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Angular Signals in practice: Building a signal-first form in Angular

18 June 2026 at 10:00

Understanding a reactivity model in the abstract is useful, but it is ultimately incomplete without seeing how it shapes real application code. Concepts such as state, derivation, and explicit dependencies only become meaningful when they influence how forms are built, validated, and maintained in practice.

In two previous articles, “Angular Signal Forms: From event pipelines to signal-driven state” and “Angular Signals explained: How pull-based reactivity changes how we model state,” we reframed form behavior as a state-driven problem and examined Angular Signals as a pull-based reactivity model well-suited to that kind of work. The natural next step is to apply those ideas to an actual Angular form and observe how the architecture changes when state becomes the primary concern.

This article focuses on a concrete example: a modest but realistic registration form. Rather than introducing new concepts, the goal here is to make earlier ideas tangible. We will see how a signal-backed model reshapes validation, interaction state, and submission logic, and how much coordination logic simply disappears when form behavior is expressed declaratively.

The focus here is not on novelty or completeness, but on making the underlying ideas easier to reason about. By walking through a signal-first form from model definition to submission, we can evaluate whether this approach truly reduces complexity and where it introduces new trade-offs that teams should understand before adopting it more broadly.

Read the series:

Implementing a signal-first registration form

With the conceptual groundwork in place, we can now turn theory into a concrete implementation. In this section, we will build a fully working registration form using Angular’s Signal Forms API. This example is deliberately modest in scope, but it is designed to serve as the foundation for the rest of the series. Each subsequent article will extend this same example rather than introducing a new one.

The form collects an email address, a password, a confirmation password, and explicit acceptance of terms. While simple on the surface, this structure allows us to explore field-level validation, cross-field constraints, interaction state, and submission behavior, all without reverting to event-driven form logic.

Project setup and structure

The example assumes a standard Angular application created with the Angular CLI and configured to use Signals (Angular 17+). The Signal Forms APIs (Angular 21+) live under @angular/forms/signals, which must be explicitly imported.

https://github.com/sonukapoor/angular-signal-forms

The folder structure is intentionally conservative:

src/
  app/
    registration/
      registration.component.ts
      registration.component.html
      registration.model.ts

Separating the model from the component keeps form state independent of presentation. This becomes increasingly valuable as the form grows or is reused across multiple components.

Defining the form model

We begin by defining the shape of the data that the form collects. This is a plain TypeScript interface with no Angular dependencies. Treating the form model as a simple data structure reinforces the idea that the form’s values are just state.

// registration.model.ts
export interface RegistrationData {
  email: string;
  password: string;
  confirmPassword: string;
  acceptedTerms: boolean;
}

This interface mirrors what would typically be sent to a back-end API. There is no duplication of state, no separate “form value” object, and no mapping required at submission time.

Creating the signal-backed form

The form itself is created in the component using a writable signal as the source of truth. The form() function attaches form semantics validation, field state, and submission to that signal.

// registration.component.ts
import { CommonModule } from "@angular/common";
import { Component, signal } from "@angular/core";
import {
  email,
  form,
  FormField,
  required,
  submit,
} from "@angular/forms/signals";
import { RegistrationData } from "./registration.model";

@Component({
  selector: "app-registration",
  imports: [FormField, CommonModule],
  templateUrl: "./registration.html",
  styleUrl: "./registration.css",
})
export class Registration {
  readonly model = signal({
    email: "",
    password: "",
    confirmPassword: "",
    acceptedTerms: false,
  });

  readonly registrationForm = form(this.model, (schema) => {
    required(schema.email, { message: "Email is required" });
    email(schema.email, { message: "Enter a valid email address" });

    required(schema.password, { message: "Password is required" });
    required(schema.confirmPassword, {
      message: "Please confirm your password",
    });

    required(schema.acceptedTerms, {
      message: "You must accept the terms to continue",
    });
  });

  async onSubmit(event?: Event) {
    event?.preventDefault();

    await submit(this.registrationForm, (value) => {
      console.log(value());
      // Mock Server Call
      return Promise.resolve([
        {
          kind: "EmailAlreadyExists",
          field: this.registrationForm.email,
          error: { kind: "server", message: "Email already taken" },
        },
      ]);
    });
  }
}

Several design decisions are worth noting.

First, the model signal is defined as read-only. All mutations to the model occur through form bindings, not ad hoc assignments in the component. This keeps the component declarative and avoids the temptation to manipulate form state imperatively.

Second, validation is declared in one place. The schema function describes constraints on the model without introducing control trees, validator arrays, or observable pipelines. Angular takes responsibility for re-running validation whenever the model changes.

Finally, submission logic is explicit. The submit() helper ensures that the form is valid before invoking the callback, and it passes the current model value directly. There is no need to check flags or manually extract values.

Binding the form to the template

With the form defined, the next step is to bind it to the template. Signal Forms provide the [formField] directive, which connects an input element directly to a field in the form schema.





  
@if (registrationForm().errors().length > 0) {
@for (error of registrationForm().errors(); track error.message) {

{{ error.kind }}

}
}
@if ( registrationForm.acceptedTerms().invalid() && registrationForm.acceptedTerms().touched() ) {

{{ registrationForm.acceptedTerms().errors()[0].message }}

}
@if ( registrationForm.confirmPassword().invalid() && registrationForm.confirmPassword().touched() ) {

{{ registrationForm.confirmPassword().errors()[0].message }}

}
@if ( registrationForm.password().invalid() && registrationForm.password().touched() ) {

{{ registrationForm.password().errors()[0].message }}

}
@if ( registrationForm.email().invalid() && registrationForm.email().touched() ) {

{{ registrationForm.email().errors()[0].message }}

}

What stands out here is the absence of indirection. Each input binds directly to a field. Validation state is accessed through signals such as invalid() and touched(). Error messages are read from a structured error object, not reconstructed manually.

This template contains no subscriptions, no async pipes, and no event handlers for value changes. The UI simply reflects the current form state.

Interaction state and user experience

One of the common criticisms of declarative form models is that they obscure user interaction logic. Signal Forms address this directly by exposing interaction metadata as signals.

The touched() signal determines whether a field has been interacted with. By combining it with invalid(), we control when validation messages appear. This logic remains purely declarative: the template describes when errors should be visible, and Angular ensures the signals stay up-to-date.

The disabled state of the submit button is derived from registrationForm.invalid(). There is no need to manually enable or disable it in response to events. If the form becomes valid, the button is enabled automatically.

Why this scales

Even at this early stage, several advantages of a signal-first form model are apparent. The form’s behavior is expressed in terms of state and derivation, not events. The model, validation rules, and UI bindings are clearly separated. There is no duplication of logic between the component and the template.

