Run prettier on backlog folder

2026-05-04 21:22:27 +01:00
parent 7bf89d9a1d
commit dc3b31378d
177 changed files with 2594 additions and 936 deletions
@@ -2,9 +2,10 @@
 id: doc-1
 title: Alternatives for reducing collection chat token usage
 type: other
-created_date: '2026-05-02 16:12'
-updated_date: '2026-05-04 06:55'
+created_date: "2026-05-02 16:12"
+updated_date: "2026-05-04 06:55"
 ---
+
 # ML-156 Research: Alternatives for reducing collection chat token usage

 Research document for [ML-156 - Explore alternatives to reduce token usage when providing collection context to LLM for collection chat](backlog://task/ML-156).
@@ -14,11 +15,12 @@ Research document for [ML-156 - Explore alternatives to reduce token usage when
 ## Current state

 **Token flow per new collection chat:**
+
 1. `Collection.collection_summary/0` runs on mount via `start_async`
 2. Loads ALL records: `from(r in Record, where: not is_nil(r.purchased_at), order_by: [order_alphabetically()], select: ^essential_fields())`
 3. Groups by `musicbrainz_id`, formats each group as `"Artist - Title (year, formats) [genre1, genre2]"`
 4. Builds stats header: `"# Stats: N releases, M artists\nGenres: ...\nFormats: ...\nEras: ..."`
-5. Returns `{stats + "\n\n" + catalog, group_count}` 
+5. Returns `{stats + "\n\n" + catalog, group_count}`
 6. Stored in `@collection_summary` assign on the LiveView
 7. Passed to Chat component as `chat_context={@collection_summary}`
 8. When user sends first message, `do_send_message` calls `chat_module.stream_response(messages, chat_context, callback)`
@@ -30,6 +32,7 @@ Research document for [ML-156 - Explore alternatives to reduce token usage when
 **For 500 releases:** ~9,000 input tokens for catalog alone + ~100 tokens for stats + ~500 tokens for prompt template = ~9,600 tokens

 **Key files:**
+
 - `lib/music_library/collection.ex:203-235` — `collection_summary/0` (loads and formats all records)
 - `lib/music_library/chats/collection_chat.ex:18-31` — `build_instructions/2` (embeds summary in prompt)
 - `lib/music_library/chats/prompt.ex` — `Prompt.build/2` (wraps in identity + approach)
@@ -41,6 +44,7 @@ Research document for [ML-156 - Explore alternatives to reduce token usage when
 ### Streaming architecture constraints

 The Chat component dispatches streaming to a `Task.Supervisor` child:
+
 ```elixir
 Task.Supervisor.start_child(MusicLibrary.TaskSupervisor, fn ->
  case chat_module.stream_response(stream_messages, chat_context, fn chunk ->
@@ -65,17 +69,20 @@ The callback sends `[chunk: chunk]` updates; `update/2` in the Chat component ac
 **Token savings:** ~9,000 → ~100 input tokens (~99% reduction)

 **Pros:**
+
 - Simplest possible change; < 10 lines of code
 - No architectural changes needed
 - No streaming infrastructure changes
 - The LLM can still answer statistical questions ("what's my most common genre?", "how many jazz records do I have?")

 **Cons:**
+
 - LLM loses ability to answer specific questions ("do I have Kid A?", "which Radiohead albums do I own?", "show me my 90s electronic albums")
 - User experience degrades for record-specific queries
 - The LLM will hallucinate or say "I don't have access to your specific collection" frequently

 **Impact on code:**
+
 1. `CollectionChat.build_instructions/2` — remove `#{collection_summary}` interpolation, keep only stats
 2. `Collection.collection_summary/0` — could be simplified to return only stats (or keep as-is, the function is also tested independently)

@@ -88,7 +95,9 @@ The callback sends `[chunk: chunk]` updates; `update/2` in the Chat component ac
 **Token savings:** ~9,000 → ~100 input tokens base + tool call overhead + tool results (~200-500 tokens when actually searching)

 **How it works:**
+
 1. Add a tool definition to the Responses API request:
+
 ```json
 {
  "type": "function",
@@ -97,7 +106,7 @@ The callback sends `[chunk: chunk]` updates; `update/2` in the Chat component ac
  "parameters": {
    "type": "object",
    "properties": {
-      "query": {"type": "string", "description": "Search query"}
+      "query": { "type": "string", "description": "Search query" }
    },
    "required": ["query"]
  }
@@ -114,6 +123,7 @@ The callback sends `[chunk: chunk]` updates; `update/2` in the Chat component ac
 3. The Chat component's streaming architecture needs to handle this multi-turn flow.

