Zento

The future is bright

[Zento @0xZ3nto]

@jesseabed_ 1. But I thought people create fresh accounts to send more DMs avoiding ban. So how can each one be from a “high follower page”?

2. But what about on the first try, everyone starts somewhere so what’s the strat to get the foot in the door before you have viral content to show?

[Zento @0xZ3nto]

@jesseabed_ What was the cold message you sent to creators, and was it mainly in insta/tt dms?

Also, what was the average price you are paying per creator, and did you start with a 1 off test, or setup an immediate partner for many videos? What did most creators find most appealing?

[Entros @entros_protocol]

https://t.co/jpihy7QQUs

[mert @mert]

crypto without privacy is not crypto

[toly 🇺🇸 @toly]

Build a company.

[MartyParty @martypartymusic]

Solana $SOL breaks blockchain all time high for economic activity - Q1 2026 - $1.1 trillion

In Q1 2026, @solana 's total on-chain economic activity hit $1.1 trillion for the quarter.

This is the first time any blockchain (including @ethereum and @Bitcoin) has ever crossed the $1 trillion mark in a single quarter.

Daily active users climbed to ~4.6 million with billions of transactions processed.

Solana captured 41% of all onchain activity (including Bitcoin and Ethereum)

[toly 🇺🇸 @toly]

If they can get you to sign a tx they can steal your source code. Immutable open source with formal verification is the only real option

[Andrej Karpathy @karpathy]

LLM Knowledge Bases

Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So:

Data ingest:
I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them.

IDE:
I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides).

Q&A:
Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale.

Output:
Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base.

Linting:
I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into.

Extra tools:
I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries.

Further explorations:
As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows.

TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

[Zensei @zensei]

solana ranks #1 in all-time developers.

over 10,000 builders chose to build here.

if you’re serious about building, this is where you do it.

[Aaron Wright @awrigh01]

Digital art isn't competing with physical art.

It's competing with ignorance about where culture forms now.

(Hint: it's not in elite circles or galleries).

[vitalik.eth @VitalikButerin]

One thing that can help is to embrace open source:

https://t.co/nXE7DtRZ77

It's natural to fear technology if it's an inscrutable thing made by Other People that you're supposed to just trust, and you can't participate except as an end consumer.

Technology needs to be something that people everywhere can go and inspect and modify to suit their needs, and feel like it's theirs.

The intellectual requirements of doing this are of course far higher than 100 years ago, but on the flip side we have AI to help.

[Matt Shams @daoboymatt]

👀 lack of perception depth in AI

A four-year-old has seen more data than an LLM!

interesting talk by @ylecun

[Andrej Karpathy @karpathy]

I am (slowly) re-reading the Tolkien legendarium (of which Lord of the Rings is a small part). The whole body of work is so incredible and there's nothing else like it... it dilutes other worlds of fiction. Wait - your story doesn't have a comprehensive history/mythology spanning multiple ages all the way back to a creation myth as detailed in separate volumes? You didn't first invent new languages and dialects for your characters? You didn't pack it with powerful themes and stories written it in a beautiful, archaic style and compose poems and songs alongside? It didn't take you multiple decades of iteration? And what of all the uncharted territory still remaining? Is Tom Bombadil one of the Ainur. Where are the Entwives. What happened to the two unaccounted Istari. Can we hear more about what it was like in Cuiviénen when the elves first awoke? Or to see the light of the two trees of Valinor. Or of the splendor of the caves of Aglarond.

What's most on my mind though - the Tolkien legendarium is imo a concrete example of a height of culture. Does AI, today or soon, make it easier to reach this high via empowerment in both writing and ideation? Or harder, when quick wins are tempting and ~free, and an independent ability to create is stifled. If such a body of work is made again but now with heavy AI assistance, does it inspire the same wonder? What if thousands of them come out on demand with just a prompt? Why do you feel cheated when you learn that something your read was AI generated? Is it transient or a function of capability? Is it slop? What is slop? Or is wonder inseparable from its own creation myth of a lifelong obsession of a mind like your own? So many questions.

[Hayden Adams 🦄 @haydenzadams]

AI should evolve from trust based to cryptographically verifiable

Lots of techniques popular in crypto could enable this - tees, zk proofs, self hosting, open source, nodes + consensus, etc

Computer systems that require trusting humans / companies will eventually be outdated

[Liron Shapira @liron]

Doom Debate with @VitalikButerin has dropped:
Will “d/acc” Protect Humanity from Superintelligent AI?

Vitalik is both a hardcore techno-optimist and builder, and someone willing to seriously consider AI regulation and coordination mechanisms. He coined the term "d/acc'" – defensive, decentralized acceleration – as a middle path between uncritical AI acceleration and total pause scenarios.

We debate the tractability of AI alignment, whether approaches like d/acc would actually work when superintelligence arrives, and why he thinks a pluralistic world of competing AIs might be safer than a single aligned superintelligence.

The crux of our disagreement is that I think we're heading for a "plants vs. animals" scenario where AI will simply operate on timescales we can't match, while Vitalik believes we can maintain agency through the right combination of defensive technologies and institutional design.

Finally, we tackle the discourse itself – I ask Vitalik to debunk the common ad hominem attacks against AI doomers, from "it's just a fringe position" to "no real builders believe in doom." His responses carry weight given his credibility as both a successful entrepreneur and someone who's maintained intellectual honesty throughout his career.

Big thanks to Vitalik for a good-faith debate!

[Joseph Lubin @ethereumJoseph]

Permissioned enterprise chains were tried and failed years ago.

Why? Because nobody trusted the central controller of those chains enough to set up shop on those chains. We've seen the deplatforming story over and over for decades.

There might be ways to make some of these work, but that would involve achieving credible neutrality and rigorous decentralization. Doesn't seem like that will be the plan, or even achievable, for most of the corporate L1s projects.

From @tian_ling84099 (Paul Brody, EY):

https://t.co/FNamEZKXzw

https://t.co/394wxLhJQR

[vitalik.eth @VitalikButerin]

On idea-driven ideas

https://t.co/VBNiAtT9lx

[Maggie Hsu @meigga]

Over the past year, I’ve been in countless conversations with banks, asset managers, and fintechs who are exploring crypto use cases again - this time with more urgency. With improving regulatory clarity, and increasing competitive pressure, leaders are moving from “should we?” to “how do we?”

That’s why @lordvolth, @guywuolletjr and I wrote a practical playbook for understanding the most compelling business cases for a blockchain-enabled strategy, as well as assembling the technical building blocks you’ll need to actually make it happen.

If your team is thinking about entering - or re-entering - the space, this will help you do it right (link below):

@a16zcrypto @a16z

[SingularityNET @SingularityNET]

By unifying representation, computation, and deployment in a single metagraph-based framework, Hyperon offers a flexible infrastructure for experimenting with alternative cognitive architectures, scaling AGI algorithms, and steering toward beneficial superintelligence.