As the form grows, this structure holds. Additional fields introduce additional schema entries and template bindings, not new subscription logic. Cross-field validation can be added declaratively. Asynchronous validation and persistence can be layered on without rewriting the core model.

Most importantly, the form remains inspectable. At any point during execution, the model signal reflects the current state of the form. Derived state validity, errors, and UI flags can be understood by reading the code, not by tracing runtime behavior.

What we did not solve yet (and why)

At this stage, it would be easy to walk away with the impression that Signal Forms eliminates most of the hard problems associated with form handling. That impression would be misleading. What we have built so far is intentionally incomplete, not because the approach falls short, but because introducing too much too early obscures the value of the underlying model.

One area we have deliberately postponed is cross-field validation that expresses richer business rules. Many real-world forms depend on relationships between fields rather than isolated constraints. Password confirmation is a familiar example, but more complex scenarios quickly arise in enterprise applications. While Signal Forms support these patterns, introducing them before establishing a clear understanding of derived state risks turns validation back into an imperative exercise rather than a declarative one.

We have also avoided asynchronous validation. Server-backed checks introduce latency, partial failure, cancellation, and race conditions. These are not trivial concerns, and treating them casually often leads to subtle bugs and confusing user experiences. Although Signal Forms provide the necessary hooks to model asynchronous behavior, doing so responsibly requires a careful discussion of pending state, effects, and life-cycle boundaries. That discussion belongs in its own article.

Another omission is persistence and synchronization. Many forms need to autosave drafts, synchronize state with local storage, or react to changes by triggering external side effects. These behaviors are not part of the form state itself; they are consequences of state changes. Treating them as such is essential to keeping the architecture comprehensible. Introducing persistence too early would blur the distinction between state and reaction that this article has worked to establish.

Finally, this article has not addressed migration and interoperability. Few teams are starting from a blank slate. Most will adopt Signal Forms incrementally within applications that already rely on reactive forms or template-driven forms. Hybrid approaches, bridging strategies, and gradual refactors are all critical topics, but they presuppose familiarity with both paradigms. Addressing migration before establishing a solid signal-first mental model would undermine that foundation.

These omissions are intentional. A form architecture that tries to do everything at once often ends up doing nothing clearly. By focusing on the core ideas of state, derivation, and declarative validation, we create a base that can absorb additional complexity without collapsing under it.

Signal Forms in the context of Angular’s evolution

To fully appreciate Signal Forms, it helps to step back and view them not as an isolated feature, but as part of a broader shift in Angular’s design philosophy.

For much of its history, Angular emphasized declarative templates paired with imperative coordination in component classes. RxJS became the backbone of that coordination, providing a powerful abstraction for handling asynchronous workflows, user input, and external events. This model scaled well, but it also encouraged developers to express state indirectly through streams and subscriptions.

Signals represent a deliberate recalibration. They re-center Angular’s reactivity model around state and derivation, rather than events and emissions. This shift is visible across the framework: in component inputs, change detection, and now forms. Signal Forms are not an attempt to replace everything that came before; they are an attempt to make the most common use case, modeling and deriving state, simpler and more explicit.

Framed this way, the design of Signal Forms aligns more closely with state-driven form behavior. The requirement to start with a model signal reflects the idea that the state should have a single, inspectable source of truth. Schema-based validation aligns with the notion that constraints are properties of state, not behaviors triggered by events. Field state exposed as signals reinforces the idea that validity, errors, and interaction metadata are derived values that should be read, not managed.

It is also worth noting that Signal Forms do not attempt to abstract away form behavior. They do not hide form state behind opaque classes or life-cycle hooks. They do not require developers to think in terms of control hierarchies or subscription graphs. Instead, they expose form behavior directly, making it easier to reason about how values, validation, and UI feedback relate to one another.

This approach aligns closely with other recent changes in Angular, including the introduction of modern template control flow and a stronger emphasis on explicit data dependencies. Together, these features point toward a framework that favors clarity over indirection and composition over orchestration.

Importantly, Signal Forms are still evolving. Their APIs may change, and their surface area will almost certainly expand. That is precisely why grounding them in first principles matters. Developers who understand why Signal Forms work the way they do will be far better equipped to adapt as the APIs mature.

This article has intentionally avoided duplicating documentation or enumerating every available feature. Instead, it has focused on establishing a conceptual framework that makes the official APIs feel intuitive rather than surprising. When viewed this way, Signal Forms are not a new way to write forms; they are a clearer expression of what forms have always been.

A new way to think about forms

Building the registration form in this article reveals a quiet but important shift. The reduction in complexity does not come from fewer features or simpler requirements. It comes from expressing form behavior in terms of state and derivation rather than orchestration and reaction.

By treating the data model as the single source of truth, validation rules as declarative constraints, and UI behavior as derived from current conditions, much of the coordination logic that typically surrounds forms becomes unnecessary. There are fewer subscriptions to manage, fewer flags to synchronize, and fewer life-cycle concerns to reason about. Form behavior becomes easier to inspect because it is visible directly in the relationships between values.

This approach does not eliminate the hard problems associated with forms. Asynchronous validation, persistence, and interoperability with existing Angular Forms APIs still require careful design. What changes is where that complexity lives. Instead of being interwoven with state representation, those concerns are layered explicitly on top of a clear foundation.

Signal-first forms are not a universal replacement for existing patterns, nor are they a shortcut to simpler applications. They are, however, a strong example of how aligning APIs with first principles can reduce cognitive overhead and improve maintainability over time. For teams building large, state-heavy forms, this alignment can make the difference between code that merely works and code that continues to evolve without friction.

Why AI coding debt is different

18 June 2026 at 10:00

In hardware, when you ship something broken, the consequences are severe and often irreversible. That’s the world I worked in for years, in verification roles at Mellanox and later at Alibaba. The stakes forced the industry to build a rigorous verification culture. You proved designs worked before they left the building.

In software, verification disciplines look like CI/CD pipelines, static analysis, canary deployments, and observability. But those systems were built around code written at human speed, with human comprehension baked into the process. AI code generation has broken that assumption. The writing process can no longer be trusted to carry institutional knowledge and judgment into the codebase. The industry is being pushed toward the kind of rigorous verification culture that hardware engineers have practiced for decades.

Enterprises are generating code faster than at any point in history. Google recently disclosed that 75% of the company’s new code is now AI-generated. Meta has set internal targets requiring most of its engineers to generate the majority of their committed code with AI tools by mid-2026. The velocity gains are significant. But a growing body of evidence suggests the industry is accumulating a new form of technical debt, one that is less visible than the traditional kind and harder to unwind. It’s also preventable, and the organizations that get ahead of it will have a meaningful advantage over those that don’t.

