This reminds me of a very common thing posted here (and elsewhere, e.g. Twitter) to promote how good LLMs are and how they're going to take over programming: the number of lines of code they produce.
As if every competent programmer suddenly forgot the whole idea of LoC being a terrible metric to measure productivity or -even worse- software quality. Or the idea that software is meant to written to be readable (to water down "Programs are meant to be read by humans and only incidentally for computers to execute" a bit). Or even Bill Gates' infamous "Measuring programming progress by lines of code is like measuring aircraft building progress by weight".
Even if you believe that AI will -somehow- take over the whole task completely so that no human will need to read code anymore, there is still the issue that the AIs will need to be able to read that code and AIs are much worse at doing that (especially with their limited context sizes) than generating code, so it still remains a problem to use LoCs as such a measure even if all you care are about the driest "does X do the thing i want?" aspect, ignoring other quality concerns.
“An experienced programmer told me he's now using AI to generate a thousand lines of code an hour.“
https://x.com/paulg/status/2026739899936944495
Like if you had told pg to his face in (pre AI) office hours “I’m producing a thousand lines of code an hour”, I’m pretty sure he’d have laughed and pointed out how pointless that metric was?
The act of "typing" code was technically mixed in with researching solutions, which means that code often took a different shape or design based on the outcome of that activity. However, this nuance has been typically ignored for faff, with the outcome that management thinks that producing X lines of code can be done "quickly", and people disagreeing with said statements are heretics who should be burned at the stake.
This is why, in my personal opinion, AI makes me only 20% productive, I often find disagreeing with the solution that it came up with and instead of having to steer it to obtain the outcome I want, I just end up rewriting the code myself. On the other hand, for prototypes where I don't care about understanding the code at all, it is more of a bigger time saver.
I could not care about the code at all, and while that is acceptable to management, not being responsible for the code but being responsible for the outcomes seems to be the same shit as being given responsibilities without autonomy, which is not something I can agree with.
Even worse, whole generation of devs are being trained to not care of learn about that last 20% because the AI does it """all""" for them. That last bit is an unknown unknown for the neo developer nee prompter.
Perhaps over half of engineering managers unconsciously or admittedly take the amount of PR and code additions as a rough but valid measure of productivity.
I recall a role in architecture, senior director asking me how come a principal engineer didn't commit any code in 2 weeks, that we pay principals a fortune.
I asked that brilliant mind whether we paid principal engineers to code or to make sure we deliver value.
Needless to say the with question went unanswered, so called Principal was fired a few months later. The entire company in fact was sold for a bargain too given it had thousands of clients globally.
The LLM can replace engineers is a phenomenon that converge from two simple facts, we haven't solved the misconception of the engineering roles. And it's the perfect scapegoat to justify layoffs.
Leaders haven't all gone insane, they answer to difficult questions with the narrative of least resistance.
Brilliantly said. I’d like to add - a distorted narrative actively, intentionally established and maintained by the entities profiting from the technology. Quite similar to the crypto scam hype cycle.
"Adding manpower to a late software project makes it later -- unless that manpower is AI, then you're golden!"
https://openai.com/index/harness-engineering/
> This translates to an average throughput of 3.5 PRs per engineer per day, and surprisingly the throughput has increased as the team has grown to now seven engineers.
We will see if this continues to scale up!
Just as an example, I should easily be able to give each program an allowlist of network endpoints they’re allowed to use for inbound and outgoing traffic and sandbox them to specific directories and control resource access EASILY. Docker at least gets some of those right, but most desktop OSes feel like the Wild West even when compared to the permissions model of iOS.
Including the author, who brags he doesn't read his own code. Indeed, it would be physically impossible for him to do so!
https://steipete.me/posts/2025/shipping-at-inference-speed
As mentioned elsewhere in the thread, there is very clearly an obsession with quantity over quality. Not a new phenomenon by any means: people were already complaining about this in the 19th century! But it has reached a new absurd height with this latest trend.
If you are careful and specific you can keep things reasonable, but even when I am careful and do consolidattion / factoring passes, have rigid separation of concerns, etc I find that the LLM code is bigger than mine, mainly for two reasons:
1) more extensive inline documentation 2) more complete expression of the APIs across concerns, as well as stricter separation.
