💻 Matthew Ford

The shine is starting to wear off vibe coding. I know, because I’ve been there. One moment I’m in Cursor, impressed by what a large language model can produce. The next, I’m staring at the clock, realising I’ve lost hours because it couldn’t navigate the complexity of the codebase I’m working on - and I find myself back in PyCharm, questioning the value of the detour. One answer that’s emerging is "Spec-First Development". You don’t toss vague instructions at an AI and hope for magic. Instead, you craft the clearest, most rigorous specification you can - and let the agent build from that. Better still, treat specifications as code artefacts: versioned, under source control, and continually refined with compacted, curated context. For me, this isn’t a radical shift. I’ve long worked with test-driven development, where the unit test is the specification. And LLMs thrive on this. Unit tests aren’t just notes in prose; they’re executable specifications - formal, computable, and unforgiving in all the right ways. That’s the deeper lesson: when you hand a spec to an LLM, the more formalisation, the better. Natural language is fine, but structure wins. Code loves tests. Maths loves notation. Business, however, has no equivalent - no unit test for strategy documents, compliance rules, or process design. Enter ontologies and knowledge graphs. They give us a way to formalise business semantics, capturing domains in rigorous, computable detail. Paired with an LLM, they don’t just guide the generation - they also validate it. The future of agentic coding - and of LLMs in business more broadly - won’t be built on “vibes.” It will be driven by how well we can formalise intent: transforming ambiguity into something structured, testable, and executable. ⭕ Spec-kit: https://lnkd.in/dPZCgUeq ⭕ What is an Ontology: https://lnkd.in/ePS7ha8z ⭕ What is a Knowledge Graph: https://lnkd.in/e5ed_f8g

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He Built an Enterprise-Level Software for over a Year. Zero Users. Last week, I sat in a coffee shop with a brilliant friend who showed me something incredible. An enterprise software he'd been building for over a year. Beautifully designed. Thoughtfully architected. Genuinely solves a real problem. I asked him: "How many users do you have?" "None yet. I'm still working on it." "Does anyone know this exists?" "...No." I looked at him and said, "Your first 10 users could've come from your network. But nobody knows your solution exists."? Here's what kills me about this: We resist showing up because we're afraid of crickets. "I don't get engagement." "I don't know what to post." "Nobody cares what I have to say." Meanwhile, someone in your network has the exact problem your solution solves. The numbers don't lie: My second open-source project? The first 5 users came directly from my network. Now it has: • 26 active users • 5 countries • 426 actions taken (times someone actually used a feature) You know I love sharing receipts, but none of that happens if I stay invisible. Let's talk about what 3 likes actually means: In an attention economy where everyone is drowning in content, 3 people stopped scrolling, read your entire post, and chose to acknowledge it. That's not failure. That's three humans who gave you their most precious resource: attention. Leave metrics, focus on connections Don't know what to post? Just be yourself and tell people what you care about. The project you're building. The problem you're solving. etc You don't need profound wisdom. You need an authentic presence. Start small if you need to: → Comment thoughtfully on others' posts → Document what you're building etc Visibility isn't all-or-nothing. It's showing up consistently in whatever way feels natural. What showing up has done for me: • People recognise me from LinkedIn at events and random places • I get DMs with opportunities • The last 3 speaking gigs came from my network • Referrals I've never applied to speak at any event. Visibility did that. The voice in your head: If something inside you is saying, "I should be out there," stop ignoring it. Your ideas, your work, your perspective have value. Quieting that voice doesn't protect you. It just keeps you invisible. The cost of invisibility? Zero impact. "Your silence doesn't serve anyone, including you." Focus on value, not clout The authority comes from consistent value, not chasing viral moments. You don't need permission to be visible: You don't need thousands of followers. You don't need perfect content. You don't need to go viral. You just need to stop being invisible to the people who are already looking for what you offer. It's perfectly fine if you choose not to show up. Not everyone needs to be online, constantly sharing. But if that voice is telling you that you DO need to be out there, then get out there

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Leading a technical team is VERY different to being a Senior Developer, don't you think? Reflecting on more than 30 years of hindsight, there are a few things that I wish I had understood better and learned more quickly back in the day. So I thought I would share them and maybe help others learn to be better Technical Leaders, more quickly. That's why I've put together this guide, that pulls that experience together and you can get your copy for FREE ➡️ https://lnkd.in/ekRVdi6T In my experience, the best leaders in software engineering are the servant leaders who clear obstacles, create safe environments for experimentation, and focus relentlessly on enabling their teams to succeed. Leadership is about recognising that the success of the organisation depends on the people closest to the work having the freedom and support to do their best. If you want high-performing teams, your job as a leader is to build trust, remove friction, and help brilliant engineers thrive. ❗ See more of my advice how to be a great tech leader in the guide here ➡️ https://lnkd.in/ekRVdi6T

