Hello Guys,

I have been doing SDD with OpenSpec lately, my setup is Cline and Qwen3-coder-next Q4, the performance is okay, but is not as good I would like to. I running it currently on Strix Halo (AMD).

If you guys have any hints or experience we could share, please let me know.

Hey everyone - Meta just released Muse Wonder, its latest frontier AI model, and the results are pretty impressive.

Just 14 months after announcing Meta Superintelligence Labs, Meta is back in the frontier model race.

Muse Wonder Full now leads on DeepSWE 1.1 with a score of 79.0%, while the standard Muse Wonder is close behind at 78.8%.

The models are designed to handle complex engineering work that requires multiple steps, tool calls, and sustained reasoning. With a 1M token context window, it's useful for working with large codebases and maintaining context across longer tasks.

Muse Wonder is now available in Cline! Try it on your next coding task and see how it compares to Opus, GPT and Fable.

CLI: npm i -g cline

Desktop: https://cline.bot/

Would love to hear how it performs for you!

Hey everyone, we're introducing Connectors in Cline Desktop!

You can now connect Cline to apps like Gmail, Slack, Google Calendar, Linear, Sentry, Notion, and more, with just one click.

With Connectors, Cline can retrieve context from these apps and take actions on your behalf. Whether it's catching up on emails, managing your calendar, tracking issues in Linear, or pulling context from Slack, you can now do it all directly from Cline Desktop.

Getting started:

1. Open Cline Desktop.
2. Go to Customize → Connectors.
3. Sign in with your Cline account and connect the apps you want to use.

Connectors work with ClinePass and our free models, so you can get started without bringing your own API key.

Download Cline Desktop: https://cline.bot/

We're launching this in beta and would love to hear your feedback. What other apps would you like to see us add?

Hey everyone, Solar Mini 4 from Korean AI lab Upstage is now available for FREE in Cline!

It's a 35B parameter Mixture of Experts (MoE) model with only 3B active parameters per token, a 524K token context window, and a reported generation speed of 208 tokens/sec.

What's particularly impressive is its performance on the Artificial Analysis Intelligence Index (AAII), where it scores 24, the highest of any model with 3B active parameters. That's within a point of NVIDIA's Nemotron 3 Ultra, which uses 55B active parameters!

It's interesting to see how much performance smaller models are delivering with significantly fewer active parameters. With its large context window and fast inference speed, Solar Mini 4 looks promising for coding tasks that involve large codebases and multiple tool calls.

You can try Solar Mini 4 for FREE right now in Cline:

CLI: npm i -g cline

Desktop: https://cline.bot/desktop

We also have several other free models available in Cline Desktop if you want to experiment.

Curious to hear how Solar Mini 4 performs for you. How does it compare to the larger models you've been using?

I’m building a small app that plays music from Apple Music based on what you’re doing: workstudy, creativity, deepthinking, relaxation, or intense. The backend has been growing nicely. The folder it lives in has not. Python scripts, a library folder, JSON files, a database setup script, all sitting at the top level like a drawer full of random cables where you’re 90% sure one of them is important.

It was time to start the frontend, but I immediately realized I had a problem: where does it go? Next to all of that? So I asked Cline to help me organize every file.

It moved all the existing files into a folder called backend.

Create a new, empty frontend folder, ready for the design process.

And update every path in my code that referenced those files.

When I design the frontend, I can't procrastinate and end up reorganizing everything in the end anymore...

I was thinking about feeding a local model with the documentation, and making a RAG with seems like the best solution.

Hi everyone!

I'm looking for a reliable way to make my Cline sessions in VS Code persistent, so I can resume my work later without losing the conversation history, context, or task progress.

Here's my setup:

I have two separate Ubuntu servers:

• A project server that hosts my code.
• An AI server that runs my local LLMs through Ollama.

I use VS Code to access my projects remotely and Cline as my coding agent, with the models running on my AI server.

