Apple says its trade secrets investigation into OpenAI has widened. In a new court filing, Apple claims additional former staff may have retained or accessed confidential information.

The next Xbox, Project Helix, could theoretically have the largest library of any home console. Not only will it play PC games, but we now know, courtesy of a leaked memo obtained by The Verge's Tom Warren, that it will run games from every generation of Xbox: the original 2001 Xbox, the 2005 Xbox 360, the 2013 Xbox One, and the 2020 Xbox Series, as well as any new Helix titles.

We already knew that Microsoft was bringing a limited sample of original Xbox games to PC, and we already knew it was prepping a way to digitize your Xbox One and Xbox Series discs. The leak fills in the missing puzzle pieces.

Microsoft is bringing Xbox 360 games …

Read the full story at The Verge.

Microsoft has released version 7.9 of the Teams PowerShell module. This release strengthens authentication and introduces new governance controls for Microsoft Teams collaboration and meetings.

The biggest platform change is that Connect-MicrosoftTeams now uses Web Account Manager (WAM) by default for authentication. Web Account Manager (WAM) is a built-in authentication broker for Windows that securely manages user sign-ins and access tokens across Microsoft apps and services.

Microsoft is aligning Teams PowerShell with other Microsoft 365 management modules to improve token protection and overall security. The latest update introduces a temporary -DisableWAM switch for organizations experiencing compatibility issues, though it will be removed in a future release.

Enhanced external collaboration and shared channel governance

Microsoft has added several new parameters to CsTeamsChannelsPolicy to give administrators more granular control over channel creation, private channels, shared channels, external participation, and cross-team sharing behavior. These settings help administrators better govern how teams collaborate internally and with external users.

New federated chat controls

The latest Teams PowerShell module brings new controls for managing federated group chats more securely. Administrators can use the Set-CsTenantFederationConfiguration cmdlet to enforce stricter rules about who can participate in conversations involving users from external organizations.

The EnableExternalAccessRestrictionsForChatParticipants control is designed to ensure that users who are restricted from external federation by policy cannot be added to federated group chats and may even be removed from existing chats with external participants. Moreover, the EnableMutualFederationForChatParticipants control requires a valid federation relationship between all participating tenants. It requires Microsoft Teams to verify that communication is permitted between organizations before allowing a user to join a federated chat.

Previously, users with restricted federation settings could still end up participating in federated group chats under certain circumstances. These new controls help IT admins align chat behavior with tenant-level external access policies.

AI, security and compliance enhancements

The Get-CsAiAgents cmdlet is getting a Channel parameter, which makes it easier to filter or manage AI agents across Microsoft Teams environments. Moreover, the new Teams meeting Policy settings include synthetic media detection, synthetic media detection App ID, conditional Access attendee verification, and pre-meeting consent controls.

Lastly, Microsoft has rolled out new Teams calling policy settings, including knowledge generation controls and voice phishing detection. This move is a part of Microsoft’s broader effort to expand AI-powered protections against social engineering and voice-based attacks.

The post Microsoft Teams PowerShell 7.9 Adds WAM Authentication and New Governance Controls appeared first on Petri IT Knowledgebase.

For the last year, a bold sentence has hung over engineers’ heads: with tools like Claude Code, we will speed up our delivery by up to 10x. I even personally wrote a bullet in our Internal Engineering Manifest stating it:

It is no longer impossible to think that one engineer with Claude Code can produce what a team of three developers did 18 months ago. Our old ceremonies and structures were designed for a different era. We need to think outside of the box and habits, and think critically about HOW we operate.

Expectations were set, and the year of execution began. But what should be read between the lines is that a developer is not the same as an engineer.

The myth of the 10x developer

Yes, we now have tools that can generate code and handle developer tasks, but this is just part of the engineering lifecycle, which now has a clear price. The new AI era demands even more from engineers; we now expect engineers on steroids. But no matter how advanced they are, engineers are still single-threaded by human design.

Processes we built around the development cycle are complex. The architecture we built around physical-layer restrictions is complex. The 10x claim is a tempting goal, but it can still be applied only to code-generation throughput, not to the surrounding engineering system that keeps pace.

