Tau’s Post

Sharing My Latest Project: a Duck Meme Bot I’m pleased to share a fun project I’ve been developping called *Duck Meme Bot*. This bot automatically gathers the latest duck memes from X and posts them to a Telegram channel. It’s been an interesting journey combining Twitter/X’s API with Telegram using Python, and I’m excited to see how the community can help make it even better. Why This Project? As an aspiring developer, I’m always looking for creative ways to bring a bit of joy to people’s day, and who doesn’t love a good duck meme? This project also gave me a great opportunity to explore API integrations, automation, and continuous deployment using GitHub Actions. What’s Involved: - Python for scripting - Twitter/X API for sourcing memes - Telegram Bot API for sharing them in a channel - GitHub Actions for automated deployment and workflow management - VS Code as a coding platform Take a Look at the Code: The project is open-source and available on my GitHub. I’d love for you to check it out, try it, and even contribute. Whether it’s refining the code, suggesting new features, or just providing feedback, your insights would be greatly appreciated! 👉 GitHub Repo: https://lnkd.in/gJid49xk Join the Collaboration: If you’re interested in Python, APIs, or just enjoy a good meme, I encourage you to explore the repo and share any ideas or improvements you might have. Let’s work together to make this project even more fun and effective! Looking forward to your thoughts and contributions! #Python #APIs #Automation #OpenSource #GitHub #TelegramBot #TwitterAPI #CommunityProject

Hey everyone, I'm excited to share some great news from the world of coding! GitHub has just released a new AI tool that can automatically fix code vulnerabilities. This is a game-changer for developers and security teams alike. The tool, which combines GitHub's existing products Copilot and CodeQL, can find and fix vulnerabilities in code with incredible accuracy. It can fix over two-thirds of the vulnerabilities it finds and covers more than 90% of alert types in the languages it supports. This means that developers will spend less time manually fixing bugs, freeing them up to focus on more creative and strategic tasks. Security teams will also benefit from this tool, as it will help them to identify and address vulnerabilities more quickly and efficiently. Overall, this is a major development that has the potential to significantly improve the security of code. I'm excited to see how this tool is used in the future! What are your thoughts on this new AI tool? Let's discuss in the comments below! #github #ai #coding #security #developer #devops

Ever dreamed of a tool that could fix code vulnerabilities as you type? GitHub just made it a reality. --- GitHub's latest innovation blends AI smarts with real-time code analysis: - 🛠️ Automatically resolves code vulnerabilities. - 🤖 Powered by GitHub Copilot & CodeQL for real-time vulnerability detection. - 📚 Languages Covered: JavaScript, TypeScript, Java, Python. - 💡 More than 90% of alerts handled across supported languages. The bottom line: Coders can focus more on creativity, less on fixing bugs. --- For developers, this means a seismic shift: No more pausing your flow to fix vulnerabilities. GitHub's AI has your back, automating the tedious parts of code security. --- How does it work? AI Autofix combines CodeQL's deep code analysis with GitHub Copilot's agility, offering solutions often without needing any manual edits from developers. Essentially, it's like having a security expert and a coding assistant in one. --- The promise is bold: GitHub's system can accurately fix over two-thirds of detected vulnerabilities. And that's just for starters. --- Why is GitHub rolling out such powerful tools to everyone? It's all about empowering developers to produce secure code faster, while allowing security teams to zoom out and strategize against bigger threats. --- This tool isn't just a leap forward for GitHub; it's a leap forward for software development: As AI integration in coding becomes norm, expect to see more secure, robust applications. The future? A world where coding is more about creation, less about correction. --- What's your take on AI's role in coding and security? Will tools like GitHub's AI Autofix change the game? #ai #github #codeql

