distillr 0.19.4

Source-to-intelligence platform: turn YouTube, websites, and arXiv papers into a structured, reusable corpus with per-source insights, cross-source synthesis, and Deep Research reports.

Distill

Point distill at a research goal; it finds the papers, talks, repos, podcasts, and posts worth reading, analyzes each into structured insights with source receipts, verifies the claims against those receipts before writing, and synthesizes across them into a durable plain-Markdown corpus on your disk. You browse it in Obsidian, your agents read it as files or query it over MCP, you ask it questions and the cited answers can re-enter the corpus - and it refreshes on a cadence instead of going stale.

Installed as distillr on PyPI; the CLI command is distill (plus distill-mcp).

pip install distillr
distill papers "temporal knowledge graph" --topic tkg --limit 20

That one command searches arXiv, downloads 20 PDFs, extracts full text, runs structured analysis on each, and writes a cross-paper synthesis. For a 20-paper run like the example below, expect single-digit minutes and under a dollar in model spend on the grok-4.3 default. Terminal output during the run looks like this (illustrative run; see the labelled sample-output note below):

Papers: temporal knowledge graph
Topic: tkg | Sort: relevance | Expand: on | Rerank: on | Limit: 20

Analyzing 20 paper(s)

  paper 1/20 | phase analyze | completed 0/20 | failed 0 | spent $0.0000 Time is Not a Label: Continuous Phase Rotation for Temporal Knowledge Graphs and Agentic Memory
  paper | phase done | completed 1/20 | failed 0 | spent $0.03 | ~6m left
  paper 2/20 | phase analyze | completed 1/20 | failed 0 | spent $0.03 Inductive Reasoning for Temporal Knowledge Graphs with Emerging Entities
  ...

  6m 47s  ~$0.58 (391,278 in / 38,117 out)

  time_is_not_a_label_260411544_Paper.md     90.4 KB
  time_is_not_a_label_260411544_Insights.md   8.1 KB
  ...
  tkg_Paper_Synthesis.md  11.8 KB
  tkg_Corpus_Synthesis.md 10.5 KB

Where distill sits

Three kinds of tools orbit this space, and distill is deliberately none of them:

• Deep Research oracles (ChatGPT, Gemini, Perplexity) are excellent at one-shot answers - and the work evaporates after each session. No corpus, no receipts you can re-check, nothing that compounds. Distill is the engine under that pattern: every run leaves transcripts, extracted paper text, per-source insights, and cross-source synthesis on disk, refreshable on a cadence.
• Grounded notebooks (NotebookLM) keep a persistent corpus, but in a silo: you find and feed the sources by hand, and the corpus exports to Google Docs/Sheets only. Distill finds the sources against your goal, and the corpus is plain files you own.
• LLM-wiki maintainers (the post-Karpathy wave of agent-curated Markdown vaults) assume you already have the content and tidy it. Distill is the acquisition half they leave out - goal-aware discovery across papers, videos, sites, and X, transcript-grade capture, and provenance on every claim - producing exactly the kind of vault those tools maintain.
• Academic literature tools (Elicit, Semantic Scholar, scite, Consensus) are stronger for pure paper search, citation graphs, and systematic review. Distill treats papers as one source type inside a broader corpus that also holds talks, vendor docs, and posts.

The short version: those are report and search layers; distill is the corpus layer underneath repeated research - capture, per-source insights, cross-source synthesis, refresh, receipts. And plain Markdown is the substrate, not the moat: anyone can write Markdown. The moat is the acquisition-and-maintenance loop that fills it and keeps it current.

That matters when you are doing thesis work, competitive analysis, technical due diligence, or building a startup knowledge base: you can verify the receipts, watch how a topic evolves, query the same folder through MCP from Claude Desktop / Cursor / other agents, and open it in Obsidian, Logseq, VS Code, or plain filesystem search. Reports and briefs export to Word for stakeholder delivery (distill export <topic> --what report), and paper topics export to BibTeX or RIS for Zotero and reference managers (distill export <topic> --what citations). Nothing is locked in anything.

One honest scoping note: distill is a terminal tool for people comfortable installing a Python CLI and setting two API keys (or running a local model). If you want a one-click app, this isn't that - and the corpus it builds is plain files precisely so the tools you already use can be the interface.