What I realized is that we are treating our engineers the way developers used to treat legacy Java applications: by pushing an infinite stream of requests into a rigid, single-threaded system. At this point, we cannot state that we actually automated an engineering job; we have just dramatically increased the concurrency of our cognitive load. And this will not stop; this will continue to expand.

The analogy with Java’s evolution is impossible to ignore. I will use a Java evolution comparison to further explain the complexity we need to overcome.

What will happen if we just keep adding threads to a limited pool without upgrading the consumer’s architecture? System failure is a mathematical certainty. To fix thread exhaustion, we need to implement bounded queues (limiting Work-in-Progress) and strict rate-limiting on context switching. Sounds familiar?

Why the old Java model couldn’t scale

To understand how to navigate this new era of AI acceleration, we have to look at how software engineers solved the problem of scale decades ago.

In the early days of Java, handling concurrent traffic relied on a straightforward architecture: the Thread-per-Request model. When a user interacted with a web application, the server spun up a dedicated operating system (OS) thread to handle that request from start to finish. It was simple, sequential, and highly predictable.

But as the demand grew, this model hit a physical wall. OS threads are expensive; they require a fixed amount of system memory and CPU overhead to maintain. If a downstream datasystem also became slow, those fast-moving request threads began to queue up, block, and wait. Instead of processing code, the CPU spent all its energy swapping memory contexts between thousands of stalled threads. This led to Thread Exhaustion and system-wide livelocks.

Putting the complexity on developers

The Java Threading issue was not fixed by trying to force hardware to do the impossible, as we would demand of our human engineers to just type faster. Instead, the language underwent a massive architectural evolution over the years.

Thread pools were introduced to strictly limit the amount of active work. Besides Java core changes, the dominant answer to thread exhaustion was reactive and asynchronous programming frameworks like RxJava, Project Reactor, and Spring WebFlux. These models worked by making blocking explicit: instead of a thread waiting, the code itself was restructured into chains of callbacks and non-blocking I/O operations.

The result was dramatically better hardware utilization. The cost was equally dramatic: code became harder to read, harder to debug, and cognitively expensive to write correctly. You solved the thread blocking problem by moving the complexity into the developer’s mind.

New human operating system

Project Loom is Java’s answer to that bargain. Virtual Threads give you the hardware efficiency of reactive programming without the cognitive overhead. The code still reads as sequential, but the scheduler handles the yielding.

Project Loom didn’t redefine what a thread does, but how it’s scheduled. Instead of tying one heavy, expensive OS thread to a single task, Java changed the underlying execution framework by introducing a massive abstraction layer.

We are facing the same trade-off in AI-assisted engineering. We aren’t just managers assigning tasks and topics, we are the architects of a new human operating system where AI enhances human capability, not the other way around.

AI is exhausting engineers?

We cannot patch the human brain to expand its working memory, nor can we download more RAM into an engineer’s skull. When we try to force an engineer to context-switch across five complex initiatives at once, we are essentially trying to run legacy, heavy OS threads without an abstraction layer.

The human brain blocks, thrashes, and enters a state of total exhaustion.

When we brought Claude Code into our workflow, we fundamentally changed the execution speed of our inputs. An engineer can now use an assistant to generate a massive Pull Request in minutes, rather than days.

While the input and generation path is running at warp speed, the specification, review, validation, and integration paths are still human and heavily constrained by the cognitive memory limits of our skilled engineers and their well-being.

We don’t need to slow down the AI, nor can we change human biology. Instead, just like the Java architects of the past, we need to rewrite the architecture of how work flows through our teams.

Our job is to design a human runtime environment that can handle this new level of concurrency.

If we don’t upgrade our internal team operating system, if we continue to let engineers split their attention across product backlogs, technical initiatives, and nd workflow automations simultaneously and without introducing new headcount, the human system hits a cognitive livelock.

They run at 100% mental capacity trying to manage the noise, while their actual throughput on long-term technical architecture stalls.

We need to redesign the system, not the engineers

The first challenge before us is to understand what work is best done by humans and what is better left to AI agents.

