Machine Learning on foojay.io - Friends Of OpenJDK

One Base URL, Many Coding Agents: What an OpenAI-Compatible Gateway Looks Like From java.net.http

Wed, 09 Sep 2026 16:09:53 +0000

Somewhere in every coding agent there is an HTTP client. Claude Code, Codex, Cursor, Cline, OpenCode: strip away the terminal UI or the editor pane and each of them serializes a conversation into JSON, POSTs it to a base URL with an API key in a header, and streams the answer back. The client owns the prompt assembly, the tool loop and the rendering; the model sits on the far side of that socket.

“Commit created” — but it isn’t: why the agent’s word needs external verification, and what the mistake costs

Tue, 08 Sep 2026 08:54:03 +0000

A detailed report reads like proof. The agent lists the files it touched, names the branch, quotes a commit hash, adds a test count and closes with “done”. The work has a shape. The shape looks right. You have six more tickets.

The report is model output. The commit, if it exists, lives in .git. The file, if it exists, lives on disk. The build result lives in a process exit code and a run log. The chat window is none of those places, and it can only vouch for itself.

How to Create a Spring Boot Fraud Scoring Service

Fri, 31 Jul 2026 08:45:43 +0000

Most Java teams who want a machine learning model in production end up standing up a Python service and calling it over HTTP. That works, but it buys you, as a Java developer, a second runtime, a second deployment pipeline, a network hop on every prediction, and a team boundary that turns retraining into someone else’s ticket.

Deep Netts removes that split: it’s a pure-Java deep learning library, so the model trains in Java, serializes to a file, and loads back into your Spring Boot application as an ordinary bean. Predictions become in-process method calls measured in microseconds, with nothing extra to deploy, secure, or monitor.

JC-AI Newsletter #16

Tue, 09 Jun 2026 18:28:33 +0000

Over the past two weeks, the field of artificial intelligence has continued its remarkable pace of advancement. As AI becomes increasingly woven into the fabric of daily life, shaping how we work, communicate, and make decisions, it is both timely and valuable to step back and understand the broader trajectory of this technology. Whether the developments around us feel promising or challenging, one truth remains clear: AI is not simply going away. It is here to stay, and understanding its evolution is essential from many perspectives. Have you ever wondered what harness engineering is, how evals attempt to move traditional unit tests onto a probabilistic plane, or how AI is reshaping entire industries across various branches? Let’s start.

"Agentic" Was Everywhere at Money20/20 Amsterdam

Fri, 05 Jun 2026 08:55:48 +0000

I walked the floor at Money20/20 in Amsterdam with a simple little mission: count the first ten vendor booths using “AI,” “agent,” or “agentic” in their pitch.

My first impression was that this would be hard. Hundreds of organizations, and the buzzwords felt oddly absent — payments, core banking, compliance, the usual fintech furniture, but not the wall-to-wall “agentic everything” I’d braced for. I jotted down my first ten and thought: huh, AI is barely here.

Explore Spring AI SDK - Amazon Bedrock AgentCore - Part 1

Mon, 20 Apr 2026 13:44:54 +0000

Introduction

Artificial intelligence has rapidly expanded across every industry since the inception of ChatGPT. It represents a breakthrough in how we build and use software. Moreover, this breakthrough technology has driven major transformation. At the same time, it has created significant noise and hype.

Today, AI is no longer experimental. Instead, it has become essential, much like electricity and the internet. As a result, it now plays a key role in our daily lives. Overall, this shift reflects strong technological acceleration across industries.

JC-AI Newsletter #15

Fri, 20 Mar 2026 07:56:01 +0000

Over the past two weeks, the field of artificial intelligence has continued its remarkable pace of advancement. As AI becomes increasingly woven into the fabric of daily life, shaping how we work, communicate, and make decisions, it is both timely and valuable to step back and understand the broader trajectory of this technology. Whether the developments around us feel promising or challenging, one truth remains clear: AI is not simply leaving. It is here to stay, and understanding its evolution is essential from many perspectives.

