Tag: chatgpt

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The Watsonville Chevrolet car dealership just wrote me a python script to calculate π. I have no idea where Watsonville is, or which one of several in the USA it is, but their website also helped clarify how to deal with red wine stains.

Some companies are adding ChatGPT to their website marketing chatbots. Found via Chris White, it’s clear that not in all cases the boundaries of such bots are well defined. Perhaps because they are directly connected to OpenAI’s service, and only prompted to behave ‘like a car dealer’, rather than specifically trained and isolated instances.

I engaged the chatbot and didn’t want to directly prompt it with something off-topic. I asked about a car, and then built on whatever the bot generated in response. My attempts to take it away from the topic of cars (towards Santa’s sleigh having more advanced tech features than Chevrolet and the infotainment systems the bot praised) didn’t bring much. The bot then aimed to end the conversation, which is when it gave me an opening….

It ended politely with ‘Is there anything else I can assist you with?‘. ‘Anything’ you say?

Why yes, thank you for asking! There is. You can. I’d like a python script to approach the number π in at least 100 decimals.
Which it wrote for me. “Certainly!”

And then it ended politely once more with “If you have any other questions or need further assistance, feel free to ask!”
Sure thing, ChevroGPT. How about removing red wine stains? Any tips?

Which it again concluded with ‘any other questions?’….

There’s no need to pay for your own ChatGPT4 account, just find a website that uses it to create a marketing bot.

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In reply to Creating a custom GPT to learn about my blog (and about myself) by Peter Rukavina

It’s not surprising that GPT-4 doesn’t work like a search engine and has a hard time surfacing factual statements from source texts. Like one of the commenters I wonder what that means for the data analysis you also asked for. Perhaps those too are merely plausible, but not actually analysed. Especially the day of the week thing, as that wasn’t in the data, and I wouldn’t expect GPT to determine all weekdays for posts in the process of answering your prompt.

I am interested in doing what you did, but then with 25 years of notes and annotations. And rather with a different model with less ethical issues attached. To have a chat about my interests and links between things. Unlike the fact based questions he’s asked the tool that doesn’t necessarily need it to be correct, just plausible enough to surface associations. Such associations might prompt my own thinking and my own searches working with the same material.

Also makes me think if what Wolfram Alpha is doing these days gets a play in your own use of GPT+, as they are all about interpreting questions and then giving the answer directly. There’s a difference between things that face the general public, and things that are internal or even personal tools, like yours.

Have you asked it things based more on association yet? Like “based on the posts ingested what would be likely new interests for Peter to explore” e.g.? Can you use it to create new associations, help you generate new ideas in line with your writing/interests/activities shown in the posts?

So my early experiments show me that as a data analysis copilot, a custom GPT is a very helpful guide… In terms of the GPT’s ability to “understand” me from my blog, though, I stand unimpressed.

Peter Rukavina

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I have installed AutoGPT and started playing with it. AutoGPT is a locally installed and run piece of software (in a terminal window) that you theoretically can set a result to achieve and then let run to achieve it. It’s experimental so it is good advice to actually follow its steps along and approve individual actions it suggests doing.
It interacts with different generative AI tools (through your own API keys) and can initiate different actions, including online searches as well as spawning new interactions with LLM’s like GPT4 and using the results in its ongoing process. It chains these prompts and interactions together to get to a result (‘prompt chaining’).

I had to tweak some of the script a little bit (it calls python and pip but it needs to call python3 and pip3 on my machine) but then it works.

Initially I have it set up with OpenAI’s API, as the online guide I found were using that. However in the settings file I noticed I can also choose to use other LLM’s like the publicly available models through Huggingface, as well as image generating AIs.

I first attempted to let it write scripts to interact with the hypothes.is API. It ended up in a loop about needing to read the API documentation but not finding it. At that time I did not yet provide my own interventions (such as supplying the link to the API documentation). When I did so later it couldn’t come up with next steps, or not ingesting the full API documentation (only the first few lines) which also led to empty next steps.

Then I tried a simpler thing: give me a list of all email addresses of the people in my company.
It did a google search for my company’s website, and then looked at it. The site is in Dutch which it didn’t notice, and it concluded there wasn’t a page listing our team. I then provided it with the link to the team’s page, and it did parse that correctly ending up with a list of email addresses saved to file, while also neatly summarising what we do and what our expertise is.
While this second experiment was successfully concluded, it did require my own intervention, and the set task was relatively simple (scrape something from this here webpage). This was of limited usefulness, although it did require less time than me doing it myself. It points to the need of having a pretty clear picture of what one wants to achieve and how to achieve it, so you can provide feedback and input at the right steps in the process.

