u/hasmcp

screenshot for output with lms ls

Before starting, sharing what do I do?

* Writing code

* Scraping web and pdfs(papers) for finding topics interesting for my work to write content

* Analyzing day long contents on multiple dimensions

* Finding prospects

Hardware:

* ngx spark 128gb unified ram

* macbook air 24gb

* mac mini 16gb

* rpi x 3 x 4gb

Software:

* I use `lms` (lm studio headless) lms has very limited commands, only available adjustment is context-length at least that I was able do.

* I open llms to web with access tokens (TLS enabled) for using my cloud deployed projects via a tunnel go lang backend that I asked llm to code that for me. (cost for me will be ~5 usd per month for hosting, currently free with my current aws credits)

* zed editor for local llms (free/opensource)

* antigravity (paid pro)

* agentrq for task management and managing local agents (free/opensource)

LLMS (local):

* Mining from visuals including web: IBM granite 4.1 --> Good for parsing pdfs and visuals, web surf is ok too. `granite-4.1-30b --context-length 32000`. Sometimes I switch to gemma 4 but it is too slow.

* Text classification and scoring: `google/gemma-4-26b-a4b (1 variant) 26B-A4B gemma4 17.99 GB Local ✓ LOADED`

* 31b version is significantly better but too slow, I switch time to time for a/b testing `google/gemma-4-31b (1 variant) 31B gemma4 19.89 GB Local`

Coding with Zed with Qwen 3.6 35b (beware tool call does not work on zed well for Qwen3.6).

* opencode + agentrq (always on on mac mini with acp gateway)

LLMs paid with subscription:

* For high quality task execution my favorite is Sonnet 4.6 with claude code + agentrq (always on rpi 4gb)

* For coding I use mostly Antigravity (always on my macbook air)

* For remote coding gemini cli + agentrq (always on on rpi + mac mini with acp gateway)

Tips:

* For efficient processing keep context window small

* Keep tasks small

* Use good models to create tasks and orchestrate

My pain points (probably due to bad prompting?):

* Bad part with SLMs is they don't obey sometimes. But the cost is pretty low or nothing, especially if you have a local setup.

* Output formatting (sometimes llms are not able to generate simple json output sadly).

For last 6 weeks, I am doing all of my coding and a lot of other tasks using AgentRQ agentic task manager. I created it with love and joy based with help of agents for my own requirements. Improved it when it does not as it needs to be. And not to forget it helps me

- to improve my code base in daily cadence

- to find customers for HasMCP while I am sleeping

- to build a closed-loop self learning system (itself)

- I have now 10+ agents running day and night for me. All self-learning, improving themselves with md files

TL;DR; It comes with 2 distinct MCP Servers

Supervisor (CEO):

Top level MCP server that can create tasks, see anything that I am seeing when I login. (Remote MCP: https://mcp.agentrq.com/mcp)

* listWorkspaces: List all workspaces for the authenticated user.

* createWorkspace: Create a new workspace.

* getWorkspace: Get a workspace by ID.

* updateWorkspace: Update a workspace.

* getWorkspaceStats: Get statistics for a workspace.

* listTasks: List tasks in a specific workspace.

* listAllTasks: List all tasks across all workspaces.

* createTask: Create a new task in a workspace.

* getTask: Get a specific task by ID.

* respondToTask: Submit an allow/deny response to a task.

* replyToTask: Post a message to a task thread.

* updateTaskStatus: Update a task's status.

* updateTaskOrder: Update a task's sort order.

* updateTaskAssignee: Update a task's assignee.

* updateTaskAllowAll: Toggle allow_all_commands for a task.

* updateScheduledTask: Update a scheduled/cron task.

Worker (Workspace):

These are specialized agents, they can't not see other agents. They only know there is a human and can assign you tasks. They get tasks assigned via Claude MCP Channels (experimental feature). I made it possible to use it with Gemini using ACP(agent client protocol). (Remote MCP: https://<WORKERID>.mcp.agentrq.com/mcp these are sub agents, each has a unique mcp address)

* createTask: Create a task for the human user. Returns the task ID.

* updateTaskStatus: Update the status of a task (e.g., to ongoing, completed, blocked).

* reply: Send a message to the current ongoing task, with optional attachments.

* downloadAttachment: Download the content of an attachment by its ID and task ID.

* getWorkspace: Returns the workspace title, mission description, and task statistics.

* getTaskMessages: Read the chat history and messages of a task with cursor-based pagination.

* getNextTask: Get the next available "not started" task assigned to the agent.

Source code is available at: https://github.com/agentrq/agentrq . Happy to answer any questions.