The following is a quick ramble before I get into client work, but might give you an idea of how AI is being used today in companies. If you have an questions about Generative AI, let me know!
The work to make the OpenAI API (built on Nelson Ferraz's OpenAPI::Client::OpenAI module) is going well. I now have working example of transcribing audio using OpenAI's whisper-1 model, thanks to the help of Rabbi Veesh.
Using a 7.7M file which is about 16 minutes long, the API call takes about 45 seconds to run and costs $0.10 USD to transcribe. The resulting output has 2,702 words and seems accurate.
Next step is using an "instruct" model to appropriately summarize the results ("appropriate" varies wildly across use cases). Fortunately, we already have working examples of this. Instruct models tend to be more correct in their output than chat models, assuming you have a well-written prompt. Anecdotally, they may have smaller context windows because they're not about remembering a long conversation, but I can't prove that.
Think about the ROI on this. The transcription and final output will cost about 11 cents and take a couple of minutes. You'll still need someone to review it. However, think of the relatively thankless task of taking meeting minutes and producing a BLUF email for the company. Hours of expensive human time become minutes of cheap AI time. Multiply this one task by the number of times per year you have to do it. Further, consider how many other "simple tasks" can be augmented via AI and you'll see why it's becoming so powerful. A number of studies show that removing many of these simple tasks from people's plates, allowing them to focus on the "big picture," is resulting in greater morale and productivity.
When building AI apps, OpenAPI::Client::OpenAI should be thought of as a "low-level" module, similar to DBIx::Class. It should not be used directly in your code, but hidden behind an abstraction layer. Do not use it directly.
I tell my clients that their initial work with AI should be a tactical "top-down mandate/bottom-up implementation." This gives them the ability to start learning how AI can be used in different parts of their organization, given that marketing, HR, IT, and other departments all have different needs.
Part of this tactical approach is learning how to build AI data pipelines. With OpenAI publishing their OpenAPI spec, and with Perl using that, we can bring much of the power of enterprise-level AI needs to companies using Perl. It's been far too long that Perl has languished in the AI space.
Next, I need to investigate doing this with Gemini and/or Claude, but not now.
Note, if you're not familiar with the BLUF format, it's a style of writing email that is well-suited for email in a company that is sent to many people. It's "bottom-line up front" so that people can see the main point and decide if the rest of the email is relevant to them. It makes for very effiicient email communication.
submitted by /u/OvidPerl
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Hi all,
Nelson Ferraz has been working with generative AI for a while. I've started collaborating with him on his OpenAI modules. He wrote a module named OpenAI::API, but it required manually writing the code for all of the behavior. With the size of the OpenAI API, the rapid evolution, of said API, the birth of new models and the deprecation of old models, this approach turned out to be unmaintainable.
Thus, that module was deprecated in favor of Nelson's OpenAPI::Client::OpenAI module. Throw the 13K+ lines OpenAPI spec for OpenAI at it and it just works. Further, the module is pretty much a single Perl class rather than a bunch of hand-crafted code.
CPAN authors know it can be hard to keep modules up-to-date (mea culpa, mea culpa!) and this module is no exception. I need this module so I offered to collaborate and created a PR to update it to version 2.0.0 of the OpenAI spec. It now passes all the tests (for those wondering, you need an OpenAI key and it costs $0.04 USD to run the test suite).
In trying to build a Whisper pipeline for that, I found that I couldn't. There was a PR for Whisper support for the older module, but for the newer one, I can't figure out how to get it to issue a request with multipart/form-data support. I've noted the issue in the PR.
If anyone would like to see OpenAI support for Perl, we would dearly love to collaborate with you to make this happen.
submitted by /u/OvidPerl
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Mo utilities for email.
Changes for 0.02 - 2024-04-26T23:02:53+02:00
- Add tests for error parameters.
- Rewrite the tests so that the functional tests are first and then the errors.
Perl.social Code of Conduct
I've posted this on reddit and wanted a discussion here too for those not on reddit for whatever reason:
reddit.com/r/perl/comments/1bl…
The gist though is that I've gotten another request for a proper CoC/ToS that would be acceptable to the community since i've been negligent in doing so. I've decided that a slightly modified version from the mastodon CoC might be a good starting point and I'll post that content in a reply to this so that it doesn't flood everyone's feeds with a giant wall of text immediately.
So hear me out...
This idea is stupid. But on Star Trek (VOY, TNG, and DS9 at least), they measured their data as "quads". ( memory-alpha.fandom.com/wiki/Q… ). This was never defined because it's just Sci-Fi and doesn't need a real definition. But... what if they're quad-floats aka 128bit floating point values. This would mean then that all the storage could be done as LLM or other neural network style models, and vector embeddings and such. Given what we've got today with transformer style models for doing translation, chat, etc. If you had ultrapowerful computers that could do these calculations with such gigantic precision then you'd be able to store very accurate data and transform it back and forth from vector embeddings and other fancy structures. It'd enable very powerful searches, and the kind of analysis we're trying to use LLMs for and see them use in the shows when talking to the computers. This would also explain a lot about the universal translators from ENG onward, and could even help make sense of Darmok and Jalad at Tenagra. And then Voyager even has bio-neural circuitry for doing things faster, some kind of organic analog computing doing stuff "at the edge". Using weights and embeddings to do things with them and have them react by programming them with a machine learning model at each node could easily explain how that could work too.
This idea honestly feels too stupid to be real but it could explain so much.
Perl.social server upgrades
perlbot and related status
Ryan Voots
in reply to Ryan Voots • •COC/TOS
Borrowing many things from the Mastodon CoC as a astarting point (github.com/mastodon/mastodon/b…).
I am removing a few things from it, not because I don't think they're good ideas or anything but also because I want to limit the scope
of the initial discussion and the amount of work for myself as I'm still currently the only moderator but once the community there gets larger
or it changes that I'm not the only one maintaining things, we will hold another discussion about everything.
I've changed a few things also, specifically to add stronger language that any moderators
MUST document why an action was taken. This doesn't necessarily mean that I believe
that those reasons must be immediately given to an affected user, but that they must
be available when requested. Specifically I'm thinking of not informing in the context
of bots, spam, illegal or otherwise legally actionable content (i.e. something that's going to get me a subpeona or court case).
Other proposed ideas:
1) Some kind of regular discussion, maybe annually? on ToS/CoC type things
1a) The idea being that we require a regular discussion of anything that's
happened over the last time period to avoid it being possible for something
happening being "swept under the rug" or "falling through the cracks" because
it didn't get the proper time given to it previously. How this should be done
I have no good recommendations for, likely creating a group on perl.social to
host the conversation each time?
2) ?
Contributor Covenant Code of Conduct
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Violating these terms may lead to a permanent ban.
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Consequence: A permanent ban from any sort of public interaction within the
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This Code of Conduct is adapted from the Contributor Covenant,
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contributor-covenant.org/versi…contributor-covenant.org/versi….
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Community Impact Guidelines were inspired by
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For answers to common questions about this code of conduct, see the FAQ at
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