u/Material-Trouble-415

We've been trying to scale up our content output over the last few months using claude and chatgpt for first drafts. the speed of getting a structured article is awesome, no doubt about it.

but honestly, the editorial phase is absolutely breaking my team right now. the ai just loves to drop random statistics, percentages, and case study names that look incredibly official but are entirely made up. it confidently quotes industry data that sounds perfect, but when we actually try to find the source to verify it, nothing exists.

It feels like my editors are no longer editing for tone or readability, they basically turned into fulltime investigators who have to manually google every single sentence to make sure we dont accidentally deliver a complete lie to a client.

if you're running a content team and using ai, how are you managing this workflow? do your editors just check everything line by line manually, or do you have a better process to catch these Halucinations? because rn, the time spent on manual verification is eating up all the time we supposdly saved on drafting lol.

I run a small content agency and we've been trying to use claude and chatgpt to help scale up our blog posts and client research lately. for draft generation and outlines it's great, saves a ton of time.

but man, the fact-checking part is becoming an absolute nightmare. the models just casually make up statistics, quote fake reports from huge brands, and invent urls that look completely real but lead to a 404 page. yesterday it confidently quoted a 2026 hubspot state of marketing report with specific percentages that turned out to be totally fabricated when i checked.

if a client catches a fake fact in an article we deliver, it completely ruins our trust. but right now, having my editors manually google every single stat and link to make sure the ai didn't hallucinate it is taking so much time that it defeats the whole point of using ai in the first place.
When I ask the other AI , to cross check specific facts or LLM council to do that , they also hallucinate , non existing URLs.

how are you guys dealing with this in your seo workflows? do you use any tools to automate verifying links and claims, or are you just stuck manually checking everything line by line? need some ideas because this is killing our productivity ngl.

i'm tinkering with a small side project idea but i'm honestly not sure if it's actually worth investing my time into, so i wanted to ask people who actually build stuff in this space.

basically it's a simple app that scans ai-generated video scripts or lore files to automatically catch hallucinations, fake citations, and weird madeup facts before you use them for a video or a stream.

i know a lot of ai vtuber channels rely on llms to generate content or character backgrounds, and having your bot confidently lie about something can kinda ruin the immesion or just make editing a nightmare.

do you guys actually run into this issue often, or do you just manually skim through everything anyway? just trying to see if this is a real pain point for creators or if im just trying to build something nobody really needs lol. any honest thoughts would be great tbh.

so i ve been spending a lot of time lately messing around with automation pipelines and llms, mostly trying to generate research pieces, scripts, and overall text. The major roadblock is always the same. the output looks super clean and professional, but then i realize the ai completely made up a statistic or cited a source that doesnt exist.

manually fact-checking every single generation is absolute brain rot and totally defeats the purpose of automating things in the first place.

i'm tempted to build a simple verification engine that could automatically cross text outputs against real data and flag potential hallucinations. but before i dive into vibe coding this over the next few weeks and wasting my time, i wanted to do a quick sanity check with you guys.

i d love to chat about how you're currently dealing with hallucinations, and i can hit you up first once i have a rough prototype ready to break.

i've been trying to finish a basic side project over the last two weeks using pretty much just cursor and claude. at first it felt like magic, ngl. blew through the basic setup in like an hour.

but now im realizing i spend way more time reading through massive context walls and arguing with the prompt than actually building anything. it feels like i traded syntax errors for just babysitting an AI that constantly apologizes and then makes the same mistake three times in a row.

how are you guys actually staying productive with this? are you still writing core logic by hand and just using AI for boilerplate, or are you pushing through the prompts? genuinely curious because the fatigue is real

Hi everyone,

I’m currently at a career crossroads and I’d really appreciate some outside perspective.

I’m based in Europe and my background is in mechatronics engineering. I worked for around 8 years as an R&D/project engineer in an industrial company.

After that, I spent several years as a mobile field service engineer in laboratory diagnostics / medical-lab equipment. That involved travelling to customer sites, repairs, preventive maintenance, troubleshooting under pressure, user support, operator training, documentation, and dealing with urgent technical issues.

The problem is that this type of work eventually took a serious toll on my health. Constant travel, unpredictable working hours, pressure from customers, stress, and lack of routine affected me both mentally and physically. I developed somatic anxiety symptoms and recurring abdominal issues. At some point I had to leave, because my body basically told me that I couldn’t continue like that anymore.

Now I’m looking for a calmer, more sustainable career direction. Ideally something remote or hybrid, with more predictable hours and less constant pressure. I’m not looking for a glamorous corporate career at all costs. I’m looking for work where I can use my technical background, earn a stable income, and not destroy my health further.

Recently I’ve also been learning AI automation / no-code tools such as n8n, Make, Notion, basic integrations and workflow automation. I’m interested in this area, but I’m still trying to understand whether it is a realistic career path or more of a useful additional skill.

