Danny

@mad-scientist

14 Following

92 Followers

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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).

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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?

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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.

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🚀 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.

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Did anyone build an auto-cross-chain import/export set of smart contracts? i.e. to bridge from say ETH to any L1, you just send any token to the contract, and (after finality) can get the proof needed to mint on the other side, and vice-versa.

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Who's fine-tuning this model to become an Ethereum core-dev? Imagine this beast working 24/7 on specing and implementing the latest experimental EIPs, well ahead of schedule, in all clients. Getting up in the morning to find out that the LLM has spun 2 new dev-nets, and another EIP works without issues.

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Is anyone trying to fine-tune a strong model to be a 10X Ethereum core developer? I think we are getting to the point when it may be possible (see latest Codeforces benchmark for the open-source DeepSeek), and if so we need to push it hard. Imagine having a @karalabe.eth level developer coding 24/7, now imagine having 100s of them. Cutting time from idea&research to execution.

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What's the advantage over Volitions?

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IMO the big advantage (and it's big) of native rollups over execution sharding isn't in the (current) implementation details, but in who is executing. Native rollups can be perused by anyone, allowing for quick parallel iterations by multiple teams, looking for good tech but also PMF. This much surface bigger area for innovation is more likely to succeed.

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The core validium trust assumption is not the operator staying around, but the data being available. If for example the state update requires a signature from an external DA committee, then the operator of the validium can't freeze user funds. They will get the data from the committee and submit to exit.

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that's such a 90s kids thing to say

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Is it just me, or do TradFi trading system aren't even close to modern DEXs/CEXs in UX?

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now imagine the future when liquidity will be shared, and there will be no need to bridge to base.

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It's almost 2025, and Amazon is still not accepting crypto/stables. What's wrong with them?

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Long 2Xbitcoin for every 1Xshort MSTR, with a ~3X leverage on capital, seems like the obvious thing ATM. Any good platforms where I can do this in isolation with shared collateral between the positions? May be able to compromise for 0.5XSMST, but the daily thing adds more risk.

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Wow. It hurts me that hobbiests can't reasonably run the biggest open models at home, but to the next best thing - @venice-ai, when can I try this?

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sounds like an arbitrage opportunity. how consistent is it?

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Geth has a "freezer" function, where older data requiring less access is stored on HDD, allowing to effectively run a full node with much less SSD storage. Have you tried this for the archive nodes?

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1. Top researchers and maybe EF outlining, in a single consensus document, standards for best-practices for bridges. Pushing for stage-2 equivalent for bridges on L2beat (currently it doesn't look good there). Maybe even publicly endorsing bridges that did a good job. 2. If/when large institutions ask, let the EF advise them on best practices (in a neutral way, equal access to all).

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