We were missing the feeling of a “whiteboard session” with another dev where you leave with a deep understanding of a system, so we built this app for ourselves. Whiteboard plugs into the tools you already use - e.g. Claude Code, Codex, etc. – and gives your agent an SDK to draw on an in-app canvas to describe its work. We began with an MVP based on HTML artifacts and started rethinking the app as we ran into limitations:

1. Built on top of CodeOSS: We found that in pure HTML tools it was hard to connect a spec or diagram to code. In Whiteboard, when you click on visualizations like a sequence diagram, an entity relationship diagram, or a quote from the agent’s trace, you can jump to the underlying code directly. When navigating code, you get keybindings and LSP support from VSCode out of the box. We’ve found this is especially valuable because tradeoffs are often only discovered after a first pass at implementation (re: slop)

2. Semantic diff viewer: we wrote a semantic, AST-aware diff viewer in Rust so you can only view the code changes which are relevant to you [1]. We’ve set up some sane defaults: large added functions are summarized as pseudocode, and things like unit tests and large documentation changes are collapsed / hidden. This is all customizable with a WASM-based plugin system.

3. Decision Log: We found it difficult to reason about what set of decisions our agents made autonomously. So we built tools for agents to query and link their own traces to the Whiteboard, so you can understand how the requirements that you set were implemented, and understand what decisions your agent made autonomously.

Here’s a quick demo video explaining more: https://www.youtube.com/watch?v=ChPn3ftULWE

Folks at companies like Salesforce and Modal are using Whiteboard today as a review tool for architecture or spec-level changes – really any change where they want to be involved:

1. Reviewing your own coding agent’s work: because Whiteboard makes it easier to review large amounts of code, folks will typically have their AI agents create a prototype and a corresponding Whiteboard session so they can iterate on the design.

2. Reviewing other people’s changes: We’ve found that Whiteboard is particularly helpful when composed with tools like Greptile. For example, you can run an automated code reviewer on small changes and escalate to a Whiteboard session for the changes that require human judgement.

Why we built this: we’re four buddies from college who quit our jobs as tech leads right before agentic coding became industry standard. As we iterated towards an MVP for a previous idea, we struggled to maintain a comprehensible codebase while reaping all the velocity benefits of agentic coding. As more PRs were merged without our understanding, we felt a ‘cognitive debt’ begin to seep in, until it became difficult for us to even contribute to the system [2].

We’re releasing our desktop app under an MIT license. Please poke through and feel free to contribute! Eventually we’ll charge companies for a hosted web version that manages whiteboard session creation alongside features like trajectory storage and multiplayer reviews. Everything will always remain self-hostable.

Thanks for reading, and we hope you try it out! We would love to hear any feedback and to learn from your expertise.

Here’s are the project links again: https://github.com/devdotfast/whiteboard, and you can install (for MacOS + Linux) at https://install.dev.fast

[1] diffs library: https://github.com/devdotfast/diffr [2] Credit for the term ‘cognitive debt’ goes to https://www.geoffreylitt.com/2026/07/02/understanding-is-the... [sidharthkmenon]

I just open sourced the DSL that our harness in grep.ai uses to turn repeatable parts of agent work into workflows. You can combine tool calls, code, Jev-powered system one decisions for things like routing and screening evidence, and agents when a step needs more investigation.

Our harness uses the traces and retro notes agents leave behind when doing a job to figure out which parts can become a workflow. The idea is to make the work easier to understand and avoid paying for a full agent loop where one isn’t needed. For example, a research workflow can split a question into subquestions, send agents to research them in parallel, use Jev to screen the evidence, and have another agent write the report. You can inspect the steps, evaluate the evidence screening separately, or change one agent without rebuilding everything.

The DSL and examples are in our GitHub. There’s a scripted demo you can run without API keys: https://github.com/Parcha-ai/agentrun

You can also use it as a Pi extension to build, inspect, and run workflows: https://github.com/Parcha-ai/agentrun#use-it-in-pi

I would love to hear if this is useful to others.

More background on how AgentRun works in this video: https://www.youtube.com/watch?v=vOVhtGjtwpg. Or read about our use cases in this article: https://x.com/MiguelriosEN/status/2101029313906987422. [miguelrios]

Find duplicate code blocks meaningful to the language (classes/functions), not just lines.

Find near-duplicates: ignore whitespace, strings, high level AST nodes such as function and names.

Find structurally similar code: anonymize identifiers, constants, etc.

Pull requests welcome: This is very much an proof of concept - I'm happy with it, but I haven't supported very many languages at present.

Languages supported: astro, bash, css, go, html, javascript, lua, markdown (plus codeblocks), python, sql, typescript, java, kotlin, rust, yaml [91awebsi]