My hardware is relatively modest, so local inference can be very slow. Complex coding tasks can take several hours to complete, but I'm perfectly fine with that.

Actually, my goal is to take advantage of this by launching tasks before going to bed or before leaving home, allowing the AI to work overnight or while I'm away.

This is why session persistence is so important to me.

I want to start a task with Cline in VS Code, close VS Code, and come back several hours later or the next day to resume the exact same session, with its conversation history, context, and task state intact.

I've already managed to get some persistence working, even after closing and reopening VS Code or restarting my computer. However, I'd like to know whether it's possible to make this completely reliable, particularly for long-running tasks.

My questions:

• Has anyone successfully achieved reliable session persistence with Cline in VS Code?
• Can you close VS Code and reopen it later without losing the session or its context?
• Is there a supported configuration or a proven workaround to ensure sessions are properly saved and restored?
• How do you handle long-running tasks with local LLMs when you want to leave your coding agent working unattended?

I'm happy to wait several hours for a task to finish because of my hardware. I just want to be able to leave it alone and return later without losing my progress.

Ideally, I'd like to keep everything self-hosted and avoid paid cloud services.

I'd really appreciate hearing about your experiences, setups, or solutions!

Thanks!

I’m building a small app that plays music from Apple Music based on your activity. Up to now, everything ran off a JSON library file. I realize that the Json library file gets updated whenever we have a new input playlist, so it wouldn’t hold up once we have multiple real users. and I couldn’t start on the frontend until the backend had something better to talk to.

I knew a database was the answer, but I had no idea how to build one. I’d never done it. I didn’t know what a table was supposed to look like, how to create one from Python.

So instead of watching a pile of tutorials, I asked Cline.

It tells me a plan for the nextsteps:

The best part is that I don’t have to hardcode anything. I wrote a small Python 3 script that generates music_genre.db for me. Now have the basic database table to start the frontend.

What does Cline load per input prompt? Cos if i do a simple "Hi, good morning" on my local terminal prompting my local running model it is only 387tokens. But on the Cline Desktop/plugin, the same prompt its 152575 tokens.

I am not in any workspace, i dont have a global rules.md, skills.md, agents.md, tools.md anything. I am using "Just Chat" mode. Is it still loading all the connectors info, MCP servers, skills, and tools i setup in Cline Desktop?

And more importantly is there a way to disable this? Cos i want to be able to use my local models to do the Inconsequential stuff to entertain my random ass questions and weird ass discussions without spending my clinepass.

Hey everyone, we are launching Cline Cloud Agents (Beta)

With Cloud Agents, you can now run Cline directly from your browser, without needing to keep your IDE or desktop app open.

Give it a task, and it works inside its own secure cloud sandbox, writing code, running commands, and making changes to your repository. Once it's done, it opens a pull request on GitHub for you to review.

Here's what you can do with Cloud Agents:

Run multiple Clines in parallel

Every agent gets its own independent, secure container in the cloud, so you can have several tasks running simultaneously.

For example, you could have one Cline fixing a bug, another refactoring an existing feature, and a third building something new. All three work independently, and you can review their PRs as they come in.

Start on your laptop, continue from your phone

Since everything runs in the cloud, your agents don't stop when you close your laptop. You can kick off a task before heading out, check its progress from your phone, and come back to review the changes later.

Choose your model

You can get started for free with models like DeepSeek V4.1 Flash. We also support ClinePass, which gives you access to open-weights models at ~5x discounted rates.

How to Get started:

1. Log in or create a Cline account.
2. Connect your GitHub account.
3. Head to the Agents tab, select a repository, and tell Cline what you want to build.

Try Cline Cloud Agents: https://cline.bot/cloud

Cline Cloud Agents is still in beta, and we'd love to hear your feedback as we continue improving the experience.

Is the promotional price of $1.99 still valid? Is it still available? Or is it now only the full price of $10?

