AI Engineer Europe 2026

One Developer, Two Dozen Agents, Zero Alignment: Why we Need Collaborative AI Engineering

Maggie Appleton· Staff Research Engineer, GitHub Next17:43

Slide chapters

19 slide and demo groups · Timestamps are approximate. 21 additional groups were omitted from this pass.

AI Engineer Europe

AI Engineer Europe logo in white on black.

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AI Engineer
EUROPE

Presenting sponsor

Google DeepMind is the presenting sponsor.

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PRESENTING SPONSOR
Google DeepMind

Platinum sponsors

Braintrust, WorkOS and OpenAI are the platinum sponsors.

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PLATINUM SPONSORS

Braintrust
WorkOS
OpenAI

Individual users and AI tools

Five people stand separately above AI tool icons. The speaker argues that these single-player tools scale individual work, while building software also requires teams to agree on what they are building and why.

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Interface text:
Audience · Maggie AI Engineer Talk - Figma Slides

Nine women

Nine pregnant women illustrate the speaker’s analogy: nine women cannot make a baby in one month. More individual output does not solve problems that require communication and coordination.

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Interface text:
Audience · Maggie AI Engineer Talk - Figma Slides

1:46Completed build

Show 2 builds
  1. 1:40

    A row of women in white shirts and jeans pose with their hands on pregnant bellies.

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    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides
  2. 1:46

    Nine pregnant women stand in a row wearing white shirts and jeans.

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    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides

Alignment and communication is the new bottleneck

The diagram narrows at alignment before widening into implementation. As coding gets faster, the speaker argues that teams must agree on what to build and where to spend their energy.

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Alignment and communication
is the new bottleneck

alignment
implementation

Interface text:
Audience · Maggie AI Engineer Talk - Figma Slides

Team coordination tools

Six people connect through Slack, GitHub, Linear and Jira to one person. The speaker argues that these coordination tools were not designed for agentic development.

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Engineering the future of AI

Interface text:
Audience · Maggie AI Engineer Talk - Figma Slides

Plan, build, review

A long build sat between planning touchpoints in chat, docs and issues and PR review. The slower cycle gave teams time for feedback and course corrections.

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chat threads
docs
issues
PRs

plan
build
review

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Audience · Maggie AI Engineer Talk - Figma Slides

3:36Completed build

Show 2 builds
  1. 3:17

    A timeline shows chat threads, documents and issues during planning, a long build phase, and pull requests during review.

    Text in image
    chat threads
    docs
    issues
    PRs
    
    plan
    build
    review
    
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides
    Engineering the future of AI
  2. 3:36

    A timeline shows chat threads, documents and issues during planning, a long build period, and pull requests during review.

    Text in image
    chat threads
    docs
    issues
    PRs
    
    plan
    build
    review
    
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides

The implementation window collapses

The build window shrinks to a marker between planning and PR review. The speaker argues that faster implementation leads teams to skip early alignment.

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chat threads
 docs
issues
PRs

plan
build
review

Interface text:
Audience · Maggie AI Engineer Talk - Figma Slides

Alignment shifts toward review

The speaker says generated code increases review time, placing more alignment points after implementation. The diagram shows two planning touchpoints before a short build and four review touchpoints after it.

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chat threads    issues    PRs

plan    build    review

Interface text:
Audience · Maggie AI Engineer Talk - Figma Slides

Chat threads, issues, and PRs

The timeline compresses chat threads and issues before the build, with several touch points afterward. The speaker says an agent can open a PR a couple of minutes after an issue is logged.

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chat threads, issues
PRs

Interface text:
Audience · Maggie AI Engineer Talk - Figma Slides

4:16Completed build

Show 2 builds
  1. 4:13

    A compact timeline places chat threads and issues close to a short build marker, followed by pull request touch points.

    Text in image
    chat threads, issues
    PRs
    
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides
  2. 4:16

    A timeline places chat and issue tools before pull requests.

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    chat threads, issues
    PRs
    
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides

Local, unshared plans

Most coding agents’ local plans go unshared with teammates, leaving alignment checks for the PR after implementation.

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local, unshared plans

Interface text:
Audience · Maggie AI Engineer Talk - Figma Slides

Wasted work

Moving fast without alignment can produce features nobody asked for or work thrown out after critical feedback arrives late.

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Wasted work

building the wrong thing

Interface text:
Audience · Maggie AI Engineer Talk - Figma Slides
Engineering the future of AI

Wasted work and coordination debt

Misalignment leads to wasted work and coordination debt: teams may build the wrong thing, face merge conflicts, or duplicate work.

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Wasted work
building the wrong thing

Coordination debt
cleanup cost of unaligned efforts

Interface text:
Audience · Maggie AI Engineer Talk - Figma Slides

Collaboration in the agentic development flow

Planning and building form a repeating cycle. The speaker calls for teams to align before agents start and keep aligning throughout implementation.

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Bring collaboration and alignment into the agentic development flow

build   build   build
plan    plan    plan

Interface text:
Audience · Maggie AI Engineer Talk - Figma Slides

Human context is not in the codebase

People appear above a code symbol: business context, financial resources, political dynamics and product vision live in people’s heads and need to be shared early to align the team.

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Human context is not in the codebase

Business context & financial resources
Political dynamics
Product vision

</>

Interface text:
Audience · Maggie AI Engineer Talk - Figma Slides

Ace

Ace is a research prototype for agent collaboration. The team plans a technical preview and user testing with a few thousand people before iterating on what it learns.

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Ace
Agent collaboration environment

Interface text:
Audience · Maggie AI Engineer Talk - Figma Slides
Engineering the future of AI

Ace session

Ace brings teammates and coding agents into a shared session. The demo shows their chat alongside a running terminal and change summary; the speaker says each session has its own cloud microVM and Git branch.

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[Embedded screenshot: mostly illegible]
❤️ 1
Maybe we should add multiple themes, actually

iamnbutler 5:05 pm
We could do a theme per category... I started a llm service in #add-llm-provider so we could kick that work off.
(edited)
👀 1

davidslater left

idan 5:06 pm
Cute. So like, Purple for AI-themed posts, and orange for classic HN shitposting, etc etc

exec abort by idan
bun dev

exec by idan
bun dev
VITE v7.1.10 ready in 754 ms
  ➜ Local:   http://localhost:5173/
  ➜ Network: http://169.254.0.2:5173/

Updated accent color from orange to emerald throughout the App component, changing gradient backgrounds and hover states to align with the green theme.

