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Followers
71
Account age
9 yrs
💰 Suggested sponsorship rate
$40 – $65 / sponsored post
Estimated from audience size & typical reach — every deal is negotiable and held in SocialDB escrow.
🧰 Free analysis for dehora
📊 Post engagement
1
Avg engagement / post
1.4%
Engagement vs followers
Apr 2017
On Mastodon since
🔥 Top post: Batch focused semantics and Hadoop focused mechanics, perhaps na · 1 likes + reposts
📊 Activity & format
Posting cadence
0.07 / week
A lower-frequency account — each post lands with more weight.
Content mix
Mostly text
Recent: 8 text · 4 image · 0 video.
Follower / following
1.4×
Follows 52 back. A more reciprocal / networked account.
Recent posts
View on Mastodon ↗
Some thoughts on Steve Yegge's Welcome to Gas Town: https://dehora.net/journal/2026/2/initial-thoughts-on-welcome-to-gas-town
Hey folks, I am switching instances to @dehora@hachyderm.io.
If you’re following, I gather you should be copied along and see no real change, but you might have to re-follow.
Where I’m following you, I'm using the export/import feature an…
Watching The Peripheral and it's really, quite good [1], while wanting to say, Eli Goree, is a standout performer among standout performances.
----
[1] A strong adaptation of @GreatDismal. Aside from having that noir style dialog even in t…
Baked cod from #georgesfishshop #stepaside with black pudding and red pepper
One takeaway is it requires intentional design to get good data 'off of' clients. In that sense, The Unified Logging Infrastructure for Data Analytics at Twitter feels very much like a letter to a constant future reader.
Representing data as Unicode code points to reduce the volume a map reduce has to rip through, then reverse encoding post reduce. That's just a very cool hack.
Representing client interaction space as a tree grammar where each possible interaction is a tuple. If your site or app has a design system this property can fall out of that.
Back to basics:
The Unified Logging Infrastructure for Data Analytics at Twitter, Lee, Lin, Liu, Lorek, Ryaboy, 2012
https://arxiv.org/abs/1208.4171
On browsing through order histories and owned books earlier, I noticed I bought Domain Driven Design in 2003. Yet one wish remains an update to Debasish Ghosh's Functional and Reactive Domain Modeling, or even a language neutral (patterned…
As a for example on the general idea, imagine a source whose each event emitted is well formed and valid, but which does not emit all the events it should emit, systematically doesn’t emit a kind of event, etc. They would form a sort of ‘w…
An observation, and hard learning about event stream processing is quality can't be reasoned about per event, instead it's established in the aggregate. This probably generalises—is this event source reliable when differentiated over time?
Baked cod from #georgesfishshop #stepaside with black pudding and red pepper
One takeaway is it requires intentional design to get good data 'off of' clients. In that sense, The Unified Logging Infrastructure for Data Analytics at Twitter feels very much like a letter to a constant future reader.
Representing data as Unicode code points to reduce the volume a map reduce has to rip through, then reverse encoding post reduce. That's just a very cool hack.
Representing client interaction space as a tree grammar where each possible interaction is a tuple. If your site or app has a design system this property can fall out of that.
Back to basics:
The Unified Logging Infrastructure for Data Analytics at Twitter, Lee, Lin, Liu, Lorek, Ryaboy, 2012
https://arxiv.org/abs/1208.4171
On browsing through order histories and owned books earlier, I noticed I bought Domain Driven Design in 2003. Yet one wish remains an update to Debasish Ghosh's Functional and Reactive Domain Modeling, or even a language neutral (patterned…
As a for example on the general idea, imagine a source whose each event emitted is well formed and valid, but which does not emit all the events it should emit, systematically doesn’t emit a kind of event, etc. They would form a sort of ‘w…
An observation, and hard learning about event stream processing is quality can't be reasoned about per event, instead it's established in the aggregate. This probably generalises—is this event source reliable when differentiated over time?
🐘 Community & instance
Home server
mastodon.cloud
A large general-interest Mastodon server. On the fediverse the server a creator picks hints at their community — this one spans all topics.
On Mastodon since
Apr 2017
An OG fediverse account from Mastodon's early years — a long, established presence.
💡 Facts
🗓️Joined Mastodon in 2017 — 9 years ago.
👁️Averages 1 views per post.
📤Posts about 0.1× per week.
🕵️ Fake follower check
Estimated80/100
Excellent
Credibility score
97%
Real
Real audience
Low
Fake-follower risk
High
Data confidence
- Est. 97% real, active audience · Low fake-follower risk.
- Strong engagement (~1.4% of followers engage each post) — an active, real audience.
- Established account (9+ years old).
Heuristic estimate from engagement, follower ratios, account age & growth — a screening signal, not a guarantee.
About
Still figuring it out. 🇮🇪
(Currently, posting over on hachyderm.io as @dehora.)
📸 Gallery
🔀 Audience overlap
EstimatedEstimated shared audience with similar creators — useful for avoiding overlap (or doubling down) when planning a campaign.
Dimitar Charov
18%
Oliver
17%
Alan Braggins
15%
Daniel Schwarz :toad:
12%
🌼jensebluemchen🌼
11%
Teometric :toad:
11%
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