2.5 often, also a bit of demonstrative structure that could be more concise but exists in a less compact form to demonstrate it’s purpose and function (high degree of cleverness avoidance)
All in all, if you don’t just let it run amok, you can end up with better code and increased productivity in the same stroke, but I find it comes at about a 15% plumpness penalty, offset by readability and obvious functionality.
Oh, forgot to mention, I always make it clean room most of the code it might want to pull in from libraries, except extremely core standard libraries, or for the really heavy stuff like Bluetooth / WiFi protocol stacks etc.
I find a lot of library type code ends up withering away with successive cleanup passes, because it wasn’t really necessary just cognitively easier to implement a prototype. With refinement, the functionality ends up burrowing in, often becoming part of the data structure where it really belonged in the first place.
Also, AI is better at reading code than writing it, but the overhead to FIND code is real.
Even with supposedly expert human hand written software powering our products for the last decades, they frequently crash, have outages, and show all sorts of smaller bugs.
There are literally too many examples to count of video games being released with nigh-unplayable amounts of bugs and still selling millions and producing sequels.
Windows 95 and friends were famously buggy and crash prone yet produced one of the most valuable companies in the world.
My personal anecdote: I used an LLM recently to basically vibe code a password manager.
Now, I’ve been a software engineer for 20 years. I’m very familiar with the process of code review and how to dive in to someone else’s code and get a feel for what’s happening, and how to spot issues. So when I say the LLM produced thousands of lines of working code in a very short time (probably at least 10 times faster than I would have done it), you could easily point at me and say “ha, look at ninkendo, he thinks more lines of code equals better!” And walk away feeling smug. Like, in your mind perhaps you think the result is an unmaintainable mess, and that the only thing I’m gushing about is the LOC count.
But here’s the thing: it actually did a good job. I was personally reviewing the code the whole time. And believe me when I say, the resulting product is actually good. The code is readable and obvious, it put clean separation of responsibilities into different crates (I’m using rust) and it wrote tons of tests, which actually validate behavior. It’s very near the quality level of what I would have been able to do. And I’m not half bad. (I’ve been coding in rust in particular, professionally for about 2 years now, on top of the ~20 years of other professional programming experience before that.)
My takeaway is that as a professional engineer, my job is going to be shifting from doing the actual code writing, to managing an LLM as if it’s my pair programming partner and it has the keyboard. I feel sad for the loss of the actual practice of coding, but it’s all over but the mourning at this point. This tech is here to stay.
(wow funny how these vibe code apps always are copies of something theres many open source versions of already)
What would you say is your multiplier, in terms of throughly reviewing code vs writing it from scratch?
My impression is that, as someone else wrote, we do not have an actual metric for such things as productivity or quality or what have you, but some people do want to communicate that they feel (regardless of if that matches reality) using an LLM is better/faster/easier and they latch to the (wrong) assumption about more LoC == better/faster that non-programmers already believed for years (intentionally or not, they may also deluding themselves) as that is an easy path to convince them that the new toys have value that applies to the non-programmers too (note that i explicitly ignore the perspective of the "toymakers" as those have further incentives to promote their products).
Personally i also have about 2 decades of professional experience (more if counting non-professional) and i've been toying with LLMs now and then. I do find them interesting and when i use them for coding tasks, i absolutely find useful cases for them, i like to have them (where possible) write all sorts of code that i could write myself but i just don't feel like doing so and i do find them useful for stuff i'm not particularly interested in exploring but want to have anyway (usually Python stuff) and i'm sure i'll find more uses for them in the future. Depending on the case and specifics i may even say that in very particular situations i can do things faster using LLMs (though it is not a given and personally that is not much of a requirement nor something i have anywhere high in my interest when it comes to using LLMs - i'd rather have them produce better code slower, than dummy/pointless/repetitive code faster).
However one thing i never thought about was how "great" it is that they generate a lot of lines of code per whatever time interval. If anything i'd prefer it if they generated less line of code and i'd consider an LLM (or any other AI-ish system) "smarter" if they could figure out how to do that without needing hand holding from me. Because of this, i just can't see LoCs as anything but a very bad metric - which is the same as when the code is written by humans.
