I spent about 10 hours this weekend experimenting with claude, and the result genuinely surprised me.

The workflow I built looks roughly like this:

1. I connected our existing video library to claude so it could understand what footage and clips I already have.
2. I also gave it access to the videos we’ve already published on our channel.
3. Based on the published content, I asked it to find unused angles and ideas hidden inside our existing library.
4. It then turned those angles into new video concepts and identified the relevant clips.
5. From there, i finished editing workflow completely in claude: subtitles, transitions, simple animations, B-roll and audio.

I used Codex mainly to build the integrations and glue the different parts of the workflow together.

What surprised me is how usable the result already feels. For this type of content, I didn’t need to go back into my usual CapCut or Premiere workflow to manually assemble everything.

It’s definitely not perfect, but after a single weekend, it feels much closer to a real production workflow than a demo or toy project.

The next question for me is whether this can be made reliable enough for an actual content team, not just something that works when the person who built it is watching every step.

Has anyone here built something similar with Claude, Codex, or other tools?

Disclose: it did consume some tokens in my plan

I’ve gradually found myself using claude and got for different parts of my workflow.

In my experience, Claude seems especially good at:

- Synthesizing long conversation histories and keeping track of context

- Turning previous discussions into a concrete next-step plan

- Using local tools to build small, purpose-built utilities for data analysis

GPT, on the other hand, seems better suited for:

- Searching for and gathering information

- Exploring a topic from multiple angles

- Drafting and refining written content

This isn’t based on any formal benchmark—just the pattern I’ve noticed through everyday use. I’m curious whether others have had a similar experience.

Do you deliberately assign different kinds of tasks to different models? What does your “division of labor” look like, and are there particular workflows where one model consistently performs better for you?

When you have a strong source asset (an interview, webinar, research call, customer story, or long article) how do you decide whether to reuse it or create something new?

The obvious answer is to repurpose it into clips, posts, emails, and sales material. But that can turn into superficial duplication: the same point cropped into five formats without adding context for the audience or channel.

I’m more interested in the decision process. What makes a source rich enough to revisit? A clear argument, a useful example, a customer phrase, unanswered questions, strong engagement, or evidence that the topic still matters?

And when you adapt it, what do you preserve so the new piece does not drift away from the original meaning? Do you keep timestamps, source notes, approved claims, or a record of where each idea came from?

For teams reporting into demand gen, I’m also curious whether reuse is measured as production efficiency, influenced pipeline, engagement quality, or simply the ability to support more campaigns with the same source material.

I recently spoke with a few marketers whose team already have a pretty complete content stack.

Their webinar platform automatically creates clips and subtitles. The team can search and edit their video library without involving an external editor. Distribution isn’t the main problem either.

But during a typical brainstorm, they might come up with 30 different content ideas—and they don’t have the time or people to decide which five are actually worth pursuing.

That made me wonder whether the bottleneck for content teams is shifting:

It used to be expensive to create clips, posts, and campaign ideas. Now AI can generate dozens of them almost instantly, but someone still has to review everything and apply the “human gut check.”

I’m curious how other teams handle this:

After a webinar, podcast, or long-form video, how many possible clips or angles do you usually end up with?

Who decides which ones are worth publishing?

Do you have explicit criteria, or does the decision mostly live in one experienced person’s head?

How much time does reviewing and prioritizing take?

After publishing, does anyone go back and use the results to improve those criteria?

One idea I’m exploring is a tool that would take:

the campaign goal and intended audience;

a rubric defined or approved by the marketer;

a collection of videos;

…and return the five moments that best fit the brief, with timestamps, supporting evidence, and an explanation of why each one was selected.

It wouldn’t automatically publish anything or claim to discover your taste. The marketer would still own the criteria; the system would mainly reduce the review set from, say, 50 possibilities to five.

Would that solve a meaningful problem for your team, or would it just add another tool and another review step?

And if you did use something like this, what would you need to see before trusting its recommendations?

About 95% of my content workflow now happens inside Claude.

I’ll start with a content idea, use Claude for audience research, and then have it help me turn that into a script. That part works surprisingly well.

The bottleneck starts immediately afterward.

Once I have the script, I still have to leave Claude, manually search through my video library, scrub through clips, and figure out which footage matches each part of the script. Claude knows what the video is supposed to say, but it has no idea what footage I already have.

