标签：#elm

Programming languages are fascinating. Learning how they work shouldn’t feel like fighting a dragon. On Monday, August 3, 2026, I’m launching Tiny Interpreters , a new blog and newsletter about learning how programming languages work, one tiny interpeter at a time. We’ll begin by building the interpreters in Elm, one language feature at a time. It’s for Elm developers, functional programmers, and anyone curious about programming languages who would rather begin with something small and understandable than confront an entire compiler at once. Each interpreter will introduce one carefully chosen idea and follow it through the language’s design and implementation. We’ll build up our understanding gradually, allowing the deeper ideas to emerge from programs we can see, run, and reason about. Tiny Interpreters is the path into programming languages I wish I could have followed when I first tried to learn the subject. It took me years—and one unsuccessful encounter with a dragon—to find that path for myself. My first encounter with the dragon I became interested in programming languages when I discovered that studying, designing, and building them brought together several fields I enjoyed: mathematics, computer science, and software development. My university didn't offer a course in programming languages or compiler construction while I was there, so I had to explore the subject on my own. A professor whose opinion I respected pointed me towards Compilers: Principles, Techniques, and Tools , better known as the Dragon Book—the textbook the university had used when it still offered the compilers course. I ordered a copy through the university bookstore. When it arrived, I eagerly began working through it, but I didn't get very far. I learned a great deal about parsing and syntax-directed translation, but I still lacked a clear framework for understanding how programming language features were designed, implemented, and made to work together. In hindsight, the Dragon Book

LLM agents burn a ridiculous number of tokens on redundancy: opening the same files again and again, trying a patch, failing, then wandering back through the repo like they’ve never seen it before. A July 2026 paper, ContextSniper: AntTrail's Token-Efficient Code Memory for Repository-Level Program Repair , puts real numbers behind that waste. In repository-level repair, agents keep dragging in irrelevant code and logs. ContextSniper tackles that with a context layer built around tiered memory and an intention-aware context gate that filters low-value regions before they ever reach the model. That gate alone cut tokens by 51.5% on one host agent and 38.9% on Claude Code, while submitted-resolution rates stayed basically in the same neighborhood. The gate is the interesting part, because it is not tied to that paper’s exact system. It is a more general idea, and it is starting to show up across agent architectures. At heart, the gate is just a classifier. Given a request, it has to decide what kind of retrieval will answer the question cheapest: symbol lookup, semantic search, graph impact, mutation prep, or something else. That leads to the practical question the paper does not really answer: Do you need another LLM call just to decide what context to retrieve? We tested that directly. Five ways agents get code into context Before you can gate anything, you need a retrieval strategy. Most current systems fall into one of five rough families: Grounded read-only retrieval: parse the code and return exact symbol source by name. Byte-precise, no synthesis. Graph code intelligence: model calls, imports, entities, and dependencies as a graph, then traverse it. Embedding / RAG search: use vector similarity over chunks. Whole-repo packers: compress or dump the repo into the context window. Mutate / execute runtimes: retrieve context, then modify or run code. None of these is magic. Graphs are great for relationships, but they can drift away from source. RAG is useful, but f

Originally published on DevToolHub . Helm 4 shipped in November 2025. Eight months later, most teams are still running Helm 3 in production CI/CD because it works. But Helm 3's final feature release lands September 9, 2026, and security patches stop completely on February 10, 2027. This helm 4 migration is simpler than it looks. Your charts don't need rewriting — Helm 3 Chart API v2 charts are fully compatible with Helm 4. But the automation around Helm has four real breaking points that fail silently if you don't know where to look. [IMAGE: articles/images/2026-07-05-helm-4-migration-guide-featured.png | alt: "helm 4 migration flow from Helm 3 to Helm 4 upgrade path"] Why This Helm 4 Migration Matters Now The EOL timeline has three stages, and they matter differently based on your situation: September 9, 2026 — Final Helm 3 feature release (limited to Kubernetes client library updates only after this date) February 10, 2027 — All security patches stop If your organization runs regulated workloads with requirements around supported software, February 2027 is your hard deadline. But waiting until then means doing this migration under pressure, after 14 months of Helm 4 fixes shipped without you tracking them. The better path: upgrade now, before September, so you're on supported software when new Kubernetes releases land and need updated client libraries. What Actually Broke: The Four Real Changes 1. Post-renderers require plugin registration In Helm 3, you could pass any executable directly to --post-renderer : helm install myapp ./chart --post-renderer ./scripts/mutate.sh Helm 4 drops this. Post-renderers must now be registered as named Helm plugins and referenced by plugin name: helm install myapp ./chart --post-renderer my-post-renderer If your pipeline calls --post-renderer ./path/to/script.sh , it fails on Helm 4. The error message doesn't say "plugin required," so this is easy to miss in a quick smoke test. To wrap an existing script as a plugin, create a plug

Early in building our cloud infrastructure, we had a problem nobody talks about — because it happens so slowly you almost don't notice it. We had eight separate Helm charts. One for services that needed KEDA scaling. One for standard HPA. One for backends that exposed HTTP. One for workers that didn't. One for Azure Functions. One for frontends. Eight charts, all living in the same repository, all drifting apart from each other. The charts started as copies of each other. Over time each one picked up its own fixes, its own conventions, its own slightly-different take on security contexts and ServiceAccount annotations and rolling update strategy. Nobody made a decision to diverge. It just happened. Every time we fixed something in one chart — say, wiring up Azure Workload Identity to every ServiceAccount — we had to remember to propagate that fix to seven others. Sometimes we did. Sometimes we didn't. We'd find out when something broke in an unexpected way six weeks later. Helm chart drift is more dangerous than dependency drift. At least with a dependency, you know what version you're on. With eight loosely related charts, you just don't know what you don't know. This is the story of how we replaced all eight with a single versioned chart, published to an OCI registry, and consumed by 70+ services through ArgoCD multi-source Applications — and what that structure forced us to think clearly about. The Two-Questions Framework The first thing we had to do was figure out why we had eight charts in the first place. What was actually different between services that justified a different chart? We landed on two questions: Does it expose HTTP? — This determines whether it needs an ingress, a Service, liveness/readiness probes on an HTTP path. What drives its scaling? — Standard CPU/memory HPA, or event-driven scaling via KEDA (Azure Service Bus, Event Hubs)? That's it. Everything else — security contexts, Workload Identity, pod anti-affinity, rolling update strategy, how s

Platform Engineering Labs has announced a major update to its open-source Infrastructure-as-Code platform, formae, introducing full Kubernetes support, native Helm integration, direct .tfvars compatibility, and a new public plugin hub aimed at simplifying cloud-native infrastructure management By Craig Risi