Blogroll

Investors are thinking twice about the stocks that have most benefited from the AI boom.

The 20 most valuable chip stocks have lost $1.3 trillion over the past week after a big sell-off, according to analysis by CNBC.

According to the outlet's data, Nvidia led in losses after investors liquidated $238 billion since the market closed on Friday. There's perhaps no bigger sign that investors are getting cold feet when it comes to artificial intelligence, as Nvidia has benefited more than any other company from the AI boom. 

Win an Apple Watch in the Mashable Big Guessing Game!

Other companies in the memory space have also taken a big hit. SK Hynix lost $176 billion. Samsung is down $173 billion. Taiwan Semiconductor Manufacturing Co. lost $119 billion. Micron shed $113 billion. And AMD is down $110 billion.

Tech companies have seen demand in memory and storage skyrocket as AI companies buy out supply to power their insatiable compute needs. Due to this, RAM and SSD storage supply has dwindled for everyday consumers. Consumer tech companies like Apple have been forced to institute price hikes on their products as a result.

Despite this, however, investors are seemingly starting to question their AI-related investments.

“This decline appears to be driven largely by sentiment rather than fundamentals,” Morningstar's Chief Equity Strategist Michael Field told CNBC. “Simply put, it’s loss of confidence,” he added. “We continue to see upside in many AI names, but these are growth stocks, and, as such, much of their value comes from cash flows expected far out into the future, which requires a lot of faith from investors.”

Investor concern surrounding AI technology seems rooted in the fact that while these companies make billions of dollars, they're spending way more than they make. According to recent reporting from the Financial Times and Ed Zitron, AI giant OpenAI had a net loss of $38.5 billion last year. Just this week, OpenAI announced that it will spend $750 billion on infrastructure through 2030. Finally, Google recently experienced its first-ever negative cash flow quarter, thanks to spending on AI infrastructure.

Of course, investor sentiment on AI can turn around quickly.

I've had Roku devices in my house for years, going all the way back to when it was just a little box sitting next to the TV. At this point, I've got several Roku TVs and a few sticks spread across different rooms, and for the most part, I've always used them the same way. Open an app, pick something to watch, and move on. It works, so I never really thought much about what else was there.

If you own an old Samsung Galaxy phone you aren't using anymore and need a secondary PC, consider converting it into a mini PC instead of buying one. The only prerequisite is support for Samsung DeX. While you might think that a desktop experience on an Android phone would be dragged down by all kinds of limitations and annoyances, I can attest that it's much better than I expected.

Most people think of the built-in Windows Snipping Tool as the little thing that pops up when you press the "Print Screen" button, but the truth is that this app can do far more than take screenshots.

The luxury sedan segment has long been dominated by familiar names. Buyers shopping for a flagship model have traditionally gravitated toward the Mercedes-Benz S-Class, BMW 7 Series, Audi A8, or Lexus LS, expecting class-leading comfort, cutting-edge technology, and impeccable craftsmanship.

My trash day reminder is one of my favorite automations. Using an integration that pulls the information from an official website, Home Assistant always knows which trash collection is due. Even using spoken reminders, however, we still sometimes forget to put the trash out on the right day, so I decided to build something that would be harder to ignore.

Android is in a strange place right now. New phones have never been better, yet it's harder than ever to justify buying the latest one. You're better off keeping your current device until it's no longer up to the job. And when you do need to replace it, buy an older model instead.

Netflix is arguably the most popular streaming service in the world. However, it's not the only streamer that boasts great movies. HBO Max, Hulu, Paramount+, and Prime Video all have vast libraries with terrific movies in various genres. If you know where to look, you'll find plenty of certified classics.

Scaling fidelity over sheer count, targeting the capabilities agents actually lack, and evolving with the models they train. At a glance

We built twelve training worlds for computer-use agents: ten deep domain worlds and two capability worlds, each drilling a single control rendered in many forms (date pickers and nested filters). Depth is what makes them worth training on: these worlds reproduce an application’s real behavior, come seeded with realistic data, and keep state coherent across screens and users. Trained on all twelve, a 9B model nearly doubles its base score (36.5% to 67.1%), coming within fourteen points of GPT-5.4. The experiment taught us several lessons: 

• High simulation fidelity is a must-have; shallow worlds hurt the agent. Trained on shallow and deep builds of the same sites, the model regressed on the shallow ones but improved on the deep ones.
• Agents often struggle with the same challenging UI elements, like date pickers and nested filters. Drilling those controls in varied forms taught the model to operate them in domains it never saw in training.
• Co-evolving the model, the world, and the verifier improves all of them. As the world grows more correct and its tasks grow harder, the model climbs with it.
• Reinforcement learning against the worlds pushes the agent past imitation. Using the grounded verifier as the reward, RL lifts held-out performance and teaches the agent to reach the goal in fewer steps.
• We’re releasing four of the worlds with their code, data, and grounded graders, to support research on high-fidelity computer-use worlds.
  Github:  microsoft/Echoverse: Deep, Evolving Environments for Computer-Use Agents (opens in new tab)
  Hugging Face: microsoft/Echoverse · Datasets at Hugging Face (opens in new tab)
  Technical Report: https://www.microsoft.com/en-us/research/publication/echoverse-deep-evolving-environments-for-training-computer-use-agents-at-scale/

A computer-use agent learns the results of what its actions do only where they have real consequences. A click changes saved state, a message reaches a real person, or a page that refuses to move tells the agent its last move did nothing. A screenshot can show what an interface looks like, but only a working world shows what an action caused.

The consequences worth learning from are stateful, and most of them sit behind a login. The work people want automated lives in closed systems: email and chat, banking, health records, the internal consoles for cloud and ML. You cannot train an agent against the live versions of these. Every attempt writes to a real account, there is no reset between tries, and the true state stays hidden behind the screen. So you rebuild the system as a synthetic world where the database is yours: the state is real and changes for real, but it is safe to break, quick to reset, and graded from the data rather than a screenshot.

