Skip to main content

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.

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 32 launches
AI Coding Tools

DeepSeek Harness developer preview

DeepSeek opened the DeepSeek Harness developer preview and published its source code on August 13, 2026. The official landing page and repository describe an agent harness built around…

Source-verified Free: Unknown API: Unknown Open: Yes
Creator coverageStrong demoClear use caseVideo demo
Launch readiness
9.1 / 10
Demo evidence
7.5 / 10
Creator-story fit
Medium
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: 2026-08-13.
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.

This is a substantive launch for developers because DeepSeek published both a runnable package and the source behind a composable agent runtime. Treat it…

AI Agents

DeepSeek V4-Flash-0731 launches in API beta with native Codex support

DeepSeek released DeepSeek-V4-Flash-0731 as the official V4-Flash API public beta. The checkpoint keeps the preview model’s architecture and size but adds new post-training, native Responses API support, and…

Source-verified Free: No API: Yes Open: Yes
Clear use caseVideo demoDeveloper-friendlyTraction signal

Worth testing for cost-sensitive coding-agent workloads because it combines native Codex support, a one-million-token context window, very low API pricing and MIT-licensed weights. Treat…

AI Coding Tools

GitHub Copilot CLI security review command

GitHub added a dedicated security review command to Copilot CLI.

Recheck due Free: Unknown API: Unknown Open: No
Clear use caseVideo demo
Launch readiness
6.7 / 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: 2026-06-12.
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 a dedicated security review command to Copilot CLI in its June 10, 2026 changelog (github.blog). Developers and Security-Minded Engineering Teams get an…

AI Agents

GitHub Copilot Chat agent-session search

GitHub updated Copilot Chat so it can search and query past Copilot cloud agent sessions in chat.

Recheck due Free: Unknown API: Unknown Open: No
Clear use caseVideo demo
Launch readiness
6.5 / 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: 2026-06-12.
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 updated Copilot Chat to search and query past Copilot cloud agent sessions, per the June 10, 2026 changelog (github.blog). Developers and Engineering Teams…

AI Coding Tools

Claude Fable 5 and Mythos-class model launch

Anthropic announced Claude Fable 5 alongside Claude Mythos 5 as a Mythos-class model release for hard knowledge work and coding tasks.

Recheck due Free: Unknown API: Yes Open: No
Clear use caseVideo demoTraction signal
Launch readiness
8.4 / 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: 2026-07-10.
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 announced Claude Fable 5 alongside Claude Mythos 5, a Mythos-class release for hard knowledge work and coding that drew same-day coverage from TechCrunch…

AI Coding Tools

North Mini Code open-source coding model

Cohere announced North Mini Code, a small open-source agentic coding model for developers and coding agents.

Recheck due Free: Yes API: Yes Open: Yes
Clear use caseVideo demoTraction signal
Launch readiness
7.7 / 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: 2026-06-12.
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 North Mini Code, a small open-weight agentic coding model with downloadable weights on Hugging Face and an available API (cohere.com). Developers, AI…

AI App Builders

ChatGPT Sites

OpenAI launched ChatGPT Sites in preview for eligible ChatGPT Business workspaces, allowing teams to create, host, refine, deploy, and share lightweight websites and web apps through ChatGPT and…

Recheck due Free: No API: No Open: No
Clear use caseVideo demoDeveloper-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.

OpenAI launched ChatGPT Sites in preview for eligible ChatGPT Business workspaces — teams create, host, refine, and share lightweight websites and web apps through…

AI Agents

OpenAI expands Codex for every role, tool, and workflow

OpenAI expanded Codex across roles, tools, and workflows, positioning it as a broader professional agent for software-adjacent and knowledge-work tasks.

Recheck due Free: No API: Yes Open: No
Clear use caseVideo demoCreator-friendlyDeveloper-friendly
Launch readiness
6.1 / 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.

OpenAI expanded Codex across roles, tools, and workflows, positioning it as a broader professional agent for software-adjacent knowledge work (openai.com). Developers, Product Managers, Data…

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

Kiro Web launches autonomous coding workflows from the browser

Kiro launched Kiro Web in preview, letting paid users start browser-based sessions where Kiro can write code, coordinate across repositories, and open pull requests.

