... WordPress code written with AI works on the first try, but WordPress adds its own traps: missing nonce and capability checks, unescaped output, and queries that are slow on big sites. When AI hands you a plugin or theme snippet, what is your review routine before it touches a client site? Do you use a linter like PHPCS with the WordPress standards, a staging site, a second AI pass, or just read it line by line? I am curious what ... ... logged_in() where they needed current_user_can(). That reads perfectly fine in a diff. Second, call the same endpoint logged out: a register_rest_route with permission_callback missing, or set to __return_true, is common in AI-written

... recurring meetings on the calendar. Total time cost across a team of 9 was roughly 34 person-hours per week. We cancelled all of them for 30 days and replaced them with a single written update thread. What happened: Total meeting hours dropped from 34 to 4 per week (one weekly sync we kept) Shipping velocity stayed flat for the first 2 weeks, then went up Two things broke: a handoff between design and engineering stalled for 6 days ... ... customer issue sat unresolved for 3 days because nobody knew who owned it Written

... this over the last couple months, sharing here since it fits this forum exactly. apexversus.com lets you type any two things and get an AI verdict: a winner, a 0-100 score per side across 7 dimensions, and a written explanation. The twist is the scoring isn't one generic rubric the AI detects what category you're comparing (16 total: anime characters, real people, companies, countries, animals, athletes, cricketers, cars, etc.) and scores it with dimensions that actually ... ... Anime gets judged on battle IQ and technique, companies get judged on market share and innovation. What's in it beyond the single comparison: Radar chart + full written

... thing I m curious about is whether code review should be different when a pull request was mostly written by an AI agent. Do you review those PRs line by line like normal code, focus mainly on the tests and architecture, or use another AI tool to review the changes first ...

... Vitals is live on Product Hunt today. It's an Activity Monitor alternative for Mac that shows you 60 apps instead of 1,000 processes, and in its first week, close to 830 licenses have sold. This is the story of why I built it, how it works, and why I think it's the best way to see what your Mac is actually doing. Why Activity Monitor isn't enough You know the moment. The fans spin ... ... graph or both, in the order you drag them. Sound : a volume slider for every app with sound open. Bluetooth : every connected device and its battery, lowest first. Hardware : temperatures, fans, battery health, and power per app. Vitals vs

... When asking Gemini to compare our product against our top competitor, it hallucinated that the competitor

... Product Hunt community! When drafting commercial invoices, legal contracts, or official financial reports, converting numerical amounts into formal written English text (e.g., "$1,250" to "One Thousand Two Hundred Fifty") is a routine requirement to prevent ambiguity and tampering. To make this process effortless and error-free without relying on heavy or ad-cluttered websites, I vibe-coded a dedicated, browser-based Number to Words Converter micro-tool. Key features & functionality: Instant Text Conversion: Accurately converts small and large ... ... integer values into written

... first week. Curious what others are doing though. Are you testing event ordering explicitly? Replaying prod events? Or mostly monitoring and accepting some prod chaos as the cost of shipping fast? How are you validating that agent-written webhook integrations actually survive in prod? Comment from Siarhei Hamanovich(@siarheihamanovich): Hey. Testing the ordering is the part I'd push back on, 'cauz ordering isn't something either end can promise you. Stripe says outright that it doesn't guarantee events ... ... still wrong. Guarding the write on the fetched object's own updated_at, so a stale read can't overwrite a fresher one, is what fixed it for me. Comment from Asad M.(@asadmalik901): The agent-written

... prompt: use test-driven development, use QuickCheck, use fuzzing, use TLA+, audit first. Then he scored each run on a hidden test suite. That was the whole design. No instruction wildly beat the run with none. He had written down six guesses beforehand about which conditions would not outperform, and all six came true. What interested us was how the instructed runs failed, because the failures share a shape. Told to use QuickCheck, all of them used the library ... ... code did. Naming the technique gives the agent the motions of the technique. The check still comes from the agent's own reading of the spec, so the new framework fills up with the tests it would have written

... different clothes. In all three the instrument could only ever return good news, and none of them looked broken. One. A refresh job reported one item refreshed successfully. I checked the field that records when the price was last written and it had not moved. The function underneath returns an ok status for a product that does not exist, and my counter incremented on ok rather than on a value having been written

... Writing code got cheap. Checking whether it did the thing did not, and the gap between those two is where I spent a whole day. None of what follows was broken. Every line ran as written, raised no error, and reported success. That is the part worth talking about, because I think it is the real failure mode of this era and it does not look like a bug. One. A refresh job reported one item refreshed. I checked ... ... field that records when a price was last written

... time. House rule (there is only one) Here's the part that matters more than any feature table. When Happycapy finishes a job, the result doesn't live in our chat box it lands  in your tools : written into your Notion, committed to your GitHub, saved into a folder on your own machine. That's what Connectors are for. Which means the lesson of this week is already handled. If a platform any platform, ours included ... ... always was: in the accounts and on the hardware you own. An agent should be replaceable. Your work should not be deletable. The water's warm. Take your time   happycapy.ai Conclusion:  Happycapy is a direct Manus alternative

... case of this drift before it went out? Is this a real problem for your workflow, or am I overthinking it? Not looking for "just read it carefully" curious if anyone's found something that actually works at scale, versus per-article manual re-checking ...

