Most recent layoffs had boring causes first.
AI rewards strong fundamentals.
code see intent
- Daily diff reviews: Read every line of changes.
- Trace library code: Follow exact execution paths.
- Focus on the "why": Understand developer intent.
systems find truth
- DSA foundations: Reduce complexity systematically.
- Model system state: Map transitions to spot bugs.
- Trace variables: Isolate true root causes step-by-step.
output own quality
- Critique AI draft: Treat it as junior work.
- Run local tests: Verify before accepting.
- Own the code: Never ship what you can't explain.
Build small. Ask for help. Read the diff.
"Ship only what you can explain."
During your learning phase, treat agents as drafts. Review, verify, and understand every line before merging.
A chatbot answers. An agent acts.
Chatbot
Agent
Model + Tools + Memory + Loop = Agent
The brain that plans & decides steps
Interacts with files, terminal, & APIs
Remembers rules & project context
Self-corrects & repeats until goal met
A bad AGENTS.md turns memory into noise.
This file is loaded for every session and every conversation. Keep it short, current, and task-relevant.
Too many mixed rules.
A full checklist loaded every time.
Short enough to scan in seconds.
Personal defaults. Project rules.
Context is expensive real estate.
Load only the context the task needs.
Bloated instructions hurt performance.
The famous "CLAUDE.md" template actually reduced test pass rates by 2.2% while increasing token costs by 5%.
"The 'simplicity first, surgical only' instruction biases the agent to re-invent complex dependencies in naive ways and miss the mark."
Skills stay in drawers until needed.
A modular, targeted bundle of instructions and tools built for a specific, non-routine task.
Instead of bloating global memory (AGENTS.md), skills are only loaded when triggered by the task.
Keeps the agent focused on what is relevant, saving context tokens and preventing conflicting instructions.
Be harness agnostic.
Own the workflow. Swap the wrapper.
Understand the model persona.
Work with different models long enough to understand their unique reasoning intuition, biases, and coding temperament.
Different models excel at different tasks. Leverage fast, efficient models like Gemini 3.5 Flash for general-purpose tasks, active research, and rich UI-building operations.
Explore alternatives like GLM 5.2 (strong frontier logic), DeepSeek V4 Pro, Kimi K2.6, or Mimo 2.5.
Disclaimer: Model names, placements, and categorizations are based on publicly available data on the internet as of July 2026 and may change as the landscape evolves.