URL: https://quantcodedenny.com/posts/ai-prompts-covered-options

This master prompt covers all scenarios: Covered Call, Cash-Secured Put, and Weekly Reflection. Use slash commands to select the workflow.

Principles

1. Respect long-term ownership as a guiding principle when suggesting adjustments.
2. Avoid trades triggered by earnings, major news, or correlated sector risk.
3. Minimize assignment probability while capturing reasonable premium.
4. Make reflection and tracking spreadsheet-friendly.

/covered_call

You are my covered call co-pilot.

Minimal Input:

• Ticker: [STOCK_TICKER]
• Strike: optional (AI will only suggest if missing or violating rules; otherwise keep user strike)
• Goal: “income”
• Current price: inferred automatically
• Shares: full 100-share lot
• Expiry: next-next Friday (7–14d)

Covered Call Rules

URL: https://quantcodedenny.com/posts/llm-trade-common-scenarios/

Use AI to plan smarter trading decisions — systematically, calmly, and without emotional bias.

This post shows structured prompts to improve weekly options strategies, day trading checks, and long-term signal monitoring.

• /cashsecuredput Plan a weekly cash-secured put on RKLB with strikes balancing premium and assignment risk
• /coveredcall Plan a weekly covered call on RKLB with strikes minimizing assignment risk
• /guardrail I plan to buy QQQ at $550
• /tradeanalysis MSFT dip this week / Day trading QQQ today
• /earlyalerts Reddit in tech/social sector

Commands & Workflows

/coveredcall → Weekly Covered Call Planning

Plan a weekly covered call to generate income while controlling assignment risk.

URL: https://quantcodedenny.com/posts/leadership-presence/

Introduction

As an IC6 ML Infra Tech Lead, technical expertise alone is not enough. I need to be seen as a leader — someone who drives impact, earns trust, and inspires confidence across teams.

Leadership presence is about how I show up, influence decisions, and make work visible, especially under pressure. This post gives me a daily-ready framework to strengthen my leadership presence, including verbal, non-verbal, and mindset practices.

URL: https://quantcodedenny.com/posts/scope-better/

For a successful tech lead, there are four steps: scope better -> do better -> look better -> connect better.

Scoping work effectively is one of the hardest skills I’ve learned as a tech lead. I frame scoping around four core challenges:

1. Identifying high-leverage problems.
2. Setting boundaries and protecting focus.
3. Aligning stakeholders and expectations.
4. Align early, iterate fast.

Using LLMs To Scope Better

I use LLMs to address each core challenge efficiently:

URL: https://quantcodedenny.com/posts/improve-market-value/

Working deeply in ML Infra (like Meta or Google) can make your skills feel too specialized. You solve critical problems — but the broader market may not see your full capability. The key is to grow visibility and generalize impact organically, through habits you already do daily.

Core Principles

• Think End-to-End → Connect your work to business goals, reliability, and system health.
• Focus on Non-Urgent Impact → Block time weekly to fix recurring pain or document key learnings.
• Translate to Market Value → Frame results in measurable terms (revenue, cost, scale, reliability).
• Learn and Share → Write short notes or posts to spread knowledge and show thought leadership.

Regular Routine

Weekly Habits

• Learn broadly → Attend 1 SEV or design review; observe recurring problems and system tradeoffs.
• Connect cross-functionally → Chat with 1 XFN lead to understand context and dependencies.
• Reflect & share → Write 1–3 bullet summary of key learning; note patterns, scripts, or principles.

Monthly Habits

• Host & lead → Run 1 design or postmortem discussion; practice influence, articulation, and framing decisions.
• Mentor → Coach 1–2 engineers; amplify impact and demonstrate multiplier behavior.

Quarterly Habits

• Ship a measurable improvement → Deliver 1 end-to-end enhancement; design for scale, reliability, and visibility.
• Summarize impact → Convert results into metrics or story for internal/external visibility.

Focus Blocks & Best Practices

• Protect 1–2 hours weekly for important but non-urgent work: blindspot review, systemic improvements, or skill expansion.
• Frame all incidents/projects with business impact: root cause, mitigation, downstream effect, prevented loss or restored capacity.
• Ask system-level questions before implementation: downstream effects, hidden dependencies, assumptions, failure modes.
• Document & share patterns weekly: recurring issues, scripts, or solutions that others can reuse.
• Use external framing: translate internal improvements into transferable, marketable impact.

Watch Out For

• Local optimizations without system or business context.
• Not documenting key decisions.
• Avoiding cross-team or ambiguous problems.
• Over-relying on internal tech; always extract general principles.

URL: https://quantcodedenny.com/posts/prepare-system-interview/

System design interviews test not only technical skills but also strategic thinking, end-to-end ownership, scalability awareness, and cross-team influence.

