research

every post and study we have published, newest first. negative and incomplete results included.

27 of 27 write-ups, newest first

november 2026

  1. a 25 ms commit window for t3 code: 22% fewer sqlite commits on real agent turnst3 code commits once per streamed event. grouping plain provider updates for up to 25 ms cut commits per stored event by 22% on 156 real co…
  2. the free stack every contributor already has17 free llm lanes, used all at once: 149 small tasks in 1.4 s. a case study in speeding up open-source work.
  3. a chart is not a web pagesame model, same data: an openui board took 7.2× fewer output tokens and finished 2.5× sooner than an html page.

october 2026

  1. 100 Android chats: a send is not a captured replyAn operational run of the reusable uiautomator2 workflow, with dispatch, result capture, and verification kept separate.
  2. what we have sent to cua, and what is still only a fixturea living record of our trycua/cua work: merged fixes, open rfcs, and experiments that have not earned a claim yet.
  3. the guard passed. the gpu did not help.bend agreed with the oracle. the best cpu algorithm made the large-batch chart beside the point.
  4. the memory study, october updatethe real-source gate stayed closed, the handoff froze, and the replay caught a real error.
  5. offline fan-out is not a phone fleetthe git rollback was real. the screens and preview failures were synthetic.
  6. the proof passed. the work was still stranded.three statecharts, then a replay of the things our agents actually left behind.
  7. same model, four harnessesthe fast profile matters. so does the difference between an engine and its cli.
  8. perfect recall, stale firstthree native retrieval surfaces found the fixture. none put the new fact first.
  9. we predicted the next action. nobody committed it.the frozen baseline is a negative result. the pull-first replacement is still a proposal.
  10. an agent api that does not steal the foregroundthe bounded phone run worked. that says nothing about the quality of health advice.
  11. voice parity failed. the fixes still mattered.we scored the audio, kept the failures, and did not call a partial rerun a pass.
  12. the tap was fast. proving it landed cost more.we kept the lexical matcher and did not ship the on-phone model tier.
  13. studydoes optchat stay small after fifty tool calls?the transcript kept growing and the derived view stayed smaller. context bytes only, no answer-quality claim.
  14. studycan four agents remember the same thing?optmem foundations landed and optchat split off; the four-harness recall cohort is still partial.
  15. claude code savings, re-measured496 pre-registered runs against the current z0 integration: one lever holds everywhere, one depends on the task, one barely shows up.

september 2026

  1. the boundary worked. the model study did not run.deterministic integration evidence is useful. it is not an answer-quality result.
  2. where do claude code tokens actually go?one night of dogfooding z0intelligence inside claude code, measured with reported usage and checked outcomes.
  3. three small decisions, three pre-registered barsno small model beats the 8b at forming z0's own calls, and 'auto-merge on green' fails on our own history.
  4. four pre-registered bars, four missesa keyless gpu tier below its bar, a learned router that loses to a generic one, and an authority check that belongs on the tool call.
  5. a format check is not a faithfulness checka local worker passed our offload bar, then failed on faithfulness. the learning loop does not have the data yet.
  6. studyhow do you learn the frontier fast?the research trail behind z0intelligence: classifiers, calibration, routing, conditional compute and tiny specialists.
  7. studywhat actually deserves a model?we kept trying to make routing smarter. the useful part was learning which decisions should not be model problems.
  8. How z0evals Fits Into Zer0a federated evaluation layer built from the fixtures and contracts the rest of the zer0 ecosystem already owns.
  9. studyhow much of the llm do we actually need?phase 1b: which local model routes best, and why the compiler turned out to be the intelligence.