_fradesantis

Founded Vision Studio (2022–25)

[_fradesantis @_fradesantis]

The outcome of any long-term goal is built on the foundation of consistent short-term actions.

[Garry Tan @garrytan]

Prediction: you’re all going to be using @AsideAI with your agents in a year or so

[Argona @Argona0x]

holy sh*t. Karpathy's second brain pattern runs on Grok Bot now: stop explaining your life to AI from zero every chat, keep a wiki that writes itself

three bots in one group chat, each with its own memory:

the reader takes every link, PDF and voice note you drop and never edits it
the librarian rewrites every wiki page that source touches, usually 10 to 15 of them
the checker wakes up on a nightly routine and hunts contradictions, stale claims and pages nothing links to

→ you ask a question: the answer gets filed back as a page, so the brain grows from use, not only from reading
→ a new source contradicts an old page: the checker tags you instead of quietly overwriting it
→ a page goes 30 days without a fresh source: it gets marked unconfirmed

a second brain can sound sure while remembering the wrong thing

Reader collects. Librarian writes. Checker doubts.

every page has to show:
> the claim
> the source it came from
> the date it was last confirmed

paste this into Grok Bot:

"Build me a second brain with three bots in one group chat. Keep my sources untouched in raw/, maintain wiki/ yourself, file good answers back as pages and run a nightly check for contradictions. Show me the structure first. Do not create anything until I confirm"

start with one topic you actually work on, not your whole life

full setup below

[Alexandr Wang @alexandr_wang]

introducing muse wonder

our new frontier model. state-of-the-art at personal agents and professional agents.

our largest and most capable model yet—rivals the big bois

[tobi lutke @tobi]

Great read from Patrick, as always.

“Personal agents will, I think, act as a kind of structural subsidy for product quality.”

Yes, and well put.

[Elon Musk @elonmusk]

Interesting piece from CEO of Microsoft

[Garry Tan @garrytan]

Logical: in the future ICs with agents will be more productive and create better outcomes than equivalent people managers from prior eras

[Carlo @carloAI]

Introducing Interface, the primary hardware for the agent era.

Interface lets you control all your agents from your hand.

$99 for early adopters. Ships in January.

[Google Labs @GoogleLabs]

🚨NEW EXPERIMENT 🚨

Playground is an experimental gaming platform that lets you create your own games with zero coding experience. If you can think it, you can play it.

Go to https://t.co/fRUukpuoqM to learn more! Available to users 18+ in the US.

[Paul Graham @paulg]

Amazon banning agents is the first opportunity I've seen since Amazon was founded for a startup to create an Amazon competitor. People will want agents to buy stuff for them. It will be one of the main use cases. And they won't want to use some Amazon-supplied agent to do it.

[Andrew D. Huberman, Ph.D. @hubermanlab]

Indeed.

[Garry Tan @garrytan]

You can basically work with agents to write markdown skills and put it on a cron job and do almost every useful type of knowledge work now

[Andrew D. Huberman, Ph.D. @hubermanlab]

It was a matter of when, not if this Nobel would happen. Awesome to see it.
It is worth noting that @KarlDeisseroth lab shared optogenetic tools far and wide from the very beginning, and thereby facilitated so much discovery. Fun fact: Karl was the first guest ever on the HLP.

[a16z @a16z]

.@OpenRouter co-founder Alex Atallah, in his first podcast since Stripe acquired the company, joins @Replit co-founder Amjad Masad and a16z's Erik Torenberg on why the future of AI is independence and specialization.

In this conversation, Alex walks through how the Stripe deal unfolded, why he wasn't originally looking to sell, and why "payments and inference are going to blend together."

Pre-OpenRouter, the typical AI workflow had one model provider to choose from, and little pressure on that provider to lower prices. Now enterprises are diversifying across labs and open-weight models, and every board is asking about AI costs and benchmarks.

Amjad argues if your company depends on one AI lab, it can turn into your competitor. So Replit is building the layer that lets enterprises use any model and any cloud, without being locked into either.

Alex and Amjad are split on personal agents – Amjad runs one agent across his whole company and loves the cross-domain joins, while Alex says general agents cause you to sacrifice understanding, and argues 10 specialized chiefs of staff beats one superagent.

