chasingtherain

rain chaser

[chasingtherain @chasingtherain2]

Endless capital, limited BTC

[Carl ₿ MENGER ⚡️🇸🇻 @CarlBMenger]

When endless money printing meets 21 million #Bitcoin. Y’all ain’t ready for what’s comin’

[Chamath Palihapitiya @chamath]

https://t.co/vPrPSUjTJG

[Crypto Rover @cryptorover]

Bitcoin NEVER bottomed above the Realized Price.

This time won't be different.

[Stanford NLP Group @stanfordnlp]

The “problem” with CS336 is not the ~22 hours of videos but the larger number of hours it takes to do the assignments.

But that is where most of the real learning occurs.

We’re reminded of @karpathy’s seminal tweet:

https://t.co/fvSeE2bDkE

2026 site: https://t.co/E1pzUSC6Tr

[Alex Lieberman @businessbarista]

I stole this idea and now use it with every single employee.

It’s the best illustration I’ve seen of teaching someone to be high agency.

It says there are 5 levels of work:

Level 1: “There is a problem.”

Level 2: “There is a problem, and I’ve found some causes.”

Level 3: “Here’s the problem, here are some possible causes, and here are some possible solutions.”

Level 4: “Here’s the problem, here’s what I think caused it, here are some possible solutions, and here’s the one I think we should pick.”

Level 5: “I identified a problem, figured out what caused it, researched how to fix it, and I fixed it. Just wanted to keep you in the loop.”

Using this framework, here’s what I say to every new employee…

You will live at Level 4 from Day 1 and as we build trust you will rise to Level 5.

Being high agency doesn’t just mean tackling problems in this way. It means your entire way of working should be oriented to being a Level 4+ employee.

Plz feel free to steal it as well.

And ty @stephsmithio for the framework!

[Alisa Liu @alisawuffles]

I'm joining OpenAI next week!🥹 The job search turned out to be really challenging but also super rewarding, so I wrote a small blog to share what I learned along the way and hopefully make the process a little less mysterious for the next person. https://t.co/6FigSBdenD

[Shann³ @shannholmberg]

what is agent looping

for the last two years we prompted agents one task at a time. that is starting to change

instead of asking an agent to build the landing page and then driving every step yourself, you set up a loop that handles discovery, planning, the work, checking, and iterating until the goal is met

looping is a setup you build. almost any agent harness can run it, it just depends on how you wire it up

at its simplest, looping is one agent working on itself:

> researches
> drafts
> checks the draft against a goal
> fixes what is weak
> runs that cycle again until the work clears the requirements

you are not prompting each step anymore. the agent repeats the cycle for you

the bigger version is a fleet looping. you give an orchestrator agent a goal, it breaks the goal into pieces, hands each piece to a specialist agent, and those specialists hand smaller jobs to their own subagents

the whole tree keeps looping through discovery, planning, execution, and verification until the goal is met

one agent looping is like a person redoing their own draft. a fleet looping is a whole team running a project end-to-end

you create a goal, and the system runs the loop until it finishes within the reqs you set

open and closed looping:

OPEN LOOPING is exploratory. it still has conditions and a goal, but you give the agent or the fleet a wide space to move in. it can try different paths, discover things, build something you did not fully spec out

this is the exciting end, it is what Peter and others are doing, and tbh it is where I want to spend more time

the catch is cost, an open loop with real room to explore burns an insane amount of tokens. for the 90 percent of people without an unlimited budget it is not runnable yet, and pointed at projects with a loose standard it turns into a slop machine

CLOSED LOOPING is bounded. a human designs the end-to-end path first:

> clear goal
> defined steps
> an eval at each step
> a point where it stops or hands back to you (and feeds back performance data)

the agents still loop, but inside framework you built. it gets better every run because each pass feeds the next, and it runs on a normal budget because the path is tight.

for most marketing work, closed is the one that pays off today.

> the orchestrator owns the goal
> the specialists own the steps
> the subagents do the narrow work
> an eval gate make sure its not slop

[Reads with Ravi @readswithravi]

James Clear on having high standards:

[Dave Kline @dklineii]

Major career cheat code: Be easy to work with. Calm when others panic. Positive in the face of pessimists. Reliable. Consistent. It doesn't take talent. It just takes intention. The world bends toward people who make everyone around them better.

[Blake Burge @blakeaburge]

Major cheat code for life: Be fully where your feet are. When you're at work, work. When you're with family, be with family. When you're resting, rest. Most people are physically present and mentally everywhere else.

