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.
DeepSeek opened the DeepSeek Harness developer preview and published its source code on August 13, 2026. The official landing page and repository describe an agent harness built around…
Source-verifiedFree: UnknownAPI: UnknownOpen: Yes
Creator coverageStrong demoClear use caseVideo demo
Launch readiness
9.1 / 10
Demo evidence
7.5 / 10
Creator-story fit
Medium
Score definitions and rubric
These are launch-record readiness heuristics, not product ratings.
Launch readiness
How complete and reviewable the launch record is, not the quality of the product.
Inputs and weights: Launch date 15%; qualifying source 10%; what launched 10%; demo 15%; category 10%; audience 10%; editorial assessment 10%; traction evidence 10%; creator or audience fit 10%.
Evidence inputs: Reviewed launch metadata, public source links, demo links, taxonomy, audience, editorial notes, and recorded traction signals.
Demo evidence
Whether the record contains useful, reviewable demonstration evidence; it is not a rating of product output quality.
Inputs and weights: Working demo URL 45%; video walkthrough 25%; clear description of what launched 10%; audience 10%; editorial assessment 10%.
Evidence inputs: Demo and video URLs plus the reviewed launch description, audience, and editorial notes.
Creator-story fit
Whether a launch has enough demonstrable evidence and audience relevance for a useful creator story; it does not predict views or guarantee coverage.
Inputs and weights: Demo evidence 25%; visual creator category 15%; audience 15%; editorial assessment 15%; traction evidence 10%; pricing clarity 10%; API or open-weight evidence 10%.
0.0–10.0. A present qualifying input receives its published weight; a missing input receives zero. Scores are rounded to one decimal.
Assigned by
Suggested by the deterministic field-completeness helper and assigned or approved by a Kingy editorial reviewer.
Rubric and check date
Rubric version P0-2026-08-10. The record’s “Last verified” date is the score check date. Checked: 2026-08-13.
Confidence and missing data
Confidence depends on source completeness. “Not scored yet” means no reviewed value; “Needs review” means the value or score set failed validation.
Freshness
Recalculate after a material launch, source, demo, pricing, audience, or traction change and during the record freshness review.
Disputes
Use “Suggest a correction” on the record and cite the relevant evidence. Commercial relationships cannot buy or alter a score.
This is a substantive launch for developers because DeepSeek published both a runnable package and the source behind a composable agent runtime. Treat it…
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 feedback. The…
Source-verifiedFree: NoAPI: YesOpen: Yes
Clear use caseDeveloper-friendlyGitHub tractionTraction signal
The CLI and SDK make Codex Security easier to insert into repeatable engineering workflows and expose useful integration primitives such as typed findings, SARIF,…
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…
Source-verifiedFree: YesAPI: NoOpen: No
Clear use caseDeveloper-friendlyGitHub tractionTraction signal
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.…
OpenClaw v2026.7.1 shipped major Control UI and onboarding overhauls, major updates to the official iOS, Android, and macOS apps, expanded model and provider support, and stronger Codex and…
OpenClaw v2026.7.1 is a substantive platform release rather than a narrow patch: the interface, onboarding, companion apps, and provider and coding-agent paths move together.…
On July 7, 2026, GitHub announced that the GitHub Copilot app is available on every Copilot plan across macOS, Windows, and Linux.
Recheck dueFree: YesAPI: UnknownOpen: Yes
Clear use caseGitHub tractionTraction signal
Launch readiness
7.3 / 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%.
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 made the Copilot app available on every plan across macOS, Windows, and Linux — including Copilot Free and GitHub Education (github.blog). Developers, Students,…
Builder.io published its visual-plan and visual-recap launch article on June 24, 2026, alongside open-source skills for coding-agent workflows.
Recheck dueFree: YesAPI: UnknownOpen: Yes
Clear use caseGitHub tractionTraction signal
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%.
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.
Builder.io published open-source visual-plan and visual-recap skills on June 24, 2026, giving reviewers architecture, scope, and risk artifacts before merging coding-agent work (github.com/BuilderIO/skills). AI…
Mem0 launched the Mem0 Plugin for Pi Code on June 24, 2026.
Recheck dueFree: YesAPI: YesOpen: Yes
Clear use caseGitHub tractionTraction signal
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%.
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.
