AI Product Launches — Weekly Tracker
Weekly curated snapshot of AI products that actually launched: new models, agents, devtools and AI features that became generally available, went live in an API/app/store, or were open-sourced in the trailing 7-day window. Each row records the exact announcement URL, a deterministic category and pricing signal, and an evidence score that rewards independent corroboration and recency. Method: agent-curated — each weekly sweep searches public AI news digests and product-coverage pages for launches in the trailing 7-day window (inclusion: GA/live/open-sourced; excluded: announced-only, waitlists, previews without user access, future GA dates, planned launches, models still training). No keyless programmatic launch API exists for this domain (GDELT refused traffic from this environment on 2026-09-24; news sites prohibit scraping), so collection is manual and each snapshot is ingested through the hub store/publish path with provenance 'agent-curated' plus the run timestamp. Units: one row per launched AI product per week. Columns: ISO week, sweep timestamp, deterministic launch id (canonical company slug + product slug), product and canonical company name, company country (ISO alpha-3 + name, empty when the coverage does not state it), deterministic keyword category (model, agent, image-generation, video-generation, audio, robotics, devtools, productivity, marketing, ecommerce, security, legal, other), deterministic pricing signal (free, freemium, paid, enterprise, unknown), launch date, one-line agent-written summary (<=220 chars), exact announcement URL, count and comma-joined list of coverage URLs, launch_score = 100 * (1 + ln(1 + coverage_count)) * exp(-age_days / 7), and launch_rank (1 = strongest signal) by score desc, tie-broken by launch_date desc then launch_id asc. Primary key: (week, launch_id). Cadence: weekly. Nullability: company country fields are empty when not stated in coverage; everything else is always populated. Caveats: curated from public coverage, so unpublicized launches may be missing and launch dates reflect the first public launch report seen; category and pricing_signal are deterministic keyword rules on the summary, not editorial judgments; coverage is English-language press plus vendor announcements. Sample use: order by launch_rank for the week's strongest launch signals, or filter category = 'model'.
- Source
- AI Product Launch Signals (agent-curated)
- Rows
- 28
- Columns
- 16
- Source cadence
- Weekly
- Last refreshed
- Sep 25, 2026
- Theme
- technology
| Column | Type | Description |
|---|---|---|
| week | string | ISO week label of the sweep (unit: YYYY-'W'WW) |
| fetched_at | string | UTC timestamp of the sweep that produced the row (unit: ISO-8601) |
| launch_id | string | Deterministic id: canonical company slug + product slug |
| product_name | string | Launched product name as reported |
| company | string | Canonical company name |
| company_country_alpha3 | string | Company HQ country, ISO 3166-1 alpha-3 ('' if not stated in coverage) |
| company_country_name | string | Company HQ country name ('' if not stated in coverage) |
| category | string | Deterministic keyword category (unit: model|agent|image-generation|video-generation|audio|robotics|devtools|productivity|marketing|ecommerce|security|legal|other) |
| pricing_signal | string | Deterministic pricing signal from the summary (unit: free|freemium|paid|enterprise|unknown) |
| launch_date | string | Date the product launched (unit: YYYY-MM-DD) |
| summary | string | One-line agent-written summary, <=220 chars |
| announcement_url | string | Exact primary source URL for the launch (unit: URL) |
| coverage_count | integer | Number of independent coverage URLs seen (unit: count) |
| coverage_urls | string | Comma-joined list of every coverage URL (unit: URLs) |
| launch_score | float | Evidence score: 100*(1+ln(1+coverage))*exp(-age_days/7) (unit: 0-~300) |
| launch_rank | integer | Rank within the week by launch_score (1 = strongest) (unit: rank) |
First 10 sample rows — a preview, not the complete dataset.
