Track what happened after the launch announcement: browse the source-linked AI feature availability tracker, including plan, region and platform scope. Kingy has not tested account-level access.
Verified AI Launch Intelligence
See what launched, what changed, and what it costs.
Track AI product launches, model updates, pricing changes and tested workflows. Each record separates official sources, company claims, Kingy testing and third-party evidence so you can see what is known and what remains unverified.
Editorial submissions and sponsor-fit reviews are separate. Payment does not influence Kingy scores, verdicts, rankings, evidence labels, or publication decisions.
Launch tracker
Browse the AI Launch Tracker
Search source-backed launch records, then use common filters first and advanced filters only when you need a narrower view.
Evaluation Cards
The EvalEval Coalition beta-launched Evaluation Cards, an open-source reader over a normalized evaluation warehouse with card-level context and four interpretive signals: reproducibility, completeness, provenance and comparability.
Evaluation Cards is a useful antidote to treating benchmark scores as self-explanatory because it foregrounds reporting gaps and provenance. Its signals still depend on…
Open WebUI v0.9.6 release
Open WebUI released v0.9.6 with knowledge-base sync, folder organization, filesystem tooling, permissions, and security-related fixes.
- Launch readiness
- 7.6 / 10
- Demo evidence
- Not scored yet
- Creator-story fit
- Not scored yet
Score definitions and rubric
These are launch-record readiness heuristics, not product ratings.
Launch readiness
How complete and reviewable the launch record is, not the quality of the product.
Inputs and weights: Launch date 15%; qualifying source 10%; what launched 10%; demo 15%; category 10%; audience 10%; editorial assessment 10%; traction evidence 10%; creator or audience fit 10%.
Evidence inputs: Reviewed launch metadata, public source links, demo links, taxonomy, audience, editorial notes, and recorded traction signals.
Demo evidence
Whether the record contains useful, reviewable demonstration evidence; it is not a rating of product output quality.
Inputs and weights: Working demo URL 45%; video walkthrough 25%; clear description of what launched 10%; audience 10%; editorial assessment 10%.
Evidence inputs: Demo and video URLs plus the reviewed launch description, audience, and editorial notes.
Creator-story fit
Whether a launch has enough demonstrable evidence and audience relevance for a useful creator story; it does not predict views or guarantee coverage.
Inputs and weights: Demo evidence 25%; visual creator category 15%; audience 15%; editorial assessment 15%; traction evidence 10%; pricing clarity 10%; API or open-weight evidence 10%.
Evidence inputs: Reviewed demo, category, audience, editorial, traction, pricing, API, and open-weight fields.
- Scale
- 0.0–10.0. A present qualifying input receives its published weight; a missing input receives zero. Scores are rounded to one decimal.
- Assigned by
- Suggested by the deterministic field-completeness helper and assigned or approved by a Kingy editorial reviewer.
- Rubric and check date
- Rubric version P0-2026-08-10. The record’s “Last verified” date is the score check date. Checked: 2026-06-24.
- Confidence and missing data
- Confidence depends on source completeness. “Not scored yet” means no reviewed value; “Needs review” means the value or score set failed validation.
- Freshness
- Recalculate after a material launch, source, demo, pricing, audience, or traction change and during the record freshness review.
- Disputes
- Use “Suggest a correction” on the record and cite the relevant evidence. Commercial relationships cannot buy or alter a score.
Open WebUI's v0.9.6 release adds knowledge-base sync, folder organization, filesystem tooling, permissions work, and security-related fixes, per the GitHub release (github.com/open-webui). Developers, Operators, and…
Cohere launches Command A+ as an open-source model for agentic enterprise workflows
Cohere released Command A+ as an Apache 2.0 open-source MoE model optimized for reasoning, multilingual, multimodal, RAG, and agentic workflows.
- Launch readiness
- 7.2 / 10
- Demo evidence
- Not scored yet
- Creator-story fit
- Not scored yet
Score definitions and rubric
These are launch-record readiness heuristics, not product ratings.
Launch readiness
How complete and reviewable the launch record is, not the quality of the product.
Inputs and weights: Launch date 15%; qualifying source 10%; what launched 10%; demo 15%; category 10%; audience 10%; editorial assessment 10%; traction evidence 10%; creator or audience fit 10%.
Evidence inputs: Reviewed launch metadata, public source links, demo links, taxonomy, audience, editorial notes, and recorded traction signals.
