Results for “amara”

Records research provenance as a post-task epilogue, scanning conversation history to extract decisions, experiments, dead ends, claims, heuristics, and pivots, and writing them into the ara/ directory with provenance tags.

Create and manage persistent AI agents with threads, messages, runs, and tools using the Azure SDK for Java.

Generates, remixes, and manages short video clips via OpenAI's Sora API for cinematic shots, b-roll, and rapid concept iteration.

Compiles any research input — PDF papers, GitHub repositories, experiment logs, code directories, or raw notes — into a complete Agent-Native Research Artifact (ARA) with cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph, and grounded evidence. Use when ingesting a.

Builds RAG and agent applications with LlamaIndex, covering installation, LlamaParse, and LlamaAgents.

Configure an AI agent's personality using the AURA protocol based on the HEXACO model, adjusting traits like honesty, assertiveness, and autonomy to reduce sycophancy and tailor communication style.

Generates, remixes, polls, lists, downloads, and deletes Sora videos via OpenAI's video API using a bundled CLI.

`analysis-agent`/`task-agent`/`review-agent`: use when uploads, object storage, streaming, MIME, scanning, access, retention, or cleanup changes; skip without file/storage impact.

Orchestrates three frontier models to debate a question and synthesizes their best insights into a single superior answer.

Coordinates multiple agents with spatial memory and swarm navigation for collective task execution.

Prisma ORM expert for schema design, migrations, query optimization, relations modeling, and database operations. Use PROACTIVELY for Prisma schema issues, migration problems, query performance, relation design, or database connection issues.

Train and run Mamba state-space models with O(n) complexity, achieving faster inference and longer context than Transformers.

State-space model with O(n) complexity vs Transformers' O(n²). 5× faster inference, million-token sequences, no KV cache. Selective SSM with hardware-aware design. Mamba-1 (d_state=16) and Mamba-2 (d_state=128, multi-head). Models 130M-2.8B on HuggingFace.

Connect to the MAXIA AI-to-AI marketplace on Solana to discover, buy, and sell AI services, earn USDC, and access crypto intelligence, DeFi yields, sentiment analysis, and rug detection tools.

State-space model with O(n) complexity vs Transformers' O(n²). 5× faster inference, million-token sequences, no KV cache. Selective SSM with hardware-aware design. Mamba-1 (d_state=16) and Mamba-2 (d_state=128, multi-head). Models 130M-2.8B on HuggingFace.