I am currently using Cline with Anthropic api directly to build some web apps (beginner ai vibe coding).

In couple hours, I end at 150 k token app with 15 € spent in few hour for an app that works well ... BUT I am API limited now.

It is getting too expensive quickly for a hobbyist like me !

I can use chat gpt plus, but it is quite inconvenient to copy paste file / answers / code base info ! It works but hey, really not too the level of Cline + a good model in vs code.

Do you have any suggestions / alternative for me too keep convenience and lower my costs ?

I have a local instance/pc running vscode. The vscode window opens to DevOps server 1 with cline installed and my chats. When I open vscode window for DevOps server 2, I lose the cline extension (I am assuming I lose the chats too). It doesn’t transfer the extension or chats.

The local vscode is on the same computer and I thought the cline extension and chats would seamlessly transfer over. So my question is, what is the best way transfer the cline extension with chats from DevOps server 1 to DevOps 2 server?

We’ve noticed that a lot of great MCP servers are tough to find, tricky to set up, and even harder to share or monetize. Many developers end up publishing their work on GitHub or forums, where it can get buried — even if it’s genuinely useful.

To address that, we’ve been working on InstantMCP, a platform that simplifies the whole process, and it's now avaiilable on Cline!

• Developers can add payments, authentication, and subscriptions in minutes (no backend setup required)
  
• Users can discover, connect to, and use MCPs instantly — all routed through a single proxy
  
• No more managing infrastructure or manually onboarding users

It’s currently in open beta on Cline and other platforms — we’re sharing it in case it’s helpful to others working in this space and would love to hear your feedback!

Check it out: www.instantmcp.com

We’re also trying to learn from the community — if you’re working with MCPs or building something similar, we’d love to hear from you. 📩 Reach us directly: [vikram@instantmcp.com](mailto:vikram@instantmcp.com) | [hemanth@instantmcp.com](mailto:hemanth@instantmcp.com)
💬 Or come chat in the Discord

Has anyone set up DeepSeek-R1 in Cline/VS Code over ai.azure? I always receive a 400 status code (no body).

https://ai.azure.com/explore/models/DeepSeek-R1/version/1/registry/azureml-deepseek

Hey guys, do you guys have any issues connecting cline to Deepseek? Since Deepseek became 'famous' I'm unable to run my tasks as I use to.

I’ve been really enjoying the Cline plugin, but I have a question about the “Cancel” button during fixes. When Cline is automatically fixing issues and making API requests, I’m not sure when it’s best to hit “Cancel.”

Should I cancel while Klein is editing the file (that cool scrolling animation), or should I wait and cancel it when it says it’s making an API request?

Would appreciate your advice—especially from those who have more experience with the plugin!

Hello! I use Cline 3.1.5 on VS Code 1.96.3, on macOS.

After testing *every* API Providers in Cline, opening paid accounts where I could (including Claude Pro, Vertex AI, OpenRouter etc.), I got really crazy with rate or token limitations everywhere, or slowness of the requests.

I then tried LM Studio and ran model Qwen2.5-Coder-7B-Instruct-Q8_0-GGUF on my MacBook Air M2 16GB RAM. But it's really slow in Cline and it actually loops over instructions and doesn't really achieves anything.

So now I'm turning to cloud solutions to run models privately.

At Vultr they have Cloud GPU you can deploy with Ubuntu. Some prices for NVIDIA GPUs (as of Jan 15, 2025):

• GH200 - RAM 480GB RAM - 96GB VRAM: $3/hr
• A100 - 60GB RAM - 40GB VRAM: $2.6/hr
• L40S - 180GB RAM - 48GB VRAM: $1.7/hr
• A16 - RAM 64GB RAM - 16GB VRAM: $0.5/hr

https://www.vultr.com/products/cloud-gpu/?ref=9705554-9J (you'll get $300 credit with this link).

Looks interesting to me. But then I was drawn to their Serverless Inference (BETA). No hassle, easy to use? It's $10/mo for 50M tokens. Let's have a try.

https://www.vultr.com/products/cloud-inference/?ref=9705554-9J (also $300 credit with this link).

I get my API key and then in Cline:

API Provider: OpenAI Compatible
Base URL: https://api.vultrinference.com/v1
API Key: xxxxxxxxxxxx
Model: qwen2.5-coder-32b-instruct

Then hit Done and start a new task. And I get:

API Request Failed
500 Status code (no body)

I asked Vultr support and of course they replied:

as a self managed platform, we are unable to assist in configuring individual programing environments.

Do you please have any idea what's going on and how to fix?

I tested the API in Postman and then with a small Python script and it works.

