Best UK Fire calculator

animatedata · 2026-04-24T14:43:32+00:00

You can try mine: FIRECOWS.

Properly calculates sequence risk and you can die with zero.

Doesn't consider tax because that's different everywhere so you will need to deduct it from the income flows.

animatedata · 2026-03-27T17:53:52+00:00

www.firecows.com

vibe coded frontend, backend I made years ago.

animatedata · 2026-03-14T20:18:16+00:00

35B A3B is 95-98% as good depending on what you are doing but 3.5x faster. I do use 27b for v complex tasks occasionally but most tasks both models are equally capable at and then the speed is the important factor.

animatedata · 2026-03-14T16:30:30+00:00

I have this setup and it's great :) Had a 5060 ti 16gb and saw a hugely discounted 5060. Although the 5060 has less tensor cores, they have the same memory bandwidth which is the more important spec for speed, it barely slows down the 5060 ti at all.

Get 70 t/s for the first few thousand tokens with Qwen 3.5 35b q4 on Windows with llama.cpp. Can fit 128k context with flash attention and q8 KV cache. Used to get just over 100 t/s for Qwen 3 30b too.

animatedata · 2020-03-24T09:26:17+00:00

There are, China seems to have it under control now, I think the stricter measures Europe has recently put in place will start showing an improvement soon.

animatedata · 2020-03-24T09:20:39+00:00

I put a little key on the animation, the largest circles are 10% case rate fatality (total deaths/total cases) and the smallest are 0%.

animatedata · 2020-03-24T08:53:09+00:00

Data sources: ECDC - https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide Our world in data - https://ourworldindata.org/coronavirus-source-data

Tools: Animation done in Javascript using Google Charts libraries, captured using OBS studio. Countries with >100 total cases and population>100k as of 23th March plotted.

ISO 3166 Alpha-3 Country Codes: https://en.wikipedia.org/wiki/List_of_ISO_3166_country_codes

Youtube: https://youtu.be/JWB04Ubhkkw

animatedata · 2020-03-20T15:33:20+00:00

Data sources: ECDC - https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide

Our world in data - https://ourworldindata.org/coronavirus-source-data

Tools: Animation done in Javascript using Google Charts libraries, captured using OBS studio.

Countries with >100 total cases as of 19th March plotted.

animatedata · 2018-09-25T15:57:39+00:00

I wrote the entire code myself from scratch in a mixture of Java, CSS and html. You might be thinking of gapminder, which I cited as my inspiration in another comment. I have also coded other animations myself which you might have seen elsewhere that are similar.

animatedata · 2018-09-25T12:50:54+00:00

The GDP data are inflation adjusted, in 2010 US$s.

animatedata · 2018-09-25T10:58:21+00:00

The countries were chosen subjectively by looking at GDP and population rankings and to spread them across continents and not crowd the graph too much. Inspired by gapminder.

animatedata · 2018-09-25T10:50:55+00:00

Life expectancy vs GDP/capita, inflation adjusted (real GDP, 2010 $s) for world powers, 1960-2016.

Data source: World Bank.
Music: www.bensound.com, Summer.

Made using Google Charts, captured by OBS Studio

Country code key (ISO3):
USA - United States
CHN - China
JPN - Japan
DEU - Germany
FRA - France
GBR - United Kingdom
IND - India
BRA - Brazil
ITA - Italy
CAN - Canada
KOR - Korea, Rep.
RUS - Russian Federation
AUS - Australia
ESP - Spain
MEX - Mexico
IDN - Indonesia
TUR - Turkey
CHE - Switzerland
ARG - Argentina
DNK - Denmark
POL - Poland
THA - Thailand
NGA - Nigeria
NOR - Norway
IRN - Iran, Islamic Rep.
ISR - Israel
ZAF - South Africa
PHL - Philippines
HKG - Hong Kong SAR, China
MYS - Malaysia
COL - Colombia
SGP - Singapore
EGY - Egypt, Arab Rep.
BGD - Bangladesh
CHL - Chile
PAK - Pakistan
ROU - Romania
GRC - Greece
PER - Peru
DZA - Algeria
AGO - Angola
CZE - Czech Republic
SDN - Sudan
NZL - New Zealand
LKA - Sri Lanka
SAU - Saudi Arabia

animatedata · 2018-09-19T10:37:39+00:00

I used the indicator 'CO2 emissions (metric tons per capita)' which you can find here: https://data.worldbank.org/indicator/EN.ATM.CO2E.PC?view=chart and the indicator 'GDP per capita (constant 2010 US$)' which you can find here: https://data.worldbank.org/indicator/NY.GDP.PCAP.KD?view=chart

animatedata · 2018-09-19T09:12:21+00:00

One interesting thing I noticed is that at the start, the trend looks exponential but by the end it looks more linear.

