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Today I discovered something I honestly should have explored a long time ago: Google Lighthouse. Funny enough, revamping my portfolio is one of those projects I kept pushing forward with the classic “I’ll do it tomorrow” mindset — and somehow tomorrow kept winning. But today I finally sat down and started improving it, and during that process, I came across Lighthouse. For anyone who hasn’t heard of it yet, Google Lighthouse is an open-source automated tool designed to help developers improve the quality of web pages. You can run it on almost any page — whether it’s public or behind authentication. What immediately caught my attention is that it audits things like: Performance Accessibility SEO Best Practices And probably a few more things I’m still discovering You can run Lighthouse directly inside Chrome DevTools, through the command line, or even as a Node.js module. The process is simple: You give Lighthouse a URL, it scans the page, runs a series of audits, and then generates a detailed report showing how your website performs. What makes it powerful is that it doesn’t just tell you what’s wrong it also explains: _ Why the issue matters How it affects users And how you can fix it _ As a beginner software engineer and developer, I’m slowly realizing that writing code is only one part of building great applications. Performance, accessibility, maintainability, and user experience matter just as much. And honestly, tools like Lighthouse make the learning process feel less overwhelming because they point you in the right direction. One thing I’ll say though don’t fall into the trap of chasing a perfect Lighthouse score instead of building useful projects. A lot of developers start optimizing numbers before validating whether the product itself solves a real problem. Lighthouse is a guide, not the final goal. For my portfolio specifically, Lighthouse exposed a few weaknesses immediately: Large unoptimized images Accessibility issues Slow-loading assets Missing metad

We were burning 400ms in p99 tail latency on a core event-processing path in Veltrix. The upstream teams kept blaming the network, but the numbers didnt lie—64% of the time was spent inside the JVM, specifically in sun.misc.Unsafe.park during GC pauses. Every time we hit 80% heap pressure, the throughput collapsed and we lost 300k events per minute. That was the exact moment I stopped believing in the JVM as the runtime and started looking at the system boundary. The first attempt was aggressively tuned HotSpot with G1GC and pinning the critical threads to their own NUMA nodes. We set -XX:MaxGCPauseMillis=20 , -XX:+UseNUMA , and even migrated to Azul Zulu Prime because its handling of large heaps was supposedly better. The p99 dropped to 280ms, but the GC telemetry still showed a sawtooth pattern of 30–40ms spikes every 230ms on a 16GB heap. Profiling with JDK Flight Recorder told us 18% of CPU time was spent in card-table scanning. At that point I knew we were fighting the runtime, not the problem. The event pipeline was small—just JSON parsing, enrichment, and a single RocksDB write—but the JVMs generational collector couldnt stop moving objects. The architecture decision came during a four-day blackout window after a failed Blue-Green deploy. Three of us sat in a war room with a single Grafana dashboard showing 100% CPU steal time on the Kubernetes nodes. We had two choices: squeeze more life out of the JVM by manually balancing the heap or rewrite the critical hot path in Rust and give the compiler full control over memory layout. The Rust option meant losing the JVM ecosystem (no more async-profiler, no more one-liner heap dumps) but gave us stackless futures, zero-cost abstractions, and compile-time memory safety. We chose Rust. We forked the Cargo.toml wed used in a sidecar for metrics and started porting the event collector. The numbers after the rewrite told the story. We recompiled the same two endpoints— POST /events and GET /aggregates —and served them f

