u/flatrive

been going deeper into AI-heavy video workflows lately and something keeps coming up. people talk a lot about the AI features in Premiere and DaVinci, Firefly-powered Generative Extend, Speech to Text, auto captions, Neural Engine, stuff like Magic Mask and voice isolation, but way less about how you actually trigger and manage those features fast when you're mid-edit. that's where I started looking at programmable shortcut keyboards and macro pads, things like Editors Keys or Logickeyboard setups. honest take after messing around with it for a bit: the keyboard itself isn't doing anything AI. it's just reducing the friction between you and the AI tools already in your software. so if you've got frequently used AI actions mapped to physical keys you can hit without thinking, you do stay in flow better. less hunting through menus, less breaking concentration. worth being clear though, not every AI feature is a clean one-key trigger. a lot of them still open dialogs or need a bit of manual review before you, commit, so the keyboard gets you to the door faster but doesn't skip the process entirely. for repurposing workflows where you're doing a lot of repeated actions across clips, that muscle memory stuff genuinely adds up. and in 2026 where near-real-time previews are more common, the bottleneck is less often the AI inference and more often you, navigating, reviewing, correcting. that's actually where a macro pad starts to make more sense than it did a couple years ago. but if you're expecting the keyboard to make the AI smarter or faster at generating, nah, it won't. it only speeds up the human side of the loop. the more interesting question for me is whether optimising that human side is still worth the setup, cost, or if agentic editing tools are moving fast enough that the whole interaction model shifts again soon. curious if anyone here has actually built out a macro-heavy keyboard setup for AI video, work and whether it made a real difference or felt like overkill at this point.

been sitting with this question for a while now because I use AI tools pretty heavily in my own work and the honest answer is. it's complicated. for straight production tasks, yeah, things that used to eat half my day are basically gone. background removal, resizing variations, rough layout options, quick image gen for mood boarding. a lot of that is handled now across tools like Figma AI, Canva, Framer, and others, and I genuinely don't miss doing it manually. that said, quality still varies depending on the tool and the workflow, so "automated" doesn't always mean "done", you're still reviewing and correcting more than people admit. but here's where I think people get the framing wrong. the tools are fast, but they're not making the calls. someone still has to know what looks right, what fits the brief, what the client actually needs versus what they said they need. I've seen AI outputs that are technically fine but completely miss the point of the project. that gap between "generated something" and "solved something" is still very much a human job. if anything I spend more time thinking now and less time pushing pixels, which honestly feels like a net positive. one thing that doesn't get talked about enough is how much design systems work and brand consistency still matter inside these AI-assisted workflows. the tools are getting better at working within component libraries and flagging accessibility stuff like contrast issues or missing alt text, which is genuinely useful. but keeping everything coherent across a project? still on you. where I do think there's a real shift happening is at the entry level. a lot of the work that used to be how junior designers built skills and got reps in is getting absorbed by these tools. that's worth talking about honestly because it changes how people develop and where they build judgment. curious whether others have noticed this in practice, either in your own path or watching newer designers come up.

been noticing a shift lately where the interesting AI stuff isn't really "AI writes your caption" anymore. it's more like taking one piece of content and running it through a multimodal workflow, script, voiceover, visuals, short-form cuts, all from the same source asset. or using it to pull the structural pattern out of your best-performing posts and apply that skeleton to new topics. still needs a human pass for quality and brand voice, but the starting point is genuinely different. the Washington Post experimenting with personalized audio briefings built from their own reporting was, an early example that felt like more than just automation, it was a format shift. curious whether that kind of thing has become more common or if it quietly got shelved. right now the stuff I'm most interested in is AI video and avatar workflows, repurposing, a long webinar or podcast into a series of short spokesperson-style explainers without reshooting anything. some teams are doing this really well, others are clearly just vibing and hoping nobody notices the uncanny valley. I'm way more interested in workflows that enable something that wasn't feasible before, not just ones that shave 10 minutes off a first draft. what's the most genuinely interesting use you've come across, something you built yourself, or seen someone else actually pull off?