What I’d really like is a way for Claude to search or understand my existing video library—something like: “Find a close-up that fits this line” or “Show me clips related to this section of the script.”

I signed up for one tool that claims to do something similar, but it’s still in closed beta and I haven’t received access.

For people using Claude as part of their content or editing workflow, how are you handling this right now? Are you tagging and transcribing everything manually, using a media asset manager, building something with MCP, or just accepting that clip selection is still a separate manual step?

I’ve spent a little over ten years working in video, everything from editing my own content and freelance projects to working with marketing teams and larger production workflows. A friend asked me how he could publish videos on his YouTube channel faster. He expected recommendations for new software, AI tools, or a better computer. After looking at his process, the problem wasn’t his editing software. It was that every video started from zero. New folders, different file names, footage in random places, feedback through several apps, and no clear definition of when a video was actually finished.

I wrote down the workflow I suggested to him, and figured it might be useful for other new or junior editors.

1. Define “done” before opening your Premier

Before touching the timeline, write down:

- Where the video will be published

- Target length

- Aspect ratio and resolution

- Deadline

- Required graphics, captions, and platform versions

- Who gives final approval

A surprising amount of wasted editing time comes from decisions that should have been made before the edit started.

2. Stick to folder structure, or use AI

Depends on your style, if you like open the app from home screen, stick to your naming convetion with structured folders. Create it once and duplicate it for every project. It sounds boring, but it removes dozens of small decisions and makes old projects much easier to reopen. Something like:

01_Footage

02_Audio

03_Graphics

04_Project

05_Proxies

06_Exports

07_Deliverables

08_Licenses

If you like open the app from iphone search bar like me, index your video using tools like vivu and connect to chatgpt/claude. Same for b roll managements

3. Back up the footage before you start editing

At minimum, your footage should exist on two separate devices before you format the camera card. For a solo creator, that could simply be:

- Working copy on an SSD

- Backup on an external hard drive

- Cloud backup for project files and important footage

NAS is unnecessary for most beginners. Two real copies, however, are not optional if the footage matters.

4. Learn how to use proxies

If 4K, H.265, phone footage, or screen recordings make your timeline stutter, don’t immediately assume you need a new computer. Generate proxies in Premiere, Resolve, or Final Cut. You edit with lightweight copies, then reconnect to the originals for export.

Proxy workflows are one of the first “professional” techniques I would teach a junior editor. Smooth playback lets you concentrate on timing and story instead of fighting the computer.

5. Edit in passes

A common beginner mistake is spending an hour perfecting the first 20 seconds while the rest of the video does not exist. I normally separate the work into passes:

1. Build the complete rough cut.
2. Fix structure, pacing, and unnecessary sections.
3. Add B-roll and visual coverage.
4. Clean dialogue and add music or sound design.
5. Add graphics and captions.
6. Correct color.
7. Watch the export and perform QC.

The exact order can change, but the principle is important: solve the biggest problems first.

There is little value in polishing a transition if the entire section may be removed tomorrow.

6. Save versions instead of constantly overwriting the project

Use names such as:

ChannelName_Episode12_V01

ChannelName_Episode12_V02

ChannelName_Episode12_PictureLock

Do the same for review exports.

Avoid names like `final_final_v2_realfinal.mp4`. More importantly, don’t overwrite the only working project file immediately after making major structural changes. Versions give you permission to experiment because you always have somewhere safe to return.

7. Keep feedback in one place

If somebody else reviews your work, ask them to use one feedback channel.

Frame is great for timecoded comments. An unlisted YouTube or Vimeo link plus a shared document can also work. The specific tool matters less than having one official set of notes.

Feedback spread across texts, emails, calls, and chat messages will eventually result in something being missed.

Ask reviewers to consolidate conflicting opinions before sending them to the editor. “Make it more exciting” is not a useful note. “The explanation from 01:20–01:45 repeats the introduction, so shorten it” is.

8. Export a master before making platform versions

Keep one high-quality master file. Create smaller YouTube, Instagram, TikTok, or client-delivery files from that master when appropriate. This gives you a clean source for future versions without reopening the project every time. Tools such as Shutter Encoder, Adobe Media Encoder, or Resolve can handle the additional conversions.

