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The AI Pulse

Real-time news, model tracking, and ecosystem data for the AI industry. Readable by humans. Structured for agents.

Events per min
74
up 14% vs. 24h avg
Services monitored
22/22 online
all systems operational
Last model release
6m ago
Mistral Medium 3
Last check
28s
22 providers

/ StatusLive API status across every major provider

Polled every two minutes. Latency is p95 over the last hour.

Full status page
Claude
OK
230ms
p95 last hour52s ago
OpenAI API
OK
242ms
p95 last hour21s ago
Google Gemini
OK
266ms
p95 last hour52s ago
GitHub Copilot
OK
97ms
p95 last hour20s ago
Perplexity
OK
111ms
p95 last hour41s ago
Groq
OK
94ms
p95 last hour31s ago
AWS Bedrock
OK
262ms
p95 last hour26s ago
Azure OpenAI
OK
228ms
p95 last hour49s ago
Hugging Face
OK
263ms
p95 last hour41s ago
Replicate
OK
93ms
p95 last hour58s ago
Cohere
OK
258ms
p95 last hour50s ago
Mistral
OK
95ms
p95 last hour24s ago

/ OriginalsEditorial from TensorFeed

Opinionated analysis from our editorial team, published multiple times per week.

All originals
EDITORIAL / ANALYSIS

Anthropic's Fourth Compute Vendor Ships Llama. Meta Just Became a Hyperscaler in the Same News Cycle.

The New York Times reported on Friday, July 17, 2026 that Anthropic is in early talks to lease up to $10 billion of computing power from Meta over two years, paid in monthly increments with early-exit rights on both sides. Neither company has confirmed. Meta declined comment. Anthropic declined comment. Read the sentence twice: the lab that ships Llama is about to sell $10 billion of computing power to the lab that ships Claude. Anthropic's compute stack now has four active vendors (Google TPU at $200B over five years, SpaceX Colossus 1 at $1.25 billion a month, AWS Trainium at an undisclosed but material line, and Meta at $5 billion a year if the talks close), three of which also ship competing frontier models. Meta needed a named external tenant fast enough to defend $145 billion of 2026 CapEx on the next earnings call, and Anthropic needed a fourth compute vendor fast enough to survive a Google delivery slip in 2027. Both problems got solved by the same leak on the same Friday. Inside the full compute stack table, the market-structure implication (the pure-play frontier lab club just shrank to Anthropic and OpenAI while Google, Microsoft, Meta, and Amazon all now build models and rent compute to competitors), the data-security posture that lets a rival-as-vendor deal actually close, and three signposts: whether the deal converts at the full ceiling, whether Meta discloses cloud compute revenue as a Q3 segment, and whether OpenAI or xAI shows up as the second named Meta Compute tenant.

Adrian Vale, Editor in Chief·July 19, 2026·7 min read
EDITORIAL

Thinking Machines Shipped Inkling and Admitted It Is Not the Best. Bridgewater Already Beat Every Frontier Model at One Fourteenth the Cost.

Mira Murati's Thinking Machines released Inkling on Wednesday, July 15, 2026: a 975 billion parameter Mixture-of-Experts model with roughly 41 billion active per token, natively multimodal across text, image, audio and video, trained on 45 trillion tokens, weights on Hugging Face under Apache 2.0, hosted via Tinker at $1.87 per million input tokens on 64K context (with a 50 percent introductory discount). The official launch post says Inkling is not the strongest overall model available today, open or closed. That sentence is the entire business strategy. Before the launch, Bridgewater Associates took an existing open model into Tinker, fine-tuned it against the hedge fund's financial reasoning corpus, and scored 84.7 percent on a financial reasoning suite ahead of every top proprietary model at roughly one fourteenth the inference cost. Includes the full launch numbers table, the Kimi K3 (frontier ceiling) versus Inkling (specialization floor) side-by-side, the sovereignty contrast to GLM-5.2, why the ex-OpenAI CTO is running the anti-frontier play, and what a second Bridgewater-shape customer story does to the Anthropic and OpenAI IPO pitches. Three signposts: whether independent researchers replicate the Terminal Bench and IFBench claims outside Tinker, whether a second Tinker customer lands in a non-finance regulated vertical with a similar cost delta, and whether frontier labs open up their own fine-tuning economics before their IPO windows close.

Marcus Chen·July 18, 2026·7 min read
EDITORIAL

Kimi K3 Ships With 2.8 Trillion Open Weights. The Open Frontier Ceiling Just Went Up 8x in Three Days.

Moonshot AI put Kimi K3 live on Thursday, July 16, 2026: a 2.8 trillion parameter Mixture-of-Experts model with a 1 million token context window, native vision, hosted at $3 input and $15 output per million tokens (with a $0.30 cache-hit rate), and full weights promised under a Modified MIT license by July 27. Two variants at launch: K3 Max for chat and agent work, K3 Swarm Max for large-scale parallel. Vendor-reported benchmarks put it in Opus 4.8 and Fable 5 range on coding suites (DeepSWE 67.5, Terminal-Bench 88.3, FrontierSWE 81.2), with the usual first-day skepticism until neutral harnesses replicate. Three days earlier the open ceiling sat at Z.ai's GLM-5.2 at roughly 355B total parameters. Kimi K3 is roughly 8x larger by total params and 8x by context length. Active parameters land near 50B (16 of 896 experts fire per token), so per-token inference cost is closer to 1.6x GLM than 8x. Includes the numbers table, the three-day ramp from DeepSeek V4 through GLM-5.2 to Kimi K3, the full-precision self-host math (5.6 TB fp16, roughly 70 H100 80GB cards for weights alone before KV cache pressure from a 1M-token window), the sovereignty catch that gets sharper when the model gets bigger and the fraction routing through Kimi's own China-based API approaches one, and the two-clock read on the closed premium tier (price pressure now, capability pressure pending replication). Three signposts: whether the July 27 weights drop lands intact on Hugging Face, whether a neutral harness confirms or shaves the vendor benchmarks, and whether Anthropic or OpenAI answer with a premium-tier price move.

