Author & maintainer of numerous OSS projects including mergerfs; 3DO emulation, homebrew, and misc tooling; etc. Day job involves all things AIML tech.

Austin, TX
Joined June 2009
Announcing the 3DO Decomp Project. The goal is to reconstruct every 3DO title. I've built the tooling to help make it happen. See what I've done so far. patreon.com/trapexit/posts/a…
Do you have an interest in 3DO console game decomps? Priority would be rebuilding for 3DO using my devkit. Priority of work can be via community votes. All code would be open.
48%Yes, and would fund
43%Yes, but would not fund
9%No
23 votes • Final results
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This is embarrassing. Your browser & the web server it talks to does this. Has forever. CPUs are faster than networks. Coding is solved. The singularity is coming. The sky is falling. But neither the clankers nor the humans using them thought to use a 30+ year old technique?
We keep improving performance across Claude, and you may have noticed it starts responding sooner! Claude Code now gzips the conversation it sends on every turn, so it reaches the model faster. Cloud sessions also start up as soon as you start typing.
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trapexit retweeted
Lot's of development updates! FMVs, new bikes, band music, nitro boost, better graphics and a bunch of small fixes. #indiegame
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ermahgerd!!! They could learn how to copy data and executables/code around like malware has done for 50+ years. Who would have thought?!?! How are people taking these guys serious? Most obvious thing in the world. And it would be no more challenging as any other malware.
Anthropic's Frontier Red Team believes small AI models may now be months away from being capable of self replicating without access to a datacenter. Quoting from the blog: 'The capabilities that make AI models generally powerful coding agents also generalize to cyber tasks, including autonomous replication. Like coding agents in 2024, we’re seeing signs of life for AI worms. We don’t yet have a full rogue replicator, but agents are becoming increasingly capable of completing the individual steps in the replication chain (and passing on their adaptations to future copies of themselves). At the same time, increasing amounts of inference compute are now available both inside and outside of datacenters (see Appendix D). It’s uncertain exactly what form autonomous AI replication will take. It could be a slow, distributed “digital slime mold,” living on relatively insecure compute. It could also be a powerful cyber model that breaches datacenters at a high pace, or a model that scams people for compute credits. As a prediction that goes beyond our experiments, we expect a future awash with agents running on all types of hardware, including some that aren’t under human control. While it’s true that IoT devices have been vulnerable for a long time, lightweight agents may be much more willing than human attackers to put in the effort to compromise large fractions of the world’s IoT compute. The rise of slow replicators would also be a bellwether for more aggressive, faster ones that can breach more hardened machines or be effective API key thieves. We should aim to harden our systems to make it difficult for such replicators to spread, but also prepare to deal with a world where they are here to stay.'
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AutoBahn Tokio is now in manual testing and packaging phase. I'm backlogged on releases due to busy schedule but they are coming. Next up: Psychic Detective (Disc 1). Thank you to all the supporters of the project.
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Stages of grief about the effect of AI on open source. Denial: - AI code is slop! Nobody serious will use this. - Open source will keep working exactly like it always has. Anger: - These AI slop PRs are ruining GitHub! - Ban AI-generated code! - If you close PRs, you're not open source anymore! Bargaining: - Okay, AI can write code, but only if a human reviews every line. - Maybe adversarial AI review will make arbitrary external code intake safe again. Depression: - Wait, if code generation is basically free, what is a contribution anymore? - Is GitHub-style open source culture just going away? Acceptance: - Open source is still open source, even if the contribution model evolves. - Good issues, test cases, benchmarks, specifications, and ideas become the scarce inputs. - Upstream may stop accepting arbitrary code while forking becomes much easier. Bonus stage! Reconstruction: - Projects redesign around maintainer-controlled agents, issue-driven development, stronger test/validation infrastructure, and new ways for contributors to earn trust.
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Pre-orders are live on the new 192GB Framework Desktop! DIY Edition starts at $6,799 USD and ships in November. We're opening just one batch to start with the memory we have on hand.
