Track what happened after the launch announcement: browse the source-linked AI feature availability tracker, including plan, region and platform scope. Kingy has not tested account-level access.
Verified AI Launch Intelligence
See what launched, what changed, and what it costs.
Track AI product launches, model updates, pricing changes and tested workflows. Each record separates official sources, company claims, Kingy testing and third-party evidence so you can see what is known and what remains unverified.
Editorial submissions and sponsor-fit reviews are separate. Payment does not influence Kingy scores, verdicts, rankings, evidence labels, or publication decisions.
Launch tracker
Browse the AI Launch Tracker
Search source-backed launch records, then use common filters first and advanced filters only when you need a narrower view.
GitHub Mobile Copilot cloud agent merge-conflict fix
In the latest iOS and Android production builds, GitHub Mobile can prefill a pull-request comment asking Copilot cloud agent to resolve merge conflicts; the user reviews and submits…
The mobile shortcut is useful for starting work, not for proving that a conflict was resolved correctly. GitHub prepopulates a request from the pull-request…
GitHub Copilot in VS Code June 2026 releases
GitHub summarized Copilot changes across Visual Studio Code 1.123 through 1.127, including integrated browser interaction, parallel agent sessions, chat organization, AI-credit visibility, model-provider discovery and Autopilot behavior.
The June roundup shows VS Code becoming an orchestration surface for multiple agents, browser checks and model choices rather than a single coding chat.…
Codex as an agent provider in GitHub Copilot for JetBrains
GitHub added Codex as an optional public-preview agent provider in GitHub Copilot for JetBrains IDEs and shipped related agentic enhancements including hooks support, richer MCP server management and…
Provider choice inside JetBrains can reduce tool switching, but Codex, Claude and Copilot modes should not be treated as equivalent harnesses. The release is…
LeRobot v0.6.0
Hugging Face released LeRobot v0.6.0 with world-model policies, a reward-model API, six new simulation benchmark integrations, a deployment rollout CLI, DAgger-style human correction, richer dataset tooling, FSDP and…
LeRobot v0.6.0 connects evaluation, deployment, intervention data and retraining more coherently than earlier releases, and its source material is unusually detailed. Breadth is also…
Copilot usage metrics review-cycle fields
On July 7, 2026, GitHub added avg_pull_requests_minutes_to_review and avg_pull_requests_review_cycles fields to the totals_by_ai_adoption_phase breakdown in Copilot usage metrics.
- 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: 2026-07-08.
- 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.
GitHub added average-minutes-to-review and review-cycle fields to the adoption-phase breakdown in Copilot usage metrics (github.blog). AI Platform Teams and Engineering Leaders measuring whether Copilot…
GitHub Copilot Agent Session Streaming
GitHub launched a public preview that lets eligible GitHub Enterprise Cloud owners stream Copilot agent session records across supported clients to an audit destination or retrieve the previous…
GitHub Copilot agent session streaming provides a valuable evidence feed across clients, but it is a public preview with narrow tenant eligibility, a 48-hour…
Genkit Agents API
Google introduced the Genkit Agents API in preview for TypeScript and Go, with a shared chat interface, streaming, server- or client-managed state, snapshots, human interrupts, detached tasks and…
- Launch readiness
- 7.6 / 10
- Demo evidence
- Not scored yet
- Creator-story fit
- Not scored yet
Score definitions and rubric
These are launch-record readiness heuristics, not product ratings.
Launch readiness
How complete and reviewable the launch record is, not the quality of the product.
Inputs and weights: Launch date 15%; qualifying source 10%; what launched 10%; demo 15%; category 10%; audience 10%; editorial assessment 10%; traction evidence 10%; creator or audience fit 10%.
Evidence inputs: Reviewed launch metadata, public source links, demo links, taxonomy, audience, editorial notes, and recorded traction signals.
Demo evidence
Whether the record contains useful, reviewable demonstration evidence; it is not a rating of product output quality.
Inputs and weights: Working demo URL 45%; video walkthrough 25%; clear description of what launched 10%; audience 10%; editorial assessment 10%.
Evidence inputs: Demo and video URLs plus the reviewed launch description, audience, and editorial notes.
Creator-story fit
Whether a launch has enough demonstrable evidence and audience relevance for a useful creator story; it does not predict views or guarantee coverage.
