Yury Jansen 🇧🇷 🇧🇷 🇧🇷

Paraibano de Campina Grande. Juntos, somos mais fortes.

[SONIA @S0N_IA]

ESTO ES ORO PURO

alguien recopiló todo lo que necesitas para dominar claude en una sola guía, y es GRATIS

Cómo construir tu primer agente de IA (Guía completa)

si esto hubiera existido hace un año, mi primera app me habría tomado un día, no 2 semanas

guárdalo cuanto antes.

[Nico @nicos_ai]

Google lanzó la herramienta que todo desarrollador pedía desde hace años.

Se llama CodeWiki.

Pegas cualquier repositorio y la IA lo convierte automáticamente en una documentación interactiva.

No solo resume el código:

• Genera diagramas automáticamente
• Explica cómo funciona cada parte
• Crea tutoriales paso a paso
• Detecta arquitectura y dependencias
• Y hasta monta un chatbot que entiende tu código completo

Básicamente:
convierte proyectos imposibles de entender en algo que cualquier desarrollador puede navegar en minutos.

Enlace abajo👇

[Swati Gupta @hrswatigupta]

INSTEAD OF WATCHING NETFLIX TONIGHT.

Spend 1 hour with this.

Claude AI FULL COURSE that teaches you how to BUILD and AUTOMATE anything.

The people who watch this tonight will wake up tomorrow with a new skill.

Watch it and bookmark it now

[Anatoli Kopadze @AnatoliKopadze]

Google Brain founder, Andrew Ng:

"Prompting will be dead in 6 months, graphs are what's replacing it."

In 2 hours at Stanford he shows how to build agents that work and improve entirely on their own.

The first 10 minutes cover what most $500 courses never do.

Watch the lecture first, then read the guide below on how to build a system that improves itself.

[Lunar @LunarResearcher]

Google just released a free 2-hour course on full Graph Engineering.

How to go from one prompt to 100 agents running inside one graph:

17:44 - Build your first AI agent
39:30 - Run agents with loop engineering
1:12:38 - Turn agent loops into graphs
1:34:26 - Build agents that throttle themselves
1:55:05 - Orchestrate the full multi-agent system

Most people build one agent and stop there.

Google is teaching the full stack:

Prompt → Agents → Loops → Graphs → Multi-Agent Systems

Single agents are the old workflow.

Self-regulating agent graphs are the new one.

This 2-hour watch is worth more than most paid agent engineering courses.

Bookmark and watch it today

Then read the full architecture below ↓

[Shann³ @shannholmberg]

this is my AI marketing engine

say I have an idea I want to turn into a campaign. a guide, a cohort, a webinar, something we have been planning for a while, or just something cool I came across and want to build around.

it usually starts as one thing, an idea or an evergreen piece, and this engine is how that one thing becomes a full campaign and fans out across every vertical

the engine is a graph, a general step-by-step the idea moves through. at each step I can swap the harness, the loop, the tool, or the model to fit the campaign

right now I'm testing gstack, superpowers, and matt's skills, engineer tools I'm bending toward marketing

the models can write and design well now. what takes work is the context you feed them, the routing per job, and the evals that catch what is weak

there are eval stops all through the graph. both human and other agents evaluating and reviewing output, and then looping it back if it doesnt

here is the path it travels, from a raw idea down to a shipped campaign

PLANNING

1. the idea in
I dictate the whole thing out loud, every half-formed thought, and let the skill bundle catch the mess and hand me back a starting brief. this is the karpathy point, get it out of your head first and clean it up after

2. ideation
off that brief it opens the idea into angles and directions to choose from. I throw most of them out

3. research and context
this is where I pull context, and how much I need depends on the campaign, sometimes a ton of internal history, sometimes barely any. the internal side is our company brain (gBrain), the voice, the past campaigns, what converted, the offers, the ICP. externally I pull the market, the competitors, the hooks working this week

4. synthesis
different models merge all of that into a draft plan. the plan itself, the architecture and the trade-offs, runs on opus 5, and the narrow work underneath gets cheap fast models. that split is model routing