Demo evidence
Whether the record contains useful, reviewable demonstration evidence; it is not a rating of product output quality.
Inputs and weights: Working demo URL 45%; video walkthrough 25%; clear description of what launched 10%; audience 10%; editorial assessment 10%.
Evidence inputs: Demo and video URLs plus the reviewed launch description, audience, and editorial notes.
Creator-story fit
Whether a launch has enough demonstrable evidence and audience relevance for a useful creator story; it does not predict views or guarantee coverage.
Inputs and weights: Demo evidence 25%; visual creator category 15%; audience 15%; editorial assessment 15%; traction evidence 10%; pricing clarity 10%; API or open-weight evidence 10%.
Evidence inputs: Reviewed demo, category, audience, editorial, traction, pricing, API, and open-weight fields.
- Scale
- 0.0–10.0. A present qualifying input receives its published weight; a missing input receives zero. Scores are rounded to one decimal.
- Assigned by
- Suggested by the deterministic field-completeness helper and assigned or approved by a Kingy editorial reviewer.
- Rubric and check date
- Rubric version P0-2026-08-10. The record’s “Last verified” date is the score check date. Checked: 2026-06-08.
- Confidence and missing data
- Confidence depends on source completeness. “Not scored yet” means no reviewed value; “Needs review” means the value or score set failed validation.
- Freshness
- Recalculate after a material launch, source, demo, pricing, audience, or traction change and during the record freshness review.
- Disputes
- Use “Suggest a correction” on the record and cite the relevant evidence. Commercial relationships cannot buy or alter a score.
Cohere released Command A+ as an Apache 2.0 open-source MoE model built for reasoning, multilingual, multimodal, RAG, and agentic enterprise workflows (cohere.com). Developers, Enterprises,…
n8n AI agents guide and workflow launch coverage
n8n published its AI agents guide showing how to build and deploy agent workflows using n8n.
- Launch readiness
- 6.9 / 10
- Demo evidence
- Not scored yet
- Creator-story fit
- Not scored yet
Score definitions and rubric
These are launch-record readiness heuristics, not product ratings.
Launch readiness
How complete and reviewable the launch record is, not the quality of the product.
Inputs and weights: Launch date 15%; qualifying source 10%; what launched 10%; demo 15%; category 10%; audience 10%; editorial assessment 10%; traction evidence 10%; creator or audience fit 10%.
Evidence inputs: Reviewed launch metadata, public source links, demo links, taxonomy, audience, editorial notes, and recorded traction signals.
Demo evidence
Whether the record contains useful, reviewable demonstration evidence; it is not a rating of product output quality.
Inputs and weights: Working demo URL 45%; video walkthrough 25%; clear description of what launched 10%; audience 10%; editorial assessment 10%.
Evidence inputs: Demo and video URLs plus the reviewed launch description, audience, and editorial notes.
Creator-story fit
Whether a launch has enough demonstrable evidence and audience relevance for a useful creator story; it does not predict views or guarantee coverage.
Inputs and weights: Demo evidence 25%; visual creator category 15%; audience 15%; editorial assessment 15%; traction evidence 10%; pricing clarity 10%; API or open-weight evidence 10%.
Evidence inputs: Reviewed demo, category, audience, editorial, traction, pricing, API, and open-weight fields.
- Scale
- 0.0–10.0. A present qualifying input receives its published weight; a missing input receives zero. Scores are rounded to one decimal.
- Assigned by
- Suggested by the deterministic field-completeness helper and assigned or approved by a Kingy editorial reviewer.
- Rubric and check date
- Rubric version P0-2026-08-10. The record’s “Last verified” date is the score check date. Checked: 2026-06-24.
- Confidence and missing data
- Confidence depends on source completeness. “Not scored yet” means no reviewed value; “Needs review” means the value or score set failed validation.
- Freshness
- Recalculate after a material launch, source, demo, pricing, audience, or traction change and during the record freshness review.
- Disputes
- Use “Suggest a correction” on the record and cite the relevant evidence. Commercial relationships cannot buy or alter a score.
n8n published its AI agents guide, showing how to build and deploy agent workflows using the platform's existing open-source nodes and integrations (n8n.io). Automation…
Daily radar
Latest verified AI launches
A Radar edition publishes only when the day has enough source-ready signal; the tracker continues to show the newest verified records.
