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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.

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 1–16 of 16 launches
AI Agents

Humalike social behavior plugin for Hermes Agent

The open plugin connects Hermes to Humalike’s behavioral APIs. It can decide when the agent should join a conversation, rewrite replies to match a group’s style, build a…

Source-verified Free: Yes API: Yes Open: Yes
Clear use caseBeginner-friendlyCreator-friendlyDeveloper-friendly

Humalike x Hermes addresses a concrete need: Agent quality in group settings depends on timing, restraint, and social context as well as answer quality.…

AI Agents

Creed portable Markdown context for AI agents

Creed launched a portable Markdown-based context file that gives supported AI agents a user-controlled record of preferences, projects, and working style.

Source-verified Free: Yes API: Unknown Open: Yes
Clear use caseBeginner-friendlyBusiness-friendlyDeveloper-friendly

Creed addresses a concrete need: Users increasingly move among several assistants, while the context each one learns usually stays trapped in a product. The…

AI Agents

Deck AI chief of staff with a delegated inbox

Deck launched an AI chief of staff with its own inbox, letting a user copy selected email threads, assign follow-up work, and schedule recurring routines without granting full…

Source-verified Free: Yes API: Unknown Open: Unknown
Clear use caseBeginner-friendlyBusiness-friendly

Deck addresses a concrete need: Giving an assistant its own inbox offers a narrower permission model than granting access to an entire mailbox, while…

AI Agents

Fuzzy AI relationship-first sales workspace

Fuzzy AI launched a relationship-first sales workspace that combines prospect research, LinkedIn engagement, personalized outreach, sequencing, and human-reviewed replies.

Source-verified Free: No API: Unknown Open: Unknown
Clear use caseBeginner-friendlyBusiness-friendly

Fuzzy AI addresses a concrete need: Fuzzy reflects a shift from high-volume AI prospecting toward systems that try to coordinate research, public engagement, and…

AI Agents

Genspark.ai Series B extension funding announcement

Genspark announced a $100 million Series B extension that it says brought the round to $485 million at a $2.6 billion post-money valuation, alongside a chief revenue officer…

Source-verified Free: Yes API: No Open: No
Clear use caseVideo demoBeginner-friendlyBusiness-friendly

The extension increases Genspark’s stated capacity to pursue enterprise distribution, but capital raised is not product validation. The funding, valuation, ARR, investor, model-count and…

AI Agents

OpenAI Partner Network

OpenAI launched a global program for firms that build, sell and deliver AI solutions, with Select, Advanced and Elite tiers, a public partner directory, planned specializations and a…

Source-verified Free: No API: No Open: No
Clear use caseBeginner-friendlyBusiness-friendly

The Partner Network gives enterprises a more structured way to discover OpenAI-focused delivery firms and gives partners clearer tiers and program infrastructure. A directory…

AI Agents

Replit Agent adds custom Shopify storefront creation

Replit added a Shopify workflow where users can design and launch a custom storefront by chatting with Replit Agent, including generating a front end, creating a Shopify store,…

Recheck due Free: Yes API: No Open: No
Clear use caseVideo demoBeginner-friendlyCreator-friendly
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: 2026-06-08.
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.

Replit added a Shopify workflow where users design and launch a custom storefront by chatting with Replit Agent — generating the front end, creating…

AI Agents

Replit SEO Agent launches to improve web and AI-search discoverability

Replit introduced SEO Agent, which scans published apps, identifies discoverability issues, and can apply fixes for web search and AI search visibility.

Recheck due Free: Yes API: No Open: No
Clear use caseVideo demoBeginner-friendlyBusiness-friendly
Launch readiness
6.4 / 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.

Replit introduced SEO Agent, which scans published apps, flags discoverability issues, and can apply fixes for web and AI search visibility (replit.com). Replit Builders…

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

Lindy Assistant launch

Lindy launched Lindy Assistant for inbox, calendar, meeting prep, meeting notes, and follow-up workflows.

Recheck due Free: Yes API: Yes Open: No
Clear use caseVideo demoBeginner-friendlyBusiness-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: 2026-06-24.
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.

Lindy launched Lindy Assistant, packaging inbox, calendar, meeting prep, notes, and follow-up workflows on top of its no-code agent platform (lindy.ai). Operators, Founders, and…

AI Agents

Manus joins Meta announcement

Manus announced it was joining Meta while continuing current services and working on more powerful general AI agent capabilities.

Recheck due Free: No API: No Open: No
Clear use caseVideo demoBeginner-friendlyCreator-friendly
Launch readiness
5.9 / 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.

Manus announced it is joining Meta, saying current services continue while it works on more powerful general-agent capabilities (manus.im). Operators, Founders, and Creators using…

AI Agents

Gumloop Agents launch

Gumloop announced that Gumloop Agents were coming out of beta as AI-powered reasoning engines that can use tools to solve open-ended tasks.

Recheck due Free: Yes API: Yes Open: No
Clear use caseVideo demoBeginner-friendlyBusiness-friendly
Launch readiness
7.0 / 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: 2026-06-24.
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.

Gumloop moved Gumloop Agents out of beta — reasoning engines that use tools to tackle open-ended tasks inside its no-code workflow builder (gumloop.com). Operators,…

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 Agents

Introducing ChatGPT agent

OpenAI introduced ChatGPT agent as a unified agentic system combining research, browser action, code execution, and connected app workflows.

Recheck due Free: No API: No Open: No
Clear use caseBeginner-friendlyBusiness-friendlyTraction signal

A key agent-era launch because it reframed ChatGPT as a task executor rather than only a chat interface.

AI Agents

Zapier AI Agents launch

Zapier introduced AI agents that can work across connected business apps and automate delegated tasks.

Recheck due Free: Yes API: Yes Open: No
Clear use caseVideo demoBeginner-friendlyBusiness-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: 2026-06-24.
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.

Zapier introduced AI agents that work across its connected business apps, automating delegated tasks through the integration network teams already use (zapier.com). Automation Teams,…

AI Agents

Relay.app AI Agent steps launch

Relay.app launched AI Agent steps, DALL-E integration, data formatting steps, and expanded integrations for workflow automation.

Recheck due Free: Yes API: No Open: No
Clear use caseVideo demoBeginner-friendlyBusiness-friendly
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: 2026-06-24.
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.

Relay.app shipped AI Agent steps alongside DALL-E integration, data formatting, and expanded integrations in its June 2024 product update (relay.app). Operators, Small Teams, and…

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 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
AI Security

Microsoft Project Perception

Summary: Microsoft announced Project Perception, an agentic security system that coordinates specialized red, blue, and green agents inside Microsoft Defender.

Why it matters: The launch brings attack simulation, defense testing, and threat-scenario planning into one Microsoft Defender workflow. Its practical effectiveness and cost remain unproven until security…

Pricing
Microsoft says Project Perception uses consumption-based, pay-as-you-go pricing measured in Security Compute Units (SCUs). Different agents consume SCUs at different rates; Microsoft did not publish numeric SCU rates in the checked launch sources.
Verification
Source-verified; 3 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.