The base of the lamp has two slider buttons. One toggle adjusts the warmth, from cold white light all the way to red. One adjusts the intensity, from ultra-bright down to a glareless glow. Hard taps on each button skip ahead, while holding the toggle down on one side or another adjusts the light settings quite slowly—slowly enough I at first sometimes question whether it’s happening.

The maximum brightness is 1,000 lumens—the approximate intensity of a 75-watt incandescent bulb. At this brightness, the battery lasts about five hours. At a lower intensity, this can extend to as long as a dozen hours.

Red Shift

Photograph: Matthew Korfhage

There’s an added feature I have come to appreciate at night, which is the red-light mode. There’s little evidence that blue light from your little smartphone is keeping you awake at night. But numerous studies do show that blue light wavelengths can affect melatonin levels and thus your body’s circadian rhythm, while red light doesn’t do this.

Red light therapy is, of course, the province of TikTok as much as science—a field where wild exaggerations live alongside legitimate uses and benefits. For every sleep study showing that red light is superior to blue light when it comes to melatonin levels, there’s another showing that red light is associated with “negative emotions” before bed.

So I can only offer my own experience, which is that Edge Light Go’s red reading light offers me a pleasant liminal space between awake time and sleepy time, one not offered by a basic nightstand lamp. It allows me to sort of bask in a darkroom space that still lets me see and read, and drift off a little easier.

If I fall asleep, the light has an automatic 25-minute shut-off, which means I won’t do what I far too often do, which is drift off while reading and then wake up, alarmed, to a room filled with bright light in the middle of the night.

Caveats and Quirks

Photograph: Matthew Korfhage

This said, for all the virtues of portability, the Edge Light Go does not boast a base that’s heavy enough to stop the lamp from tipping over if I bend it forward from its lowest hinge. This can be an annoyance when trying to use the lamp as a reading light from a bedside table or the arm of a couch.

Volvo’s parent company has launched a new electric sedan in China that hits a familiar sore spot for US car shoppers.

The Geely Galaxy A7 EV pairs a clean, mainstream shape with a claimed 550km of CLTC range, and it enters the market at a price that still looks strikingly low by Western EV standards. It also appears set to stay far away from US dealerships.

That low headline number needs some caution though. Car News China reports a cheaper entry point, but the launched EV trims are higher, starting at 112,800 yuan, or about $16,530, and rising to 119,800 yuan. That is still aggressive pricing for a sedan of this size, just not quite the jaw-dropping bargain the earliest figure implied.

Cheap price, messy rollout

Once you get past the pricing confusion, the core package looks solid. The A7 EV uses a 58.05 kWh LFP battery and a front-mounted 160 kW motor, with Geely claiming 550km on the CLTC cycle. Reports say there’s a smaller-battery version, so there’s still some room for Geely to clear up how broad the lineup may become.

The rest of the car sounds more mature than bargain-bin. The EV gets restrained exterior styling, a 14.6-inch touchscreen, a digital instrument display, and an interior layout that reads like normal family transport instead of a stripped-down cost cutter. That matters because the real appeal here is not novelty. It is normality at a low price.

Why this one won’t reach you

For American readers, the frustrating part is how familiar this story has become.

China keeps producing lower-cost EVs that look usable and complete, while the US market rarely gets anything close to this price in a new electric sedan.

Nothing tied to the A7 EV points to a US launch, so this one looks like another car Americans will only watch from afar.

What happens before this goes on sale

The next question is whether the EV version can help revive momentum for the wider Galaxy A7 line.

Geely delivered 15,230 A7 units in China in the first quarter of 2026, but that total was down 59.4% from the prior quarter.

If this EV lands with buyers, it will matter as more than a fresh trim. It will show how quickly China’s lower-cost EV market is sharpening up.

If you’ve ever signed up for an app and then spent the next five minutes hunting for a six-digit code buried in your inbox, you know how painful the process is. I especially despise the magic sign-up link that websites send, as they sometimes fail to work if my default browser isn’t Google Chrome.

Thankfully, Google is fixing that with a new verified email credential for Android, and it’s a genuinely smart solution. 

So what’s actually broken with OTPs?

