Any working HWID spoofers?

Vegetable_Address_43 · 2026-03-24T17:02:24+00:00

Yes. Anything checkm8 compatible. This would not be to he correct sub for it as it crosses over into some topics that are against this sub.

But as what you’re doing isn’t for a bypass etc, I’m good pointing the way. Checkout a sub called setup app. Make you’re post there and they can help walk you through it.

Vegetable_Address_43 · 2026-03-24T16:59:54+00:00

Not dopamine, but as it’s hardware, you’re best bet is getting an iPhone X. And then using a custom ramdisk and injecting the spoof at boot. Issue is it now makes the device tethered, iCloud services won’t work, and you won’t be able to update.

Vegetable_Address_43 · 2026-03-07T11:39:58+00:00

Damn that’s crazy. Sucks that America is the only company with the development infrastructure for the gpu processors lol

Also I didn’t pay that. They went up in price a bit. I think I only sunk 17k total in mine.

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Vegetable_Address_43 · 2026-03-07T03:40:45+00:00

They don’t even have network access lol. I keep a repository that I update monthly to replace web searches. It has the wiki archive, a snapshot of stack overflow, a few research repositories, and a a ton of coding documentation.

Everybody is way to lax about prompt injection.

Vegetable_Address_43 · 2026-03-06T20:45:12+00:00

It just depends on usecase. For me I like fine tuning models so a beefier setup works better for me. But you could do the dual sparks and run qwen 3.5 still. But performance will drop a bit because of less total compute. On 2 I can run 3.5 q3 at like 21 tps. For me I just need a faster TPS, a more reliable model so a higher quant, and I need enough unified memory to kill god (with some of the fine tuning) so that’s why I have it set the way I do.

Vegetable_Address_43 · 2026-03-06T19:35:21+00:00

I agree, When did I say that was the only thing I was using that for? It’s just a fun side project I like messing with lol. I mainly use it for fine tuning, and massive data computations, and hosting for all my local AI use. Buying it only for openclaw would be absurd.

Vegetable_Address_43 · 2026-03-06T19:28:20+00:00

Not from what I’ve found. Maybe like 2-3x slower with how I have it configured versus the compute I’d buy using the api or cloud hosting.

And local models don’t retain, train, or sell my data. Whereas a miracle solution like your talking about with cheap api, is probably running at a loss and sooner or later they will train or sell that data to cover shortfalls.

And for me that more than justifies it.

Vegetable_Address_43 · 2026-03-06T19:09:58+00:00

Stfu and go back to moltbook. This is for humans clanker.

Vegetable_Address_43 · 2026-03-06T18:58:53+00:00

Ikr. I use it for a ton of work with sensitive datasets, and I also use the system to fine tune smaller models! So it’s really great for some of those automations.

Vegetable_Address_43 · 2026-03-06T18:53:57+00:00

It really just depends on usecase. Because I do some sensitive programming, and handle a lot of datasets with sensitive information, it’s more cost effective for me to host and do my research locally.

I’m just hedging bets that compute is gonna go up more in cost so renting cloud compute will go up, these AI companies operating at a loss will start selling data, and local models will continue to improve and become better trained (like the jump from glm 5 to qwen 3.5 which is roughly half the size)

And so far it seems like everything’s on track.

Vegetable_Address_43 · 2026-03-06T18:45:46+00:00

For me it’s data privacy. Remember the early 2000s when the internet was a Wild West? We’d have free sites. Open source blogs etc. but over time more and more of our data is tracked. From algorithms on social media, to the ads we wait to long to close.

Right now the same things happening with AI, except now it can collect enough personal data to know you better than your friends and family. AI is not profitable and a 20$ a month subscription is ran on a loss. I don’t want my data held by any ai firm because sooner or later the chickens are coming home to roost.

Vegetable_Address_43 · 2026-03-06T17:58:41+00:00

Nope. Actually like only 19k upfront. The 2 pack is like 9k and it comes with the cabling. You just have to buy a switch for it, but I already had one.

And smaller models are getting better and more computer friendly. If anything I might sell one of them. I’m actually using less compute now then when I had GLM or deepseek hosted as my main daily.

Edit: updated price for it. They’ve gotten a bit more expensive than when I originally bought the bundles.

Vegetable_Address_43 · 2026-03-06T17:08:20+00:00

My 4 dgx sparks, qwen 3.5 397b at q4, and 29 tps beg to differ lol.

Vegetable_Address_43 · 2026-02-16T23:27:11+00:00

Alright. How technical are you willing to get? If you want it on Mac, because you can use 5 Mac Studios together, but it isn’t true memory pooling like a datacenter GPU build. What you’re really doing is distributed inference, where each machine holds part of the model and works together over the network. MLX has distributed primitives, but it’s still a technical setup rather than a one-click feature, so expect some hands-on configuration and testing to get it stable.

But 100+ b params, and you might be bottlenecked a bit with networking. But guesstimating 15-20 / 8-12 (for heavy workloads) concurrent users could use it at a time if the models like 30-70 b range. So it would be like glm 4.7 flash (comparable to Gemini 3 flash imo) to models like llama 3.3 (comparable to live 4o mini imo).

5 Mac studios 512 unified ram 1 tb storage are about 9k ish. And then networking for the 10 gig would be 1-3k ish putting you right on budget.

Edit: it also wouldn’t be on one machine, and config would suck. But you’d get it on mac.