 **Pros:**
+
 - Maximal token efficiency — only pay for what's actually needed
 - LLM can answer arbitrary specific questions with real data
 - Scales to any collection size
@@ -121,6 +131,7 @@ The callback sends `[chunk: chunk]` updates; `update/2` in the Chat component ac
 - Leverages the existing `tools` infrastructure in the Responses API request

 **Cons:**
+
 - **Significantly more complex** — requires:
  - Changes to `OpenAI.API.chat_stream/6` to support function calls in streaming mode
  - Changes to `decode_responses_event/2` to handle function call SSE events
@@ -132,6 +143,7 @@ The callback sends `[chunk: chunk]` updates; `update/2` in the Chat component ac
 - More error states to handle (function execution failures, parse errors)

 **Implementation scope:**
+
 1. **Tool definition module** — New module ~30 lines defining the OpenAI function tool schema
 2. **Function executor** — New module or function in `CollectionChat` that executes the tool call (~20 lines)
 3. **Streaming changes in `OpenAI.API`** — New `chat_stream_with_tools/7` or modify `chat_stream/6` to accept tools and handle function call events (~80 lines)
@@ -150,12 +162,14 @@ The callback sends `[chunk: chunk]` updates; `update/2` in the Chat component ac
 **Token savings:** ~9,000 → ~400 tokens (summary text + stats)

 **Pros:**
+
 - Good middle ground — compact but informative
 - The LLM has a narrative understanding of the collection
 - No streaming architecture changes needed
 - Single one-time cost to generate (amortized across many chats)

 **Cons:**
+
 - Still doesn't give the LLM ability to answer specific queries ("do I have Kid A?")
 - Summary can go stale if not regenerated on collection changes
 - Requires an initial LLM call to generate the summary (token cost + latency)
@@ -164,6 +178,7 @@ The callback sends `[chunk: chunk]` updates; `update/2` in the Chat component ac
 - Adds a new background job / async concern

 **Implementation scope:**
+
 1. Store the cached summary (new DB field on a canonical collection `Chat` record, or a new schema)
 2. A function/worker to generate the LLM summary (calls OpenAI, stores result)
 3. Invalidation triggers (PubSub on record add/edit/delete, regenerate async)
@@ -179,11 +194,13 @@ The callback sends `[chunk: chunk]` updates; `update/2` in the Chat component ac
 **Token savings:** ~9,000 → ~100 tokens base + tool results (0-500 tokens per use)

 **Pros:**
+
 - Best of both worlds: statistical awareness always available, specific lookup on demand
 - Token-efficient — base cost is minimal
 - LLM knows to reach for the tool when appropriate

 **Cons:**
+
 - Same implementation complexity as Alternative B for the tool infrastructure
 - Slightly more prompt engineering to ensure the model uses the tool appropriately

@@ -198,6 +215,7 @@ Upload the collection catalog as a file to OpenAI, create a vector store, and us
 **Token savings:** ~9,000 → ~100 tokens base + retrieval overhead (~200-500 when model searches)

 **How it works:**
+
 1. Format collection catalog as text (same as current `collection_summary/0` output)
 2. Upload to OpenAI: `POST /v1/files` with `purpose: "assistants"` → `file_id`
 3. Create vector store: `POST /v1/vector_stores` → `vector_store_id`
@@ -208,6 +226,7 @@ Upload the collection catalog as a file to OpenAI, create a vector store, and us
 **Critical difference from Alt B:** No SSE event handling changes, no orchestration loop, no custom tool execution. `file_search` works exactly like `web_search_preview` (already in use) — OpenAI handles retrieval automatically.

 **Pros:**
+
 - ~150 lines vs ~250 for Alt B
 - No changes to `decode_responses_event/2` or Chat streaming loop
 - Semantic search (finds "upbeat 80s rock" not just keywords)
@@ -215,6 +234,7 @@ Upload the collection catalog as a file to OpenAI, create a vector store, and us
 - Reusable for artist bios, notes, etc.

 **Cons:**
+
 - File can go stale if not updated on collection change
 - 2-3 new API endpoints needed (Files API, Vector Stores API)
 - Vector store indexing is async (may need brief poll)
@@ -226,13 +246,13 @@ Upload the collection catalog as a file to OpenAI, create a vector store, and us

 ## Comparison

-| Criterion | A (stats-only) | B (custom func) | C (cached) | E (file_search) |
-|---|---|---|---|---|
-| Implementation complexity | ★☆☆☆☆ | ★★★★☆ | ★★★☆☆ | ★★☆☆☆ |
-| Response quality | ★★☆☆☆ | ★★★★★ | ★★★☆☆ | ★★★★★ |
-| Token efficiency | ★★★★★ | ★★★★★ | ★★★★☆ | ★★★★★ |
-| Infrastructure risk | ★★★★★ | ★★★☆☆ | ★★★★☆ | ★★★★☆ |
-| Reusability | ★☆☆☆☆ | ★★★★☆ | ★★☆☆☆ | ★★★☆☆ |
+| Criterion                 | A (stats-only) | B (custom func) | C (cached) | E (file_search) |
+| ------------------------- | -------------- | --------------- | ---------- | --------------- |
+| Implementation complexity | ★☆☆☆☆          | ★★★★☆           | ★★★☆☆      | ★★☆☆☆           |
+| Response quality          | ★★☆☆☆          | ★★★★★           | ★★★☆☆      | ★★★★★           |
+| Token efficiency          | ★★★★★          | ★★★★★           | ★★★★☆      | ★★★★★           |
+| Infrastructure risk       | ★★★★★          | ★★★☆☆           | ★★★★☆      | ★★★★☆           |
+| Reusability               | ★☆☆☆☆          | ★★★★☆           | ★★☆☆☆      | ★★★☆☆           |

