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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.

Server-rendered fallback. Checking the live launch index…
Showing 1–18 of 26 launches
AI Coding Tools

GitHub Copilot Cost Centers AI Credit Pools

GitHub added AI credit pools to cost centers on July 2, 2026 so eligible enterprises can cap how much included Copilot AI credit usage a group draws from…

Recheck due Free: No API: Yes Open: No
Clear use caseBusiness-friendly
Launch readiness
6.8 / 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: 2026-07-09.
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.

GitHub added AI credit pools to cost centers, letting eligible enterprises cap how much included Copilot usage a group draws from the shared pool…

AI Agents

GitHub Copilot for Jira reaches GA: June 25, 2026 launch record

GitHub Copilot for Jira reached general availability, adding real-time Copilot cloud-agent progress inside Jira, post-session steering and simplified onboarding for connected GitHub repositories.

Recheck due Free: No API: Unknown Open: No
Clear use caseBusiness-friendlyDeveloper-friendly

The GA release closes useful workflow gaps by returning agent progress and follow-up control to Jira. The integration also moves ticket context into a…

AI Coding Tools

strictKnownMarketplaces for Copilot CLI and VS Code

GitHub launched public-preview strictKnownMarketplaces support in enterprise-managed settings for Copilot CLI and Visual Studio Code, restricting plugin installation to explicitly configured marketplace sources.

Recheck due Free: No API: No Open: No
Clear use caseBusiness-friendly

strictKnownMarketplaces for Copilot CLI and VS Code provides a useful fail-closed plugin-source boundary, including complete lockdown with an empty list. The public preview does…

AI Agents

OpenAI Ona Acquisition for Codex

OpenAI announced an agreement to acquire Ona, subject to customary closing conditions, with the stated intent to bring Ona’s secure, persistent, customer-controlled cloud execution and orchestration technology into…

Recheck due Free: No API: No Open: No
Clear use caseBusiness-friendlyDeveloper-friendly

Ona gives OpenAI a credible route toward persistent, governed execution for Codex, but the announcement is a transaction and product-direction statement, not proof that…

AI Agents

Vibe gets to work.

Mistral AI relaunched Le Chat as Vibe, a unified agent for long-horizon work and coding with Work Mode, Code Mode, VS Code support, CLI updates, and mobile access.

Recheck due Free: Yes API: Yes Open: No
Clear use caseVideo demoBusiness-friendlyDeveloper-friendly
Launch readiness
7.1 / 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.

Mistral relaunched Le Chat as Vibe, a unified agent for long-horizon work and coding with Work Mode, Code Mode, VS Code support, CLI updates,…

AI Agents

Introducing Claude Opus 4.8

Anthropic released Claude Opus 4.8 with improved coding, agentic task performance, professional work quality, effort controls, and Claude Code dynamic workflows.

Recheck due Free: No API: Yes Open: No
Clear use caseVideo demoBusiness-friendlyDeveloper-friendly
Launch readiness
7.9 / 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: 2026-06-08.
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.

Anthropic released Claude Opus 4.8 with improved coding, agentic task performance, effort controls, and Claude Code dynamic workflows, priced at $5 per million input…

AI Agents

Introducing GPT-5.5

OpenAI released GPT-5.5, a frontier model for agentic coding, computer use, knowledge work, and research workflows across ChatGPT, Codex, and the API.

Recheck due Free: No API: Yes Open: No
Clear use caseBusiness-friendlyDeveloper-friendlyTraction signal
Launch readiness
8.0 / 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.

OpenAI released GPT-5.5, a frontier model for agentic coding, computer use, and research workflows, shipping across ChatGPT, Codex, and the API with documented rates…

AI Agents

Claude Opus 4.7 launches as an Anthropic frontier model update for agent work

Anthropic released Claude Opus 4.7 as a frontier Claude update relevant to demanding coding, reasoning, and agentic tasks.

