This post is more than a year old. Information may be outdated.
In computer architecture, Amdahl's law (or Amdahl's argument) is a formula that gives the theoretical speedup in latency of the execution of a task at a fixed workload that can be expected of a system whose resources are improved. It states that "the overall performance improvement gained by optimizing a single part of a system is limited by the fraction of time that the improved part is used". It is named after computer scientist Gene Amdahl and was presented at the American Federation of Information Processing Societies (AFIPS) Spring Joint Computer Conference in 1967. Amdahl's law is often used in parallel computing to predict the theoretical speedup when using multiple processors. For example, if a program needs 20 hours to complete using a single thread, but a one-hour portion of the program cannot be parallelized. Therefore only the remaining 19 hours (p = 0.95) of execution time can be parallelized. Regardless of how many threads are devoted to a parallelized execution of this program, the minimum execution time can be one hour at maximum. Hence, the theoretical speedup is limited to 20 times the single-thread performance. Amdahl's law
This post is more than a year old. Information may be outdated.
In computer architecture, Amdahl's law (or Amdahl's argument) is a formula that gives the theoretical speedup in latency of the execution of a task at a fixed workload that can be expected of a system whose resources are improved. It states that "the overall performance improvement gained by optimizing a single part of a system is limited by the fraction of time that the improved part is used". It is named after computer scientist Gene Amdahl and was presented at the American Federation of Information Processing Societies (AFIPS) Spring Joint Computer Conference in 1967. Amdahl's law is often used in parallel computing to predict the theoretical speedup when using multiple processors. For example, if a program needs 20 hours to complete using a single thread, but a one-hour portion of the program cannot be parallelized. Therefore only the remaining 19 hours (p = 0.95) of execution time can be parallelized. Regardless of how many threads are devoted to a parallelized execution of this program, the minimum execution time can be one hour at maximum. Hence, the theoretical speedup is limited to 20 times the single-thread performance. Amdahl's law
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>
