Prashant P. Naikar

Digital Marketer,

[DAN KOE @thedankoe]

The fastest way to change your life is to rip yourself out of your (physical and digital) environment. Change everything overnight. The places you go, the accounts you follow, the info you consume, etc. It's difficult but it absolutely works.

[Anatoli Kopadze @AnatoliKopadze]

She's 22 and a16z just led her $21M round. Her AI is live inside the Fortune 100.

"I work every waking second, 7 days a week, and I've never been happier."

17 minutes of an unreal story, from youngest hedge fund quant to funded founder in 2 years.

worth more than most of the startup advice on your feed.

watch it, then read the article below.

[Phosphen @phosphenq]

She's 22. Her AI runs inside the Fortune 100. a16z just led her $21M raise.

"Put up the most minimal fake version you probably can of something and then go sell it."

In 17 minutes, MIT grad Jessica Wu reveals the YC playbook that got her here in 24 months.

From youngest hedge fund quant to founder + the trick her batchmates missed + the Christmas Day deployment + the 10-year founder test

Worth more than a year of YC advice on your timeline

[himanshu @himanshustwts]

since a good bunch of discourse is going on around "how to do research", these pieces are quite worth a read.

https://t.co/pA0MkOMlKS

https://t.co/rw9uMiwlCj

https://t.co/H1AGvnb7LP

https://t.co/FTyAabr9Rx

[Chris Tate @ctatedev]

Introducing Generative UI for Claude Code, Codex and Pi

Charts, forms, 3D, anything

Your agent renders real UI for users while it works in a sandbox

Powered by AI SDK's experimental HarnessAgent + json-render

[bbshare @shadouyoua]

硅谷PayPal帮主彼得·蒂尔认为，AI正终结过去200年，以数学能力为核心的精英体系。工程、量化等“数学型人才”的逻辑优势，正在被AI迅速替代，其职业护城河也正在消失。未来真正的竞争力，在于叙事、社交语境理解，以及把AI嵌入社会系统的语言型人才，这才是人类独特优势。

工业革命让肌肉贬值，AI革命正在让“纯计算的大脑”贬值。未来的胜出者，是那些能用语言驾驭算法、用叙事锚定价值的社会系统操盘手。

作为程序员： 别只埋头代码，而是要多练自然语言描述问题；学产品思维和用户研究，理解非技术需求；开发时优先考虑伦理、社会影响；选领域深耕；把代码变成“能讲故事”的系统。未来你的优势是“代码+叙事”。所以，年轻人别慌着放弃STEM，也别觉得学文科就躺赢了。真正该练的，是用AI思考更深的问、讲出更动人的故事、构建更复杂的社会系统。蒂尔指出了方向，但真正的游戏，才刚刚开始。

说白了，需要文理兼修，才能走得更远。

[Luc @investingluc]

My high-level workflow for finding the strongest themes + scanning for which stocks to actually enter:

1)
- go to finviz, click maps in the nav bar
- click "themes" in the left panel
- select 1-week (or month) from the dropdown
- observe what's strongest/greenest

2)
- go back to the nav bar
- click screener
- click "theme" box
- scroll all the way down, click custom
- select the strongest themes from step 1

3)
- add supporting filters
- performance
- market cap
- relationship with moving avgs
-etc (see mine attached)

Super high level, but this should give you a good starting point.

- Luc

[Shann³ @shannholmberg]

why you should create specific open and closed Hermes agent profiles

a profile is its own agent, with its own config, skills, memory, and model.

most users run everything through one default agent and set it up again each session.

so I build two kinds of profiles, open and closed. they work in opposite ways, explained here:

the open profile

this is your research agent, the one you open when you don't know what you'll need yet.

> lots of tools: web search, scrapers, transcription, doc parsing, and access to your past research
> research skills on top, like a /last30days that runs a whole sequence with one command
> output in whatever you need: markdown for briefs, json to feed other agents, html to read

its built to move fast and go down paths you didn't plan for.

the catch is cost, give it real room to explore and it burns a lot of tokens, and if the goal is loose it just makes slop fast. use it for one-off work, not for jobs that repeat.

the closed profile

this is the opposite, one job, kept narrow, run over and over. a good one has the same things you'd give a person doing that job:

> the context and data the job runs on
> what good looks like, and why
> the tools it can use, and what it must not touch
> a check at each step, and a human sign-off before anything ships
> memory, so each run makes the next one sharper

it runs on a schedule and on a normal budget, because the path is tight. and it gets better each run, because every pass feeds the next.

the depth comes from running the exact same setup over and over, and that only happens if you save it as a profile instead of rebuilding it each week.

one example I run watches around 15 accounts in my niche. it pulls the week's posts, finds the angles getting traction, and drops a report into the content brain. same setup every time, sharper every week.

once you have a few, they stop being separate tools. an orchestrator passes work between them, you manage them one by one or from a kanban board, and the whole set starts to run like an agent company.