Mem0 launched its Plugin for Pi Code on June 24, 2026, adding persistent, scoped memory so coding agents retain project context, decisions, and preferences…
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,…
Source-verifiedFree: YesAPI: NoOpen: No
Clear use caseDeveloper-friendlyGitHub tractionTraction signal
Agent Finder addresses a real scaling problem: agents should not carry every possible integration in context. Its strongest controls are registry choice, managed settings…
The Go Team launched a public GET-only v1beta pkg.go.dev API for structured search and package ecosystem metadata, including packages, modules, symbols, versions, known vulnerabilities and imported-by relationships, with…
Source-verifiedFree: YesAPI: YesOpen: Yes
Clear use caseDeveloper-friendlyGitHub tractionTraction signal
The pkg.go.dev API replaces brittle HTML scraping with an official structured interface and gives AI coding tools a cleaner evidence source. It remains v1beta,…
The Qwen team released Qwen3, a new generation of open-weight models with reasoning, coding, math, and general capability improvements.
Recheck dueFree: YesAPI: YesOpen: Yes
Clear use caseBusiness-friendlyDeveloper-friendlyGitHub traction
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%.
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.
The Qwen team released Qwen3, a new generation of open-weight models with improvements in reasoning, coding, math, and general capability, published to a public…
StackBlitz launched Bolt.new, a browser-based AI app builder that can generate, run, edit, and deploy full-stack web apps using WebContainers.
Recheck dueFree: YesAPI: NoOpen: No
Clear use caseVideo demoBeginner-friendlyCreator-friendly
Launch readiness
7.5 / 10
Demo evidence
Not scored yet
Creator-story fit
Not scored yet
Score definitions and rubric
These are launch-record readiness heuristics, not product ratings.
Launch readiness
How complete and reviewable the launch record is, not the quality of the product.
Inputs and weights: Launch date 15%; qualifying source 10%; what launched 10%; demo 15%; category 10%; audience 10%; editorial assessment 10%; traction evidence 10%; creator or audience fit 10%.
Evidence inputs: Reviewed launch metadata, public source links, demo links, taxonomy, audience, editorial notes, and recorded traction signals.
Demo evidence
Whether the record contains useful, reviewable demonstration evidence; it is not a rating of product output quality.
Inputs and weights: Working demo URL 45%; video walkthrough 25%; clear description of what launched 10%; audience 10%; editorial assessment 10%.
Evidence inputs: Demo and video URLs plus the reviewed launch description, audience, and editorial notes.
Creator-story fit
Whether a launch has enough demonstrable evidence and audience relevance for a useful creator story; it does not predict views or guarantee coverage.
Inputs and weights: Demo evidence 25%; visual creator category 15%; audience 15%; editorial assessment 15%; traction evidence 10%; pricing clarity 10%; API or open-weight evidence 10%.
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.
StackBlitz launched Bolt.new, a browser-based AI app builder that generates, runs, edits, and deploys full-stack web apps using WebContainers — with a public repository…
No source-ready records are tagged for today yet, so this section is showing the latest public launch records available in the tracker.
AI Coding Tools
DeepSeek Harness developer preview
Summary: DeepSeek opened the DeepSeek Harness developer preview and published its source code on August 13, 2026. The official landing page and repository describe an agent harness…
Why it matters: This is a substantive launch for developers because DeepSeek published both a runnable package and the source behind a composable agent runtime. Treat it…
Pricing
The source code is published under the MIT License and the documented local start path does not list a purchase price. The reviewed official sources do not advertise a paid hosted tier. Local use requires a compatible Node.js runtime and the user's own model or service costs may still apply.
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.
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.
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.
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.
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.
Future-dated migration, shutdown, and retirement records are separated from today's launches and latest-record lists. These dates are upcoming operational events, not launches that have already happened.
AI Developer Tools
Google Imagen 4 API shutdown window: August 17, 2026
Summary: Google announced that imagen-4.0-generate-001, imagen-4.0-ultra-generate-001 and imagen-4.0-fast-generate-001 are deprecated. Its deprecations table lists August 17, 2026 as the earliest possible shutdown date and recommends gemini-3.1-flash-image.
Why it matters: Plan against August 17 while preserving Google’s wording: the deprecations page says listed dates are the earliest possible shutdown dates and that exact timing…
Pricing
There is no separate migration fee. Replacement Gemini API image generation follows current Google pricing and quota terms; teams should compare the selected model, image output, prompts and workload mix on the live pricing page.
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.
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…
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.