Amit Pal

Hi folks, more about pointers in C which are undeniably powerful, but they can also be tricky for even seasoned developers. 🤯 Avoid common pitfalls and write safer, more robust code with these tips: 1️⃣ Understand Scope & Lifetime: 🤔    - A pointer's value is only valid as long as the memory it points to exists. Keep track of object lifetimes to prevent dangling pointers (pointing to  freed memory!) leading to crashes. 2️⃣ Null Pointers Are Your Friends: ✅    - Always initialize pointers to `NULL` when not pointing to a specific location. Check for `NULL` before dereferencing to avoid segmentation faults.  "Null checks are your safety net!"  3️⃣ Beware of Dangling Pointers: 💀   - When memory is freed, the pointer becomes invalid. Reusing it can lead to unpredictable behavior. Double-check deallocation and pointer usage. 4️⃣ Pass by Reference with Care: ⚠️   - Be mindful when passing pointers as arguments. Modifying a passed pointer might unintentionally change original data. Use const qualifiers for  safety. 5️⃣ Deep Dive into Documentation: 📚   - C libraries often have complex pointer interactions. Consult documentation thoroughly to understand how memory management works for specific  functions and structures. What's your biggest pointers challenge? Share in the comments! Let's learn from each other's experiences. 👇 #CProgramming #SoftwareDevelopment #MemoryManagement #Pointers

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5 questions in 4 minutes: 1. What does a senior software engineer do? 2. How do you motivate your team? 3. Individual Contributor or Engineering Manager? 4. How do you stand out in Big Tech? 5. Is ChatGPT affecting the job market? Hemant Pandey, senior software engineer at Meta, shares his thoughts. Let me know your thoughts in the comments. Cheers!

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In 2021, I tried to understand how tech compensation really works - at least in the Netherlands. I published "the trimodal nature of software engineering salaries" article. Time for an update: Back then, based on ~100 data points (and my own experience as a hiring manager,) I suspected that there are three, distinctively different compensation bands for software engineers: 1. Local companies (Tier 1) Most local businesses that benchmark to the numbers on most salary comparison sites. They usually offer close to – and sometimes slightly above – what government positions advertise for senior engineering roles. 2. Ambitious local companies (Tier 2) Startups, scaleups, and more ambitious local companies that want to hire and retain the best people, locally. 3. Companies benchmarking across a region (Tier 3). Big Tech companies, hedge funds and scaleups that hire across regions, frequently hire from abroad, and often compete with fellow Big Tech giants for talent. Curiously, the third tier (Tier 3) seemed to be missing from most public compensation data sources. Since publishing the model, I've both received lots of feedback suggesting it applies to the US, Canada, UK, Europe and many other countries. I've since then also collected far more data points, manually tagged companies by tier, and can finally confirm that yes, this model holds up when compared with actual data. Here is the longer analysis (and the stats behind this graph below) in yesterday's The Pragmatic Engineer issue. Read it here: https://lnkd.in/e7FV4gBB

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Finally my talk is up! A short story of how we built and scaled #GraphQL at Dream11

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🕒 Looking for a reliable, open-source time tracker? Meet Kimai 💡! It's a powerful, free tool that handles freelance and business projects with ease. 🚀 Kimai's impressive features include: ✅ JSON API, invoicing, data exports ✅ Multi-timer & punch-in punch-out mode ✅ Tagging, multi-user, multi-timezones, multi-language (30+ translations!) ✅ Authentication via SAML/LDAP/Database, two-factor authentication (2FA) ✅ Customizable role and team permissions ✅ Responsive design, user/customer/project specific rates ✅ Advanced search & filtering, money and time budgets ✅ Advanced reporting, plugin support ...and so much more! 🔧 System requirements: PHP 8.1.3, MariaDB/MySQL, webserver & subdomain, PHP extensions 🚀 Either set up natively with Git and Composer, or use Docker containers. Host it on your local machine, shared hosting, Synology, or hosted environments. If you'd rather not host yourself, check out the Cloud version! 🔄 Updating and maintaining Kimai is a breeze, with regular release versions and easy-to-follow upgrade steps 📚. 👉🏼 [Documentation](https://lnkd.in/g8xuNwVg), [blog](https://lnkd.in/gSyf9hpk), and [plugins](https://lnkd.in/g3_caFCF) are all available. #OpenSource #TimeTracking #Kimai #Productivity #TechCommunity

