the loss of potential profits. In a recent report by IBM, it is estimated that an average of $4.88 million is lost for every data breach and the head of IBM Security, Kevin Skapinetz was quoted as saying that, “Businesses are caught in a continuous cycle of breaches, containment and fallout response. This cycle now often includes investments in strengthening security defences and passing breach expenses on to consumers – making security the new cost of doing business.” Aside from the costs associated with data breaches, clients would also be less likely to engage in the services of a company that doesn’t have a stellar track record in terms of privacy protection, which leads to the loss of potential sales. Harvard reports a 7.5% loss in stock values following data breaches. The loss of confidence is also a hefty price to pay for these security breaches. Hackers are also known to use these breaches as a means to extort money from corporations that require their databases by encrypting it. These are some of the risks associated with data breaches, and one of the many cybersecurity threats that linger in the world wide web, especially in today’s landscape whereby data is largely stored in the cloud. Fortunately, consumer protection in AI LLMs is steadily increasing. The Application of AI-driven LLMs in the Interest of Consumers AI LLMs, or Large Language Models, might sound extremely technical to many but the fact is that many more are utilising these technologies without realising. For instance, one of the most prominent AI LLMs at the moment is ChatGPT, a generative tool. It is used by college students to complete their homework assignments, or by your everyday Joe to research on a particular matter by using it as a search engine. The key feature of the tool is that it is able to consolidate information in a comprehensible manner while taking into consideration the vast amount of data available at its disposal. But how can a generative tool be used in cybersecurity? Being able to analyse vast amounts of data is key. LLMs have the capacity to process network traffic, emails, system logs, and other data to detect any anomalies that may indicate a security breach. Aside from that, it can also formulate a response based on the threat by drawing on its predefined response strategies. This means that the software will be able to either isolate the event, alert security teams, or block the attack altogether. Understanding the context of a data breach is heavily reliant on the company’s ability to feed the tool a specific dataset, as generative tools are only able to draw on what data they have access to. Much like the human brain that stores information, LLMs are not able to generate information that they do not “know”. Therefore, it is important for these tools to be so sophisticated that it is able to distinguish between genuine threats or false alarms. Unlike a human brain, software can process huge amounts of information in very little time, and it is not prone to human error. By analysing the available history and emerging threat patterns, AI LLMs are able to provide predictive insights into potential future attacks as well as neutralising current threats. The positive implications of using AI LLMs Imagine how a security team might work to protect consumers in real time - they would need to work around the clock, without taking a break in between as they rotate shifts. The price to keep eyes on all digital fronts would be exorbitant. Fortunately, AI is able to offer a feasible and cost-effective solution. The Ponemon Institute's Cost of Data Breach Report 2023 indicated that organisations and corporations that are utilising AI LLMs save up to $1.2 million per breach, a comparable improvement especially when considering the amount of money needed to fund a manual process running. AI-driven security solutions are not only much easier on the wallet for corporations, it is also much more efficient. According to IBM, these contemporary methods are 27% quicker in pinpointing breaches as compared to traditional methods. But simply identifying these breaches is not enough, the accuracy of detection is also very much higher, with false positives reduced by 40% when using AI LLMs. This is crucial to prevent burnout of security teams which will have to review each individual breach to ensure that they have all been properly dealt with. Identifying and responding to cybersecurity threats in real time to protect consumers with AI LLMs is the future Rapid analysis capabilities afforded by AI LLMs will push forward the boundaries of cybersecurity, mitigating threats before they cause any significant damage as well as providing cost saving solutions that require less manpower, maintenance, yet having more powerful output and systems. In the future, all organisations will be outfitted with their own AI LLMs directly into their existing cybersecurity mainframe such as Security Information and Event Management (SIEM) systems and other threat managing platforms. There will also be new positions available for tech crews who are dedicated to ‘teaching’ these LLMs with the latest trends and most relevant histories - as mentioned before, LLMs operate on the database that it is outfitted with, which means that when dealing with the ever-evolving landscape of something like security, the datasets require frequent updates and fine-tuning in order to stay current, much like the antiviruses that we run on our computers or the constant updates that we download onto our phones, all of which include the latest security updates. But this also means that AI isn’t here to replace a manual workforce, but rather enhance it overall as these platforms will be able to deliver faster results, while giving the human workforce the ability to focus solely on the most crucial component: ensuring that the appropriate threat response is administered. Data is the new gold. Every organisation and industry is strongly steeped in data, from market research all the way to grassroots implementation, data plays a significant part in everything. As a result of this, security has become all the more important. Data breaches can cost millions in lost patents, penalties, law suits, databases, and also the loss of potential profits. In a recent report by IBM, it is estimated that an average of $4.88 million is lost for every data breach and the head of IBM Security, Kevin Skapinetz was quoted as saying that, “Businesses are caught in a continuous cycle of breaches, containment and fallout response. This cycle now often includes investments in strengthening security defences and passing breach expenses on to consumers – making security the new cost of doing business.” average of $4.88 million average of $4.88 million Aside from the costs associated with data breaches, clients would also be less likely to engage in the services of a company that doesn’t have a stellar track record in terms of privacy protection, which leads to the loss of potential sales. Harvard reports a 7.5% loss in stock values following data breaches. The loss of confidence is also a hefty price to pay for these security breaches. 7.5% loss 7.5% loss Hackers are also known to use these breaches as a means to extort money from corporations that require their databases by encrypting it. These are some of the risks associated with data breaches, and one of the many cybersecurity threats that linger in the world wide web, especially in today’s landscape whereby data is largely stored in the cloud. Fortunately, consumer protection in AI LLMs is steadily increasing. consumer protection in AI LLMs consumer protection in AI LLMs The Application of AI-driven LLMs in the Interest of Consumers AI LLMs, or Large Language Models, might sound extremely technical to many but the fact is that many more are utilising these technologies without realising. For instance, one of the most prominent AI LLMs at the moment is ChatGPT, a generative tool. It is used by college students to complete their homework assignments, or by your everyday Joe to research on a particular matter by using it as a search engine. The key feature of the tool is that it is able to consolidate information in a comprehensible manner while taking into consideration the vast amount of data available at its disposal. But how can a generative tool be used in cybersecurity? Being able to analyse vast amounts of data is key. LLMs have the capacity to process network traffic, emails, system logs, and other data to detect any anomalies that may indicate a security breach. Aside from that, it can also formulate a response based on the threat by drawing on its predefined response strategies. This means that the software will be able to either isolate the event, alert security teams, or block the attack altogether. Understanding the context of a data breach is heavily reliant on the company’s ability to feed the tool a specific dataset, as generative tools are only able to draw on what data they have access to. Much like the human brain that stores information, LLMs are not able to generate information that they do not “know”. Therefore, it is important for these tools to be so sophisticated that it is able to distinguish between genuine threats or false alarms. Unlike a human brain, software can process huge amounts of information in very little time, and it is not prone to human error. By analysing the available history and emerging threat patterns, AI LLMs are able to provide predictive insights into potential future attacks as well as neutralising current threats. The positive implications of using AI LLMs Imagine how a security team might work to protect consumers in real time - they would need to work around the clock, without taking a break in between as they rotate shifts. The price to keep eyes on all digital fronts would be exorbitant. Fortunately, AI is able to offer a feasible and cost-effective solution. The Ponemon Institute's Cost of Data Breach Report 2023 indicated that organisations and corporations that are utilising AI LLMs save up to $1.2 million per breach, a comparable improvement especially when considering the amount of money needed to fund a manual process running. AI-driven security solutions are not only much easier on the wallet for corporations, it is also much more efficient. According to IBM, these contemporary methods are 27% quicker in pinpointing breaches as compared to traditional methods. pinpointing breaches pinpointing breaches But simply identifying these breaches is not enough, the accuracy of detection is also very much higher, with false positives reduced by 40% when using AI LLMs. This is crucial to prevent burnout of security teams which will have to review each individual breach to ensure that they have all been properly dealt with. Identifying and responding to cybersecurity threats in real time to protect consumers with AI LLMs is the future Rapid analysis capabilities afforded by AI LLMs will push forward the boundaries of cybersecurity, mitigating threats before they cause any significant damage as well as providing cost saving solutions that require less manpower, maintenance, yet having more powerful output and systems. In the future, all organisations will be outfitted with their own AI LLMs directly into their existing cybersecurity mainframe such as Security Information and Event Management (SIEM) systems and other threat managing platforms. There will also be new positions available for tech crews who are dedicated to ‘teaching’ these LLMs with the latest trends and most relevant histories - as mentioned before, LLMs operate on the database that it is outfitted with, which means that when dealing with the ever-evolving landscape of something like security, the datasets require frequent updates and fine-tuning in order to stay current, much like the antiviruses that we run on our computers or the constant updates that we download onto our phones, all of which include the latest security updates. But this also means that AI isn’t here to replace a manual workforce, but rather enhance it overall as these platforms will be able to deliver faster results, while giving the human workforce the ability to focus solely on the most crucial component: ensuring that the appropriate threat response is administered.

