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Learning rate tuning

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Running2

Widen the model hidden dimension

Working...

Learning rate tuning

Increase the Muon matrix learning rate

Working...

Waiting for review2

Learning rate tuning

Add a learning rate warmup phase

PR is ready•

Learning rate tuning

Tune the WSD warmdown ratio

Waiting for CI•

Done3

Switch the MLP activation to GELU

42m ago•

Establish the training baseline

3h ago•

Add QK-norm and value embeddings

1h ago•

main

Launchpad

0

Screen Reader Optimized

Ln 231, Col 29

Spaces: 2

UTF-8

{ } TypeScript JSX

Cognition Platform (Enterprise)

Windsurf - Settings

Agent Command Center

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autoresearch

Establish the training baseline

Add QK-norm and value embeddings

A world-class IDE

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exactly when you need it.

Read, trace, and debug every change your agents ship.

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autoresearch

train.py

      self.mlp = MLP(config)
117
  def forward(self, x, ve, cos_sin, window_size):
      x = x + self.attn(norm(x), ve, cos_sin, window_size)
      x = x + self.mlp(norm(x))
      return x
122
123
124class GPT(class) GPT: type[GPT]Go to GPTImplements the full transformer language model defined in train.py .•Builds wte embedding, Block layer list, and output head.•Handles value embeddings, rotary embeddings, and windowed attention patterns.•Provides init_weights , _precompute_rotary_embeddings , and FLOP estimation utilities.(nn.Module):
  def __init__(self, config):
      super().__init__()
      self.config = config
      self.window_sizes = self._compute_window_sizes(config)
      self.transformer = nn.ModuleDict({
          "wte": nn.Embedding(config.vocab_size, config.n_embd),
          "h": nn.ModuleList([Block(config, i) for i in range(config.n_layer)]),
      })
      self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
      self.resid_lambdas = nn.Parameter(torch.ones(config.n_layer))
      self.x0_lambdas = nn.Parameter(torch.zeros(config.n_layer))
      # Value embeddings
      head_dim = config.n_embd // config.n_head
      kv_dim = config.n_kv_head * head_dim

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micrograd

Implement backprop

implement backprop from scratch — a minimal scalar autograd engine, like micrograd

I'll build a single `Value` type that records the computation graph as expressions run, then backpropagates with reverse-mode autodiff. Starting from an empty repo with the node scaffold — the data, its gradient, and the edges to its parents.

engine.py+9

Each forward op returns a new `Value` and stashes a local `_backward` closure — the chain rule for that op, pushing the output gradient into its inputs.

engine.py+17

engine.py+13

Now the backward pass itself: topologically order the graph from the output, seed its gradient to 1, and run the stored closures in reverse so each node is fully accumulated before its parents are reached.

engine.py+12

Sanity-checking the gradients against PyTorch on a small expression with a reused variable, so the accumulation is actually exercised.

test_engine.py+9

python -m pytest test_engine.py -q

Done — a ~50-line autograd engine, written from scratch. `backward()` topo-sorts the graph and applies each op’s local gradient in reverse, accumulating with `+=` so reused sub-expressions sum correctly. The gradient check matches PyTorch to 1e-6.

Code

Agents

Local

micrograd

Spaces

Onboarding Flow Redesign

feat/onboarding-flow-design

One Space for every agent

Use Spaces to share context and Git worktrees across all your agents.

Supercomplete

autoresearch

train.py

  def __init__(self, config):
      super().__init__()
      self.config = config
      self.window_sizes = self._compute_window_sizes(config)        self.window_sizes = self._compute_window_sizes(config)
121
  def _compute_window_sizes(self, config):
      pattern = config.window_pattern.upper()
      long_window = config.sequence_len
      short_window = long_window // 2
      char_to_window = {"L": (long_window, 0), "S": (short_window, 0)}
      
      for i in range(config.n_layer):        for i in range(config.n_layer):
          char = pattern[i % len(pattern)]            char = pattern[i % len(pattern)]
          window_sizes.append(char_to_window[char])            window_sizes.append(char_to_window[char])
      return window_sizes        return window_sizes

Tab, Tab, Ship

Supercomplete predicts your next thought, not just your next edit.

