The Kindle Scribe is worth considering if you’re heading back to school, as its large 10.2-inch screen can display textbooks and ebooks, or let you jot down handwritten notes during class. Now through August 8th, the first-gen model (with a Premium Pen) is down to just $149.99 with 16GB of storage in refurbished condition at Woot when you apply the promo code KINDLE25 at checkout. That’s $220 off its original launch price from 2022 and even cheaper than the current Kindle Paperwhite, though you’ll only get Woot’s 90-day warranty. Woot doesn’t specify the exact condition of its refurbished devices, but it does offer a brief explanation here of what to expect.

Kindle Scribe (16GB)

Where to Buy:

• $369.99 $149.99 at Woot (with code KINDLE25)

The Kindle Scribe features a sharp 300ppi E Ink display that’s glare-free and supports adjustable color temperatures, making it easy to read whether you’re outside in the sun or studying late at night. Like all other Kindles, the Scribe gives you quick and easy access to Amazon’s massive store of digital books, so you can switch between assigned reading and your favorite novels without carrying multiple books or devices. It’s not waterproof like the Kindle Paperwhite, though, so you’ll want to keep it away from the pool and be careful when it rains.

It’s also a solid digital notebook, with the device offering a variety of pre-made templates for everything from lined paper and planners to to-do lists. After updating the software, the original Kindle Scribe also includes the same AI-powered note-taking features as the newer models, allowing you to jot down notes directly on compatible ebook pages, summarize and refine your handwritten notes, generate AI-powered summaries, and more.

Read our Kindle Scribe (2022) review.

Alongside the models and workflows, the project releases training data and evaluation methods intended to help the broader research community build and study open agentic systems. Artificial intelligence is rapidly moving beyond static question-answering toward autonomous agents that can plan, reason, and act across complex, multistep environments. These systems can fix bugs in complex codebases, navigate the web on a user’s behalf, and manage workflows involving calendars and email.  

While there is excitement around agentic AI’s capabilities, the research community faces a persistent bottleneck. Building state-of-the-art agentic systems often requires proprietary infrastructure, including custom sandboxes, closed training pipelines, and proprietary datasets that most researchers and practitioners cannot access or reproduce.

To address this gap, we introduce Orchard (opens in new tab), an open-source framework for scalable agentic modeling. At the center of Orchard is Orchard Env, a lightweight, Kubernetes environment that provides reusable isolated components for running and building agents at scale—from collecting training data to reinforcement learning rollouts and evaluation.  

Unlike many existing frameworks, Orchard Env is designed to support different agent systems and task types without modification. The same service can support software-engineering agents, web-browsing agents, and personal-assistant agents across domains. 

To demonstrate this approach, we are releasing three domain-specific training recipes—Orchard-SWE, Orchard-GUI, and Orchard-Claw. (opens in new tab) We are also releasing the training data and evaluation methods used to build them.

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Environment layer that scales across types of tasks

The central idea behind Orchard is that the runtime environment should be a standalone, reusable service rather than infrastructure embedded inside a specific training framework. Orchard Env’s Kubernetes foundation enables it to create, manage, and remove thousands of isolated components in parallel.

The system is designed to work across tasks like coding, web browsing, using tools. It is also designed to work across different agent systems, along with stages of the training and evaluation process, including data distillation and reinforcement learning rollouts.

This flexibility makes Orchard practical at a research scale. Teams can introduce new benchmarks, agent systems, or training algorithms without rebuilding the underlying infrastructure from scratch.

Orchard also makes it possible to train agents inside any harness. Today’s most capable agents rarely run as a bare model. They operate through sophisticated harnesses—such as Claude Code, Codex, and OpenClaw—that manage multi-turn reasoning, tool use, and connections to external systems. Open training tools usually cannot handle these stateful, multi-process harnesses, forcing researchers to train on a simplified stand-in and then deploy in the real setting, which creates a mismatch. Orchard closes this gap: a lightweight proxy records the harness’s own model calls as training data while each rollout runs in its own container, so an agent can be trained end-to-end directly in the harness that it will be deployed with—OpenClaw, Codex, ZeroClaw, or others—and across several harnesses.

