🎯 esengine/DeepSeek-Reasonix landed on trending. Worth a proper look.

🔗 https://github.com/esengine/DeepSeek-Reasonix
📝 DeepSeek-native AI coding agent for your terminal. Engineered around prefix-cache stability — leave it running.
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Reasonix is a DeepSeek-native AI coding agent designed for terminal use, focusing on prefix-cache stability to keep token costs low. This legacy TypeScript line is in maintenance mode, with active development moved to the Go rewrite in the main-v2 branch.

Key features include:
- Cache stability as an invariant in the loop design
- DeepSeek-only for byte-stable prefix-cache mechanics
- Real-time feedback with token costs and cache hits

To get started, install Reasonix globally with npm install -g reasonix or run it once with npx reasonix code. The reasonix command launches the coding agent in the current directory, while other subcommands like chat, run, and doctor provide additional functionality.

The desktop client offers a GUI over the same loop, with a prerelease version available for download. Configuration is done through a JSON file at ~/.reasonix/config.json and per-project overrides.

Audience: Developers, especially those familiar with DeepSeek and interested in AI-powered coding tools.

One-liner takeaway: Reasonix is your AI coding companion, designed to optimize token costs and streamline your development workflow with DeepSeek-native stability.

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🧠 Channel: https://t.iss.one/GithubRe

💡 EveryInc/compound-engineering-plugin just hit the trending charts — here's why it matters.

🔗 https://github.com/EveryInc/compound-engineering-plugin
📝 Official Compound Engineering plugin for Claude Code, Codex, Cursor, and more
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Compound Engineering Plugin makes each unit of engineering work easier than the last. This plugin, available on GitHub, provides AI skills to plan, review, and codify knowledge, inverting traditional development's complexity accumulation.

Key features include:
- Brainstorming and planning before writing code
- Reviewing to catch issues and calibrate judgment
- Codifying knowledge for reusability
- Supporting multiple platforms, including Codex, Claude, and Cursor

To use, simply install the plugin from the marketplace or via command line, then invoke skills with $skill-name (e.g. $ce-plan) or /skill-name (e.g. /ce-brainstorm).

Technical highlights include a self-contained install, with no separate custom-agent install required, and support for multiple editors and CLI tools.

The target audience is developers and engineers looking to streamline their workflow and improve quality.

With Compound Engineering, you can automate your development process and make each unit of work easier than the last - try it out and discover a whole new way to code!

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🧠 Channel: https://t.iss.one/GithubRe

⚡ huangruiteng/loopx is making waves. Here's the full picture.

🔗 https://github.com/huangruiteng/loopx
📝 Lightweight loop engineering state kernel for long-running AI agent teams. Agent-loop agnostic across Codex, Claude Code, and other coding agents, with durable goals, quota-aware auto-wake, executable todos, evidence logs, and verifiable handoffs.
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LoopX is a lightweight, local control plane for loop engineering, designed to keep long-running AI agent work reviewable, restartable, and easier to hand off across turns, tools, and agents. It maintains a compact, durable control state, including objectives, gates, todos, evidence, and quota, ensuring that human judgment is always involved when needed. Key features include a simple, agent-agnostic design, support for multiple AI runtimes (e.g., Codex, Claude Code), and a quota system that decides whether a turn should deliver, ask, wait, or stop. Usage involves installing LoopX, connecting it to your project, and using the loopx command to manage the loop. Technical highlights include a small core tick, deliberate separation of concerns, and a focus on local-first design. LoopX is suitable for developers, researchers, and operators working with long-running AI agent projects, such as multi-day engineering, research, or experiments. In short, LoopX helps you keep the loop moving while keeping human judgment in the driver's seat.

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🧠 Channel: https://t.iss.one/GithubRe

🔍 Deep-diving into TencentCloud/TencentDB-Agent-Memory — fresh off the trending list.

🔗 https://github.com/TencentCloud/TencentDB-Agent-Memory
📝 TencentDB Agent Memory is a team-level memory hub for AI Agents — turning conversations, docs, and code into four reusable memory assets (Chat Memory, Skill, LLM-Wiki, Code-Graph) that are governed, shared, and equipped across agents and frameworks.
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The TencentDB Agent Memory is a cutting-edge solution designed to enhance the capabilities of AI agents by providing them with a robust memory system. This system combines symbolic short-term memory and layered long-term memory to enable agents to learn from workflows, retain task context, and reuse past experiences. By offloading heavy tool logs and condensing them into compact Mermaid symbols, the system reduces token usage and improves task success rates.

The key features of TencentDB Agent Memory include:

* Memory layering: a hierarchical approach to memory formation and recall, allowing for progressive disclosure and heterogeneous storage.
* Symbolic memory: a Mermaid symbol graph that encodes task state transitions, enabling precise and concise memory representation.
* Context offloading: the ability to offload full tool logs to external files, reducing token cost while preserving traceability.

The system has been integrated with OpenClaw and Hermes agents, with impressive results, including a 61.38% reduction in token usage and a 51.52% improvement in pass rate.

To get started with TencentDB Agent Memory, users can follow the Quick Start guide, which provides step-by-step instructions for installing and configuring the plugin with OpenClaw or Hermes agents.

In summary, TencentDB Agent Memory is a powerful solution that enables AI agents to remember what's important, so humans can focus on what truly matters – and that's a game-changer!

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🧠 Channel: https://t.iss.one/GithubRe