Designer Robbie Tilton released Compositor on September 18, 2026 — a free, open-source image compositing editor he originally built to get himself off an Adobe Photoshop subscription, then decided to release publicly. The detail worth noting for anyone tracking how AI actually gets used in real, shipped indie software: he planned the app's feature mapping with GPT-6 Astra before implementing and hand-tuning the details, rather than either building it entirely by hand or having Astra generate the whole thing unsupervised. The headline comparison he posted is genuinely striking on its own: the entire app is 12MB; Photoshop, on his machine, is 6,455MB.
TL;DR
| Question | Answer |
|---|---|
| What is it? | Compositor — a free, open-source image compositing editor |
| Who built it? | Designer Robbie Tilton, released September 18, 2026 |
| Why? | To escape an Adobe Photoshop subscription; open-sourced after building it for himself |
| AI's role | Planned feature mapping with GPT-6 Astra, then implemented and hand-tuned details himself |
| Size comparison | 12MB (Compositor) vs. 6,455MB (Photoshop, on Tilton's machine) |
| Scope | Essential compositing tools only, not a full Photoshop replacement |
| Where to get it | robbietilton.com/compositor |
How Astra was actually used
The specific, useful detail here is how Tilton used AI in the build process, since it's a more grounded answer than most "built with AI" claims give. Asked directly how he mapped out Photoshop's feature set for the rebuild, his answer: "Just laid out a plan w Astra telling it what mattered to me most. Lots of tweaks after implementation though to get details right." That's a planning-and-scaffolding role — using Astra to help translate "here's what I personally need from an image editor" into a concrete feature list and initial structure — followed by substantial manual refinement, rather than a claim that Astra autonomously produced a finished, polished application. It's a useful, honest data point on where AI-assisted app development actually sits for a real, shipped, non-trivial desktop application today: valuable for planning and acceleration, not yet a substitute for the manual detail work that makes an editor actually feel right to use.
The size comparison, and why it's more than a flex
12MB versus 6,455MB is a genuinely dramatic ratio, and it's worth being precise about what it does and doesn't prove. It doesn't mean Compositor is "better" than Photoshop in any general sense — Photoshop's size reflects decades of accumulated features, format support, plugin infrastructure, and enterprise capability most individual users never touch, not pure bloat. What the comparison does illustrate well: a tool built to cover exactly one person's actual, essential workflow — Tilton specifically describes it as having "all the essential tools I need for compositing, with none of the BS" — can be dramatically smaller and, for that specific narrow use case, arguably more usable than a general-purpose tool trying to serve every possible user's needs simultaneously. That's a real, recurring pattern in the current wave of AI-assisted indie software: individuals building narrowly-scoped, personally-tailored tools that compete with bloated incumbent software specifically by not trying to do everything.
The honest caveat Tilton himself included
Worth highlighting because it's an unusually self-aware admission from someone releasing their own tool: Tilton directly acknowledged that manual, pixel-level image compositing is "kind of archaic in 2026," and that he's still using this workflow specifically because AI image generation and editing tools aren't yet pixel-perfect enough for the level of detail control his work requires. That's a useful, grounded calibration point on the current state of AI image editing broadly — even someone actively building AI-assisted tools and clearly comfortable with the technology judges that, for his specific professional compositing needs, a traditional manual editing workflow (just built smaller and leaner) still beats what generative AI image tools can currently deliver.
The broader pattern: indie tools built to replace bloated incumbents
Compositor fits into a recognizable, recurring pattern in 2026's software landscape worth naming explicitly: individual developers and designers building small, sharply-scoped tools specifically to replace a much larger, general-purpose incumbent application they only use a fraction of. The economics here are genuinely different than they were even a couple of years ago — planning and scaffolding a real, usable desktop application used to require either significant engineering time an individual designer typically didn't have, or hiring a developer, both of which made "just build my own tool" impractical for most non-engineers with a specific, narrow need. Using an AI model to handle the planning and initial structure lowers that barrier meaningfully, even when — as Tilton's own account makes clear — real manual refinement work is still required to get the details right. The interesting long-term question this raises isn't really about Compositor specifically, but about how many other bloated, subscription-based incumbent tools across other categories (video editing, audio production, project management) are similarly ripe for a narrowly-scoped, personally-built replacement now that the planning and scaffolding cost has dropped this much.