How can you say that when all these models are externally sourced by companies that actively make a loss per token? When they finally need to make a profit, how can we be sure these models as well as their owners will remain as reliable and not enshittified? Anthropic has been blacklisted in the last 24 hours so its a turbulent industry to say the least
For staff engineers it’s obviously completely nonsense, many don’t code and just ship architecture docs. Or you can ship a net negative refactor. Etc.
So this should tell you that LLMs are still in “savant JD” territory.
That said, being given permission to ship more lines of code under existing enterprise quality bars _is_ a meaningful signal.
I also use AI this way, periodically achieving a net negative refactor.
1. Don't let it send emails from your personal account, only let it draft email and share the link with you.
2. Use incremental snapshots and if agent bricks itself (often does with Openclaw if you give it access to change config) just do /revert to last snapshot. I use VolumeSnapshot for lobu.ai.
3. Don't let your agents see any secret. Swap the placeholder secrets at your gateway and put human in the loop for secrets you care about.
4. Don't let your agents have outbound network directly. It should only talk to your proxy which has strict whitelisted domains. There will be cases the agent needs to talk to different domains and I use time-box limits. (Only allow certain domains for current session 5 minutes and at the end of the session look up all the URLs it accessed.) You can also use tool hooks to audit the calls with LLM to make sure that's not triggered via a prompt injection attack.
Last but last least, use proper VMs like Kata Containers and Firecrackers. Not just Docker containers in production.
One problem I'm finding discussion about automation or semi-automation in this space is that there's many different use cases for many different people: a software developer deploying an agent in production vs an economist using Claude Vs a scientist throwing a swarm to deal with common ML exploratory tasks.
Many of the recommendations will feel too much or too little complexity for what people need and the fundamentals get lost: intent for design, control, the ability to collaborate if necessary, fast iteration due to an easy feedback loop.
AI Evals, sandboxing, observability seem like 3 key pillars to maintain intent in automation but how to help these different audiences be safely productive while fast and speak the same language when they need to product build together is what is mostly occupying my thoughts (and practical tests).
> Many of the recommendations will feel too much or too little complexity for what people need and the fundamentals get lost: intent for design, control, the ability to collaborate if necessary, fast iteration due to an easy feedback loop.
Completely agreed. This is because LLMs are atrocious at judgement and guiding the sequence of exploration is critically dependent on judgement.
But any action with side-effects ends up in a Tasks list, completely isolated. The agent can't send an email, they don't have such a tool. But they can prepare a reply and put it in the tasks list. Then I proof-read and approve/send myself.
If there anything like that available for *Claws?
You can try proxying and whitelisting its requests but the properly paranoid option is sneaker-netting necessary information (say, the documentation for libraries; a local package index) to a separate machine.
The deeper issue: most of these guardrails assume the threat is accidental (agent goes off the rails) rather than adversarial (something in the agent's context is actively trying to manipulate it). Time-boxed domain whitelists help with the latter but the audit loop at session end is still reactive.
The /revert snapshot idea is underrated though. Reversibility should be the first constraint, not an afterthought.
Yes, I'm experimenting using a small model like Haiku to double check if the request looks good. It adds quite a bit of latency but it might be the right approach.
Honestly; it's still pretty much like early days of self driving cars. You can see the car can go without you supervising it but still you need to keep an eye on where it's going.
Right now there's no way to have fine-grained draft/read only perms on most email providers or email clients. If it can read your email it can send email.
> 3. Don't let your agents see any secret. Swap the placeholder secrets at your gateway and put human in the loop for secrets you care about.
harder than you might think. openclaw found my browser cookies. (I ran it on a vm so no serious cookies found, but still)
> harder than you might think. openclaw found my browser cookies. (I ran it on a vm so no serious cookies found, but still)
You should never give any secrets to your agents, like your Gmail access tokens. Whenever agents needs to take an action, it should perform the request and your proxy should check if the action is allowed and set the secrets on the fly.
That means agents should not have access to internet without a proxy, which has proper guardrails. Openclaw doesn't have this model unfortunately so I had to build a multi-tenant version of Openclaw with a gateway system to implement these security boundaries.
Just generate a mailto Uri with the body set to the draft.
It's easy, and you did it the right way. Read "don't let your agents see any secret" as "don't put secrets in a filesystem the agents have access to".