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🔧 Ever found yourself in a situation where tracking time spent on projects or tasks became a challenge? Meet Kimai, an open-source, professional-grade timetracker that can help you manage your time more efficiently! 🌟 Key Features: ✅ JSON API ✅ Invoicing ✅ Data exports ✅ Multi-timer & punch-in/punch-out mode ✅ Tagging ✅ Multi-user, multi-timezone, multi-language (30+ translations!) ✅ SAML/LDAP/Database authentication ✅ Two-factor authentication (2FA) ✅ Customizable role & team permissions ✅ Responsive design ✅ User/customer/project specific rates ✅ Advanced search & filtering ✅ Money & time budgets ✅ Advanced reporting ✅ Plugin support ...and so much more! 📚 I've personally found Kimai to be an invaluable tool in organizing my projects and tracking time. With its robust feature set and ease of use, it has made time management a breeze! 💬 I'd love to hear your thoughts and experiences with time-tracking tools. Let's start a conversation and learn from each other. Don't forget to include the hashtags #Kimai #TimeTracking when sharing your thoughts!

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LangChain recently released their ebook "Definitive Guide to Testing LLM Applications" (https://lnkd.in/dgrtbzM8), a read that I totally recommend 🤩 Testing LLM applications should be an integral part of every stage of the development lifecycle, from design to pre-production, and even post-production. It allows us to identify issues early, refine models continuously, and ensure that applications perform as expected in real-world scenarios. 💡 While platforms like LangSmith by LangChain provide excellent tools for LLM testing, there is also other alternatives that might be worth checking out: * DeepEval (https://lnkd.in/dHRGRTCp) focuses on confidence-based evaluation, helping to measure how certain the model is in its responses. * Ragas (https://lnkd.in/d3bB5mDR) offers a stable, open-source solution for rigorous LLM evaluation, making it easy to integrate into existing workflows. * TruLens (https://www.trulens.org/) provides a framework for interpretability and understanding of LLM decisions, which is crucial for transparency and debugging. Incorporating diverse testing methodologies ensures that LLMs are not just powerful, but also reliable and aligned with the desired outcomes. As we push the boundaries of what LLMs can do, let's not forget to test, iterate, and refine at every step. 🚀

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Most big tech Software Engineers in Switzerland enjoy compensations ranging between $200k to $500k a year in their 20s and early 30s. 🇨🇭💰 If this fact seems unbelievable to you, it might indicate a gap in your understanding of salaries within big tech companies. Given the wealth of information available on this topic, I'll link to some resources that can hopefully illuminate: - Clement Mihailescu: A famous YouTuber who focuses on salary transparency within Big Tech companies (search "Clément Mihailescu salaries" on YouTube). - Rahul Pandey and Alex Chiou: Founders of Taro (YC S22), a mentorship platform designed for those aiming to join and thrive in big tech. They openly discuss salaries in big tech, including outlier cases of young engineers earning $800k+. - Gergely Orosz: Author of The Trimodal Nature of Software Engineering Salaries in the Netherlands and Europe, article that highlights the distinct salary brackets in big tech compared to the broader market. - Zach Wilson: A big tech data engineer who has openly shared his salary journey, reaching $500k+/year within just a few years in the industry. - Ryan Peterman: Often talks about salaries in big tech and ways to get in such companies and move up the ladder. - Levels.fyi by Zaheer Mohiuddin and Zuhayeer Musa: The ultimate resource for salary data across big tech companies, categorized by location, seniority level, and company (look for salaries of Google, NVIDIA, Meta, etc in Zurich). - Blind: For further evidence, join Blind and delve into thousands of discussions about big tech salaries. - You can also check out my salary sharing document for devs in Europe to get insights on community-sourced compensation data. Big Tech companies, being multinationals, adjust their pay scales based on location. In Switzerland, their salary bands are comparable to those in the USA. If you're curious about my journey landing a big tech role in Switzerland, check out the comments.

