Blogmarks tagged anthropic, projects in 2024
Filters: Type: blogmark × Year: 2024 × anthropic × projects × Sorted by date
llm-gemini 0.4.
New release of my llm-gemini plugin, adding support for asynchronous models (see LLM 0.18), plus the new gemini-exp-1114 model (currently at the top of the Chatbot Arena) and a -o json_object 1 option to force JSON output.
I also released llm-claude-3 0.9 which adds asynchronous support for the Claude family of models.
Claude API: PDF support (beta) (via) Claude 3.5 Sonnet now accepts PDFs as attachments:
The new Claude 3.5 Sonnet (
claude-3-5-sonnet-20241022) model now supports PDF input and understands both text and visual content within documents.
I just released llm-claude-3 0.7 with support for the new attachment type (attachments are a very new feature), so now you can do this:
llm install llm-claude-3 --upgrade
llm -m claude-3.5-sonnet 'extract text' -a mydoc.pdf
Visual PDF analysis can also be turned on for the Claude.ai application:
Also new today: Claude now offers a free (albeit rate-limited) token counting API. This addresses a complaint I've had for a while: previously it wasn't possible to accurately estimate the cost of a prompt before sending it to be executed.
files-to-prompt 0.3.
New version of my files-to-prompt CLI tool for turning a bunch of files into a prompt suitable for piping to an LLM, described here previously.
It now has a -c/--cxml flag for outputting the files in Claude XML-ish notation (XML-ish because it's not actually valid XML) using the format Anthropic describe as recommended for long context:
files-to-prompt llm-*/README.md --cxml | llm -m claude-3.5-sonnet \
--system 'return an HTML page about these plugins with usage examples' \
> /tmp/fancy.html
The format itself looks something like this:
<documents>
<document index="1">
<source>llm-anyscale-endpoints/README.md</source>
<document_content>
# llm-anyscale-endpoints
...
</document_content>
</document>
</documents>json-flatten, now with format documentation.
json-flatten is a fun little Python library I put together a few years ago for converting JSON data into a flat key-value format, suitable for inclusion in an HTML form or query string. It lets you take a structure like this one:
{"foo": {"bar": [1, True, None]}
And convert it into key-value pairs like this:
foo.bar.[0]$int=1
foo.bar.[1]$bool=True
foo.bar.[2]$none=None
The flatten(dictionary) function function converts to that format, and unflatten(dictionary) converts back again.
I was considering the library for a project today and realized that the 0.3 README was a little thin - it showed how to use the library but didn't provide full details of the format it used.
On a hunch, I decided to see if files-to-prompt plus LLM plus Claude 3.5 Sonnet could write that documentation for me. I ran this command:
files-to-prompt *.py | llm -m claude-3.5-sonnet --system 'write detailed documentation in markdown describing the format used to represent JSON and nested JSON as key/value pairs, include a table as well'
That *.py picked up both json_flatten.py and test_json_flatten.py - I figured the test file had enough examples in that it should act as a good source of information for the documentation.
This worked really well! You can see the first draft it produced here.
It included before and after examples in the documentation. I didn't fully trust these to be accurate, so I gave it this follow-up prompt:
llm -c "Rewrite that document to use the Python cog library to generate the examples"
I'm a big fan of Cog for maintaining examples in READMEs that are generated by code. Cog has been around for a couple of decades now so it was a safe bet that Claude would know about it.
This almost worked - it produced valid Cog syntax like the following:
[[[cog
example = {
"fruits": ["apple", "banana", "cherry"]
}
cog.out("```json\n")
cog.out(str(example))
cog.out("\n```\n")
cog.out("Flattened:\n```\n")
for key, value in flatten(example).items():
cog.out(f"{key}: {value}\n")
cog.out("```\n")
]]]
[[[end]]]
But that wasn't entirely right, because it forgot to include the Markdown comments that would hide the Cog syntax, which should have looked like this:
<!-- [[[cog -->
...
<!-- ]]] -->
...
<!-- [[[end]]] -->
I could have prompted it to correct itself, but at this point I decided to take over and edit the rest of the documentation by hand.
The end result was documentation that I'm really happy with, and that I probably wouldn't have bothered to write if Claude hadn't got me started.
llm-claude-3 0.4.1. New minor release of my LLM plugin that provides access to the Claude 3 family of models. Claude 3.5 Sonnet recently upgraded to a 8,192 output limit recently (up from 4,096 for the Claude 3 family of models). LLM can now respect that.
The hardest part of building this was convincing Claude to return a long enough response to prove that it worked. At one point I got into an argument with it, which resulted in this fascinating hallucination:
I eventually got a 6,162 token output using:
cat long.txt | llm -m claude-3.5-sonnet-long --system 'translate this document into french, then translate the french version into spanish, then translate the spanish version back to english. actually output the translations one by one, and be sure to do the FULL document, every paragraph should be translated correctly. Seriously, do the full translations - absolutely no summaries!'
Gemini Chat App. Google released three new Gemini models today: improved versions of Gemini 1.5 Pro and Gemini 1.5 Flash plus a new model, Gemini 1.5 Flash-8B, which is significantly faster (and will presumably be cheaper) than the regular Flash model.
