Flam

@flamappofficial

The AI-native content format powering the internet beyond videos.

San Francisco

flamapp.ai

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Flam
@flamappofficial
Sep 1
Indic languages are the toughest to crack for a multilingual LLM. India speaks 22 languages. AI breaks on almost all of them. The best English models struggle with Hindi and barely understand Odia, Assamese or Sanskrit. The specialists that do speak them well are too slow for
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Flam
@flamappofficial
Aug 20
Most models translate first and think second. That's why they drift out of your language mid-sentence and break the formats your product depends on. Rupam on how Falcon gets to first token in 29ms on average and why that number doesn't move when the language does.
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Flam
@flamappofficial
Aug 10
Inside Falcon - the tokenizer. With Rupam, Senior AI Engineer at Flam. Blink. That took about 100 milliseconds. In that time Falcon reads text, splits it and has its first token out in just 30ms, in whichever language you asked. Switch languages and the number doesn't move.
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Flam
@flamappofficial
Aug 5
Introducing Falcon: the brain. An LLM built on a sparse MoE, 26B parameters with only ~4B active per token, grounded in your knowledge base, with time to first token at 30ms. The semantic router is what does the work, it decides which experts are needed before processing
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Flam
@flamappofficial
Jul 24
Two AI models power every Flam visual agent. Fantom: identity preservation and motion transfer. One face shot and one voice sample is enough to reconstruct textures, expressions, and movement at human fidelity. Falcon: the brain. An LLM trained on Indic datasets, grounded in
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