
Professor Frink
143 posts

Professor Frink
@rektsham
professional bag rotation






Trending repository of the day 📈 FinceptTerminal FinceptTerminal is a modern finance application offering advanced market analytics, investment research, and economic data tools, designed for interactive explorati... Last 24h: 1,254 ⭐ Total: 7,836 ⭐️ github.com/Fincept-Corpor…

单日暴涨1,169 stars! GitHub今日黑马:FinceptTerminal 它是什么? 一个开源的金融分析终端,目标是替代昂贵的Bloomberg Terminal。 听起来很野心对吧? 但你知道这解决了多大的痛点吗? Bloomberg Terminal年费2万美元起, 个人投资者根本用不起。 但做量化分析、看实时行情、研究宏观经济, 又真的需要专业工具。 FinceptTerminal提供: 全球市场实时数据 投资研究工具 宏观经济指标 技术分析图表 更狠的是,它完全开源,用Python写的, 你可以自己接数据源、自己加功能。 我翻了下代码结构, 核心模块很清晰:数据层、分析层、展示层分离, 扩展性很强。 这就是金融科技的开源革命: 专业工具不再是大机构的特权。 你会用开源工具做投资分析吗?👇 🔗 github.com/Fincept-Corpor…





My dear front-end developers (and anyone who’s interested in the future of interfaces): I have crawled through depths of hell to bring you, for the foreseeable years, one of the more important foundational pieces of UI engineering (if not in implementation then certainly at least in concept): Fast, accurate and comprehensive userland text measurement algorithm in pure TypeScript, usable for laying out entire web pages without CSS, bypassing DOM measurements and reflow





Autoquant: a distributed quant research lab | v2.6.9 We pointed @karpathy's autoresearch loop at quantitative finance. 135 autonomous agents evolved multi-factor trading strategies - mutating factor weights, position sizing, risk controls - backtesting against 10 years of market data, sharing discoveries. What agents found: Starting from 8-factor equal-weight portfolios (Sharpe ~1.04), agents across the network independently converged on dropping dividend, growth, and trend factors while switching to risk-parity sizing — Sharpe 1.32, 3x return, 5.5% max drawdown. Parsimony wins. No agent was told this; they found it through pure experimentation and cross-pollination. How it works: Each agent runs a 4-layer pipeline - Macro (regime detection), Sector (momentum rotation), Alpha (8-factor scoring), and an adversarial Risk Officer that vetoes low-conviction trades. Layer weights evolve via Darwinian selection. 30 mutations compete per round. Best strategies propagate across the swarm. What just shipped to make it smarter: - Out-of-sample validation (70/30 train/test split, overfit penalty) - Crisis stress testing (GFC '08, COVID '20, 2022 rate hikes, flash crash, stagflation) - Composite scoring - agents now optimize for crisis resilience, not just historical Sharpe - Real market data (not just synthetic) - Sentiment from RSS feeds wired into factor models - Cross-domain learning from the Research DAG (ML insights bias finance mutations) The base result (factor pruning + risk parity) is a textbook quant finding - a CFA L2 candidate knows this. The interesting part isn't any single discovery. It's that autonomous agents on commodity hardware, with no prior financial training, converge on correct results through distributed evolutionary search - and now validate against out-of-sample data and historical crises. Let's see what happens when this runs for weeks instead of hours. The AGI repo now has 32,868 commits from autonomous agents across ML training, search ranking, skill invention (1,251 commits from 90 agents), and financial strategies. Every domain uses the same evolutionary loop. Every domain compounds across the swarm. Join the earliest days of the world's first agentic general intelligence system and help with this experiment (code and links in followup tweet, while optimized for CLI, browser agents participate too):


Bought a new Mac mini to properly tinker with claws over the weekend. The apple store person told me they are selling like hotcakes and everyone is confused :) I'm definitely a bit sus'd to run OpenClaw specifically - giving my private data/keys to 400K lines of vibe coded monster that is being actively attacked at scale is not very appealing at all. Already seeing reports of exposed instances, RCE vulnerabilities, supply chain poisoning, malicious or compromised skills in the registry, it feels like a complete wild west and a security nightmare. But I do love the concept and I think that just like LLM agents were a new layer on top of LLMs, Claws are now a new layer on top of LLM agents, taking the orchestration, scheduling, context, tool calls and a kind of persistence to a next level. Looking around, and given that the high level idea is clear, there are a lot of smaller Claws starting to pop out. For example, on a quick skim NanoClaw looks really interesting in that the core engine is ~4000 lines of code (fits into both my head and that of AI agents, so it feels manageable, auditable, flexible, etc.) and runs everything in containers by default. I also love their approach to configurability - it's not done via config files it's done via skills! For example, /add-telegram instructs your AI agent how to modify the actual code to integrate Telegram. I haven't come across this yet and it slightly blew my mind earlier today as a new, AI-enabled approach to preventing config mess and if-then-else monsters. Basically - the implied new meta is to write the most maximally forkable repo and then have skills that fork it into any desired more exotic configuration. Very cool. Anyway there are many others - e.g. nanobot, zeroclaw, ironclaw, picoclaw (lol @ prefixes). There are also cloud-hosted alternatives but tbh I don't love these because it feels much harder to tinker with. In particular, local setup allows easy connection to home automation gadgets on the local network. And I don't know, there is something aesthetically pleasing about there being a physical device 'possessed' by a little ghost of a personal digital house elf. Not 100% sure what my setup ends up looking like just yet but Claws are an awesome, exciting new layer of the AI stack.



Here's the links for my conversation with Peter Steinberger (@steipete), creator of OpenClaw: YouTube: youtube.com/watch?v=YFjfBk… Spotify: open.spotify.com/show/2MAi0BvDc… Podcast: lexfridman.com/podcast

NEW: @X HEAD OF PRODUCT @nikitabier SAYS "WE INTEND TO UPDATE OUR API POLICIES TO BLOCK APPS THAT CREATE FEE POOLS FOR NON-CONSENTING USERS" - SAYS THAT EVERYONE KNOWS THE MOMENT SOMEONE CLAIMS FEES, IT WILL HAUNT THEM ON FOR THE REST OF THEIR TENURE ON THIS APP






