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.
RayNeo iO
Announced August 21, 2026, RayNeo iO is now orderable on the official USD storefront. RayNeo says the Polar Night version is expected to begin shipping gradually September 22;…
- Launch readiness
- 8.0 / 10
- Demo evidence
- 6.8 / 10
- Creator-story fit
- Medium
Score definitions and rubric
These are launch-record readiness heuristics, not product ratings.
Launch readiness
How complete and reviewable the launch record is, not the quality of the product.
Inputs and weights: Launch date 15%; qualifying source 10%; what launched 10%; demo 15%; category 10%; audience 10%; editorial assessment 10%; traction evidence 10%; creator or audience fit 10%.
Evidence inputs: Reviewed launch metadata, public source links, demo links, taxonomy, audience, editorial notes, and recorded traction signals.
Demo evidence
Whether the record contains useful, reviewable demonstration evidence; it is not a rating of product output quality.
Inputs and weights: Working demo URL 45%; video walkthrough 25%; clear description of what launched 10%; audience 10%; editorial assessment 10%.
Evidence inputs: Demo and video URLs plus the reviewed launch description, audience, and editorial notes.
Creator-story fit
Whether a launch has enough demonstrable evidence and audience relevance for a useful creator story; it does not predict views or guarantee coverage.
Inputs and weights: Demo evidence 25%; visual creator category 15%; audience 15%; editorial assessment 15%; traction evidence 10%; pricing clarity 10%; API or open-weight evidence 10%.
Evidence inputs: Reviewed demo, category, audience, editorial, traction, pricing, API, and open-weight fields.
- Scale
- 0.0–10.0. A present qualifying input receives its published weight; a missing input receives zero. Scores are rounded to one decimal.
- Assigned by
- Suggested by the deterministic field-completeness helper and assigned or approved by a Kingy editorial reviewer.
- Rubric and check date
- Rubric version P0-2026-08-10. The record’s “Last verified” date is the score check date. Checked: 2026-08-24.
- 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.
Paid coverage: RayNeo paid Kingy for the September 9 video. No affiliate compensation, supplied product or access, or other benefit was received. Work began…
Attentive AI Grow
Attentive announced the general availability of AI Grow on August 19, 2026. The product analyzes browsing and shopping behavior among identified and logged-out visitors to decide when and…
- Launch readiness
- 8.5 / 10
- Demo evidence
- 7.5 / 10
- Creator-story fit
- Medium
Score definitions and rubric
These are launch-record readiness heuristics, not product ratings.
Launch readiness
How complete and reviewable the launch record is, not the quality of the product.
Inputs and weights: Launch date 15%; qualifying source 10%; what launched 10%; demo 15%; category 10%; audience 10%; editorial assessment 10%; traction evidence 10%; creator or audience fit 10%.
Evidence inputs: Reviewed launch metadata, public source links, demo links, taxonomy, audience, editorial notes, and recorded traction signals.
Demo evidence
Whether the record contains useful, reviewable demonstration evidence; it is not a rating of product output quality.
Inputs and weights: Working demo URL 45%; video walkthrough 25%; clear description of what launched 10%; audience 10%; editorial assessment 10%.
Evidence inputs: Demo and video URLs plus the reviewed launch description, audience, and editorial notes.
Creator-story fit
Whether a launch has enough demonstrable evidence and audience relevance for a useful creator story; it does not predict views or guarantee coverage.
Inputs and weights: Demo evidence 25%; visual creator category 15%; audience 15%; editorial assessment 15%; traction evidence 10%; pricing clarity 10%; API or open-weight evidence 10%.
Evidence inputs: Reviewed demo, category, audience, editorial, traction, pricing, API, and open-weight fields.
- Scale
- 0.0–10.0. A present qualifying input receives its published weight; a missing input receives zero. Scores are rounded to one decimal.
- Assigned by
- Suggested by the deterministic field-completeness helper and assigned or approved by a Kingy editorial reviewer.
- Rubric and check date
- Rubric version P0-2026-08-10. The record’s “Last verified” date is the score check date. Checked: 2026-08-24.
- 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.