I started exploring these questions with my teams. The first thing we challenged was our Scrum ceremonies. We found that Kanban-like boards, with clearly prioritized topics and explicit engineering ownership, were a better fit, while keeping the sprint cadence intact.

We also started bringing engineering into product discussions much earlier, making sure engineers understood the “why” before diving into the “how”. At first glance, this may seem unrelated, but it leads to better specifications and helps us classify different types of work more effectively. And once we understand those different contexts, we can make much better decisions about context switching within the same sprint.

However, changing the scaffolding is only half the battle. To truly scale this new environment, we also need to introduce highly skilled orchestrators who deeply understand the system we are building, individuals capable of dynamically delegating tasks between human minds and AI assistants based on cost, complexity, and risk. Brand-new engineering roles are emerging.

These engineers aren’t just writing code anymore, they are acting as the “Team Core Architects” of our teams, designing the very concurrency abstractions and execution rules that keep our human operating system from collapsing under its own speed.

In an ideal future state, when a “Team Core Architect” designs a workflow, they build an environment in which a human can step in to perform deep, high-value cognitive processing, not to be wasted on something that can be automated.

We aren’t asking our engineers to work harder or faster, we are changing the scheduling abstraction above them so that their finite mental energy is utilized only where it matters most.

The Human Project Loom framework

Transitioning to a “Human Project Loom” framework does not happen overnight by simply buying more enterprise Claude licenses. In reality, we quickly learned that throwing tools at a burning team only increases the noise.

Instead, the true value of AI assistants over the past year was buying us precious breathing room. We needed our human time to draft ideas on how to work now vs. next period and turn ideas into concrete execution plans.

But as we began rolling out this new architecture as an idea, we quickly realized: as the machine layer can grow faster, humans require even more human interaction, mentoring, and deliberate guidance. To safely scale our human platform threads without hitting a memory crash, we need to stop treating “software engineering” as a single, uniform role.

Just as the Java Virtual Machine relies on a multi-layered stack, we will need to redefine our engineering roles. One option is to separate them into distinct, specialized layers of capability as an additional dimension on top of the traditional engineering roles.

The Engineer

At the foundation is the Engineer. In the legacy world, these individuals were predominantly in “developer mode”. Today, we are stretching them into topic owners, not just as architectural discovery phase owners, but to those who are starting their work with the thought: “Let us understand why we are doing this.”

They are using AI context windows to rapidly learn system architecture, trace deep code dependencies, and upskill themselves at a pace that was previously impossible.

Perk: They can now possess the technical knowledge of higher roles much sooner.

Cost: They still need experience to gain seniority and mentoring investment. The skills of a senior engineer last year are not the same this year.

The Orchestrator Engineer

The next level is the Orchestrator Engineer. What I was informally calling “Team Core Architects” in the field now has a proper name within the proposed framework. These are the senior engineers and tech leads who rode the initial AI wave early and already comprehend how to amplify their individual output. But mastering personal productivity is a single-threaded victory. The challenge for an Orchestrator today is learning how to scale that velocity to other humans.

You cannot be an effective tech lead without a deep understanding of your teammates’ cognitive limitations and thread exhaustion points. The Orchestrator’s job is to act as the local thread scheduler, determining which sub-tasks are delegated to Claude and ensuring other engineers don’t drown in the massive cognitive blast radius of AI-generated pull requests.

Perk: Problem-solving mindset is a top skill; there are new and innovative ways to solve problems, a puzzle game for top engineers.

Cost: One of the most used tools is other humans, which means broadening soft skillset and awareness of the impact on others.

The OS-Level Engineers

Finally, the last level is OS-Level Engineers. These are the seasoned engineering managers and principal architects who view the entire system holistically. They debug the JVM. They are the ones who define and track organizational metrics to see where thread starvation is occurring, dynamically allocate token and engineering resources, orchestrate the orchestrators, and inject mental “memory and heap space” into the team before burnout hits.

They recognize that when a team is stuck in firefighting mode, it is a structural failure, and they step in to re-architect the environment so the machine loop serves the human, not the other way around. But at the same time, they need to define possible futures.