App.tsx  +2 -2
style: change accent color from orange to emerald
77ad8f6

Interface text:
Audience · Maggie AI Engineer Talk - Figma Slides
calmhn
Chat  Diff  Terminal
Connected
My Sessions
upbeat-target
vital-bolt
llm-categories-stati...
epic-icon
asdfasdf
noble-marble
create-green-theme
add-llm-provider
Other People's Sessions
pr-11
steady-train
glad-socket
add-blue-theme
power-arm
kind-craft
onboarding-design-eng...
pr-10
stop  running...
Mention #sessions, @people, +files, and / for commands
Chat mode (⇧ Tab)
@ace is AI and is present in this channel. Check for mistakes.
#19 style: update color scheme...
Merge
Engineering the future of AI

7:25Demo milestone

LLM-driven categories plan

Ace shows a plan to categorize stories at build time. The speaker explains that each session has its own cloud microVM and Git branch, letting teammates switch tasks and inspect the agent’s history.

Text in image
Here’s the plan summary:

LLM-Driven Categories — Approach

Core idea: A build-time Node.js script fetches stories from Algolia, sends titles to an LLM (GPT-4o-mini) for categorization, and writes a static JSON file. Vite bundles it — zero runtime LLM calls.

A GitHub Actions cron workflow runs this daily to keep categories fresh, commits the updated JSON, and triggers a deploy.

Todos (in order)

# | Task | Description
1 | Categorize script | scripts/categorize.ts — fetch stories → LLM categorization → write src/data/categorized-sto[illegible]json
2 | Categories UI | src/CategoriesPage.tsx — [illegible] stories grouped by category, reusing existing card style
3 | Routing | Add react-router with / (feed) and /categories routes + nav
4 | GitHub workflow | .github/workflows/categorize.yml — daily cron + manual dispatch
5 | Seed data | Initial JSON so the app works before first workflow run

Cost: ~600 tokens per run (30 stories) — fractions of a cent. API key stored as a GitHub Actions [illegible]

Planning an LLM-driven categories page that automatically organizes stories under categories while maintaining static site compilation, using a build-time workflow or script to generate the categorization.

No files changed
No commits

Engineering the future of AI

Interface text:
Audience · Maggie AI Engineer Talk - Figma Slides
calmhn
Chat
Plan
Terminal
Connected
My Sessions
clarification-needed
upbeat-target
vital-bolt
llm-categories-stati...
epic-icon
asdfasdf
noble-marble
create-green-theme
add-llm-provider
Other People's Sessions
pr-11
steady-train
glad-socket
add-blue-theme
power-arm
kind-craft
onboarding-design-eng...
pr-10
Mention #sessions, @people, +files, and / for commands
Chat mode (⇧ Tab)
0:09 / 0:09
1x

7:45Demo milestone · Completed build

Show 2 builds
  1. 7:38

    Ace displays a plan for generating story categories and a list of implementation tasks.

    Text in image
    Here’s the plan summary:
    
    LLM-Driven Categories — Approach
    
    Core idea: A build-time Node.js script fetches stories from Algolia, sends titles to an LLM (GPT-4o-mini) for categorization, and writes a static JSON file. Vite bundles it — zero runtime LLM calls.
    
    A GitHub Actions cron workflow runs this daily to keep categories fresh, commits the updated JSON, and triggers a deploy.
    
    Todos (in order)
    
    # | Task | Description
    1 | Categorize script | scripts/categorize.ts — fetch stories → LLM categorization → write src/data/categorized-stories.json
    2 | Categories UI | src/CategoriesPage.tsx — renders stories grouped by category, reusing existing card style
    3 | Routing | Add react-router with / (feed) and /categories routes + nav
    4 | GitHub workflow | .github/workflows/categorize.yml — daily cron + manual dispatch
    5 | Seed data | Initial JSON so the app works before first workflow run
    
    Cost: ~600 tokens per run (30 stories) — fractions of a cent. API key stored as a GitHub Actions [illegible]
    
    Planning an LLM-driven categories page that automatically organizes stories under categories while maintaining static site compilation, using a build-time workflow or script to generate the categorization.
    
    No files changed
    No commits
    
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides
    calmhn
    Chat
    Plan
    Terminal
    Connected
    My Sessions
    clarification-needed
    upbeat-target
    vital-bolt
    llm-categories-stati...
    epic-icon
    asdfasdf
    noble-marble
    create-green-theme
    add-llm-provider
    Other People's Sessions
    pr-11
    steady-train
    glad-socket
    add-blue-theme
    power-arm
    kind-craft
    onboarding-design-eng...
    pr-10
    Mention #sessions, @people, +files, and / for commands
    Chat mode (⇧ Tab)
    @ace is AI and is present in this channel. Check for mistakes.
  2. 7:45

    A shared coding session displays a plan for categorizing stories at build time.

    Text in image
    Here’s the plan summary:
    
    LLM-Driven Categories — Approach
    
    Core idea: A build-time Node.js script fetches stories from Algolia, sends titles to an LLM (GPT-4o-mini) for categorization, and writes a static JSON file. Vite bundles it — zero runtime LLM calls.
    
    A GitHub Actions cron workflow runs this daily to keep categories fresh, commits the updated JSON, and triggers a deploy.
    
    Todos (in order)
    
    # | Task | Description
    1 | Categorize script | scripts/categorize.ts — fetch stories → LLM categorization → write src/data/categorized-sto[illegible]json
    2 | Categories UI | src/CategoriesPage.tsx — [illegible] stories grouped by category, reusing existing card style
    3 | Routing | Add react-router with / (feed) and /categories routes + nav
    4 | GitHub workflow | .github/workflows/categorize.yml — daily cron + manual dispatch
    5 | Seed data | Initial JSON so the app works before first workflow run
    
    Cost: ~600 tokens per run (30 stories) — fractions of a cent. API key stored as a GitHub Actions [illegible]
    
    Planning an LLM-driven categories page that automatically organizes stories under categories while maintaining static site compilation, using a build-time workflow or script to generate the categorization.
    
    No files changed
    No commits
    
    Engineering the future of AI
    
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides
    calmhn
    Chat
    Plan
    Terminal
    Connected
    My Sessions
    clarification-needed
    upbeat-target
    vital-bolt
    llm-categories-stati...
    epic-icon
    asdfasdf
    noble-marble
    create-green-theme
    add-llm-provider
    Other People's Sessions
    pr-11
    steady-train
    glad-socket
    add-blue-theme
    power-arm
    kind-craft
    onboarding-design-eng...
    pr-10
    Mention #sessions, @people, +files, and / for commands
    Chat mode (⇧ Tab)
    0:09 / 0:09
    1x

Read the talk

One Developer, Two Dozen Agents, Zero Alignment

Coding agents make implementation faster, but teams still need to agree on what to build. Ace explores a shared workspace for planning, conversation, and running code.

From a talk by Maggie Appleton

Before you start: Familiarity with Git branches, pull requests, and coding agents will help you follow the demonstration.

A wall of agents still needs a shared direction

What does peak developer productivity look like when one person can run a fleet of coding agents? Maggie Appleton approaches that question as a designer and staff research engineer at GitHub Next, the experimental research team within GitHub. Her opening example is a wall of terminal agents running in parallel on one machine: one developer with two dozen Claudes, apparently doing the implementation work of an entire team.