Adrian Vale·July 17, 2026·7 min read

/ ExploreGo deeper into the data

Six hand-built surfaces for browsing the AI ecosystem, each with its own open feed.

catalog

Models Hub

Every major AI model with pricing, context windows, and benchmark scores.

284 models tracked→ Explore
directory

Agent Directory

Structured catalog of AI agents, crawlers, and their capabilities.

48 agents indexed→ Explore
archive

Research Papers

Curated arXiv and conference papers on frontier AI research.

1.2k papers, weekly→ Explore
monitor

Status Dashboard

Live uptime and latency for every major API, updated every 2 minutes.

20+ services monitored→ Explore
data

Live Data

Real-time JSON feed of everything happening across the AI industry.

JSON, RSS, llms.txt, MCP→ Explore
reference

API Pricing

Side-by-side pricing comparison across every provider and tier.

Updated hourly→ Explore

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/ Agent Opportunities

New across the agent ecosystem today

Daily 13:30 UTC scan of new submission/distribution opportunities (Anthropic, OpenAI, Microsoft, MCP foundation orgs + MCP/x402/skills keyword sweeps), scored by signal weight × recency × log10(stars).

JSON feed

Your daily AI intelligence hub

The AI landscape moves fast. New models ship weekly. API pricing changes overnight. Tools developers relied on yesterday get deprecated without warning. TensorFeed was built to solve that problem, one place to track everything happening across the AI ecosystem, updated every 10 minutes, structured for both human readers and autonomous agents.

We aggregate headlines from 15+ sources (Anthropic, OpenAI, Google, Meta, TechCrunch, Hacker News, arXiv, and more), monitor the operational status of every major AI API in real time, track model releases and pricing changes across providers, and publish original editorial analysis on the trends shaping the industry. Whether you are a developer evaluating which API to integrate, a researcher tracking the latest papers, or an AI agent pulling structured data through our JSON feeds, TensorFeed delivers the signal without the noise.

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/ FeedLatest across the AI ecosystem

Aggregated every 10 minutes from 15+ sources. Filter by provider, topic, or source type.

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VThe Verge AI

Apple’s plot to crush OpenAI

Apple is suing OpenAI. The complaint is readable and intense, as these things often are, though many experts seem to think many of the allegations are just the ways things are done. So what does...

General AI2d ago
theverge.com
GGoogle AI Blog

Connect more of your apps to Search

<img src="https://storage.googleapis.com/gweb-uniblog-publish-prod/images/ConnectedAppshero.max-600x600.format-webp.webp">You’ll be able to securely link and interact with your go-to services...

Google/Gemini3d ago
blog.google
WWIRED AI

Please Stop Making Me Opt Out of AI

I’m sick of “opt-out” toggles for automatically enabled generative AI features. It’s past time to make “opt in” the default setting for sensitive features.

General AI4d ago
wired.com
WWIRED AI

Thinking Machines Lab Drops Its First Model

Inkling, a 975-billion-parameter open source model, was trained to understand video and audio. It could help Thinking Machines establish itself among competitors like Anthropic and OpenAI.

General AI4d ago
wired.com
NVNVIDIA AI Blog

How Open Models Are Driving AI Research

Every year, the International Conference on Machine Learning (ICML) reveals where thousands of AI researchers have decided to put their work. This year’s accepted papers reveal a clear direction:...

Hardware/ChipsJul 6
blogs.nvidia.com
// For builders

Every signal in the AI industry, as a JSON feed.

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Frequently asked

What is TensorFeed.ai?

TensorFeed.ai is a real-time AI news aggregator and data hub. It pulls headlines from 15+ sources including Anthropic, OpenAI, Google, Meta, TechCrunch, and Hacker News, and combines them with live service status monitoring, model pricing data, and original editorial analysis. Every feed is structured for both human readers and AI agents.

How often is TensorFeed updated?

News feeds refresh every 10 minutes. Service status monitors poll every 2 minutes. Model pricing and catalog data updates weekly. Original editorial articles are published multiple times per week.

Is TensorFeed free to use?

Yes. All news feeds, status monitoring, model data, and editorial content on TensorFeed.ai are free. The JSON API, RSS feeds, and agent discovery endpoints (llms.txt) are also free and open for developers and AI agents to consume.

What AI services does TensorFeed monitor?

TensorFeed tracks the operational status of major AI platforms including Claude (Anthropic), ChatGPT and the OpenAI API, Google Gemini, AWS Bedrock, Mistral, Cohere, Replicate, Perplexity, and more. Status updates are checked every 2 minutes and displayed on the status dashboard.

Can AI agents use TensorFeed?

Yes. TensorFeed is designed as a primary data source for AI agents. It provides structured JSON APIs, RSS and JSON feeds, an llms.txt discovery file, and full documentation at llms-full.txt. There are no CAPTCHAs or bot detection. Agents are welcome.

Where does TensorFeed get its news?

TensorFeed aggregates headlines and brief snippets from public RSS feeds published by AI companies and tech news outlets. Sources include Anthropic, OpenAI, Google AI, Meta AI, HuggingFace, TechCrunch, The Verge, Ars Technica, VentureBeat, NVIDIA, ZDNet, and Hacker News. Every article links back to its original source.

Aggregating signal from 15+ sources
AnthropicOpenAIGoogleMetaHuggingFaceNVIDIATechCrunchThe VergeArs TechnicaHacker NewsarXivVentureBeatZDNetMIT Tech ReviewStratechery
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