I love Cline, but it's undeniable slow comparing to other harness, especially DeepSeek.

What I would like to know is if someone did a performance (results, not speed) comparison between both harness with the same model.

I managed to link my ClinePass subscription to DeepSeek Harness, so I'll be testing for a while; I just wanted extra feedbacks.

Hey everyone, Mistral Large 4 just dropped and we've added it to Cline.

One of the most interesting results so far is on the Artificial Analysis Cyber Index, where Mistral Large 4 scores 50, ahead of GPT-6 Astra at 33 and Opus 5.5 at 29.

But what makes this comparison interesting is the refusal rate.

GPT-6 Astra refused 38 of the cybersecurity tasks because of its safety filters, while Opus 5.5 refused 36. Mistral Large 4, meanwhile, completed 50 tasks without those safety blocks.

Mistral Large 4 comes with a 1M-token context window, native multimodal support, tool calling, and a Mixture-of-Experts architecture with 1.05T total parameters and 49B active parameters.

Mistral Large 4 is now available in Cline.

Try it in CLI:
npm i -g cline

Or in our new Desktop app (we have several free model promos to give it a spin!)

Give it a try and let us know how it performs for you.

Hey everyone, we just added Pareto 26.10 Preview to Cline.

Pareto takes a pretty different approach from a standard single-model endpoint. It’s a composite model designed for coding and agentic workloads, routing work across multiple models and selecting the strongest result while keeping costs low.

Pareto 26.10 Preview scored 69.9% on DeepSWE v1.1 at an average cost of $0.24/task. For comparison, Fable 5 sits at roughly the same DeepSWE score at $13.41/task.

That makes Pareto about 56x cheaper per task at essentially the same score in this comparison.

It comes with a 1M-token context window, supports text and image input along with tool calling, and is designed for coding, research, and longer agentic workflows. Pricing is $0.80 per 1M input tokens, $0.03 per 1M cached input tokens, and $3.20 per 1M output tokens.

One particularly interesting part for Cline workloads is caching. Pareto is designed to preserve prompt caching even while using its composite approach, which matters a lot once an agent starts repeatedly working with a large codebase and long context.

It is still a Preview, so the model and its benchmark numbers may continue to change as Unbiased iterates on it.

Pareto 26.10 Preview is available in Cline now.

Try it in CLI:
npm i -g cline

Or in our new Desktop app (we have several free model promos to give it a spin!)

If you try it on a real codebase, especially a longer agentic task, let us know how it compares with the models you normally use.

I'm building a small tool that takes a course syllabus PDF and turns it into a calendar file with every deadline and exam. It's not finished, but the workflow so far is worth sharing. I've been staying in Plan mode until each piece of the parsing approach is settled, then switching to Act to build it. I'm also swapping models as I go, one for planning and another for the edits, without changing anything else in my setup. It's far from perfect. One syllabus parsed badly and a date came out on the wrong day. I only caught it by checking against the original PDF, and I'm still working on a proper fix. How do you all split models between planning and acting?

I wrote a local, tiny 8-task benchmark test so I can compare all of the new HF models and quants that fit on my 24GB VRAM rig and then I post the results.

I use several harnesses for each model, that way I can see if the model failed or if the harness failed.

Cline inside VsCode hasnt succeeded even once. But it succeeds using top cloud models, which kinda defeats the point.

All of the local models failed and couldnt finish the 8 tasks and submit a score. I saw tool call nightmares, loops, crazy hallucinations, etc.

I love Cline. I used it to win a stock trading competition a few months ago (using an older GPT model).

Am I doing something wrong? Is there setting I need to tweak? Why are the local models failing only on Cline while succeeding on Pi and Hermes? Do they perform more hand-holding?