By a world we mean three things bound together: an environment (the application, its state, and the actions that change it), the tasks that set goals in it, and a verifier that grades the outcome against ground truth. The community is now good at making them: pipelines stand up an application, seed it, generate tasks, and attach verifiers, yielding hundreds of environments and thousands of checkable tasks. This work builds on that progress. However, once worlds are plentiful and its internal structure becomes the bottleneck: regardless of whether state stays coherent across users and screens, workflows keep their dependencies, a weak skill recurs in enough forms to generalize, and success is judged by outcome or by appearance.

Our bet, the one Echoverse tests, is that the real leverage comes less from adding worlds than from a loop that keeps improving the ones you already have. It treats building the environment and training the model as one process, not two stages: run a model in a world, find where it fails, make the world, its tasks, and its verifiers more faithful or more demanding there, train on the sharper signal, and repeat. Ordinary fine-tuning improves only the model. Here the same graded run that measures the model also improves the world that judged it, so a static benchmark saturates while the loop compounds.

Three levers keep that loop productive, none of them raw environment count. Depth: behaviorally faithful worlds for the domains that matter, including the closed and proprietary ones. Capability targeting: narrow worlds built around the exact interaction a model keeps failing. Co-evolution: improving the environment, its tasks, and its verifiers on every graded run, not just the model.

Figure 1: The learning loop: every graded run is read twice. Surviving failures become model training data, and defects in the world, its tasks, or its verifier become repairs. The same graded run that measures the model also sharpens the world. Why synthetic, and why deep?

Open, login-free sites might seem to remove the need for synthetic worlds, but they make a poor training ground for a different reason: they will not hold still. Pages get redesigned, listings and dates roll forward, and hosts throttle or block automated traffic, so a benchmark that is pinned to them drifts, and no two runs face the same site. An occasional evaluation can absorb that; training cannot, since it runs the same task thousands of times and needs the same world each time. A synthetic world is fixed in time and data: the calendar does not move, the seed data does not churn, and a task means the same thing on the thousandth rollout as on the first. We trade a little surface realism for a world we fully control.

Control is only the floor. A world can be perfectly stable and still be hollow, so what earns training time is depth: not its page count but how faithfully it preserves the causal structure of the work.   Five properties set the bar: behavioral fidelity (controls, permissions, and errors follow the product’s logic); coherent state (a sent message appears for its recipient, a cancelled meeting clears both calendars); workflow depth (an early choice constrains what happens later); authoritative verification (application state, not pixels); and domain value (the workflow is worth improving). In the systems that matter most, the difficulty lives in permissions, shared state, and audit histories: exactly the structure a shallow clone skips. Above this bar, more environments add variety; below it, they add noise.

How the Echoverse factory works?

Echoverse is a single pipeline with two outputs: full domain worlds that preserve workflow depth, and capability worlds that vary one diagnosed interaction. Both lean on the fact that we own the database underneath, so success is a property of the app’s own state, not a model’s read of a screenshot.

Building the world

The pipeline expands a handful of seed scenarios into a spec, then compiles it into machine-checkable claims about routes, state, and behavior. Only then does it generate the app: a FastAPI and SQLite backend under a React interface. A fresh app is a hypothesis, not a world: the builder runs every claim against the running environment, repairing the database, backend, or frontend until each passes, then writes a readiness record that separates hard blockers from advisory risks. A world with open blockers does not advance. Depth here is not a promise in a prompt; it is the list of claims the world has been shown to pass.

Growing the corpus

A world that builds cleanly is still not training data. We reground each task on the live database, drawing goals from entities that actually exist, then send every goal through a panel of analyzers: are its entities real, is the goal plausible, does its difficulty match the work, and, the sharpest test, can an agent driving the real UI complete it? That last check runs in the browser, catching goals no interface can satisfy before a model ever sees them. A generated goal is a claim; a solve against the real app is proof.

Every failure becomes an issue tagged by the layer that must change: database, backend, frontend, task text, or verifier. Layer-specific fixers apply the repair, re-check it against the running app, and roll it back if it regresses. The loop re-scores against database ground truth until the pass rate stops climbing, and each surviving task is exported carrying the exact check that grades it. Those tasks become training data through one process: GPT-5.4 solves each task, a verifier keeps the trajectories that pass ground truth, and those become the supervised fine-tuning (SFT) data behind every experiment below.

Building the world and growing the corpus are not two stages but rather one loop: most defects belong to the world, so we re-version the environment with every iteration. Harder tasks expose gaps in the world, and a sturdier world can carry harder tasks, so each round leaves both stronger.

Figure 2: The environment factory: the two loops behind every world. Phase 1 expands a handful of seeds into an app, then repairs the database, backend, and frontend until it passes machine-checkable claims. Phase 2 regrounds tasks on live data, runs a panel of analyzer and layer-specific fixer agents, and re-scores against database ground truth until the pass rate plateaus. Many of those fixes land in the world itself (dashed arrow).  The verifier is grounded in the database

Every task carries its own answer key, a value or a state change minted from the real database by a SQL query at generation, true by construction and re-checked after the agent finishes. A read is graded on semantic equivalence to the stored value ($288 for $287.62 passes); a write on a real before/after database diff, so claiming a ticket was closed fails unless the row flipped; a read_write scores the lower of the two. Grading is hard to game, grounded rather than labelled, and uniform across an EchoStay booking, an EchoForge issue, and an EchoBank transfer.

Full domains carry the workflow

The domains with the most consequential work are the hardest for public benchmarks to reach: closed, proprietary systems where the difficulty lives in permissions, shared state, and history, not layout. A faithful clone has to reproduce that. What matters is not the pixels but that an action’s consequences reach across screens and users, so a task can run a real workflow and be graded on the state it leaves behind.