[The Boring Marketer @boringmarketer]

if you want Fable level performance NOW, the answer is to build your own coding harness

here's how I'm doing it

(in a lightweight Pi-native way rather than a heavy Superpowers/Compound clone)

first, I think we're going to see PERSONAL HARNESSES that derisk single model reliance, help users save money, and squeeze top frontier performance out of homegrown systems

The best engineers I know already do this...

1) Use Pi, the open source coding harness that you can make your own and access any model from

2) Set 5.5 codex as your default model

3) Deeply analyze publicly available system prompts (guess which model I looked at?)

4) Feed Codex context on Loop Engineering and other engineering skill repos

5) As it to improve efficiency as a lot of engineering repos are super heavy and bloated/slow

6) Ask it to use FUSION via Openrouter for planning, logic, and review tasks

7) Ask it to use Kimi k2.7 code for subagents/execution

8) Have it dog food the harness until it's dialed in then unleash it on a repo for a deep analysis

[Hasan Toor @hasantoxr]

Here's the part that blew my mind:

In a real use case, you can start with ChatGPT, and switch the model to Claude midway.

As you can see in the demo below, I started with ChatGPT to ask about content creation, and in the middle I switched to Claude so I could get a proper answer without losing the context.

[swyx @swyx]

## On Loopcraft

One might argue the entire game of the next century is to be able to stack loops as effectively as possible.

In the early days of each phase, it will be valuable to know when to go **DOWN** a loop when things go wrong (for reliability)…

but it will probably be more valuable to know how to go **UP** a loop as models improve (for leverage).

If you don’t figure out how to do this, don’t be salty when you lose to those that do.

[rody @0x_rody]

Anthropic's Claude Code engineer Sid Bidasaria:

"Stop babysitting your agents. If you're watching Claude work, you've already lost the leverage."

In 37 minutes, he shows how to set up agents that run without supervision, and the config below takes it to your phone.

Watch the talk, then grab the full setup below👇

[Xudong Han @Xudong07452910]

最近看到 Anthropic 研究员 Vivek @itsreallyvivek 的一篇长帖，讲怎么真正训练自己的研究能力。

我最有感触的一点是：很多时候，我们以为自己在做研究，其实只是在追热点、刷 Arxiv、看别人讨论什么，然后把那些问题接过来继续做。

这当然也能产出东西，但很容易变成一种科研角色扮演：看起来很忙，输入很多，笔记很多，实验很多，可心里其实并不确定自己到底在追问什么。

Vivek 这篇帖提醒我，研究能力不是某种玄学天赋，而是一套很朴素的习惯：自己选问题，实验前先预测结果；要多读读原paper；把假设、结果和想法变化都写下来，盯着失败案例，而不是只盯着损失曲线。

越往后越觉得，做研究真的很像训练自己。

你给大脑喂什么输入，它就会长出什么 taste；你愿不愿意面对错误，决定了你进步的速度；你写不写下来，决定了你是在诚实迭代，还是说在靠记忆去美化自己。

同质化输入，只会带来同质化输出。

真正拉开差距的可能是谁能在很长时间里，慢慢训练出属于自己的问题感、判断力和纠错系统。

这篇帖很适合迷茫的时候读一遍。

[WILL NESS @N3sOnline]

Used @mattpocockuk /teach skill and gave it access to a chess engine and an API to fetch all of my recent games.

It now will fetch hundreds of my recent games, analyze them, then build custom lessons with visuals to help teach me mental models to improve the weakest points of my games. Interactive visuals, custom chess puzzles from my actual games, quizzes.

This is the new way to learn things.

