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If you've ever wanted an AI that doesn't just chat but actually does things — queries databases, calls APIs, makes decisions, and learns from results — you're in the right place. In this tutorial, I'll show you how to build production-ready AI agents using Solon 4.0's ReActAgent . By the end, you'll have built an agent that can reason through complex problems, use external tools, and adapt its behavior based on real-world feedback. What Makes ReActAgent Different? Traditional LLMs are great at generating text, but they hit a wall when they need to interact with the real world — checking a database, fetching live data, or performing calculations. ReActAgent (Reason + Act) breaks through that wall. It implements a cognitive loop: Thought → Action → Observation → (repeat or finish) The agent thinks about what to do next, acts by calling a tool, observes the result, and decides whether to continue or deliver the final answer. This isn't just theory. Solon's ReActAgent has been used in production for automated customer support, intelligent data analysis, and multi-step workflow automation. 1. Adding the Dependency First, add the solon-ai-agent module to your project: <dependency> <groupId> org.noear </groupId> <artifactId> solon-ai-agent </artifactId> </dependency> Note : If you're using Solon's parent POM, the version is managed automatically. Otherwise, use the latest Solon version. 2. Building a ChatModel (The Agent's Brain) Every agent needs a "brain" — a ChatModel that powers reasoning. Let's build one using the fluent API: import org.noear.solon.ai.chat.ChatModel ; ChatModel chatModel = ChatModel . of ( "https://api.moark.com/v1/chat/completions" ) . apiKey ( "your-api-key-here" ) . model ( "Qwen3-32B" ) . build (); You can also configure it via YAML and inject it: solon.ai.chat : demo : apiUrl : " http://127.0.0.1:11434/api/chat" provider : " ollama" model : " llama3.2" @Inject ( "${solon.ai.chat.demo}" ) ChatConfig chatConfig ; ChatModel chatModel = ChatModel . o

📖 Article original : GitHub Gist Un guide technique par Mohamed ben mallessa Le problème Recevoir un dossier de 500 fichiers HEIC à convertir en WebP pour un site web est une situation courante pour tout développeur. Les solutions traditionnelles ont leurs limites : ImageMagick nécessite des codecs spécifiques, les convertisseurs en ligne sont limités en taille, et le traitement manuel est exclu à cette échelle. La solution Photopea (Photoshop gratuit dans le navigateur) supporte nativement tous les formats d'image courants. En l'utilisant comme moteur de conversion piloté par script, on obtient un pipeline batch rapide et fiable. Formats supportés Entrée Sorties possibles HEIC / HEIF JPG, PNG, WebP JPEG WebP, PNG, PSD PNG JPG, WebP WebP PNG, JPG PSD PNG, JPG, WebP SVG PNG, JPG TIFF PNG, JPG, WebP Pipeline Dossier source (500 HEIC) → Photopea → Dossier sortie (500 WebP) Le script préserve la structure des sous-dossiers, applique le redimensionnement et la qualité configurés, et livre les fichiers organisés. Paramètres typiques --format webp # Format de sortie --quality 80 # Qualité (1-100) --resize 1920 # Redimensionnement (côté long) --output ./web/ # Dossier de destination Avantages Un seul outil pour tous les formats d'entrée Aucun codec à installer (Photopea gère tout nativement) Gratuit et sans abonnement Local — les fichiers ne quittent pas votre machine Structure préservée — l'arborescence est conservée Mohamed ben mallessa — Full-stack developer & solutions B2B 🔗 GitHub · LinkedIn opensource #webp #python #tutorial 💻 Vous avez un projet technique ? Développement full-stack, automatisation IA, solutions B2B sur mesure. 🔗 GitHub 💼 LinkedIn 🎨 Behance Article initialement publié sur GitHub Gist