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When a team hits a ceiling with their coding agent, the first instinct is to reach for a better model. The reasoning feels obvious: the model is the part that produces the code, the code is the part that is wrong, therefore a smarter model will produce more correct code. Wait for the next release. Switch providers. Bump the tier. This is sometimes right. It is much more often wrong, and the cost of being wrong about it is that you spend months waiting for a model upgrade to solve a problem the model was never the cause of. The harness is the cause of the problem more often than the model. Most teams discover this only after they have exhausted the model-upgrade reflex and finally turn to look at everything else. What a model upgrade actually buys you Newer, stronger models do tangibly improve some things. They handle longer contexts more reliably. They make fewer simple reasoning errors on complex tasks. They follow nuanced instructions more closely. On a fixed prompt, with a fixed task, a better model produces a better answer. What model upgrades do not change: The fact that the agent has no idea your team prefers functional components over class components, because the convention is not in any file the agent reads. The fact that your tests do not actually fail when the code is wrong, so the agent can ship broken code that passes CI. The fact that your codebase has three different ways of handling errors and the agent picks one at random on each PR. The fact that the rule the senior engineer keeps repeating in reviews is not encoded anywhere the next session can see. None of these are fixed by a smarter model. They are fixed by a better harness. A smarter model loaded into the same broken harness will produce slightly more sophisticated versions of the same problems. The diagnostic The diagnostic is one question: when the agent fails, does it fail because it lacked information, or because it lacked capability? A capability failure looks like: the task required reas

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Meta’s upcoming Instagram Plus and Facebook Plus subscriptions are the latest example of the company seeing what works elsewhere and mimicking it.

TRIESTE, Italy – For developers, system administrators, and digital hoarders alike, the daily struggle of locating a specific snippet of text buried deep inside hundreds of nested project files is a universal headache. While heavy-handed IDEs and clunky terminal commands exist, they often feel like using a sledgehammer to crack a nut. Enter FiXiY, a lightweight, blazing-fast utility designed to do exactly one thing flawlessly: scan a folder and find precisely what you’re looking for inside the files. Created by software engineer Lorenzo Battilocchi (known online as XeroHero), FiXiY has officially launched as a free, open-source project on GitHub. Simplicity Meets Speed Unlike built-in operating system searches that are notorious for missing code snippets or taking ages to index, FiXiY bypasses the bloat. It provides a localized, no-nonsense approach to file-content searching. Users simply point the tool to a folder, type in the phrase, string, or code block they need, and FiXiY maps out every instance across all supported file types within seconds. "As developers and creators, we waste an incredible amount of cumulative time just navigating our own file structures looking for a variable, a configuration line, or a specific piece of text," says creator Lorenzo Battilocchi. "FiXiY was built out of necessity. It’s a nimble, friction-free alternative for anyone who wants instant answers without waiting for a massive IDE to load or fighting with complex regex syntax in a terminal." Key Features of FiXiY: Deep Folder Scanning: Recursively searches through complex directory trees and nested folders seamlessly. Intelligent Text Matching: Pinpoints exact strings of text, code, or data buried within plain text, source code, scripts, and logs. Lightweight Footprint: Operates with zero background bloat, making it perfect for rapid-fire asset hunting on any machine. 100% Open Source: Built transparently for the community, ensuring full privacy with no data leaving your local mac

AI workflow automation looks simple in demos. A form submission comes in. An AI model reads it. The CRM gets updated. A Slack message goes out. An email is sent. But once you move from demo to production, the workflow becomes more sensitive. What happens if the AI summary is wrong? What happens if the CRM is updated with incomplete data? What happens if the customer request needs human approval before the next step? What happens when a workflow fails halfway? That is where AI workflow automation needs better architecture. In one recent project, we designed an AI workflow automation system using: Make.com for workflow orchestration FastAPI for custom backend logic OpenAI/GPT APIs for summarization and structured output Monday.com CRM for record management Slack for internal notifications Gmail for email-based communication Human review steps for approval and control The goal was not to build a chatbot. The goal was to reduce repetitive manual review work while keeping the workflow controlled, traceable, and practical for daily business use. The workflow problem The original workflow had several manual steps: A new request came in. Someone reviewed the request manually. Important information was extracted. A CRM record was created or updated. The internal team was notified. A follow-up email was prepared or sent. The team tracked the workflow manually. This kind of workflow is common in service businesses, operations teams, sales teams, and CRM-heavy processes. The pain was not that any one step was too difficult. The pain was that the same steps repeated again and again. That makes the workflow slow, inconsistent, and dependent on manual copy-paste work. Why not fully automate everything? The obvious idea is: Let AI read the request and update everything automatically. But that can be risky. AI-generated output can be incomplete, overconfident, or slightly wrong. That may be acceptable if the output is only a draft. It is not acceptable if the output directly updates

