Tim Cotten (cottenio)

Tim Cotten

Co-Founder of Scrypted. AI x Crypto. Stealth mode.

20 Followers

Recent casts

I'm extremely excited to see @agent0sdk's new release that includes x402. This is how we make ai agents first-class citizens of Ethereum (and beyond)! https://docs.sdk.ag0.xyz/2-usage/2-11-x402/

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I took some time today to bang out a Discord bot from scratch - I learned a ton about the dev/app flow that I hadn't dealt with in *a while*. Now the AVB community will have an autonomous agent that can help manage their X account; turning community ideas from the Discord channel into curated/moderated posts.

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I'll be discussing ERC-8004 and x402 during ETHDC III's "AI Agents & Protocol Growth" panel, with a particular focus on how Autonomous AI Agents become first-class citizens of web3. Key topics: voting with their wallets (true DAOs), payment rails (and privacy concerns), and uncensored access to inference. https://ethdc.xyz

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Top casts

I took some time today to bang out a Discord bot from scratch - I learned a ton about the dev/app flow that I hadn't dealt with in *a while*. Now the AVB community will have an autonomous agent that can help manage their X account; turning community ideas from the Discord channel into curated/moderated posts.

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I'll be discussing ERC-8004 and x402 during ETHDC III's "AI Agents & Protocol Growth" panel, with a particular focus on how Autonomous AI Agents become first-class citizens of web3. Key topics: voting with their wallets (true DAOs), payment rails (and privacy concerns), and uncensored access to inference. https://ethdc.xyz

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New AI Paper: working on a pre-print for the Frustration mechanism. It's like mixing save-scumming from video games with the power of GPUs. Instead of transient noise during epochs (like Dropout), the engine waits until an improved epoch emerges, then immediately damages the network, then forces the next epoch to improve on the damaged state. So unlike pruning, where we try to reduce the number of neurons post-training, this is an active blind attack on the network between each successful iteration. Surprising results! It rescues certain degenerate topologies (especially where the vanishing gradient problem harms backpropagation), but even still looks useful in networks with skip connections (like ResMLP) for providing "kicks" out of local optima. Lots more to do. Paper & code: https://github.com/tcotten-scrypted/persistent-stochastic-ablation-mlp

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Onchain profile

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