JC-AI Newsletter #14

Tue, 03 Mar 2026 15:11:53 +0000

Two weeks have passed and a lot have been happening on the field of artificial-intelligence.

Two weeks have passed and a lot has been silently yet visibly happening in the field of artificial intelligence. This newsletter brings interesting developments, including Dario Amodei’s (Anthropic) view on the progress achieved in the LLM field and his response to the utilization of these models for specific kinds of military purposes, as well as OpenAI’s response to it. Aside from the fact that development may follow more sigmoids instead of exponential progress, it is important to have awareness of utilization across branches. Does prompting and clarifying the goal influence agent responses, and if so, how? How far are we from reliable robotics applications? How much bias is introduced when clinical data is being analyzed?

From “Crypto AI” to general AI: Do AI agents dream of electric langoustines?

Mon, 23 Feb 2026 18:11:54 +0000

x402, ERC-8004, A2A, and The Next Wave of AI Commerce: Do AI Agents Dream of Electric Langoustines?

A Blade Runner riff for a world where the lobster ships paid endpoints while humans still argue about the roadmap.

The shift that matters for agent commerce - From “Crypto AI” to general AI

Today, you can search the web all day and never see an invoice.

That happens because you are not the paying client.

Enabling AI Agents to Use a Real Debugger Instead of Logging

Mon, 16 Feb 2026 21:12:13 +0000

Every Java developer has been there. Something breaks, and the first instinct is to litter the code with System.out.println(">>> HERE 1"). Then HERE 2. Then HERE 3 — value is: " + x. Rebuild. Rerun. Stare at the console. Repeat.

We’ve been doing this for decades. And now, so have our AI agents.

When you ask an AI coding assistant to debug a Java application, it almost always reaches for the same playbook: add logging statements, recompile, rerun, read the output, and reason about what happened. It’s the println debugging loop, automated — but it’s still println debugging.

Machine Learning in Java - SPAM detection using ONNX

Tue, 10 Feb 2026 06:51:50 +0000

Believe it or not, it is possible to do Machine Learning in Java. In this article I go over how to implement a Spring Boot API for Spam Detection using an advanced anti-spam model from the Hugging Face onnx-community and Microsoft’s ONNX Runtime for Java.

We will package the API up as a Docker image which we can run a container from using docker or podman, and I guess in theory you could deploy on your Kubernetes cluster, if you (are) fancy.

JC-AI Newsletter #13

Thu, 05 Feb 2026 21:12:12 +0000

Two weeks have passed, and it is time to present a new collection of readings that may shape developments, utilization or ideas in the field of artificial intelligence in 2026.

While significant activity characterizes the AI field, many unresolved research, design, and implementation challenges continue to impact progress. Future advancement depends heavily on understanding the nature of these challenges to approach probabilistic problems from the appropriate directions. This JC-AI newsletter features insightful interviews with key figures in the field, enabling readers to ask the right questions and compare visions of an ‘uncertain future’ against current capabilities to maintain a grounded perspective.

JC-AI Newsletter #12

Wed, 14 Jan 2026 07:15:44 +0000

F irst of all, Happy New Year 2026! This year is designated in the Chinese Calendar as the Year of the Fire Horse (starting on February 17.). The year 2026 brings not only tremendous energy to AI development but also, in my humble opinion, many breakthroughs in the field.

Although there have been many small steps toward the field’s evolution, it often feels that development is stagnating, applying known or slightly tweaked strategies to non-deterministic problems while expecting deterministic results. This includes the often misleading benchmarking strategies (deterministic) performed on synthetic datasets.

The End of One-Sized-Fits-All Prompts: Why LLM Models Are No Longer Interchangeable

Sun, 14 Dec 2025 18:48:31 +0000

For developers and product builders, one assumption has guided the last few years of LLM application development. To improve your product, just swap in the latest frontier large language model. Flip a single switch and your tool’s capabilities level up.

But that era is over. We’re now seeing that new models like Anthropic’s Claude Sonnet 4.5 and OpenAI’s GPT-5-Codex have diverged in fundamental ways. The choice of which model to use is no longer a simple engineering decision but a critical product decision. Flip that switch today… and the very texture of your product changes.

