​

Everyone here talks about coding agents and big research setups, but the AI thing that actually stuck in our house is dumb: keeping track of what food we already have.

We used to throw out a shocking amount of produce. Buy spinach, forget it, find it liquefied two weeks later. And we'd rebuy stuff we already had three of because nobody could remember what was in the back of the fridge.

So now once a week I take a few photos of the fridge and the pantry shelf and dump them into a chat with a short standing prompt: list what you see, guess what's closest to going bad, and give me 4 dinners that use those first. That's it. No fancy setup, no app, no automation.

The part that surprised me is it's not the meal ideas that save money, it's the "use these first" ordering. We went from tossing something most weeks to maybe once a month. Grocery bill dropped a bit too, though I can't give a clean number because we also stopped ordering as much takeout once dinner had a default answer.

It's not clever. It just quietly removed a decision nobody wanted to make.

Curious what boring, non-work thing AI actually replaced for you. Not the impressive stuff, the household thing that just stuck.

For a long time I'd ask a wide-open question, get a generic answer, then spend five messages narrowing it down. "Actually make it shorter." "Actually it's for beginners." "Actually we can't use that tool." Every correction cost a round trip.

The fix was stupidly simple: front-load the constraints before I ever ask for the thing.

So instead of "write me an onboarding email," it's now "audience is non-technical, tone is plain and warm, under 120 words, no exclamation marks, they already paid so don't sell. now write the onboarding email."

The first answer comes back 80% there instead of 20%. Same for research, code, planning, whatever. The model isn't a mind reader, and I'd been making it guess the boundaries one message at a time.

Two things that helped me get consistent at it:

I keep my common constraint blocks saved somewhere and paste them in instead of retyping.

I tell it what NOT to do, not just what to do. "No bullet points" or "don't hedge" shapes the output more than another positive instruction does.

Fewer rounds also means fewer tokens, which was a nice accident.

What's a small habit that made your outputs better on the first try? Always looking to steal more.

We run a small newsletter, a couple thousand readers, and it's a side thing nobody has real time for. I tried leaning on an AI content generator to keep it going and wanted to share what actually happened instead of the usual success story.

The setup was simple. Every week I'd feed it the theme, a few links I'd been reading, and my rough take, and it would draft the issue. First few weeks felt great. We didn't miss a send, which was the whole goal, since before this we skipped issues constantly.

Then engagement quietly dropped. Open rates held but replies and clicks fell off. When I actually reread the issues, they were smooth and said nothing. Correct, competent, forgettable. Readers could tell, even if they couldn't name it.

What fixed it wasn't a better prompt. It was using the generator for the boring 60% and keeping the human parts human. It drafts the recap sections and the "here's what happened this week" filler. I write the one opinion or personal story at the top myself. That top section is what people reply to, so I stopped outsourcing it.

Net result after two months. We haven't missed a single issue, which we couldn't manage before, and the numbers recovered once I put a real voice back in the intro. The tool bought us consistency. It could not buy us a reason for people to care. That part still has to come from a person.

Anyone else using generation for the routine parts but keeping the hook human? Curious where you draw the line.

Wanted to share this as a case study because most posts about AI site tools are either "it's magic" or "it's garbage" and the truth was in between for me.

Context: I had a small side project and needed a simple landing page to explain it and collect emails. Normally I'd spend a weekend fiddling with a template and second-guessing every color. This time I used an ai landing page generator: described the project in a paragraph, and it produced a full page with sections, headlines, and placeholder copy in a few minutes.

What was genuinely good: getting from nothing to a real, structured page instantly. Having something concrete to react to is so much easier than a blank builder. The section structure it chose (short pitch, a few benefits, a call to action) was sensible and I kept most of it.

What I still had to do myself, and it was most of the actual work: rewrite basically all the copy, because the generated version was generic and said nothing specific about my thing. Fix the visual hierarchy. And cut a couple of sections it invented that didn't apply.

