Most lists of startup credits are written to be skimmed by a person who is already interested. They show a logo, a headline number, and a button. What they rarely show is the sentence on the vendor page that the number came from, the day someone read it, or what happens when the vendor changes the terms three weeks later.
That gap matters more now that software agents read these pages on their own. An agent that recommends a $15,000 credit has to know whether the offer is still open, whether the eligibility test can be evaluated from facts it can actually see, and whether the vendor will answer a machine at all.
Sourcey (sourcey.com) is an attempt to publish that information as a dataset rather than a landing page. Its startup credits registry describes itself as the open registry of startup programs, with every fact carrying the URL it came from and the date it was read. The design choice is worth examining because it changes what a reader, human or machine, can check without trusting the publisher.
What the registry contained on 20 October 2026
On 20 October 2026 the published release was sha256:1472317002b32cb99007465cbbc46040f1273cf238934bd6287dfca20916cc02, and the artifact digest for the dataset file was sha256:1c6801f9ac13adcd61b1d4402da0c8c46156d2b4d19924bc22968427bc111dd2. Those two strings are the point of the exercise. They let anyone confirm that the file they downloaded is the file the publisher signed, without a login and without a screenshot.
Inside that release, the registry listed 557 companies and 619 offers, of which 616 were marked active. The remaining three were split between ended and paused programs, which is itself a useful signal: a directory that never removes anything is not a record of the market, it is a record of the archive.
The offers are spread across 24 categories. Hosting and infrastructure is the largest with 84 companies, followed by AI and machine learning with 72, developer tools and authentication and security with 37 each, and data and analytics with 34. The categories are a rough map, not a taxonomy, but they explain why the registry reads the way it does: the density of credits is highest exactly where early teams spend their first money.
The number that matters more than the count
A count of offers is easy to inflate. The harder question is whether an offer's eligibility can be evaluated without a human reading a paragraph.
The statistics page answers that directly. Of the 616 active offers, 108, or 17.53 percent, express every recorded eligibility condition as a rule software can evaluate. The same page is careful about what that number does not mean. Missing company facts can still leave the answer unknown, and vendor approval remains a separate step even when the rule is fully structured.
That is an honest framing. A registry that claimed 100 percent machine-readable eligibility would be describing its own optimism, not the vendors' paperwork. The published figure says that roughly one in six offers is ready for an automated decision today, and the rest still need a person to interpret a page.
Fifty report cards on whether an agent can do the job
Eligibility is only one half of the problem. The other half is whether a service can be used by software at all: whether an agent can find the interface, get access, onboard, pay, confirm what happened, and keep the access alive.
Sourcey publishes agent readiness report cards for that question. On 20 October 2026 the page carried 50 report cards, each covering one job such as creating a repository, sending an email, or creating a database deployment. Each card records the date it was probed and grades the six steps on the publisher's published grading rules.
The distribution in that set was two A+ grades, seven A, sixteen B+, nineteen B, two C, three D, and one F. The GitHub REST API sat at A+ on a probe dated 12 October 2026. A failing grade is not a verdict on the product; it means the vendor's public documents did not give an agent enough to complete that specific job.
Why the packaging is the interesting part
The registry is published as one YAML file per company in a public repository, with a catalog verifier that checks the files before release. The data is licensed CC BY 4.0 and the code is MIT. The dataset file carries its own release identifier and artifact digest, and the repository documents how the files are validated.
That combination is what makes the registry usable in an agent pipeline. A model can fetch the dataset, check the digest, read a single company file, and quote the observation date next to the number. If the vendor changes the terms, the next release carries a different digest and the change is visible instead of silent.
None of this removes the need for judgment. A structured eligibility rule can still be wrong, and a B grade can still describe a service that works fine with a person in the loop. What the registry changes is the cost of checking. Instead of trusting a directory's summary, a reader can follow the provenance chain back to the vendor page and the day it was read.
For teams that compare startup programs, and for agents asked to recommend one, that is the difference between a claim and a citation.
Sources:
Startup credits registry, eligibility statistics, agent readiness report cards, the published dataset, and the source repository.ers are spread across 24 categories. Hosting and infrastructure is the largest with 84 companies, followed by AI and machine learning with 72, developer tools and authentication and security with 37 each, and data and analytics with 34. The categories are a rough map, not a taxonomy, but they explain why the registry reads the way it does: the density of credits is highest exactly where early teams spend their first money.
The number that matters more than the count
A count of offers is easy to inflate. The harder question is whether an offer's eligibility can be evaluated without a human reading a paragraph.
The statistics page answers that directly. Of the 616 active offers, 108, or 17.53 percent, express every recorded eligibility condition as a rule software can evaluate. The same page is careful about what that number does not mean. Missing company facts can still leave the answer unknown, and vendor approval remains a separate step even when the rule is fully structured.
That is an honest framing. A registry that claimed 100 percent machine-readable eligibility would be describing its own optimism, not the vendors' paperwork. The published figure says that roughly one in six offers is ready for an automated decision today, and the rest still need a person to interpret a page.
Fifty report cards on whether an agent can do the job
Eligibility is only one half of the problem. The other half is whether a service can be used by software at all: whether an agent can find the interface, get access, onboard, pay, confirm what happened, and keep the access alive.
Sourcey publishes agent readiness report cards for that question. On 20 October 2026 the page carried 50 report cards, each covering one job such as creating a repository, sending an email, or creating a database deployment. Each card records the date it was probed and grades the six steps on the publisher's published grading rules.
The distribution in that set was two A+ grades, seven A, sixteen B+, nineteen B, two C, three D, and one F. The GitHub REST API sat at A+ on a probe dated 12 October 2026. A failing grade is not a verdict on the product; it means the vendor's public documents did not give an agent enough to complete that specific job.
Why the packaging is the interesting part
The registry is published as one YAML file per company in a public repository, with a catalog verifier that checks the files before release. The data is licensed CC BY 4.0 and the code is MIT. The dataset file carries its own release identifier and artifact digest, and the repository documents how the files are validated.
That combination is what makes the registry usable in an agent pipeline. A model can fetch the dataset, check the digest, read a single company file, and quote the observation date next to the number. If the vendor changes the terms, the next release carries a different digest and the change is visible instead of silent.
None of this removes the need for judgment. A structured eligibility rule can still be wrong, and a B grade can still describe a service that works fine with a person in the loop. What the registry changes is the cost of checking. Instead of trusting a directory's summary, a reader can follow the provenance chain back to the vendor page and the day it was read.
For teams that compare startup programs, and for agents asked to recommend one, that is the difference between a claim and a citation.
Sources:
Startup credits registry, eligibility statistics, agent readiness report cards, the published dataset, and [the sou
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