Orchard-SWE: Advancing open-source software engineering agents

Software engineering is one of the most demanding settings for autonomous agents. It requires multi-step reasoning over real codebases, tool use, and the ability to recover from mistakes. Orchard-SWE is our training workflow for this domain. It is built using the Mini-SWE-Agent framework, designed to autonomously solve software engineering tasks, and evaluated on the widely used SWE-bench Verified benchmark, which tests a model’s ability to navigate, diagnose, and repair real-world codebases.

To train the system, we distilled 107,000 agent interactions from two advanced open-weight models (MiniMax-M2.5 and Qwen3.5-397B) covering a broad range of GitHub Issues. The training process uses credit-assignment supervised fine-tuning: rather than discarding attempts where the agent failed to fully resolve an issue, the system learns from the productive portions of those partial attempts, expanding the amount of useful training data available to the model.

Reinforcement learning comes next, but its feedback is sparse—an agent usually learns only whether its final patch passed or failed the hidden tests. We start with Balanced Adaptive Rollout, designed to make the most of these infrequent success signals, and then add two “dense reward” techniques for richer guidance: on-policy distillation, in which a stronger teacher model scores the agent’s decisions step by step, and a process reward model, in which an AI judge rewards sound problem-solving process—writing tests that reproduce the bug, verifying the fix, and checking that existing behavior still works—independent of whether the final tests passed. 

Finally, we train a value model on past rollouts to rerank candidate solutions. Reinforcement learning generates many practice trajectories that are normally discarded; instead, trajectories from 20 prior experiments train a compact 4-billion-parameter value model that recognizes high-quality solutions, and at problem-solving time it scores several candidate answers and picks the best one. Together, these techniques take Orchard-SWE from a 61.4% baseline on SWE-bench Verified to 69.1% with Balanced Adaptive Rollout and 69.7% with the dense-reward techniques—a new state of the art among open-source models of comparable size (roughly 3 billion active parameters)—rising to 73% with value-model reranking, approaching frontier systems more than 10 times larger, as shown in Figure 1. 

Orchard-GUI: A lightweight browser agent for real-world web tasks

Web navigation presents a different set of challenges. Agents must interpret visual layouts, interact with dynamic interfaces, and complete open-ended tasks described only in natural language.

Orchard-GUI trains a 4-billion-parameter vision-language model as a browser agent using a relatively small amount of supervision: 400 distilled demonstrations combined with 2,200 open-ended training tasks. Despite this limited training data, the resulting model achieves strong results across several web-navigation benchmarks: 74.1% on WebVoyager, 67.0% on Online-Mind2Web, and 64.0% on DeepShop, for an average of 68.4%, as shown in Figure 1.

These results place Orchard-GUI among the strongest open-source web agents to date while remaining competitive with larger proprietary models. The results also suggest that with the right training approach and environment, small open models can perform well on real-world web tasks.

Orchard-Claw: Personal assistant agents for everyday productivity

Many of the most impactful agentic applications involve everyday productivity tasks, including reading and drafting emails, managing calendars, searching for information, and coordinating across tools. Orchard-Claw focuses on personal-assistant tasks by training an agent on just 200 synthetic tasks. Evaluated on Claw-Eval, a benchmark covering realistic productivity workflows, it successfully completes 59.6% of tasks when given up to three attempts. That increases to 73.9% when paired with the stronger ZeroClaw agent system.

Because Orchard can train agents directly inside real deployment harnesses, Orchard-Claw is trained across several of them—including ReACT, ZeroClaw, OpenClaw, and Codex—rather than a single simplified loop. Training inside these real harnesses substantially improves the agent’s reliability; under the Codex harness, for example, its success rate rises from 18.6% for the untrained model to 51.5% after Orchard training. 

Implications and the road ahead

Orchard’s results reinforce a broader point: the environment layer matters. By making the underlying infrastructure open, lightweight, and reusable, Orchard lowers the cost of agentic AI research. Teams no longer need to build custom isolated environments from scratch or depend on proprietary cloud services. The same Orchard Env can be used to generate training data, run reinforcement learning rollouts, and evaluate final models without rebuilding the system each time.

Looking ahead, we see reusing training experience as a promising direction toward cumulative agent learning. Instead of discarding trajectories once a training run finishes, we treat them as persistent assets—for example, distilling them into reusable value models. This enables agentic experience to accumulate over time, allowing each new generation of agents to inherit and extend the knowledge acquired by previous ones, rather than starting from scratch. 