💡𝗚𝗼𝗹𝗮𝗻𝗴 𝗧𝗶𝗽: 𝗨𝘀𝗲 `𝘀𝘆𝗻𝗰.𝗥𝗪𝗠𝘂𝘁𝗲𝘅` 𝗳𝗼𝗿 𝗥𝗲𝗮𝗱-𝗪𝗿𝗶𝘁𝗲 𝗟𝗼𝗰𝗸𝗶𝗻𝗴! 🔔 sync.RWMutex is a specialized mutex that allows multiple goroutines to read from shared data concurrently, while ensuring exclusive access for writing. It improves performance when read-heavy operations are involved, as multiple readers can proceed simultaneously without blocking each other. ⚙ 𝗛𝗼𝘄 𝗜𝘁 𝗪𝗼𝗿𝗸𝘀: 🔹 𝗥𝗟𝗼𝗰𝗸() 𝗮𝗻𝗱 𝗥𝗨𝗻𝗹𝗼𝗰𝗸(): These methods allow multiple goroutines to read the shared data simultaneously. When a goroutine calls RLock, other readers can still acquire the read lock, but writers will be blocked. 🔹 𝗟𝗼𝗰𝗸() 𝗮𝗻𝗱 𝗨𝗻𝗹𝗼𝗰𝗸(): These methods are used to acquire the write lock. When a goroutine acquires the write lock, no other goroutines (neither readers nor writers) can proceed until the lock is released. 🔹 𝗖𝗼𝗻𝗰𝘂𝗿𝗿𝗲𝗻𝗰𝘆 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁: In read-heavy workloads, using RWMutex allows concurrent reads, improving performance compared to using a regular sync.Mutex where every read would block all other operations. 🔎 𝗪𝗵𝘆 𝗨𝘀𝗲 𝘀𝘆𝗻𝗰.𝗥𝗪𝗠𝘂𝘁𝗲𝘅? ➖ 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗥𝗲𝗮𝗱-𝗪𝗿𝗶𝘁𝗲 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀: Allows multiple reads to happen concurrently, which is efficient for data that is frequently read but occasionally written. ➖ 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗱 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: Reduces contention and improves performance in scenarios where reads vastly outnumber writes, such as cache lookups or shared configuration access. ➖ 𝗧𝗵𝗿𝗲𝗮𝗱-𝗦𝗮𝗳𝗲: Provides safe access to shared data in concurrent environments, ensuring data consistency while optimizing read operations. 🔐 sync.RWMutex ensures efficient and safe read-write access, making it a great tool for optimizing performance in concurrent Go applications. How often do you use it RWMutex? --- For more Golang content, consider to follow, like and repost ➡️✔️♻️ #go #golang #softwareengineering #softwaredevelopment #gophers

11 Steps to go from Junior to Senior Developer. Courtesy of ByteByteGo 1. Collaboration Tools Software development is a social activity. Learn to use collaboration tools like Jira, Confluence, Slack, MS Teams, Zoom, etc. 2. Programming Languages Pick and master one or two programming languages. Choose from options like Java, Python, JavaScript, C#, Go, etc. 3. API Development Learn the ins and outs of API Development approaches such as REST, GraphQL, and gRPC. 4. Web Servers and Hosting Know about web servers as well as cloud platforms like AWS, Azure, GCP, and Kubernetes 5. Authentication and Testing Learn how to secure your applications with authentication techniques such as JWTs, OAuth2, etc. Also, master testing techniques like TDD, E2E Testing, and Performance Testing 6. Databases Learn to work with relational (Postgres, MySQL, and SQLite) and non-relational databases (MongoDB, Cassandra, and Redis). 7. CI/CD Pick tools like GitHub Actions, Jenkins, or CircleCI to learn about continuous integration and continuous delivery. 8. Data Structures and Algorithms Master the basics of DSA with topics like Big O Notation, Sorting, Trees, and Graphs. 9. System Design Learn System Design concepts such as Networking, Caching, CDNs, Microservices, Messaging, Load Balancing, Replication, Distributed Systems, etc. 10. Design patterns Master the application of design patterns such as dependency injection, factory, proxy, observers, and facade. 11. AI Tools To future-proof your career, learn to leverage AI tools like GitHub Copilot, ChatGPT, Langchain, and Prompt Engineering. PS. Entered the last chapter of my 20s today. Thank you, Jesus! 🚀 Let's do big things. #Developer #ByteByteGo #JuniorDeveloper #SeniorDeveloper

Did Stack Overflow contributors work against their future interests? Back in 2012, while I was only working as a teaching assistant in the Computer Science department, we had a pause in our commercial projects. I wasn’t coding at all during those days. My great friend Danilo Vulović told me how he started to reply to Stack Overflow questions and was harvesting upvotes. So I started too. Oh boy, how fun it was! Not only did I improve my programming skills by trying to find solutions as fast as possible, giving decent descriptions to OPs, and commenting on suggested solutions, but I also had a sense of helping and giving back to the community I had only taken from before. We even started a race to see who would have the higher reputation by the end of that year. In the next two years, I really committed my time to contributing to Stack Overflow. Even today, my reputation there is 8500+ points, and I always advocate to my students and colleagues to upvote useful answers and show appreciation to the contributor. But where does this lead? I can’t help but think how such massive data, available from Stack Overflow and other software development forums, actually helped LLMs be trained for code generation. It had everything that was needed: the context, different potential solutions, and the best-accepted solution. If I were to think about what would be the best dataset to use to train AI to code, it would be Stack Overflow. Obviously, GitHub has much more data, and with pull requests and comments, there is a lot of context tied to the code as well. Hmm, did Microsoft purchase GitHub only to have access to training data for AI? Ultimately, my thinking is, could it be that all of us contributors to Stack Overflow, forums, open source projects, and users of free and public repositories helped build tools that will be seen as our replacements? I still think that I should teach someone to understand the Dijkstra shortest path algorithm instead of just asking Copilot to embed it into the code. But today, everybody is asking Copilot or ChatGPT and not on Stack Overflow, which might reduce the quality of data for future training. Stack Overflow definitely shut down their questions feed for access from outside of the website to prevent giving its data for training for free. What are your thoughts?

