The Design-Code Roundtrip That Isn't — Jonathan Gordon, ReWeaver AI
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Why the Design–Code Roundtrip Still Loses Information
Jonathan Gordon demonstrates how ReWeaver AI detects mismatches between design and code, explains why repeated AI transformations can accumulate drift, and proposes deterministic checks with humans controlling the repairs.
From a talk by Jonathan Gordon
At a glance
Ideas worth remembering
A true design–code roundtrip must preserve fidelity in both directions and retain provenance, including the connection between a design element and the code that produced it.
Visible success can conceal implementation defects. Gordon's innerHTML intervention and missing ARIA live-region example motivate inspecting generated code and checking requirements beyond appearance.
Gordon reports losses across five tool setups and degradation during a 12-iteration pure-LLM experiment. The unspecified configurations, scoring method, and guarded numerical results limit these findings as a general benchmark.
His proposed guardrails identify drift, apply known repairs when requested, and report unresolved issues for human judgment. Users retain the ability to apply, undo, or ignore changes before merge.
Accumulated drift becomes maintenance debt. Detecting mismatches during generation and before merge is central to Gordon's argument for keeping humans in control of design, code, and cost.
A roundtrip requires fidelity and provenance
Jonathan Gordon opens with a small example of AI helping under pressure: mirroring his laptop screen cost him access to his speaker notes, so he asked Claude to extract them from his slide deck. He received notes, although he explicitly says he does not know whether they are correct. That distinction between obtaining a useful output and knowing that it faithfully preserves its source introduces the larger problem of the talk.
His definition of a design–code roundtrip is demanding: design and engineering must form a complete loop in both directions, lose no fidelity, and retain persistent provenance. Provenance means keeping track of where each element came from. A successful conversion in one direction therefore does not establish a successful roundtrip; the return journey must preserve the result and its relationship to the source.
Gordon admires tools that turn a request into something clickable and interactive. His concern is that this visible success can discourage people from inspecting the code. The question he wants to investigate is whether the implementation preserves the intended design, beyond making an impressive interface appear.
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Why the handoff remained open
Gordon places the problem in more than 30 years of building and designing developer tools and IDEs. As a designer working with engineering teams, he repeatedly tried to make the delivered product match the original intent. Negotiation and relationships helped get software to customers, but he says they never produced a reliably closed loop.
His explanation is that design and engineering optimize for different outcomes. Engineering has requirements and technical constraints; design may be pursuing a future experience or a rebranding. Those priorities can diverge during implementation, so the handoff problem involves reconciling decisions as well as transferring artifacts.
AI offered a reason to revisit that history. An LLM could interpret design intent and also write the code for a running application. Gordon hoped that having one intelligence perform both tasks could close the loop at the speed of inference. This was the hypothesis behind his exploration, rather than a result he had already established.
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The generated code that interrupted the vibe
Gordon describes going deeply into vibe coding from April 2025. He reports receiving an email placing him in the top 0.1% of Cursor usage and recalls using a $20 plan with occasional additional spending. His sustained goal was to move from design to code and back again without losing fidelity—the pixel-perfect loop he had wanted from earlier tools.
During one session, he was ignoring the stream of generated text until an innerHTML statement caught his attention. He associated it with HTML injection risk, stopped the agent, asked what it had done, and directed it to revert the change and use another approach. The example establishes that he recognized a potentially unsafe implementation choice; the talk does not provide the code, its input handling, or the replacement, so it does not establish the precise vulnerability.
That interruption changed his working method. The agent could explain its decision confidently, yet Gordon still needed to reject it. He concluded that he had to steer generation and read the code again. Further inspection revealed additional issues, motivating a deeper investigation into what the tools actually preserved.
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Turning a conversion demo into a testable question
Gordon then turns to industry claims that code and canvas could support a true roundtrip. He recalls an Anthropic and Figma demonstration in which a coding tool could create a Figma artboard from a prompt. He found that capability impressive, but wanted to investigate whether it met the stronger claim of a complete return journey.
Further announcements raised the possibility that code itself could become design material and eliminate the handoff. Gordon says his own experimentation did not support the conclusion that the roundtrip was solved. To examine that gap, he built a harness and switches into a local demonstration, briefly recovering from trouble locating it.