The ten Echo domains span communication, technical work, regulated records, community, media, and travel. Where a rich public dataset exists we build on it: EchoStay is seeded from InsideAirbnb, so its listings, hosts, reviews, and amenities are real rather than invented, and EchoForum sits on a public forum corpus of 2.55 million comments. Where none exists, as with mail, calendar, banking, and health records, a seeding pipeline generates the state under strict constraints, dense and internally consistent, not a handful of placeholder rows.

Workflow categoryEnvironmentsDepth the world has to carryCommunication & coordinationEchoMail, EchoCalendar, EchoChatShared threads, schedules, participants, permissions, historiesTechnical creation & operationsEchoML, EchoForgeArtifacts, configuration, dependencies, roles, multi-stage changesRegulated records & transactionsEchoBank, EchoCareBalances or records, authorization, audit history, consequential writesCommunity, media & travelEchoForum, EchoTunes, EchoStayPersistent preferences, social state, search, booking, account actionsTable 1: The ten full-domain environments of the Echo family, grouped by the work they represent. Each is a faithful stand-in for a widely used product, named for the workflow rather than the brand.

That accumulated state is what makes an action’s consequences reach across screens and users. A booking in EchoStay moves through search, listing, availability, and payment across roughly 87 routes and 23 tables, but not a single confirmation screen; an EchoMail thread carries intent from draft through delivery, reply, and label state; an EchoCare order writes each change to an audit trail. The tasks are expensive because of it, often five to twenty actions deep, and finished only when the underlying state has changed.

Figure 3: Per-domain detail across the Echo suite. Each ships as a self-contained, fully-interactive clone of the app it models, with its own backend, seeded database, and feature surface. Counts are grounded database state, not mockups. Capability worlds isolate one skill

Not every weakness represents a missing domain; some are caused by a single control that the agent cannot reliably operate. Picture an agent booking a trip: it searches, filters, opens the right listing, then stalls at the date picker, unable to turn “the second week of March” into the right clicks on an unfamiliar calendar. Building another booking site would not fix that. The skill is learned only when the control itself appears in enough forms, and date pickers and nested filter-and-search are ubiquitous on the live web, rendered a hundred different ways, exactly the variability a single deep app cannot supply.

So we isolate the control and widen the interaction, mass-producing it across layouts, states, and constraints, then generating grounded tasks over each. The datepicker world renders one date control as six core widgets across 10 contexts and holds out 10 new unseen ones, from calendar heatmaps to scroll wheels and fiscal-quarter pickers; its hardest tasks turn transcription into reasoning, resolving “the last Thursday of January 2026” or “10 business days after a start date” to one exact, widget-reachable date. The nested-filter world varies 20 widget families and holds out nine compound-panel families as out-of-distribution, grading every submission by whether the filtered results actually meet the requested conditions, judged by the app’s own logic rather than by appearance.

Figure 4: Every widget family the two skills cover, split into training (in-distribution) and evaluation-only (held out): nested filters, 20 families plus 9 held-out compound panels; date pickers, 6 core types across 10 contexts plus 10 held-out widgets. Figure 5: Date pickers and nested filters themed across domains: nested filters over six verticals, from real estate to pet adoption; date pickers over ten contexts, from scheduling to insurance. What deeper, targeted worlds change

More trajectories do not automatically provide more training signal. What matters is depth: whether an episode carries a task through the dependent steps of a real workflow rather than just rehearsing an action in isolation. Two experiments make the difference concrete from opposite ends: one goes deeper on a whole domain, the other narrows to a single broken skill.

Shallow worlds backfire; deep worlds transfer

A shallow world is the cheap option. It stands up fast and looks convincing, but it only rehearses isolated, correct-looking clicks. Train on that and the model will pick up the wrong reflexes, over-stepping and looping and repeating dead actions, because nothing in the easy world ever punished them. A deep world costs more, but its trajectories carry the dependent structure that transfers to the live site.

To isolate that, take two live WebVoyager domains, Allrecipes and Hugging Face, and compare three checkpoints: the base model and two trained on shallow-world and deep-world trajectories built for those domains. The shallow world poses short, self-contained tasks; the deep world poses tasks that run across dependent steps, where an early action changes the state, options, and verification available later. Both give the model the same domain exposure, so only depth differs, and evaluation uses tasks from the public WebVoyager benchmark for these domains, run on the live sites outside any training world.

On Allrecipes, the shallow world pulls the model down, 80.0% to 75.0%; on Hugging Face it stays flat at 48.0%. Only the deep world improves both, lifting Allrecipes to 85.0% and the harder Hugging Face split to 65.0%. With exposure held equal, the gap is depth: the deep model loops less, and of the 37 Hugging Face tasks, those that exhaust their step budget fall from 15 to nine. What separated the two was not how much the model saw, but whether what it saw preserved the structure of the work.

Figure 6: Deep versus shallow worlds for two live WebVoyager domains, with identical domain exposure and different task depth. Deep lifts both; shallow drops below base on Allrecipes and stalls on Hugging Face. Precision about one skill

The datepicker and nested-filter worlds drill exactly the controls our evaluations flagged, and the two skills reinforce each other. Datepicker training lifts datepicker evaluations (in-distribution 60.0% to 82.6%, held-out layouts 34.0% to 54.0%); filter training lifts held-out filters 62.8% to 84.1%. Gains that hold on forms never trained on indicate that the model learned a rule, not a layout. The skills transfer across each other rather than competing: training either one alone still lifts the other, and training both is the best all-rounder on every split. Against GPT-5.4 as a frontier reference, that combined model already edges ahead on nested filters and closes most of the datepicker in-distribution gap, trailing clearly only on held-out datepickers. And the rule reaches the open web, lifting Online-Mind2Web 29.5% to 34.3% on sites it never saw. 

Figure 7: Targeted training, targeted gains: training either date pickers or nested filters lifts both controls, including held-out widgets and compositions neither was trained on, and training both is the best all-rounder on every split. Higher is better. From synthetic worlds to the live web

Three models run through the rest of this section. Base is Qwen3.5-9B given only a handful of synthetic trajectories, just enough to align a general model to the browser action space. Our model is that same 9-billion-parameter network trained on the full synthetic corpus. GPT-5.4 is a far larger frontier model, included as a reference ceiling.