The data efficiency demonstrated by Orchard-GUI suggests that larger-scale web agents could be trained without requiring large amounts of manually created training data. By releasing the complete Orchard stack, including the environment service, training pipelines, and training datasets, we hope to help the broader research community build more capable open agents more quickly. 

Acknowledgements

We thank the teams at Microsoft Research and collaborating institutions for their contributions to Orchard, as well as the open-source community whose benchmarks and tools made this research possible.

The post Orchard: An open framework for scalable agentic AI appeared first on Microsoft Research.

Microsoft is making passwordless sign-ins more secure and accessible for desktop users by extending Windows Hello Enhanced Sign-in Security (ESS) to compatible external fingerprint readers. This update enables hardware-backed biometric authentication on more Windows 11 devices, which helps protect sensitive data from sophisticated cyberattacks.

Windows Hello Enhanced Sign-in Security (ESS) is an advanced security feature in Windows 11 that strengthens biometric sign-ins by creating a protected pathway between users’ fingerprint reader or facial recognition camera and the operating system. ESS uses hardware-backed protections, such as the Trusted Platform Module (TPM) and Virtualization-Based Security (VBS), to isolate sensitive biometric data and authentication processes from potential malware and unauthorized access. This capability makes it harder for an attacker with elevated privileges to intercept, manipulate, or spoof biometric credentials.

Previously, devices with built-in fingerprint sensors could take advantage of enhanced Sign-in Security (ESS). However, users with external USB fingerprint readers often couldn’t access the same level of protection. Consequently, desktop users and those with docked laptops had to choose between the convenience of biometric sign-in and the stronger security safeguards provided by ESS.

How does Enhanced Sign-in Security protect biometric authentication?

Microsoft’s latest update (KB5101684) addresses this gap by allowing compatible external fingerprint readers to work within the ESS framework. This release brings secure, passwordless authentication to a much wider range of Windows 11 devices.

“Windows Hello Enhanced Sign-in Security (ESS) now supports external fingerprint sensors. With this update, this secure sign-in option extends beyond devices with integrated fingerprint sensors to desktop PCs and other Windows 11 PCs, including Copilot+ PCs. To get started, connect a supported ESS fingerprint reader,” Microsoft explained.

What this update means for IT administrators

Microsoft has started rolling out this optional update to PCs running Windows 11 versions 25H2 and 24H2. Users can get started with fingerprint authentication by connecting a reader that supports Enhanced Sign-in Security and configuring it under Settings > Accounts > Sign-in options.

For IT administrators, this update simplifies the task of balancing security and user convenience. With support for ESS-certified external fingerprint readers, IT teams can now deploy passwordless authentication more broadly without weakening security policies. It helps organizations improve user experience while maintaining stronger protection against credential theft and biometric-related attacks.

The post Windows Hello Enhanced Sign-In Security Now Supports External Fingerprint Readers appeared first on Petri IT Knowledgebase.

At the WeAreDevelopers Conference in Berlin, I met with Zohar Elkayam (Principal Solutions Architect, Aerospike) to talk about one of the most common mistakes engineering teams still make: choosing a database because it’s familiar, instead of choosing it for the actual problem they need to solve.

It sounds like a small decision at first, but as Zohar explains, it can become a costly one later when teams have to deal with scale, reliability, latency, and re-architecture.

We also discussed what changes when you build for real-time AI workloads, and why predictable performance matters far more than averages when your users expect speed every time.

When teams pick a database today, what do they most often get wrong?

Zohar: Most people start by thinking about the databases they already know and have used in previous roles, rather than what they actually need for the specific use case in front of them.

As a result, we often see customers choose something familiar instead of evaluating variables such as latency, speed, scale, reliability, and consistency, and selecting the right solution for their particular problem.

That becomes a major issue when they later have to revisit their decision and re-architect or refactor the solution. It can be time-consuming, costly, and extremely difficult.

As AI apps get more real-time and data-heavy, how should developers and CTOs rethink database architecture?

Zohar: Traditional web applications focused on human interaction and often relied on caches and in-memory data. When we talk about real-time systems and AI, however, we are talking about high-throughput, low-latency workloads that consume large amounts of data and need it immediately.

Because of this, teams sometimes use solutions that no longer fit their needs. They require systems that are reliable, predictable, fast, and scalable, which is something we see all the time. Aerospike was built for exactly that use case: real-time workloads, low latency, predictability, and high throughput.

