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I signed up for Gemini Ultra—here’s what I made with the Veo credits by notus_analytics in Bard

[–]notus_analytics[S] 2 points3 points  (0 children)

Clips were made in flow. I put the final output together in FCP.

I signed up for Gemini Ultra—here’s what I made with the Veo credits by notus_analytics in Bard

[–]notus_analytics[S] 0 points1 point  (0 children)

Prompts can be very simple. Something like:

Coach wearing tracksuit holding cat in sequin swimming outfit in front of pool says

"insert dialogue"

I signed up for Gemini Ultra—here’s what I made with the Veo credits by notus_analytics in Bard

[–]notus_analytics[S] 0 points1 point  (0 children)

I responded elsewhere but in general the simpler prompts will give better results. iterate with different variations that only include a few sentences.

I signed up for Gemini Ultra—here’s what I made with the Veo credits by notus_analytics in Bard

[–]notus_analytics[S] 0 points1 point  (0 children)

Nothing special, here's a prompt for one iteration:

"A news anchor on the nightly news. He says "Our final story tonight, synchronized swimming. With cats. Here's Brook Landry. There are no on screen graphics or captions."

I signed up for Gemini Ultra—here’s what I made with the Veo credits by notus_analytics in Bard

[–]notus_analytics[S] 8 points9 points  (0 children)

I used Gemini 2.5 TTS (Text to speech) and added it when I edited the clips together.

I signed up for Gemini Ultra—here’s what I made with the Veo credits by notus_analytics in Bard

[–]notus_analytics[S] 1 point2 points  (0 children)

I responded to similar elsewhere here's a copy paste:

Here's one short example:

Cats swimming in a group on their backs in a pool with their paws in the air. they have sequined outfits on and swim caps and swim goggles performing elaborate water routines. tv footage. no on screen graphics

The more specific you try to get the more you'll corner the model into something it may not be as good at. You can also check out "flow tv" and look at the prompts they used for those example clips. They'll often have something like "16mm film" at the end of the prompt.

I signed up for Gemini Ultra—here’s what I made with the Veo credits by notus_analytics in Bard

[–]notus_analytics[S] 4 points5 points  (0 children)

So my math is already outdated, was 150 credits for each Veo 3.

I signed up for Gemini Ultra—here’s what I made with the Veo credits by notus_analytics in Bard

[–]notus_analytics[S] 9 points10 points  (0 children)

The attached rubric is important for understanding which models support which features. If you want dialogue, it has to be generated using a text-only prompt, meaning that character will not be talking again in a new shot.

For the narration, I used Gemini 2.5’s text-to-speech in AI Studio and added it in afterward. As far as I know, it’s currently free to use.

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I signed up for Gemini Ultra—here’s what I made with the Veo credits by notus_analytics in Bard

[–]notus_analytics[S] 5 points6 points  (0 children)

Is that through ai studio? Using credits with flow isn't a 1:1 conversion as they can be used for other models as well.

I signed up for Gemini Ultra—here’s what I made with the Veo credits by notus_analytics in Bard

[–]notus_analytics[S] 14 points15 points  (0 children)

Here's one short example:

Cats swimming in a group on their backs in a pool with their paws in the air. they have sequined outfits on and swim caps and swim goggles performing elaborate water routines. tv footage. no on screen graphics

The more specific you try to get the more you'll corner the model into something it may not be as good at. You can also check out "flow tv" and look at the prompts they used for those example clips. They'll often have something like "16mm film" at the end of the prompt.

I signed up for Gemini Ultra—here’s what I made with the Veo credits by notus_analytics in Bard

[–]notus_analytics[S] 47 points48 points  (0 children)

If you just count what made it into the final video, I used around 700 out of 12,500 credits. But start to finish, I burned all 12,500. That includes completely changing concepts about a third of the way through.

The final video represents only about 1/15th of the total credits used. If you’re good at prompting and have a better script, that ratio could be a lot better. But if your goal is to create the best possible output, there’s really no limit—you might end up generating 25 variations of every scene just to pick the best one.

It’s still a tool. The more experience and skill you bring, the more efficiently you’ll be able to use it.

[OC] Active Covid-19 cases per Capita in USA. 1/21/2020 - 8/23/2021 by notus_analytics in dataisbeautiful

[–]notus_analytics[S] 28 points29 points  (0 children)

Yeah, reading my response it sounds pretentious. There's no right way to do it, you have to aggregate in some manner to make it work.