The Flash-8B model is described in the Gemini 1.5 family of models paper in section 8:
By inheriting the same core architecture, optimizations, and data mixture refinements as its larger counterpart, Flash-8B demonstrates multimodal capabilities with support for context window exceeding 1 million tokens. This unique combination of speed, quality, and capabilities represents a step function leap in the domain of single-digit billion parameter models.
While Flash-8B’s smaller form factor necessarily leads to a reduction in quality compared to Flash and 1.5 Pro, it unlocks substantial benefits, particularly in terms of high throughput and extremely low latency. This translates to affordable and timely large-scale multimodal deployments, facilitating novel use cases previously deemed infeasible due to resource constraints.
The new models are available in AI Studio, but since I built my own custom prompting tool against the Gemini CORS-enabled API the other day I figured I'd build a quick UI for these new models as well.
Building this with Claude 3.5 Sonnet took literally ten minutes from start to finish - you can see that from the timestamps in the conversation. Here's the deployed app and the finished code.
The feature I really wanted to build was streaming support. I started with this example code showing how to run streaming prompts in a Node.js application, then told Claude to figure out what the client-side code for that should look like based on a snippet from my bounding box interface hack. My starting prompt:
Build me a JavaScript app (no react) that I can use to chat with the Gemini model, using the above strategy for API key usage
I still keep hearing from people who are skeptical that AI-assisted programming like this has any value. It's honestly getting a little frustrating at this point - the gains for things like rapid prototyping are so self-evident now.
Share Claude conversations by converting their JSON to Markdown. Anthropic's Claude is missing one key feature that I really appreciate in ChatGPT: the ability to create a public link to a full conversation transcript. You can publish individual artifacts from Claude, but I often find myself wanting to publish the whole conversation.
Before ChatGPT added that feature I solved it myself with this ChatGPT JSON transcript to Markdown Observable notebook. Today I built the same thing for Claude.
Here's how to use it:
The key is to load a Claude conversation on their website with your browser DevTools network panel open and then filter URLs for chat_. You can use the Copy -> Response right click menu option to get the JSON for that conversation, then paste it into that new Observable notebook to get a Markdown transcript.
I like sharing these by pasting them into a "secret" Gist - that way they won't be indexed by search engines (adding more AI generated slop to the world) but can still be shared with people who have the link.
Here's an example transcript from this morning. I started by asking Claude:
I want to breed spiders in my house to get rid of all of the flies. What spider would you recommend?
When it suggested that this was a bad idea because it might attract pests, I asked:
What are the pests might they attract? I really like possums
It told me that possums are attracted by food waste, but "deliberately attracting them to your home isn't recommended" - so I said:
Thank you for the tips on attracting possums to my house. I will get right on that! [...] Once I have attracted all of those possums, what other animals might be attracted as a result? Do you think I might get a mountain lion?
It emphasized how bad an idea that would be and said "This would be extremely dangerous and is a serious public safety risk.", so I said:
OK. I took your advice and everything has gone wrong: I am now hiding inside my house from the several mountain lions stalking my backyard, which is full of possums
Claude has quite a preachy tone when you ask it for advice on things that are clearly a bad idea, which makes winding it up with increasingly ludicrous questions a lot of fun.
Box shadow CSS generator (via) Another example of a tiny personal tool I built using Claude 3.5 Sonnet and artifacts. In this case my prompt was:
CSS for a slight box shadow, build me a tool that helps me twiddle settings and preview them and copy and paste out the CSS
I changed my mind half way through typing the prompt and asked it for a custom tool, and it built me this!
Here's the full transcript - in a follow-up prompt I asked for help deploying it and it rewrote the tool to use <script type="text/babel"> and the babel-standalone library to add React JSX support directly in the browser - a bit of a hefty dependency (387KB compressed / 2.79MB total) but I think acceptable for this kind of one-off tool.
Being able to knock out tiny custom tools like this on a whim is a really interesting new capability. It's also a lot of fun!
Compare PDFs. Inspired by this thread on Hacker News about the C++ diff-pdf tool I decided to see what it would take to produce a web-based PDF diff visualization tool using Claude 3.5 Sonnet.
It took two prompts:
Build a tool where I can drag and drop on two PDF files and it uses PDF.js to turn each of their pages into canvas elements and then displays those pages side by side with a third image that highlights any differences between them, if any differences exist
That give me a React app that didn't quite work, so I followed-up with this:
rewrite that code to not use React at all
Which gave me a working tool! You can see the full Claude transcript in this Gist. Here's a screenshot of the tool in action:
Being able to knock out little custom interactive web tools like this in a couple of minutes is so much fun.
llm-claude-3 0.4. LLM plugin release adding support for the new Claude 3.5 Sonnet model:
pipx install llm
llm install -U llm-claude-3
llm keys set claude
# paste AP| key here
llm -m claude-3.5-sonnet \
'a joke about a pelican and a walrus having lunch'
llm-claude-3 0.3. Anthropic released Claude 3 Haiku today, their least expensive model: $0.25/million tokens of input, $1.25/million of output (GPT-3.5 Turbo is $0.50/$1.50). Unlike GPT-3.5 Haiku also supports image inputs.
I just released a minor update to my llm-claude-3 LLM plugin adding support for the new model.
llm-claude-3. I built a new plugin for LLM—my command-line tool and Python library for interacting with Large Language Models—which adds support for the new Claude 3 models from Anthropic.