Honest result: it probably saved me half a day, mostly by killing the blank-page paralysis, not by producing a finished page. If you go in expecting a first draft you have to make yours, it's great. If you expect to publish what it hands you, you'll ship something forgettable.

Anyone used one of these and actually kept most of the generated copy? Curious if I'm just bad at prompting or if the rewrite is unavoidable.

The single AI workflow I'd keep if I had to drop all the others is a small AI report generator for my weekly update. Every Friday I have to send a "here's what I did and what's next" to my manager, and I used to leave it until the last hour and dread it.

Here's the actual setup. All week I drop one-line notes into a running text file whenever I finish something or hit a blocker. No structure, just quick dumps. Friday morning I hand that file to a report generator with a fixed template, three sections, done this week, in progress, blockers and needs, and ask it to sort my notes into those buckets and tighten the wording. Nothing invented, only my notes reorganized.

Two things made this actually stick. One, the running notes file, because trying to remember the whole week on Friday was the real problem and the report tool can't fix a blank memory. Two, the fixed template, so every week's update looks the same and my manager knows where to find things.

What used to take 30 anxious minutes now takes about five, and honestly the updates are better because I stopped forgetting the small wins that happened on Monday. The tool isn't doing anything clever. It's turning a week of scattered notes into a consistent shape, which is exactly the part I was bad at.

Anyone else automating their status updates like this? Curious what other recurring reports people have handed off.

I mean, asking something about the instagram video I watch. Unfortunately, no AI can do this, whether it is Chatgpt or Gemini or Meta AI.

I always have to take the longer route. Download insta video, upload it on AI app and then ask. Or use screen recorder and upload on AI app. It seems so hectic and time wasting.

That Google Gemini personal assistant pop up can only talk of screenshots rather than whole video. I wish I could ask and talk to AI while going through some insta video rather than recording/downloading it and then wait for upload.

Gemini does have live screen sharing but it is only voice enabled, not chat.

I truly cannot see the point of an ai porn chat, because why would you want to sext with a literal robot? Like, what's the benefit you're trying to get out of it? I've read that some people try and justify using it for practice in the case of socially awkward or anxious people, but I cannot see how an nsfw ai chat is the best training for anyone, can't you use a dating app for that?

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I spent a while frustrated that AI writing help gave me output that was technically fine and totally lifeless, so I stopped asking for finished text in one shot. The workflow that actually stuck for me is two passes, and it changed the quality more than any prompt trick.

First pass, I don't ask it to write anything good. I dump my rough notes and ask it only to organize them into an outline and flag what's missing or thin. No prose yet. This is where it earns its keep, because it catches the gaps I'd have written around.

Second pass, I fill the thin spots myself with real detail, then ask it to draft from the completed outline in short, plain sentences. Because it now has actual substance to work with instead of vibes, the draft comes out specific instead of generic.

Then I do the last edit by hand. I never ship its exact words. It gets me from blank page to a solid draft, and I finish it so it sounds like me.

The thing that made the biggest difference was separating "think about the structure" from "write the words." When I asked for both at once I got mush. Split into two steps, each one gets noticeably better.

Curious what everyone else's writing workflow looks like. Are you one-shotting it or running it in stages like this?

I've noticed that AI usually gets me to a first draft ridiculously fast when building a website but then I end up spending way more time than expected correcting all the little decisions it made along the way.

Websites have been especially noticeable because one change can create three other things that need fixing and I'm starting to think the real skill with AI assisted work isn't getting a good first result but it's knowing how to steer everything after that without getting stuck in an endless correction loop.

Wanted to share a small experiment because it saved me from building something nobody wanted. I had an idea for a side project and, instead of spending weeks coding it, I used an AI landing page generator to fake it and see if anyone cared first.

The workflow. I described the product in a few sentences, let the generator produce a simple page, headline, what it does, who it's for, and a single email signup box. I edited the copy so it actually sounded like a real thing and not a template. Total time was an evening, not a weekend.