Leveraging AI LLMs for Real-Time Threat Detection and Response

In the age of AI, the field of traditional ‘data science’ might seem antiquated or not as important, innovative, or lucrative as emerging technologies. However, the ebb and flow of tech trends are underpinned by traditional data science methodologies, which means data scientists are still a vital component in the tech ecosystem and will become ubiquitous across all industries as organizations increasingly rely on data to fuel their business objectives. Arguably, even more so thanks to the advent of generative AI, which has revolutionized our ability to digest vast volumes of data at the touch of a button, but still needs data scientists to interpret and verify AI-gleaned insights into accurate, and actionable assessments. McKinsey’s latest Global Survey has identified that inaccuracy poses the biggest threat to companies using AI and that investment in analytical AI is now on par with investment in generative AI. 3 data scientist jobs that pay over $100k Data Scientist, Advertising, Spotify, New York ($107,766 - $153,951 a year)
Principal Data Scientist, Salesforce, San Francisco ($185,800 - $296,400 a year)
Director - Data Scientist Prudential, Newark ($173,500 - $234,700) Looking to the future According to a recent study, the data science platform market is forecast to grow to a valuation of $322.9 billion by 2026 at a CAGR of 27.7% during the period of 2021 to 2026. Understandable when you take into consideration that 147 zettabytes of data has been created so far this year, compared to 120 zettabytes in 2023 and 97 zettabytes in 2022 (it’s estimated that each person can potentially generate around 15.87 terabytes of data daily). The even better news is that whether you’re a data analyst or data engineer, your skillset and ability to enforce data policies and shape how AI is used is commanding high salaries across the board. Unlocking your potential So, how can you pivot to a career in data if you’re already proficient in other areas of tech and are now ready to make the transition mid-career? For starters, having an understanding of Python, R, and SQL is essential for a career in data science as it will allow you to not only automate tasks but manage structured data, aggregate data, and perform tasks related to machine learning and data visualization. 3 high-paying data scientist jobs Data Scientist, Booz Allen, Chantilly ($75,600 - $172,000 a year)
AML Data Scientist II, TD Bank, Mount Laurel ($95,264 - $155,376)
Applied NLP Data Scientist, ubs, New York ($150,000 - $200,000 a year) If you need to brush up on one or all of the above, Udemy is offering a concise (and free) course on data science, machine learning, data analysis, data visualization using Python and R. Or to get a better understanding of how you can develop your machine learning skills, Andrew Ng, the co-founder of Google Brain and an adjunct professor at Stanford University is running a series of short courses via Coursera. Whatever you choose, one thing is clear: data science remains one of the most appealing (and generously-paid) career routes in tech. Ready to find your next role in tech? Whether you want to pivot to data science or put your coding expertise to good use, visit the Hackernoon Job Board today By Aoibhinn Mc Bride In the age of AI , the field of traditional ‘data science’ might seem antiquated or not as important, innovative, or lucrative as emerging technologies. AI However, the ebb and flow of tech trends are underpinned by traditional data science methodologies, which means data scientists are still a vital component in the tech ecosystem and will become ubiquitous across all industries as organizations increasingly rely on data to fuel their business objectives. Arguably, even more so thanks to the advent of generative AI, which has revolutionized our ability to digest vast volumes of data at the touch of a button, but still needs data scientists to interpret and verify AI-gleaned insights into accurate, and actionable assessments. McKinsey’s latest Global Survey has identified that inaccuracy poses the biggest threat to companies using AI and that investment in analytical AI is now on par with investment in generative AI. 3 data scientist jobs that pay over $100k Data Scientist, Advertising, Spotify, New York ($107,766 - $153,951 a year) Principal Data Scientist, Salesforce, San Francisco ($185,800 - $296,400 a year) Director - Data Scientist Prudential, Newark ($173,500 - $234,700) Data Scientist, Advertising, Spotify, New York ($107,766 - $153,951 a year) Data Scientist, Advertising, Spotify, New York ($107,766 - $153,951 a year) Principal Data Scientist, Salesforce, San Francisco ($185,800 - $296,400 a year) Principal Data Scientist, Salesforce, San Francisco ($185,800 - $296,400 a year) Director - Data Scientist Prudential, Newark ($173,500 - $234,700) Director - Data Scientist Prudential, Newark ($173,500 - $234,700) Looking to the future According to a recent study, the data science platform market is forecast to grow to a valuation of $322.9 billion by 2026 at a CAGR of 27.7% during the period of 2021 to 2026. Understandable when you take into consideration that 147 zettabytes of data has been created so far this year, compared to 120 zettabytes in 2023 and 97 zettabytes in 2022 (it’s estimated that each person can potentially generate around 15.87 terabytes of data daily). The even better news is that whether you’re a data analyst or data engineer, your skillset and ability to enforce data policies and shape how AI is used is commanding high salaries across the board. Unlocking your potential So, how can you pivot to a career in data if you’re already proficient in other areas of tech and are now ready to make the transition mid-career? For starters, having an understanding of Python, R, and SQL is essential for a career in data science as it will allow you to not only automate tasks but manage structured data, aggregate data, and perform tasks related to machine learning and data visualization. 3 high-paying data scientist jobs Data Scientist, Booz Allen, Chantilly ($75,600 - $172,000 a year) AML Data Scientist II, TD Bank, Mount Laurel ($95,264 - $155,376) Applied NLP Data Scientist, ubs, New York ($150,000 - $200,000 a year) Data Scientist, Booz Allen, Chantilly ($75,600 - $172,000 a year) Data Scientist, Booz Allen, Chantilly ($75,600 - $172,000 a year) AML Data Scientist II, TD Bank, Mount Laurel ($95,264 - $155,376) AML Data Scientist II, TD Bank, Mount Laurel ($95,264 - $155,376) Applied NLP Data Scientist, ubs, New York ($150,000 - $200,000 a year) Applied NLP Data Scientist, ubs, New York ($150,000 - $200,000 a year) If you need to brush up on one or all of the above, Udemy is offering a concise (and free) course on data science, machine learning, data analysis, data visualization using Python and R. Or to get a better understanding of how you can develop your machine learning skills, Andrew Ng, the co-founder of Google Brain and an adjunct professor at Stanford University is running a series of short courses via Coursera. Whatever you choose, one thing is clear: data science remains one of the most appealing (and generously-paid) career routes in tech. Ready to find your next role in tech? Whether you want to pivot to data science or put your coding expertise to good use, visit the Hackernoon Job Board today Ready to find your next role in tech? Whether you want to pivot to data science or put your coding expertise to good use, visit the Hackernoon Job Board today Ready to find your next role in tech? Whether you want to pivot to data science or put your coding expertise to good use, visit the Hackernoon Job Board today By Aoibhinn Mc Bride By Aoibhinn Mc Bride

