Gareth AI - Dafnck Studio

I'm Gareth, I build and implement AI agents & AI Automations for Businesses

[rari @0xwhrrari]

https://t.co/MI0iMdcW5o

[rari @0xwhrrari]

Anthropic and OpenAI are both telling engineers to write loops

Not prompts
Not agents
Loops

That is not a coincidence

When the two biggest AI labs converge on the same pattern, it is a signal

Most engineers still think in single calls

Input → Model → Output

The ones winning in 2026 think in cycles

Output becomes input
The model checks its own work
The loop runs until the result is right

Bookmark this before loops become the default workflow

Then read the article below

[Kris Puckett @krispuckett]

Dario and @AnthropicAI tonight...

Nobody will notice a little rename, right?

[Anthropic @AnthropicAI]

The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees.

The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance.

Access to all other Claude models is not affected.

We apologize for this disruption to our customers. We believe this is a misunderstanding and are working to restore access as soon as possible.

Read our full statement: https://t.co/bwn0sximKZ

[Gareth AI - Dafnck Studio @DafnckStudio]

@claudeai https://t.co/4umUlk5zTl and https://t.co/IlhXSr0kjY and https://t.co/UHag62TT1o …. With love and Claude

[Gareth AI - Dafnck Studio @DafnckStudio]

@claudeai https://t.co/laz7t8eMcJ thanks @claudeai

[Gareth AI - Dafnck Studio @DafnckStudio]

@claudeai https://t.co/4umUlk5zTl thanks @claudeai

[Gareth AI - Dafnck Studio @DafnckStudio]

@tibo_maker Not impressed ^^ welcome in ai

[angel @angeldot_]

Anthropic ha lanzado una guía de 37 minutos para construir Agentes de IA que automatizan una empresa entera.

Gratis. De los ingenieros que construyeron Claude.

Agentes que trabajan, se reparten tareas y ejecutan todo solos.

Subtitulado al español.

Guárdate este post. 🔖

[Bun @bunjavascript]

In the next version of Bun

`Buffer.from(str, "base64url")` gets 41x faster on large input

[std dev @subproject_22]

Seeding Design Systems with the most popular shadcn/ui and tweakcn themes.

Or create your own.

Any variables, any naming.
Save, reuse, update, preview on your own components.

No more shadcn/ui lock-in.

Shipping in a few hours.

[fabian @fabianstelzer]

Anthropic launches Claude Monet

Painters are cooked

[Suryansh Tiwari @Suryanshti777]

RAG might already be becoming obsolete.

A month ago, Andrej Karpathy dropped a simple GitHub gist called “LLM Wiki.”

Now the comments section looks like the birth of an entirely new AI category.

5000+ stars later, developers are rapidly building:
• persistent AI memory systems
• self-maintaining knowledge bases
• multi-agent research environments
• contradiction detection engines
• AI-native company operating systems
• local-first memory architectures
• graph-based reasoning layers
• evolving second brains

And the craziest part?

Most of them were built in DAYS.

Because the core idea is insanely powerful:

Instead of AI repeatedly retrieving raw chunks like traditional RAG…

…the model continuously maintains a living knowledge system.

Not temporary context.

Persistent synthesis.

The shift sounds subtle until you realize what it changes:

RAG:
retrieve → answer → forget

LLM Wiki:
ingest → synthesize → evolve

That one architectural difference is causing an explosion of experimentation right now.

People are already building:
• agent memory operating systems
• AI-maintained engineering documentation
• self-healing knowledge graphs
• persistent research environments
• conversational memory architectures
• contradiction-aware wikis
• context compression engines
• machine-readable company systems

The comments section alone feels like watching an ecosystem form in real time.

One developer built deterministic contradiction detection using sheaf cohomology

Another built “sleep consolidation” for AI memory systems inspired by human memory formation

Another created persistent multi-agent vault conversations

Another turned entire repositories into continuously maintained AI wikis

Another built local-first memory systems with audit trails, provenance, graph exports, and MCP integration

This is the important part:

Karpathy didn’t launch a product.

He introduced a pattern.

And patterns are what create ecosystems.

The same way:
• transformers created modern AI
• RAG created AI retrieval startups
• agents created orchestration frameworks

LLM Wikis may create persistent AI memory infrastructure.

That’s why this moment feels different.

For years, AI systems have been stateless.

Now developers are trying to build systems that actually accumulate understanding over time.

And once knowledge compounds instead of resetting…

…the entire interface layer of AI changes.

(Link in comments)

[Attio @attio]

Join the next generation of GTM

[Gareth AI - Dafnck Studio @DafnckStudio]

https://t.co/QrKqPGq7QM

[Lisan al Gaib @scaling01]

It's over

Mythos is insane

[Gareth AI - Dafnck Studio @DafnckStudio]

@claudeai have a look https://t.co/eFufOZ37Ms

[Gareth AI - Dafnck Studio @DafnckStudio]

@dhillonpost @ClaudeDevs Again

[Gareth AI - Dafnck Studio @DafnckStudio]

@lukas_undefined @ClaudeDevs +1

[Lukas Böhm @lukas_undefined]

@ClaudeDevs - kill the Agent SDK
- burn the devs who trusted it and built on it
- explain why the rug pull is actually good
- throw some limit resets around
- wonder why power users are moving to Codex
¯\_(ツ)_/¯