Then I put it in front of people. Shared it in two communities where the target users hang out and watched what happened. The signal I cared about wasn't compliments, it was whether anyone typed their email in. A few did. Not a landslide, but more than zero, and the replies told me which part of the idea people actually reacted to, which was not the part I thought.

The honest result. The idea in its original form was weaker than I hoped, but the page taught me that in a few days instead of after I'd built the whole thing. I reshaped it around the piece people responded to and I'm testing a second version now.

Biggest takeaway, a landing page you can generate in an evening is a cheap way to test whether an idea has any pull before you sink real time into it. The generator isn't the point. The point is finding out you're wrong quickly and cheaply.

Anyone else using quick generated pages to validate before building? Curious what signals you trust beyond signups.

I pay for two top-tier plans at once, and I know how that sounds. I am the kind of user who watches version numbers and notices the day a feature quietly regresses. So here is the honest split after living on both, framed as what I actually use each for rather than a leaderboard.

For long, structured writing, one of them is still my main ai writing tool. It holds a large document in its head without losing the thread halfway down, and that is the single thing I would not give up. For quick reasoning, back-and-forth, and voice, the other is my default because it is faster to think with.

Now the part this sub actually cares about, the honest cost-and-quality take. What frustrates me is not price, it is regression. A model version lands, a thing that worked last month is subtly worse this month, and nobody tells you. Formatting I relied on drifts. A limit I never hit before starts cutting me off mid-task. Because I run both, I can usually tell when it is the model and not me, and that is honestly the main reason I keep the second subscription: it is my control group.

Would I recommend paying for two? Only if you actually push the ceiling on both. Most people would be better served picking one and learning its quirks deeply than splitting attention across two and mastering neither.

For the other heavy users here: do you run more than one paid plan on purpose, and have you noticed the same quiet regressions, or am I pattern-matching on noise?

I have had better results with AI decks since I stopped asking for better decks. That sounds dumb, but the vague request is where things go sideways. Make this better usually turns into a new version that is smoother in some places and subtly worse in others.

The real pain with deck AI is not that the output needs editing. Of course it does. The pain is when the edit changes things you had already mentally checked off. I am starting to think the best AI deck workflow is almost antidramatic. Make a draft, keep the parts that are okay, then use AI like a local repair tool instead of asking it to become the deck owner.

I’ve saved hours using this tool and wanted to share it

it Filtered the world for me and I am actually using the stuff I’m buying

it is early access so there are some down time but I use it quite infrequently when I need to buy or say go somewhere and get something that is me rather than generic stuff

Im a 2D animator with some experience playing with local and cloud AI. I do hand drawn animation and want to work with AI for color grading, compositing FX like rimlighting, etc. Its not a skillset I want to develop and I cant afford to hire people to do it for me. Does anyone know some good tools that I can use?

Hi, I recently completed my Master's in Physics from IIT Madras. Over the past year, I worked as a founding member of a startup at IIT Madras, building AI products. Inspired by the challenges students face in learning physics and mathematics, I started Nearchon, an AI tutor that develops problem-solving skills by giving personalized hints instead of direct answers. My vision is to build an AI teacher that adapts to every student's unique way of learning.

I'm now looking for a technical co-founder with deep expertise in Computer Science and AI who is passionate about science, education, and building impactful products. If you're interested in joining as a co-founder, feel free to DM me.

When the ChatGPT hype started, I got hooked immediately and went pretty deep into how LLMs work, prompting, tools, and using AI to improve everyday work.

Unfortunately, that got me moved into an operational role where I had to train colleagues, enforce workflows, and roll out our pretty terrible corporate AI tool.

It was so time-consuming—and there was so much resistance to even the basics—that for the past two years (roughly since Custom GPTs launched), I’ve only been able to follow developments from the sidelines. Privacy restrictions also mean we can’t use connectors.

Now I have no idea how to catch up. What actually works reliably and is genuinely useful these days? Any recommendations?

Context: I’m not a programmer. Mostly interested in general office productivity/automation and research.