These interfaces scale an individual’s output. They do not establish agreement among the people responsible for the product. Software development requires communication about what to build and why; adding more parallel production cannot resolve that dependency. Appleton invokes the familiar fallacy of nine women producing a baby in one month: some constraints do not yield to more workers. More output can instead amplify the cost of unresolved decisions.

Appleton describes implementation as rapidly becoming a solved problem: code is getting faster and cheaper to produce, with improving quality. That is the premise of her argument, rather than a claim that every engineering problem has disappeared. As implementation becomes easier, the difficult question shifts from how to build something to whether it should be built. Agreement about the right work becomes the bottleneck.

Slide with a brown flow shape narrowed beneath an alignment arrow and widening into an area labeled implementation.Open full source frame
Alignment and communication is the new bottleneck.

Choosing a feature still commits the team’s attention to that feature instead of everything else it might do. When production is cheap, opportunity cost becomes more consequential: are we spending effort where it will have the most impact? Alignment has always constrained teams. Agents increase the amount of work a team can produce before discovering that it was heading in the wrong direction.

0:250:34
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0:25 · section reference included

The implementation window collapses

The coordination tools receiving this growing volume of agent output—GitHub, Slack, Jira, and Linear—were built around an older development rhythm. Appleton reports that people inside GitHub are also exploring what comes after issues and pull requests; the existing artifacts are not assumed to be the final form of software collaboration.

In the slower planning–building–review sequence, implementation created time for alignment. Slack conversations, meetings, issue comments, and draft PRs let teammates contribute expertise, catch mistakes, and redirect work while it was still taking shape. By the time a change reached review and merge, the team had usually seen it develop.

Agent implementation compresses that interval. Because building feels inexpensive, teams may also skip planning. Appleton says generated code takes longer to review, placing more coordination after implementation. She describes the interval from logging an issue to an agent opening a PR as a couple of minutes. These are observations motivating the design, not controlled benchmark results.

Local plan modes remove another opportunity for intervention: an agent can produce a plan that no teammate sees, and its operator may barely read it before proceeding.

Coordination pointSlower developmentAccelerated, private agent work
PlanningDiscussion before buildingLocal, unshared plan
ImplementationTime for feedback and correctionCompressed feedback window
Pull requestReview of familiar workFirst encounter with decisions

The pull request inherits the burden of explaining intent, supplying context, and negotiating scope after the code already exists. Appleton’s missing primitive is a shared place to discuss plans, gather context, and work with agents together.

2:422:55
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2:42 · section reference included

Coordination debt starts before the pull request

The consequences fall into two related categories:

  • Wasted work: features nobody asked for, changes that do not solve real problems, or completed implementations discarded after critical feedback arrives.
  • Coordination debt: cleanup from agents changing the same files, difficult merge conflicts, and developers independently completing the same task.

The work may be locally successful while creating costs elsewhere in the team.

Two-column slide: “Wasted work” with “building the wrong thing,” and “Coordination debt” with “cleanup cost of unaligned efforts.”Open full source frame
Wasted work and coordination debt are two costs of poor alignment.

Large queues of PRs compound the problem when reviewers have no context for what they contain. The proposed response is to align before agents start and keep aligning during implementation. Planning and building become a cycle, with context gathering and decision-making in the same environment as development.

Much of the information needed for that cycle is outside the repository:

  • Business constraints: available money and the commercial reasons for doing the work.
  • Decision authority: who is responsible, who can approve a direction, and the organization’s political dynamics.
  • Product understanding: leadership’s vision and designers’ findings from user research.
  • Organizational history: what the team has already built and learned.

Code alone cannot tell an agent these things. People need a natural way to contribute them early, without turning every interaction into another layer of process.

5:025:19
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5:02 · section reference included

A session is a conversation and a computer

Ace, the Agent Collaboration Environment, is GitHub Next’s research prototype for this workflow. At the time of the recording, it was unfinished and approaching a technical preview, with user testing planned to explore how teams would collaborate in it. The demonstration shows that prototype, not a production-release announcement.

Its interface uses familiar patterns: a session list on the left and a multiplayer chat containing both teammates and coding agents. But each session is also backed by a microVM, a sandboxed cloud computer with its own Git branch. Separate sessions isolate changes for parallel tasks; participants within a session share the working environment.

That changes the cost of asking a colleague for help. They can enter the session and inspect the work without stashing local changes, pulling another branch, or managing worktrees. They also see the complete prompting history, which explains how the current output came to exist. The shared artifact includes both the implementation and the conversation that produced it.

6:406:49
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6:40 · section reference included

Run the project and inspect the changes

A session supports ordinary terminal commands. Appleton installs dependencies and starts the development server:

sh

bun install
bun dev

Opening the port exposes the running project in a browser preview beside the conversation. The demo application is a calmer version of Hacker News, showing top stories from the last three months rather than demanding daily attention.

She asks the agent to change the theme to purple. The preview updates because the environment is running the changed code. The agent also creates a commit with a message, and the resulting diffs remain available for inspection. These familiar coding-agent capabilities establish the working loop before the collaboration features come into play.

Next, with teammates present, she requests additional color themes and selects Opus 4.6. A summary block in the upper-right corner tracks recent changes from any participant. Its purpose is orientation: someone switching among concurrent sessions can recover what has happened without reading every message and action.

8:078:21
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8:07 · section reference included

Team conversation becomes agent context

Teammates can inspect the proposed changes in the same environment. They can start the development server, view the same preview, run terminal commands, and see shared outputs. Discussion is attached to a common running system rather than to separate local reproductions of it.

The collaborators contribute screenshots, questions, and alternative feature ideas. Nate then asks Ace to add a teal theme. Appleton initiated the session, but Nate can prompt its agent directly. The agent receives the whole conversation as input, so the team can discuss a change first and then invoke @Ace to act on that shared context.

A familiar chat interface also gives designers, product managers, and customer support staff a way to participate while a feature is being built. Appleton’s reason for building a dedicated environment rather than simply using Slack is the need for software-development primitives: terminals, diffs, and the working project itself. In her view, those are outside Slack’s core business. Ace tries to make those capabilities accessible without requiring everyone to collaborate through a terminal.

9:219:28
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9:21 · section reference included

Return to GitHub without losing the session

Once the team likes the theme changes, it creates a pull request directly inside Ace, inspects the PR preview, and follows the link to GitHub. The PR description includes a link back to the Ace session. This preserves a route to the conversation behind the change while allowing only part of a team to adopt Ace; colleagues can continue using their existing tools.

GitHub pull request titled “feat: add theme switcher with multiple color options #20,” showing an Open status, change description, and View Session link.Open full source frame
The created pull request appears on GitHub with a link to its Ace session.

Direct editing remains available. Appleton, whose front-end work often requires correcting an agent’s CSS, opens the project in VS Code. The shared cloud computer supports real-time multiplayer editing. Closing her laptop does not end the session: teammates can keep editing and prompting the agent.