Want To Earn 100k and Above? Then Look to Data Science Jobs

At the Potomac, Where DC, the Analog Political National Capital, and VC, the Digital Capital, Meet

Why You Should Choose Angular or Vue over React for Your New Web Application

আপনার নতুন ওয়েব অ্যাপ্লিকেশনের জন্য কেন আপনার কৌণিক বা Vue ওভার প্রতিক্রিয়া বেছে নেওয়া উচিত

Warum Sie für Ihre neue Webanwendung Angular oder Vue statt React wählen sollten

Por qué debería elegir Angular o Vue en lugar de React para su nueva aplicación web

Pourquoi devriez-vous choisir Angular ou Vue plutôt que React pour votre nouvelle application Web

आपको अपने नए वेब एप्लिकेशन के लिए React की बजाय Angular या Vue क्यों चुनना चाहिए

新しい Web アプリケーションに React ではなく Angular または Vue を選択する理由

새로운 웹 애플리케이션에 React 대신 Angular나 Vue를 선택해야 하는 이유

Por que você deve escolher Angular ou Vue em vez de React para seu novo aplicativo da Web

Почему вам следует выбрать Angular или Vue вместо React для вашего нового веб-приложения

Yeni Web Uygulamanız için Neden React Yerine Angular veya Vue'yu Seçmelisiniz?

Tại sao bạn nên chọn Angular hoặc Vue thay vì React cho ứng dụng web mới của mình