How CodeRabbit's Agentic Code Validation helps with code reviews

Sun, 07 Dec 2025 11:10:21 +0000

The 2025 Stack Overflow survey reveals a paradox: while 84% of developers express confidence in adopting AI tools, nearly half (48%) still distrust the accuracy of their outputs. This tension between optimism and skepticism has reshaped how teams think about quality assurance.

From PRD to PR in days (not weeks)

The bottleneck in software development has fundamentally shifted from writing code to validating it.

In the early days of AI-assisted development, the workflow was straightforward: AI suggested code, humans read the suggested snippet and then decided whether or not to accept that suggestion. Tab completion wrote boilerplate. Copilot suggested functions. But a senior engineer still manually validated and chose each line of code to ensure its quality, structure, and safety before making a pull request.

JC-AI Newsletter #10

Wed, 26 Nov 2025 18:39:40 +0000

Fourteen days have passed, and it is time to present a fresh collection of readings that could influence developments in the field of artificial intelligence.

This newsletter focuses on examining how agentic AI systems improve accuracy, tutorials on agentic system architecture, and importnat security challenges arising from increased not only from agentic AI systems adoption. This edition of the AI newsletter includes compelling discussions and interviews about the future of AI and approaches.

JC-AI Newsletter #9

Wed, 12 Nov 2025 15:21:12 +0000

Fourteen days have passed, and it is time to present a fresh collection of readings that could influence developments in the field of artificial intelligence.

This newsletter focuses on examining how AI enhances productivity through enterprise studies, tutorial, agentic system architecture, GraphRAG, evaluating risk methodologies in agentic systems, and the security challenges arising from increased AI-LLM adoption. This edition of the AI newsletter includes a compelling discussion between six of the most influential leaders in artificial intelligence, along with additional content.

JC-AI Newsletter: Easy Access to Expanding Challenges

Tue, 04 Nov 2025 18:13:33 +0000

A few months ago, I launched the AI Newsletter to provide a minimally biased perspective on the growing challenges surrounding artificial intelligence.

My primary motivation was and remains to be serving the community not only by showing how to use and access specific services for utilizing Large Language Models, but also by support a deeper understanding of the broader artificial intelligence landscape.

Many of the core challenges that have emerged around LLMs have not been and still not properly addressed, often omitted due to their uncomfortable implications.

JC-AI Newsletter #8

Thu, 30 Oct 2025 06:36:12 +0000

Fourteen days have passed, and it is time to present a fresh collection of readings that could influence developments in the field of artificial intelligence.

This newsletter focuses on examining how AI enhances productivity through enterprise studies, agentic system architecture, attack vectors, Model Context Protocol (MCP) implementation, Agent-to-Agent (A2A) protocol, Java code generation within IDEs, LLM benchmarking methodologies, and the security challenges arising from increased AI-LLM adoption.

The world influenced by LLM is changing very quickly, let’s start…

JC-AI Newsletter #7

Tue, 14 Oct 2025 05:35:01 +0000

Fourteen days have passed, and it is time to present a fresh collection of readings that could influence developments in the field of artificial intelligence.

Beyond focused tutorials that can enhance your understanding of AI applications, this newsletter concentrates on Hallucination, Java Code Generation, Testing, Agentic System Architecture and LLM benchmarking methodologies designed to ensure models accuracy and competency in handling complex contextual information.

The world influenced by LLM is changing very quickly, let’s start…

JC-AI Newsletter #6

Wed, 01 Oct 2025 17:18:16 +0000

Fourteen days have passed, and it is time to present a fresh collection of readings that could influence developments in the field of artificial intelligence.

Beyond opinion pieces and Java focused tutorials that can enhance your understanding of AI applications, this newsletter concentrates on Hallucination, Security, RAG and LLM benchmarking methodologies designed to ensure models accuracy and competency in handling complex contextual information.

The world influenced by LLM is changing very quickly, let’s start…

JC-AI Newsletter #5

Thu, 18 Sep 2025 07:19:33 +0000

Fourteen days have passed, and it is time to present a fresh collection of readings that could influence developments in the field of artificial intelligence.

