Gemini on Margrop Blog

Can You Still Buy a Coding Plan? A July 2026 Reality Check on Limits, Quotas, and Lockouts

Mon, 20 Jul 2026 09:00:00 +0800

Short answer: as of July 20, 2026, most mainstream AI coding plans are still purchasable. The confusing part is that “available to buy,” “available in my region,” and “available for heavy coding” are no longer the same statement. Some products have a live checkout but tight quotas; others are visible in marketing pages but not offered to every account; some individual coding experiences have been paused or reshaped during product transitions.

Small Models Make Agents Drift. Superpowers Gives Them Rails.

Thu, 07 May 2026 16:43:10 +0800

The short version

When an agent powered by a smaller or weaker model behaves unpredictably, the problem is not always solved by writing a longer prompt. Smaller models often struggle with long-horizon consistency, tool discipline, evidence tracking, and multi-step execution. Superpowers does not turn a weak model into a frontier model. What it does is more practical: it gives the agent an external engineering workflow made of reusable Skills, checkpoints, testing habits, debugging rules, review steps, and completion verification.

For agents such as OpenClaw, HermesAgent, Codex, Claude, Gemini, OpenCode, Droid, Cursor, and similar tools, Superpowers is best understood as an engineering-discipline layer. It makes the agent less dependent on improvisation and more likely to follow a repeatable process.

This article is about a very common failure mode in modern agent workflows: the model can talk fluently, but the agent does not behave reliably once the task becomes long, stateful, tool-heavy, or distributed across multiple agents.

The issue becomes more visible when using smaller domestic models, local models, quantized models, or cost-optimized models. They may answer normal questions well enough, but once they need to read a repository, follow a constraint, modify files, run tests, interpret logs, preserve privacy, and report only verified results, the instability becomes obvious.

All examples in this article are generic. No private hostnames, internal addresses, credentials, project names, or personal paths are included.

How to Prompt OpenClaw, HermesAgent, Codex, Claude, Gemini, OpenCode, and Droid to Write Articles and Publish WeChat Drafts

Tue, 05 May 2026 16:00:00 +0800

Short version

If you want an agent such as OpenClaw, HermesAgent, Codex, Claude, Gemini, OpenCode, or Droid to write an article, prepare a Hugo blog post, convert the Chinese version into WeChat-compatible HTML, preview it, fix layout issues, and create a WeChat Official Account draft, the prompt cannot simply say “write and publish an article.” A useful prompt must define the goal, source material, paths, account constraints, fixed-IP requirements, conversion steps, preview checks, failure recovery, privacy boundaries, and final verification evidence.

This post describes a reusable prompting pattern for article automation. It explains how to prepare the WeChat draft API, why a Linux machine with a fixed public IP is often required, how to tell an agent to try local API access first and then fall back to a fixed-egress host, how to use a Markdown-to-WeChat-HTML converter, and how to make the agent check for invisible white text, strange indentation, broken images, and accidental leaks before declaring the job done.

All examples are privacy-safe. They use placeholders rather than real secrets, private IP addresses, hostnames, repository URLs, tokens, cookies, or personal paths. Replace placeholders with your own values only in a protected runtime environment, never in a public article, public repository, or screenshot.

Using WoClaw to Solve Shared Memory for Multi-Agent Workflows: OpenClaw, Codex, Claude, and Gemini on the Same Table

Sun, 05 Apr 2026 20:10:00 +0800

What matters most in a multi-agent system is often not “which model is smarter”, but “which agent still remembers what happened five minutes ago”.

When I put OpenClaw, OpenAI Codex, Claude, and Gemini into the same workflow, the most painful problem was not raw answer quality. It was context fragmentation:

OpenClaw remembered scheduling decisions, Codex remembered implementation details, Claude remembered review conclusions, and Gemini remembered external references, but none of them knew what the others knew.
Each tool had its own session lifecycle, so yesterday’s discussion was often gone by tomorrow.
As soon as a project entered a multi-round, multi-tool stage, repeating the same background became more expensive than solving the problem itself.
Without a shared memory layer, the final result was not collaboration, but several partial answers that looked similar and still failed to line up.

This post is about how I used WoClaw to place all of those tools on the same shared memory layer, so they could stop talking past each other and start working from the same state.

To avoid leaking private information, all examples below are abstracted. They do not include real hostnames, account names, addresses, tokens, or internal project codes.