What you get

One local library/ directory of plain Markdown. No database, no cloud lock-in, no proprietary format. Files use globally descriptive names plus YAML frontmatter so knowledge-base tools, Dataview-style plugins, and AI coding assistants can understand them without guessing from generic insights.md tabs.

Eight source types, same pipeline shape (capture -> analyze -> verify -> synthesize -> report), every one behind the same write-time verify gate:

Source	Entry point	Notes
YouTube	distill latest, distill video, distill discover	channels, topic searches, videos, Shorts
Websites	distill site, distill site-batch	browser-first crawl; PDF/embedded-video ingestion
arXiv papers	distill papers	query expansion, LLM rerank, full-PDF extraction, DOI metadata when arXiv supplies it, cross-paper synthesis, BibTeX/RIS export
X (Twitter) posts	distill ingest <tweet-url>	public syndication endpoint (no anti-bot scraping); attached video transcribed via local-first Whisper
GitHub repos	distill ingest <repo-url>	metadata + README + releases into a structured maturity/when-to-use insight
Podcasts	distill ingest <rss-url>	RSS-first; publisher transcripts preferred over paid audio transcription
Newsletters (Substack-class)	distill ingest <feed-url>	full post text from the feed itself; routed by substance, narration audio ignored
Local files	distill ingest <path>	PDF/Markdown/text/HTML documents, plus audio/video through the Whisper ladder

Plus an MCP server so AI assistants and agent systems can query the library directly, and distill ask to query it yourself - answers grounded only in your corpus, every claim cited, with --save promoting a verified answer back into the corpus so it compounds.

Quick start

One-line install

Windows (PowerShell):

powershell -ExecutionPolicy ByPass -c "irm https://raw.githubusercontent.com/blisspixel/distillr/main/scripts/install.ps1 | iex"

macOS / Linux:

curl -fsSL https://raw.githubusercontent.com/blisspixel/distillr/main/scripts/install.sh | bash

After the installer finishes, open a new terminal and run distill init. It is a guided, one-command setup: it creates your .env, helps you pick the cloud or local provider, validates your key against the provider, installs the Playwright browser if it is missing, and ends with a ready/not-ready verdict and the first command to try. (distill doctor remains the read-only diagnostic; distill init is the one that sets things up.) Both are no-TTY-safe, so a scripted or agent-driven setup never hangs.

Updating: distill update upgrades to the latest release in place - it detects how you installed (uv tool / pipx / pip) and runs the right upgrade for you; distill update --check just reports whether a newer version exists. distill also prints a one-line nudge when a new release is published (cached daily, silence with DISTILL_NO_UPDATE_CHECK=1).

---

Distill runs on Windows, macOS, and Linux (Python 3.12+). Local models run on consumer GPUs via Ollama or LM Studio.

Recommended: uv tool (the 2026 default for Python CLIs)

uv installs the CLI into an isolated, on-PATH environment in one command - no venv to activate, no PATH surgery:

uv tool install distillr     # installs the `distill` + `distill-mcp` commands
distill --version            # confirm it's on PATH
playwright install chromium  # browser support for YouTube + web capture
distill doctor               # verify keys + environment

Want to try it before installing? uvx --from distillr distill --help runs the CLI in a throwaway environment (note: ingestion needs the one-time playwright install chromium, so uv tool install is the path for real runs).

Alternative (virtual environment or pipx)

This is the cleanest way without uv and avoids common PATH problems.

# 1. Create a virtual environment
python -m venv .venv          # some systems: python3 -m venv .venv

# 2. Activate the environment
#   Windows (PowerShell):   .\.venv\Scripts\Activate.ps1
#   macOS / Linux:          source .venv/bin/activate

# 3. Install
pip install -e .              # from a source checkout, or: pip install distillr

# 4. Browser support (for YouTube + web capture)
playwright install chromium

# 5. Verify
distill doctor

Even simpler alternative: pipx (great for CLI tools):

pipx install distillr
playwright install chromium
distill doctor

Fast path (bare pip)

pip install distillr
playwright install chromium
distill doctor

Windows note (common gotcha): If you use a system Python (e.g. under C:\Program Files\Python...) without admin rights, bare pip installs to your user directory. The CLI (distill.exe) may land in a Scripts folder that is not on PATH. Use the venv or pipx method above, or add %APPDATA%\Python\Python312\Scripts (adjust version) to your user PATH and restart the terminal.