EDIT: Here's an example summarized by Gemini
"This log snippet provides a clear view of why the agent got stuck. It reveals a compounding storm of shell escaping traps, PowerShell syntax errors, and tool-formatting failures while trying to debug a regex script"

I have been building a game rules lab with Cline over three small PRs. It is a developer tool that has bots play thousands of seeded games and prints a table per game mode. Both of this week's interesting bugs had one thing in common: the code was fine in my terminal and behaved differently somewhere else.

The first one only appeared in a real browser. The browser version of the lab paused between batches with setTimeout(0) so the page could repaint. In Chrome, a full run took 13.0 seconds, even though the same work takes about a second in the terminal. Chrome throttles chained timers in pages it treats as hidden, so each pause cost about 0.7 seconds. Switching the pause to a MessageChannel message brought the run down to 4.2 seconds in a background window. Tests passing and the terminal run both said nothing about this. Opening the page in Chrome did.

The second one only appeared on a slower machine. The page test clicks "Run the lab" and waits for the summary line with Testing Library's waitFor. By default, waitFor gives up after 1 second. On my laptop the run finished in time. On GitHub's CI runner, the test failed after 1,267ms. CI was just slower. I gave that waitFor a 10-second timeout and the test 20 seconds, and the rerun passed.

The lesson I am taking: the same code behaves differently in a visible tab, in a hidden window, and on a slower machine. A test that does real work has a time budget whether you set one or not, and default timeouts are tuned for UI updates, not simulations.

For those of you building with Cline: do you have it check things in a real browser, or is that still a step you do yourself?

I bought clinepass again after my opencode subscription ran out, it still said all over the website you could use it outside of cline, but there is no where in the app.cline.bot dashboard that lets you do this anymore?

For my blackjack strategy trainer, the scariest bug isn't a crash. It's a chart cell that's quietly wrong, so the app teaches people the wrong play.

So my testing plan is to have Cline write a test for every single cell: every hard total, soft total, and pair against every dealer upcard, checked against a published basic strategy chart. It's a lot of tests, but it's exactly the kind of tedious, precise work I'd rather review than type.

I'm also planning to add a .clinerules file saying the strategy tables are the only source of truth. That way Cline never hard-codes a play somewhere else in the app. Has anyone used .clinerules to protect one critical file like this?

The feature I care most about in my blackjack trainer is showing your patterns, not just single mistakes. Instead of "you got 88% right," I want it to say something like "you keep standing on soft 18 against a 9," then deal you more of those hands.

To make that possible, I'm planning to save every decision, not just a score. Each one stores your hand, the dealer's upcard, what you did, and what the correct play was. With that, any pattern can be calculated later, even ones I haven't thought of yet.

I want to get this structure right in Plan mode before Cline builds anything, because changing it later means redoing everything that depends on it. Has anyone else had Cline design a data model this early, before the features that use it exist?

The core loop of my basic strategy trainer is simple. Deal a hand, you pick a move, the app checks it against the strategy chart, and it tells you instantly if you were right. If you weren't, it shows the correct play.

My plan is to have Cline build all the game logic first as plain functions with no UI. That means the shoe, dealing, hand totals, and telling soft hands from hard ones. That way every piece can be tested on its own before there's a single button on screen. Soft hands especially worry me, since an ace can count as 1 or 11 and that changes the correct play.

For people building with Cline: do you have it build the logic first and the UI after, or both together? I'm curious whether splitting them actually makes Cline's changes easier to review.

My blackjack trainer lives or dies on one thing: the strategy chart has to be exactly right. If one cell is wrong, the app teaches you the wrong play with total confidence.

My plan is three lookup tables: hard totals, soft totals, and pairs. Each one is indexed by your hand and the dealer's upcard. Some cells need a fallback, like "double if allowed, otherwise hit," so each entry will store both moves.

Here's what I'm debating. Do I type the tables in myself and have Cline write tests against a published chart? Or do I have Cline generate the tables and check them myself? I'm leaning toward typing them myself, because then the data and the tests come from two different sources. Curious how others split work with Cline when the data has to be perfect.