 ---

@@ -2,9 +2,10 @@
 id: doc-2
 title: Prevent pi from accessing sensitive files
 type: other
-created_date: '2026-05-03 13:32'
-updated_date: '2026-05-04 06:55'
+created_date: "2026-05-03 13:32"
+updated_date: "2026-05-04 06:55"
 ---
+
 # Implementation Analysis: Prevent pi from accessing sensitive files

 ## Summary
@@ -13,14 +14,14 @@ The task requires a **declarative** way to intercept pi tool calls that would ac

 ## Sensitive File Categories (for this project)

-| Category | Patterns | Examples |
-|----------|----------|----------|
-| Environment secrets | `.env`, `.env.*`, `.envrc` | `.env`, `.env.production` |
-| Key files | `*.pem`, `*.key`, `*.key.pub` | SSH keys |
-| Secret/credential files | `*secret*`, `*credential*`, `*credentials*` | `secrets.yml`, `credentials.json` |
-| Config dirs | `.ssh/`, `.aws/`, `.gnupg/` | SSH config, AWS credentials |
-| Production config | Any path known to contain secrets | `rel/`, production env vars |
-| Pi session files | `~/.pi/agent/sessions/` | Session JSONL files (contain tool outputs) |
+| Category                | Patterns                                    | Examples                                   |
+| ----------------------- | ------------------------------------------- | ------------------------------------------ |
+| Environment secrets     | `.env`, `.env.*`, `.envrc`                  | `.env`, `.env.production`                  |
+| Key files               | `*.pem`, `*.key`, `*.key.pub`               | SSH keys                                   |
+| Secret/credential files | `*secret*`, `*credential*`, `*credentials*` | `secrets.yml`, `credentials.json`          |
+| Config dirs             | `.ssh/`, `.aws/`, `.gnupg/`                 | SSH config, AWS credentials                |
+| Production config       | Any path known to contain secrets           | `rel/`, production env vars                |
+| Pi session files        | `~/.pi/agent/sessions/`                     | Session JSONL files (contain tool outputs) |