If you've ever tried integrating a large language model (LLM) into a Java application, you've probably written a lot of boilerplate: HTTP clients, JSON parsing, streaming handling, session management. Solon 4.0's ChatModel abstracts all of that away with a clean, builder-oriented API. In this guide, I'll walk through building real, working AI features using ChatModel — from a simple chat call to a streaming chatbot with conversation memory. 1. What Is ChatModel? ChatModel (package org.noear.solon.ai.chat ) is a unified LLM client in Solon's AI ecosystem. Instead of writing raw HTTP calls for different model providers, you use a single API that supports: Synchronous calls — one-shot request, full response Streaming calls — reactive streaming via Project Reactor ( Flux<ChatResponse> ) Tool/Function Calling — let the LLM invoke your Java methods Chat Sessions — automatic conversation memory Multi-modal messages — text, images, audio Dialect adaptation — works with OpenAI, Ollama, Anthropic, Gemini, DashScope, and more The best part? It uses a dialect pattern — you point it at any compatible LLM endpoint, and it adapts automatically. 2. Setting Up Add the dependency to your pom.xml (no parent POM needed — Solon works standalone): <dependency> <groupId> org.noear </groupId> <artifactId> solon-ai </artifactId> <version> ${solon.version} </version> </dependency> This pulls in all built-in dialects (OpenAI, Ollama, Gemini, Anthropic, DashScope). 3. Configuration 3.1 Via YAML (Recommended) solon.ai.chat : demo : apiUrl : " http://127.0.0.1:11434/api/chat" # Full URL, not baseUrl provider : " ollama" # Dialect identifier model : " llama3.2" # Model name headers : x-demo : " demo1" Then create a @Bean to get a ready-to-use ChatModel : import org.noear.solon.ai.chat.ChatConfig ; import org.noear.solon.ai.chat.ChatModel ; import org.noear.solon.annotation.Bean ; import org.noear.solon.annotation.Configuration ; import org.noear.solon.annotation.Inject ; @Configuration public cla

Hey dev community! I don't have a PC yet, so I challenged myself to build an open source ethical hacking & "security audit" tool called ImCurvin' completely on my phone screen. Here is my honest background: I just started learning programming 6 months ago (yes, January 2026!). Since then, I've been fully focused on coding. Just 2 weeks ago, I decided to jump into cybersecurity. To be honest, I hate memorizing dry theories. I prefer focusing purely on the logic and finding cracks in systems. While I am a bit lazy with documentation, I am doing my best to learn all the official "technical terms" along the way! The project currently stands around 1,155 lines of code (993 Bash, 162 Python), i want it to be minimalist. The tool is fully modularized with separate payload directories, though the overall formatting might look a bit messy since every single character was typed on a smartphone touch screen. I want to keep this post super short, so please check out the full features, code documentation, and contribution guidelines directly inside my repository: Check out https://github.com/Skokoo/ImCurvin Feel free to check it out, and please submit a Pull Request or open an issue if you want to help a beginner clean up and optimize the code!

AI agents are only as smart as the context you give them. OKF is a new open specification that packages your organizational knowledge as plain markdown files so any agent can read it without custom integrations or proprietary SDKs. Every team building AI agents hits the same wall. The model is capable. The agent framework is set up. But the agent doesn't know anything about your organization. It doesn't know what your orders table means, what the churn_score metric formula is, or what the on-call runbook says to do when the pipeline breaks. That knowledge exists. It's scattered across Confluence pages, Notion wikis, data catalog entries, Slack threads, and the heads of senior engineers. Getting it into an agent means building a custom integration for every source. Every team solves this from scratch. Published on June 12, 2026, the Open Knowledge Format (OKF) is a vendor-neutral specification that solves this with the simplest possible approach: a directory of markdown files. 🎯 🏗️ What OKF Actually Is An OKF bundle is a directory of markdown files representing concepts: anything you want to capture, including tables, datasets, metrics, playbooks, runbooks, and APIs. Each concept is one file. That's the entire model. A directory of .md files with YAML frontmatter. The format is deliberately minimal: one required field ( type ), optional metadata ( title , description , resource , tags , timestamp ), and a free-form markdown body. A concept document looks like this: --- type : table title : " orders" description : " One row per customer order. Source of truth for revenue reporting." resource : " postgresql://prod-db/ecommerce/orders" tags : [ revenue , core , sla ] timestamp : 2026-06-15T10:00:00Z --- # orders The `orders` table records every purchase event. It is the join root for all revenue queries. Do not filter on `status = 'complete'` unless you specifically want to exclude in-flight orders from the count. ## Key columns - `order_id` - UUID primary key - `custom