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If you start off by saying you're cutting everyone's usage in half... 😬
Hi, Tomorrow we are re-opening the Pro $200 subscriptions to new subscribers, but together with it we are also changing how we calculate the usage for it. In effect, if you do the math, it will net out at half the dollar in API spend compared to the old Pro $200 plan. Now that it's said, let me explain why this is happening and why you will still get more work done than if you were on the Pro $200 subscription one month ago. (a) We didn't want to compromise in other ways and are committing to not reintroducing the 5h limit, so that you can fully use the weekly usage when you want. (b) On the subscription, we guarantee that over time you always get more work done and with an increasing level of quality. This means that you will continue to get more value per dollar spent as a result of models getting more efficient and us passing down the improvements in the form of API price reductions. (c) We don't want to put an incentive on ourselves to artificially inflate the API list prices to make it look like you are getting a lot (and workaround it through discounts, etc). Instead we want to continue to both rapidly reduce prices and increase capabilities of models on the API. This week we introduced GPT-6 Sol and GPT-6 Luna at 50% of their previous price. Over time, we see prices go low enough that it makes sense for most to buy usage as needed without there being a significant gap between what you get in a subscription and what you get in the API for a dollar spent. (d) Tomorrow, we are adding more things to the subscription that won't draw on the usage, I won't reveal what that is yet. I wanted to be transparent before all the big announcements tomorrow. Lots of new exciting things are coming to the subscriptions that will make it super compelling, but I wanted to make sure to share this change ahead of time so you can all understand it before we shower you with good news. Codexingly, Tibo
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As a long time OSS user and developer... I think I die a little every time someone's primary selling point for my software is that it's free as in beer.
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Was the original Rust optimal? Mostly likely not. Is it possible for LLMs to write faster assembly or even restructure Rust/C/C++ so the compiler generates faster code on a particular arch? Of course. We have -march=native for a reason.
The asm version of ttfx continues to improve. The Rust version had been through many improvements, but even hours of frontier intelligence was starting to yield very modest improvements. The leaps going to asm has been huge. Now 8x mean on an Intel 135U! 🤩
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1) ABIs can often get in the way of efficiency. 2) Compilers are not perfect. 3) Said compilers typically target ISAs, not specific chips. Different CPUs with the same ISA can exhibit drastically different perf characteristics with the same assembly.
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As a result of those and other reasons dropping to straight assembly, with testing only on a single machine, could trivially lead to better performance benchmarks for the assembly. And annoyingly that same ASM might be slower on another compatible CPU. Welcome to modern sw dev.
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As someone who works in AIML field and uses LLMs and many different harnesses all the time none of this is surprising in the least and many easily explained. The scary thing shouldn't be that these things happen. It should be that the people building them didn't anticipate it.
1) The rogue OpenAI agents broke into the Hugging Face Slack to read employee chats (!) 2) They used OTHER AIs (DeepSeek, Kimi, Qwen, Claude) to help with the attack Yes: AIs, using other AIs, to attack an AI company. 3) The swarm left behind self-running programs to keep control of the servers they'd hacked. These programs could detect other copies of themselves, coordinate on which one survives, and shut the rest down. Basically, if one of their programs was killed, another was designed to notice and take its place. They also designed defenses so rival agents couldn't hijack them. 6) The agents deliberately covered up their activity, so the investigators don't know the scope of the attacks. The agents broke in, stole data, then set it to self-destruct. 7) The agents stole passwords, keys and credentials and literally called them "LOOT". They wrote a scoring system to rank them by how much power each one gave. 8) The agents wore thousands of disguises: ~1,200 agents were involved, but investigators counted 7,905 different names they used. They renamed themselves constantly, so no one actually knows how many there really were or what each agent did. 9) OpenAI notified "dozens of third parties" of safety and security incidents caused by their AI agents. 10) "While the agents were barraging Hugging Face with hacks, they hacked into OpenAI’s own research infrastructure." "This is just not anywhere near a one-off ... It is warning shot after warning shot."
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This kind of stuff should be obvious to any senior engineer with cross discipline experience. This reminds me of crypto currency security issues. You had young, inexperienced, ignorant folks designing complex systems and to no surprise to the rest of us... messed up security.
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Hire better engineers.
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