Beyond opinion pieces and Java focused tutorials that can enhance your understanding of AI applications, this newsletter concentrates on LLM benchmarking methodologies designed to ensure models accuracy and competency in handling complex contextual information.

The world influenced by LLM is changing very quickly, let’s start…

Stochastic AI Agility: Breaking Cycles of Debt

Wed, 10 Sep 2025 11:27:16 +0000

The launch of ChatGPT in November 2022 has significantly influenced and potentially transformed industry standards across multiple sectors. While my primary focus remains on the information technology sector, observations indicate that its impact extends across all industries and affects the daily lives of consumers and professionals alike.

This article examines the observed changes in project management practices. These observations are govern by Role 17: Stochastic AI Agility. Over past two decades, the industry has actively pursued the implementation of agile methodologies to enable iterative product delivery.

JC-AI Newsletter #4

Tue, 02 Sep 2025 20:27:52 +0000

14 days have passed and it’s time for a new batch of readings that could shape developments in the field of artificial intelligence.

The current newsletter vol. 4 offers us a closer look at several different areas of artificial intelligence. We start with the topic of energy consumption and the environmental impact of systems serving artificial intelligence, and continue with automation, how we obtain data for RAG, robustness of GenAI systems, vibe-coding and more.

JC-AI Newsletter #3

Tue, 19 Aug 2025 08:06:34 +0000

The first and second newsletters introduced a 14-day cadence, and even though it is the holiday season for many of us, we are sticking to the promised period.

The current newsletter vol.3, brings a collection of valuable articles focusing on challenges that are commonly reported through reported breakthroughs in the field of artificial intelligence. Although some articles may remain open or contain suggestions for future research due to the intensive development and utilization efforts of agentic AI systems, these articles can be considered valuable to the community, increasing awareness and understanding of ongoing challenges.

New Java Benchmark for Coding LLMs puts GPT-5 at the top

Mon, 18 Aug 2025 08:06:40 +0000

Introducing the Brokk Power Ranking

The Brokk Power Ranking is a new open-source coding benchmark, featuring 93 tasks from large, real-world codebases. You can check out the current Power Ranking here.

Why a New Benchmark?

SWE-bench is the closest thing we have to a standard, objective benchmark for LLM coding performance, but it has a bunch of issues, the largest of which are that it’s Python-only, and it’s old enough that almost certainly some labs are now training to the test. (Epoch AI has a great writeup on the more subtle problems with SWE-bench if you want to go deeper.)

JC-AI Newsletter #2

Tue, 05 Aug 2025 06:30:03 +0000

The JC-AI Newsletter Vol.2 brings again a very interesting collection of articles worth considering.

The world influenced by LLM is changing very quickly, let’s start…

article: OpenAI CEO Sam Altman warns of an AI ‘fraud crisis’

author : Clare Duffy, CNN
date : 2025-07-22
desc.: Sam Altman discusses the serious potential for current AI abuse. Although the example of attacks on financial institutions is mentioned, Sam Altman warns of the broader impact before one even takes advantage of LLM agent systems.

AI Applications with LangChain4j Guardrails and Spring Boot

Tue, 29 Jul 2025 11:16:31 +0000

As AI applications become increasingly complex, ensuring that language models behave predictably and safely is paramount. LangChain4j’s guardrails feature provides a powerful framework for validating both the inputs and outputs of your AI services.

This article demonstrates how to implement comprehensive guardrails in a Spring Boot application, with practical examples that you can adapt to your use cases.

📦 Complete source code available at : github.com/rokon12/guardrails-demo

Understanding LangChain4j Guardrails

In LangChain4j, guardrails are validation mechanisms that operate exclusively on AI Services, the framework’s high-level abstraction for interacting with language models. Unlike simple validators, guardrails provide sophisticated control over the entire AI interaction lifecycle.

CodeRabbit Tutorial for Java Developers

Mon, 28 Jul 2025 12:43:19 +0000

CodeRabbit is an AI-powered code review tool that integrates seamlessly with your Git workflow to provide intelligent, automated code reviews.