The humble OTP has been the backbone of email verification forever, but it comes with real problems. You leave the app, open your inbox, find the email, copy the code, and come back. 

It’s a long process that not only hurts consumers but also app developers. The number of steps required may cause a user to leave the app mid-sign-up, meaning the app loses potential users before they even try it.

iOS fixed this issue by directly allowing users to sign in via Apple account. Recently, it also added a feature to autofill OTPs from emails, just as Android supports OTP autofill from messages. 

Now, Google is also creating a seamless signup process that doesn’t require users to jump between apps. 

How does the new system work?

Google now issues a cryptographically verified email credential directly to Android devices through the Credential Manager API. When an app needs to confirm your email, it can pull that credential directly using the Credential Manager API. 

A small prompt appears on screen showing what information is being requested. You tap to confirm, and the app gets your verified email. No switching apps, no codes, no delay.

Google recommends pairing this with passkey creation, so the first sign-up becomes the last time a user has to do anything manual. 

The same can also be used for account recovery and re-authentication of sensitive actions, including setting changes, updating profile details, and more. 

The best part is that the new feature supports Android 9 and later devices, so you don’t need the best new Android smartphones to enjoy this quality-of-life improvement.

Are there any restrictions?

There are a few restrictions. The feature currently works only with regular consumer Google Accounts, not Workspace accounts. It also only works with Gmail accounts, and not with third-party email accounts that you might have used to create your Google account.

OpenAI introduced a new paradigm and product today that is likely to have huge implications for enterprises seeking to adopt and control fleets of AI agent workers.

Called “Workspace Agents,” OpenAI’s new offering essentially allows users on its ChatGPT Business ($20 per user per month) and variably priced Enterprise, Edu and Teachers subscription plans to design or select from pre-existing agent templates that can take on work tasks across third-party apps and data sources including Slack, Google Drive, Microsoft apps, Salesforce, Notion, Atlassian Rovo, and other popular enterprise applications.

Put simply: these agents can be created and accessed from ChatGPT, but users can also add them to third-party apps like Slack, communicate with them across disparate channels, ask them to use information from the channel they’re in and other third-party tools and apps, and the agents will go off and do work like drafting emails to the entire team, selected members, or pull data and make presentations.

Human users can trust that the agent will manage all this complexity and complete the task as requested, even if the user who requested it leaves.

It’s the end of “babysitting” agents and the start of letting them go off and get shit done for your business — according to your defined business processes and permissions, of course.

The product experience appears centered on the Agents tab in the ChatGPT sidebar, where teams can discover and manage shared agents.

This functions as a kind of team directory: a place where agents built by coworkers can be reused across a workspace. The broader idea is that AI becomes less of an individual productivity trick and more of a shared organizational resource.

In this sense, OpenAI is targeting one of office work’s oldest pain points: the handoff between people, systems, and steps in a process.

OpenAI says workspace agents will be free for the next two weeks, until May 6, 2026, after which credit-based pricing will begin. The company also says more capabilities are on the way, including new triggers to start work automatically, better dashboards, more ways for agents to take action across business tools, and support for workspace agents in its AI code generation app, Codex.

For more information on how to get started building and using them, OpenAI recommends heading over to its online academy page on them here and its help desk documentation here.

The Codex backbone

The most significant shift in this announcement is the move away from purely session-based interaction. Workspace agents are powered by Codex — the cloud-based, partially open-source AI coding harness that OpenAI has been aggressively expanding in 2026 — which gives them access to a workspace for files, code, tools, and memory.

OpenAI says the agents can do far more than answer a prompt. They can write or run code, use connected apps, remember what they have learned, and continue work across multiple steps.

That description lines up closely with the capabilities OpenAI shipped into Codex just six days ago, including background computer use, more than 90 new plugins spanning tools like Atlassian Rovo, CircleCI, GitLab, Microsoft Suite, Neon by Databricks, and Render, plus image generation, persistent memory, and the ability to schedule future work and wake up on its own to continue across days or weeks.

Workspace agents inherit that plumbing. When one pulls a Friday metrics report, it is effectively spinning up a Codex cloud session with the right tools attached, running code to fetch and transform data, rendering charts, writing the narrative, and persisting what it learned for next week.