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I just helped a client find their dream developer in a place their competitors would never think to look. Last month I was doing a demo for a company trying to hire Ruby developers. They'd been posting on job boards for 3 months with zero qualified candidates. I asked them a simple question: "Do you know anyone in the Ruby community?" Turns out their CTO had been active in a LinkedIn group for Ruby developers for years, but they'd never thought to tap into it for recruiting. So we helped them reach out to the group leaders. When the group leader vouched for someone's technical abilities, that recommendation carried serious weight—way more than any resume or coding test could. Three weeks later, they had 5 qualified candidates in their pipeline. Most recruiters are sitting on goldmines and don't even realize it. We've been so focused on employee referrals that we're missing these massive professional communities where people already know and trust each other's work. Let me give you the five types of communities that have generated the best results for our clients.   • LinkedIn professional groups - Find the groups specific to your roles and get to know the leaders. They know everyone's actual capabilities because they've seen people's work in action.   • Industry specific networks - We've had healthcare clients absolutely kill it with communities like NurseDash. These professionals know each other's clinical skills and work styles in ways that go way beyond what you can see on a LinkedIn profile.   • Skill based communities on platforms like Reddit - When someone refers from a machine learning community, they're vouching for proven project experience. They've seen this person's code and watched them solve real problems.   • Geographic and local networks - Members meet face to face, collaborate on regional projects, and cultural fit comes built in because they understand the local business environment.   • Alumni networks from universities and bootcamps - These connections last forever and members understand learning styles and career paths in ways that skill assessments completely miss. Relationship strength matters way more than frequency of contact. You might not have talked to someone in 6 months, but if you worked on a meaningful project together, your recommendation still carries real weight. Most companies limit themselves to employee networks and then wonder why their referral programs fail. The companies winning right now are expanding into these broader professional communities where trust and professional credibility already exist. Bottom line is this: 60% of jobs get filled through networking anyway. Why not make it intentional and tap into communities where people actually know each other's work quality? What communities have been most valuable for your sourcing?

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AI won't make your team faster on its own. Better developer experience will. I sat down with Laura Tacho, CTO at DX, to talk about what actually improves engineering team performance and what just looks like it does. We covered: 1. Why most metrics fail (and how to use them properly). 2. What developer surveys can tell you that GitHub stats never will. 3. How to track DevEx without a fancy tool, even at a 10-person startup. 4. The real impact of AI on engineering workflows (spoiler: it’s not replacing your team). The big takeaway? Developer performance isn’t about shipping faster. It’s about removing friction. Whether it’s bad tooling, unclear goals, or noisy metrics, the teams that win are the ones that make it easier to do great work.

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The sequence matters: 1. Build with no-code 2. Find paying customers 3. Hit limitations 4. Then hire engineers Not: 1. Search for CTO 2. Spend 6 months pitching technical co-founders 3. Build something nobody wants 4. Wonder why it failed The market doesn't care about your tech stack. It cares if you solve a problem worth paying for.

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Yes, the market is tough for QAs. But our Engenious University students are still getting hired. Last month alone, 5 of our AI & LLM Testing students landed new roles. 👉 One was laid off from a manual QA role 9 months ago 👉 One switched careers from a hostile work environment 👉 One had outdated manual QA experience. So what changed? ✅ They upskilled in testing GenAI apps with tools like Promptfoo ✅ Got mentorship on job search & interview strategy ✅ Worked with a real US-based AI startup through our internship training It’s 2025. Most people are still applying outdated manual skills and strategies from 2017. That doesn’t work anymore. That’s exactly why we built the AI Career Accelerator: to help testers master the #1 in-demand skill for 2025: testing AI Apps and LLMs. 🚀 This is the last cohort of 2025, and we have just 10 seats left. Check out Oct AI Testing Cohort: https://lnkd.in/e5nRzJ5k We kick off next week. Excited to help the next wave of QA professionals become AI QA leaders. #AITesting #Promptfoo #QAjobs #TechCareers #AIQA

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Myth busted: "Saving Developers time doesn't mean they're more productive" I hear this objection all the time, followed by: "You don't know what they do with all the time you've saved them" Yes, I do. You would too if you asked them. We asked this as a part of our research for the 2025 Atlassian State of DevEx report. Here’s how developers are reinvesting time saved by using AI: 1. Improving code quality 2. Developing new features 3. Improving culture 4. Documentation A few things jumped out: - Turns out when you reduce friction, developers spend time improving quality and shipping more features... Who would've thought? - “Improving code quality” probably wouldn’t have made this list a few years ago ... Devs are spending a lot of time improving the quality of AI-generated code, puts question marks over time savings. - Documentation... Really? No one likes doing it, AI is good at it… maybe it’s fixing AI-generated docs? This data shows if you improve Developer Experience, remove friction, and save devs time, they will invest their time back into shipping high-quality software, faster. #DevOps #DeveloperExperience #AI

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