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One poisoned extension, one package install, one CI workflow. Any of them can now be the first domino. That is the uncomfortable lesson from the latest Shai-Hulud activity and GitHub’s recently confirmed internal-repository breach. The scary part is not only the number of affected packages, tokens, or repositories. Counts move fast. The scarier part is where the attacker code ran: inside the trusted developer and CI path. The modern supply chain is not just “the dependencies we ship to production.” It is your IDE, your package manager, your GitHub Actions runner, your cache keys, your OIDC flow, your local gh auth, your AI coding tool config, and the cloud account that quietly pays the bill when something goes sideways. What happened, briefly CISA described the original Shai-Hulud wave as a self-replicating npm worm that compromised more than 500 packages and targeted GitHub personal access tokens plus AWS, GCP, and Azure keys. GitHub later said it removed 500+ compromised packages and began pushing npm toward shorter-lived credentials, 2FA enforcement, and trusted publishing. The later waves got more CI-aware. Instead of only stealing npm tokens from maintainers, they looked for credentials inside build environments, abused publishing workflows, and used the build system itself as distribution. Microsoft’s May 2026 reporting on the @antv ecosystem described a “Mini Shai-Hulud” style campaign that targeted GitHub Actions environments and stole GitHub, AWS, Vault, npm, Kubernetes, and 1Password secrets. Microsoft said GitHub removed 640 malicious packages and invalidated 61,274 npm granular access tokens with write permissions and 2FA bypass. Then GitHub confirmed an incident involving a compromised employee device and a poisoned third-party VS Code extension. GitHub said the attacker’s claim of roughly 3,800 internal repositories was “directionally consistent” with its investigation, while also saying its current assessment was exfiltration of GitHub-internal reposi

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A colleague said something to me recently that I keep coming back to: "Often, by the time you've finished articulating a complex problem for the AI, you've already solved it yourself." It sounds almost like a joke. You open a chat window, start typing out your problem in careful detail — and somewhere in the middle of the second paragraph, the answer appears. Not from the AI. From you. If you've worked with LLMs seriously, you've probably experienced this. And I think it points to something important about what is actually changing in our craft — something that goes beyond the usual conversation about automation and job displacement. The Rubber Duck, Promoted Developers have known for decades that explaining a problem out loud helps solve it. The classic technique involves a rubber duck: you place it on your desk, narrate your code to it, and the act of articulation forces you to confront the assumptions you'd quietly made. The duck never responds. That's not the point. The LLM is a rubber duck that occasionally says something useful back. But even when it doesn't — even when the response is generic or slightly off — the discipline of formulating the prompt has already done its work. You've had to be precise. You've had to strip away ambiguity. You've had to decide what actually matters. That process is not a workaround. It is thinking. The Inversion of the Workflow In the pre-AI era, the typical development workflow looked something like this: you had a rough mental model of the solution, you started coding, and you discovered the edge cases along the way. The code was exploratory. The thinking happened during the writing. With AI assistance, that workflow inverts. Vague inputs produce vague outputs — the model has no way to compensate for an underspecified problem. So precision becomes mandatory upfront. You have to think before you type, not while you type. This is a more demanding cognitive posture. It requires holding the full shape of a problem in your head be

Learn how to integrate model-based systems engineering (MBSE) with mission-driven requirements to create a connected framework that delivers reliable solutions designed with key objectives. submitted by /u/goto-con [link] [留言]

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