u/Rude_Context_4844

the message said they came across my profile and thought i'd be a great fit for an opportunity at a company they're partnering with. I recognized the company name. I recognized the JD. It is my old job. I held the role for three years. They laid me off in a restructuring. The role has been open since February. I know because I occasionally look out of morbid curiosity. I did not reply immediately. I made some tea. I thought about it. Then I wrote back asking if the role came with a 40% increase from my previous salary there to account for inflation and the trauma. I have not received a reply yet.

Not even in a malicious way necessarily, but it already feels harder to tell what was written, edited, or assisted by AI sometimes.

Feels like in a few years most online content will probably involve AI somewhere in the process without people thinking twice about it.

This is stressing me out so Bad

Submitted a Research Paper last week and my professor said it came back with a high similarity score on the school's system. I wrote every single word myself, I just used a lot of the same sources everyone in the class was using

I went back and read my paper again and yeah some of the phrasing near my citations probably looks similar but I cited everything properly

Does running it through a separate plagiarism checker help at all? Like if I can show her a different report would that matter? OR is there a way to see exactly which parts are triggering it so I can explain myself

I wonder which technologies people in the future will look back on the same way we look at the early internet now - rough around the edges, but clearly the start of something massive.

Everyone talks about AI constantly, but I’m more interested in technologies that are quietly improving in the background without much mainstream attention yet.

Could be something practical, weird, or even something most people would consider boring right now but potentially huge later on.

i’ve bounced between all of these over the last year trying to book trips, so i figured i’d share a raw breakdown. honestly, there is no "best" overall tool anymore it completely depends on how you search.

Seats.aero

blazing fast and perfect if you are a power user who knows exactly what routings to look for. the UI is basically a giant spreadsheet which scares some people off, but it's incredibly efficient. the biggest downside is it relies heavily on cache, so you can definitely get burned by phantom availability on highly contested routes.

FlightPoints:

Cleanest UI by far with elite filtering. Their Chrome extension overlays award prices right inside Google Flights (saves so much tab switching). The mobile app is also top-tier, you get instant push notifications when seats drop, which completely eliminates the need to sit glued to your laptop, the main downside is the free tier is locked to a 60-day window, so you have to pay to search schedule open. honestly though, that's the standard with basically all these tools now.

Point.me

the ultimate beginner tool. if your P2 doesn't know what an alliance is, use this. it holds your hand through the entire transfer and booking process. the trade-off is that it's agonizingly slow because it live-searches step-by-step. you will be staring at loading screens for a few minutes per search. it's also the priciest option.

AwardTool

the best option if your dates are hyper-flexible. they have a really nice visual calendar UI, and the multi-airport mega search works great for broad strokes when you just want to get to europe "sometime in may." the interface can feel a little cluttered compared to the others, but the calendar view is top tier for flexible planning.

TL;DR:

it just depends on your workflow. seats.aero for raw speed, point.me for total beginners, awardtool for flexible calendars, and flightpoints for specific filtering/alerts. what is everyone else daily driving right now?

A few years ago the idea sounded dystopian to most people, but now a lot of people already casually talk to AI for advice, brainstorming, emotional support, or just boredom.

Feels like society crossed the “this is weird” phase surprisingly quickly.

Not even talking about replacing human relationships — more like AI becoming a normal background presence in everyday life the same way social media quietly did.

Curious where people think this goes in the next 5–10 years

If I know something big is going to happen, I end up paying more attention to how the story gets there instead of just waiting for twists.

A lot of stories honestly become better on rewatch/reread anyway, so spoilers never bothered me much.

Apparently this is unpopular because everyone treats spoilers like psychological warfare now.

So It’s our first time visiting London! and the kids ( a 9 year old, a 12 year old plus their best friend) are VERY excited and already got a full list of things they want to see, so we’re trying to stay somewhere central to make it easy to get around without exhausting them. At the same time, us adults don’t really want to be righttttt in the middle of the busiest areas if it’s loud/overwhelming at night. So based on my research, I think we' be looking to stay somewhere near Marylebone, Mayfair, and Soho (please advise if these are correct locations too if Im wrong here). It’s just hard to get a sense of what they’re actually like to stay in.