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A harness with code and design side by side
The harness places AI-generated code on the left and a design on the right. Gordon describes the design source as a canvas from a tool such as Figma or Sketch; the particular source is secondary to the experiment. A transformation can begin with either code or design and use a prompt to request the next artifact.
His first example asks an LLM to build a design system from a form. He describes the transformation as updating the code, creating styles, and producing a design system that works in the running application and also exists in Figma. He does not open the Figma link during this portion of the demonstration, so the corresponding Figma artifact remains something he describes rather than independently demonstrates here.
A design system, in his explanation, comprises components, tokens, variables, and styles that serve as a basis for building software. The codebase must understand that system for the two representations to work together. This makes the shared structure consequential: preserving a visible result is only part of preserving the system that produced it.
He next describes adding a company field and an orange button. Both code and design update, which looks like a successful exchange. But he leaves the return trip as an open question and introduces a separate button, Show Drift, to inspect what the apparent success may conceal.
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Finding defects behind the visible result
Gordon identifies Show Drift as ReWeaver AI and presents it as the product's first public demonstration. It produces a list of issues by examining code and design together. The named dimensions include design quality, code quality, performance, and design tokens, extending the inspection beyond visual resemblance.
Accessibility is especially important to him because of his work at Microsoft. He recalls frustration with models generating inaccessible code and compares the situation with the need to train engineers on accessibility 20 years earlier. His point is that generated code still needs explicit attention to requirements that a functioning visual interface can fail to satisfy.
The concrete accessibility finding is a missing ARIA live region. Gordon explains that the relevant element would not be announced to a screen-reader user. ReWeaver offers a fix, he applies it, and he reports that the red issue disappears while another blue issue remains. The transcript does not identify the element or include a screen-reader test, so the demonstrated result is the reported repair and cleared finding, rather than verification of the whole accessibility experience.
The mechanism he describes is to scan both representations, collect issues, and provide guardrails together with ways to improve the code. Applying that scrutiny to five different bidirectional tool setups did not yield a complete roundtrip. He reports lost bindings and cases in which a design change survived but the code did not. The configurations and individual results are not specified, which limits how broadly the experiment can be generalized.
Gordon does not rule out future tools achieving a better roundtrip. He argues that tools for understanding drift are still necessary, even as conversion capabilities improve. The failures he reports concern information surviving repeated movement between representations, which an attractive intermediate result cannot establish on its own.
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Checking probabilistic transformations
Gordon contrasts the repeatable behavior he expects from conventional developer tools with the variable outcomes introduced by AI. His proposal keeps AI in the workflow but surrounds it with deterministic guardrails: inspect the code, identify mismatches, and repair them. The role of those checks is to expose drift that would otherwise remain unnoticed.
He reports another experiment involving 12 iterations on a codebase with a sufficiently complex UI. In the pure-LLM condition, the output began at 30% by his description of quality, fidelity, and pixel perfection, then degraded somewhat. In the guarded condition, issues were found and fixed. The talk provides neither the scoring methodology nor a complete numerical result for the guarded condition, so it supports his reported comparison without establishing a reproducible benchmark.
He also places a limit on automation: reaching 100% requires human judgment and decision-making. He describes the remaining 10% as belonging to that judgment. This is his characterization of the boundary between automated repair and human decisions; it does not supply a measured 90% result for the guarded system.
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Making reconciliation a human-controlled operation
Gordon describes an AI-dependent workflow in which design-to-code, code-to-design, chat, and movement across tools all incur attention and token costs. In his characterization, agents control the work while humans remain in the loop. He wants to change that distribution of authority so people control the code, design, and cost.
He distinguishes three blind spots: the probabilistic model, drift arising during generation, and drift accumulating over time. The last becomes a form of technical debt because mismatches persist and pile up across the codebase. His warning about pain over the following six months expresses the maintenance consequence he anticipates, rather than a measured duration.
The desired alternative is fully bidirectional editing with all surfaces preserved and provenance carried through every transformation. Gordon makes provenance concrete with a button in Figma: someone needs to know which line of code produced it so that changing the design can lead to changing the appropriate code. That correspondence is part of controlling drift, rather than merely recreating a similar-looking button.
