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.
GitHub Issues adds agent automation controls
GitHub added public-preview approval, confidence, and rationale controls for agent-driven changes to issue labels, fields, types, status, and assignees.
Agent automation controls in GitHub Issues addresses a concrete need: The controls make issue automation more observable and reviewable, but GitHub explicitly says approvals…
OpenAI Presence managed enterprise agents
Each deployment begins with a defined job and limited access to the knowledge and systems required for that job. Companies set policies, approved actions, and escalation conditions. Simulations…
OpenAI Presence addresses a concrete need: The launch packages agent design, deployment controls, operational evaluation, and post-launch improvement into one managed enterprise offering rather…
Rex governed agents for order-to-cash work
Rex launched a set of governed AI agents for collections, customer-portal work, accounts-receivable inboxes, cash application, and dispute handling.
Rex addresses a concrete need: Order-to-cash work crosses inboxes, finance systems, and customer portals, making it a useful test of whether agentic software can…
Lunen enterprise agent orchestration layer
Lunen.ai launched an early-access enterprise orchestration layer for defining agents in plain language and governing their tools, data access, approvals, schedules, and audit trails.
Lunen.ai addresses a concrete need: Enterprises experimenting with agents need controls at the action layer, not only prompt guidelines. The main limitation is this:…
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.
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…
Replay QA autonomous web application testing loop
Replay QA launched an autonomous web-app testing loop that explores a URL or connected repository, records browser execution, finds bugs, and returns root-cause analysis with suggested fixes.
Replay QA addresses a concrete need: AI-assisted development has shortened build cycles without eliminating QA work; Replay QA packages browser execution evidence and debugging…
Nautis AI-native operating system for startups
Nautis launched an AI-native operating system that brings a startup's planning, fundraising, finance, contacts, documents, hiring, and operating context into one workspace.
Nautis addresses a concrete need: Startup operations are often fragmented across documents, spreadsheets, CRMs, and chat tools; Nautis is a current example of products…
QApilot CoWork
QApilot introduced CoWork for converting manual mobile test cases into executable iOS, Android, and Flutter automation with AI planning and human approval.
- 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: 2026-07-24.
- 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.
QApilot introduced CoWork, converting manual mobile test cases into executable iOS, Android, and Flutter automation with AI planning and human approval (qapilot.io). Mobile QA…
BrowserAct
BrowserAct launched a browser-automation platform spanning an agent CLI and hosted workflow product, with isolated browser sessions, reusable profiles, proxy options, network evidence and human takeover for compatible…
BrowserAct’s launch combines useful session isolation, agent commands and human takeover, but stealth browsers, proxies and reusable profiles increase both capability and misuse risk.…
Hang Ten Systems Seed funding announcement
Hang Ten Systems announced $32 million in Seed funding.
- Launch readiness
- 5.1 / 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.
Hang Ten Systems raised a $32 million seed round led by Mayfield to help enterprises adopt AI, according to its BusinessWire announcement (businesswire.com). AI…
Gemini Enterprise Workflow Agents
Google made Gemini Enterprise Workflow Agents generally available with an allowlist, enabling authorized users to create, import, update and run triggered sequences that mix AI automation, connected actions…
- 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: 2026-07-28.
- 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.
Gemini Enterprise Workflow Agents offer a more governable structure than an improvised multi-step chat, but GA does not mean open access: the Cloud project…
ChatGPT Scheduled Tasks
OpenAI added a dedicated Scheduled page and improved one-time, recurring, and monitoring tasks in ChatGPT.
- 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: 2026-07-10.
- 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.
OpenAI added a dedicated Scheduled page and improved one-time, recurring, and monitoring tasks in ChatGPT, documented on its help-center pages (help.openai.com). ChatGPT Users who…
Microsoft Copilot Cowork
Microsoft made Copilot Cowork generally available worldwide inside Microsoft 365 Copilot for complex, multi-step work.
- Launch readiness
- 7.8 / 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: 2026-07-10.
- 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.
Microsoft made Copilot Cowork generally available worldwide inside Microsoft 365 Copilot, moving the assistant toward delegated multi-step work across documents, communications, and organizational context…
GitHub Agentic Workflows
GitHub released Agentic Workflows in public preview, compiling natural-language Markdown into standard GitHub Actions workflows that run coding agents for issue triage, CI analysis, documentation and other repository…
- 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: 2026-07-28.
