Archives

A Swift debounce investigation through task reuse, runtime metadata, generic specialization, lock placement, and callback latency.

How I moved project memory into shared repository files so Claude Code and Codex can read and maintain the same notes.

Show tool calls and progress in Telegram with native thinking blocks, animated action icons, and a Stop button.

Dynamic Profiles turn one LanguageModelSession into a declarative agent runtime: per-phase models, history transforms, and two orchestration patterns.

Private Cloud Compute gives apps a 32K-context server model with reasoning and no API keys and makes the per-user daily quota a UI design problem.

WWDC26 turned Foundation Models into Swift's general LLM client: a rebuilt on-device model, a server model with no API keys, and the first real agent primitives.

After nine posts building a Claude Code-style agent by hand in Swift, I opened up JetBrains Koog framework and realized how much of what I'd hand-rolled was already someone else's problem. Here's what the framework gave me for free, and what it changed about how I think about agents.

The biggest gains in AI agent performance come from the system around the model, not the model itself.

How we restructured Claude Code's CLAUDE.md files in a monorepo to cut 73% of always-loaded context without losing a single fact.

Swift actors for background execution — the one type that truly needs an actor, with notification injection before each API call.

A file-based task DAG with dependency resolution — durable planning that survives context compaction.

A three-layer compression strategy — micro-compaction, auto-compaction, and manual compaction — for infinite conversations.

Two-layer knowledge injection — cheap awareness in the system prompt, full skill bodies loaded on demand via tool results.

Context isolation through recursive agent loops — spawning child agents with restricted tool sets for focused subtasks.

A Codable todo system with validation constraints and nag reminders — giving the agent a structured notepad it writes for itself.

Dictionary-based tool dispatch, path sandboxing, and the read/write/edit file tools that let the agent work with code.

The agent loop that drives everything — how a single stopReason check turns an API client into an autonomous agent.

Setting up a two-target SPM package, strict concurrency, and AsyncHTTPClient — the foundation for a CLI agent that never needs restructuring.