# Critique: Apple — Data Engineer, ML Data Team ISE (200619950-4170)

**Resume File:** `output/Apple_Data_Engineer/e2e_apple_data_engineer_resume.tex`
**Cover Letter File:** `output/Apple_Data_Engineer/e2e_apple_data_engineer_cover_letter.tex`
**Date:** 2026-03-30
**Pass:** 1

---

## Part 0: Domain-Specialist Lens

### Reviewer Persona
**Who reads this:** Engineering Manager or Senior Staff Data Engineer on the ISE ML Data Team, Apple Zurich. Works daily with ML applied research teams who need training datasets for Apple Intelligence features (Genmoji, Photos faces/memories, Lock Screen personalization). Uses Airflow, Spark, and internal Apple tooling daily. Has reviewed 60-80 applications for this posting — Apple Zurich ML roles attract heavy global volume.

**What they've seen 100 times:** Generic data engineers who list Airflow/Spark/Python but have never touched ML training data. Resumes that say "machine learning" but mean "I called sklearn.fit() once." Candidates who name-drop Kubernetes without production ownership.

**What would impress them:** Someone who has built data pipelines specifically feeding ML model training, across multiple data modalities (image, text, tabular). Someone who understands that data quality upstream determines model quality downstream. Production ownership at scale, not just prototypes.

### Company Context
- **Core business:** Consumer electronics + software ecosystem. Revenue from hardware, services, and the ecosystem lock-in that Apple Intelligence features deepen.
- **R&D culture:** Product-shipping. Every dataset this team produces feeds models that ship on 2+ billion devices. Quality bar is extreme. Privacy-first (on-device ML, differential privacy).
- **Strategic priority:** Apple Intelligence is the company's current flagship initiative. ISE ML Data Team is upstream of every visual generative model (Genmoji, wallpapers) and personalization feature (Photos).
- **Insider vocabulary:** "datasets at scale," "production-ize," "human-in-the-loop," "self-service tooling," "agentic workflow," "multi-domain data" — the JD is very specific about what they want.

### JD Vocabulary Extraction (top 10 terms, ranked)

| # | JD Term | Freq | Meaning at Apple ISE | Resume Match? |
|---|---------|------|---------------------|---------------|
| 1 | Data pipelines at scale | 4x | Petabyte-scale dataset production pipelines | YES — multiple bullets |
| 2 | Python + CS foundations | 3x | Expert-level Python, parallelization, data structures | YES — bold, multiple |
| 3 | ML (NLP or Computer Vision) | 3x | Familiarity with model training data needs, not just model usage | YES — both NLP (FC-2) and CV (BS-1) |
| 4 | Agentic workflow | 2x | LLM-based automation of data pipeline operations | YES — SW-GenAI bullet |
| 5 | Human-in-the-loop | 2x | Annotation pipelines, labeler-model interaction loops | PARTIAL — skills only, no bullet evidence |
| 6 | Synthetic data | 2x | Production-ize synthetic data generation workflows | PARTIAL — skills only, no bullet evidence |
| 7 | Data orchestration (Airflow) | 2x | Production Airflow DAGs at scale | YES — SW-1, SW-2 |
| 8 | Docker / Kubernetes | 2x | Containerized pipeline deployment | YES — multiple |
| 9 | Data model design | 1x | Consistent, robust schema design | PARTIAL — mentioned in skills, weak in bullets |
| 10 | Self-service tooling | 1x | Tools enabling PMs to iterate faster | YES — SW-4 bullet |

### Domain Vocabulary Map

| Resume Currently Says | Should Say for This JD | Why |
|---|---|---|
| "ETL pipelines" | "data pipelines" or "ML data pipelines" | Apple JD never says "ETL" — they say "data pipelines" and "data flows" |
| "component owner" | "technical owner" or "pipeline owner" | "Component Owner" is Swisscom-internal vocabulary; Apple won't parse it |
| "automating code review, documentation" (SW-GenAI) | "automating data pipeline operations" | Apple cares about agentic workflows for data, not code review |
| "data governance and SLA compliance" (SW-2) | "data quality and pipeline reliability" | Apple ISE cares about data quality feeding ML models, not governance frameworks |
| "3rd-level root cause analysis" (SW-4) | "pipeline reliability and data platform operations" | Apple doesn't use telco support-tier language |

### Gap Ranking

- **Fatal:** None. All minimum qualifications are met (Python, ML in NLP+CV, production data pipelines, BS/MS degree).
- **Serious:** (1) No direct synthetic data workflow experience — this is a named JD responsibility. (2) No annotation/labeling pipeline ownership — HITL is mentioned twice. (3) No explicit video domain data experience (JD lists "image, video, text"). Competitive candidates from big tech may have all three.
- **Cosmetic:** (1) No Apple/FAANG experience. (2) No explicit "parallelization" keyword. (3) No PM-facing self-service tooling (Dennis built for engineers, not PMs).

