sunforever

🚀 How should LLMs sample on hard reasoning problems during post-training and inference where direct rollouts rarely produce a correct answer? Best-of-N (e.g., GRPO) and tree search share two limitations: 🔻 Verification signals are sparse 🔻 Candidates stay within the model's own distribution We introduce BES: Bidirectional Evolutionary Search — a search framework that couples forward candidate evolution with backward goal decomposition. ✅ Works for both post-training and inference.

我说了在支付宝买美股基金不丢人。。。 但丢人的是，买了半天不知道自己买的是什么 建信新兴市场混合一天涨12.43%，兄弟们都吃爽了，押注存储、AI算力、光通信，重仓三星、海力士、英伟达、台积电、康宁。 表现很猛。 很多兄弟问：但支付宝上关于纳斯达克的基金，名字都差不多，到底有啥区别呢？ 第一类：市场基准型 代表：纳斯达克100ETF联接、纳斯达克100指数。 目标只有一个：复制指数表现，不做任何主观判断。 跟踪的是纳斯达克100指数，100家最大的非金融公司 苹果、微软、英伟达这些科技巨头在里面。 星巴克、可口可乐这些消费股也在里面。 ETF联接和指数基金，本质一样，手段不同。 ETF联接是间接投资，先买海外上市的ETF。 指数基金更直接，可能直接买成分股。 对投资者来说，结果差不多，都是跟着指数涨跌。 收费低，透明度高，适合不想操心的。 第二类：主动管理型 代表：纳斯达克精选股票 关键词是精选。 基金经理自己挑股票，不跟踪任何指数。 看的是自己的研究、判断、眼光。 目标是打败市场，比指数涨得更多。 可能远超指数，也可能大幅跑输。 完全取决于基金经理的能力。 收费比被动基金高，因为有人替你操心。 适合相信基金经理比自己强的。 第三类：策略增强型 代表：纳斯达克科技市值加权ETF联接。 也是跟踪指数，但不是传统宽基指数。 是在纳斯达克100的基础上，再套一层筛选规则。 科技两个字意味着，非科技行业的公司被剔除了。 星巴克、可口可乐这些消费股，不要了。 只留下纯粹的科技企业。 更集中，更纯粹，波动也更大。 涨的时候比纳指100涨得多。 跌的时候也比纳指100跌得狠。 适合看好科技赛道、能承受波动的。 ---- 兄弟要明白支付宝上的美股基金，名字都差不多，但底层逻辑完全不同。 被动型适合懒人，主动型适合相信基金经理的，策略型适合看好单一赛道的。 没有哪种更好，只有哪种更适合你。 但前提是，你得先搞清楚自己买的是什么。 不然跟闭着眼睛下注，没区别。

Anthropic pays $750,000+ a year for engineers who can build LLM architectures from scratch. Stanford taught the entire thing in 1 hour lecture & released it for free. Bookmark & watch this today before someone takes it down.

想快速上手 Codex？这个教程算是目前最省心的一份了。 从头到尾三十八分钟，零基础直接跟，手把手带你把这套工具用明白。多版本怎么装、语音对话、从 GitHub 上拉项目、插件配置、MCP 对接、自动化测试，还有搭建工作流，这些实操环节全都有。 全程用的都是最新版本，纯中文讲解，讲得通俗，不绕弯子，新手也能一遍看懂、上手就用。

This Chinese developer launched Llama 70B locally on a MacBook on a plane and for a full 11 hours without internet ran client projects. He was sitting by the window on a transatlantic flight with a MacBook Pro M4 with 64 GB of memory. WiFi on board cost $25 for the flight. He declined. No cloud API, no connection to Anthropic or OpenAI servers, no internet at all. Just a local Llama 3.3 70B on bf16 and his own orchestrator script. The model runs through llama.cpp. Generation speed, 71 tokens per second. Context around 60,000 tokens. Memory usage, 48.6 GiB out of 64. Battery at takeoff, 3 hours 21 minutes. And he gave the orchestrator this system prompt before takeoff: "You are an offline orchestrator running on a single MacBook. There is no network. The only resources you have are local files in /Users/dev/work, the Llama 70B inference server at localhost:8080, and a battery budget of 3 hours 21 minutes. Process the queue at /Users/dev/work/queue.jsonl (one client task per line). For each task: draft → run local evals → save artefact to /Users/dev/work/done/. Save context checkpoints every 12 tasks so you can resume after a battery swap. Stop only on empty queue or when battery drops below 5%." So the system knows exactly what resources it is running on. It knows it has no connection to the outside world for the next 11 hours. It knows it has finite memory and a finite battery. It knows the human will not intervene until the plane lands. The system runs in 1 loop. Takes a task from the queue, runs it through inference, saves the artifact, writes a checkpoint. Task after task, just like that. And only when the battery drops below 5% does the orchestrator automatically pause, waits for the laptop to switch to the backup power bank, and continues from the last checkpoint. Here is what the system actually writes in his log during the flight: "saved context checkpoint 8 of 12 (pos_min = 488, pos_max = 50118, size = 62.813 MiB)" "restored context checkpoint (pos_min = 488, pos_max = 50118)" "prompt processing progress: n_tokens = 50 / 60 818" "task 37016 done | tps = 71 s tokens text → /Users/dev/work/done/proposal_westside.md" Outside the window, clouds, blue sky, and no WiFi. On the tray, 1 MacBook, an open terminal on 2 screens, and an inference server on localhost. From what I have observed, this is the cleanest offline AI workflow I have seen in the past year: 11 hours of flight, $0 for WiFi, and the entire client queue closed before landing.

There is a reason Wedbush is in my Top 3 for worst biotech analysts of all time.. $NTLA I’ll say this one more time: NO HAE PATIENT GIVES A SHIT AT WHAT THE BLINDED PERIOD SHOWS “We continue to view the inability to discontinue dosing… a commercial limitation…” WTF 🤬 ZERO patients went back to their prophylactic medication once allowed to after Week 28 and safety was essentially perfect. The month of Week 36 saw 32 patients experience a total of 1 attack. Baseline values would’ve expected ~112 attacks per month (a 99.1% decrease in attacks). Does Wedbush understand how to look at data? Or do they just use AI to sift through surface level information 🤷🏻‍♂️ “Nierengarten kept his neutral rating and 12 price target on Intellia stock.”