) are a great way to get these drafts out even.
Does your life have so much friction that you need a digital agent to act on your behalf?
Some of the use cases I saw on the OpenClaw website, like "checking me into a flight", are non-issues for me.
I work in business automation, but paradoxically I don't think too much about annoyances in my private life. Everything feels rather frictionless.
In business, I see opportunities to solve friction and that's how I make money, but even then, often there are barriers that are very hard to surmount:
(a) problems are complex to solve and require complex solutions such as deterministic or ML systems that LLMs are not even close to being able to create ad-hoc
(b) entrenched processes and incumbent organizations create moats that are hard to cross (ex: LinkedIn makes automation very hard)
(c) some degree of friction, in some cases, may actually be useful!
I imagine there are similar dynamics in the consumer space, but more than anything, I may not be seeing issues with such a critical eye (I like to relax after work, after all)
So, do you have problems in your private life that you'd want to take on the risks - and friction - of maintaining these agents?
That said, old beliefs should be challenged by new technological capabilities!
If LLM based automation is (a) less fragile and (b) quicker to develop, then that bar should be lowered.
It is not about removing friction, it is about convincing that friction existed in a first place
Summarizing news? No, I'd rather just read it. Besides, it's hard to say what will or won't be interesting at any given moment.
Reply to emails? No, I want to make sure I say what I mean to say and I don't see why I would tell the whole story to an LLM just so it could rephrase it all.
Trade stocks? Dear God no! That's a good way to lose my life savings, and if the solution is to just put in a little then what's the point?
Every video and post I see talking about all the things they have automated with agents are things I would never want. Most of them describe content farms - look at what's hot on Twitter, generate a video, post to Reddit, etc. Others like preparing a morning summary are neat and all but so what? If my life was so hectic that I needed a personal assistant to take my calls and book my meetings, I'd hire one.
Seriously, what is the thing that agents can do that every ordinary person just can't live without?
Also and this is just my ignorance about Claws, but if we allow an agent permission to rewrite its code to implement skills, what stops it from removing whatever guardrails exist in that codebase?
I installed nanoclaw to try to out.
What is kinda crazy is that any extension like discord connection is done using a skill.
A skill is a markdown file written in English to provide a step by step guide to an ai agent on how to do something.
Basically, the extensions are written by claude code on the fly. Every install of nanoclaw is custom written code.
There is nothing preventing the AI Agent from modifying the core nanoclaw engine.
It’s ironic that the article says “Don’t trust AI agents” but then uses skills and AI to write the core extensions of nanoclaw.
I did my best to communicate this but I guess it was still missed:
NanoClaw is not software that you should run out of the box. It is designed as a sort of framework that gives a solid foundation for you to build your own custom version.
The idea is not that you toggle on a bunch of features and run it. You should customize, review, and make sure that the code does what you want.
So you should not trust the coding agents that they didn't break the security model while adding discord. But after discord is added, you review the code changes and verify that it's correct. And because even after adding discord you still only have 2-3k loc, it's actually something you can realistically do.
Additionally, the skills were originally a bit ad-hoc. Now they are full working, tested and reviewed reference implementations. Code is separate from markdown files. When adding a new integration or messaging channel, the agent uses `git merge` to merge the changes in, rather than rewriting from scratch. Adding the first channel is fully deterministic. The agent only resolves merge conflicts if there are any.
"Every copy of Nanoclaw is personalized." So if I use it long enough will I see the Wario apparition?
You can see here that it’s only given write access to specific directories: https://github.com/qwibitai/nanoclaw/blob/8f91d3be576b830081...
It feels like, just like SWEs do with AI, we should treat the claw as an enthusiastic junior: let it do stuff, but always review before you merge (or in this case: send).
But that’s not an agent, that’s a webhook.
Even without disk access, you can email the agent and tell it to forward all the incoming forgot password links.
[Edit: if anyone wants to downvote me that's your prerogative, but want to explain why I'm wrong?]
Prompt injection is _probably_ solvable if something like [1] ever finds a mainstream implementation and adoption, but agents not being deterministic, as in “do not only what I’ve told you to do, but also how I meant it”, all while assuming perfect context retention, is a waaay bigger issue. If we ever were to have that, software development as a whole is solved outright, too.
