tim ganiev (@postimortem) header
tim ganiev (@postimortem) profile picture

patterning

residual stream
Joined April 2014
made a mac app for myself and it got good enough to share it's called arden. my personal assistant, if you will: it remembers and does stuff for me, and tells me what's happening and what i need to do. and do most of it. spent most of this year on it and i'm sharing it just because i can lol p.s. thanks claude for this (and other) video haha
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the last neuron left my body
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negative agency
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> simpler architectures typically win in the end humanity just should do complex over-engineered stuff to understand simple thing from the first principles at least i can see it everywhere, not only in ml.
I would love to believe this, and genuinely hope it's a breakthrough However, I'm pretty bearish on it for a number of reasons - people have tried training on bytes many times; examples include ByT5, CANINE, Perceiver, Byte Latent Transformer (BLT), and many more - none have succeeded in replacing a tokenizer + Transformer, the main reason being that these are too complex to work with in practice. - Hierarchical networks are no different: they consist of various (5 !!) submodules, which are just way too hard to serve at scale - They use State-Space Machine (SSM) layers, none of which are adopted by major AI labs so far due to the complexity, although Qwen-4 might be different - So far, the bitter lesson has taught us that simpler architectures typically win in the end
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sometimes it's just a product of external conditions tho. e.g. some numerical instability
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complexity is a part of exploration stage
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don't think it's even fair
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they're doing this to me at work tomorrow
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tim ganiev retweeted
Replying to @alexandr_wang
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hyrum's law + occam's razor is probably the best mental toolkit for building interfaces, from ui to internal apis
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i was looking for a job and then i found a job and heaven knows i'm miserable now
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what's the best UI to review the traces? ideally label them
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just wrote my own btw
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alright i just can't work with sol / astra, they have some issues with context attention
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the hell is this preview bro
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holy "Note the graph is in log scale"
Replying to @valvesxyz
I like to look at claims like this in case I can improve Ghostty. I grabbed your source and immediately spotted the issue: your read loop overwrites a 64 KiB ring. Everything older is discarded unparsed per tick. You're faster because you're not actually terminal emulating. It means the number isn't a throughput number. In a kitten benchmark run at the rate you're claiming, roughly 6.6 MB arrives per 60 Hz tick and 64 KiB of it gets parsed. You're read(2)-ing 100% of the data and parsing about 1% of it. There are other minor issues (a small fixed scrollback in a preallocated ring buffer, no grapheme clustering, throwing away data like OSC 8 URIs, etc.), but the major one is simply that you're discarding ~99% of the input. For plain text, e.g. your cat benchmark, the final screen ends up looking right, but the scrollback is missing 99% of the file and each retained chunk starts mid-line. As soon as there are escape sequences in the dropped spans, your emulator is fully out of sync and invalid: a cut inside a CSI prints the parameters as text, and any SGR, mode, title, or alt-screen change in the gap is just... gone. FYI, this is why I don't like when people point at refterm as a terminal benchmark. refterm implements a TINY subset of what a terminal emulator must support, it's closer to a fast file reader, not a terminal emulator. I also don't mean any shade by this. I like to look at technology objectively to try to learn something and improve my own craft and work. So I'm sharing what I found. I modified your code very slightly to parse on every full buffer (rather than per tick). And the results are attached. Both use main commit in each repo at the time of this post. Note the graph is in log scale, the discrepancy between performance for things like Kitty images is measured in GB/s.
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was fascinated by all these "found X neuron" folks and tried to find something in my old "orange cat llm" found 0 neurons
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