been thinking about this a lot after seeing that point-and-click post getting heaps of traction here. the concept sounds doable on paper, right? backgrounds, props, inventory items, all generated. but then you actually try to keep a character looking the same across 30 scenes and it gets messy fast. style drift, lighting inconsistencies, slightly different face geometry every other screen. the thing is, how much cleanup you're dealing with depends a lot on which tools and workflows you're actually using. reference conditioning, LoRAs, inpainting, pose control, these have gotten genuinely better at holding identity and style together compared to even a year ago. but "better" doesn't mean "solved." some tools give you much stronger consistency controls than others, so blanket statements about what AI can or can't do here age badly pretty quickly. the workflow that seems to actually work in practice is still hybrid rather than pure AI output. generate a bunch of candidates, pick the best, upscale, paint over the weird bits, standardize the palette. some teams also lean on iterative inpainting rather than just upscaling, which can save a few rounds of manual correction. either way, the cost and time savings are real but they're not as dramatic as the pitch makes them sound. worth flagging too: if you're building something commercial, the IP and training data questions are not resolved. style similarity claims and asset ownership are still genuinely murky depending on your jurisdiction, so that's a risk layer on top of the production challenges. I reckon "perfect" is probably the wrong frame anyway. "good enough for a solo dev with no art budget" is a more honest target, and for that, yeah AI art can genuinely get you there. the harder question is whether the consistency and art direction needed to make an adventure game, feel cohesive can be handled without someone who actually has a visual brain steering the whole thing. curious if anyone here has shipped or nearly shipped something in this genre using mostly AI art, and where the pipeline actually broke down for you.

been thinking about this a lot lately because I keep jumping between AI-generated video and more traditional animation workflows depending on the project. the speed difference is genuinely hard to ignore at this point, AI gets you to, a reviewable cut in hours where traditional might take days just for one revision pass. that gap hasn't really closed, it's just become the new normal for most people working at any reasonable scale. but there's still this ceiling I keep hitting with AI where anything requiring a subtle emotional beat in close-up just. falls flat. the timing is close but not quite right. like the motion synthesis has gotten genuinely impressive for crowds, background action, medium-distance human behavior, that stuff reads convincingly now. it's the asymmetrical micro-expressions and the weight of a held moment where traditional still wins pretty clearly. what I've landed on is using AI for basically everything in the early stages, pitch reels, background action, costume and environment variations, and only pulling in traditional techniques when a shot actually needs to carry emotional weight. feels like most people doing this seriously have converged on something similar, hybrid isn't really a hot take anymore, it's just the workflow. curious where others actually draw the line though. is there a specific shot type or project context where you've switched back to traditional and felt it was clearly worth the extra time and cost? or has anyone found AI tools that are actually closing the gap on close-up emotional work?

been thinking about this a lot lately, especially watching tools like Runway and Kling improve with motion consistency and character persistence over the past 6 months or so. the short-form and ad side is already pretty clearly disrupted. small teams can now produce stuff that would've needed a decent budget even 2 years ago. but I reckon the feature film side is a different story, at least for now. the more interesting question to me isn't whether AI replaces directors or cinematographers, it's what happens to the middle layer. the mid-budget production companies, the VFX houses doing bread-and-butter compositing work, the localization teams. McKinsey flagged this too, that value redistributes away from traditional studios toward smaller creators and integrated platforms. that tracks with what I'm seeing. the studios that survive probably look more like curators and IP managers than production houses. the Hollywood strikes fallout still hangs over all of this too. likeness rights, disclosure, job displacement for editors and VFX artists. those conversations aren't close to resolved. so even if the tech gets there, the legal and labor frameworks might slow adoption more than the quality gaps will. curious whether people here think the business model shift happens gradually through workflow tools, or whether, there's a sharper break at some point when the quality threshold gets crossed for narrative content.

Running 12–15 clip batches for ad creative using Kling and Veo. Around clip 7–8, the aesthetic drifts — color grading shifts, lighting temperature changes, motion feel stops matching earlier clips despite identical prompts.

What I've tried:

Locking seeds (unreliable — depends on whether the model actually supports seed spec)

Splitting into smaller batches of 6 — drift still happens, just later

Phygital+ (AI video tool built for ads/marketing) — first 6 clips held tighter visually, but tail end still wandered past clip 10

My best guess is the style reference gets reinterpreted when model context resets mid-batch. But I'm also not sure Kling or Veo actually support true style chaining in no-code pipelines, so this might partly be a setup issue on my end.

What I want to know:

Is this a known ceiling with chained video pipelines?

Is there a way to aggressively anchor style reference past the halfway point?