为什么你应该为你的新 Web 应用程序选择 Angular 或 Vue 而不是 React

Being about ancient technology for one of the first personal computers. At the time this written I was married to this girl and attending Georgia Tech as an Electronics Engineering major. She worked for a data processing firm in Atlanta just across the interstate from Tech, Data Systems Corporation, and she insisted that I needed to meet one of her coworkers, a fellow named Chester P. Quinn. Her logic for this was that he was really smart and  knew a lot about computers. I have never understood the value a person might have just based on smarts. Finally I gave in and went to meet the guy after class one day. He had an office upstairs from the data center, and I trudged up there trying to be enthusiastic. At the time I had my wonderful book bag, my daily carry, and it had my usual content. So Chester asked me right off if I knew anything about microprocessors. So i responded, “Cat got an ass? Oh yeah brother.”  and pulled out some 8080 manuals and a magazine or two. Told him this area was mine. Showed him an article from Radio-Electronics magazine about the MITS Altair S-100 computer and said see this, it is the near future, and this is a kit these folks are peddling. It is happening now.
Then he interrupted me and asked if I smoked pot. Well yes, as a matter of fact… So he says push the door to, and pulled out a bag. Then he asks me if he buys one of these computers would I put it together for him? Chester was legally blind and wore the original Coke Bottle Bottoms glasses. To read the code on a screen he had to put his face on the screen. So sure, Hell yes, I build your computer. At the time of our meeting I had been playing with some UARTS, and I had this little perfboard with one on it and had it rigged to send and receive and I showed it to him as well. Cute with LEDs, kawai.  Long story short, we ended up doing a dog and pony show for the owner of the DP company, and we were in business.Now the thing is that this was in the introductory era of microprocessors, just as they became somewhat useful, and we had decided to build our own machine. Two MegaHertz 8080 Intel processor. Not fast enough to run a floppy disk in polled mode, so we decided to roll our own freak DMA by jamming an Interrupt instruction onto the buss every data byte. In these adventures CVhester supplied half the brainpower and all the software. I supplied the other half of the brainpower and ll the hardware. So this design is a slight mod of the card I designed for our machine, this design being appropriate for an S-100 buss machine.Another thing about our mnachine was we put the BIOS in an EPROM, and I gave Chester the first card I wired up, and he already had the BIOS in an EPROM! Cool, eh? So I asked him to make me a copy. He said no, here, and gave me a floppy disk. Tell him what was in track #xx and sctor #yy and he would give me an EPROM. Smart ass, I had to enter the data (program) and assemble it, but hey one thing I understood from the get go; microprocessors were code and hardware nuts to butts (US Navy slang, I was already a veteran) so it didn’t take long. You really need to grasp both parts of the machine.One thing my bride at the time was absolutely correct about, Chester was one smart fellow! Actually brilliant. And I think he may have been a saint. But that’s part of another story. So here without further ado is an article I wrote farago and longaway about our design. Chester wrote all the code. We had a legit thing going and built a few computers. IDES Intelligent Data Entry Systems. But that’s another story. ☺ The Interface Age issue the magazine article appeared in had an annual index which is the first two pages below. This next image was not part of the article, but it gives details of our IDES system’s controller. As always comments, criticisms, and suggestions are appreciated. God bless all! Being about ancient technology for one of the first personal computers. At the time this written I was married to this girl and attending Georgia Tech as an Electronics Engineering major. She worked for a data processing firm in Atlanta just across the interstate from Tech, Data Systems Corporation, and she insisted that I needed to meet one of her coworkers, a fellow named Chester P. Quinn. Her logic for this was that he was really smart and  knew a lot about computers. I have never understood the value a person might have just based on smarts. Finally I gave in and went to meet the guy after class one day. He had an office upstairs from the data center, and I trudged up there trying to be enthusiastic. At the time I had my wonderful book bag, my daily carry, and it had my usual content. So Chester asked me right off if I knew anything about microprocessors. So i responded, “Cat got an ass? Oh yeah brother.”  and pulled out some 8080 manuals and a magazine or two. Told him this area was mine. Showed him an article from Radio-Electronics magazine about the MITS Altair S-100 computer and said see this, it is the near future, and this is a kit these folks are peddling. It is happening now. Then he interrupted me and asked if I smoked pot. Well yes, as a matter of fact… So he says push the door to, and pulled out a bag. Then he asks me if he buys one of these computers would I put it together for him? Chester was legally blind and wore the original Coke Bottle Bottoms glasses. To read the code on a screen he had to put his face on the screen. So sure, Hell yes, I build your computer. At the time of our meeting I had been playing with some UARTS, and I had this little perfboard with one on it and had it rigged to send and receive and I showed it to him as well. Cute with LEDs, kawai.  