Some directions I’ve been considering:

• technical support / application support,
• customer support for technical/SaaS products,
• technical writing / documentation,
• QA / software testing,
• implementation specialist,
• product support specialist,
• AI automation / no-code automation,
• operations / process automation,
• technical training or instructional content,
• some bridge role between engineering, IT, documentation and support.

I would prefer to avoid heavy sales roles and avoid going back into constant field travel.

Has anyone here made a similar transition from field service, engineering, or hands-on technical work into something calmer, remote/hybrid, or more sustainable?

Which roles would you realistically recommend for someone with my background?

I’d be grateful for any practical advice, job titles to search for, industries to look into, skills to build, or examples of paths that worked for you.

Cześć,

jestem obecnie trochę na zawodowym rozdrożu i szukam porady od osób, które może były w podobnej sytuacji albo znają realia rynku.

Z wykształcenia jestem inżynierem mechatroniki. Przez około 8 lat pracowałem jako inżynier w dziale R&D przy maszynach przemysłowych. Później przez kilka lat pracowałem jako mobilny inżynier serwisu w diagnostyce laboratoryjnej - sprzęt medyczny/laboratoryjny, wyjazdy do klientów, naprawy, przeglądy, kontakt z użytkownikami, szkolenia.

Problem w tym, że poprzedni tryb pracy mocno odbił się na moim zdrowiu - psychicznie i fizycznie. Ciągłe wyjazdy, presja czasu, nieprzewidywalne godziny, odpowiedzialność za sprzęt u klienta, stres i brak stabilnego rytmu doprowadziły u mnie m.in. do nerwicy somatycznej i nawracających problemów z brzuchem. Doszedłem do momentu, w którym musiałem z tego odejść, bo organizm zaczął mówić „stop”.

Teraz szukam spokojniejszego kierunku zawodowego. Najchętniej czegoś zdalnego albo hybrydowego, z bardziej przewidywalnym rytmem pracy. Nie zależy mi na wielkiej karierze korporacyjnej za wszelką cenę - bardziej na pracy, która pozwoli normalnie funkcjonować, zarabiać stabilnie i nie rozwalać dalej zdrowia.

Mam doświadczenie techniczne, analityczne, serwisowe, projektowe. Dobrze czuję się w rozwiązywaniu problemów, analizie, tworzeniu instrukcji/procedur, pracy z systemami, dokumentacją techniczną, szkoleniami. Ostatnio uczę się też automatyzacji AI/no-code, m.in. n8n, Make, Notion, podstaw integracji i workflow.

Zastanawiam się, w którą stronę najlepiej pójść, żeby realnie wykorzystać moje doświadczenie, ale nie wracać do tak wyniszczającego trybu pracy. Myślałem m.in. o:

• technical support / application support,
• QA / testowanie,
• customer success w firmie technicznej, ale bez agresywnej sprzedaży,
• AI automation / no-code automation,
• praca związana z Notion, n8n, Make, automatyzacją procesów,
• szkolenia techniczne lub tworzenie materiałów edukacyjnych,
• jakieś stanowiska pomostowe między inżynierią, IT i obsługą techniczną.

Czy ktoś z Was przeszedł podobną zmianę z pracy terenowej/serwisowej/inżynierskiej do spokojniejszej pracy zdalnej lub biurowej?

Jakie stanowiska polecilibyście osobie z takim doświadczeniem?

Co ma realny sens w 2026 roku, a co wygląda dobrze tylko na papierze?

Będę wdzięczny za konkretne sugestie, ścieżki wejścia, nazwy stanowisk, branże albo przykłady firm, w których warto szukać.

Hey guys. Quick question for those who have used both.

I have been diving deep into AI automation for the past 6 weeks. I just finished a pretty solid project using Make, Supabase and WeWeb for the front end. It works really well, but now I am looking into n8n and wondering if I should pivot. I actually just signed up for a course to learn it properly over the next few weeks.

Before I go all in, I would love to hear your honest thoughts. Is n8n a big upgrade over Make for this kind of stack? What are the actual advantages when building heavier AI workflows?

I hear a lot about self hosting and better error handling, but I want to know how it feels in day to day dev work. Any tips from people who made the jump are highly appreciated!

Quick follow up question for those of you working in the space.

I've been diving into AI automation for the past 6 weeks. I just finished my first project using Make, Supabase, and WeWeb for the UI, plus Notion to handle all the documentation as the app got bigger.

My next step is getting deep into n8n. I just signed up for a course and plan to spend the next couple of weeks building out some smaller projects to pad out my portfolio.

As for the AI side, I've tested ChatGPT, Gemini, and Claude. Claude has been my daily driver lately, honestly mostly because its MCP integration makes it super reliable for my workflow.

Am I on the right path with this stack? Is there anything crucial I'm missing right now? I'd love to hear from folks who are further along and have actually managed to transition into doing AI automation for their day job. Any tips or reality checks are appreciated!