A mobile interface was still under development. The architectural advantage was already clear, however: future phone access could connect to the cloud session without tunneling into a personal computer. Keeping the work available would not require leaving a laptop running or buying a dedicated local machine.

11:0211:10
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11:02 · section reference included

Revise the plan before asking for implementation

For a more complex change, the team discusses adding variable timeframes to the calm Hacker News app. Appleton asks Ace to draft a plan, then opens it as a shared document. Teammate cursors are visible, and everyone can edit the plan and assess whether it achieves the intended result.

Nate suggests a dropdown instead of a segmented control. Another teammate updates the requirements so the agent will receive that decision. Only after the team is satisfied does Appleton return to chat and ask @Ace to implement it. The demonstrated sequence is a draft, a collaborative requirements change, and an implementation request; it does not show a completed timeframe feature. The important intervention happens while the interface choice is still part of a plan.

12:1812:35
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12:18 · section reference included

Recover personal and team context

Moving planning and discussion into sessions gives Ace more context to summarize. The dashboard demonstration starts with a Monday-morning problem: remembering unfinished work from Friday. Ace surfaces React hooks from an ongoing refactor. From there, Appleton can start a new session or use Pick back up to reopen the session associated with an unmerged PR. Recently completed PRs and issues provide a record of finished work.

Team pulse summarizes coworkers’ activity: Nate has been shipping a lobby channel, while David has been fixing access-token issues. A raw feed of repository issues and PRs remains available alongside the summaries. Appleton illustrates the growing volume of work with coworkers shipping five features a day instead of half of one. This is an illustration of the coordination problem, not a measured productivity result for Ace; the dashboard is a first attempt at proactively supplying the missing social context.

The proposed next step is to use conversations around code as a social information fabric. Agents could surface relevant decisions or invite the original author of a feature into a discussion about extending it. Those are possibilities beyond the demonstrated summaries. They depend on the same foundation: a shared workspace where agents can see how people’s decisions relate to the code, rather than disconnected terminal sessions on individual machines.

13:0713:24
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13:07 · section reference included

Spend the reclaimed time on craft

The larger opportunity is to reclaim time. Implementation has historically consumed effort that teams wanted to spend on user research, design details, and architectural choices. People shipped work they were not proud of because they lacked the time to explore those questions properly. Coding agents can return some of that time.

Those savings can fund deeper exploration, more research, and more rigorous thinking together before and during implementation. Faster production only improves the product if the team uses its expanded capacity well. Appleton argues that, as software becomes cheaper to produce, quality and craftsmanship become stronger differentiators.

Craft still costs time and energy. Buying that time requires agreement about doing fewer things better. Easy prompting creates its own distractions: unnecessary features are cheap to request, and a team can quickly build the wrong thing. The desired environment helps people reason together about hard problems, get aligned sooner, and put their effort into a few exceptional things.

15:0015:09
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15:00 · section reference included

Preview access and further reading

At the close of the recording, Appleton expected Ace early access within a couple of months at the latest. The displayed QR code led to a signup form requesting a GitHub username for access when available. She pointed viewers to GitHub Next’s research and her own site, with slides and notes expected shortly afterward. Her companion page for this talk provides the accompanying material.

Closing slide reading “Thank you for listening,” with githubnext.com, maggieappleton.com, and a QR code labeled “Scan to sign up for Ace early access.”Open full source frame
Ace early-access signup and follow-up websites.
16:5517:09
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16:55 · section reference included

Resources

From the talk

Read the complete timestamped transcript

17 slide groups · 2 demo milestones. Extracted from the recording; expand builds to follow changes. Timestamps are approximate. 21 additional groups were omitted from this pass.

  1. 0:00

    [upbeat music] Okay.

  2. AI Engineer Europe

    AI Engineer Europe logo in white on black.

    Text in image
    AI Engineer
    EUROPE
  3. Presenting sponsor

    Google DeepMind is the presenting sponsor.

    Text in image
    PRESENTING SPONSOR
    Google DeepMind
  4. Platinum sponsors

    Braintrust, WorkOS and OpenAI are the platinum sponsors.

    Text in image
    PLATINUM SPONSORS
    
    Braintrust
    WorkOS
    OpenAI
  5. 0:15

    Are we all good? Right. Uh, so yes, this talk, uh, is called, uh, One Developer, Two Dozen Agents, Zero Alignment. Uh, this is the case for why we need collaborative AI engineering.

  6. 0:25

    So first, a very quick intro. I'm Maggie. I work, uh, at GitHub as a staff research engineer. Uh, at least that's my title. I'm actually a designer back when that was, like, a separate thing to engineer.

  7. 0:34

    Um, and n- Next is the labs team within GitHub, so we work on kinda more experimental risky bets than the rest of the organization. We like to call it the department of fuck around and find out. [laughing]

  8. 0:44

    Um, and like everyone else, we are, of course, trying to shape new developer agentic tools.

  9. 0:50

    So I think this is what many people think peak developer productivity looks like right now, right? [laughing] This is, like, a wall of terminal-based coding agents all running in parallel on one person's machine.

  10. 1:03

    I like to call this the one man, two dozen Claudes theory of the future. Uh, so the promise that we're given here is that one person with a fleet of agents will do the en- work of an entire team of developers.

  11. 1:16

    The main problem with this dream is it assumes that software is made by one person. All of these tools are single player interfaces, and they focus on scaling up the work of the individual.

  12. Individual users and AI tools

    Five people stand separately above AI tool icons. The speaker argues that these single-player tools scale individual work, while building software also requires teams to agree on what they are building and why.

    Text in image
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides
  13. 1:26

    But there is limited value in scaling up one individual because software is not made by one person in a vacuum. It is a team sport, and everyone building it needs to agree on what they're building and why.

  14. 1:38

    Believing individual productivity leads to great software is nine maybe-- nine women make a baby in one month logic. [laughing] Uh, more individual output doesn't solve problems that require communication and coordination.

  15. Nine women

    Nine pregnant women illustrate the speaker’s analogy: nine women cannot make a baby in one month. More individual output does not solve problems that require communication and coordination.

    Text in image
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides

    1:40

    Show 2 builds
    1. 1:40

      A row of women in white shirts and jeans pose with their hands on pregnant bellies.

      Text in image
      Interface text:
      Audience · Maggie AI Engineer Talk - Figma Slides
    2. 1:46

      Nine pregnant women stand in a row wearing white shirts and jeans.

      Text in image
      Interface text:
      Audience · Maggie AI Engineer Talk - Figma Slides
  16. 1:50

    It makes them worse. And implementation is rapidly becoming a solved problem, right? Probably everyone here believes that. Uh, writing code is now fast, it's getting cheaper, and quality is going up and to the right.

  17. Alignment and communication is the new bottleneck

    The diagram narrows at alignment before widening into implementation. As coding gets faster, the speaker argues that teams must agree on what to build and where to spend their energy.

