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Recommendations

Engineering cost recommendations with evidence and math

A dashboard shows how much was spent. Nythrex recommendations tell you where money produced no result and what to do about it. Every recommendation is the result of a named rule over your records: with evidence, arithmetic and a check of whether the fix worked.

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Recommendations7

AIAlready spent15 sessions

AI sessions with no commit$37.00

5 of 15 finished AI sessions produced no commit — half of the AI spend for the period.

How it was calculated

  • A session counts as finished after a few hours without activity.
  • Small sessions below the threshold are not counted.
  • This is an upper bound: research and reading code land here too.

What you can do. Check whether long sessions end in a commit and whether the hook that signs commits with the session is installed.

The rules are real; the examples are demo data.

Rules that already work

Recommendations are grouped by area: AI, CI, cloud and process. Below are the rules from the example above, with what each of them counts.

Nythrex recommendation rules
RuleAreaWhat it counts
AI sessions with no commitAIA session counts as finished after a few hours without activity.
Expensive model on helper requestsAIThe same tokens re-priced at the next model tier down.
Context written to cache againAIA rewrite is a large cache write after a pause longer than the cache lifetime.
Several workflows per pushCIThe most expensive workflow of each commit is treated as necessary.
Failed and repeated runsCIFailed means a failure or timed_out conclusion.
Cloud spend with no ownerCloudThe total across subscriptions and resource groups not assigned to any project.
Abandoned workProcessA unit is abandoned if its pull request was closed without a merge or has had no activity for a long time.

How every recommendation is built

  • Records

    Links to the specific sessions, runs or resource groups the conclusion is based on.

  • Arithmetic

    How the amount was obtained: which records were counted, which were left out and why it is an upper bound, if it is one.

  • Basis

    “Already spent” or “estimate” — so that the potential is never confused with the actual.

  • Next step

    What exactly can change: a setting, a process, or a check of whether the resources are still needed.

What recommendations do not do

  • they do not promise savings — a potential amount is never added to what was saved;
  • they do not rate people or look for someone to blame — the rules look at process and settings;
  • they do not invent conclusions from too small a sample;
  • they need nothing to be run — they fire on their own after every data refresh.
The first recommendations appear as soon as the rules have enough records after you connect your sources. Until then the product shows how many more are needed.

Frequently asked questions

Are the recommendations generated by a language model?

No. Every recommendation is the result of a deterministic rule: the same records always give the same recommendations. Plain-language wording is optional, and the model receives only the facts of one recommendation — no records and no code.

What if there is too little data?

The rule stays silent and says how many more records it needs. Nythrex does not draw a confident conclusion from three data points.

How do I check that a recommendation worked?

Mark it as fixed. After at least a week a before-and-after comparison on the same records appears.

Can I get recommendations by email?

Yes, a weekly digest — optional, with one-click unsubscribe in every email.

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