Understand what makes this debt different

The standard narrative frames this as AI writing bad code. That’s not quite right. The more precise problem is cognitive debt: the loss of understanding of how and why software was built the way it was.

When a human writes code, something else happens alongside the typing. They simulate edge cases, reason through dependencies, and make judgment calls grounded in organizational context, including the business requirements behind a feature, the best practices the team has established, and the reasoning behind past architectural choices. That cognitive loop is how institutional knowledge gets built. When AI writes the code, you can get output that is syntactically correct, passes CI, ships cleanly, and leaves no one holding the mental model. The code works until something changes or breaks, and then the team is excavating a black box.

This is distinct from traditional technical debt, which is messy code. Cognitive debt is invisible code that functions but that nobody truly owns. And it compounds faster, because the same velocity that makes AI generation attractive is what prevents anyone from stopping to build the understanding that maintainability requires.

GitClear’s analysis of 211 million changed lines of code across major repositories found that during 2024, duplicate code blocks of five or more lines increased eightfold, while refactoring dropped from 25% to under 10% of all code changes. Refactoring is the slow, unglamorous work that keeps codebases healthy, and developers are doing far less of it. Google’s 2024 DORA report found that a 25% increase in AI adoption correlates with a 7.2% decrease in delivery stability. DORA analysts note that the root cause isn’t flawed code per se; AI inflates batch sizes, and larger changesets have always been riskier to ship.

These findings aren’t indictments of AI-assisted development. They’re diagnostics, and they point toward a specific set of fixes.

Close the context gap first

In a survey of 609 developers we conducted last year, 65% said AI misses relevant context during critical tasks like refactoring, writing tests, or reviewing code. Context is the primary driver of AI code quality, and it’s where most enterprise organizations are underinvesting.

When an AI tool generates code without access to your organization’s architectural decisions, historical pull requests, security policies, or existing module patterns, you get solutions that are locally correct but globally incoherent. Closing that gap requires context engineering: ensuring the tools and agents you use have access, at the right moment, to the right organizational knowledge, and the judgment to determine what is actually relevant for a given task. A retrieval system that surfaces too much irrelevant context can degrade output quality as readily as one that surfaces too little. The specific tooling matters less than the discipline. Context infrastructure needs to be actively maintained, not indexed once and forgotten.

Build this infrastructure before you scale AI generation. Retrofitting is significantly harder. Treat it the way you treat your CI pipeline, as a prerequisite for safe production deployment.

Consider what happens when a team has built this context infrastructure well. Their code review tooling knows about a deprecated internal API, because that deprecation decision lives in months of past pull request discussions that have been indexed and surfaced. When generated code references the old API, the review flags it. Without that context layer, the same mistake gets waved through every time. That’s the kind of institutional knowledge that evaporates when humans stop writing every line of code, and that you have to actively work to preserve.

Build a verification layer that matches your generation velocity

Almost all of the investment in AI-assisted development has gone into generation. Very little has gone into verification. That imbalance is where the tech debt accumulates.

I think of these as the blue team and the red team. The blue team covers code generation, autocomplete, and agentic coding. It’s getting the headlines, the budgets, and the product launches. The red team covers integrity checks, behavior coverage, and alignment with organizational standards. In most organizations, it’s an afterthought. A CI pipeline catches obvious failures. A code review might happen, but reviewers are overwhelmed by the volume of AI-generated output and cannot meaningfully evaluate all of it. The result is code with a veneer of having been reviewed without anyone having actually understood it.

The Crowdstrike outage of 2024 is worth keeping in mind here. AI didn’t generate the problematic code, but the incident illustrated what happens when a single software error propagates through production systems without sufficient verification. That exposure multiplies when code is being generated faster than humans can understand it.

A real verification layer means automated analysis that evaluates whether generated code aligns with your organization’s best practices, architectural standards, and compliance requirements. It means test coverage that reflects intended behavior, not only the happy path the AI chose to generate tests for. And it means traceability: a connection between the requirement and the implementation, so that six months from now, someone can understand what the code does and why it exists.

The numbers support investment here. In the same developer survey, teams that integrated AI into their code review workflow saw quality improvements in 81% of cases, compared to 55% for comparable teams without it. 

Make ownership non-negotiable

Every piece of AI-generated code in production needs an accountable human who understands it well enough to maintain it. This is harder than it sounds, and it’s where most organizations are falling short.

The same velocity that makes AI generation attractive also creates pressure to skip the slow work of genuine comprehension. A developer reviews a 500-line pull request that an AI generated in three minutes and faces a real choice: spend two hours actually understanding it, or approve it because it looks right, passes the test, and “LGTM” (looks good to me). 

Real ownership means slowing down generation velocity enough to allow for meaningful review, and being explicit with your team that this is the right trade-off. When that doesn’t happen, you’ve started building your next legacy system.

What to do this quarter

The good news is that none of this requires a multi-year transformation. The structural problems are real, but they have concrete solutions, and engineering leaders can make meaningful progress on all three fronts without waiting for the next budget cycle.

  • Audit your context infrastructure. If your AI tools are generating code without access to your organization’s architectural decisions, deprecated APIs, and security policies, fix that before expanding generation velocity further. The quality of context determines the quality of output.
  • Invest in the red team. Establish an automated code quality and governance layer that evaluates generated code against your specific organizational standards, beyond functional correctness. This is a distinct investment from your code generation tooling and needs to be treated as such.
  • Establish ownership explicitly. Every AI-generated feature in production should have a human owner who genuinely understands it. Make that a formal requirement.

The organizations that get this right will find that AI generation becomes far more reliable once it has a verification layer underneath it. The ones that don’t will keep shipping faster while understanding their systems less, until the accumulated debt forces a reckoning. That’s a solvable problem. The question is whether you solve it now or later.

—

New Tech Forum provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss emerging enterprise technology in unprecedented depth and breadth. The selection is subjective, based on our pick of the technologies we believe to be important and of greatest interest to InfoWorld readers. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Send all inquiries to doug_dineley@foundryco.com.

Building a signal-first form in Angular

18 June 2026 at 10:00

Understanding a reactivity model in the abstract is useful, but it is ultimately incomplete without seeing how it shapes real application code. Concepts such as state, derivation, and explicit dependencies only become meaningful when they influence how forms are built, validated, and maintained in practice.