Any fixes or workarounds appreciated.

been thinking about this a lot lately because I keep bouncing between the two depending on the project. built a general-purpose assistant a while back and it was honestly pretty fast to get running, but the moment someone wanted it to do something specific to their industry it started falling apart. hallucinations on domain-specific stuff, wrong terminology, weird edge cases it just couldn't handle. switched to building something more focused for a doc processing use case and the accuracy, difference was pretty noticeable once I got the right data and the right rules in there. the tradeoff I keep running into is setup time vs long-term reliability. general bots you can prototype in a day but they feel a bit fragile in production. specialized ones take longer to scope and build but clients actually trust the output more, and that trust gap is apparently pretty significant, some benchmarks are putting specialized tools 20-40% ahead of generalists on accuracy for enterprise tasks, which honestly tracks with what I've been seeing firsthand. also noticing this is getting more loaded in regulated spaces. healthcare and finance clients especially are asking harder questions about auditability and compliance now that EU AI Act enforcement is tightening around specialized models. so the build decision isn't just a performance call anymore, it's a paper trail call too. and the integration piece matters heaps more with specialized tools, like you can't just dump, outputs into a spreadsheet, it has to connect to whatever system the industry already runs on. that scoping conversation alone adds time before you write a single line. curious if others have found a point where fine-tuning a general model actually gets you, close enough to purpose-built, or if that's mostly a shortcut that catches up with you later. feels like the answer changes depending on how niche the domain is and how much the client cares about explainability.

been going down a rabbit hole on this lately because a client asked me to put together a rough budget comparison for their product videos. the per-minute pricing looks amazing on paper at first glance, but once you factor in revision rounds, prompt iteration time, and the fact that, your usable output rate can be closer to 1-in-6 or 1-in-10 on a good day, the real cost per finished asset creeps up fast. like, Kling is sitting around $0.07/sec for 1080p clips right now, which sounds cheap until you're burning through credits on failed generations. Veo 3 is the one people keep citing for cinematic quality, but if you're accounting for the, outputs that don't make it, some folks are reporting $600+ to get five minutes of actually usable footage. that's not a bargain, that's just a different kind of production budget. the subscription tools like HeyGen or Synthesia still make the most sense if you're doing real volume, like ongoing ad creative where you need 20+ variations a month. the math works there. for a one-off hero video it usually doesn't, especially once you hit credit limits mid-revision cycle. what I'm trying to figure out is how people are actually accounting for this in a marketing budget. do you treat it like a software cost or a production cost? because those sit in completely different line items and get scrutinized differently by finance. and has anyone had to justify the quality tradeoff to a client or stakeholder who either thinks AI video is basically free or thinks it's a brand liability? curious how those conversations are going for people actually doing this at scale.

been putting more time into nTop lately for some denser geometry work and the implicit modeling stuff genuinely holds up well for lattices and TPMS structures. the boolean ops don't fail on complex implicits the way traditional CAD does, which is a real thing once you've lost an hour to a mesh error mid-session. no argument there. but the part that keeps catching me is what happens when you try to get those complex geometries out the other end. high surface area parts can still generate pretty hefty mesh files depending on how you're exporting. Simplify Mesh by Threshold is still in the toolkit but it's trial-and-error in a way that gets old fast. that said, the Implicit Body by Voxel Grid block has been genuinely useful for this - you get tighter file sizes with, deviation tolerances you can actually control, which is a step up from just hoping the simplify pass doesn't wreck your surface quality downstream. if you're not using that yet it's worth a look before you go deep on the threshold approach. also been looking at Field Optimization and while the point-wise lattice optimization is interesting, the lack of overhang or, extrusion constraints feels like a gap if you're trying to close the loop on actual printability rather than just shape. that part still feels workflow-specific in a way that requires a lot of manual patching. curious whether others are hitting the same walls or found cleaner ways through it. specifically around managing file size and surface fidelity on anything with real geometric complexity before it gets to simulation or manufacturing.

curious what people here would consider genuinely workflow-changing vs just convenience stuff. asking because I've been going deep on automating the creative side of things lately, specifically around asset production, content repurposing, and, cross-platform delivery, and the gap between "nice to have" and "can't imagine working without this" is way bigger than I expected. for me the one that actually stuck was automating the resize and reformat pipeline for assets across different placements. sounds boring but it was eating maybe 2-3 hours a week of just dumb, repetitive, work, and once that was gone I stopped dreading the production side of projects entirely. the other one was setting up a trigger-based approval flow instead of chasing people through slack and email. that one probably saves more mental energy than actual time, which honestly matters more at this point. what's interesting to me is how both of those changed the shape of the work, not just the speed. the resize pipeline meant I stopped context-switching into production mode mid-creative-flow. the approval flow meant decisions actually moved instead of dying in someone's inbox. neither is flashy but both are load-bearing. what's the automation you'd actually miss if it disappeared tomorrow? not the stuff you demo to people, just the one that quietly changed how your day runs.