Does the skill survive the open web? We evaluate our model, unchanged, on WebVoyager and Online-Mind2Web, benchmarks it never trained on. They barely overlap with what we built: both are dominated by open, public sites and read-mostly browsing, while our worlds train login-gated, write-heavy workflows. A large jump was never the point; direction is. The frozen model clears base on both, WebVoyager 66.5% to 71.5% and Online-Mind2Web 40.5% to 43.4% (without BrowserBase, 50.9% to 55.6% and 29.5% to 37.2%), reported through BrowserBase because a hosted browser strips the datacenter bot-blocks and rate limits that otherwise depress every agent’s score. With no live-web data in the mix, this is transfer, not memorization.

Figure 8: Synthetic training transfers to the live web. The full-corpus model, on two benchmarks it never trained on, clears base on both; scores run through BrowserBase to remove datacenter bot-blocks.

The modest live-web gain is a coverage effect, not a ceiling: aim at a live domain and it grows. EchoForge, our code-hosting world, is the same kind of app as GitHub, one of the live sites WebVoyager tests. Add EchoForge to the training mix and the live GitHub score climbs 58.5% to 63.4%, with the overall live scores rising too (WebVoyager 50.9% to 52.9%, Online-Mind2Web 29.5% to 31.1%). The average simply reflects that most of what we built sits in domains these benchmarks never touch.

Figure 9: Closing the gap to the frontier, per environment. The green bar is the gain from base to our model; the faded remainder is the distance still to GPT-5.4. Our model surpasses GPT-5.4 on EchoBank and both nested filters and closes most of the gap elsewhere; each model’s exact score is labelled on the right.

The domains we built, most of them closed and login-gated, tell the opposite story. Across all fourteen, the model nearly doubles base, 36.5% to 67.1%, and where base was weakest it climbs three- to nine-fold, with EchoCalendar, EchoML, EchoChat, EchoCare, EchoForge, and EchoForum all moving from single or low double digits into the forties through sixties. That puts a 9-billion-parameter model within fourteen points of GPT-5.4 on the average (67.1% against 80.7%). On EchoMail, EchoBank, and both nested filters, it matches or beats the far larger frontier model outright, trailing by only a few points on in-distribution datepickers. What gets a 9B model this close is not scale but training data that is deep, targeted, and checkable, exactly what the factory is built to produce. 

What scaling buys, and what it doesn’t

We scaled two axes separately: more trajectories through a fixed set of environments, drawn in equal numbers from each, and more distinct environments. They behave differently. More trajectories on the same worlds keep lifting the in-domain average, though the gains keep shrinking, while transfer to the live web flattens outright: from 6,400 to 20,000 trajectories, WebVoyager holds steady (54.8% to 55.6%) and Online-Mind2Web slips (40.1% to 37.2%). Since every point samples the worlds equally, this is no artifact: each environment holds only so much transferable skill, and once a model has drawn it out, more rollouts mostly polish what it already does. 

Scaling environments produces the opposite result. The average keeps climbing as breadth grows, and WebVoyager reaches its best only with the full set. For generalization, the lever is diversity, not volume. A model reaches sites it never saw by training across many kinds of work, not by seeing one kind many more times. 

Even so, scale itself is not the lever on either axis. A large trajectory budget spent on shallow worlds, or graded against the wrong answer, moves the synthetic number and goes nowhere on the live web. What travels is inside each trajectory: depth that preserves a real workflow, targeting that drills the control an agent fails, and database-grounded grading that keeps the signal honest.

Figure 10: Two scaling axes, scored without BrowserBase. Left: more trajectories on a fixed set of worlds, drawn in equal numbers from each, with the x-axis spaced by actual trajectory count. The synthetic average keeps rising, but live-web transfer saturates, WebVoyager flat and Online-Mind2Web slipping past 6,400 trajectories. Right: more environments, where breadth keeps the synthetic average and WebVoyager climbing. Diversity of environments, not trajectory volume, is what carries skill to unseen sites. The model is not the only thing that learns

The score an agent earns is never the model alone. It comes from a coupled stack: the agent, the environment, the task, and the verifier. A zero can mean the agent failed, or the control is broken, or the requested state is impossible, or the verifier checks the wrong thing. Reading every zero as model supervision trains on defects that should have been repaired. So, we read every graded rollout as a test of the whole stack and let the whole stack learn. The environment improves as broken controls and wiring get fixed, the tasks as goals are re-grounded and made harder, the verifier is fixed when it drifts out of sync with the data. Only failures that survive all three become model curriculum.

EchoStay made this visible. Its failures traced to the world, not the agent: a guest-count control silently broke booking tasks, so a correct booking could never register. Fixing it raised the share of those bookings that could be completed at all from 48% to 78%, recovering 15 of the 24 that had been blocked. The same loop finds different faults elsewhere: EchoForum needed frontend fixes and a page-load speedup, which took one failing set of 37 tasks from 0 solved to 36; EchoChat’s verifier had drifted out of sync with the data, and realigning it lifted the share of gradable tasks from 34% to 99%; EchoCare needed one state-wiring fix; EchoForge had the backend logic but no UI control to reach it.

As the world sharpens, the model climbs with it. Re-running the loop on EchoStay across two rounds, the model trained on its corpus more than doubles, from 16.2% to 38.5%, two-thirds of the distance to GPT-5.4’s 50.4%. The model is not the only thing that learns; it is the thing that compounds once everything under it learns.

Figure 11: Co-evolution lifts the model on EchoStay. As the world went from v1 to v2, the model trained on it more than doubled, from 16.2% to 38.5%, a separate measure from the world’s own solve rate. Higher is better. 

That boundary between repairing the world and teaching the model is easy to hold inside a controlled environment, where both are inspectable. The live web erases it: there is no world to repair mid-task, so when an action lands on nothing, correctness rests entirely on the agent noticing and choosing differently. That is the last thing a world has to teach, and where the live web is least forgiving.