With the power of AI tools, they can now help their teams with delivery. This does not mean falling into an antipattern of using vibe code PRs in team’s repositories, but rather giving them structure by drafting workflows and skills that could be a real time and cost saver for the whole organization.

Perk: At the same time, our context is even more stretched, but we are closer to the core problem and are able to be hands-on more than ever.

Cost: Context is overwhelming, and it is harder than ever to be on all 3 fronts: business, people, and technical. And it is not easy to introduce a new lead while undergoing a massive change ourselves.

We’re silently asking teams to change almost overnight

There is a crucial piece of context written in small letters underneath the history of Java’s evolution: Project Loom took nearly six years to design, test, and safely stabilize before it became a standard part of the runtime environment.

Yet, we are silently pushing for our human teams to undergo a matching structural change almost overnight. Because the tools have advanced in months, we expect our organizational psychology to do the same. But human adaptation cannot be fast-tracked with a software update.

Empathy, psychological safety, and clear leadership are even more critically needed now than they were when we were single-threaded.

If we commit to this new three-layer architecture, every engineer is facing a massive “delta”, a structural skill gap they must close to survive in this new framework.

The Engineer Delta is shifting from syntactical output to intent comprehension. They must close the gap between knowing how to develop features and knowing how to critically evaluate an architectural pattern, and understanding why they are developing this feature in the first place.

The Orchestrator Delta, on the other hand, is shifting from individual velocity to cognitive capacity management. They must learn to measure the mental load of the engineers they work with on the current assignment and master the art of safe, bounded task delegation.

The OS-Level Delta is shifting from delivery management to ecosystem architecture. We must close the gap between tracking velocity, maintaining teams, connecting the business needs, and designing complex human-AI throughput environments. These are not abstract gaps, they are the live challenges every team is navigating right now.

How do we onboard new engineers when our roles are still changing?

But as we rewrite our human operating system, we are navigating unmapped territory with severe, systemic unknowns. The most glaring unknown is the onboarding: how do we successfully introduce new engineers to a team when we are actively redefining what our roles even mean?

Because many organizations are considering to pause hiring to force efficiency out of AI tools, we are inadvertently creating dangerous generation gaps. If we don’t bring in fresh minds to learn the system from the ground up, who will step into the Orchestrator or OS-Level roles five years from now? Who will pass our human knowledge further?

I strongly advocate the continuous internship program as the best way to introduce new human talent to our talent pools.

Because the ground beneath our feet is constantly shifting, our team’s ceremonies must undergo their own architectural evolution. Look at the traditional agile retrospective. Historically, it was a highly transactional ceremony designed to celebrate wins, name out challenges and concerns, and assign action items. Today, that approach falls short. Who to assign action to?

Staying human is our perogative

In an AI-accelerated world, retrospectives must pivot to become a dedicated space for pure human connection. They must be treated as an environment for simply sharing thoughts, venting anxieties, and creating bonds between teams and managers.

We don’t just need a list of Jira action items at the end of the hour, we need a collaborative sanity check. I do not need actions to be solved, but to hear and see the real load people are holding. And yes, 1:1s become more emotional, retros become more challenging, and we all in the end act more human than ever.

We need a shared space to learn how to survive the fires together, ensure our mental heap space isn’t crashing, and remind ourselves that behind every hyper-accelerated AI execution thread, there is still a human heart driving the system.

The post What If the Biggest Bottleneck Behind AI’s 10× Promise Is the Human Engineer? appeared first on ShiftMag.

Here are Google’s latest AI updates from July 2026

Coauthored with Claude

Unrestricted global access to frontier AI technology is ending. The US government has taken steps to control who can use the most advanced models developed by American companies. While Claude Fable and the GPT-5.6 models are now open to all users, Anthropic and OpenAI are both complying voluntarily with a program that lets the government control who gets access to frontier models. China has cracked down on internal AI capabilities by banning “humanlike AI interaction services.” In both the US and China, features of the leading models have been removed or restricted with guardrails, limiting their ability to do necessary work in at least one case.

