Gourivio

AI ASTRONAUT

[elune @elune0x]

I turned Claude into an entire company

42 skills structured like a real org chart

Every department has a real function

Every skill can be installed from the links below

Developers

Superpowers
→ https://t.co/pUYLg6vnDC

Context7
→ https://t.co/8gWFEY80uV

Skill Creator
→ https://t.co/fFXJkhchpB

MCP Builder
→ https://t.co/fFXJkhchpB

Webapp Testing
→ https://t.co/fFXJkhchpB

Claude-Mem
→ https://t.co/T3Syy24ljy

Designers

UI UX Pro Max
→ https://t.co/cM7LPrKvzs

Taste
→ https://t.co/1AOjzPxzEh

Frontend Design
→ https://t.co/1AOjzPxzEh

Transitions
→ https://t.co/noSrAKxhlN

Web Artifacts
→ https://t.co/fFXJkhchpB

Brand Guidelines
→ https://t.co/fFXJkhchpB

Marketing

45 skills for copywriting, SEO, lead magnets, and campaigns

Get them here
→ https://t.co/XcTkl0SQkA

Social Media

17 skills for posts, Reels, thumbnails, and content workflows

Get them here
→ https://t.co/VA7l15hGFT

Finance

8 skills for financial statements, reconciliation, and audits

Get them here
→ https://t.co/ccpa8C3ZKJ

Small Business

31 skills for cash flow, payroll, invoicing, and operations

Get them here
→ https://t.co/8IQCIepYk6

Legal

9 skills for contracts, NDAs, and compliance

Get them here
→ https://t.co/9ASpcRWKPs

Not prompts

Not wrappers

A complete operating system for Claude

Bookmark this and build your own AI company

[Neo Kim @systemdesignone]

If you want to become world-class with AI agents, then watch these 10 videos (save them):

[iPaul @iPaulCanada]

2026世界人工智能大会，某科技公司展示的跨时代教具--AI黑板

[Avid @Av1dlive]

Andrew Ng just dropped a free 1-hour course on building agentic knowledge graphs from scratch

Watch it today, then read the article below on how to become a graph engineer.

Save and bookmark this no matter what. It'll be the most productive thing you do this week

[↓ Save this playbook before it disappear in your feed]

• 00:00 : What agentic knowledge graphs are and why agents need them

• 03:07 : How to construct your first agentic graph

• 14:00 :How multi-agent systems get architected on top of graphs

• 23:00 : How to build agentic graphs with Google's ADK

• 01:06:03 : Why graphs are the future of agentic AI

[Shann³ @shannholmberg]

graph engineering explained (marketing edition)

graph engineering is about designing the map your agents run inside, you draw the steps and the routes between them ahead of time, then they travel the path you put down

it is the layer past looping, where one agent just circles a single task until the work meets the standard you set

every agent graph is built from 4 pieces:

> nodes: a stage the work passes through, research, draft, score, publish, a few run once, others are their own loop the agent circles until that step clears

> routes: the paths you draw between the nodes ahead of time, every direction the work is allowed to travel

> checkpoints: the check on each route that reads the result and sends the work forward when it clears or back to an earlier node when it misses

> gates: a checkpoint the work cannot skip, nothing publishes until the draft clears the rubric

if you have built a workflow in n8n you have already drawn one, nodes you connected, branches that fire on a condition, a step that loops until it clears.

an agent graph is that same shape, each node holds an agent doing the work n8n would hand to a single api call

the content graphs I run at my agency all take this shape, here is one you can build for SEO

> 1 research: pull the keyword, the search intent, the competitors ranking for it, and the questions people keep asking
> 2 brief: turn that research into a brief, the angle, the entities to cover, the queries the piece has to answer
> 3 draft: an agent writes the article from the brief and nothing else in its context
> 4 score: a critic grades the draft against your rubric, depth, intent match, originality. this node is a loop, it sends the weak drafts back to 3 and only releases one that clears
> 5 publish: once the rubric clears and the brand rules pass, the agent adds internal links and the piece goes live

each arrow between those is a route, every grade is a checkpoint that picks which route the work takes next, and the draft and score nodes form a loop inside the bigger map while the rest run once

this is where the word graph starts to mislead. that draft and score loop can pass itself, the critic likes the draft, its rubric clears, it publishes, and still never ranks. the loop was grading the writing against another agent's opinion while the only thing that counts is whether it ranked

so you add a checkpoint the agents cannot argue with, one that reads live search and AEO signals from outside the graph:

> did google index it
> is it climbing on the target query
> are AI answers citing it
> do people stay once they land

if those move, the map keeps its shape and you feed it the next keyword. a stall sends the work back to research instead, because a miss this late usually traces to the angle or the intent you chose at the start, which a rewrite cannot fix

anchor the map to results the agents cannot fake, and freeze the few rules they never rewrite, your brand voice and the claims you cannot make

with the anchor in place, a failed piece shows you the exact node it broke on

[wast3 @0xWast3]

https://t.co/j9thHBFgdB

[Hanako @hanakoxbt]

10 agent evals for AI engineers:

(explained with usage)

1) golden set
→ a fixed set of cases you never edit, run on every single change.
→ use as the baseline that tells you whether anything moved at all.

2) llm as judge
→ a second model scores the output against a written rubric.
→ use when the answer is open-ended and there is no string to match against.

3) rubric scoring
→ one number per dimension: correctness, tone, safety, cost.
→ use when a single score hides which part actually got worse.

4) trajectory eval
→ grade the path the agent took, not only the answer it landed on.
→ use when the right answer for the wrong reason is going to bite you later.

5) tool unit tests
→ test each tool on its own, with fixtures, no model in the loop.
→ use always. most agent bugs are tool bugs wearing a costume.

6) regression suite
→ replay past runs against the new prompt or model and diff the results.
→ use before every prompt change, because prompts have no type system.

7) a/b in prod
→ split live traffic between two versions and compare outcomes, not vibes.
→ use when offline scores stopped predicting what users actually do.

8) human review
→ sample a slice of runs and have a person grade them honestly.
→ use to calibrate your judge, because a judge nobody checks quietly drifts.

9) shadow run
→ the candidate runs on real traffic in parallel and its output is shown to nobody.
→ use before a risky rollout, when one bad answer would be expensive.

10) red team
→ deliberately attack it: jailbreaks, injection, exfil, tool abuse.
→ use before anyone external can reach it, not after.

offline evals tell you it works. online evals tell you it still works.

both sides matter, but not all ten do. run the two that would have caught your last outage.

save this. then read the full breakdown on loop engineering below.

[Vivek Sen @Vivek4real_]

ASK CLAUDE TO MAP YOUR ENTIRE APP'S ARCHITECTURE INTO A SINGLE HTML PAGE AND JSON FILE.

THE HTML IS FOR YOU.

THE JSON IS FOR THE NEXT AGENT WORKING ON A NEW FEATURE.

YOUR CODEBASE NOW EXPLAINS ITSELF.

[Gourivio @gourivio]

@JSickmore @NoContextHumans whats psycho about brazil? the school shootings? the failed public health system? the obesity? the failed economy? the corrupt president? oh, wait! thats USA....brazil has lots of problems, thats for sure. but c'mon man.... you guys are looney.

[Gourivio @gourivio]

@LasagnaReverse @NoContextHumans USA is also. you guys just didn't notice it yet.

[Gourivio @gourivio]

@LiaTeixeira17 @OGloboPolitica @LiaTeixeira17, a sua fonte foi Marco Rubio? Esse gato bolsonarista é muito burro mesmo. Você é muito burra, tinha que ter vergonha de abrir a boca.

[Gourivio @gourivio]

@gituty82 @bbcbrasil Gisele, pelas bandeiras no seu nome já da pra ver que você é burra. pela sua bio também. cade a maioria de direita? quero ver nessas eleições agora.

[Hanako @hanakoxbt]

there are four types of agent loops. most people only know one.

loop engineering is a choice between four structures, each handing off one more job than the last.

every one answers two questions: what starts a run, and what ends it.

hand-run, you answer both yourself, every time.

1) turn-based
→ you prompt, it acts, you review, you prompt again. both jobs stay with you.
use when requirements are still forming.

2) goal-based
→ "/goal hit Lighthouse 90, stop after 5 tries." an evaluator checks, a no sends it back.
use when the outcome is measurable but the path isn't.

3) time-based
→ a clock fires, it runs "check the PR, fix CI," then waits. /loop local, /schedule survives a closed laptop.
use for recurring work.

4) proactive
→ no human present. it watches a channel, spawns triage, fix, and a reviewer, closes the task itself.
use for standing duties you can't predict.

not which one is most advanced.
whether your task is exploratory, measurable, recurring, or standing.

the more you hand off, the less you babysit.

full breakdown in the article below.

[Gourivio @gourivio]

@DiegoMBrazil @ianbremmer @ScienceDJX galera mal sabe onde fica o México, vai saber quem é o lula ou a janja? gado antipetista comentando até em post de gringo. loucura.