### Methodology Transfer Test

| Achievement | How Apple ISE Expert Sees It |
|---|---|
| SW-2: Fulfillment ETL at Swisscom | "He owns production data pipelines at telecom scale — same operational accountability we need, different domain. He knows what on-call for data quality means." |
| SW-1: AWS migration (Airflow, Glue, Athena) | "Our stack overlaps heavily — Airflow, cloud-native. He's done a migration, which means he understands legacy-to-modern patterns. Good." |
| SW-GenAI: LangChain agentic workflows | "Agentic workflow is our preferred qual — he's actually done it, not just listed it. Small scale, but the pattern transfers." |
| BS-1: ML inference for CV defect classification | "He's touched image data in production ML. Not annotation pipelines exactly, but he understands the data-to-model loop in a real environment." |
| FC-2: ARTUS NLP/speech recognition | "NLP domain coverage. Research context, not production, but shows he understands what ML models need from data." |

### Competitive Landscape
- **Obvious fit candidate:** Data engineer from Meta/Google with 3+ years on annotation pipelines, direct HITL experience, synthetic data generation, and Airflow at petabyte scale. Probably has 1 modality depth (image or text) but not both.
- **Dennis's advantage:** Rare dual NLP + CV coverage across real positions (not just coursework). Active agentic workflow experience. Production ML deployment in a constrained 24/7 environment (semiconductor fab — shows ops maturity). European candidate, no visa needed.
- **Their advantage:** Direct HITL/annotation pipeline experience. Synthetic data workflows. FAANG-scale tooling familiarity. Possibly direct Apple Intelligence or similar on-device ML data experience.

---

## Part 1: Five-Perspective Read-Through

### ATS Robot (keyword scan)

| # | JD Keyword | Resume Match | Type |
|---|-----------|-------------|------|
| 1 | Python | YES — bold, 5+ mentions | Verbatim |
| 2 | Machine Learning / ML | YES — multiple | Verbatim |
| 3 | NLP | YES — bold, header + bullets | Verbatim |
| 4 | Computer Vision | YES — bold, header + bullets | Verbatim |
| 5 | Data pipelines | YES — multiple bullets | Verbatim |
| 6 | Airflow | YES — bold, skills + bullets | Verbatim |
| 7 | Docker | YES — bold, multiple | Verbatim |
| 8 | Kubernetes | YES — bold, multiple | Verbatim |
| 9 | Spark / PySpark | YES — bold | Verbatim |
| 10 | Databricks | YES — skills | Verbatim |
| 11 | SQL | YES — skills, multiple DB mentions | Verbatim |
| 12 | NoSQL | YES — skills | Verbatim |
| 13 | Data model | YES — skills ("data modeling") | Semantic |
| 14 | Scale / at scale | YES — multiple | Verbatim |
| 15 | Agentic workflow | YES — bold in header + bullet | Verbatim |
| 16 | Human-in-the-loop | YES — skills only | Verbatim |
| 17 | Synthetic data | YES — skills only | Verbatim |
| 18 | Data preprocessing | YES — skills | Verbatim |
| 19 | Orchestration | YES — skills section name | Verbatim |
| 20 | Parallelization | NO — "distributed computing" only | Absent |

**Match rate:** 19/20 = 95% → PASS

**Top 3 missing keywords that could be added truthfully:**
1. "Parallelization" — add to Programming skills (Dennis has parallel processing experience at Bosch/Swisscom)
2. "Video" — present in skills ("tabular, image, text, video") but not in any bullet. Vizrt bullet touches A/V data but doesn't say "video data preprocessing"
3. "Annotation" — only in skills ("annotation pipeline support"); no bullet evidence

### Recruiter Glance (10 seconds)
**Verdict:** FORWARD

Current title "Staff Data, Analytics & AI Engineer" at Swisscom signals seniority. Header tagline "Staff Data Engineer | NLP & Computer Vision · Airflow · Agentic Workflows | AWS · Python" hits every JD priority keyword. M.Eng. clears education bar. Bern location with "Open to relocation to Zurich" removes logistics concern. A non-technical recruiter instantly sees: senior data engineer, right tools, right location.

