Simon Willison’s Weblog

Redis is open source again (via) Salvatore Sanfilippo:

Five months ago, I rejoined Redis and quickly started to talk with my colleagues about a possible switch to the AGPL license, only to discover that there was already an ongoing discussion, a very old one, too. [...]

I’ll be honest: I truly wanted the code I wrote for the new Vector Sets data type to be released under an open source license. [...]

So, honestly, while I can’t take credit for the license switch, I hope I contributed a little bit to it, because today I’m happy. I’m happy that Redis is open source software again, under the terms of the AGPLv3 license.

I'm absolutely thrilled to hear this. Redis 8.0 is out today under the new license, including a beta release of Vector Sets. I've been watching Salvatore's work on those with fascination, while sad that I probably wouldn't use it often due to the janky license. That concern is now gone. I'm looking forward to putting them through their paces!

See also Redis is now available under the AGPLv3 open source license on the Redis blog. An interesting note from that is that they are also:

Integrating Redis Stack technologies, including JSON, Time Series, probabilistic data types, Redis Query Engine and more into core Redis 8 under AGPL

That's a whole bunch of new things that weren't previously part of Redis core.

I hadn't encountered Redis Query Engine before - it looks like that's a whole set of features that turn Redis into more of an Elasticsearch-style document database complete with full-text, vector search operations and geospatial operations and aggregations. It supports search syntax that looks a bit like this:

FT.SEARCH places "museum @city:(san francisco|oakland) @shape:[CONTAINS $poly]" PARAMS 2 poly 'POLYGON((-122.5 37.7, -122.5 37.8, -122.4 37.8, -122.4 37.7, -122.5 37.7))' DIALECT 3

(Noteworthy that Elasticsearch chose the AGPL too when they switched back from the SSPL to an open source license last year).

# 5:19 pm / open-source, salvatore-sanfilippo, redis, vector-search

Making PyPI’s test suite 81% faster (via) Fantastic collection of tips from Alexis Challande on speeding up a Python CI workflow.

I've used pytest-xdist to run tests in parallel (across multiple cores) before, but the following tips were new to me:

• COVERAGE_CORE=sysmon pytest --cov=myproject tells coverage.py on Python 3.12 and higher to use the new sys.monitoring mechanism, which knocked their test execution time down from 58s to 27s.
• Setting testpaths = ["tests/"] in pytest.ini lets pytest skip scanning other folders when trying to find tests.
• python -X importtime ... shows a trace of exactly how long every package took to import. I could have done with this last week when I was trying to debug slow LLM startup time which turned out to be caused be heavy imports.

# 9:32 pm / performance, pytest, pypi, python

Expanding on what we missed with sycophancy. I criticized OpenAI's initial post about their recent ChatGPT sycophancy rollback as being "relatively thin" so I'm delighted that they have followed it with a much more in-depth explanation of what went wrong. This is worth spending time with - it includes a detailed description of how they create and test model updates.

This feels reminiscent to me of a good outage postmortem, except here the incident in question was an AI personality bug!

The custom GPT-4o model used by ChatGPT has had five major updates since it was first launched. OpenAI start by providing some clear insights into how the model updates work:

To post-train models, we take a pre-trained base model, do supervised fine-tuning on a broad set of ideal responses written by humans or existing models, and then run reinforcement learning with reward signals from a variety of sources.

During reinforcement learning, we present the language model with a prompt and ask it to write responses. We then rate its response according to the reward signals, and update the language model to make it more likely to produce higher-rated responses and less likely to produce lower-rated responses.

Here's yet more evidence that the entire AI industry runs on "vibes":

In addition to formal evaluations, internal experts spend significant time interacting with each new model before launch. We informally call these “vibe checks”—a kind of human sanity check to catch issues that automated evals or A/B tests might miss.

So what went wrong? Highlights mine:

In the April 25th model update, we had candidate improvements to better incorporate user feedback, memory, and fresher data, among others. Our early assessment is that each of these changes, which had looked beneficial individually, may have played a part in tipping the scales on sycophancy when combined. For example, the update introduced an additional reward signal based on user feedback—thumbs-up and thumbs-down data from ChatGPT. This signal is often useful; a thumbs-down usually means something went wrong.

But we believe in aggregate, these changes weakened the influence of our primary reward signal, which had been holding sycophancy in check. User feedback in particular can sometimes favor more agreeable responses, likely amplifying the shift we saw.

I'm surprised that this appears to be first time the thumbs up and thumbs down data has been used to influence the model - they've been collecting that data for a couple of years now.

I've been very suspicious of the new "memory" feature, where ChatGPT can use context of previous conversations to influence the next response. It looks like that may be part of this too, though not definitively the cause of the sycophancy bug:

We have also seen that in some cases, user memory contributes to exacerbating the effects of sycophancy, although we don’t have evidence that it broadly increases it.

The biggest miss here appears to be that they let their automated evals and A/B tests overrule those vibe checks!

One of the key problems with this launch was that our offline evaluations—especially those testing behavior—generally looked good. Similarly, the A/B tests seemed to indicate that the small number of users who tried the model liked it. [...] Nevertheless, some expert testers had indicated that the model behavior “felt” slightly off.

The system prompt change I wrote about the other day was a temporary fix while they were rolling out the new model:

We took immediate action by pushing updates to the system prompt late Sunday night to mitigate much of the negative impact quickly, and initiated a full rollback to the previous GPT‑4o version on Monday

They list a set of sensible new precautions they are introducing to avoid behavioral bugs like this making it to production in the future. Most significantly, it looks we are finally going to get release notes!

We also made communication errors. Because we expected this to be a fairly subtle update, we didn't proactively announce it. Also, our release notes didn’t have enough information about the changes we'd made. Going forward, we’ll proactively communicate about the updates we’re making to the models in ChatGPT, whether “subtle” or not.

And model behavioral problems will now be treated as seriously as other safety issues.

We need to treat model behavior issues as launch-blocking like we do other safety risks. [...] We now understand that personality and other behavioral issues should be launch blocking, and we’re modifying our processes to reflect that.

This final note acknowledges how much more responsibility these systems need to take on two years into our weird consumer-facing LLM revolution:

One of the biggest lessons is fully recognizing how people have started to use ChatGPT for deeply personal advice—something we didn’t see as much even a year ago. At the time, this wasn’t a primary focus, but as AI and society have co-evolved, it’s become clear that we need to treat this use case with great care.

# 4:57 pm / ai-personality, openai, ai, llms, ai-ethics, generative-ai, chatgpt, postmortem