Lisa Dean

95% of mid-level developers are stuck in tutorial hell. The other 5%? They get promoted to senior within 18 months. After conducting 500+ technical interviews and mentoring dozens of developers, I've noticed something disturbing: Most developers do: -Collect courses like Pokemon cards -Chase every new framework and library -Panic in interviews because they can't articulate what they actually know Meanwhile, the ones who get promoted do: -Audit their learning vs. application ratio weekly -Focus on solving business problems, not mastering tools -Crush interviews because they can explain their thinking process 📌 The reality is: Your promotion isn't blocked by what you don't know. It's blocked by what you haven't applied. Here's what I see in interviews: Candidates who've done 50 algorithms courses but can't solve a simple array problem under pressure. Why? Because learning without application creates knowledge without confidence. Senior engineers aren't walking encyclopedias. They're problem-solvers who can take their existing knowledge, apply it under pressure, and communicate their thought process clearly. The 80/20 rule of senior engineering: 80% of your interview success comes from 20% of your core knowledge, applied consistently. Ready to break the learning loop AND ace your next interview? My complete guide shows you exactly how to demonstrate your skills when it matters most: https://lnkd.in/d64MhyMr

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Developers average 8–15 meetings per week. Many of those meetings shouldn't include them. Some shouldn't exist at all. But we accept by default-the invite arrives, we click yes, another hour disappears. Learning to decline meetings is a career skill. Here's what I tell the teams I work with: Decline status updates where you have nothing to add. Decline large meetings where your role is unclear. Decline recurring meetings that lost their purpose. Decline meetings that are actually email. The hardest one? Declining back-to-back chains that eliminate all focus time. Not because any single meeting is bad-but because the pattern destroys your ability to do actual work. Here's how to decline professionally: → Give a brief reason: "I'm protecting focus time for [project]" → Offer an alternative: "Can we move this to [specific time]?" → Suggest async: "Could you send this as a doc? I'll review and follow up" Most people think saying no to meetings will hurt their career. The opposite is true. Protecting your time to do high-impact work is what gets you promoted. Sitting in meetings where you add no value doesn't. What's one meeting on your calendar this week you should probably decline?

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As a dev who's constantly context-switching between projects and frameworks, I spend way too much time digging through official documentation for the simplest answers. Most of the time, a single clear example would get me unstuck, but good luck finding one in the "official" sources. Formal docs often seem written for folks who live and breathe the ecosystem. Meanwhile, I’m juggling multiple mental models, trying to reload context like it’s a cached webpage. Take Python’s 𝓂𝒶𝓍() function docs: a dense wall of text about positional-only parameters, iterables, and keyword arguments. Critical details? Sure. But if I just need to recall how to use a custom key for sorting, it’s overkill. What if the docs led with something like this? 𝘮𝘢𝘹(4, 6) # 𝘙𝘦𝘵𝘶𝘳𝘯𝘴 6 𝘮𝘢𝘹([1, 2, 3]) # 𝘙𝘦𝘵𝘶𝘳𝘯𝘴 3 𝘮𝘢𝘹(['𝘹', '𝘺', '𝘢𝘣𝘤'], 𝘬𝘦𝘺=𝘭𝘦𝘯) # 𝘙𝘦𝘵𝘶𝘳𝘯𝘴 '𝘢𝘣𝘤' (𝘭𝘰𝘯𝘨𝘦𝘴𝘵 𝘴𝘵𝘳𝘪𝘯𝘨) 𝘮𝘢𝘹([]) # 𝘙𝘢𝘪𝘴𝘦𝘴 𝘝𝘢𝘭𝘶𝘦𝘌𝘳𝘳𝘰𝘳: 𝘮𝘢𝘹() 𝘢𝘳𝘨 𝘪𝘴 𝘢𝘯 𝘦𝘮𝘱𝘵𝘺 𝘴𝘦𝘲𝘶𝘦𝘯𝘤𝘦 𝘮𝘢𝘹([], 𝘥𝘦𝘧𝘢𝘶𝘭𝘵=5) # 𝘙𝘦𝘵𝘶𝘳𝘯𝘴 5 Boom—context restored in 10 seconds flat. No PhD required.

