Lior Alexander · pinion

Lior Alexander@LiorOnAI

You can now give infinite memory to Claude Code. Claude-Mem just released a free open source memory plugin by thedotmack. It saves context so Claude resumes work without reexplaining everything. 𝗣𝗲𝗿𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗺𝗲𝗺𝗼𝗿𝘆 𝗳𝗼𝗿 𝗖𝗹𝗮𝘂𝗱𝗲 𝗖𝗼𝗱𝗲 Claude-Mem records what Claude does during coding sessions. It stores tool usage, observations, and summaries locally. Future sessions reuse that compressed context automatically. How memory stays efficient. → Claude saves short semantic summaries, not raw transcripts. → Retrieval uses search before loading full details. → Tokens stay low even with long histories. What Endless Mode changes. • Up to 95 percent fewer tokens per session. • About 20 times more tool calls before context limits. • Enabled from the beta channel. What you control. • Local SQLite storage only. • Private tags exclude sensitive data. • Configurable context injection.

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▶@abouelatta_ali After a couple hours of work, I finally finished developing my first ever skill. :D Claude’s frontend skill tells the AI to "pick an extreme aesthetic" and "be creative." The problem tho is LLMs are just based on probability. Without strict rules, they statistically default to the most likely patterns, that's where AI slop comes from. To get clean and production-grade UI, you need to override these biases with some engineering constraints. I open-sourced Taste-Skill to fix this. :) Check it out! https://github.com/Leonxlnx/taste-skill (still early lots of improvements are on the way)@LexnLin tool is live, show me what you cook: https://ascii.0xbalance.xyz/@0xLauge I took the @karpathy autoresearch loop and pointed it at markets. 25 AI agents debate macro, rates, commodities, sectors, and single stocks daily. Every recommendation scored against real outcomes. Worst agent by rolling Sharpe gets its prompt rewritten by the system. Keep or revert. Same loop, prompts are the weights, Sharpe is the loss function. Trained the agents on 18 months of market data. 378 iterations. 54 prompt modifications, 16 survived. The system learned which agents to trust using Darwinian weights — geopolitical, commodities, and the @BillAckman quality compounder rose to the top. The agents even figured out their own portfolio manager was the weakest link before we did! Deployed the trained agents. +22% in 173 days. Best pick: AVGO at $152, held for +128%. The final prompts are evolutionary products — shaped by market feedback, not human intuition. Now running live with my own capital. https://github.com/chrisworsey55/atlas-gic Part hedge fund, part research experiment :)@Chris_Worsey