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This issue isn’t new — it’s a cycle that re-emerges every few years, affecting companies that lack a technically savvy full-time CTO who can vet options and identify long-term, stable solutions. Without this key role, companies risk wasting millions redeveloping the same products repeatedly. In my early career, I experienced this firsthand while establishing the technology stack for Animal Care Technologies. We invested heavily in developing services and web applications using Cold Fusion, only to replace them with stateful web services powered by SOAP as the data transfer protocol. Over time, the industry went in a different direction again towards REST, and I found myself on the losing side of a community-wide shift. The problem cycle generally follows this pattern: - New tech emerges and quickly gains traction. - Jobs are created in the new field. - Inexperienced developers take on leadership roles and struggle to vet options for longevity, given the overwhelming number of choices. Fast forward a few years, and we'll see this same post written about the next big thing: ML platforms for low-code/no-code applications. This pattern is familiar to anyone who’s been in the industry for a while. As Matthieu Petruzzi illustrates, the hype around web apps began with the browser wars (WebKit vs. Gecko vs. MSHTML), and we're only now seeing a sense of maturity as developers settle into their respective camps. Before the rush to web apps, there was the explosion of data storage frameworks, with ORMs like Hibernate, Entity Framework, and ActiveRecord, as well as NoSQL implementations like MongoDB. This was also when cloud infrastructure started to take off, with larger data storage players like Google BigTable and Amazon DynamoDB. And before that, the major focus was on data transfer architectures like REST, SOAP/WCF, and Pub/Sub. So, what’s the solution to this revolving door problem? Companies must invest in a technically seasoned, full-time engineer — ideally, a CTO — to make informed, strategic decisions and avoid the costly pitfalls of redeveloping their tech stack every few years. #CTO #Leadership #TechStrategy

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💡 Golden Paths: guiding developers towards excellence, also on day 2 Ever heard of "Golden Paths"? These are the optimal routes that developers can follow to achieve the best results with minimal friction. While tools like Backstage provide solid templates to get started on day one, they often fall short when it comes to ongoing maintenance and day 2 changes. At Amplication, we’ve taken Golden Paths to the next level by empowering platform teams to continuously update architectural standards and configurations across all services. Whether it’s a new code best practice, an updated Terraform module, or a refined Helm chart, platform teams can make these changes centrally. This means that development teams receive alerts highlighting technical debt that needs attention or pull requests with the updated code. This keeps every application aligned with evolving standards, without disrupting ongoing projects. This isn’t about limiting creativity; it’s about removing obstacles so your team can focus on innovation rather than wrestling with setup, configuration, or fragmented updates. With Amplication, platform teams maintain control over infrastructure standards, while developers enjoy a consistent, streamlined experience throughout the entire development lifecycle. I firmly believe that clear guidance and adaptable tools empower developers to do their best work and accelerate the entire development lifecycle. If you want to learn more, visit https://lnkd.in/ewBq5TTK and try it out for yourself, or use the link on my profile to schedule a demo.