 ## Available Pi Extension Mechanisms

@@ -37,6 +38,7 @@ pi.on("tool_call", async (event, ctx) => {
 ```

 **Key capabilities:**
+
 - `event.toolName` — identifies the tool (`read`, `bash`, `grep`, `find`, `ls`, `write`, `edit`)
 - `event.input` — tool parameters (mutable, can be patched)
 - `isToolCallEventType("read", event)` — type-narrows for specific tools
@@ -46,15 +48,15 @@ pi.on("tool_call", async (event, ctx) => {

 ### Tools that can access sensitive files

-| Tool | Danger | Input fields to check |
-|------|--------|----------------------|
-| `read` | **High** — reads file content into LLM context | `input.path` |
-| `bash` | **High** — can `cat`, `grep`, `curl` any file | `input.command` (regex match) |
-| `grep` | **High** — searches file contents | `input.path` |
-| `find` | **Low** — lists filenames only | `input.path` (directory listing) |
-| `ls` | **Low** — lists directory contents | `input.path` |
-| `write` | **Medium** — could overwrite sensitive files | `input.path` |
-| `edit` | **Medium** — could modify sensitive files | `input.path` |
+| Tool    | Danger                                         | Input fields to check            |
+| ------- | ---------------------------------------------- | -------------------------------- |
+| `read`  | **High** — reads file content into LLM context | `input.path`                     |
+| `bash`  | **High** — can `cat`, `grep`, `curl` any file  | `input.command` (regex match)    |
+| `grep`  | **High** — searches file contents              | `input.path`                     |
+| `find`  | **Low** — lists filenames only                 | `input.path` (directory listing) |
+| `ls`    | **Low** — lists directory contents             | `input.path`                     |
+| `write` | **Medium** — could overwrite sensitive files   | `input.path`                     |
+| `edit`  | **Medium** — could modify sensitive files      | `input.path`                     |

 ### `tool_result` event (secondary approach)

@@ -85,6 +87,7 @@ pi.registerTool({
 **Description:** Single extension listening on `tool_call`, checking paths/commands against a configuration file of protected patterns. Blocks matching calls.

 **Implementation:**
+
 1. Create `.pi/extensions/sensitive-file-guard.ts` (or `index.ts` in a directory)
 2. Create `.pi/sensitive-paths.json` — declarative config listing protected patterns
 3. In `tool_call` handler:
@@ -94,6 +97,7 @@ pi.registerTool({
   - Block if match, notify in UI

 **Sample config file (`.pi/sensitive-paths.json`):**
+
 ```json
 {
  "blocked_paths": [
@@ -109,16 +113,12 @@ pi.registerTool({
    ".gnupg/",
    "config/*.secret.exs"
  ],
-  "blocked_commands": [
-    "cat .env",
-    "cat ~/.ssh",
-    "printenv",
-    "cat ~/.aws"
-  ]
+  "blocked_commands": ["cat .env", "cat ~/.ssh", "printenv", "cat ~/.aws"]
 }
 ```

 **Pros:**
+
 - Minimal code (~60 lines of TypeScript)
 - Declarative configuration file (easy to audit and update)
 - Works with ALL tools (read, bash, grep, find, ls, write, edit)
@@ -128,6 +128,7 @@ pi.registerTool({
 - Fails safe (blocks before any file access)

 **Cons:**
+
 - Can't sanitize partial output (but blocking prevents all access)
 - Regex-based command scanning for bash has edge cases
 - `find`/`ls` only blocked at directory level, not per-file
@@ -141,17 +142,20 @@ pi.registerTool({
 **Description:** Override the `read` and `bash` tools with custom implementations that include path checking. Delegates to original implementation for allowed paths.

 **Implementation:**
+
 1. Create `.pi/extensions/sensitive-file-guard/` directory
 2. Override `read` tool: check paths, delegate allowed reads to original implementation
 3. Override `bash` tool: scan commands, delegate allowed commands to original implementation
 4. Optionally override `grep`, `find`, `ls`, `write`, `edit`

 **Pros:**
+
 - Full control (can log, can sanitize output, can modify behavior)
 - No event overhead per tool call
 - Can add audit logging

 **Cons:**
+
 - Must reimplement or re-wrap built-in tool logic (~200+ lines)
 - Must maintain compatibility with pi updates
 - More complex testing
@@ -170,10 +174,12 @@ pi.registerTool({
 Same as Route A, plus a `tool_result` handler that scans output for sensitive patterns and redacts them.

 **Pros:**
+
 - Most comprehensive protection
 - Can allow partial access with sanitization

 **Cons:**
+
 - Most complex
 - `tool_result` sanitization is error-prone (false positives/negatives)
 - Redacted content still consumes context tokens
@@ -195,14 +201,14 @@ Route A (pure `tool_call` event interception with declarative config) is the **s

 ## Architectural Impact

-| Touchpoint | Impact |
-|------------|--------|
-| `.pi/extensions/` | New extension file added |
-| `.pi/sensitive-paths.json` | New config file (declarative rules) |
-| `tool_call` event | New subscriber, no breaking changes |
-| Built-in tools | Unchanged (event interception is non-invasive) |
-| `ctx.ui` | Used for notification only (no blocking dependency) |
-| Other extensions | Compatible — tool_call handlers chain in load order |
+| Touchpoint                 | Impact                                              |
+| -------------------------- | --------------------------------------------------- |
+| `.pi/extensions/`          | New extension file added                            |
+| `.pi/sensitive-paths.json` | New config file (declarative rules)                 |
+| `tool_call` event          | New subscriber, no breaking changes                 |
+| Built-in tools             | Unchanged (event interception is non-invasive)      |
+| `ctx.ui`                   | Used for notification only (no blocking dependency) |