From SFT to RL: Turning worlds into RLEs

Every result so far comes from imitation: the 9B model copies the trajectories GPT-5.4 got right. Imitation inherits a ceiling, though: a clean demonstration never shows how to recover from a mistake or when to stop, the failures that break agents in the wild. Reinforcement learning optimizes the outcome we grade and lets the model learn from its own trajectories, not a teacher’s.

But reinforcement learning needs an RL environment (RLE) it can drive at scale. Each rollout needs a reset to a known state, throughput to sample in parallel, and a reward it can trust, and a run replays the same task thousands of times. The live web is not an RLE: it will not reset, so no two rollouts begin alike; it throttles and blocks automated traffic well before RL’s scale; and it exposes no ground truth, only a screenshot a second model must judge, so the reward is as noisy as the judge and a policy learns the judge’s blind spots rather than the task. Echoverse is an RLE by construction. Every world is a self-contained app we snapshot and reset per rollout, run in parallel, and grade from its own database, so the verifier that filtered the SFT data returns a grounded, verifiable reward rather than one inferred from pixels. The same worlds that benchmark an agent train one.

Figure 12: Reinforcement learning on an Echoverse RLE. From the SFT policy we roll out a group of trajectories in one world; each is a sequence of act and execute steps that changes the database. A grader, the same grounded verifier that filtered the SFT data, sits outside the environment and scores each rollout’s final database state into a reward. The group of rewards updates the policy, and the loop repeats across every training world.

We take the SFT model as the starting policy and run RL against five worlds: EchoBank, EchoForge, EchoForum, EchoStay, and EchoTunes. Tasks come from the harder end of each world, where the SFT policy still leaves headroom, and each update draws on several graded rollouts. Each rollout earns two rewards: a trajectory reward from our database-grounded verifier (LLM judge GPT-4.1), and a dense per-step reward from a multimodal judge that grades each screenshot (GPT-4.1 vision). We train on roughly 100 tasks per world beyond the SFT data, for two epochs. On a held-out set of 25 tasks per world, the judged score rises from 58% to 69%. The teacher taught it what to do; the world taught it when to stop, when to recover, and when to give up.

Figure 13: Reinforcement learning on five worlds, over twoepochs. Left: the held-out judge score (25 tasks per world) climbs from 58% to 69%. Right: the critic’s mean score, the RL reward signal, trends up through training. The reward sums a trajectory reward from our database-grounded verifier (LLM judge GPT-4.1) and a dense per-step reward from a multimodal judge (GPT-4.1 vision). Where this leaves us

A world is no longer a fixed benchmark you score against; it is a training surface you keep improving, where the same graded run that measures the model also sharpens the world that judged it. Deep worlds transferred where shallow clones pulled capability down; one widget rebuilt in a hundred forms taught a skill that reached the live web; co-evolution moved both sides at once; and reinforcement against the same worlds pushed the agent past imitation, lifting held-out performance and trimming wasted steps.

The durable advantage is not the largest inventory of synthetic websites. It is a factory that diagnoses what an agent cannot yet do, builds or repairs the world that teaches it, protects the capability already earned, and runs the loop again. The next turns scale three fronts at once. First, more deep worlds for the closed domains public benchmarks cannot reach. Second, more capability worlds for the interactions models keep failing. And, above all, more reinforcement against those grounded worlds: longer runs, harder tasks, and wider reward exploration that push the agent’s behavior and its performance further than imitation ever could. The levers compound: deeper and broader worlds make stronger RL, stronger RL boosts the agent, and every round exposes the next capability to build.

We are releasing a piece of the factory: environment code and graded test tasks for four worlds, two deep domains (EchoStay and EchoForge) and two capability worlds (the datepicker and nested-filter, each with an in-distribution and a held-out split). Every task carries the database-grounded verifier that scores it, so the same worlds can benchmark an agent or train one. Code and tasks: https://aka.ms/echoverse

When worlds grow at the frontier of an agent’s competence, evaluation stops being a scoreboard and becomes the engine that decides what to build next: worlds that keep learning alongside the agents they train.

Acknowledgments

We thank Alexey Taymanov, Andrew Zhao, Aravind Rajeswaran, Corby Rosset, Hussein Mozannar, Luiz Do Valle, Sara Abdali, Spencer Whitehead, Vibhav Vineet, Zach Nussbaum, Yadong Lu, Pashmina Cameron, Rafah Hosn, and Chinmay Karkar for their valuable help, insightful discussions, and continued support throughout this work.

Opens in a new tab

The post Echoverse: Deep, evolving environments for computer-use agents appeared first on Microsoft Research.

Critical coverage usually draws an angry email or two. For Ina and David Steiner, it led to live cockroaches, a fetal pig, and a bloody Halloween mask.

On Monday, July 27, eBay and several former executives agreed to pay $55.7 million to resolve the couple's lawsuit over the 2019 corporate harassment campaign.

The Steiners are the married founders of EcommerceBytes, a news site covering eBay and the broader ecommerce industry. They filed the civil case in 2021 after members of eBay's security team sent them threats, disturbing packages, and unwanted visitors in an effort to influence the site's reporting.

Of the $55.7 million settlement, $48.7 million will go directly to the Steiners. EBay will pay $46.15 million, former CEO Devin Wenig will pay $2 million, former senior vice president Wendy Jones will pay $500,000, and former chief communications officer Steve Wymer will pay $50,000. The remaining $7 million will go to nonprofit organizations. EBay will contribute $6 million, while Wenig will donate another $1 million to a group protecting First Amendment rights in Ina Steiner's name.

The agreement also allows the Steiners to keep talking publicly about the case. It contains no confidentiality provision, a priority for the couple because they wanted the settlement to discourage other corporations from trying to intimidate journalists over critical coverage.

To understand why the Steiners considered that transparency so important, it helps to go back to how the campaign began.