5. the sign-off
nothing crosses into execution until I sign it off. I read the draft against our marketing protocols, the voice rules, the brand, the SOPs, and I cut, sharpen, or send it back

EXECUTION

6. handing it to the build
once the plan clears the sign-off it goes into the build, and the idea splits into all the parts a campaign needs. here I run two shapes depending on the job. when a piece is one task that has to clear a bar I run a loop, the agent drafts, checks itself, fixes, and keeps circling until it is good.

the bigger many-part pieces I build as a graph, drawing the steps and routes ahead of time so the agents travel the map I laid down. it is usually a bit of both

7. the models doing it
routing runs in execution too, you do not pay opus prices to resize a thumbnail for example

8. what stays with people
some of it I coordinate, some assets I make myself, and the work that needs taste, a relationship, or a client in the room I hand to the agency team

that one idea comes out the other end as a full campaign across every vertical, landing pages, blogs and guides, video scripts, email, PR, paid, and the social cuts

then the results come back in, what got bookmarked, what converted, what died, and that updates the brain for the next campaign

everyone has the same models, so the edge is the graph, the brain, and the protocols, and those you have to build yourself

[Roan @RohOnChain]

Don't waste 2 years learning to build AI agents.

An Anthropic engineer who built Claude Code tells you what to learn from scratch instead.

60 minutes course. Free:

00:00 - AI agent architecture
24:47 - LangGraph AI agent
29:15 - building AI agents live

Prompting is the old job. Building AI agent loops is the new one.

Bookmark now & watch it. Then build your own AI agent with the guide below.

[Kanika @KanikaBK]

Andrew Ng dropped a 12-page PDF that changed how I think about building agentic systems and I have been building with agents long enough to know that does not happen often.

Most people chasing better model outputs are solving the wrong problem because the architecture underneath the model matters more than the model itself and this PDF explains exactly why in four design patterns that stack on each other in a way that finally made the whole picture click.

Reflection first, where the agent reviews its own work against a rubric and you get a ten to thirty percent quality lift on day one without changing anything else.

Then tool use so the agent stops guessing and starts checking real data.

Then planning so when something breaks the agent reroutes on its own instead of failing silently.

Then multi-agent where you split roles so different checks catch different errors.

And at the top a graph that externalises shared state so loops can run overnight and the agent picks up exactly where it left off because the graph remembers everything the agent forgets.

My article is the practical thirty minute version of building your first knowledge graph from scratch so you have something working by the time you finish reading.

Full guide below.

[NO1ennn @N01ennn]

AN ANTHROPIC DEVELOPER TOLD ME "MOST PEOPLE WILL NEVER BUILD THIS. NOT BECAUSE IT'S HARD. BECAUSE THEY DON'T BELIEVE A FOLDER CAN OUT-THINK THEM"

the graph you're looking at is my vault after five weeks. every dot is something I wrote. every line is a connection Claude found on its own

I never linked anything. I never sorted anything. I dropped raw files into a folder and let Claude read them. it organized the whole thing, connected it, and now it maintains it while I sleep. zero effort from me

but the graph is not the point. it just looks good in a screenshot

the point is what happens when you ask it a question. "what do I keep getting wrong?" "where am I repeating the same mistake?" "what did I write four months ago that I no longer believe?"

and it answers. from my own words, with dates, with sources. no hallucination, no google. just me talking to the sharpest version of my own memory

the developer said one more thing that stuck. "the people who build this never go back. the people who don't will keep re-explaining themselves to a chatbot that forgets them every 24 hours"

five weeks in, he was right. I stopped starting from zero every morning

[CyrilXBT @cyrilXBT]

Andrew Ng:

“AI agents are doing almost 100% of my tasks now - the hype has exceeded my expectations.

in 3-6 months, we’ll all be building graphs to orchestrate self-improving agents. No more prompting.”

In a 40-minute talk, Andrew Ng explains how to build self-improving agentic systems from scratch.

