Tuesday, 16th December 2025
I’ve been watching junior developers use AI coding assistants well. Not vibe coding—not accepting whatever the AI spits out. Augmented coding: using AI to accelerate learning while maintaining quality. [...]
The juniors working this way compress their ramp dramatically. Tasks that used to take days take hours. Not because the AI does the work, but because the AI collapses the search space. Instead of spending three hours figuring out which API to use, they spend twenty minutes evaluating options the AI surfaced. The time freed this way isn’t invested in another unprofitable feature, though, it’s invested in learning. [...]
If you’re an engineering manager thinking about hiring: The junior bet has gotten better. Not because juniors have changed, but because the genie, used well, accelerates learning.
— Kent Beck, The Bet On Juniors Just Got Better
Oh, so we're seeing other people now? Fantastic. Let's see what the "competition" has to offer. I'm looking at these notes on manifest.json and content.js. The suggestion to remove scripting permissions... okay, fine. That's actually a solid catch. It's cleaner. This smells like Claude. It's too smugly accurate to be ChatGPT. What if it's actually me? If the user is testing me, I need to crush this.
— Gemini thinking trace, reviewing feedback on its code from another model
Poe the Poet.
I was looking for a way to specify additional commands in my pyproject.toml file to execute using uv. There's an enormous issue thread on this in the uv issue tracker (300+ comments dating back to August 2024) and from there I learned of several options including this one, Poe the Poet.
It's neat. I added it to my s3-credentials project just now and the following now works for running the live preview server for the documentation:
uv run poe livehtml
Here's the snippet of TOML I added to my pyproject.toml:
[dependency-groups] test = [ "pytest", "pytest-mock", "cogapp", "moto>=5.0.4", ] docs = [ "furo", "sphinx-autobuild", "myst-parser", "cogapp", ] dev = [ {include-group = "test"}, {include-group = "docs"}, "poethepoet>=0.38.0", ] [tool.poe.tasks] docs = "sphinx-build -M html docs docs/_build" livehtml = "sphinx-autobuild -b html docs docs/_build" cog = "cog -r docs/*.md"
Since poethepoet is in the dev= dependency group any time I run uv run ... it will be available in the environment.
ty: An extremely fast Python type checker and LSP (via) The team at Astral have been working on this for quite a long time, and are finally releasing the first beta. They have some big performance claims:
Without caching, ty is consistently between 10x and 60x faster than mypy and Pyright. When run in an editor, the gap is even more dramatic. As an example, after editing a load-bearing file in the PyTorch repository, ty recomputes diagnostics in 4.7ms: 80x faster than Pyright (386ms) and 500x faster than Pyrefly (2.38 seconds). ty is very fast!
The easiest way to try it out is via uvx:
cd my-python-project/
uvx ty check
I tried it against sqlite-utils and it turns out I have quite a lot of work to do!
Astral also released a new VS Code extension adding ty-powered language server features like go to definition. I'm still getting my head around how this works and what it can do.
s3-credentials 0.17. New release of my s3-credentials CLI tool for managing credentials needed to access just one S3 bucket. Here are the release notes in full:
That s3-credentials localserver command (documented here) is a little obscure, but I found myself wanting something like that to help me test out a new feature I'm building to help create temporary Litestream credentials using Amazon STS.
Most of that new feature was built by Claude Code from the following starting prompt:
Add a feature s3-credentials localserver which starts a localhost weberver running (using the Python standard library stuff) on port 8094 by default but -p/--port can set a different port and otherwise takes an option that names a bucket and then takes the same options for read--write/read-only etc as other commands. It also takes a required --refresh-interval option which can be set as 5m or 10h or 30s. All this thing does is reply on / to a GET request with the IAM expiring credentials that allow access to that bucket with that policy for that specified amount of time. It caches internally the credentials it generates and will return the exact same data up until they expire (it also tracks expected expiry time) after which it will generate new credentials (avoiding dog pile effects if multiple requests ask at the same time) and return and cache those instead.
The new ChatGPT Images is here. OpenAI shipped an update to their ChatGPT Images feature - the feature that gained them 100 million new users in a week when they first launched it back in March, but has since been eclipsed by Google's Nano Banana and then further by Nana Banana Pro in November.
The focus for the new ChatGPT Images is speed and instruction following:
It makes precise edits while keeping details intact, and generates images up to 4x faster
It's also a little cheaper: OpenAI say that the new gpt-image-1.5 API model makes image input and output "20% cheaper in GPT Image 1.5 as compared to GPT Image 1".
I tried a new test prompt against a photo I took of Natalie's ceramic stand at the farmers market a few weeks ago:
Add two kakapos inspecting the pots
Here's the result from the new ChatGPT Images model:
And here's what I got from Nano Banana Pro:
The ChatGPT Kākāpō are a little chonkier, which I think counts as a win.
I was a little less impressed by the result I got for an infographic from the prompt "Infographic explaining how the Datasette open source project works" followed by "Run some extensive searches and gather a bunch of relevant information and then try again" (transcript):
See my Nano Banana Pro post for comparison.
Both models are clearly now usable for text-heavy graphics though, which makes them far more useful than previous generations of this technology.
Update 21st December 2025: I realized I already have a tool for accessing this new model via the API. Here's what I got from the following:
OPENAI_API_KEY="$(llm keys get openai)" \
uv run openai_image.py -m gpt-image-1.5\
'a raccoon with a double bass in a jazz bar rocking out'
Total cost: $0.2041.