The Once and Future Perl

Damian on top form as always. The modules this talk is based on are, of course, all both brilliant and incredibly useful. But the thing that's really impressed me here is the way he has taken some of his modules from a couple of decades ago and replaced them with calls to LLMs. That's food for thought.

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The Once and Future Perl - Damian Conway - TPRC 2024

#tprc24 #perl #rakuRetroemotions, statistical outliers, lunar excursions, Lorentz contraction, extrasolar planets, atomic clocks, lucky bullets, Greek mythol...

^YouTube

PSA: to get Net::SSLeay to build on a new M3 MBP I had to do export OPENSSL_PREFIX=/opt/homebrew/opt/openssl/include/openssl/ to get it to build on a new M3 MBP with an OpenSSL that was installed via brew install openssl

Filed an issue at https://github.com/radiator-software/p5-net-ssleay/issues/482

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Build fails on new M3 MBP due to apparent change to OpenSSL include directory · Issue #482 · radiator-software/p5-net-ssleay

On a new M3 MBP to get Net::SSLeay to build I had to do export OPENSSL_PREFIX=/opt/homebrew/opt/openssl/include/openssl/ to get it to build with an OpenSSL that was installed via brew install opens...

^GitHub

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.

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BLUF: THE method for better and effective written communication

Communication in remote work is all about written communication. That’s the big shift that happened regarding how we communicate. The Economist and Information Week are two examples of many…

^{Gabriela De Luca (Medium)}

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.

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Nelson Ferraz

CPAN Author

^metacpan.org

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.