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+
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
NVIDIA, Telecom Leaders Build AI Grids to Optimize Inference on Distributed Networks
Meta
Four MTIA Chips in Two Years: Scaling AI Experiences for Billions
Our latest investment in open source security for the AI era
Microsoft
Microsoft announces new solutions, infrastructure at NVIDIA GTC
OpenAI
From model to agent: Equipping the Responses API with a computer environment
Anthropic
Anthropic invests $100 million into the Claude Partner Network
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.
Qwen3.5-35B-A3B-Uncensored-HauhauCS-Aggressive momentum +4%
HauhauCS • Qwen3.5-35B-A3B-Uncensored-HauhauCS-Aggressive
Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled momentum +9%
Jackrong • Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled
s2-pro momentum +30%
fishaudio • s2-pro
OmniCoder-9B momentum +26%
Tesslate • OmniCoder-9B
xAI raised $6B (Series C)
Funding Radar • xAI
AI Out Of Control? Meta Reportedly Confirms Data Exposure Triggered By Internal Tool
Times Now News • AI News
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 ChecksBuilder Brief
Get the launches, benchmarks, pricing moves, and outages that matter before your app breaks.
Get AI intelligence briefs
Receive high-signal updates on companies, funding, and models.
Or stay in the loop on X
Follow @_MomentumTraderFunding 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
Apple
NVIDIA
Microsoft
Market data delayed. For informational purposes only.
Latest Research
View allUnified Spatio-Temporal Token Scoring for Efficient Video VLMs
Token pruning is essential for enhancing the computational efficiency of vision-language models (VLMs), particularly for video-based tasks where temporal redundancy is prevalent. Prior approaches typically prune tokens either (1) within the vision transformer (ViT) exclusively for unimodal perceptio...
arXivUniversal Skeleton Understanding via Differentiable Rendering and MLLMs
Multimodal large language models (MLLMs) exhibit strong visual-language reasoning, yet remain confined to their native modalities and cannot directly process structured, non-visual data such as human skeletons. Existing methods either compress skeleton dynamics into lossy feature vectors for text al...