🤝
Make a deal with Lucian Ghinda
🔒 Paid proposals held safely in escrow — released only when the work's approved.
Followers
1K
Account age
4 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 Lucian Ghinda
📊 Post engagement
1
Avg engagement / post
0.1%
Engagement vs followers
Apr 2022
On Mastodon since
🔥 Top post: Read the full article here: https://allaboutcoding.ghinda.com/s · 1 likes + reposts
📊 Activity & format
Posting cadence
85.7 / week
A very active account — posts many times a day.
Content mix
Mostly text
Recent: 10 text · 2 image · 0 video.
Follower / following
1.7×
Follows 598 back. A more reciprocal / networked account.
Recent posts
View on Mastodon ↗
The comment and abstraction problems reveal something deeper: LLMs optimize for explicitness and abstraction while human maintainers want conciseness and clarity.
This tension might be fundamental, not something we can prompt away with ea…
How does this scale to teams?
This workflow is optimized for solo or very small teams. Coordinating multiple developers all running competing agents is a different adaptation. One that needs a different appoach. I will say we are entering…
What is the cost vs time trade-off? Running 4+ agents in parallel plus multi-panel reviews has real API costs. For this project I run some analysis compared with other projects in the past without LLMs and even if the cost of tokens were h…
Some open questions:
What types of work does this approach NOT work for? So far I found it struggles with deep architectural decisions that need sustained context across multiple sessions.
But of course this was my role to drive and decid…
2. Abstract concepts: They invented terms like "grafting cases" or "sticky clause" instead of using clear domain names. I fought this constantly to ensure PRs were readable without conversation context.
Two persistent problems across all agents:
1. Excessive comments: All agents, especially Claude, added too many comments. I created a skill specifically to remove most of them and still got a lot more than I would have expected in the fina…
My personal rule: try 3-4 prompt iterations with an agent before switching to manual editing. This saves time when the codebase has solid tests.
Claude won most often, Codex came second, occasionally Cursor or Amp had the best implementation. But almost always, no matter who won, the others found important things I added to the final version.
6. I read the final report and decide what changes to accept
7. Final round where I read the code and give feedback
8. All PRs were reviewed by colleagues before merging.
4. Before final review, run multi-panel agent review: four agents checking idiomatic Ruby and Rails patterns, codebase conventions, test design, security, observability, and adversarial scenarios
5. An orchestrator combines reviews, matche…
1. Four agents (Claude, Codex, Cursor, Amp) each implement the same feature independently in a backoff session orchestrated by a Claude session
2. I read all implementations and run automated comparison
3. Pick one as the main PR and cherr…
Claude won most often, Codex came second, occasionally Cursor or Amp had the best implementation. But almost always, no matter who won, the others found important things I added to the final version.
6. I read the final report and decide what changes to accept
7. Final round where I read the code and give feedback
8. All PRs were reviewed by colleagues before merging.
4. Before final review, run multi-panel agent review: four agents checking idiomatic Ruby and Rails patterns, codebase conventions, test design, security, observability, and adversarial scenarios
5. An orchestrator combines reviews, matche…
1. Four agents (Claude, Codex, Cursor, Amp) each implement the same feature independently in a backoff session orchestrated by a Claude session
2. I read all implementations and run automated comparison
3. Pick one as the main PR and cherr…
🐘 Community & instance
Home server
ruby.social
Their home server on the fediverse — the instance a creator picks signals the community they belong to.
✅ 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
Apr 2022
An established account with real history on the platform.
💡 Facts
🗓️Joined Mastodon in 2022 — 4 years ago.
👁️Averages 1 views per post.
📤Posts about 85.7× per week.
🛡️ Audience credibility
52/100
Fair
Credibility score
76%
Mixed
Real audience
Medium
Fake-follower risk
High
Data confidence
- Est. 76% real, active audience · Medium fake-follower risk.
- Low engagement (~0.1% of followers engage each post) — a sign of an inflated or inactive audience.
- Verified account.
- Established account (4+ years old).
Heuristic estimate from engagement, follower ratios, account age & growth — a screening signal, not a guarantee.
About
Product Engineer, Senior Ruby Developer | Co-creator of Ideatify | Curator of https://shortruby.com
| Always looking on the bright side
🔀 Audience overlap
EstimatedEstimated shared audience with similar creators — useful for avoiding overlap (or doubling down) when planning a campaign.
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