Worth more than a $500 agentic course.

Watch this video, then read the article below on how to become a graph architect.

[rari @0xwhrrari]

your agent is already a graph

you just cannot see the bugs yet

7 graph rules for production agents

1) type the state

every node reads and writes a contract

→ https://t.co/VDhbkYNWlE

2) draw topology first

if you cannot see the paths you cannot debug the run

→ https://t.co/gFBE1b4nGX

3) make execution durable

a failed worker resumes from state instead of starting from zero

→ https://t.co/QST61rS1LN

4) put a budget on every cycle

an edge that can loop needs a counter and an exit

→ https://t.co/KNyTGswAV3

5) retrieve relationships

chunks tell you what exists

edges tell you how it connects

→ https://t.co/kVdpP7tAJk

6) store provenance

every answer points back to the nodes and edges that produced it

→ https://t.co/i8rz4NXfFL

7) trace the full path

one run id across every node tool and branch

→ https://t.co/CM9UwOPyRE

linear agents look simple until they fail

graphs look complex until you have to debug them

save this

then read article below

[CyrilXBT @cyrilXBT]

Sam Altman:

"We're going to see 10-person billion-dollar companies pretty soon."

"If I were 22 right now, I'd feel like the luckiest kid in history."

Most people will read this, feel inspired for 3 minutes, and go back to what they were doing.

The ones who act will build a one-person company this weekend.

One tool. Claude Cowork. Full operation.

This is the exact playbook ↓

[0xRafy @0xRafy]

Head of Claude Code Boris Cherny:

"Opus 5 just won't stop. no /goal, no /loop needed. it just knows it needs to do the task"

he asked Claude to rewrite an app in one prompt. two weeks later - still running. thousands of agents spawned

Anthropic runs 20-30 daily routines across every codebase. Claude maintains itself now

deleted 80% of the system prompt. "the model is more intelligent without these prompts"

~35mins, free

watch today, then read how to go from loops to Graph architect in the article below ↓

[0xMarioNawfal @RoundtableSpace]

YOU INSTALLED CLAUDE CODE AND STOPPED THERE.

HERE ARE 22 SKILLS ACTUALLY WORTH ADDING:

BUILD

1. superpowers — plans and debugs before writing
https://t.co/KmQXWSKn20

2. gstack — 23 skills in one setup
https://t.co/VwLP1EgOkN

3. caveman — direct answers with no padding
https://t.co/FwevIKFJqD

4. ponytail — says less and writes the line
https://t.co/TK7280uBYT

5. codex — runs Codex inside Claude Code
https://t.co/RDRzUA9Hnj

6. i-have-adhd — stops Claude burying the answer
https://t.co/S0wjVl2GML

DESIGN

7. ui-ux-pro-max — working palettes, fonts, and layouts
https://t.co/gJCiZV6oNk

8. taste-skill — makes websites look expensive
https://t.co/ztWqKeWQzI

9. impeccable — adds the final front-end polish
https://t.co/5vOBt41YqB

10. hyperframes — turns web pages into videos
https://t.co/AjQyL5PJMs

11. emil — creates polished interface animations
https://t.co/vUkk72OSSp

12. gsap — builds smooth web animations
https://t.co/Y1olkCBvUl

RESEARCH

13. skill-creator — helps you build custom skills
https://t.co/9OaIQ0zJhm

14. graphify — turns notes into visual knowledge maps
https://t.co/RW6p7OmQmz

15. last30days — researches what the internet discussed recently
https://t.co/ZiXtpEwaOE

16. agent-browser — lets Claude operate websites
https://t.co/ce8KyrSxPm

17. find-skills — finds the right skill for any task
https://t.co/iIUcctHops

18. claude-hud — shows what Claude is doing live
https://t.co/Oph2nCYpPt

MARKETING

19. remotion — builds complete videos with React
https://t.co/CinAtWQ3qR

20. marketingskills — 40 marketing skills in one pack
https://t.co/g3c5WLDOar

21. humanizer — removes obvious AI writing patterns
https://t.co/VnkiSTSgaA

22. social-media-skills — a free pack for creating content
https://t.co/RWUs7KV4Wy

MOST INSTALL WITH:

npx skills add [repo]

YOU WILL NOT USE ALL 22 DAILY.

