Kotlin's DeepRecursiveFunction turns deep recursion into a heap-allocated trampoline while keeping direct-style code, built on suspend functions. This post explains why that works, then builds a Scala 3 macro that does the same trick with no coroutines and no Project Loom, by rewriting a recursive method into a TailRec state machine at compile time.
Recently, Wasm is used in different areas: safely running AI-generated code, server-side applications with ultra-fast cold starts, and language-independent plugin systems. What Wasm itself can do is just a pure computation. It cannot affect the outside world directly. Things like file I/O, clocks, and networking have to go through interfaces imported from the host. So unless the host explicitly gives it access, a Wasm module cannot perform external effects. This is why Wasm is often described as a sandbox.
Alerts show symptoms, not causes. Grafana RCA workbench puts related services, infrastructure components, and telemetry on one shared timeline — so you can follow the failure chain instead of jumping between dashboards.
Nearly a decade after PSD2 reshaped EU digital payments, PSD3 and PSR are coming. Here's what changes, why it matters for FinTech companies, and how to start preparing before the 2028 deadline.
Grafana comes in three flavours: open source, self-hosted Enterprise, and fully managed Cloud. This practical comparison explains the real differences in governance, operational responsibility, integrated observability workflows, support, and total cost of ownership — and tells you when each option is the right fit.
A curated guide to the most valuable FinTech conferences taking place between September and December 2026, covering events across the US, Europe, the Middle East, and Asia.
Daml is a functional language for writing smart contracts on Canton Network — a distributed ledger built for regulated, multi-party workflows. This guide covers Daml syntax, templates, choices, testing with Daml Script, and local debugging tools.
Learn how to build a natural language search feature using AI and Amazon Bedrock. Translate user intent into secure, structured SQL or GraphQL queries without training custom LLMs.
Evaluating agentic systems is a multi-dimensional challenge that requires a multi-layered framework. A robust evaluation system must verify both functional and non-functional requirements. The system should verify whether an agent is fulfilling its core objectives, while performing efficiently, safely and with low latency. Furthermore, the system must account for scalability while ensuring the agent's underlying reasoning remains transparent and traceable. Those different evaluation layers allow, to evaluate the model both from the technical and strategic business perspectives.
A monthly reading roundup - no chasing every model, no top-50 lists, no marketing. One thread running through it all: the best model on the planet can be switched off on a Friday afternoon, so the real asset is what you build around it. You lease the model, but the process is yours.
Diffusion models became de facto standard in image generation. While remarkably powerful, they rely on a multi-step process of iterative denoising, which takes time. Text generation is also processed in a sequential manner, however the text can be streamed and visualized token by token, giving a natural, real-time feeling, similar to how humans read or write a text. With images, we must wait for the final denoising step to complete before a usable result appears. This makes optimizing diffusion models for speed and efficiency a critical challenge.
Depending on the team and project, developers may work from well-defined requirements or help shape solutions from the ground up. In developer tooling, the latter tends to happen particularly often. We spoke with Karol Skóra about what surprised him most after moving from backend development into tooling and why communication may be just as important as technical expertise.
