Mira
peeps in 2025: he's so broke he can't even afford to breathe anymore guess who has talix pro now 🎧
wait, i think i should create a separate channel to push the changelogs and updates
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would you like to test talix?
Anonymous Poll
61%
yep. setup some sorta wait-list
11%
nah. will wait for proper launch
28%
what's talix
Forwarded from Talixi
Talix — from imagined roots blending "talent" + "elixir". It's the rare essence of hidden genius. I have always felt that learning shouldn't be dull, and the maximum value of learning is directly proportional to how enjoyable you make the journey. Instead of a one-size-fits-all traditional approach, Talix uses personalization to ensure that every material is suitable for the learner based on their interests.
[Take a look at its inception]
[Take a look at its inception]
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Talixi
you can toggle web search and upload your PDFs or images, so that whatever material you generate will be up-to-date and context aware with even your custom prompt parameter per lesson level
so this is the stuff we introduced lately. i think it's pretty cool
Talixi
you can toggle web search and upload your PDFs
So.. the technical implementation no one asked for, but for the few subsets of nerds here
Unlike traditional "inaccurate" PDF parsers that often lose formatting or table structures, Talixi treats your files as multimodal inputs. We upload the raw PDF buffer directly to Gemini. The model sees the document layout, diagrams, and tables exactly as a human would (aka utilizing RAG). Then the AI uses its internal vision and document understanding to extract key topics, notable facts, and page-specific highlights as structured data (using zod for validation). So this just eliminates the need for external OCR libraries or complex text-extraction scripts.
Also the web search isn't just a simple link scraper; it uses native Google Search Grounding using built-in gemini tool calling capability (`google_search` tool). When the search grounding context is toggled on, the AI generates a specific search query based on your lesson topic, executes it via Google's live search engine, and retrieves "Grounding Metadata." Then the system extracts structured citations (URLs and snippet previews) directly from the Google Search results, which are then displayed in the sources section of your lesson.
With that said, you can now literally build an efficient multimodal AI workflow by following this approach.
Unlike traditional "inaccurate" PDF parsers that often lose formatting or table structures, Talixi treats your files as multimodal inputs. We upload the raw PDF buffer directly to Gemini. The model sees the document layout, diagrams, and tables exactly as a human would (aka utilizing RAG). Then the AI uses its internal vision and document understanding to extract key topics, notable facts, and page-specific highlights as structured data (using zod for validation). So this just eliminates the need for external OCR libraries or complex text-extraction scripts.
Also the web search isn't just a simple link scraper; it uses native Google Search Grounding using built-in gemini tool calling capability (`google_search` tool). When the search grounding context is toggled on, the AI generates a specific search query based on your lesson topic, executes it via Google's live search engine, and retrieves "Grounding Metadata." Then the system extracts structured citations (URLs and snippet previews) directly from the Google Search results, which are then displayed in the sources section of your lesson.
With that said, you can now literally build an efficient multimodal AI workflow by following this approach.
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