Yolo Group

Tuesday means YouTube video day! One of the last AWS videos for a while and then we move into Data Science and AI. And what a way to ease into Data Science and AI... with a video on Analytics and Data Science in Amazon Web Services (AWS) Watch the video here: https://lnkd.in/dztwADgj

A guy reached out to me on Upwork. New profile. 1 review. Already 1 hire. Payment verified. Looked legit enough. He sent me a GitHub repo and asked me to explore it before we kicked off the project. Said he wanted it as the base for what we’d build together. I was on a walk by Shnelli lake, so I pulled up my AI assistant, dropped the repo link, and typed “analyze this, do NOT run anything” three times (i am running it in an isolated environment). Just to be safe. It flagged a suspicious package almost immediately: tailwind-clamps-line. The screenshot tells the rest of the story. The lake was beautiful though.

I built AI coding agents that don't need babysitting. For months, I watched developers struggle with AI coding tools. The pattern was always the same: 1. Open chat 2. Explain the task (again) 3. Wait while it "thinks" 4. Review the output 5. Repeat for the next task It's like having a junior dev who can only work on one thing at a time—and you have to watch them do it. That's not scaling. That's just... slightly faster coding. So I built Camelot. Instead of chat, you get a Kanban board. Instead of one agent, you run multiple in parallel. Instead of black-box execution, you see real-time streaming of what each agent is doing. And most importantly: agents propose, you approve. Nothing ships without your sign-off. The result? A 3-person team shipping like a 10-person team. Agents handle bugs and routine tasks while you focus on architecture and product. It's open-source, self-hosted, and built with Elixir/Phoenix for real-time collaboration. If you're tired of babysitting AI, star the repo for early access: 🏰 https://lnkd.in/dBEeqjeF #AI #DeveloperTools #OpenSource #ElixirLang #StartupLife

By profession, I work as a B2B Support Specialist. But beyond work, I’m deeply passionate about creativity. From cooking and filming different recipes 🍽️, creating artwork 🎨, to writing content and creating videos for that content✍️—I enjoy expressing ideas in different forms. If you’re interested in Art, Food and thoughtful content, feel free to check out my Instagram and connect! Instagram Account @palette_by_zakera https://lnkd.in/drBFtF5K

AI is quietly reshaping how we work — not in the future, but right now. Lately, a few trends have stood out to me: 🔹 LLMs as building blocks APIs from OpenAI, Google, and Anthropic are becoming part of everyday development. From generating code to drafting test cases and even writing migration scripts — it’s like having a teammate that never sleeps. 🔹 AI in CI/CD Tools like GitHub Copilot X and CodeQL are evolving into smart gatekeepers, catching issues before they ever reach production. 🔹 Privacy-first AI More teams are bringing models like Llama 3 and Mistral in-house to keep sensitive data behind their own walls. 🔹 Explainable AI for ops Modern observability is going beyond metrics — helping teams understand why a model made a decision, which is crucial when things break. 🔹 AI-driven UI evolution Interfaces are starting to adapt based on real user behavior, reducing the need for constant feature shipping. ─── Curious to hear how others are approaching this: • What’s the most useful AI shortcut you’ve added to your workflow? • How are you handling data privacy with hosted models? • Would you ship LLM-generated code without manual review — why or why not? • What’s been your biggest challenge taking an AI experiment to production? #AI #MachineLearning #LLM #SoftwareEngineering #DevOps #DataPrivacy

Today, DesignWorks is a 40-member team, with an alumni community of over 100 architects, designers and interns. Over time, we have engaged with colleagues across different formats: long, mid and short-term roles, as well as task or gig-based associations. As the practice has evolved, we have tried to be more intentional about our team members’ professional development. This is what we have attempted so far: ▪️ Access: Enable access to people, information and resources within the studio, during one’s time here and beyond. ▪️ Structure: Orient team members to the systems that support our work, while remaining open to questioning and refining them. ▪️ Autonomy: Delegate responsibility and allow individuals the space to take ownership of their work. ▪️ Breadth & Depth: Share references and experiences from within design and outside it, including through our newsletter DWell, to expand one’s personal and professional horizons. ▪️ Studio Forums: Create opportunities to present, review and discuss ongoing and completed work. ▪️ Industry Engagements: Facilitate interactions with the larger professional ecosystem through workshops, conversations and collaborations. ▪️ Mentorships: Offer guidance on academic and professional trajectories through ongoing dialogue. ▪️ Assessment Matrix: Develop a basic framework for self-assessment and structured feedback. Creating the conditions for individuals to grow and for the studio to function as a collective requires continuous thought and calibration. These ideas were not in place at the outset. They have taken shape gradually, through experience, and often through trial and error. It will be interesting to observe how we sustain these practices and how they evolve. Responses and suggestions are welcome. _____________________ #Mentorship #ArchitecturePractice #WorkCulture #PracticeManagement #ProfessionalDevelopment . . . .

When we started User Stories , it was because something felt missing. There wasn’t really a space where product, data, UX and CRM people could come together and talk about how things actually work in practice. Not theory. Real decisions, real trade-offs. That’s exactly what we’re continuing to build with this session, with speakers like Michelle Briffa, PhD. bringing a perspective you don’t usually get in typical industry talks. If you care about building better experiences and understanding users properly, you’ll get a lot out of this. It’s free to attend: https://lnkd.in/dYfq6Nvn

It’s Thursday again, which means… new video 😊 This one is all about where data actually lives in AWS. “The cloud” often sounds abstract… but in reality, your data is stored in very specific ways depending on how it’s used. In this episode I break down, simply: - S3 for storing files and data at scale - EBS for storage attached to a server - EFS for shared storage across multiple servers Once you understand this, a lot of AWS starts to click. This has been a long time in the making, so if you want to support me, I’d really appreciate a subscribe 🤍 #aws #cloudcomputing #dataengineering #datastorage #learndataeasy #awstutorial #cloudbasics #data #analytics #tech