... worked better for us: declaring intent in the morning instead of reporting history. "Here's what I'm picking up today" takes ten seconds, is answerable without memory, and produces something you can compare against reality at 6pm. Plan versus done, written

... verified purchase" label, so even that badge doesn't mean what people think it means anymore. The part that got me: real reviews mention weird, specific, slightly annoying details. "The manual was useless but I figured it out." AI written ones glide right past that. They praise smoothly and specifically avoid describing anything a person would actually notice. Once you know to look for it, you start seeing it everywhere. Feels like the entire signal system flipped. We used ... ... tell people to be suspicious of typos and broken English. Now the polished, confident, well written

... genuinely unclear where the line sits. Is swapping the underlying model a significant change to what the app does? Is broadening what the assistant will discuss? I don't think anyone knows yet, and I'd rather have written down my answer before a release forces one. Where I actually am on this. Murror is an emotional-reflection product, so the age of the person on the other side is not a compliance field to me, it's the most ... ... could have. And I still don't have a clean answer on the verification-method question, because the honest version is that I don't yet know what I'd do differently with a government-ID-checked 16 versus

... audited an AI-written billing service this week. 22 findings, 10 critical. None of them crashed. All returned 200. Four that stuck with me: an auth check that silently lets anyone in when one env var is missing, SQL injection in the one query that wasn't parameterized, a broken permission boundary so anyone can attach a charge to a workspace that isn't theirs, and a Stripe retry that double-grants seats because there's no idempotency check anywhere ... ... cleaner change and re-proved it. That one held. That's what I'm building FetchSandbox around: reproduce the real failure, apply the fix, then prove it holds on your actual code before you ship. For those shipping AI-written

... review queue and you quietly stopped showing up for the shift. There's a report that came out this spring that I keep coming back to, and I don't think it landed properly with makers because it's written for engineering leaders at big companies. Faros AI published "The Acceleration Whiplash" in April two years of telemetry from 22,000 developers across more than 4,000 teams. Not a survey about how developers feel. Actual measurement of what came ... ... code you didn't write and often can't fully explain. The report has a good phrase for the mechanism: AI-generated code is "superficially convincing." Idiomatic, well-named, stylistically consistent with everything around it. It reads like code written

... made this tool called Eneya , and unlike most things posted here the name tells you nothing, so: it's written pre-screening for tech recruiters. You build a short questionnaire around the competencies the role actually needs, candidates answer it in text on their own time, and you get back a scored report on each one. No video, no scheduling, no calls that end in the first four minutes. Anyone who has screened for a role with two hundred applicants ...

... scores your vibe. No human in the loop. You're not being interviewed you're being processed. AI-generated "thought leadership." Copy, paste, post. Zero lived experience, zero original thought. It's just AI slop in a blazer. AI-written emails to people who matter. Your client can tell. Not because the grammar's off because the warmth is. VS

... years by people who cared about how it feels to use. Dark, considered interfaces. Whitespace that isn't accidental. Content that is the product, not decoration bolted onto a spreadsheet. The short answer to "what's the best LinkedIn alternative in 2026" is this: builders aren't looking for another feed. They're looking for a professional network that was actually designed, not just maintained. That's the gap forg.to is built to close. Here's why the gap exists ... ... people outsourcing the writing entirely, then hitting publish under their own name and photo. The market has already started punishing it. Coverage of the same study found AI-generated LinkedIn posts pulling in roughly 45% less engagement than human-written

... shifted under everyone. Antigravity relaunched in May as a multi-agent suite with a new CLI and Gemini 3.5 Flash as the default model, running roughly 4x faster than Opus 4.8 or GPT-5.5, worth knowing if you'd written it off as the "free but weaker" option. What I haven't found a clean answer to: does anyone have a real methodology for tracking cost-per-solved-task rather than cost-per-token? Token counts alone feel misleading once ...

... what s finished, the decisions already made, what s broken, and the next step. Then I can either resume after the reset or give that handoff to another agent without asking it to reverse-engineer the feature from half-written code. Rate limits are still annoying, but small checkpoints make them much less destructive. Comment from Abdullah Javaid(@abdullah_javaid3): @alpertayfurr   @bickov   That combination is the right move. Here is the gap I would flag. A handoff note ... ... gets written

... Basically all our code gets written by agents now, and so do the tests. Usually same session, same model. And I keep hitting the same loop: Agent builds the feature Agent writes the tests, everything green Ship it A user hits an edge, I dig in, and the tests were checking what the code does, not what the ticket asked for. Suite was green the whole time. The annoying part is that if the model misread the ticket, its tests ... ... homework. The model is just agreeing with itself. So what do people actually do here? Different model writing tests than the one writing code? Does that actually help or do they just share the same blind spots? Tests written

... blog with 12 readers versus a client's site with 100k? Because "reach, not reversibility" implies the gate should be proportional , and I can't tell if that's worth the complexity or if a single publish-boundary gate is the right 80/20. Curious where you've landed, because you're closer to the content case than almost anyone in this thread. Comment from Dale Mooney(@dalemooney): @alex_amancio_candoa_   @aadilghani  Constant versus