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From Code to Impact: The Journey of a Software Developer 🚀 I often get asked, "How did you get to work at a top product-based company?" The answer is simpler than it sounds: Focus on building real impact, not just writing code. As a Software Development Engineer (SDE), I've learned that writing code is just the start. The true power lies in the architecture, the systems thinking, and the ability to solve real-world problems. Here are some key lessons I've picked up along the way: 1️⃣ Think Big, Break It Down The toughest problems in tech aren’t solved with simple solutions, but by breaking them into smaller, digestible pieces. When tackling large-scale systems, it's crucial to break down the problem into smaller units, focus on the core problem first, and build iteratively. Don't try to build the perfect solution right away – perfection comes through iteration. 2️⃣ Be a Constant Learner Technology evolves fast, and staying ahead of the curve requires continuous learning. Whether it’s mastering new frameworks, understanding system design patterns, or diving deep into algorithms, the key is to never stop. Learning is not just a part of the job; it's the job. 3️⃣ Collaborate, Don’t Compete Some of my best experiences have been in team environments. The most impactful code isn’t written in isolation – it’s built with and for a team. Strong collaboration, mentorship, and code reviews make you not only a better developer but also a better problem-solver. The best code is often a team effort. 4️⃣ Fail Fast, Learn Faster Failures are inevitable. But what matters is how fast you recover, adapt, and improve. In the tech world, failure isn’t the opposite of success; it’s part of it. Each bug, each deployment failure, each missed deadline teaches you something. Embrace the failures; they’re your greatest teachers. 5️⃣ Think in Scalable, Reliable, and Maintainable Systems Building products at scale isn't just about adding features – it's about thinking about longevity. We write code that should be efficient, reliable, and maintainable. Systems should be designed for the long term, where performance, security, and scalability aren't afterthoughts – they are foundational principles. In the end, software engineering isn’t just about pushing code to production – it’s about creating solutions that make a difference. The joy of seeing millions of users benefiting from the work you’ve done is unparalleled. 🚀 To all aspiring SDEs: Never stop building, never stop learning, and never underestimate the power of collaboration. If you can dream it, you can build it – just take the first step. #Tech #SoftwareEngineering #SystemDesign #Coding #GrowthMindset #ProductDevelopment #TechCommunity #EngineeringExcellence #Innovation #CareerGrowth

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*Bracing myself for the response I might get for asking this* Software Engineers: Would you ever consider working on an EQUITY ONLY basis (ie no salary, just owning a portion of the business, the value of which would increase as it progresses and scales)? Assuming: - The product is really interesting and so is the work - The product is in the "tech for good" space and would be a really positive contribution to parts of society - Seed funding is confirmed to be coming with several backers keen & they will able to offer a salary in the near future - The product is super early stage, you would be writing the software from the ground up, but the concept, design and business model once funding is received are all solid I know that there will be many who would never consider this under any circumstances and I totally get why, but just curious to hear if any of you would if it was for a product you believed in enough? And would you look at it on a full-time basis or a part-time basis? It's something I got asked about and so I thought I would throw it out to Linkedin to see what you all think rather than assuming that I know the answer on your behalf. #startup #founder #softwareengineer

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𝗪𝗵𝘆 𝗥𝗲𝗰𝗿𝘂𝗶𝘁𝗶𝗻𝗴 𝗦𝗮𝗮𝗦 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 𝗶𝘀 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 – 𝗔𝗻𝗱 𝗪𝗵𝘆 𝗜𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 As a software engineering manager in the SaaS industry, you know that finding the right talent is crucial. So does it frustrate you that the people you task with your recruiting do not understand that recruiting SaaS developers fundamentally differs from hiring regular software engineers? 𝙒𝙝𝙮 𝙩𝙝𝙚 𝘿𝙞𝙛𝙛𝙚𝙧𝙚𝙣𝙘𝙚? SaaS developers need a unique blend of skills and experience. They must be proficient in languages like JavaScript, Python, Ruby, and Node.js, and have hands-on experience with cloud infrastructure services such as AWS, Google Cloud, and Microsoft Azure. They also need to excel in API integration, database management, DevOps, and automation, and be adept with containerization technologies like Docker and Kubernetes. 𝙎𝙤𝙛𝙩 𝙎𝙠𝙞𝙡𝙡𝙨 𝙈𝙖𝙩𝙩𝙚𝙧 𝙏𝙤𝙤 Beyond technical prowess, great SaaS developers excel in problem-solving and communication. They can adapt to unexpected issues and find solutions quickly. Moreover, SaaS development is a team effort, requiring developers to work closely with designers, project managers, testers, and others. Effective communication ensures mutual understanding and alignment of technical decisions with every other aspect of the product. A top-tier SaaS developer can explain complex ideas to non-technical team members, fostering better collaboration and a unified approach to achieving common goals. 𝙏𝙝𝙚 𝘾𝙤𝙣𝙨𝙚𝙦𝙪𝙚𝙣𝙘𝙚𝙨 𝙤𝙛 𝙂𝙚𝙩𝙩𝙞𝙣𝙜 𝙄𝙩 𝙒𝙧𝙤𝙣𝙜 Hiring the wrong developer can lead to a product that is insecure, unscalable, and user-unfriendly. This not only affects your product’s performance but can also damage your company’s reputation and bottom line. 𝙒𝙝𝙮 𝙋𝙖𝙧𝙩𝙣𝙚𝙧 𝙬𝙞𝙩𝙝 𝙍𝙤𝘾𝙤 𝙍𝙚𝙘𝙧𝙪𝙞𝙩𝙢𝙚𝙣𝙩? At RoCo Recruitment, we specialize in identifying top-tier SaaS developers who bring not just technical skills, but a deep understanding of the SaaS landscape. We know what makes a great SaaS software engineer and how to attract them. Partnering with us ensures that your development team is equipped to build secure, scalable, and user-friendly products. Finding the right SaaS software developers is the only way to ensure your SaaS product is functional and competitive. Don’t leave it to chance – let RoCo Recruitment help you build a team that drives success. 𝘿𝙈 𝙞𝙛 𝙮𝙤𝙪 𝙬𝙤𝙪𝙡𝙙 𝙡𝙞𝙠𝙚 𝙩𝙤 𝙨𝙘𝙝𝙚𝙙𝙪𝙡𝙚 𝙖 𝙘𝙖𝙡𝙡 𝙩𝙤 𝙙𝙞𝙨𝙘𝙪𝙨𝙨 𝙩𝙝𝙞𝙨 𝙛𝙪𝙧𝙩𝙝𝙚𝙧 #Softwareengineering #SaaS #SaaSsoftwareengineers #RoCorecruitment #recruitment