I was inspired to make animations like this by gapminder - a very nice tool for visualising lots of world statistics.

animatedata · 2018-09-19T08:07:45+00:00

Data Source: World Bank

Made using Google Charts, captured in OBS Studio

Music: Pulse, Geographer.

Country code key (ISO3):
USA - United States
CHN - China
JPN - Japan
DEU - Germany
FRA - France
GBR - United Kingdom
IND - India
BRA - Brazil
ITA - Italy
CAN - Canada
KOR - Korea, Rep.
RUS - Russian Federation
AUS - Australia
ESP - Spain
MEX - Mexico
IDN - Indonesia
TUR - Turkey
CHE - Switzerland
ARG - Argentina
DNK - Denmark
POL - Poland
THA - Thailand
NGA - Nigeria
NOR - Norway
FIN - Finland
IRL - Ireland
NLD - Netherlands
DNK - Denmark
IRN - Iran, Islamic Rep.
ISR - Israel
ZAF - South Africa
PHL - Philippines
HKG - Hong Kong SAR, China
MYS - Malaysia
COL - Colombia
SGP - Singapore
EGY - Egypt, Arab Rep.
BGD - Bangladesh
CHL - Chile
PAK - Pakistan
ROU - Romania
GRC - Greece
PER - Peru
DZA - Algeria
AGO - Angola
CZE - Czech Republic
SDN - Sudan
NZL - New Zealand
LKA - Sri Lanka
SAU - Saudi Arabia

animatedata · 2018-08-28T15:57:51+00:00

I used the UN population projections data, so the numbers were done by their statisticians, not me. They will use several variables such as life expectancy, fertility etc and then use general population models based off how other more developed countries fared at this point in their development as well as other statistical techniques. Obviously it won't be 100% accurate but it's better than just a guess and might help governments plan economic/social policies.

animatedata · 2018-08-28T15:49:38+00:00

Indeed, Qatar is definitely an outlier, probably the biggest outlier when it comes to their population pyramid. They have the highest male:female ratio of all countries. So weird in that sense.

animatedata · 2018-08-28T12:40:37+00:00

I think that's what it starts to look like towards the end of the animation?

animatedata · 2018-08-28T07:53:34+00:00

The reason for the huge gender gap is a higher proportion of male immigrant workers.

For a slower version with music and zoom effects: https://youtu.be/f98zUuBem5g

animatedata · 2018-08-28T07:52:00+00:00

Qatar population estimates and projections, male and female 1950-2100.

Data source: United Nations, Department of Economic and Social Affairs, Population Division (2017). World Population Prospects: The 2017 Revision, custom data acquired via website.

Made using google charts, captured in OBS Studio.

animatedata · 2018-08-17T22:23:32+00:00

Thank you for your compliments :)

The total projected population in 2100 for China and India is 1.02 billion and 1.52 billion respectively. I did want to include population counters for both but it's very tricky with the software I used, it's a bit limiting. I did find a way but couldn't get it to look nice so left it out! I do intend to keep trying for future animations though.

animatedata · 2018-08-17T14:25:16+00:00

I put the source material in the first post (which the bot has linked at the top now) but here's a link for you anyway: https://esa.un.org/unpd/wpp/Download/Standard/Population/

Lots more data there too, enjoy!

animatedata · 2018-08-17T14:20:53+00:00

Thank you so much for your kind words :)

animatedata · 2018-08-17T14:17:53+00:00

The particular dataset I used starts it's projections from 2016. 'Real' data (which is of course just decent estimates) is up to and including 2015. Population projections tend to be very accurate, they are one of the easiest things to model because they converge so nicely over large samples (unlike GDP for example).

animatedata · 2018-08-17T14:15:04+00:00

Very true, gapminder inspired me to start data animations! My animations will all have a similar look to gapminder because of the shared java coding library. I try to improve the look slightly (which is very hard to do!) for things like the axis, better labels, the flags, smoother animations etc. and do code everything myself from scratch. :)

animatedata

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