A post on Hacker News last week described a technique for reverse engineering DeepSeek by prompting the model to interview itself about its own architecture and training.
The approach uses the model as both subject and interrogator. One instance generates questions about weights, tokenization, and safety layers while another responds, producing structured output without external tools.
Model: DeepSeek | Method: Self-interview | Engagement: 16 points, 2 comments on HN
Source: manish.sh article
How the Self-Interview Process Works
The technique starts with a system prompt that instructs one DeepSeek instance to act as an interviewer focused on technical internals. The second instance answers under constraints that force concrete details rather than generic refusals.
Questions target training data mixtures, context window handling, and alignment mechanisms. Responses are logged and cross-checked across multiple runs to identify consistent claims.
What the Hacker News Thread Shows
The discussion received 16 points and 2 comments. Participants noted the method's low cost compared with API scraping or weight inspection.
One comment questioned output reliability, while the other suggested combining self-interviews with activation patching for verification.
Comparison with Traditional Reverse Engineering
Standard approaches require weight access or heavy API querying. Self-interviewing needs only chat access and runs locally or via cheap endpoints.
| Method | Access Needed | Cost Level | Output Structure | Verification Ease |
|---|---|---|---|---|
| Weight inspection | Full weights | High | Precise | High |
| API scraping | Rate limits | Medium | Noisy | Low |
| Self-interview | Chat only | Low | Structured | Medium |
Pros and Cons of the Approach
- Requires no special infrastructure beyond standard inference
- Produces readable transcripts that can be parsed automatically
- Risks hallucinated internals that must be validated elsewhere
- Limited to what the model is willing or able to disclose
Who Should Try Self-Interviewing
Researchers studying closed models with chat access will find it useful for initial mapping. Teams with weight access should skip it in favor of direct inspection.
Developers building evaluation harnesses can use the transcripts as seed data for more rigorous tests.
Practical Next Steps
Clone the prompting pattern from the linked post and run paired instances on a local DeepSeek deployment. Log outputs in JSON for later analysis.
Cross-reference claims against public benchmarks or smaller open models where internals are known.
Bottom line: Self-interviewing offers a low-resource entry point for mapping model behavior when weights remain unavailable.
The method is likely to spread to other chat-only models as teams seek cheaper ways to document internals before investing in heavier analysis.
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