+| Other extensions           | Compatible — tool_call handlers chain in load order |

 ## Open Questions

@@ -2,14 +2,16 @@
 id: doc-3
 title: Force production logs to single-line format
 type: other
-created_date: '2026-05-03 13:53'
-updated_date: '2026-05-04 06:55'
+created_date: "2026-05-03 13:53"
+updated_date: "2026-05-04 06:55"
 ---
+
 # Research: Force production logs to single-line format

 ## Current state

 **Config** (`config/config.exs`):
+
 ```elixir
 config :logger, :default_formatter,
  format: "$time $metadata[$level] $message\n",
@@ -17,18 +19,19 @@ config :logger, :default_formatter,
 ```

 **Override** (`config/prod.exs`):
+
 ```elixir
 config :logger, level: :info
 ```

 **Log sources in prod**:

-| Source | Produces | Problem |
-|---|---|---|
-| `Phoenix.Logger` (telemetry) | `GET /health` + `Sent 200 in Xms` | Two separate lines for one request — can't reverse |
-| `Phoenix.Logger` (telemetry) | `CONNECTED TO Phoenix.LiveView.Socket...` | One event with embedded `\n` — 4+ lines |
-| Custom `Logger.info/error/...` | Varies | Some may be multi-line |
-| OTP / stack traces | Crash reports | Multi-line |
+| Source                         | Produces                                  | Problem                                            |
+| ------------------------------ | ----------------------------------------- | -------------------------------------------------- |
+| `Phoenix.Logger` (telemetry)   | `GET /health` + `Sent 200 in Xms`         | Two separate lines for one request — can't reverse |
+| `Phoenix.Logger` (telemetry)   | `CONNECTED TO Phoenix.LiveView.Socket...` | One event with embedded `\n` — 4+ lines            |
+| Custom `Logger.info/error/...` | Varies                                    | Some may be multi-line                             |
+| OTP / stack traces             | Crash reports                             | Multi-line                                         |

 `Phoenix.Logger` is auto-attached by Phoenix.Endpoint and handles telemetry events including `[:phoenix, :endpoint, :start]`, `[:phoenix, :endpoint, :stop]`, and `[:phoenix, :socket_connected]`.

@@ -46,14 +49,14 @@ v2 (`~> 2.0.0-rc.5`) uses `Logster.attach_phoenix_logger()` in `application.ex`

 ### Architecture impact

-| Touchpoint | Change |
-|---|---|
-| `config/prod.exs` | Add `config :phoenix, :logger, false` + Logster config + formatter config |
-| `lib/music_library/application.ex` | Add conditional `Logster.attach_phoenix_logger()` |
-| `lib/music_library_web/telemetry_log_handler.ex` | **New module** — handles socket_connected telemetry |
-| `lib/music_library/logger/single_line_formatter.ex` | **New module** — Logger.Formatter format function |
-| `mix.exs` | Add `{:logster, "~> 2.0.0-rc.5"}` |
-| Docs | Update `docs/production-infrastructure.md` with logging config |
+| Touchpoint                                          | Change                                                                    |
+| --------------------------------------------------- | ------------------------------------------------------------------------- |
+| `config/prod.exs`                                   | Add `config :phoenix, :logger, false` + Logster config + formatter config |
+| `lib/music_library/application.ex`                  | Add conditional `Logster.attach_phoenix_logger()`                         |
+| `lib/music_library_web/telemetry_log_handler.ex`    | **New module** — handles socket_connected telemetry                       |
+| `lib/music_library/logger/single_line_formatter.ex` | **New module** — Logger.Formatter format function                         |
+| `mix.exs`                                           | Add `{:logster, "~> 2.0.0-rc.5"}`                                         |
+| Docs                                                | Update `docs/production-infrastructure.md` with logging config            |

 ### Performance

@@ -2,9 +2,10 @@
 id: doc-4
 title: GitHub Actions → Tangled Spindles
 type: other
-created_date: '2026-05-03 16:57'
-updated_date: '2026-05-04 06:54'
+created_date: "2026-05-03 16:57"
+updated_date: "2026-05-04 06:54"
 ---
+
 # CI Pipeline Porting Analysis: GitHub Actions → Tangled Spindles

 ## Current State (GitHub Actions)
@@ -22,6 +23,7 @@ Key GHA features used: `jdx/mise-action`, `actions/cache`, concurrency control,
 Tangled provides **Spindles** — a CI runner service. Workflows are YAML files in `.tangled/workflows/`.

 **Available features:**
+
 - Triggers: `push`, `pull_request`, `manual` — with branch/tag glob filtering
 - Engine: `nixery` (uses Nixpkgs packages via Nixery at `nixery.tangled.sh`)
 - Dependencies: declared from nixpkgs (or custom registries)
@@ -34,6 +36,7 @@ Tangled provides **Spindles** — a CI runner service. Workflows are YAML files
 - Timeout: configurable via `SPINDLE_PIPELINES_WORKFLOW_TIMEOUT` (default 5m)

 **Missing features (vs GitHub Actions):**
+
 - ❌ No job dependencies (`needs`) — all steps in a workflow are strictly sequential
 - ❌ No parallel jobs — single linear pipeline per workflow file
 - ❌ No inter-run caching primitive — no equivalent to `actions/cache`
@@ -51,20 +54,20 @@ Tangled also provides **Webhooks** (push events, HMAC-signed, 3 retries with bac

 Nixery builds OCI images with each nixpkgs package in a **separate Docker layer**. Docker reuses pre-existing layers, so any nixpkgs dependency declared in the `dependencies` block — `mise`, `nodejs`, `shellcheck`, `hurl`, `sqlite` — is cached at the Docker level between runs.

-This is confirmed by the Tangled team: *"caching for commonly used packages is free thanks to Docker (pre-existing layers get reused)."* — [Introducing Spindle blog post](https://blog.tangled.org/ci/)
