Blogs

What do LLMs think when you don't tell them what to think about?

Together

Our interactions with large language models (LLMs) are dominated by task- or topic-specific questions, such as “solve this coding problem” or “what is democracy?” This framing strongly shapes how LLMs generate responses, constraining the range of behaviors and knowledge that can be observed.

Visit Site

Linearizing LLMs with LoLCATsTogether How speech models fail where it matters the most and what to do about itTogether Hyena Hierarchy: Towards larger convolutional language modelsTogether Kimi K3: the complete developer guideTogether How to choose the right open model for productionTogether Together AI launches Llama 3.2 APIs for vision, lightweight models & Llama Stack: powering rapidTogether Cloud Security Trends & Challenges: Complete GuideCybersecurity Exchange Digital Forensics & Emerging Technologies GuideCybersecurity Exchange Publish changes - MintlifyMintlify How we grew Mintlify by doing things that don't scaleMintlify Trained on 100,000+ Voices: Deepgram Unveils Next-Gen Speaker Diarization and Language DetectionDeepgram The Language of LGBTQ Inclusion and Allyship - Deepgram Blog ⚡️Deepgram Mind the Gap: The Chasm Between AI Fiction and Fact - Deepgram Blog ⚡️Deepgram Six Things to Consider When Using Redis on HerokuRedis How to Conduct an AI Agent Security Audit [+ Checklist]Zapier WooCommerce: App spotlightZapier Workato integrations: What's included and when to use ZapierZapier 6 ways to use the Zapier Zoho CRM integrationZapier Plumsail Documents: Zapier spp spotlightZapier Get email alerts for Facebook Messenger messagesZapier