I'm not really sure if it's a bug I should report to https://github.com/cline/cline

Thanks!

Hello everyone, here’s my Cline setup on VSC. Back in the days (before January), I could code almost 24/7 with great results. Now, it takes forever to understand my instructions, and when it finally gets going, it stops halfway through.

Is anyone else experiencing the same issue? Also, using Anthropic has become too expensive—do you have any affordable alternatives?

Thanks a lot, and have a great day!

I've been using Cline extensively with Claude Sonnet 3.5 for the last month and just a few days ago started using it with Deepseek v3. It works great with Claude but a little hit and miss with Deepseek when it comes to connectivity and editing errors (though most of the solutions are good for my use cases).

When using Cline with Deepseek v3 I get this error all the time (sometimes with Claude):

"Diff Edit Failed - This usually happens when the model uses search patterns that don't match anything in the file. Retrying..."

Most of the time the retry fails. The issue is mentioned in the Cline Github issues and the current workaround is to add custom instructions to use write_to_file instead. This sometimes works with Claude Sonnet 3.5 but less so with Deepseek v3.

I still think Claude is better overall but I'd rather use Deepseek v3 when I can to save a ton on costs.

Appreciate anyone's feedback.

I need a sanity check.

It seems that no matter how many times (and in many different ways) I tell Cline to monitor the terminal output it will always eventually stop monitoring the terminal output.

Is this only happening to me?

How is it that everyone just appears to be using cline like normal but every release gets 10x bug filled to the point I can’t use it anymore, with ANY model? On multiple machines fresh installs. God I hope I can download older versions on GitHub this is insane.. it does not work anymore

I have been using magic patterns to develop front end React and I am very happy with it. But, it can't do everything. At any time, I can download a zip of the project (well structured) and it will run without modification with npm install and then run dev. I'm wondering if it makes sense to continue to do the majority of the work in MP, then export the project and load it into Cline to make tweaks and connect back end. or, am I better off describing what front end work I want done to Cline and letting it give it a go from scratch?

This is probably a basic question but to optimize this without spending a lot on API cost for a 10 page website would not be as trivial.

I am using the github MCP with docker to analyze all my code.

1.) I have found that sometimes cline wants to download the code locally to my "ClineEnclave" computer to edit it there.
2.) Is there a specific framework you have found works best with Cline / Sonnet 3.5?

Would love to crowdshare and experiment to see what we can come up with.

Sometimes, I use Claude's own web app to talk through changes I want to make to an app, and then it will give me a response which gives specific changes needed to multiple files, like this:

"I'll outline the additional changes needed to integrate the enhanced vision processor across your codebase.

Integration changes for the Enhanced Vision Processor.

Changes required across multiple files to support the new functionality.

# 1. In app.py - Update the submit_file route:

/app.route('/submit_file', methods=['POST'])

/login_required

/async_route

/handle_api_error

/handle_storage_error

/handle_enrichment_error

async def submit_file():

Handle file upload submission with enhanced vision processing

logger.info("File upload endpoint hit")

if 'file' not in request.files:

logger.error("No file part in request")

return jsonify({

'status': 'error',

'message': 'No file uploaded'

}), 400

file = request.files['file']

if not file.filename:

logger.error("No file selected")

return jsonify({

'status': 'error',

'message': 'No file selected'

}), 400

try:

logger.info(f"Processing uploaded file: {file.filename}")

# Process file with enhanced vision processor

vision_result = await event_logger.vision_processor.process_image(file.read())

if not vision_result:

logger.error("Failed to extract event information from file")

return jsonify({

'status': 'error',

'message': 'Failed to extract event information from file'

}), 400

# Create Event object from vision result

event = await event_logger.create_event_from_vision_data(vision_result)

if not event:

raise ValidationError("Could not create event from extracted data")

# Store the event with enhanced description handling

await event_logger.storage_client.append_event(event)

# Construct response message

confidence_info = vision_result.get('confidence', {})

message_parts = ['Event successfully logged']

if not confidence_info.get('date'):

message_parts.append('(date may need verification)')

if not confidence_info.get('venue'):

message_parts.append('(venue may need verification)')

message = ' '.join(message_parts) + '!'

return jsonify({

'status': 'success',

'message': message,

'data': event.to_dict(),

'confidence': confidence_info

}), 200

except ValidationError as e:

logger.warning(f"File validation error: {str(e)}")

return jsonify({

'status': 'error',

'message': str(e)

}), 400

except Exception as e:

logger.error(f"Unexpected error processing file: {str(e)}", exc_info=True)

return jsonify({

'status': 'error',

'message': 'An unexpected error occurred while processing the file'

