u/anshchauhann

For me the thing that held up was locking the character as one fixed reference node feeding every shot with a pinned seed, rather than training a separate LoRA per character. That kept the same face and outfit across a whole sequence better than re-prompting each shot.

Setup, in case it helps: Flux for stills, Wan for i2v, multi-shot. The drift problem was the usual one where a full sequence ends up looking like three different people.

What I compared in ComfyUI:

• Character LoRA: strongest identity lock if you have the data and time to train. Overkill for a one-off character, and slow to iterate.
• IPAdapter + fixed seed as a reference node: lighter, and the part that actually worked for me. Save it as a node group and reuse it across shots so the character comes from one source, not a fresh roll each time.
• Straight re-prompting: fine for one shot, falls apart across a sequence.

I also tried a hosted node tool OpenCreator that keeps the character as one locked node in the browser. It held consistency similarly and skipped the local setup, but you get less low-level control than a full ComfyUI graph, and it's not local. For anything I want to really tune, I stay in ComfyUI.

Tradeoff: the reference-node route is more wiring than just prompting, and LoRA is more upfront still. But re-prompting every shot never held for me past 2-3 shots.

For the ComfyUI folks: are you getting better multi-shot consistency from a LoRA or from a locked IPAdapter reference node? Anything that survives batch generation without the face wandering?

Seedance 2.0 is probably still my favorite video model for short clips, but testing prompts gets really expensive fast.

Sometimes the first output is good, but most of the time I need to adjust camera movement, timing, subject action, lighting, or reference image. That means the real cost is not one generation.

I’m currently looking at cheaper Seedance 2.0 providers. Loova ai seems to have one of the better prices I’ve found (720p from $0.1/s), especially if you do a lot of image to video tests.

Anyone else optimizing for cost while still keeping quality decent?

I was recently invited to join the Vizard alpha test, and honestly, it made me think the most interesting use case for AI video agents right now isn’t “type one sentence and get a finished video.”

It’s rough cuts, or first assembly.

I work around video editing, so I’m usually pretty skeptical when AI video tools pitch themselves as replacing editors. That’s a hard sell for me. Editing has too many decisions that aren’t just about executing instructions. You’re judging pacing, context, emotional continuity between shots, when to leave a pause in, and when to cut a line that looks unnecessary on paper but actually carries a certain tone.

But rough cuts are different.

One thing current AI video agents can already handle pretty well is finding specific content inside very long videos. Anyone who edits long-form content probably knows that a huge chunk of the time is spent before the actual editing even starts.

Say you have a 1 to 2 hour interview, livestream replay, or podcast, and the client asks for a certain point, a product explanation, a reaction, or a specific example. The normal workflow is to scan through the whole thing, mark moments as you go, then come back and cut. I don’t know how other people feel about that part, but it drives me insane.

Trying to locate a few specific lines inside a massive transcript or timeline is super mechanical, but you can’t really skip it.

That’s where the video agent thing actually starts to make sense to me. I can just tell it what I’m looking for in natural language. Something like “find the parts where they talk about personal finance planning,” and it starts working through the video almost like Codex does with code. Meanwhile I can just sit there, scroll TikTok for a bit, and come back to the relevant clips.

Based on my recent experience with agent tools, I think the realistic role right now is assistant editor. Not replacing the editor, but handling a lot of the dirty work. That could mean finding specific moments, removing bad takes, adding tedious captions, motion graphics, or b-roll, and making small visual tweaks through conversation. I feel like there’s still a lot of untapped potential with Vizard Agent. Since it works through natural language prompts, in theory, a lot of video editing, generation, effects, and other actions could be handled just by prompting it.

Curious how other people are thinking about AI video agents. Do you see them as actually useful yet, or still mostly hype?

When doing weekly video repurposing, I keep wondering if the order of the workflow matters more than the tool itself.

Let’s say you have one 20-minute video every week and need to turn it into 5 TikToks, Reels, or Shorts. Do you usually watch the whole video first, list out 8 to 10 possible clips, then narrow it down to 5? Or do you just open the editor and start cutting as you watch?

I’m trying to avoid that annoying thing where you watch the same video three times and still don’t know which parts are actually worth turning into Shorts.

Curious how people organize the order of clip selection, captioning, vertical cropping, and all the other steps.

I used to think sleep was either good or bad.

Lately I have been noticing there is a lot in between. The room being a little too bright, some noise outside, my phone too close, my brain still hanging onto the day. None of it feels like a huge problem by itself, but together it adds up.