+This is confirmed by the Tangled team: _"caching for commonly used packages is free thanks to Docker (pre-existing layers get reused)."_ — [Introducing Spindle blog post](https://blog.tangled.org/ci/)

 ### What is NOT cached (fetched/compiled fresh every run)

-| Artifact | Reason |
-|----------|--------|
-| Elixir 1.20.0-rc.4, Erlang 28.5 | Not in nixpkgs; `mise install` downloads fresh each run |
-| `mix deps` + `_build` | No directory-level cache primitive in Spindle |
-| `npm` packages + asset builds | Same — no cache primitive |
-| Fluxon private Hex repo packages | Fetched each run |
+| Artifact                         | Reason                                                  |
+| -------------------------------- | ------------------------------------------------------- |
+| Elixir 1.20.0-rc.4, Erlang 28.5  | Not in nixpkgs; `mise install` downloads fresh each run |
+| `mix deps` + `_build`            | No directory-level cache primitive in Spindle           |
+| `npm` packages + asset builds    | Same — no cache primitive                               |
+| Fluxon private Hex repo packages | Fetched each run                                        |

 ### Within a run

-Per the blog post: *"the `/tangled/workspace` and `/nix` volumes persisted across steps."* So within a single workflow run, compiled artifacts are available to subsequent steps. But between runs, everything resets except the Nixery Docker layers.
+Per the blog post: _"the `/tangled/workspace` and `/nix` volumes persisted across steps."_ So within a single workflow run, compiled artifacts are available to subsequent steps. But between runs, everything resets except the Nixery Docker layers.

 ### Potential caching strategy: Custom Nix derivation for Elixir 1.20.0-rc.4

@@ -74,20 +77,21 @@ Similarly, Erlang 28.5 could be pinned via a custom derivation if not in nixpkgs

 ## Dependency Availability in Nixpkgs

-| Tool | In nixpkgs stable? | Version | Notes |
-|------|-------------------|---------|-------|
-| `elixir` | ✅ | 1.18.4 / 1.19.5 | **Not 1.20.0-rc.4** — must use mise or custom Nix derivation |
-| `erlang` | ✅ | via beam packages | OTP versions tied to package sets |
-| `nodejs` | ✅ | Yes | |
-| `mise` | ✅ | 2025.11.7 | Can install exact Elixir/Erlang/Node from mise.toml |
-| `shellcheck` | ✅ | Yes | |
-| `hurl` | ✅ (likely) | | |
-| `sqlite` | ✅ | Yes | |
-| `credo` | ❌ | Elixir mix task | Must install via mix after Elixir is available |
-| `sobelow` | ❌ | Elixir mix task | Same |
-| `mix_audit` | ❌ | Elixir mix task | Same |
+| Tool         | In nixpkgs stable? | Version           | Notes                                                        |
+| ------------ | ------------------ | ----------------- | ------------------------------------------------------------ |
+| `elixir`     | ✅                 | 1.18.4 / 1.19.5   | **Not 1.20.0-rc.4** — must use mise or custom Nix derivation |
+| `erlang`     | ✅                 | via beam packages | OTP versions tied to package sets                            |
+| `nodejs`     | ✅                 | Yes               |                                                              |
+| `mise`       | ✅                 | 2025.11.7         | Can install exact Elixir/Erlang/Node from mise.toml          |
+| `shellcheck` | ✅                 | Yes               |                                                              |
+| `hurl`       | ✅ (likely)        |                   |                                                              |
+| `sqlite`     | ✅                 | Yes               |                                                              |
+| `credo`      | ❌                 | Elixir mix task   | Must install via mix after Elixir is available               |
+| `sobelow`    | ❌                 | Elixir mix task   | Same                                                         |
+| `mix_audit`  | ❌                 | Elixir mix task   | Same                                                         |

 The workflow pattern with mise:
+
 1. Add `mise` as a nixpkgs dependency (cached via Docker layers ✅)
 2. `mise install` to fetch Elixir 1.20.0-rc.4, Erlang 28.5, Node 25.9.0 (NOT cached ❌)
 3. Run existing mise tasks
@@ -95,30 +99,35 @@ The workflow pattern with mise:
 ## Implementation Routes

 ### Route A: Pure Spindle — Single Linear Workflow
+
 One `.tangled/workflows/ci.yml` that runs lint → test → deploy sequentially.

 **Pros:** Simplest — one file, fully self-contained on Tangled.
 **Cons:** Slowest (no parallelism between lint+test), deploy runs on every PR push to main, no separation of concerns.

 ### Route B: Multiple Spindle Workflows — Split CI/CD
+
 Separate files: `lint.yml`, `test.yml`, `deploy.yml`. Deploy is manual-trigger only.

 **Pros:** Lint and test can trigger on PRs, deploy is intentional. Better separation of concerns.
 **Cons:** No enforcement that test passes before manual deploy. Lint and test still run sequentially within each workflow.

 ### Route C: Spindle for CI + Webhook for Deploy
+
 Spindle handles lint+test on PRs. A Tangled webhook on push to main triggers Coolify deployment via an external endpoint.

 **Pros:** Deploy runs independently. Can add gating logic in webhook receiver.
 **Cons:** Requires a webhook receiver (external service). More infrastructure.

 ### Route D: Spindle for All — Lean Pipeline (Accept Gaps)
+
 Full pipeline on Spindle but accept the missing features. Configure longer timeout, accept sequential runs, use manual deploy trigger.

 **Pros:** Fully native Tangled experience.
 **Cons:** All gaps accepted as-is. May frustrate PR workflow.

 ### Route E: Mirror to GitHub + Keep Actions
+
 Mirror the Tangled repo to GitHub (or use Tangled as a mirror of GitHub) and keep existing Actions.

