portico-cli 0.1.7

Render any input as a portico – a three-layer ASCII visualization.

Try it locally • Try it online • Example • How it works

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About

portico renders input as a three-layered visual abstraction.

portico renders an arbitrary input (text, code, URL, repo) as a portico: a three-layer ASCII visualization with a roof (the unifying idea), pillars (the load-bearing components), and a base (the foundation everything rests on). An LLM decides what each layer means for the specific input; the renderer is a pure function from JSON to ASCII.

1. The LLM reads your input, classifies it, and decomposes it into three layers _ii^: roof, pillars, base.
   
2. The renderer, a pure function from JSON to ASCII, turns those layers into a portico.
   
3. The output is a tiny monument of abstraction that helps clarify concepts.

Glyph	Layer	Meaning
^	Roof	The unifying idea
ii	Pillars	The load-bearing components
_	Base	The foundation everything rests on

Try it locally

Install

uv tool install portico-cli

portico README.md
portico https://example.com/article
portico ./src --no-legend
echo "your text here" | portico -

Try it online

Run portico in your browser, no install required:

▶ Try demo on Hugging Face

The space uses 🦙 Llama 3.3 70B via Groq. Paste input and render.

Example

portico "https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)"

── encyclopedia article: Transformer ─────────────────────────────────────

                                   ◆ ◆
                               ^^^  ▲  ^^^
     ╔══════════════════════════════════════════════════════════════╗
     ║                  Attention Is All You Need                   ║
     ╚══════════════════════════════════════════════════════════════╝
  ////º~~º~~º~~º~~º~~º~~º~~º~~º~~º~~º~~º~~º~~º~~º~~º~~º~~º~~º~~º~~º~\\\\
   ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░
       ▀██▀          ▀██▀          ▀██▀          ▀██▀          ▀██▀
        ██            ██            ██            ██            ██
        ██            ██            ██            ██            ██
      RNN to         Core        Training    Variants and     Broad
   Transformer   Architecture    Paradigm     Efficiency   Applications
        ██            ██            ██            ██            ██
        ██            ██            ██            ██            ██
       ▄██▄          ▄██▄          ▄██▄          ▄██▄          ▄██▄
   ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░
  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
╔════════════════════════════════════════════════════════════════════════╗
║                          Multi-head Attention                          ║
╚════════════════════════════════════════════════════════════════════════╝

legend:
  ^  Attention Is All You Need: Replacing recurrence with multi-head self-
     attention enables parallel, scalable sequence modelling.
  ii RNN to Transformer: Sequential limitations of RNNs and seq2seq models
     motivated the shift to parallel attention.
  ii Core Architecture: Tokenization, positional encoding, encoder and decoder
     layers form the transformer's structure.
  ii Training Paradigm: Large-scale self-supervised pretraining followed by
     task-specific fine-tuning drives performance.
  ii Variants and Efficiency: Encoder-only, decoder-only, and encoder-decoder
     designs, plus optimizations like FlashAttention and KV caching, adapt the
     architecture to diverse needs.
  ii Broad Applications: Transformers have expanded from NLP to vision, audio,
     robotics, and multimodal generation.
  _  Multi-head Attention: Scaled dot-product multi-head attention is the
     mathematical substrate every transformer component rests on.

─────────────────────────────────────────────────────── built with _ii^ ──

Run with claude-sonnet-4-6.

Pipeline

Five stages run in strict order. Each owns one responsibility and hands a typed value to the next. The CLI (cli.py) is the only place that wires them together and translates exceptions into exit codes.