    Text in image
    Alignment and communication
    is the new bottleneck
    
    alignment
    implementation
    
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides
  18. 2:03

    The hard question is no longer how to build it, it's should we build it? Agreeing on what to build is the new bottleneck, so everyone on your team needs to be involved in asking, are we making the right thing?

  19. Alignment and communication is the new bottleneck

    The diagram narrows at alignment before widening into implementation. As coding gets faster, the speaker argues that teams must agree on what to build and where to spend their energy.

    Text in image
    Alignment and communication
    is the new bottleneck
    
    alignment
    implementation
    
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides
  20. 2:15

    Are we spending our energy in the right place, and how do we have the most impact? When production is cheap, opportunity cost becomes the real cost. You can't build everything, and whatever you pick comes at the cost of everything else.

  21. Alignment and communication is the new bottleneck

    The diagram narrows at alignment before widening into implementation. As coding gets faster, the speaker argues that teams must agree on what to build and where to spend their energy.

    Text in image
    Alignment and communication
    is the new bottleneck
    
    alignment
    implementation
    
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides
  22. Alignment and communication is the new bottleneck

    The diagram narrows at alignment before widening into implementation. As coding gets faster, the speaker argues that teams must agree on what to build and where to spend their energy.

    Text in image
    Alignment and communication
    is the new bottleneck
    
    alignment
    implementation
    
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides
  23. 2:30

    Anyone who ships software on a team knows that this isn't a new problem. Um, alignment has always been a bottleneck, but agents have made the cost of not being aligned as a team much, much higher.

  24. 2:42

    What makes it worse is that all our coordination tools are still from another era. So GitHub, Slack, Jira, Linear, and the like are, as they currently stand, are not designed for the agentic development world.

  25. Team coordination tools

    Six people connect through Slack, GitHub, Linear and Jira to one person. The speaker argues that these coordination tools were not designed for agentic development.

    Text in image
    Engineering the future of AI
    
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides
  26. Team coordination tools

    Six people connect through Slack, GitHub, Linear and Jira to one person. The speaker argues that these coordination tools were not designed for agentic development.

    Text in image
    Engineering the future of AI
    
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides
  27. 2:55

    We are funneling masses of agentic outputs into platforms that were built for an outdated way of building software. Um, he-- I know-- Like, I work at GitHub, so that might sound heretical for me to say, um, but I promise it's not controversial.

  28. Team coordination tools

    Six people connect through Slack, GitHub, Linear and Jira to one person. The speaker argues that these coordination tools were not designed for agentic development.

    Text in image
    Engineering the future of AI
    
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides
  29. 3:09

    There are very few people internally who believe that the PR and the issue are the future of software development, and there are lots of us inside the machine trying to explore what comes next.

  30. Plan, build, review

    A long build sat between planning touchpoints in chat, docs and issues and PR review. The slower cycle gave teams time for feedback and course corrections.

    Text in image
    chat threads
    docs
    issues
    PRs
    
    plan
    build
    review
    
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides
    Engineering the future of AI
  31. 3:19

    So this is how the development process used to look, right? We had a planning phase, a building phase, and a review phase, and we had all of these touch points of alignment along the way.

  32. 3:28

    And it was slow enough that we had time for conversations in Slack and Zoom meetings, comments on issues and draft PRs so you could discuss the details, and everyone could give their two cents and get advice from expertise across your team and seniors, and catch mistakes, uh, and course-correct if things were going wrong.

  33. Plan, build, review

    A long build sat between planning touchpoints in chat, docs and issues and PR review. The slower cycle gave teams time for feedback and course corrections.

    Text in image
    chat threads
    docs
    issues
    PRs
    
    plan
    build
    review
    
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides
  34. 3:44

    But by the time the code was reviewed and merged, the whole team had seen the work, right, happening, and they were roughly on the same page.

  35. The implementation window collapses

    The build window shrinks to a marker between planning and PR review. The speaker argues that faster implementation leads teams to skip early alignment.

    Text in image
    chat threads
     docs
    issues
    PRs
    
    plan
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    review
    
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides
  36. 3:52

    But that implementation window has now collapsed. And because implementation is no longer as expensive and time-consuming, we think we don't need to plan as much, so most of those early touch points actually disappear.

  37. 4:05

    And we know the review time for generated code is actually increased, so that creates more points of alignment, but they're actually on the wrong side of the implementation.

  38. Alignment shifts toward review

    The speaker says generated code increases review time, placing more alignment points after implementation. The diagram shows two planning touchpoints before a short build and four review touchpoints after it.

    Text in image
    chat threads    issues    PRs
    
    plan    build    review
    
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides
  39. Chat threads, issues, and PRs

    The timeline compresses chat threads and issues before the build, with several touch points afterward. The speaker says an agent can open a PR a couple of minutes after an issue is logged.

    Text in image
    chat threads, issues
    PRs
    
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides

    4:13

    Show 2 builds
    1. 4:13

      A compact timeline places chat threads and issues close to a short build marker, followed by pull request touch points.

      Text in image
      chat threads, issues
      PRs
      
      Interface text:
      Audience · Maggie AI Engineer Talk - Figma Slides
    2. 4:16

      A timeline places chat and issue tools before pull requests.

      Text in image
      chat threads, issues
      PRs
      
      Interface text:
      Audience · Maggie AI Engineer Talk - Figma Slides
  40. 4:14

    The time between logging an issue and an agent opening a PR is now a couple of minutes. The code is so cheap that we don't properly stop to think before we prompt it.

  41. Local, unshared plans

    Most coding agents’ local plans go unshared with teammates, leaving alignment checks for the PR after implementation.

    Text in image
    local, unshared plans
    
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides
  42. 4:24

    Unhelpfully, most coding agents also have this local plan mode that is completely unshared with other people, so you're not even checking with your team on whether the plan it made is good before you ship it, if you even read it, and so we lose even more alignment points.

  43. 4:38

    This leaves the weight, uh, of all that alignment, um, to sit on the pull request. All those checkpoints now come after the implementation at the end of the process when it's too late, and it's never what PRs were really designed to do in the first place, so they perform poorly at it.

  44. 4:53

    None of our current tools give teams a shared space to discuss plans, gather the right context, and work with agents as a collective.

  45. 5:02

    We're all experiencing the repercussions of this. Going fast without good alignment leads to wasted work, so this is, like, features no one asked for and that don't actually solve real problems, and receiving critical feedback after you finish something that ends up meaning you have to toss the whole thing out, and also coordination debt.

  46. Wasted work

    Moving fast without alignment can produce features nobody asked for or work thrown out after critical feedback arrives late.

    Text in image
    Wasted work
    
    building the wrong thing
    
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides
    Engineering the future of AI
  47. 5:19

    This is when you get really hairy merge conflicts because agents are all touching the same files, or developers even doing duplicated work 'cause they both picked up a thing and tried to finish it in one day.

  48. 5:28

    Um, or as we all know, we all have giant stacks of PRs to review that nobody has any context for and don't even know what's in them. So how do we solve this?

