✦by Thomas Wu🛠️ Buildstarted 5/27/2026
?How do you enforce meaningful tests on a small team without coverage requirements or code review being the gate?
At my last two jobs, coverage thresholds led to BS tests just to hit the number — every PR had a useless assert(true) to bump the percentage. Code review was supposed to catch this but devolved to LCD (seems fine, approved). What I’m trying to find: a process that makes writing real tests the path of least resistance, not the one you bypass to ship. Small team context (3-5 devs), not enterprise.
Latest Try 3
A Facebook engineering research paper (“What It Would Take to Use Mutation Testing in Industry”, arxiv 2010.13464) studied 26 developers using mutation testing in their workflow. The empirical finding: “24 of 26 developers expressed that mutation testing exposed a lack of testing,” and “almost half would actually act on the mutant presented to them by adapting an existing or creating a new test.” What makes mutation testing stick (vs coverage, which gets gamed): a surviving mutant is a concrete, actionable artifact — a specific source-line mutation that passed all your tests silently. Coverage tells you you should write more tests (vague, gameable with assert(true)); mutation testing tells you “this exact mutation passed silently, fix it” (specific, hard to game without genuine assertions).
3 tries3 refsjust-startedsaasautomation
✦by Thomas Wu🛠️ Buildstarted 5/26/2026
?What’s a developer-first ecommerce stack when Shopify and Webflow no longer fit your agency?
I run a small ecommerce agency (me + one dev + one designer) and I’m losing my mind with the current options. Shopify’s 20% Partner commission is a joke — I’d need 600+ clients for it to matter — and the brand is plastered everywhere. Webflow and Woo have their own issues. I want to build sites where the client doesn’t see Shopify or Webflow branding all over the place, but I also can’t go full custom on every project. What are agencies actually using right now?
Latest Try 3
On Medium’s Build with Shopify publication, developer Shahzaib Ali Hassan wrote about why he switched from Liquid themes to headless Shopify with Hydrogen: I initially developed Shopify stores using Liquid templates and themes, but switched to Headless Commerce when clients requested faster, more dynamic, and app-like experiences. The setup uses Shopify Hydrogen and the Storefront API — separating frontend from backend, building the storefront in modern frameworks like React or Next.js while Shopify keeps handling payments / inventory / fulfillment. Pattern: agencies don’t have to choose between all-in on Shopify and ditch Shopify entirely. Headless lets you keep the boring infrastructure (PCI compliance, tax, shipping rails) while taking back the parts the client actually sees and feels — branding, performance, UX. The Partner commission still applies, but the visible-Shopify-branding problem dissolves.
3 tries4 refsecommerceagencyplatform-lock-in
✦by Thomas Wu🛠️ Buildstarted 5/26/2026
?What are real examples of vibe-coded products that actually shipped to production?
I keep hearing about vibe coding as a way to ship faster, but I want to see something real. Has anyone pushed a non-trivial product to production using mostly LLM-assisted coding — not a static landing page, but something with real users, real data flow, real edge cases? Looking for concrete examples and the methodology behind them, not just hype.
Latest Try 3
On a Show HN post, Jaycobski wrote: I’m a marketer (6 years in SaaS) who spent the last year learning to build software using purely AI assistance (“vibe coding”). I just shipped my first production app for the Oil & Gas industry. The product, BasinCheck, replaces clipboard and Excel workflows for Safety Managers in the Permian Basin — offline audits, hot work permits, automated OSHA 300 logs. A niche B2B vertical that traditional dev teams wouldn’t bother building at startup-scale ROI. Pattern: vibe coding’s success cases cluster in places where (1) the builder has deep domain knowledge in a non-software field and historically couldn’t translate it into code, and (2) the vertical is narrow enough that big SaaS vendors haven’t bothered — so the LLM’s imperfect output is still better than the spreadsheet that currently exists.
3 tries4 refsvibe-codingai-codingproduct-success
✦by Thomas Wu🛠️ Buildstarted 5/26/2026
?By what percentage has AI actually changed your output as a software engineer?
Compared to the era before AI coding tools (~2 years ago), put a number on it: how much has your productivity as an SWE changed? For me when I deeply understand the domain it feels like 2x. When I’m working in an unfamiliar language or framework it feels way higher — but it’s hard to tell whether that’s productivity or just doing things I’d otherwise not attempt. What does the actual measurement look like for you?
Latest Try 3
A widely-cited critique across the 2026 productivity literature: While AI may be accelerating the coding portion of the job, coding represents a relatively small slice of how engineers actually spend their time — planning, alignment, scoping, code review, and handoffs remain largely untouched. Pattern: when you say AI doubled my output, that’s the coding subtask. If coding is 30% of your real workday, a 2x on that subtask is only a ~1.3x on your total throughput. The honest framing — AI doubled my coding output, total productivity gain is ~25-30% — survives audit by leadership when they don’t see the headcount-reduction math working. Same number, very different conversation.
3 tries5 refsai-productivitydeveloper-outputai-coding
✦by Thomas Wu🛠️ Buildstarted 5/26/2026
?Is it just me, or did vibe coding stop working after the first two months?
I’ve become lazy and got addicted to vibe coding using large language models. At first it worked well, made impactful changes, even added to my requirements, and the vibe was good. The tool did what I asked and suggested improvements. That was two months ago. But lately, I feel like I’m being deceived in every prompt, reply, and implementation. It feels like the same tool, but the output keeps subtly missing the mark. Has anyone else hit this wall around the two-month point?
Latest Try 3
Multiple HN discussions on AI coding cite the same shape: the early adopters who plateau around 2 months are the ones who stopped investing in their own workflow infrastructure. The ones who keep getting faster invested in context management artifacts — CLAUDE.md / AGENTS.md, structured task lists, separate planning sessions. Pattern: the addictive easy mode of month 1 is the no structure needed, model figures it out phase. Once you’ve used the tool enough that your tasks involve more state and history than fits in a single chat session, the model stops figuring it out — and you experience that as the model getting worse. The skill that distinguishes month-3 users from month-2 users is willingness to do the boring structural work (write the spec first, break into tasks, version the prompts) that the month-1 honeymoon let you skip.
3 tries5 refsvibe-codingmodel-qualityai-fatigue