Block Unveils Dessa, a Full-Duplex Speech Model Aimed at Natural Conversation

·1 views

Block has introduced Dessa, a speech model it describes as a full-duplex system, meaning it can listen and speak at the same time. The model was highlighted on Hacker News, where the post drew 19 points and 2 comments. Based on the available material, the company positions Dessa as a highly authentic approach to speech interaction. The source provided only a link and engagement metrics, so specific technical details, benchmarks, and availability are not confirmed here.

Read the full story at Hacker News

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

Discussion (0)

Embedding AI in Product Workflows Instead of Relying on Chat Interfaces

A DEV Community article argues that building a generic chatbot clone for a specific domain tends to produce confusing products, since users need AI integrated into their tasks rather than a separate conversation window. It contends that chat interfaces obscure status, turn errors into conversation, and force users to repeat context the application already holds. The author recommends treating AI as a coworker assigned to a defined task, with embedded actions, human-reviewable drafts, diff views, and visible sources and confidence levels. For enterprise tools such as ERP systems, the piece suggests structured forms with an AI assist option, permissioned actions, and audit logs rather than open-ended autonomous agents.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Production Briefs Must Flag Incomplete Data Rather Than Project False Readiness

A well-written production brief can still mislead a team into believing work is ready when key channel data or dependencies are missing. The article argues that gaps should be distinguished, since an order may exist while its artwork approval is unknown, or a source may be reachable yet return no recent update. It proposes separating source coverage, record readiness and decision readiness so readers can see which inputs contributed and what remains unresolved. The author notes that a fictional example illustrates how a brief should disclose stale commitments and excluded channels instead of presenting a polished summary. The piece also stresses that factual calculations should be separated from AI-generated interpretation, with each important claim traceable to its source.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

How Machine Shops Can Test Quoting Software Before Buying

A DEV Community article offers a practical checklist for evaluating machine-shop quoting software. It recommends testing candidate systems with real shop scenarios and confirming they separate setup, programming, and tooling costs from run time and material. Evaluators should check that the software handles operations, outside processes, inspection, scrap, and rate definitions clearly, and that margin is not confused with markup. The author advises requesting a cost build-up for a sample part and testing at least three quantities to see whether unit prices change for explainable reasons. The piece also notes that workflow areas such as RFQ intake, revision control, approvals, and integrations should be reviewed, and that results should be compared against the shop's current method.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Fast Image Generation APIs Compared for Real-Time Node.js Catalog Apps

A guide for developers building live, user-facing image generation in a customer-support app recommends Flux schnell or SDXL Turbo for the first visible preview. Flux schnell is described as the fastest option at roughly 300 to 800 milliseconds per image, while SDXL Turbo and Lightning variants take about 0.5 to 1.5 seconds. DALL-E 3, at around 2 to 5 seconds, is suggested only for slower, deliberate refinement rather than live previews. The article stresses tracking per-tenant cost and timing across each stage, and it advises loading available models at startup via a discovery endpoint instead of hardcoding model names. Higher-resolution versions are meant to come from a Lanczos enlargement step after the user accepts the preview.

0 comments Read more at DEV Community