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There’s a quiet trend happening - Rust engineers leaving comfortable FAANG roles for small, high-ownership teams. Not because they want ping-pong tables or startup chaos - but because they want meaning. They want to build things where their fingerprints actually show up in the product. Every week I hear the same line: “I just miss building.” That’s why early-stage Rust hiring is taking off. The best engineers want to feel the impact of their work again. If you were choosing between stability and impact, which way would you lean right now? 🦀

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Anybody else get referred to as 'the linter' by their coding agents? 😃 For example, "The linter must have reverted the changes. That's fine." A linter is an automated tool that flags and fixes code issues. So apparently I've been demoted from developer to... automated tooling! #AI #CodingAgents #DeveloperHumor

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💥 “Sometimes it’s not about fixing code — it’s about being the person your client can count on.” Last week, one of my clients accidentally broke their live server while changing DNS settings. Within minutes, he emailed me in panic — and I jumped right in to help. I diagnosed the issue, fixed the DNS mapping, restored the server, and got everything back online almost instantly. A few hours later, he sent me a $30 bonus with a note: “Thanks for handling the emergency so quickly.” Moments like these remind me — It’s not just about your skills or code quality. It’s about trust, reliability, and how you make clients feel safe when things go wrong. That’s how you build long-term relationships, not just projects. #ClientTrust #FreelancerLife #UpworkJourney #WebDeveloper #LaravelDeveloper #WordPressDeveloper #VuejsDeveloper #FullStackDeveloper #FreelanceSuccess #ClientRelationship #DeveloperMindset #CodingLife #RemoteWork #WorkEthic #FreelanceTips #EntrepreneurMindset #ArhamNatiq #MuhammadArham #FreelanceJourney #MotivationForDevelopers

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The best devs I’ve worked with don’t just build what’s in the ticket. They ask: - “Why are we doing this?” - “Is this actually solving the problem?” - “Could this be simpler?” And they *suggest* things. They care about the impact-not just the implementation. If you’re in a remote role and want to grow, that’s the mindset to adopt. It’s not just about being productive-it’s about being intentional and proactive.

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I’ve been experimenting a bit with Kiro in a couple of Spring Boot projects recently, mostly to see how well these setups hold up in a real codebase. What stood out pretty quickly is that the problem isn’t really code generation. That part works surprisingly well. The issue is that the model has no idea how your project is actually structured. In a real system, “good code” is mostly about implicit decisions — where logic lives, how boundaries are enforced, how tests are written. None of that is visible to the model. So you get code that looks fine in isolation, but doesn’t quite fit once you integrate it. I tried to make some of that explicit by adding a small .kiro/ overlay to the repo. Nothing fancy — a few steering files, some basic skills, a couple of hooks. What surprised me is how much difference even very simple, very local rules make. Something like “controllers don’t call repositories directly” goes a long way. The more I worked with this, the more it felt like the real problem isn’t the model, but how understandable the system is in the first place. If the structure is unclear, AI amplifies that and if it’s explicit, it becomes a lot more useful. Curious how others are approaching this in real projects. I’ll drop the link to the template in the comments, as always: feedback is appreciated!

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Most recruiters don’t speak to 20-30 engineers a week unless they have a role. I do and always will! Most weeks, I’ll conservatively speak to 20-30 engineers across the ‘DevOps’ community. Not pitching jobs. Just understanding: - What they’re building, or what they’d like to build next - What problems they enjoy solving (and what might make them leave to solve something new) - What kind of teams they want to work in, or stay away from - What their ideal role looks like, the kind that would actually tempt them into a conversation Over time, that builds a real map of the market. And more importantly..... TRUST. So when I do call, people answer. This means when I take an intake call for a role, there are usually 2-3 senior engineers or leaders who come to mind immediately. Not because I’ve just searched. Because we’ve already spoken. For hiring managers, that means no wasted time, just: ✅ Strong, relevant profiles who actually care about your challenges, team, and company ✅ Quality you can trust, backed by real conversations and references (no one is more than one person away from me) ✅ 2-3 candidates within 24-48 hours ✅ Engineers who are genuinely engaged in the interview process ✅ No risk of fake profiles DanOps has always been trusted because of its proximity to the DevOps community. And that’s something you can’t fake.

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The best developers I’ve ever worked with frequently said: “I don’t know, but I’ll find out.” They didn’t bluff. They didn’t hide behind jargon. They didn't bs. They were honest. They were curious. They focused on solving the problem They were not focussed on protecting their ego. Your job isn’t to know everything, it's to create business value and you can't do that by bluffing your way through everything. Saying “I don’t know” doesn’t make you look weak. I actually respect it. Pretending you do? That’s what does damage. There isn't much worse in an interview when someone is trying to make up the answer instead of saying: 'I don't know, but if in the job i guarantee id work it out by...'

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“Vibe coding has massive technical debt. A train wreck waiting to happen.” I hate these takes. Just teach the people how to make good software that has a 3rd party auth, rls, no keys exposed and encourage them to keep it simple. Technical debt is not a problem if you structure it right. It’s not a problem if the WHOLE APP can be recreated in an hour by another AI 6 months from now. Too much technical debt? Fine just make it again. No one here is vibe-coding an autopilot for an F-35, we’re making simple workflow apps. We gotta stop acting like vibe coding is going to make the sky fall and just teach people how to use it well. Ai agents are here to stay and it is a massive unlock for professionals who have real product experience and have seen portions of their careers wiped away by AI, so just help everyone build good software!

