Getting something working is easy now.
Launching it, scaling it, and keeping it alive is not.
Every answer comes straight from the source. Free, no sign‑up.
Prebuilt agents that specialize in security, UI/UX, debugging, data and LLM engineering — each grounded in real research.
When context overflows, the AI compresses it — and accuracy drops. Keep the context in DataPilot, so tokens go to building.
… such compression methods will lead to performance degradation as the necessary compression grows. …
arXiv 2310.08560 · Packer et al. →Every answer stays faithful to the context you provide — which minimizes the risk of hallucinations. Cited to the exact source, so you can check.
An LLM answer is trustworthy when it stays faithful to your sources — not guessed from memory ❝ Survey of Hallucination · p.5
… Faithfulness is defined as staying consistent and truthful to the provided source — an antonym to “hallucination.” …
arXiv 2202.03629 · ACM Computing Surveys →Implement proven advanced methods.
Implement the auth practices these papers require — and keep testing until the results match what’s described.
Validate the credentials first, then fork. On success: issue a long, random session token and regenerate it on login — that’s what closes session fixation — set HttpOnly and Secure on the cookie, apply both idle and absolute timeouts, and log every auth event. On failure: increment the counter and lock out once it crosses your threshold.
23 sources in this library
Create in-depth reports.
Which accounts are at risk of churning this quarter?
Six accounts show the pattern that preceded your last five churns: logins down >40% and an open support ticket past 14 days. Northwind ($82K ARR) is the biggest exposure — usage halved since the admin left in May. Joined from your CRM, product logs and Zendesk; no SQL, and every row is one click away.
Discover & create new ideas.
What do these papers say happens to you inside a black hole?
Inside a black hole, spacetime curvature becomes so extreme that time and space effectively swap roles, and any object crossing the event horizon is inevitably pulled toward a central singularity where known physics breaks down.
Turn examples into a workable simulation.
Turn Example 2.1.1 on page 121 into something I can actually run.
The block on the incline, built from the page itself — same forces (N, friction, mg), same angle. Change θ or the friction and watch what the equations do. The problem you were re-reading, running.
Every one of these started as papers or a repository in DataPilot.
Every behaviour traces back to a page — the event horizon, the accretion disc, the spiral arms.
3D creatures that learn to walk from scratch — the algorithm comes straight from the papers.
Cells that grow, move and evolve on their own — one library of papers, two different builds.
Connect a repository and ask it — DataPilot draws the high-level architecture of a complex build.
The tunnel’s lensing follows James et al. 2015 — the physics traces back to the papers.
Same loop you get: read the research, then build.
Understand the research, and you can build almost anything. That’s what DataPilot does for me — I built DataPilot with it, and every idea since.
And the reading runs on our hardware — your AI receives 849 tokens instead of 16,334, measured on a real paper here.
On paid plans your AI connects directly — context flows automatically, no more copying.
If you work inside one AI with no documents, a bigger plan is simpler.
Your ideas, planned and started. No card required.
Everything you need to build and launch.
Includes 1M tokens & 5 GB of data monthly · auto-refresh & cloud backup.
Everything in Founder — plus a dedicated consultant.
Free forever · cancel anytime · 1-1 consultation and Founders’ Session are included with annual billing.
Type yours. Watch it begin. Free.
Enter your email — we’ll create your free workspace, train your file, and open your page.
Free · no credit card · your data stays private