Cline’s bet: give the model context, keep the developer in control

Turn missing requirements into a plan

Ask an agent to build a website, and consequential decisions remain unstated: which pages should exist, and what should the theme look like? Cline, an open-source coding agent, gives those questions a place before implementation. Its plan mode reads files, gathers context, and asks for missing requirements. Switching to act mode directs it to execute the plan through commands and file edits.

The plan-and-act interface grew out of a user habit: first ask Cline to write a Markdown architecture plan, then tell it to carry out that plan. Discord conversations helped turn this workflow into explicit modes, saving users from repeatedly typing the same instructions. Developer involvement can concentrate in planning, followed by execution with automatic approval and occasional course correction.

At the time of the July 2025 Latent Space conversation with Saoud Rizwan and Pash, Cline shipped as a VS Code extension with editor, terminal, browser, and external-service access. Pash described a broader ambition: a modular foundation for agents beyond coding, with a command-line interface (CLI) and software development kit (SDK) planned.

Build around the model’s new capabilities

Cline’s origins trace back to June 2024 and a Build with Claude hackathon. A guest recalls beginning development shortly after a Claude 3.5 release, inspired by an Anthropic model-card addendum describing a model running in a tool-calling loop. That suggested a product built around iterative task execution: the model could call tools and take successive steps toward a result.

The other enabling capability was retrieving details from a long context—the material available to the model during a request. The guest recalls Claude 3.5 finding granular information with roughly 90% of its 200,000-token context window occupied, where earlier models tended to favor the beginning or end. That experience encouraged a recurring design choice: keep prompts general, provide substantial context, and let users discover workflows.

MCP provides a shared protocol for connecting agents to tools, services, and APIs. Those connections let Cline’s general prompts support work beyond application development. Nick Baumann, described as working on product marketing, reportedly used a Reddit MCP server to gather content and another server to publish tweets. A participant also built a conference presentation by combining a Cline rules document for styling with a Limitless transcription of spoken ideas and an agenda; Cline implemented the deck using a JavaScript presentation library.

Use the editor as the agent’s workbench

Shipping as an extension supplied both capabilities and distribution. VS Code provided local file access, a terminal, and diff views for inspecting changes. Its marketplace brought users, while installation inside an existing editor reduced the onboarding and organizational approval friction described by the guests. Cline could also run inside Cursor or Windsurf.

Maintaining an editor fork would mean tracking upstream changes, merge conflicts, and evolving backend services. The guests preferred to spend that effort on the agent loop—the model’s repeated use of tools to advance a task—and support for different model families. Their bet was that programming would increasingly involve directing agents in natural language, with less manual editing and tab completion.

Connect services without losing sight of the work

A debugging example recounted in the conversation shows MCP’s appeal. A developer retrieved a buggy Cloudflare Worker’s stack trace through Sentry MCP, had Cline repair the code, closed the issue through GitHub MCP, and deployed the fix to Cloudflare. Natural-language instructions inside the editor coordinated work across services that the developer otherwise would have operated separately.

That reach makes visible execution valuable. Cline exposes prompts, tool calls, errors, and explanations so developers can intervene when a task drifts. The product argument is that catching a wrong turn during execution can save the retries required after an unattended agent spends hours pursuing a misunderstanding.

Early MCP support required teaching the model how to build integrations. Much of Cline’s system prompt reportedly explained the new protocol and server construction. Watching Cline build servers helped users understand how agents connected to APIs. The subsequent MCP Marketplace addressed discovery and setup: its one-click installation had Cline read a GitHub-linked README, install the server, and get it running.

Integrations supplied several kinds of capability: - 21stDev’s Magic MCP provided relevant UI component examples to guide implementation, with paid access through an API key. - Local Unity and Ableton servers connected the agent to desktop applications for creating 3D objects or music. - Context Seven let the agent retrieve documentation for popular libraries through natural-language queries.

Remote servers promised simpler sign-in instead of manual API-key management, but desktop integrations gave local servers a continuing role. The guests expected both forms to coexist. Cline had not begun hosting remote MCP servers itself and was exploring support and marketplace listings.

