Programmers

Sometimes AI is so much like a human programmer.

First they tell you something is near impossible.

You point out a possible way to do it.

Then they say it might be possible but it's really hard and complicated.

You point out a simple way to do it.

Finally they admit that, ok, it's actually easy.

I guess they won't be taking my job this month :-)

AI Coding Tools

A few people have asked about what tools I'm using for AI coding so I figured I'd snapshot what I'm using right now. Given how fast AI is changing it will probably be different six months from now. I haven't tested different tools extensively so don't take this as expert advice, just one data point.

For tab completion I've been using Amp Tab which is the tab completion part of Amp which is Sourcegraph's second generation AI coding tool (after Cody). Amp itself is a little too aggressive for my style of programming. I prefer to review changes closely before applying them. Currently Amp Tab is experimental and is still free. My understanding is that it's similar to Cursor, which I haven't tried because I prefer to use standard VSCodium. Even Amp Tab can be a little aggressive for me sometimes. I have to be careful about hitting tab to indent a line since it's liable to go and make changes to my code.

For investigating, reviewing, or writing code I've been using Cline as a VSCode extension. Cline is open source and lets you pick your AI model and provider. I've been using OpenRouter so I can try out different models. You can also use Cline as the provider which I might have done if I'd realized it before I signed up with OpenRouter.

Cline has a "planning" mode which is basically read-only, and an "act" mode where it makes code changes. Even in act mode I require approval for code changes.

As far as models, I started out with Claude Sonnet 3.5 and progressed to 3.7 and now 4. I've also tried a few others like GPT-5 and grok-code-fast-1. There isn't a huge difference, they can all do well or mess up badly but I tend to go back to Claude Sonnet 4 even though it's one of the more expensive ones. The last few months I've been spending about $50 per month on model usage. It's worth it for me, as much for the learning experience as for the actual code produced. If you didn't want to (or couldn't afford to) spend money on it, there are usually free or cheap options. 

For general research I've been using Gemini 2.5 Pro, mostly because it's included with our company Gmail/Google accounts. It works well to research algorithms or data structures.

The big question these days is whether programmers are actually more productive using AI. There have been studies that show that although programmers feel they're more productive, they're actually not. It sounds a bit like multi-tasking. I wouldn't say it's made a huge difference to my productivity. Some types of tasks go quicker, but for others AI can become a big time waster. I would say the quality of my code might be slightly higher from having more tests and more reviews.

OverIter Cur Deleted

gSuneido has had a long standing assertion failure that happened maybe once a month which equates to about once per million user hours. We tried multiple times but were never able to recreate it.

I’ll try to explain the scenario. Indexes are stored in the database file as btrees. While the database is running, index updates are buffered. An Overlay consists of the base btree plus layers of updates. When a transaction commits it adds an ixbuf layer to the Overlay’s that it updated. Background threads merge the layers and update the btree. OverIter handles iterating over an Overlay. It merges iterators for each layer. This is fundamentally straightforward but as always, the devil is in the details. The ixbuf layers from the transactions include updates and deletes as well as additions. So the merging has to combine these. For example, combining an add followed by several updates. The final piece of the puzzle is that concurrent modifications can occur during iteration.

"OverIter Cur deleted" means that the current value of the iterator is marked as deleted. This should never happen. The iterator is designed to skip deleted records.

The error occurred again recently and I decided to see what Claude (Sonnet 4) could do with it. It kept saying "now I see the problem", but when I'd tell it to write a test to make it happen it couldn't. It became obvious it wasn't going to spot the bug by just looking at the code so I got it writing tests. It wrote a lot of them, and they all passed. That was actually kind of nice since it meant the code was fairly robust. I wouldn't have been surprised if other bugs had showed up with the intense testing.

Finally, it wrote a random stress test that caused the assertion failure. I was cautiously optimistic. Sadly, it turned out it was generating invalid test data and that was what was triggering the assertion failure. Once I corrected the data generation, then the test passed. It was possible that the bug was actually leading to bad data which was leading to the assertion failure but that seemed unlikely since bad data would cause other problems.

Back to the drawing board. I continued extending the test to cover more scenarios. Eventually I managed to recreate the error with legitimate data and actions. After that it was a matter of extracting one failing case from the random test. Claude added print statements, looked at the result, and wrote a test for that specific sequence.

Once I had a relatively simple failing test, Claude claimed to find the bug right away. I was skeptical since it had claimed to find the bug many times already. I worked on extending the test to cover more of the scenarios. Sure enough, it started failing again. Claude came up with another fix. But the test kept failing. The proposed changes would fix certain cases but break other cases. No combination of the “fixes” solved all the problems.

Eventually Claude proposed rewriting one of the key functions. I was even more skeptical but the code looked reasonable and it was simpler and clearer than the old code. It wasn't very efficient but it wasn't hard to tell Claude how to optimize it. But it still didn’t fix all the problems. I dug into the other “fix” and realized it was on the right track but wasn’t quite complete. A little back and forth came up with a solution here as well. And finally I had a version of the code that passed all the tests.