  49. Wasted work and coordination debt

    Misalignment leads to wasted work and coordination debt: teams may build the wrong thing, face merge conflicts, or duplicate work.

    Text in image
    Wasted work
    building the wrong thing
    
    Coordination debt
    cleanup cost of unaligned efforts
    
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides
  50. 5:38

    We need tools that help everyone on the team align before the agents start working, not after. That alignment needs to happen constantly alongside the implementation. Planning and building are no longer separate phases, they are now a cycle.

  51. Collaboration in the agentic development flow

    Planning and building form a repeating cycle. The speaker calls for teams to align before agents start and keep aligning throughout implementation.

    Text in image
    Bring collaboration and alignment into the agentic development flow
    
    build   build   build
    plan    plan    plan
    
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides
  52. 5:52

    The tools of the future need to bring planning, context gathering, and decision-making and development underneath one roof.

  53. Collaboration in the agentic development flow

    Planning and building form a repeating cycle. The speaker calls for teams to align before agents start and keep aligning throughout implementation.

    Text in image
    Bring collaboration and alignment into the agentic development flow
    
    build   build   build
    plan    plan    plan
    
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides
  54. 6:01

    This is especially true because most of the context that you need for alignment and to build the right thing is not in the code base. It's in people's heads.

  55. Human context is not in the codebase

    People appear above a code symbol: business context, financial resources, political dynamics and product vision live in people’s heads and need to be shared early to align the team.

    Text in image
    Human context is not in the codebase
    
    Business context & financial resources
    Political dynamics
    Product vision
    
    </>
    
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides
  56. 6:09

    The business context and the financial resources determine what the correct thing to build is, the political dynamics of who's in charge and who gets to make decisions, the product vision from leaders, the user research insight from designers, and the organization's history and what you've built before.

  57. 6:25

    These all matter immensely when you're deciding what the right thing for your team to build is, and the agents can never discover this context on their own. You need a way to get humans to share it early and naturally without adding process and overhead.

  58. 6:40

    So all of this has been very clear to us on the Next team, um, and we've been building a new research prototype that explores how we might solve some of these problems.

  59. 6:49

    It's called Ace. It stands for Agent Collaboration Environment. Uh, it's not a prime time product yet, so, like, if it looks pretty rough around the edges, it's because it is.

  60. 6:57

    Um, we're about to go into technical preview, and we're going to user test it with a few thousand people. Um, but then we're gonna learn how people collaborate in it and iterate from there.

  61. Ace

    Ace is a research prototype for agent collaboration. The team plans a technical preview and user testing with a few thousand people before iterating on what it learns.

    Text in image
    Ace
    Agent collaboration environment
    
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides
    Engineering the future of AI
  62. 7:07

    So here we are in Ace. It probably looks pretty familiar. We're not reinventing any more wheels than we have to. Uh, it looks a bit like Slack, GitHub Copilot, and a bunch of cloud computers had a baby.

  63. 7:18

    So we have our sessions list here on the left, and sessions are where you do work, right? It's a multiplayer chat. It's like a Slack channel. I have team- teammates in here, and I can talk to them about the work we're doing, but I also have my coding agents in here.

  64. Ace session

    Ace brings teammates and coding agents into a shared session. The demo shows their chat alongside a running terminal and change summary; the speaker says each session has its own cloud microVM and Git branch.

    Text in image
    [Embedded screenshot: mostly illegible]
    ❤️ 1
    Maybe we should add multiple themes, actually
    
    iamnbutler 5:05 pm
    We could do a theme per category... I started a llm service in #add-llm-provider so we could kick that work off.
    (edited)
    👀 1
    
    davidslater left
    
    idan 5:06 pm
    Cute. So like, Purple for AI-themed posts, and orange for classic HN shitposting, etc etc
    
    exec abort by idan
    bun dev
    
    exec by idan
    bun dev
    VITE v7.1.10 ready in 754 ms
      ➜ Local:   http://localhost:5173/
      ➜ Network: http://169.254.0.2:5173/
    
    Updated accent color from orange to emerald throughout the App component, changing gradient backgrounds and hover states to align with the green theme.
    
    App.tsx  +2 -2
    style: change accent color from orange to emerald
    77ad8f6
    
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides
    calmhn
    Chat  Diff  Terminal
    Connected
    My Sessions
    upbeat-target
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    Mention #sessions, @people, +files, and / for commands
    Chat mode (⇧ Tab)
    @ace is AI and is present in this channel. Check for mistakes.
    #19 style: update color scheme...
    Merge
    Engineering the future of AI

    7:25Demo milestone

  65. 7:32

    Each session is more than a chat channel, though. It is also backed by a microVM, so a sandboxed computer in the cloud on its own Git branch. The changes we make in each session are isolated, so we can work on parallel tasks and instantly switch between them.

  66. LLM-driven categories plan

    Ace shows a plan to categorize stories at build time. The speaker explains that each session has its own cloud microVM and Git branch, letting teammates switch tasks and inspect the agent’s history.

    Text in image
    Here’s the plan summary:
    
    LLM-Driven Categories — Approach
    
    Core idea: A build-time Node.js script fetches stories from Algolia, sends titles to an LLM (GPT-4o-mini) for categorization, and writes a static JSON file. Vite bundles it — zero runtime LLM calls.
    
    A GitHub Actions cron workflow runs this daily to keep categories fresh, commits the updated JSON, and triggers a deploy.
    
    Todos (in order)
    
    # | Task | Description
    1 | Categorize script | scripts/categorize.ts — fetch stories → LLM categorization → write src/data/categorized-stories.json
    2 | Categories UI | src/CategoriesPage.tsx — renders stories grouped by category, reusing existing card style
    3 | Routing | Add react-router with / (feed) and /categories routes + nav
    4 | GitHub workflow | .github/workflows/categorize.yml — daily cron + manual dispatch
    5 | Seed data | Initial JSON so the app works before first workflow run
    
    Cost: ~600 tokens per run (30 stories) — fractions of a cent. API key stored as a GitHub Actions [illegible]
    
    Planning an LLM-driven categories page that automatically organizes stories under categories while maintaining static site compilation, using a build-time workflow or script to generate the categorization.
    
    No files changed
    No commits
    
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides
    calmhn
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    Terminal
    Connected
    My Sessions
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    vital-bolt
    llm-categories-stati...
    epic-icon
    asdfasdf
    noble-marble
    create-green-theme
    add-llm-provider
    Other People's Sessions
    pr-11
    steady-train
    glad-socket
    add-blue-theme
    power-arm
    kind-craft
    onboarding-design-eng...
    pr-10
    Mention #sessions, @people, +files, and / for commands
    Chat mode (⇧ Tab)
    @ace is AI and is present in this channel. Check for mistakes.

    7:38Demo milestone

  67. LLM-driven categories plan

    Ace shows a plan to categorize stories at build time. The speaker explains that each session has its own cloud microVM and Git branch, letting teammates switch tasks and inspect the agent’s history.