I have not done anything dramatic. Just small boring changes. Phone away from the pillow, room a little cooler, curtains closed better. For light and sound, I sometimes use my Sleenova sleep mask with low white noise, mostly so I am not using my phone for noise.

The part that seems to help is having less stuff pulling at my attention before sleep. Not total silence, not a perfect dark room, just fewer little things keeping me half-alert.

I still have bad nights, but I wake up less often feeling like I slept for hours and somehow did not rest. Maybe for light sleepers it is more about lowering the number of small interruptions than finding one big fix.

I've been building multi-shot videos in ComfyUI (Flux for the key frames → Wan 2.x for image-to-video → upscale → stitch) and I keep hitting the same wall: reuse.

The blocker: each new video I end up half-rebuilding the graph, and when a shot needs to change I re-run nodes in a way that knocks downstream shots out of sync. When I want 10 variations of the same idea, I'm cloning and hand-editing the graph 10 times. I can see the dependencies — that's the whole reason I moved off one-prompt-one-clip end-to-end tools — but I haven't found a clean way to make the whole pipeline repeatable and batchable.

What I'm trying to figure out, and would love your setups for:

How do you parametrize a workflow so one graph drives many shots/variations (prompt lists, batch nodes, wildcards)?

Best way to lock an upstream node (character/style) so editing shot 3 doesn't re-roll shots 1–2–4?

Do you save sub-graphs / node groups as reusable templates, or go full API/script?

For context, I also poked at a hosted node/canvas tool (OpenCreator) to see how they handle reuse — it's the same node-workflow idea but browser-based with models bundled; trade-off is less low-level control and it's not local. I'd rather solve this in ComfyUI if I can.

What does your repeatable multi-shot setup look like?

Okay, I've literally been falling down a rabbit hole for the past few days trying to find a decent IPTV service, and at this point my brain is fried. 😵

Every other website out there claims to be the "#1 provider in 2026," but half the comment sections look completely bot-filled, and the reviews are all over the place. One person swears a service is flawless, then the very next comment says it stopped working after 5 days and support ghosted them.

I came across a service called Xerquvatv on another forum, but honestly at this point I don't really trust any of these random sites. No clue if it's actually legit or just another sketchy name floating around in the crowd.

I'm based in the USA, and I really don't need anything wild. I don't need 50,000 random channels or some complicated tech setup with 10 different apps. I literally just want something stable, decent picture quality, that doesn't sit there buffering every 2 minutes while I'm trying to chill on the couch at night. 🥲

If anyone here has actually been using an IPTV service for a while in the USA and it genuinely works, what would you honestly recommend?

Has anyone here personally tried Xerquvatv? Is it worth giving a shot, or should I keep looking?

Would really appreciate real experiences from regular people — please no promo replies or copy-paste affiliate spam. 🙏

Something that's bugged me for a while building agents: ask one a factual question like how many GitHub stars or Twitter followers a company has, and it'll give you a clean, confident, completely fabricated number. For a competitive-research use case that's worse than useless, because the answer looks authoritative and is wrong.

So I went the other direction and built a competitive-intelligence tool surface where the model isn't allowed to produce the numbers at all. The agent calls one tool with a competitor's URL, and behind that tool it fans out to 15-plus real data sources in parallel: DataForSEO for traffic, Wayback Machine for site history, Twitter/X for followers and who's amplifying them, Product Hunt votes, GitHub stars, Google Trends. The response comes back as structured JSON, so the model receives real values it can summarize and reason over. It never invents the follower count, it just narrates what the API returned. The verdict and the scoring sit on top of verified data, not in place of it.

A few design things I had to think through that might be useful if you're building tool surfaces. One, the tool is intentionally coarse, one call does the whole report rather than fifteen chatty sub-calls, because round-tripping every source through the model wastes tokens and invites it to "help" by filling gaps. Two, partial failure is the normal case when you depend on that many third parties, so it returns whatever sections completed instead of failing the whole call, and marks what's missing so the model doesn't silently treat absent data as zero. Three, every field carries its provenance, which is what actually kills the hallucination, the model can't confuse its own guess with a measured value because the measured value is right there.

I built this (it's from the Gingiris team), so this isn't a neutral post, and it has a free tier so you can try the tool call without paying. Happy to drop the link in the comments rather than clutter the post.

For folks building agents that touch real-world facts: how are you stopping the model from inventing numbers when a tool returns partial or empty data?
That's the failure mode I'm least happy with so far.

Alright, fellow resellers, let's talk about the photo process. If you're like me, you've probably spent way too many hours trying to get that perfect shot of thrifted items to make them pop online. I mean, the listing game is already a hustle, with 30-40 items going up daily. Time is money, and the photo step was eating a chunk of it.