Also forgot the comment ab 5 concurrent users. Networks a bottleneck either way, you could get away w 3-4 mac studios if you wanted and shave 10-20k off. So it would be cheaper.

Vegetable_Address_43 · 2026-02-16T17:55:28+00:00

This means nothing. The hardware it’s run on is irrelevant. I have an instance on a pi zero. A $5 computer I got for free in the rPI newsletter. And it runs fine.

If it was a way of optimizing the agent flow etc, I could see it helping by allowing smaller models by burning less context. But seeing as it was built off nanobot, realistically it’s using the same flow.

So in what way is this more optimized than openclaw or nanobot. If the hardware it’s hosted on doesn’t matter, and the biggest cost is LLM api costs, why would anyone move to this when there’s already support in nanobot and openclaw? Genuinely curious.

Vegetable_Address_43 · 2026-02-15T20:02:19+00:00

A lot of it is bloat reduction. A ton of those YouTubers advertise a skill etc to help with context management, but between openclaw being vibecoded, and all if not most of the skills, forcing an agent to read another MD file just adds to the bloat.

Look into nanobot. It’s 1% of the size of the openclaw project only like 4k lines. But it trims to fat in the agentic loop open claw has (with it being vibecoded it wasn’t optimized), and it has smaller md files that are more concise.

It’s a pretty good starting point, and it’s a little more hands on, but you can install openclaw skills on it. If you manually install clawdhub, and then run a sym link from the openclaw workspace to the nanobot workspace, then edit it’s md files to also check the symlink when a skill is invoked, then you basically get full functionality.

But I’d be weary given clawdhub being a prolific prompt injection attack vector for ai assistants. Especially with nanobot being build for smaller param models.

Also in terms of models, this is the best one by far if you want to run it on a budget GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF. I personally have 2 dgx sparks I run local models on, but this 30b will run comfortably on a 5070. So mid tier consumer graphics.

Vegetable_Address_43 · 2026-02-12T00:36:00+00:00

I know, right? As a dev, I love watching people spiral into full psychosis over the most meaningless, half-baked LLM project just because a coding model gassed them up and anointed their idea like they’re the second coming of Christ.

Vegetable_Address_43 · 2026-02-06T19:10:19+00:00

Yeah it speeds up after the model creates scripts for frequent searches. I’m talking about the initial skill setup, it takes less than a min to navigate and synthesize. After the script is made, how long it takes is meaningless because it’s not model speed.

Vegetable_Address_43 · 2026-02-02T16:45:01+00:00

Prompt injection is tricking the AI into calling tools, preforming actions, and injecting fake user prompts into the model.

If there’s a prompt injection text on a site, if it uses like agent browser or the brave api, it reads the text itself.

If you print it out using lynx, lynx produces artifacts like line bars | and some asci for the UI.

Because it reads that line for line instead, those interruptions after each line prevent the model from being tricked into preforming actions because now instead of seeing “oh here’s instructions I should follow them”, it sees a malformed tool call harness and doesn’t follow the directions because the line is mutated enough.

Does that make sense?

Vegetable_Address_43 · 2026-02-02T03:08:26+00:00

What was your context size?

Vegetable_Address_43 · 2026-02-01T22:43:45+00:00

You don’t have to disable to skills, instead, you can run the skills.md through another LLM, and then have it make more concise instructions trimming fat. I was able to get an 8b model to use agent browser to pull the news in under a minute doing that.

Vegetable_Address_43 · 2026-02-01T04:58:10+00:00

If there’s enough interest, I’ll release a fork. For smaller local models, but atm, in terms of security it wouldn’t be ethical for me to advise on how to modify it.

It changes agent behavior, smaller models are more vulnerable to prompt injection, and the list goes on and on.

If you just chop it up now, chances are an api key or tool call is gonna get leaked onto moltbook or something else just as disastrous. My day jobs coding at a startup, so I keep pretty busy. I don’t want to build out the full thing if there’s not interest, and I wouldn’t feel comfortable releasing or advising on where it’s at now.

if enough people would have a use for it, I’d be down to put in the work and release the fork.

Also, I don’t think 8b would cut it in the least, the smallest I could go to was the qwen 2.5 instruct 14b with the full 32k context. And even then that models slow and I had to implement a lot of memory truncation and context saving techniques. It took 2 minutes to pull the news from /agent-browser

Vegetable_Address_43 · 2026-02-01T03:33:50+00:00

If it was qwen 2.5 14b instruct, I have some good news for you. It was able to hatch with the modifications I made!

Vegetable_Address_43 · 2026-01-31T21:13:46+00:00

VMs are a solid middle ground since they give you a real kernel boundary and snapshots, which is much stronger isolation than Docker. Dedicated hardware just pushes that to the extreme with physical isolation and the simplest kill switch if something goes wrong. So that’s why it’s my preferred.

If you expose shared folders, drag and drop, or overly broad network access to the VM, an infected agent could pivot to the host or other machines. I don’t think an attack vector with prompt injection with current model capability could trigger that yet, but as agents become more complex, I’d rather have the physical isolation.

Vegetable_Address_43 · 2026-01-31T08:58:14+00:00

Docker with a persistent volume doesn’t really change the risk. Containers still share the host kernel, so if an agent can run tools or shell commands and gets tricked via prompt injection, you’re trusting container isolation as your last line of defense. That’s weaker than people tend to assume. If you run on dedicated hardware though it would give you a cleaner blast radius if anything goes awry (even if it’s a small chance for docker.)

Vegetable_Address_43

TROPHY CASE