[girish @googrish]

“don’t train your own model” is common ai advice. it's wrong. your token bill's the proof.

today, we’re excited to launch castform into open preview. castform is the easiest way for you to train your own model, on your own data.

open-weights models are performant and much cheaper. when trained on your task & proprietary data, they beat closed models. the thing standing between you and that was weeks of plumbing & years of ml expertise.

with castform, model training is as simple as prompt engineering. @castformai

bring your agent traces or raw corpora. castform turns it into training data, picks the right algorithmic recipes, manages gpus, and gives you an ide to watch and chat with your model as it learns.

see what you can build with castform👇

[Tabish @0xTab]

I used @Remotion to vibe code all of the graphics here, super cool tool. If you’re an OpenSpec fan please like & RT the post below!

[Serena @369Serena]

Serenity 火了以后，很多人第一反应是去抄作业。

但其实我们可以给 Codex 它装上这些投资类 Skills 和 AI 投研框架后

它可以变成一个股票研究助理：

帮你拆产业链、读财报、看情绪、查风险、做估值、写反方报告，甚至把策略拿去回测。

先收藏这 5 个 GitHub 项目!
别让 AI 直接替你买股票，先让它帮你把研究做扎实。

1️⃣ UZI-Skill⭐ 2.4K

中文投资者最值得先看的一个!

它把 A股、港股、美股分析做成了一套完整流程：深度分析、陷阱排查、龙虎榜、投资大佬评审团、22维数据、180条量化规则。

适合拿来做个股初筛、风险排查和中文市场研究。

GitHub：
https://t.co/1Dq2yXMLhq

2️⃣ TradingAgents⭐ 83.7K
GitHub 上最火的 AI 金融交易框架之一。
它把一个投研团队拆成多个 Agent：基本面分析、情绪分析、新闻分析、技术分析、研究员、交易员、风控团队。

如果你想看看“AI 投研团队”到底怎么协作，这个项目最有代表性。

GitHub：
https://t.co/N0VMCVroA9

3️⃣ Serenity Skill⭐ 731

最近 X 上讨论很多的投资 Skill。

它复用 Serenity 的核心研究路径：从热点出发，拆产业链，找供应链瓶颈，再回到股票和基金方向，检查公告、财报、客户、产能和风险。
适合研究 AI 半导体、CPO、机器人、算力、电力设备、创新药这类“产业链很长、信息很乱”的方向。

GitHub：
https://t.co/YGgiXR6aZr

4️⃣ Buffett Skills⭐ 512

这个 Skill 适合在你心动之前泼一盆冷水。

它会逼你检查：

这家公司是不是好生意？
有没有护城河？
现金流可靠吗？
管理层可信么？
价格有没有安全边际？

适合长期投资者做质量审查。

GitHub：
https://t.co/pDMtWc6LTB

5️⃣ QuantDinger ⭐ 7.4K

一个更完整的 AI 量化交易工作台。

它覆盖从 AI 研究、策略代码、回测、模拟盘，到实盘执行和监控的完整链路。

如果你不满足于“让 AI 分析股票”，想把想法变成策略并验证，QuantDinger 更适合继续研究。

GitHub：
https://t.co/cZqOdURO5u

别让 AI 替你买股票。

让 Codex 替你拆产业链、读财报、查证据、找风险、写反方报告、做回测。

这才是普通投资者最该抄走的用法。
#codex #skills #Serenity

[Ole Lehmann @itsolelehmann]

luckily bookmark rot is an easy problem to fix now

here's how to turn every X bookmark you've ever saved into a second brain your agent has full context on:

1. export your bookmarks. i use twitter-web-exporter (free userscript) or the BookmarkSave extension. you get one file with every bookmark + the full text + the author + the link

2. drop that file into a folder. if you already run an llm wiki / obsidian vault, drop it straight in so your bookmarks join the rest of your knowledge

3. point your agent at the folder (claude code, codex, hermes, whatever you run) and tell it: "read this export and turn every bookmark into its own markdown note with the original link and a couple of topic tags"

that's it, your agent has read all of it.

now you can ask "what have i saved about pricing" or "pull everything i bookmarked on claude code" and it answers across the whole pile

takes maybe 10 minutes

after that they actually get used, and every new bookmark folds into the same brain instead of rotting in a tab you never open again

[K三 凯 @kaikaibtc]

