Janitha B Rathnayake

[Rahul Kumar @RahulTechX]

One of my friend makes $1.2 million a year as an Anthropic engineer.

I asked him how he learned prompting so well. He sent me a video that was never supposed to get out. Their core team's prompting playbook.

You won’t find anything better about prompting than this 30 minutes video.

I watched it last night. Halfway through, I realized I've been using Claude completely wrong for two years.

Watch it, then read the article below.

[Dhruv kumar @Dhruvkumar16797]

Anthropic engineer: "at Anthropic, we don't write prompts anymore. we build loops"

in 42 minutes, she shows how the Claude team creates loops that prompt themselves

if this cost $400, people would call it the best agents course of the year

it's free

watch it,

[Jouhatsu | AI Influence Operator @Jouhatsu_ai]

Anthropic vient de publier un guide de 37 minutes expliquant comment créer des Agents IA capables d’automatiser une entreprise entière.

( THREAD🧵 La suite de la vidéo est dans les commentaires. )

C’est gratuit et ça vient directement des ingénieurs qui ont créé Claude.

Des Agents qui fonctionnent réellement, délèguent des tâches et accomplissent leur travail de manière autonome.

🔖 Mets ce guide en signet. Tu risques d’en avoir besoin plus vite que prévu.

[Dhruv kumar @Dhruvkumar16797]

🚨Leave Netflix tonight.

Watch this 2 h 34 min Stanford class.

It's the clearest, most complete, and brutally honest explanation out there on how ChatGPT and Claude are really built.

From Tokenization and BPE to the Transformer architecture, the training pipeline, and the next-token decoder. No fluff. No marketing. Just the truth.

Doesn't matter if you've never touched a line of AI code or if you spend your days launching Agents: by the end, you'll suddenly connect a ton of pieces you've been trying to fit together for years.

The real core boils down to this:

How text turns into numbers the model can "eat" (BPE tokenization)
The sole mission of a language model: predict the next token
How the Transformer uses Attention so tokens can pass messages to each other
In training, the NLL loss pushes the probability of the entire sequence
In generation, the decoder builds the response token by token

The big-picture view that takes most people years to form… this class delivers it to you complete in one sitting.

Free up your time.
This could be, no exaggeration, the most valuable class you watch this month.

[Janitha B Rathnayake @JanithaBR]

@AzzamAmeen What about universities ???

[Janitha B Rathnayake @JanithaBR]

@TechKatha ���ටේ උන්ව ආතල් එකට ගන්න දවස😂😂

[Janitha B Rathnayake @JanithaBR]

#first move in tweeter👍👍