planetis-m

Instead of using std/json in Nimony to build JSON strings from a DOM, let's use an approach similar to the one used by the httpheaders module, with compile-time validation of string literals.

JSON will be supplied as string literals, and when an expression involves string concatenation with runtime values, the compile-time plugin should be able to infer the necessary information and inject the appropriate runtime operations.

For example:

json"{'key':" & myArr & "}"
json"{'key': [1, 0, " & myInt & "]}"
json"{" & myKey & ":[1]}"

planetis-m

New Fedora Workstation Installation Guide

Download and Boot Fedora ISO

1. Download the latest Fedora Workstation ISO from Fedora Downloads.
2. Burn the ISO to a USB drive using Fedora Media Writer or Rufus.
3. Boot from the USB drive and start the live installation.

Installation (Manual Partitioning)

planetis-m

Nimony OpenAPI Auto-Projection: Finished Design

This is the intended product shape, constrained to ordinary Nimony plugins. There is no compiler pass, compiler modification, privileged semantic hook, or post-semantic mutation of an already-used nominal type.

What application code looks like

The normal application uses complete-looking generated types and ordinary field syntax:

planetis-m

How to use Crush with the Alibaba Cloud Token Plan

Crush (charmbracelet) natively supports both Anthropic and OpenAI-compatible APIs, so the token plan works out of the box. Add this to ~/.local/share/crush/crush.json under providers:

"bailian-token-plan": {
  "name": "Alibaba Cloud Model Studio (Token Plan)",
  "type": "anthropic",
  "base_url": "https://token-plan.ap-southeast-1.maas.aliyuncs.com/apps/anthropic",
  "api_key": "YOUR_TOKEN_PLAN_KEY",

planetis-m

OpenRouter Provider Fidelity Policy

Updated: 19 Aug 2026 Primary criterion: model fidelity. Rule: model labs serving their own models are automatically Allowed. Secondary infrastructure providers must show roughly ≥90% of first-party/reference capability on same-checkpoint comparisons. A clear sub-90% result moves a secondary provider to Ignored. Providers without enough clean evidence stay in Hold / Unproven.

OpenRouter endpoint-specific AutoExacto results take priority over brand reputation, speed, or generic infrastructure quality.

1. Allowed

planetis-m

## Standalone benchmark for the hottest Responses API path: decoding a realistic
## response body and extracting what a consumer needs (first text, first function
## call, usage tokens, parsing call arguments).
##
## Strategies:
##   jsonx typed    : package `fromJson(body, ResponseResult)` + accessor scans
##                    (identical logic to `openai/responses` accessors; schema-only
##                    import avoids linking relay)
##   std/json typed : `parseJson(body).to(MyFlatTypes)` into flat own types
##                    (`type` fields as strings, `JsonNode` for open payloads,

planetis-m

Game System Design Protocol for AI Agents (Nim)

Directive

When asked to architect, implement, or review a game system in Nim, follow this protocol sequentially. Do not skip steps. Do not introduce abstractions not prescribed here unless the user explicitly requests them.

---

Phase 1: Classify the Subsystem

planetis-m

Architecting Games in Nim: A Pragmatic Guide

1. The Core Recommendation

Start simple. Stay simple. Most games never need ECS.

The game we just built — a survival arena with hundreds of agents, projectiles, particles, spatial queries, and camera-following — runs in a fraction of a millisecond using nothing more exotic than seq[Agent] and seq[Color]. The entire architecture is plain Nim: types, arrays, and procs. There is no framework, no query layer, no entity manager, no component registration, no archetype migration.

This is not a compromise. It is the optimal answer for the vast majority of games.

planetis-m

Results are stable across 3 runs. The pattern holds:

System	ECS-Arch	MI-AoS	Consistency
ai_update	~2.13	~1.71-2.93	Noisy — genuine tie (data fits L2)
move	~0.76	~1.19	Stable — Arch wins 1.6x
combat	~0.27	~0.45	Stable — Arch wins 1.6x
projectile	~1.12	~0.74	Stable — AoS wins 1.5x
cull	~2.8	~0.55	Rock solid — AoS wins 5x
despawn	~0.002	~0.001	Stable — AoS wins 2x

planetis-m

## Archetype ECS vs MI-AoS Benchmark
## ==================================
## Two approaches for a mixin-based entity simulation with cross-entity refs:
##
##   1. ECS-Archetype — SoA columns within archetype buckets, no signature scan
##   2. MI-AoS        — per-archetype value arrays (composition), direct field access
##
## Scenario: Tactical field simulation
##   Building   {pos, sprite}                        — static, can be a parent
##   Unit       {pos, worldPos, vel, hp, sprite,