Inputs and weights: Demo evidence 25%; visual creator category 15%; audience 15%; editorial assessment 15%; traction evidence 10%; pricing clarity 10%; API or open-weight evidence 10%.
Evidence inputs: Reviewed demo, category, audience, editorial, traction, pricing, API, and open-weight fields.
- Scale
- 0.0–10.0. A present qualifying input receives its published weight; a missing input receives zero. Scores are rounded to one decimal.
- Assigned by
- Suggested by the deterministic field-completeness helper and assigned or approved by a Kingy editorial reviewer.
- Rubric and check date
- Rubric version P0-2026-08-10. The record’s “Last verified” date is the score check date. Checked: 2026-07-28.
- 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.
Genkit Agents API removes repeated full-stack agent plumbing while leaving teams in control of runtime and state ownership. The preview can introduce breaking changes…
GitHub Desktop 3.6 Copilot Integration
GitHub released Desktop 3.6 with Git worktrees, Copilot SDK-based commit authoring, repository instruction support, model selection, bring-your-own-key options and AI-assisted merge-conflict explanations and suggestions.
Desktop 3.6 combines three practical improvements—worktrees, better commit assistance and conflict help—without removing the Git review boundary. Worktrees can still share resources, and a…
strictKnownMarketplaces for Copilot CLI and VS Code
GitHub launched public-preview strictKnownMarketplaces support in enterprise-managed settings for Copilot CLI and Visual Studio Code, restricting plugin installation to explicitly configured marketplace sources.
strictKnownMarketplaces for Copilot CLI and VS Code provides a useful fail-closed plugin-source boundary, including complete lockdown with an empty list. The public preview does…
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.…
Google Ads API v24.2 AI Transparency Features
Google announced Google Ads API v24.2 on June 24, 2026, including AI Transparency: Synthetic Content Labeling.
- 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: 2026-06-25.
- 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 announced Google Ads API v24.2 on June 24, 2026, adding AI Transparency synthetic-content labeling with release notes, samples, and updated client libraries (developers.google.com).…
Gemini 3.5 Flash Computer Use
Google launched public preview support for the Computer Use tool in Gemini 3.5 Flash on June 24, 2026.
- Launch readiness
- 7.6 / 10
- Demo evidence
- Not scored yet
- Creator-story fit
- Not scored yet
Score definitions and rubric
These are launch-record readiness heuristics, not product ratings.
Launch readiness
How complete and reviewable the launch record is, not the quality of the product.
Inputs and weights: Launch date 15%; qualifying source 10%; what launched 10%; demo 15%; category 10%; audience 10%; editorial assessment 10%; traction evidence 10%; creator or audience fit 10%.
Evidence inputs: Reviewed launch metadata, public source links, demo links, taxonomy, audience, editorial notes, and recorded traction signals.
Demo evidence
Whether the record contains useful, reviewable demonstration evidence; it is not a rating of product output quality.
Inputs and weights: Working demo URL 45%; video walkthrough 25%; clear description of what launched 10%; audience 10%; editorial assessment 10%.
Evidence inputs: Demo and video URLs plus the reviewed launch description, audience, and editorial notes.
Creator-story fit
Whether a launch has enough demonstrable evidence and audience relevance for a useful creator story; it does not predict views or guarantee coverage.
Inputs and weights: Demo evidence 25%; visual creator category 15%; audience 15%; editorial assessment 15%; traction evidence 10%; pricing clarity 10%; API or open-weight evidence 10%.
Evidence inputs: Reviewed demo, category, audience, editorial, traction, pricing, API, and open-weight fields.
- Scale
- 0.0–10.0. A present qualifying input receives its published weight; a missing input receives zero. Scores are rounded to one decimal.
- Assigned by
- Suggested by the deterministic field-completeness helper and assigned or approved by a Kingy editorial reviewer.
- Rubric and check date
- Rubric version P0-2026-08-10. The record’s “Last verified” date is the score check date. Checked: 2026-06-25.
- 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 shipped public-preview Computer Use support in Gemini 3.5 Flash on June 24, 2026, with dedicated API docs and a live browser demo (ai.google.dev).…
Latitude V2 Agent Monitoring
Latitude launched V2 for AI-agent monitoring, combining production traces, session search, behavior-pattern signals, recurring-failure discovery, alerts and a coding-agent handoff supplied with issue context.