Recheck due Free: No API: Yes Open: No
Clear use caseVideo demo
Launch readiness
6.6 / 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: 2026-06-08.
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.

AWS launched Kiro Web in preview, letting paid users start browser-based sessions where Kiro writes code, coordinates across repositories, and opens pull requests (kiro.dev).…

AI Agents

Cursor Composer 2.5 launches with better sustained long-running agent work

Cursor released Composer 2.5, describing it as a substantial improvement over Composer 2 for sustained long-running tasks, instruction following, and collaboration.

Recheck due Free: No API: No Open: No
Clear use caseVideo demo
Launch readiness
6.2 / 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.

Anysphere released Cursor Composer 2.5, calling it a substantial improvement over Composer 2 for sustained long-running tasks, instruction following, and collaboration (cursor.com). Cursor users…

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

Cursor Cloud Agents add computer use for testing and demos

Cursor updated Cloud Agents so they can use their own isolated computers to test changes, run software, and produce videos, screenshots, and logs for review.

Recheck due Free: No API: No Open: No
Clear use caseVideo demo

This was a meaningful coding-agent update because verification artifacts make cloud agents easier to trust and review.

AI Agents

Cognition launches Devin 2.2 with computer use, self-verification, and autofix

Cognition released Devin 2.2 with desktop computer use, end-to-end testing, self-verification, review autofix, faster startup, and a redesigned interface.

Recheck due Free: Yes API: No Open: No
Clear use caseVideo demo

This was a major Devin update because it tightened the full loop from code generation to computer-use testing and autofix.

AI Agents

OpenAI launches GPT-5.3-Codex-Spark for real-time coding in Codex

OpenAI released GPT-5.3-Codex-Spark, a smaller ultra-fast Codex model designed for real-time coding collaboration and low-latency edits.

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

Codex-Spark matters because speed changes how coding agents feel in interactive sessions.

AI Agents

OpenAI launches GPT-5.3-Codex for frontier agentic coding work

OpenAI introduced GPT-5.3-Codex, describing it as a more capable agentic coding model for Codex, long-running tasks, and broader professional computer work.

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

A major agentic coding model release because OpenAI positioned it as moving Codex from code generation toward broader computer work.

AI Agents

OpenAI releases the Codex app for managing multiple coding agents

OpenAI released the Codex app for macOS as a command center for running long-horizon and background coding-agent tasks, reviewing diffs, and using skills and automations.

Recheck due Free: No API: Yes Open: No
Clear use caseVideo demo

The Codex app is important because it makes multi-agent software work feel manageable from a dedicated desktop surface.

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 Coding Tools

DeepSeek Harness developer preview

Summary: DeepSeek opened the DeepSeek Harness developer preview and published its source code on August 13, 2026. The official landing page and repository describe an agent harness…

Why it matters: This is a substantive launch for developers because DeepSeek published both a runnable package and the source behind a composable agent runtime. Treat it…

Pricing
The source code is published under the MIT License and the documented local start path does not list a purchase price. The reviewed official sources do not advertise a paid hosted tier. Local use requires a compatible Node.js runtime and the user's own model or service costs may still apply.
Verification
Source-verified; 6 sources
AI Voice/Audio Tools

gamedai live AI sports radio

Summary: gamedai says its live AI sports-radio service went live on August 7, 2026 for the NFL preseason. The launch page describes coverage for all 16 Week…

Why it matters: gamedai is an interesting vertical-agent launch because it joins live data, retrieval, voice generation, and an evaluation layer around one narrow listener job. The…

Pricing
The official homepage lists a free Casual tier, Premium at $9.99 per month with a seven-day free trial, and a $99.99 one-time Lifetime unlock. The reviewed Join Now link returned a deployment-not-found page, so signup and billing availability were not independently confirmed.
Verification
Source-verified; 4 sources
AI Agents

Liquid AI LFM2.5-2.6B

Summary: Liquid AI released the base and post-trained LFM2.5-2.6B checkpoints on August 4, 2026. The post-trained text model targets agentic workloads and ships in native, GGUF, MLX,…

Why it matters: LFM2.5-2.6B is a credible option for high-volume, privacy-sensitive local agents where compact size and tool use matter more than frontier reasoning. Treat it as…