For anyone who’s done a family friendly luxury hotel in London, where did you stay, or rather where would you recommend we stay as first-timers, that gives a nice balance of walkability and a slightly calmer vibe? (if thats even possible in London lol!)). Thanks in advance!

Trying to “vibe code” my own AI SDR has been… an experience 😅

I thought it would be a fun weekend project, like stitch together GPT for copy, some scraping tools for leads, and my email platform to fire sequences automatically. Fast forward a few weeks and it’s basically a fulltime job keeping it all running. Between broken scrapers, weird edge-case responses from GPT, and having to babysit deliverability, I’m starting to realize maybe there’s a reason people pay for tools that already do this. Has anyone else gone down this rabbit hole and survived? Or did you just bite the bullet and use something that's alreay on the market?

I've been following this sub for about a year and I work with LLMs daily for content strategy and product work. I've tried most of the frameworks people recommend here. chain of thought, tree of thought, few-shot examples, role-based prompting, the mega-prompts with 15 sections.

they all work to varying degrees. but after months of experimenting I keep coming back to the same conclusion: the biggest lever is just the amount of relevant context in the prompt, not the structure around it.

a sloppy, unstructured prompt that includes all the relevant details about my specific situation consistently outperforms a beautifully structured prompt that's missing context. I've tested this across probably 300+ prompt-output pairs at this point.

I think what happens is people read about frameworks and then spend their energy structuring the prompt while still being vague about the actual content. "you are an expert marketing strategist. use chain of thought reasoning. think step by step." ok but I still didn't tell you anything about my company, my audience, my constraints, or what I've already tried. the framework is a nice wrapper around an empty box.

one thing I've noticed about my own behavior: when I type prompts I keep them short because typing is slow and I subconsciously trim. when I talk through what I want out loud and paste the transcription as my prompt, I include 3-4x more context naturally. I've been using an AI voice dictation tool called Willow Voice for this and the prompts end up being full paragraphs with all the messy background details that actually make the output specific. but you could get the same effect by just forcing yourself to type more. the input method matters less than the input quantity.

I'm not saying frameworks are useless. few-shot examples genuinely help for specific formats. but I think we'd help more people by telling them "give the model way more context" instead of "try this 12-step prompting framework."

am I oversimplifying or do others see the same thing?

finding datasets isn’t that hard, but finding ones that are actually reliable, well-documented, and usable (without a paywall) is a different story.

obviously there’s government portals, World Bank etc but even their pretty hit or miss depending on data structure and maintainance

where do you consistently go when you need solid datasets?not just a big list of datasets but sources you actually trust for things like documentation, clear definitions / methodology, reasonably up-to-date data something you’d feel comfortable citing or building on?

Please drop links to if you can, always looking to build a better mental list of go-to sources.

I've been tracking my prompt engineering experiments for about 4 months. running the same tasks through claude and gpt-4o with different prompt styles. I have a spreadsheet with 200+ prompt-output pairs rated on a 1-10 quality scale.

the single biggest predictor of output quality is not the framework (chain of thought, few-shot, role-based, etc). it's prompt length, specifically the amount of domain-specific context included.

my data shows:

- prompts under 50 words: average quality rating 5.2/10

- prompts 50-150 words: average quality rating 7.1/10

- prompts over 150 words: average quality rating 8.4/10

the structure of the prompt matters but it's secondary. a well-structured 30-word prompt still underperforms a messy 200-word prompt that includes all the relevant context.

what I think is happening: when you type a prompt, you unconsciously compress. you leave out details you think are obvious. but those details are exactly what the model needs to produce something specific. the longer prompts just have more of the right information.

I've experimented with different ways to get more detail into prompts faster. one thing that helped is talking through what I want out loud first using an AI voice dictation tool called Willow Voice, then pasting the transcription as my prompt or cleaning it up slightly. not because dictation is magic but because speaking is 3x faster than typing so I naturally include more context without it feeling tedious. it formalizes the rambling thoughts into something the model can actually use.

but even without dictation the core finding holds. if you're getting generic outputs, before trying a new framework, just try giving the model 3x more context about your specific situation. constraint details, audience info, examples of what you do and don't want. that alone will probably do more than any prompting template.

Has anyone else tracked this systematically? curious if prompt length correlates with quality across different use cases or if I'm overfitting to my own workflow.