The Transcript
RT @TheTranscript_: $QCOM CEO talks of memory shortages:

"While our near-term handsets outlook is impacted by industry-wide memory supply constraints, we are encouraged by end-consumer demand for premium and high-tier smartphones & remain on track to achieve our FY 29 revenue goals"

Qualcomm CEO: "We are pleased to deliver strong quarterly results, with record total company revenues. Our momentum across personal, industrial and physical AI is growing..."

$QCOM: -9% AH https://t.co/QXxlynWpWq - The Transcript
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Dimitry Nakhla | Babylon Capital®
RT @DimitryNakhla: Reiterating my thoughts on $CSU.

The reason $CSU is being sold (multiple compression due to AI fears & potential uncertainty surrounding its moat, among other things) 𝙘𝙤𝙪𝙡𝙙 𝙚𝙣𝙙 𝙪𝙥 𝙗𝙚𝙞𝙣𝙜 𝙩𝙝𝙚 𝙚𝙭𝙖𝙘𝙩 𝙧𝙚𝙖𝙨𝙤𝙣 𝙞𝙩𝙨 𝙘𝙖𝙥𝙞𝙩𝙖𝙡 𝙖𝙡𝙡𝙤𝙘𝙖𝙩𝙞𝙤𝙣 𝙚𝙣𝙜𝙞𝙣𝙚 𝙜𝙚𝙩𝙨 𝙗𝙚𝙩𝙩𝙚𝙧 𝙖𝙜𝙖𝙞𝙣

𝐓𝐡𝐞 𝐢𝐫𝐨𝐧𝐲: 𝐭𝐡𝐞 𝐬𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐫𝐞-𝐫𝐚𝐭𝐢𝐧𝐠 𝐰𝐞’𝐫𝐞 𝐬𝐞𝐞𝐢𝐧𝐠 𝐭𝐨𝐝𝐚𝐲 (𝐩𝐚𝐫𝐭𝐥𝐲 𝐝𝐫𝐢𝐯𝐞𝐧 𝐛𝐲 𝐀𝐈 𝐝𝐢𝐬𝐫𝐮𝐩𝐭𝐢𝐨𝐧 𝐟𝐞𝐚𝐫𝐬) 𝐦𝐚𝐲 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐢𝐦𝐩𝐫𝐨𝐯𝐞 $𝐂𝐒𝐔’𝐬 𝐨𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐲 𝐬𝐞𝐭

When public multiples compress, private-market deal pricing often follows — creating better entry points and potentially higher IRRs for $CSU’s next wave of acquisitions

So yes, one of the common critiques many investors will cite is that $CSU returns on capital have been trending down over the last few years

Yet, given this set up, perhaps it will trend higher over the next few years

At 2,845, $CSU looks like an interesting contrarian opportunity amid peak fear - Dimitry Nakhla | Babylon Capital®
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Quiver Quantitative
President Trump's meme coin has now fallen 95% from its all-time high.

Melania Trump's meme coin has fallen 99%.

Still have not seen any politicians buying either of them.
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Dimitry Nakhla | Babylon Capital®
RT @DimitryNakhla: $FICO at a 3.62% FCF yield while growing FCF at >20% CAGR — risk/reward looks compelling 💵 https://t.co/BAbK3DL3YF
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Javier Blas
OIL MARKET: Saudi Arabia has again reduced the price of its flagship export grade Arab Light to Asia.

For the first time since late 2020, state-owned Aramco is selling Arab Light at parity with the regional benchmark, rather than a premium.
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The Transcript
$UPS CEO: Amazon volume down 1M daily pieces in 2025, another 1M to go in 2026.

“By the end of the year, we… reduced Amazon’s volume in our network by approximately 1 million pieces per day… we intend to glide-down another 1 million pieces per day.”
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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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