# Goku Mohandas (@GokuMohandas) on x

- id: koinon:creator:x_gokumohandas
- url: https://koinonlink.com/kol/x/GokuMohandas
- schema_version: 1.4

## Identity
- platform: x
- handle: GokuMohandas
- platform_url: https://twitter.com/GokuMohandas
- entity_type: creator
- person_id: Cross-platform identity not resolved for this creator.

## Reach
- followers: 14248
- following: 130
- posts_total: 958
- posts_observed: 14
- posts_per_day: 0.232
- account_age_days: 4134
- verified: true
- size_tier: koc

## Engagement
- status: peak_post_only
- peak_post_engagement_rate: 0.01362
- best_post_interactions: 194
- measurement_source: x_post_interactions
- measurement_formula: interactions on one post — likes, or likes + reposts + replies depending on the discovery path (the two are not distinguishable in the stored data)
- comparable_pool: peak_interactions
- status_note: We have only the single best observed post, so no mean exists. peak_post_engagement_rate is an UPPER BOUND, systematically higher than an average — never compare it against an average rate.
- 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: ai_tools
- category_confidence: 0.95
- category_source: derived_from_recent_content
- topics: End-to-end LLM workflows, OSS LLM fine-tuning and serving, RAG application improvements, Prompt engineering and preprocessing
- bio: I like making things.

## Audience (inferred)
- status: present
- description: AI/ML engineers and developers building LLM applications, RAG systems, and fine-tuning/serving open-source models for industry use
- roles: dev
- seniority: practitioner
- pain_points: Fine-tuning and serving OSS LLMs to match or beat closed-source performance, Improving RAG output quality via chunking, preprocessing, and prompt experiments, Designing reliable end-to-end LLM workflows for real customer deployments
- posts_analysed: 3
- 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: 88
- authenticity_basis: engagement_observed
- credibility_score: 88
- flagged_for_review: false
- koinon_bd_grade: A — 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_gokumohandas#1e5955df] https://t.co/X7Wtupv48Z (https://twitter.com/GokuMohandas/status/2066853420326384055)
- [x_gokumohandas#2e2fd575] Excited to share our end-to-end LLM workflows guide that we’ve used to help our industry customers fine-tune and serve OSS LLMs that outperform closed-source models in quality, performance and cost. https://t.co/u9hvVj7E24 1/🧵
- [x_gokumohandas#247e1b7d] It's been nice to see small jumps in output quality in our RAG applications from chunking experiments, contextual preprocessing, prompt engineering, fine-tuned embeddings, lexical search, reranking, etc. but we just added Mixtral-8x7B-Instruct to the mix and we're seeing a 🤯 ste

## 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=76, method=llm_persona_from_recent_content
- audience.roles: source_type=ai_inferred, confidence=0.4, freshness_days=76, method=llm_persona_from_recent_content
- audience.seniority: source_type=ai_inferred, confidence=0.4, freshness_days=76, method=llm_persona_from_recent_content
- commercial.audience_buyer_fit: source_type=ai_inferred, confidence=0.6, freshness_days=77, 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=77, method=llm_scorer
- quality.anomaly_flags: source_type=heuristic, confidence=0.5, freshness_days=108, 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=0.5, freshness_days=77, 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=108, 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=108, method=platform_profile

## Freshness
- profile_checked_days_ago: 108
- content_analysed_days_ago: 77
- audience_updated_days_ago: 76

## AI retrieval
- indexing_requests: 9
- 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_gokumohandas
- Request removal: https://koinonlink.com/claim?k=x_gokumohandas
- Terms: https://koinonlink.com/for-ai