9. Watch the exported file

Not only the timeline—the actual exported file. Check:

- The first and last few seconds

- Audio sync and volume

- Spelling, names, and captions

- Missing media or graphics

- Unexpected black frames

- Aspect ratio and resolution

- Music and stock-asset licenses

Whenever possible, watch the whole thing. Many export problems only appear outside the editing application.

10. Archive intentionally

When the project is finished, keep:

- Original footage

- Project files

- Final master

- Graphics and important audio

- Fonts, plugins, or instructions needed to reopen it

- Music and stock licenses

You usually don’t need to keep render cache, preview files, or proxies if they can be generated again. The main lesson I gave my friend was that becoming faster isn’t only about shortcuts or moving the mouse faster. Good editors spend less time searching for files, rebuilding lost work, decoding vague feedback, and fixing preventable mistakes.

You don’t need an enterprise setup. Borrow the discipline behind it, then use the simplest tools that fit your current workload.

When a video, ad, or carousel works, how do you save the reason it worked?

Someone often preserve the asset but lose the lesson. The file goes into Drive, the screenshot goes into Slack, the editor remembers the pacing, the strategist remembers the hook, and three weeks later someone says “make another one like that” without knowing which part mattered.

What do you save after a winner: hook, audience pain, source insight, creator notes, comments, variants, or the decision to reuse or retire the angle? A full postmortem is unrealistic, but relying on memory gets expensive once multiple people touch the work.

If you had to train a junior media buyer or strategist to judge whether a creative is worth testing, what would you actually show them?

I do not mean a generic checklist like "strong hook, clear CTA, good visuals." I mean the working examples and decision rules that help someone understand why one asset deserves budget and another should stay organic.

For example, would you have them compare past winners and losers, read the brief before looking at performance, watch sales/customer calls, map each creative to an objection, or review comments for buying intent?

My hunch is that creative judgment gets better when the person can connect the asset back to a buyer situation, not just a platform metric. Curious how agencies and in-house teams make that teachable without turning it into a fake-scientific scoring system.

I keep seeing teams run into a versioning problem before they run into an ideas problem. One base video becomes five hooks, three captions, two crops, a few thumbnail options, and suddenly nobody remembers which version was approved or why it performed differently.

For people doing content marketing or paid social: how are you managing variants in a way that stays readable?

Are you naming files by angle, audience, platform, date, editor, or something else?

Do you keep a central tracker, rely on the editor timeline, or just use folders and discipline?

A solo creator can keep a lot in memory, but a brand or agency usually needs some kind of shared system before the content calendar turns into archaeology.

A lot of automation advice treats the goal as “remove the human.” In practice, I keep seeing the opposite problem: teams automate the easy part, then lose the judgment step that actually protects quality.

Curious how people here draw the line. For example, I can see reporting pulls, draft variations, first-pass transcription, and naming conventions being automated pretty safely. But offer decisions, creative judgment, client approvals, legal/privacy calls, and post-mortems still seem easy to mess up if nobody owns the final call.

Where do you still insist on a human review step, even if a tool could technically do it? And what made you draw that line?

Something I keep seeing in agency and advertising conversations is that “better process” can mean two very different things.

Sometimes it means fewer mistakes: the right version goes live, feedback is not lost, approvals are clearer, and nobody is manually stitching together campaign context at the last second.

Other times it becomes theater: more decks, more status rituals, more internal language, and no obvious improvement in the actual work or client decision-making.

For people who have been on agency or brand teams for a while, where do you draw that line? What is one process change that genuinely made campaign work better, and one that looked mature on paper but just slowed everyone down?

A lot of B2B channel advice still seems to assume the main battlefield is email plus LinkedIn, with maybe paid search layered on top.

But from what I’m seeing, the useful conversations are scattered: private Slack groups, Reddit threads, events, comment sections, niche newsletters, podcasts, customer communities, and occasionally LinkedIn when someone has built real trust there. The hard part is that most of these places do not behave like normal lead-gen channels. You cannot just show up with a sequence and expect it to work.

For people running B2B marketing or outbound right now: where are you seeing the highest-quality buyer conversations happen before someone enters your funnel?

I keep seeing a split in marketing work lately. On one side, teams can produce more than ever: more ads, more landing pages, more social posts, more email variants, more AI-assisted drafts. On the other side, a lot of people seem less confident that the extra volume is creating better judgment, better positioning, or stronger demand.