Pricing
The model weights are downloadable under Liquid AI's LFM1.0 license. Liquid AI did not announce a paid hosted API price for this release, and the reviewed Hugging Face page says no inference provider currently deploys the model. Self-hosting avoids a vendor per-token fee but still carries hardware, electricity, engineering, and support costs.
Verification
Source-verified; 5 sources
AI Agents

DeepSeek V4-Flash-0731 launches in API beta with native Codex support

Summary: DeepSeek released DeepSeek-V4-Flash-0731 as the official V4-Flash API public beta. The checkpoint keeps the preview model’s architecture and size but adds new post-training, native Responses API…

Why it matters: Worth testing for cost-sensitive coding-agent workloads because it combines native Codex support, a one-million-token context window, very low API pricing and MIT-licensed weights. Treat…

Pricing
$0.0028 per 1M cached input tokens; $0.14 per 1M uncached input tokens; $0.28 per 1M output tokens. DeepSeek says future peak-hour prices will be 2× regular rates, with no effective date announced.
Verification
Source-verified; 9 sources
AI Coding Tools

OpenAI releases Codex Security CLI and TypeScript SDK in limited beta

Summary: OpenAI published the Codex Security command-line client and TypeScript SDK. The CLI supports repository and change review, bulk scans, history, CI workflows, SARIF output and false-positive…

Why it matters: The CLI and SDK make Codex Security easier to insert into repeatable engineering workflows and expose useful integration primitives such as typed findings, SARIF,…

Pricing
OpenAI does not publish self-serve Codex Security pricing in the reviewed documentation. CLI and SDK access is limited to approved beta customers and partners through an OpenAI account team. Commercial terms, quotas and any Trusted Access for Cyber requirement are account-specific.
Verification
Source-verified; 6 sources
AI Developer Tools

Model Context Protocol 2026-07-28 specification goes final

Summary: The final Model Context Protocol 2026-07-28 specification became the current authoritative protocol version. It replaces connection-level state with self-contained requests, moves capability negotiation to each request…

Why it matters: The 2026-07-28 MCP specification makes a consequential architectural trade: stateless, self-contained requests can simplify scaling and recovery, but implementations must now negotiate and validate…

Pricing
MCP is an open protocol specification, not a paid hosted product. Implementations, infrastructure, models, connectors and support can carry separate costs. The official specification and governance pages are the authority for the protocol boundary.
Verification
Source-verified; 4 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 Developer Tools

Google Imagen 4 API shutdown window: August 17, 2026

Summary: Google announced that imagen-4.0-generate-001, imagen-4.0-ultra-generate-001 and imagen-4.0-fast-generate-001 are deprecated. Its deprecations table lists August 17, 2026 as the earliest possible shutdown date and recommends gemini-3.1-flash-image.

Why it matters: Plan against August 17 while preserving Google’s wording: the deprecations page says listed dates are the earliest possible shutdown dates and that exact timing…

Pricing
There is no separate migration fee. Replacement Gemini API image generation follows current Google pricing and quota terms; teams should compare the selected model, image output, prompts and workload mix on the live pricing page.
Verification
Source-verified; 5 sources
AI Agents

OpenAI Assistants API migration deadline: August 26, 2026

Summary: OpenAI’s Assistants API reaches its shutdown deadline on August 26, 2026. OpenAI recommends Responses API and Conversations API, with documented mappings from Assistants to configuration or…

Why it matters: Treat August 26 as an operational deadline, not a feature launch or a find-and-replace exercise. OpenAI documents object and orchestration changes, including explicit tool-loop…

Pricing
There is no separate migration fee. Replacement API usage is billed under current OpenAI API pricing, while engineering, regression testing, data migration, observability and rollback work remain implementation costs.
Verification
Source-verified; 6 sources
AI Developer Tools

Google retires Gemini Robotics ER 1.6 Preview on August 31

Summary: Google announced that gemini-robotics-er-1.6-preview will shut down on August 31, 2026, and its deprecation table names gemini-robotics-er-2-preview as the replacement.

Why it matters: Google announced that gemini-robotics-er-1.6-preview will shut down on August 31, 2026, and its deprecation table names gemini-robotics-er-2-preview as the replacement. Kingy’s action: Replace the…

Pricing
No pricing change announced
Verification
Source-verified; 2 sources

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

Source-checked AI launch and product intelligence. See the public archive for the latest edition and cadence.