2025-12-07 02:30:56
The Unexpected Effectiveness of One-Shot Decompilation with Claude
Chris Lewis decompiles N64 games. He wrote about this previously in Using Coding Agents to Decompile Nintendo 64 Games, describing his efforts to decompile Snowboard Kids 2 (released in 1999) using a "matching" process:The matching decompilation process involves analysing the MIPS assembly, inferring its behaviour, and writing C that, when compiled with the same toolchain and settings, reproduces the exact code: same registers, delay slots, and instruction order. [...]
A good match is more than just C code that compiles to the right bytes. It should look like something an N64-era developer would plausibly have written: simple, idiomatic C control flow and sensible data structures.
Chris was getting some useful results from coding agents earlier on, but this new post describes how a switching to a new processing Claude Opus 4.5 and Claude Code has massively accelerated the project - as demonstrated started by this chart on the decomp.dev page for his project:
Here's the prompt he was using.
The big productivity boost was unlocked by switching to use Claude Code in non-interactive mode and having it tackle the less complicated functions (aka the lowest hanging fruit) first. Here's the relevant code from the driving Bash script:
simplest_func=$(python3 tools/score_functions.py asm/nonmatchings/ 2>&1) # ... output=$(claude -p "decompile the function $simplest_func" 2>&1 | tee -a tools/vacuum.log)
score_functions.py uses some heuristics to decide which of the remaining un-matched functions look to be the least complex.
Via Hacker News
Tags: games, ai, prompt-engineering, generative-ai, llms, ai-assisted-programming, coding-agents, claude-code
2025-12-06 22:40:46
If you work slowly, you will be more likely to stick with your slightly obsolete work. You know that professor who spent seven years preparing lecture notes twenty years ago? He is not going to throw them away and start again, as that would be a new seven-year project. So he will keep teaching using aging lecture notes until he retires and someone finally updates the course.
— Daniel Lemire, Why speed matters
Tags: productivity
2025-12-05 14:03:29
TIL: Subtests in pytest 9.0.0+
I spotted an interesting new feature in the release notes for pytest 9.0.0: subtests.I'm a big user of the pytest.mark.parametrize decorator - see Documentation unit tests from 2018 - so I thought it would be interesting to try out subtests and see if they're a useful alternative.
Short version: this parameterized test:
@pytest.mark.parametrize("setting", app.SETTINGS) def test_settings_are_documented(settings_headings, setting): assert setting.name in settings_headings
Becomes this using subtests instead:
def test_settings_are_documented(settings_headings, subtests): for setting in app.SETTINGS: with subtests.test(setting=setting.name): assert setting.name in settings_headings
Why is this better? Two reasons:
I had Claude Code port several tests to the new pattern. I like it.
Tags: python, testing, ai, pytest, til, generative-ai, llms, ai-assisted-programming, coding-agents, claude-code
2025-12-05 12:28:05
Thoughts on Go vs. Rust vs. Zig
Thoughtful commentary on Go, Rust, and Zig by Sinclair Target. I haven't seen a single comparison that covers all three before and I learned a lot from reading this.One thing that I hadn't noticed before is that none of these three languages implement class-based OOP.
Via Hacker News
Tags: go, object-oriented-programming, programming-languages, rust, zig
2025-12-05 09:19:26
The Resonant Computing Manifesto
Launched today at WIRED’s The Big Interview event, this manifesto (of which I'm a founding signatory) encourages a positive framework for thinking about building hyper-personalized AI-powered software - while avoiding the attention hijacking anti-patterns that defined so much of the last decade of software design.This part in particular resonates with me:
For decades, technology has required standardized solutions to complex human problems. In order to scale software, you had to build for the average user, sanding away the edge cases. In many ways, this is why our digital world has come to resemble the sterile, deadening architecture that Alexander spent his career pushing back against.
This is where AI provides a missing puzzle piece. Software can now respond fluidly to the context and particularity of each human—at scale. One-size-fits-all is no longer a technological or economic necessity. Where once our digital environments inevitably shaped us against our will, we can now build technology that adaptively shapes itself in service of our individual and collective aspirations.
There are echos here of the Malleable software concept from Ink & Switch.
The manifesto proposes five principles for building resonant software: Keeping data private and under personal stewardship, building software that's dedicated to the user's interests, ensuring plural and distributed control rather than platform monopolies, making tools adaptable to individual context, and designing for prosocial membership of shared spaces.
Steven Levy talked to the manifesto's lead instigator Alex Komoroske and provides some extra flavor in It's Time to Save Silicon Valley From Itself:
By 2025, it was clear to Komoroske and his cohort that Big Tech had strayed far from its early idealistic principles. As Silicon Valley began to align itself more strongly with political interests, the idea emerged within the group to lay out a different course, and a casual suggestion led to a process where some in the group began drafting what became today’s manifesto. They chose the word “resonant” to describe their vision mainly because of its positive connotations. As the document explains, “It’s the experience of encountering something that speaks to our deeper values.”
Tags: ai, alex-komoroske, ai-ethics
2025-12-05 07:57:34
Django 6.0 includes a flurry of neat features, but the two that most caught my eye are background workers and template partials.
Background workers started out as DEP (Django Enhancement Proposal) 14, proposed and shepherded by Jake Howard. Jake prototyped the feature in django-tasks and wrote this extensive background on the feature when it landed in core just in time for the 6.0 feature freeze back in September.
Kevin Wetzels published a useful first look at Django's background tasks based on the earlier RC, including notes on building a custom database-backed worker implementation.
Template Partials were implemented as a Google Summer of Code project by Farhan Ali Raza. I really like the design of this. Here's an example from the documentation showing the neat inline attribute which lets you both use and define a partial at the same time:
{# Define and render immediately. #}
{% partialdef user-info inline %}
<div id="user-info-{{ user.username }}">
<h3>{{ user.name }}</h3>
<p>{{ user.bio }}</p>
</div>
{% endpartialdef %}
{# Other page content here. #}
{# Reuse later elsewhere in the template. #}
<section class="featured-authors">
<h2>Featured Authors</h2>
{% for user in featured %}
{% partial user-info %}
{% endfor %}
</section>You can also render just a named partial from a template directly in Python code like this:
return render(request, "authors.html#user-info", {"user": user})
I'm looking forward to trying this out in combination with HTMX.
I asked Claude Code to dig around in my blog's source code looking for places that could benefit from a template partial. Here's the resulting commit that uses them to de-duplicate the display of dates and tags from pages that list multiple types of content, such as my tag pages.
Tags: django, python, ai, generative-ai, llms, ai-assisted-programming, htmx, coding-agents, claude-code