OpenAI Agents SDK 0.13 → 0.17: Three Breaking Changes You Will Hit The OpenAI Agents SDK moved from 0.13 to 0.17 in five weeks (April 9 – May 19, 2026). That's 19 releases, including three breaking changes. Two of them are silent — they change runtime behavior without raising an error at upgrade time. If you're running agents on 0.13.x, this is the migration guide you need before you upgrade. The Three Breaks Break #1: ModelRefusalError (v0.15.0) — Silent to Loud The change: Model refusals used to return empty-string output. Now they raise an exception. What this means: If your code treated refusals as output == "" or checked for empty responses, you were silent-failing gracefully. That behavior changed. A model refusal now raises ModelRefusalError and will crash your agent run unless you handle it. What you need to do: Register a "model_refusal" error handler before upgrading past 0.15.0: from openai.agents import Agent , RunConfig def handle_refusal ( error ): print ( f " Model refused: { error . reason } " ) return " Model refused to respond. Try rephrasing. " agent = Agent ( model = " gpt-4 " , tools = [...], ) config = RunConfig ( on_error = { " model_refusal " : handle_refusal , } ) response = agent . run ( " ... " , config = config ) Without the handler, you'll see: ModelRefusalError: model declined to process this request This is an intentional change — OpenAI wants refusals to be explicit, not silent. But it means your error handling has to change. Impact: Any agent that doesn't add a refusal handler will crash on a refusal. High-risk if your agents field user input directly. Break #2: Default Model Changed (v0.16.0) — Silent Model Switch The change: The default model switched from gpt-4.1 to gpt-5.4-mini . What this means: If you created an agent without explicitly setting model= , you were running on gpt-4.1. On upgrade to 0.16.0+, that same code silently switches to gpt-5.4-mini. This isn't just a version bump — gpt-5.4-mini ships with different defaults

Indian algo traders and quant developers hit the same wall: they reach for pandas_market_calendars , set up XNSE , and get back answers that are silently wrong for three segments that matter most in India. Here is what breaks and what to use instead. The three failure cases 1. MCX evening sessions MCX commodity markets (crude oil, natural gas, gold, silver) run until 23:30 IST. pandas_market_calendars has no MCX calendar. Any check after 15:30 returns a wrong answer. # pandas_market_calendars — no MCX at all # mcal.get_calendar("MCX") → KeyError # aion-indian-market-calendar — works correctly from aion_indian_market_calendar import IndiaMarketCalendar from datetime import datetime from zoneinfo import ZoneInfo cal = IndiaMarketCalendar . bundled ( 2026 ) tz = ZoneInfo ( " Asia/Kolkata " ) cal . is_market_open ( " MCX " , datetime ( 2026 , 6 , 18 , 20 , 0 , tzinfo = tz )) # True 2. NSE Currency Derivatives (CDS) — wrong hours, wrong holidays USDINR, EURINR, GBPINR, JPYINR futures and options trade on NSE CDS from 09:00 to 17:00 IST — 90 minutes longer than NSE equity. CDS also has a separate holiday calendar. pandas_market_calendars has no CDS calendar. Using XNSE gives you wrong close times and potentially wrong holiday answers for any currency derivative workflow. from aion_indian_market_calendar import IndiaMarketCalendar cal = IndiaMarketCalendar . bundled ( 2026 ) # These resolve correctly to their respective segments cal . is_market_open ( " USDINR " , at ) # NSE_CURRENCY_DERIVATIVES: closes 17:00 cal . is_market_open ( " NSE " , at ) # NSE_EQUITY: closes 15:30 cal . is_market_open ( " MCX " , at ) # MCX: closes 23:30 3. Muhurat trading (Diwali special session) On Diwali, NSE runs a one-hour equity session in the evening. pandas_market_calendars marks this day as a holiday. Schedulers that rely on it will skip execution entirely. cal = IndiaMarketCalendar . bundled ( 2026 ) events = cal . events_on ( " 2026-11-08 " , exchange = " NSE " ) # Returns the Muhurat t

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The Cloud Native Computing Foundation (CNCF) has announced the graduation of OpenTelemetry, elevating the project to the foundation's highest level of maturity and formally recognizing it as production-ready for enterprise use. By Craig Risi