In two previous articles, “Angular Signal Forms: From event pipelines to signal-driven state” and “Angular Signals explained: How pull-based reactivity changes how we model state,” we reframed form behavior as a state-driven problem and examined Angular Signals as a pull-based reactivity model well-suited to that kind of work. The natural next step is to apply those ideas to an actual Angular form and observe how the architecture changes when state becomes the primary concern.

This article focuses on a concrete example: a modest but realistic registration form. Rather than introducing new concepts, the goal here is to make earlier ideas tangible. We will see how a signal-backed model reshapes validation, interaction state, and submission logic, and how much coordination logic simply disappears when form behavior is expressed declaratively.

The focus here is not on novelty or completeness, but on making the underlying ideas easier to reason about. By walking through a signal-first form from model definition to submission, we can evaluate whether this approach truly reduces complexity and where it introduces new trade-offs that teams should understand before adopting it more broadly.

Implementing a signal-first registration form

With the conceptual groundwork in place, we can now turn theory into a concrete implementation. In this section, we will build a fully working registration form using Angular’s Signal Forms API. This example is deliberately modest in scope, but it is designed to serve as the foundation for the rest of the series. Each subsequent article will extend this same example rather than introducing a new one.

The form collects an email address, a password, a confirmation password, and explicit acceptance of terms. While simple on the surface, this structure allows us to explore field-level validation, cross-field constraints, interaction state, and submission behavior, all without reverting to event-driven form logic.

Project setup and structure

The example assumes a standard Angular application created with the Angular CLI and configured to use Signals (Angular 17+). The Signal Forms APIs (Angular 21+) live under @angular/forms/signals, which must be explicitly imported.

https://github.com/sonukapoor/angular-signal-forms

The folder structure is intentionally conservative:

src/
  app/
    registration/
      registration.component.ts
      registration.component.html
      registration.model.ts

Separating the model from the component keeps form state independent of presentation. This becomes increasingly valuable as the form grows or is reused across multiple components.

Defining the form model

We begin by defining the shape of the data that the form collects. This is a plain TypeScript interface with no Angular dependencies. Treating the form model as a simple data structure reinforces the idea that the form’s values are just state.

// registration.model.ts
export interface RegistrationData {
  email: string;
  password: string;
  confirmPassword: string;
  acceptedTerms: boolean;
}

This interface mirrors what would typically be sent to a back-end API. There is no duplication of state, no separate “form value” object, and no mapping required at submission time.

Creating the signal-backed form

The form itself is created in the component using a writable signal as the source of truth. The form() function attaches form semantics validation, field state, and submission to that signal.

// registration.component.ts
import { CommonModule } from "@angular/common";
import { Component, signal } from "@angular/core";
import {
  email,
  form,
  FormField,
  required,
  submit,
} from "@angular/forms/signals";
import { RegistrationData } from "./registration.model";

@Component({
  selector: "app-registration",
  imports: [FormField, CommonModule],
  templateUrl: "./registration.html",
  styleUrl: "./registration.css",
})
export class Registration {
  readonly model = signal({
    email: "",
    password: "",
    confirmPassword: "",
    acceptedTerms: false,
  });

  readonly registrationForm = form(this.model, (schema) => {
    required(schema.email, { message: "Email is required" });
    email(schema.email, { message: "Enter a valid email address" });

    required(schema.password, { message: "Password is required" });
    required(schema.confirmPassword, {
      message: "Please confirm your password",
    });

    required(schema.acceptedTerms, {
      message: "You must accept the terms to continue",
    });
  });

  async onSubmit(event?: Event) {
    event?.preventDefault();

    await submit(this.registrationForm, (value) => {
      console.log(value());
      // Mock Server Call
      return Promise.resolve([
        {
          kind: "EmailAlreadyExists",
          field: this.registrationForm.email,
          error: { kind: "server", message: "Email already taken" },
        },
      ]);
    });
  }
}

Several design decisions are worth noting.

First, the model signal is defined as read-only. All mutations to the model occur through form bindings, not ad hoc assignments in the component. This keeps the component declarative and avoids the temptation to manipulate form state imperatively.

Second, validation is declared in one place. The schema function describes constraints on the model without introducing control trees, validator arrays, or observable pipelines. Angular takes responsibility for re-running validation whenever the model changes.

Finally, submission logic is explicit. The submit() helper ensures that the form is valid before invoking the callback, and it passes the current model value directly. There is no need to check flags or manually extract values.

Binding the form to the template

With the form defined, the next step is to bind it to the template. Signal Forms provide the [formField] directive, which connects an input element directly to a field in the form schema.





  
@if (registrationForm().errors().length > 0) {
@for (error of registrationForm().errors(); track error.message) {

{{ error.kind }}

}
}
@if ( registrationForm.acceptedTerms().invalid() && registrationForm.acceptedTerms().touched() ) {

{{ registrationForm.acceptedTerms().errors()[0].message }}

}
@if ( registrationForm.confirmPassword().invalid() && registrationForm.confirmPassword().touched() ) {

{{ registrationForm.confirmPassword().errors()[0].message }}

}
@if ( registrationForm.password().invalid() && registrationForm.password().touched() ) {

{{ registrationForm.password().errors()[0].message }}

}
@if ( registrationForm.email().invalid() && registrationForm.email().touched() ) {

{{ registrationForm.email().errors()[0].message }}

}

What stands out here is the absence of indirection. Each input binds directly to a field. Validation state is accessed through signals such as invalid() and touched(). Error messages are read from a structured error object, not reconstructed manually.

This template contains no subscriptions, no async pipes, and no event handlers for value changes. The UI simply reflects the current form state.

Interaction state and user experience

One of the common criticisms of declarative form models is that they obscure user interaction logic. Signal Forms address this directly by exposing interaction metadata as signals.

The touched() signal determines whether a field has been interacted with. By combining it with invalid(), we control when validation messages appear. This logic remains purely declarative: the template describes when errors should be visible, and Angular ensures the signals stay up-to-date.

The disabled state of the submit button is derived from registrationForm.invalid(). There is no need to manually enable or disable it in response to events. If the form becomes valid, the button is enabled automatically.

Why this scales

Even at this early stage, several advantages of a signal-first form model are apparent. The form’s behavior is expressed in terms of state and derivation, not events. The model, validation rules, and UI bindings are clearly separated. There is no duplication of logic between the component and the template.

As the form grows, this structure holds. Additional fields introduce additional schema entries and template bindings, not new subscription logic. Cross-field validation can be added declaratively. Asynchronous validation and persistence can be layered on without rewriting the core model.

Most importantly, the form remains inspectable. At any point during execution, the model signal reflects the current state of the form. Derived state validity, errors, and UI flags can be understood by reading the code, not by tracing runtime behavior.

What we did not solve yet (and why)

At this stage, it would be easy to walk away with the impression that Signal Forms eliminates most of the hard problems associated with form handling. That impression would be misleading. What we have built so far is intentionally incomplete, not because the approach falls short, but because introducing too much too early obscures the value of the underlying model.

One area we have deliberately postponed is cross-field validation that expresses richer business rules. Many real-world forms depend on relationships between fields rather than isolated constraints. Password confirmation is a familiar example, but more complex scenarios quickly arise in enterprise applications. While Signal Forms support these patterns, introducing them before establishing a clear understanding of derived state risks turns validation back into an imperative exercise rather than a declarative one.

We have also avoided asynchronous validation. Server-backed checks introduce latency, partial failure, cancellation, and race conditions. These are not trivial concerns, and treating them casually often leads to subtle bugs and confusing user experiences. Although Signal Forms provide the necessary hooks to model asynchronous behavior, doing so responsibly requires a careful discussion of pending state, effects, and life-cycle boundaries. That discussion belongs in its own article.

Another omission is persistence and synchronization. Many forms need to autosave drafts, synchronize state with local storage, or react to changes by triggering external side effects. These behaviors are not part of the form state itself; they are consequences of state changes. Treating them as such is essential to keeping the architecture comprehensible. Introducing persistence too early would blur the distinction between state and reaction that this article has worked to establish.

Finally, this article has not addressed migration and interoperability. Few teams are starting from a blank slate. Most will adopt Signal Forms incrementally within applications that already rely on reactive forms or template-driven forms. Hybrid approaches, bridging strategies, and gradual refactors are all critical topics, but they presuppose familiarity with both paradigms. Addressing migration before establishing a solid signal-first mental model would undermine that foundation.

These omissions are intentional. A form architecture that tries to do everything at once often ends up doing nothing clearly. By focusing on the core ideas of state, derivation, and declarative validation, we create a base that can absorb additional complexity without collapsing under it.

Signal Forms in the context of Angular’s evolution

To fully appreciate Signal Forms, it helps to step back and view them not as an isolated feature, but as part of a broader shift in Angular’s design philosophy.

For much of its history, Angular emphasized declarative templates paired with imperative coordination in component classes. RxJS became the backbone of that coordination, providing a powerful abstraction for handling asynchronous workflows, user input, and external events. This model scaled well, but it also encouraged developers to express state indirectly through streams and subscriptions.

Signals represent a deliberate recalibration. They re-center Angular’s reactivity model around state and derivation, rather than events and emissions. This shift is visible across the framework: in component inputs, change detection, and now forms. Signal Forms are not an attempt to replace everything that came before; they are an attempt to make the most common use case, modeling and deriving state, simpler and more explicit.

Framed this way, the design of Signal Forms aligns more closely with state-driven form behavior. The requirement to start with a model signal reflects the idea that the state should have a single, inspectable source of truth. Schema-based validation aligns with the notion that constraints are properties of state, not behaviors triggered by events. Field state exposed as signals reinforces the idea that validity, errors, and interaction metadata are derived values that should be read, not managed.

It is also worth noting that Signal Forms do not attempt to abstract away form behavior. They do not hide form state behind opaque classes or life-cycle hooks. They do not require developers to think in terms of control hierarchies or subscription graphs. Instead, they expose form behavior directly, making it easier to reason about how values, validation, and UI feedback relate to one another.

This approach aligns closely with other recent changes in Angular, including the introduction of modern template control flow and a stronger emphasis on explicit data dependencies. Together, these features point toward a framework that favors clarity over indirection and composition over orchestration.

Importantly, Signal Forms are still evolving. Their APIs may change, and their surface area will almost certainly expand. That is precisely why grounding them in first principles matters. Developers who understand why Signal Forms work the way they do will be far better equipped to adapt as the APIs mature.

This article has intentionally avoided duplicating documentation or enumerating every available feature. Instead, it has focused on establishing a conceptual framework that makes the official APIs feel intuitive rather than surprising. When viewed this way, Signal Forms are not a new way to write forms; they are a clearer expression of what forms have always been.

A new way to think about forms

Building the registration form in this article reveals a quiet but important shift. The reduction in complexity does not come from fewer features or simpler requirements. It comes from expressing form behavior in terms of state and derivation rather than orchestration and reaction.

By treating the data model as the single source of truth, validation rules as declarative constraints, and UI behavior as derived from current conditions, much of the coordination logic that typically surrounds forms becomes unnecessary. There are fewer subscriptions to manage, fewer flags to synchronize, and fewer life-cycle concerns to reason about. Form behavior becomes easier to inspect because it is visible directly in the relationships between values.

This approach does not eliminate the hard problems associated with forms. Asynchronous validation, persistence, and interoperability with existing Angular Forms APIs still require careful design. What changes is where that complexity lives. Instead of being interwoven with state representation, those concerns are layered explicitly on top of a clear foundation.

Signal-first forms are not a universal replacement for existing patterns, nor are they a shortcut to simpler applications. They are, however, a strong example of how aligning APIs with first principles can reduce cognitive overhead and improve maintainability over time. For teams building large, state-heavy forms, this alignment can make the difference between code that merely works and code that continues to evolve without friction.

Confined Migration Causes DNA Damage in Neurons

18 June 2026 at 01:09

In a groundbreaking study published in Nature, researchers have illuminated the subtle yet profound effects of confined cellular migration on the genomic integrity of developing neurons. This investigation, spearheaded by Zhang et al., delves into the enigmatic landscape of DNA double-strand breaks (DSBs) triggered by migration through physically restrictive environments—an event that is essential during brain development. The study reveals that such DNA damage, although non-lethal, accumulates in specific chromatin regions and leads to long-lasting transcriptional changes, shedding new light on neural physiology and potentially neurodegenerative processes.

The team utilized END-seq, a sophisticated genome-wide technique designed to detect double-strand breaks with base-pair precision. They focused on cerebellar granule neurons (CGNs), which were forced to migrate through micro-pores measuring either 3 micrometers or 8 micrometers. This experimental setup mimics the physical confinement neurons experience during their natural migration in the developing brain. Surprisingly, the analysis showed no enrichment of DSBs at well-known genetic hotspots or regulatory motifs, suggesting a non-random but unconventional pattern of DNA breakage.

