Rahul @aestheticoder

If you think code no longer matters, read this 👇😉 martinfowler.com/articles/what-…

Why doesn’t Java’s String class have a built-in .reverse() / .reversed() method yet? 🤔

Java is setting the standards for #AI infrastructure⚡ Benchmark of MCP server implementations across 4 major languages puts #MCP #Java & #SpringAI at the top of the performance charts: sub-ms latency, highest throughput, and the best CPU efficiency 📖 tmdevlab.com/mcp-server-per…

A good collection of Modern Java snippets :) javaevolved.github.io

I've mentioned how Java is catching up to Scala multiple times now. This video perfectly summarizes the recent progress - from null-restricted types to value classes and beyond. 👇 youtube.com/watch?v=UuQQVY…

I see your 2 GB, 45 seconds to start Java & Spring app, and I raise you a 135 MB, 0.097 s to start Java & Spring app with @GraalVM ;)

Peter Steinberger 🦞 @steipete

4 months ago

We should make EnterpriseClaw just for the lolz. Java 21, Spring Boot, 14 abstract factory beans, 2GB Docker image, takes 45 seconds to start, AbstractSingletonProxyFactoryAgentClawResponseHandlerBeanDefinitionRegistryPostProcessorImpl .java

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#Java generics solve more problems than most developers realize. This article shows how much safer and clearer your code becomes when you lean on them properly. bit.ly/4re1Q0w bit.ly/4re1Q0w

Comes the news that Anthropic has vibecoded an entire C compiler. It's an impressive stunt, but even as bullish as I am on AI-assisted coding I wouldn't want to use it for production. The reason is auditability. I don't actually know, not having seen the source code, but so far what usually happens when you vibecode something this size is you get a gigantic hairball. Functional, maybe, but not something that a human can practically speaking comprehend or modify. So, what happens when it breaks? How can I know that when it's driven into a odd region of its behavioral space it won't do something alien and bizarre? How do I develop confidence in it when I can't read the code and nobody else can either? In the last quarter century, we've come to almost equate being auditable with being open-source. And that made sense as long as human brains were producing code that could be comprehended by other human brains. We're not in that world anymore. We have to grapple with the possibility that LLMs coding at scale will produce open source that is as opaque as a binary blob. Even if this compiler isn't actually the first example of that, it's going to happen. It's not good enough to say that we have AIs to help us with this problem. Because at a significantly high degree of scale and opacity, the human being is going to be hard put to figure out what questions it ought to be asking its assistant to do a proper audit. To solve this problem, we're going to have to figure out how to tell LLMs to design very large codebases so that they're not only fit for purpose, but readable by human beings as well.

@Grady_Booch Same is happening with Gemini in Intellij. Agent mode & ask mode both maintain different context it seems, the code modified/added by Ask mode is getting overwritten by the agent mode. It doesn't give a shit what was there earlier.

There are more things in computing, Dario, that are dreamt of in your philosophy. Respectfully, @AnthropicAI's @DarioAmodei is profoundly wrong. At best, this is a shift in levels of abstraction and a reduction in friction: nothing more, nothing less. Valuable? Yes. An earthshaking discontinuity? No. Consider the vast breadth of software. Some domains - such as simple apps that sit on top of CRUD backends at scale - are certainly amenable to automation largely because a) they are architecturally simple b) most of their essential design decisions are already manifest in the libraries that underpin them, c) if we are talking UI elements, even these are incremental adaptations to existing patterns, and d) speaking of patterns, most AI coding assistants have been trained on a multitude of such use cases, so what is really happening is the delivery of the common mediocrity of that trained data which is undeniably useful but most certainly not a state change in development. The word of computing is much bigger than web-centric software-intensive systems at scale.

Wes Roth @WesRoth

5 months ago

"Software Engineering Will Be Automatable in 12 Months," Anthropic CEO Dario Amodei predicts that AI models will be able to do 'most, maybe all' of what software engineers do end-to-end within 6 to 12 months, shifting engineers to editors.