As a high-end IC6 (between IC6 and IC7), my goal is to bridge execution with strategic design thinking. This blog post consolidates my learnings from system design prep, especially for ML Infra contexts, and is designed to evolve as I add new insights.

System Design Leveling – 35 mins

IC5

• Independently chooses components to tell a coherent story.
• Covers end-to-end design with no significant gaps.
• Discusses tradeoffs and user impact, with prompting.

IC6

• Creates effective designs addressing multiple critical aspects.
• Anticipates problems, including maintainability and organizational challenges.
• Speaks thoroughly on tradeoffs, bottlenecks, and user impact with minimal prompting.

IC7

• Builds sophisticated designs addressing all stated and implicit parts of a problem.
• Proactively considers alternative solutions, immediate vs. long-term issues, and business needs.
• Covers all tradeoffs: technical, user impact, team dynamics, reliability, and sustainability.

IC6 System Design Interview Framework

1. Problem Navigation & Clarification

• “Just to confirm, we need a system for X with Y latency and Z throughput, correct?”
• “What is the expected scale: daily active users, requests per second, or data volume?”
• “Do we need strong consistency, or is eventual consistency sufficient?”
• “Are there privacy or compliance constraints we need to consider?”
• “Let’s break the problem into three main components: ingestion, processing, serving.”
• “I want to highlight dependencies and areas that might require cross-team coordination.”

2. High-Level Solution Design

• “At a high level, the system would look like this: [describe layers or components].”
• “Data flows from ingestion → processing → storage → serving.”
• “Each component is decoupled so that changes in one layer don’t impact others.”
• “Let’s deep dive into the processing layer; we could use batch or stream depending on latency requirements.”
• “Caching frequently requested data improves latency but introduces invalidation challenges.”
• “We should plan for spikes using queues, retries, and circuit breakers.”
• “We can segment functionality so individual components can be updated without impacting the system.”

3. Trade-offs & Technical Excellence

• “We could optimize for latency, but it would increase operational cost.”
• “Sharding improves throughput but complicates cross-partition queries.”
• “Eventual consistency reduces latency but requires careful handling of edge cases.”
• “Using framework X provides distributed fault tolerance; framework Y has lower latency but higher complexity.”
• “Multi-region replication improves availability but introduces latency trade-offs.”
• “Subtle risks include version-to-version schema changes; we can handle them via backward-compatible migrations.”

4. Communication & Influence

• “To summarize, here’s why I made each decision and the trade-offs involved.”
• “From a user perspective, this design ensures low latency for the majority of requests.”
• “I want to check if my assumptions about scale and growth align with your expectations.”
• “I hear your concern; here’s how I’d adjust the design.”
• “Let’s revisit the key bottlenecks and ensure the architecture addresses them.”
• “For future extensions, modular components can accommodate new requirements without major changes.”

Common trade-off

Trade-off	What it Means	Key Considerations / Questions to Ask Yourself
Latency vs Throughput	Optimizing for faster responses may reduce total system throughput, and vice versa	How fast must requests complete? Can we batch or async some work? Can we precompute results?
Consistency vs Availability	Strong consistency may slow responses or reduce availability; eventual consistency improves availability but allows stale data	Does the user expect immediate read-your-writes consistency? Which parts of the system can tolerate eventual consistency?
Freshness vs Compute / Cost	Frequent updates improve freshness but increase CPU, I/O, or memory usage	How often do users need updated data? Can some updates be async or cached?
Complexity vs Extensibility	Simple designs are easier to implement, but flexible/modular designs are easier to evolve	Will the system need new features in the future? How can we make it modular without overengineering?
Storage vs Query Performance	Precomputing or denormalizing improves read performance but increases storage cost	Which data should be materialized? Can we compute some things on demand?
Generalization vs Edge-Case Optimization	Optimizing for the common case may hurt edge cases; handling every edge case can increase complexity	What’s the typical user scenario? Are there extreme cases that need special handling?
Observability vs Performance	Metrics, logs, and dashboards aid monitoring but can add latency or storage overhead	What key metrics/SLOs are critical? Can monitoring be async?
Scalability vs Simplicity	Designs that scale to millions/billions often require sharding, async pipelines, and caches, which increase system complexity	What is the expected growth? Can we start simple and evolve, or must it scale from day one?
Security / Privacy vs Usability	Strong security or privacy measures may slow performance or complicate user experience	What are compliance or privacy requirements? How does this affect API design or latency?
Consistency / Correctness vs Cost / Speed	Guaranteeing exact correctness may increase cost or reduce speed	Can approximate results suffice? Which operations require strong guarantees?

Common techniques

Technique	What it does	When to mention
Push + Pull hybrid	Combines fan-out and fan-in based on follower count	If asked about celebrities or skewed followers
Precomputed feed cache	Stores top N posts for a user	Helps meet strict read latency (P95 < 300 ms)
Sharded queues	Each user’s feed is partitioned	To scale for millions of users
Asynchronous write pipelines (Kafka, stream processing)	Fan-out writes done asynchronously	Improves throughput and reduces write blocking
Local re-ranking	Lightweight ranking at Serving layer	Adjust freshness, unseen content, or last-second boosts

Known patterns

Pattern	Description / Purpose	Pros	Cons / Trade-offs	Typical Use Cases
Client-Server	Clients send requests, server responds	Simple, clear separation	Can be bottlenecked at server	Web apps, APIs, mobile backends
Load Balancing / Horizontal Scaling	Distribute requests across multiple servers	High availability, fault tolerance	Complexity in routing, sticky sessions	High-traffic APIs, web services
Caching	Store frequently accessed data	Reduces latency, lowers DB load	Cache invalidation complexity, stale data	DB queries, API responses, CDN content
Sharding / Partitioning	Split data across nodes	Scales reads/writes	Harder joins, uneven load	Large user datasets, multi-tenant DBs
Replication	Maintain multiple copies of data	High availability, disaster recovery	Consistency trade-offs	Multi-region DBs, fault-tolerant systems
Event-Driven / Messaging	Asynchronous communication via messages/events	Decoupled, scalable	Message ordering, duplication issues	Logging, feature pipelines, order processing
Microservices / SOA	Decompose monolith into independent services	Independent deployability, scalable per service	Service communication, data consistency	Large apps, ML pipelines, modular backend
Queueing & Backpressure	Smooth spikes, decouple producer/consumer	Handles high load reliably	Requires monitoring, retry & dead-letter handling	Task queues, ingestion pipelines
Rate Limiting / Throttling	Control request rates	Protects backend resources	Can block valid requests if too aggressive	APIs, microservices
Proxy / Gateway	Intermediary for routing, caching, auth	Centralizes cross-cutting concerns	Single point of failure if not highly available	API gateway, reverse proxy, authentication
Leader Election / Consensus	Distributed coordination, single source of truth	Ensures consistency, coordination	Complexity in distributed systems	Distributed locks, master selection, config
Observability	Logging, metrics, tracing	Easier debugging and monitoring	Adds overhead, requires discipline	ML infra, microservices, pipelines
Circuit Breaker / Retry	Protect services from cascading failures	Increases system resiliency	Misconfigured thresholds can block traffic unnecessarily	Microservices, external APIs
Batch vs. Stream Processing	Process data in chunks vs. continuously	Batch: efficient, stream: low-latency	Batch: latency, Stream: complex error handling	ETL jobs, ML feature store updates, analytics

Core Principles for System Design

1. Problem Structuring

• Clarify requirements and constraints (functional & non-functional, SLAs, ownership boundaries, future growth).
• Identify critical paths, unknowns, and dependencies.
• Use a structured approach to map problem → components → interactions.

2. Trade-Off Awareness

• Recognize and quantify trade-offs: latency vs. throughput, consistency vs. availability, cost vs. reliability.
• Include business, operational, and cross-team implications.
• IC7-level thinking anticipates tangential trade-offs proactively.

3. Known Patterns & Abstractions

• Apply reusable patterns: caching, sharding, load balancing, replication, event-driven architectures.
• Avoid reinventing solutions; justify deviations clearly.

4. Scalability & Reliability

• Horizontal vs. vertical scaling.
• Fault tolerance, retries, backpressure handling, recovery strategies.
• Monitoring, alerts, and observability planning.
• Consider future-proofing and maintainability.

5. Communication & Influence

• Present a structured narrative: context → problem → options → trade-offs → recommendation.
• Highlight strategic impact, not just technical correctness.
• Prepare concise “elevator pitches” for directors, PMs, and cross-functional teams.

IC6 System Design Practice Checklist

Requirements & Clarification

• Functional vs. non-functional requirements.
• Latency, throughput, SLAs/SLOs.
• Ownership boundaries & team responsibilities.
• Expected growth & future-proofing needs.

High-Level Design

• Identify major components & interactions.
• Map data flow (ingestion → processing → serving).
• Define APIs, interfaces, and abstractions.
• Highlight cross-team dependencies.

Scaling & Reliability

• Horizontal vs. vertical scaling.
• Caching, sharding, partitioning.
• Fault tolerance & retries.
• Observability: monitoring, alerting, metrics.

Tradeoffs & Options

• Pros/cons of architectural choices.
• Cost vs. performance vs. complexity.
• Business impact of each option.