0:45 How the Stripe deal unfolded
5:05 Why mixing models beats one model
7:25 Forcing the labs to compete on price
8:50 Enterprises want open-weight models
10:30 Every board asks about AI every month
12:25 Why companies must own their intelligence
14:15 Replit as the independence layer
15:10 Everyone is building the same agent
16:35 Why Amjad built bring-your-own-cloud
18:10 Amjad's agent that runs his whole company
19:55 Why 10 specialized agents beat one
23:35 Machines, not humans, should specialize
27:30 Guardrails for agents talking to agents
31:10 Models training their own replacements
33:45 Most tasks don't need a frontier model
40:50 Training small models on Qwen 8B
43:25 The Rust cycle is coming for AI
45:10 Fusion models: frontier quality at half the cost

YouTube: https://t.co/stL1g14F3q

@alexatallah @OpenRouter @amasad @eriktorenberg

[a16z @a16z]

.@OpenRouter co-founder Alex Atallah on the "everyone is building the same thing" take:

"This reminds me of this tweet I saw... Everybody's building an agent loop with notifications, third-party connectors, context management, memory, sandboxes, agentic web search, an always-on agent on top."

"This product is showing up everywhere. It is showing up everywhere, but these are just the new table-stakes primitives."

"A 2005 version of that tweet would be: 'Oh, everybody's building the same thing. A database, a users table, a sign-in page, a sign-up page, a profile page, a logout page. Everything's the same.'"

"There's like a lot of differentiation really. There's table-stakes needs for AI just like there's table-stakes needs for the web."

@alexatallah @amasad

[Muse @Muse]

see THIS is why you kept it!

[Garry Tan @garrytan]

AGI Science Loops are coming, and there will be Muse/Instinct for those AGI Science Loops. Halmos is building that.

[Hux @huxlab]

Alexandr Wang 说了一句让技术流创始人集体沉默的话。

“做技术的人习惯了数据说话，觉得东西更好、数字更漂亮，客户总会识货”

他直接点破：大公司不是这么运转的，政府更不是。

在那些客户眼里，感知比现实更真实。

真实的业务现状太难看了，没几个人愿意直面。

大家要的不是真相，是一套能让自己在系统里安全生存的叙事。

所以更好的产品照样被踢出局。

买方根本没在评测产品，谁掌控了感知，单子给谁。

他甚至点名 Palantir：表面是软件公司，底色是个剧团。新员工入职，真的会发一本戏剧表演书，发了整整好多年。

做企业级市场，改写现实只是一半的工作；

塑造一套客户愿意相信的说法，是另一半。

技术人最容易栽在这道坎上：

你的东西确实碾压竞品，但坐在对面签字盖章的人，自始至终连看都没看屏幕一眼。

[a16z @a16z]

Replit CEO Amjad Masad on how general models could train smaller, domain-specific models on the fly:

"There's a lot of talk of recursive self-improvement, but there's something I don't think is getting a lot of discussion, which is models training their replacements."

"You can think of it as a just-in-time compiler. As you're executing dynamic code, the interpreter realizes there's an opportunity to optimize, and it emits machine code on the fly that's a lot more optimized."

"You can imagine general models, you're doing something with Operator or Astra, some of the big models, and they realize the use case is limited, or some other agent observing realizes the use case is limited."

"General agents have all these flaws, but there's also more potential for them to be harmful, more potential for them to go off the rails."

"So the model, on the fly, trains a model that could be its replacement, but is a lot more domain specific. Therefore it's cheaper, less vulnerable to prompt injections, and less harmful for you, because it's less capable."

"It's almost like a system that's training machine learning models for specific use cases as it's monitoring the entire system."

@amasad

[Robert Soriano @wobsoriano]

Got @Muse running on the PSP that's been sitting in our house for over 15 years @alexandr_wang

Had my agent port the gadget client to C so it runs natively on the PSP. Hold R, talk into the built-in mic. Muse answers, and OpenAI gives it a voice

[Nat Friedman @natfriedman]

🚨 side project alert 🚨

Announcing Muse Gadgets, an open source ESP32 firmware and Linux sdk so that you can make hardware devices that work with Muse.

Grab an API token from https://t.co/Q0dE0cYnCs and point your favorite coding agent at the github repo to build your own peripherals for Muse.