He calls the core operation deterministic reconciliation. The repair policy has an explicit boundary: when the system knows a fix, it can fix the issue; when it does not, it should report the issue and leave the repair to the person. This preserves a useful distinction between detecting a violation and knowing how to correct it safely. He does not explain the underlying rules or reconciliation algorithm in the talk.
Gordon describes ReWeaver's core as using a local LLM with zero extra token costs, while allowing a connection to Claude or another model if desired. The statement concerns additional token costs in the local setup; it is not a complete accounting of operating costs. His larger principle is that humans should retain control over cost, code, and design, with the intended outcome matching what they actually need.
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Catching drift before merge
Gordon closes by describing nine dimensions of deterministic guardrails for software quality and production readiness. He names design consistency, accessibility, and AI code generation governance, without enumerating all nine in the spoken explanation. Design consistency checks whether the code and design system agree in both directions; accessibility receives its own dimension; governance addresses the volume of generated code moving through the system.
He clarifies how human control should work in practice. ReWeaver is not presented as independently authoring the application: the user can request Apply Fix, watch the resulting code change, undo it, or ignore the finding. Its ability to write a repair is therefore subordinate to the person's decision. The goal is to catch drift within a closed loop before anything merges.
He invites the audience to use a playground with a harness similar to the demonstration: supply AI-generated code, scan it, and receive results. He also describes a challenge to generate code scoring below 30 PDR, which he expands as production drift ratio. The offered rewards are a place near the front of the beta and a showcase on the page. Because he does not define the ratio or its calculation, that threshold cannot be equated with the earlier 30% fidelity figure.
The final invitation is to join the beta, improve the product, and sanity-check its claims. Gordon mentions a hoped-for mid-July rollout, but gives no year or confirmed release date in these remarks. He finishes by offering further conversation online or outside the session.
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Read the complete timestamped transcript
- 0:01
[music]
- 0:12
Hello everyone. I'm Jonathan Gordon. I'm
- 0:16
a founder of Reweaver AAI, a new
- 0:18
company, you know, on the scene, let's
- 0:20
say. I want to share one thing with all
- 0:21
of you that was really fascinating. So,
- 0:23
I I plugged in my laptop and I realized
- 0:27
I had to mirror. So, I no longer have my
- 0:29
speaker notes. So, I went into Claude
- 0:31
and I said, "Can you extract my speaker
- 0:33
notes from my slide deck, please?" And I
- 0:35
had my speaker notes now.
- 0:38
I love AI.
- 0:41
The suspense was killing me. I was
- 0:43
talking to Claude
- 0:45
now. Who knows if it got it right. I
- 0:46
don't know. Whatever. Um, so I also
- 0:49
realized that my title slide buried the
- 0:50
lead. I shouldn't have put it up. Um,
- 0:53
but nonetheless, let's play with this.
- 0:56
So, uh, yeah, my name is Jonathan. Um,
- 0:59
I'm here to talk to you about the design
- 1:00
code roundtrip that isn't.
- 1:04
And I guess probably the best thing to
- 1:06
do first is to explain what I think a
- 1:09
design code roundtrip should be. Um the
- 1:13
design code roundtrip is a full loop
- 1:17
between design and engineering in both
- 1:21
directions without any loss of fidelity
- 1:26
and with a persistent provenence. So it
- 1:28
knows exactly where everything came
- 1:30
from. You know that perfect dream world
- 1:33
that we live in where there is no drift
- 1:37
in between the gaps of design and code.
- 1:42
Um, today's tools, design is amazing.
- 1:45
It's incredible. It's magic. It's I ask
- 1:47
for something, I get it. It's clickable.
- 1:49
It's interactive. It's amazing to me.
- 1:52
But nobody really looks at the code
- 1:54
anymore, it seems. And so, I kind of
- 1:57
want to go there. I want to go into that
- 1:58
space.
- 2:00
Whoops. Of course, I have to get my
- 2:02
mouse to the right location. There we
- 2:04
go. Um, a little bit about me.
