OpenCode AI: Local Coding Agent Setup in 14 Steps [2026]

Running a coding agent entirely on your own hardware stopped being a weekend hobby project sometime in 2026. Searches for “ai coding” climbed to roughly 90,500 a month, up about 400 percent year over year and 308 percent over the prior three months, according to Rising Trends’ October 2026 AI trend tracker. “Local llm” searches reached about 246,000 a month in the same window. Those two numbers together point at the same shift: developers want agent-style coding help, and a growing share of them want it without sending their codebase to someone else’s server.

This tutorial builds that setup from scratch. You will install OpenCode, the open-source terminal coding agent, point it at Alibaba’s Qwen3.8 27B model running locally through LM Studio, and confirm the whole chain works by having the agent read, edit, and run code in a real project folder. No subscription, no per-token invoice, and no code leaving your machine unless you choose to route a task to a cloud model later. By the end you will also know how to keep the agent running on a remote box over SSH, how to sandbox what it can touch, and what to do when any part of the stack refuses to cooperate.

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What You’re Building: A Local Three-Piece Coding Stack

The setup Has three moving parts, and each one does a specific job. OpenCode is the agent: it reads your repository, decides what to change, writes the edits, and runs shell commands when a task calls for it. LM Studio is the host: it downloads, loads, and serves the model on your machine, and exposes an OpenAI-compatible API so other tools can talk to it. Qwen3.8 27B is the model doing the actual reasoning and code generation, a 27-billion-parameter open-weight release from Alibaba that landed in August 2026 as the newest entry in the Qwen3 coding line.

None of these three pieces is new on its own. Terminal coding agents, OpenAI-compatible local servers, and open-weight models have all existed separately for a couple of years at this point. What changed in 2026 is that the pieces finally fit together cleanly enough that a developer without a background in machine learning infrastructure can wire them up in under an hour, using nothing but a GUI download, a config file edit, and a couple of terminal commands. That is the gap this tutorial is built to close: not inventing a new piece of software, but showing the exact sequence that turns three separate downloads into one working agent.

It also helps to be clear about what this setup is not. It is not a drop-in replacement for a frontier hosted model on your hardest problems, and it is not a zero-maintenance system, since a model update, a config typo, or a quantization choice can quietly degrade output quality in ways that are easy to miss if you are not paying attention. Think of it as a second, lower-stakes lane for your coding work, one that happens to cost nothing per request and keeps every line of code on a machine you control.

Why Developers Are Choosing Local Over Cloud in 2026

The pull toward local setups is not really about saving a few cents per request, though that matters too once an agent is running dozens of edit cycles a day. It is about control. A local model does not log your proprietary code to a vendor’s servers, does not go down when a provider has an outage, and does not change its behavior overnight because a company pushed a silent model swap. For teams working on regulated codebases, or just developers who want a coding assistant that behaves the same way every single time, that predictability is the draw. Stack Overflow’s 2025 Developer Survey found that 84 percent of developers already use or plan to use AI tools in their workflow, and 51 percent of professional developers use AI tools daily, numbers that help explain why “keep it local” has become its own category instead of a niche preference. Read the breakdown at Stack Overflow’s 2025 AI survey results.

Where OpenCode Fits in the Agent Ecosystem

OpenCode has grown fast this year. Its September 28, 2026 release, version 1.18.33, added Claude Opus 5.5 and GPT-6 to its hosted “Zen” model list, bringing the project’s total provider count past 75. That breadth matters for this tutorial because it means the exact same agent you configure for a local Qwen3.8 model today can be pointed at a hosted model next week without changing how you work, just by swapping a provider entry. If you want the fuller picture of that provider expansion, our earlier coverage of OpenCode 2.0’s API access to 75-plus AI models walks through how that list grew.

Prerequisites and Hardware Requirements

Before you install anything, confirm your machine can actually carry a 27-billion-parameter model. Qwen3.8 27B needs roughly 18 GB of memory at 4-bit quantization, and that figure is before you add headroom for context window and runtime overhead. In practice that means a 24 GB GPU, or a Mac with at least 32 GB of unified memory, is the realistic floor for running this model comfortably alongside an operating system, a browser, and an IDE.

