Task Contract
Keeps the goal, scope, constraints, and done criteria visible before the model starts working.
Private closed beta · Early access soon
The Context Layer for AI Coding
Local-first context, validation, and evidence for LLM-powered coding workflows.
Reality Graph is being tested in real development workflows before it opens to a small group of early users.
Built carefully: private beta first, public claims only when they are earned.
The problem
AI coding tools are powerful. The workflow around them still breaks.
Claude Code, Cursor, Copilot, and Antigravity raised the ceiling on what a model can do. But the loop around them tends to break in the same places, in the same order:
- 01
The task gets vague
A request that's clear in your head reaches the model underspecified. The goals, constraints, and done-criteria stay implicit.
- 02
Context gets noisy
The wrong files, stale logs, and half-related history crowd out the few things that actually matter.
- 03
Output looks plausible
The result reads convincingly, which makes it harder to notice what's quietly wrong.
- 04
Validation is scattered
Tests, risks, and acceptance criteria live in different places, so nothing checks the change as a whole.
- 05
Review takes too long
Reviewers rebuild intent from the diff alone, paying again for context that was never written down.
- 06
Evidence is missing
When something breaks later, there's no record of what changed, what was checked, or why it shipped.
Where a control layer steps in
- prepare context
- keep boundaries visible
- preserve validation
- record evidence
A high-level shape, not a feature list. A human stays in control throughout.
The thesis
Stronger models are not the whole answer.
AI coding also needs a better operating layer. One that understands the task, keeps boundaries visible, prepares focused context, preserves validation, and records evidence.
What Reality Graph adds
A control layer around the AI coding run.
Reality Graph does not replace your AI coding tool. It sets up the run around it: the task, context, memory, validation, and evidence. That way the human can review what happened with more confidence.
Context Packs
Prepares focused task context instead of pushing vague prompts, repo noise, and scattered history into the run.
Project Memory
Brings back useful project rules, decisions, and repeated lessons when they are relevant to the current task.
Validation Map
Connects the task to tests, risks, and review criteria before the output is trusted.
Evidence Trail
Makes it easier to see what changed, what was checked, and what still needs human review.
Human Control Gates
Keeps Reality Graph advisory by default. Risky actions stay visible instead of becoming invisible automation.
How it works
- 1Start with a task
- 2Reality Graph prepares the run
- 3Your AI coding tool works
- 4You review validation and evidence
The AI coding tool still does the coding. Reality Graph keeps the surrounding workflow structured, reviewable, and human-controlled.
Want to follow the beta, or test it when it opens?
Join early accessThe need
Why AI coding still needs a control layer
Illustrative workflows and external signals, not Reality Graph performance claims.
Developer trust gap
AI coding tools are widely used, but trust in their output remains limited. That points to a need for better review, validation, and evidence.
AI adoptionlimited trustreview needevidence
Context chaos
A messy prompt, repo noise, terminal logs, and a vague task rarely add up to focused context. Illustrative, not a numeric claim.
promptrepo noiseterminal logsvague taskfocused context
Evidence gap
The hard part is not only generating code. It is knowing what happened across diffs, tests, and risks, and whether it is safe to ship.
AI outputgit difftestsriskshuman review
Stack Overflow Developer Survey 2025 reports that more developers distrust AI tool accuracy than trust it, with only a small share highly trusting outputs. Source: Stack Overflow Developer Survey 2025
Where a control layer helps
Illustrative, not a Reality Graph performance claim.
Bring the few things that matter into focus and leave the noise out. Instead of pushing a whole repo, stale logs, and a vague prompt into the run, a control layer prepares a tight, task-shaped slice of context before the model starts.
- Pull in the files, rules, and history that relate to the task
- Keep out repo noise, dead code, and unrelated logs
- Carry the goal and constraints alongside the context
Keep risks, tests, and acceptance criteria visible while the work happens, not after the fact. A control layer ties the change back to how it will be checked, so output that only looks plausible isn't mistaken for output that's verified.
- Connect the task to the tests and checks that cover it
- Surface risky areas before the output is trusted
- Make acceptance criteria explicit, not implicit
Capture what changed and why, so a run can be reviewed and understood later. A control layer keeps a trail across diffs, tests, and decisions, instead of leaving reviewers to rebuild intent from the diff alone.
- Record what changed and what was checked
- Keep the reasoning attached to the result
- Make a run reviewable long after it finishes
Keep a human in the loop: advisory by default, with no autonomous commits. A control layer makes risky actions visible and easy to approve or reject, instead of turning them into invisible automation.
- Stay advisory unless a human approves
- Make risky steps visible at decision points
- Leave the final call with the developer
External market and problem signals, not Reality Graph performance claims. Source: Stack Overflow Developer Survey 2025
Testing Reality Graph in real development workflows.
Join early access if you want to follow the beta or test it when it opens.
In motion
See the idea in motion.
A short, optional explainer: scattered context resolving into a structured map. No sign-up needed to watch.
What Reality Graph is
A context, validation, and evidence layer around the tools you already use.
Focused context
Prepares focused context for the AI coding tools you already use.
Visible validation
Keeps risks, tests, and acceptance criteria visible.
Evidence trail
Makes AI-assisted runs easier to review and understand.
Local-first boundary
Built around local workflows and code privacy.
Human control
Advisory by default. No autonomous commits.
Current status
In private closed beta.
Reality Graph is being refined through real AI-assisted development workflows. The next step is a small early-access group before a broader open beta.
Private closed betaEarly users soonOpen beta planned
Who should join
Built for people who feel this problem deeply.
AI-coding power users
For developers who use AI coding tools daily and keep fighting context, validation, or review friction.
Technical reviewers
For people who need to understand what changed, why it changed, and whether it is safe to trust.
Small technical teams
For teams exploring AI-assisted development but unwilling to give up control, evidence, or local-first boundaries.
Boundaries
Built with clear boundaries.
Reality Graph is designed as a control layer for AI coding workflows, not another autonomous coding agent. It keeps context, validation, and evidence visible while humans stay in control.
- Works with existing AI coding tools
- Human-controlled by default
- No launch claims or fake benchmarks
Early access
Want to test it when it is ready?
Join the early access list. I'm looking for a small number of technical users and teams who feel this problem deeply and will tell me the truth about it.
FAQ
Questions, answered plainly.
- What is Reality Graph?
- Reality Graph is a local-first context, validation, and evidence layer for LLM-powered coding workflows. It works alongside the AI coding tools you already use to keep context focused, validation visible, and evidence easy to review.
- What does Reality Graph add?
- It adds a control layer around AI coding runs: task boundaries, focused context, project memory, validation awareness, and an evidence trail. The AI coding tool still does the coding; Reality Graph keeps the workflow easier to review.
- Is Reality Graph an AI coding agent?
- No. Reality Graph is a control layer, not an autonomous coding agent. It is advisory by default and does not write or commit code on its own, so you stay in control.
- Which tools is Reality Graph designed to work with?
- It is designed to work with existing AI coding tools and local development workflows, rather than replacing them.
- Is Reality Graph available now?
- Not yet. Reality Graph is in private closed beta and is being tested in real development workflows. A small early-access group comes first, with an open beta planned later.
- How can I get early access?
- Join the early access list, or reach out to the founder directly. I'm looking for a small number of technical users and teams who feel this problem deeply.
Founder note
“I'm building Reality Graph because I kept hitting the same wall: the model was powerful, but the workflow around context, validation, and evidence was too fragile to trust.”