- Launch readiness
- 7.6 / 10
- Demo evidence
- Not scored yet
- Creator-story fit
- Not scored yet
Score definitions and rubric
These are launch-record readiness heuristics, not product ratings.
Launch readiness
How complete and reviewable the launch record is, not the quality of the product.
Inputs and weights: Launch date 15%; qualifying source 10%; what launched 10%; demo 15%; category 10%; audience 10%; editorial assessment 10%; traction evidence 10%; creator or audience fit 10%.
Evidence inputs: Reviewed launch metadata, public source links, demo links, taxonomy, audience, editorial notes, and recorded traction signals.
Demo evidence
Whether the record contains useful, reviewable demonstration evidence; it is not a rating of product output quality.
Inputs and weights: Working demo URL 45%; video walkthrough 25%; clear description of what launched 10%; audience 10%; editorial assessment 10%.
Evidence inputs: Demo and video URLs plus the reviewed launch description, audience, and editorial notes.
Creator-story fit
Whether a launch has enough demonstrable evidence and audience relevance for a useful creator story; it does not predict views or guarantee coverage.
Inputs and weights: Demo evidence 25%; visual creator category 15%; audience 15%; editorial assessment 15%; traction evidence 10%; pricing clarity 10%; API or open-weight evidence 10%.
Evidence inputs: Reviewed demo, category, audience, editorial, traction, pricing, API, and open-weight fields.
- Scale
- 0.0–10.0. A present qualifying input receives its published weight; a missing input receives zero. Scores are rounded to one decimal.
- Assigned by
- Suggested by the deterministic field-completeness helper and assigned or approved by a Kingy editorial reviewer.
- Rubric and check date
- Rubric version P0-2026-08-10. The record’s “Last verified” date is the score check date. Checked: 2026-07-28.
- 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.
Latitude V2 creates a useful closed loop from production traces to recurring-failure signals and a proposed repair. Kingy did not connect telemetry, measure cluster…
AgentX Agent Evaluation Framework
AgentX launched an AI-agent evaluation workflow for building test suites, tracing failures, comparing models on quality, cost, and latency, and suggesting fixes before production deployment.
- Launch readiness
- 7.2 / 10
- Demo evidence
- Not scored yet
- Creator-story fit
- Not scored yet
Score definitions and rubric
These are launch-record readiness heuristics, not product ratings.
Launch readiness
How complete and reviewable the launch record is, not the quality of the product.
Inputs and weights: Launch date 15%; qualifying source 10%; what launched 10%; demo 15%; category 10%; audience 10%; editorial assessment 10%; traction evidence 10%; creator or audience fit 10%.
Evidence inputs: Reviewed launch metadata, public source links, demo links, taxonomy, audience, editorial notes, and recorded traction signals.
Demo evidence
Whether the record contains useful, reviewable demonstration evidence; it is not a rating of product output quality.
Inputs and weights: Working demo URL 45%; video walkthrough 25%; clear description of what launched 10%; audience 10%; editorial assessment 10%.
Evidence inputs: Demo and video URLs plus the reviewed launch description, audience, and editorial notes.
Creator-story fit
Whether a launch has enough demonstrable evidence and audience relevance for a useful creator story; it does not predict views or guarantee coverage.
Inputs and weights: Demo evidence 25%; visual creator category 15%; audience 15%; editorial assessment 15%; traction evidence 10%; pricing clarity 10%; API or open-weight evidence 10%.
Evidence inputs: Reviewed demo, category, audience, editorial, traction, pricing, API, and open-weight fields.
- Scale
- 0.0–10.0. A present qualifying input receives its published weight; a missing input receives zero. Scores are rounded to one decimal.
- Assigned by
- Suggested by the deterministic field-completeness helper and assigned or approved by a Kingy editorial reviewer.
- Rubric and check date
- Rubric version P0-2026-08-10. The record’s “Last verified” date is the score check date. Checked: 2026-07-16.
- 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.
AgentX launched an agent-evaluation framework that builds test suites, traces failures, compares models on quality, cost, and latency, and suggests fixes before deployment, shipping…
Cloudflare Temporary Accounts for AI Agents
Cloudflare added 60-minute Temporary Accounts to Wrangler so an unauthenticated coding agent can deploy and revise supported Workers resources, return a preview and claim URL, and let a…
- Launch readiness
- 7.6 / 10
- Demo evidence
- Not scored yet
- Creator-story fit
- Not scored yet
Score definitions and rubric
These are launch-record readiness heuristics, not product ratings.