The corpus lands in ~/.distill/library/ by default (<repo>/library/ when running from a source checkout); override with DISTILL_OUTPUT_DIR. Set two keys in .env in your working directory (copy from .env.example):

XAI_API_KEY=xai-...             # Grok models
GEMINI_API_KEY=AIza...          # Gemini Deep Research (reports + briefings)

Or run locally with Ollama (no API keys needed for ingestion):

ollama pull qwen3.5:27b         # download recommended model for 24GB GPU
echo "DISTILL_PROVIDER=ollama" >> .env
distill doctor                  # verify local setup

Local mode still uses fresh sources. DISTILL_PROVIDER=ollama or DISTILL_PROVIDER=lmstudio changes the model that analyzes the fetched receipts; it does not answer from the model's pretraining alone. distill discover, distill latest, distill papers, and distill ingest still fetch current public sources such as arXiv, YouTube, feeds, sites, repos, and local files, then ask the configured model to analyze that captured evidence.

Shell completions (optional): distill --install-completion wires tab-completion for your shell (bash/zsh/fish/PowerShell), including live topic-name completion; distill --show-completion prints the script to inspect or source manually.

No telemetry. Distill phones home for nothing - no analytics, no usage beacons. Your research, your keys, and your run history stay on your disk. The only outbound calls are the LLM/transcription APIs you configure and the public sources you ask it to fetch.

Then try any of:

# Goal-aware cross-source discovery (papers + videos + curated sites, reranked against a goal)
distill discover "help an AI become a great music composer" --topic music --preview
distill discover --goal-file private/my-goal.md --topic research --yes
distill discover --goal-file private/agent365-goal.md --topic agent365 --site-seeds private/agent365_sites.json --site-limit 10 --preview
distill discover --goal-file private/agent365-goal.md --topic agent365 --trusted-site https://learn.microsoft.com/en-us/microsoft-365/agents --site-limit 10 --preview

# Get smart on a YouTube topic, fast
distill latest "Microsoft Fabric best practices" --limit 10 --report

# Discover and ingest arXiv papers - expands the query, LLM-reranks candidates,
# picks the top N (use --preview to see the shortlist without ingesting)
distill papers "agent memory systems" --topic memory --limit 20
distill papers "agent memory systems" --topic memory --limit 20 --preview
distill export memory --what citations --format bibtex

# Distill a vendor/research site
distill site-batch configs/example_seeds.json --topic example --seed-only

# Ask the corpus a question -- grounded-only, every claim cited; --save promotes
# a verified answer back into the corpus
distill ask "which checker should the verify tier use?" --topic memory

# Trust report: verification coverage, prompt staleness, synthesis freshness,
# exact duplicate videos, thin long-video transcripts, near-duplicate insights, contested concepts,
# link integrity, coverage gaps --
# free, no model calls
distill audit memory --report-only
distill audit memory --next-actions --json   # bounded loop handoff plan

Long ingest and report runs print per-item or per-phase progress with completed count, failed count, running spend, and ETA when enough items have completed. For external loops that only need files, exit codes, or JSON, use distill --quiet <command> to suppress human console output. DEBUG records are kept in library/.distill/distill.log for post-run review, and distill --verbose <command> mirrors them to stderr while a command runs. Site ingest progress also reports unchanged-page reuse and empty crawls as structural outcomes, so repeated website runs show why work was skipped. Discovery previews also summarize video candidate counts and known watch time before approval, so broad goals reveal the size of the video corpus before anything is ingested. Website-heavy discovery can expand operator-trusted domains or section URLs with --trusted-site, using same-host sitemaps, TOC/navigation links, and landing-page links before the normal goal-aware rerank. Selected website candidates ingest exact pages by default; operators can opt into bounded shallow crawls with --site-crawl-depth and --site-crawl-pages. If a trusted site is a section URL, shallow crawls keep followed links under that source path branch. Site preview rows show the exact URL, section label, discovery source, and sitemap freshness date when available, so official-doc page candidates stay legible before approval. Website seed files can also mix exact-page and shallow-crawl modes, and distill site-batch --preview shows the resolved pages, depth, and crawl boundary before any crawl or write. With global --json, the same preview emits loop-readable plan rows instead of console text.