From critical coverage to criminal charges

When eBay's security team began targeting the Steiners in 2019, the couple had already spent two decades covering the company and other online marketplaces, such as Amazon and Etsy. Through EcommerceBytes, they reported on the issues affecting online sellers, including fees, policy changes, and the executives making those decisions.

The internal lead-up to the campaign only became clear years later — the Steiners initially filed their civil lawsuit in July 2021 and amended it in March 2023. Ina Steiner later told Wired in July 2026 that the litigation gave the couple access to roughly 68,000 documents showing how eBay executives discussed the site behind the scenes.

Those records traced a steady escalation. According to the amended complaint, Wenig sent Wymer a link to an April 10, 2019, EcommerceBytes article about his compensation. Wymer responded, "We are going to crush this lady."

The following month, Jones allegedly asked eBay security chief Jim Baugh to address criticism of the company "off the radar" and told him she did not want to know the details. Then, on Aug. 1, 2019, EcommerceBytes published an article questioning Wenig's handling of eBay's litigation against Amazon. Within half an hour, Wenig allegedly told Wymer that if they were ever going to "take her down," referring to Ina Steiner, "now is the time."

This Tweet is currently unavailable. It might be loading or has been removed.

Four days later, the harassment campaign allegedly began. According to eBay's admissions to federal prosecutors, members of its security team targeted the Steiners between Aug. 5 and Aug. 23, 2019. Anonymous accounts on what was then Twitter criticized EcommerceBytes and threatened to show up at the couple's home in Natick, Massachusetts.

The campaign quickly reached their front door. In addition to the cockroaches, fetal pig, and bloody mask, the group allegedly sent live spiders, fly larvae, a funeral wreath, and a book about surviving the death of a spouse. Pornographic magazines addressed to David were allegedly delivered to a neighbor, while Craigslist ads allegedly invited strangers to the Steiners' home for sex, a block party, and an estate sale. One night, an emergency plumber even arrived unannounced.

As the messages and deliveries continued, the language inside eBay remained aggressive. On Aug. 11, Wymer allegedly told Baugh, "I want to see ashes. As long as it takes. Whatever it takes." The group also took the harassment offline — several members of eBay's security team allegedly traveled from California to Massachusetts, followed the Steiners in a rented van, and attempted to install a GPS tracker on their car.

This Tweet is currently unavailable. It might be loading or has been removed.

They also had an unusual plan for how the campaign would end. EBay security manager Brian Gilbert, a former police captain, was allegedly supposed to approach the Steiners and offer to stop the attacks his colleagues were secretly carrying out. EBay would then appear to have solved a problem its own employees had created.

That plan unraveled when the Steiners realized they were being followed and contacted local police. Once members of the group learned they were under investigation, they allegedly made false statements, deleted digital evidence, and falsified records in an attempt to hide eBay's involvement.

This Tweet is currently unavailable. It might be loading or has been removed.

Wenig, Wymer, and Jones were not criminally charged, and Wenig has maintained that he was requesting a communications response and knew nothing about the harassment. EBay's internal investigation found his messages inappropriate but said it uncovered no evidence that he authorized the security team's actions.

Federal prosecutors eventually charged seven former eBay employees and contractors in 2020, all of whom eventually pleaded guilty. Four received prison sentences between July 2021 and October 2022, including Baugh, who was sentenced to 57 months in September 2022. Two others received one year of home confinement later that year. The final defendant, Gilbert, was sentenced in July 2024 to time served and one year of supervised release.

This Tweet is currently unavailable. It might be loading or has been removed.

The consequences also reached eBay itself. In January 2024, federal prosecutors charged the company with six felony offenses, including stalking, witness tampering, and obstruction of justice. EBay admitted to a detailed account of the campaign, paid the maximum penalty of $3 million, and agreed to retain an independent compliance monitor for three years.

The new settlement, though, resolves the Steiners' civil claims against eBay, Wenig, Jones, and Wymer. The couple also reached separate settlements with the other former employees named in the lawsuit, although those terms were not disclosed.

EBay says it has changed

In a public statement published July 28, 2026, the company called what happened “wrong, reprehensible and should never have happened.” It condemned the employees who pleaded guilty and acknowledged the “unprofessional tone” of communications involving Wenig, Wymer, and Jones.

eBay also pointed to the changes it has made since 2019, including bringing in new leaders and strengthening its policies, internal controls, and employee training.

Wenig, meanwhile, continues to maintain that he knew nothing about the campaign. In a statement provided to Mashable, the former CEO said he was “saddened” by what the Steiners endured, “especially because it occurred during my time as CEO of eBay.” He said the harassment “was deliberately done in secret and without my knowledge.”

After the packages, threats, surveillance, criminal cases, and six years of litigation, the campaign still failed at its original goal. EcommerceBytes remains online, and Ina Steiner got to publish the news of eBay's settlement herself. Talk about closure!

Agentic AI. Claws. Vibe coding. AGI.

AI buzzwords come and go pretty quickly these days, and now there's a new AI trend to know: super apps.

What is a super app?

A super app puts a variety of AI tools into one agentic app. This means that AI chatbots (like ChatGPT) exist within the same space as AI agents, i.e. tools that execute multi-step requests with a degree of autonomy.

The idea is simple: Soon, you'll no longer be bouncing between your AI chatbot, coding assistant, and agentic tools. "Super apps" will centralize all of these tools within a single app. A super app, if you will.

Why are people talking about super apps?

In an earning call this week, Microsoft CEO Satya Nadella said that a new super app will be central to Microsoft's upcoming AI strategy.

"This quarter, we also introduced Autopilots, autonomous, long-running agents with full enterprise compliance, including an always-on personal agent powered by OpenClaw," Nadella said. "And this quarter we will bring these Copilot experiences together, including Code, in one 'super app' spanning both consumer and commercial experiences."

Nadella said that the super app would be released by the end of summer 2026.

Microsoft isn't leading the charge on super apps; they're just the latest company to hop on the trend. AI companies, including OpenAI and Anthropic, are also focusing efforts on super apps.