AI Grow is a commercially relevant launch because it applies AI to an established subscriber-acquisition workflow, is generally available, and is measured with cohort-level…
ChatGPT for Teens
OpenAI announced ChatGPT for Teens on August 18, 2026. Eligible users aged 13 to 17, including accounts the system estimates are under 18, are placed into a distinct…
- Launch readiness
- 8.4 / 10
- Demo evidence
- 6.8 / 10
- Creator-story fit
- Medium
Score definitions and rubric
These are launch-record readiness heuristics, not product ratings.
Launch readiness
How complete and reviewable the launch record is, not the quality of the product.
Inputs and weights: Launch date 15%; qualifying source 10%; what launched 10%; demo 15%; category 10%; audience 10%; editorial assessment 10%; traction evidence 10%; creator or audience fit 10%.
Evidence inputs: Reviewed launch metadata, public source links, demo links, taxonomy, audience, editorial notes, and recorded traction signals.
Demo evidence
Whether the record contains useful, reviewable demonstration evidence; it is not a rating of product output quality.
Inputs and weights: Working demo URL 45%; video walkthrough 25%; clear description of what launched 10%; audience 10%; editorial assessment 10%.
Evidence inputs: Demo and video URLs plus the reviewed launch description, audience, and editorial notes.
Creator-story fit
Whether a launch has enough demonstrable evidence and audience relevance for a useful creator story; it does not predict views or guarantee coverage.
Inputs and weights: Demo evidence 25%; visual creator category 15%; audience 15%; editorial assessment 15%; traction evidence 10%; pricing clarity 10%; API or open-weight evidence 10%.
Evidence inputs: Reviewed demo, category, audience, editorial, traction, pricing, API, and open-weight fields.
- Scale
- 0.0–10.0. A present qualifying input receives its published weight; a missing input receives zero. Scores are rounded to one decimal.
- Assigned by
- Suggested by the deterministic field-completeness helper and assigned or approved by a Kingy editorial reviewer.
- Rubric and check date
- Rubric version P0-2026-08-10. The record’s “Last verified” date is the score check date. Checked: 2026-08-24.
- 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.
A material ChatGPT update for teens and families because it attaches defined learning features and default protections to a distinct under-18 experience. Kingy has…
WethosAI Twins
WethosAI launched Twins on August 18, 2026 as part of its Human Context Platform. The company says organizations can use the capability through its SaaS product, APIs, enterprise…
- Launch readiness
- 8.2 / 10
- Demo evidence
- 7.5 / 10
- Creator-story fit
- Medium
Score definitions and rubric
These are launch-record readiness heuristics, not product ratings.
Launch readiness
How complete and reviewable the launch record is, not the quality of the product.
Inputs and weights: Launch date 15%; qualifying source 10%; what launched 10%; demo 15%; category 10%; audience 10%; editorial assessment 10%; traction evidence 10%; creator or audience fit 10%.
Evidence inputs: Reviewed launch metadata, public source links, demo links, taxonomy, audience, editorial notes, and recorded traction signals.
Demo evidence
Whether the record contains useful, reviewable demonstration evidence; it is not a rating of product output quality.
Inputs and weights: Working demo URL 45%; video walkthrough 25%; clear description of what launched 10%; audience 10%; editorial assessment 10%.
Evidence inputs: Demo and video URLs plus the reviewed launch description, audience, and editorial notes.
Creator-story fit
Whether a launch has enough demonstrable evidence and audience relevance for a useful creator story; it does not predict views or guarantee coverage.
Inputs and weights: Demo evidence 25%; visual creator category 15%; audience 15%; editorial assessment 15%; traction evidence 10%; pricing clarity 10%; API or open-weight evidence 10%.
Evidence inputs: Reviewed demo, category, audience, editorial, traction, pricing, API, and open-weight fields.
- Scale
- 0.0–10.0. A present qualifying input receives its published weight; a missing input receives zero. Scores are rounded to one decimal.
- Assigned by
- Suggested by the deterministic field-completeness helper and assigned or approved by a Kingy editorial reviewer.
- Rubric and check date
- Rubric version P0-2026-08-10. The record’s “Last verified” date is the score check date. Checked: 2026-08-24.
- 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.