- 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 Agentic Workflows offers credible defense in depth through read-only defaults, sandboxing, a network firewall, safe outputs, compile-time validation and threat scanning. Those controls…
Microsoft Scout launches as an always-on personal Autopilot agent
Microsoft introduced Scout, its first Autopilot agent, designed to stay active in the background, connect across Microsoft 365 apps, and help coordinate work under enterprise controls.
- 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: 2026-06-08.
- 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.
Microsoft introduced Scout, its first Autopilot agent — designed to stay active in the background, connect across Microsoft 365 apps, and coordinate work under…
CoSupport AI 2.0 adds decision logs and action-capable support workflows
CoSupport says version 2.0 replaces its earlier single-pass retrieval flow with multi-step search and validation. The update adds per-reply decision logs, an Agentic API for actions in connected…
Watch. CoSupport AI 2.0 is a substantive operations-focused update, particularly for teams that need decision visibility and actions through Shopify or Stripe. Its performance,…
Google introduces Gemini Spark as a 24/7 personal AI agent
Google introduced Gemini Spark as a 24/7 personal AI agent alongside a more agentic Gemini app, Daily Brief, and other I/O 2026 Gemini updates.
- 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: 2026-07-10.
- 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.
Google introduced Gemini Spark as a 24/7 personal AI agent alongside a more agentic Gemini app and Daily Brief from I/O 2026 (blog.google). Consumers…
Microsoft makes Copilot’s agentic capabilities in Word, Excel, and PowerPoint generally available
Microsoft announced general availability of agentic Copilot capabilities in Word, Excel, and PowerPoint, enabling multi-step, app-native actions inside documents, spreadsheets, and presentations.
This is a major mainstream agent launch because it brings agentic behavior into Office apps used by billions of people.
Daily radar
Latest verified AI launches
A Radar edition publishes only when the day has enough source-ready signal; the tracker continues to show the newest verified records.
Daily AI Launch Radar: August 12, 2026 — Quiet Launch Window
Daily Kingy AI Launch Radar for 2026-08-12: a source-checked quiet-day edition explaining what did not clear the publication bar and why.
No source-ready records are tagged for today yet, so this section is showing the latest public launch records available in the tracker.
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
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
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
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
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
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.
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
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
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
Publishing
The Kingy Brief
Source-checked AI launch and product intelligence. See the public archive for the latest edition and cadence.
Awards archive
Launches of the Week is paused
Prior editions remain available as an archive. Kingy is not currently promising a new weekly awards edition.
Category navigation
Find AI launches by market, workflow, and source signal
The Kingy AI Referral Index
AI assistants sent 1.3279% of kingy.ai's own traffic. Published studies put the web-wide figure anywhere from under 1% to 25%. The index reconciles them, catalogued by method.
Methodology
How Kingy AI Scores Launches
Kingy AI scoring is designed for launch discovery, editorial review, creator planning, and buyer trust checks.
Kingy score
A directional editorial score for launch clarity, source quality, audience fit, pricing visibility, demo usefulness, and practical buyer or creator value.
Demo score
A signal for whether the product can be shown, tested, explained, or compared without relying on vague announcement copy.
YouTube potential
A creator-fit signal for demos, tutorials, before-and-after workflows, explainers, reviews, and audience-specific product education.
Source verification
Records distinguish verified, needs verification, and founder submitted status using official URLs, source links, last-verified dates, and correction paths.
Pricing clarity
Pricing signals favor launches with visible pricing, free-plan status, pricing pages, API access notes, or clear uncertainty when pricing is not public.
Use-case clarity
Launches are easier to evaluate when the audience, workflow, category, demo, and alternative comparison are obvious from public sources.
Founder and sponsor fit
Founder submissions and sponsor interest are routing signals only; editorial review still checks source quality, public claims, demo clarity, and usefulness.
Verification labels
Verified means enough public source evidence is present, needs verification means the record needs more checking, and founder submitted means the entry came through the submission path.
Scores are editorial signals, not scientific benchmarks, paid placements, or guarantees.
Founder submission
Submit Your AI Launch
Founders can send a launch for editorial review when the public source trail is ready to verify.
What to include
- Product name
- Website
- Launch date
- What launched
- Official source links
- Pricing page
- Demo/video
- Company info
- Category
- Public funding info
- Screenshots/media
Privacy note
Do not submit secrets, unreleased financials, private customer data, or regulated personal data.
Submit only public product, company, source, pricing, demo, and media details that Kingy AI can review without logging into private systems.
Sponsor path
Sponsor Kingy AI launch coverage
Launching an AI product that needs clear demos, creator education, and buyer trust? Sponsor a Kingy AI video or launch feature.