SShortSingh.
TopTechnologyAI & MLStartupsProgrammingScienceBusinessFinanceCrypto & Web3World
Back to feed
Programming

Romi cuts Voice AI latency by rethinking async tools, dedup layers, and model choice

0
·1 views

Romi, an ADHD-focused voice AI product, has shared a series of engineering improvements aimed at reducing conversational latency. The team switched tool execution from synchronous to asynchronous, allowing database writes to happen in the background without pausing the voice pipeline. Seven deduplication layers were trimmed down to two after they were found to slow processing and conflict with each other. The team also replaced a 'thinking' model with one optimised for speed and accurate tool calls, eliminating unnecessary reasoning delays. A new Push-to-Talk toggle was added alongside Natural Flow mode, giving users more control in noisy environments or when thinking aloud.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingDEV Community ·

How to Monitor Docker Container CPU and Memory Usage Without Extra Tools

Docker includes a built-in 'docker stats' command that provides real-time CPU and memory metrics for running containers, requiring no additional software. Developers can customize output using format flags or capture periodic snapshots via shell scripts for lightweight historical logging. Setting memory limits on production containers is strongly advised, as unlimited containers can exhaust host RAM and trigger kernel OOM kills. For more robust monitoring, Google's open-source cAdvisor tool runs as a container and exposes Prometheus-compatible metrics for long-term storage and alerting. Pairing cAdvisor with Grafana enables full dashboard visibility, helping teams correlate performance issues with deployment events over time.

0 comments Read more at DEV Community
0
ProgrammingDEV Community ·

Developer Builds MVP Weather Chatbot Using Node.js and Agentic Tool-Use Architecture

A developer has published Mausam AI, an open-source MVP chatbot built with Node.js that fetches real-time weather, temperature, and humidity data for any city. The project addresses a core limitation of large language models — their lack of real-time data — by integrating an external weather API called wttr.in. The bot uses an agentic loop architecture that forces the AI to follow a strict sequence of steps: START, PLAN, TOOL, and OUTPUT, with all responses structured as JSON. A maximum iteration cap of five cycles is enforced to prevent infinite loops or hallucinations during the tool-calling process. The project is explicitly described as a conceptual demonstration and is not intended for production use, with source code available on GitHub.

0 comments Read more at DEV Community
0
ProgrammingDEV Community ·

Google Apps Script Turns Drive into Serverless AI Vector Search Engine via Apache Iceberg

A developer has published a serverless multimodal vector search architecture that uses Google Apps Script, Apache Iceberg, and BigQuery to eliminate the need for dedicated vector databases like Pinecone or Milvus. The system, built around an engine called IcebergApp.js, ingests heterogeneous Google Drive content — including Docs, Sheets, Slides, binary images, and web-fetched files — into a unified Apache Iceberg table stored as open Parquet files on Google Cloud Storage. Gemini's text-embedding-004 API generates 768-dimensional vector embeddings on the fly, while BigQuery executes cosine distance queries directly against the Parquet files, returning ranked results in 0.3 to 0.5 seconds. Each search result retains the original Google Drive file ID, enabling one-click navigation back to the live collaborative document. The approach is presented as a zero-maintenance, zero-subscription alternative to conventional RAG pipelines, avoiding vendor lock-in while keeping embeddings within core analytical storage.

0 comments Read more at DEV Community
0
ProgrammingDEV Community ·

How VSporte Replaced vMix-Based Broadcast Graphics With a Web-Overlay System

VSporte, a sports broadcasting software company, spent several years migrating its on-air graphics workflow away from vMix's proprietary title system after hitting consistent performance ceilings. The original setup stored graphics as gtzip files inside vMix, requiring manual data entry for each match and growing memory usage as client template packs expanded to 20–50 elements. vMix's own documentation warns against exceeding 20 open templates and notes that a single animated intro can consume nearly 2.5 GB of graphics memory, limits VSporte regularly breached. The engineering team's solution was to build a proprietary graphics control platform that renders overlays as web pages fed into vMix as an input layer, keeping vMix as the final mixer while removing graphics from its memory footprint entirely. According to the company's lead architect, each fix in the multi-step migration exposed a new bottleneck, making the process incremental rather than a single overhaul.

0 comments Read more at DEV Community
Romi cuts Voice AI latency by rethinking async tools, dedup layers, and model choice · ShortSingh