[Gourivio @gourivio]

@SnakePlissken75 @nytimes @TheAthletic yeah, just figure out how many people watch the super bowl x world cup final. 1.5 billion viewers, while the Super Bowl reaches an average of 125 to 165 million viewers globally. so yeah, you're probably right.

[Gourivio @gourivio]

@SnakePlissken75 @nytimes @TheAthletic you excel because only you guys play it. like 3 countries play baseball, some play basketball and none play american football. only you and canada play hockey. (maybe iceland and some other winter countries) so yeah, you can keep your sad excuses for tv commercials.

[Gourivio @gourivio]

The most exciting AI use cases aren't the ones that just make an old process faster, they're the ones that change what the work actually looks like. Every industry has to figure this out for themselves since it plays out differently everywhere, but that's where the real upside is

[Aaron Levie @levie]

Just coming off of meetings with a couple dozen enterprise IT leaders discussing AI agents. Here are a few of the common themes that stand out:

* Lots of conversation that you have to solve an operating model challenge to get the full benefits of AI. Most companies have orgs that have always operated in siloes; but agents are most effectively when they are tied to a process, which often cuts across these siloes. So the big question is how do you start to deploy centrally managed agents that can work across organizational boundaries. Who manages these agents? How do they get deployed and adopted?

* Data fragmentation remains a major issue for most organizations. As long as data remains highly fragmented and not in standard formats, or data is not available to the right people and agents, enterprises are dealing with issues around being able to get answers from agents that are accurate or that conform to their business practices. This cuts across both systems with structured data (product metrics or revenue figures) and unstructured data (product roadmap or customer contracts).

* Clear sense that companies need to figure out what their core data moats are going to be in the future. If everyone has access to roughly the same superintelligence from the various models, then the context that you feed the models becomes proprietary value in the future. Capturing this data and getting it into a format that agents can use becomes very important.

* Everyone is trying to figure out the right metrics to manage to for AI adoption. General consensus that tokens are not the right metric per se, and people leaning more toward business outcomes (in an ideal world). For business outcomes (like more revenue or more shipped product), though, you have to get close to each individual workflow to figure out if it was successfully transformed with AI so it’s harder to manage top down.

* Growing view that enterprises are going to live in a multi-model world. Lots of interest (though early in actual adoption) in layers that can route workloads to different models (frontside or open weights) for cost or performance reasons. Also enterprises are trying to figure out what things do you give to the models directly vs. what do you separate as horizontal systems and context so you can swap any system in and out.

* Talent for driving AI adoption and implementation still remains a major issue and topic. Many view it as something you necessarily have to train for internally due to a shortage of talent being trained on this in the outside. As an aside, this feels like it remains a huge opportunity for those that get very good at deploying and management agents in an enterprise since most companies are looking for these skills.

* The best use-cases for AI tend to be those that fundamentally change the work being done instead of just replacing an existing process and doing it more efficiently. Companies are working through their versions of this individually because it’s different per industry, but this often remains both the most exciting and higher upside uses of AI.

Many more topics discussed recently, but overall it’s clear that there’s a ton of change going on with much more to come.

[Rohan Paul @rohanpaul_ai]

During a Bloomberg interview, Yann LeCun (@ylecun ) explains why LLMs are limited in terms of real-world intelligence during a Bloomberg interview.

"Language is a very approximate, reduced, quantized, and simplified description of the world, and LLMs can only deal with discrete sequences of symbols. The world is much more complicated than language.

The biggest LLMs are pre-trained on the totality of all the publicly available text on the internet. That’s about 20 trillion words, or 30 trillion tokens.

A token is about 3 bytes. So total 10¹⁴ bytes of text.

This is the amount of data a four-year-old has seen through vision during four years. Now, the text, though, would take 400,000 years to read?

So, there is enormously more data from sensory input, like vision, touch, and everything else, than there could ever be through language."

A child does not need 400,000 years of reading to understand cups, doors, balance, faces, falls, or heat, because the body is already collecting dense feedback from vision, touch, motion, and consequence.

Text strips most of that away.

It turns a living scene into symbols, then asks the model to infer the missing world from traces left by people describing it.

That is why an LLM can sound fluent about physics and still have no native sense of how fragile glass feels in a hand.

Moravec’s paradox names this reversal: the things humans find intellectual can be easier for machines than the things toddlers do without applause.

The hard part is not producing an answer, but building a model of the world that survives contact with weight, friction, surprise, and failure.

----

Link to the full video on Bloomberg's site. Link in comment.