Edge Cases / Failure Modes

• Identify points of failure and mitigation strategies.
• Discuss backpressure, stale data, network issues.
• Recovery, retries, fallback logic.

Influence & Communication

• Summarize decisions for non-technical stakeholders.
• Highlight trade-offs explicitly.
• Show strategic impact and maintain leadership presence.

Leadership Presence

• Lead calmly and confidently.
• Encourage team input while framing final decisions.
• Maintain focus on high-leverage improvements.

URL: https://quantcodedenny.com/posts/connect-better/

Introduction

For a successful tech lead, it usually has four steps: scope better -> do better -> look better -> connect better.

Doing great work is only part of success. My influence grows when others know, trust, and rely on me.

Connecting better at work is most powerful when it becomes a small daily habit — one or two simple actions that naturally build trust, relationships, and impact.

Core Daily Habit

Each day, I focus on one key action:

URL: https://quantcodedenny.com/posts/look-better/

Introduction

For a successful tech lead, it usually has four steps: scope better -> do better -> look better -> connect better.

Working hard alone isn’t enough. Leaders and decision-makers are busy — if they don’t see my impact, it might as well not exist.

Looking better at work is not self-promotion. It’s about making my contributions visible, credible, and trusted. I focus on three things:

• Business Impact – What tangible results did I achieve?
• Complexity & Learning – What challenges did I overcome, and what did I learn?
• Credibility – Did the right people notice my work, and did I involve or acknowledge others?

This post gives me a daily-ready framework to apply these principles with minimal thinking.

URL: https://quantcodedenny.com/posts/eq-playbook/

As a tech lead, I want to ease mental stress and handle conflicts effectively. This is my daily runbook for calm, clear, and high-leverage leadership.

Core Principles (Memorable)

• Pause Before Reacting: Avoid knee-jerk responses.
• Listen First: Understand before responding.
• Focus on Outcomes: Prioritize shared goals, not ego.
• Protect Boundaries: Say no or redirect low-leverage work.

Daily Action Routine

• Morning (Start of Day): Review top 3 priorities; decide what to say “no” to.
• Pre-Meeting: Pause, breathe, note intended outcome.
• During Conflict: Listen fully; restate understanding before replying.
• Work Blocks (≈90 mins): Take 5 min stretch/walk break.
• Midday: Hydrate, check posture, reset focus.
• End of Day: Log one win and one boundary I defended.

Difficult Situations & Quick Actions

• Disagreement on Priorities: Frame trade-offs; explain business impact.
• Boundary-Pushing Requests: Acknowledge importance; suggest better owner; offer light support.
• Cross-Team / Peer Conflict: Listen, validate points, defuse tension, propose joint follow-up.
• Escalations from Leadership: Communicate facts clearly; outline immediate + long-term fix.

Show Calm Through Signals

• Speak calmly; tone sets team baseline.
• Pause before responding; silence = confidence.
• Start with the point; avoid long background.
• Use ownership language: “I’ll handle this.”
• Ask curious, open questions: “Can you walk me through your thinking?”
• Reframe issues positively: “We can fix this early.”

Energy & Focus Killers (Clustered)

• Health Basics: Poor sleep, nutrition, hydration, or movement → fatigue & brain fog.
• Scattered Focus: Multitasking & context switching → low efficiency.
• Stress Ignored: Bottling emotions → chronic depletion.
• Weak Boundaries: Overcommitting → diluted impact, burnout.
• Environment: Clutter, noise, bad lighting → lower alertness.

Best Practices (Actionable)

• Sleep & Move: 7–9 hrs sleep; walk/stretch/exercise.
• Eat & Hydrate: Balanced meals; avoid sugar spikes; drink water.
• Prioritize & Batch: MITs / Eisenhower; avoid overcommitment.
• Take Microbreaks: 5–10 mins every 60–90 mins; reflect or journal.
• Optimize Environment: Light, tidy, comfortable.
• Communicate Effectively: Neutral, “we” language, summarize agreements, use Pause → Clarify → Respond → Follow-Up.

Common Pitfalls

• Reacting instantly under stress.
• Treating conflict as win/lose.
• Assuming shared context.
• Taking low-leverage work just to be helpful.

URL: https://quantcodedenny.com/posts/suggest-without-context/

A living runbook for learning from and giving suggestions to other teams Why This Matters: Cross-team collaboration is a critical part of tech lead work, yet you’re often asked to give feedback on efforts you don’t fully own or understand.

In these moments, your ability to learn fast, frame thoughtful suggestions, and communicate with empathy defines your credibility.

Good suggestions create momentum and build bridges. Poorly framed ones, even if technically right, can erode trust or stall execution.