Why "archaic" workflows sometimes outlast the hype cycle
Tilton's framing of manual compositing as "archaic in 2026" but still necessary is worth sitting with a bit longer, because it cuts against a common assumption in AI discourse that a traditional workflow surviving alongside newer AI tools means the AI tools simply haven't caught up yet, and will inevitably replace it once they do. That's sometimes true, but it's also possible that certain workflows — precise, detail-controlled pixel editing being one plausible example — represent a genuinely different kind of task than the one generative AI image tools are actually optimized for, where "generate something plausible from a description" and "make this exact, specific change to this exact pixel region" are different problems requiring different tools, not a maturity gap on a single continuum. Tilton building a leaner version of the traditional tool, rather than waiting for AI image editing to catch up, is itself a bet on the second interpretation — that manual, precise compositing remains a distinct and durable need worth optimizing directly, not a stopgap.
Honest limitations
- No Windows availability confirmed — a direct request for a Windows build in the replies wasn't answered, suggesting the initial release may be platform-limited.
- This is one developer's personal tool, open-sourced, not a company-backed product with a committed roadmap or support structure.
- The exact division of labor between Tilton and Astra beyond "planning" isn't fully detailed — how much of the actual implementation code was AI-assisted versus hand-written isn't specified.
- Feature scope is intentionally narrow — it covers Tilton's own compositing needs specifically, not a general Photoshop replacement for arbitrary use cases.
Why open-sourcing it matters beyond generosity
It's worth noting why Tilton's decision to open-source Compositor, rather than keeping it as a private personal tool, is significant beyond simple generosity. A closed, personal tool built by one designer for their own workflow inevitably encodes that one person's specific assumptions and priorities — open-sourcing it invites exactly the kind of scrutiny and divergent use cases (the Windows request in the replies, potential feature additions from other contributors) that tends to reveal whether a tool's design generalizes beyond its original author's needs or was really only ever suited to one person's specific workflow. That's a useful test for anyone considering open-sourcing their own AI-assisted personal tool: the real value of doing so isn't just sharing something useful, it's inviting the kind of real-world stress-testing from strangers with different needs that a solo project, however well it works for its creator, never actually receives on its own.
What this means for builders
Compositor is a clean, concrete example of a pattern worth studying if you're considering building your own narrowly-scoped tool to replace a bloated incumbent: use an AI model to help plan the essential feature set from your own actual workflow rather than trying to replicate a general-purpose competitor's entire surface area, then do the manual refinement work yourself once the scaffolding exists. The 12MB-versus-6.4GB gap is the visible result of that discipline — cutting scope aggressively to match one real use case, not a claim that AI made the whole build effortless. If you're weighing whether your own workflow has a similarly bloated incumbent worth replacing with something narrower, Tilton's process is a reasonable template to follow directly: list the specific tasks you actually perform in the existing tool, feed that list to a model for an initial feature-mapping and architecture pass, then budget real time afterward for the manual tuning that turns a functional first draft into something you'd actually want to use daily rather than expecting the first AI-assisted pass to be the finished product.
Related on explainx.ai
- What is vibe coding? A complete guide
- Top 10 things to build with GPT-6 Astra
- What is harness engineering? The layer that turns a model into an agent
- GPT-6 Astra's launch: every benchmark, the pricing, and the ARC-AGI controversy
- The AI Builder Workshop
- Primary source: Robbie Tilton on X · Compositor
This post is sourced to Robbie Tilton's own September 18, 2026 announcement on X. App size figures, feature scope, and the description of Astra's role in planning are Tilton's own stated claims; explainx.ai has not independently tested the application.