Connect your repo. Brainsless identifies the agents, models, tools, and supporting code it can find. Inspect the source references and choose which agent or operation to test.
Review the rules found in your prompts and add your own. Brainsless prepares cases with concrete inputs and expected behavior. Rules needing an owner, fixture, or browser evidence stay visible as gaps.
Read what happened in each conversation: the request, the reply, the check, and the evidence. Compare agents, inspect recorded latency, and see which cases passed, failed, or could not be settled.
The World Compiler prepares your app in an isolated sandbox using your repository and configured environment. Run ready cases and supported test suites there. Startup checks show which endpoints and dependencies are available.
Sandy lives beside the World. Ask her to inspect the evidence and propose a repair plan. Approve the plan, reproduce the failure, apply the sandbox edit, and verify it. Review the patch and compare the saved runs.
Your repository supplies the app code and startup configuration. Brainsless prepares a sandbox, checks the available endpoints, and runs selected cases against the app that starts there.
Read the runtime receipts alongside the replies. Missing credentials, unavailable dependencies, and unobserved tool activity remain evidence gaps, so you can tell what the run actually tested.
A component can report success while the conversation still fails the person asking for help.
Cases come from your reviewed rules, source findings, and supported workflows. Select an agent, a group, or the available suite. Each report separates the cases you ran from the catalog and the work still awaiting inputs.
A plausible patch is only a proposal. Sandy can inspect the source and write a regression check that reproduces the failure before editing the app.
Follow the approved repair through its check, edit, restart, and verification. Rerun the cases and compare unchanged contracts with the earlier run. Unsettled results and regressions stay in the report.
Connect supported production sources to inspect recorded sessions, errors, latency, model and tool activity in your World. See which observed usage patterns your tests cover and where the gaps remain.
The visible Production tab refreshes every 15 seconds; provider connections sync about every five minutes. Sandbox results measure the selected cases. Conversion, retention, and customer outcomes need their own production evidence.
Turn a business goal into a concrete behavior to test. These are examples of what to examine; improvements in customer metrics require measurement in production.
Answers from your data instead of guessing.
Cites the source, or says it does not know.
Solves it on the first reply instead of looping.
Escalates only what a human needs to see.
Finishes the task without handing it off.
Answers the objection instead of discounting.
Checks the order before it approves a refund.
Says what it cannot do instead of stalling.
Confirms the fix worked before it closes.
Sends the easy turns to a smaller model.
Calls the tool with the schema it was given.
Runs the first setup step instead of linking docs.
How do you know a new version of the Driver is better than the last one? Not from the road. Twenty billion simulated miles against two hundred million real ones. One event, replayed with the traffic, the timing, the weather, the road users changed. Situations that never happened anywhere: a flooded street, an elephant on the road.
Bring that discipline to your AI app: exercise difficult situations in a sandbox, inspect the full exchange, and test an approved repair. Keep the baseline and rerun together. Connected production observations help you decide which situations to test next.
Frontier models have converged. What moves the numbers now is everything built around them. Same model, unchanged weights, every time. Hover a row.
Across public harnesses, HAL measures a single model swinging up to 48 points. Public benchmarks, because coding is where the receipts are public. The mechanism is identical whatever the job.
We're building a future where anyone shipping AI can see how their system behaves, change it on purpose, and know the change is an improvement before a customer ever meets it. We are researchers and engineers, and we publish what we learn: five papers so far on long-context attention, memory systems, and what it costs to serve models at this size, each with the code and the hardware bill to reproduce it.
faster attention at a million tokens, from the law that a model reads a small, fixed number of memories per word
of ordinary RAM runs a million-token cache, instead of $180k of GPUs
tokens is where retrieval collapses, and why the memory we build is structured
tokens per second, single stream, trillion-parameter model on four GPUs, reproducible for about fifteen dollars
of what a model reads to write a single word comes from one small shared piece — 7% of the file — that everyone still ships at full size
A saved run keeps its case catalog, results, recorded conversations, source revision, and available request measurements. Review the sandbox patch separately. Compatible reruns compare unchanged contracts, with missing evidence and changed scope called out.
Teams whose AI talks to customers, makes decisions, moves money, handles sensitive information, or operates critical workflows. If a wrong answer costs you money, trust, or a regulator's attention, the behavior belongs under version control.
Brainsless prepares cases from your reviewed rules, source findings, and supported workflows. You can inspect the input and expected behavior. A case runs when its owner, prerequisites, and evidence requirements are available; the report shows what still needs attention.
No. Sandy proposes changes to your app code, prompts, or configuration inside the sandbox. You review the plan and patch, then inspect the reproducing check and verification results.
Brainsless does not fine-tune models on your repository or conversations. You choose which production sources to connect. Requests use the configured model providers; their data handling and the Brainsless privacy policy apply.
Configure a model your app supports, rerun the same selected cases, and compare the recorded behavior and request latency. Provider cost estimates appear where a rate card is available. Your saved cases provide an app-specific benchmark.
Start with your repository and a recorded run. Ask Sandy to inspect a failure and prepare a plan, then approve its execution in the sandbox. Follow the reproducing check, patch, and verification, and use the next run to see what improved or regressed.
Export the report as JSON with its recorded cases, conversations, and analysis. Review the source changes as a patch. Your repository remains yours; World access and evaluation runs follow the allowance you purchase.
One full run of your app every month is free, with the whole report and the fix it proposes. Ship is $99 a month for ten runs, two apps and a check on every push to your main branch. Prove is $250 a month for thirty runs, five apps, and a gate that stops a pull request merging until your app holds. Everyone on your team is included on both. Every line is on the pricing page. Your app answers on your own model key, and that usage is yours.
Own how your AI behaves.
Connect your repo. Understand the failures. Review a repair and keep the proof.