START WITH SUPERPOWERS, CAVEMAN, AND LAST30DAYS.

[Rajesh Agarwal @Rajesh992510253]

Claude FULL COURSE 1 HOUR (Build & Automate Anything)

[EDU @edusoyy]

El equipo de IA aplicada de Anthropic acaba de soltar un taller de 25 minutos donde te enseña como se usa Claude Code de verdad

He visto cursos de 500 dolares que no explican lo que ellos sueltan en los primeros 5 minutos

Arquitectura, como conectar tus herramientas entre si, por que se rompen los flujos que arma la mayoria y el modelo mental completo

Todo en un video y completamente gratis

Funciona seas programador, principiante o alguien que lleva meses usando Claude

[MIKE @mikenevermiss]

Ex-Google engineer built a graph-based workflow that made Claude Code do nearly 10 days of work in about an hour.

Here's the exact setup he uses.

step 1.

create a separate Git worktree for every task.

run:

git worktree add ../task-1 -b task-1

open a new terminal, cd into it, and launch Claude Code.

repeat this for every agent.

each one gets its own branch, so nothing conflicts.

step 2.

give every Claude agent a different task.

same repository.

different branches.

they all work in parallel.

step 3.

once they're done, review the pull requests.

keep the good ones.

merge what works.

step 4.

add a review agent.

paste each PR into Claude and ask:

"does this implementation match the original specification"

every agent now has another agent reviewing its work before it gets merged.

step 5.

scale it up.

48GB of RAM can comfortably run around 20 Claude Code agents.

128GB can handle roughly 50.

most engineers run Claude Code once and wait.

he runs dozens of Claude Code agents in parallel, then merges the best results.

this 60-minute lecture is one of the best free resources on graph engineering and multi-agent workflows.

bookmark it and watch it below.

[KingWilliam @kingwilliam_]

https://t.co/AspsoV0LUg

[Antonio Costa | IA @xetgepete]

🚨 URGENTE: O Claude agora pode CRIAR um canal do YouTube do zero e levá-lo à monetização em apenas 90 dias. 100% gratuito.

Aqui estão 8 prompts para conseguir isso:

[Nico @nicos_ai]

RAG vs. Graph RAG, explicado de forma visual.

El RAG tradicional tiene varias limitaciones.

Imagina que quieres resumir la biografía de una persona y que cada capítulo del documento habla de un logro diferente.

Con un RAG clásico, esto es complicado porque solo recupera los fragmentos (top-k) que considera más relevantes. El resultado suele perder contexto y dejar información importante fuera.

Graph RAG resuelve este problema.

Cómo funciona?

→ Convierte los documentos en un grafo de entidades y relaciones.
→ Durante la búsqueda, recorre ese grafo para recuperar todo el contexto conectado.
→ Envía ese contexto completo al LLM para generar una respuesta mucho más precisa.

En el ejemplo de una biografía, cada logro está conectado al nodo de esa persona.

En lugar de recuperar unos pocos fragmentos aislados, Graph RAG recorre el grafo y obtiene todos los logros relacionados antes de generar el resumen.

El resultado es una respuesta mucho más completa que la de un RAG tradicional.

Además, Graph RAG aprovecha mejor una de las mayores fortalezas de los LLM: razonar sobre datos estructurados y relaciones entre entidades.

Y una vez tienes la arquitectura adecuada, el siguiente paso es optimizar la eficiencia.

Muchas implementaciones de RAG siguen dependiendo en gran medida de la búsqueda vectorial, una capa que puede reducir su consumo de memoria hasta 32 veces mediante binary quantization.

En el artículo explico cómo funciona todo el proceso y cómo implementar este tipo de arquitectura.

Si quieres profundizar en Graph Engineering, échale un vistazo a este artículo: