Technology is the sum of techniques, skills, methods, and processes used in the production of goods or services or in the accomplishment of objectives, such as scientific investigation. Technology can be the knowledge of techniques, processes, and the like, or it can be embedded in machines to allow for operation without detailed knowledge of their workings.
Best Prepaid Phone Plans for 2026Fri, 06 Mar 2026 18:06:00 +0000 You may not need all the features and expense of an unlimited plan from the big carriers. A prepaid plan with fixed costs could be a better choice.
Apple thinks it can lure in the 'Apple curious' for $599Fri, 06 Mar 2026 19:49:58 +0000 Apple has made it pretty clear that it wants to siphon off Android and Windows users, and it's doing it by adopting an aggressive, "budget-friendlier" model across nearly its entire ecosystem.
Apple is using $599 devices to grow its ecosystem
When I first entered the Apple ecosystem, it was when I bought an iPhone 4 in 2011 — I got it right after the 4s made its debut. I don't remember exactly what I paid, but I know it was less than the initial $199 price tag.
And back then, I thought that was a completely asinine amount of money to pay for a phone. Fortunately, or maybe unfortunately, I had more money in my pocket than brains in my head, so I bought it just the same.
AirPods Pro 3 long-term review: Holding the newest AirPods Pro
In my original review of Apple's latest earbuds, I largely praised them for improving audio quality, ANC, as well as adding new features. Now that the initial excitement has subsided, let's examine the changes that have stood out.
I went from the AirPods Pro 2 to the AirPods Pro 3. This wasn't a major jump by any means, but I felt it was worth it, especially since the battery life on my years-old pair had deteriorated, and I was able to pass them down to my partner.
Apple vice president of industrial design Molly Anderson said in a rare March 6 solo interview that the MacBook Neo retains its MacBook identity despite its $599 starting price. Apple introduced the MacBook Neo on March 4 as its most affordable Mac laptop.
The MacBook Neo uses the A18 Pro processor instead of the Apple Silicon M-series chips found in other Macs. Apple is targeting students and first-time Mac buyers who might otherwise choose inexpensive Windows laptops or Chromebooks.
Save up to $100 on Apple's new M4 iPad Air at AmazonFri, 06 Mar 2026 17:19:14 +0000 Apple's brand-new M4 iPad Air is already discounted, with Amazon launching preorder deals on both 11-inch and 13-inch models for the weekend.
Save up to $100 on iPad Air M4 preorders.
Despite being announced mere days ago, Amazon is already issuing preorder deals on the 2026 iPad Air with M4. Save up to $100 on preorders ahead of the official March 11 release.
Both notebooks use the standard M5 chip with a 10-core CPU, integrated graphics, and Apple's unified memory architecture. The MacBook Air uses a fanless chassis with passive cooling, while the MacBook Pro uses active cooling to sustain higher performance during longer workloads.
For everyday tasks like web browsing, document editing, and light creative work, the two machines are very similar in short bursts. Heavier tasks such as compiling code, exporting video, or rendering graphics are where MacBook Pro's thermal design begins to matter.
New iPads mean new accessories & ESR has productivity-boosting optionsFri, 06 Mar 2026 14:42:57 +0000 The iPad Air has been upgraded with the M4, so pair it or Apple's other tablets with accessories that enhance your workflows. ESR's magnetic keyboard cases are a great place to start.
ESR has accessories ready for your new iPad. Image source: ESR
ESR's iPad accessory line combines form and function to ensure your devices look great while keeping you productive. Choose one of the cases with a removable magnetic keyboard, a strong screen protector, and a digital stylus with Find My support, all from ESR.
Having a thin and light iPad that works great as a handheld tablet or a sturdy laptop doesn't have to be an either/or choice. ESR's cases let users have it all in a convenient form factor.
MacBook Neo was the most exciting of Apple's launches
It wasn't a full week of launches, but Apple certainly packed a lot into its three days of announcements. Practically all of it was predicted, but still there are stand-out releases like the MacBook Neo.
That seems to be polarizing people, as some regard Apple's compromises as going too far. It's certainly not for everyone, but then Apple has aimed at everybody by also updating its MacBook Air and MacBook Pro models.
Sydney Opera House to be lit up by art created on iPadFri, 06 Mar 2026 13:14:30 +0000 Apple and the Sydney Opera House are collaborating on a series of creativity projects for young people, including the chance to have iPad-created art projected on the famous building.
How the new artwork will look when projected onto the Sydney Opera House — image credit: Apple
Just as it did for Christmas with its UK headquarters, Apple is inviting people to submit artwork on the iPad to the Sydney Opera house. It's part of a 12-month collaboration which will see Apple supporting arts programming, including a new international children's festival later in 2026.
"For 50 years, Apple has been at the forefront of empowering creativity, providing tools that allow people to imagine, design, and share their unique visions with the world," said Greg Joswiak, Apple's senior vice president of Worldwide Marketing, in a statement. "We are thrilled to be working with such an iconic Australian cultural landmark to help inspire the next generation of creatives."
MacBook Neo starts seeing shipping delaysFri, 06 Mar 2026 12:30:11 +0000 Most of the 512GB configurations of the MacBook Neo are now back-ordered between one and two weeks, although both silver models remain available for March 11, 2026.
The Citrus MacBook Neo is now on backorder
The only conclusion you can truly draw from shipping delays is that if you want a device whose date has slipped back, you're going to have to wait. That is what's now happening with the new MacBook Neo.
Since Apple will never reveal how many devices it has made in which configurations, it can't really be said whether one version of the MacBook Neo is more popular than another. But you can say which colors are selling out.
Inside Control Center on macOS TahoeFri, 06 Mar 2026 12:16:15 +0000 Apps you don't want can clog up your Mac menubar, while ones you do can't be added. Here's how and why to use Control Center to make that menubar work for you, the way you want.
If only Apple would let you have two rows of menubar app icons you don't recognze
Especially if you have a MacBook Pro or MacBook Air with a small display — and a notch — you will have hit problems with menubar apps and controls. Quite apart from some apparently hiding behind that notch, there are others you want but there isn't enough room for them.
Or even on a large display, you might shrug and let any app go in the menubar if it likes. But the result there is a huge number of apps, making it hard to find the one you actually need — especially since macOS will keep moving them around.
One day ago, you could have spent $14,099 on a maxed-out Mac Studio. That included $4,000 on a 512GB RAM option, but that is now gone.
At the same time, Apple has raised prices on what RAM upgrades it still offers. The configurations depend on which processor you select, but for one example, it now costs an extra $400 than it did to upgrade from 96GB RAM to 256GB RAM.
How to backup your iPhone or iPadMon, 31 Mar 2025 13:53:05 +0000 It's not enough to just let your iPhone back up to iCloud, not when you need to be sure you won't lose anything important. Here are your options for backing up iOS devices.
Steve Jobs once made a point of telling an audience that he hadn't backed up in all the years he had been at NeXT. However, that wasn't because backing up is unimportant. It was because it was so important that every NeXT employee's data was automatically backed up for them.
Maybe Jobs brought that ethos to Apple, because the company has worked to progressively make backups easier - and unthinkingly so. You see this whenever you change iPhones and all of your apps, and their data, appear on the new one.
But as convenient as that is, it also makes you unthinkingly assume that your data is safe. It isn't, not unless you take positive steps - and regularly.
If you've been following AppleInsider long enough, you might have heard of Apple EVT/DVT collector Kosutami. They're a great social media follow for pictures of products that have never seen the light of day, but occasionally, they share a possible leak.
On Thursday, Kosutami shared that the "HomePad" was coming in fall. They're a little more old school in their leaking, providing only a cryptic message.
My home networking setup has evolved twice in 2025, first to move away from an aging Linksys setup, then a second time to test a more powerful tri-band system. While the first upgrade was a night and day improvement, the second was more incremental, if welcome.
I had a lot of success using TP-Link's $250 BE5000 dual-band mesh routers, but I knew I'd benefit from something higher up in the lineup. Many, if not most, users won't need to make the jump, but I've been testing the BE14000 system and it has smoothed out the wrinkles.
MacBook Neo has an A18 Pro processor and that should be plenty for most users
The MacBook Neo is a new and somewhat controversial product in Apple's lineup. It cuts out a lot of premium features associated with modern Macs to achieve the low price.
One of the cost-saving features is the A18 Pro processor originally revealed for the iPhone 16 Pro. Early benchmarks first discovered by MacRumors show what are nearly identical specs to the iPhone 16 Pro but with a Mac identifier 17,5.
M5 Max MacBook Pro benchmarks show a powerful processor
Product reviewers are likely running benchmarks on the recently announced MacBook Pros, which means they've begun showing up in Geekbench. The iPad Air with M4 has already had its benchmarks revealed, and the latest ones discovered in the public database are for the M5 Max MacBook Pro.
A post by Reddit user One_TrackMinded in the r/macbookpro subreddit revealed the results for Mac 17,7. It seems these results could be for a 16-inch MacBook Pro with the M5 Max.
After years of rumors, the budget MacBook was revived on March 4, 2026. The MacBook Neo is its name, and people are already losing their minds over one key cost-cutting decision.
The MacBook Neo has the A18 Pro at its heart, the same chip that powered the iPhone 16 Pro. It's something that had been rumored, yet still seems to have blindsided some of those looking to create a fuss on social media.
Ternus discussed Apple's strategy in an interview published March 5, one day after Apple announced the MacBook Neo on March 4. The new laptop starts at $599, or $499 for education buyers, making it the lowest-priced Mac notebook Apple has ever sold.
"We saw an opportunity here to really just reach a lot more people than we ever have before," Ternus said. "MacBook Neo is solid, reliable, and durable, all the things you want a Mac to be. And yeah, that's an amazing price point."
Save $300 on select MacBook Air laptops - Image credit: Apple
With a $100 price increase on the M5 MacBook Air that was announced this week, Amazon's blowout M4 deal is especially enticing at $300 off. Pick up the 13-inch M4 model with 24GB of unified memory and 512GB of storage for just $1,099 in the Sky Blue finish. This configuration comes with the 10-core GPU too, for added performance.
Rumors of Apple's entry into the affordable laptop market were accurate, they just took years to come true. March 4, 2026, saw Apple launch a laptop at the impossibly low price of just $599. Or $100 less, if you're buying as a student or teacher.
Apple is more accustomed to the other end of the market. The one where a $1,100 MacBook Air is considered a good value and a MacBook Pro is just the cost of doing business.
When you think of Apple TV, you probably don't immediately think of true crime. While the streaming giant has shown a deep love for sci-fi and psychological thrillers, it has typically allowed Netflix and Hulu to corner the true crime market.
That may not be the case anymore. Apple TV is gearing up to release a new three-part docuseries, "Twisted Yoga," which tells the tale of an international sex trafficking network masquerading as a collection of tantric yoga studios.
iPhone 17e vs iPhone 16e: Apple's low-end, comparedThu, 05 Mar 2026 15:00:25 +0000 The iPhone 17e is here, and it brings a performance boost and MagSafe to Apple's value-focused smartphone. Here's how it compares to its predecessor, the iPhone 16e.
iPhone 17e [left], iPhone 16e [right]
The iPhone 16e was introduced in February 2025 as a replacement for the iPhone SE line, becoming the budget option of the iPhone roster. There was a suspicion that Apple could turn the once-periodic iPhone SE updates into a yearly affair under the e-line, and Apple has done just that.
One year later, the iPhone 17e now becomes the choice for consumers with a more limited budget. The model that provides iPhone 17-like performance but with enough money to buy accessories and other essentials of life.
Apple's physical multi-color logo, in New York City on March 4, 2026
It's true that Apple has announced a week of launches. There currently still two days left to go.
Based on past experience, and the fact that on Wednesday, Apple hosted creators including our own Andrew O'Hara, Apple has probably now wrapped that up.
Feds call Anthropic supply-chain risk, tech companies aren't happy about itThu, 05 Mar 2026 01:39:16 +0000 A group that includes Apple, Google, and others has sent a letter to the US Department of Defense concerning Anthropic's supply-chain risk designation, clearly concerned about how that might affect future tech contracts.
The tech industry could be affected by arbitrary use of supply-chain risk designations
Anthropic took a moral stand against the United States government's request for unrestricted access to AI tools. The Trump administration retaliated by ordering all government entities to stop using Claude and designated the company as a supply-chain risk.
The designation is usually reserved for foreign national entities that pose a threat to United States infrastructure.
I got my first BenQ ScreenBar all the way back in February of 2020. While I thought it was somewhat novel, I told myself, "Surely this isn't going to be something I use regularly."
I didn't like the utilitarian aesthetic, and I'd spent years illuminated only by my monitor. Was there ever going to be a real reason that I'd need to have a light attached directly to my monitor?
The MacBook Neo is a brand new category in the lineup that has a new-to-Apple moniker to go with it. It's not just a new laptop, but a new market for the premium product seller.
According to an interview with Director of Mac Product Marketing, Colleen Novielli conducted by Techradar, the MacBook Neo is meant to be competitive in the $599 price range. It brings Apple's attention to detail and premium materials to a product nearly half the price of the MacBook Air.
Now, the company has implemented an additional AI-related safeguard, referred to as Transparency Tags. They're essentially disclosure labels that let music distributors and record labels indicate specific content was made with the help of artificial intelligence.
MacBook Neo vs M5 MacBook Air: Budget notebooks comparedThu, 05 Mar 2026 18:47:24 +0000 The MacBook Neo is Apple's new lowest-cost MacBook, taking the role from the MacBook Air. Here's where the differences are looking at the spec sheets of the the two least-expensive MacBook lines.
MacBook Neo [left], M5 MacBook Air [right]
Early March has seen Apple launch two new value-focused MacBook options for consumers on a budget. Built to reduce the entry cost as much as possible, it's an unusual model that uses Apple Silicon still, but a chip designed for the iPhone and iPad.
This is a big shake-up in Apple's pricing strategy. It that replaces the MacBook Air as the entry-level MacBook option, which it has served as for quite a few years.
Apple updates iOS, macOS Tahoe to 26.3.1 to support new Studio DisplaysWed, 04 Mar 2026 19:28:41 +0000 Right after concluding its week of product launches, Apple has rolled out iOS 26.3.1, iPadOS 26.3.1, visionOS 26.3.1, and macOS 26.3.1 updates, adding support for its updated Studio Display and the new Studio Display XDR.
Apple's two new Studio Displays — image credit: Apple
Apple periodically releases smaller updates for its operating systems, fixing bugs and adding support for new products. Wednesday's updates firmly fall into the latter category.
The updates, rolling out to iPhone, iPad, Apple Vision Pro, and Mac, brings macOS Tahoe up to version 26.3.1, with iOS 26.3.1, iPadOS 26.3.1, and visionOS 26.3.1 also released at the same time.
How to use more than one external monitor on MacBook NeoWed, 04 Mar 2026 17:36:44 +0000 The MacBook Neo supports only one external display, and doesn't have Thunderbolt, but you can still get more ports and more monitors from a dock. Here are some of our favorites.
DisplayLink docks for MacBook Neo
Users can add more displays using DisplayLink docks. These docks have a macOS driver that sends compressed video over USB-C instead of relying on the laptop's native display pipeline.
Apple introduced MacBook Neo as its most affordable Mac laptop, powered by the A18 Pro chip and equipped with two USB-C ports. It supports one external display at up to 4K resolution at 60Hz alongside the built-in display.
Today, the company announced Claude Marketplace, a new offering that lets enterprises with an existing Anthropic spend commitment apply part of it toward tools and applications powered by Anthropic's Claude models but made and offered by external partners including GitLab, Harvey, Lovable, Replit, Rogo and Snowflake.
According to Anthropic’s Claude Marketplace FAQ, the program is designed to simplify procurement and consolidate AI spend. Anthropic says the Marketplace is now in limited preview and that enterprises interested in using it should reach out to their Anthropic account team to get started.
For customers interested in the Marketplace, Anthropic says purchases made through it “count against a portion of your existing Anthropic commitment,” and that the company will manage invoicing for partner spend — meaning enterprises can use part of their existing Anthropic commitment to buy Claude-powered partner solutions without separately handling partner invoicing.
In effect, Anthropic is positioning Claude Marketplace as a more centralized way for enterprises to procure certain Claude-powered partner tools.
Yet, the whole point of Anthropic's Claude Code and Claude Cowork applications for many users was that they could shift enterprise spend and time away from current third-party software-as-a-service (Saas) apps and instead, they could "vibe code" new solutions or bespoke, AI-powered workflows. This idea is so pervasive that prior Claude integrations have on several recent occasions caused a major selloff in SaaS stocks after investors thought Claude could threaten the underlying companies and applications. Claude Marketplace seems to be pushing against that idea, suggesting current SaaS apps are still valuable and perhaps even more useful and appealing to enterprises with Claude integrated into them.
The launch raises a broader question about how enterprises will choose to use Claude: directly through Anthropic’s own products and APIs, or through third-party applications that embed Claude for more specialized workflows.
Tool integration
Model and chat platforms have always sought to offer integrations, aiming to cut the time users spend building their app versions.
OpenAI added third-party apps into ChatGPT and launched a new App Directory in December 2025. This brought in offerings from companies such as Canva, Expedia and Figma that users can invoke by using "@" mentions while prompting on the chatbot.
However, three months in, it’s unclear exactly how many people use ChatGPT Apps, particularly in enterprises — will Claude's Marketplace be able achieve more success here, given rising enterprise adoption of Claude and Anthropic products?
Other AI tool marketplaces have also cropped up. Lightning AI launched an AI Hub last year following similar moves from AWS and Hugging Face. Many AI marketplaces, such as Salesforce's, focus on surfacing AI agents that may already have the capabilities customers need.
How does Anthropic's solution stand out from these? Asked for comment a spokesperson responded:
"Claude is a model — it reasons, writes, analyzes, and codes. But Harvey isn't just Claude with a legal prompt. It's a purpose-built platform built for how legal teams actually work — with the domain expertise, workflow integrations, compliance infrastructure, and institutional knowledge that enterprises require. Same with Rogo for finance, Snowflake for enterprise data, or GitLab for software development. These partners have spent years building the product layer on top of Claude that makes it useful for specific industries and workflows.
That's actually the point. Thousands of businesses use Claude to power their products — and the best ones have built something Claude alone can't replicate. Claude Marketplace isn't Anthropic trying to replace those products. It's Anthropic investing in them — making it easier for enterprises to access the best Claude-powered tools without managing a separate procurement process for each one. Claude is the intelligence layer. Our partners are the product."
Native vs app
Enterprise users adapted their Claude or ChatGPT platforms to recognize preferences, connect to their data sources and retain context. So much of how people use enterprise AI these days focuses on customizability, on making the system work for their needs.
Platforms like OpenClaw also allowed people to set up autonomous agents that can have full access to their computers to complete tasks and execute workflows. In other words, Claude and other platforms can already do much of the work that these new third-party Marketplace tools enable — provided they have the right context and data.
However, third-party tools and integrations allow enterprise users to avoid doing the work themselves and instead invoke an existing tool to handle it. For those whose businesses are built around specific, tool-based workflows, the Marketplace may be exactly the right AI integration for them. In addition, there's also a good chance that enterprises already paying for Claude may now take advantage of the new Marketplace to explore third-party tools and services they wouldn't have otherwise.
While it’s still unclear what Claude Marketplace would look like in action, it’s possible that, with these tools, enterprises could use Claude as an orchestrator, where the platform acts as a command center that taps the right tool and accesses the right context without constantly prompting.
Observers noted that Claude Marketplace offers enterprises a way to “pre-approve” apps, bypassing the often long and cautious approval process.
Some people noted that Anthropic’s move tracks with how many businesses will want to work directly with the platforms without requiring users to move to their separate offerings.
Anthropic's biggest challenge with Claude Marketplace, however, is adoption. Many of the partners for its launch already have enterprise customers who deploy their tools through an API or already connect via MCP or other protocols for context.
Some users may have already vibe-coded apps that tap into these integrations. It's now a matter of enterprise users showing they want to use these new tools within their Claude workflows.
As models get smarter and more capable, the "harnesses" around them must also evolve.
This "harness engineering" is an extension of context engineering, says LangChain co-founder and CEO Harrison Chase in a new VentureBeat Beyond the Pilot podcast episode. Whereas traditional AI harnesses have tended to constrain models from running in loops and calling tools, harnesses specifically built for AI agents allow them to interact more independently and effectively perform long-running tasks.
Chase also weighed in on OpenAI's acquisition of OpenClaw, arguing that its viral success came down to a willingness to "let it rip" in ways that no major lab would — and questioning whether the acquisition actually gets OpenAI closer to a safe enterprise version of the product.
“The trend in harnesses is to actually give the large language model (LLM) itself more control over context engineering, letting it decide what it sees and what it doesn't see,” Chase says. “Now, this idea of a long-running, more autonomous assistant is viable.”
Tracking progress and maintaining coherence
While the concept of allowing LLMs to run in a loop and call tools seems relatively simple, it’s difficult to pull off reliably, Chase noted. For a while, models were “below the threshold of usefulness” and simply couldn’t run in a loop, so devs used graphs and wrote chains to get around that. Chase pointed to AutoGPT — once the fastest-growing GitHub project ever — as a cautionary example: same architecture as today's top agents, but the models weren't good enough yet to run reliably in a loop, so it faded fast.
But as LLMs keep improving, teams can construct environments where models can run in loops and plan over longer horizons, and they can continually improve these harnesses. Previously, “you couldn't really make improvements to the harness because you couldn't actually run the model in a harness,” Chase said.
LangChain’s answer to this is Deep Agents, a customizable general-purpose harness.
Built on LangChain and LangGraph, it has planning capabilities, a virtual filesystem, context and token management, code execution, and skills and memory functions. Further, it can delegate tasks to subagents; these are specialized with different tools and configurations and can work in parallel. Context is also isolated, meaning subagent work doesn’t clutter the main agent’s context, and large subtask context is compressed into a single result for token efficiency.
All of these agents have access to file systems, Chase explained, and can essentially create to-do lists that they can execute on and track over time.
“When it goes on to the next step, and it goes on to step two or step three or step four out of a 200 step process, it has a way to track its progress and keep that coherence,” Chase said. “It comes down to letting the LLM write its thoughts down as it goes along, essentially.”
He emphasized that harnesses should be designed so that models can maintain coherence over longer tasks, and be “amenable” to models deciding when to compact context at points it determines is “advantageous.”
Also, giving agents access to code interpreters and BASH tools increases flexibility. And, providing agents with skills as opposed to just tools loaded up front allows them to load information when they need it. “So rather than hard code everything into one big system prompt," Chase explained, "you could have a smaller system prompt, ‘This is the core foundation, but if I need to do X, let me read the skill for X. If I need to do Y, let me read the skill for Y.'"
Essentially, context engineering is a “really fancy” way of saying: What is the LLM seeing? Because that’s different from what developers see, he noted. When human devs can analyze agent traces, they can put themselves in the AI’s “mindset” and answer questions like: What is the system prompt? How is it created? Is it static or is it populated? What tools does the agent have? When it makes a tool call, and gets a response back, how is that presented?
“When agents mess up, they mess up because they don't have the right context; when they succeed, they succeed because they have the right context,” Chase said. “I think of context engineering as bringing the right information in the right format to the LLM at the right time.”
Listen to the podcast to hear more about:
How LangChain built its stack: LangGraph as the core pillar, LangChain at the center, Deep Agents on top.
Why code sandboxes will be the next big thing.
How a different type of UX will evolve as agents run at longer intervals (or continuously).
Why traces and observability are core to building an agent that actually works.
Enterprise AI applications that handle large documents or long-horizon tasks face a severe memory bottleneck. As the context grows longer, so does the KV cache, the area where the model’s working memory is stored.
A new technique developed by researchers at MIT addresses this challenge with a fast compression method for the KV cache. The technique, called Attention Matching, manages to compact the context by up to 50x with very little loss in quality.
While it is not the only memory compaction technique available, Attention Matching stands out for its execution speed and impressive information-preserving capabilities.
The memory bottleneck of the KV cache
Large language models generate their responses sequentially, one token at a time. To avoid recalculating the entire conversation history from scratch for every predicted word, the model stores a mathematical representation of every previous token it has processed, also known as the key and value pairs. This critical working memory is known as the KV cache.
The KV cache scales with conversation length because the model is forced to retain these keys and values for all previous tokens in a given interaction. This consumes expensive hardware resources. "In practice, KV cache memory is the biggest bottleneck to serving models at ultra-long context," Adam Zweiger, co-author of the paper, told VentureBeat. "It caps concurrency, forces smaller batches, and/or requires more aggressive offloading."
In modern enterprise use cases, such as analyzing massive legal contracts, maintaining multi-session customer dialogues, or running autonomous coding agents, the KV cache can balloon to many gigabytes of memory for a single user request.
To solve this massive bottleneck, the AI industry has tried several strategies, but these methods fall short when deployed in enterprise environments where extreme compression is necessary. A class of technical fixes includes optimizing the KV cache by either evicting tokens the model deems less important or merging similar tokens into a single representation. These techniques work for mild compression but “degrade rapidly at high reduction ratios,” according to the authors.
Real-world applications often rely on simpler techniques, with the most common approach being to simply drop the older context once the memory limit is reached. But this approach causes the model to lose older information as the context grows long. Another alternative is context summarization, where the system pauses, writes a short text summary of the older context, and replaces the original memory with that summary. While this is an industry standard, summarization is highly lossy and heavily damages downstream performance because it might remove pertinent information from the context.
Recent research has proven that it is technically possible to highly compress this memory using a method called Cartridges. However, this approach requires training latent KV cache models through slow, end-to-end mathematical optimization. This gradient-based training can take several hours on expensive GPUs just to compress a single context, making it completely unviable for real-time enterprise applications.
How attention matching compresses without the cost
Attention Matching achieves high-level compaction ratios and quality while being orders of magnitude faster than gradient-based optimization. It bypasses the slow training process through clever mathematical tricks.
The researchers realized that to perfectly mimic how an AI interacts with its memory, they need to preserve two mathematical properties when compressing the original key and value vectors into a smaller footprint. The first is the “attention output,” which is the actual information the AI extracts when it queries its memory. The second is the “attention mass,” which acts as the mathematical weight that a token has relative to everything else in the model’s working memory. If the compressed memory can match these two properties, it will behave exactly like the massive, original memory, even when new, unpredictable user prompts are added later.
"Attention Matching is, in some ways, the 'correct' objective for doing latent context compaction in that it directly targets preserving the behavior of each attention head after compaction," Zweiger said. While token-dropping and related heuristics can work, explicitly matching attention behavior simply leads to better results.
Before compressing the memory, the system generates a small set of “reference queries” that act as a proxy for the types of internal searches the model is likely to perform when reasoning about the specific context. If the compressed memory can accurately answer these reference queries, it will very likely succeed at answering the user's actual questions later. The authors suggest various methods for generating these reference queries, including appending a hidden prompt to the document telling the model to repeat the previous context, known as the “repeat-prefill” technique. They also suggest a “self-study” approach where the model is prompted to perform a few quick synthetic tasks on the document, such as aggregating all key facts or structuring dates and numbers into a JSON format.
With these queries in hand, the system picks a set of keys to preserve in the compacted KV cache based on signals like the highest attention value. It then uses the keys and reference queries to calculate the matching values along with a scalar bias term. This bias ensures that pertinent information is preserved, allowing each retained key to represent the mass of many removed keys.
This formulation makes it possible to fit the values with simple algebraic techniques, such as ordinary least squares and nonnegative least squares, entirely avoiding compute-heavy gradient-based optimization. This is what makes Attention Matching super fast in comparison to optimization-heavy compaction methods. The researchers also apply chunked compaction, processing contiguous chunks of the input independently and concatenating them, to further improve performance on long contexts.
Attention matching in action
To understand how this method performs in the real world, the researchers ran a series of stress tests using popular open-source models like Llama 3.1 and Qwen-3 on two distinct types of enterprise datasets. The first was QuALITY, a standard reading comprehension benchmark using 5,000 to 8,000-word documents. The second, representing a true enterprise challenge, was LongHealth, a highly dense, 60,000-token dataset containing the complex medical records of multiple patients.
The key finding was the ability of Attention Matching to compact the model’s KV cache by 50x without reducing the accuracy, while taking only seconds to process the documents. To achieve that same level of quality previously, Cartridges required hours of intensive GPU computation per context.
When dealing with the dense medical records, standard industry workarounds completely collapsed. The researchers noted that when they tried to use standard text summarization on these patient records, the model’s accuracy dropped so low that it matched the “no-context” baseline, meaning the AI performed as if it had not read the document at all.
Attention Matching drastically outperforms summarization, but enterprise architects will need to dial down the compression ratio for dense tasks compared to simpler reading comprehension tests. As Zweiger explains, "The main practical tradeoff is that if you are trying to preserve nearly everything in-context on highly information-dense tasks, you generally need a milder compaction ratio to retain strong accuracy."
The researchers also explored what happens in cases where absolute precision isn't necessary but extreme memory savings are. They ran Attention Matching on top of a standard text summary. This combined approach achieved 200x compression. It successfully matched the accuracy of standard summarization alone, but with a very small memory footprint.
One of the interesting experiments for enterprise workflows was testing online compaction, though they note that this is a proof of concept and has not been tested rigorously in production environments. The researchers tested the model on the advanced AIME math reasoning test. They forced the AI to solve a problem with a strictly capped physical memory limit. Whenever the model’s memory filled up, the system paused, instantly compressed its working memory by 50 percent using Attention Matching, and let it continue thinking. Even after hitting the memory wall and having its KV cache shrunk up to six consecutive times mid-thought, the model successfully solved the math problems. Its performance matched a model that had been given massive, unlimited memory.
There are caveats to consider. At a 50x compression ratio, Attention Matching is the clear winner in balancing speed and quality. However, if an enterprise attempts to push compression to extreme 100x limits on highly complex data, the slower, gradient-based Cartridges method actually outperforms it.
The researchers have released the code for Attention Matching. However, they note that this is not currently a simple plug-and-play software update. "I think latent compaction is best considered a model-layer technique," Zweiger notes. "While it can be applied on top of any existing model, it requires access to model weights." This means enterprises relying entirely on closed APIs cannot implement this themselves; they need open-weight models.
The authors note that integrating this latent-space KV compaction into existing, highly optimized commercial inference engines still requires significant effort. Modern AI infrastructure uses complex tricks like prefix caching and variable-length memory packing to keep servers running efficiently, and seamlessly weaving this new compaction technique into those existing systems will take dedicated engineering work. However, there are immediate enterprise applications. "We believe compaction after ingestion is a promising use case, where large tool call outputs or long documents are compacted right after being processed," Zweiger said.
Ultimately, the shift toward mechanical, latent-space compaction aligns with the future product roadmaps of major AI players, Zweiger argues. "We are seeing compaction to shift from something enterprises implement themselves into something model providers ship," Zweiger said. "This is even more true for latent compaction, where access to model weights is needed. For example, OpenAI now exposes a black-box compaction endpoint that returns an opaque object rather than a plain-text summary."
Google senior AI product manager Shubham Saboo has turned one of the thorniest problems in agent design into an open-source engineering exercise: persistent memory.
The project serves as a practical reference implementation for something many AI teams want but few have productionized cleanly: an agent system that can ingest information continuously, consolidate it in the background, and retrieve it later without relying on a conventional vector database.
For enterprise developers, the release matters less as a product launch than as a signal about where agent infrastructure is headed.
The repo packages a view of long-running autonomy that is increasingly attractive for support systems, research assistants, internal copilots and workflow automation. It also brings governance questions into sharper focus as soon as memory stops being session-bound.
What the repo appears to do — and what it does not clearly claim
The repo also appears to use a multi-agent internal architecture, with specialist components handling ingestion, consolidation and querying.
But the supplied materials do not clearly establish a broader claim that this is a shared memory framework for multiple independent agents.
That distinction matters. ADK as a framework supports multi-agent systems, but this specific repo is best described as an always-on memory agent, or memory layer, built with specialist subagents and persistent storage.
Even at this narrower level, it addresses a core infrastructure problem many teams are actively working through.
The architecture favors simplicity over a traditional retrieval stack
According to the repository, the agent runs continuously, ingests files or API input, stores structured memories in SQLite, and performs scheduled memory consolidation every 30 minutes by default.
A local HTTP API and Streamlit dashboard are included, and the system supports text, image, audio, video and PDF ingestion. The repo frames the design with an intentionally provocative claim: “No vector database. No embeddings. Just an LLM that reads, thinks, and writes structured memory.”
That design choice is likely to draw attention from developers managing cost and operational complexity. Traditional retrieval stacks often require separate embedding pipelines, vector storage, indexing logic and synchronization work.
Saboo's example instead leans on the model to organize and update memory directly. In practice, that can simplify prototypes and reduce infrastructure sprawl, especially for smaller or medium-memory agents. It also shifts the performance question from vector search overhead to model latency, memory compaction logic and long-run behavioral stability.
Flash-Lite gives the always-on model some economic logic
That is where Gemini 3.1 Flash-Lite enters the story.
Google says the model is built for high-volume developer workloads at scale and priced at $0.25 per 1 million input tokens and $1.50 per 1 million output tokens.
The company also says Flash-Lite is 2.5 times faster than Gemini 2.5 Flash in time to first token and delivers a 45% increase in output speed while maintaining similar or better quality.
On Google’s published benchmarks, the model posts an Elo score of 1432 on Arena.ai, 86.9% on GPQA Diamond and 76.8% on MMMU Pro. Google positions those characteristics as a fit for high-frequency tasks such as translation, moderation, UI generation and simulation.
Those numbers help explain why Flash-Lite is paired with a background-memory agent. A 24/7 service that periodically re-reads, consolidates and serves memory needs predictable latency and low enough inference cost to avoid making “always on” prohibitively expensive.
Google’s ADK documentation reinforces the broader story. The framework is presented as model-agnostic and deployment-agnostic, with support for workflow agents, multi-agent systems, tools, evaluation and deployment targets including Cloud Run and Vertex AI Agent Engine. That combination makes the memory agent feel less like a one-off demo and more like a reference point for a broader agent runtime strategy.
The enterprise debate is about governance, not just capability
Public reaction shows why enterprise adoption of persistent memory will not hinge on speed or token pricing alone.
Several responses on X highlighted exactly the concerns enterprise architects are likely to raise. Franck Abe called Google ADK and 24/7 memory consolidation “brilliant leaps for continuous agent autonomy,” but warned that an agent “dreaming” and cross-pollinating memories in the background without deterministic boundaries becomes “a compliance nightmare.”
ELED made a related point, arguing that the main cost of always-on agents is not tokens but “drift and loops.”
Those critiques go directly to the operational burden of persistent systems: who can write memory, what gets merged, how retention works, when memories are deleted, and how teams audit what the agent learned over time?
Another reaction, from Iffy, challenged the repo’s “no embeddings” framing, arguing that the system still has to chunk, index and retrieve structured memory, and that it may work well for small-context agents but break down once memory stores become much larger.
That criticism is technically important. Removing a vector database does not remove retrieval design; it changes where the complexity lives.
For developers, the tradeoff is less about ideology than fit. A lighter stack may be attractive for low-cost, bounded-memory agents, while larger-scale deployments may still demand stricter retrieval controls, more explicit indexing strategies and stronger lifecycle tooling.
ADK broadens the story beyond a single demo
Other commenters focused on developer workflow. One asked for the ADK repo and documentation and wanted to know whether the runtime is serverless or long-running, and whether tool-calling and evaluation hooks are available out of the box.