Further classification of the DSBs unveiled a striking preference for heterochromatic regions, particularly lamina-associated domains (LADs), which are generally repressive chromatin zones linked to the nuclear periphery. The clusters of DSBs were notably depleted from active promoters and enhancers, indicating that the migratory stress preferentially compromises regions of genomic inactivity or tightly packed chromatin. Intriguingly, the damage was also significantly enriched in repetitive DNA elements, especially long interspersed nuclear elements (LINEs), hinting at a possible vulnerability of retrotransposons under mechanical strain.

The authors also analyzed chromatin loop anchor sites, which typically harbor proteins such as CTCF and cohesin subunit RAD21, but found no significant alteration in DSB accumulation within these sites. Additionally, they examined transcription start sites and gene bodies without detecting marked shifts in DNA damage correlating with transcriptional activity or gene length, including those of notably long genes linked to neuronal connectivity. This evidence points towards a topological and functional disconnect between confined migration-induced DNA breaks and classical topoisomerase II beta (TOP2β) hotspots involved in gene regulation.

Experimentally, the research extended beyond in vitro models; the team studied cerebellar cortex tissues from genetically modified mice deficient in DNA Ligase IV (LIG4), a key enzyme in non-homologous end joining (NHEJ), a primary DNA repair pathway. These mice exhibited accumulation of unrepaired DSBs, which, akin to their in vitro counterparts, showed no consistent site-specificity but were enriched in similar inert chromatin domains and repetitive sequences. The lack of damage enrichment in actively transcribed regions contrasted with the typical topological DNA breaks observed in genome regulation, affirming the unique nature of migration-induced genomic insults.

To explore the long-term consequences of these persistent DSBs, the researchers performed bulk RNA sequencing on cerebellar tissue from control and LIG4-deficient mice at two months of age. The analysis uncovered 336 genes significantly differentially expressed. Notably, genes involved in neuronal differentiation and synaptic function were downregulated, whereas genes associated with stress and immune responses, particularly those expressed by microglia and immune cells, were upregulated. These transcriptional alterations suggest that incomplete DSB repair following migration influences neuronal function and potentially primes an inflammatory-like gene expression environment.

Further validation through immunofluorescence staining confirmed the decreased expression of MYO15A—a gene critical to neuronal physiology—in the granule neurons of mutant mice. Concurrently, CD86, a marker of immune activation, was elevated in cerebellar microglia. However, contrary to expectations, classical hallmarks of microglial activation and neuroinflammation were absent, hinting at a nuanced immune response possibly distinct from pathological states. Moreover, the gene expression profile differed markedly from normal ageing patterns in the cerebellum, underscoring a unique molecular signature tied to DNA damage rather than degeneration or senescence.

Intriguingly, a notable overlap was detected between the gene clusters upregulated in LIG4-deficient mice and those observed in cortical neurons exhibiting γH2AX foci—a marker of DNA damage—in early neurodegeneration models. This parallel points to a mechanistic link between migration-induced DNA damage and neurodegenerative processes, positing confined migration as a previously underappreciated source of genomic stress that might influence disease susceptibility later in life.

The findings challenge traditional notions that DNA damage in neurons is predominantly tied to transcriptional mechanics or enzymatic activity at gene regulatory elements. Instead, the physical constraints of cellular migration emerge as a novel driver of genome instability, especially within transcriptionally silent and structurally repressive chromatin. These insights compound our understanding of neurodevelopmental biology and illuminate how mechanical forces can precipitate subtle genomic alterations with lasting transcriptional consequences.

Methodologically, the integration of END-seq with chromatin accessibility assays like ATAC-seq, histone modification ChIP-seq, and RNA sequencing provided a multidimensional approach to dissecting the genome-wide effects of confined migration. This comprehensive epigenomic profiling enabled the precise localization of DSBs and their contextual annotation within chromatin states, revealing the vulnerability of specific nuclear compartments to migratory stress.

By linking persistent DNA lesions to transcriptional rewiring, the study suggests a model in which unresolved damage in perinuclear heterochromatin may disrupt chromatin organization or nuclear architecture, ultimately modulating gene expression networks critical for neuronal maturation and function. The absence of typical DNA damage markers at regulatory elements also highlights that different mechanisms underpin the observed breaks, possibly related to mechanical strain or physical deformation during migration rather than enzymatic topological activity.

The clinical and biological implications of these findings are profound. They provoke questions about how developmental neuronal migration, a fundamental process shaping brain circuitry, might inadvertently introduce genomic perturbations that contribute to neurodevelopmental disorders or sensitize neurons to later insults. Additionally, they raise the prospect that interventions aimed at enhancing DNA repair capacity or modulating nuclear mechanics could mitigate such damage, offering new therapeutic avenues for neuroprotection.

Ultimately, this study redefines our understanding of the interplay between physical cellular processes and genome stability in the nervous system. It underscores the importance of considering mechanical forces as integral contributors to genomic integrity and neuronal health, bridging gaps between developmental biology, genomics, and neurodegeneration research. As research advances, these revelations pave the way for novel strategies to preserve neuronal function amidst the inherent challenges of brain development.


Subject of Research: Genomic integrity and DNA damage induced by confined migration in developing neurons.

Article Title: Confined migration induces non-lethal DNA damage in developing neurons.

Article References:
Zhang, Z., Canela, A., Kurisu, J. et al. Confined migration induces non-lethal DNA damage in developing neurons. Nature (2026). https://doi.org/10.1038/s41586-026-10648-8

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41586-026-10648-8

Cortical Development Dynamics in Autism Models

17 June 2026 at 23:07

Emerging insights into autism spectrum disorder (ASD) have been propelled forward by an innovative study examining cortical development dynamics using multiple mouse models. This work delves deeply into the transcriptional landscapes shaped by mutations implicated in ASD, revealing a complex interplay of stage-specific and genotype-specific molecular signatures. Employing cutting-edge network colocalization analyses and protein–protein interaction mapping, the study illuminates how biological processes converge and diverge during neurodevelopment in mutant genotypes.

Fundamentally, the research builds upon gene set enrichment analysis (GSEA), which previously highlighted overlapping pathways across various ASD-related mutations. The researchers expanded on this by applying network colocalization analysis to protein interaction networks, allowing functionally related proteins to be grouped into clusters through sophisticated averaging of random walks. This methodological advancement enables a nuanced assessment of expression changes aggregated at the systems-level rather than focusing solely on individual genes.

At the heart of their findings is the identification of distinct yet overlapping patterns of molecular dysregulation during critical developmental stages, particularly embryonic day 14.5 (E14.5), postnatal day 4 (P4), and postnatal day 14 (P14). At E14.5, the study reveals that mutations such as those in HnrnpU and Kdm6b robustly influence pathways tied to the actin cytoskeleton, cell proliferation, and division within excitatory neurons. Notably, these effects also extend, to a lesser degree, to Cul3 mutants, aligning with earlier evidence suggesting these genes’ involvement in neurodevelopmental regulation.

