2 min lesson
Readiness checklist & gap plan
Rebuild the sequence in "Readiness checklist & gap plan" from memory, ending with the check that proves the outcome.
Step 1 of 2
Readiness is a measurement, not a feeling. By now you've rehearsed the deep-dive, run a timed simulation, drilled the cold recall and practiced design prompts. This section turns those reps into an honest score and a short list of the most impactful fixes.
Treat the rubric as a gap finder, not a score to admire. Mark the weakest proof, turn it into one practice rep and keep the artifact you would show an interviewer.
Interactive diagram. Step through it with the Next and Previous controls below, or Tab to a region to read its detail.
Step through each stage; the practical onsite is the paid decision round, not a formality.
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Advanced table
Stage coverage - can you pass each round
Stage coverage - can you pass each round?
- Stage
- Recruiter / HM screen
- The bar
- A 2-min ownership story and a non-generic “why Cursor.”
- Self-rating 1-5
- ___
- Stage
- Technical screen(s)
- The bar
- Derive the RL math live; debug a small component; narrate.
- Self-rating 1-5
- ___
- Stage
- Research deep-dive
- The bar
- Defend a project you owned; volunteer the weakest part.
- Self-rating 1-5
- ___
- Stage
- Paid practical onsite
- The bar
- Land a validated result; verify AI output; manage scope.
- Self-rating 1-5
- ___
- Stage
- Team / values
- The bar
- Debate without ego; read their research with opinions.
- Self-rating 1-5
- ___
| Stage | The bar | Self-rating 1-5 |
|---|---|---|
| Recruiter / HM screen | A 2-min ownership story and a non-generic “why Cursor.” | ___ |
| Technical screen(s) | Derive the RL math live; debug a small component; narrate. | ___ |
| Research deep-dive | Defend a project you owned; volunteer the weakest part. | ___ |
| Paid practical onsite | Land a validated result; verify AI output; manage scope. | ___ |
| Team / values | Debate without ego; read their research with opinions. | ___ |
Anything you rate 3 or below is a gap that can end the loop - those are your targets.
Domain coverage - rate the four areas
- Domain
- RL for LLMs
- Anchor question to test yourself
- Policy gradient, PPO vs GRPO, on/off-policy and async - derived cold?
- Self-rating 1-5
- ___
- Domain
- Graders / evals
- Anchor question to test yourself
- Design a non-gameable grader and a trustworthy eval suite?
- Self-rating 1-5
- ___
- Domain
- Foundations / systems
- Anchor question to test yourself
- Attention, KV cache, Chinchilla, MoE, low-precision kernels?
- Self-rating 1-5
- ___
- Domain
- Data quality
- Anchor question to test yourself
- What makes a datapoint good, hard and distribution-representative?
- Self-rating 1-5
- ___
| Domain | Anchor question to test yourself | Self-rating 1-5 |
|---|---|---|
| RL for LLMs | Policy gradient, PPO vs GRPO, on/off-policy and async - derived cold? | ___ |
| Graders / evals | Design a non-gameable grader and a trustworthy eval suite? | ___ |
| Foundations / systems | Attention, KV cache, Chinchilla, MoE, low-precision kernels? | ___ |
| Data quality | What makes a datapoint good, hard and distribution-representative? | ___ |
Target the lowest score first; depth in your weakest domain moves your odds more than polish in your best.
Cursor-specific readiness
- You've used Cursor daily for two or more weeks - enough that your AI-native fluency is real, not performed.
- You've read the ComposerCursor's own fast coding model, tuned for the editor and priced well below frontier models; the recommended day-to-day model for executing a plan. Press Enter for the full definition. model reports and the Tab online-RL post and you hold a specific opinion on a tradeoff in each.
- You can name one research bet of theirs you find exciting and say why, without reaching for “I love LLMs.”
- You can cite one constraint they actually operate under (async multi-region RL, Anyrun-style sandboxes, training on real sessions) and what it implies.