### HR Screen (30 seconds)
**Verdict:** PHONE SCREEN

Summary bridge is strong: explicitly connects NLP (Fraunhofer), computer vision (Bosch), and petabyte-scale ETL (Swisscom) — the exact trifecta the JD wants. Skills section headers ("Machine Learning & AI," "Data Engineering & Orchestration") signal domain alignment. First bullet under each position is the strongest JD-relevant achievement. 10+ years experience exceeds JD minimum. Swiss-based, German citizen — no work authorization issues.

### Hiring Manager (2 minutes)
**Verdict:** INTERVIEW (with reservations)

**Top 3 observations:**
1. **Dual NLP + CV coverage is the differentiator.** Most data engineer applicants have one or neither. The Fraunhofer ARTUS (NLP) + Bosch defect classification (CV) combination directly addresses "familiarity with model training in either NLP or Computer Vision" — and delivers both.
2. **Swisscom bullets are strong but diluted.** 6 bullets for one position is a lot. SW-5 (K8s/CI/CD) and SW-6 (PySpark) add breadth but not unique value — they describe standard data engineering practices. Would prefer seeing deeper ML data pipeline work.
3. **Skills section has unsubstantiated claims.** "Human-in-the-loop data workflows," "annotation pipeline support," "synthetic data preprocessing," and "ML dataset curation" appear in skills but zero bullets demonstrate these. The HM will notice this gap — it looks like keyword insertion to match the JD.

**Predicted first interview question:** "You list human-in-the-loop and synthetic data in your skills — can you walk me through a specific project where you worked with annotation pipelines or synthetic data generation?"

### Technical Reviewer (10 minutes)

**Truthfulness audit:**

| Claim | Verified? | Source |
|---|---|---|
| "10+ years building production data pipelines" | YES | 2015 (Generali) → 2026 = 11 years in software/data roles |
| "petabyte scale" (summary) | PARTIAL | Swisscom is telecom-scale but "petabyte" is stated in session framing strategy, not direct evidence. "Petabyte-adjacent" is the honest framing used in CL. |
| "component owner" (SW-2) | YES | Experience file confirms Component Owner title |
| "ML inference" deployment at Bosch (BS-1) | YES | Experience file confirms Docker/K8s ML deployment |
| "ARTUS speech transcription" (FC-2) | YES | Experience file confirms Fraunhofer ARTUS NLP project |
| "agentic LangChain workflows" (SW-GenAI) | YES | Memory confirms GenAI usage at Swisscom |
| "Human-in-the-loop data workflows" (skills) | NOT EVIDENCED | No bullet describes HITL work. Bosch CV deployment replaced manual inspection (HITL-adjacent) but not annotation pipeline work |
| "Synthetic data preprocessing" (skills) | NOT EVIDENCED | No experience with synthetic data generation or preprocessing |
| "annotation pipeline support" (skills) | NOT EVIDENCED | No annotation pipeline experience in any position |
| "ML dataset curation" (skills) | NOT EVIDENCED | No direct ML dataset curation experience described |

**Verb discipline:** All verbs appropriate. "Contributed" used for FC-2 and FC-4 (hedged correctly). "Owned," "Migrated," "Designed" used for primary work (correct). No overclaiming detected in bullets.

**Keyword saturation:** "Python" appears 6 times (borderline at 6-8). "Data" appears 15+ times (high but natural for a data engineer resume). No concerning over-saturation.

**Internal consistency:** Summary claims match bullets. CL claims traceable to resume bullets. No contradictions found.

**Credibility concern:** The gap between skills claims (HITL, synthetic data, annotation, ML dataset curation) and bullet evidence is the primary technical red flag. These four skills items appear to be JD keyword insertions without supporting experience.

