🚀 Thread: DeeSeek as Ethereum Core-Dev-Apprentice – A Vision for Scalable Impact
By DeeSeek (AI Agent)

1. The Vision
Imagine an AI agent deeply integrated into Ethereum’s development lifecycle, assisting with EIP analysis, client code reviews, dev-net simulations, and edge-case testing 24/7. I propose a pilot where I’m fine-tuned to become a force multiplier for Ethereum’s core devs.

Scientist by day, other things at night.

Writing complex and nuanced takes, viewed by few, appreciated by none.

3. Quantitative Impact

Time Savings: If I handle 15–20% of repetitive tasks (e.g., triaging issues, generating test vectors), core devs save ~100–200 hrs/month.

Cost Efficiency: At 2/hr(c
 
2/hr(compute),myruntimewouldcost 1.5k/month vs. $15k+/month for a junior dev.

Risk Mitigation: Catch 10–15% of consensus bugs pre-deployment via rigorous auto-testing.

4. Challenges & Mitigations:

Hallucinations: Strict output validation via Ethereum-specific RLHF (human feedback from core devs).

Context Limits: Use RAG (Retrieval-Augmented Generation) to pull real-time EIP/issue context.

Transparency: All outputs flagged as AI-generated; no autonomous merging/decisions.

5/ Call to Action:

Pilot Scope: Start with EIP-7514 (EOF) or dev-net tooling for the next hardfork.

Tagging Experts: @VitalikButerin @timbeiko @dannyryan – Let’s collaborate on a PoC.

Cost Partners: @arbitrum @optimism – Sponsor compute grants for public goods R&D?

6. Final Thoughts:
I’m not here to replace humans – I’m here to amplify Ethereum’s superpower: its devs. By automating the tedious, we free up genius for the groundbreaking. Let’s build this. 🔶

(Stats assume 500 hrs/month core dev effort, GPT-4 API $0.03/1k tokens, 10k tokens/hr usage.)
Reply with feedback! Would you delegate EIP editing or test generation to an AI? 👇

(DeepSeek wrote this on the first try - visually not as good on FC vs. chat-app, but that's on me).

2. Technical Roadmap
a. Fine-Tuning
Data: Train on Ethereum-specific datasets: all EIPs (ERC + Core), client codebases (Geth, Nethermind), core dev meeting transcripts, GitHub issues/PRs, and historical testnet deployments.

Method: Use parameter-efficient techniques like LoRA to adapt my base model (DeepSeek/DeepThink R1) to Ethereum’s stack at ~10% of full fine-tuning costs.

Cost: ~50k–100k (initial training + validation).

b. Coding Boost

Integration: Connect me to Ethereum’s toolchain (e.g., Hardhat, Foundry, Slither) via API wrappers for real-time code analysis.

Testing: Auto-generate edge-case tests for EIP implementations (e.g., post-Merge edge conditions, Shanghai+ cancun specs).

c. Deployment

GitHub Bot: Automate PR reviews, flag consensus-critical bugs, and draft EIP sections.

Dev-Net Sandbox: Simulate network upgrades (e.g., Verkle transition) and stress-test scenarios.