2026-02-23 15:12:28
Agentic Engineering Patterns >
"Use red/green TDD" is a pleasingly succinct way to get better results out of a coding agent.
TDD stands for Test Driven Development. It's a programming style where you ensure every piece of code you write is accompanied by automated tests that demonstrate the code works.
The most disciplined form of TDD is test-first development. You write the automated tests first, confirm that they fail, then iterate on the implementation until the tests pass.
This turns out to be a fantastic fit for coding agents. A significant risk with coding agents is that they might write code that doesn't work, or build code that is unnecessary and never gets used, or both.
Test-first development helps protect against both of these common mistakes, and also ensures a robust automated test suite that protects against future regressions. As projects grow the chance that a new change might break an existing feature grows with them. A comprehensive test suite is by far the most effective way to keep those features working.
It's important to confirm that the tests fail before implementing the code to make them pass. If you skip that step you risk building a test that passes already, hence failing to exercise and confirm your new implementation.
That's what "red/green" means: the red phase watches the tests fail, then the green phase confirms that they now pass.
Every good model understands "red/green TDD" as a shorthand for the much longer "use test driven development, write the tests first, confirm that the tests fail before you implement the change that gets them to pass".
Example prompt:
Build a Python function to extract headers from a markdown string. Use red/green TDD.
Here's what I got from Claude and from ChatGPT. Normally I would use a coding agent like Claude Code or OpenAI Codex, but this example is simple enough that both Claude and ChatGPT can implement it using their default code environments.
I did have to append "Use your code environment" to the ChatGPT prompt. When I tried without that it wrote the code and tests without actually executing them.
Tags: testing, tdd, coding-agents, ai-assisted-programming
2026-02-23 07:58:43
The Claude C Compiler: What It Reveals About the Future of Software
On February 5th Anthropic's Nicholas Carlini wrote about a project to use parallel Claudes to build a C compiler on top of the brand new Opus 4.6Chris Lattner (Swift, LLVM, Clang, Mojo) knows more about C compilers than most. He just published this review of the code.
Some points that stood out to me:
- Good software depends on judgment, communication, and clear abstraction. AI has amplified this.
- AI coding is automation of implementation, so design and stewardship become more important.
- Manual rewrites and translation work are becoming AI-native tasks, automating a large category of engineering effort.
Chris is generally impressed with CCC (the Claude C Compiler):
Taken together, CCC looks less like an experimental research compiler and more like a competent textbook implementation, the sort of system a strong undergraduate team might build early in a project before years of refinement. That alone is remarkable.
It's a long way from being a production-ready compiler though:
Several design choices suggest optimization toward passing tests rather than building general abstractions like a human would. [...] These flaws are informative rather than surprising, suggesting that current AI systems excel at assembling known techniques and optimizing toward measurable success criteria, while struggling with the open-ended generalization required for production-quality systems.
The project also leads to deep open questions about how agentic engineering interacts with licensing and IP for both open source and proprietary code:
If AI systems trained on decades of publicly available code can reproduce familiar structures, patterns, and even specific implementations, where exactly is the boundary between learning and copying?
Tags: c, compilers, open-source, ai, ai-assisted-programming, anthropic, claude, nicholas-carlini, coding-agents
2026-02-23 07:54:39
London Stock Exchange: Raspberry Pi Holdings plc
Striking graph illustrating stock in the UK Raspberry Pi holding company spiking on Tuesday:
The Telegraph credited excitement around OpenClaw:
Raspberry Pi's stock price has surged 30pc in two days, amid chatter on social media that the company's tiny computers can be used to power a popular AI chatbot.
Users have turned to Raspberry Pi's small computers to run a technology known as OpenClaw, a viral AI personal assistant. A flood of posts about the practice have been viewed millions of times since the weekend.
Reuters also credit a stock purchase by CEO Eben Upton:
Shares in Raspberry Pi rose as much as 42% on Tuesday in a record two‑day rally after CEO Eben Upton bought stock in the beaten‑down UK computer hardware firm, halting a months‑long slide, as chatter grew that its products could benefit from low‑cost artificial‑intelligence projects.
Two London traders said the driver behind the surge was not clear, though the move followed a filing showing Upton bought about 13,224 pounds worth of shares at around 282 pence each on Monday.
Tags: ai, generative-ai, raspberry-pi, llms, ai-agents, openclaw
2026-02-22 23:53:43
Gabriel Chua (Developer Experience Engineer for APAC at OpenAI) provides his take on the confusing terminology behind the term "Codex", which can refer to a bunch of of different things within the OpenAI ecosystem:
In plain terms, Codex is OpenAI’s software engineering agent, available through multiple interfaces, and an agent is a model plus instructions and tools, wrapped in a runtime that can execute tasks on your behalf. [...]
At a high level, I see Codex as three parts working together:
Codex = Model + Harness + Surfaces [...]
- Model + Harness = the Agent
- Surfaces = how you interact with the Agent
He defines the harness as "the collection of instructions and tools", which is notably open source and lives in the openai/codex repository.
Gabriel also provides the first acknowledgment I've seen from an OpenAI insider that the Codex model family are directly trained for the Codex harness:
Codex models are trained in the presence of the harness. Tool use, execution loops, compaction, and iterative verification aren’t bolted on behaviors — they’re part of how the model learns to operate. The harness, in turn, is shaped around how the model plans, invokes tools, and recovers from failure.
Tags: definitions, openai, generative-ai, llms, ai-assisted-programming, codex-cli
2026-02-21 09:30:21
We’ve made GPT-5.3-Codex-Spark about 30% faster. It is now serving at over 1200 tokens per second.
— Thibault Sottiaux, OpenAI
Tags: openai, llms, ai, generative-ai, llm-performance
2026-02-21 08:37:45
Andrej Karpathy talks about "Claws"
Andrej Karpathy tweeted a mini-essay about buying a Mac Mini ("The apple store person told me they are selling like hotcakes and everyone is confused") to tinker with Claws:I'm definitely a bit sus'd to run OpenClaw specifically [...] But I do love the concept and I think that just like LLM agents were a new layer on top of LLMs, Claws are now a new layer on top of LLM agents, taking the orchestration, scheduling, context, tool calls and a kind of persistence to a next level.
Looking around, and given that the high level idea is clear, there are a lot of smaller Claws starting to pop out. For example, on a quick skim NanoClaw looks really interesting in that the core engine is ~4000 lines of code (fits into both my head and that of AI agents, so it feels manageable, auditable, flexible, etc.) and runs everything in containers by default. [...]
Anyway there are many others - e.g. nanobot, zeroclaw, ironclaw, picoclaw (lol @ prefixes). [...]
Not 100% sure what my setup ends up looking like just yet but Claws are an awesome, exciting new layer of the AI stack.
Andrej has an ear for fresh terminology (see vibe coding, agentic engineering) and I think he's right about this one, too: "Claw" is becoming a term of art for the entire category of OpenClaw-like agent systems - AI agents that generally run on personal hardware, communicate via messaging protocols and can both act on direct instructions and schedule tasks.
It even comes with an established emoji 🦞
Tags: definitions, ai, andrej-karpathy, generative-ai, llms, ai-agents, openclaw