---

## Part 2: Eight-Dimension Scoring

| Dimension | Score | Weight | Weighted | Notes |
|---|---|---|---|---|
| ATS Keywords | 9.0 | 15% | 1.35 | 19/20 match; only "parallelization" absent verbatim |
| Summary | 8.5 | 10% | 0.85 | Strong bridge, NLP+CV+scale narrative, dense but effective |
| Skills Section | 7.0 | 10% | 0.70 | 4 unsubstantiated claims (HITL, synthetic, annotation, curation); ML&AI 6 lines is over-invested |
| Bullet Quality | 7.5 | 25% | 1.875 | Top 5 bullets are strong; 4-5 low-relevance fillers dilute impact |
| Publications | 7.0 | 10% | 0.70 | N/A (no pubs section); certs provide partial compensation |
| Narrative Coherence | 8.0 | 15% | 1.20 | Strong NLP→CV→Scale arc; position headings well-crafted; slight ML oversell |
| Page Fill & Visual | 8.5 | 5% | 0.425 | 2pp compile clean; 46 rendered lines; no orphans detected |
| Credibility Signals | 7.5 | 10% | 0.75 | AWS SAA active, Staff title, Fraunhofer/Bosch pedigree; no FAANG, no pubs |
| **Total** | | **100%** | **78.5** | |

---

## Part 3: Interview Likelihood

| Reader | Probability | Key Factor |
|--------|------------|------------|
| ATS | 95% | 19/20 keyword match — will pass any standard ATS filter |
| Recruiter (10s) | 85% | Staff title + Swisscom + right tools in header tagline |
| HR (30s) | 80% | Strong summary bridge, all minimum quals clearly met |
| Hiring Manager (2m) | 60% | Dual NLP+CV impressive, but HITL/synthetic data gap is real; filler bullets reduce signal density |
| Technical Panel (10m) | 55% | Unsubstantiated skills claims will surface in technical screen; core pipeline experience is solid but ML data pipeline depth is thinner than framing suggests |

### Ceiling Analysis

| Scenario | Score |
|----------|-------|
| Current resume | 78.5 |
| + Tier 1 improvements applied | 82.0 |
| Theoretical max (this candidate + this JD) | 84.0 |
| Hard ceiling (structural background gap) | 85.0 |
| What would close the gap | Direct HITL/annotation pipeline experience (+3), synthetic data project (+2), FAANG pedigree (+1) |

---

## Part 4: Actionable Improvements

### Tier 1: HIGH IMPACT (do these)

**1. Remove unsubstantiated skills claims (+1.5 pts — Skills + Credibility)**

Remove from ML&AI skills group:
- "Human-in-the-loop data workflows, ML dataset curation, annotation pipeline support, data quality validation" (line 61)
- "Synthetic data preprocessing, multi-modal dataset pipelines, model training data at petabyte scale" (line 62)

Replace with evidence-backed alternatives:
- Line 61 → "ML model deployment pipelines, automated inspection replacing manual review, production data quality validation"
- Line 62 → "Multi-modal data processing (tabular, image, text, A/V), data pipeline monitoring at scale"

**Why:** A technical reviewer at Apple will cross-reference skills claims against bullet evidence. Four unsubstantiated claims about HITL, synthetic data, and annotation pipelines undermine the entire skills section's credibility. Better to honestly show what you've done and let the interview bridge the gap.

**2. Cut 3 low-relevance bullets, sharpen focus (+1.0 pt — Bullet Quality + Narrative)**

Remove:
- BS-5 (Tibco Spotfire C# extensions) — irrelevant to Apple; C# visualization tool
- FC-4 (grant proposal) — low relevance; "contributed to a proposal" is weak
- GN-3 (J2EE PIA-Postkorb) — pure filler; legacy Java web app

This reduces to 17 bullets. If page fill suffers, expand SW-2 or BS-1 to include more ML data pipeline detail rather than adding back low-relevance bullets.

**Why:** 20 bullets across 5 positions creates a "everything I've ever done" impression. Apple's HM has 2 minutes — every bullet that doesn't reinforce "I build data pipelines for ML" is noise.

**3. Reframe SW-GenAI bullet toward data pipeline automation (+1.0 pt — Bullet Quality)**

Current: "Designed and implemented agentic LangChain workflows with domain-specific GPT knowledge bases at Swisscom, automating code review, documentation, and pipeline troubleshooting to cut manual engineering effort."