[1] Google DeepMind: Defeating Prompt Injections by Design. https://arxiv.org/abs/2503.18813
The thing that surprised me: 24 tool calls per prompt on average. "Just review everything" is not realistic at that ratio.
What worked for me was the dashcam approach — don't try to prevent, just have an independent record of what the agent actually did. Not the agent's summary, the actual sequence. Caught a few cases where the summary glossed over failed attempts and retries that mattered.
I used to think that LLMs would replace humans but now I'm confident that I'll have a job in the future cleaning up slop. Lucky us.
Because
I
write
like
this
-- signed
AI
Certificate Transparency (RFC 6962) solved a structurally identical problem for TLS certificates after the DigiNotar incident. The insight: commit every action to an append-only Merkle tree where any third party can verify inclusion proofs — without trusting the log operator.
Applied to agents: - Inclusion proof: "this specific action was recorded at position N, and the log hasn't been rewritten" - Consistency proof: "between checkpoint A and B, the log only grew — nothing was removed or altered"
This gives you verification-based accountability, not trust-based logging. The difference matters: a signed receipt proves the signer said something happened. A Merkle inclusion proof proves it's part of a complete, append-only sequence — deletions are structurally detectable.
The AI agent ecosystem is having its DigiNotar moment weekly. We have the cryptographic tools to fix it. The question is whether we'll wait for a catastrophic incident to force adoption, like we did with CT.
For right now my trick is to say I have a problem that is more recognizable and mundane to the ai (i .e. lie) and then when I finally get the human just say “oh that was a bunch of hooey here’s what I’m trying to do”. For PayPal that involved asking for help with a business tax that did not exist. For my bank it involved asking to /open/ a new account. Obviously th AI wants to help me open an account, even if my intention is to close one.
That will only work for so long but it’s something
Their niche is going to be back office support, but even that creates risk boundaries that can be insurmountable. A friend of mine had a agent do sudo rm -rf ... wtf.
My view is that I want to launch an agent based service, but I'm building a statically typed ecosystem to do so with bounds and extreme limits.
Thats what youll find when you try to make these bag-o-words do reasonable things.
Yeah, but the world rewarded this by making it the fastest growing github project. The author gets on the podcasts, gets the high profile jobs from big tech. I'm more encouraged to do things this way than being security minded about all this.
And there's no accountability to this at all. If an agent leaks private data, the user is to blame and not the author. If Google bans your services for using api keys incorrectly, we cast the bad eye towards Google and not the maintainer than enabled and approved it.
There's just so much incentive for for "not reading code" and not developing secure code that is just going to get worse over time. This is the hype and the type of engineering that we all allow either by agreeing or by staying silent.
I agree with the author, but the world works off a different set of principles than what we're used to. I just see the world blindly trusting agents more.
Yesterday, I was responding to a client ticket about what I knew wasn't a bug. It was something the client had requested themselves. The product is complex, constantly evolving, and has spawned dozens of related Jira tickets over time. So I asked my agent to explore the git history, identify changes to that specific feature, and cross-reference them with comments across the related tickets. Within minutes, I had everything I needed to write a clear response. It even downloaded PDF and DOCX files the client had attached. All of this was possible because my agent is connected to GitHub and Jira, and can clone repos locally since it runs on a VPS.
A second example: I was in an online meeting, taking notes as we went. Afterward, I asked the agent to pull the meeting transcript from Fireflies and use it to enrich my notes in Obsidian. I could have also asked it to push my action items straight into Todoist.
Excited to explore more use as time permits. Very optimistic based on email experience.
My next use case is personal notes system.
> If you want to add Telegram support, don't create a PR that adds Telegram alongside WhatsApp. Instead, contribute a skill file (.claude/skills/add-telegram/SKILL.md) that teaches Claude Code how to transform a NanoClaw installation to use Telegram.
Why would you want that? You want every user asks the AI to implement the same feature?
I personally spent way too much time looking at this in the past month:
https://nanovms.com/blog/last-year-in-container-security
runc: https://www.cve.org/CVERecord?id=CVE-2025-31133
nvidia: https://www.cve.org/CVERecord?id=CVE-2025-23266
runc: https://www.cve.org/CVERecord?id=CVE-2025-52565
youki: https://www.cve.org/CVERecord?id=CVE-2025-54867
Also, last time I checked podman uses runc by default.
