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Vorflux has opened its new cloud platform, allowing users to run tasks from a plan to a merged PR on a dedicated machine. It can plan, build, test, and review on its own. > Diff reviews are done by a different model family than the one that wrote it... show more

🚨 AI News | TestingCatalog

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Vellum has launched Ambient Assistants, adding vellum assistant as a floating pill on top of any Mac app, where it sees the screen in real time and can draw on top of it to point at what matters. Context that used to be taken from screenshots, pasted links, and long prompts now comes straight from whatever is open on screen. > Users can hand off a task by voice without leaving the app they use, while Vellum assistant works on it in the background. > In unfamiliar tools, Vellum assistant can circle the exact button, setting, or metric to use, directly on screen. > Users can show a routine on screen and talk it through, so the assistant can run it later after its plan is approved.

Vellum has launched Ambient Assistants, adding vellum assistant as a floating pill on top of any Mac app, where it sees the screen in real time and can draw on top of it to point at what matters. Context that used to be taken from screenshots, pasted links, and long prompts now comes straight from whatever is open on screen. > Users can hand off a task by voice without leaving the app they use, while Vellum assistant works on it in the background. > In unfamiliar tools, Vellum assistant can circle the exact button, setting, or metric to use, directly on screen. > Users can show a routine on screen and talk it through, so the assistant can run it later after its plan is approved.

🚨 AI News | TestingCatalog

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Introducing Pages on Superset mobile Your agents can now deliver interactive demos, mockups, and reports as Pages you can review from your phone. Ask an agent to build a feature, compare designs, or investigate a problem. It can put the result in a Page with the context you need to review it and give feedback. A few ways to use it: • Feature work: try an interactive demo, then inspect the code in the PR. • Design reviews: compare mockups and before-and-after screenshots. • Investigations: explore findings, charts, and recommendations in one place. To try it, ask your agent to build a feature and create a Page for you to review.

Introducing Pages on Superset mobile Your agents can now deliver interactive demos, mockups, and reports as Pages you can review from your phone. Ask an agent to build a feature, compare designs, or investigate a problem. It can put the result in a Page with the context you need to review it and give feedback. A few ways to use it: • Feature work: try an interactive demo, then inspect the code in the PR. • Design reviews: compare mockups and before-and-after screenshots. • Investigations: explore findings, charts, and recommendations in one place. To try it, ask your agent to build a feature and create a Page for you to review.

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The Matrix launch video lands on one line; in Matrix, anyone can become a CEO. That’s the real claim. Not one assistant doing one task. A company that keeps running after the first prompt, routing intent through a CEO Office, into OKRs, down to departments that do the work and send back proof to review. Connect Codex, point your leftover credits at it, and the first agent company is live.

The Matrix launch video lands on one line; in Matrix, anyone can become a CEO. That’s the real claim. Not one assistant doing one task. A company that keeps running after the first prompt, routing intent through a CEO Office, into OKRs, down to departments that do the work and send back proof to review. Connect Codex, point your leftover credits at it, and the first agent company is live.

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🚨 AI News | TestingCatalog

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Code reviews Codex goes beyond static analysis—it checks a PR against its intent, reasons across the codebase and dependencies, and can run code to validate the behavior of changes. Set it up to auto-review new PRs in your repo, or tag @ codex.