Component	Version Used Here	Where It Comes From	Cost
OpenCode	v1.18.33 (Sept 28, 2026 release)	GitHub, npm package opencode-ai	Free, MIT license
LM Studio	Latest desktop release	lmstudio.ai	Free, closed-source app
Qwen3.8 27B (4-bit quant)	Released August 2026 by Alibaba	Model browser inside LM Studio	Free, open weights
GPU or unified memory	24 GB GPU, or Mac with 32 GB unified memory	Your own hardware	One-time hardware cost
Operating system	Linux, macOS, or Windows with WSL2	–	–

You will also want a terminal you are comfortable in, Node.js installed if you plan to use OpenCode’s npm package, and roughly an hour set aside. The model download alone can take a while depending on your connection, so it is worth kicking that off before you read the rest of this guide.

Minimum vs Recommended Specs

There is a real difference between a machine that can technically load Qwen3.8 27B and one that can run it comfortably while you are also doing everything else a normal workday requires. The 18 GB figure cited above is the model’s footprint alone, loaded and idle, before a single token of context has been added. Open a browser with thirty tabs, an IDE, and a Slack client alongside a model sitting that close to your total memory ceiling, and you will see swapping and slowdowns that have nothing to do with the agent itself. Treat the 24 GB GPU or 32 GB unified memory figures as the point where the stack stops fighting the rest of your operating system for resources, not as a hard technical minimum you can shave closer to the edge without consequence.

If you are on the edge of these numbers, the context length setting inside LM Studio is your main lever. A smaller context window frees up memory for the rest of your system but limits how much of a file or project the agent can reason about in a single pass. Start conservative, confirm the stack runs without crashing, then raise the context length gradually while watching for out-of-memory errors rather than guessing at a number up front.

Step 1 to 3: Install LM Studio and Download Qwen3.8 27B

Step 1. Download LM Studio for your platform from its official site and install it like any other desktop application. LM Studio ships as a standard GUI app on Windows, macOS, and Linux, and the install process is the usual drag-to-Applications or run-the-installer routine, nothing scripted required at this stage.

Step 2. Open LM Studio and use the built-in model search to find Qwen3.8 27B. Search for the model identifier directly rather than browsing, since the catalog is large:

Qwen3.8-27B-Instruct-GGUF

Pick the 4-bit quantized build unless you have more than 24 GB of VRAM to spare. The 4-bit version is the one that fits the roughly 18 GB memory figure mentioned above, and it is the version most people running this stack on a single consumer GPU or an Apple Silicon Mac will end up using.

Step 3. Let the download finish, then open the My Models tab, select the downloaded Qwen build, and click Load Model. LM Studio will let you set the context length before loading. Set it high enough to hold a reasonable chunk of your codebase in a single pass, but remember every token of context eats into the same memory budget as the model weights themselves, so this is a balance, not a one-size value.

Step 4 to 6: Enable LM Studio’s Local API Server

Step 4. With the model loaded, switch to the Developer tab inside LM Studio. This is where the local server toggle lives, and it is separate from the chat interface most people see first when they open the app.

Step 5. Flip on the local server. LM Studio exposes an OpenAI-compatible API at http://localhost:1234/v1 once this is active, meaning any tool built to talk to OpenAI’s API shape, including OpenCode, can talk to your local model without a custom integration.

Step 6. Confirm the server actually sees your loaded model before moving on to the agent side of the setup. Open a terminal and run:

curl http://localhost:1234/v1/models

A working server returns a JSON object listing your loaded model by its identifier, something close to this shape:

{
  "object": "list",
  "data": [
    {
      "id": "qwen3.8-27b-instruct",
      "object": "model",
      "owned_by": "organization"
    }
  ]
}

If that command hangs, times out, or returns a connection error, stop here and fix it before installing OpenCode. Everything downstream depends on this endpoint responding correctly.

Step 7 to 9: Install and Configure OpenCode

Step 7. Install OpenCode. The project ships as the npm package opencode-ai, which is the fastest path if you already have Node.js on your machine:

npm install -g opencode-ai

Step 8. Navigate into the project you want the agent working on, then launch it once to let it generate its default configuration folder:

cd ~/projects/my-app
opencode

Exit out once the interface loads. You now have a config file to edit.

