Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

🧵1/2 MIT's Charles Leiserson covers the progression from source code to compilation, machine code, hardware interpretation, and ultimately, execution. 👇Full MIT Performance Engineering of Software Systems Course 👇

115,218 görüntüleme • 2 yıl önce •via X (Twitter)

10 Yorum

stratejake profil fotoğrafı
stratejake2 yıl önce

I still think about this course on like a weekly basis.

chrinovic マーク profil fotoğrafı
chrinovic マーク2 yıl önce

Gold 🙌

unprofessor profil fotoğrafı
unprofessor2 yıl önce

2018! Hope he's doing ok. Was always nice when I was in grad school.🫡 He wasn't exactly the low level guy tho. Glad he's keeping at it.

Igor Komolov 𝕏 profil fotoğrafı
Igor Komolov 𝕏2 yıl önce

Anthony Hopkins took a page from this man

0rbit4l profil fotoğrafı
0rbit4l2 yıl önce

this looks so amazing; thanks for sharing!

Erick Wesonga profil fotoğrafı
Erick Wesonga2 yıl önce

@rattibha

Umer Saeed profil fotoğrafı
Umer Saeed2 yıl önce

#56155717130 #futurenowpk #machinelanguage @threadreaderapp unroll

Thread Reader App profil fotoğrafı
Thread Reader App2 yıl önce

Your thread is going viral! #TopUnroll 🙏🏼@_UmerSaeed for 🥇unroll

Matt profil fotoğrafı
Matt2 yıl önce

gold

Ozlem Peksoy Bishop profil fotoğrafı
Ozlem Peksoy Bishop2 yıl önce

this is so good indeed

Benzer Videolar

In 2025, it was rational to be skeptical about whether AI would change the future of software development. In 2026, it's not, anymore. With Charity Majors: Timestamps: 00:00 Intro 02:56 How Parse led to Honeycomb 06:00 The limits of individual productivity metrics 09:08 How Charity’s perspective on AI has evolved 13:50 Rewriting code vs. editing code 19:20 Production as a stage of development 22:14 Code reviews 26:56 Non-deterministic systems 31:11 Sensible uses of AI 37:41 The two AI camps 44:40 Why AI works so well for building software 49:42 DevOps 55:13 Modern observability 1:00:40 Handling context overload 1:01:56 What’s new in Observability Engineering’s 2nd edition 1:07:45 What effective leadership looks like 1:10:25 Engineering management: what is changing? 1:16:31 Junior engineers 1:18:01 AI fatigue 1:21:39 Book recommendations Brought to you by: • Antithesis — turbocharge testing of your systems by running your whole system under aggressive fault injection. Teams like Jane Street, and the etcd community rely on Antithesis. • Buildkite — the CI platform trusted by OpenAI, Anthropic, Cursor, Meta, Uber, NVIDIA, Airbnb and many more. Engineered to absorb whatever your coding agents throw at the build queue. • WorkOS — make your app and agents Enterprise Ready, with SSO, SCIM, RBAC, and more. 1. The question engineers need to answer: what would it take for you to be fully comfortable shipping code you have not read? Charity believes it is a “when” and not an “if” that professional software engineers will ship code they never looked at – and thus do not understand – to production. Engineering is building the systems that validate this code, and allow shipping with full confidence. 2. AI could have the software industry go through the “pets” to “cattle” change that compute infra went through in the 2010s. Up to now, writing software from scratch was far more expensive than editing existing software. But now, generating hundreds of variants of a function can be done faster than how long it would take you to hand-write it once. Charity believes that we might be at the beginning of the transition from “pets” to “cattle” that happened at the hardware infrastructure layer. Before the 2010s, configuring and repairing individual servers was commonly done. But with tools like Terraform and Kubernetes, individual servers having issues are no longer fixed up: they are re-created instead. Charity thinks the same might happen with code, sooner rather than later. When there’s an issue with the code, generate new code that solves it, and is verifyably correct.​ 3. Non-deterministic systems require more engineering discipline versus before. With code written by AI, we’re reducing the trust in the code (because we no longer wrote it), so we need to increase trust at the other part of the development process. Specifically, at validation: with things like tests, evals, and conformance testing.

Gergely Orosz

30,495 görüntüleme • 1 ay önce