An automatic video recap can start with a game, interview, documentary, vlog, or film. The detail that carries the story changes with the source. In a sports recap it might be a defensive move that opens a pass; in an interview it might be the qualification that makes a quote accurate.
Take an hour of footage and a three-minute target for a first-time viewer. My first requirement would be to list the moments that change what happens next, then point the editor to the visual or spoken evidence for each. In the fictional film example in the diagram, a shot of the mark on a key explains why the detective chooses that door.
I would ask the automatic editor for a first version with editable shots, narration and captions. Then I d ask a reviewer why the next action happened. If their answer depends on seeing the original, restore the missing evidence or adjust its timing.
For makers who ship video tools, which input would you ask editors to supply first: must-keep story beats, source timestamps, or a short note explaining the audience? What would you leave for review after the first edit?
Hey all, we just released a few updates to product hubs with a focus on converting visitors to your website/apps/social, and providing the right information to community members who are researching new products. As an example, here's Notion's product hub: https://www.producthunt.com/prod... (you can access your product hubs in the new sidebar on the homepage) The header was re-focused on driving conversions, followers, and saves. We ve removed upvote counts and focused on reviews + followers on the hub pages; while upvotes are important for launch day, we ve found that they re not necessarily a highly correlated signal for overall product quality, especially for community members researching products months (or years) after a launch. The updated sidebar displays links, product info, and makers more prominently, and the rest of the page has been simplified with an additional focus on reviews (currently sorted by recent, so make sure you re encouraging your community to leave reviews). As always, Product Hunt is in a constant state of change, so let us know what you think about these changes and if you have any suggestions or recommendations moving forward!
I've tried Reddit posts, a few Discord communities, and cold DMs. Most responses were polite silence. The few people who did respond gave feedback that was too vague to act on. "Looks cool." "Nice idea." Nothing that changed what I built.
The problem isn't finding people it's finding people who care enough to tell you something uncomfortable.
So for those who got past this stage: where did your first 10 users who actually gave actionable feedback come from? Not "post on Twitter," a specific channel, a specific approach, a specific thing that worked. And the harder follow up: what did you do with the vague feedback before you found the useful feedback?
So, been running a AI startup for more than a year now and the one thing i always thought or was puzzled was when naturally customers think AI should be cheaper than human hours even though AI's throughtout or the benefits is 100x. So we have been doing it the 'cost+' pricing way, but to be very frank this is working and is helping us be in control of the margins, but also exploring outcome based pricing in certain use cases. Will have this thread open to have discussions around this topic, also if i find newer ways or use cases where outcome based pricing works, happy to explore.