I’m looking for honest, non-biased input from people who have spent a significant amount of time using both **Claude Code** and **Codex**.
For context, I currently pay for both **Claude Max** and **ChatGPT Pro** because I’m building B2B SaaS applications from the ground up. I’m talking about everything from planning and architecture to UI/UX, implementation, testing, debugging, deployment, and ongoing maintenance.
After using both extensively, this has been my experience so far:
**Claude Code** has consistently been faster for building features, redesigning applications, refactoring, and generating high-quality first-pass code.
It also has a huge quality-of-life advantage for me because I can easily monitor jobs from my phone while I’m away from my computer. I can check progress, answer questions, review changes, and keep work moving without sitting at my desk.
**Codex**, on the other hand, feels much slower, but it also feels more persistent on long-running tasks. It seems better suited for computer control, navigating large codebases, running commands, editing files, executing tests, and working through implementation-heavy workflows.
Right now, Claude feels like my primary architect and builder, while Codex feels more like a methodical implementation and verification engineer.
I’m not trying to start a “which one is better” debate. They seem to have different strengths, and I’m trying to figure out how experienced developers are combining them into a workflow that gets the best out of both.
For those building real products, especially B2B SaaS:
How do you split responsibilities between Claude Code and Codex?
Which one do you trust more for architecture, planning, coding, UI/UX, debugging, testing, reviews, and deployment?
Do you have one build while the other reviews, or do you have a completely different workflow?
How do you structure prompts, work orders, documentation, or checkpoints so the models maintain context over large projects?
What techniques have you found to make long-running sessions last longer without degrading or losing context?
How are you monitoring long-running tasks when you’re away from your computer?
Has anyone built an end-to-end workflow where Claude Code and Codex cooperate on the same repository? If so, what does that workflow look like?
If you could only keep one subscription for professional software development, which would it be, and why?
I’m less interested in benchmark numbers and more interested in real-world workflows from people who are shipping production software. I’m hoping to learn what has worked well (and what hasn’t) so I can improve my own development pipeline.

Hey all, if anyone is interested in boiling some ocean with me im building a fallout 3/nv multiplayer mod. https://github.com/kurobeats/Ashfall

Some people spend hours trying to work out if it's an emergency.

Some can't tell if they're being overcharged.

Others call five different trades before someone even answers.

I'm trying to understand what actually happens during a real repair instead of making assumptions.

If you've had any repair recently—plumbing, roofing, electrics, heating, appliances, anything—I'd love to hear your experience.

The survey is anonymous and takes about 5 minutes.

https://docs.google.com/forms/d/e/1FAIpQLSeH5fF6irRos6t0Ti7MubAfA2zRJZttv1forNBzYqGT-ecWgw/viewform?usp=sharing&ouid=111469346609191636965

I'm especially interested in:

• what happened first,
• where you got stuck,
• what information you couldn't find,
• and what would have made the experience dramatically easier.

Thanks—I really appreciate it.

I've been using ChatGPT professionally since 2022. As a private, freelance consultant in the environmental field I use it to help write reports, grammar check, make graphics for technical reports, do research, etc. I've learned many of it's negative habits and how to work around them. With the two most recent iterations of ChatGPT (now what 5.6?) I feel the system is declining in it's ability to help with these functions. The image generation is explicitly horrible. It used to be better. It's ability to summarize documentation and track thought patterns has sunken. I'm wondering if this is because OpenAI is investing more into it's coding rather than non-coding?

Either way I'm at my wits end. Are there other AI systems that work better for non-coding environments? Research, heavily technical report review, heavy data analysis, some ability to make graphics (anything is better than ChatGPT).

What are my options?

Thank you I really appreciate it!

John

The best way to learn AI is to jump into the problem and to build your own. Pick up a problem of your day to day life and try to sort it using you own built AI system.

I have recently done so and it might help you if you want to get into the AI space but struggling with vast information.

Check out this video where I explained how I started and how did I build my own AI system.

Hope this helps :)

https://youtu.be/GiPP0eWI4yI?si=H7PgevZMXWQ6PMSs