Based on the supplied materials, the answer is effectively both: the memory-agent example itself is structured like a long-running service, while ADK more broadly supports multiple deployment patterns and includes tools and evaluation capabilities.
The always-on memory agent is interesting on its own, but the larger message is that Saboo is trying to make agents feel like deployable software systems rather than isolated prompts. In that framing, memory becomes part of the runtime layer, not just an add-on feature.
What Saboo has shown — and what he has not
What Saboo has not shown yet is just as important as what he's published.
The provided materials do not include a direct Flash-Lite versus Anthropic Claude Haiku benchmark for agent loops in production use.
They also do not lay out enterprise-grade compliance controls specific to this memory agent, such as: deterministic policy boundaries, retention guarantees, segregation rules or formal audit workflows.
And while the repo appears to use multiple specialist agents internally, the materials do not clearly prove a larger claim about persistent memory shared across multiple independent agents.
For now, the repo reads as a compelling engineering template rather than a complete enterprise memory platform.
Why this matters now
Still, the release lands at the right time. Enterprise AI teams are moving beyond single-turn assistants and into systems expected to remember preferences, preserve project context and operate across longer horizons.
Saboo's open-source memory agent offers a concrete starting point for that next layer of infrastructure, and Flash-Lite gives the economics some credibility.
But the strongest takeaway from the reaction around the launch is that continuous memory will be judged on governance as much as capability.
That is the real enterprise question behind Saboo's demo: not whether an agent can remember, but whether it can remember in ways that stay bounded, inspectable and safe enough to trust in production.
What's old is new: the command line — the original, clunky non-graphical interface for interacting with and controlling PCs, where the user just typed in raw commands in code — has become one of the most important interfaces in agentic AI.
That shift has been driven in part by the rise of coding-native tools such as Claude Code and Kilo CLI, which have helped establish a model where AI agents do not just answer questions in chat windows but execute real tasks through a shared, scriptable interface already familiar to developers — and which can still be found on virtually all PCs.
For developers, the appeal is practical: the CLI is inspectable, composable and easier to control than a patchwork of custom app integrations.
Now, Google Workspace — the umbrella term for Google's suite of enterprise cloud apps including Drive, Gmail, Calendar, Sheets, Docs, Chat, Admin — is moving into that pattern with a new CLI that lets them access these applications and the data within them directly, without relying on third-party connectors.
The project, googleworkspace/cli, describes itself as “one CLI for all of Google Workspace — built for humans and AI agents,” with structured JSON output and agent-oriented workflows included.
In an X post yesterday, Google Cloud director Addy Osmani introduced the Google Workspace CLI as “built for humans and agents,” adding that it covers “Google Drive, Gmail, Calendar, and every Workspace API.”
While not officially supported by Google, other posts cast the release as a broader turning point for automation and agent access to enterprise productivity software.
Now, instead of having to set up third-party connectors like Zapier to access data and use AI agents to automate work across the Google Workspace suite of apps, enterprise developers (or indie devs and users, for that matter) can easily install the open source (Apache 2.0) Google Workspace CLI from Github and begin setting up automated agentic workflows directly in terminal, asking their AI model to sort email, respond, edit docs and files, and more.
Why the CLI model is gaining traction
For enterprise developers, the importance of the release is not that Google suddenly made Workspace programmable. Workspace APIs have long been available. What changes here is the interface.
Instead of forcing teams to build and maintain separate wrappers around individual APIs, the CLI offers a unified command surface with structured output.
Installation is straightforward — npm install -g @googleworkspace/cli — and the repo says the package includes prebuilt binaries, with releases also available through GitHub.
The repo also says gws reads Google’s Discovery Service at runtime and dynamically builds its command surface, allowing new Workspace API methods to appear without waiting for a manually maintained static tool definition to catch up.
For teams building agents or internal automation, that is a meaningful operational advantage. It reduces glue code, lowers maintenance overhead and makes Workspace easier to treat as a programmable runtime rather than a collection of separate SaaS applications.
What developers and enterprises actually get
The CLI is designed for both direct human use and agent-driven workflows. For developers working in the terminal, the README highlights features such as per-resource help, dry-run previews, schema inspection and auto-pagination.
For agents, the value is clearer still: structured JSON output, reusable commands and built-in skills that let models interact with Workspace data and actions without a custom integration layer.
That creates immediate utility for internal enterprise workflows. Teams can use the tool to list Drive files, create spreadsheets, inspect request and response schemas, send Chat messages and paginate through large result sets from the terminal. The README also says the repo ships more than 100 agent skills, including helpers and curated recipes for Gmail, Drive, Docs, Calendar and Sheets.
That matters because Workspace remains one of the most common systems of record for day-to-day business work. Email, calendars, internal docs, spreadsheets and shared files are often where operational context lives. A CLI that exposes those surfaces through a common, agent-friendly interface makes it easier to build assistants that retrieve information, trigger actions and automate repetitive processes with less bespoke plumbing.
The important caveat: visible, but not officially supported
The social-media response has been enthusiastic, but enterprises should read the repo carefully before treating the project as a formal Google platform commitment.
The README explicitly says: “This is not an officially supported Google product”. It also says the project is under active development and warns users to expect breaking changes as it moves toward v1.0.
That does not diminish the technical relevance of the release. It does, however, shape how enterprise teams should think about adoption. Today, this looks more like a promising developer tool with strong momentum than a production platform that large organizations should standardize on immediately.
This is a cleaner interface, not a governance bypass
The other key point is that the CLI does not bypass the underlying controls that govern Workspace access.
The documentation says users still need a Google Cloud project for OAuth credentials and a Google account with Workspace access. It also outlines multiple authentication patterns for local development, CI and service accounts, along with instructions for enabling APIs and handling setup issues.
For enterprises, that is the right way to interpret the tool. It is not magic access to Gmail, Docs or Sheets. It is a more usable abstraction over the same permissions, scopes and admin controls companies already manage.
Not a rejection of MCP, but a broader agent interface strategy
Some of the early commentary around the tool frames it as a cleaner alternative to Model Context Protocol (MCP)-heavy setups, arguing that CLI-driven execution can avoid wasting context window on large tool definitions. There is some logic to that argument, especially for agent systems that can call shell commands directly and parse JSON responses.
But the repo itself presents a more nuanced picture. It includes a Gemini CLI extension that gives Gemini agents access to gws commands and Workspace agent skills after terminal authentication. It also includes an MCP server mode through gws mcp, exposing Workspace APIs as structured tools for MCP-compatible clients including Claude Desktop, Gemini CLI and VS Code.
The strategic takeaway is not that Google Workspace is choosing CLI instead of MCP. It is that the CLI is emerging as the base interface, with MCP available where it makes sense.
What enterprises should do now
The right near-term move for enterprises is not broad rollout. It is targeted evaluation.
Developer productivity, platform engineering and IT automation teams should test the tool in a sandboxed Workspace environment and identify a narrow set of high-friction use cases where a CLI-first approach could reduce integration work. File discovery, spreadsheet updates, document generation, calendar operations and internal reporting are natural starting points.
Security and identity teams should review authentication patterns early and determine how tightly permissions, scopes and service-account usage can be constrained and monitored. AI platform teams, meanwhile, should compare direct CLI execution against MCP-based approaches in real workflows, focusing on reliability, prompt overhead and operational simplicity.
The broader trend is clear. As agentic software matures, the command line is becoming a common control plane for both developers and AI systems. Google Workspace’s new CLI does not change enterprise automation overnight. But it does make one of the most widely used productivity stacks easier to access through the interface that agent builders increasingly prefer.
Actually, GPT-5.4 comes in two varieties: GPT-5.4 Thinking and GPT-5.4 Pro, the latter designed for the most complex tasks.
Both will be available in OpenAI's paid application programming interface (API) and Codex software development application, while GPT-5.4 Thinking will be available to all paid subscribers of ChatGPT (Plus, the $20-per-month plan, and up) and Pro will be reserved for ChatGPT Pro ($200 monthly) and Enterprise plan users.
ChatGPT Free users will also get a taste of GPT-5.4, but only when their queries are auto-routed to the model, according to an OpenAI spokesperson.
The big headlines on this release are efficiency, with OpenAI reporting that GPT-5.4 uses far fewer tokens (47% fewer on some tasks) than its predecessors, and, arguably even more impressively, a new "native" Computer Use mode available through the API and its Codex that lets GPT-5.4 navigate a users' computer like a human and work across applications.
The company is also releasing a new suite of ChatGPT integrations allowing GPT-5.4 to be plugged directly into users' Microsoft Excel and Google Sheets spreadsheets and cells, enabling granular analysis and automated task completion that should speed up work across the enterprise, but may make fears of white collar layoffs even more pronounced on the heels of similar offerings from Anthropic's Claude and its new Cowork application.
OpenAI says GPT-5.4 supports up to 1 million tokens of context in the API and Codex, enabling agents to plan, execute, and verify tasks across long horizons— however, it charges double the cost per 1 million tokens once the input exceeds 272,000 tokens.
Native computer use: a step toward autonomous workflows
The most consequential capability OpenAI highlights is that GPT-5.4 is its first general-purpose model released with native, state-of-the-art computer-use capabilities in Codex and the API, enabling agents to operate computers and carry out multi-step workflows across applications.
OpenAI says the model can both write code to operate computers via libraries like Playwright and issue mouse and keyboard commands in response to screenshots. OpenAI also claims a jump in agentic web browsing.
Benchmark results are presented as evidence that this is not merely a UI wrapper.
On BrowseComp, which measures how well AI agents can persistently browse the web to find hard-to-locate information, OpenAI reports GPT-5.4 improving by 17% absolute over GPT-5.2, and GPT-5.4 Pro reaching 89.3%, described as a new state of the art.
On OSWorld-Verified, which measures desktop navigation using screenshots plus keyboard and mouse actions, OpenAI reports GPT-5.4 at 75.0% success, compared to 47.3% for GPT-5.2, and notes reported human performance at 72.4%.
On WebArena-Verified, GPT-5.4 reaches 67.3% success using both DOM- and screenshot-driven interaction, compared to 65.4% for GPT-5.2. On Online-Mind2Web, OpenAI reports 92.8% success using screenshot-based observations alone.
OpenAI also links computer use to improvements in vision and document handling. On MMMU-Pro, GPT-5.4 reaches 81.2% success without tool use, compared with 79.5% for GPT-5.2, and OpenAI says it achieves that result using a fraction of the “thinking tokens.”
On OmniDocBench, GPT-5.4’s average error is reported at 0.109, improved from 0.140 for GPT-5.2. The post also describes expanded support for high-fidelity image inputs, including an “original” detail level up to 10.24M pixels.
OpenAI positions GPT-5.4 as built for longer, multi-step workflows—work that increasingly looks like an agent keeping state across many actions rather than a chatbot responding once.
Tool search and improved tool orchestration
As tool ecosystems get larger, OpenAI argues that the naive approach—dumping every tool definition into the prompt—creates a tax paid on every request: cost, latency, and context pollution.
GPT-5.4 introduces tool search in the API as a structural fix. Instead of receiving all tool definitions upfront, the model receives a lightweight list of tools plus a search capability, and it retrieves full tool definitions only when they’re actually needed.
OpenAI describes the efficiency win with a concrete comparison: on 250 tasks from Scale’s MCP Atlas benchmark, running with 36 MCP servers enabled, the tool-search configuration reduced total token usage by 47% while achieving the same accuracy as a configuration that exposed all MCP functions directly in context.
That 47% figure is specifically about the tool-search setup in that evaluation—not a blanket claim that GPT-5.4 uses 47% fewer tokens for every kind of task.
Improvements for developers and coding workflows
OpenAI’s coding pitch is that GPT-5.4 combines the coding strengths of GPT-5.3-Codex with stronger tool and computer-use capabilities that matter when tasks aren’t single-shot.
GPT-5.4 matches or outperforms GPT-5.3-Codex on SWE-Bench Pro while being lower latency across reasoning efforts.
Codex also gets workflow-level knobs. OpenAI says /fast mode delivers up to 1.5× faster performance across supported models, including GPT-5.4, describing it as the same model and intelligence “just faster.”
And it describes releasing an experimental Codex skill, “Playwright (Interactive)”, meant to demonstrate how coding and computer use can work in tandem—visually debugging web and Electron apps and testing an app as it’s being built.
OpenAI for Microsoft Excel and Google Sheets
Alongside GPT-5.4, OpenAI is announcing a suite of secure AI products in ChatGPT built for enterprises and financial institutions, powered by GPT-5.4 for advanced financial reasoning and Excel-based modeling.
The centerpiece is ChatGPT for Excel and Google Sheets (beta), which OpenAI describes as ChatGPT embedded directly in spreadsheets to build, analyze, and update complex financial models using the formulas and structures teams already rely on.
The suite also includes new ChatGPT app integrations intended to unify market, company, and internal data into a single workflow, naming FactSet, MSCI, Third Bridge, and Moody’s.
And it introduces reusable “Skills” for recurring finance work such as earnings previews, comparables analysis, DCF analysis, and investment memo drafting.
OpenAI anchors the finance push with an internal benchmark claim: model performance increased from 43.7% with GPT-5 to 88.0% with GPT-5.4 Thinking on an OpenAI internal investment banking benchmark.
Measuring AI performance against professional work
OpenAI leans on benchmarks intended to resemble real office deliverables, not just puzzle-solving. On GDPval, an evaluation spanning “well-specified knowledge work” across 44 occupations, OpenAI reports that GPT-5.4 matches or exceeds industry professionals in 83.0% of comparisons, compared to 71.0% for GPT-5.2.
The company also highlights specific improvements in the kinds of artifacts that tend to expose model weaknesses: structured tables, formulas, narrative coherence, and design quality.
In an internal benchmark of spreadsheet modeling tasks modeled after what a junior investment banking analyst might do, GPT-5.4 reaches a mean score of 87.5%, compared to 68.4% for GPT-5.2.
And on a set of presentation evaluation prompts, OpenAI says human raters preferred GPT-5.4’s presentations 68.0% of the time over GPT-5.2’s, citing stronger aesthetics, greater visual variety, and more effective use of image generation.
Improving reliability and reducing hallucinations
OpenAI describes GPT-5.4 as its most factual model yet and connects that claim to a practical dataset: de-identified prompts where users previously flagged factual errors. On that set, OpenAI reports GPT-5.4’s individual claims are 33% less likely to be false and its full responses are 18% less likely to contain any errors compared to GPT-5.2.
In statements provided to VentureBeat from OpenAI and attributed early GPT-5.4 testers, Daniel Swiecki of Walleye Capital says that on internal finance and Excel evaluations, GPT-5.4 improved accuracy by 30 percentage points, which he links to expanded automation for model updates and scenario analysis.
Brendan Foody, CEO of Mercor, calls GPT-5.4 the best model the company has tried and says it’s now top of Mercor’s APEX-Agents benchmark for professional services work, emphasizing long-horizon deliverables like slide decks, financial models, and legal analysis.
Pricing and availability
In the API, OpenAI says GPT-5.4 Thinking is available as gpt-5.4 and GPT-5.4 Pro as gpt-5.4-pro. Pricing is as follows:
Another important note: with GPT-5.4, requests that exceed 272,000 input tokens are billed at 2X the normal rate, reflecting the ability to send prompts larger than earlier models supported.
In Codex, compaction defaults to 272k tokens, and the higher long-context pricing applies only when the input exceeds 272k—meaning developers can keep sending prompts at or under that size without triggering the higher rate, but can opt into larger prompts by raising the compaction limit, with only those larger requests billed differently.
An OpenAI spokesperson said that in the API the maximum output is 128,000 tokens, the same as previous models.
Finally, on why GPT-5.4 is priced higher at baseline, the spokesperson attributed it to three factors: higher capability on complex tasks (including coding, computer use, deep research, advanced document generation, and tool use), major research improvements from OpenAI’s roadmap, and more efficient reasoning that uses fewer reasoning tokens for comparable tasks—adding that OpenAI believes GPT-5.4 remains below comparable frontier models on pricing even with the increase.
The broader shift
Across the release and the follow-up clarifications, GPT-5.4 is positioned as a model meant to move beyond “answer generation” and into sustained professional workflows—ones that require tool orchestration, computer interaction, long context, and outputs that look like the artifacts people actually use at work.
OpenAI’s emphasis on token efficiency, tool search, native computer use, and reduced user-flagged factual errors all point in the same direction: making agentic systems more viable in production by lowering the cost of retries—whether that retry is a human re-prompting, an agent calling another tool, or a workflow re-running because the first pass didn’t stick.
Most enterprise RAG pipelines are optimized for one search behavior. They fail silently on the others. A model trained to synthesize cross-document reports handles constraint-driven entity search poorly. A model tuned for simple lookup tasks falls apart on multi-step reasoning over internal notes. Most teams find out when something breaks.
Databricks set out to fix that with KARL, short for Knowledge Agents via Reinforcement Learning. The company trained an agent across six distinct enterprise search behaviors simultaneously using a new reinforcement learning algorithm. The result, the company claims, is a model that matches Claude Opus 4.6 on a purpose-built benchmark at 33% lower cost per query and 47% lower latency, trained entirely on synthetic data the agent generated itself with no human labeling required. That comparison is based on KARLBench, which Databricks built to evaluate enterprise search behaviors.
"A lot of the big reinforcement learning wins that we've seen in the community in the past year have been on verifiable tasks where there is a right and a wrong answer," Jonathan Frankle, Chief AI Scientist at Databricks, told VentureBeat in an exclusive interview. "The tasks that we're working on for KARL, and that are just normal for most enterprises, are not strictly verifiable in that same way."
Those tasks include synthesizing intelligence across product manager meeting notes, reconstructing competitive deal outcomes from fragmented customer records, answering questions about account history where no single document has the full answer and generating battle cards from unstructured internal data. None of those has a single correct answer that a system can check automatically.