Daily AI Launch Radar: Mistral Large 4 weights, Copilot Fusion and Replit Cloud Agents
Mistral releases Large 4 weights, GitHub launches Copilot Fusion and Replit expands Cloud Agents. Access, pricing and license details.
No source-ready records are tagged for today yet, so this section is showing the latest public launch records available in the tracker.
Mistral Large 4 (Le Chonk) weights
Summary: Mistral made improved Large 4 weights downloadable on October 29, 2026, following its October 6 public preview. The repository documents text and image input, text output,…
Why it matters: Mistral Large 4 gives teams a documented choice between an API and downloadable weights. Evaluate the commercial-license restriction, infrastructure cost and your own task…
- Pricing
- Mistral's USD model documentation lists $0.70 input, $0.07 cached input and $2.10 output per million tokens. Self-hosting costs are separate.
- Verification
- Source-verified; 4 sources
GitHub Copilot Fusion
Summary: GitHub announced Copilot Fusion general availability on October 28, 2026, on all Copilot plans in VS Code, the Copilot app, Copilot CLI and JetBrains IDEs. Users…
Why it matters: GitHub Copilot Fusion gives existing Copilot users an orchestration option in supported clients. Confirm your plan’s models and effort settings, monitor credit use and…
- Pricing
- Each underlying model is billed at its standard rate with no separate Fusion charge. Official docs specify a 30% paid-plan model-cost discount through November 30, 2026. Multiple-model tasks can consume more credits.
- Verification
- Source-verified; 2 sources
Gemini 4 Carbon
Summary: Google announced Gemini 4 Carbon on October 28, 2026. Access starts with a subset of cyber defenders and is to expand gradually to trusted Fairwind partners.…
Why it matters: Gemini 4 Carbon is currently relevant to teams admitted to Google’s controlled cyber-defense rollout. Broader developer, business and consumer access is a future step.…
- Pricing
- Announced introductory API rates are US$2 per million input tokens and US$10 per million output tokens, with cached input 95% below the input rate. Google lists later rates of US$4 and US$20 after the introductory period, without a confirmed expiry here. These announced rates do not establish a generally callable public API.
- Verification
- Source-verified; 2 sources
Replit Cloud Agents for company codebases
Summary: Replit announced the company-codebase workflow on October 27, 2026; its article was updated October 29. The article says all Pro and Enterprise customers can use it.…
Why it matters: Replit Cloud Agents give eligible teams a workflow for proposing code changes through engineer review. Confirm plan access, usage terms and repository permissions before…
- Pricing
- Availability is verified for paid Pro and Enterprise plans. The reviewed public pricing page did not expose plan amounts or a Cloud-Agent-specific usage rate. Check current pricing and account terms; no unlimited or included-usage claim is made.
- Verification
- Source-verified; 2 sources
GPT-6.1 Terra
Summary: GPT-6.1 Terra launched on October 26, 2026. OpenAI lists access for Plus, Pro, Enterprise and Edu users in Work and Codex, and API model gpt-6.1-terra. It…
Why it matters: GPT-6.1 Terra can be evaluated on a bounded text-and-image workflow with approval before desktop or browser actions. Standard API rates are US$1 per million…
- Pricing
- Standard API rates are US$1 input, US$0.05 cached input and US$5 output per million tokens. API usage is separate from subscriptions. Cache writes cost US$1.25 for 30 minutes or US$2.50 for 12 hours per million tokens. Above 272K input tokens, the full request uses 2× input/cache and 1.5× output rates. Batch/Flex, regional processing and Fast mode have separate adjustments.
- Verification
- Source-verified; 3 sources
Claude Duet
Summary: Anthropic introduced Claude Duet on October 21, 2026. The initial research preview starts with US Pro and Max subscribers through a waitlist. Anthropic lists separate iOS,…
Why it matters: Claude Duet is a preview for US Pro and Max subscribers who receive access to evaluate proactive checks and delegated work across connected apps.…
- Pricing
- During the research preview, Duet is included with eligible Pro and Max plans and shares their usage limits. Background checks count toward those limits, and larger tasks can require more work. This is a paid-plan, waitlisted preview, not an unlimited or universally free offer.
- Verification
- Source-verified; 2 sources
Plan ahead
Upcoming Deadlines
Future-dated migration, shutdown, and retirement records are separated from today's launches and latest-record lists. These dates are upcoming operational events, not launches that have already happened.