When I started, I was using my phone for photos, which worked okay but never really hit the mark. Not only did it take forever to set up, but I also ran into issues with "not as described" claims because the photos didn't fully capture the item's details. Plus, trying to get clean shots on a white background without a fancy setup felt impossible.

About six months ago, I stumbled upon a tool that made this part of my workflow less painful. I don't want to sound like I'm pushing anything, but Snappyit has been pretty useful for me, especially their ghost mannequin feature (snappyit.ai/ghost-mannequin). It lets me remove the mannequin or model from the shot, giving a 3D effect that shows how the clothes fit without any distractions. It’s not perfect—sometimes it struggles with really complex patterns or textures, but it usually gets the job done faster than my old setup.

Getting clean shots without a studio has been a game changer in terms of saving time. I just snap a quick pic with my phone, upload it, and in a few seconds, I have a photo ready for my listing. I can’t say it’s flawless every time, but it’s better than juggling lights and backdrops all afternoon. Plus, it’s cheaper than hiring a photographer or renting studio space. Most of the time, I’m paying about 10-30 cents per image, which is way better than the $15-50 I’d shell out otherwise.

The real kicker is how much time it saves. What used to take a whole morning now gets done in under an hour, and I can focus more on sourcing and listing, which is where the real money's at. It also reduces the risk of returns because buyers can see more detail in the clothes, so they know exactly what they're getting.

Now, I’m not saying it’s the perfect solution for everyone. If you’re dealing in high-end items or really intricate designs, you might still need a little extra editing or a better setup. And sometimes, the AI messes up with weird angles or shadows, so it's not foolproof. But for typical thrifted finds, it usually works well enough for me.

So, if you're struggling with the photo process and need something to speed things up, it might be worth checking out tools that offer ghost mannequin effects. It’s made a noticeable difference in my workflow, but do your research and see what fits your needs best.

TL;DR: Streamlining the photo process with ghost mannequin shots saves time and reduces

If you had to pick one AI music model today, what would it be? Suno, Udio, Stable Audio, JazzCat, or something else? Curious what everyone's using most often.

I keep seeing people who are mostly index-fund investors talk about buying SPCX now, and I get it. SpaceX might be one of the few companies where the hype is actually attached to a real business.

But after a $75B IPO, a first-day close around $2.1T, and a run to roughly a $2.6T market cap within the first few trading days, this is not “getting in early.” The exceptional future is already the base case in the price.

That doesn’t mean it can’t work. Great companies can stay expensive for a long time. But buying SPCX here is still a concentrated bet that SpaceX will beat the market from your entry price.

That’s why I’m trying to separate “I want exposure to the space theme” from “I need to chase SPCX right after the IPO.” A space-themed ETF, a small RKLB position, or even a tiny SpaceX-related prediction market position all feel easier to size than pretending I know the right entry on a company already valued in the trillions.

I’ve been watching some of those contracts on moomoo mostly as a sanity check, not as a replacement for owning the stock. If I’m wrong, I’d rather be wrong with small money than turn post-IPO FOMO into a real portfolio position.

I’m not saying nobody should buy it. I’m just saying it feels weird to call broad index funds boring/risky and then treat a post-IPO SpaceX buy like it’s the conservative move.

Anyone else trying to separate “amazing company” from “good entry point” here?

Maybe a dumb question, but can someone explain the new prediction markets tab on moomoo? I get stocks, options, ETFs, etc., but this feels like something different. I'm seeing contracts on the World Cup, elections, SpaceX IPO, and a bunch of other events.

Is this basically options, except the underlying "asset" is an outcome instead of a stock? Or am I thinking about it completely wrong? Curious if anyone here actually uses it and what the appeal is.

not saying this is a migraine cure. it is not. but i’ve started paying more attention to the first few minutes after waking, and i think i was underestimating how much load gets added there.

on fragile mornings, before i even know whether it will become an attack day, my system already feels sensitive. light feels sharper. sound lands harder. moving too quickly feels wrong. and then the alarm hits, usually as the first real input of the day.

i used to think only the volume mattered. now i think the onset matters too. a sound that starts suddenly feels different from a sound that fades in gradually, even if the final volume is not that different.

one small change i made was dimmer light, slower movement, no immediate phone checking, and a fade-in alarm. it didn’t change migraine itself, but it removed one sharp input from the morning. i’m trying to think of mornings less as “wake up and push through” and more as a vulnerable transition