Did you read gwerns post on it by any chance?
1,589 karma · joined June 2, 2015
The advertiser can buy logit "boosts" to specific tokens in a given context (the more preceding context, the cheaper). Once an activation has happened, dont do it again for the session.
The LLM will end up pushing a specific product contextual to the conversation. It just needs this slight nudge at the logit level to start talking about it and will fill in the rest.
> not wanting to give the user the feeling they are doing something definitive
In their refusal, while (accidental) acceptance is always definitive...
EDIT: In another thread this is called the ratchet of consent. Apt.
I feel this pattern is really abusive. Like I am offering to slap you in the face, giving you two answer options: Yes and "Maybe later/no, thank you". As though you have any obligation to be forced to answer this question again at some late time, or to be very polite while you refuse to be slapped in the face. The correct answer is obviously "No, fuck off".
Given a certain world state (including a tool's internal state), its effects back on the world state (as initiated by me) are at some leve of description understandable, expected and repeatable. Swing hammer, drive nail into wood. Make slicing motion with knife, cut meat. Type ' find /path/to/some/dir -name "keyword"', find files with keyword. Point harness at codebase with prompt 'fix bug X', actually fix bug X.
All these examples are at some level of description incredibly complex (think of all particles interacting at the (sub-)atomic level even when using a hammer to only drive a nail into some wood), and of course all the electrons flowing through the GPUs doing matrix multiplications in order to fix bug X, but at some level of description (the one I just used) they are also incredibly simple and understandable.
Intelligence is rather nebulous (and as used by OpenAI/Anthropic, quite threatening), but I don't think this definition of a tool precludes it to be "intelligent". They feel more orthogonal. The intelligence (or perhaps capability) feels like it is related to the size of the chunk of the world state that it can take into account and affect, while still resulting in understandable, expected and repeatable effects. LLMs, when properly harnessed, are pretty great at this currently and we are still discovering what they are consistently capable of.
Calling harnessed LLMs tools is perhaps also a more grounding frame specifically to counter-act the anthropomorphizing framing that OpenAI and Anthropic consistently go for in their game of AI-doom-chicken talk. The tool framing is in that sense maybe a (self-)jedi-mind-trick.
How does stagehand deal with complex http/websocket request/response and or console message filtering? https://docs.stagehand.dev/v4/reference/page#on E.g. I would like a script that tracks all communication that matches a specific filter (implemented as an anonymous function/lambda). This filter may look at patterns in the url, but sometimes needs to do a deeper inspection of also the payload (if the url does not carry enough information in itself).
I've found playwright to be prohibitively slow at this, not only because of the round-trip latency, but just the simple fact that it needs to pump the complete response to my filter function, which then proceeds to read only a couple of bytes to make the filtering decision. There are a lot of cases where I am only interested in around 1% of the total requests processed by the filter, which makes this behaviour massively wasteful.
Ideally I would like to run this filter in the browser as well. It currently simply searches the first 100 bytes (usually enough) for a given substring, but a more flexible filter would be good, perhaps even a filter func that is eval'ed in the extension? From the documentation, I don't see this use-case is currently supported. Are there any plans along these lines? :)
LG is truly an adversial party in all of this.
To not know who made and who approved a set of mutations on data can easily become equally as mind-blowingly stupid as not knowing who made mutations to code. Code is a subset of data after all and search over (provenance of) data can be implemented as DAG traversal.
Not tracking data changesets like code changesets is certainly a choice, not really a constraint anymore. A similar choice I feel is implied by "extreme scale of text searching".
> no they will not all add the telemetry you wish they did
...is just a failure of the corporate policy surrounding data handling. Is git-for-data already considered telemetry?
Of course the truth is provenance of data is something best institutionally forgotten as quickly as possible. The only thing that matters is it's there, that the data has no history, and that's why it can be used in whatever way deemed necessary.
MacOS is, in my experience, incredibly clunky due to this mismatch. You can fix some (e.g. use AltTab for sane alttabbing), but not others (how can i prevent the next application window popping up when minimizing one)
The link is annoying enough to find that I can imagine "Mea Culpa" being an effective enough strategy for businesses moving into the ML/AI field, changing their tune after they get caught, but matured their own software to stand on its own feet.
I would say when this comes to pass, we are already 5 years along?
> Part of it is knowing that whatever sort of enshittification the cloud providers do, my local programming environment won’t ever be less effective than it is today locally.
I think this is quite understated. It basically is freedom from a growingly antagonistic relationship between you and some remotely hosted API managed by faceless corporates at the whims of their board, shareholders and governments.. It really is such a mental burden to need to constantly manage this relationship (watermarks, silent downgrades, random false refusals, downtimes, model sunsets, changing ToS's, fucking ads). These companies will need to squeeze you for every cent that they can before open-weight models are simply good enough for the valuable tasks we can throw at them.
To have your own hardware is to no longer have this mental burden.
Not sure exactly how Zed works, but wouldn't the results of the `ls` tool call be fed back into claude?
https://huggingface.co/unsloth/Qwen3.8-Flash-Next-GGUF https://unsloth.ai/docs/models/qwen3.8-next
> You will need at least 75 GB of RAM or unified memory to run the model. Its smallest 1-bit quantized version is larger than usual because of the model’s architecture so 1-bit isn't really 1-bit at all. However, this also means the quantization is less aggressive, allowing the model to retain more of its original accuracy than more heavily quantized models.
Lots of RAM required even for the 1-bit, which is already downloadable. Interested to see how well this one works compared to Ornith1.5-35B-A3B I've been running (and quite happy about).
Edit: but llama-cpp does not yet support it.
It probably could, but the point is this would require additional tokens, blowing up the comparison. The token output of LLMs and "token output" of speech are simply at different abstraction levels. Hence my comparison to the LLM brain driving the robot harness to produce speech etc. This would be more comparable, and also look significantly worse than "only" the 22x less efficient number.