Launch readiness
How complete and reviewable the launch record is, not the quality of the product.
Inputs and weights: Launch date 15%; qualifying source 10%; what launched 10%; demo 15%; category 10%; audience 10%; editorial assessment 10%; traction evidence 10%; creator or audience fit 10%.
Evidence inputs: Reviewed launch metadata, public source links, demo links, taxonomy, audience, editorial notes, and recorded traction signals.
Demo evidence
Whether the record contains useful, reviewable demonstration evidence; it is not a rating of product output quality.
Inputs and weights: Working demo URL 45%; video walkthrough 25%; clear description of what launched 10%; audience 10%; editorial assessment 10%.
Evidence inputs: Demo and video URLs plus the reviewed launch description, audience, and editorial notes.
Creator-story fit
Whether a launch has enough demonstrable evidence and audience relevance for a useful creator story; it does not predict views or guarantee coverage.
Inputs and weights: Demo evidence 25%; visual creator category 15%; audience 15%; editorial assessment 15%; traction evidence 10%; pricing clarity 10%; API or open-weight evidence 10%.
Evidence inputs: Reviewed demo, category, audience, editorial, traction, pricing, API, and open-weight fields.
- Scale
- 0.0–10.0. A present qualifying input receives its published weight; a missing input receives zero. Scores are rounded to one decimal.
- Assigned by
- Suggested by the deterministic field-completeness helper and assigned or approved by a Kingy editorial reviewer.
- Rubric and check date
- Rubric version P0-2026-08-10. The record’s “Last verified” date is the score check date. Checked: 2026-07-27.
- 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.
Temporary Accounts remove real signup friction and delete unclaimed previews after 60 minutes. The claim URL and temporary token are sensitive values, so production…
GitHub Copilot AI Credits Usage Metrics API
GitHub added the ai_credits_used field to enterprise and organization Copilot usage-metrics API reports, exposing an overall per-user total in the one-day and 28-day user endpoints.
Per-user AI-credit totals give administrators a practical way to spot consumption concentration and build internal reporting. The field is deliberately coarse: GitHub says it…
GitHub Copilot Auto Mode
GitHub made automatic model selection generally available in Copilot Chat on github.com and GitHub Mobile for all Copilot plans.
- 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: 2026-07-09.
- Confidence and missing data
- Confidence depends on source completeness. “Not scored yet” means no reviewed value; “Needs review” means the value or score set failed validation.
- Freshness
- Recalculate after a material launch, source, demo, pricing, audience, or traction change and during the record freshness review.
- Disputes
- Use “Suggest a correction” on the record and cite the relevant evidence. Commercial relationships cannot buy or alter a score.
GitHub made automatic model selection generally available in Copilot Chat on github.com and GitHub Mobile for all plans, with a documented 10% billing discount…
GitHub Copilot Agent Finder
GitHub released Agent Finder for Copilot, which accepts a plain-language task, searches a selected public or private Agentic Resource Discovery registry, and returns ranked MCP servers, skills, canvases,…
Agent Finder addresses a real scaling problem: agents should not carry every possible integration in context. Its strongest controls are registry choice, managed settings…
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.
Cloudflare Clef / Clef-flash
Summary: Cloudflare released Clef and Clef-flash, decision models that produce structured choices and probabilities. The family is hosted on Workers AI and distributed as Apache-2.0 weights for…
Why it matters: Clef and Clef-flash give developers hosted and local options for structured decisions. Compare them on your own decision tasks; Cloudflare’s benchmark results are vendor…
- Pricing
- Hosted inference is subject to Workers AI usage billing; open weights are distributed under Apache 2.0 and local inference requires compute. The announcement does not establish a universal free hosted plan or self-service fine-tuning price.
- Verification
- Source-verified; 1 source
Strands Decider 2B
Summary: The Strands team released Decider 2B, a small decision model that chooses among defined outputs and assigns probabilities. The release includes model weights, code, data and…
Why it matters: Strands Decider 2B fits experiments that need a model to choose among defined options. It is not a general chat or coding model; evaluate…
- Pricing
- The release offers public code, weights and data for local experimentation. Running or training it requires local or rented compute; no hosted API price or hosted free plan is established by the announcement.