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OpenAI Developers

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How Did Pi Network Build a Huge User Base Before it Had a Fully Open Blockchain? Launched in 2019 by Stanford graduates, Pi (Pi Network) made crypto participation possible through a smartphone app instead of expensive mining hardware. But what exactly made its model different? (1) It made mining accessible through smartphones. Pi allowed users to earn Pi by checking into the app and starting a mining session. The process was designed to consume very little battery and computing power compared with traditional proof of work mining. This removed one of the biggest barriers to crypto mining, expensive hardware and high electricity costs. (2) It turned users into part of the network's growth engine. Pi introduced referral bonuses that encouraged existing users to invite friends and family. Users could also build security circles by adding people they trusted to the network. This created a built in growth mechanism that helped Pi expand its community rapidly. (3) Early Pi mining was not traditional blockchain mining. Before Open Network, users were not securing a public blockchain through conventional proof of work mining. Instead, the mobile app helped establish user participation and prepare the ecosystem before the network became publicly accessible. (4) Pi built the community before opening the network. Pi spent years operating through different development stages before launching its Open Network in February 2025. A firewalled Enclosed Mainnet had already gone live in December 2021, giving the project a working but closed blockchain years before it opened externally. During that period, the project focused heavily on growing its user base and developing its ecosystem. This flipped the usual crypto growth model, where networks often launch first and try to attract users afterward. (5) The Open Network changed what users could actually do. With Open Network, Pi became externally connected to the broader blockchain ecosystem. Users were now able to engage with Pi beyond the closed system it had been previously in, while developers were free to create applications on the network. This made the token and its massive community more realistic.

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BSCN

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Devin for Terminal is the first CLI agent with its own dedicated virtual machine. You can start a project locally and hand it off to Devin to test it end-to-end. Devin for Terminal is super performant, and it works with all frontier models. Try it out today.

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Devin for Terminal is the first CLI agent with its own dedicated virtual machine. You can start a project locally and hand it off to Devin to test it end-to-end. Devin for Terminal is super performant, and it works with all frontier models. Try it out today.

Devin for Terminal is the first CLI agent with its own dedicated virtual machine. You can start a project locally and hand it off to Devin to test it end-to-end. Devin for Terminal is super performant, and it works with all frontier models. Try it out today.

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Smart contracts on Internet Computer can issue encryption keys they cannot read A canister on DFINITY Foundation keeps its state in the clear on the replica, so node operators can read it even though the public cannot. vetKeys change that. A canister asks the subnet for a key, and a quorum of nodes derives and encrypts it under a transport key that the user generates fresh for that session. A single node cannot derive the key, and the user can verify it came out of the protocol correctly and wasn't tampered with. Neither the nodes nor the canister ever hold the raw key. The key is not stored anywhere. It gets rebuilt on demand from the same recipe every time. A user can sign in on a new phone but will get the same key back. Cryptography doesn't decide who can ask for a key. The app does, in its own code, and each key request costs the app a small fee. It has been live on mainnet since July 2025

BSCN

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Wajo has launched Fo, an AI agent that can place calls, send emails, and pay with its own card to complete everyday errands. > Fo can send emails from its own address and check out on its own using a single-use card for each purchase. > Handle the back-and-forth with vendors over calls, emails, and group chats. > When a task requires a person, Fo is able to hand it over to one of its trained executive assistants. In Wajo's tests on tasks with setbacks, such as sold-out slots or unresponsive vendors, Fo completed 71% of tasks without help and stayed within user-approved limits on 94% of test runs.

🚨 AI News | TestingCatalog

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this is f*cking beyond comprehension. Google engineers just shipped the entire agent lifecycle in one release: build, scale, govern. and every piece answers a specific way agents die in production > context layers (Static, Turn, User, Cache): you decide what the model carries between turns, so token spend stops being a mystery > a self heal plugin: the agent notices a tool call failed and retries it a different way instead of dying mid run > adk deploy: one command from your laptop to the managed runtime, no packaging, no infra ticket > Go joins Python and Java, with its own A2A SDK then the part nobody builds for themselves: > a dashboard on token consumption, latency, error rates and tool calls: the four things that actually kill an agent > a traces tab that opens the real sequence of actions the agent took, step by step > a playground wired to the deployed agent, past sessions included, so debugging is not a redeploy loop > an Evaluation Layer with a User Simulator, because you cannot unit test a non deterministic system and the part that decides whether it ever ships: > agents get native identities as first class IAM principals: least privilege applies to them like it does to people > Model Armor screens prompt injection, tool calls and responses, inline for Gemini or over REST > Security Command Center inventories every agentic asset and flags data exfiltration by an agent ADK is already at 7 million downloads. the runtime has a free tier, and express mode runs off a Gmail address. the prototype was never the hard part.

