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Hi, I’m a Principal Architect with 15+ years of experience designing and delivering scalable, resilient, high-availability Java SaaS platforms. My work sits at the intersection of distributed systems, real-time data platforms, and the emerging enterprise generative AI stack. I enjoy turning complex technical challenges into secure, maintainable systems that create measurable business value. Architectural Foundation I lead technical direction for platforms built with JDK 17–25 and Spring Boot 3.x, with a strong focus on microservices, transactional consistency, and operational resilience. My experience includes: Designing services around ACID transaction requirements Managing distributed workflows with Saga and Outbox patterns Applying strategic Domain-Driven Design (DDD) Defining bounded contexts that align software architecture with business capabilities Using Architecture Decision Records (ADRs) to make technical decisions transparent and durable Real-Time and Distributed Systems I build the “nervous systems” of enterprise platforms using Kafka, Redis, and MongoDB. My focus is on event-driven architectures that support real-time processing, high throughput, low latency, and global availability. I am particularly interested in infrastructure-aware design: making sure application architecture, data flow, deployment topology, and observability work together rather than being treated as separate concerns. Generative AI and Agentic Systems A significant part of my current work involves AI/ML and generative AI initiatives, especially Retrieval-Augmented Generation (RAG) and agentic workflows for enterprise use cases. Areas I am actively exploring include: JVM-native inference: Running inference with ONNX Runtime to reduce network overhead and improve predictability Agent orchestration: Building production-ready workflows with LangChain4j and Spring AI Build vs. buy decisions: Evaluating emerging AI platforms against enterprise requirements AI guardrails: Designing secur

The HIPAA-compliant backend for AI builders Discussion | Link

Most engineering organizations now pay several vendors for AI coding assistants, each one bills differently, and no single person in the company can answer the simplest question: what did our AI coding tools actually cost this month, and what did we get for it? This guide is the practical answer — the metrics that matter, the ways teams track spend, a step-by-step setup, and an honest maturity model for governing it. The short answer To track AI code assistant spend across every vendor, pull cost and usage from each tool's admin or billing API, normalize it into one model — because every vendor bills on a different unit and a different clock — and map it to your teams and cost centers. The four approaches teams use are manual spreadsheets, each vendor's native dashboard, an open-source usage CLI, and a dedicated AI spend management platform. Only the last gives finance, engineering, and IT one live number plus forecasting, anomaly detection, and per-developer and per-pull-request cost. If you only do three things: inventory every assistant in use, including shadow tools bought on personal cards; connect each vendor read-only and normalize to a common cost model; and instrument the leading indicators — premium-model mix, token or credit runway, and idle seats — because they move before the invoice does. What "AI code assistant spend" means AI code assistant spend is the total cost an organization pays across all of its AI coding tools — commonly GitHub Copilot, Cursor, Anthropic Claude, OpenAI, and others teams connect — including per-seat license fees, metered token or credit consumption, premium-model surcharges, and the hidden cost of idle or duplicate licenses. It sits at the application layer, which distinguishes it from general cloud cost (compute, storage, networking), and it concerns money and utilization, which distinguishes it from AI model governance and its focus on model risk and compliance. Why it's genuinely hard to track (and got harder in 2026) There

The agentic IDE for designers and programmers Discussion | Link

Build live analytics with coding agents Discussion | Link

Let non-engineers build secure internal tools Discussion | Link

Agent Skills turn repeated prompts into reusable, inspectable workflows. This collection includes five small skills for work that comes up often when building with OpenCode or Claude Code: reviewing public copy, checking text limits, capturing public webpages as PDFs, sending task notifications, and structuring research for later use. The full index is available at Published Agent Skills . For OpenCode, Skills can live in ~/.config/opencode/skills/ or project-level locations supported by your setup. Claude Code can discover Skills from .claude/skills/ . Put a Skill folder in the right location, restart the agent session if needed, and it becomes available when the task matches its description. 1. AI Writing Detector Skill AI Writing Detector Skill reviews English and Brazilian Portuguese copy for patterns that make AI-written text feel generic. It checks common issues such as repeated sentence rhythm, filler phrases, excessive formatting, and em-dash use. It also ships with a CLI and MCP server for text and file linting. Useful prompts: Review this README introduction with the anti-ai-tells Skill. Keep the technical facts, flag generic wording, and suggest direct replacements. Run the writing linter on this release post, then rewrite only the passages that need attention. This is useful before publishing documentation, launch posts, landing pages, and changelogs. 2. Text Counter Skill Text Counter Skill gives exact counts for characters, words, sentences, paragraphs, lines, graphemes, bytes, and phrase occurrences. It helps when "roughly under the limit" is not enough. Useful prompts: Write a 155-character meta description for this package and verify its exact character count. Reduce this GitHub issue title to 80 characters without removing the error code. Count the phrase "OpenCode" in this Markdown file. The Skill makes counting rules explicit. That avoids surprises with spaces, Unicode characters, emoji, or repeated phrases. 3. HTML to PDF Skill HTML to PDF Skill