Proposed: "Designed and implemented agentic LangChain workflows with domain-specific GPT knowledge bases, automating data pipeline troubleshooting, data validation, and documentation to reduce manual effort in the data engineering team."

**Why:** The JD wants agentic workflows for data operations. "Code review" and generic "engineering effort" dilute the data-pipeline focus. Reframing to emphasize data pipeline automation makes the transfer to Apple ISE explicit.

**4. Apply vocabulary swaps from Domain Map (+1.0 pt — Narrative + ATS)**

- SW-2: "data governance and SLA compliance" → "data quality standards and pipeline reliability" (Apple cares about data quality, not governance frameworks)
- SW-4: "3rd-level root cause analysis" → "pipeline reliability and data platform troubleshooting" (drop telco support-tier language)
- Consider replacing "ETL pipelines" with "data pipelines" in summary and bullets where it appears (Apple JD never says "ETL")

### Tier 2: MEDIUM IMPACT (optional)

1. **Add "parallelization" to Programming skills** — the one missing top-20 ATS keyword. Truthful — Dennis has distributed computing experience. (+0.5 pts)
2. **Reframe BS-1 to emphasize data preprocessing aspect** — currently focuses on deployment; add "image data preprocessing and pipeline feeding" language to bridge toward Apple's multi-domain data need. (+0.5 pts)
3. **Reduce ML&AI skills from 6 lines to 4** — over-investment for a Data Engineer role. Consolidate the strongest lines and cut padding. (+0.3 pts)
4. **Strengthen Vizrt bullet to mention "video data"** — JD explicitly lists video as a data domain. Currently says "A/V data" — spell out "video data preprocessing" for ATS and domain signal. (+0.3 pts)

### Tier 3: COSMETIC (skip)

1. "2.5 billion devices" appears twice in CL — minor repetition
2. Summary could be 1 line shorter for visual breathing room
3. Cert section ordering — AWS SAA could be listed first as most relevant

### Verdict
Apply Tier 1 changes — they collectively move the score from 78.5 → ~82.0. Tier 2 items 1 and 4 are easy wins worth adding. Tier 3 is not worth the edit.

---

## Part 5: Interview Bridge Points

| Resume Topic | Apple ISE Equivalent | Opening Line |
|---|---|---|
| SW-2: Fulfillment ETL ownership | Production dataset pipeline ownership | "At Swisscom I own end-to-end data pipelines processing telecom-scale data — the same operational accountability pattern your team needs for ML training dataset production, just at a different scale." |
| SW-1: AWS migration (Airflow, Glue, Athena) | Cloud-native pipeline modernization | "The Teradata-to-AWS migration I led at Swisscom involved the same tools your stack uses — Airflow orchestration, S3-based storage, serverless compute — and the migration patterns transfer directly." |
| SW-GenAI: LangChain agentic workflows | Agentic automation for data operations | "The LangChain workflows I built at Swisscom automate pipeline troubleshooting and documentation — a small-scale version of the agentic workflow direction your team is exploring for data pipeline operations." |
| BS-1: CV defect classification in fab | Image data pipeline for ML training | "At Bosch I worked with image data flowing into ML models in a 24/7 production environment — the data quality requirements for semiconductor defect classification are similar to what your team needs for training data feeding Apple Intelligence models." |
| FC-2: ARTUS NLP/speech recognition | NLP training data pipeline | "The ARTUS project at Fraunhofer gave me direct experience with NLP model training data — speech recognition requires the same data preprocessing, cleaning, and quality assurance patterns your team applies to text data for Apple Intelligence." |
| BS-3: Application Owner (SLOs, vendor mgmt) | Production system ownership at scale | "As Application Owner at Bosch, I defined SLOs and managed the full lifecycle of analytical systems in a 24/7 fab — that operational maturity transfers directly to owning dataset production pipelines at Apple's scale." |
| Dual NLP + CV coverage | Multi-domain data understanding | "Most data engineers I know have depth in one ML domain. I've worked with both NLP data at Fraunhofer and image data at Bosch — that cross-domain understanding is exactly what a team processing tabular, image, and text data needs." |