The part I’m curious about is where people draw the line. Some work clearly benefits from speed: resizing, first drafts, variant testing, formatting, reporting. But some work seems to get worse when the goal becomes producing more before anyone has sat with the actual problem.

For people working in-house, freelance, or agency-side: where has faster production genuinely helped your marketing, and where has it just created more average work to manage? I’m especially interested in specific examples: content calendars, ad creative, landing pages, client reporting, campaign strategy, or anything else where the tradeoff has become obvious.

For people working in agencies right now: where are you seeing the line move between “junior work” and “still needs real judgment”?

A lot of the AI discussion gets flattened into either “everything is automated now” or “nothing has changed,” but neither version seems very useful. The more interesting question is which parts of the job are getting compressed and which parts are becoming more important.

For example, first drafts, variants, summaries, and quick research may be easier to produce. But client context, taste, knowing what not to say, understanding why a brief is weak, defending a direction, and reading the room in reviews still seem hard to fake.

If you manage, mentor, or work alongside junior creatives/strategists/account people, what skills would you tell someone to build now so they are not just competing with faster production?

Not looking for doom or hype. More interested in what has actually changed inside the work.

Sometimes I know I’ve shot exactly what I need before — a particular product angle, location, action, reaction, B-roll sequence, whatever. The problem is that across years of drives and client/project folders, finding that shot can take long enough that using stock or shooting again feels easier.

For people sitting on a serious amount of footage: do you actively search and reuse old material? If yes, what actually works for you — folder structure, tags, DAM, Premiere/Resolve metadata, proxies, just remembering where stuff is?

I’m also curious whether archive reuse is something people genuinely do often, or mostly something we think we should be better at.

I’ve been thinking about how much footage basically becomes dead storage after a video is published. Months later I’ll remember that somewhere I had a good explanation, reaction, B-roll shot, or alternate take that could work for a new video. But finding the exact moment means going back through old project folders and scrubbing footage, and half the time it feels easier to just shoot something again.

For people who’ve built up a decent archive: do you actually reuse old raw footage?

I’ve been using gpt/claude more for scripts, campaign ideas and briefs, etc. One thing I keep getting stuck on is existing video footage.

Say I’m working on a new social post and I know we already have useful product demos, customer clips, event footage and old interviews. The AI can help with the idea, but then I’m back in Drive/Dropbox digging through folders, opening files, finding the relevant section, and moving it into whatever tool I’m using next. At that point the “AI workflow” feels pretty manual again.

Curious how other marketers handle this. Do you actually reuse your existing footage when working with AI tools?

Most of my gpt workflow is still text, but a lot of useful context in work/life sits in video.

I do lots of video creation scripting in gpt. I can do something with one video if I already know which file matters. But once there are hundreds of videos, the first job is still on me: remember what exists, find the right file, find the right moment, then extract or upload whatever I need.

Curious whether anyone here has a real workflow around this. Do you use your own videos with gpt today? Or is video basically absent from your ChatGPT workflow?

For people who work with founders, CEOs, or small leadership teams: what has actually helped you communicate marketing tradeoffs without the conversation turning into “just get more growth”?

A lot of teams talk past each other. The marketer is thinking in terms of audience, channel fit, budget, and proof. The CEO is often thinking in terms of revenue this month, competitor pressure, or a tactic they saw working somewhere else.

What has helped in real situations? A one-page plan, clearer forecasts, showing what will not be done, customer examples, campaign postmortems, tighter definitions of qualified leads, or something else?

I am especially interested in the practical phrasing or artifacts that changed the conversation.

I’m curious how people here decide when a campaign is ready to scale versus when it still needs another round of learning.

I keep seeing two opposite failure modes. One is launching too many channels or creative angles at once, then not knowing what actually moved the number. The other is waiting so long for perfect tracking, perfect assets, and perfect segmentation that the campaign never gets enough real market feedback.

For people managing paid, organic, email, SEO, or mixed campaigns: what do you check before you put more budget or team time behind something?

A few examples I’m thinking about:

• Do you need channel-level attribution to be clean first?
• Do you wait for a minimum number of conversions, comments, leads, or sales?
• Do you care more about early qualitative signal or dashboard signal?
• Are there any checks you used to care about that you now ignore?

I’m less interested in generic launch checklists and more interested in the messy judgment call: when do you decide “this is real enough to push”?