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The reason why RAM has become four times more expensive is that a huge amount of RAM that has not yet been produced was purchased with non-existent money to be installed in GPUs that also have not yet been produced, in order to place them in data centers that have not yet been built, powered by infrastructure that may never appear, to satisfy demand that does not actually exist and to obtain profit that is mathematically impossible.

Yesterday we had a tech town hall in Zoho where we did a code review of the C++ code generated by the Claude Opus 4.5 model. It went on for hours late evening. I now have a much clearer understanding of what these models do well: they are able to stitch together systems well, taking data from one system, reshape it and pass it to another system. There is often a lot of such "glue code" in these systems and that is not very complicated but it is very tedious. In general, AI models have "memorized" all the open source too and they are able to recall patterns from them (with some possibility of hallucination). They are also able to stitch various open source pieces together well. Our senior engineer had guided ("orchestrated" is the right word) this process. When the AI was stuck he helped "unstuck" it. This was a very vital contribution and without his experienced guidance, the AI output would not be useful. We examined several C++ files with thousands of lines of code in each, looking for what I consider complex code. Most of it was straightforward glue code and only a tiny part of it was complex. I suspect that the AI generated code tended to be needlessly verbose but I have to study it more to be sure. On the whole, I am both impressed and not super awed. I believe we can do better 😏

Dear influencers: if you are neither a Java nor have ever written applications in Java or with the Java ecosystem, stop commenting about Java or anything that involves Java. Your lack of knowledge in the Java ecosystem is showing and is very telling. #Java #smh

@mario_casari Now, tell me how you instantiate UserInfo class.

When you know your worth, they’ll call you difficult. When you draw boundaries, they’ll call you arrogant. The world demands submission disguised as humility. It’s never your pride they hate, it’s your independence. The world fears the man who can’t be guilted into servitude.

Develop the ability to be disliked and free yourself from the prison of other people’s opinions.

Gave a junior engineer a basic html/css/js app and they didn't know what to do with it. Busy trying to run npm install and looking for package.json What have we done? 🤦‍♂️

I keep screaming, that you need both curiosity and a high bias for action to make an impact. But what does that really mean and why does it matter? Let's break it down combinatorially :) 1. Neither Curiosity nor Bias for Action - Stagnation This is the absolute worst case. No curiosity means no learning. No action means no execution. This way, you stay stuck with no growth, no relevance, just fading into the background. 2. Curiosity without Bias for Action - Analysis Paralysis Because you are curious, you will ask great questions, dive deep into concepts, and accumulate knowledge. But without action, it's all theoretical. Because you have been learning a lot, overthinking kicks in, procrastination follows, and nothing gets built. Many brilliant minds fall into this trap - they understand problems deeply but never take the steps to solve them. This also leads to frustration and you start to build a negative outlook towards life and others. 3. Bias for Action without Curiosity - Thoughtless Execution You move fast but without questioning whether you're moving in the right direction or are taking the shorter path or not. That means inefficiencies, repeated mistakes, and wasted effort. You are effectively working hard, but not smart. 4. Curiosity and a High Bias for Action - Impact This is where the magic happens. Curiosity helps you learn and innovate, while a high bias for action turns those insights into reality. You prototype, experiment, and iterate quickly. I've seen this firsthand - some of the best ideas I've had only became impactful because I acted on them sooner; and some of my biggest mistakes? Moving fast without questioning enough. The world doesn't reward just thinkers or just doers - it rewards those who learn fast and act faster.

Recently I worked on a perf improvement on @QuarkusIO to speed up Jackson's serialization. Since during this process I learned a lot about (and had fun with) Jandex, Gizmo and Quarkus extensions, I decided to share my experiences with these technologies quarkus.io/blog/quarkus-m…

NEW POST Generative AI may be more useful to understand code rather than generate it. Birgitta Böckeler explores this by picking an issue from an open-source hospital management system and seeing how AI helps her tackle it. martinfowler.com/articles/explo…