$praxist-takeover

Treat the current directory as the existing runnable research project. Verify
the baseline and its evaluation path before changing anything.

Optimize <primary metric and direction> while preserving <key constraints>.
Use <peer count> peers for up to <generation count> generations within
<time or cost budget>. Use the runtime and model provider selected during
setup. <Allow or disable> literature search, <enable or disable> QD, and
<enable or disable> generation-zero DIG.

Do not download new datasets or replace required project assets. Build a
separate task harness with explicit metric directions, baseline provenance,
protocol-integrity checks, evidence maturity rules, and justified retention
lanes. After readiness checks pass, <launch immediately in detached mode / ask
for confirmation>. Report the task path, run ID, evidence contract, generation
close policy, and monitor command.

Q1. What is Praxist?

Praxist is an autonomous research system for measurable research problems that can be executed on a computer. It turns an already runnable project into a continuous, evidence-driven research run.

Across successive generations, parallel research agents develop candidate solutions; evaluators convert results into structured evidence; and a planning panel synthesizes that evidence into the research agenda for the next generation. The cycle continues until the search converges or the budget is exhausted.

You provide a runnable project and a measurable objective. Praxist orchestrates the research process that searches for the best-performing solution.

Q2. How is Praxist different from manual tuning or AutoML?

AutoML tunes parameters within a predefined search space. Praxist runs the full research loop.

Parallel research agents can change methods, architectures, and strategies. Evidence from evaluation shapes the agenda for the next generation, while the Deep Innovation Gate (DIG) and Quality-Diversity (QD) allocation help the system escape local optima.

Praxist is closer to a self-directing research team than a search tool. If your researchers are already iterating on a problem manually, Praxist takes over the iteration loop itself.

Q3. Is my project a good fit for Praxist?

Praxist delivers the most value when three conditions are met:

• The objective is measurable: there is at least one metric that meaningfully distinguishes better from worse, with a clear optimization direction.
• The project already runs: the baseline code, environment, and required data or simulator are in place and work without Praxist.
• The best path forward is unknown.

If a prerequisite is missing, Praxist stops and tells you exactly what is needed. It will not silently download unspecified datasets, invent a simulator, or fabricate baseline performance. That is a deliberate design principle.

Q4. Do I need an API key, and what will it cost?

No API key is required in Codex-native mode; Praxist uses your authenticated Codex session. We also recommend using your own API key to access supported model APIs.

API costs are set by the provider and vary by model and usage. Total cost also depends on parallelism, the number of generations, and evaluation runtime. For cost-sensitive runs, start with a small representative workload before scaling up.

Q5. How does Praxist protect my code and data?

Praxist provides three layers of protection:

• Project isolation: Praxist does not modify your original project. Run artifacts are stored separately.
• Credentials: API keys are entered through a masked local prompt and are not exposed in commands, shell history, or conversations.
• Data collection: Praxist does not collect data used in your experiments. It collects only limited system-level operational information, which you can disable at any time.

Q6. How can I trust that a reported improvement is real?

Praxist uses three safeguards:

• Preregistration: Metrics, evaluation protocols, baselines, and acceptance thresholds are defined before the run.
• Consistent evaluation: Every candidate is measured through the same evaluator, and invalid or suspicious results are excluded.
• End-to-end provenance: Every reported improvement includes the evidence and lineage needed to inspect and reproduce it.

We recommend reviewing what the selected solution changed and testing it again in your own environment. Praxist's results are designed to be verifiable, and your own validation should be the final test.

Q7. What if Praxist does not improve the result?

Praxist does not guarantee a specific metric improvement. It provides a rigorous research process and auditable evidence.

If a run does not meet its target, you still receive a negative-result evidence package, an audit report, and recommendations on whether to stop or redirect the research.

A negative result can still be valuable: it rules out tested approaches with evidence and helps prevent further investment in an unproductive direction.

Q8. Is Praxist open source, and what terms apply to its outputs?

Praxist is licensed under the Fair Source License Agreement 1.0. The precise description is source-available: the complete source code is publicly available and may be viewed, downloaded, and modified. Subject to the license terms, Praxist may be used for internal business purposes and deployed within your own organization.

Organizations with aggregate annual revenue, including revenue from affiliates, below US$1 million may use Praxist commercially at no charge. Once annual revenue reaches or exceeds that threshold, the organization must contact the Licensor, Sapient Intelligence Pte Ltd, to negotiate a Commercial License.

The revenue threshold does not apply to qualifying teaching and academic research conducted by institutions of higher education, public research institutions, and nonprofit academic research organizations.

Generated outputs: no attribution is required for internal use. If an output is published externally or otherwise made available to third parties, the product-name attribution "Praxist by Sapient Intelligence" must be retained.

This FAQ is a summary only. If it conflicts with the Fair Source License Agreement 1.0, the terms of the license agreement control.