OpenAI Agent Builder shutdown deadline: November 30, 2026
Summary: OpenAI announced the Agent Builder lifecycle deadline: the visual workflow builder is deprecated and scheduled to shut down on November 30, 2026. OpenAI documents two migration…
Why it matters: Export the workflow, select the Agents SDK or a suitable Workspace Agent, and test tools, state, permissions and guardrails before the November 30 shutdown.…
- Pricing
- Review replacement API costs or Workspace Agent plan requirements before migrating. This record does not estimate your workflow’s implementation or operating cost.
- Verification
- Source-verified; 3 sources
OpenAI reusable prompt objects and v1/prompts are scheduled to shut down November 30, 2026
Summary: OpenAI announced reusable-prompt deprecation on June 3, 2026. The documented November 30 shutdown covers reusable prompt objects and v1/prompts. Move managed prompt content into application-owned code…
Why it matters: Treat saved OpenAI prompt objects as a migration dependency before November 30. Move their content and variables into application code, then validate the generated…
- Pricing
- Pricing unknown
- Verification
- Source-verified; 5 sources
Three GPT Image API models are scheduled to retire December 1, 2026
Summary: OpenAI schedules gpt-image-1-mini, gpt-image-1.5 and chatgpt-image-latest for API removal on December 1, 2026. Its current deprecation table recommends gpt-image-2.5-sunburst or gpt-image-2.5-flare.
Why it matters: OpenAI schedules gpt-image-1-mini, gpt-image-1.5 and chatgpt-image-latest for API removal on December 1, 2026. Its current deprecation table recommends gpt-image-2.5-sunburst or gpt-image-2.5-flare. Inventory the three…
- Pricing
- Review the current price of the chosen replacement and measure representative jobs. This lifecycle record does not establish workload cost or promise a cost-neutral migration.
- Verification
- Source-verified; 5 sources
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Explore launch categories, methodology and participation Awards archive, category paths, methodology, submissions, sponsorship and newsletter
Awards archive
Launches of the Week is paused
Prior editions remain available as an archive. Kingy is not currently promising a new weekly awards edition.
Category navigation
Find AI launches by market, workflow, and source signal
The Kingy AI Referral Index
AI assistants sent 1.3279% of kingy.ai's own traffic. Published studies put the web-wide figure anywhere from under 1% to 25%. The index reconciles them, catalogued by method.
Methodology
How Kingy AI Scores Launches
Kingy AI scoring is designed for launch discovery, editorial review, creator planning, and buyer trust checks.
Kingy score
A directional editorial score for launch clarity, source quality, audience fit, pricing visibility, demo usefulness, and practical buyer or creator value.
Demo score
A signal for whether the product can be shown, tested, explained, or compared without relying on vague announcement copy.
YouTube potential
A creator-fit signal for demos, tutorials, before-and-after workflows, explainers, reviews, and audience-specific product education.
Source verification
Records distinguish verified, needs verification, and founder submitted status using official URLs, source links, last-verified dates, and correction paths.
Pricing clarity
Pricing signals favor launches with visible pricing, free-plan status, pricing pages, API access notes, or clear uncertainty when pricing is not public.
Use-case clarity
Launches are easier to evaluate when the audience, workflow, category, demo, and alternative comparison are obvious from public sources.
Founder and sponsor fit
Founder submissions and sponsor interest are routing signals only; editorial review still checks source quality, public claims, demo clarity, and usefulness.
Verification labels
Verified means enough public source evidence is present, needs verification means the record needs more checking, and founder submitted means the entry came through the submission path.
Scores are editorial signals, not scientific benchmarks, paid placements, or guarantees.
Founder submission
Submit Your AI Launch
Founders can send a launch for editorial review when the public source trail is ready to verify.
What to include
- Product name
- Website
- Launch date
- What launched
- Official source links
- Pricing page
- Demo/video
- Company info
- Category
- Public funding info
- Screenshots/media
Privacy note
Do not submit secrets, unreleased financials, private customer data, or regulated personal data.
Submit only public product, company, source, pricing, demo, and media details that Kingy AI can review without logging into private systems.
Sponsor path
Sponsor Kingy AI launch coverage
Launching an AI product that needs clear demos, creator education, and buyer trust? Sponsor a Kingy AI video or launch feature.