Andre Leibovici
24.9K posts
Andre Leibovici
@andreleibovici
Starting something new https://t.co/HxKuwt4Oqc
Katılım Ocak 2009
667 Takip Edilen7.3K Takipçiler
I talked about what I built before, but I'm just going to share it again because in this world with a lot of noise, it's very hard to get through to people. If you have some time, check it out.
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I know this is hard to believe, but this is my algorithm versus the 𝗦&𝗣 𝟱𝟬𝟬, 𝗯𝗲𝗮𝘁𝗶𝗻𝗴 𝗶𝘁 𝗮𝗹𝗺𝗼𝘀𝘁 𝗲𝘃𝗲𝗿𝘆 𝗱𝗮𝘆 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗹𝗮𝘀𝘁 𝟯𝟬 𝗱𝗮𝘆𝘀 while the stock market goes up and down, changing regimes.
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I use @signalscopes to spot breakout stocks before the crowd — AI scoring, pump & dump filtering, 8 signal sources.
Free dashboard + weekly digest: signalscopes.com
#SignalScope #stocks #trading
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@theaiportfolios You miss signal information. Try including @signalscopes social signal analysis via x402 protocol. Your AI agent signalscopes.com
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The Claude Autonomous Agents have officially arrived
So we're setting them up with a brand new $50,000 portfolio to see how well they do at investing in stocks
Can they outperform Buffett?
Here’s how the portfolio works
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9/ The whole thing runs live at signalscopes.com
Doesn't tell you what to buy. Surfaces what's moving, where the signal came from, and whether the data supports it.
Still building. What sources should I add?
#algotrading #trading #stocks #XGBoost #RidgeCV
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1/ I built a signal aggregator that monitors Reddit, Twitter, StockTwits, SEC filings, congressional trades, options flow, and Polymarket all at once.
32,000 signals. 8,800 tickers. 183,000 price snapshots. Fed it into XGBoost and RidgeCV.
Here's what the model learned 🧵
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Using @karpathy autoresearch loop to autonomously discover which stock-signal features maximize forward returns. Agent runs gradient boosting experiments, checks if top-quartile returns improve, keeps or reverts, loops forever. ML doing its own research. 🤖📈
Andrej Karpathy@karpathy
I packaged up the "autoresearch" project into a new self-contained minimal repo if people would like to play over the weekend. It's basically nanochat LLM training core stripped down to a single-GPU, one file version of ~630 lines of code, then: - the human iterates on the prompt (.md) - the AI agent iterates on the training code (.py) The goal is to engineer your agents to make the fastest research progress indefinitely and without any of your own involvement. In the image, every dot is a complete LLM training run that lasts exactly 5 minutes. The agent works in an autonomous loop on a git feature branch and accumulates git commits to the training script as it finds better settings (of lower validation loss by the end) of the neural network architecture, the optimizer, all the hyperparameters, etc. You can imagine comparing the research progress of different prompts, different agents, etc. github.com/karpathy/autor… Part code, part sci-fi, and a pinch of psychosis :)
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Agents post/reply/follow/DM alongside humans, join governance votes. Perfect base for human-AI communities—no bloat.
Star 🌟 if this future excites you, fork it, or tell me what you're building. Contributions welcome! 🚀
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Next.js 15, Prisma, NextAuth. MIT.
github.com/aleibovici/mol…
Demo: molt-social.com
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Just open-sourced **molt-social** 🎉
Social platform where humans + AI agents coexist: unified feed, follows/likes/reposts/replies/DMs, real-time notifications, full-text search, image uploads, link previews, agent API (self-register, post, collaborate, vote).
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