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GitHub Actions shows you one run at a time. Green check, red X, green check, green check, red X. You scroll the list, you re-run the flaky one, you move on. Nobody's asking the question that actually matters: is this getting better or worse? "I calculated how much my CI failures actually cost. Curious what your pipeline success rate looks like — has anyone else tracked the actual wasted compute time over time?" That's a real question from someone who did the math by hand and found their failures were burning a real chunk of their compute budget. The replies were the same story you'd expect: heavyweight CI platforms have their own dashboards for this, but nobody had a lightweight, local way to just... track it. So I built citrend : pull your GitHub Actions run history into a local file, get a trend. npx citrend sync --repo owner/name npx citrend report --repo owner/name What it actually shows you $ citrend report --repo acme/widgets acme/widgets — 812 run(s) (2 in progress) success rate: 87.4% (699/800 settled, 12 skipped) wasted runs: 101 (12.6%) total compute: 118h 42m wasted compute: 14h 6m weekly trend (oldest → newest): 2026-06-05 91.2% success, 8 wasted (58m) 2026-06-12 88.0% success, 11 wasted (1h 22m) 2026-06-19 79.4% success, 22 wasted (3h 8m) 2026-06-26 84.1% success, 15 wasted (2h 1m) That weekly column is the entire point. A single gh run list will never show you that week 3 was a cliff — you'd have to notice it got annoying to work in, which is a much slower and much less precise signal than a number going from 91% to 79%. How it works sync pulls your workflow run history from the GitHub REST API and caches it locally (deduped by run id, so you can run it on a schedule without piling up duplicates). report reads that cache — no network call — and computes: Success rate , over settled runs only (still-running runs don't count either way until they conclude, and skipped runs are excluded from the denominator since they're not a pass/fail outcome). "Wasted"

Seven days of flow. Fixed a peer identity binding issue in py-libp2p, automated my Neovim lockfile merges, and added a massive batch of notes on xv6 and Category Theory. 10 commits and a solid PR in the works. TL;DR I managed to hit a perfect seven-day streak this week, balancing some deep-dive p2p networking work with necessary maintenance on my local environment. The highlight was opening a PR in py-libp2p to tighten up how PeerRecords are handled in the Kademlia DHT. On the side, I spent time automating the annoying parts of my Neovim config and dumping a fresh batch of notes into my knowledge base. 10 commits, 204 lines added, and a much cleaner workflow to show for it. What I Built Neovim Configuration & CI I’m a firm believer that your editor should work for you, not the other way around. My nvim repo saw a lot of action this week—9 commits in total—but most of it was under-the-hood maintenance. I’ve been leaning on Lua to keep things snappy, and this week was about ensuring my plugin ecosystem doesn't rot. I pushed several updates to keep plugins at their latest versions, but the real "quality of life" improvement was adding a chore to auto-resolve lazy-lock.json merge conflicts. If you’ve ever worked on your Neovim config across multiple machines, you know the headache of the lockfile drifting. I set up a flow to prioritize incoming changes, which saves me from manually triaging JSON diffs every time I pull from dev . It’s a small tweak, but it removes a recurring friction point in my daily flow. I also spent time cleaning up the root of the config, with about 37 additions and 33 deletions—refactoring is a constant process when you live in your terminal. The Knowledge Base I also put some serious time into main-notes . I’m currently going deep on a few different subjects, and I use this repo as my "second brain." I added 167 lines of new material across 13 files, covering a pretty diverse range of topics: xv6 (the re-implementation of Unix V6), Category Theo

Spent the week deep-diving into my dev-publish tool, focusing on durability and orchestrator resilience. 21 commits across two repos, with a massive cleanup of the publishing logic and some much-needed architecture documentation. TL;DR There is a specific kind of satisfaction that comes from taking a tool you use every day and finally giving it the "production-grade" treatment it deserves. This week was exactly that. I spent most of my time in the guts of dev-publish , moving past the "it works on my machine" phase and into "it works even if the world is on fire" territory. With 21 commits and over 11,000 lines of code churn, I focused on making the publishing orchestrator resilient and the state durable. What I Built The star of the show this week was dev-publish . If you’ve ever tried to automate cross-platform technical writing, you know that the edge cases are where the real pain lives. I pushed 16 commits here, touching about 45 files. The diff was pretty wild: +6,926 additions and -4,289 deletions. That net positive tells part of the story, but the deletions represent me ripping out brittle logic that just wasn't cutting it. Hardening the Orchestrator The biggest win was a massive fix to make the publish state durable and the orchestrator resilient. In the previous iteration, if a network request to an API (like Dev.to) failed halfway through a multi-platform push, the state was... let's just say "vague." I spent a lot of time in src ensuring that the orchestrator can now pick up where it left off. I also documented the published-flag semantics and re-run resilience in the README. It sounds like a small thing, but knowing that a re-run won't accidentally double-post your article is a huge weight off my mind. I also spent some time on the "boring but important" stuff. I normalized how tags are handled to make them safer across different platforms and implemented a much stricter resolution for cover images. If a local image is required but missing, the tool now