# MemTensor/MemT-1.0 > A family of memory foundation models with native long-term memory: dynamic local memory and trainable hyper memory retain history without accessing the original context, cutting storage roughly 66x versus full-context KV cache at 128K. - Magnitude: 4.8 out of 10 — Early signal - Stars: 140 total · +20 stars measured 2026-10-20, 06:37–08:46 UTC, ≈ 81 by evening - Star trust: star growth looks organic - Category: Language models · Language: Python · Created: 2026-10-19 · Last push: 2026-10-20 - GitHub: https://github.com/MemTensor/MemT-1.0 · Page: https://gitnova.dev/en/r/MemTensor/MemT-1.0 ## Useful for - Serve MemT-1.0-Nano with SGLang and run multi-turn conversations with persistent memory - Build a long-horizon coding agent that reuses stored procedural experience via native memory ## Why it’s here - Star-counter measurements on 2026-10-20 (UTC), 06:37–08:46: 120 → 140 stars (+20). This is the change over that interval. - Estimated end-of-day forecast: about +81 stars, using observed gains and the previous day. - The repository is 1 day old and already has 140 stars. With less than two weeks of history, there's no usual pace to compare the spike against yet. - Top new repositories this week: #154. ## Star trust Star growth looks organic. Star-trust labels are heuristics based on the repository’s behavior, not a check of every stargazer. ## Numbers - Forks: 2 - Issues and pull requests: 1 - Watchers: 0 - Average over the last week: 102 per day - Usual pace: too little history (under two weeks) - Stars in the last hour (measured): 10 ## Stars per day, last 2 days (oldest → newest, today is partial) 2026-10-19 … 2026-10-20: 123, 20 ## Spotted in now - Top new repositories this week: #154 ## More in this category 1. **mistralai/timestral** — 5.8 · Early signal · Language models · Python · +57 stars measured 2026-10-20, 00:22–08:45 UTC, ≈ 170 by evening A family of pretrained foundation models for multivariate probabilistic time series forecasting with covariate support. Enables zero-shot forecasting on CPU or GPU without training. Full card: https://gitnova.dev/en/r/mistralai/timestral.md 2. **solidsf-inc/solidcv** — 5.3 · Early signal · Language models · Rust · +13 stars measured 2026-10-20, 00:23–08:46 UTC, ≈ 73 by evening A pure-Rust OpenCV replacement with zero FFI: classical 2D/3D vision, stereo reconstruction, and mechanistic interpretability for LLMs/VLMs with hardware kernels for Apple Silicon and NVIDIA. Full card: https://gitnova.dev/en/r/solidsf-inc/solidcv.md 3. **headroomlabs-ai/headroom** — 5.1 · Breakout · Language models · Python · +208 stars measured 2026-10-20, 00:23–08:46 UTC, ≈ 525 by evening A local library, proxy and MCP server that compress tool outputs, logs, files and RAG chunks before they reach the LLM, cutting token usage without changing answers. Full card: https://gitnova.dev/en/r/headroomlabs-ai/headroom.md 4. **Niko1221/Strata** — 4.8 · Steady · Language models · C++ · +380 stars measured 2026-10-20, 00:23–08:46 UTC, ≈ 974 by evening A C++ local inference engine that runs the 125B MoE model Qwen3.8-Flash-Next on a regular PC with one NVIDIA GPU (12-24 GB) and 64 GB RAM, exposing an OpenAI/Anthropic-compatible API on localhost. Full card: https://gitnova.dev/en/r/Niko1221/Strata.md 5. **mudler/LocalAI** — 4.5 · Breakout · Language models · Go · +33 stars measured 2026-10-20, 00:24–08:46 UTC, ≈ 102 by evening LocalAI is an open-source AI engine that runs LLMs, vision, voice, image and video models on any hardware, including CPU-only, behind an OpenAI-compatible API. Full card: https://gitnova.dev/en/r/mudler/LocalAI.md --- Magnitude (0–10) measures how fast and how unusually interest in a repository is growing right now. It is not a quality score. Days are UTC. “So far today” is a fact; “expected by the end of the day” is a forecast. Summaries and use cases are written by an LLM (DeepSeek V4.1 Flash) from the README and may be inaccurate: verify specific claims (benchmarks, speed, hardware) in the repository itself. Data as of 2026-10-20 08:57 UTC, updated every 30 minutes.