Show HN: Cheap-IM – CPU-only voice agent approximating Thinking Machines' demo
A new CPU-only voice agent called Cheap-IM replicates the behaviors of Thinking Machines' demo using off-the-shelf components. It performs tasks such as real-time speech recognition, live translation, and background processing on a standard laptop. The project showcases how commodity models can be integrated to achieve complex functionalities with minimal resources.
- ▪Cheap-IM runs on a single CPU laptop and utilizes a Python event loop to manage tasks.
- ▪The system incorporates local speech and vision processing, including person detection and translation capabilities.
- ▪Users can interact with the agent while it performs background tasks like searching and chart generation.
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Story provenance
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Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/kouhxp/cheap-im |
| Publication time | Sun, 17 May 2026 23:49:30 +0000 |
| Retrieval time | 2026-05-18T00:03:21.133Z |
| Last seen | 2026-05-18T00:03:21.133Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | swmcU5x7iDYR · 2 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
cheap-im A CPU-only voice agent that replicates the surface behaviors of Thinking Machines' Interaction Models demo (May 2026) — real-time speech, vision-keyed proactivity, live translation, mid-conversation background tasks — on a laptop, with off-the-shelf parts and minimal LLM calls. The point isn't to match Thinking Machines' architecture. They trained a 276B MoE from scratch on continuous audio+video with 200ms micro-turns. This project glues commodity models together with a Python event loop and shows how close a careful harness can get on the four behaviors that demo highlighted. Speech and vision are local (Silero VAD, Kroko ASR, YOLO11 pose, Piper TTS); LLM calls go to DeepInfra (Llama-3.1-8B-Instruct-Turbo for the foreground, DeepSeek-V3.2 for structured background work).
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.