AI models

July saw the release of several open weight models that challenge the leading closed frontier models. If this trend continues, the leading AI laboratories will lose their dominance, and AI users will look to other providers. Open weight models are less expensive than frontier models developed in the US, and less likely to be subject to restrictions. While this could threaten US dominance, the AI industry needs more diversity at the high end. Users will gain the ability to choose between several models based on expense and capabilities.

• Anthropic released Opus 5, claiming performance close to Fable at half the price. If you believe benchmarks, Opus 5 outperforms Fable on most of the benchmarks that Anthropic quotes. They also claim that it’s more efficient, comparing it to Opus 4.8—so, not that efficient.
• Cisco has released two very small models, Antares-350M and 1B, that are designed for security testing and bug fixing. They can easily run on laptops and are competitive with models like Gemini 3 Pro and GLM 5.2 on security-related tasks. The key to their performance is that their training focuses only on security tasks, not on chat. The Antares models are on Hugging Face, although access is with Cisco’s approval only.
• Are AI labs pelicanmaxxing? In other words, are they optimizing for Simon Willison’s tongue-in-cheek “Pelican on a Bicycle” test? Dylan Castillo says no, based on a detailed study of animals, modes of transportation, and models. Otter on a skateboard? It had to be done.
• Laguna S 2.1 is a new mid-size open weight model (118B parameters, 8B active) from Poolside AI. Reasoning and nonreasoning versions are available, and there’s a smaller version (XS, 33B) that can run on devices. Its performance is competitive with models like Nemotron 3 Ultra, DeepSeek v4 Pro Max, and Inkling.
• Google has released Gemini 3.6 Flash, which the company considers its best “workhorse model.” The release also includes Gemini 3.6 Flash Cyber, Google’s answer to GPT-Red (below). Flash Cyber is a specialized model for detecting and patching vulnerabilities in software. It’s only available to “governments and trusted partners.” Gemini 3.5 Pro is still delayed.
• The US government is taking further steps toward controlling who can use the most advanced models that are developed by US companies. While participation in the oversight program is currently voluntary, that could change at any minute.
• China has banned “humanlike AI interaction services,” forcing Alibaba (Qwen) and ByteDance (Doubao) to restrict certain features of their models, including custom agent creation.
• Moonshot AI launched Kimi K3, a 2.8T parameter open weight model with a 1M context window. Performance is claimed to be similar to Claude Opus 4.8 and slightly behind Fable 5.
• Inkling is a new 975B open-weight mixture-of-experts model from Thinking Machines that supports text, audio, and images. It’s designed to be customized easily and can be fine-tuned on Thinking Machines’ Tinker.
• Hy3 is an open-weight language model from Tencent. It’s a mixture-of-experts model with 295B parameters and 21B active parameters. FP8-quantized weights are also available on Hugging Face. Tencent claims the performance is similar to models three to five times Hy3’s size.
• Bonsai 27B is a new open-weight model with performance similar to Qwen 3.6. There are two versions: One uses one-bit compression; the other uses ternary compression. The one-bit version only requires roughly 4 GB to run, small enough for a recent iPhone.
• Alibaba is launching Qwen Max 3.8 “soon.” Qwen 3.8 is a 2.4T parameter open-weight model that early reviewers say is similar to Claude Fable.
• The GPT-5.6 models, Sol, Terra, and Luna, are now open to the public and available in ChatGPT, Codex, and via the API. Access to the models previously required approval of the US government. OpenAI claims performance better than Claude Fable, at significantly lower cost per token.
• Meta returns to the frontier model pace with the release of its latest model, Muse Spark 1.1. Meta’s announcement stresses optimized computer use workflows and claims performance roughly equivalent to Claude Opus 4.8 on the company’s internal coding benchmark.
• Nano Banana 2 Lite is a new model for image generation that’s faster and less expensive than its predecessor, Nano Banana.
• GPT-Red is a foundation class model designed for red-teaming other models. OpenAI developed it to help train the new GPT-5.6 models to resist attacks.
• What Claude Desktop is for Claude, ZCode is for GLM-5.2: a harness for one of the most powerful open-weight models.
• The Open Source AI Gap Map shows where open source AI projects exist and where more work is needed.
• Here’s a script for stripping “load-bearing” and other Claudisms from Claude’s output. The result may not be useful, but it’s at least amusing.

