AI workflow software for continuing work across engineers, models and infrastructure.
Cantos connects a job’s inputs, decisions and results to reusable procedures. Second Run, its execution-and-reuse capability, runs supported procedures, reuses verified results and identifies what needs requalification when dependencies change.
This example continues our own Chapek research. Explore the record, change an assumption, or download the handover and run its next action. Run instructions
The recommendation in use
What changed since it was written
Changed inputs are linked to their previous versions in the history.
Work already performed
Continue the case
The changed commitment basis is ready for your assessment. The record carries the outstanding inputs and measurements into the next step.
Open decisions and evidence
What you can do next
next.py.Command and request
What has to be true for this customer?
Test it against an opportunity
More detail (optional)
The first target to test
DRAFTWhat would change this recommendation?
A customer may already have adequate continuity tooling, lack recurring tasks, prohibit the required data access, need a different operational boundary, or have too little demand for a dedicated allocation. A well-evidenced alternative should replace this hypothesis through your own conclusion.
Your conclusion
After you change the prospect or the server numbers, write the conclusion you draw. It is kept as a new version beside the earlier one.
Write your own conclusion
Sign off a version (optional)
What one server costs per useful result.
What each useful result costs depends on how many results the server delivers and how much of the month the customer’s work fills. Use observed throughput and demand to assess the allocation.
Change any input below. The table “Same commitment, different productive use” shows what every 1,000 results cost if the customer fills 25%, 50%, 75% or all of the month. To keep the numbers with the prospect, use them in the recommendation below the instrument.
Quick test: one prospect on the server in use
Two answers. The price per result appears as you type; it is priced by the same engine as the instrument below.
One result = one finished, checked piece of their work: a reviewed change case, a resolved ticket, a processed document.
Numbers the recommendation uses
IN USEWhere these numbers come from
Measure first, on a Chapek node
Chapek’s paid trial month is the natural place to take these three measurements.
Run, reuse and continue.
Compare initial execution, reuse, price changes, model changes and the repaired runner. Open a procedure to inspect its basis and next action.
Recorded run narrative
Runs
Each retained procedure
Each procedure links its declared inputs and execution basis to a retained result. Open its details for the exact task, computation key and previous bindings.
Read here. Run from the package.
The browser displays the results and runs the record rules and capacity calculations. The downloadable package runs these retained procedures through integration/work.py. Follow the setup, commands and expected results.
What the browser does execute: the ResearchCore record rules (revisions, dependency checks, sign-offs, checksums) and the Compute engine inside the server instrument. Both are embedded unchanged and listed under Keep & share.
The illustrative workload
What a cold successor needs
Each accepted piece of work should leave whoever picks it up next ahead.
Expensive reasoning and GPU work can produce a tested procedure and a usable record. The next suitable task can reuse them. New conditions expose the unresolved work rather than silently inheriting old conclusions.
Customer-owned state
Authoritative sources, revisions, permissions, accepted decisions, exceptions and unfinished obligations.
Keep the record portable and the actual systems of record authoritative. Reconstructible indexes and caches remain secondary.
Qualified procedures
Inputs, applicability conditions, code, model and runtime pins where relevant, validation checks and known failure cases.
Supported checks run locally; changed conditions identify the work needing fresh investigation.
Proposed Chapek operation
Chapek would maintain the agreed environment, qualified changes, recovery and support, while the customer keeps consequential authority.
Model work runs on Chapek capacity by default. Retained procedures make each later run quicker to check, so the customer gets more accepted work from the same server.
One workflow, continued
This example demonstrates the record and handover. The proposed engineering workflow connects the customer’s sources and checks to Chapek’s execution interfaces.
Evidence behind the work.
Demand locates questions. Benchmarks identify candidate configurations. Workload tests establish bounded outcomes. Market and failure history say what must be rechecked. Their evidence classes stay separate.
How the evidence changes the strategy
Sources and boundaries
Each source retains its date, evidence class and link to the original material.
Your work stays yours, and travels with you.
Keep it, send it, continue it
Everything you record is saved in this browser automatically. Save a copy gives you this page back with your work inside: it opens on any computer, offline, and you can send it on.
Questions, or want to go further: Jonathan Sandhu · bigbirdreturns@proton.me. The email opens with your prospect filled in; attach your saved copy.
More options
Inspect the record file
Saved copies and records include what you enter. Browser storage is unencrypted. Checksums detect changes relative to the kept record; they do not authenticate the publisher or grant execution authority.
Exact implementation boundary
The embedded ResearchCore 1.0.2 owner is unchanged. SHA-256:
2bfcac9b188d56c91c4d98831e5bfae2dc2b06ed99632eb093d912eb1214d170The server instrument is the Cantos dedicated-capacity page from commit 7f0ec5a, embedded byte-identical except its saved-input slot. SHA-256 of the unchanged page:
960b7f25e6c408f53cf44e3453eff375c819bbe3dfd572258e4800692ec57f17Its charging calls the Cantos Compute engine, Git blob 21956dbce1449a3bbfdedfce3a5064e5b8d87495. The quick test and every kept server number are computed by that engine. There is no background worker, model call, telemetry, rental, automatic scoring or deployment.