As reported by Reuters, OpenAI has already launched its super app to employees. Meanwhile, competitor Anthropic is also combining its agentic Cowork tool with its Claude chatbot, the company recently announced.

Super apps could also accomplish something else for AI companies. Because users can access apps like InstaCart, Uber, Zillow, and OpenTable directly within ChatGPT, these agentic super apps could absorb as much of your online activity as possible into a single company's app.

Elon Musk reportedly wants to turn X into an everything app as well, and with super apps, his biggest AI rivals appear to be moving in the same direction.

Disclosure: Ziff Davis, Mashable’s parent company, in April 2025 filed a lawsuit against OpenAI, alleging it infringed Ziff Davis copyrights in training and operating its AI systems.

Toyota has spent decades building a reputation for vehicles that last. From dependable powertrains to strong resale values and low running costs, the brand has become one of the most trusted names for buyers who want a vehicle they can keep for years.

The Venn diagram of Spotify Premium users and runners is in for a treat. Running Mode is now available in the Spotify mobile app, matching song tempos to the different phases of a run.

Spotify's Fitness Hub rolled out in April of this year, partnering with Peloton and digital fitness instructors to put workouts directly in the app. Running Mode is now apart of the Fitness Mode, but a more self-guided experience.

The feature is available to Premium users on iOS in the U.S., Canada, the U.K., Ireland, Australia, New Zealand, and Sweden. It lets you choose from 25 curated running presets or build a custom workout from scratch. You can tailor the playlist to match your run length, beats per minute, and preferred genre, and it generates a personalized playlist based on your selections. If you need a boost, Running Mode also has optional audio cues to provide a little motivation.

This is not Spotify’s first attempt to keep pace with runners. In 2015, the company introduced Spotify Running, which used a phone’s sensors to detect a runner’s tempo and select music that matched it. Spotify retired that feature in 2018. Unlike its predecessor, the new Running Mode does not detect when someone speeds up or slows down — runners choose their desired tempo and workout structure before taking off.

Running Mode builds on Spotify's existing AI feature, Prompted Playlist, which allows users to provide a playlist description that Spotify uses to curate music. As Spotify positions itself to be an a one-stop-shop where you can listen to music, watch podcasts, and even buy books, Running Mode is a natural next step.

Are you ready for some more fun projects to tackle with your Raspberry Pi this weekend? Well, you're in luck, and I actually have one project that's sure to bring you down the path of nostalgia before it's all over.

Tinder has paused an AI-powered Photo Enhance tool that some users said altered their appearance, NewsNation reported.

Former user Jenny Rubin told NewsNation that the tool edited her photo without her opting in. "It was extremely violating," she told NewsNation's Jesse Weber. "It felt pretty uncanny, to be honest. Those are not my teeth. That’s not what my mouth looks like." Tinder had apparently changed the photo of her from a toothless smile to one with teeth.

SEE ALSO: You can now find local events on Tinder — and match with other attendees

Rubin deleted the app and made a TikTok about it, and others told her the app did the same thing. Tinder users also told NBC News that the app changed facial features and even skin tone, and also added features that weren't previously there.

"Photo Enhance is a tool that helps improve the technical quality of your photos. It's designed to enhance overall photo quality — for example, making your profile photo clearer and better lit — not to make you look different," Tinder's page on Photo Enhance states. The feature is supposed to "sharpen blurry images, adjust brightness and exposure, and improve clarity," the page reads. "It’s not designed to add or remove people, objects, or backgrounds, apply cosmetic effects, or change facial features or skin tone appearance."

A Tinder spokesperson reiterated that the feature isn't designed to change someone's appearance. "We know that profiles with clear, well-lit first photos have a much better experience, so our goal was to help make that easier for people on Tinder. The feature is in a test phase in limited markets and is available to a subset of profiles that had a detectable technical issue on the first photo, and more than 90 percent of people who receive an enhanced photo keep it," the spokesperson told Mashable over email.

According to the page, the suggested enhancement can be disabled, and the photo can be reverted to its original.

Tinder introduced the feature in March, along with a ton of other AI-powered features, including a matchmaker called Chemistry. At the same time, Tinder launched Photo Insights, which uses AI to scan users' camera rolls to determine interests based on patterns. The app previously introduced Photo Selector, an AI tool to help pick the best photos for a user's profile, in 2024.

Photo Enhanced is now paused in the 13 U.S. states where it was available. Tinder told NBC News and Mashable that the unintended changes impacted a small number of users. "Feedback data shows less than 0.5 percent of enhanced photos were flagged as not looking like the user, though not all of that feedback reflects a change as visible as what's been reported," the spokesperson told Mashable. It's paused to investigate the causes and strengthen safeguards.

When you use CarPlay, you probably just start some music, enter a destination, and drive off. However, there is a lesser-known feature that can make your CarPlay experience a lot more personal and fun: setting a custom greeting or sound when it connects to your car.

SAVE $160: The Samsung internal SSD 990 with 2TB is on sale at Amazon for $369.99, down from the list price of $529.99. That's a 30% discount and the lowest we've ever seen at Amazon.

Opens in a new window Credit: Samsung Samsung internal SSD 990 $369.99 at Amazon
$529.99 Save $160   Get Deal

According to the experts, the memory shortage is not letting up anytime soon. It's become a challenge to find affordable SSDs, and setting up a NAS at home or the office comes with a steep price tag these days. If you're in need of storage, consider this Amazon deal.

As of July 30, the Samsung internal SSD 990 with 2TB is on sale at Amazon for $369.99, down from the list price of $529.99. That's a 30% discount and the lowest we've ever seen at Amazon.

If your PC or laptop has been asking for more storage, this Samsung deal is one to hop on. The Samsung 990 SSD offers 2TB of storage and read speeds up to 7,250 MB/s. Write speeds reach up to 6,450MB per second. Those speeds will help with downloading games quickly, installing updates, and transferring high-resolution files without waiting around.