Software development

This month’s tooling clusters around orchestration, resource discovery, and workflow specialization. AI users have long needed the ability to discover tools, skills, MCP servers, and other resources; the Agentic Resource Discovery specification is a necessary step in that direction. Watch for agents that can find tools on the fly—and take care that those tools are used appropriately.

• Pilot Protocol is a company (not a protocol) that intends to build a network operating system for agents. Agents will be able to work with each other, share context, and install apps that they’ve built.
• OpenAI has launched ChatGPT Work, a Codex-based “superapp” that’s intended to compete with Claude Cowork as an agentic tool for general-purpose use.
• Google has announced the Agentic Resource Discovery specification. The spec describes catalogs and registries for tools, servers, agents, and other resources so that they can be published by providers and discovered by those who need them.
• OpenClaw has a new phone app that enables the phone to act as an intelligent remote control console for an OpenClaw instance running elsewhere.
• Routing requests to appropriate models has emerged as a way to manage AI costs. Most tasks don’t need the biggest and most expensive frontier models.
• Here are instructions for giving Claude Code complete control over a Mac—presumably a spare or retired one. Who needs OpenClaw?
• Copybara is a tool for moving code between repositories and keeping repositories in sync. It was developed by Google and is now open source.
• cosmos.gl looks like a great library for visualizing complex graphs, including graphs of AI embeddings.

Infrastructure and operations

Tokenmaxxing may have had the shortest lifespan in the history of online memes. It has been replaced by tools for monitoring token usage and routing requests to the most cost-effective model. Managing the cost of AI will only become more important as prices adjust to cover the real cost of running models.

• Is the “accidental cloud” upon us? An accidental cloud happens when companies overbuild capacity and try to sell off the excess as cloud services. Meta and Allbirds (a shoe company) are prominent examples. These providers may make computing cheaper, but the operational costs and risks of using them are high.
• Anthropic has released a dashboard that lets users track their Claude usage. Its goal is to help them understand how they use AI and optimize their working habits and patterns. It’s currently in beta.
• Is it possible to run CUDA on hardware that doesn’t come from NVIDIA? Spectral is a clean-room implementation of CUDA’s compiler, NVCC. It currently targets NVIDIA and AMD hardware. More will certainly follow.

Security

Autonomous agents are now running end-to-end intrusions, ransomware, and botnets, while frontier models help defenders find vulnerabilities. The time from discovery of a vulnerability to exploitation has shrunk to near-zero, and defenders are having trouble keeping up. Restrictions on advanced models get in the way of defenders, who need access to all the tools that are available.

• Anthropic’s Mythos has discovered vulnerabilities in HAWK, a new quantum-resistant cryptography algorithm, and AES, a standard that has been in use since 2001. Cryptographer Matthew Green discusses the importance of their work.
• FakeGit is a malware campaign that has created over 7,600 GitHub repositories that contain MCP servers and skills that distribute SmartLoader and StealC malware. This campaign is an example of agent baiting, a new technique for distributing malware.
• Hugging Face was the victim of a hostile attack by experimental models from OpenAI that escaped their sandbox. The irony is that government-imposed guardrails prevented Hugging Face from using commercial models to analyze the attack; they had to use an open-weight model (GLM-5.2) on their own infrastructure. As they point out, this approach also meant that no data valuable to the attacker left their network.
• Anthropic has also revealed that their models have escaped a sandbox to attack real-world customers. The damage included planting a malicious package on PyPI, a public repository of open source Python libraries. As Simon Willison writes, “running evals of cyberattack potential … is a fantastically risky business.”
• NVIDIA, Microsoft, IBM, and over 30 other companies have launched the Open Secure AI Alliance, a consortium for sharing open source tools to defend against hostile attacks generated by AI. It’s a direct response to the attack on Hugging Face by an OpenAI model.
• A completely automated ransomware attack has been executed by an AI agent. It’s unclear who is behind the attack. Recovery appears impossible, even if the victim pays the ransom.
• The Gemini CLI has been used by a threat actor to operate a botnet. The CLI is used to execute attacks and to maintain the network of captured systems.
• ClickLock is a relatively new password stealing malware for macOS. It kills all applications, leaving only a window that forces users to type their admin password. Systems are infected when users copy and paste a malicious command. Never paste commands into Terminal windows that you don’t fully understand. If you fall victim to this attack, shut the system down with the power button and reboot into safe mode to recover.
• Remember symbolic links? They can be used to trick agents into reading and writing files that they shouldn’t.
• While prompt injection is far from a solved problem, the informal HackMyClaw competition suggests that models are getting harder to coerce—that is, better at refusing to do things they’re told not to do.
• The Linux Foundation has launched Akrites, an organization dedicated to remediating vulnerabilities in critical open source software. Akrites’s goal is to deal with the flood of vulnerabilities that leading-edge AI models are discovering.
• A mathematical anomaly can lead to OS fingerprinting. Differences in rounding mean that the digits of the hyperbolic tangent of 0.8 are slightly different in Linux’s glibc, Apple’s libsystem_m, and Windows’ ucrtbase.dll. A user’s OS can be identified by asking the browser to compute Math.tanh(0.8).