 **Pros:** Zero CI changes. All existing features preserved.
@@ -2,9 +2,10 @@
 id: doc-5
 title: Log Line Copy in Prod-Logs Extension
 type: other
-created_date: '2026-05-03 21:06'
-updated_date: '2026-05-04 06:55'
+created_date: "2026-05-03 21:06"
+updated_date: "2026-05-04 06:55"
 ---
+
 # Implementation Analysis: Log Line Copy in Prod-Logs Extension

 ## Problem Summary
@@ -28,13 +29,15 @@ The `/prod-logs` extension (`.pi/extensions/prod-logs/index.ts`) displays log li
 ## Route A: Cursor + Toggle Selection + setEditorText

 ### Concept
+
 Add a visual cursor line. Enter to copy single line. Space to toggle line selection (multi-select mode). `y` to copy all selected lines and exit. After exit, call `ctx.ui.setEditorText()` with the copied text.

 ### Changes to LogViewer
+
 1. Add `cursorIndex: number` (absolute line index into `this.lines`)
 2. Add `selectedIndices: Set<number>` (absolute indices)
 3. New key bindings:
-   - `Enter` → copy cursor line, `done(line)` 
+   - `Enter` → copy cursor line, `done(line)`
   - `Space` → toggle selection of cursor line
   - `y` → copy selected lines (or cursor line if none selected), `done(text)`
 4. Render changes:
@@ -44,6 +47,7 @@ Add a visual cursor line. Enter to copy single line. Space to toggle line select
 5. Help text update: add "y copy · Space select · Enter copy line"

 ### Return value handling
+
 ```typescript
 const copiedText = await ctx.ui.custom<string | null>(...);
 if (copiedText !== null) {
@@ -52,12 +56,14 @@ if (copiedText !== null) {
 ```

 ### Pros
+
 - No external dependencies
 - `setEditorText` is already available in pi's ExtensionAPI
 - Aligns with the "paste" use case (text in editor = ready to send or copy)
 - Simple, contained change to one file

 ### Cons
+
 - Modifies editor content (replaces whatever the user had typed)
 - Not a true "system clipboard" copy — the user can't paste outside pi without an extra step
 - Toggle-based selection for multi-line is somewhat unusual UX (vs. range selection)
@@ -67,9 +73,11 @@ if (copiedText !== null) {
 ## Route B: Visual Mode (Vim-style) + setEditorText

 ### Concept
+
 Vim-inspired visual mode: `v` enters linewise visual selection, `j`/`k` extend the range, `y` copies and exits. Enter still copies single line. Selection is always a contiguous range.

 ### Changes to LogViewer
+
 1. Add `cursorIndex: number` (absolute)
 2. Add `visualMode: boolean` + `visualAnchor: number` (start of visual selection)
 3. New key bindings:
@@ -84,11 +92,13 @@ Vim-inspired visual mode: `v` enters linewise visual selection, `j`/`k` extend t
 5. Help text: show visual mode help when active

 ### Pros
+
 - Familiar UX for vim users
 - Contiguous range selection is intuitive
 - Same `setEditorText` approach — no external deps

 ### Cons
+
 - Slightly more complex state machine (normal mode vs. visual mode)
 - Doesn't allow non-contiguous selection (but the user said "contiguous")
 - Still modifies editor content
@@ -98,19 +108,23 @@ Vim-inspired visual mode: `v` enters linewise visual selection, `j`/`k` extend t
 ## Route C: System Clipboard + Either Selection Model

 ### Concept
+
 Either selection model (A or B), but instead of `setEditorText`, write to the system clipboard. Requires a package like `clipboardy`.

 ### Additional Changes
+
 - Add `clipboardy` dependency (or use `node:child_process` with `pbcopy`/`xclip`)
 - On copy: `clipboardy.writeSync(text)` or similar
 - No need to change return type — can stay `void` since we don't pass text back

 ### Pros
+
 - True "copy" behavior — paste anywhere
 - Doesn't touch editor content
 - Works across applications

 ### Cons
+
 - External dependency (`clipboardy` — native module, may have install issues)
 - Platform-specific behavior (macOS `pbcopy`, Linux `xclip`/`wl-copy`, Windows)
 - pi extensions run in the pi process; clipboard access may have security implications
@@ -121,6 +135,7 @@ Either selection model (A or B), but instead of `setEditorText`, write to the sy
 ## Recommendation: Route B (Visual Mode + setEditorText)

 **Rationale:**
+
 1. **No external dependencies** — `setEditorText` is already available
 2. **Familiar UX** — vim-style visual mode is intuitive for developers
 3. **Contiguous selection** — matches the user's "contiguous lines" description
@@ -136,6 +151,7 @@ Either selection model (A or B), but instead of `setEditorText`, write to the sy
 ## Outstanding Question for User

 How should the copied text be placed in the editor?
+
 - **Append** to current editor content (preserves what's there)
 - **Replace** current editor content (clean slate)

@@ -2,9 +2,10 @@
 id: doc-6
 title: Programmatic Access to Production Logs
 type: other
-created_date: '2026-05-04 06:43'
-updated_date: '2026-05-04 06:55'
+created_date: "2026-05-04 06:43"
+updated_date: "2026-05-04 06:55"
 ---
+
 # Implementation Route Analysis: Programmatic Access to Production Logs

 ## Decision: Route B — Pi Tool via `pi.registerTool()` ✓ SELECTED
@@ -28,19 +29,23 @@ The LLM needs a tool it can call to retrieve production logs directly into its c
 ## Route A: Tidewave MCP Tool (Elixir-side)

 ### Description
+
 Add a new MCP tool `get_production_logs` to Tidewave that calls the Coolify API from Elixir using `Req`.

 ### Pros
+
 - Architecturally consistent with existing MCP tools
 - Req is battle-tested in this codebase
 - Accessible by any MCP client

 ### Cons
+
 - Must forward Coolify env vars to Phoenix app runtime
 - Rewrites the API call in Elixir (no code reuse from existing `fetchLogs`)
 - New Elixir module to maintain

 ### Verdict
+
 **Rejected** in favor of Route B. The pi tool approach is simpler, reuses existing code and credentials directly, and keeps the change contained to a single file.

 ---