SEE ALSO: This week's best SSD deals include a World Cup edition Sandisk and a 2TB Lexar

For gamers who are used to long loading times or even need to delete old games, upgrading to an additional 2TB could be well worth the money. Since the Samsung internal SSD 990 with 2TB is on sale for the lowest we've ever seen and RAMageddon isn't gonna end soon, jump on this deal.

Walking through the aisles of a video store used to be half the fun of a movie night. You judged films by their cover art, stumbled across forgotten classics, and often left with something you never planned to watch.

A lot of my smart home automations feel a little bit like magic. When I saw a post on Reddit, however, I realized that I might be able to bring some genuine magic to Home Assistant. I bought an official Harry Potter wand and set it up to work as a magical smart home controller for my kids.

At a glance

• Self-supervised. EvoLib enables large language models to learn from their own experience during inference, without requiring ground-truth labels or external feedback.
• From experience to knowledge. EvoLib transforms past attempts into reusable skills and reflective insights that can be applied to future tasks.
• Knowledge that evolves. Useful skills and insights are continually refined, consolidated, and reweighted, turning instance-specific observations into increasingly general knowledge over time.
• Learning that transfers across tasks. By turning experience into reusable knowledge, EvoLib helps AI models learn from past successes and failures and evolve the knowledge that has the highest potential on improving future performance.
• Built for today’s AI models. As EvoLib does not require model updates, it can be applied to any black-box language models and AI systems deployed through APIs.

Memory has become an important AI agent capability: the ability to store and retrieve past experiences. But memory alone is not learning. A collection of past conversations, reasoning traces, or action histories can quickly grow into a vast archive of experiences, making it difficult to identify the most relevant knowledge for a new task—let alone refine and evolve this knowledge to improve performance over time.

Humans learn differently. We do not remember every detail of our past experiences. Instead, we remember what matters: strategies that work, mistakes to avoid, and skills that transfer across situations. Over time, these lessons are refined into increasingly general and reusable knowledge. This ability to transform experience into transferable, evolving knowledge is one of the foundations of human learning.

In our recent paper, Test-Time Learning with an Evolving Library, we explore how AI systems can learn from experience in a similar way. We introduce EvoLib, a framework that transforms raw experience into an evolving library of knowledge. Rather than treating memory as a growing archive of past experiences, EvoLib extracts reusable knowledge from those experiences and continually refines it as new experiences arrive. Through the evolution of library, skills become more general, insights become more accurate, and downstream performance gets improved consistently over time. In this way, AI agents can continually learn from accumulating experience without updating the underlying model.

How EvoLib Works

Unlike traditional AI memory systems that store raw experiences as static information, EvoLib is built around the idea of evolving knowledge. In EvoLib, a unit of knowledge can take the form of a reusable skill distilled from a successful solution or a reflective insight learned from mistakes. Rather than simply accumulating more memories over time, EvoLib continually refines, consolidates and reweights existing knowledge as new experiences arrive. Concretely, we design the following mechanisms around knowledge evolution:

• Consolidation. As new knowledge is extracted from recent experience, EvoLib retrieves similar knowledge from the library and tries to consolidate it with the new knowledge into a more general and reusable one. This allows knowledge to move beyond individual experiences and become applicable across tasks.
• Weighting mechanism. EvoLib continually updates the importance of each knowledge unit based not only on its immediate utility on the current task, but also on how much it contributes to generating useful knowledge on future tasks. Over time, knowledge with the greatest long-term impact naturally becomes more prominent in the library.

Figure 1. EvoLib transforms raw experiences into reusable skills and insights, then continually evolves them through consolidation and dynamic weighting. Key Results

To evaluate EvoLib, we tested it across a diverse set of challenging tasks with different types of experiences and demands for learning:

• Solving mathematical reasoning problems
• Writing code to perform the given tasks under efficiency constraints
• Making decisions to explore and interact with an environment to perform long-horizon tasks

Across these tasks, EvoLib consistently outperforms the top retrieval-based memory approaches and other abstract memory mechanisms with more efficient token usage.

We also evaluated how effectively EvoLib converts test-time compute into performance gains through continually evolving knowledge. Figure 2 compares EvoLib against both compute scaling methods that perform each task in isolation and strong memory-based learning approaches. Each curve shows how performance improves as the amount of test-time compute increases.

Across all three benchmarks, EvoLib achieves higher performance throughout most of the compute range and improves performance more rapidly with increasing compute.

These results suggest that the key to better learning may not simply be storing more memories or spending more compute. Instead, the greatest gains come from transforming experience into reusable knowledge that can be continually refined and applied across tasks.

Figure 2. Across all tasks, EvoLib converts test-time compute into performance gains more effectively than existing methods. Robustness to random task order

A natural question is whether such learning depends heavily on the order in which tasks are encountered. In the real world, an AI system may face diverse types of tasks in arbitrary order, and a useful learning framework should be robust to the randomness in task order. To evaluate this, we measured the task performance on the same set of heterogeneous tasks but with different task orders. We found that EvoLib consistently improves over existing memory-based learning approaches and maintains stable performance across different orderings. This indicates that EvoLib can continually learn from diverse tasks even when they are interleaved, suggesting its practical advantage in real-world scenarios where an agent must handle and learn from a mixed stream of heterogeneous user requests without relying on a structured curriculum.

As AI systems take on longer-running and more complex tasks, learning from experience will become increasingly important. The future of AI may depend not only on larger models and more computation, but also on mechanisms that allow systems to continually accumulate, refine, and reuse knowledge.

EvoLib is one step toward that vision. By transforming experience into evolving knowledge, it enables AI systems to continually improve and adapt after deployment. Rather than repeatedly starting from scratch, future AI systems may be able to build upon an evolving library of reusable skills and insights, much like humans do.

Code and experiment results are available on GitHub (opens in new tab) to support future research on memory and knowledge evolution in AI systems.

Opens in a new tab

The post EvoLib: Turning experience into evolving knowledge appeared first on Microsoft Research.