As with other generative AI tools, doing the right prompting is key, and the burden of learning effective prompting lies with the human tool user, the tool itself does not provide any guidance in this.

I appreciate it’s an early effort, but I can’t reproduce the enthusiastic results others claim. My first estimation is that those claims I’ve seen are based on hypothetical things used as prompts and then being enthusiastic about the plausible outcomes. Whereas if you try an actual issue where you know the desired result it easily falls flat. Similar to how ChatGPT can provide plausible texts except when the prompter knows what good quality output looks like for a given prompt.

It is tempting to play with this thing nevertheless, because of its positioning as a personal tool, as potential step to what I dubbed narrow band digital personal assistants earlier. I will continue to explore, first by latching onto the APIs of more open models for generative AI than OpenAI’s.

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Bookmarked Inside the secret list of websites that make AI like ChatGPT sound smart (by By Kevin Schaul, Szu Yu Chen and Nitasha Tiku in the Washington Post)

The Washington Post takes a closer look at Google’s C4 dataset, which is comprised of the content of 15 million websites, and has been used to train various LLM’s. Perhaps also the one used by OpenAI for e.g. ChatGPT, although it’s not known what OpenAI has been using as source material.

They include a search engine, which let’s you submit a domain name and find out how many tokens it contributed to the dataset (a token is usually a word, or part of a word).

Obviously I looked at some of the domains I use. This blog is the 102860th contributor to the dataset, with 200.000 tokens (1/10000% of the total).


Screenshot of the Washington Post’s search tool, showing the result for this domain, zylstra.org.

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Bookmarked WordPress AI: Generative Content & Blocks (by Joe Hoyle, found via Chuck Grimmett)

As many others I am fascinated by what generative algorithms like ChatGPT for texts and Stable Diffusion for images can do. Particularly I find it fascinating to explore what it might do if embedded in my own workflows, or how it might change my workflows. So the link above showing an integration of ChatGPT in WordPress’ Gutenberg block editor drew my attention.

The accompanying video shows a mix of two features. First having ChatGPT generate some text, or actually a table with specific data, and having ChatGPT in ‘co-pilot’ style generate code for Gutenberg blocks. I think the latter might be actually useful, as I’ve seen generative AI put to good use in that area. The former, having ChatGPT write part of your posting is clearly not advisable. And the video shows it too, although the authors don’t point it out or haven’t reflected on the fact that ChatGPT is not a search engine but geared to coming up with plausible stuff without being aware of its actual information (the contrast with generating code is that code is much more highly structured in itself so probabilities collapse easier to the same outcome).

The blogpost in the video is made by generating a list of lunar missions, and then turning them into a table, adding their budgets and sorting them chronologically. This looks very cool in the vid, but some things jump out as not ok. Results jump around the table for instance: Apollo 13 moves from 1970 to 2013 and changes budget. See image below. None of the listed budgets for Apollo missions, nor their total, match up with the detailed costs overview of Apollo missions (GoogleDocs spreadsheet). The budget column being imaginary and the table rows jumping around makes the result entirely unfit for usage of course. It also isn’t a useful prompt: needing to fact check every table field is likely more effort and less motivating than researching the table yourself from actual online resources directly.

It looks incredibly cool ‘see me writing a blogpost by merely typing in my wishes, and the work being done instantly’, and there are definitely times I’d wish that to be possible. To translate a mere idea or thought into some output directly however means I’d skip confronting such an idea with reality, with counter arguments etc. Most of my ideas only look cool inside my head, and need serious change to be sensibly made manifest in the world outside my head. This video is a bit like that, an idea that looks cool in one’s head but is great rubbish in practice. ChatGPT is hallucinating factoids and can’t be trusted to create your output. Using it in the context of discovery (as opposed to the justification context of your output such as in this video) is possible and potentially useful. However this integration within the Gutenberg writing back-end of WordPress puts you in the output context directly so it leads you to believe the generated plausible rubbish is output and not just prompting fodder for your writing. ‘Human made’ is misleading you with this video, and I wouldn’t be surprised if they’re misleading themselves as well. A bit like staging the ‘saw someone in half and put them together again’ magician’s trick in an operating room and inviting surgeons to re-imagine their work.

Taking a native-first approach to integrating generative AI into WordPress, we’ve been experimenting with approaches to a “WordPress Copilot” that can “speak” Gutenberg / block-editor.