Rewire anxiety for good Discussion | Link

Talk through your app, your coding agents get the plan Discussion | Link

As a developer, I constantly end up searching for small tools to do random things. Format JSON, decode JWTs, generate UUIDs, encode URLs, compare text, convert data, and so on. I got tired of opening a different website every time, so I started building my own collection. That's CtrlTool. It currently has 132 free tools for developers and everyday tasks, with a focus on keeping them fast, simple, and easy to use. A lot of the tools process data directly in the browser when possible. https://ctrltool.wtf It's still very new, so I'd love to hear what tools you think are missing.

With smarter gym tracking, new health alerts, and offline Gemini, the Pixel Watch 5 fine-tunes a winning formula—for a price.

A richup like browser monopoly game Discussion | Link

Google’s new voice typing is pure magic, but subpar gaming performance and a useless rear LED keep these flagships from true greatness.

I Built a Claude Code Skill That Reverse-Engineers Undocumented APIs Because "the docs are in the code" is not a documentation strategy. The Week I Lost to Grepping I joined a new team last month. Day 1 task: add a feature to the billing service. Day 1 reality: I opened the API docs and realized they were from 2022. Half the routes had been rewritten. The other half never had docs to begin with. So I did what every backend dev does. I grepped. grep -r "app.get|app.post|router." src/ --include="*.js" Four hours later, I had a notebook full of endpoints, a headache, and zero confidence that I had found everything. I found routes that worked but were not documented. I found docs for routes that did not exist anymore. I found one GET /invoices/:id endpoint with zero auth checks that had been sitting there since 2022. This is normal. And it should not be. The Idea What if I could drop a single file into a repo and have Claude Code map the entire API layer for me? Not from annotations. Not from existing OpenAPI specs. From the actual code. So I built it. Meet API Archaeologist API Archaeologist is a Claude Code / Codex CLI skill that reads your source code and reverse-engineers your API layer. It finds: • Internal endpoints — REST, GraphQL, gRPC, WebSockets • External integrations — third-party APIs, webhooks, SDK clients • Auth flows — JWT, OAuth, API keys, session cookies, RBAC • Security gaps — unauthenticated routes, hardcoded secrets, missing rate limits • Dead code — auth middleware with no endpoints, orphaned routes And it generates two things: API_DISCOVERY.md — A complete catalog with Mermaid diagrams openapi-draft.yaml — A draft OpenAPI spec How It Works The skill is just a SKILL.md file. Claude Code reads it and follows the instructions. It: Discovers route definitions Traces handlers, DTOs, middleware, services, and database calls Maps authentication and authorization Finds external API calls and integrations Flags potential security and reliability risks Gene

Frontier Intelligence for Long-Running Agents Discussion | Link

Highlight and summarize any page, PDF, or video in Firefox Discussion | Link

I tested the top-of-the-line Ecovacs Winbot W2S Omni window-cleaning robot on my Victorian home, and it was an unmitigated disaster.

The Dell XPS 13 and MacBook Neo are both premium-feeling laptops with only 8 GB of RAM. Which $700 laptop is the better buy?

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

AI coding agents are useful, but team collaboration around them can become messy very quickly. A common shortcut is to share an account, API key, or long-lived access token so another teammate can run a task. It may feel convenient, but it creates avoidable security, ownership, and review problems. A better approach is to separate the task from the account that executes it. The person requesting the work prepares a complete, portable task. The person running it uses their own authorized AI-agent subscription and returns the result with evidence. Here is a practical way to structure that workflow. Why shared AI accounts create problems When several people use the same AI account, it becomes difficult to answer basic operational questions: Who initiated a specific action? Which person approved the resulting changes? What project context was exposed? Who is responsible for reviewing the output? What happens when a teammate changes roles or leaves? Shared credentials also tend to spread. A password may end up in a private message, a token may be copied into a local configuration file, or a browser session may remain active on an unmanaged device. Even when everyone involved is trusted, the process itself is difficult to audit. The goal should not be to share access more efficiently. It should be to share the work without transferring the account. Treat the task as a portable unit A useful AI task should make sense outside the original conversation in which it was created. Someone receiving the task should be able to understand: the desired outcome; the relevant project context; the boundaries of the work; the evidence required for completion; the decisions that still need human review. This turns the request into a portable unit of work rather than a fragment of chat history. For example, instead of writing: Update the import flow. Write something closer to: When a user uploads a CSV containing duplicate email addresses, show a validation summary before importing any re