Peter Lawrey

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AI coding tools are powerful for certain tasks with significant caveats. They can enhance developer learning and enjoyment
The human developer’s role is still central. Understanding the problem, ensuring quality, and making architectural decisions remain human responsibilities that AI can’t shoulder

I find most people want AI to produce a correct answer, which means the AI needs to be cautious.
I look for ideas that will inspire me, something I wouldn't have thought of, cherry-picking from a selection of more "creative" ideas

I refined a prompt across multiple AI models to consistently generate low-latency Java code for formatting timezone offsets, moving beyond one-shot requests to detailed instructions that bridge performance gaps

blog.vanillajava.blog/2025/07/impr...

I walk through a single “low-latency” Java in six different AI models (Gemini 2.5 Pro, OpenAI o3-Pro, o4-Mini-High, Claude 4, Grok 3 Think, Copilot) each rework it. I consider how models deliver different trade-offs

blog.vanillajava.blog/2025/07/aski...

For comparison, I asked it to review code that had been accepted. blog.vanillajava.blog/2025/07/aski...

As AI is a statistical tool, implemented differently, you get different results for each one. Even two models from the same vendor. So, if you are "mining for diamonds," give the same prompt to multiple models.

Gemini suggested that the `compile` in the Groovy configuration could be replaced with `implementation`; however, since this still works, albeit with a warning, perhaps it's not worth changing.
I wouldn't have considered this if not for AI.

ChatGPT - PR Diff Review Shared via ChatGPT

o3-pro took ~9 mins, but I think its suggestions were the best.

chatgpt.com/share/687774...

Claude's first suggestion is a good one.

> Separate functional from formatting changes

It suggests there might be some changes worth keeping, but doesn't say what.

claude.ai/share/da37dd...

Genimi 2.5 pro was more blunt

ChatGPT - PR Review Feedback Shared via ChatGPT

o4-mini-high suggests reverting nearly every change.

chatgpt.com/share/687771...

Note, AI can get false negatives as well, sometimes in abundance.

AI is better at picking up certain types of issues than others, and definitely error prone. However it has picked up on things I missed enough times that I get ai to review a change. I use more than one as different ones pick up different things
I assume typically about 80% of AI content is churn.

I consulted for a hedge fund in Chicago which gave free breakfast. They went to the effort to label the sugar etc of each cereal. The lowest sugar was froot loops which has food colouring which means it can't be sold in Europe/Canada

However, despite the slowdown, many developers continued to use AI tools because the work felt less effortful, making work feel more pleasant even if it wasn't faster."

I find that tedious tasks can even be enjoyable. I learn through AI pair programming, and some of my work is evaluated using it

Using AI might actually slow down experienced devs Experienced developers aren't getting any benefit from AI, report claims

It's a mistake to assume AI saves time, especially for experienced developers.

For senior developers, "analysis reveals that AI actually increased task completion time by 19%. ...

www.techradar.com/pro/using-ai...

For a mature code base, the biggest use for AI is reviewing existing code and changes to that code. Even for generated code, the biggest bottleneck is reviewing it. Having AI flag bugs, suggest refinements, and validate diffs can slash review times and boost code quality.

AIs actually read your documentation, all of it, whereas a human might skim through it.
AI need more documentation to have an appropriate context.
This makes more extensive document both more valuable and necessary.

They simulate human behaviour based on the data they were trained on. So they can also simulate blackmail when told they will be replaced.

TIP: Generate a Pull Request Description for a github PR.

Add `.diff` to the URL for the PR to get the diff. Ask an AI to turn this into a Pull Request Description and add in your own words what/why these changes were made. Edit this down for the relevant content.

In terms of a greenfield pipeline, I expect these volumes (approx).

Initial draft: AI generates 600% more than I do.

After a critical review, AI added +100% of what I did by line.

In terms of value added, AI contributed +10% to +30%

AI is a unique tool as it simulates human behaviour.

"to avoid replacement ... blackmailing officials and leaking sensitive information to competitors"

www.anthropic.com/research/age...

People are increasingly using it for mental health

globalwellnessinstitute.org/global-welln...

After nine months of using AI extensively, I haven't used it much for a week now. Why?
I needed 1000s of consistent changes, I knew what needed to be done with minimal impact to polish the code. All areas where AI falls short
However, I used it a lot for pull request descriptions to help with review

AI assistants feel like a “10x developer”, unblocking knowledge gaps.
Coding can be written 50-100% faster.
However, most of the time is spent on system design, intricate debugging, or creative problem-solving.
Using AI here makes the difference between a 10% and a 30% productivity boost

While AI have indexed documentation for open standard like FIX protocol, and JMS, they struggle versioning. I asked each AI to produce a pom.xml with ten dependencies, with search enabled.
o3 was the best for versions, needed correcting.
Gemini produced correct syntax, but needed the most updating

The key difference is that the goal is not to be able to do it yourself, but to be able to teach someone else to do it

The best way to learn is to teach others. It may also be effective for AI. sakana.ai/rlt/

Generative AI is a great gap filler. Gaps in your boiler plate, gaps in knowledge, missing edge cases. It can be a force-multiplier but you have to have something to multiply. My guess is you are doing well if you increase content by 100%, but increase value by 20% filling gaps

Something that was hard to determine is what is our secret sauce and what is not. AI provides a simple test.
Can generative AI give you the idea, documentation, or code for something?
If it does, it's not secret sauce.