All the cool kids are doing it
18 days ago
- #productivity
- #LLMs
- #coding
- Author hasn't invested much time in LLM tools due to mixed impressions and priorities.
- Previous client used LLMs heavily but didn't see significant productivity or code quality improvements.
- Author's value came from reading, digesting code, identifying unnecessary work, and refactoring—areas where LLMs currently fall short.
- No immediate worry about job security; LLMs may increase short-term work by generating code that needs performance tuning.
- Best practices for LLMs change rapidly, making current skills potentially obsolete soon.
- Author prefers deep, narrow problems and has tacit knowledge, making it hard to delegate to LLMs or juniors.
- LLMs are likened to managing eager junior developers, which the author finds frustrating.
- Research bottleneck is motivation, not code production; LLMs might add frustration without clear benefits.
- Cost is a concern—LLMs become more expensive with more use, unlike other tools that amortize.
- LLMs change frequently and are unstable, making them a risky long-term investment.
- Tried using LLMs for search and research but found fact-checking their output more work than human output.
- LLM code review services sometimes catch mistakes but often bluff or are wrong about dataflow.
- Potential in using LLMs for fuzzing by generating plausible-but-wrong code.
- Machine transcription has improved but still requires corrections.
- Unconvinced about LLMs for coding; waiting for tools to improve or become commoditized.
- Ironically, finds LLMs useful for explaining assembler syntax, which is hard to Google.