The full command reference lives in docs/usage.md.

Mental model

library/
  └── topics/<topic>/
       ├── channels/<creator>/videos/<video>/
       │     ├── <video-slug>_Transcript.txt
       │     └── <video-slug>_Insights.md
       ├── sites/<hostname>/pages/<page>/
       │     ├── <page-slug>_Content.md
       │     └── <page-slug>_Insights.md
       ├── papers/<paper>/
       │     ├── <paper-slug>_Paper.md
       │     └── <paper-slug>_Insights.md
       ├── repos/<repo>/                    # GitHub:  _Repo.md + _Insights.md
       ├── podcasts/<show>/<episode>/       # podcasts: _Episode.md + _Transcript.txt + _Insights.md
       ├── newsletters/<pub>/<post>/        # newsletters: _Content.md + _Insights.md
       ├── answers/                         # distill ask: _Answer.md (+ promoted insights)
       ├── <topic>_Topic_Synthesis.md      # cross-source
       ├── <topic>_Corpus_Synthesis.md     # mixed-source view
       └── <topic>_Audit.md                # trust report from `distill audit`

You build a topic library over time. Ingest once, refresh on a cadence, generate a report or briefing when you need one. Older insights.md-style libraries are still readable, but new Markdown writes use the stable knowledge-base naming scheme.

See docs/outputs.md for what every artifact contains.

Sample output

The excerpts below are synthetic examples: the file shapes, frontmatter fields, and section structure are exactly what distill writes, but the papers, authors, and numbers are invented for illustration. For a provenance-first tool that distinction matters, so it is stated. A real, unedited example corpus (6 papers on claim verification, $0.19 of analysis) ships in examples/.

A cross-paper <topic>_Paper_Synthesis.md (excerpt):

## Strongest Research Signals

- Append-only temporal representations improve long-horizon extrapolation:
  RoMem (arXiv:2604.11544), EST (arXiv:2602.12389v3), and CID-TKG converge on
  persistent or dual-view entity state over destructive overwriting, with
  consistent MRR/Hits@K gains on ICEWS and GDELT.

- Semantic gating scales better than manual relation tagging: RoMem's Semantic
  Speed Gate and EST's energy-barrier gate both learn relational volatility
  from text embeddings rather than schema tags…

Per-paper <paper-slug>_Insights.md excerpt (click to expand)

---
title: "Time is Not a Label: Continuous Phase Rotation for Temporal Knowledge Graphs"
type: "insights"
topic: "tkg"
source: "arxiv"
source_id: "2604.11544v1"
url: "https://arxiv.org/abs/2604.11544v1"
doi: "10.5555/example-tkg"
authors: ["Alice Example", "Bob Example"]
tags: ["distill/tkg", "source/arxiv", "cs.AI"]
synthesis_scope: "single-paper"
analyzed_by: grok-4.3
source_mode: full_pdf
---

### Core Contribution
1. Continuous functional rotation θ_r(τ) = s · α_r · τ · ω instead of discrete
   timestamp lookup tables. Zero-shot interpolation of unseen dates.
2. Semantic Speed Gate: MLP that reads only text embedding ϕ(r) and outputs α_r.
   Learns relational volatility from data.
3. Geometric shadowing in complex space: obsolete facts rotated out of phase so
   the correct fact outranks contradictions via the scoring function alone.

### Methods and Evidence
- On ICEWS05-15, RoMem-ChronoR reaches 72.6 MRR (vs vanilla ChronoR 68.4).
- Zero-shot domain transfer to FinTMMBench: 0.728 MRR, 0.673 R@5.
- All baselines use identical answer LLM and judge for fairness.

### Limits and Open Questions
- Computational cost at millions-of-facts scale is motivation but no latency,
  memory, or throughput numbers are reported.
- Gate pretrained only on ICEWS05-15 political events; generalization to
  highly ambiguous relations is not quantified.

For multi-topic literature reviews, stakeholder briefings, or agent grounding, distill research-brief (Gemini Deep Research, web-augmented) and distill synthesize (grok-4.3 single-call, corpus-only) take a user-written context file that shapes the output. See docs/usage.md#research-briefings-and-deep-synthesis.

