... 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

... 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

... 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

... feed ships with real settings - freshness window, follow boost, social signal weight. You tune the algorithm, not the other way around. 2. Fame-weighted curation: established accounts' votes move posts further than day-old accounts'. It's written in the ToS, not hidden. 3. Creators sell products and subscription tiers wallet-to-wallet over BTCPay (BTC, LTC, USDT, Lightning). We never hold funds. The ToS and privacy policy are written

... 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 ...

... wiped every decision the agent had already made. What fixed it was keeping those decisions outside the model. I run a folder of markdown memory files with a small index loaded at the start of each session. Decisions get written there as they happen, so the session after the reset starts from the file instead of from whatever survived in context. Gal's point about a second model not knowing the first one's choices stops applying once the choices ... ... trusting they will reconstruct it later from the transcript. Comment from Alexander Bickov(@bickov): @alpertayfurr   @abdullah_javaid3  The real gap left standing is the decision that was still being formed when the cut happened, nothing 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

... rather than hold the line forever. A stalled job that took the cautious path is recoverable. A stalled job that just sat there is a support ticket. Comment from Dale Mooney(@dalemooney): @alex_amancio_candoa_   @aadilghani  Constant versus invariant is the useful thing here and I am taking it. One addition: you cannot separate them by observation, but you can by deliberately violating the constant once. A property that survives a test faking an unattended caller ... ... shape. The only artefact that encodes meaning is a test. If escalate means two things, what makes the fork visible is a test asserting that escalate on the unattended path denies, because then the second meaning exists as a written

... before my last two launches I wasted weeks building before checking who else was in the space. don t be me. here s the 30-min competitor check I do now, all free tools: 1. search PH archives (5 min) search your keywords on Product Hunt. sort by launch date. note how the top 5 positioned themselves the gaps in their taglines are your opening. 2. check who s actually alive (10 min) a launch from 2023 means nothing ... ... signals: are they hiring? shipped anything in the last 6 months? active changelog? I use exploreyc.com for this (free db of 8,600+ YC / a16z / PH startups with hiring status) but LinkedIn works too. 3. read the comments on competitor

... budget. Is that dishonest, or is it no different from using a framework, a library, or a junior dev and not itemizing every line's origin? Nobody used to ask what percentage of a website a developer typed themselves versus copied from Stack Overflow or scaffolded with a generator. But this feels different somehow, maybe because the speed and volume are so much higher, or because "AI-written

... Four sentences a  user  would recognise, in the order they'll ship. I comment until it's right "drop revoke for v1, it's not load-bearing" and the agent regenerates the list. No code has been written. No branch has been cut. I'm editing outcomes, not architecture, and it costs me a coffee's worth of attention. Get the blueprint right and you've done 90% of your job for the week. Everything after is yeses ... ... pings me instead of flailing forever. So by the time a milestone reaches my review, something already tried five times to break it and failed. That quietly changes what my review even  is . The real bottleneck on AI-written

... years as a 3D artist, never touched Xcode or written a line of code before this year. In February I opened Cursor and just started typing what I wanted to build. 7 months later, backend on Cloudflare Workers, iOS app in SwiftUI, multi-model AI routing (GPT, Claude, Gemini), all written

... what the actual job entails. Developers have noticed. Only 12% of developers use LinkedIn as their main source of technical information, according to a Stack Overflow survey. Most are on the platform purely because they feel they have no alternative, not because they find it genuinely useful. The Fake Everything Problem LinkedIn has a trust problem that is getting dramatically worse, not better. In the second half of 2024, LinkedIn identified and removed 80.6 million fake accounts at the point ... ... Cringe Content Problem Every developer knows this feeling. You open LinkedIn hoping to find a job, make a connection, or learn something useful. Instead, you scroll through: A founder revealing the three lessons they learned from almost going bankrupt, written

... workflow question I keep going back and forth on, and how I landed. I ship a lot of AI-written code as a solo founder, and I hit the wall you probably know: if a human has to review every agent PR, you didn't buy leverage, you hired yourself as a full-time code reviewer. So I stopped asking "should I approve this PR" and started asking two things instead: how confident am I it works ... ... gate the whole point is to gate by the cost of being wrong, not to remove the gate. So I'm curious how the rest of you draw the line: what do you still review by hand on agent-written

... editorial identities : ChatGPT Search is encyclopedic. It favors Wikipedia for Education, Recommendations, Comparison, and Purchase queries. If your content reads like a reference section, it surfaces here. Perplexity is video-anchored. It leads with YouTube for Education and Recommendations. Written content alone leaves citations on the table. Google AI Overviews is video-biased across six of seven intents. The one exception is Navigational queries, where brand-owned domains take the top slot. Google AI Mode is the most exploratory engine ... ... shows you your visibility, share of voice, and citation rates across each platform individually . This allows you to see exactly where you win and where you are invisible, with the granularity required to understand each platform's editorial identity. Competitor

... would have changed their mind never reached them. The numbers Average original architecture score across all 59 audits: 3.4 out of 10. Average rebuilt score after structural correction: 9 out of 10. Same content. Different sequence. No new copy written from scratch. The problem was never the words. It was always the order. What I built from these patterns After 59 teardowns, the most common question I received was: " How do I know if my email has this problem ... ... competitor