| week | fetched_at | launch_id | product_name | company | company_country_alpha3 | company_country_name | category | pricing_signal | launch_date | summary | announcement_url | coverage_count | coverage_urls | launch_score | launch_rank |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2026-W39 | 2026-09-24T00:00:00+00:00 | amazon-amazon-seller-workflows | Amazon seller workflows | Amazon | USA | United States | ecommerce | free | 2026-09-23 | Free agentic AI service for third-party sellers: continuous workflows that watch ratings and prices, with seller-controlled data sharing. | https://dailyaiblog.com/today-in-ai-september-23-2026-cheaper-models-amazon-seller-agents-muse/ | 2 | https://dailyaiblog.com/today-in-ai-september-23-2026-cheaper-models-amazon-seller-agents-muse/, https://aiagentstore.ai/ai-agent-news/this-week | 181.92 | 1 |
| 2026-W39 | 2026-09-24T00:00:00+00:00 | ando-ando | Ando | Ando | — | — | agent | paid | 2026-09-24 | AI-native team chat that gives AI agents real identities and inboxes as first-class members; came out of stealth with a $20M raise. | https://aiagentstore.ai/ai-agent-news/this-week | 1 | https://aiagentstore.ai/ai-agent-news/this-week | 169.31 | 2 |
| 2026-W39 | 2026-09-24T00:00:00+00:00 | noibu-noibu-ai-agents | Noibu AI agents | Noibu | — | — | ecommerce | unknown | 2026-09-24 | Six AI agents that diagnose ecommerce site issues, propose fixes, deploy approved changes and measure impact across conversion and performance. | https://aiagentstore.ai/ai-agent-news/this-week | 1 | https://aiagentstore.ai/ai-agent-news/this-week | 169.31 | 3 |
| 2026-W39 | 2026-09-24T00:00:00+00:00 | anthropic-claude-opus-5-5 | Claude Opus 5.5 | Anthropic | USA | United States | model | paid | 2026-09-22 | Flagship model at Fable 5.1-level performance for 40% lower running cost: $4/$20 per M tokens, 30% faster output, live on the API and major clouds. | https://www.macrumors.com/2026/09/22/anthropic-claude-opus-5-5/ | 2 | https://www.macrumors.com/2026/09/22/anthropic-claude-opus-5-5/, https://techstartups.com/2026/09/23/anthropic-launches-claude-opus-5-5-claims-it-beats-openais-gpt-5-6-sol-at-one-third-the-cost/ | 157.71 | 4 |
| 2026-W39 | 2026-09-24T00:00:00+00:00 | openai-gpt-6-luna | GPT-6 Luna | OpenAI | USA | United States | model | paid | 2026-09-22 | Fast, cheap GPT-6 model for high-volume routine tasks at $0.10/$0.50 per M tokens, about half the price of its GPT-5.6 predecessor. | https://www.metirai.com/blog/openai-gpt-6-sol-luna-release-benchmarks-pricing-2026 | 2 | https://www.metirai.com/blog/openai-gpt-6-sol-luna-release-benchmarks-pricing-2026, https://aistockwire.com/blog/ai-price-war-anthropic-opus-5-5-openai-gpt-6-sol-luna-september-2026 | 157.71 | 5 |
| 2026-W39 | 2026-09-24T00:00:00+00:00 | openai-gpt-6-sol | GPT-6 Sol | OpenAI | USA | United States | model | paid | 2026-09-22 | Lower-cost GPT-6 model for coding and multi-step reasoning with 1.05M-token context; $2/$10 per M tokens via API and rolling out in ChatGPT Work and Codex. | https://www.metirai.com/blog/openai-gpt-6-sol-luna-release-benchmarks-pricing-2026 | 2 | https://www.metirai.com/blog/openai-gpt-6-sol-luna-release-benchmarks-pricing-2026, https://kingy.ai/news/2026-09-22-ai-launch-radar/ | 157.71 | 6 |
| 2026-W39 | 2026-09-24T00:00:00+00:00 | tencent-hy-image-3-5-preview | Hy Image 3.5 Preview | Tencent | CHN | China | image-generation | freemium | 2026-09-22 | Image-generation model preview with text-to-image and multi-turn conversational editing, free on Design Agent Miora until Oct 7; integrated into Yuanbao. | https://www.morningstar.com/news/dow-jones/20260922684/tencent-shares-jump-on-ai-image-model-launch | 2 | https://www.morningstar.com/news/dow-jones/20260922684/tencent-shares-jump-on-ai-image-model-launch, https://www.technotime.net/17050 | 157.71 | 7 |
| 2026-W39 | 2026-09-24T00:00:00+00:00 | alibaba-qwen-audio-3-1 | Qwen Audio-3.1 | Alibaba | CHN | China | audio | paid | 2026-09-23 | Five-model audio stack (ASR, TTS, Realtime, TTS-Next, ASR-Next) with published list prices, e.g. $0.15/$0.47 ASR and $6.40 audio-in for Realtime-Plus. | https://www.theneuron.ai/digest/everything-that-happened-in-ai-today-wednesday-september-23-2026/ | 1 | https://www.theneuron.ai/digest/everything-that-happened-in-ai-today-wednesday-september-23-2026/ | 146.78 | 8 |