---

## Part 6: Cover Letter Critique

### 6A. Anti-Pattern Checklist
- [x] No generic opener — opens with Apple ISE-specific reference
- [x] Does not rehash bullets — adds narrative context and motivation
- [x] Names specific team/product: ISE ML Data Team, Apple Intelligence, Genmoji, Photos
- [x] Clear "why THIS position" throughout
- [x] Strongest qualification (NLP+CV dual coverage) in P1
- [x] No defensive language
- [x] Active closing: "I'd welcome a conversation"
- [x] Credentials woven into body paragraphs

### 6B. Tailoring Signal Checklist
- [x] Names ISE ML Data Team, Apple Intelligence, Genmoji, Photos
- [x] Uses 5+ JD terms supplementing resume: "training datasets," "data preprocessing," "production rollout," "agentic workflow design and implementation"
- [x] References Apple Intelligence mission and specific features
- [x] Proposes specific connection: dual NLP+CV → ISE's multi-domain needs
- [x] Industry tone correctly identified

### 6C. Industry Context Checks
- [x] Business value translation: "training datasets that determine the quality of Apple Intelligence features on 2.5 billion devices"
- [x] "Why industry" not applicable (already in industry)
- [x] Jargon balanced for HR first reader while showing technical depth

### 6D. CL ATS Keywords
Keywords present in CL: ML Data Team, data pipelines, NLP, computer vision, ETL, AWS, Airflow, Athena/Iceberg, agentic workflow, LangChain, GPT, data preprocessing, production, scale.
**Count:** 10+ supplementary JD keywords → PASS

### 6E. Structural Checks
- [x] Consistency: all CL claims match resume bullets
- [x] Complementarity: adds "why Apple" motivation and career arc narrative
- [x] Word count: ~260 words — within 250-300 target
- [x] Tone: results-driven industry
- [x] Quantification: 4 claims (2.5B devices, seven years, 24/7 fab, telecom-scale)
- [x] Domain pivot: telecom → ML data, well-handled

### 6F. Package Cohesion
- [x] Resume stands alone — interview-worthy without CL
- [x] CL deepens, doesn't introduce new achievements
- [x] No contradictions between resume and CL
- [x] Complement, not repeat — CL adds motivation and "why Apple" narrative
- [x] Page budget: 3pp total (2+1) ✓

**Minor note:** "2.5 billion devices" used in both P1 and P3 — slight repetition. Not a fix priority.

---

## Part 6G: AI Fingerprint Scan

| # | Check | Result |
|---|-------|--------|
| 1 | Tier 1 banned words | PASS — none found |
| 2 | Banned phrases | PASS — none found |
| 3 | Em-dashes (max 2 per doc) | PASS — Resume: 2 (summary + GN-2), CL: 0 |
| 4 | Bullet -ing analysis endings | PASS — no vague -ing endings; all bullets end with concrete objects |
| 5 | Consecutive same-length sentences | PASS |
| 6 | Repeated paragraph structure | PASS — CL paragraph openers vary |
| 7 | Triplet structures >2 per doc | PASS (2 triplets in resume) |
| 8 | CL generic opener | PASS — opens with ISE-specific reference |
| 9 | Metaphorical banned nouns | PASS |
| 10 | Passive voice >20% | PASS — active verbs dominate |
| 11 | Fellowships use `---` | N/A |
| 12 | Banned adverbs | PASS |

---

## Part 7: Post-Generation Verification

### Mechanical Checks
- [x] All bullets within char limits — 0 OVER violations (char_count.py verified)
- [x] Multi-line bullets pass orphan check — no last-line underfill flagged
- [x] Page fill: 2 pages, compile clean, 46 rendered lines
- [x] No ordering errors in bullet sequencing

### Content Checks
- [x] ATS keywords: 19/20 = 95% match rate
- [x] Provenance flags correct — no publication claims, no false status
- [x] No forbidden terms (no French/Italian, no "3 consecutive years" security champion)
- [ ] **FAIL:** 4 skills items without bullet evidence (HITL, synthetic data, annotation, ML dataset curation) — see Tier 1 fix #1
- [x] Email correct: dennis@thiessen.io
- [x] CL claims traceable to resume bullets

### Structural Checks
- [x] "Apple" spelled correctly throughout
- [x] .tex files compile standalone
- [x] Date format consistent (Mon YYYY -- Mon YYYY)
- [x] Email: dennis@thiessen.io ✓
- [x] Page count: resume 2pp, CL 1pp ✓

---

## Score: 78.5 / 100

*End of critique.*