    Text in image
    Here’s the plan summary:
    
    LLM-Driven Categories — Approach
    
    Core idea: A build-time Node.js script fetches stories from Algolia, sends titles to an LLM (GPT-4o-mini) for categorization, and writes a static JSON file. Vite bundles it — zero runtime LLM calls.
    
    A GitHub Actions cron workflow runs this daily to keep categories fresh, commits the updated JSON, and triggers a deploy.
    
    Todos (in order)
    
    # | Task | Description
    1 | Categorize script | scripts/categorize.ts — fetch stories → LLM categorization → write src/data/categorized-sto[illegible]json
    2 | Categories UI | src/CategoriesPage.tsx — [illegible] stories grouped by category, reusing existing card style
    3 | Routing | Add react-router with / (feed) and /categories routes + nav
    4 | GitHub workflow | .github/workflows/categorize.yml — daily cron + manual dispatch
    5 | Seed data | Initial JSON so the app works before first workflow run
    
    Cost: ~600 tokens per run (30 stories) — fractions of a cent. API key stored as a GitHub Actions [illegible]
    
    Planning an LLM-driven categories page that automatically organizes stories under categories while maintaining static site compilation, using a build-time workflow or script to generate the categorization.
    
    No files changed
    No commits
    
    Engineering the future of AI
    
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides
    calmhn
    Chat
    Plan
    Terminal
    Connected
    My Sessions
    clarification-needed
    upbeat-target
    vital-bolt
    llm-categories-stati...
    epic-icon
    asdfasdf
    noble-marble
    create-green-theme
    add-llm-provider
    Other People's Sessions
    pr-11
    steady-train
    glad-socket
    add-blue-theme
    power-arm
    kind-craft
    onboarding-design-eng...
    pr-10
    Mention #sessions, @people, +files, and / for commands
    Chat mode (⇧ Tab)
    0:09 / 0:09
    1x

    7:45Demo milestone

  68. 7:47

    If I want to tap one of my teammates on the shoulder and get their thoughts on a feature I'm building, nobody has to stash their Git changes and, like, pull down a new branch or, like, wrestle with local worktrees.

  69. LLM-driven categories plan

    Ace shows a plan to categorize stories at build time. The speaker explains that each session has its own cloud microVM and Git branch, letting teammates switch tasks and inspect the agent’s history.

    Text in image
    Here’s the plan summary:
    
    LLM-Driven Categories — Approach
    
    Core idea: A build-time Node.js script fetches stories from Algolia, sends titles to an LLM (GPT-4o-mini) for categorization, and writes a static JSON file. Vite bundles it — zero runtime LLM calls.
    
    A GitHub Actions cron workflow runs this daily to keep categories fresh, commits the updated JSON, and triggers a deploy.
    
    Todos (in order)
    
    # | Task | Description
    1 | Categorize script | scripts/categorize.ts — fetch stories → LLM categorization → write src/data/categorized-sto[illegible]json
    2 | Categories UI | src/CategoriesPage.tsx — [illegible] stories grouped by category, reusing existing card style
    3 | Routing | Add react-router with / (feed) and /categories routes + nav
    4 | GitHub workflow | .github/workflows/categorize.yml — daily cron + manual dispatch
    5 | Seed data | Initial JSON so the app works before first workflow run
    
    Cost: ~600 tokens per run (30 stories) — fractions of a cent. API key stored as a GitHub Actions [illegible]
    
    Planning an LLM-driven categories page that automatically organizes stories under categories while maintaining static site compilation, using a build-time workflow or script to generate the categorization.
    
    No files changed
    No commits
    
    Engineering the future of AI
    
    Interface text:
    Audience · Maggie AI Engineer Talk - Figma Slides
    calmhn
    Chat
    Plan
    Terminal
    Connected
    My Sessions
    clarification-needed
    upbeat-target
    vital-bolt
    llm-categories-stati...
    epic-icon
    asdfasdf
    noble-marble
    create-green-theme
    add-llm-provider
    Other People's Sessions
    pr-11
    steady-train
    glad-socket
    add-blue-theme
    power-arm
    kind-craft
    onboarding-design-eng...
    pr-10
    Mention #sessions, @people, +files, and / for commands
    Chat mode (⇧ Tab)
    0:09 / 0:09
    1x

    7:56Demo milestone

  70. 7:57

    I just jump into their session, and I see what they're doing in a click, and this includes their entire prompting history with the agent, so I have the context about how they got to the current outputs.

  71. 8:07

    Just like a local machine, I can run terminal commands in this session. Here, I'm gonna run bun install and bun dev to get my current project running. And you're gonna see in a minute, uh, my live preview in the browser on the side is gonna pop up when I open the port.

  72. 8:21

    Um, the demo project we have here is a calm version of Hacker News, so it only shows you the top three stories from the last-- or top stories from the last three months, which is a bit more chill than every day.

  73. 8:30

    Um, and I'm going to ask the agent to change the color theme to purple here, and you'll see in a second it instantly appears in my preview, right? It's just running the code.

  74. 8:38

    Uh, and the agent has also made, uh, an automatic commit for me with a nice commit message, and I can open the diffs and see the diffs, all kind of standard things you would expect from coding agents.

  75. 8:47

    So let's say we wanna do some real work. Uh, I have my teammates here in this session with me, uh, and I'm gonna ask Ace to add some additional color themes to my app.

  76. 8:55

    We're going to, um, pick which model we want to use, and obviously it's Opus 4.6, and then Ace is gonna get started. Um, we also have this handy summary brock-- block in the top right-hand corner.

  77. 9:06

    Uh, this keeps me up to date with the latest changes in this session, whether they're from me or someone else, which means I can switch between lots of people's sessions that are running in parallel and always stay oriented about what's happening so that you don't get overwhelmed with the amount of noise and activity.

  78. 9:21

    But the more important thing is I want to talk to my teammates, right? I wanna discuss what changes we're making, so I can ask them what they think of the current changes.

  79. 9:28

    They can spin up the dev server themselves because, remember, we're all working on the same computer in the cloud. This is no problem. We can all see the same preview.

  80. 9:36

    We can all write terminal commands and see the shared outputs. No one is going to say, "This doesn't work on my machine."

  81. 9:44

    So my teammates, Nate and Adan, they're jumping in here. They've taken some screenshots. They're suggesting some alternative features, asking questions. And now what we're about to see is that A, um, Nate is going to ask the Ace agent to make changes in a minute.

  82. 9:59

    Where's Nate? There's Nate. [chuckles] So he said, "Ace, let's add a teal theme too." Um, so I actually kicked off this session, but Nate is now prompting the agent, so this is truly multiplayer.

  83. 10:08

    Both of us are sharing this, uh, coding session. Uh, the agent can also read our whole conversation. That is all input to the prompt, so we can talk about things up ahead and just say, "@Ace, do it."