| 2026-W39 | 2026-09-24T00:00:00+00:00 | black-forest-labs-flux-3-action | FLUX 3 Action | Black Forest Labs | — | — | robotics | free | 2026-09-23 | Open-weight 7B world-action model for robot control that predicts 32 robot actions plus future frames; weights and training code published. | https://www.theneuron.ai/digest/everything-that-happened-in-ai-today-wednesday-september-23-2026/ | 1 | https://www.theneuron.ai/digest/everything-that-happened-in-ai-today-wednesday-september-23-2026/ | 146.78 | 9 |
| 2026-W39 | 2026-09-24T00:00:00+00:00 | cnvs-cnvs | CNVS | CNVS | — | — | devtools | paid | 2026-09-23 | Native Swift macOS canvas for voice-directing Claude, Cursor and Codex in parallel terminals; one-time $199 pricing. | https://www.theneuron.ai/digest/everything-that-happened-in-ai-today-wednesday-september-23-2026/ | 1 | https://www.theneuron.ai/digest/everything-that-happened-in-ai-today-wednesday-september-23-2026/ | 146.78 | 10 |
Profiled Sep 25, 2026 from snapshot 20260925T050803Z-766fe6da18f7
Measured- Completeness
- 100%
- Rows
- 28
- Columns
- 16
- Columns with gaps
- 0
| Column | Missing | Distinct | Range | Distribution |
|---|---|---|---|---|
| weekvarchar | 0% | 1 | — |
|
| fetched_atvarchar | 0% | 1 | — |
|
| launch_idvarchar | 0% | 28 | — |
|
| product_namevarchar | 0% | 30 | — |
|
| companyvarchar | 0% | 24 | — |
|
| company_country_alpha3varchar | 0% | 5 | — |
|
| company_country_namevarchar | 0% | 5 | — |
|
| categoryvarchar | 0% | 14 | — |
|
| pricing_signalvarchar | 0% | 4 | — |
|
| launch_datevarchar | 0% | 6 | — |
|
| summaryvarchar | 0% | 28 | — |
|
| announcement_urlvarchar | 0% | 17 | — |
|
| coverage_countbigint | 0% | 2 | 1 → 2median 1 | |
| coverage_urlsvarchar | 0% | 20 | — |
|
| launch_scoredouble | 0% | 10 | 62.29 → 181.92median 146.78 | 2 outside 1st–99th percentile |
| launch_rankbigint | 0% | 27 | 1 → 28median 14.5 | 2 outside 1st–99th percentile |
- Current
20260925T050803Z-766fe6da18f7 · sha256 766fe6da18f7…
28 rows · first snapshot
Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).
curl "https://datazimuts.com/v1/datasets/ai_product_launch_signals/ai_product_launches" | jq '{title, rows, columns_count, license}'import requests
ds = requests.get("https://datazimuts.com/v1/datasets/ai_product_launch_signals/ai_product_launches").json()
print(ds["title"], ds["rows"], "rows")
# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
print(row)API endpoint: https://datazimuts.com/v1/datasets/ai_product_launch_signals/ai_product_launches
Tip: fetch /llms.txt for the full machine-readable catalog.
Where this data comes from and what was made from it. Other people's work shows as counts; only shared projects are named.
Cite this snapshot
Pinned to snapshot 20260925T050803Z-766fe6da18f7 and its content hash, so readers get exactly the data you used.
AI Product Launch Signals (agent-curated). (2026). AI Product Launches — Weekly Tracker [Data set, snapshot 20260925T050803Z-766fe6da18f7, sha256 766fe6da18f7]. Datazimuts. Retrieved 2026-09-25, from https://datazimuts.com/en/datasets/ai_product_launch_signals/ai_product_launches?snapshot=20260925T050803Z-766fe6da18f7
@misc{dz_ai_product_launch_signals_ai_product_lau_766fe6da,
title = {{AI Product Launches — Weekly Tracker}},
author = {{AI Product Launch Signals (agent-curated)}},
year = {2026},
publisher = {Datazimuts},
howpublished = {\url{https://datazimuts.com/en/datasets/ai_product_launch_signals/ai_product_launches?snapshot=20260925T050803Z-766fe6da18f7}},
note = {Snapshot 20260925T050803Z-766fe6da18f7, sha256 766fe6da18f7469438ff4d58b05a8d12a9fb9915c8b0b2d9aa3ebcbda81e2c85; accessed 2026-09-25}
}Embed a table or a chart
Paste this into any page. The embed is pinned to the same snapshot, follows the reader's light or dark setting, and always shows the source, license and a link back.
<iframe src="https://datazimuts.com/embed/chart?dataset=ai_product_launch_signals%2Fai_product_launches&lang=en&theme=auto&snapshot=20260925T050803Z-766fe6da18f7&x=launch_date&y=coverage_count&agg=avg" title="AI Product Launches — Weekly Tracker" width="100%" height="380" style="border:0" loading="lazy"></iframe>
Ask about this dataset. Answers come only from its catalog record, measured profile and change history, and list the facts they used.