March 2026
9 posts: 2 entries, 2 links, 3 quotes, 2 notes
Context Windows Are Limited by Atoms, Not Bits
There is a popular narrative in tech right now: AI progress is exponential, context windows will grow to infinity, and all vertical AI products will soon be replaced by general-purpose AI that can use all the context of your entire business. This implies that the big players like Anthropic, OpenAI, and Google, with their general-purpose agents like Claude Cowork, ChatGPT, or Gemini, will subsume all software.
[... 828 words]I think actually what being an IC across this past year has taught me, is that it actually just gave me a lot of skills that I don't think I would've gained if I was just managing throughout this year.
— Jenny Wen, Lenny's Podcast interview. Wen left a director role at Figma to return to IC design work at Anthropic.
Hyphens and Dashes
With AI tools becoming widely adopted, em-dashes have become a telltale sign of AI-generated content. Claude and ChatGPT seem to love them, which is unfortunate, because it’s now made everyone suspicious of a perfectly good punctuation mark. But this doesn’t change the fact that most people (non-native and native English speakers alike) never learned or understood the difference between hyphens (-), en-dashes (–), and em-dashes (—) in the first place. I now frequently see correct usage (AI-generated) and incorrect usage mixed in the same document, which happens when people do not understand the difference and revise a piece of AI-generated text.
[... 659 words]Em-dashes have become a telltale sign of AI-generated text, which has created some funny side effects.
I now frequently see correct and incorrect usage of hyphens and dashes mixed in the same piece of text. This happens when someone revises a piece of AI-generated text but doesn't understand the difference between hyphens, en-dashes, and em-dashes.
It's also pretty obvious that some people have started find-replacing all em-dashes with single hyphens (-) or double hyphens (--) to hide that they used AI. Which, of course, is its own tell.
But this still doesn't hide the most obvious giveaway, which isn't the em-dash itself. LLMs almost always put spaces around em-dashes: word — word instead of word—word. My guess is that models are heavily trained on news data, where the AP style guide, most commonly used in journalism, recommends spaces around em-dashes. Books and most professional writing use them without spaces.
So if you're taking your writing seriously, there's no way around learning how to use hyphens, en-dashes, and em-dashes correctly. I wrote a short post explaining the differences on my blog: Hyphens and Dashes
The reason why Nvidia can move so fast is because we always have a unifying theory for the company, which is my job [as the CEO of the company]. I need to come up with a unifying theory for what's important and why things connect together and how they connect together and then create an organization, an organism that's really, really good at delivering on that unifying theory.
— Jensen Huang, Stratechery interview with Nvidia CEO Jensen Huang
How do I keep a sane mind? Well, it's important to be married to someone sane.
I mean, it sounds like a strange compliment to describe someone as sane, but the older you are, the more you realize that's actually a fairly unique quality. And so if you're married to someone sane, and as long as you don't both freak out at the same time, then there's always someone to calm the other one down. Right?
That's the advantage. So I recommend to everyone, marry someone sane.
— Paul Graham, The Social Radars podcast interview with Paul Graham, Y Combinator founder.
One big danger of AI tools in the workplace is how much easier they make it to pursue side quests.
Side quests used to be self-regulating. You'd think "wouldn't it be cool to try this?", estimate the effort at half a day, and move on. Now you tell yourself "it takes only five minutes", and decide to just go for it out of curiosity, but it's of course never just five minutes.
The result is that you can get to the end of a day having completed ten low-priority items on your todo list very efficiently, while making zero progress on the one high-priority item that needed your attention most. Your only real defense is knowing what the highest-priority item on your list is and holding yourself accountable for making progress on it.
Andrej Karpathy on Code Agents, AutoResearch, and the Loopy Era of AI (No Priors Podcast). Andrej Karpathy is always worth listening to because he has the time to experiment and tinker with the latest developments in a way that most people working at companies don't. He effectively lives a few months in the future compared to the rest of us.
Two things stuck with me from this conversation. First, Karpathy frames Claws (from OpenClaw) as another layer of the AI stack: LLMs → Agents → Claws. I have never actually set up a Claw yet, but the persistent memory architecture and how "your Claw" gets to know you over time are things I want to experiment with, as this is directly relevant to what we're working on at Ren as the product becomes more agentic.
Second, his work on AutoResearch. We've discussed the concept internally at Ren multiple times over the past few months, but never found the time to actually try it. We have a concrete problem that would lend itself well to this approach: building a more efficient multi-label classifier. We currently use a relatively heavy model for it, we have abundant training data, and the objective is clear (maximize precision/recall/F1 for a given latency budget). We could just let an AutoResearch system loose on this task. What I'm missing is knowing how to set up a sandbox that's safe enough but has sufficient permissions for the agent to carry out the research on its own. The meta task would then be similar to Claws: build a system in a few markdown files that defines how the agent approaches and documents its research.
From skeptic to true believer: How OpenClaw changed my life (Lenny’s Podcast). This is the podcast on OpenClaw I listened to this weekend after the Karpathy episode. I think I understood the appeal of a proactive system that works independently from the start, but I haven't bought into the hype so far. However, I feel that these two podcasts together have started changing my mind—not because of a single capability, but because of the apparent emergent behavior that arises once a Claw has context about you and access to real tools. Agents, as we typically think of them, are reactive: you give them a task, and they execute what they are asked to do. But I now fully realize that Claws are persistent and have personalities of their own. They run in the background, build up memory over time, check in on a schedule, and start acting on your behalf without being prompted.
Claire Vo, who was apparently a big OpenClaw skeptic when it launched, now manages nine agents across multiple Mac Minis for both personal and professional use.
The first thing that stood out to me in this conversation is how well the onboarding is apparently done. Instead of structured forms and settings pages, your Claw just asks you who it is and who you are, and you figure it out together through conversation, as if you hired a new employee. The second thing I learned is how well-crafted the default behavior of the Claw appears to be. The Claw's behavior emerges from some simple markdown files ("soul document"), but the defaults are apparently surprisingly thoughtful and lead to a really pleasant behavior. It sounds like this is something anyone working in product right now should experience firsthand.
I'm now genuinely intrigued to try it myself. To really get the full experience, you clearly need to run it on a separate machine, both for security and because you don't want to think about whether your laptop is online. I should really try setting one up on my Raspberry Pi, or just buy a Mac Mini for it. The other thing I don't really have yet is a clear use case for a Claw. I wonder whether I should try to come up with one before getting started, or whether this is something you just have to go for, because the onboarding seems good enough that the use case will emerge during the setup process.