Step 9. Open OpenCode’s configuration file and add a custom provider entry pointing at your LM Studio server. OpenCode supports a custom baseURL field specifically so it can reach model servers running locally or elsewhere on your network, which is the hook you need here:

{
  "provider": {
    "local-qwen": {
      "npm": "@ai-sdk/openai-compatible",
      "options": {
        "baseURL": "http://localhost:1234/v1"
      },
      "models": {
        "qwen3.8-27b-instruct": {}
      }
    }
  }
}

Save the file. OpenCode’s provider system treats this local entry exactly the same way it treats a hosted provider like Anthropic or OpenAI, which is the whole point of the OpenAI-compatible API standard LM Studio implements.

Step 10 to 12: Connect the Agent and Run Your First Task

Step 10. Relaunch OpenCode from inside your project directory and select the local-qwen provider and the qwen3.8-27b-instruct model from the provider picker. If the model does not appear, double check the provider name in your config matches what you selected and that LM Studio’s server is still running.

Step 11. Give the agent a real, scoped task rather than something open-ended. A good first test is a bug fix or a small refactor where you already know what a correct answer looks like, so you can judge the result on sight. Something like:

Find the function that parses the CSV upload in src/import.py.
It throws on files with a trailing blank line. Fix it and add a
test case covering a file that ends in a blank line.

Step 12. Watch the agent work. It will read the relevant file, propose a diff, and, depending on your permission settings, either ask before running the new test or run it automatically. A typical first output looks like a standard unified diff plus a short note on what changed, for example a stripped blank-line check added to the parser loop and a new test function appended to the existing test file. Review every diff before accepting it, the same way you would review a pull request from a junior engineer you just met.

A Complete Working Project: Local Agent, Start to Finish

To see the full stack in action on something slightly bigger than a one-line fix, walk through this end-to-end example. Start with a small Node.js Express API with no tests and ask the agent to add a test suite:

Add a Jest test suite for the three routes in src/routes/users.js.
Cover the happy path and the 404 case for each route. Install any
missing dev dependencies and run the suite when you're done so I
can see it pass.

With the local-qwen provider selected, OpenCode reads users.js, infers the route signatures, writes a users.test.js file, updates package.json with the Jest dependency if it is missing, and runs npm test to confirm the suite passes before handing control back to you. The entire exchange, from prompt to a green test run, happens without a single API call leaving your machine. That is the complete local loop this tutorial is built around: read, write, execute, verify, all on hardware you own.

One developer who documented a near-identical build with a different coding agent on top of the same Qwen3.8 and LM Studio pairing found the local stack handled a full small web app build end to end, a useful data point if you are wondering whether 27 billion parameters is enough model for real work. You can read that build log at this fully local coding agent write-up on Dev.to.

Local Model Server Options: LM Studio vs Ollama vs llama.cpp vs vLLM

LM Studio is the easiest entry point for this tutorial because it is a GUI app with a one-click local server toggle, but it is not the only option, and OpenCode does not care which one you pick as long as it speaks an OpenAI-compatible or Anthropic-compatible shape. Here is how the main options compare.

Tool	Interface	API Style	Best Fit
LM Studio	Desktop GUI	OpenAI-compatible, localhost:1234/v1	Developers who want a visual model manager with no setup scripting
Ollama	Command line	OpenAI and Anthropic-compatible endpoints	Scripted workflows and quick model swaps from the terminal
llama.cpp	Library or compiled binary	Raw local inference, OpenAI-compatible server mode available	Advanced users who want direct control over quantization and runtime flags
vLLM	Server framework	OpenAI-compatible, built for throughput	Self-hosted VMs or remote GPU boxes serving multiple concurrent sessions

All four of these can serve models from the Qwen3-Coder family on hardware you control, so switching between them later is mostly a matter of re-pointing OpenCode’s baseURL at a new port, not rebuilding your whole config.

Local vs Cloud: What You Actually Trade Away

Before you commit to running this stack daily, it helps to see the trade-off laid out plainly rather than discovering it halfway through a deadline. Local and cloud coding agents are not competing on the same axis, they are optimized for different priorities, and knowing which axis matters for a given task is more useful than picking one approach as a permanent default.