                           ┌──────────────────────────────┐
                           │         LLM provider         │
                           │   claude / openai / gemini   │
                           └──────────────────────────────┘
                              ▲                          ▲
                              │ (if oversized)           │ (analyze + retry)
  input   ┌────────┐   ┌──────┴─────┐   ┌────────┐   ┌───┴────┐   ┌──────────┐   ASCII
  ─────▶  │ loader │──▶│ summarizer │──▶│  cache │──▶│analyzer│──▶│ renderer │ ──────▶
          └────────┘   └────────────┘   └────────┘   └────────┘   └──────────┘
          loaders/     summarize.py     cache.py     analyzer.py  render/

• loader – reads the input (text, file, directory, URL, or repo) into a normalized LoadedInput.
• summarizer – chunks oversized inputs and recursively summarizes them via the LLM.
• cache – hashes (text, provider, model) to JSON on disk; on hit, skips the analyzer.
• analyzer – prompts the LLM for a three-layer decomposition; validates and retries on bad JSON.
• renderer – turns the analyzer's JSON into ASCII; pure function, never calls the LLM.

Inputs

• Raw text or stdin
• Local files and directories
• URLs (page content is extracted)
• Git repositories

When an input doesn't fit a three-layer shape – poems, flat lists, gibberish – portico refuses honestly rather than fake one.

Customization

Flag	What it does
--no-legend	Hide the per-layer summary (legend renders by default)
--reapex=N	Pin the apex to seed N (random by default; pool of 600+ variants)
--json	Emit the analyzer's JSON instead of rendering
--diagnose	Print a pipeline report (input type, model, fit quality) and exit

Run portico --help for the full list.

Apex

The apex is the ornament crowning the portico -- picked at render time from a pool of 600+ variants.
🎲 --reapex=SEED pins a specific composition to reproduce.

portico https://0trm.blog/data-science-at-camp-nou/ --reapex=0

── essay: Data Science at Camp Nou ─────────────────

                       ▲ * ▲
                    ~~~  ▲  ~~~
     ╔════════════════════════════════════════╗
     ║         Data-Driven Ticketing          ║
     ╚════════════════════════════════════════╝
  ////º~~º~~º~~º~~º~~º~~º~~º~~º~~º~~º~~º~~º~\\\\
   ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░
        ▀██▀            ▀██▀            ▀██▀
         ██              ██              ██
         ██              ██              ██
     Analytics    Experimentation    Predictive
         ██              ██           Modeling
         ██              ██              ██
         ██              ██              ██
        ▄██▄            ▄██▄            ▄██▄
   ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░
  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
╔══════════════════════════════════════════════════╗
║                   Fan Behavior                   ║
╚══════════════════════════════════════════════════╝

portico https://0trm.blog/data-science-at-camp-nou/ --reapex=1

── essay: Data Science at Camp Nou ─────────────────

                        ◆ ◆
                    ═══  ◆  ═══
     ╔════════════════════════════════════════╗
     ║         Data-Driven Ticketing          ║
     ╚════════════════════════════════════════╝
  ////º~~º~~º~~º~~º~~º~~º~~º~~º~~º~~º~~º~~º~\\\\
   ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░
        ▀██▀            ▀██▀            ▀██▀
         ██              ██              ██
         ██              ██              ██
     Analytics    Experimentation    Predictive
         ██              ██           Modeling
         ██              ██              ██
         ██              ██              ██
        ▄██▄            ▄██▄            ▄██▄
   ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░
  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
╔══════════════════════════════════════════════════╗
║                   Fan Behavior                   ║
╚══════════════════════════════════════════════════╝

portico https://0trm.blog/data-science-at-camp-nou/ --reapex=7

── essay: Data Science at Camp Nou ─────────────────

                       · · ·
                    ░░░  ▲  ░░░
     ╔════════════════════════════════════════╗
     ║         Data-Driven Ticketing          ║
     ╚════════════════════════════════════════╝
  ////º~~º~~º~~º~~º~~º~~º~~º~~º~~º~~º~~º~~º~\\\\
   ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░
        ▀██▀            ▀██▀            ▀██▀
         ██              ██              ██
         ██              ██              ██
     Analytics    Experimentation    Predictive
         ██              ██           Modeling
         ██              ██              ██
         ██              ██              ██
        ▄██▄            ▄██▄            ▄██▄
   ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░
  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
╔══════════════════════════════════════════════════╗
║                   Fan Behavior                   ║
╚══════════════════════════════════════════════════╝

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License

MIT

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