From my perspective, when CTOs evaluate this kind of solution, they need to think about the future. They should consider what they need today, but also where the product and its infrastructure will need to be at the next stage. That is especially important for AI applications and real-time applications in general.

Many teams are adding vector search, graph databases, and real-time pipelines to their stack. When is that the right choice?

Zohar: When many customers think about AI, their first reaction is: ‘This is what my competitor is doing, so I need to do it too. Sometimes, when we examine the use case, we find it provides no real value. Teams choose to do it simply because everyone else is doing it.

These solutions shine when they provide a competitive advantage, and AI can be integrated into the system in a way that creates long-term value. If you integrate a graph database or vector-search solution simply because someone else is doing it, you are going to have a very difficult time.

From that point onward, everything you do can become a technological hurdle, which is exactly where you do not want to be. You need to focus on what will create the most value. If AI is one of those things, go for it. If it is not, you should probably consider other solutions.

What should engineering teams measure if they really want to understand database performance at scale?

Zohar: When it comes to database performance, predictability is the number-one factor. It should be the industry standard for anything involving low latency and high throughput.

Focusing on the average can put you in a dangerous position. Think of a swimming pool with an average depth of 30 centimeters: you can still drown in the deep end.

You need to think differently about performance. Focus on predictability and metrics such as P99 or even P99.9. Measure them at the application level, the database level, and across the overall user experience, because that is what will make your life easier later.

If you measure only the average, 50 percent of your users will experience latency worse than that figure. If you measure P99, the 99th percentile, only 1 percent of users will experience worse latency.

A long performance tail can be highly problematic for some use cases. Our main focus is providing long-term predictability at the high end, including P99.9 and beyond.

That predictability cannot depend on memory or caches. It needs to hold when reading from disk, NVMe, or other storage, without relying on prior activity, cache hits, or warming data into memory.

What do teams give up or gain when they move fast with managed tools versus building something custom?

Zohar: If you are a startup building a proof of concept or just getting started, using off-the-shelf tools is perfectly fine. Over the long term, however, those tools can impose strict limitations. They can be expensive, slow, or unreliable, and they may change without your consent or even your knowledge. They can help you build quickly and get to market fast.

But once you reach a more established stage, you need to find a different way to handle things.

That may mean building your own solutions, adopting a data platform, and unifying your stack. You look for ways to differentiate your product from competitors and make it more scalable, faster, and more valuable.

It all comes down to value. If your solution provides something no one else can, and that differentiation comes from a different architecture or infrastructure, then it makes sense.

Ultimately, it is not one approach or the other. You need to combine them: use common tools to solve common problems, while applying the right technologies to the uncommon, differentiating parts of each use case.

What database and infrastructure skills should software engineers focus on to stay relevant as AI changes development?

Zohar: I think AI is a major accelerator for developers, SREs, and DevOps teams, dramatically speeding up their work. Even so, people need to stay mindful and continue developing deep expertise in their field when working with AI.

It is like generating an image with AI. You enter a prompt and get a result, but it may not be exactly what you wanted or imagined. To the human eye, the problem is often immediately obvious: the image may show six fingers, strange features, or something else you did not expect. The same applies when you are writing code or analyzing logs.

You need enough expertise to evaluate the response critically. You have to ask why: Why did the AI give me that answer? Where did it come from? What did I learn, and how can I improve my solution?

Instead of using AI only to generate and build things, use it to learn. You can still use it to generate code or solve problems, but you need to be able to read the output, understand it, and guide the AI so that it produces results that make sense for you.

When you build an agentic system, use multiple agents, and pass outputs from one to another, you can sometimes lose that visibility. But if you preserve it over the long term, CTOs, developers, newcomers, and junior engineers can go very far, provided they understand what is actually happening.

From my perspective, AI is where the industry is heading. I use it every day and across many parts of my work, but I always remain critical. I plan before I act. It is not about wasting tokens; it is about creating value through my work.

The post Choose a Database Because It Fits the Workload, Not Because It’s Familiar appeared first on ShiftMag.

Today, with CSS gap decorations fully supported in Chrome and Edge, starting with version 149, you can now very easily style gaps, and with a lot of control.

Gap Decorations Are Now Available, Here’s What’s New originally handwritten and published with love on CSS-Tricks. You should really get the newsletter as well.