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When building LLM Agents communications having a well defined registries for functions/APIs they can call and for agents themselves is imperative. Depending on how you build the architecture around inter-bot communication you will reduce - 1 - Initial prompt engineering (Shorter prompts) 2 - The possibility of agent not getting to the answer for your query I post on X too - https://lnkd.in/gd9AkqTX

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🚀 Exciting News for Developers! Join us on Friday, November 15th for an in-depth session on the upcoming new major version of the Fluent UI Blazor library. This release brings groundbreaking Web Components and major architectural innovations to take your web development to the next level. With Vincent Baaij https://www.dotnetconf.net 🔍 What to Expect: Overview of New Features: Explore the latest enhancements, including cutting-edge Web Components. Architectural Changes: Understand the performance, scalability, and maintainability improvements in the new architecture. Migration Guide: Yes, there will be breaking changes! But we’ll guide you with practical tips and best practices to smoothly transition your projects. Hands-On Demos: Watch the new features in action with live coding demonstrations. 📅 Don’t miss out! Mark your calendar and get ready for this transformative session. #Blazor #FluentUI #WebDevelopment #WebComponents #Architecture #Migration #LiveCoding #TechTalk #WebInnovation

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Zookeeper is a highly-available coordination service that can be leveraged to conduct leader election in distributed systems. Lets take a look at how this is done in a step-by-step process. The root node creates a persistent znode for election & all the nodes in the system start joining this znode as its children to put their candidacy as a leader. As zookeeper maintains a global order, it assigns each znode an id based upon their insertion order. This is the first step of the process. In next step, each node queries the children of /election znode & figures out if the znode associated with it is the one with the smallest id in list of children. This is step two of the process. In the final step, if the znode associated with a node is the one with smallest id then it means that this node is the leader node. If it is not the case then that node understands that it is the follower node & prepares itself to start receiving instruction from the leader node. This is how Zookeeper assists in leader-election by a three step process. Credits to Michael P. for explaining this concept in his distributed systems course. #zookeeper #distributedsystems

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Using GPT's API has been a practice for almost all software engineers. But the struggle was real, because the output was uncertain. One of the discussions with Ammar Mohanna, PhD, he stressed enough on the importance of having deterministic outputs from these models, before passing the resutls to the end user (Thank you for the lightening discussion back then) The struggle is real when the developers want json outputs from any LLM ( GPT, Llama, Mistral, you name it). Woke up on an email for the output pydantic parser (found in the image below). While I think it's a life saver, it still raises my curiosity about how they were able to make it? I remember trying different prompts for months now and at many times I had to send the request again (Sneak Peak: It happend also in a client demo :))) ) What do you think? #ai #FakkerAI #gpt #pydanticmodels #chatgpt #llama

