Stop reading 60 pages to find out if ChatGPT hallucinated your citation.
Otio answers from your uploaded sources. Every citation links to the exact passage. Click. Verified.
Free plan is a real product. Not a trial.
Paid plans from $7/mo — and right now 50% off Otio Go for 3 months with code GO50V2
One tab. Every AI model. All your sources loaded.
Try it here 👇
otio.ai/?ref=brookswha…
I wasted months re-explaining the same PDFs to ChatGPT every single session.
Upload. Explain. Close tab. Repeat from zero.
Found something that actually remembers.
And my whole research workflow changed overnight 👇
Nellie, a vet student at Adelaide University, runs multiple AI models side-by-side in Otio when she's decoding journal articles for research papers.
She highlights a confusing section, asks the same question to @claudeai , @OpenAI , and Gemini at once, and compares their answers in parallel. Every response traces back to the exact quote in the source; no rereading the same sentence twelve times hoping it clicks.
She organises her workspace by topic: uni research in one, life admin in another. All her sources stay in one place. All her models run in the same thread.
That's the difference between four tabs and one workspace.
Original video by Nellie (@nellieforellies on Instagram)
Jenni AI's claim confidence feature checks whether your citations match your claims - after you've already written the sentence.
Otio solves the upstream problem: making sense of 80 sources before you write the first line.
Different workflow stage. Different tool.
Otio is the AI research assistant for iPhone and desktop - built to summarize articles, YouTube videos, and PDFs across every AI model, in one place.
Upload anything: PDFs, web pages, YouTube links, podcasts, or notes. Chat across all your sources at once, with every answer
You can't run the same prompt through Claude and GPT side-by-side in ChatGPT or Claude. You lose one model's angle.
30 transcripts + 6 months of Slack exports. One question, two models, both cited. Different themes surfaced.
Google just dropped Gemma 4 12B - multimodal model that ditches the vision encoder and supposedly runs on 16GB of VRAM.
HN commenters are split: half excited about a locally runnable multimodal model, half calling the memory claim a marketing spin once you factor in quantization and context overhead.
blog.google/innovation-and…
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