Factor	Local (OpenCode + Qwen3.8 + LM Studio)	Cloud-hosted agent
Per-request cost	None beyond electricity and hardware you already own	Per-token billing, scales with usage
Data privacy	Code and prompts never leave your machine	Code and prompts transit a third-party API
Uptime dependency	Depends only on your own hardware	Depends on the provider’s service status
Raw model capability	Limited by what fits on consumer hardware	Access to the largest frontier models
Setup effort	One-time install and config, roughly an hour	Usually an API key and a few minutes
Works offline	Yes, once the model is downloaded	No, requires an active connection

Most developers who settle into this stack long term end up using it as one option among several rather than their only agent. Routine, well-scoped tasks go to the local setup. Anything requiring deeper reasoning, unfamiliar frameworks, or a model with a longer track record on genuinely hard problems goes to a hosted provider. The config work Makes that switch a matter of picking a different provider entry, not rebuilding your whole workflow.

What Kind of Coding Tasks Qwen3.8 27B Handles Well

A 27-billion-parameter model is not going to match a frontier model’s reasoning depth on a genuinely hard architectural problem, and it is worth setting that expectation before your first session rather than after a frustrating one. Where it holds up well is on bounded, well-specified work: fixing a bug you can describe precisely, writing a test suite for code that already exists, renaming and refactoring within a single file or a small set of related files, and generating boilerplate like route handlers, data models, or configuration files that follow an established pattern elsewhere in your codebase.

Where it tends to struggle is the opposite kind of task: open-ended design questions, multi-service changes that span several repositories, or anything that requires holding a very large amount of unfamiliar context in mind at once. If you find yourself writing an increasingly long, increasingly careful prompt trying to get a correct result out of the local model, that effort is usually a signal the task has outgrown a 27B model’s practical ceiling, not a signal to keep refining the prompt. At that point, switching the same OpenCode session to a hosted provider, using the multi-provider config covered later Is faster than continuing to fight the smaller model.

It is also worth testing the model on a handful of tasks from your own codebase before trusting it with anything that matters. Every codebase has its own conventions, and a model’s output quality on unfamiliar patterns can vary more than generic benchmarks suggest. Twenty minutes spent running the agent through a few low-stakes tasks you already know the answer to will tell you more about whether this setup fits your specific work than any general claim about model capability.

Keeping the Agent Running Remotely with tmux and SSH

Running the model on your laptop is fine for a quick test, but a laptop that goes to sleep or loses Wi-Fi kills a long-running agent task halfway through. The common fix is to move the whole stack, or just the model server, onto a Linux VM or a spare desktop with a GPU, and keep the agent session alive inside a tmux session over SSH so it survives a dropped connection.

ssh dev-box
tmux new -s local-agent
cd ~/projects/my-app
opencode

Detach from the tmux session with its standard detach key combination and disconnect from SSH whenever you want, the agent keeps running on the remote box. Reconnect later with tmux attach -t local-agent and you are back exactly where you left off. This pattern, a plain VM plus SSH plus tmux plus whichever agent CLI you prefer, is popular precisely because it needs no extra platform or orchestration layer on top. A detailed walkthrough of this exact approach, including how to point the agent at a model server running on a different machine entirely, is covered in this self-hosted AI coding agent infrastructure guide.

Security and Sandboxing Your Local Agent

A coding agent that can edit files and run shell commands is useful precisely because it has real power over your system, which is also exactly why it needs boundaries. Running everything locally removes one category of risk, your code and prompts never transit a third-party API, but it does not remove the risk of the agent itself running a destructive command inside your project.

Set OpenCode to prompt before executing shell commands rather than running them automatically, at least until you trust how it behaves on your codebase. Work inside a dedicated git worktree or branch so a bad edit is a quick git checkout away from being undone, not a manual cleanup job. Keep secrets and API keys out of the files the agent can read by using environment variables loaded outside the project directory, not values hardcoded into config files sitting in the repo. This matters even more when you eventually mix local and hosted providers in the same OpenCode config, since a misconfigured provider entry can expose a cloud API key to logs or to a model you did not intend to share it with. Our coverage of the OpenCode console breach that exposed OpenAI and Anthropic keys is a useful reminder of what that failure mode looks like in practice, and why key hygiene is not optional once an agent has shell access.

Common Pitfalls When Running a Local AI Coding Agent

Most of the problems people run into with this stack are not exotic. They are small, specific mistakes that are easy to make once and hard to diagnose later because the error message rarely points directly at the real cause. The list below covers the ones that come up most often, roughly in the order they tend to bite a first-time setup.