Long story short, we ended up doing a dog and pony show for the owner of the DP company, and we were in business. Now the thing is that this was in the introductory era of microprocessors, just as they became somewhat useful, and we had decided to build our own machine. Two MegaHertz 8080 Intel processor. Not fast enough to run a floppy disk in polled mode, so we decided to roll our own freak DMA by jamming an Interrupt instruction onto the buss every data byte. In these adventures CVhester supplied half the brainpower and all the software. I supplied the other half of the brainpower and ll the hardware. So this design is a slight mod of the card I designed for our machine, this design being appropriate for an S-100 buss machine. Another thing about our mnachine was we put the BIOS in an EPROM, and I gave Chester the first card I wired up, and he already had the BIOS in an EPROM! Cool, eh? So I asked him to make me a copy. He said no, here, and gave me a floppy disk. Tell him what was in track #xx and sctor #yy and he would give me an EPROM. Smart ass, I had to enter the data (program) and assemble it, but hey one thing I understood from the get go; microprocessors were code and hardware nuts to butts (US Navy slang, I was already a veteran) so it didn’t take long. You really need to grasp both parts of the machine. One thing my bride at the time was absolutely correct about, Chester was one smart fellow! Actually brilliant. And I think he may have been a saint. But that’s part of another story. At the time this written I was married to this girl and attending Georgia Tech as an Electronics Engineering major. She worked for a data processing firm in Atlanta just across the interstate from Tech, Data Systems Corporation, and she insisted that I needed to meet one of her coworkers, a fellow named Chester P. Quinn . Her logic for this was that he was really smart and  knew a lot about computers. I have never understood the value a person might have just based on smarts. Finally I gave in and went to meet the guy after class one day. He had an office upstairs from the data center, and I trudged up there trying to be enthusiastic. At the time I had my wonderful book bag, my daily carry, and it had my usual content. So Chester asked me right off if I knew anything about microprocessors. So i responded, “Cat got an ass? Oh yeah brother.”  and pulled out some 8080 manuals and a magazine or two. Told him this area was mine. Showed him an article from Radio-Electronics magazine about the MITS Altair S-100 computer and said see this, it is the near future, and this is a kit these folks are peddling. It is happening now. Chester P. Quinn Then he interrupted me and asked if I smoked pot. Well yes, as a matter of fact… So he says push the door to, and pulled out a bag. Then he asks me if he buys one of these computers would I put it together for him? Chester was legally blind and wore the original Coke Bottle Bottoms glasses. To read the code on a screen he had to put his face on the screen. So sure, Hell yes, I build your computer. At the time of our meeting I had been playing with some UARTS, and I had this little perfboard with one on it and had it rigged to send and receive and I showed it to him as well. Cute with LEDs, kawai.  Long story short, we ended up doing a dog and pony show for the owner of the DP company, and we were in business. Now the thing is that this was in the introductory era of microprocessors, just as they became somewhat useful, and we had decided to build our own machine. Two MegaHertz 8080 Intel processor . Not fast enough to run a floppy disk in polled mode, so we decided to roll our own freak DMA by jamming an Interrupt instruction onto the buss every data byte. In these adventures CVhester supplied half the brainpower and all the software. I supplied the other half of the brainpower and ll the hardware. So this design is a slight mod of the card I designed for our machine, this design being appropriate for an S-100 buss machine. Two MegaHertz 8080 Intel processor Another thing about our mnachine was we put the BIOS in an EPROM, and I gave Chester the first card I wired up, and he already had the BIOS in an EPROM! Cool, eh? So I asked him to make me a copy. He said no, here, and gave me a floppy disk. Tell him what was in track #xx and sctor #yy and he would give me an EPROM. Smart ass, I had to enter the data (program) and assemble it, but hey one thing I understood from the get go; microprocessors were code and hardware nuts to butts (US Navy slang, I was already a veteran) so it didn’t take long. You really need to grasp both parts of the machine. One thing my bride at the time was absolutely correct about, Chester was one smart fellow! Actually brilliant. And I think he may have been a saint. But that’s part of another story. So here without further ado is an article I wrote farago and longaway about our design. Chester wrote all the code. We had a legit thing going and built a few computers. IDES Intelligent Data Entry Systems . But that’s another story. ☺ IDES Intelligent Data Entry Systems The Interface Age issue the magazine article appeared in had an annual index which is the first two pages below. Interface Age This next image was not part of the article, but it gives details of our IDES system’s controller. As always comments, criticisms, and suggestions are appreciated. God bless all!

