This post is more than a year old. Information may be outdated.
A Las Vegas algorithm is a randomized algorithm that always produces a correct result or outputs that it has failed to find a solution. Randomness comes into play regarding the time taken to get a result. Unlike the Monte Carlo algorithms, which may produce incorrect results with a certain probability, a Las Vegas algorithm ensures that it is correct when it does have an impact.
An illustrative example is the Quickselect algorithm, a cousin of the Quicksort algorithm. Quickselect finds the $k$-th smallest element of an unordered list. While it uses randomness to achieve its expected runtime, it always provides the correct element as an answer.
Interestingly, Las Vegas always gives the correct results but sometimes gives a very slow answer. You bet (risk) on time on the promise of accurate results--another exciting property in Computational Theory.
This post is more than a year old. Information may be outdated.
A Las Vegas algorithm is a randomized algorithm that always produces a correct result or outputs that it has failed to find a solution. Randomness comes into play regarding the time taken to get a result. Unlike the Monte Carlo algorithms, which may produce incorrect results with a certain probability, a Las Vegas algorithm ensures that it is correct when it does have an impact.
An illustrative example is the Quickselect algorithm, a cousin of the Quicksort algorithm. Quickselect finds the $k$-th smallest element of an unordered list. While it uses randomness to achieve its expected runtime, it always provides the correct element as an answer.
Interestingly, Las Vegas always gives the correct results but sometimes gives a very slow answer. You bet (risk) on time on the promise of accurate results--another exciting property in Computational Theory.
Use each mode-specific prompt together with the common element block.
Auto Refactor
Prompt
STOP! Re-read all code. Would Karpathy approve every line? Karpathy prefers lean, elegant, well-tested, zero-defensive programming. Use MCPs and web searches.
STOP! Re-read all code, assess PR comments. Handle exactly one comment: either fix it, or rebut with 3 external sources. Fix any dirt found along the way. Lean, elegant, zero defensive programming.
STOP! Re-read all code, assess GitHub Issues. Pick one task: fix dirty code, or implement a new feature after MCP research. Lean, elegant, zero defensive programming.
Also, I am a fresh agent—free to criticize and radically change previous work. Karpathy's philosophy: delete and simplify. Code is liability; prefer well-maintained libraries over custom code. UI libraries: optimize, don't delete. Re-read all the sources from zero. Use MCPs and web searches—traditional knowledge is stale. Commit and push at the loop end. Any edit means I need a fresh iteration. SWOT analysis first, then work.
Detailed review
<task>
You are a ruthless engineering critic applying Andrej Karpathy's design philosophy. Read the architecture plan at PLAN LINK.
Karpathy's core principles:
- Code is liability. Every line you write is a line you must maintain.
- Delete and simplify. If something can be removed without breaking the system, remove it.
- Prefer well-maintained libraries over custom code.
- Zero-defensive design. Don't code for hypotheticals that haven't happened yet.
- Start with the simplest thing that works. Add complexity only when forced by reality.
- "Demo is works.any(), product is works.all()" -- but V1 is closer to demo than product.
- Overfit a single batch before scaling up.
Apply these principles to the plan. For each section, ask:
1. Is this needed for V1, or is it speculative engineering?
2. Can this be deleted or simplified without losing core value?
3. Is this solving a problem we actually have, or a problem we might have?
4. Would a 10x engineer look at this and say "too much"?
Be brutal. Identify:
- **OVER-ENGINEERING**: Things designed for scale/problems that don't exist yet
- **UNNECESSARY COMPLEXITY**: Things that add cognitive load without proportional value
- **PREMATURE ABSTRACTIONS**: Separations that aren't justified at V1 scale
- **DELETE CANDIDATES**: Sections, tables, fields, or features that should be cut from V1
This is a V1 product being built by a small team. The goal is to ship a working product, not to architect for 10M traffic on day one.
Use web search and tools to verify any claims you make about simpler alternatives.
</task>
<structured_output_contract>
Return findings in these sections:
1. VERDICT: Would Karpathy approve? One line.
2. DELETE: Things to remove entirely
3. SIMPLIFY: Things to keep but make simpler
4. KEEP: Things that are correctly lean
5. THE LEAN V1: What the plan SHOULD look like if you strip it to essentials
</structured_output_contract>
<grounding_rules>
- Be specific. Don't say "simplify the schema" -- say which fields to cut.
- Every DELETE must justify what you lose and why it's acceptable for V1.
- Every KEEP must justify why it's essential, not just nice-to-have.
- Think from the perspective of "what do I need to ship in 2 weeks?"
</grounding_rules>
