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  1. Article: Your Next DSL Author Is a Language Model

    Typed Domain Grounding (TDG) embeds a DSL inside a mainstream language the LLM already knows (e.g., Kotlin) and uses the host compiler as an oracle. The author describes five building blocks—embedding, choosing a host language with high training‑data frequency, compiler‑driven type safety, a generate‑compile‑repair loop, and an on‑demand teaching tool—and shows measured results from kUML, a Kotli…

    InfoQinfoq.com18 min
  2. Use Curiosity, Craft, and Care to Decide What AI Should Write

    The post proposes a three‑principle framework—Curiosity, Craft, and Care—to decide how much AI should author each artifact in a software development workflow. It argues that AI can be used aggressively for exploratory, disposable outputs (Curiosity) but should be limited for artifacts that commit the team to decisions (Craft) and for communications that require personal ownership (Care). The auth…

    Atomic Objectatomicobject.com4 min
  3. Quiz: How to Get Started With Ollama

    This is a 10‑question quiz that checks your grasp of installing Ollama, pulling models, choosing between chat and generate APIs, and managing multi‑turn conversations in Python. It reinforces the basics of running LLMs on your own hardware for privacy and offline use.

    Real Pythonrealpython.com1 min
  4. Article: Beyond Relevance: A Governance-First Architecture for Enterprise Personalization

    The article proposes a governance‑first architecture for enterprise personalization, where policy‑driven steps (memory, journey graph, AI routing, scoring, trust checks, outcome simulation) shape the recommendation before it is returned. A reference FastAPI implementation demonstrates the pattern with external YAML policies and optional LLM assistance.

    InfoQinfoq.com19 min
  5. Article: Architecting Secure and Scalable Facial Verification Systems

    A real‑world post‑mortem of a high‑volume face verification service that moved from a naïve synchronous API to an async, layered pipeline (edge validation, preprocessing, decoupled detection/verification, decision engine) to achieve 8.5k rpm, p99 < 1.8 s, 30 % cost savings, and strict privacy controls.

    InfoQinfoq.com15 min