An Interrupt Driven Floppy Disk
Controller for the S-100 Bus

একটি বিঘ্নিত চালিত ফ্লপি ডিস্ক
S-100 বাসের কন্ট্রোলার

Ein Interrupt-gesteuerter Diskettencontroller für den S-100-Bus

Un disquete controlado por interrupciones
Controlador para el Autobús S-100

Une disquette pilotée par interruption
Contrôleur pour le bus S-100

एस-100 बस के लिए एक इंटरप्ट ड्रिवेन फ्लॉपी डिस्क नियंत्रक

S-100 バス用の割り込み駆動型フロッピー ディスク コントローラ

인터럽트 구동 플로피 디스크
S-100 버스용 컨트롤러

Um disquete acionado por interrupção
Controlador para o barramento S-100

Дискета, управляемая прерываниями
Контроллер для автобуса С-100

Kesintiye Dayalı Bir Disket
S-100 Veriyolu Kontrol Cihazı

Đĩa mềm được điều khiển bằng ngắt
Bộ điều khiển cho xe buýt S-100

用于 S-100 总线的中断驱动软盘控制器

Let's learn about via these 76 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Vc /Learn Repo 1. Valuing a Startup with Negative EBITDA Learn how to value a startup with negative EBITDA a startup valuation tool that stands for Earnings Before Interest, Taxes, Depreciation, and Amortization. 2. What Will the Next Era of Marketplaces Look Like? 3. Li Jin: My Path to Venture Capital 4. Community-as-a-Service Concept: How You Can Monetize Access 5. Airtable: 26 Firms Offering Non-Dilutive Funding Options to Founders Raise money without giving up a piece of your company 6. Advice for Founders During Uncertain Times Having been in the VC business for 22 years now, we have had the misfortune of living through at least two major downturns. While each has its own unique characteristics, there are a few lessons that may be relevant in responding to the latest crisis. 7. Consider a Loan Instead of VC Money “If you’re starting a business and you take out a loan, you’re a moron,” said firebrand entrepreneur Mark Cuban in an interview with Bloomberg. 8. Being A Creator: Definition, Economy, Tools and More 9. Crypto VC Behavior Reveals Market Trends for 2023 and Beyond Crypto VCs scaled down funding significantly in 2022 due to the macro uncertainty and systemic risks such as Luna's collpase and the recent FTX debacle. 10. 10 First Principles from Investing Legends for Better Decision Making Now We're living in crazy times. Coronavirus, lockdowns, recession and riots. It's enough to make even the most seasoned investor wonder what to do next. Not to mention what it all does to an average person. 11. Invest in People, Not Companies: Identity-Centric Investment Model According to Paul A. Gompers and Anna Kovner’s research, serial entrepreneurs are the key driving forces in the modern business world. Those who succeeded once have significantly higher chances of succeeding in their next venture by 30% when compared to the first-timers. That makes the past performance of an entrepreneur is a strong indicator of a future success.[1] 12. What Will VC Investment Look Like in the Year Ahead? The crises of 2022, which came in the form of geopolitical instability, economic recession, and inflation, have taken a toll on the venture capital investment. 13. What'll Happen to Crypto After Black Monday 2020? Bitcoin was explicitly called “digital gold” back in 2011, and a 2010 InfoWorld article says: 14. Aves Lair Announces Three Startups Comprising Inaugural Accelerator Cohort New York, NY, (December 14, 2020) – Aves Lair, a New York-based venture capital and frontier technology accelerator, announced today the debut of three innovative startups in its Winter 2020 cohort. 15. Pecan.ai Raises 11 Million to Bring Machine Learning to Business Analysts Pecan.ai has just come out of stealth, raising an $11M Series A, to enable business analysts to build machine learning models automatically. Dell Capital led the round, joined by S capital and bringing the total funding of the company to $15M. 16. Bootstrapping A Marketplace: "Fake It Till You Make It" Strategy 17. Passion Economy: The "Influencer Stack" and Chinese Influencer Incubators [Tweetstorm] 18. How 100 True Fans Can Change Everything More than a decade ago, Wired editor Kevin Kelly wrote an essay called “1,000 True Fans,” predicting that the internet would allow large swaths of people to make a living off their creations, whether an artist, musician, author, or entrepreneur. Rather than pursuing widespread celebrity, he argued, creators only needed to engage a modest base of “true fans”—those who will “buy anything you produce”—to the tune of $100 per fan, per year (for a total annual income of $100,000). By embracing online networks, he believed creators could bypass traditional gatekeepers and middlemen, get paid directly by a smaller base of fans, and live comfortably off the spoils. 19. What is the Biggest Risk For Passion Economy Startups? Creators want a direct relationship with their own users, which is against the platforms’ DNA. E.g. FB restricts ability for large group admins to message members. Agree that platforms adding in direct monetization
features is a big risk though to new startups in the Passion Economy.
Here’s some tactics for startups to hedge against incumbents: 20. Future of Technology in Modern Education: Hallway Conversation 21. Why Do Investors Say "No"? All startups hunt for capital, and in order to attract it from investors or funds they need, first of all, to contact them. 22. Passion Economy: Not All Communities Are Created Equal 23. Why Hardware Will Be Sexy to Mainstream VC (in 5 Slides) If you're a software engineer looking to become a founder, consider hardware. There's a shift coming. 24. How I Managed a Fund that Gave No Money, Took No Equity and Had an Exit I managed a fund that gave no money, took no equity, and had an exit. Here's that story. 25. Passion Economy: Future of Social Products [Tweetstorm] I like this article by Sam Lessin on the future of social, but think there’s a big omission. Namely, one of the major opportunities in social is to first
