buildOne report1 publisher Moving off three retiring OpenAI API models by April 2027 starts with finding every caller
OpenAI will remove gpt-5.3-codex, gpt-5.1 and gpt-5.4-nano from its API on April 1, 2027, according to a dev.to migration guide. The guide treats that date as the end of a staged move that starts by finding every caller, including the ones grep cannot see.
Reality
- Evidence45
- Adoption
- Not scored
- Hype gap0
- Incentives
- Not scored
- Confidence50
buildConfirmed6 publishers The lab says Claude leads 26% of its model research and collaborates on about 90%, with both tiers defined by how closely a human directs each task, and it wants rival labs publishing the same measure.
Perspective Coverage
6 publishers
- Builder
- Builder 39%
- Operator
- Operator 33%
- Investor
- Investor 28%
Reality
- Evidence50
- Adoption65
- Hype gap+30
- Incentives70
- Confidence60
OpenAI puts a GPT-5.4 reviewer where Codex used to stop and ask a human
Auto-review, shipped in Codex last week, hands escalation requests at the sandbox boundary to a separate GPT-5.4 Thinking call that approves about 99 percent of them and cuts human stops roughly 200-fold.
Publishers:alignment.openai.com
Reality
- Evidence38
- Adoption42
- Hype gap+22
- Incentives78
- Confidence55
California's transparency law makes a developer's own safety policy enforceable against it, and the Midas Project built its case entirely from OpenAI's Frontier Governance Framework and the system cards published after it.
Reality
- Evidence64
- Adoption45
- Hype gap+18
- Incentives70
- Confidence62
buildOne report1 publisher A memory layer beat CLAUDE.md by 22.2 points, and 54 of 72 test pairs never moved
Deterministic checkers replaced the judge model, and the project instruction file finished below having no instruction file at all. The measurable part of context engineering turns out to be narrow.
Reality
- Evidence44
- Adoption10
- Hype gap+26
- Incentives78
- Confidence40
GLM-5.3 Buys Buyers Time: Z.ai's Coding Model Cuts Tokens, Not the Closed-Model Lead
Z.ai says its 743B-parameter GLM-5.3 hits 34.5% on its own code bench using 22% fewer output tokens than GLM-5.2. The weights are still two weeks out.
Reality
- Evidence32
- Adoption18
- Hype gap+38
- Incentives76
- Confidence36