DeepSeek Harness
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DeepSeek Harness (DSH) launches on GitHub with plugin ecosystem
DeepSeek Harness (DSH), a framework for running and evaluating DeepSeek models, has been released on GitHub. The release includes a curated ecosystem of plugins, tools, and infrastructure, with sources from the dsh-external/hub catalog and the public dsh-plugin topic. Developers can now access a structured harness for DeepSeek models, though detailed documentation is still sparse.
DeepSeek Harness (DSH), a framework for running and evaluating DeepSeek models, has been released on GitHub. The release includes a curated ecosystem of plugins, tools, and infrastructure, with sources from the dsh-external/hub catalog and the public dsh-plugin topic. Developers can now access a structured harness for DeepSeek models, though detailed documentation is still sparse.
Fulmar: native macOS app wrapping DeepSeek Harness agent runtime with access, model-routing and recovery controls
Fulmar is a native Mac app that wraps the DeepSeek Harness (DSH) agent runtime, which handles conversations, tools and tasks. It adds management controls the raw runtime lacks: choosing local or cloud model routes with explicit consent, Keychain credential storage, workspace/tool access limits, and recovery checkpoints. It targets developers running file- and command-capable agents on macOS who want oversight and safety.
Fulmar is a native Mac app that wraps the DeepSeek Harness (DSH) agent runtime, which handles conversations, tools and tasks. It adds management controls the raw runtime lacks: choosing local or cloud model routes with explicit consent, Keychain credential storage, workspace/tool access limits, and recovery checkpoints. It targets developers running file- and command-capable agents on macOS who want oversight and safety.
Indie 2.6B 'Engram' model offloads knowledge to a 4.3B table; training passes 100M tokens
A solo developer training a small 'Engram' model posted an update on r/LocalLLaMA after roughly a week. The model pairs a 2.6B-parameter transformer with a 4.3B context-aware Engram table, aiming to test whether knowledge can be offloaded from network weights to a lookup table. At just 104M training tokens, completions show the Engram table already improving factual recall, and the developer is seeking a compute sponsor to finish training.
A solo developer training a small 'Engram' model posted an update on r/LocalLLaMA after roughly a week. The model pairs a 2.6B-parameter transformer with a 4.3B context-aware Engram table, aiming to test whether knowledge can be offloaded from network weights to a lookup table. At just 104M training tokens, completions show the Engram table already improving factual recall, and the developer is seeking a compute sponsor to finish training.
Headlong: A Microharness for Persistent Agents
Headlong is a microharness designed for persistent agents, introduced on laude.org. It provides a lightweight framework for building and running agents that maintain state over time. The tool aims to simplify the development of long-running AI agents.
Headlong is a microharness designed for persistent agents, introduced on laude.org. It provides a lightweight framework for building and running agents that maintain state over time. The tool aims to simplify the development of long-running AI agents.
LLM Honeypot: Detect AI bots by serving hidden text
A web page that serves invisible text to detect LLM-powered crawlers. When an AI bot reads the hidden content and uses it in responses, the honeypot reveals the bot's behavior. It helps website owners identify unauthorized AI scraping.
A web page that serves invisible text to detect LLM-powered crawlers. When an AI bot reads the hidden content and uses it in responses, the honeypot reveals the bot's behavior. It helps website owners identify unauthorized AI scraping.