When that same agent is deployed to a Slack channel, it is a Codex instance listening for mentions and threading its work back in.

This is the technical decision enterprise buyers should focus on. Building an agent on a code-execution substrate rather than a pure LLM-call-and-response loop is what gives workspace agents the ability to do real work — transforming a CSV, reconciling two systems of record, generating a chart that is actually correct — rather than describing what the work would look like.

Persistence and scheduling

In earlier AI assistant models, progress paused when the user stopped interacting. Workspace agents change that by running in the cloud and supporting long-running workflows. Teams can also set them to run on a schedule.

That means a recurring reporting agent can pull data on a set cadence, generate charts and summaries, and share the results with a team without anyone manually kicking off the process.

Here at VentureBeat, we analyze story traffic and user return rate on a weekly basis — exactly the kind of recurring, multi-step, multi-source task that could theoretically be automated with a single workspace agent. Any enterprise with a weekly reporting rhythm pulling from dynamic data sources is likely to find a use for these agents.

Agents also retain memory across runs. OpenAI says they can be guided and corrected in conversation, so they improve the more a team uses them.

Over time they start to reflect how a team actually works — its processes, its standards, its preferred ways of handling recurring jobs — which is a meaningfully different proposition from the static instruction-set GPTs that preceded them.

The integrated ecosystem

OpenAI’s claim is that agents should gather information and take action where work already happens, rather than forcing teams into a separate interface. That point becomes clearest in the Slack examples. OpenAI’s launch materials show a product-feedback agent operating inside a channel named #user-insights, answering a question about recent mobile-app feedback with a themed summary pulled from multiple sources.

The company’s demo lineup walks through a sample team directory of agents: Spark for lead qualification and follow-up, Slate for software-request review, Tally for metrics reporting, Scout for product feedback routing, Trove for third-party vendor risk, and Angle for marketing and web content.

OpenAI also shared more functional examples its own teams use internally — a Software Reviewer that checks employee requests against approved-tools policy and files IT tickets; an accounting agent that prepares parts of month-end close including journal entries, balance-sheet reconciliations, and variance analysis, with workpapers containing underlying inputs and control totals for review; and a Slack agent used by the product team that answers employee questions, links relevant documentation, and files tickets when it surfaces a new issue.

In a sense, it is a continuation of the philosophy OpenAI espoused for individuals with last week’s Codex desktop release: the agent joins the workflow where work is already happening, draws in context from the surrounding apps, takes action where permitted, and keeps moving.

From GPTs to a broader agent push

Workspace agents are not a standalone launch. They sit inside a roughly 12-month arc in which OpenAI has been systematically rebuilding ChatGPT, the API, and the developer platform around agents.

Workspace agents are explicitly positioned by OpenAI as an evolution of its custom GPTs, introduced in late 2023, which gave users a way to create customized versions of ChatGPT for particular roles and use cases.

However, now OpenAI says it is deprecating the custom GPT standard for organizations in a yet-to-be determined future date, and will require Business, Enterprise, Edu and Teachers users to update their GPTs to be new workspace agents.

Individuals who have made custom GPTs can continue using them for the foreseeable future, according to our sources at the company.

In October 2025, OpenAI introduced AgentKit, a developer-focused suite that includes Agent Builder, a Connector Registry, and ChatKit for building, deploying, and optimizing agents.

In February 2026, it introduced Frontier, an enterprise platform focused on helping organizations manage AI coworkers with shared business context, execution environments, evaluation, and permissions.

Workspace agents arrive as the no-code, in-product entry point that sits on top of that stack — even if OpenAI does not explicitly describe the architectural relationship in its materials.

The subtext across all three launches is the same: OpenAI has decided that the future of ChatGPT-for-work is fleets of permissioned agents, not single chat windows — and that GPTs, its first attempt at letting businesses customize ChatGPT, were not enough.

Governance and enterprise safeguards

Because workspace agents can act across business systems, OpenAI puts heavy emphasis on governance. Admins can control who is allowed to build, run, and publish agents, and which tools, apps, and actions those agents can reach.

The role-based controls are more granular than the ones most custom-GPT rollouts ever had: admins can toggle, per role, whether members can browse and run agents, whether they can build them, whether they can publish to the workspace directory, and — separately — whether they can publish agents that authenticate using personal credentials.

