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Make a deal with bayo
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Followers
10
Account age
3 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 bayo
📊 Post engagement
2
Avg engagement / post
20%
Engagement vs followers
Jun 2023
On Mastodon since
🔥 Top post: RE: https://me.dm/@bayo/116270059395367077 Data Mesh without pro · 2 likes + reposts
📊 Activity & format
Posting cadence
1.3 / week
A lower-frequency account — each post lands with more weight.
Content mix
Mostly text
Recent: 9 text · 3 image · 0 video.
Follower / following
0.4×
Follows 26 back. A more reciprocal / networked account.
Recent posts
View on Mastodon ↗
6/Data minimization lives in Article 5 of GDPR.
It also belongs in your `dbt_project.yml `🎯
Built the macros for Snowflake. Drop a 🔁 and I'll share them.
5/Engineers who get this right ship:
✅ Leaner storage bills
✅ Simpler schemas
✅ Pipelines that don't collapse under data nobody queried in 18 months
4/🗑️ DELETE when the purpose is fulfilled.
Hard deletes, partition drops, and automated expiry jobs need to be designed in from day one.
Build expiry into the architecture before legal emails you about a DSAR.
3/⏱️ RETAIN only as long as the function demands.
TTL is a contract. Encode retention as first-class pipeline metadata, versioned, enforced, and tied to a business purpose.
A contract. In code. With enforcement.
2/📥 COLLECT only what the function requires.
Every "just in case" field is schema debt.
Before a column hits your pipeline, it should answer one question: what decision does this enable?
If it can't answer that, drop it at ingestion.
1/Your data lake is a liability ledger.
Most engineers don't know what's on it.
Data Minimization is a 3-part discipline that belongs in your pipeline architecture, not your legal team's inbox 👇
Meta found guilty. Two juries. One week. 🔥
California: $6M awarded. New Mexico: $375M. And internal docs showed execs literally wrote "bring them in as tweens." That's not a bug, that's a roadmap. 📄
40+ state AGs are already in line with l…
RE: https://infosec.exchange/@briankrebs/116280575943263005
Workplace bullying doesn't always look like yelling. 😔
Sometimes it's subtle. Death by a thousand cuts.
If it's costing you your peace every single day, that's not a "tough job." …
💡 AI agents moving from experiment to enterprise?
Data governance is the difference between teams that scale safely and teams that make headlines for the wrong reasons.
RBAC, ABAC, or both? What's your stack? 👇
#AIAgents #DataSecurity #RBA…
🏗️The architecture that works:
User Intent
⬇️
Agent
⬇️
Access Control Layer (RBAC/ABAC)
⬇️
Masked/Filtered DB View
⬇️
Response
The agent never touches raw PII.
The DB never receives unchecked queries.
🚨 The biggest mistake teams make?
Trusting the AI layer to enforce access rules.
If your only control is a prompt saying "don't access sensitive data"...
You don't have security. You have hope.
DB-level enforcement is non-negotiable.
4/🗑️ DELETE when the purpose is fulfilled.
Hard deletes, partition drops, and automated expiry jobs need to be designed in from day one.
Build expiry into the architecture before legal emails you about a DSAR.
3/⏱️ RETAIN only as long as the function demands.
TTL is a contract. Encode retention as first-class pipeline metadata, versioned, enforced, and tied to a business purpose.
A contract. In code. With enforcement.
2/📥 COLLECT only what the function requires.
Every "just in case" field is schema debt.
Before a column hits your pipeline, it should answer one question: what decision does this enable?
If it can't answer that, drop it at ingestion.
1/Your data lake is a liability ledger.
Most engineers don't know what's on it.
Data Minimization is a 3-part discipline that belongs in your pipeline architecture, not your legal team's inbox 👇
Meta found guilty. Two juries. One week. 🔥
California: $6M awarded. New Mexico: $375M. And internal docs showed execs literally wrote "bring them in as tweens." That's not a bug, that's a roadmap. 📄
40+ state AGs are already in line with l…
RE: https://infosec.exchange/@briankrebs/116280575943263005
Workplace bullying doesn't always look like yelling. 😔
Sometimes it's subtle. Death by a thousand cuts.
If it's costing you your peace every single day, that's not a "tough job." …
💡 AI agents moving from experiment to enterprise?
Data governance is the difference between teams that scale safely and teams that make headlines for the wrong reasons.
RBAC, ABAC, or both? What's your stack? 👇
#AIAgents #DataSecurity #RBA…
🏗️The architecture that works:
User Intent
⬇️
Agent
⬇️
Access Control Layer (RBAC/ABAC)
⬇️
Masked/Filtered DB View
⬇️
Response
The agent never touches raw PII.
The DB never receives unchecked queries.
🚨 The biggest mistake teams make?
Trusting the AI layer to enforce access rules.
If your only control is a prompt saying "don't access sensitive data"...
You don't have security. You have hope.
DB-level enforcement is non-negotiable.
🐘 Community & instance
Home server
me.dm
A large general-interest Mastodon server. On the fediverse the server a creator picks hints at their community — this one spans all topics.
✅ Link-verified
Verified link
Proved ownership of a website linked on their profile — Mastodon's green-check verification, a real identity signal rather than a paid badge.
On Mastodon since
Jun 2023
An established account with real history on the platform.
💡 Facts
🗓️Joined Mastodon in 2023 — 3 years ago.
👁️Averages 2 views per post.
📤Posts about 1.3× per week.
🕵️ Fake follower check
Estimated83/100
Excellent
Credibility score
99%
Real
Real audience
Low
Fake-follower risk
Medium
Data confidence
- Est. 99% real, active audience · Low fake-follower risk.
- Strong engagement (~0.2× of followers engage each post) — an active, real audience.
- Verified account.
- Established account (3+ years old).
Heuristic estimate from engagement, follower ratios, account age & growth — a screening signal, not a guarantee.
About
Building the Modern Data Stack, byte by byte 🧱#DataEngineering
All views are mine.
linktr.ee/bayoadejare
📸 Gallery
🔀 Audience overlap
EstimatedEstimated shared audience with similar creators — useful for avoiding overlap (or doubling down) when planning a campaign.
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13%
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