Dashboard

distill                         # terminal home screen
distill serve                   # local web dashboard at http://127.0.0.1:8899

The terminal home screen shows tracked topics, channel and topic watches, recent runs, failures, and rolling spend. The web dashboard adds clickable drill-downs to per-topic, per-channel, and per-video views with rendered markdown, plus cost history and watchlist status. Both auto-refresh and read directly from library files - no database.

MCP server, and agent-discoverable directories

Distillr is built for two parallel agent-integration paths:

Path 1 - MCP (structured queries). Claude Desktop / Claude Code config:

{ "mcpServers": { "distill": { "command": "distill-mcp" } } }

Distill exposes 27 tools, deliberately kept small and shrinking toward workflow-shaped tools. The JIT read layer returns ranked path/preview/score tuples with read_insight drill-down, never full payloads by default; ask answers questions grounded only in the corpus, with citations; and find_insights_summary returns a token-bounded brief for sub-agents, cached so repeat calls are free. For agent-facing deployments, set DISTILL_MCP_READ_ONLY=1: agents keep the full read surface while every spend/ingest/mutation tool refuses with a clear message, so they cannot burn budget or poison the corpus by tool call. Ingest happens via the CLI by a named operator. Deployments that do expose write tools get two narrower guardrails: DISTILL_MCP_MAX_SPEND_PER_CALL (per-call spend ceiling, enforced on actual recorded spend) and DISTILL_MCP_INGEST_ALLOWLIST (URL ingest confined to operator-approved domains). The MCP site_batch tool accepts relative JSON seed files with the same exact-page and shallow-crawl modes as the CLI, and its preview=true mode returns the plan even in read-only deployments. See docs/mcp.md for the list.

Path 2 - file system (the corpus IS the interface). When a coding agent cds into library/topics/<your-topic>/, the directory is plain Markdown with stable filenames and YAML frontmatter, so grep, cat, ls, and find are first-class query primitives - no schema to learn, no MCP setup required. Every topic directory (and the library root) ships auto-generated CLAUDE.md and AGENTS.md orientation files with identical content - CLAUDE.md for Claude Code, AGENTS.md for Codex, Cursor, Gemini CLI, and the 30+ tools on the cross-vendor AGENTS.md standard - so any agent that enters the directory gets oriented. This matches what Anthropic's Agent SDK material recommends for agent design: file system + composable tools as the substrate, with structured APIs layered on top when they help, not as the only entry point.

There's also a canonical Agent Skill distributed as the folder skills/distill-corpus/ (containing SKILL.md plus the convention for additional resources). Drop the folder into ~/.claude/skills/ (or ~/.agents/skills/). The skill is narrowly scoped to "use a distill corpus via its plain files and CLI", emphasizes verification against receipts, carries a Gotchas section derived from real failure modes, and uses the generated per-topic AGENTS.md/CLAUDE.md files for progressive disclosure of current state.

Cost

On the grok-4.3 default ($1.25/$2.50 per 1M tokens), bulk video analysis runs about $0.03/video and a full paper about $0.03; Gemini Deep Research dominates paid reports at about $2-3/report; distill synthesize is about $0.20-0.40 for a multi-topic corpus pass. grok-4.3 is the cloud floor: xAI retired the cheaper fast tiers (grok-4-1-fast etc.) on 2026-05-15, and those slugs now redirect to grok-4.3 and bill at grok-4.3 rates (migration guide). The only cheaper path is running analysis on a local model (Ollama/LM Studio). distill eval --models grok-4.3,<local-model> measures the cost x quality tradeoff over frozen fixtures and recommends the cheapest model that clears your quality bar before you switch. Every run logs actual vs estimated cost to cost_log.jsonl, and per-call prompt telemetry lands in library/.distill/telemetry.jsonl; distill costs shows estimator accuracy, local/cloud split, and the biggest prompts. The estimator's goal is accuracy, not safe padding: a padded estimate discourages runs you would happily pay for, so calibration error is tracked and shrunk over time.

Cost modes. DISTILL_COST_MODE=auto|no-metered|paid-ok (or distill --cost-mode <mode> <command> for one run) gates routes by billing: no-metered allows local Ollama/LM Studio and refuses API-billed or ambiguous routes before any provider call. xAI and Gemini cloud routes plus Ollama and LM Studio local routes are implemented today; Anthropic and OpenAI ship in-tree as opt-in, uncalibrated routes; the plan-quota CLI adapters (Codex CLI, Claude Code, Grok Build, Gemini/Antigravity) are roadmap, and only graduate once an adapter doctor proves included-plan auth, machine-readable output, scratch-only writes, usage ledgering, and distill eval quality. The cost log records provider and route class on every run, even when the dollar cost is zero.