我把Serenity和Crux这两个美股狂魔，蒸馏成了一个自动投研的Agent

上周我扒了 Serenity（@aleabitoreddit）的 1073 条推文，5 个月 32 倍。

这次我又扒了第二个人——Crux Capital（@crux_capital_），五个月 647 条原帖，126 个价值票。

Serenity窄而深：找一个制胜点，押死。
Crux 宽而有纪律：画一张六层栈，每层按执行力排仓位。

看完我有点难受，不是因为收益图太夸张。是我发现，这两个人干的根本不是传统意义上的“选股”。

他们是在搭一条投研流水线， AI 负责搬砖，人负责最后拍板。

1⃣ 他们的方法论：先拆机器，再找堵点

他们看一个赛道，不先问“谁会涨”。

先问的是：

这台机器到底长什么样？
BOM 里最难替代的零件是什么？
如果其中一家明天停产，下游要等多久？

这个问题一问出来，视角就完全变了。

大家还在盯 GPU、HBM、电力这些第一层热点时，
他们已经往下抠第二层、第三层：

激光器、InP 衬底、外延、测试、连接、冷却、配电……

Serenity 最狠的地方，是死盯单点故障。
Crux 更像工程师，先画一整张栈，再给每层贴标签：

谁是龙头，
谁是瓶颈，
谁只是会讲故事，
谁又是那种没人聊、但每台机器都得用的“无聊必需品”。

2⃣AI 在他们手里，不是军师，是苦力

普通人用 AI 做投资，喜欢问：

“这票能买吗？”
“帮我总结一下财报。”
“这个赛道有没有机会？”

这种问法，说到底还是在等答案。

他们不是这么用的，他们让 AI 去干最脏最累的活：

拆机器，
顺着零件找供应商，
再找供应商的供应商，
横着读多家公司财报和电话会，
把“产能打满”“认证快结束”“明年放量”这些零碎信号拼成一张图。

说白了，AI 没替他们赚钱，AI 只是把研究量的上限硬抬上去了。

3⃣所以我把这套东西蒸馏成了一个 skill

https://t.co/V6OUO1IuJZ

它不干一件很俗的事：上来就给你甩股票代码。

它先跑五步：

L1 先确认终端，到底研究的是哪台机器、哪套系统
L2 再画供应链栈，搞清楚谁在第几层
L3 找真正的卡点，卡的是产能、认证、热、电、良率，还是材料
L4 用财报、产业新闻、研报交叉验证
L5 先给方向，再给名字，最后才谈仓位

我蒸馏的不是他们买了什么，我蒸馏的是他们怎么提问，怎么排除，怎么把热闹拆成一个个能验证的环节。

4⃣举个例子，人形机器人接下来该买什么

大部分人第一反应都是特斯拉、英伟达、总龙头。
但这个 skill 不会先看这些。

它会先问：

如果 Optimus 真开始放量，最先紧张的是哪一层？

如果谐波减速器已经被市场盯得很紧，
那下一步就该继续往旁边看：

每个关节都离不开的力传感器，有没有被认真定价？

重点不是马上得出“买谁”。
重点是先知道：你该查哪一层。

顺着这套方法继续往后推，
我现在最想深挖的，反而不是更拥挤的 GPU 叙事。
而是AI factory 的供电 + 液冷 + 物理交付层。

原因很简单。

芯片再猛，电接不进去也白搭。
柜子再多，热散不掉也白搭。
模型再聪明，机房交不出来，还是得排队。

这条线不性感，不热闹，也不太适合吹牛。
但真缺了它，项目就只能往后拖。

最后

看完 Serenity 和 Crux，我最大的感受其实挺扎心的：

AI 没有让投研变轻松。
它只是先把偷懒的人淘汰了。

以后谁再跟我说
“AI 会让投资越来越简单”

我大概只会回一句：

简单个屁，马太效应罢了，你有的他给你更多，你没有的，他把你已有的都给抢走。

[Claudio Drews @ClaudioDrews25]

Just open-sourced **Memory OS** — a complete hierarchical persistent memory architecture for the Hermes Agent. 🪽

🧠 6 layers, fully local:
• Structured facts + trust scoring with feedback loop
• Hybrid vector search (Qdrant + BM25)
• Self-curating LLM Wiki
• Semantic deduplication + surgical context injection

Built with love for the Hermes ecosystem.

Repo: https://t.co/cYgdZmXSsw

@NousResearch @Teknium