[Blake Burge @blakeaburge]

Underrated life advice: Make yourself easy to root for. Be kind. Be reliable. Celebrate other people’s wins. Work hard without complaining. Carry good energy into rooms. You'll be shocked by how many doors open for you by making life better for others.

[George Mack @george__mack]

The high agency triangle

[Dave Kline @dklineii]

While most leaders worry about AI, a few are becoming the AI expert in their company. They're making themselves irreplaceable. It's hard, but it's not complicated. Here's the 5-step plan to become that leader before the year is out:

[Jen Zhu @jenzhuscott]

As I build my own 2nd brain 🧠 on Obsidian using @karpathy ‘s wiki idea, it suddenly dawned on me - one day when we r gone, our kids could inherit an interactive map to your mind, passion, obsessions, work, fascinations…

It’s kind of beautiful way to think abt your 2nd 🧠.

[James Clear @JamesClear]

A Monday morning question for you:

The common narrative is that kids learn faster than adults, but if you watch any toddler they spend a large portion of the day attempting things that are on the edge of their ability.

How much time have you spent on the edge of your ability today?

[elvis @omarsar0]

Building a personal knowledge base for my agents is increasingly where I spend my time these days.

Like @karpathy, I also use Obsidian for my MD vaults.

What's different in my approach is that I curate research papers on a daily basis and have actually tuned a Skill for months to find high-signal, relevant papers.

I was reviewing and curating papers manually for some time, but now it's all automated as it has gotten so good at capturing what I consider the best of the best. There are so many papers these days, so this is a big deal.

You all get to benefit from that with the papers I feature in my timeline and on @dair_ai.

The papers are indexed using @tobi qmd cli tool (all of it in markdown files along with useful metadata). So good for semantic search and surfacing insights, unlike anything out there.

I am a visual person, so I then started to experiment with how to leverage this personal knowledge base of research papers inside my new interactive artifact generator (mcp tools inside my agent orchestrator system). The result is what you see in the clip.

100s of papers with all sorts of insights visualized. I keep track of research papers daily, so believe me when I tell you that this system is absolutely insane at surfacing insights. This is the result of months of tinkering on how to index research and leverage agent automations for wikification and robust documentation.

But this is just the beginning. The visual artifact (which is interactive too) can be changed dynamically as I please. I can prompt my agent to throw any data at it. I can add different views to the data. Different interactions. I feel like this is the most personalized research system I have ever built and used, and it's not even close.

The knowledge that the agents are able to surface from this basic setup is already extremely useful as I experiment with new agentic engineering concepts. I feel like this knowledge layer and the higher-level ones I am working on will allow me to maximize other automation tools like autoresearch. The research is only as good as the research questions. And the research questions are only as good as the insights the agents have access to.

Where I am spending time now is on how to make this more actionable. I am obsessed about the search problem here. The automations, autoresearch, ralph research loop (I built one months ago) are easier to build but are only as good as what you feed them.

Work in progress. More updates soon. Back to building.

[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.

[James Clear @JamesClear]

Many situations in life are similar to going on a hike: the view changes once you start walking.

You don't need all the answers right now. New paths will reveal themselves if you have the courage to get started.

[JUMPERZ @jumperz]

karpathy is showing one of the simplest AI architectures that actually works..

dump research into a folder, let the model organise it into a wiki, ask questions, then file the answers back in.

the real insight is the loop...every query makes the wiki better. it compounds.. now thats a second brain building itself.

i think this is so good for agents if applied right

instead of pulling from shared memory every session, they build a living knowledge base that stays.

your coordinator is not just coordinating tasks anymore.. it is maintaining institutional knowledge so every execution adds something back to the base.

the bigger implication is crazy tho.

agents that own their own knowledge layer do not need infinite context windows, they need good file organisation and the ability to read their own indexes.

way cheaper, way more scalable, and way more inspectable than stuffing everything into one giant prompt.

[Dan McAteer @daniel_mac8]

This is amazing. Do this.

[Suni @suni_code]

GitHub — version control (free)
Claude — coding ($20/mo)

Namecheap — domain ($12/yr)
Cloudflare — DNS (free)
Vercel — deploy (free)

Clerk — auth (free)
Supabase — backend + database (free)
Upstash — Redis (free)
Pinecone — vector DB (free)

Resend — emails (free)
Stripe — payments (2.9% per transaction)

PostHog — analytics (free)
Sentry — error tracking (free)

Total cost to run a startup: ~$20/month

No servers.
No DevOps team.
No funding required.

Just an idea and WiFi.

There has never been a cheaper time to build. 🚀
Today is the best time to bet on yourself and build the things ⭐