been going deeper into generative design for AM lately and the same issues keep coming up. stair-stepping artifacts, excessive support material, surface quality on self-supporting structures that just looks rough. Fusion 360's generative design explorer does topology optimization and has overhang minimization baked in, which helps somewhat, but I'd be cautious about claiming any specific, "newer solver" is meaningfully ahead on support reduction, the geometry-first output problem still feels pretty real from what I've seen and what people are reporting. MSC ApexGD is another one that comes up for stress-based strut optimization, though I haven't put serious time into it either. the bigger frustration is that most of these tools still treat geometry as the primary, deliverable and don't account for anisotropy or thermal distortion until way later in the process. there's been some interesting movement around ML-aided topology prediction that apparently helps with stair-stepping specifically, and parametric/geometry-aware approaches are getting more attention, for cutting down support bloat, but it still feels like process-level stuff rather than something baked into the solver from the start. curious whether people here are actually constraining for anisotropy and thermal behavior upfront, or just running verification after the fact and iterating until it's acceptable. and whether anyone's found a workflow that genuinely reduces back-and-forth with the printer rather than just adding more steps to the pipeline.

something I keep running into: you bring up a UX concern in a backend meeting and suddenly the room gets a bit tense. not because anyone's being difficult, just that the framing lands wrong. 'designer is telling us how to build it' energy when that's not the intent at all. what's actually helped me is sharing stuff early and rough, not polished. when you bring a finished Figma file to a dev it reads like a spec to hit, but when you bring a, half-baked wireframe and say 'here's the user problem I'm trying to solve, what breaks on your end?' it becomes a different conversation entirely. this matters even more now that a lot of what we're building has AI-driven or context-aware behavior baked in. those flows are genuinely hard to prototype cleanly, so getting backend in the room early with something scrappy is basically required. you can't hand off a polished spec for a system that adapts at runtime and expect everyone to just figure it out. a few other things that have helped: being upfront about what's a must-have vs what I'm still figuring out. devs are pretty good at working with uncertainty when you're honest about it, it's the false confidence that causes friction. design systems help too because then you're both looking at the same source of truth instead of interpreting a screenshot differently. and when there are user-controlled behaviors involved, like motion preferences or personalization settings, flagging those, early means the backend team can actually plan for them instead of finding out at handoff. the 'overstepping' thing mostly goes away when the framing shifts from 'here's what it should look like' to 'here's what the user is trying to, do, how do we get there together.' curious if others have found a specific ritual or meeting format that actually made this click with their team.

been thinking about this a lot lately because most of the conversation around generative design tools still seems aimed at solo users or small studios. once you scale up to a proper team the problems shift completely. it's less about which tool you pick and more about who owns the approved models, how you stop people going rogue with, random vendor solutions, and whether there's any actual feedback loop between the people doing the work and whoever set up the systems. what's interesting is that the teams handling this well in 2025 and into this year, aren't just adding AI on top of existing workflows, they're redesigning the workflows around it. the ones that seem to get the most out of it have some kind of centralized group managing AI tooling rather than every sub-team doing their own thing. keeps outputs consistent and avoids the chaos of five different pipelines producing five different quality levels. there's also a real push now toward building in evaluation layers, basically structured ways to check quality across rapid iterations, rather than just eyeballing it, which matters a lot when you're moving fast and the prompts are doing heavy lifting. the other thing that seems to matter heaps is requiring everything to expose standard APIs from day one. otherwise you're locked in and any model swap becomes a nightmare. with AI-embedded tooling becoming more common across the board, that interoperability question is only getting more urgent. curious if anyone here has actually worked on a team at that scale. what broke first?

Freelance AI designer here, mostly doing brand kits and social content for small DTC clients. Two to four deliverables per client per week, nothing huge.

Constraints: solo operation, no dev support, clients expect turnaround in 24 hours, and I can't spend $80/month stacking five separate tool subscriptions.

I've tried Midjourney pipelines with saved prompt templates and Flux through ComfyUI, but both fall apart the second a client wants a variation on last month's campaign. Rebuilding node graphs or re-prompting from memory is eating way more time than it should. I tested Phygital+ briefly for one client's banner series and the brandbook upload actually, helped keep colors consistent, but I haven't gone deep enough to know if it scales.

What I care most about: reusability across projects, not having to re-explain the brand to the model every, single time, reasonable pricing, and something I can hand off notes on if I ever bring in a contractor.