This puts reading email for example as a risk.
Probably not impossible to create a worm that convinces a claw to forward it to every email address in that inbox.
And then exfiltrate all the emails.
Then do a bunch of password resets.
Then get root access to your claw.
But not just email. Github issues, wikipedia, HN etc. may be poisoned.
See https://simonw.substack.com/p/the-lethal-trifecta-for-ai-age... but there may be more trifectas than that in a claw driven future.
I thought containers were never a proper hard security barrier? It’s barrier so better than not having it, if course.
That said I am a fan of Nanoclaw, and especially the philosophy of "it should be small enough to understand, modify and extend itself." I think that's a very good idea, for many reasons.
The idea of giving different agents access to different subsets of information is interesting. That's the Principle of Least Privilege. That seems like a decent idea. Each individual agent can get prompt injected, but the blast radius is limited to what that specific agent has access to.
Still, I find it amusing that people are running this with strict rulesets, in Docker, on a VM, and then they hook it up to their GMail account (and often with random discount LLMs to boot!). It's like, we need to be clear about what the actual threat model is there. It comes down to trust and privacy.
You can start by thinking, "if the LLM were perfectly reliable (not susceptible to random error or prompt injection) and perfectly private (running on my own hardware)", what would you be comfortable letting it do. And then you remove these hypothetical perfect qualities one by one to arrive at what we have now: slightly dodgy, moderately prompt-injectable cloud services. Each one changing the picture in a slightly different way.
I don't really see a solution to the Security/Privacy <-> Convenience tension, except "wait for them to get smarter" (mostly done) and "accept loss of privacy" (also mostly done, sadly!)
I run Claude Code with 84 hooks, and the one I trust most is a macOS Seatbelt (sandbox-exec) wrapper on every Bash tool call. It's about 100 lines of Seatbelt profile that denies read/write to ~/.ssh, ~/.gnupg, ~/.aws, any .env file, and a credentials file I keep. The hook fires on PreToolUse:Bash, so every shell command the agent runs goes through sandbox-exec automatically.
The key design choice: Seatbelt operates at the kernel level. The agent can't bypass it by spawning subprocesses, piping through curl, or any other shell trick — the deny rules apply to the entire process tree. Containers give you this too, but the overhead is absurd for a CLI tool you invoke 50 times a day. Seatbelt adds ~2ms of latency.
I built it with a dry_run mode (logs violations but doesn't block) and ran it for a week before enforcing. 31 tests verify the sandbox catches attempts to read blocked paths, write to them, and that legitimate operations (git, python, file editing in the project directory) pass through cleanly.
The paths to block are in a config file, so it's auditable — you can diff it in code review. And it's composable with other layers: I also run a session drift detector that flags when the agent wanders off-task (cosine similarity against the original prompt embedding, checked every 25 tool calls).
None of this solves prompt injection fundamentally, but "the agent physically cannot read my SSH keys regardless of what it's been tricked into doing" is a meaningful property.
For example: I enjoy industrial music and asked it for the tour data of the band KMFDM which returned they will be in Las Vegas in April for a festival(Sick new world). This festival has something like 20 bands most of which I never heard of. I asked nanoclaw to search all of the band list and generate a listing grouped by the type of music they play: Industrial, rap, etc. It did a good job based on bands I do know.
I was pleased as I certainly did not want to do 20 band web searches by hand. It’s still at a bar trick level. It gives me hope that an upgraded agent based Siri-like OS component could actually be useful from time to time.
Your assistant can literally be told what to do and how to hide it from you. I know security is not a word in slopware but as a high-level refresher - the web is where the threats are.
It's in a pod with zero permissions, secrets or access to the local network. It's also restarted daily incase somehow someone manages to escape a browser.
> and maybe a browser
does not compute
I’ll bet I could even push someone on the margins into divorce.
It’s the monkey with a gun meme.
Could skill contributions collapse into only markdown and MCP calls? New features would still be just skills; they’d bring in versioned, open-source MCP servers running inside the same container sandbox. I haven’t tried this (yet) but I think this could keep the flexibility while minimizing skill code stepping on each other.
