jmoral4 / readittome

Python and .NET app to read detailed summaries of web pages, forums, and research papers.

ReadItToMe

Why use ReadItToMe rather than a screen reader? I built this tool for two major use cases.

1. Reading research papers and large web content in a smart way (don't read ads, don't read menus, etc etc).
2. Reading large forums and summarizing the findings, consensus, insights, etc.

In these cases, it blows a standard screenreader out of the water.

Features

• Support for OpenAI models (GPT-3/GPT-4)
• Support for Anthropic models (Claude-2/3)
• Support for Ollama Models (Mistral, Llama2, etc)

Optional CLI usage

Specify Url

py main.py --url "https://example.com/page"

Specify a filename (to reuse the same file. By default one file per webpage is generated)

py main.py --fixed-filename "summary.mp3"

Specify a 'playlist' or file with multiple urls, one per line, to process. Can be combined with --silent and --download-only to setup a playlist for later listening.

py main.py --playlist \your\directory\playlist.txt

Save the AI generated summaries for later viewing

py main.py --save-summaries \output\dir

Flags

• --silent (Don't vocalize the actions being performed)
• --download-only (Only download the audio files, don't play them back (useful for bulk creating a playlist))

Example (Download a playlist for use in a media player)

py main.py --playlist C:\git\HNplaylist.txt --download-only --silent

Setup

• Copy or Rename config.example.json to config.json
• Add your keys for models. OpenAI key is required for OpenAi's natural text to speech which is the main feature of this app. (may support other platforms in the future)
• Add your output directory - this is where audio files generated for playback will be stored
• Add your selected model and model type for text summarization (openai, claude, ollama)

Technical Decisions

Disclaimer: I'm not a daily Python coder but ironically the core implementation is in Python via experimentation and backported to C# via Claude 3.0 and hand fixup.

• Opted to use Pygame for audio playback in Python as it provided the most seamless user experience (other approaches required convoluted FFMPEG setup on Windows)
• Opted for OpenAI's voice - I personally enjoy the natural way they sound including vocal mannerisms.

Practical Notes

• MAX_RESPONSE_TOKENS has a very strong effect on how thorough or concise the summary is. At 720, you'll get a reasonable and detailed overview if the story is brief. I personally use 3072 since I use it for large stories or HackerNews threads. Expand this if you prefer a deeper dive - to the limits of your model. Of course, this has a direct effect on cost-per-query.
• In general, requires models with 16k+ context sizes in order to be useful (GPT-3.5-turbo, GPT-4-Turbo, Claude-2, Claude-3)
• Not all Ollama models support large context sizes.
• In practice Mistral was passable but most small/medium models (7B or less) did poorly or required tweaking to deliver useful summaries. YMMV!
• Claude-3 and GPT-4 did exceptionally well due to the large context sizes and recollect quality

Roadmap

• Chromium and FF based plugins (investigating)
• Better tested support for specific local models (ollama, oobabooga, or anything that supports the OpenAI api)
• Support for multiple audio generation models