Will

tech cto

[Mario Nawfal @MarioNawfal]

Will Smith eating pasta is now the official benchmark for AI progress.

This is actually insane.

[Mike Investing @MrMikeInvesting]

We are currently in a “once in a lifetime” AI super cycle…

Phase 1 was: (already gone)
Semiconductors ~ $NVDA, $AMD, $INTC, $ARM

Phase 2 is: (passing by now)
Memory ~ $MU, $SNDK, $WDC
Photonics ~ $AAOI, $AEHR, $LITE, $MRVL

The current phase is Neo Cloud/AI infrastructure:
$IREN, $NBIS, $CRWV, $CIFR, $APLD

Next wave (many will miss)
Rare Earths ~ $USAR, $MP, $UUUU, $FCX
Power & Cooling~ $VRT, $CEG, $OKLO, $OSS

Finally it all concludes with these 3 sectors:
Robotics ~ $TSLA, $PATH, $SERV
Space ~ $RKLB, $ASTS, $PL, $LUNR
Drones ~ $ONDS, $AVAV, $LMT

Many will make generational wealth from this AI super cycle over the next 7 months.

Save this to look back on later…

[Will @ConceptNA]

@hthieblot teaching old people at mid size firms how to use AI

[Will @ConceptNA]

rip tmux and cmux

[Claude @claudeai]

We've redesigned Claude Code on desktop.

You can now run multiple Claude sessions side by side from one window, with a new sidebar to manage them all.

[Andrew @Tradesdontlie]

Claude controlling @tradingview live — switching symbols, writing Pine Script, batch scanning futures, replay trading, drawing levels. All from the terminal. Still rough edges but the vision is clear.

[Avi Chawla @_avichawla]

Researchers built a new RAG approach that:

- does not need a vector DB.
- does not embed data.
- involves no chunking.
- performs no similarity search.

And it hit 98.7% accuracy on a financial benchmark (SOTA).

Here's the core problem with RAG that this new approach solves:

Traditional RAG chunks documents, embeds them into vectors, and retrieves based on semantic similarity.

But similarity ≠ relevance.

When you ask "What were the debt trends in 2023?", a vector search returns chunks that look similar.

But the actual answer might be buried in some Appendix, referenced on some page, in a section that shares zero semantic overlap with your query.

Traditional RAG would likely never find it.

PageIndex (open-source) solves this.

Instead of chunking and embedding, PageIndex builds a hierarchical tree structure from your documents, like an intelligent table of contents.

Then it uses reasoning to traverse that tree.

For instance, the model doesn't ask: "What text looks similar to this query?"

Instead, it asks: "Based on this document's structure, where would a human expert look for this answer?"

That's a fundamentally different approach with:

- No arbitrary chunking that breaks context.
- No vector DB infrastructure to maintain.
- Traceable retrieval to see exactly why it chose a specific section.
- The ability to see in-document references ("see Table 5.3") the way a human would.

But here's the deeper issue that it solves.

Vector search treats every query as independent.

But documents have structure and logic, like sections that reference other sections and context that builds across pages.

PageIndex respects that structure instead of flattening it into embeddings.

Do note that this approach may not make sense in every use case since traditional vector search is still fast, simple, and works well for many applications.

But for professional documents that require domain expertise and multi-step reasoning, this tree-based, reasoning-first approach shines.

For instance, PageIndex achieved 98.7% accuracy on FinanceBench, significantly outperforming traditional vector-based RAG systems on complex financial document analysis.

Everything is fully open-source, so you can see the full implementation in GitHub and try it yourself.

I have shared the GitHub repo in the replies!

[Will @ConceptNA]

@WSBgod just dropped

[аlina🥂 @mercedarri]

SABRINA CARPENTER AND LEWIS HAMILTON?!?!?!

[𝙠 🇨🇦🇪🇸🇲🇽🏴󠁧󠁢󠁥󠁮󠁧󠁿 @lecvsn]

leaked footage of the 2024 british gp

[Will @ConceptNA]

@blurcloud @TheAmiraLuna did you get an earring ??

[Formula 1 @F1]

LEWIS: "It's so important for kids out there to see this...

"Don't listen to anyone who tells you you can't achieve something. Dream the impossible. Speak it into existence. You've got to work for it, chase it, and never give up!"

#TurkishGP 🇹🇷 #F1

[Will @ConceptNA]

@yoongilolcats ??

[Will @ConceptNA]

@Skyrosaur NUT

[Will @ConceptNA]

@Skyrosaur @TheAmiraLuna WHAT ARE THOZE SANDALS BROOOOOOOO

[Will @ConceptNA]

@NICKMERCS @Skyrosaur you need his workout plan bro

[Will @ConceptNA]

@Faiiko_R6 Concept aqua

[Faiiko @FaiikoDGAF]

Comment Name + Colour
(RT and must be following 👍)

[Will @ConceptNA]

@PlayVALORANT #bigbrain

[Will @ConceptNA]

@PlayVALORANT #cypher