Underestimating memory headroom. Loading Qwen3.8 27B at 4-bit quantization eats roughly 18 GB on its own, and setting a large context window on top of that without leaving overhead is the single most common cause of a model that crashes or silently truncates responses mid-task.
Testing the agent before testing the server. If you skip the curl http://localhost:1234/v1/models check and go straight to configuring OpenCode, you will waste time debugging the wrong layer when the real problem is that LM Studio’s server never started.
Letting the agent run shell commands unsupervised from the first session. A model you have not worked with yet does not have a track record on your codebase. Start with manual approval for every command and loosen that only once you know its behavior.
Mismatching quantization to the task. A heavily quantized model saves memory but can produce noticeably worse code on complex refactors. If output quality drops on a hard task, that is a signal to try a less aggressive quantization before assuming the model itself is the wrong choice.
Forgetting the provider name has to match exactly. A typo between the provider key in your OpenCode config and the one you select in the interface is a quiet failure that looks like a connection problem but is really just a string mismatch.
Running the model server on a machine with a firewall blocking the port. If you move the model server to a remote box, port 1234 (or whichever port you configure) needs to be reachable from the client machine, which usually means an SSH tunnel or an explicit firewall rule, not just opening the port to the open internet.

Troubleshooting Your Local Setup

Most problems in this stack trace back to one of three layers: the model server, the network path between the agent and the server, or the agent’s own configuration. Work through these in order before assuming something is fundamentally broken. Isolating which of the three layers is actually failing before you start changing settings will save you from the common trap of fixing a config file when the real problem was that the server process had silently stopped, or restarting the server when the real problem was a firewall blocking the port on a remote box.

“Connection refused” when you curl localhost:1234. LM Studio’s local server is not running. Reopen the Developer tab and confirm the toggle is on, and confirm a model is actually loaded first.
The model loads but every response comes back empty. This usually means the context window was set too small for the prompt plus the project files being read. Increase the context length in LM Studio’s model settings and reload.
OpenCode says it cannot find the local-qwen provider. Check for a typo between the provider key in your config file and the name shown in OpenCode’s provider picker. These have to match character for character.
Everything runs on CPU and generation is painfully slow. Run nvidia-smi (or Activity Monitor’s GPU tab on a Mac) while the model is generating to confirm the GPU is actually being used. If it shows near-zero utilization, LM Studio likely fell back to CPU inference because the GPU offload setting was left too low.
The agent hangs on large file edits. Very large files pushed into a limited context window can stall generation. Point the agent at specific functions or line ranges instead of asking it to process an entire multi-thousand-line file at once.
Out-of-memory crash partway through a response. This is the 18 GB memory floor catching up with you. Close other GPU-hungry applications, drop to a smaller context length, or move to a machine with more VRAM.
An SSH session drops and the agent task disappears with it. This is exactly what the tmux pattern described earlier solves. If you are not already running the agent inside tmux on a remote box, that is the fix, not a one-off workaround.
Mixing local and cloud providers causes authentication errors. A local provider entry needs no API key, but a cloud provider entry in the same config does. Confirm the right keys are set as environment variables for the cloud entries specifically, without accidentally applying them to the local one.
Token generation is noticeably slower than you expected. Local inference on consumer hardware is rarely as fast as a dedicated cloud inference cluster. This is a known tradeoff of the local approach, not necessarily a misconfiguration, though checking GPU offload settings is still worth doing first.

Advanced Tips: Routing Between Local and Cloud Models

Once the local stack is working, the next useful step is deciding when to actually use it versus reaching for a hosted model. A practical pattern many developers settle into is running routine, low-risk tasks, formatting fixes, test scaffolding, small bug fixes, through the local Qwen3.8 setup, and reserving hosted models for tasks that genuinely need a larger model’s reasoning depth, like a tricky architectural refactor across many files.

OpenCode’s multi-provider config makes this easy, since you can register both a local-qwen entry and a hosted entry like Anthropic or Google Gemini in the same file and switch between them per session. If you want a working example of wiring a cloud API key into an agent config correctly, our Gemini API key setup tutorial covers that process step by step, and it applies the same way whether you are feeding a key to OpenCode or any other agent framework. For a broader look at routing a single workflow across multiple providers rather than picking one, see our guide to building an AI router across Claude, ChatGPT, Gemini, and Copilot.