this is f*cking beyond comprehension. Google engineers just shipped the entire agent lifecycle in one release: build, scale, govern. and every piece answers a specific way agents die in production > context layers (Static, Turn, User, Cache): you decide what the model carries between turns, so token spend stops being a mystery > a self heal plugin: the agent notices a tool call failed and retries it a different way instead of dying mid run > adk deploy: one command from your laptop to the managed runtime, no packaging, no infra ticket > Go joins Python and Java, with its own A2A SDK then the part nobody builds for themselves: > a dashboard on token consumption, latency, error rates and tool calls: the four things that actually kill an agent > a traces tab that opens the real sequence of actions the agent took, step by step > a playground wired to the deployed agent, past sessions included, so debugging is not a redeploy loop > an Evaluation Layer with a User Simulator, because you cannot unit test a non deterministic system and the part that decides whether it ever ships: > agents get native identities as first class IAM principals: least privilege applies to them like it does to people > Model Armor screens prompt injection, tool calls and responses, inline for Gemini or over REST > Security Command Center inventories every agentic asset and flags data exfiltration by an agent ADK is already at 7 million downloads. the runtime has a free tier, and express mode runs off a Gmail address. the prototype was never the hard part.

NO1ennn

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TESLA HALTED MODEL S AND MODEL X PRODUCTION TO BUILD AN ARMY OF OPTIMUS ROBOTS The Fremont assembly line was torn down in 46 days. In its place, Tesla is building a line for humanoid production, aiming for a million units a year A humanoid robot is a body shaped like a human. Physical AI is the intelligence that controls that body Walking and making coffee is often just imitation learning from a scripted routine. But once the environment shifts, the learned trick stops working Language models had the entire internet to train on. Robotics has nothing close to that scale of data, which is why one giant brain hasn't worked for anyone yet The industry is moving toward modularity instead - separate models for vision, movement, and planning, each improved on its own The real question is no longer whether a robot can move impressively. It's whether it can pull its sensors into one picture of the world and adapt to whatever wasn't scripted for it

TESLA HALTED MODEL S AND MODEL X PRODUCTION TO BUILD AN ARMY OF OPTIMUS ROBOTS The Fremont assembly line was torn down in 46 days. In its place, Tesla is building a line for humanoid production, aiming for a million units a year A humanoid robot is a body shaped like a human. Physical AI is the intelligence that controls that body Walking and making coffee is often just imitation learning from a scripted routine. But once the environment shifts, the learned trick stops working Language models had the entire internet to train on. Robotics has nothing close to that scale of data, which is why one giant brain hasn't worked for anyone yet The industry is moving toward modularity instead - separate models for vision, movement, and planning, each improved on its own The real question is no longer whether a robot can move impressively. It's whether it can pull its sensors into one picture of the world and adapt to whatever wasn't scripted for it

iamigorekk

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Super excited about Hydra-0 from Hongyu Li and team! The key idea is to use flow as a shared visual interface across embodiments/objects for controllable video generation, allowing a single generalist world model to learn from human, handheld-gripper, and robot interaction data. My favorite result is the video below: start from a real video of a human doing the task (left), extract the desired object flow, and condition the model on that flow (right). The model then hallucinates a plausible robot motion that could produce the same object motion. Very cool glimpse of how a generalist world model can bridge human demonstrations and robot control.

Super excited about Hydra-0 from Hongyu Li and team! The key idea is to use flow as a shared visual interface across embodiments/objects for controllable video generation, allowing a single generalist world model to learn from human, handheld-gripper, and robot interaction data. My favorite result is the video below: start from a real video of a human doing the task (left), extract the desired object flow, and condition the model on that flow (right). The model then hallucinates a plausible robot motion that could produce the same object motion. Very cool glimpse of how a generalist world model can bridge human demonstrations and robot control.

Yunzhu Li

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