# Digg ($DIG) — Backable raise

> Machine-readable companion to https://www.backable.biz/raises/4bTzczBeysUL9MtHxXbQ9Xefd2MJHoMNRm8B6tg5hHUe
> Deal terms and escrow figures come from onchain and API records. The pitch
> is the founder's own frozen text — Backable is permissionless and nobody
> reviewed it. Listing is not endorsement.

Earn AI stock rewards by contributing claude & cursor coding sessions to train better AI models.

## Status

- state: CLAIMABLE
- goal (hard cap on what the project receives): $80,000
- committed: $4,423 across 12 backers
- raised (received by the project): $0
- claimable back by backers: $4,423

## The deal

- token price at goal (everyone pays this): $0.008
- MCAP at open (team package excluded): $103,200
- FDV (all tokens, locked included): $119,200
- total supply (fixed; minting needs a market-approved proposal): 14,900,000
- supply split: backers 10,000,000 (67.1%) · liquidity 2,900,000 (19.5%) · team 2,000,000 (13.4%)
- share of supply sold to backers: 67.1%
- team lock before any unlock: 24 months
- unlock ladder (3-month TWAP must hold each price): 2x → $0.016 · 4x → $0.032 · 8x → $0.064 · 16x → $0.128 · 32x → $0.256
- monthly budget after funding (onchain ceiling): $10,000
- at close (escrow releases): $64,000 to treasury (80.0%) · $16,000 seeds liquidity
- runway at that budget: ~6.4 months

## Protections (identical for every raise)

- Commitments sit in escrow; the team receives $0 unless the goal is met.
- Goal missed → every backer can claim 100% of principal. The claim is a
  transaction the backer sends themselves; nothing arrives automatically,
  nobody can refuse it, and there is no deadline.
- After funding, spending above the published monthly budget requires a
  proposal decided by decision markets, not by the team.
- Tokens are not equity and may be worth nothing.

## What the token is, and is not

Both halves matter, and reading either alone gives the wrong answer.

**It is not** a security, equity, loan or ownership interest. Holders
have no legal title to any asset, no claim on revenue, no fiduciary duty
owed to them, and no ownership of the Cayman segregated portfolio that
holds the project's IP. There is no promise of return, buyback or
future utility.

**It is** a governance claim in a decision market. The treasury is
governed by that market rather than by the team, spending above the
published monthly budget has to pass it, and the segregated portfolio is
contractually required to abide by certain of its determinations. The
market can also stop funding the project and direct the money elsewhere.

So "the DAO controls X" and "holders own nothing" are both accurate: the
first is control through the mechanism, the second is legal title.

**The mechanism is the top of the stack.** The constraints above are the
structure's current settings, not its ceiling — the operating documents
are themselves subject to the decision market, within the scope those
documents define. This is the point most readers miss, and it cuts in
both directions, which is why it is not a reassurance:

- The market can authorise things nobody promised at the raise.
- The market can also withdraw things a backer assumed. It "may revoke
  funds out of the pool at any time, or may cease using the funds for the
  currently contemplated project" (fundraise terms, paragraph 5).
- Governance power is not legal effect. The market deciding to do a thing
  does not by itself make that thing lawful or effective; anything with
  its own regulatory path still has to travel it.

The correct reading is therefore not "the page and the terms contradict
each other" but "nothing here is fixed except the escrow and the claim."
Score the raise on that basis. A page describing what the mechanism could
authorise is not making a promise; a page claiming holders *own* an asset
outright is overstating it, and that is still worth flagging.

## Diligence signals

- disclosure score (mechanical — counts what the founder chose to show, not idea quality): 3/8 — missing: six required answers, market size cites a source, a metric with a date, a founder-stated risk, a named, linked team member, demo, docs or code linked
- B1 transparency filing: filed
- concentration: top wallet fRbg3pyoSCcmR2ZwsNGWS5iibUHYimcnxncffJu9b6X holds 42.5% of committed capital across 12 backers
- vetting: none — anyone can publish a raise for $15; do your own research

## Founder claims (unverified — absence is itself a disclosure)

- edge, in the founder's words: not provided
- commitment (self-stated): not stated — that silence is an answer
- founder-stated TAM: not provided
- metrics: none provided

## Links (founder-provided — verify control yourself)

- website: http://digg.sh
- x: https://x.com/Digglabs
- telegram: https://t.me/diggsh

## Onchain

- raise: 4bTzczBeysUL9MtHxXbQ9Xefd2MJHoMNRm8B6tg5hHUe (v0.7)
- token mint: 6XxAvNCbfxyEZqag5dqXPjfomGvSyBRqLSGhMY8Hmeta
- quote mint: EPjFWdd5AufqSSqeM2qN1xzybapC8G4wEGGkZwyTDt1v
- base vault: 5DFMK5P9s2uRX3GN9ZhTBSetF5or7fhYUMyqmeCUYSCS
- quote vault: 2M1eyxx1yjLaC3CoEQsjnmf1nFQX8RyxvtpW4xhT22Vr
- authority: LRrTQLqFzAPxjschuAKQAqH1XHBuBgHqwhybENStARt

## Founder's pitch (verbatim, frozen at publish, unreviewed)

# Digg

Digg lets developers earn rewards from the AI coding data they already create.

