# Mikhail Rogov (@i_mika_el) on x

- id: koinon:creator:x_i_mika_el
- url: https://koinonlink.com/kol/x/i_mika_el
- schema_version: 1.4

## Identity
- platform: x
- handle: i_mika_el
- platform_url: https://twitter.com/i_mika_el
- website: https://github.com/creynir/x-builder
- entity_type: creator
- person_id: Cross-platform identity not resolved for this creator.

## Reach
- followers: 2123
- following: 1460
- posts_total: 11930
- posts_observed: 11
- posts_per_day: 63.797
- account_age_days: 187
- verified: true
- size_tier: nano

## Engagement
- status: not_collected
- status_note: Engagement has not been collected for this creator yet. This is a gap in our collection, NOT a statement that the creator has no engagement. Follower count is known, so it is measurable.
- unit_note: engagement_rate and peak_post_engagement_rate are ratios (0-1); avg_interactions_per_post and best_post_interactions are absolute counts. Read status before reading any number: a null rate has several different causes and status says which one applies here.

## Content
- category: software
- subcategory: devtools
- category_confidence: 0.9
- category_source: derived_from_recent_content
- topics: AI agents and CLI tooling replacing IDEs, LLM code generation policies and review practices, Refactor vs rewrite debates in real codebases, Side project economics and cost tracking, Public building and reply-driven growth on X
- bio: Dad of two. AI Engineering Lead. 
Built software for unicorns. 
Now starting from 0 in public to own my time.

## Audience (inferred)
- status: present
- description: Software developers and AI engineers who build with agents, manage side projects, and grow in public on X
- roles: dev
- seniority: practitioner
- pain_points: AI agent compute costs spiraling out of control in CI or production, Deciding whether to refactor legacy code or do a full rewrite, Balancing side-project spending against actual revenue, Scaling personal audience growth without burning out on replies
- posts_analysed: 6
- confidence: 0.4

## Commercial (inferred)
- audience_buyer_fit: 0.9
- promotion_intent: 0.8
- open_to_collaboration: false
- contact: Koinon does not publish creator contact details.

## Quality signals
- authenticity_score: 39
- authenticity_basis: profile_shape_only
- credibility_score: 39
- flagged_for_review: false
- koinon_bd_grade: C — measures Koinon partner-outreach fit, not creator quality
- interpretation: Signals for triage, not verdicts. A flag means the profile is worth a manual check.

## Evidence
- [x_i_mika_el#35cb03fa] half of devs: never rewrite. refactor, respect the scars. other half: burn it down. has a rewrite ever actually worked for you? 😅 (https://twitter.com/i_mika_el/status/2072754322991968256)
- [x_i_mika_el#50274d44] rust just banned LLM-written code in their repos. LLM review and analysis: allowed. writing: no. so, genuinely asking: what’s your actual system for when the AI writes vs when you do? mine is “vibes” and I know that’s not a system 😅
- [x_i_mika_el#dbc11760] CLI agents already won. the IDE is a legacy interface — most devs just haven’t noticed yet.
- [x_i_mika_el#59149a8a] side project math, be honest: total spent vs total earned 😅 I’ll go first: 1759 / 0 usd
- [x_i_mika_el#cacf2996] genuinely asking: how do you keep AI agent costs sane once they’re running in CI? mine are starting to look like a second salary 😅
- [x_i_mika_el#ae94ceb0] 96 days on this app. 0 → 3,400 followers. what actually worked, with numbers: replies. 500-600 a day. every week i kept that volume, growth compounded. every week i slacked, it stalled — on a 1-week delay, like

## Provenance
Every inferred field below carries how it was produced. Facts stay facts; inferences stay inferences.

- audience.description: source_type=ai_inferred, confidence=0.4, freshness_days=78, method=llm_persona_from_recent_content
- audience.roles: source_type=ai_inferred, confidence=0.4, freshness_days=78, method=llm_persona_from_recent_content
- audience.seniority: source_type=ai_inferred, confidence=0.4, freshness_days=78, method=llm_persona_from_recent_content
- commercial.audience_buyer_fit: source_type=ai_inferred, confidence=0.6, freshness_days=78, method=llm_scorer — Relative signal within Koinon indexing, not a market-wide score.
- commercial.promotion_intent: source_type=ai_inferred, confidence=0.6, freshness_days=78, method=llm_scorer
- quality.anomaly_flags: source_type=heuristic, confidence=0.5, freshness_days=111, method=profile_snapshot_and_engagement_ratio_heuristic — Means "worth a manual check", NOT confirmed fraud. Flags can be wrong and are not a verdict about the person.
- quality.authenticity_score: source_type=heuristic, confidence=0.25, freshness_days=78, method=Profile-shape heuristic from a single snapshot (follower count, following ratio, account age, posting volume, verification), reduced for engagement red flags where post engagement was collected. Where only a follower count is available the score saturates at 58. — The input set differs by platform; do not compare this score across platforms.
- quality.koinon_bd_grade: source_type=ai_inferred, confidence=0.5, freshness_days=111, method=llm_partner_fit_classifier + fit_score >= 0.6 AND authenticity >= 60 => A; fit_score >= 0.4 AND authenticity >= 45 => B; else C. authenticity < 30 or a promotional-pattern flag => quarantine. — Koinon partner-outreach fit, not creator quality.
- reach.followers: source_type=observed, confidence=0.95, freshness_days=111, method=platform_profile

## Freshness
- profile_checked_days_ago: 111
- content_analysed_days_ago: 78
- audience_updated_days_ago: 78

## AI retrieval
- indexing_requests: 11
- user_triggered_requests: 0
- note: Being indexed in bulk is not the same as being retrieved to answer a question.

## This profile
- Claim or correct: https://koinonlink.com/claim?k=x_i_mika_el
- Request removal: https://koinonlink.com/claim?k=x_i_mika_el
- Terms: https://koinonlink.com/for-ai