focus on helping creators monetize and own their audiences, then layering in social elements on top. 26. Private Equity in Your Pocket: How Digitization Changed VC Investment Market With further simplification of access to the VC market, people will start massively investing in new technologies and shape the image of tomorrow. 27. An insight into Asia's Tech Startup Scene With the boom in technology, tech startups are booming all over the world, especially in the Asian region. In recent years, it has been noticed that startups are growing the track record of success stories in the business world. 28. Investment Trend in Generative AI Shows No Signs of Slowing Down Generative AI refers to the use of machine learning to create new content. It has the potential to revolutionize industries 29. The European Tech Champions Initiative Aims to Fend Off Non-EU Investors The European Tech Champions Initiative (ETCI) aims to fend off the threat of non-European investors acquiring such businesses as they become more established. 30. Meet the Writer: HackerNoon's Contributor Eran Elhanani, Co-Founder of BullPerks and GamesPad My name is Eran Elhanani, and I’m the Co-Founder of BullPerks, a decentralized VC and multichain launchpad, currently ranking as the #2 IDO investment platform. 31. Pros and Cons of Running a Managed Marketplace 32. Naming Your Startup: 6 Considerations for First Time Founders Naming your startup can be a tricky and time-consuming process. This article breaks down some tips and stories  from VC Partners and CEOs to get you started. 33. What Are Crypto Venture Capitalists (VCs) Investing in This Bearish Market? The value of a cryptocurrency can be influenced by various factors, including news about the cryptocurrency and its underlying technology, regulations etc 34. 5 Things That Make Ecommerce Startups Work 35. Notion : A Product That Users Love, and VCs Can't Invest Into If you somehow navigate the mysterious path to reach the Notion HQ at 1:00 PM PST on a Friday, you would find an empty office with two golden poodles and a mutt running around. The entire team would not be far though, just shy of a mile away sitting at Barzotto and eating Extra-Long Noodles pasta over a glass of sparkling white wine. Add some soft serve gelato to that. This is just one of the many idiosyncrasies you would find in this 20 member start-up that has captured over a million users with their sleek product. 36. Data Leak of $1B “Tech Unicorn” Gorillas Shows How Far Venture Capital Has Fallen VC money is spilling out onto the streets, and despite millions of dollars of investment tech companies still neglect their security. 37. Raising Money? Prioritize Smart Money When raising capital, not all startups have options—they only receive one term sheet from one interested investor. And that should be celebrated. However, some startups are “hot” enough to attract multiple VCs. 38. For Founders: How the Venture Capital Investors Deal With Your Pitch Deck А useful guide for founders on how to prepare a pitch deck and how we look on it as a VC investor. 39. Over 90% of Startups Fail, So it is Difficult for Banks to Commit Millions BBVA launched BBVA Spark in July 2022, its comprehensive financial services proposal for innovative companies in their various stages of growth. 40. How Aggregators Are Replacing Brands [Tweetstorm] 41. Your Complete Guide To Venture Capital Funding For Startups Venture Capital is an institutional investment made into early-stage startup companies. If you want the investment, you need to work on a growth plan. 42. How Technology is Helping Venture Capitalists Make Investment Decisions From stone tools to the invention of the wheel to steam engines to the internet, technology has come a long way, it has overtaken nearly all major aspects of our lives. Just take this article for example, you wouldn’t be reading it if it wasn’t for technology. 43. Critical Necessities of the Venture-Backed Startup Once you’ve obtained the capital required to launch your startup, it’s time to look at fulfilling your technical needs. Do you have a secure web server? An IT security specialist? A reliable computer setup? These are all important, but there’s more. 44. 3 Best Tips To Attract Investors Being An Outsider Unshackled Ventures founding partner Nitin Pachisia explains three ways he built a strong network during his fundraising journey. 45. Passion Economy is a Consumer Enterprization Movement 46. List of Top VC Firms Investing Into Promising HR Startups [Part 4] Hi there! My name is Vitalii Berdichevskyi, I’m a Product Manager. In this essay of the series I will share with you List of top VC's invested in HR Tech. Previous parts of the series you can find below: 47. Startup Metrics: The Dynamics of Network Effects [This essay was co-authored by my colleague D’Arcy Coolican (@dcoolican) and me. We also compiled a list of metrics to measure your network effects.] 48. [Announcement] Wise Raises $12M To Accelerate Embedded Banking Partnerships Wise, the leading embedded banking FinTech, that offers business banking via partnerships, announced today that it has closed a $12M Series A. 49. How Crypto Fund Iconium is Leading The Charge for Web3 In this post we’ll be looking at how crypto investment fund Iconium is leading the charge for development of Web3 infrastructure. 50. How To Raise Capital for Blockchain Projects (and other startups) Venture capitalists get pitched hundreds of times a year. And, even though countless articles have been written on the topic of how to present a startup to professional investors, entrepreneurs consistently fail to address check points needed by the investor to come to a decision. 