GitHub introduces AI tool to fix code vulnerabilities automatically. #AIcodingfixes 🤝 Follow us on Discord 🔜: https://lnkd.in/gt823Zd3 🤝 Follow us on Whatsapp 🔜 https://wapia.in/wabeta _ ❇️ Summary: GitHub has launched a public beta of its code scanning autofix feature, combining real-time assistance with analytical prowess to tackle vulnerabilities in code. The tool aims to solve over two-thirds of detected vulnerabilities across major programming languages, leveraging CodeQL and GitHub Copilot. Despite high confidence in autofix suggestions, GitHub acknowledges some margin of error due to AI limitations. The company plans to expand language support and encourages user feedback for further enhancements. This innovation represents a significant step towards automating security in the coding process. Hashtags: #chatGPT #AIcodefixer #GitHubsecurity

My #opensource history has a lot connection to where the water spins in the other direction, and #golang. Chris Mazur dared to host my first talk on #webassembly a couple years ago, in Wellington. This was on #wazero.. pure go runtime (against the odds in a rust ecosystem). This year, Katie Fry dares to host my first talk on #genai in go (against the odds in a python ecosystem), in Sydney. To write a first talk for me is a mammoth task. I get really nervous and do tons of research, ask my friends, look at a million demos. I have 300 browser tabs, tons of example code locally, several rambling text editor windows. Co-located with an Elastic meetup, my presentation at Katie's is all about Go, and I feel good about it. I had a quite decent plan together, even if later versions go farther. Yesterday, I had some initial scratches towards a real outline. This morning, Philippe Charrière pointed me to his 3 month old baby, https://lnkd.in/eTCRpNPr. This is one of his finest works: it is another improvement on his developer empathetic approach to #opensource. His examples are simple, tiered and top-notch. As GenAI is such a vast and drifty space, this is even more important for noobs. Of course, I was unsurprised this integrates with #webassembly functions via Extism. So, when you ask a question in a way the LLM understands a tool is relevant, those functions can be in any language, not just #tinygo, but anything that works in wasm, like rust. I chuckled stumbling on this, unsurprised as I worked with Phillipe earlier with his capsule FaaS project. TL;DR; I basically threw out my examples and will start over and use parakeet. This doesn't imply you will end with parakeet, but frankly there are far worse ways to start in Go. Who knows where you will end, but starting is the most important part. I'm hoping folks in Sydney enjoy this as much as I do, and also hope this turns into another successful way to relate a new tech, yet again starting where the water spins in the other direction. https://lnkd.in/ebZfF9Va

The article discusses a coding test for Meta's Llama 3 large language model, focusing on implementing JSON persistence. It details setting up a C# project using Newtonsoft.Json to serialize and deserialize objects, specifically a Car class. The test showcases how the model handles JSON data storage and retrieval, emphasizing practical implementation in .NET environments. The process reveals both the capabilities and limitations of Llama 3 as a coding tool, particularly in handling complex data structures like enums within JSON serialization. https://lnkd.in/ddK_k-sk

My first LinkedIn post. Increasing deep breaths. lol. I should write more here. Phew. Anyways, Random but, I think one of the most important skills a developer can develop (see what I did there?), is to develop a knack for simplicity. Everytime I write a function, and it works, I always, always take a step back and ask myself, can this be made even simpler? What can I take away while maintaining correct functionality? And this isn't just a question about using fancy syntax to condense three lines into one: think a ternary operator instead of an if...else block. (sometimes I find all that fancy syntax can make things less simple even...) Big O aside, it's about coming back to your code later on, on a Saturday night when you're knackered, or on a Tuesday afternoon when you need to push a hotfix to prod, and looking at your code and understanding what you were doing when you wrote it. But it's also about someone else coming to your code and understanding what you wrote. Because it's never really only ever about just you. A litmus test I usually use is to ask myself, if I were to onboard someone unto this project, how easy would it be for them to look at this and understand what I was doing? If I wasn't here, can this be maintained? If someone handed this to me to maintain, will I pull my hair out and die away from frustration? It's easy sometimes to get sucked into the whirlpool of meeting deadlines and stuff and to just crank that feature out, no matter how spaghettified the code is; but mahn, it only really ever leads to a descent into madness when its time to maintain. Coding is hard, yes. No one is really ultimately that 10x engineer, we all make mistakes ear and dear. Lol. But we don't have to make it harder. We can always try. We can always, always, refactor. Sayonara, fam. Stay safe and don't die. Cheers.