Month: January 2026

Last month, I spent 3 hours trying to write a decent cold email template. Three. Whole. Hours. The AI kept spitting out generic garbage that sounded like every other “Hey [FIRST_NAME], hope this email finds you well”… Then I changed one thing in my prompt. One thing. Suddenly, the AI was writing emails that actually … Read more

This document was adapted from Dagger Directives for a monorepo that uses Bazel Build, and is provided for ease of use in other organizations. Contents This document is extensive, and while each directive is simple, the broader architecture they promote may be unclear; therefore, an end-to-end example is provided to aid comprehension, and the underlying … Read more

After spending months studying transformer architectures and building LLM applications, I realized something: most explanations are overwhelming or missing out some details. This article is my attempt to bridge that gap — explaining transformers the way I wish someone had explained them to me. For an intro into what Large language model (LLM) means, refer … Read more

We interviewed a dozen(ish) expert tech bloggers over the past year to share perspectives and tips beyond Writing for Developers. The idea: ask everyone the same set of questions and hopefully see an interesting range of responses emerge. They did. You can read all the interviews here. We’ll continue the interview series (and maybe publish some book … Read more

There’s a strange feeling that washes over you when you witness something you’ve created take on a life of its own. It’s not just pride; it’s a deep, almost philosophical resonance. That’s the feeling I’ve carried since my “Auto-Painter Robot Brain” completed its first masterpiece. What began as a simple coding exercise evolved into a … Read more

How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## The Hidden Cost of AI: Why It’s Making Workers Smarter, but Organisations Dumber By @yuliiaharkusha [ 8 Min read ] AI … Read more

Throughout 2025, the PVS-Studio team has been actively checking open-source C# projects. Over the year, we discovered plenty of defects. So, we picked the ten most interesting ones from this huge variety. We hope you find this roundup interesting and useful. Enjoy! How did we compile the top? There are several criteria the project code … Read more

A leading Shenzhen manufacturing enterprise uses #ApacheDolphinScheduler to standardize data pipelines and deploy dozens of factories in a single day—with visual DAGs, template-based workflows, and decentralized scheduling.

Internal API platforms are no longer plumbing. This article explains how a platform-led API strategy reduces complexity, governs AI execution, and accelerates business velocity.

AI features don’t fail at launch, but they perform the best on day one. Unlike traditional software, AI systems deteriorate due to model drift, stale data, and evolving user behaviour. Early launch metrics do not quite capture the nuances of AI features and can create a false sense of success. After launch, PMs are likely … Read more

IAM often creates a false sense of security. Hidden anti-patterns like excessive privileges, shared identities, and stale credentials silently grow, increasing breach risk unless continuously monitored and governed.

AI is no longer a future consideration in marketing. It is already embedded across workflows, quietly shaping how campaigns are created, optimized, and scaled. For many teams, AI has become part of the default operating model rather than an experimental layer. Yet despite similar access to tools, outcomes vary widely. Some teams use AI to … Read more

Cloud migrations break in places that dashboards do not measure. AI exposes every inconsistency your architecture has accumulated and the next phase belongs to teams that tighten the weak links, steady the routing paths, and keep the system coherent when the pressure rises.

Reinforcement learning only works in pricing when the system learns from real consequences, and the hard part is not the algorithm but aligning rewards, defining states, and managing exploration safely, which ultimately turns pricing into a living decision loop rather than a prediction task.

For a long time, I assumed that high Lighthouse scores were mostly the result of tuning. Compressing images, deferring scripts, fixing layout shifts, adjusting themes, swapping plugins, and repeating the cycle every time a new warning appeared. Over time, that assumption stopped matching what I was seeing in practice. The sites that consistently scored well … Read more

I didn’t notice it at first. It wasn’t a demo video or a flashy product launch. It was a normal support call. I asked a question, paused instinctively — and the answer came back so fast that I actually stopped mid-breath. No lag. No dead air. No “one moment while I check that for you.” … Read more