For readers with serious multi-GPU hardware who want to see what sits above Qwen3.8 27B, Meta released Muse Spark Open on October 15, 2026, an open-weight model with 816 billion total parameters, 42 billion active parameters, and a 1-million-token context window. It is not a realistic target for the single-GPU or single-Mac setup this tutorial describes, but it is worth knowing it exists as the ceiling of what “local” can mean once you have the hardware budget for it. Coverage of its run recipes and hardware floor is available at Developers Digest’s local AI tutorials hub.

If local setup turns out to be more friction than your workflow needs, it is worth knowing the inverse path exists too. Developers moving the other direction, from a local or IDE-bound agent into a cloud-native workflow, can find a structured walkthrough in our Cursor to Claude Code migration tutorial, which covers a comparable config-swapping exercise from the opposite side.

Why This Setup Beats a Subscription for Some Teams, and When It Doesn’t

It is worth being honest about where a local setup makes sense and where it does not. If your work involves sensitive client code, regulated data, or an air-gapped environment, local inference is close to the only option that satisfies those constraints while still getting agent-style help. If your hardware budget tops out at a laptop with 16 GB of RAM and no discrete GPU, this entire stack is simply off the table, and a hosted model remains the faster path to the same productivity.

Stack Overflow’s 2025 survey also found that 31.1 percent of developers use AI agents at work daily, weekly, or at least occasionally, against 37.9 percent who do not use agents and have no plans to start. That split suggests agent adoption is still a choice, not a default, and the local-versus-cloud decision sits inside that broader choice rather than being a separate question. The same survey found 82 percent of developers used OpenAI’s GPT models for development work over the past year, a reminder that whatever local setup you build, most of the ecosystem around you is still cloud-first, and your agent configuration should probably be flexible enough to talk to both worlds rather than lock into only one.

Frequently Asked Questions

Do I need an internet connection once the local agent is set up?

No, not for the local inference path itself. Once LM Studio has downloaded Qwen3.8 27B and OpenCode is configured to use your local provider, the model runs entirely offline. You only need a connection if OpenCode is also configured with a cloud provider you choose to switch to, or when checking for software updates.

Can I run this setup on a laptop without a dedicated GPU?

Technically yes, LM Studio will fall back to CPU inference, but it will be slow enough to undermine the point of using an agent at all. The realistic floor for comfortable performance is a 24 GB GPU or a Mac with at least 32 GB of unified memory, as outlined in the prerequisites section above.

Is OpenCode free to use with a local model?

Yes. OpenCode itself is MIT-licensed and free, and Qwen3.8 27B is distributed as open weights with no usage fee. LM Studio is also free to download and run. The only cost in this stack is the hardware you already own, there is no subscription or per-token charge tied to the local path.

How does Qwen3.8 27B compare to a model like GPT-6 for coding tasks?

Qwen3.8 27B is a far smaller model than the frontier hosted options, and it will generally underperform them on the hardest reasoning-heavy tasks. What it offers instead is zero marginal cost, full data privacy, and no dependency on a provider’s uptime, which is a worthwhile trade for routine coding work even if it is not a universal replacement for a frontier cloud model on the most demanding tasks.

What happens if I close my laptop while the agent is mid-task?

If everything is running locally on that laptop, the task stops. This is exactly the problem the tmux-and-SSH pattern covered earlier solves: move the model server and the agent onto a remote machine, run the agent inside a tmux session, and closing your laptop simply disconnects your view of a task that keeps running on the remote box.

Can I use Ollama instead of LM Studio for this tutorial?

Yes. Ollama, like LM Studio, can serve Qwen3-Coder family models through an OpenAI-compatible or Anthropic-compatible endpoint. You would swap the baseURL in OpenCode’s provider config to Ollama’s local endpoint instead of LM Studio’s, and the rest of the agent-side setup stays the same.

Does running the agent locally protect me from all security risks?

No. Local inference removes the risk of your code or prompts being logged by a third-party API, but it does not remove the risk of the agent itself running a destructive command inside your project if left unsupervised. Set OpenCode to confirm shell commands before executing them, and work inside a dedicated branch or worktree, as covered in the security section above.

OpenCode AI: Local Coding Agent in 14 Steps, 90 Min [2026]