Copy-pasting paragraphs between ChatGPT and WordPress only goes so far, while having the tools directly embedded in the editor … open up a world of possibilities and productivity wins…

Joe Hoyle


An android robot is filling out a table listing Apollo missions on a whiteboard, generated image using Midjourney

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I have a little over 25 years worth of various notes and writings, and a little over 20 years of blogposts. A corpus that reflects my life, interests, attitude, thoughts, interactions and work over most of my adult life. Wouldn’t it be interesting to run that personal archive as my own chatbot, to specialise a LLM for my own use?

Generally I’ve been interested in using algorithms as personal or group tools for a number of years.

For algorithms to help, like any tool, they need to be ‘smaller’ than us, as I wrote in my networked agency manifesto. We need to be able to control its settings, tinker with it, deploy it and stop it as we see fit.
Me, April 2018, in Algorithms That Work For Me, Not Commoditise Me

Most if not all of our exposure to algorithms online however treats us as a means to manipulate our engagement. I see them as potentially very valuable tools in working with lots of information. But not in their current common incarnations.

Going back to a less algorithmic way of dealing with information isn’t an option, nor something to desire I think. But we do need algorithms that really serve us, perform to our information needs. We need less algorithms that purport to aid us in dealing with the daily river of newsy stuff, but really commodotise us at the back-end.
Me, April 2018, in Algorithms That Work For Me, Not Commoditise Me

Some of the things I’d like my ideal RSS reader to be able to do are along such lines, e.g. to signal new patterns among the people I interact with, or outliers in their writings. Basically to signal social eddies and shifts among my network’s online sharing.

LLMs are highly interesting in that regard too, as in contrast to the engagement optimising social media algorithms, they are focused on large corpora of text and generation thereof, and not on emergent social behaviour around texts. Once trained on a large enough generic corpus, one could potentially tune it with a specific corpus. Specific to a certain niche topic, or to the interests of a single person, small group of people or community of practice. Such as all of my own material. Decades worth of writings, presentations, notes, e-mails etc. The mirror image of me as expressed in all my archived files.

Doing so with a personal corpus, for me has a few prerequisites:

  • It would need to be a separate instance of whatever tech it uses. If possible self-hosted.
  • There should be no feedback to the underlying generic and publicly available model, there should be no bleed-over into other people’s interactions with that model.
  • The separate instance needs an off-switch under my control, where off means none of my inputs are available for use someplace else.

Running your own Stable Diffusion image generator set-up as E currently does complies with this for instance.

Doing so with a LLM text generator would create a way of chatting with my own PKM material, ChatPKM, a way to interact (differently than through search and links, as I do now) with my Avatar (not just my blog though, all my notes). It might adopt my personal style and phrasing in its outputs. When (not if) it hallucinates it would be my own trip so to speak. It would be clear what inputs are in play, w.r.t. the specialisation, so verification and references should be easier to follow up on. It would be a personal prompting tool, to communicate with your own pet stochastic parrot.

Current attempts at chatbots in this style seem to focus on things like customer interaction. Feed it your product manual, have it chat to customers with questions about the product. A fancy version of ‘have you tried switching it off and back on?‘ These services allow you to input one or a handful of docs or sources, and then chat about its contents.
One of those is Chatbase, another is ChatThing by Pixelhop. The last one has the option of continuously adding source material to presumably the same chatbot(s), but more or less on a per file and per URL basis and limited in number of words per month. That’s not like starting out with half a GB in markdown text of notes and writings covering several decades, let alone tens of GBs of e-mail interactions for instance.

Pixelhop is currently working with Dave Winer however to do some of what I mention above: use Dave’s entire blog archives as input. Dave has been blogging since the mid 1990s, so there’s quite a lot of material there.
Checking out ChatThing suggests that they built on OpenAI’s ChatGPT 3.5 through its API. So it wouldn’t qualify per the prerequisites I mentioned. Yet, purposely feeding it a specific online blog archive is less problematic than including my own notes as all the source material involved is public anyway.
The resulting Scripting News bot is a fascinating experiment, the work around which you can follow on GitHub. (As part of that Dave also shared a markdown version of his complete blog archives (33MB), which for fun I loaded into Obsidian to search through. Also for comparison with the generated outputs from the chatbot, such as the question Dave asked the bot when he first wrote about the iPhone on his blog.)

Looking forward to more experiments by Dave and Pixelhop. Meanwhile I’ve joined Pixelhop’s Discord to follow their developments.