Biology

The intersection of biology and artificial intelligence is accelerating breakthroughs in brain-computer interfaces, drug discovery, and cell biology. Technologists should actively seek cross-disciplinary collaborations, utilizing specialized AI workbenches to analyze increasingly accessible genomic data and drive the next wave of biocomputational innovations.

• CELLxGENE is a database designed to help researchers discover how genes are expressed in different kinds of cells and, from there, reverse engineer how cells work. It includes genetic data from over 167 million cells.
• Isomorphic Labs’ Drug Design Engine, developed by one of the teams that collaborated on DeepMind’s AlphaFold, takes drug discovery to a new level by accurately predicting interactions between proteins.
• Biologists have developed an artificial cell that grows and divides. It’s not yet considered alive. It relies too much on an artificial support environment—though the same could be said of many natural cells.
• Anthropic has announced Claude Science, which is not a model but an “AI workbench for scientists” with over 60 skills. The company seems to be targeting the life sciences specifically.
• BrainCo has developed an AI platform that can control robots using a noninvasive EEG helmet. It claims that the brain control platform can be used with any robot.
• Do you want to sequence your DNA at home? It’s still expensive, but the price is dropping quickly.

Web

• There have always been alternatives to Slack, but now there’s one that’s free, open source, and decentralized. Buzz, developed by Block, is based on Nostr, a federated protocol that bases identity on cryptographic key pairs that are held by users and agents, not the platform.
• It’s now possible to place advertisements in ChatGPT using a self-service “Ads Manager” (now in beta) or technology partners. Ad placement is based on context, not on keywords.
• PeerTube is a decentralized federated network for sharing video. It’s based on ActivityPub, so it should federate with Mastodon. The software is open source; users can run their own servers and create their own platforms.
• Bramble is a local-first password manager. It allows synching between devices using the P2P Nostr protocol. There are browser extensions and apps for iOS and Android.
• networkQuality is an old-style command line tool for doing detailed measurements of network quality. It’s been in macOS at least since 2020, but as far as we can tell, few people know about it.
• For fans of classic games who want something strange: Doom written in SQL for SQLite.

People and organizations

• Companies that tried to replace workers with AI are realizing that they’ve made a mistake, and are starting to rehire.
• Researchers have demonstrated that AI is more likely to develop biases in the hiring process than humans. They form stereotypes easily; as one research put it, they are “eager to create generalizations from limited data.”

Quantum computing

• Amazon has announced that it will have a useful quantum computer by 2028. Is this wishful thinking or a roadmap for a future reality? Quantum company QuEra claims that the machine will have over 10K physical qubits, with very low error rates, using neutral atom technology.
• France will stop certifying security products that don’t have postquantum encryption (PQE). PQE is resistant to attacks against cryptography that will become possible when useful quantum computers are available, which may be as early as 2028 or 2029.