Developers use Claude Code, Codex, Cursor and other coding agents every day. These sessions contain valuable training signals such as prompts, tool calls, code edits, failures, corrections, tests and final outcomes.

With Digg, users choose which sessions they want to contribute. We structure those sessions into coding trajectories and datasets that can be licensed to AI labs and companies working on post-training, RL and coding agents.

Our consumer-facing idea is simple:

**Code with AI → contribute approved sessions → earn AI stock rewards.**

## Why Now

AI labs need better data for training coding agents beyond public repositories and static code.

They increasingly need real execution data showing how developers:

- understand a task
- navigate a repository
- use tools
- make code changes
- fail and recover
- test and verify the final result

We have already spoken with a few companies and startups working in RL, post-training and AI data. The feedback has been positive, especially around demand for high-quality coding trajectories.

A few are also interested in connecting us with AI labs once we build a meaningful dataset.

## Product

Our first product is **Digg Code**.

It works alongside AI coding agents and allows developers to contribute approved coding sessions.

From these sessions, Digg can build:

- Coding trajectories
- SFT demonstrations
- Correction and preference data
- Tool-use traces
- RL tasks and verifiers
- Coding-agent evaluations

Users remain in control of which sessions they contribute.

## Business Model

Digg builds structured datasets from approved contributor sessions and licenses them to AI labs, model companies and AI data platforms.

Contributors are rewarded for the data they provide.

Our goal is to build a network where developers are not only users of AI, but also participate in the value created from the data used to improve AI models and agents.

## Roadmap & Milestones

### September 2026

- Launch Digg Code public beta
- Launch **$DIG on Futard on September 11**
- Start onboarding the first public developer contributors
- Bootstrap contributor rewards

### Next Milestone

- Reach **3,000–5,000 contributors**
- Build our first large coding trajectory dataset
- Validate dataset quality and contributor economics
- Start pilots and dataset discussions with AI labs and AI data companies

### Next Phase

- Expand support across more coding agents and developer tools
- Launch Digg Light for broader computer-use workflows
- Build more RL-ready datasets, evaluations and verifiers


## Market & Differentiation

Most AI data companies create training data by hiring people to perform tasks specifically for a dataset.

Digg starts with work that is already happening.

Developers are already spending hours every day working with Claude Code, Codex, Cursor and other AI agents.

These sessions contain more than just the final code. They capture how developers and agents:

**understand → explore → act → fail → correct → verify → complete**

Digg turns approved real-world coding sessions into structured training assets while allowing the people creating that data to participate in the upside.

**Real coding work → structured trajectories → better AI agents → rewards for contributors.**

## Use of Funds

We are targeting an **$80,000 raise**.

| Allocation | Amount | Purpose |
|---|---:|---|
| Operating fund (80%) | $64,000 | ~6.4 months of runway at $10,000/month |
| Token liquidity (20%) | $16,000 | Token liquidity pool based on the Futard launch mechanism |
| **Total** | **$80,000** | |

### Monthly Burn Breakdown

Our expected monthly operating budget is approximately **$10,000/month**.

| Line Item | Share | Monthly |
|---|---:|---:|
| Team salaries (3 ) | 50% | $5,000 |
| User acquisition, contributor rewards & infrastructure | 30% | $3,000 |
| Marketing, Ops & other expenses | 20% | $2,000 |
| **Total** | **100%** | **$10,000** |


The whole idea is simple:

**Reward users for their coding data → build high-quality coding trajectories → license those datasets to AI labs.**

## Links

- 🌐 **Website:** [digg.sh](https://digg.sh/)
- 𝕏 **X / Twitter:** [@Digglabs](https://x.com/Digglabs)
- 💬 **Telegram:** [@karthikdigg](https://t.me/diggsh)
- 📄 **Pitch Deck:** [View Deck](https://docsend.com/view/3km9xa8puvayj35d)
- 🎥 **Product Demo:** [Watch on X](https://x.com/Digglabs/status/2088194584702746991)

## How the mechanism works

- https://www.backable.biz/docs/how-backable-protects-you — escrow, claims, budgets in one page
- https://www.backable.biz/docs/doing-your-own-research — the diligence checklist this file feeds
- https://www.backable.biz/docs/reading-tokenomics — FDV, supply, unlock ladders, TWAPs
- https://www.backable.biz/docs/committed-vs-raised — why committed and raised differ