"Doing reinforcement learning in a world where you don't have a strict right and wrong answer, and figuring out how to guide the process and make sure reward hacking doesn't happen — that's really non-trivial," Frankle said. "Very little of what companies do day to day on knowledge tasks are verifiable."
The generalization trap in enterprise RAG
Standard RAG breaks down on ambiguous, multi-step queries drawing on fragmented internal data that was never designed to be queried.
To evaluate KARL, Databricks built the KARLBench benchmark to measure performance across six enterprise search behaviors: constraint-driven entity search, cross-document report synthesis, long-document traversal with tabular numerical reasoning, exhaustive entity retrieval, procedural reasoning over technical documentation and fact aggregation over internal company notes. That last task is PMBench, built from Databricks' own product manager meeting notes — fragmented, ambiguous and unstructured in ways that frontier models handle poorly.
Training on any single task and testing on the others produces poor results. The KARL paper shows that multi-task RL generalizes in ways single-task training does not. The team trained KARL on synthetic data for two of the six tasks and found it performed well on all four it had never seen.
To build a competitive battle card for a financial services customer, for example, the agent has to identify relevant accounts, filter for recency, reconstruct past competitive deals and infer outcomes — none of which is labeled anywhere in the data.
Frankle calls what KARL does "grounded reasoning": running a difficult reasoning chain while anchoring every step in retrieved facts. "You can think of this as RAG," he said, "but like RAG plus plus plus plus plus plus, all the way up to 200 vector database calls."
The RL engine: why OAPL matters
KARL's training is powered by OAPL, short for Optimal Advantage-based Policy Optimization with Lagged Inference policy. It's a new approach, developed jointly by researchers from Cornell, Databricks and Harvard and published in a separate paper the week before KARL.
Standard LLM reinforcement learning uses on-policy algorithms like GRPO (Group Relative Policy Optimization), which assume the model generating training data and the model being updated are in sync. In distributed training, they never are. Prior approaches corrected for this with importance sampling, introducing variance and instability. OAPL embraces the off-policy nature of distributed training instead, using a regression objective that stays stable with policy lags of more than 400 gradient steps, 100 times more off-policy than prior approaches handled. In code generation experiments, it matched a GRPO-trained model using roughly three times fewer training samples.
OAPL's sample efficiency is what keeps the training budget accessible. Reusing previously collected rollouts rather than requiring fresh on-policy data for every update meant the full KARL training run stayed within a few thousand GPU hours. That is the difference between a research project and something an enterprise team can realistically attempt.
Agents, memory and the context stack
There has been a lot of discussion in the industry in recent months about how RAG can be replaced with contextual memory, also sometimes referred to as agentic memory.
For Frankle, it's not an either/or discussion, rather he sees it as a layered stack. A vector database with millions of entries sits at the base, which is too large for context. The LLM context window sits at the top. Between them, compression and caching layers are emerging that determine how much of what an agent has already learned it can carry forward.
For KARL, this is not abstract. Some KARLBench tasks required 200 sequential vector database queries, with the agent refining searches, verifying details and cross-referencing documents before committing to an answer, exhausting the context window many times over. Rather than training a separate summarization model, the team let KARL learn compression end-to-end through RL: when context grows too large, the agent compresses it and continues, with the only training signal being the reward at the end of the task. Removing that learned compression dropped accuracy on one benchmark from 57% to 39%.
"We just let the model figure out how to compress its own context," Frankle said. "And this worked phenomenally well."
Where KARL falls short
Frankle was candid about the failure modes. KARL struggles most on questions with significant ambiguity, where multiple valid answers exist and the model can't determine whether the question is genuinely open-ended or just hard to answer. That judgment call is still an unsolved problem.
The model also exhibits what Frankle described as giving up early on some queries — stopping before producing a final answer. He pushed back on framing this as a failure, noting that the most expensive queries are typically the ones the model gets wrong anyway. Stopping is often the right call.
KARL was also trained and evaluated exclusively on vector search. Tasks requiring SQL queries, file search, or Python-based calculation are not yet in scope. Frankle said those capabilities are next on the roadmap, but they are not in the current system.
What this means for enterprise data teams
KARL surfaces three decisions worth revisiting for teams evaluating their retrieval infrastructure.
The first is pipeline architecture. If your RAG agent is optimized for one search behavior, the KARL results suggest it is failing on others. Multi-task training across diverse retrieval behaviors produces models that generalize. Narrow pipelines do not.
The second is why RL matters here — and it's not just a training detail. Databricks tested the alternative: distilling from expert models via supervised fine-tuning. That approach improved in-distribution performance but produced negligible gains on tasks the model had never seen. RL developed general search behaviors that transferred. For enterprise teams facing heterogeneous data and unpredictable query types, that distinction is the whole game.
The third is what RL efficiency actually means in practice. A model trained to search better completes tasks in fewer steps, stops earlier on queries it cannot answer, diversifies its search rather than repeating failed queries, and compresses its own context rather than running out of room. The argument for training purpose-built search agents rather than routing everything through general-purpose frontier APIs is not primarily about cost. It is about building a model that knows how to do the job.
LICURV explores instant kitchen cleaning with compact dishwasher and five-second washFri, 06 Mar 2026 08:37:06 +0000 As traditional dishwasher products continue to focus on large capacity and built-in installation, smaller and more immediate cleaning solutions are emerging as a new direction in the market. Chinese tech startup LICURV has introduced a compact dishwasher that attempts to offer an alternative approach for household kitchens through a different technical design. The company says […]
Xiaomi begins limited closed beta of OpenClaw-like mobile AI agent Xiaomi miclawFri, 06 Mar 2026 06:14:15 +0000 Xiaomi has begun a limited closed beta for Xiaomi miclaw, a mobile AI agent system built on its MiMo large model, according to an announcement on March 6. The system is designed to interpret user intent and execute tasks by calling apps, system tools, and ecosystem services with user authorization. The project is intended to […]
Li Auto said to plan two-wheel robot launch this yearFri, 06 Mar 2026 05:44:01 +0000 Li Auto is said to be preparing to release a two-wheel robot designed for factory manufacturing scenarios later this year, according to people familiar with the matter. Sources said the company has been developing robotics products for nearly a year under an internal team code-named “Nexus.” The unit is led by He Junpei, a former […]
Bilibili posts first full-year profit as ads and user engagement growFri, 06 Mar 2026 05:02:29 +0000 Bilibili reported its 2025 fourth-quarter and full-year results on March 5, achieving its first full-year profit under U.S. GAAP. The company posted annual revenue of RMB 30.35 billion ($4.2 billion), up 13% year-on-year, with adjusted net profit reaching RMB 2.59 billion ($360 million). Advertising revenue rose 23% to RMB 10.06 billion ($1.39 billion) in 2025, […]
BYD unveils 1,500 kW ultra-fast EV charger, plans 20,000 flash charging stations in ChinaFri, 06 Mar 2026 02:01:47 +0000 BYD on Thursday unveiled in Shenzhen what it said is the world’s most powerful mass-produced single-gun ultra-fast charging pile, delivering up to 1,500 kW. The charger features a rail-mounted T-shaped suspended design and is equipped with a zero-gravity charging gun that can be operated with one hand. It also supports simultaneous dual-gun charging without power […]
Google DeepMind executive invites Qwen team to join amid leadership changes at QwenFri, 06 Mar 2026 01:49:47 +0000 A senior member of the Google DeepMind development team, Omar Sanseviero, publicly invited members of Qwen to join the company in a post on the social media platform X. “Qwen friends: if any of you want a new home to build great models and contribute to the open models ecosystem, please reach out! Lots of […]
Momenta Confidentially Files for Hong Kong IPOThu, 05 Mar 2026 09:33:53 +0000 Momenta has confidentially filed for an initial public offering in Hong Kong, according to people familiar with the matter. The autonomous driving startup is working with China International Capital Corp. (CICC) and Deutsche Bank on the planned listing. The company may seek to raise at least $1 billion in the offering, though deliberations are ongoing […]
Lenovo issues price increase notice; some PC models rise over $140Thu, 05 Mar 2026 06:58:17 +0000 Lenovo recently issued a price increase notice to its channel partners, with the retail prices of some computer products rising by over 1,000 yuan ($140). This adjustment mainly targets offline store networks and involves mid-to-high-end models across the ThinkPad, IdeaPad, and Legion series. The price hike is mainly driven by a surge in global memory […]
