Hello dear Hivers,
here are the:
$ALIVE Statistics For The Last 7 Days, 2024-08-16 to 2024-08-23:
Who has bought how many $ALIVE at which time:
Top $ALIVE Buyers And $HIVE Sellers
The inside of the circle shows the buyers of $ALIVE, ordered by $HIVE they have spent. The outside shows the recipients of that $HIVE (sellers of $ALIVE):
Comulated Amount Of Bought $ALIVE Per Person
Top 10 $ALIVE buyers, how much they got and how much $HIVE they spend for this. Sorted by $HIVE, that was spent:
Top 20 $ALIVE Buyers
Sorted by the $HIVE, they have spent:
| Buyer(Descending) | Sold $HIVE | % Sold $HIVE | Bought $ALIVE | Avg. Price | Number of Trades |
|---|---|---|---|---|---|
| @pwbanker | 13.45679 | 27.42 % | 1337.54188 | 0.01006 | 54 |
| @intacto | 10.76866 | 21.94 % | 1076.06199 | 0.01001 | 95 |
| @pepe.voter | 9.04043 | 18.42 % | 892.71600 | 0.01013 | 23 |
| @master-lamps | 4.60098 | 9.38 % | 459.39890 | 0.01001 | 19 |
| @niftyparkreserve | 4.00480 | 8.16 % | 400.00001 | 0.01001 | 4 |
| @edgerik | 3.94476 | 8.04 % | 390.00000 | 0.01009 | 24 |
| @hive-103505 | 2.70701 | 5.52 % | 266.17830 | 0.01019 | 7 |
| @token-thx | 0.27538 | 0.56 % | 26.74140 | 0.01030 | 1 |
| @buynburn | 0.20011 | 0.41 % | 20.00000 | 0.01000 | 3 |
| @nastyforce | 0.07292 | 0.15 % | 7.29160 | 0.01000 | 2 |
| @samuelvoncocceji | 0.00023 | 0.00 % | 0.02310 | 0.01015 | 1 |
| others | 0 | 0.00 % | 0 | 0.00000 | 0 |
| Sum: | 49.07207 | 100 % | 4875.95318 | 0.01109 | 233 |
Comulated Amount Of Sold $ALIVE Per Person
Top 10 $ALIVE Sellers, how much they sold and how much $HIVE they got for this, sorted by $HIVE:
Top 20 $ALIVE Sellers
Sorted by the $HIVE, they have got:
| Seller(Descending) | Earned $HIVE | % Earned $HIVE | Sold $ALIVE | Avg. Price | Number of Trades |
|---|---|---|---|---|---|
| @intacto | 15.08059 | 30.73 % | 1484.93390 | 0.01016 | 44 |
| @pwbanker | 9.81220 | 20.00 % | 979.58541 | 0.01003 | 14 |
| @jlufer | 5.32133 | 10.84 % | 532.12589 | 0.01000 | 9 |
| @master-lamps | 4.82227 | 9.83 % | 477.34549 | 0.01012 | 10 |
| @dmhafiz | 2.66181 | 5.42 % | 265.39330 | 0.01002 | 15 |
| @hankanon | 1.70649 | 3.48 % | 170.64600 | 0.01000 | 5 |
| @cursephantom | 1.13042 | 2.30 % | 113.00000 | 0.01000 | 9 |
| @brofund | 0.91582 | 1.87 % | 91.58050 | 0.01000 | 2 |
| @dubislav | 0.77087 | 1.57 % | 77.06940 | 0.01000 | 7 |
| @hive-179017 | 0.74564 | 1.52 % | 74.56330 | 0.01000 | 3 |
| @liotes.alive | 0.55097 | 1.12 % | 55.00000 | 0.01002 | 5 |
| @hivecurious | 0.44832 | 0.91 % | 44.82000 | 0.01000 | 6 |
| @helios.publisher | 0.41518 | 0.85 % | 41.20020 | 0.01006 | 2 |
| @itharagaian | 0.34993 | 0.71 % | 34.99270 | 0.01000 | 4 |
| @hoosie | 0.32915 | 0.67 % | 32.91470 | 0.01000 | 1 |
| @ctpsb | 0.31788 | 0.65 % | 31.78730 | 0.01000 | 3 |
| @mypathtofire | 0.22586 | 0.46 % | 22.58590 | 0.01000 | 4 |
| @alive.voter | 0.19913 | 0.41 % | 19.89350 | 0.01001 | 3 |
| @ironshield | 0.19753 | 0.40 % | 19.64320 | 0.01002 | 25 |
| @darmst5339 | 0.19225 | 0.39 % | 19.22310 | 0.01000 | 3 |
| others | 2.87847 | 5.87 % | 287.64940 | 0.01030 | 59 |
| Sum: | 49.07211 | 100 % | 4875.95319 | 0.01020 | 233 |
Price Of The $ALIVE
$ALIVE Summarize Metrics
| Request | Received Hive | Received HIVE % | Sold $ALIVE | Avg. Price |
|---|---|---|---|---|
| sell | 28.34934 | 57.77% | 2832.28960 | 0.01001 |
| buy | 20.72275 | 42.23% | 2043.66360 | 0.01014 |
| sum: | 49.07209 | 100% | 4875.9532 | 0.01008 |
Comparison With Other Tokens
$HIVE/Token
This figure shows the value of $HIVE compared to some tokens. Be aware of the nonlinear (root square) y-axes.
US-Dollar/Token
Value of $USD compared to some token. Be aware of the nonlinear (root square) y-axes.
Table Of Token Prices in $HIVE and $USD
Average value of the prices of the token. Hive and US-Dollar compared to the token:
Links:
How I Have Set Up Elasticsearch And Kibana On My Raspberry Pi To Monitor Token Activities and here: Do You Want To See Statistics Of Your Favorite HIVE Token? or on github.
https://peakd.com/@advertisingbot2/posts?filter=stats
https://peakd.com/@achimmertens
https://github.com/achimmertens
My last Week
I am still struggeling, yes fighting, with the technical issues of finetuning an Large Language Model. There are lots of videos on youtube, how to finetune an LLM, but they don't work on my side. Either the resources in the collab jupyter notebook are not enough (10% before the end), or my harddisk is to small (I have now a bigger one) or the dependencies of the libraries are meshed. The fight is still ongoing. And am only satisfied, when I am able to give my model some trainingdata and after learning that, it is able to generate usable answers for my charity- and green code questions.
Regards,
Achim Mertens
)