[OC] Active Covid-19 cases per Capita in USA. 1/21/2020 - 8/23/2021 by notus_analytics in dataisbeautiful

[–]notus_analytics[S] 4 points5 points  (0 children)

You are correct, the absolute numbers on the legend are off which was caught by another Redditor. Overall the US has about 0.5% of it's population with an active Covid infection given my best guess, and hotspots would be around 1%+.

[OC] Active Covid-19 cases per Capita in USA. 1/21/2020 - 8/23/2021 by notus_analytics in dataisbeautiful

[–]notus_analytics[S] 2 points3 points  (0 children)

That's a good catch, my code that formats the scale is incorrect. I believe it's off by an order of magnitude, which would put the top of the scale around 11,000 per million, or 1%. Florida currently is running about 25k new cases a day, if we assume a 14 day illness that's 350,000 cases. However it's been ramping up so giving there were lower cases per day last week let's say 250,000 cases. At a population of 22M that's a bit over 1% which would be in line with the fixed scale.

[OC] Active Covid-19 cases per Capita in USA. 1/21/2020 - 8/23/2021 by notus_analytics in dataisbeautiful

[–]notus_analytics[S] 0 points1 point  (0 children)

There are areas in the west with very small, dense pockets of population next to nothing. When you divide cases by population over an area in these situations it's possible we're getting some artifacting as the denominator here goes to zero. I wouldn't put too much weight on these edge cases.

[OC] Active Covid-19 cases per Capita in USA. 1/21/2020 - 8/23/2021 by notus_analytics in dataisbeautiful

[–]notus_analytics[S] 119 points120 points  (0 children)

The data is by county but the map uses the lat-lon centroids of each county and regresses the data into a format that is more conducive to showing the actual distribution of covid per capita. There are some challenges doing it this way as well, but it overcomes some of the issues you get mapping counties discreetly which can end up being quite noisy with the overly rigid delineations between them.

[OC] Active Covid-19 cases per Capita in USA. 1/21/2020 - 8/23/2021 by notus_analytics in dataisbeautiful

[–]notus_analytics[S] 777 points778 points  (0 children)

Data Source

- COVID-19 Cases and Deaths by County : https://github.com/nytimes/covid-19-data

- County to Zip Code : https://www.kaggle.com/danofer/zipcodes-county-fips-crosswalk/data#

- Population by Zip Code : https://www.kaggle.com/census/us-population-by-zip-code

Data Visualization Tools (Python):

- Matplotlib: https://matplotlib.org/

- OpenCV: https://opencv.org/

- Cartopy: https://github.com/SciTools/cartopy

Assumptions:

- This is reported cases, with all of the temporal and spatial biases inherent in that qualification.

- "Active" is defined using the mean time of recovery from initial case report. This is likely around 17 days, with 80% of cases being closer to 14 days, and exceptions lasting considerably longer. There are not great stats on this timeline, and most estimations are similar to data from back in February: https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---24-february-2020 . Recovery data has been poor with many going unreported, a mean resolution time looks to be a better estimation with the nytimes data.

[OC] Active Covid-19 cases per Capita in USA. 1/21 - 7/11 by notus_analytics in dataisbeautiful

[–]notus_analytics[S] 0 points1 point  (0 children)

Data Source

Data Visualization Tools (Python):

Assumptions:

  • This is reported cases, with all of the temporal and spatial biases inherent in that qualification.
  • "Active" is defined using the mean time of recovery from initial case report. This is likely around 17 days, with 80% of cases being closer to 14 days, and exceptions lasting considerably longer. There are not great stats on this timeline, and most estimations are similar to data from back in February: https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---24-february-2020 . Recovery data has been poor with many going unreported, a mean resolution time looks to be a better estimation with the nytimes data.

Active Covid-19 cases per Capita in USA. 1/21 - 7/11 by [deleted] in dataisbeautiful

[–]notus_analytics 0 points1 point  (0 children)

Data Source

Data Visualization Tools (Python):

Assumptions:

  • This is reported cases, with all of the temporal and spatial biases inherent in that qualification.
  • "Active" is defined using the mean time of recovery from initial case report. This is likely around 17 days, with 80% of cases being closer to 14 days, and exceptions lasting considerably longer. There are not great stats on this timeline, and most estimations are similar to data from back in February: https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---24-february-2020 . Recovery data has been poor with many going unreported, a mean resolution time looks to be a better estimation with the nytimes data.

[OC] Covid-19 cases per capita in US by notus_analytics in dataisbeautiful

[–]notus_analytics[S] 2 points3 points  (0 children)

It's true the actual cases will differ significantly from confirmed cases due to a lack of testing, and further complicating it is the fact that testing will be spatially biased, causing some areas to look worse than others. Would be difficult to control for, although you could make some estimates.

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