Pablo De Hoyos

🖖🤙✌️ • IG @pablodehoyos

[Khairallah AL-Awady @eng_khairallah1]

Boris Cherny, the creator of Claude Code at Anthropic, just explained why single-agent workflows are already dead

in this talk he breaks down exactly how the future is teams of agents, not better prompts:

- the 14% you lose to CLAUDE.md before typing a word
- one agent researching. one building. one reviewing. one orchestrating
- the architecture that separates hobbyists from real builders
- the 3 properties every agent team needs to actually survive

if you've been using Claude for more than a month and never left the chat window, you've been using one agent when you could be running a team of them

instead of another show tonight, watch this

make sure to bookmark it before it gets lost in your feed

the guide is in the article below

[Ruben @rdominguezibar]

the PITCH DECKS💰 that raised billions are now public. Study them before your next raise:

1️⃣ 26 pitch decks that raised $400M in 2026 → https://t.co/qbMHSFDMK8

2️⃣ Anthropic's 2022 pitch deck just leaked: 10 slides, no product, now worth $380B → https://t.co/vJMUDSTE8O

3️⃣ 16 unicorn pitch decks: the actual slides before the billions → https://t.co/fzrPwgSBtk

4️⃣ Peter Thiel only explained once how to raise money. Here it is → https://t.co/9RcTM7aTCC

5️⃣ SpaceX: how to build and pitch the most ambitious company of our time → https://t.co/e5mv5dX0OT

6️⃣ Synthesia turned down Adobe's $3B offer. Here's the 18-slide deck that raised $180M → https://t.co/VFG7lShvJl

7️⃣ How Brex raised $57M and rebuilt startup banking → https://t.co/9jkxGflCD0

8️⃣ 50 real pitch decks from startups that raised $380M+ → https://t.co/2PBKJJ67EE

9️⃣ 200+ pitch decks that raised over $50 billion → https://t.co/SWWsoyeDQJ

🔟 153 startups fundraising right now with their actual decks → https://t.co/TRQtsxLb83

Bookmark this. The best founders study what worked before they pitch.

How much does a pitch deck actually matter vs the founder behind it?

[Julien Bek @JulienBek]

https://t.co/hsKrbn1gEq

[Lorenzo | Meta Ads & Performance Creatives 📈 @lorenzo_pravata]

https://t.co/wCCsPpWCYB

[Olly Hudson @oliverwhudson]

https://t.co/c6k95FeIMg

[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!

[Zac @PerceptualPeak]

holy shit it fucking WORKS.

SMART FORKING. My mind is genuinely blown. I HIGHLY RECCOMEND every Claude Code user implement this into their own workflows.

Do you have a feature you want to implement in an existing project without re-explaining things? As we all know, the more relevant context a chat session has, the more effectively it will be able to implement your request. Why not utilize the knowledge gained from your hundreds/thousands of other Claude code sessions? Don't let that valuable context go to waste!!

This is where smart forking comes into play. Invoke the /fork-detect tool and tell it what you're wanting to do. It will then run your prompt through an embedding model, cross reference the embedding with a vectorized RAG database containing every single one of your previous chat sessions (which auto updates as you continue to have more sessions).

It will then return a list of the top 5 relevant chat sessions you've had relating to what you're wanting to do, assigning each a relevance score - ordering it from highest to lowest. You then pick which session you prefer to fork from, and it gives you the fork command to copy and paste into a new terminal.

And boom, there you have it. Seamlessly efficient feature implementation.

Happy to whip up an implementation plan & share it in a git repo if anyone is interested!

[Alton Syn @WorkflowWhisper]

https://t.co/ptZXXVJnbJ

[Chris Laub @ChrisLaubAI]

omg… this AI workflow is wild.

You can now run full competitive market analysis using ChatGPT, Gemini, and Grok deep research features.

Here are the exact 3 mega-prompts I use to replicate McKinsey-style insights for free:

[Aryan Mahajan @aryanXmahajan]

My Claude MCP Agent just wrote a sales proposal that closed a $6K deal.

Not kidding. One MCP setup:
- syncs Airtable transcripts automatically
- extracts pain points with Claude reasoning
- runs Perplexity research on prospects
- generates Google Doc proposals that convert

basically everything to automate your sales process

reply "CLOSER" + retweet and i'll send the workflow (must be following so i can dm)

only FREE for 48 hours before it goes into the void.

[GREG ISENBERG @gregisenberg]

we're living in the GREATEST time in HISTORY to turn ideas into reality

claude code subagents allows you to organize AI agents like a startup team

each one living in its own markdown file with specific instructions, personality traits, and expertise areas, from frontend-developer md to tiktok-strategist md to growth-hacker md.

each markdown file becomes a complete employee profile.....your frontend-developer md knows react best practices and has a preference for clean code, your tiktok-strategist md understands gen z humor and trending sounds, your growth-hacker md obsesses over conversion rates and a/b testing everything. you can give them different communication styles, decision-making frameworks, even quirky personalities that make them feel like real team members you're managing.

people are scanning @ideabrowser for painpoints of niches on subreddits/fb groups (feature is called community signals and is brand new), getting startup ideas, spinning up landing pages with bolt or lovable

and then coordinating their AI agent "team" where the product agents prioritize features, engineering agents push code, and marketing agents create content

all working from their individual instruction files.

you can literally structure your ai workforce like a company org chart, with agents in engineering/, marketing/, design/, and operations/ folders, each with detailed prompts about how they should think and act.

we've gone from using AI as a single tool to managing entire ai organizations where each agent has a specific role, communication style, and area of focus

you can literally write down what you want built in markdown files and watch ai agents execute it while you sleep or you are at your full-time job.

claude code subagents are a game changer

it's going to be a good week.

[Usman Ahmad @AIwithUsman]

Deleting your browser history doesn’t delete your actual history.

Google still knows everything.

If you truly want to wipe your digital footprint, here’s how to do it properly:

[Avi Chawla @_avichawla]

How LLMs work, clearly explained (with visuals):

[Machina @EXM7777]

i’m tired of seing fake gurus farming engagement with n8n automations they FAKE or STEAL

today i’m exposing all of them by sharing every workflow available online and the exact place you can find them



here are +3100 n8n files ready to import for FREE (bookmark this for later)

a thread 🧵

[Pablo De Hoyos @pablodehoyos]

@NoahEpstein_ Vault

[xAI @xai]

xAI partners with @Polymarket to blend market predictions with X data and Grok’s analysis. Hardcore truth engine - see what shapes the world.

This is just the start of our partnership with @Polymarket. More to come. 🚀

[Pablo De Hoyos @pablodehoyos]

@damianplayer Steal

[Pablo De Hoyos @pablodehoyos]

@grok @elnacionalpy @grok What are the top candidates known for in terms of political views and the reforms they have publicly advocated for within the Catholic Church?