pinturicchio

[Enkk @Carboaceto]

Sono anni che ripeto che "bisogna fare alfabetizzazione" sull'AI. Beh, questo è il mio tentativo. Due anni di divulgazione riassunti in 50 minuti, con un linguaggio semplice e con animazioni per facilitare la comprensione.
https://t.co/0GlybTv5bp

[Sebastian Raschka @rasbt]

After 18 months of writing, coding, and experimenting, Build a Reasoning Model (From Scratch) is
finally out!

My first copies just arrived! 📚

440 full-color pages. Inference scaling, reinforcement learning, and distillation from scratch.

[🏴‍☠️ The Pirate 🏴‍☠️ @Pinperepette]

https://t.co/GWJU6GDHf6

[🏴‍☠️ The Pirate 🏴‍☠️ @Pinperepette]

ogni giorno esce qualcosa sugli agenti, molte cose interessanti, molte inutili 😅...
poi ogni tanto arriva una PR come quella di @antirez
e cambia davvero il modo in cui li pensi. Ne parlo su -------> https://t.co/AwqKaaNLiJ

[🏴‍☠️ The Pirate 🏴‍☠️ @Pinperepette]

Sono partito da un seggio elettorale e sono arrivato alla Victoria Line. Il filo e' lo stesso: le code non funzionano come pensi. https://t.co/nH10JSV9q9

[Claude @claudeai]

Ads are coming to AI. But not to Claude. Keep thinking.

[DAIR.AI @dair_ai]

Many are trying to code with agents to boost velocity.

But at what cost?

The default assumption is that AI coding tools are additive: IDE assistants help, and autonomous agents help more. Stack them together, get more productivity.

But nobody had measured whether this is actually true in production repositories.

This new research presents the first large-scale causal study of autonomous coding agent adoption in open-source projects, analyzing repository-level outcomes across development velocity and software quality.

The methodology: staggered difference-in-differences with matched controls using the AIDev dataset.

Repositories are split into two groups: agent-first (AF), where agents are the first AI tool adopted, and IDE-first (IF), where repositories already used AI IDEs like Copilot or Cursor before adopting agents.

AF repositories see massive front-loaded gains: +36% commits and +77% lines added on average. At adoption month, the spike hits +111% commits and +216% lines added. These gains persist.

But IF repositories see almost nothing: +4% commits and +1% lines added. The short-lived bump at adoption quickly fades, and by month 6, lines added turn negative (-45%).

The quality findings are worse. Regardless of prior AI exposure, agent adoption increases static-analysis warnings by ~18% and cognitive complexity by ~35%. These effects are persistent. AF repositories reach +49% complexity by month 5. IF repositories hit +44-51% and stay there.

Autonomous agents introduce complexity debt even when velocity advantages fade. Teams already using AI IDEs face coordination and integration bottlenecks that limit throughput, but still accumulate the maintainability risks.

Coding agents are powerful but risky accelerators. Substantial velocity gains materialize only when agents are a project"s first AI tool. Prior AI IDE exposure moderates the benefits but not the quality risks. Selective deployment and strong quality safeguards are essential.

Paper: https://t.co/6lVAuUPxvh

Learn to build with AI agents in our academy: https://t.co/zQXQt0PMbG

[＊ @jaraaxz]

Best of Spalletti ball so far https://t.co/RsrFuIHLsg

[DAN KOE @thedankoe]

https://t.co/7l7Jef99QZ

[James Zou @james_y_zou]

Today in @NatureMedicine we report that AI can predict 130 diseases from 1 night of sleep🛌

We trained a foundation model (#SleepFM) on 585K hours of sleep recordings from 65K people—brain, heart, muscle & breathing signals combined.

AI learns the language of sleep🧵

[Andrej Karpathy @karpathy]

The hottest new programming language is English

[Imade. @ImadeIyamu]

OpenAI's Research Residency Program just opened (Relocation assistance is available)

6-month program designed to identify, mentor, and develop exceptional individuals

Compensation: $18,300 per month

https://t.co/navlUWTkPa

[Tech with Mak @techNmak]

There are 2 career paths in AI right now:

The API Caller: Knows how to use an API. (Low leverage, first to be automated, $150k salary).

The Architect: Knows how to build the API. (High leverage, builds the tools, $500k+ salary).

Bootcamps train you to be an API Caller. This free 17-video Stanford course trains you to be an Architect.

It's CS336: Language Modeling from Scratch.

The syllabus is pure signal, no noise: 
➡️ Data Collection & Curation (Lec 13-14)
➡️ Building Transformers & MoE (Lec 3-4)
➡️ Making it fast (Lec 5-8: GPUs, Kernels, Parallelism)
➡️ Making it work (Lec 10: Inference)
➡️ Making it smart (Lec 15-17: Alignment & RL)

Choose your path.

(I will put the playlist in the comments.)

♻️ Repost to save someone $$$ and a lot of confusion.
✔️ You can follow @techNmak, for more insights.

[Marco @Marco_Acortes]

Corona virus....its coming

[Zaka✨ @zakariamlk]

I am very satisfied watching this video

[Scatto @matscattolin]

Francisco Barido, a thread.

[pinturicchio @MarcoLeto4]

@kabarza_ X xx

[Alessà Yildiziano @Yildizianismo]

Quanto cazzo è bello non essere loro.

[sabir hussain @sabir_huss50540]

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[pinturicchio @MarcoLeto4]

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