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Most people think the cloud saves them money. Not with Kafka. ❌ Storage costs alone are 32x more expensive than what they should be. Even a miniscule cluster costs hundreds of thousands! That’s right. Let’s run the numbers. Assume a small Kafka cluster consisting of: 🔷 6 brokers 🔷 35 MB/s of produce traffic 🔷 a basic 7-day retention on the data (the default setting) With this setup: 1. 35MB/s of produce traffic will result in 35MB of fresh data produced. 👌 2. Kafka then replicates this to two other brokers, so a total of 105MB of data is stored each second - 35MB of fresh data and 70MB of copies 🤔 3. a day’s worth of data is therefore 9.07TB (there are 86400 seconds in a day, times 105MB) ✅ 4. we then accumulate 7 days worth of this data, which is 63.5TB of cluster-wide storage that's needed 😳 Now, it’s prudent to keep extra free space on the disks to give humans time to react during incident scenarios, so we will keep 50% of the disks free. ☝️Trust me, you don't want to run out of disk space over a long weekend. 63.5 times two is 127TB - let’s just round it to 130TB for simplicity. That would have each broker have 21.6TB of disk. 💡 Let’s get to pricing now. We will use AWS’s EBS HDDs - the throughput-optimized st1s. Note st1s are 3x more expensive than sc1s, but we need the extra IO throughput. st1s cost 0.045$ per GB of provisioned (not used) storage each month. That’s $45 per TB per month. 💡 We will need to provision 130TB. That’s: 🛑 $188 a day 😕 🛑 $5850 a month 😳 🛑 $70,200 a year 🤯 btw, this is the cheapest AWS region - us-east. 💡 Europe Frankfurt is $54 per TB/month which is $84,240 a year. 😭 But is storage that expensive? Hetzner will rent out a 22TB drive to you for… $30 a month. 6 of those give us 132TB, so our total cost is: 🟩 $5.8 a day 🟩 $180 a month 🟩 $2160 a year Hosted in Germany too. AWS is 32.5x more expensive! ❌ 39x times more expensive for the Germans who want to store locally. 😥 🙅‍♂️ What about Tiered Storage? It’s much, much better with tiered storage. It'd cost you around $19,200 a month in AWS, which is "just" 9x more expensive. But it comes with a lot of other benefits, so it's a trade-off worth considering. That being said, the feature is not yet GA after 6 years… so it’s understandable if you still don’t have it. In the end? It's 39x more expensive. And I haven't even begun to mention the network costs, which can cost an extra $103,000 a year. For more on that, you can see my previous detailed post on the matter here 👇 ✅ https://lnkd.in/dQDcZ-Q2 --- Did you like this type of informative content? 1. Share so your network sees it too! 💥 2. Follow me here for more 👇 ✅ Stanislav Kozlovski 3. Check out my 1-a-week 2-minute-read (no more than 476 words) highly-polished Kafka newsletter (that 5k+ engineers love) ✅ https://lnkd.in/eXpjx78p 💨 🤙

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Hot take: Building a business around open-source software is a lost battle. In last few months, we have seen how companies such as HashiCorp, Redis labs have faced the heat when they adopted business source licensing model. It resulted in their alternatives popping up within few days like OpenTofu, Dragonfly etc. There is no moat for such businesses because the underlying software is open-sourced which means new entrants can easily replicate what they are doing. They can sell it at a cheaper price or with a better ecosystem(if they are cloud vendors) or both. These businesses also face the dilemma of whether to merge a major improvement in the open-source or put it behind the SaaS offering. An alternative to this model is to build the business around consulting & training. The business keeps the software open sourced & provide consulting services to clients who want to use it in the most efficient manner. Pivotal has done a great job with Spring framework using this model. This way your revenue stream is not marginalized by new players easily & the core team can focus on improving the software without worrying whether it makes their core revenue model i.e. SaaS offering less attractive. Especially in the non-ZIRP era, companies are bound to reconsider where they are spending. Hence they will choose the option that burns the smallest hole in their pocket. #opensource

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