Recheck due Free: No API: Yes Open: No
Clear use caseBusiness-friendlyDeveloper-friendly

A frontier Claude release is relevant to the agent market because high-capability models determine what long-running agents can reliably complete.

AI Agents

Replit Agent 4 launches as a faster creative app-building agent

Replit introduced Agent 4 as its faster, more versatile app-building agent with creative workflows, design canvas, planning, parallel tasks, collaboration, and integrations.

Recheck due Free: Yes API: No Open: No
Clear use caseVideo demoBeginner-friendlyCreator-friendly

Agent 4 is important because it pushes Replit further from coding assistant toward agent-first app creation.

AI Agents

Claude Opus 4.5 launches as Anthropic’s frontier agentic model update

Anthropic released Claude Opus 4.5 as a frontier Claude model update with emphasis on advanced reasoning, coding, and agentic work.

Recheck due Free: No API: Yes Open: No
Clear use caseBusiness-friendlyDeveloper-friendly

A relevant model launch because the strongest Claude tier often becomes the default choice for demanding agent tasks.

AI Agents

Replit Agent 3 adds browser self-testing, longer autonomous runs, and agent generation

Replit launched Agent 3 with app testing in a real browser, autonomous work up to 200 minutes, and the ability to build agents and automations.

Recheck due Free: Yes API: No Open: No
Clear use caseVideo demoBeginner-friendlyBusiness-friendly

This was a meaningful autonomy jump because Replit paired generation with real browser self-testing and longer run time.

AI Coding Tools

Claude Opus 4.1

Anthropic released Claude Opus 4.1 as an upgrade to Opus 4 for agentic tasks, real-world coding, and reasoning.

Recheck due Free: No API: Yes Open: No
Clear use caseBusiness-friendlyDeveloper-friendlyTraction signal
Launch readiness
7.4 / 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.

Anthropic released Claude Opus 4.1 as an upgrade to Opus 4 for agentic tasks, real-world coding, and reasoning, at the same pricing as its…

AI Coding Tools

Cursor 1.0 with BugBot and Background Agent GA

Cursor shipped version 1.0 with BugBot for AI code review, Background Agent availability, memories, one-click MCP setup, Jupyter support, and broader AI-native IDE workflow upgrades.

Recheck due Free: Yes API: No Open: No
Clear use caseBeginner-friendlyBusiness-friendlyDeveloper-friendly

A priority AI coding record because Cursor 1.0 marked the shift from autocomplete to delegated development, review, memory, and agent workflows inside the IDE.

AI Coding Tools

Introducing Claude 4

Anthropic introduced Claude Opus 4 and Claude Sonnet 4, plus Claude Code general availability and new API capabilities for agents.

Recheck due Free: Yes API: Yes Open: No
Clear use caseBusiness-friendlyDeveloper-friendlyTraction signal

A top-tier launch for this hub because Claude 4 tied frontier models directly to developer-agent adoption.

AI Coding Tools

Build with Jules, your asynchronous coding agent

Google launched Jules in public beta as an asynchronous coding agent that reads code, plans tasks, makes changes, and integrates with GitHub workflows.

Recheck due Free: Yes API: No Open: No
Clear use caseVideo demoBeginner-friendlyBusiness-friendly

Important because Google entered the asynchronous coding-agent race with a GitHub-connected product.

AI Coding Tools

GitHub Copilot coding agent

GitHub introduced an asynchronous coding agent for GitHub Copilot, embedded in GitHub and accessible from VS Code.

Recheck due Free: No API: No Open: No
Clear use caseBusiness-friendlyDeveloper-friendlyTraction signal

Important because distribution inside GitHub may matter more than standalone agent novelty for enterprise adoption.

AI Coding Tools

Introducing Codex

OpenAI introduced Codex as a cloud-based software engineering agent that works on coding tasks in parallel in isolated environments.

Recheck due Free: No API: No Open: No
Clear use caseVideo demoBeginner-friendlyBusiness-friendly

A priority coding-agent record because Codex turned ChatGPT into an asynchronous code worker, not just an assistant.