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How I get the best out of Gemini 2.5 Pro for coding? - Gemini 2.5 Pro thinking model has proven to be the best for helping me with code writing. Here’s how I use it effectively and save myself ton of time: - Before asking it to do anything, I give as much context as possible - I tell it not to make assumptions, and to ask me if anything is unclear - I don’t let it write code immediately - I ask it to give me a plan first - I review the plan, ask for any changes, and only then I start the implementation What are your best tips to get the most out of LLM-assisted code editors?

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You don't need to hire a software farm for anything anymore. You need one developer who sits next to you for 6 weeks and learns how your business runs. Not how it looks in a deck. How it actually runs. The messy inbox system. The Notion doc nobody follows. The 4-step process your EA does manually every morning. A good AI developer, someone who works in n8n, Claude, and modern automation tools, turns that into systems that run without you. We placed one with a 12-person e-commerce brand. In 8 weeks he'd automated their order exception workflow, their influencer outreach follow-up, and their weekly reporting. The admin team is being trained in AI. Those that don’t adopt are gone. 3 tasks. Each one was eating 6-8 hours a week from someone on the team. That’s money in your pocket. 416h per year. That’s almost a full quarter's worth of work. A dev shop's savings don't matter if they can't ship. Ours do. I wrote more on this on more detail in the blog linked below: https://lnkd.in/gVpWJGZw DM me if you have been seeing AI happening in front of you and want to know how you can avoid missing out.

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Recruiting = relationships. Not résumés. Not quotas. Recruiting in BAS isn't what people think it is. It's not a numbers game. It's not "who can I fill this role with fastest." It's knowing the difference between a programmer who's done commissioning and one who hasn't. It's understanding why someone with 12 years at the same company is nervous to leave — and what actually matters to them. It's relationship-first. Always. The technical stuff just makes us better at the human part. #BASRecruiting #BuildingAutomation #HVAC

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🚀 Alright, buckle up, because .NET 10 just dropped some serious performance magic and, wow, it’s honestly pretty wild if you’re into this stuff. First off, the JIT (that just-in-time thing that makes your code actually run) has gotten a glow-up. We’re talking legit support for AVX10.2 instructions—yeah, that’s nerdy, but it basically means it chews through data way faster. Toss in struct stack allocations and slicker method inlining, and you’re looking at a runtime that’s moving like it’s had three espressos. And get this: memory management is actually smarter now. Fewer heap allocations, so your apps aren’t tripping over themselves or gobbling RAM like a starved Pac-Man. That means less crashing, better stability, and your code doesn’t feel like it’s dragging a boulder uphill. So, if you’re building stuff that actually has to scale and not just survive on your laptop—.NET 10 is kind of a game-changer. Faster apps, happier users. 👏 #runtime #optimization #softwaredevelopment #dotnet10 #notyouraverageupdate #yassineennajem

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When a dev leads with “clean architecture,” I pause. If they push for the perfect setup before writing a single feature, I ask why? It’s not that quality doesn’t matter. It’s that context does. Perfect code doesn’t help if no one needs what you built.

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Hot take: stop fighting AI-generated code style. You were never supposed to read it anyway. Saying “AI writes code not the way I like” is the same energy as “my JavaScript doesn’t compile into the assembly I’m used to.” The only difference is: you never saw that machine code. And if you did, could you even read it? But now AI writes in your language, doesn’t follow your patterns, and suddenly everyone panics. That debate is already outdated. The window for “teach AI to write code like a human” closed somewhere in early to mid 2025. The moment AI started shipping working applications and burning down real tech debt, that goal stopped mattering. Nobody serious is optimizing for that anymore. The real question now is different: -> How do you build around AI-generated code? -> How do you test it, increasingly with AI? -> How do you keep architecture clean when implementation gets messier? -> How do you manage tech debt when code volume explodes? The code is not yours anymore… Let it go. AI wrote a 3.000-line file? That’s it being polite. It would be perfectly happy with 100.000 :) To a model, your whole codebase is just text with arbitrary file boundaries. AI doesn’t use your favorite architecture pattern? Writes functional instead of OOP? Passes callbacks instead of instance refs? If it works, is testable, and doesn’t poison the system, who cares?... Two objections come up every time: -> “Our business logic is too complex, AI won’t understand it.” Maybe not fully today. Give it 2 to 3 years. The companies that adapt their processes for AI will ship like 5x faster than the ones still debating whether the code “feels right.” -> “AI makes too many bugs.” Correct. Right now, even with decent AI-assisted testing, it still produces more bugs than a strong human developer. But humans ship bugs too. The answer is not to reject AI. The answer is to upgrade your QA: graceful rollouts, automated review, faster detection, smaller deployable units. That is your engineering problem now. As a solution architect, I keep seeing teams waste weeks asking whether AI fits their current architecture. Wrong question. The real question is: what do you need to change in your architecture, your team, and your delivery model to make AI actually work? Because if you’re still asking “should we adopt AI?”, you’re already behind.

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