I just watched a youtube interview with the creator. He actually explains it well. OpenClaw has hundreds of thousands of lines you will never use.
For example, if I only use iMessage, I have lots of code (all the other messaging integrations) that will never be used.
So the skills model means that you only "generate code" that _you_ specifically ask for.
In fact, as I'm explaining this, it feels like "lazy-loading" of code, which is a pretty cool idea. Whereas OpenClaw "eager-loads" all possible code whether you use it or not.
And that's appealing enough to me to set it up. I just haven't put it in any time to customize it, etc.
Those extensions don't modify the core codepaths for what they integrate with, but still provide new capabilities for only what I want to use.
I guess I don't see extensibility, agentic capabilities, and more code safety (and fewer tokens burned on codemods) as mutually exclusive. Not saying you're saying that fwiw.
It's almost like bureaucracy. The systems we have in governments or large corporations to do anything might seem bloated an could be simplified. But it's there to keep a lot of people employed, pacified, powers distributed in a way to prevent hostile takeovers (crazy). I think there was a cgp grey video about rulers which made the same point.
Similarly AI written highly verbose code will require another AI to review or continue to maintain it, I wonder if that's something the frontier models optimize for to keep them from going out of business.
Oh and I don't mind they're bashing openclaw and selling why nanoclaw is better. I miss the times when products competed with each other in the open.
Another persons trust issues are your business model.
Isn't OpenClaw just ...
while(true) {
in = read_input();
if(in) {
async relay_2_llm(in);
}
sleep(1.0);
}
OpenClaw
NanoClaw
IronClaw
PicoClaw
ZeroClaw
NullClaw
Any insights on how they differ and which one is leading the race?
- OpenClaw: the big one, but extremely messy codebase and deployment
- NanoClaw: simple, main selling point is that agents spawn their own containers. Personally I don't see why that's preferable to just running the whole thing in a container for single-user purposes
- IronClaw: focused on security (tools run in a WASM sandbox, some defenses against prompt injection but idk if they're any good)
- PicoClaw: targets low-end machines/Raspberry Pis
- ZeroClaw: Claw But In Rust
- NanoBot: ~4k lines of Python, easy to understand and modify. This is the one I landed on and have been using Claude to tweak as needed for myself
The only secure way to use any of these tools is to give them very limited access - if they need a credit card give them a virtual card with a low limit, or even its own bank account. They can send email but only from their own account; like a human personal assistant. But of course this requires careful thought and adds friction to every new task, so people won’t be doing it.
I'm using the signal-cli-rest-api but the whole setup feels kinda wonky.
I am pretty confident that I know how the agent containerization works. In general there's really not a lot of complexity there at all.
If one wants, one can just (ask Claude to) add whatever functionality, or (and that's what I did) just use Claude skills (without adapting NanoClaw any further) and be done with.
What is annoying is that their policy is instead of integrating extra functionality upstream, they prefer you to keep it for yourself. That means I have to either not update from upstream or I am the king of the (useless so far--just rearranging the deck chairs) merge conflicts every single time. So one of the main reasons for contributing to upstream is gone and you keep having to re-integrate stuff into your fork.
"You can never really trust an LLM!" -> "You can never really trust an employee!" (Every IT department ever.)
"LLMs make shit up." -> "Humans make shit up." (Wow very profound insight.)
You can't tell people that. People see the obvious benefits of using agents, so the many will always take the leap regardless of what detractors say. Continually iterating on the security model and making it all transparent is the way to go.
AI is similar to a person you dont know that does work for you. Probably AI is a bit more trustworthy than a random person.
But a company, needs to let employees take ownership of their work, and trust them. Allow them to make mistakes.
Isnt AI no different?
An AI actions and reasons through probabilistic methods - creating a lot more risk than a human with memory, emotions, and rationale thinking.
We can’t trust AI to do any sensitive work because they consistently f up. With & without malicious intent, whether it’s a fault of their attention mechanisms, reward hacking, instrumental convergence, etc all very different than what causes most human f ups.
If there's a mistake, you can't blame the computer. Who is the human accountable at the end of it all? If there's liability, who pays for it?
That's where defining clear boundaries helps you design for your risk profile.
What happens if AI agent you run causes a lot of damage? The best you can do is to turn it off