LLM Prompt Engineering for Real-World Dev Tasks

As the year begins, one repo I keep coming back to is a design and engineering guide from a systems project (TigerBeetle's coding style) written in Zig. Even if you never touch Zig, the ideas in it are extremely valuable if you care about building reliable software. What stood out to me is how much emphasis it puts on thinking before coding. Design comes first. Every system should have clear limits. Unlimited queues, unbounded loops, or “we’ll handle it later” assumptions always come back to hurt you. It constantly asks a simple but powerful question: what could go wrong? Things like exponential algorithms, hidden memcpy costs, latency spikes, and edge cases that only show up under pressure. These aren’t theoretical problems, they happen in production. The guide strongly favors very simple and explicit control flow. No clever abstractions, no hidden behavior. Code should be easy to read, easy to reason about, and predictable. Assertions are everywhere. Validate assumptions early, pre-compute what you can, post-compute checks after critical logic, then return. Fail fast and loudly rather than letting bugs propagate silently. Variables are declared in the smallest possible scope, and the number of live variables is kept minimal. Fewer moving parts means fewer mistakes and easier reasoning. A few more ideas that resonated with me: Prefer boring, obvious code over clever solutions Make invariants explicit and enforce them Optimize for correctness first, performance second Write code that is easy to audit, not just easy to write It’s a great reminder that good software isn’t about frameworks or languages. It’s about discipline, constraints, and respect for edge cases. Highly recommend reading it with an open mind, even if you ignore the Zig specifics. https://lnkd.in/dbQ7xHyS

Your code already answers HOW it does what it does way better than any docs. But the code does not tell you WHY. Focus on this. eg, writing a comment like "isElementVisible is a function to check if the element is visible on screen using IntersectionObserver with getBoundingClienrRect fallback" does not add any new information, and requires maintenance whenever implementation changes. Here are some why questions to get you started: - Why do we need this code? - Why did we use this particular solution? - Why is the fallback necessary? The only "how" question worth answering is "how to use this code", because code does not naturally include an example of calling itself. I'll admit I'm a bit bored of talking about documentation, so this is my last bit on the topic.

tired of the friction of writing blog posts  every time i wanted to write about a project, i'd go through the same tedious steps:  → find screenshots  → copy them to the right folder  → create the mdx file with frontmatter  → remember the category/tag format  → commit everything  → start the dev server to preview  not hard. but friction kills momentum  so i built a claude code skill that does it all in one command  /blog ~/Sites/my-project  it explores the project, finds images, writes in my voice, creates the file, commits, and opens the preview  the best part? this post was written using the skill itself  skills are just markdown files with instructions. you can chain them together, make them call other skills  think about what you do repeatedly - it can probably become a one-command skill  https://lnkd.in/em5z28Hq

Go services often appear straightforward at first glance. A clean main.go file, a few handlers, and everything seems readable and organized. However, once that service is deployed in production, the narrative shifts dramatically. Context leaks emerge, goroutines accumulate, memory usage increases silently, and what once seemed like simple abstractions begin to obscure underlying complexities. In this piece, I explore the gap between aesthetically pleasing Go code and the realities of production Go systems, highlighting the lessons that are often learned through challenging experiences. If you've ever found yourself wondering, "This should be easy… why isn’t it?", this article is for you. 👉 The Hidden Complexity of Simple Go Services https://lnkd.in/daXwYgFY

This context engineering product went viral on X, and it uses Nozomio to power its up-to-date search engine for technical documentation. Continuous-Claude, a session continuity and agentic workflow system for Claude Code. Continuous-Claude solves Claude Code's context degradation problem: when conversations get long, Claude compacts (summarizes) them repeatedly until you're working with a summary of a summary. Instead, it maintains state through ledgers and handoffs: save your work to a file, /clear the context, and resume fresh with full signal. It also adds hooks for session lifecycle, spawns agents with isolated context windows, and provides token-efficient MCP script execution. They integrated Nia in two ways: - A full REST API client covering the entire Nia toolset for search, docs, code grep, packages, and deep research. - Nia is wired into their skills system, so queries like “docs” or “documentation” automatically trigger Nia-powered search across repos, docs, packages, and the web. It’s wild to randomly find your product embedded this deeply in someone else’s agent stack. More to build.

Coding is no longer about writing lines — it’s about directing intent. 🎼💻 Claude Code Update 2 reveals how agentic systems reshape developer workflows, safety, and scale. A must-read for serious engineers in 2026. Full story 👇 https://smpl.is/agc69 #SoftwareArchitecture #AgenticAI #Engineering

We ran a survey with developers to understand what they look for in technical videos. Based on the strength of the signals, here are the insights that stood out most: 1️⃣ 0% prefer videos in the 30–60 minute range Not a single respondent selected this duration. This was the strongest signal across the entire survey. 2️⃣ 100% discover technical videos on YouTube Every respondent said YouTube is where they find technical video content. 3️⃣ 51% want a complete setup walkthrough Developers want full installation, configuration, and project structure covered so they can reproduce everything on their own machine without guessing. 4️⃣ 43% want a clearly defined subject driving the video Focus matters. Developers prefer videos that stay centered on one topic or outcome. 5️⃣ 40% prefer an intermediate starting point Many developers want videos that assume some prior knowledge and move quickly into the core idea. We shared the full breakdown and what this means for technical video creators in our latest post. Link in the comments 👇

I've talked a lot about how STAN doesn't hallucinate, and it's true: I've now used STAN in quite a few complex projects and produced super tight, maintainable code with VERY little rework. I've attributed this effect to STAN's breadth of context: with a holistic view over the whole project, requirements, dev plan etc., there's very little room left for guesswork. But what I'm coming to understand is that much of the no-hallucinations effect also comes down to PROCESS. The STAN Loop is deliberately interactive. There is no "set it and forget it mode", no coding while you sleep. You're engaged in every turn, and present at every code patch. If the model strays from your intent and runs even a LITTLE off the rails--or if, having seen the next set of patches, your thinking evolves--you can interrupt the process INSTANTLY and reset with a correction or a design discussion. So while the STAN agent DOES hallucinate--though not very much--thanks to the interactive process these micro-hallucinations never get a chance to COMPOUND into actual breakage. In the end, there IS some backtracking... but it's almost entirely confined to the chat thread, and your COMMITS reflect linear progress toward a rational goal. Assuming you're actually paying attention. You know: like a real professional engineer doing real work. Give STAN a try in one of your projects! https://lnkd.in/gBvNjpNW

Coding is no longer about writing lines — it’s about directing intent. 🎼💻 Claude Code Update 2 reveals how agentic systems reshape developer workflows, safety, and scale. A must-read for serious engineers in 2026. Full story 👇 https://lnkd.in/gT3gpK-q #SoftwareArchitecture #AgenticAI #Engineering

Clean Code Tip 3: Write understandable code, part 1. Readable code alone does not guarantee comprehension. Consider the following example: incomeTax = calculateTax(income) The code above is highly readable and appears clean. However, do we truly understand it? For instance, the units of income and incomeTax are not specified. This lack of clarity prevents complete understanding. To achieve fully understandable code, every detail must be explicit so that the reader has no uncertainties. Let’s revise the previous example to enhance its clarity: incomeTaxDollars = calculateTax(incomeDollars) Unclear code is often the result of poor naming for things like variables, functions, and their parameters. If these names are not completely clear, it becomes hard to fully understand the code. You need to understand the code well before making changes, because changing code you don’t fully understand can be risky.