@@ -48,9 +53,11 @@ Add a new MCP tool `get_production_logs` to Tidewave that calls the Coolify API
 ## Route B: Pi Tool via `pi.registerTool()` ✓ SELECTED

 ### Description
+
 Add a `fetch_production_logs` tool registration to the existing `prod-logs` extension. The tool is called by the LLM directly (just like `read`, `bash`, etc.), fetches logs via the existing `fetchLogs()` function, and returns text content to the LLM.

 ### Pros
+
 - **Direct code reuse**: Same `fetchLogs()` function, same `resolveVar()` helper, same env vars
 - **No infrastructure changes**: No new Elixir modules, no credential forwarding, no config changes
 - **First-class LLM tool**: The LLM calls it natively during its agent loop, the result flows through the same context pipeline as any other tool result
@@ -58,10 +65,12 @@ Add a `fetch_production_logs` tool registration to the existing `prod-logs` exte
 - **No external dependencies**: `typebox` is already available as a pi built-in import

 ### Cons
+
 - pi-only (not accessible from other MCP clients — irrelevant for this use case)
 - Tool definition consumes a small amount of LLM context window space (same as any tool)

 ### Architecture Impact
+
 - **Modified file**: `.pi/extensions/prod-logs/index.ts` (add tool registration, ~50 lines)
 - **No changes**: No Elixir modules, no config, no schemas, no routes, no UI

@@ -2,9 +2,10 @@
 id: doc-7
 title: Pi access to production errors
 type: other
-created_date: '2026-05-04 08:07'
-updated_date: '2026-05-04 08:13'
+created_date: "2026-05-04 08:07"
+updated_date: "2026-05-04 08:13"
 ---
+
 # Implementation Routes: pi access to production errors

 ## Problem
@@ -12,6 +13,7 @@ updated_date: '2026-05-04 08:13'
 The project captures production errors via the `error_tracker` Elixir dependency (stored in `MusicLibrary.TelemetryRepo` SQLite database). Errors can currently only be viewed through the built-in web dashboard at `/dev/errors` (behind login auth, only with `:monitoring_routes` enabled). There is no programmatic access — no API, no tooling, no pi extension.

 The goal is to enable pi (the coding agent) to fetch and browse production errors. This requires three layers:
+
 1. A programmatic API to expose error data (behind auth)
 2. Pi tools the LLM can call to fetch errors
 3. A pi extension for interactive browsing
@@ -22,6 +24,7 @@ The goal is to enable pi (the coding agent) to fetch and browse production error

 **How it works:**
 Add a new API controller (`MusicLibraryWeb.ErrorsController`) with two endpoints under `/api/v1/errors`:
+
 - `GET /api/v1/errors` — list errors with optional filtering (status, muted, search) and pagination
 - `GET /api/v1/errors/:id` — single error detail with its occurrences and stacktraces

@@ -30,6 +33,7 @@ The controller queries the `error_tracker_errors` and `error_tracker_occurrences
 Pi tools (`fetch_production_errors` and `fetch_production_error`) make HTTP requests to this API using `fetch()` or `pi.exec("curl", ...)`. The pi extension builds on these tools for an interactive TUI.

 **Pros:**
+
 - Follows existing patterns exactly (see `CollectionController`, `require_api_token`)
 - Clean separation of concerns: API layer, tool layer, extension layer
 - Pi tools work remotely — no need for SSH or filesystem access to the production server
@@ -40,6 +44,7 @@ Pi tools (`fetch_production_errors` and `fetch_production_error`) make HTTP requ
 - The TelemetryRepo already exists and has the tables — no new database work

 **Cons:**
+
 - Requires a server code change and deployment
 - Adds two new routes

@@ -62,11 +67,13 @@ Pi tools execute SQL queries directly against the TelemetryRepo SQLite database
 No server-side API changes needed. The pi tools would use `pi.exec("sqlite3", ...)` or read the database file directly.

 **Pros:**
+
 - Zero server code changes
 - Immediate access to ALL data — no API shape limitations
 - Can run complex ad-hoc queries without API changes

 **Cons:**
+
 - **Production access requires SSH or filesystem access** — violates the project's existing API-based pattern for pi access (cf. `fetch_production_logs` which uses Coolify API, not SSH)
 - No auth layer — tools have full read access to the entire database
 - Tightly couples pi tools to the error_tracker schema — any migration could break tools
@@ -86,10 +93,12 @@ The direct-access approach introduces deployment friction (SSH key management, f
 Use the existing Tidewave MCP tools (`tidewave_execute_sql_query`) to query the error_tracker tables directly. This is available in dev but would need the Tidewave MCP server to be accessible in production (e.g., via SSH tunnel or a production-side MCP server).

 **Pros:**
+
 - No new code at all — uses what's already there
 - `tidewave_execute_sql_query` already understands the repo structure

 **Cons:**
+
 - **Tidewave MCP server does not run in production** — it's a development-only tool
 - Even if it did, running a dev tool against production is architecturally wrong
 - No browsing UX, no filtering, no pagination — basic SQL results only
@@ -106,9 +115,11 @@ Tidewave is a development tool. Exposing it to production would be an architectu
 Add an endpoint to the existing Coolify-like API pattern used by `fetch_production_logs`. This would require Coolify to expose error_tracker data, which it doesn't natively support. Could potentially parse production logs for error patterns, but that's unstructured and duplicative.

 **Pros:**
+
 - Consistent with the existing `fetch_production_logs` pattern

 **Cons:**
+
 - Coolify doesn't expose error_tracker data
 - ErrorTracker already has structured data — parsing it from raw logs is backwards and lossy
 - Would require Coolify API changes or custom Coolify plugin