2024 Philosophy Compass, e12950
The Coding Agent Data Deal
On user data control, coding agents as retrievers, and the value of your coding transcripts
Coding After Coders: The End of Computer Programming as We Know It - …
archived 16 Mar 2026 15:14:04 UTC
Semantic Compression
An introduction to the idea that code should be approached with a mindset towards compressing it semantically, rather than orienting it around objects.
In defense of not reading the code | Ben Shoemaker
Last week, I wrote an article whose purpose was to discuss where I saw AI-assisted coding going. That's not what generated 200 comments worth of passionate...
A sufficiently detailed spec is code
Specifications do not address the limitations of agentic coding
The 80% Problem in Agentic Coding
Managing comprehension debt when leaning on AI to code
Agentic coding and persistent returns to expertise
Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.
Astra for Coding: Why Are We Doing This Again?
Some thoughts on Astra and newfangled long-horizon models.
Background Coding Agents: Predictable Results Through Strong Feedback Loops (Part 3) | Spotify Engineering
This is part 3 in our series about Spotify's journey with background coding agents and the future of large-scale software maintenance. See also and .
Requisite variety and its implications for the control of complex systems
Recent work on the fundamental processes of regulation in biology (Ashby, 1956) has shown the importance of a certain quantitative relation called the law of requisite variety. After this relation had been found, we appreciated that it was related to a theorem in a world far removed from the biological—that of Shannon on the quantity of noise or error that could be removed through a correction-channel (Shannon and Weaver, 1949; theorem 10). In this paper I propose to show the relationship between the two theorems, and to indicate something of their implications for regulation, in the cybernetic sense, when the system to be regulated is extremely complex. Since the law of requisite variety uses concepts more primitive than those used by entropy, I will start by giving an account of that law.
How AI assistance impacts the formation of coding skills
Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.
Self-Instruct: Aligning Language Models with Self-Generated Instructions
Large "instruction-tuned" language models (i.e., finetuned to respond to instructions) have demonstrated a remarkable ability to generalize zero-shot to new tasks. Nevertheless, they depend...
Compression is prediction | ngrok blog
Compression and LLMs are trying to solve the exact same problem: predicting what comes next. Learn the fundamentals of compression and how better prediction leads to better shrinkage.