Agents on ACP

Fast ContextWhere is reconciliation scheduled?

Instant codebase context

Fast Context finds the exact files and lines your agent needs—in milliseconds.

Never miss a detail

Rapidly (or deeply) review every agent diff—before you push.

Leaderboard ranking SWE-1.6 against other coding models

Free world-class models

Unlimited access to SWE-1.6, the fastest coding model in the world.

plan.md

To build a dashboard for real-time store sales data, we will stream events from Kafka over websockets and render them onto a three.js globe.

View planImplement in Cloud

Effortless handoff to the cloud

The only IDE designed for you to close your laptop.

Customers

Teams building with Devin Desktop

Devin Desktop makes it easy to dispatch and monitor our array of agents from a single command center. We're excited to partner with Cognition to bring the agents Ramp engineers already use into one shared workspace, making it easier to jump between tasks, preserve context, and get more done.

At Harvey, we built our internal background agent, Spectre, to work across long-running engineering efforts while carrying organizational context for our legal research, engineering, product, and design teams to seamlessly collaborate. With Devin Desktop's support for custom background agents, that context now extends to every engineer's laptop, so humans and agents work from the same shared understanding instead of starting from scratch.

NVIDIA is joining Cognition's research preview for multi-agent support in Devin Desktop. Our engineers run multiple agents across complex workflows every day, and we're excited to help define how they share context and coordinate in one place.

We've been working closely with Cognition as a design partner on multi-agent support in Devin Desktop. Our engineers run multiple agents every day and Devin Desktop is the first tool that lets them manage all of them together, with shared context, from one place.

Devin Desktop gives our teams the same intelligent agent experience, but with the full permissions and flexibility of their local machines. For development work that benefits from a faster, more hands-on environment, it's a natural fit. It's snappier, it's accessible, and it fits the way a lot of our developers are working today.

Make Devin Desktop your own

Extend Devin with the tools, skills, and plugins your team already uses.

Slack

MCP Server

Search channels and messages, send and read messages, and access user profiles.

ESLint

Extension

Find and fix problems in your JavaScript and TypeScript code.

Linear

MCP Server

List, create, update, and query issues, projects, initiatives, cycles, and comments.

rust-analyzer

Language Server

Code completion, go-to-definition, and inline diagnostics for Rust.

Notion

MCP Server

Retrieve and manage pages, databases, and comments; search across your workspace.

So good you can't work without it

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Frequently Asked Questions

What is Devin Desktop?

Devin Desktop is the new name for Windsurf. We’re building on the IDE foundation of Windsurf to introduce the command center for managing all your agents in one place. The Agent Command Center (Spaces, Kanban view, and multi-agent management) is front and center, while the full IDE experience you know remains fully accessible.

Read the announcement →

How do I upgrade to Devin Desktop from Windsurf?

Devin Desktop arrives as a standard over-the-air update, so your plan, pricing, extensions, and settings all carry over automatically. You can also download the latest Devin Desktop version from the download page.

Will I lose anything if I update?

The IDE, your extensions, workflows, settings, and in-progress work will all remain intact and will be fully migrated when you update. Only the name and branding are changing.

Learn more in the docs →

Does my plan or pricing change?

No. Your current plan and pricing stay exactly the same, including legacy Windsurf Enterprise plans.

Learn more in the docs →

What is happening to JetBrains support?

Windsurf for JetBrains (IntelliJ IDEA, PyCharm, WebStorm, and more) continues to be available for download.

Get Windsurf for JetBrains →

Devin Desktop

A team of agents forevery engineer.

The power of an IDE,exactly when you need it.

All the models,All the agents.

One Space for every agent

Tab, Tab, Ship

Agents on ACP

Instant codebase context

Never miss a detail

Free world-class models

Effortless handoff to the cloud

Teams building with Devin Desktop

Make Devin Desktop your own

So good you can't work without it

Learn about our plans

Trusted by developers.

Frequently Asked Questions

A team of agents for
every engineer.

The power of an IDE,
exactly when you need it.

All the models,
All the agents.