Delving deeper, the analysis highlights a provocative role of WNT signaling, especially pronounced in HnrnpU and Kdm6b variants. WNT pathways are critical for neural patterning and synaptogenesis, and their disruption could underpin some ASD phenotypes. This novel perspective accentuates the shared mechanistic underpinnings of ASD mutations converging on fundamental developmental pathways, stressing the importance of timing within neurodevelopmental trajectories.

Postnatally, at P4 and P14, the transcriptional alterations shift noticeably towards synaptic functions, ion transport, and metabolic processes—markers of neuronal specialization and circuit maturation. The data demonstrate how the influence of mutations evolves alongside neural differentiation and maturation. Intriguingly, Kmt5b mutants exhibited an inverse pattern at P4, suggesting either a compensatory or opposing regulatory role within the shared pathways affected across mutants. This counterintuitive finding raises the possibility of complex feedback or adaptive mechanisms operating in ASD-linked genetic variants.

By P14, metabolic processes prominently emerge in the affected molecular networks, especially in Bckdk, HnrnpU, and Trip12 mutants, indicating that energy metabolism perturbations may be integral to ASD pathogenesis. These results underscore the temporal dynamics of how genetic mutations impinge upon neurodevelopment, transitioning from early cytoskeletal and proliferative effects to later synaptic and metabolic modulation.

The study also innovatively explores how differentially expressed genes from each genotype localize within the interactome network and assesses similarity across developmental stages and mutations. This reveals a compelling developmental-stage-dependent pattern where transcriptional signatures cluster more closely within each stage but become more genetically distinct by P14. Such divergence suggests that while ASD mutations initially impact common biological processes, their influence fragments into distinct genotype-specific programs as development proceeds.

These findings have broad implications for understanding ASD heterogeneity and the challenges of developing universal therapeutic interventions. The discovery of both shared and unique pathway disruptions at different developmental times suggests treatment strategies may need to be tailored to both genotype and developmental timing to maximize efficacy. Moreover, the role of metabolic dysregulation invites further exploration into metabolic interventions or dietary modulation as adjunctive strategies.

Critically, the network colocalization analysis used represents a powerful approach to dissecting complex genetic disorders. By moving beyond simple gene expression differences and integrating system-level interactions, researchers can better capture the multifaceted nature of neurodevelopmental perturbations characteristic of ASD. This systems biology approach is likely to prove invaluable in future studies dissecting the molecular bases of brain disorders.

The comprehensive nature of this study, which integrates multiple ASD-relevant mouse models, enables the identification of convergent and divergent mechanisms with precision. It highlights how individual genes implicated in autism can distinctly alter transcriptional landscapes depending on context, highlighting the multifactorial dimension of ASD. Additionally, it underscores that the timing of gene dysregulation is paramount, with early embryonic disruptions cascading into diverse neurodevelopmental consequences.

In sum, this landmark investigation offers a rich, dynamic map of the molecular trajectories altered by ASD mutations across cortical development. Its integration of genomic, proteomic, and network analyses provides a blueprint for future research aimed at unraveling the complexity of neurodevelopmental disorders. By elucidating how multiple risk genes influence overlapping yet unique biological processes during critical developmental windows, the study charts a path toward precision medicine in autism spectrum disorder.

As the field progresses, leveraging such multi-dimensional analytical frameworks will be essential for translating genetic discoveries into mechanistic insights and targeted therapies. This work also highlights the ongoing need to examine neurodevelopment through temporal and molecular lenses simultaneously, enriching our understanding of how genetic diversity shapes brain development and function. Ultimately, investigations like these nurture hope for deciphering the intricate biology underlying ASD and devising effective interventions tailored to individual developmental trajectories.

Subject of Research: Cortical development dynamics and molecular mechanisms in autism spectrum disorder mouse models.

Article Title: Cortical development dynamics across autism spectrum disorder mouse models.

Article References:
Schwarz, L.A., Dotter, C.P., Isaev, S. et al. Cortical development dynamics across autism spectrum disorder mouse models. Nature (2026). https://doi.org/10.1038/s41586-026-10679-1

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41586-026-10679-1

Scientists Discover Shared Vulnerability in Key Gut Pathogens

17 June 2026 at 19:38

Researchers at Washington University School of Medicine in St. Louis have made a groundbreaking discovery that could revolutionize the prevention of diarrheal diseases caused by enterotoxigenic Escherichia coli (ETEC) and Shigella. These two bacterial pathogens are responsible for hundreds of millions of infections annually and are leading causes of diarrheal mortality worldwide, particularly among young children. Despite decades of efforts, vaccine development has encountered significant obstacles, primarily due to the extensive strain variability that hampers the identification of universal vaccine targets.

In a recent study published in the Proceedings of the National Academy of Sciences, the WashU team, collaborating with scientists at the University of Missouri and the International Centre for Diarrhoeal Disease Research in Bangladesh, identified a shared vulnerability within these pathogens that could open the door for a combined vaccine platform. This innovative approach hinges on targeting homologous enzymes that these bacteria use to degrade the mucus lining in the human gut, a critical step in establishing infection.

For bacterial pathogens like ETEC and Shigella to cause illness, they first need to overcome the intestinal mucus barrier. This thick layer of mucus serves as a physical and biochemical shield protecting the gut epithelium and regulating the microenvironment by keeping pathogenic entities at bay while preserving the beneficial microbiota. The ability to breach this barrier is a hallmark of pathogenicity, allowing bacteria to access the underlying tissues and deploy additional virulence factors, including enterotoxins that induce diarrhea.

The study’s focal point is a family of mucin-degrading enzymes that facilitate this breaching process. The researchers detail three closely related enzymes—EatA, SepA, and Pic—that are expressed by ETEC, Shigella, and other diarrheal pathogens. EatA, an acronym for “eat away,” is produced by ETEC and was previously identified by Fleckenstein’s laboratory as a critical mucinase targeting the primary structural protein within mucus. SepA and Pic, related enzymes produced by Shigella and some other bacteria, perform analogous roles. Their enzymatic activity effectively dismantles the mucosal barrier, clearing a path for bacterial invasion.

Intriguingly, the antibodies that the team isolated from individuals infected with ETEC or experimentally exposed to the bacteria displayed a functional cross-reactivity. The antibodies not only neutralized EatA but also inhibited SepA and Pic, indicating a conserved epitope region among these mucinases. This neutralization prevents the enzymes from degrading intestinal mucus, thereby blocking the pathogens’ entry into host tissues. The concept of a single antibody interfering with multiple bacterial targets underscores a significant breakthrough in vaccine strategy.