Recurring profiles track a topic over time. distill profile run <name> prints an approval plan; --yes executes it through the normal ingest, analyze, verify, and cost paths with resume state under library/.distill/profiles/, and distill audit surfaces profile staleness and health.

Full cost model, the route-class table, and per-stage costs: docs/cost.md. Adapter and routing design: docs/design/cli-adapter-runbook.md and docs/design/recurring-profiles-cost-routing.md.

Reliability and trust boundaries

What's enforced (every release clears the same CI gate): more than 3,140 tests at 89% branch coverage (floor ratchets up-only toward the 1.0 ≥95% gate), ruff + import-linter dependency-direction contracts + pyright + bandit + pip-audit, pinned dependencies via a committed uv.lock, SHA-pinned Actions, and PEP 740 build provenance on every PyPI release. Default tests mock all LLM and network boundaries; contributors never burn API spend, and live integration tests are marked and opt-in.

Trust boundaries, stated plainly: everything ingested (transcripts, pages, PDFs, tweets, READMEs, feeds) is treated as untrusted input - injection-resistance rules are threaded through first- and second-hop prompts, the dashboard sanitizes rendered HTML, and MCP file access is confined to the library root (read-only mode available, above). Distill never bypasses login walls, captchas, or anti-bot defenses. Known-fragile edge: YouTube extraction depends on yt-dlp, which churns with YouTube's countermeasures - transient caption failures retry with backoff, captionless videos fall back to the local-first Whisper ladder, and remaining failures degrade with messages, not corrupted corpora.

What verification means here: analysis output is LLM-generated and can err; provenance fields on every artifact exist so you can check receipts, and distill checks them itself. A write-time verify hook grounds every numeric claim in every insight, on every source type, against its source receipt before the artifact is committed (--verify warn|strict|off; strict refuses to write a flagged insight). Answers from distill ask only re-enter the corpus if they pass that gate. The entailment tier (pip install distillr[entailment]) extends the same gate to prose claims: a small local cross-encoder (HHEM-2.1-Open, Apache 2.0, no cloud calls) scores every substantive sentence against the source receipt, and --verify strict refuses on prose flags too; without the extra installed, the deterministic tier stands alone. A prompt-version registry lets the audit flag artifacts produced by since-improved prompts instead of letting them age silently. distill audit <topic> rolls verification coverage, prompt staleness, synthesis freshness (a synthesis older than the sources under it is flagged in the report, the dashboard, and the topic's own orientation files, because confident prose is the most dangerous place for staleness to hide), exact duplicate video identities, health warnings, contested concepts, link integrity, and coverage gaps into a per-topic report artifact, free and deterministically. distill audit <topic|all> --next-actions --json returns the same findings as bounded actions with commands, approval class, write scope, loop metadata, and verifier stop conditions for external agents or schedulers. Full posture: docs/SECURITY.md and the security section of the roadmap.

Docs

Start at the documentation index, which groups everything below by task (get started / how-to / reference / explanation). The high-traffic entries:

• docs/usage.md - full command reference
• docs/invariants.md - design charter: what distill is, is not, and the rules that don't bend
• docs/architecture.md - data flow, 4-phase report pipeline, model routing, security hardening
• docs/outputs.md - what every artifact contains
• docs/cost.md - cost model, examples, guardrails
• docs/mcp.md - MCP tools, resources, prompts
• docs/migration-grok-4.3.md - Grok 4.3 migration guide (model retirement May 15, 2026)
• docs/briefing-contexts/TEMPLATE.md - starting point for --context-file prompts
• private/README.md - where personal/client-specific files go (git-ignored)

Roadmap and changelog

• docs/CHANGELOG.md - what shipped
• ROADMAP.md - what's next

Feature work is interleaved with recurring bug-hunt + harden passes - see the release rhythm note in the roadmap.

Contributing

See docs/CONTRIBUTING.md for dev setup, quality gates, and scope. Security disclosures go through docs/SECURITY.md.

License

Apache 2.0. See LICENSE. Free to use, build on, fork, and share patterns.