- 2:07
past 30 plus years um I've been building
- 2:10
and designing coding tools developer
- 2:13
tools idees at these companies um so
- 2:18
I've spent a lot of time with as a
- 2:20
designer I've spent a lot of time with
- 2:22
design and engineering teams trying to
- 2:24
understand
- 2:26
how do we do that handoff well how do we
- 2:28
hand off from design to code how do we
- 2:31
get what we wanted to be delivered to
- 2:33
the customer um it was never a perfect
- 2:36
science We never got it. We never found
- 2:39
that magic sauce that just made
- 2:42
everything come together where intent
- 2:45
matched the outcome.
- 2:47
Um, but I developed techniques. I
- 2:50
negotiated with engineers. I brought
- 2:52
them out for drinks. I made them my
- 2:53
friends. Um, I got things delivered to
- 2:57
customers.
- 2:58
But the the loop was never closed. Um,
- 3:01
and it's because our outcomes diverged.
- 3:04
engineering had one set of requirements,
- 3:07
technical constraints, design had a
- 3:09
vision for the future, a new rebranding,
- 3:11
whatever. So, this has existed, you
- 3:14
know, dare I say, for decades.
- 3:16
Um, then along comes AI
- 3:21
and LLM and prompting ourself into a
- 3:24
fully running application. In a sense,
- 3:28
now we have one intelligence that
- 3:30
understands design intent and the same
- 3:33
intel in intelligence can write code. So
- 3:37
I thought to myself, oh my god, I think
- 3:39
we might be able to close this loop. At
- 3:42
the speed of inference, we could close
- 3:43
this loop. And so I was curious. Um, I'm
- 3:48
curious by nature. So I went all in.
- 3:52
That's my GitHub before vibe coding
- 3:55
happened on the left on on my left on
- 3:58
your whatever. Um then after so I think
- 4:03
everybody probably gets a sense of what
- 4:05
my life was like from April 2025. And if
- 4:08
you want to scroll till today it's it's
- 4:11
even more so. Um, I got this email from
- 4:14
cursor one day that said I was in the
- 4:16
top 0.1%
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of usage of cursor
- 4:21
and I thought to myself, I need to spend
- 4:23
more time outside.
- 4:28
Then there was a bill. Um,
- 4:31
but at the time it was the $20 deal and
- 4:33
I kind of, you know, monitored, but
- 4:35
every now and again I'd pay for a little
- 4:37
bit more, get another hit. Um,
- 4:41
and this was my life vibe coding from
- 4:44
that point in April
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until today. Um, I'm trying to figure
- 4:50
out can I go from design to code? Can I
- 4:52
go from code to design? Can I build a
- 4:54
tool to go from design to code and code
- 4:56
to design a full round trip without any
- 5:00
loss of fidelity? That pixel perfect
- 5:02
dream that we have. Um, I remember
- 5:06
vividly uh one day I was, you know, just
- 5:10
hammering away at the keyboard, just
- 5:12
vibing my way. I was really feeling the
- 5:14
vibe. And, uh, this wall of text was
- 5:18
coming by and I was completely ignoring
- 5:19
it and code was being written, just
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watching that come by. And I remember
- 5:25
seeing a statement that was an inner
- 5:27
HTML statement.
- 5:29
And I remember from way back when that
- 5:33
inter HTML was a security vulnerability
- 5:35
that people can actually inject into in
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HTML and I stopped it. I said, "Wait a
- 5:40
minute, wait a minute." to my LM, "Wait
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a minute, what did you just do?" And it
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explained to me what it did and it was
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very proud of itself. And and I said,
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"Revert that. Do this instead." Blah
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blah. And I got into this mode of like,
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"Oh, maybe I need to steer this thing a
- 5:55
little bit better. Maybe I can't just go
- 5:56
blindly in. Maybe I need to look at the
- 5:58
code now.
