Quiver Quantitative
AllianceBernstein stock has risen 10% in the two weeks since we posted this report.

The market has fallen 2%.

$AB is up again today. https://t.co/y138cMZDwx
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God of Prompt
opus 4.6 is here!

this month will be incredible

Opus 4.6 is here.

The jump in autonomy is real. The biggest shift for me personally has been learning to let it run.

Give it the context, step away, and come back to something pretty amazing. The way we work alongside models is starting to completely change. - Alex Albert
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God of Prompt
openclaw + emergent = easiest way to use it for non-technies

OpenClaw broke the internet

But you DON'T need to setup any servers to use it

Here's the easiest way to run OpenClaw on a website

No Mac Minis required https://t.co/6xspOtHaxT - Alex Prompter
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God of Prompt
the agent swarms are coming

On Claude Code, we’re introducing agent teams.

Spin up multiple agents that coordinate autonomously and work in parallel—best for tasks that can be split up and tackled independently.

Agent teams are in research preview: https://t.co/LdkPjzxFZg - Claude
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God of Prompt
RT @godofprompt: instead of scrolling x, study this free course packed with INSANE value

Jason Liu just open sourced his entire paid RAG course and consulting archive. all of it. free.

for context: this is the creator of Instructor (6M+ monthly downloads, cited by OpenAI as inspiration for their structured outputs feature). former staff ML engineer at Stitch Fix. ex-Meta. a16z scout. his RAG course on Maven had 400+ engineers enrolled.

but here's what's actually interesting.

look at the highlights he chose to summarize his own course:

> product mindset over one-off implementation
> measurement, feedback loops, improvement cycles first
> synthetic eval data to break the cold start
> feedback UX that actually works
> specialized retrieval and routing instead of one-size-fits-all search

notice what's missing?

no mention of vector databases. no embedding model comparisons. no chunk size optimization. no retrieval framework shootouts.

the guy who mass-taught production RAG to hundreds of engineers is telling you the hard part was never the retrieval. it was the product thinking around it.

measurement. feedback loops. knowing what to improve and how to tell if you improved it.

everyone's out here debating pgvector vs pinecone vs weaviate. meanwhile the most credible RAG practitioner in the space just told you the answer was never in the vector store.

it was in the feedback UX.

567 Labs is done. the content lives on. go read it before you build another RAG pipeline without an eval framework. - Robert Youssef
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God of Prompt
RT @godofprompt: Steal these prompts for Perplexity Research

🔖 Bookmark for later https://t.co/wLLJHNGwOl
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God of Prompt
I don't need ChatGPT
I don't need Perplexity
I don't need DeepSeek

I got my Claude Opus 4.6.
Time to start cooking. https://t.co/3jmixVsg0A
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Javier Blas
REACTION COLUMN: Mining giants Rio Tinto and Glencore are both losers as their M&A talks to create the world’s largest miner collapse.

But I don’t think we have seen the final word on the 20-year on-and-off saga to merge both companies.

@Opinion
https://t.co/iMoHDvwEAx
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The Transcript
RT @TheTranscript_: $AMZN reporting after hours today: https://t.co/AG90XQkPOZ
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Quiver Quantitative
Elon Musk: "We are 1,000% going to go bankrupt and fail as a country without AI and robots.

Nothing else will solve the national debt."
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God of Prompt
Everyone's posting benchmarks right now.

But the scariest detail from today's drop isn't the scores.

GPT-5.3-Codex helped debug its own training, manage its own deployment, and diagnose its own test results.

OpenAI's words, not mine: "instrumental in creating itself."

Opus 4.6 agent teams are impressive. 1M context window is massive. The benchmark war is fun to watch.

But an AI model that accelerates its own development? That's not a feature update. That's a different category of thing entirely.

We just crossed from "AI that helps you code" to "AI that builds AI."

And nobody's stopping to think about what that means for the next 12 months.

Introducing Claude Opus 4.6. Our smartest model got an upgrade.

Opus 4.6 plans more carefully, sustains agentic tasks for longer, operates reliably in massive codebases, and catches its own mistakes.

It’s also our first Opus-class model with 1M token context in beta. https://t.co/L1iQyRgT9x - Claude
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