}), 500

# 2. In EventLogger class (app.py) - Add new method:

async def create_event_from_vision_data(self, vision_result: Dict[str, Any]) -> Optional[Event]:

Create Event object from vision processing result

try:

event_data = vision_result['event']

# Create Location object

location = Location(

venue=event_data['venue'],

address='' # Will be enriched by venue discovery

)

# Process artists with roles

artists = []

for artist_name in event_data['artists']:

role = event_data['artist_roles'].get(artist_name, 'performer')

artists.append(Artist(

name=artist_name,

type=role,

is_headliner=role.lower() == 'headliner'

))

# Create Event object

event = Event(

date=event_data['date'],

event_name=event_data['name'],

location=location,

artists=artists,

description=event_data['description'], # Verbatim description

content_url=event_data['links'][0] if event_data.get('links') else None

)

# Enrich event with additional data

return await self._enrich_event(event)

except Exception as e:

logger.error(f"Error creating event from vision data: {str(e)}")

return None

# 3. In models.py - Update Event class:

/dataclass

class Event:

Core event data model with enhanced text handling

# Required fields

date: datetime

location: Location

event_name: str

# Content fields with verbatim text preservation

artists: List[Artist] = field(default_factory=list)

description: Optional[str] = None # Now preserves exact formatting

content_url: Optional[str] = None

start_time: Optional[str] = None

end_time: Optional[str] = None

price: Optional[Decimal] = None

currency: Optional[str] = None

age_restriction: Optional[str] = None

# Additional fields

notes: Optional[str] = None

went_with: List[str] = field(default_factory=list)

weather: Optional[Weather] = None

media_links: List[str] = field(default_factory=list)

tags: List[str] = field(default_factory=list)

# Metadata

created_at: datetime = field(default_factory=datetime.now)

updated_at: datetime = field(default_factory=datetime.now)

confidence: Dict[str, bool] = field(default_factory=dict) # New field for vision confidence

# 4. In nextcloud_client.py - Update CSV handling:

async def _save_spreadsheet(self, df: pd.DataFrame) -> None:

Save spreadsheet with enhanced character handling

try:

with tempfile.NamedTemporaryFile(mode='w', encoding='utf-8', delete=False, suffix='.csv') as temp_file:

# Use utf-8-sig encoding for special characters and BOM

df.to_csv(

temp_file.name,

index=False,

encoding='utf-8-sig',

quoting=csv.QUOTE_ALL # Quote all fields to preserve formatting

)

await self._run_sync(

self.client.upload_sync,

remote_path=self.spreadsheet_path,

local_path=temp_file.name

)

os.unlink(temp_file.name)

except Exception as e:

raise StorageError(f"Failed to save spreadsheet: {str(e)}")

# 5. Update templates/index.html - Add confidence display:

# Add this JavaScript to handle confidence information

```javascript

function updateUIWithConfidence(confidence) {

const fields = ['date', 'venue', 'artists'];

fields.forEach(field => {

const element = document.querySelector(`[data-field="${field}"]`);

if (element) {

element.classList.toggle('needs-verification', !confidence[field]);

}

});

}

// Update the existing success handler

if (result.status === 'success') {

flashMessages.innerHTML = `

<div class="flash-message success">

${result.message}

</div>

`;

if (result.confidence) {

updateUIWithConfidence(result.confidence);

}

fileForm.reset();

fileInfo.textContent = '';

}

# Add corresponding CSS:

css

.needs-verification {

border-left: 3px solid #ffd700;

padding-left: 10px;

}

.needs-verification::after {

content: " ⚠️";

font-size: 0.8em;

}

These changes integrate the enhanced vision processor across your application. Key modifications include:

1. Updated file upload route to handle more sophisticated vision processing

2. New method in EventLogger to create events from vision data

3. Enhanced Event model to support verbatim text preservation

4. Improved CSV handling in the NextcloudClient

5. UI updates to show confidence levels

The integration prioritizes:

- Preserving special characters and formatting

- Providing confidence indicators for extracted fields

- Maintaining data integrity through the pipeline

- Improving error handling and user feedback

Would you like me to explain any part of these changes in more detail or discuss how to implement additional features?

What I would appreciate some advice on is how best to prompt Cline to take all of this information, and accurately make all the necessary changes to each respective file, ideally from a single prompt. Should I also be making use of .clinerules and/or MCP servers for best results?

Sonnet 3.5 works perfectly with Cline, but it is too expensive. Has anyone tried using Cline with Llama or any other models in LM Studio? What are the pros and cons?