A reported pull-request review workflow combined ordinary command-line access with MCP: 1. Use the installed GitHub CLI to fetch the pull request’s description, discussion, and full diff noninteractively. 2. Read surrounding files and review the change. 3. Present a proposed approval comment and request confirmation. 4. After confirmation, approve the pull request and notify the team through Slack MCP. The participants differed on Slack permissions. One preferred read-only access; another valued automated messages with approval before sending. Tool access and authority to act remained separate choices.

Make MCP convenient, payable, and trustworthy

Payments remained an open infrastructure problem. The guests envisioned payment per tool call through a unified layer, avoiding separate accounts and API keys for every provider. Discussions with Stripe and proposals involving stablecoins were exploratory. Remote aggregators offered a different convenience: exposing many servers through one URL. That simplified configuration but left unresolved how to reward better integrations and whether providers or third parties would build them.

Trust also extends beyond installation. Reviewing a server once does not secure future versions: an upstream maintainer can later introduce malicious changes. A participant described forking several servers to preserve reviewed versions. This problem qualified the convenience of marketplace installation, particularly for enterprises unwilling to trust locally installed code from unknown developers.

Despite those unresolved questions, the shared protocol reduced duplicated integration work. Agent vendors could use community-built tools instead of each implementing the same service connections inside their own product.

Choose visibility and autonomy separately

Visibility and autonomy are separate dimensions. An agent can perform substantial work while exposing its intermediate actions; a product can also emphasize the final result and reveal little execution detail. The guests positioned Cline around delegation with enough inspectability for developers to understand changes to production code.

The appropriate balance depends on the task. Scaffolding, writing tests, or launching several attempts at the same bug may require little engagement. Creativity, architectural decisions, and unstated requirements benefit from back-and-forth interaction. One participant usually approved every edit to follow the code’s development, but sometimes enabled automatic approval and reviewed the completed work afterward.

The guests saw difficult programming work shifting toward early architectural choices and how later changes build on them. They regarded many algorithmic tasks as increasingly delegable when expected behavior or tests were available. Architecture still depended on project direction and experience the model did not possess. Clarifying questions could help, but the developer needed to articulate that intent. For junior engineers, the same exploratory interaction offered a way to understand a repository and find the files relevant to a feature.

Keep exploration and editing close to the model

Cline’s open-source design had produced roughly 6,000 forks by the time of the conversation. A guest welcomed those experiments as feedback about what worked and as a way for organizations to adapt the agent. The underlying philosophy remained simple: let a capable model explore the repository, read substantial context, and apply changes directly.

That preference drove the critique of code retrieval from a vector index, discussed as RAG. This approach breaks repository files into small chunks and retrieves relevant snippets for the model. A guest argued that disconnected snippets could obscure the relationships that make code intelligible. In the team’s experience, exploration worked better: inspect folders, read files, and follow imports into related code.

Fast apply addresses a different failure. Search-and-replace editing requires a generated search block to match existing code exactly before substituting a replacement. When models struggle with that precision, a smaller fine-tuned model can take an incomplete proposed edit—with placeholders for unchanged sections—and generate the entire updated file.

That handoff creates two potential problems. The apply model can subtly change the original model’s intended edit, and generating the full file must fit its output limit. A guest cited a roughly 42,000-token file in Cline’s repository that exceeded the output capacity of the smaller apply models under discussion, requiring further workarounds.

Better direct editing could reduce the need for that layer. A guest reported an internal Claude Sonnet 4 diff-edit failure rate of about 4%, compared with rates of 20–30% around fast apply’s introduction. The dataset, sample size, failure definition, and earlier models were unspecified, limiting the comparison. The guests also retained exceptions: retrieval could suit large document collections where internal logical relationships mattered less, and fast apply could help organizations constrained to models that struggled with exact edits.

Make inference costs explicit

Letting a capable model explore extensively and edit directly can cost more. Cline’s bring-your-own-API-key approach let users connect to their chosen provider and retain existing commitments or volume discounts. The guests argued that earning no margin on inference reduced pressure to substitute cheaper models or smaller context merely to fit a fixed subscription budget.

Users could connect directly to OpenAI, Anthropic, or Bedrock, as well as use OpenRouter. The guests described model requests as bypassing Cline’s own servers and reaching the selected provider. Open source supplied another kind of transparency: developers could inspect request destinations and prompts.