Twins is an intriguing enterprise decision-rehearsal launch because WethosAI states explicit permission, access-control, and non-impersonation boundaries. The vendor has not published independent predictive-validity evidence…
Cerebras CS-4
Cerebras introduced CS-4 on August 18, 2026. The company describes a redesigned rack-scale system using three WSE-3 Turbo processors, a modular Nexus platform, native disaggregated-inference support, and first…
- Launch readiness
- 8.8 / 10
- Demo evidence
- 6.8 / 10
- Creator-story fit
- Medium
Score definitions and rubric
These are launch-record readiness heuristics, not product ratings.
Launch readiness
How complete and reviewable the launch record is, not the quality of the product.
Inputs and weights: Launch date 15%; qualifying source 10%; what launched 10%; demo 15%; category 10%; audience 10%; editorial assessment 10%; traction evidence 10%; creator or audience fit 10%.
Evidence inputs: Reviewed launch metadata, public source links, demo links, taxonomy, audience, editorial notes, and recorded traction signals.
Demo evidence
Whether the record contains useful, reviewable demonstration evidence; it is not a rating of product output quality.
Inputs and weights: Working demo URL 45%; video walkthrough 25%; clear description of what launched 10%; audience 10%; editorial assessment 10%.
Evidence inputs: Demo and video URLs plus the reviewed launch description, audience, and editorial notes.
Creator-story fit
Whether a launch has enough demonstrable evidence and audience relevance for a useful creator story; it does not predict views or guarantee coverage.
Inputs and weights: Demo evidence 25%; visual creator category 15%; audience 15%; editorial assessment 15%; traction evidence 10%; pricing clarity 10%; API or open-weight evidence 10%.
Evidence inputs: Reviewed demo, category, audience, editorial, traction, pricing, API, and open-weight fields.
- Scale
- 0.0–10.0. A present qualifying input receives its published weight; a missing input receives zero. Scores are rounded to one decimal.
- Assigned by
- Suggested by the deterministic field-completeness helper and assigned or approved by a Kingy editorial reviewer.
- Rubric and check date
- Rubric version P0-2026-08-10. The record’s “Last verified” date is the score check date. Checked: 2026-08-24.
- 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.
Cerebras CS-4 is a consequential AI-infrastructure launch with a concrete shipment window and architectural changes beyond a processor refresh. Its headline speed, throughput, and…
TruthSpine V1 launches as a local-first project-context desktop app
TimeProof Labs announced TruthSpine V1 as live after publishing its public product hub and release packages. The local-first desktop application builds a compact project context from selected sources…
TruthSpine V1 addresses a real developer problem with a concrete local-first design: preserve project decisions and sources once, then reuse compact context across agents.…
Kastra runtime authorization for AI agents
Kastra applies policies to tools, prompts, inputs, and outputs across several agent environments. Its product pages describe local enforcement, centralized policy management, audit trails, and integrations spanning coding…
Kastra addresses a concrete need: As agents gain access to files, credentials, and operational systems, authorization must happen at action time. The main limitation…
Buzzy agentic canvas for multi-scene AI video
Buzzy organizes characters, objects, locations, shots, and model outputs on a visual canvas. Makers say users can revise individual scenes, change camera angle or lighting, reuse references across…
Buzzy addresses a concrete need: The launch focuses on continuity and shot-level control, two practical constraints that become more important when AI video work…
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…
box by ASCII persistent virtual machines for coding agents
A box is a persistent Ubuntu machine with SSH and SCP access, Docker, a dedicated IPv4 address, a virtual desktop, snapshots, and disk-level forking. The official docs provide…
box by ASCII addresses a concrete need: Long-running and parallel coding agents need isolated computers with persistent state. The main limitation is this: The…
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…
Humalike x Hermes addresses a concrete need: Agent quality in group settings depends on timing, restraint, and social context as well as answer quality.…
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…
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.
Creed addresses a concrete need: Users increasingly move among several assistants, while the context each one learns usually stays trapped in a product. The…
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…
Deck addresses a concrete need: Giving an assistant its own inbox offers a narrower permission model than granting access to an entire mailbox, while…
Kogvio contextual AI browser extension
Kogvio launched a browser extension that lets users highlight text, equations, diagrams, or other visible content and ask an AI question without leaving the page.
Kogvio addresses a concrete need: Research tools often force users to copy material into a separate chat. The main limitation is this: The Chrome…
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…
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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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.
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