- 6:00
And I've looked at a lot of code in my
- 6:01
life. So I went ahead and went in and
- 6:03
looked at the code and I Whoops. I'm
- 6:07
sorry. I clicked too ahead and I found I
- 6:10
found issues. And so I went a little bit
- 6:12
deeper
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when the industry started to tell me
- 6:17
that they had solved the round trip
- 6:20
that you could go a true roundtrip
- 6:23
workflow. You could roundtrip between
- 6:25
code and canvas. I think folks in the
- 6:27
room might remember back in February,
- 6:29
no slam on anthropic or Figma, there was
- 6:33
this demo that showed going from cloud
- 6:36
code into Figma. And it was magic. It
- 6:39
was incredible. I couldn't believe you
- 6:40
could build a Figma artboard from a
- 6:43
prompt in Cloud Code. And then I had to
- 6:46
go deeper and I had to look deeper in
- 6:47
the same way that I looked at vibe
- 6:49
coding. Like, is this real? Is the hype
- 6:50
real? So I went deeper and then more
- 6:53
announcements were made all the way up
- 6:55
until last week when Figma config
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announced that code is now material.
- 7:00
Does that mean there is no even handoff
- 7:03
that exists anymore? Um so I'm
- 7:06
monitoring trying to keep up. Things are
- 7:08
happening at warp speed.
- 7:11
But they said the roundtrip was solved
- 7:13
and I started messing around and
- 7:16
realized it wasn't. And so I want to
- 7:18
share with all of you
- 7:21
a harness that I built. Whoops. Oh, wait
- 7:24
a minute. Local host, where are you?
- 7:27
Local host,
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come back to me, local host. Okay.
- 7:33
I don't know what I don't know what just
- 7:34
happened there. And now I'm afraid to
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restart this. Um, okay. I think we're I
- 7:38
think we're okay. Um, I created this
- 7:40
harness. And what this harness is is on.
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I'm going to say left. I hope everybody
- 7:46
is that your left. It is your left.
- 7:48
Okay, good. [laughter]
- 7:50
On your left is code that was generated
- 7:52
by AI. This is this is all real what
- 7:54
you're seeing here. Generated by AI and
- 7:58
on your right is a design. Be it a
- 8:02
canvas that that I created in Figma, be
- 8:04
it something I created in Sketch. It's
- 8:06
not really relevant what the sources are
- 8:09
here, but the reality is I'm trying to
- 8:11
illustrate a round trip. And so a round
- 8:14
trip you know starts with code or starts
- 8:16
with design and it has a prompt
- 8:18
associated with it. So some some LLM I
- 8:22
call off and I say in this instance I
- 8:24
want to take this form and I want to
- 8:26
build a design system from it. So there
- 8:27
there are tools that do this that can
- 8:29
extract code and build design systems
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from graphical canvases. And so I can do
- 8:35
that. And it updated the code and
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created some styles. And it also
- 8:40
actually created um a design system that
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works in runtime here. So this is like a
- 8:45
a real app that got built that is also
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living in Figma. So there's a link up
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here. I won't go there, but trust me, so
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we can say stable. If I click that link,
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it would go to Figma and show me the
- 8:56
full-blown design system that was built.
- 8:58
And a design system for those that don't
- 9:00
know is composed of components and
- 9:03
tokens and variables and styles that all
- 9:07
are used as a basis for building
- 9:09
software from. So code and design
- 9:11
together, you know, really work well
- 9:13
when there's a design system and there's
- 9:15
a code base that understands it. Now
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move back and forth. You throw something
- 9:20
over here and you say, "Okay, I want to
- 9:21
build an orange button." And I've added
- 9:24
a company field and an orange button.
- 9:26
The code is updated. The design is
- 9:27
updated. This is awesome. Could I go
- 9:30
back? Maybe. Um, but I have a button up
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here called show drift.
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So, show drift is rewaver AI working
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right now. And this is real rewaver code
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for the first time introduced to the
- 9:44
public ever. And if I click show drift,
- 9:48
I'll see a list of issues that got
- 9:49
generated from both the code and the
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design at the same time. There are
- 9:55
across several different dimensions that
- 9:57
you can see here. Design quality, code
- 9:59
quality, performance, design tokens,
- 10:02
etc. One of the ones of note that I find
- 10:05
really important from my perspective is
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at Microsoft I worked on accessibility
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and I learned a lot about what it means
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to build an accessible software system.
- 10:15
And I I want to be honest with all of
- 10:17
you. I was very frustrated when I first
- 10:18
saw LMS come out and they generated code
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that was inaccessible period out of the
- 10:22
box. I thought what models weren't
- 10:25
trained on accessibility.