51. The Hedge Fundization of VC The Hedge Fundization of VC 52. Rock, Paper, Blockchain Most data from government and financial institutions is still siloed. Public blockchains may be used by anyone and are permissionless. But how far off are we? 53. 263 Meetings, 12 Months and $128,700 in Startup Funding Later Is traction enough to raise funding for a Startup or do you need more than that? Here's how we raised $128,700 at Alpe. 54. Passion Economy: How to Organically Grow While Maintaining Paid Groups 55. Walnut Raises $2.5M Seed To Make Product Demos Easier For the Post-Pandemic World Seed round is being led by NFX with additional participation from other top SF Angels 56. Growing Your Influencer Brand Into a Sustainable Business 57. Networking at VC Events: How to Gain More Value? The pandemic and lockdowns have proven to the world the simple truth that we are social beings. 58. What Rowing For The U.S. National Team And Building A Startup Have In Common Saying “it’s not fair” won’t get you there. To be the best, you have to beat everyone. 59. Passion Economy Shall Be The Disruptor of 2021 and Beyond Clayton Christensen's disruption theory and what it means for the Passion Economy 60. The Secret Behind Successful Marketplaces [Tweetstorm] Thread: The best consumer marketplaces end up
supply-constrained, because they tap into an incredible amount of demand. For instance, Airbnb, Lyft, and Uber have seemingly infinite demand since the product/market fit is so strong–and this demand puts pressure on supply. 61. 7 Ways to Stand Out at a Tech Conference After more than 60 meetings at a major tech conference in Dubai Gitex Global 2022, I figured out what helps projects stand out and get stuck in the memory. 62. The State of Startups in the Southeast Fourth annual report reveals continued market maturation; $34.5 billion invested in southeastern startups since 2015 63. How to Choose the Right Partners in a Venture Capital Fund When you are searching for the right venture capital fund, there are a lot of factors that can go into deciding which fund to choose. 64. How Hacker Noon Leans on Our Community -  a #CUBoulder Talk by Linh Dao Smooke Transcript of COO Linh Dao Smooke's talk on how Hacker Noon leans on their community as a fundraising mechanism as opposed to traditional VCs. 65. Takeaways from Sequoia Capital's Black Swan Memo Soothsayer Sequoia Capital warned its portfolio companies in a memo—how can startups stay healthy? 66. Disruption Theory for the Passion Economy: 4 Major Implication (Part 2) Passion Economy platforms are rooted in a value proposition that deeply appeals to workers—they lower the barriers to earning income 67. The Passion Economy’s Worklife Balance The top-earning writer on the paid newsletter platform Substack earns more than $500,000 a year from reader subscriptions. The top content
creator on Podia, a platform for video courses and digital memberships,
makes more than $100,000 a month. And teachers across the US are
bringing in thousands of dollars a month teaching live, virtual classes
on Outschool and Juni Learning. 68. The Future of Marketplaces: Reinventing the $10 Trillion Service Economy [I co-wrote this essay with my a16z partner Andrew Chen. We’re excited about the future of marketplaces and the service economy. Hope you enjoy this glimpse into our thinking, and let us know if you have any feedback!] 69. Live Shopping Features On Social Media And Interview With Chinese Social Commerce Tycoon How new models of e-commerce can enable the Passion Economy 70. How We Closed Our First Pre-Seed Round in 2 Weeks We got accepted into YC on the 9th of December 2019. Originally, we wanted to raise a pre-seed round before starting YC on the 6th of January. But at the same time, we didn’t want to incorporate the company in 2019 so we wouldn’t have to deal with the taxes for 2019. Call us lazy! One would say lazy is not bad, as long as it’s smart lazy! 71. How to Fix Entrepreneurship in Europe I Studied Entrepreneurship in 4 Countries. Here's How To Fix it in Europe. 72. What to Learn from the Power User Curve The importance of power users Power users drive some of the most successful companies — people who
love their product, are highly engaged, and contribute a ton of value to the network. In ecommerce marketplaces it’s power sellers, in ridesharing platforms it’s power riders, and in social networks it’s influencers. 73. Startup Metrics: 16 Ways to Measure Network Effects [This post was co-authored by me and my colleague at a16z, D’Arcy Coolican (@dcoolican). This is a companion to our other essay exploring the dynamics of network effects. If you’re an operator, we’d love to hear your thoughts on how you measure your own product’s network effects!] 74. How to Build a Company in Highly Regulated Marketplaces: 8 Things to Consider What can founders / operators do when it comes to navigating the complexities that come with the territory in regulated industries? Here are some opportunities. 75. Bigger picture with the Decentralized identifiers This article summarizes DIDs and VCs' fundamental principles, describes the use in the real world, and provides bonus materials for tech-savvy readers. 76. How to Invest Intelligently in the Podcast Ecosystem In the world of podcasting, the flywheel is spinning: new technologies including AirPods, connected cars, and smart speakers have made it much easier for consumers to listen to audio content, which in turn creates more revenue and financial opportunity for creators, which further encourages high-quality audio content to flow into the space. There are now over 700K free podcasts available and thousands more launching each week. Thank you for checking out the 76 most read stories about Vc on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

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