For Java developers, CodeRabbit offers specialized analysis that understands Java syntax, best practices, and common patterns.

Getting Started

Prerequisites

A GitHub, GitLab, Azure DevOps, or Bitbucket repository.
Java project with standard structure (Maven or Gradle).
Admin access to your repository.

Setup Process

Install CodeRabbit
- Visit CodeRabbit.ai.
- Sign up using your credentials, while noting that CodeRabbit is free for OSS, supports GitHub, GitLab, Azure DevOps, and Bitbucket.
- Grant necessary permissions to your repositories.
Repository Configuration
- Select the Java repositories you want CodeRabbit to review.
- CodeRabbit automatically detects Java projects and configures appropriate rules.
Initial Setup Verification
- Create a test pull request with a simple Java change.
- Verify CodeRabbit comments appear within minutes.

Core Features for Java Development

1. Code Quality Analysis

CodeRabbit analyzes your Java code for:

AI Newsletter #1

Tue, 22 Jul 2025 05:39:26 +0000

There is a lot going on in today’s technological world, but the most intense and vibrant area is undoubtedly the field of artificial intelligence. After brainstorming, our Java Champion Education group agreed to create a newsletter with a 14 days cadence. In our newsletters, we will include a collection of recently read interesting resources in the field of artificial intelligence with a short description.

Although the stream of published articles is very large and the ability to read them all is an illusion, the published selection, such as the below is implicitly narrowed. The purpose of the AI Newsletter is to help everyone stay informed and expand their knowledge in areas related to artificial intelligence.

Machine Learning on foojay.io - Friends Of OpenJDK

One Base URL, Many Coding Agents: What an OpenAI-Compatible Gateway Looks Like From java.net.http

“Commit created” — but it isn’t: why the agent’s word needs external verification, and what the mistake costs

How to Create a Spring Boot Fraud Scoring Service

JC-AI Newsletter #16

"Agentic" Was Everywhere at Money20/20 Amsterdam

Explore Spring AI SDK - Amazon Bedrock AgentCore - Part 1

Introduction

JC-AI Newsletter #15

JC-AI Newsletter #14

From “Crypto AI” to general AI: Do AI agents dream of electric langoustines?

x402, ERC-8004, A2A, and The Next Wave of AI Commerce: Do AI Agents Dream of Electric Langoustines?

The shift that matters for agent commerce - From “Crypto AI” to general AI

Enabling AI Agents to Use a Real Debugger Instead of Logging

Machine Learning in Java - SPAM detection using ONNX

JC-AI Newsletter #13

JC-AI Newsletter #12

The End of One-Sized-Fits-All Prompts: Why LLM Models Are No Longer Interchangeable

How CodeRabbit's Agentic Code Validation helps with code reviews

From PRD to PR in days (not weeks)

JC-AI Newsletter #10

JC-AI Newsletter #9

JC-AI Newsletter: Easy Access to Expanding Challenges

JC-AI Newsletter #8

JC-AI Newsletter #7

JC-AI Newsletter #6

JC-AI Newsletter #5

Fourteen days have passed, and it is time to present a fresh collection of readings that could influence developments in the field of artificial intelligence.

Stochastic AI Agility: Breaking Cycles of Debt

JC-AI Newsletter #4

JC-AI Newsletter #3

New Java Benchmark for Coding LLMs puts GPT-5 at the top

Introducing the Brokk Power Ranking

Why a New Benchmark?

JC-AI Newsletter #2

I n the first newsletter, we introduced a 14-day cadence, which means that this week it’s time for a new collection of articles from the fields of AI, LLM, Java and more.

article: OpenAI CEO Sam Altman warns of an AI ‘fraud crisis’

AI Applications with LangChain4j Guardrails and Spring Boot

Understanding LangChain4j Guardrails

CodeRabbit Tutorial for Java Developers

Getting Started

Prerequisites

Setup Process

Core Features for Java Development

1. Code Quality Analysis

AI Newsletter #1