oddsnack

reconfirmed by "a private key compromise of a wallet used for internal top-up operations" from polymarket's official discord

on-chain check on the "uma ctf adapter exploit" alert from zachxbt: looks like a polymarket operator private-key compromise, not a contract bug. the drained 7702 accounts were swept by their own key-signed txs. adapter contract is intact. ordinary user funds unaffected.

.@Polymarket (supposedly) deployed a new contract on Polygon testnet a few days ago to support parlays on its platform. World Cup is a few weeks away — volume is about to go crazy. @mustafap0ly prophecy fulfilled?! amoy.polygonscan.com/address/0x5462…

@mustafap0ly @PolymarketDevs another deposit-wallet-only error: "wallet busy: active action exists". starting to think I should ditch the deposit wallet and go back to my old gnosis safe account.

@mustafap0ly @PolymarketDevs I also searched Discord and found two other users reporting what looks like the same issue within the past 15 days, but I haven’t seen any follow-up or resolution from the team.

Something seems broken in the relayer path for deposit wallet negative-risk convert transactions. One high-leg-count convert keeps returning 500 Internal Server Error on every submission. Could you please check? @mustafap0ly @PolymarketDevs

@insidersdotbot 不过还是有 split/convert 策略会算的不准，比如这种: insiders.bot/wallet/0xCB314…

用了下 @insidersdotbot ，确实是目前**唯一** polymarket 上看 split/convert pnl 比较准的。

xswl，刚好奇看了看他的 polymarket 主页，交易依然亏钱但已经每天收到返佣奖励了，真的是岳不群啊。

世界是个巨大的草台班子之，关注的技术博主突然开始经常性的发返佣链接，并且 pnl 主页开始造假。原来也是吃流量饭的啊 ctm。

@lufeieth 不会，GPT 5.4 pro 回答的极为简洁，信息密度大。你是不是用了之前版本的模型

把GPT的一个回复结果发给Claude评价，Claude打分70分，其中有句话： ”第五，格式问题依旧。 大量emoji、极度碎片化的短句、反复"👉"标记。内容密度低，适合入门科普但不适合你做投资决策参考。” 😂 大家的GPT给的回复也是碎片化断句、很多emoji吗？

LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So: Data ingest: I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them. IDE: I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides). Q&A: Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale. Output: Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base. Linting: I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into. Extra tools: I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. Further explorations: As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows. TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

就刚刚，typescript 和 py 版本的 clob SDK 2.0 都已经在 polymarket github 开源了，可以测起来了。

Polymarket 的升级看了下，主要是三点：换了新的 CTF Exchange 合约，交易前需要做一次 approval；新的 PUSD 包裹了 USDC.e 和原生 USDC；bot 和 builder 要升级到新的 clob SDK。

有什么github的替代品吗？自从github copilot在pr里开始打广告、默认使用用户的数据做训练后，这个平台就开始越走越远了。

claude code 的好处是更“说人话”，对话起来更自然。codex 是个人狠话不多的角色。

是我当时讲话太大声 😂 codex 最近更新了几个版本后，研究能力明显开始超越 claude code + opus 了。

codex plan 的研究深度很差，openai 应该没有把 chatgpt 同水平的研究能力移植到 codex 中（猜测跟这两个公司的定位有关系）。现阶段做项目特别是有一定研究深度的项目，没办法用 codex 一把梭，要跟 claude code 配合着用才行。

过去几年的经历告诉我，不需要跟任何人争，只需要默默印钱就行。

等下，我天天在用的工具（uv ruf ...)，突然就变成了 OpenAI 的了？😂