rmast

The GitHub README says the FPGA used: Alchitry Au (Artix-7 XC7A35T, 256 MiB DDR3)

That appears to be using your metaphor and pointing out how being a cyclist and using an e-bike aren’t mutually exclusive. (I do know multiple avid cyclists that own e-bikes as one of several bikes they own and ride).

Ripping off the metaphor for a moment, it was basically extending what you said to point out that being a coder and using LLMs aren’t mutually exclusive.

(Asking them to reserve erm akshully’s for someone else came across as rather dismissive sounding of their thoughts, which from a former bystander in this thread appeared legitimate and just a continuation of using your own metaphor).

A conundrum I see is that once someone releases a “solution” to a problem, it becomes much easier for someone else to come up with prompts to have AI make a clone of that solution.

Basically, the moat for many products is disappearing, and it would be relatively cheap for someone to make a clone that undercuts the cost of the first to market. The original product is charging $2? Cool, the clone will cost $1 - maybe it’s not as good due to less QA, but consumers will see the lower price and buy it.

Most of the things I work on are at least security adjacent. At some point chatting with Fable inevitably leads to it thinking about the security related aspects, tripping the safeguards.

Maybe Fable can do the same things better than other models, but having to tiptoe around to avoid tripping safeguards makes GPT 5.6 so much easier to work with that I don’t even bother with Fable (or Opus 5) now.

2 hours ago I got an email from Crowd Supply saying that my “Dabao Evaluation Board for Baochip-1x” order had shipped. Coincidence? Hmm…

Was it applying to the program for an organization or individual? The description for the individual application page makes it sound pretty straightforward compared to getting access for an organization.

I’d been thinking maybe I should move some projects from GitHub to Codeberg… but this change has a chilling effect since now whoever is making the LLM-generated decision might arbitrarily decide to ban a project based on undefined criteria, like say an em dash in a README — so projects hosted there are not in any way safe from just disappearing.

To be honest though, I don’t know what happens when something gets banned from Codeberg, or anything about the appeals process, so my brain leap to the project just disappears forever; afaik the posts I read about this policy change didn’t say. Would welcome anyone familiar with their policies to chime in and enlighten me.

I was thinking that the other definition was right and this correction was wrong.

Then I did some searching and found multiple examples of both definitions in use, making things murky.

So I turned to Merriam-Webster’s dictionary: “ of, relating to, or being a vulnerability (as in a computer or computer system) that is discovered and exploited (as by cybercriminals) before it is known to or addressed by the maker or vendor”

And of course they use an “or” to make it ambiguous as to whether the days start counting when the vulnerability becomes known, or when the vendor has addressed it.

I applied for both. Heard back from neither. Mentioned two particular projects when applying, one with 2k stars and 5M monthly downloads, and another with 2M monthly downloads.

Maybe the only way to win is to cheat.

I was using it to craft a CTF challenge for summer students involving a simulated mechanical dial safe, but with the fence replaced by a IR beam break sensor and a microcontroller handling the check + flag message display.

For generating the initial 3D simulated safe using three.js it worked well, but then modifications to print a flag tripped the safeguards; eventually got it narrowed down the part in the prompt about it being for a CTF for students, and the "thinking" for the model seems to drift to ideas of encryption/obfuscation of the safe combo so students can't just read out the answer... which makes sense logically to help force students into turning the simulated dial instead. But whatever detection Anthropic I guess just naively sees the model thinking about "encryption" and "obfuscation" without taking into account any of the context.

For writing the dummy firmware, it tripped the safeguards while thinking about how to track dial position in the firmware and output the message; however, when I left out talk about safes and just told it to write firmware for a microcontroller hooked up to an i2c display for showing a message with a beam break sensor to determine the message, and an unspecified i2c chip for getting an unspecified number (e.g. internal wheel positions) it worked fine.

An unrelated software task I asked it to write some code to translate CustomActions in a Windows MSI installer into human readable stuff, which has (exclusively?) defensive security applications for recognizing malicious behavior in an MSI installer. Maybe I'm going crazy, but I'm guessing as part of its research into MSI installer custom actions Fable found articles about analyzing malicious MSI installers, and that probably tripped the safeguards.

Overall my impression is that the safeguards are perhaps using an overzealous and naive implementation that just looks for a list of banned words in the prompt or the thinking -- which drives me crazy when the model says my prompt looks fine, and then 10 minutes in some part of the thinking trips the safeguard.

So in other words... the people Anthropic hired to do the R&D work of training a frontier model haven't finished training their replacement yet.

The things that are harder to get running in a browser via webassembly tend to have a GUI, network communication, or system calls that browsers don’t provide the APIs that are needed to support. But I’ve seen workarounds using websocket proxy servers to get around the lack of raw TCP or UDP socket access.

I’ve been surprised how easy it can be to get Python and C# code running in a browser.

It looks like most of the sources might be under the “docs” subfolder.