That last setting is the risky case, and OpenAI explicitly recommends keeping it narrowly scoped.

Authentication itself comes in two flavors, and the choice has real consequences. In end-user account mode, each person who runs the agent authenticates with their own credentials, so the agent only ever sees what that individual is allowed to see.

In agent-owned account mode, the agent uses a single shared connection so users don’t have to authenticate at run time. OpenAI’s documentation strongly recommends service accounts rather than personal accounts for the shared case, and flags the data-exfiltration risk of publishing an agent that authenticates as its creator.

Write actions — sending email, editing a spreadsheet, posting a message, filing a ticket — default to Always ask, requiring human approval before the agent executes.

Builders can relax specific actions to “Never ask” or configure a custom approval policy, but the default posture is human-in-the-loop.

OpenAI also claims built-in safeguards against prompt-injection attacks, where malicious content in a document or web page tries to hijack an agent. The claim is welcome but not yet proven in the wild.

For organizations that want deeper visibility, OpenAI says its Compliance API surfaces every agent’s configuration, updates, and run history.

Admins can suspend agents on the fly, and OpenAI says an admin-console view of every agent built across the organization, with usage patterns and connected data sources, is coming soon.

Two caveats worth flagging for security-sensitive buyers: workspace agents are off by default at launch for ChatGPT Enterprise workspaces pending admin enablement, and they are not available at all to Enterprise customers using Enterprise Key Management (EKM).

Analytics and early customer signal

OpenAI also ships an analytics dashboard aimed at helping teams understand how their agents are being used. Screenshots in the launch materials show measures like total runs, unique users, and an activity feed of recent runs, including one by a user named Ethan Rowe completing a run in a #b2b-sales channel.

The mockup detail supports OpenAI’s broader point: the company wants organizations to measure not just whether agents exist, but whether they are being used.

The clearest early-adopter signal in the launch itself comes from Rippling. Ankur Bhatt, who leads AI Engineering at the HR platform, says workspace agents shortened the traditional development cycle enough that a sales consultant was able to build a sales agent without an engineering team. “It researches accounts, summarizes Gong calls, and posts deal briefs directly into the team’s Slack room,” Bhatt says. “What used to take reps 5–6 hours a week now runs automatically in the background on every deal.”

OpenAI’s announcement names SoftBank Corp., Better Mortgage, BBVA, and Hibob as additional early testers.

The era of the digital coworker

Workspace agents do not land in a vacuum. They land in the middle of a broader OpenAI push — through AgentKit, through Frontier, through the Codex overhaul — to make agents more persistent, more connected, and more useful inside real organizational workflows.

They also land in a deeply crowded field: Microsoft Copilot Studio is wired into the Microsoft 365 base, Google is pushing Agentspace, Salesforce has rebuilt itself as agent infrastructure with Agentforce, and Anthropic recently introduced Claude Managed Agents, all different flavors of similar ideas — agents that cut across your apps and tools, take actions on schedules repeatedly as desired, and retain some degree of memory, context, and permissions and policies.

But this launch matters because it turns OpenAI’s strategy into something concrete for the teams already paying for ChatGPT, and because it quietly retires the product those teams were most recently told to standardize on.

If workspace agents live up to the pitch — shared, reusable, scheduled, permissioned coworkers that follow approved processes and keep work moving when their human is offline — it would mark a meaningful change in what workplace software does. Less passive software waiting for input, more active systems helping teams coordinate, execute, and move faster together.

The era of the digital coworker has begun. And, on OpenAI’s plans at least, the era of the custom GPT is ending.

Over the past couple of decades we’ve witnessed a whirlwind of cultural changes in the music industry, but also major changes in terms of how we find and listen to music. And there’s arguably one entity that has contributed to these shifts more than any other: Spotify — which was founded 20 years ago today (April 23). Feel old yet? I sure do.

For many music lovers out there, myself included, Spotify was their introduction to music streaming, and over the last 20 years it’s climbed to the top of the ladder, amassing over 750 million users and cementing its position as one of the best music streaming services — and in the eyes of many, the daddy of them all.

