# Rogerio Chaves (@_rchaves_) on x

- id: koinon:creator:x__rchaves_
- url: https://koinonlink.com/kol/x/_rchaves_
- schema_version: 1.4

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

## Reach
- followers: 2788
- following: 545
- posts_total: 11031
- posts_observed: 2
- posts_per_day: 1.769
- account_age_days: 6235
- 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: ai_tools
- category_confidence: 0.95
- category_source: derived_from_recent_content
- topics: token cost optimization for agents, context window traps, agent observability and monitoring, LangWatch product promotion
- bio: Building LangWatch: https://t.co/ez7kW1C6z9

create-agent-app: https://t.co/UZVdkdZDpz

## Audience (inferred)
- status: present
- description: AI agent builders and developers focused on monitoring token usage and controlling costs in agent workflows
- roles: automation
- seniority: practitioner
- pain_points: Unexpectedly high token bills from large context windows, Lack of visibility into token spend, cache hits, and tool usage per agent session, Difficulty proving or optimizing agent efficiency to justify costs
- 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: 67
- authenticity_basis: profile_shape_only
- credibility_score: 67
- 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__rchaves_#ae1c0cde] 1M CONTEXT WINDOW IS A TRAP BY BIG AI TO MAKE YOU SPEND MORE ON TOKENS tl;dr: we did an analysis with 2,451 agent sessions, each 2x context size added costs 6x the tokens, even cached, with no evidence that earlier tokens were needed in later work. We could never justify getting  (https://twitter.com/_rchaves_/status/2069793475810443652)
- [x__rchaves_#0f0d11e1] If you want to stop BigAI from sneakily increasing token cost, support us on product hunt
- [x__rchaves_#63f73b2c] Claude Code very smart. Claude Code make code. Claude Code also make bill. Big bill. -But where token go? -Did cache work? -What tool agent use? -Why agent spend 20 minutes fighting one login command? -Why agent call Bash 47 times like Bash owe him money? Before: Me open invoice.

## 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=80, 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=80, method=llm_scorer
- quality.anomaly_flags: source_type=heuristic, confidence=0.5, freshness_days=119, 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=80, 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=119, 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=119, method=platform_profile

## Freshness
- profile_checked_days_ago: 119
- content_analysed_days_ago: 80
- 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__rchaves_
- Request removal: https://koinonlink.com/claim?k=x__rchaves_
- Terms: https://koinonlink.com/for-ai