Enterprise governance was the monetization strategy. One guest recounted an unnamed Fortune 5 company with hundreds of engineers already using Cline but little visibility into their API keys, spending, or data destinations. Enterprise requests centered on security controls, guardrails, administrative insights, and invoices. Internal advocates also wanted evidence of benefit to justify spending and wider adoption; hours saved and lines written were proposed measures, not demonstrated productivity results.

This business thesis welcomed declining model costs: even free inference would benefit a company selling enterprise capabilities rather than model usage. A participant described another source of value that was harder to measure—experiments, side projects, and bug fixes that became worth attempting because Cline was available. The benefit was expanded activity, beyond time saved on already-planned work.

Move the agent beyond the editor

The forthcoming CLI was intended to support cloud processes, GitHub Actions, and additional instances launched by Cline itself. A developer would also be able to attach to a running local session and steer it. Saoud described the broader ambition as an open-source framework and SDK for interfaces including Chrome extensions, JetBrains, and Jupyter Notebooks. These integrations were planned rather than completed.

Parallel and interactive work remained complementary. Multiple agents could explore five landing-page variants, while one agent in sustained conversation could gather context and develop a complicated plan. Different forms of delegation served different kinds of work.

Preserve the task as context fills

Context management spans the whole task: choose what enters the model’s window, then condense it without losing direction. Cline’s preferred loading strategy gave the model exploration tools and navigational hints. Open editor tabs reportedly helped in internal benchmarking, though unrelated tabs could introduce distractions. A CLI lacked that editor-state signal.

Early experiments simply discarded the first half of the conversation, losing the history needed to understand how the task reached its current state. The guests called the desired continuity narrative integrity. Removing duplicate file reads helped reduce context. Asking the model to summarize relevant details, then substituting that summary for the previous context, also reportedly worked well.

Exploration could be staged before loading full files. Cline’s abstract-syntax-tree tool exposed class and function names within a directory, letting the agent assess relevance before reading implementations. A further idea, observed at other companies, represented code in a knowledge graph queried through a SQL-like language. Compact queries could support repository-wide operations such as finding functions, identifying unused ones, and removing them. This was a proposed approach to expensive, failure-prone refactors, rather than an implemented Cline capability.

A powerful tool still has to suit its model. One guest argued that model families were developing specialized tool-use tendencies, citing what they believed was Claude 4 training around grep. That raised a practical concern: a clever repository interface might work poorly with a model accustomed to a different toolset.

Separate conventions from working memory

Stable conventions such as camelCase belonged in explicit rules files, in one guest’s view. A memory system that users had to repeatedly instruct the agent to update added work. The harder open problem was retaining undocumented tribal knowledge discovered during a task without making the user request each memory save.

An internal experiment addressed task continuity with a rewritable to-do list. The agent had one tool for replacing the list, and the system periodically injected its latest state into context. This reportedly kept execution on track through repeated summarization and compaction. A guest described a complex task spanning roughly ten context-window lengths; no task specification or success rate accompanied the result, and the mechanism remained under development.

The experiment extended the memory-bank concept credited to Nick Baumann: rules instructing Cline to maintain a working scratchpad. A proposed built-in version would carry completed work, remaining tasks, file references, and general context between sessions. Tool instructions served a different purpose. One guest preferred separate rules for Cline and Cursor because they wanted the tools to behave differently within the same repository.

Build an identity—and keep the work open

Cline’s name plays on CLI and editor; the tool was previously called Claude Dev. One guest found that humanizing the agent built personal confidence in relying on it. Another connected a distinct agent identity to narrative integrity and argued that it improved results, without offering a measured comparison.

The closing hiring pitch emphasized building tools the engineers used themselves and shaping what might follow background and multi-agent systems. Hiring had largely come through friends, existing networks, and trusted former colleagues. At the time, Cline reported a 20-person team aiming to reach 100 by year-end.

Growth came with a concrete open-source tradeoff. Cline invested in research and benchmarking to reduce diff-edit failures, then released improvements that competitors could reuse. The guests defended continued openness as a way to help shape the industry, while crediting the community’s constructive feedback with improving the product and roadmap.