peter's claw is a lot more than just a wrapper around my slop.
i too had plenty of offers, but so far chose not to follow through with any of them, as i like my life as is.
also, peter is a good friend and gives plenty of credit. in fact, less credit would be nice, so i don't have to endure more vibeslopped issues and PRs going forward :)
For me it is the simplicity of it (transparent minimal system prompts and harnest), you can extend it the way you like, I don't have to install a (buggy) Electron app (CC or Codex app), it integrates where I work, because it's simple (like in a standard terminal on VS code). I'm not locked in with any vendor and can switch models whenever I want, and most importantly, I can effectively use it within apps that are themselves using it as coding agent (the meta part - like a chat UI for very specific business cases). Being in TypeScript, it integrates very well with the browser and one can leverage the browser sandbox around it.
I cannot directly answer your question, because I am looking into this topic myself currently, but I found this HN discussion from two weeks ago, which should give you more insights about pi: https://news.ycombinator.com/item?id=46844822
i'm not a member of openclaw.
i build some oss in parallel, and added 3 or so commits to the openclaw repo. and peter is taking some of the openclaw contributors with him.
What nonsense is this. You seem to be implying that contributing to an open source project creates some kind of entitlement to whatever another contributor attains. That’s not how it works.
I think it would be a very interesting discussion in how open source projects get compensated. Acting like it's shameful to discuss things, in a thread literally about someone making a massive payday by getting hired from an OS project, is odd.
It's not like it would be an impossible ask to include a stipulation to also compensate other developers, but what do I know? In fact I'm curious why this doesn't happen more, but it feels like crab bucket mentality which is the mindset VC culture has exported across the world.
Other than the response from Mario itself, pi is very frequently showcased at meetups organised by Peter/OpenClaw community, so there is definitely crediting involved.
> There is no code, there are no tools, there is no configuration, and there are no projects.
To add to this, OpenClaw is incapable of doing anything meaningful. The context management is horrible, the bot constantly forgets basic instructions, and often misconfigures itself to the point of crashing.
(5) Is there a reason why we don't investigate using a cable to pull down energy to earth? That seems to be a far more valuable and tractable problem to solve.
"Buy a mac mini, copy a couple of lines to install" is marketing fluff. It's incredibly easy to trip moltbot into a config error, and its context management is also a total mess. The agent will outright forget the last 3 messages after compaction occurs even though the logs are available on disk. Finally, it never remembers instructions properly.
Overall, it's a good idea but incredibly rough due to what I assume is heavy vibe coding.
It's been a few days, but when I tried it, it just completely bricked itself because it tried to install a plugin (matrix) even though that was already installed. That wasn't some esoteric config or anything. It bricked itself right in the onboarding process.
When I investigated the issue, I found a bunch of hardcoded developer paths and a handful of other issues and decided I'm good, actually.
Software engineers don't understand how user hostile all these AI gizmos are.
Terminals are scary. AI running local code is scary. Random Github software is scary. And in my experience, normies are far more security paranoid than developers when it comes to AI.
Normies have a much more realistic take on AI than technical people or semi-technical "power users":
* They LOVE image-generating AI and AI that messes with their own photos/videos.
* They will ask ChatGPT, Gemini, etc and just believe the result.
* They will ask Copilot to help them make a formula in Excel and be happy to be done.
The common theme here is they don't care. To them, AI is just a neat thing. It's not a huge difference in their lives. They don't think about the environmental impact much unless someone tells them it's bad, via a high-quality video stream that itself was vastly worse for the environment than any AI conversation or image generation ever could be.
They will play a game 100% made by AI because their friend said it was fun. They don't care that some AAA publisher lost a sale on their "human made for sure, just trust us :nod:" identical game because the bored person was able to pull of something good enough with little effort (and better design decisions).
They also don't care if some article or book or whatever was written partially or entirely by AI as long as it's good. The AI part just isn't important to them. Not even a little bit!
Its kind of funny how it’s the exact same discussion as we used to have about privacy in the advent of social media. "I’m not worried, I got nothing to hide!" The convenience benefits of Facebook (in the beginning, likely less nowadays) massively outweighed the privacy concerns of the layman or woman.
This is not unusual. Spotify is included because it is a relevant source of evidence as the custodian of the data. It improves the narrative that the data wasn't just indexed but obtained illegally.
It's because the higher up the stack you go, tools become more declarative and literate. Calling sort is far easier than understanding the algorithm for example.
> Calling sort is far easier than understanding the algorithm for example.
This was one of my gripes in college, why am I implementing something if I just need to understand what it does? I'm going to use the built-in version anyway.