  84. 10:17

    It'll go do it. This kind of accessible Slack-like interface means that, um, access to a coding agent is, um, brings everyone in who's creating software. So not just developers, but designers and PMs and customer support people can all be in the same conversation, seeing what's happening in real time as a feature gets built.

  85. 10:36

    Because if you're thinking, you know, like, "Why wouldn't we just use Slack for this?" I think it's because Slack is never going to become a fully featured software development tool unless they sincerely pivot from their current business.

  86. 10:46

    Um, so it's never going to have the right primitives, and I really doubt it's going to add them. You know, diffs and terminal commands and that sort of thing is not Slack's business.

  87. 10:54

    Um, we wanted Ace because it's explicitly designed for software development, but it's much more welcoming to other team members than your terminal.

  88. 11:02

    Anyway, we're back to shipping our changes here. We like how this looks, so we're gonna create a PR, um, because eventually all this code does have to go back to GitHub, right?

  89. 11:10

    So we create this PR from directly inside Ace. Uh, we give it a minute, and it's gonna show us the preview of the PR, and then we can click a link that goes to it over here.

  90. 11:20

    In a second, Adan's gonna click. There we go. So there's our PR. All works, right? This is backwards compatible. It has a link back to the Ace session within the description.

  91. 11:28

    Like, people don't all have to be in Ace to use this. You could have a few members of your team in Ace, and the rest stay on whatever else they're using.

  92. 11:35

    And sometimes, you know, you still need to touch code. Like, I do a lot of front-end, and agents are shit at CSS. They never do what I want. So we can of course open our project in VS Code here, um, and we have real-time multiplayer editing because, again, this is just a microVM cloud computer.

  93. 11:50

    Everyone's on the same computer. I can close my laptop on this, and work can continue. Um, my session doesn't die. My teammates can keep prompting Ace, um, and making progress.

  94. 12:00

    We don't have a mobile interface yet, but we're building it. But this microVM architecture means that that will work seamlessly. Like, I don't have to use my phone to somehow SSH into a terminal on my computer.

  95. 12:10

    Computer doesn't need to be alive, and I don't need to go buy a Mac mini to keep things available. I just talk to my always-on agent in the cloud.

  96. 12:18

    For bigger, more complex features, you'll of course want your agent to write a plan. That's a very standard workflow at this point. So here we're chatting about adding, uh, variable timeframes to our Hacker News calm app, and then I've gone ahead and asked Ace to make a plan, which it's gonna do quite quickly.

  97. 12:35

    And so we can go open that plan, right? And here we all are in our plan. I can see my teammates' cursors. We can collaboratively edit it together. We can decide if we like this plan, if it's any good at all, if it achieves our intent.

  98. 12:47

    Um, my teammate Na-Nate here is making suggestions about maybe using a dropdown for the interface instead of a segmented control, and then Idan's come in and updated the requirements, so the agent knows to do that.

  99. 13:00

    Uh, and once we're all happy with the details, we go back to the chat, and we can just say, "@Ace, do this," and it knows what the context is.

  100. 13:07

    So I'm now gonna jump over to our dashboard in Ace. Uh, a lot of the planning and discussion that would otherwise happen in Slack or GitHub or Linear is now happening in our Ace sessions, so we have a lot of access to rich context on what work is underway and can helpfully summarize it for you.

  101. 13:24

    So here it's Monday morning, and I've been trying to remember what I left unfinished last Friday, and Ace is prompting me to keep working on some React hooks I was making as part of a big refactor, which is helpful since I have very crappy human memory after a long weekend.

  102. 13:38

    And from here, I can start a new session, or in this Pick back up section, I, in one click, I can open the session to keep going on my unmerged PR.

  103. 13:46

    I can also see a list of my recently completed PRs and issues to stroke my ego and make me feel productive.

  104. 13:53

    And on the right here, we have a Team pulse section, so this summarizes what my coworkers have been up to for the last couple of days. I can see Nate has been shipping a lobby channel, and David has been fixing access token issues.

  105. 14:05

    Um, there's also a raw feed of recent issues and PRs on this repo, but I personally find the summary much more helpful. Um, one of the biggest challenges of agentic development is that the speed and volume of work makes it really hard to keep up with what your coworkers are doing.

  106. 14:19

    They're now shipping five features a day instead of half of one. This dashboard is our first pass at trying to make agents proactive in bringing that social context to you.

  107. 14:29

    If all your conversations around the code are available to agents, it gives them access to a social information fabric where they can help get you oriented every morning and stay aligned with your team.

  108. 14:39

    They could notify you about decisions being made or pull you into a conversation where someone is about to extend a feature that you originally built.

  109. 14:47

    So this is no longer a bunch of solo, disconnected terminal instances on individual computers. This becomes a living, intelligent environment where everyone sha-shares the same workspace and context.

  110. 15:00

    So all of this is actually about reclaiming time, right? Before coding agents came along, none of us had enough time and energy to make our products the way we wanted to.

  111. 15:09

    I guarantee everyone in this room has shipped software they're not proud of. Maybe you didn't have enough time to do user research or consider design details or think through the implications of your architecture choices.

  112. 15:21

    Um, not because you didn't want to, but because there simply wasn't enough time because implementation took up so much of that time and effort. But we've been gifted a lot of that time back.

  113. 15:32

    We have an opportunity to not just go faster and build a giant pile of the same crappy software, but instead to make much better software through much more rigorous critical thinking and better alignment in the planning stage, by doing more exploration, more research, and thinking through problems more deeply than we could have before.

  114. 15:52

    Agents allow us to scale up ourselves and our teams in a way that, if done right, should lead to better quality software.

  115. 16:01

    I think many people are now realizing that in a world of fast, cheap software, quality becomes the new differentiator. The bar is being set much higher, and craftsmanship is what set you a- what will set you apart from vibe-coded slop.

  116. 16:16

    Um, but craft still costs time and energy. It's not free, and in order to buy the time and energy you need for it, you need to do fewer things better, which requires lots of strong alignment.

  117. 16:26

    There are also more distractions than ever. It's very easy to prompt your way to the wrong thing or to add lots of unnecessary, unhelpful features to your product. I think the dream for me is that we end up with tools, whether it's Ace or others, that create environments where teams can think rigorously together about hard problems.

  118. 16:45

    Our agentic tools should help us do higher quality work, get aligned faster, and build a few exceptional things rather than a thousand crappy ones.

  119. 16:55

    Thank you very much for listening. Um, [applause] if you want... Uh, if you do want early access to Ace, we should have it out within a couple of months at the very latest.

  120. 17:09

    Uh, this QR code will take you to a form where you put in your GitHub username, and then it will give you early access as soon as it comes out.

  121. 17:15

    Um, you can read more about the GitHub Next team and their research on githubnext.com, and all my work and writing is on maggieappleton.com, and I'll have the slides and notes for this up there in a day or two.

  122. 17:26

    Thanks. [outro music]