zwdroid

[zwdroid @zwdroidai]

@Jackywine 其实，Claude 官网也不需要

[zwdroid @zwdroidai]

@bylixing @chunxiangai 写得太好了，很多体感和看法完全一致，但是没做到大佬的系统化，认真学习学习。btw， 这 token 消耗量是真恐怖，我都怀疑你在做操作系统。

[zwdroid @zwdroidai]

@elonmusk 语言的艺术：安巴尼，你别再靠垄断卡我们了，让Starlink进印度吧，对老百姓有好处。

[zwdroid @zwdroidai]

@ImperioEliteX 大多数老板，要的不是立马解决问题，而是随时 on-call 的姿态

[zwdroid @zwdroidai]

@xicilion 原来大佬也会遇到干一半返工的事🥲，这下我不用内耗了

[zwdroid @zwdroidai]

@addyosmani just for fun, or just for marketing

[zwdroid @zwdroidai]

@liujiayi1111 Claude 200x

[zwdroid @zwdroidai]

@AlchainHust @cherry_mx_reds 挺有意思

[zwdroid @zwdroidai]

大公司的协作混战进入了中小型公司，员工数量不是关键了，agent 数量才是

[lauren @poteto]

everything i know about managing agents i learned from the amazing programmers and computer scientists that came before

constraints in your codebase are freeing for both humans and agents! this has always been true, but if you were a small dev team you likely never had to think that deeply because you could trust your fellow programmers to write code and review well

but large companies have had to solve this problem since even before agents. because before you had agent slop, you had human slop. lots of engineers wrote bad code out of necessity (eg backend engineer forced to write frontend or reverse). the solution to this was constraints: lint rules, smarter compilers and diagnostics, high quality tests, investments into observability, and so on

the arrival of agents just means that big company problems are now everyone's problems. the good news is that none of this is really that novel: it's just good engineering

https://t.co/ykgu52qBqB

[zwdroid @zwdroidai]

@bojie_li 我好奇的是什么场景下的什么事情的开发可以达到这个倍数？位于这个开发环节的上下游是否也能达到这个倍数？如果达不到，那为了适配，这个环节就要减人😂

[zwdroid @zwdroidai]

@dotey 使用宝玉老师的 BaoCut 对这个访谈加字幕和翻译了下，好用啊。他们的这种探讨分享的氛围好好，没有说教和高高在上，真是只是分享。
https://t.co/6YPokn2INM

[zwdroid @zwdroidai]

先让 AI 看懂你说的，再让 AI 说你能看懂的

[Kun Chen @kunchenguid]

people who laugh and say karpathy's latest tips are outdated - i suspect the vast majority of them have not even tried the tips yet

i just tested the ASD-STE100 wording rule and it's surprisingly good at helping increase clarity of model response, even within html artifacts. but the trick is that the full ruleset is a bit too strict and you need to pick a subset

one prompt you can run super easily:

"randomly sample 10 session transcripts where i worked with you interactively within the past week. apply ASD-STE100 rules to assistant responses and analyze which rules would have increased clarity, reduced confusion and improved the conversations, then document those rules in my user level AGENTS.md"

you might be impressed!

[zwdroid @zwdroidai]

信息的承载和表现，从文本 md ，到 html，再到 video

[Andrej Karpathy @karpathy]

We'll be spending a lot more time trying to understand the outputs of language models. A few thoughts, tips & tricks:

Writing. Something I've had success with: Ask your LLM to explain something in ASD-STE100, it's a controlled language specification originally developed for aerospace maintenance documentation. LLMs well-versed in this language and it comes with heavy constraints on clean writing style that I often find a lot more readable. Sometimes I've tried to soften it a bit e.g. ask for "80% of the way to ASD-STE100" because the spec is quite stringent. But even better:

Diagrams / images. Instead of writing, ask your LLM to create a diagram. These can be a lot easier to process, parse, and understand. But even better:

Web pages. Ask for output "in HTML" to get a beautiful, interactive webpage. LLMs are getting really good at frontend and can create beautiful experiences, animations, etc. But even better:

Explainer videos. The output format I am most bullish on is fully custom / bespoke explainer videos generated on any arbitrary topic. Experiment with things like "Create a 3b1b style video explainer on X. Use my ElevenLabs API key for audio narration". (you'd need an API key for the latter or you can ask your LLM to find you decent free alternatives that use your local compute). This is actually starting to work!

In summary:
- As LLMs get better, they will do more and more of the legwork autonomously, and a lot more of our work will rise up the abstractions into oversight and understanding.
- Luckily, LLMs can help here too because as intelligence and code are increasingly abundant, you can ask for large, custom, discardable software artifacts (e.g. web apps, video explainers) that would have never made sense to create before. Push the boundaries here and you'll be surprised.

[zwdroid @zwdroidai]

加入了人为指令和语音指示牌来测试，从而避免掉可以“规则化或算法”计算的汽车避让。
Added artificial instructions and voice signage for testing, thereby avoiding car avoidance that can be calculated by "rules or algorithms"

[ollama @ollama]

Ollama now supports Jev-like decision models all locally.

Use decision models like Nimble for tasks like ticket triaging, model routing, and content moderation.

ollama pull nimble

Here’s Nimble playing Ollama racer through the new local /v1/systemone API by making decisions in real-time. 🏎️

[zwdroid @zwdroidai]

@gefei55 好有耐心

[zwdroid @zwdroidai]

@LinearUncle opus5.5 没 hold 住

[zwdroid @zwdroidai]

@xiuerold @aiwarts 那肯定不会啊

[zwdroid @zwdroidai]

@yetone 我去，这也太全了吧

[zwdroid @zwdroidai]

@KyoTA97 网络报备就是合法的啊，大公司都有专线的，外网畅通无阻的，现在中小企业做海外的都可以的

[zwdroid @zwdroidai]

送你一轮千年月，opus5.5 生成，独属中国人的浪漫，月亮。