code-assist

Chain-of-Vibes by Pete Hodgson sums up nicely the approach I take to leverage the power of agentic AI workflows while sidestepping the limitations. Treat the tools “like weirdly knowledgeable, hyper-productive junior engineers”, give enough context but otherwise limit their output. Importantly, review the outcome of each small task and make a decision:

A) Accept and commit
B) Prompt to adjust
C) Manually fix up
D) Revert and re-prompt

The last item is an important one that’s often overlooked - throwing away the result and fixing your prompt is often more time efficient, especially when learning how to prompt.

A rules-based pattern is emerging for helping agentic workflows produce better results. Examples include GreatScottyMac’s RooFlow and, Geoff Huntley’s specs and stdlib approaches.

Brendan Humphrey on Vibe Coding, aligns with my own thinking on vibe coding:

…these tools must be carefully supervised by skilled engineers, particularly for production tasks. Engineers need to guide, assess, correct, and ultimately own the output as if they had written every line themselves.

Smashing Create PR with vibe coding output amounts to an attack on the PR process:

Generating vast amounts of code from single prompts effectively DoS attacks reviewers, overwhelming their capacity for meaningful assessment

But there is still some value:

Currently we see one narrow use case where vibe coding is exciting: spikes, proofs of concept, and prototypes. These are always throwaway code. LLM-assisted generation offers enormous value in rapidly testing and validating ideas with implementations we will ultimately discard.

GitHub Copilot: The agent awakens - Just when you thought it was Cursor/Claude Desktop/Roo/Cline, m$ reminds you they’ll eat your lunch.

The “First AI Software Engineer” Is Bungling the Vast Majority of Tasks It’s Asked to Do - It took longer than a human, and failed at the vast majority of tasks.

Out of 20 tasks we attempted, we saw 14 failures, three inconclusive results, and just three successes,” the researchers found — a meager success rate of just 15 percent.

Today we’re excited to introduce Devin, the first AI software engineer.

Devin is an autonomous agent that solves engineering tasks through the use of its own shell, code editor, and web browser.

gpt-engineer - A CLI tool that allows users to specify software in natural language and have AI write and execute the code, with capabilities for code generation experiments and improvements.