I am cautiously optimistic. By this point I think the tests are fairly comprehensive. And I understand the fixes and they make sense.

As I've come to expect, my results with Claude were mixed. It definitely did some of the grunt work of writing tests. And I have to give it credit for giving up on some of my old code and writing a simpler, more correct version. But it also came up with several incorrect, or at least incomplete, fixes.

I started this thinking I'd spend a few hours playing with AI. It ended up being my main project for a week. Even though it was rare enough that it wasn't really a problem, I'm glad I finally fixed it. (I hope!)

Partnering with AI

I'm not a leading edge AI user. I have used Tabnine for the last few years, but mostly as a smarter auto-complete. Handy and time saving but nothing earth shaking. Mostly I was a skeptic. But using Cline with Claude Sonnet 4 lately has definitely changed my attitude.

One of the first things I noticed was the "rubberducky" effect. Just the effort of explaining a problem to AI often helps think of a solution. Explaining a problem to another person (or an AI) is even better because they ask questions and offer solutions. Claude often says "I see the problem" when it's totally off track. That's a little annoying, but it's no worse than what a person would do. And often those wacky ideas can spark some new ideas or avenues to explore.

A more surprising aspect is that it feels like I'm collaborating. I've been solo programming for most of my (long) career, but the last five years working remotely has exaggerated that. No one reads the majority of my code or comments on it. It's not that I'm socializing with the AI, but suddenly I have someone I can "talk" to about it. What does this do? Do we need that? What if we did xyz? Isn't that dangerous? It might not "know" the code like a true collaborator, but it's a very close approximation. And it never hurts to be exposed to other ways of doing things.

I don't vibe code with AI. I want the end code to be as good as I can make it. And with current AI that means keeping a very close watch on what it's doing. Like a beginning programmer, it seldom gets a good solution on the first try. It often requires reviewing changes closely and not being afraid to reject them. Often, you have to steer it to an elegant solution. I'm working on heavily used production code, not a throwaway toy project.

The agent approach seems to be a fundamental improvement. It's impressive to watch Claude add debugging statements, run the code, "look" at the output, and repeat until it tracks down the issue. At the same time, it can also make quite blatant mistakes so you need to be watching closely.

Honestly, I miss writing code when I'm working with AI. It's like pair programming with someone who never gives you the keyboard. Of course, there's nothing stopping me from writing some of the code myself, and I do. But when Claude can spit it out faster than I can type, it seems pointless not to let it.

One of Claude's quirks is that it has a positive tone. It's always saying "good idea" or "you're absolutely right". I found that a little goofy at first, but once I got used to it I find I like it. Who doesn't like positive feedback? I know it's meaningless, but I find when I use less positive models, I miss it.

A Bittersweet Triumph

I've been fascinated with the game of Go since I was young. As a kid I played a fair bit of chess, never seriously, but I could usually beat my peers. That was balanced by regularly getting thrashed by a 90 year old in my neighborhood that I was conscripted to play with. After I discovered Go I pretty much gave up on Chess. My business partner and I used to play a lot of Go in the early days of our company. It was a good way to occupy our time on business trips. We were never very good, and almost never played with anyone else, but it was a fun mental challenge. I still play regularly on my iPad against SmartGo Kifu and Crazy Stone (which claims to use deep learning), but only small 9x9 games. (and I'm still not any good!)

One of the things that attracted me to Go was that it wasn't amenable to the same techniques that were used in software to conquer Chess. Of course, I don't believe there is any magic in human brains, so presumably there was a way for computers to handle it. Until AlphaGo, most people believed it would be a long time, perhaps a decade, before computers could beat the best human players. So it was a bit of a shock to everyone when AlphaGo recently beat the worlds best human players.

I highly recommend the full length (90 min) documentary about AlphaGo - a Go playing computer program developed by DeepMind, a company owned by Google. (Don't be scared off by "AI". The documentary isn't technical, it's more about the people.) It's available on Netflix, iTunes, Google Play, and Amazon.



For more of the technical details, see the Nature articles. They're behind a paywall on the Nature web site, but they seem to be available on Scribd - Mastering the game of Go with deep neural networks and tree search and Mastering the game of Go without human knowledge

I've never been involved in AI, but it's always fascinated me. It was one of the original reasons I got into computers. In the early days there was a lot of hype about neural networks. But they didn't live up to their hype and there was a backlash that meant they were downplayed for many years. Recently they've seen a huge comeback and are now living up to much of those original hopes. Partly that's because of advances in hardware like GPU's.

The first AlphaGo was trained partly on human games. Its successor, AlphaGo Zero, learned completely on its own, by playing itself. It surpassed the earlier version in a matter of days. Not long after, the AlphaGo project was ended and the team was disbanded. I guess it's a done deal.