For designers doing repeat brand work solo, what setup actually holds up after month three?

been spending a fair bit of time lately going deeper into generative design for actual functional components, not just aesthetic stuff. the NASA/Autodesk A320 bracket case is the one that keeps coming up in my reading - closer to 45% weight reduction with equivalent strength, printed in AlSi10Mg aluminium. that's a real outcome, not a concept render. and the Relativity Space fuel pump consolidation is kind of absurd when you actually sit with it - they collapsed a massive, component count down to something printable as essentially a single part, which is a different category of result than just shaving weight. but honestly my experience trying to replicate even a fraction of that on smaller projects has been messier. the geometry outputs are often genuinely impressive, then you get to the print stage and the support structures become this whole separate problem. post-processing on really intricate lattice stuff is tedious in a way that doesn't always justify the weight savings at smaller scale. feels like the tooling is built around aerospace budgets and tolerances, and when you're working on, something more modest the gap between the render and the physical part can be pretty humbling. curious if anyone here has actually gotten a generative component through to something production-adjacent, or if most, of the real wins are still sitting in aerospace and automotive where the AM infrastructure is already mature. also keen to hear if anyone's tried nTopology vs the Fusion 360 workflow for, manufacturability - I keep seeing it recommended but haven't committed to learning another tool yet. and with more agentic simulation tooling starting to show up in some of these pipelines, wondering if, that's actually changing the iteration speed for anyone or if it's still mostly hype at the practical level.

been running into the same wall lately where the generative solver spits out something geometrically interesting but then it's basically, unprintable without heaps of support material, or the surface finish is rough enough that post-processing kills any time you saved upstream. feels like most tools are still optimising for shape and kind of ignoring the actual, manufacturing constraints like thermal distortion and material anisotropy until you're already committed to a direction. Fusion 360's additive solver has gotten noticeably better at reducing support requirements over the past couple of years, and tools like nTopology and MSC Apex Generative Design, have been pushing harder on AM-specific constraints, but I still find myself doing a lot of manual cleanup on lattice structures before anything goes to the printer. hybrid approach with some CNC finishing has helped on a few projects but it adds steps I'd rather not have. what I keep wishing existed is tighter feedback earlier in the loop, like printability and distortion simulation baked into the generative pass itself rather than bolted on after. some of the newer AI orchestration stuff feels promising for chaining those validation steps, together automatically, but I haven't seen a clean out-of-the-box workflow that actually does it yet. curious whether anyone's found an approach that closes the loop between simulation and printability feedback, early enough to actually change design decisions, rather than just flagging problems when you're already committed.

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been curious about this for a while. I come at nTop more from the creative/generative side than pure engineering, but I keep running into it when projects push toward actual fabrication. the implicit modeling approach makes a lot of sense on paper - especially for sidestepping the mesh reconstruction mess you usually hit after topology optimization spits something out. that part at least seems well-documented and genuinely solved. for anyone using it on real AM work though, where does it actually pull its weight in practice? the DfAM side is what I'm less sure about - nTop clearly factors in AM constraints early in the process, but I'm curious how far that actually goes. like are overhang controls and support reduction something you're actively leaning on, or is it more, of a checkbox that still needs a lot of manual cleanup before anything goes to a slicer? also curious about iteration speed at scale. the pitch is fast variant exploration and parametric flexibility, and I've seen claims of serious performance gains over traditional CAD for complex geometry - but does, that hold up when you're actually pushing weird organic forms or heavily nested lattice structures, or does it start to bog down once things get genuinely complex? basically trying to figure out where the workflow earns its keep vs. where you're still fighting it. would love to hear from people using it on actual fabrication projects right now.

been thinking about this a lot lately because I've had a few conversations with other designers where it came up. the consensus is still murky, honestly. some people are completely transparent about it, some bury it, and some just don't mention it at all and hope nobody asks. my current approach is to treat the AI stuff like any other tool and just show the process honestly. so if I used generative tools for early ideation or mood boarding, I'll include those rough outputs alongside my refinements and the final direction. the point being that the thinking and decisions are clearly mine, even if some of the raw material came from a prompt. anecdotally, showing the iteration trail tends to land better than dropping a polished final output with no context, regardless of how it was made. it also sidesteps that awkward moment where someone asks "how did you make this" and you have to do some mental gymnastics. worth noting that the transparency bar has genuinely moved. design schools like Parsons and MassArt now require explicit disclosure of AI involvement in submitted work, not just what tools you used but how and why. omitting it is being treated as an ethical issue, not just a stylistic choice. that framing is starting to bleed into professional contexts too, so getting ahead of it in your portfolio seems like the lower-risk move. the harder question I keep sitting with is about how much of the work, is actually yours when AI is doing a significant chunk of the visual heavy lifting. like if you're generating 80% of the output through prompts and doing light editing, is, the framing really the same as if you sketched it out and built it yourself? I don't think there's a clean answer, and I'm not trying to be prescriptive. but I do think the honest version of that disclosure looks different depending on where AI, sat in your process, and audiences are getting better at noticing when that's being glossed over. curious how others are handling the disclosure side of it, especially in more traditional design contexts where the reaction can still be pretty mixed.