Because that's the entire point of college. It's supposed to teach you the fundamentals - how to think, how to problem solve, how to form mental models and adapt them, how things you use actually work. Knowing how different sorting functions work and what the tradeoffs are allows you to pick the best sorting function for your data and hardware. If the tools you have aren't doing the job, you can mend them or build new tools.
So you know which sort to call because there isn't a right answer for all cases.
And so you can write your own because you're probably going to want to sort data in a specific way. Sort doesn't mean in numerical increasing or decreasing order, it means whatever order you want. You're sorting far more often than you're calling the sort function.
My degree was not specifically CS, it was a related degree, the focus was on landing jobs, but they still covered some CS concepts because some students were in fact doing a CS degree. I was more focused on show me what I need to build things. I have never had to hand-craft any algorithm in my 15 years of coding, it just makes no sense to me. Someone else figured it out, I'm contempt understanding the algorithms.
In my twenty years, I've rerolled famous algorithms "every now and then".
Its almost wild to me that you never have.
Sometimes you need a better sort for just one task. Sometimes you need a parser because the data was never 100% standards compliant. Sometimes you need to reread Knuth for his line-breaking algorithm.
My high school computer science teacher (best one I ever had) once told us this anecdote when we were learning sorting algorithms:
He was brought in by the state to do some coaching for existing software devs back in the 90s. When he was going over the various different basic algorithms (insertion sort, selection sort, etc.) one of the devs in the back of the class piped up with, "why are you wasting our time? C++ has qsort built in."
When you're processing millions of records, many of which are probably already sorted, using an insertion sort to put a few new records into a sorted list, or using selection sort to grab the few records you need to the front of the queue, is going to be an order of magnitude faster than just calling qsort every time.
Turned out he worked for department of revenue. So my teacher roasted him with "oh, so you're the reason it takes us so long to get our tax returns back."
Thinking that you can just scoot by using the built-in version is how we get to the horrible state of optimization that we're in. Software has gotten slow because devs have gotten lazy and don't bother to understand the basics of programming anymore. We should be running a machine shop, not trying to build a jet engine out of Lego.
I mean, the lesson I got from my 10X class was pretty much that: "never write your own math library, unless you're working on maintaining one yourself".
funnily enough, this wasn't limited to contributing to some popular OS initiative. You can call YAGNI, but many companies do in fact have their own libraries to maintain internally. So it comes up more than you expect.
On a higher level, the time I took to implement a bunch of sorts helped me be able to read the docs for sort(), realize it's a quicksort implentation, and make judgements like
1. yeah, that works
2. this is overkill for my small dataset, I'll just whip up basic bubblesort
3. oh, there's multiple sort API's and some sorts are in-place. I'll use this one
4. This is an important operation and I need a more robust sorting library. I'll explain it to the team with XYZ
The reasoning was the important lesson, not the ability to know what sorting is.
Cocaine (the powder) is extracted from the coca leaf, which indigenous South Americans have chewed for over 8000 years. While the synthetic drug is insanely addictive, the natural form is still commonly used as a mild stimulant, probably safer than caffeine in coffee. So yes?
I'm a huge green energy supporter, but the data belies the headline. These types of headlines are often leverage to discredit the transition.
1. US emissions didn't jump. See the first chart. The 2.4% increase easily falls within 1 standard deviation of typical changes. In that line, US emissions have remained flat since 2019.
2. The caption over that chart uses more neutral language "U.S. greenhouse gas emissions increased in 2025" instead of jumped. Which is it?
3. The 2.4% increase in emissions matches 2.4% increase in energy use nationwide.
4. The title is structured to make it sound like coal power is primarily causal of the emissions increase even though that's clearly not the case.
Unrelated point: Coal quite literally poisons the air. Why are activists so fixated on the abstract specter of climate change to convert others? I'm pretty sure we could win over lots of MAHA types with that framing.
You can try it yourself - apparently it was just this[0] single prompt:
>> This is a USB Stick of my MRI. Find all reports, find all images, use imagemagick to convert them into something useful, and get everything into a structured directory in the ./output folder that's worth retaining. Then, make an index.html that's a full exploration tool for the results. Use /frontend-skills and /generate-image skills if necessary.
> /frontend-skills you can find in the plugin marketplace, generate-image is just a small skill that allows the model to use nanobanana-pro. It used it for some diagrams.
OpenClaw is mostly a shell around this (ha!), and I've always been annoyed OpenClaw never credited those repos openly.
The pi agent repos are a joy to read, are 1/100th the size of OpenClaw, and have 95% of the functionality.
[0]: https://github.com/badlogic/pi-mono
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