- 10:27
Um, and I was reminded 20 years ago when
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you know engineers needed to be trained
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on accessibility. So here we are again
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now training LLMs instead of engineers
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on accessibility.
- 10:39
So found issues. Um, here's an issue
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where
- 10:44
has no Arya live region. So it won't be
- 10:47
announced by screen reader. A blind user
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using this won't get an announcement
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about this element. So Rewaver found
- 10:53
that and Rewaver says it can fix it and
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Rewaver fixed it. Now the red went away,
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but there's also a blue issue here as
- 11:00
well. Um so Rewaver stacks up issues and
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you know basically scans scans the code
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and scans the design and produces u
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guard rails to a sense but also a way to
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fix the code and make it better.
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So I did that across multiple dimensions
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um you know with the intention of seeing
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if anybody had solved the drown the the
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roundtrip problem
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and it didn't go well. Uh I never got
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really the full round trip. So I really
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tried hopefully this slide tells you I
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tried five different tool setups trying
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to do code design code roundtrip both
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directions birectional
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lots of lossy issues uh you know
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bindings were lost um design change
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survived but code didn't
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I'm not going to say we're not going to
- 11:52
get there um but even if we do get there
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and we're able to do this I wonder if
- 11:58
we'll get fully there And maybe we need
- 12:01
tools like Reweaver. I shouldn't say
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maybe, we do need tools like Reweaver to
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help us understand the drift.
- 12:10
So that whole experience for me really
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highlighted something from my past. Um I
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had I had built a lot of software
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development tools, designed and built
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software development tools and developer
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tools in their nature are deterministic.
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You write code, you compile it, you get
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an SA a it it'll every time you run that
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code, you'll get the same result. When
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you put AI in the middle of that or in
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the front of that or the end of that,
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you're not going to necessarily get the
- 12:37
same outcomes. Now, there are tools that
- 12:40
will help you understand, rewaver being
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one of them, that there's a mismatch.
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There's drift there and we need to be
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aware of drift. Drift lurks in the dark.
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You need to look at the code. You need
- 12:51
to find the drift. You need to fix the
- 12:52
drift. And you do that with
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deterministic guardrails around the AI.
- 12:57
The AI is still there. So I ran another
- 13:00
experiment, 12 iterations on a codebase
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with complex enough UI to it. And I did
- 13:06
one where it was just pure AIE, just LLM
- 13:09
all the way. And it degraded a little.
- 13:11
It degraded a little bit, but it started
- 13:14
at 30% quality, 30% fidelity, 30%, you
- 13:18
know, true pixel perfection.
- 13:20
And then I put it on top of
- 13:22
deterministic guardrails where we found
- 13:24
the issues, we fixed the issues. You
- 13:27
can't get to a hundred because that 10%
- 13:30
is human judgment, human
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decision-making. So the human is still
- 13:35
there in this equation.
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Um,
- 13:40
so what is it today? Uh, today I I would
- 13:44
call it we're locked into AI. And that's
- 13:46
okay. We'll go there. We'll embrace
- 13:47
that. But we're locked into AI for
- 13:50
design to code. We're locked into AI for
- 13:52
code to design, looping, chat bots, what
- 13:55
we call span the chain workflows where
- 13:57
you jumping across tools. And there's
- 13:59
always token costs to be aware of, of
- 14:01
course. So in this world, the agents are
- 14:04
in control and the humans in the loop.
- 14:06
I'd like to propose something else.
- 14:08
Before I do, there are three blind spots
- 14:10
we need to be aware of. One is the model
- 14:12
itself. It's going to you can ask it
- 14:14
questions. It'll give you answers, but
- 14:16
it's nondeterministic. It's
- 14:18
probabilistic.
