Ship AI changes with proof, not vibes
A2ZAI Checks runs evals on your repo: a PR scorecard plus a public benchmark card you can drop in READMEs and launch posts. Same site: builder radar for model launches, API shifts, pricing, and outages—so you know when to re-run.
Builder signals
5
Funding tracked
30
Models watched
5
Agents spotted
0+
Top builder-critical changes
What could break your stack this week
- highMuse-Glimmer-30B momentum +30%Meta
- highMiniMax-H3 momentum +30%MiniMaxAI
- highMiniMax-H3-Turbo-Lora momentum +30%larryvrh
- highDeepSeek-V4-Flash-0731 momentum +3%deepseek-ai
- highLTX-2.5 momentum +30%Lightricks
Sourced from the same live signal pipeline as River. Pin your evals with Checks.
Is your AI stack at risk?
Pick your providers, see critical changes in 10 seconds. Free, no signup, shareable.
Score my prompt
Compare two models on your prompt and get a shareable benchmark URL.
Live River
Quick bytes on launches, deprecations, pricing moves, benchmarks, and breakage risk.
A2ZAI Checks
GitHub-native evals that turn prompt and agent changes into PR scorecards.
Builder Stack
Discover agents today, with MCPs, plugins, SDKs, and technical products rolling in next.
Capital Radar
Funding rounds, acquisitions, and investor moves with builder-first context.
Best places to start
Choose an artifact, not a browse path
The strongest A2ZAI surfaces now end in a public object you can use, share, or route teammates into.
Utility artifact
Run Checks and publish a benchmark card
Best for shipping teams who want a PR scorecard, public benchmark artifact, and shareable proof of improvement.
Open ChecksSignal artifact
Open the live river and jump into quick bytes
Best for scanning launches, deprecations, pricing shifts, and benchmark moves, then opening the strongest byte pages.
Open Live RiverDestination artifact
Use company pages as operating dashboards
Best for sending someone one link that combines official posts, live intel, quick bytes, and capital context.
Explore company pagesCompany Spotlight
NVIDIA
Why Scaling AI Compute Performance Requires a New Power Architecture
Meta
Reimagining Independence: How Meta’s AI Models Are Helping the University of Pittsburgh Transform Assistive Robotics
Evolve your marketing with new AI tools
Microsoft
Advance Zero Trust for AI: New tools and guidance to secure AI agents and DevSecOps
OpenAI
From model to agent: Equipping the Responses API with a computer environment
Anthropic
Improving Fable 5's biology safeguards
What changed for builders
Use this stream to spot the change, then move into a stronger destination page: river for the wider feed, company pages for operator context, and Checks for a public benchmark artifact.
Muse-Glimmer-30B momentum +30%
meta-models • Muse-Glimmer-30B
MiniMax-H3 momentum +30%
MiniMaxAI • MiniMax-H3
Protiviti Member Firm for India and KIIT University partner to launch AI Center of Excellence to build India’s Future AI Workforce
Fm Live • AI News
MiniMax-H3-Turbo-Lora momentum +30%
larryvrh • MiniMax-H3-Turbo-Lora
DeepSeek-V4-Flash-0731 momentum +3%
deepseek-ai • DeepSeek-V4-Flash-0731
xAI raised $6B (Series C)
Funding Radar • xAI
Databricks raised $10B (Series J)
Funding Radar • Databricks
Perplexity raised $500M (Series B)
Funding Radar • Perplexity
A2ZAI Checks
Catch prompt and agent regressions before merge, then turn the result into a shareable benchmark card.
Explore Checks5 Things in AI Today
A fast daily read on the biggest AI stories, tools, launches, demos, and deals.
5 Things in AI Today
The biggest AI stories, tools, launches, demos, and deals in a quick daily read.
Or stay in the loop
Funding Radar
View allxAI
$6BSeries C • Foundation Models
Databricks
$10BSeries J • AI Infrastructure
Perplexity
$500MSeries B • AI Applications
Physical Intelligence
$400MSeries A • Robotics
Chosen builder wedge
A2ZAI Checks is the utility layer on top of builder radar
The site stays useful as launch radar and discovery, but the product edge is a shareable scorecard builders can produce every time they ship. Supporting surfaces like learn still exist with 15 lessons and 126+ terms, but they are now secondary to shipping workflows.
Viral Artifact
GitHub PR scorecard
A2ZAI Checks
Prompt regression check for `support-agent.yaml`
Quality
+8.4%
Latency
+220ms
Cost
-31%
Passing: `refund-policy`, `invoice-lookup`, `cancel-subscription`
Regressed: `edge-case-promotions` on `gpt-4.1-mini`
Recommendation: merge after fixing one retrieval prompt and rerunning the pack.
Public Card
Benchmark card
Repo benchmark
support-agent / checkout-recovery
Best model route
Claude Sonnet + GPT-4.1-mini fallback
Win summary
12% better success at 29% lower cost
This is the artifact that spreads on X, GitHub, and founder launches: a benchmark card builders can link to when they ship.
AI Stock Pulse
Meta
Microsoft
Apple
NVIDIA
Amazon
Market data delayed. For informational purposes only.
Latest Research
View allAdvFD: Boosting Visual Generation via Adversarial Fr'echet Distance Loss
Fréchet distance has recently emerged as an effective distribution-level objective for generator post-training, complementing the conventional sample-level diffusion and flow-matching losses. However, directly optimizing Fréchet objectives can cause Fréchet hacking. The target metrics keep improving...
arXivSurgical WAM: A World-Action Model for Data-Efficient Surgical Robot Learning
Learning reliable surgical manipulation policies is bottlenecked by the scarcity of action-labeled demonstrations: teleoperated surgical robot (e.g., dVRK) trajectories with synchronized kinematics are costly to collect, while surgical tasks demand precise contact handling, long-horizon reasoning, a...