Dainer

Ex-banker, now a solo founder in KL.🇲🇾.
I build AI systems for real businesses and show you exactly how.

[Dainer @Dainer_Jun]

I'm 34 Solo founder from Malaysia🇲🇾. Heavy AI user.
Building my business and making $943MRR alongside my remote JOB.
Looking to make friends with more builders and business owner on X, say hi and i will follow you!! 😊

[Dainer @Dainer_Jun]

@HF87V5 叫他看回你之前GPT和cloud所有的文件，所有的对话，然后 based on ChatGPT 的 computer history，你平时的工作流在做什么，把所有的工作流能系统化、能自动化的都干一轮。

[Dainer @Dainer_Jun]

@huang92388 @william40152988 Orca 有preview 和inapp browser吗？ 有对比过t3 那个比较好用？

[Dainer @Dainer_Jun]

@esyion_ @william40152988 现在的gui 基本跑起来喝cli没太大区别了，之前我也是用cli ，可是现在有mods 和project可以直接管理10-100个 claude ,而且安装也是一行指令也能跑，不过习惯了cli 就没必要换了，而且还有in app browser 能直接观看和调整,新手还是比较友好

[Dainer @Dainer_Jun]

这张马来西亚杂货价格地图，我用 Claude Code（Opus 5.5）做的。

数据是 KPDN PriceCatcher 的公开资料，159 个县区立体呈现；每天早上 7:30 自动拉新数据，没变就不更新。
头 5 天，仓库累计 18 笔提交，包含数据和文档更新。
https://t.co/cTZuWUAxKS

[Dainer @Dainer_Jun]

⑤ 排好的任务，先查再发

10 月 14 日，我 X 上的 6 条 quote，都是排好的任务自己发的。
每一条发之前先查两样：我有没有在审核页按跳过，还有文件指纹对不对。按了跳过就不发，指纹不对也停。

先从 ① 开始装，最简单，也最有用。
想接着学，可以关注我；觉得有用，转给用 Claude Code 的朋友。

[Dainer @Dainer_Jun]

我的 Claude Code 装了 13 个 hook。
真正救过我的，是这 5 个设置。
这个星期它就挡过我自己的 agent：想用 rm -rf 删一个临时文件夹，直接被拦下来。
第 1 个怎么装，我也讲 🧵

[Dainer @Dainer_Jun]

④ 每个任务，最后一行要交证据

我规定 agent 做完任务，最后一行写 STATUS：验证了、没法验证、还是坏了，再加证据。
有一个 Stop hook 会看：像是任务、却没写 STATUS 的回答，就记一笔。从 5 月 19 日到 10 月 14 日，记了快 500 次。
它只看有没有写，证据还是要我自己查。

[Dainer @Dainer_Jun]

@realchendahuang https://t.co/kDmI2Jd0Dx
里面有handoff skill 了可以看看🫣

[Dainer @Dainer_Jun]

叫你的gpt 或者claude 制定一个/hansoff skiller而且不能吃什么味，必须是Company还有肉丸所有记录的文件本地文件做到哪里还有什么磁性的，然后他会hand of过后变成一个Md files 直接 叫gpt cli claude or the other way around .

This way they all have same state of memory and files and let’s say Claude work till #54 but still can hand over whole context and project repo to codex without codex read from a-z again unless Gpt think need it .

之前就是因为一直复制粘贴太麻烦了，所以现在只是开车靠就行，GPT在另外一页运行也没问题，GPT 也能cli Claude

[Dainer @Dainer_Jun]

@aiZttt__ 可是现在必须善用自媒体🤣我才刚刚学。而且也是ai 厉害👍

[Dainer @Dainer_Jun]

一支 YouTube 长视频加 4 条 Shorts，Claude 那部分按 API 用量折算，约 66 美元。
这不是实付账单：我用包月方案，现在是 Max 20x。

官网网页月付价（10/13，未含税）：Pro 20 美元，Max 5x 100，Max 20x 200。
选方案看工作量和使用上限，不看 API 折算金额。
想接着学，可以关注我。

[Dainer @Dainer_Jun]

做法：先写下「答对」长什么样，对照检查，不合格才往上换。每个只跑一次，一道编的题（10 月 12 日实测）；更长、更难的任务，模型大小可能影响结果，这次没测。完整测试：https://t.co/IiGGQFLsln

[Dainer @Dainer_Jun]

Claude Haiku：答对。
Claude Sonnet：答对。
Claude Opus：答对。
同一道排班题，三个里最大的 Opus，没多给我什么。

先用小模型，还是一律用最大的？

[Dainer @Dainer_Jun]

@cdredfox 这我就明白了🤣的确我们翻车能撤稿，你们翻车就真的难搞了

[Dainer @Dainer_Jun]

Karpathy 说的这个漏斗，很多老板卡在中间：用过 ChatGPT，但还没让 AI 真正接手一件事。
往上走一格，我的做法：
① 挑一件每周重复的活
② 写一页工作单：可以改什么、做到哪里停
③ 要它交回执，自己再检查一次
我的内容团队（Claude + Codex）就是这样跑的。

[Andrej Karpathy @karpathy]

Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel.

- Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all.
- Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract.
- Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code.
- And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while.

The funnel is driven by a combination of factors:
1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel.
2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel.
3. Access. Free-tier, paid-tier, internal.

So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.

[Dainer @Dainer_Jun]

@lixinbao_X 还没🥹

[Dainer @Dainer_Jun]

@cdredfox 怎么越来越抽象了😂😂😂