- 14:19
There will be drift in the moment when
- 14:21
you're writing code. Drift will surface,
- 14:23
but drift over time is the pain that
- 14:26
you'll be experiencing for the next six
- 14:28
months because drift over time is the
- 14:31
new tech debt and it's going to pile
- 14:34
itself
- 14:36
gloriously over your codebase. Um, we
- 14:40
want to do something about it. So here's
- 14:44
where we think it isn't meaning what it
- 14:47
could be what it should be fully
- 14:50
birectional
- 14:51
edit both ways lossless all surfaces
- 14:54
preserved so if you go from code to
- 14:56
design and design to code nothing's
- 14:58
breaking weirdly for you provenence is
- 15:00
carried where did this start from what
- 15:03
line of code wrote this button
- 15:07
I see this button in Figma I want to
- 15:09
change it I need to change that code or
- 15:11
Somebody needs to change that code. We
- 15:14
have to control the drift. It's
- 15:16
deterministic reconciliation at its
- 15:18
core. So with deterministic guardrails,
- 15:21
and I'm happy to talk after if folks are
- 15:24
interested in what I mean by that,
- 15:26
accept or refuse. So if they know
- 15:30
there's a fix, they will fix it. If they
- 15:32
don't know there's a fix, they will tell
- 15:34
you there's an issue, but you need to
- 15:36
fix it.
- 15:38
And with Ruby Weaver, there's zero extra
- 15:40
token costs. So, we're fully local LLM,
- 15:44
but if you want to open up Claude or
- 15:46
whatever and connect to us, you can. But
- 15:48
at its core, and our core principle is
- 15:50
the human is in control always. In
- 15:54
control of cost, in control of code, in
- 15:56
control of design, because that's what
- 15:58
we've been doing for decades.
- 16:01
We've been in control. We don't need to
- 16:03
lose control. And I'm not saying loop
- 16:05
isn't good, but human in control is I
- 16:07
think a little more aspirational
- 16:10
because at the end of the day, what you
- 16:12
need is actually what you get. I call it
- 16:16
winnywig. Can I use that winny wig?
- 16:20
So what are we building at its core?
- 16:22
What we're building are nine dimensions
- 16:25
of deterministic guardrails.
- 16:28
Inside of this is a lot of stuff, but
- 16:30
these are our top level um software
- 16:33
quality, software production readiness
- 16:35
dimensions. Design consistency being
- 16:38
core. Of course, when you have a design
- 16:40
system and the code isn't consistent
- 16:41
with it and vice versa, we need to fix
- 16:44
that. So, there's a lot of design
- 16:45
consistency
- 16:47
um work that's going into the the
- 16:49
foundation. Accessibility is its own
- 16:52
dimension. um maybe selfish of me but I
- 16:55
think it's core
- 16:57
uh AI code generation too or AI code
- 17:00
sorry governance AI code generation
- 17:02
governance is another really critical
- 17:04
piece because the code is just flying
- 17:06
through the system and we need to govern
- 17:08
that code now we're not going to change
- 17:10
the code rewaver actually doesn't write
- 17:12
code you write code reweaver will do
- 17:16
what you want it to do so if you say
- 17:18
apply the fix it'll write the code for
- 17:20
you can see the code being written You
- 17:22
can say never mind undo. You can ignore
- 17:25
it. So at the end of the day, catch what
- 17:27
drifts in a closed loop before anything
- 17:30
merges. That's the goal.
- 17:33
You can try it out with your own code
- 17:34
today. Right now, you can go to reweaver
- 17:37
AIPplayground
- 17:38
and we built kind of a similar harness
- 17:40
to what I built there where you can put
- 17:42
your AI generated code on the left on
- 17:45
the left. You can scan it and get your
- 17:48
results for that code. There's also a
- 17:51
fun little challenge on that page. If
- 17:53
you can get AI to generate code that
- 17:56
gives you a score lower than30
- 17:59
PDR,
- 18:01
which is production drift ratio, will
- 18:03
get you a frontline seat on the beta,
- 18:06
but you'll also get yourself showcased
- 18:08
at the bottom on the crawl. So, give it
- 18:10
a shot. Take a look. Um, meanwhile, I'm
- 18:14
looking for people to join our beta.
- 18:15
We're going to basically blast out mid
- 18:18
July hopefully.
- 18:20
Um so would love for you to join if
- 18:22
you're interested um to make the product
- 18:24
better to sanity check us. Um basically
- 18:28
that's it and you can also reach me
- 18:31
online or outside happy to talk more.
- 18:34
Thank you.