- Verification
- Source-verified; 3 sources
Pi Durable
Summary: Earendil released Pi Durable as a new experimental package for long-running agent applications. It extends the Pi ecosystem with durable task and conversation execution rather than…
Why it matters: Pi Durable offers builders a package to explore agents that survive interruptions and run across different surfaces. Keep evaluations scoped to the experimental release…
- Pricing
- The package is MIT licensed and available through npm. The announcement does not establish a hosted-service subscription or free inference; model and infrastructure usage can incur separate charges.
- Verification
- Source-verified; 3 sources
Tavus Griffin / Griffin-Lite
Summary: Griffin and Griffin-Lite are presented as models for responsive audiovisual interaction in a restricted research preview. Access: Restricted research preview; not customer GA.
Why it matters: Griffin is research access to investigate. A customer deployment or a public price is not established.
- Pricing
- No separate event-specific price is stated in the reviewed release notice. Existing account, plan, usage or hardware terms may apply; check the official source before committing.
- Verification
- Source-verified; 2 sources
FLUX 3 Image
Summary: One API endpoint supports generation, local edits and up to ten reference images. Output reaches 4K with 15 aspect ratios; bounding boxes belong in the prompt.…
Why it matters: FLUX 3 Image offers several composition controls in one endpoint. Check them on your own reference set before replacing a production image workflow.
- Pricing
- $0.041 at 768sq, $0.048 at 1k, $0.100 at 1.5k and $0.607 at 4k per image in the release note. Resolution controls cost.
- Verification
- Source-verified; 2 sources
SynthID Bio
Summary: Google DeepMind released research on watermarking AI-generated biological sequences and structures, alongside public code and data. This record covers the research release. Access: Research release only;…
Why it matters: SynthID Bio gives researchers code and data to examine biological watermarking. Review the relevant model-weight terms before reuse; the release does not establish a…
- Pricing
- No hosted-service plan is established. Repository software uses Apache 2.0 and publication data uses CC-BY. ProteinMPNN parameters retain MIT terms; AlphaFold 3 parameters have separate terms. Research use is distinct from clinical use.
- Verification
- Source-verified; 2 sources
Plan ahead
Upcoming Deadlines
Future-dated migration, shutdown, and retirement records are separated from today's launches and latest-record lists. These dates are upcoming operational events, not launches that have already happened.
OpenAI Agent Builder shutdown deadline: November 30, 2026
Summary: OpenAI announced the Agent Builder lifecycle deadline: the visual workflow builder is deprecated and scheduled to shut down on November 30, 2026. OpenAI documents two migration…
Why it matters: Export the workflow, select the Agents SDK or a suitable Workspace Agent, and test tools, state, permissions and guardrails before the November 30 shutdown.…
- Pricing
- Review replacement API costs or Workspace Agent plan requirements before migrating. This record does not estimate your workflow’s implementation or operating cost.
- Verification
- Source-verified; 3 sources
OpenAI reusable prompt objects and v1/prompts are scheduled to shut down November 30, 2026
Summary: OpenAI announced reusable-prompt deprecation on June 3, 2026. The documented November 30 shutdown covers reusable prompt objects and v1/prompts. Move managed prompt content into application-owned code…
Why it matters: Treat saved OpenAI prompt objects as a migration dependency before November 30. Move their content and variables into application code, then validate the generated…
- Pricing
- Pricing unknown
- Verification
- Source-verified; 5 sources
Three GPT Image API models are scheduled to retire December 1, 2026
Summary: OpenAI schedules gpt-image-1-mini, gpt-image-1.5 and chatgpt-image-latest for API removal on December 1, 2026. Its current deprecation table recommends gpt-image-2.5-sunburst or gpt-image-2.5-flare.
Why it matters: OpenAI schedules gpt-image-1-mini, gpt-image-1.5 and chatgpt-image-latest for API removal on December 1, 2026. Its current deprecation table recommends gpt-image-2.5-sunburst or gpt-image-2.5-flare. Inventory the three…
- Pricing
- Review the current price of the chosen replacement and measure representative jobs. This lifecycle record does not establish workload cost or promise a cost-neutral migration.
- Verification
- Source-verified; 5 sources
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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.