A local-first agent for private and cost-effective knowledge work

Perplexity Portable Computer is presented as a local-first AI agent that runs models, conversations, tools, and task trajectories on a user’s device, using web search, external connectors, or cloud-based advisor models only with user approval. Its design pairs a compact, on-demand skill-based harness with local models such as Qwen 3.8 27B, emphasizing context efficiency, command-line connectors, self-verification, and mandatory sandboxed tool execution to reduce privacy risks and API costs. In evaluations on an NVIDIA DGX Spark, the Computer harness outperformed the general-purpose Hermes and Pi harnesses on BrowseComp web research, ParseBench-100 multimodal document understanding, and an internal 53-task knowledge-work benchmark, while generally using fewer tokens and, in two benchmarks, less time. A post-trained PPLX 27B model further improved its internal benchmark score from 82.6% to 85.4%. For more difficult coding tasks, optional escalation to a Claude Opus 5 advisor increased performance from 59.6% to 73.0% at a lower estimated cost than using the frontier model alone, though it did not eliminate the performance gap. The work argues that co-designing local hardware, models, and orchestration can make private, low-cost on-device agents increasingly viable for research, documents, data analysis, software work, and other knowledge tasks.

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