NASA announced that it will launch the Nancy Grace Roman space telescope into orbit in September 2026, eight months ahead of schedule. The new space telescope is expected to deliver 20,000 terabytes of data to astronomers over the course of its life.

That will add to 57 gigabytes of breath-taking imagery downlinked daily from the James Webb Space Telescope, which began its work in 2021, and the start of a survey later this year by the Vera C. Rubin Observatory in the mountains of Chile, which is expected to gather 20 terabytes of data each night.

For comparison, the Hubble Space telescope, once the gold standard, delivers just 1 to 2 gigabytes of sensor readings each day. It’s been a while since all those readings were pored over by hand, but like everyone else with a pile of data, astronomers are now turning to GPUs to solve their problems.

Brant Robertson, a UC Santa Cruz astrophysicist, has had a front-row seat to this step change in science while supporting or using data from these missions. Robertson has spent the past 15 years working with Nvidia to apply GPUs to the problems of understanding space, first through advanced simulations testing theories about supernnova explosions, and now developing the tools to analyze a torrent of data from the newest observatories.

“There’s been this evolution [from] looking at a few objects, to doing CPU-based analyses on large scales of the data set, to then doing GPU-accelerated versions of those same analyses,” he told TechCrunch.

Robertson and then-graduate student Ryan Hausen developed a deep learning model called Morpheus that can pore over large data sets and identify galaxies. Their early AI analysis of Webb data identified a surprising number of a specific type of disc galaxies and added a new wrinkle to theories about the development of our universe.

Now Morpheus is changing with the times: Robertson is switching its architecture from convolutional neural networks to the transformers behind the rise of large language models. That will result in the model being able to analyze several times the area than it can currently, speeding up its work.

Robertson is also working on generative AI models trained on space telescope data to improve the quality of observations collected by ground telescopes, which are distorted by Earth’s atmosphere. Despite advances in rocketry, it’s still hard to get an 8 meter mirror into orbit, so using software to improve Rubin’s observations is the next best thing.

But he’s still feeling the pressure of global demand for GPU access. Robertson has used the National Science Foundation to build a GPU cluster at UC Santa Cruz, but it is becoming outdated even as more researchers want to apply compute-intensive techniques to their work. The Trump administration proposed cutting the NSF’s budget by 50% in its current budget request.

“People want to do these AI, ML analyses, and GPUs are really the way to do that,” Robertson said. “You have to be entrepreneurial…especially when you’re working kind of at the edge of where the technology is. Universities are very risk averse because they just have constrained resources, so you have to go out and show them that, ‘look, this is where we’re going as a field.’”

There’s little point in setting up your own shed-based clean room for semiconductor purposes if you don’t try to do something practical with it. Something like responding to the RAMpocalypse by trying to make your own RAM, for example.

After all, what could be so hard about etching the same repeating structures over and over? In a recent video, [Dr. Semiconductor]’s experience doing exactly this are detailed, with actual DRAM resulting at the end.

We covered the construction of the clean room shed previously, which should provide at least the basic conditions to produce semiconductors without worrying about contaminating dies. From here the process is reminiscent of etching PCBs, with a prepared surface coated with photoresist. Using UV exposure through a mask, the pattern is etched into the photoresist and from there the pattern is subsequently etched into the wafer’s surface.

With the patterns formed, the next step is doping of the silicon in order to create the active structures, i.e. the transistors and capacitors. Doping can be done in a variety of ways, with ion implantation being the industry standard method, but a bit too expensive and bulky for a shed fab. Instead a spin-on-glass method was used. After this the remaining functional structures can be built up.

If anyone was expecting to see a DDR5 DRAM die pop out at the end, they’re bound to be disappointed. The target here was to create a 5×4 array of DRAM cells, for a dizzying 20 bits. Still, the fact that it’s possible to DIY DRAM like this at home is already pretty awesome, with clearly plenty of room to push it towards and past fabrication nodes of the 1990s and beyond.

Although the produced DRAM cells have fairly leaky capacitors, they’re good enough for their purpose, and the plan is to scale up to a large DRAM array from here. Whether the DRAM control logic will also be implemented in hardware like this remains to be seen, but the video’s ending makes it clear that the goal is to attach it to a PC somehow.