AI Coding Tools

Updated Gemini 2.5 Pro for coding and web apps

Google released early access to an updated Gemini 2.5 Pro Preview focused on coding and building rich interactive web apps.

Recheck due Free: Yes API: Yes Open: No
Clear use caseBeginner-friendlyCreator-friendlyBusiness-friendly

Important for tracking the AI coding race because this update targeted web app generation directly.

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.

No source-ready records are tagged for today yet, so this section is showing the latest public launch records available in the tracker.

AI Open-Weight Models

Cloudflare Clef / Clef-flash

Summary: Cloudflare released Clef and Clef-flash, decision models that produce structured choices and probabilities. The family is hosted on Workers AI and distributed as Apache-2.0 weights for…

Why it matters: Clef and Clef-flash give developers hosted and local options for structured decisions. Compare them on your own decision tasks; Cloudflare’s benchmark results are vendor…

Pricing
Hosted inference is subject to Workers AI usage billing; open weights are distributed under Apache 2.0 and local inference requires compute. The announcement does not establish a universal free hosted plan or self-service fine-tuning price.
Verification
Source-verified; 1 source
AI Open-Weight Models

Strands Decider 2B

Summary: The Strands team released Decider 2B, a small decision model that chooses among defined outputs and assigns probabilities. The release includes model weights, code, data and…

Why it matters: Strands Decider 2B fits experiments that need a model to choose among defined options. It is not a general chat or coding model; evaluate…

Pricing
The release offers public code, weights and data for local experimentation. Running or training it requires local or rented compute; no hosted API price or hosted free plan is established by the announcement.
Verification
Source-verified; 3 sources
AI Agents

Pi Durable

Summary: Earendil released Pi Durable as a new experimental package for long-running agent applications. It extends the Pi ecosystem with durable task and conversation execution rather than…

Why it matters: Pi Durable offers builders a package to explore agents that survive interruptions and run across different surfaces. Keep evaluations scoped to the experimental release…

Pricing
The package is MIT licensed and available through npm. The announcement does not establish a hosted-service subscription or free inference; model and infrastructure usage can incur separate charges.
Verification
Source-verified; 3 sources
Foundation Models

Tavus Griffin / Griffin-Lite

Summary: Griffin and Griffin-Lite are presented as models for responsive audiovisual interaction in a restricted research preview. Access: Restricted research preview; not customer GA.

Why it matters: Griffin is research access to investigate. A customer deployment or a public price is not established.

Pricing
No separate event-specific price is stated in the reviewed release notice. Existing account, plan, usage or hardware terms may apply; check the official source before committing.
Verification
Source-verified; 2 sources
AI Image Tools

FLUX 3 Image

Summary: One API endpoint supports generation, local edits and up to ten reference images. Output reaches 4K with 15 aspect ratios; bounding boxes belong in the prompt.…

Why it matters: FLUX 3 Image offers several composition controls in one endpoint. Check them on your own reference set before replacing a production image workflow.

Pricing
$0.041 at 768sq, $0.048 at 1k, $0.100 at 1.5k and $0.607 at 4k per image in the release note. Resolution controls cost.
Verification
Source-verified; 2 sources
AI Research Tools

SynthID Bio

Summary: Google DeepMind released research on watermarking AI-generated biological sequences and structures, alongside public code and data. This record covers the research release. Access: Research release only;…

Why it matters: SynthID Bio gives researchers code and data to examine biological watermarking. Review the relevant model-weight terms before reuse; the release does not establish a…

Pricing
No hosted-service plan is established. Repository software uses Apache 2.0 and publication data uses CC-BY. ProteinMPNN parameters retain MIT terms; AlphaFold 3 parameters have separate terms. Research use is distinct from clinical use.
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.

AI Agents

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
AI Developer Tools

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
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

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.

Brief status

The Kingy Brief

Proof Lab 01 is available as a one-off pilot. Regular email sending remains paused. Read the pilot and archive, or sign up for future editions. No recurring schedule is promised.