Luckin Coffee backer Centurium Capital wins bid for Blue Bottle, sources sayThu, 05 Mar 2026 03:33:59 +0000 Centurium Capital, the controlling shareholder of Luckin Coffee, has won the bid to acquire Blue Bottle Coffee and is expected to complete a deal with Nestlé, according to people familiar with the matter. The transaction has been signed but has not yet closed, one person close to the buyer said. Sources said Centurium Capital will […]
ByteDance’s Seedance 2.0 video model costs about $0.14 per secondThu, 05 Mar 2026 02:13:33 +0000 Volcengine, the cloud computing arm of ByteDance, has disclosed the pricing structure for its Seedance 2.0 video generation model, with costs averaging roughly one yuan ($0.14) per second. According to information published on Volcengine’s website, the Doubao-Seedance-2.0 model is priced at 28 yuan ($3.90) per million tokens for video input (video editing), and 46 yuan […]
Qwen technical lead Lin Junyang leaves Alibaba, sources sayWed, 04 Mar 2026 09:24:06 +0000 Lin Junyang, technical lead of the Qwen model team at Alibaba, has resigned from the company, according to people familiar with the matter. Lin submitted his resignation on March 3, and the departure was later communicated within the Qwen team, the sources said. Lin had led key development work on the Qwen model series under […]
Tech Odyssey Series: Bhout, the Startup That Turned a Literal Dream Into an AI Punching BagWed, 04 Mar 2026 09:15:20 +0000 In the previous episode of Tech Odyssey, we visited Startup Portugal to understand how this small European country built a tightly connected innovation ecosystem. This time, we looked at one of the startups emerging from that environment: Bhout. At first glance, the punching bag inside Bhout’s Lisbon gym looks ordinary. But once you throw a […]
Trump administration reportedly reviews whether Tencent can retain stakes in US gaming companiesWed, 04 Mar 2026 07:10:41 +0000 The Trump administration is engaged in intense discussions over whether to allow Tencent to continue holding stakes in US and Finnish gaming companies, according to Reuters. The debate centers on Tencent’s investments in Epic Games, Riot Games and Supercell, involving globally popular titles such as Fortnite, League of Legends and Clash of Clans. Sources familiar […]
MWC 2026: iFlytek releases 40g AI glasses for cross-language communicationWed, 04 Mar 2026 03:29:14 +0000 At MWC 2026, iFlytek introduced a pair of AI-powered smart glasses designed to enhance cross-language communication. The glasses feature real-time subtitle translation displayed on the lens and an integrated speaker that plays the translated audio. With advanced lip movement recognition and bone conduction microphone technology, the glasses perform well in noisy environments, enhancing translation accuracy […]
MWC 2026: ZTE debuts pet-style AI companion iMoochiWed, 04 Mar 2026 03:06:02 +0000 ZTE expanded its AI hardware lineup at the 2026 Mobile World Congress (MWC) with the launch of iMoochi, a new AI-powered companion device. Designed for users of all ages, the device is built to simulate lifelike behaviors and adjust its interaction patterns based on user engagement. Available in four distinct models, iMoochi utilizes touch sensors […]
OpenClaw launches official Weibo account in China, drawing warm welcome from domestic AI firmsWed, 04 Mar 2026 02:06:28 +0000 Open-source AI agent project OpenClaw on Tuesday launched its official Weibo account in China, attracting swift attention from the country’s AI sector, with leading domestic companies posting messages of support. Firms including Zhipu AI, Baidu, Tongyi Qianwen and Kimi welcomed the move in the account’s comment section. OpenClaw, led by an Austrian developer, focuses on […]
Xiaomi says humanoid robots begin factory trials, targets large-scale deployment within five yearsTue, 03 Mar 2026 05:41:32 +0000 Xiaomi CEO Lei Jun said Xiaomi’s humanoid robots have begun trial operations at its automobile factory, with plans to deploy large numbers of the machines across its production facilities within the next five years. In a social media post, Lei said the company’s robotics unit has made progress based on its general-purpose vision-language-action (VLA) foundation […]
MWC 2026: Tecno unveils ultra-slim modular magnetic concept phone, swappable components in minutesTue, 03 Mar 2026 02:31:21 +0000 Tecno, a subsidiary of Transsion, unveiled an ultra-thin modular magnetic concept smartphone at MWC, aiming to challenge the fixed-hardware model of today’s devices. The concept is built on a simple premise: conventional smartphones lock users into a single hardware configuration, requiring a full device upgrade to access new features. This new system instead allows users […]
MWC 2026: RayNeo launches Batman Limited Edition Air 4 Pro AR glasses, featuring optional Batman or Joker versionsTue, 03 Mar 2026 02:09:46 +0000 RayNeo on Monday unveiled the Batman Limited Edition Air 4 Pro AR glasses at MWC 2026. Compared with the standard version, the limited edition adds an exclusive light-shield cover that not only blocks ambient light but can also be used as a cosplay accessory. According to the company, the Batman Edition Air 4 Pro comes […]
MWC 2026: Lenovo showcases big-eyed AI workmate that projects and turns scanned documents into pptMon, 02 Mar 2026 05:20:50 +0000 At MWC 2026 in Barcelona, Spain, Lenovo unveiled two AI companion products — AI Workmate and AI Work Companion. Workmate resembles a desktop robotic arm, featuring a circular display on top that can show expressions. It supports voice and gesture interaction, can scan documents to generate PowerPoint presentations, and project them for display. Work Companion […]
DeepSeek plans V4 multimodal model release this week, sources sayMon, 02 Mar 2026 04:38:20 +0000 DeepSeek plans to release its V4 large language model this week, marking its first major launch since January 2025, according to people familiar with the matter. The Hangzhou-based lab is expected to unveil V4 as a multimodal model capable of generating text, images, and video. Sources said DeepSeek worked with Chinese AI chipmakers Huawei and […]
Humanoid robot maker Galbot raises RMB 2.5 billionMon, 02 Mar 2026 04:17:28 +0000 Galbot, a China-based humanoid robot developer focused on embodied AI models, has completed a new funding round totaling RMB 2.5 billion (about $350 million). Investors include the China Integrated Circuit Industry Investment Fund, Sinopec, CITIC-affiliated investment entities, SAIC Motor’s financial arm, among others. Existing shareholders also participated in the round. The company said it will […]
DiDi Autonomous Driving launches Voyager Labs for multimodal end-to-end driving researchMon, 02 Mar 2026 04:03:09 +0000 DiDi Autonomous Driving said it has established DiDi Voyager Labs, a research initiative focused on multimodal large models, world models, and reinforcement learning for end-to-end autonomous driving. The company also announced a research partnership with a team led by Professor Li Shengbo at Tsinghua University. The lab will operate under a joint framework combining dedicated […]
Alibaba unifies large model brand under QwenMon, 02 Mar 2026 03:39:29 +0000 Alibaba said it has unified its large model brand under the name “Qwen.” The Chinese brand name will be “千问大模型,” while “Qwen” will be used in English. Tongyi Lab will remain the organizational name of Alibaba’s AI research unit. The Qwen portfolio includes foundation models and domain-specific models, while the Qwen app serves as its […]
MWC 2026: Xiaomi unveils Electric Scooter 6 series with up to 75km rangeMon, 02 Mar 2026 03:13:27 +0000 Xiaomi unveiled its Electric Scooter 6 series in Barcelona ahead of MWC 2026, introducing five models including the 6 Lite, 6, 6 Pro, 6 Max, and 6 Ultra. Prices range from €329.99 to €799.99 (about $360 to $870). The top-tier 6 Ultra offers a maximum range of 75 kilometers and features 12-inch all-terrain tires with […]
MWC 2026: Huawei to open source A2A-T telecom agent protocol softwareMon, 02 Mar 2026 02:53:40 +0000 Huawei said it will launch an open source project for software supporting the A2A-T (Agent-to-Agent for Telecom) protocol during MWC 2026. The company said the move is aimed at promoting adoption of telecom-grade agent communication standards across the industry. The A2A-T protocol was previously introduced with industry partners to provide a unified interaction framework for […]
MWC 2026: Honor unveils Robot Phone with embodied AI featuresMon, 02 Mar 2026 02:46:30 +0000 Honor, a Chinese smartphone maker, introduced its “Robot Phone” ahead of MWC 2026, positioning the device as the first product under its new Alpha strategy. The smartphone features a motorized rotating flip camera, a 4DoF gimbal module reduced in size by 70%, a 200-megapixel sensor, and support for three-axis stabilization and AI-powered tracking. CEO Li […]
MWC 2026: Xiaomi unveils Vision GT concept carMon, 02 Mar 2026 02:39:22 +0000 Xiaomi unveiled its Vision GT two-door concept car at a global launch event in Barcelona ahead of MWC 2026. The company said PlayStation 5 users will be able to drive the vehicle in the racing game Gran Turismo 7. Xiaomi also introduced a dedicated driving simulator to allow consumers to experience the Vision GT concept […]
Shenzhen and the rise of Physical AI: building the factory floor of the futureMon, 02 Mar 2026 02:38:23 +0000 The first wave of AI lived inside the screen. It wrote, translated, summarized and conversed. It reshaped cognitive work and accelerated digital productivity. But a new phase is now emerging — one that extends intelligence beyond software and into the physical world.
Tencent’s TiMi denies reports of mass layoffs, says around 100 staff affected by project adjustmentMon, 02 Mar 2026 02:08:31 +0000 Recently, users on social media platforms such as Xiaohongshu alleged that Tencent’s TiMi Studio is planning to lay off several hundred employees, possibly related to the Shanhai Xunling project team. In response, a source close to TiMi told Sina Tech that the claim is not accurate. While a project previously handled by the former TiMi […]
