Sid

Open AI.
Love Open Source.

[Sid @swberry_fields8]

@Polymarket @grok who should be happy for this?

[Sid @swberry_fields8]

@Polymarket haha

[mememandir @mememandir]

Dear ministry of IT,
Scammers are reportedly using Slack & MS teams to leak NEET papers. Kindly issue a ban.

[Guillermo Rauch @rauchg]

There seem to be two main groups

1️⃣ Those who post all day long about using coding agents but don’t seem to ship anything

2️⃣ A small group whose output has dramatically increased and are constantly shipping valuable things

The irony is that the ratio of these probably remains unchanged from before AI even existed. It also seems 2️⃣ can outship 1️⃣ even more so the “ship-rich will get richer” so to speak.

[Felix Haas @felixhhaas]

High-Performance Teams in the Age of AI 🔥

I've spent a lot of time thinking about what makes teams move incredibly fast while others get stuck.

What I've seen is that performance in the age of AI has surprisingly little to do with credentials on paper and increasingly everything to do with mindset.

Here are a few observations from Lovable:

[paperpaper @paperpaper886]

最近在带入组的本科实习生，发现怎么读论文其实是科研训练里最容易被忽略的一步。
推荐一篇每个科研新人都该读的经典短文：S. Keshav 的 How to Read a Paper。

文章提出了非常实用的“三遍读论文法”：
第一遍，5 到 10 分钟快速扫读：标题、摘要、引言、章节标题、结论和参考文献。
目标是回答 5C：
Category, Context, Correctness, Contributions, Clarity。
也就是判断这篇论文是什么、和谁相关、假设是否合理、贡献是什么、写得清不清楚。
第二遍，认真读论文主线，但先跳过证明细节。重点看图表、实验设置、结果是否清楚、引用了哪些关键工作。
第三遍才进入深度理解：尝试像复现一样重建作者的思路，检查假设、方法、创新点和潜在漏洞。

放在今天看，这个方法和 AI 辅助读论文其实很契合。
第一遍可以让 AI 帮忙快速总结论文的研究问题、核心贡献和主要结论，但自己一定要判断这篇文章是否真的值得继续读。
第二遍可以让 AI 帮忙解释方法、实验设置、图表和不熟悉的概念，但不能只看 AI 总结。关键图表、实验设计和结果数字一定要回到原文核对。
第三遍可以让 AI 扮演 reviewer，帮你追问：这篇文章的假设是否成立？实验是否支持结论？有没有 missing baseline？有没有潜在的数据泄漏、评价偏差或过度 claim？

读论文不是“读完”就行。真正重要的是知道什么时候快速跳过，什么时候认真理解。
尤其在 AI 工具越来越强的情况下，科研新人更需要训练自己的判断力。
AI 可以帮你压缩信息，但不能替你决定一篇论文是否重要、是否可信、是否值得借鉴。

https://t.co/8gUc4HbLwR

[Garry Tan @garrytan]

The mistake: Using AI for simple tasks

You should use it for complex, multi-stage tasks that involve lots of people and moving parts

[Axios @axios]

Scoop: Anthropic flies staff to D.C. to clean up White House fight https://t.co/B8X8mqBTd6

[Factory @FactoryAI]

Watch @HarryStebbings ask @matanSF all the difficult questions about commoditization across the AI stack, Factory's controversial culture, or whether US startups should use Chinese models.

The 20VC episode is out now!

[dom @ryiacy]

My interpretation of this:

Right now, Anthropic and OpenAI are making a killing by selling enterprise FDE services to F500s, building workflows for them on top of proprietary models, then using the traces and context from this to build RL envs to improve the models.

This is crazy amounts of leverage - instead of buying this data they're getting paid gigantic consulting fees to extract it.

This also goes way beyond typical consulting in scope - organizations are effectively outsourcing key learning curves and domain knowledge to the AI labs.

Despite that, it's so far been worth it for them because the value of skilled FDE is so high and the ROI so fast, and orgs are willing to pay a premium for competent AI implementation.

But in the long run, one of two things happens: either orgs are gonna get hooked on this and end up paying for the model training that replaces their business, or they find a way to build and own their own model ecosystem.

What that looks like is developing some combination of AI models, evals, RL envs, and workflows. Initially probably the model will still be an off-the-shelf frontier model from a top lab.

But as firms build out more sophisticated eval / RL env (increasingly the same thing) infra, it starts to become viable to post-train an custom model on top of an OSS base. Cursor have done this successfully with their Composer model RL'd on top of Kimi.

Sidenote, this is the same conversation that a lot of national governments in Europe are having in the past week. When we look at what the rhetoric about 'sovereign AI' in the UK actually boils down to, it's doing custom post-training on top of an OSS model, and then running it on local GPUs.

Ultimately, the current feeding frenzy for AI services in all of its guises - FDE, AI consulting, etc - should raise questions about long-term sustainability. If consulting services are truly a value add and competitive advantage, then in the long term you want to in-house.

[Sid @swberry_fields8]

@mlejva I liked his speech quiet a bit you know.

[Sid @swberry_fields8]

@paraschopra I think sarvan quoted a 4 k count on GPUs.

[Sid @swberry_fields8]

@tereza_tizkova I don't but loved the hype.

[Sid @swberry_fields8]

@gabriel1 Maybe some company from india will be ahead in a few months - you never know.

[Sid @swberry_fields8]

I really loved the model!

[Claude @claudeai]

Claude Fable 5 has been out for a couple of days. Some projects people have already built with it:

[Aakash Kumar Nain @A_K_Nain]

I told you! 😝

[Gowthami @gowthami_s]

I was looking forward to the online lectures of this course. Very timely curriculum

[Tereza Tizkova, 🌁 at AI Engineer 06/29-07/02 @tereza_tizkova]

Do you use computer use agents?
If yes, for what?

[Harjot Gill @harjotsgill]

Aiden - you clearly have no idea about what you are talking about. We have more users than everyone you just mentioned (combined). See https://t.co/oXIySEAk9K

Still, I am curious how we can make it simpler and fool proof for indie developers. Maybe simpler controls and a lot of handholding can help.

[Garry Tan @garrytan]

Skill issues at big company means small new ones can eat their lunch