To unravel the structural mechanics behind this immune neutralization, the researchers leveraged cryo-electron microscopy—a state-of-the-art imaging method that preserves biomolecules in near-native states by flash-freezing samples. This technique enabled detailed visualization at atomic resolution, revealing the precise binding site of the neutralizing antibodies. Remarkably, the site of interaction was a structurally conserved region across EatA, SepA, and Pic, providing a rational basis for the design of vaccine antigens that elicit broadly neutralizing antibodies.

“The identification of this shared Achilles’ heel among diverse diarrhea-causing bacteria signifies a paradigm shift in vaccine development,” explains James M. Fleckenstein, MD, co-senior author of the study and professor of medicine at WashU. “It suggests we can target a common mechanism of gut colonization rather than chasing highly variable surface molecules, which has been the stumbling block in the field for so long.”

Further bolstering the translational potential of these findings, epidemiological data from children in Dhaka, Bangladesh, indicate that naturally acquired antibodies against EatA correlate with protection from diarrheal illness. This natural immunity validates EatA as a viable immunological target and encourages the pursuit of vaccines that induce similar protective responses.

The implications extend beyond the developing world, as enterotoxigenic E. coli has been implicated in recent foodborne outbreaks in industrialized countries such as the United States. The difficulty in distinguishing pathogenic ETEC from non-pathogenic strains in clinical settings further complicates diagnosis and containment. Moreover, reliance on antibiotics to treat infections caused by these bacteria accelerates the evolution of antibiotic resistance, posing a global public health threat.

Zachary Berndsen, PhD, assistant professor of biochemistry at the University of Missouri and co-senior author, emphasizes the rational approach to vaccine design enabled by these insights. “By pinpointing enzyme domains targeted by neutralizing antibodies, we provide a template for crafting precise immunogens that can stimulate protective immunity across multiple pathogens. This can streamline the production of vaccines that are both effective and broad-spectrum,” Berndsen says.

Development of such a vaccine could not only reduce the global burden of diarrheal diseases but also mitigate the spread of antibiotic resistance by decreasing the frequency of bacterial infections requiring treatment. Additionally, preserving the gut microbiota through targeted disruption of pathogenic mechanisms may alleviate some adverse consequences associated with broad-spectrum antibiotic use.

Going forward, the research team is focused on translating their molecular discoveries into practical vaccine candidates. This involves preclinical testing of immunogens incorporating the conserved epitopes identified, assessing their immunogenicity and protective efficacy in animal models, and progressing toward human clinical trials.

“In our ongoing battle with enteric pathogens that have coevolved with humans for millennia, blocking the enzymatic opening of the mucus gate represents a promising strategy,” Fleckenstein concludes. “If successful, this vaccine would preempt infection at its earliest and most vulnerable stage, changing the trajectory of diarrheal disease worldwide.”

The study was supported by grants from the National Institute of Allergy and Infectious Diseases and the Department of Veterans Affairs. The multidisciplinary collaboration brings together expertise in infectious diseases, structural biology, immunology, and clinical research, underscoring the necessity of integrated approaches to tackle complex global health challenges.


Subject of Research: Human tissue samples

Article Title: Human enterotoxigenic Escherichia coli (ETEC) infections elicit antibodies that broadly neutralize mucinases of pathogenic Escherichia coli and Shigella

News Publication Date: 15-Jun-2026

References:
Buckley DP, Akhtar M, Thapa M, Schmitz A, Turner J, Vickers TJ, Khatoon N, Kaisar MH, Coggin JA, Ganguli D, Sheikh A, Laird RM, Poly F, Porter CK, Ruiz-Perez F, Miller MJ, Chowdhury F, Bhuiyan TR, Qadri F, Trillo-Muyo S, Dolan B, van der Post S, Ellebedy A, Berndsen ZT, Fleckenstein JM. Human enterotoxigenic Escherichia coli (ETEC) infections elicit antibodies that broadly neutralize mucinases of pathogenic Escherichia coli and Shigella. PNAS. June 15, 2026.

Image Credits: David Hasty

Keywords: Infectious diseases, Microbial infections, Diarrhea

Decoding the Genetic Blueprint Behind Our Three-Dimensional Body

17 June 2026 at 19:23

In a groundbreaking series of experiments conducted by researchers at the University of Jena, scientists have revisited and expanded upon a century-old discovery that shapes our understanding of embryonic development. Originally unearthed by Hans Spemann and his student Hilde Mangold in 1924, the concept of the “organiser” is a pivotal developmental mechanism that directs the formation of body axes during the early stages of an embryo’s growth. Spemann and Mangold’s pioneering work involved tissue transplantation between amphibian embryos, revealing the presence of a group of cells within the blastopore that orchestrate the development of a secondary body axis. This discovery earned Spemann a Nobel Prize in 1935 but tragically Mangold passed away young, never witnessing the lasting impact of their findings.

The new research from Jena not only reaffirms these foundational insights but extends them dramatically into the early evolutionary origins of metazoans. Using delicate microsurgical manipulation, the team studied the ribbed comb jelly Mnemiopsis leidyi, a member of the ancient phylum Ctenophora. This lineage diverged from that of other animals approximately 700 million years ago, according to the latest evolutionary models. By transplanting small portions of blastoporal tissue from one comb jelly embryo into another, Jena researchers induced a secondary body axis, effectively recapitulating Spemann and Mangold’s classic findings but now in one of the earliest-diverging animal lineages. The experiment demonstrated that the organiser’s developmental blueprint is deeply conserved across vast evolutionary time frames.

What makes these experiments particularly astounding is the technical difficulty involved. Comb jelly embryos measure only about 120 micrometers in diameter—just a fraction larger than the width of a human hair—while the transplanted tissue samples were barely 20 micrometers in size. Dr. Stanislav Kremnyov, the lead experimentalist, had to master an extraordinary level of manual dexterity to integrate these tiny groups of cells into host embryos without triggering rejection or other developmental derailments. To an outside observer, it appeared as if the researchers were “dissecting clouds,” a vivid metaphor used by Prof. Andreas Hejnol, who leads the research team at Jena.

The functional transplantation of organising tissue between individuals of the same species was spectacular enough. Yet the Jena team dared to push further by performing xenotransplantations, transferring organiser tissue across animal orders into the sea anemone, a cnidarian species that diverged from ctenophores roughly 60 million years later. Remarkably, the transplanted organiser tissue also initiated axis formation in the sea anemone embryos. This unprecedented success underscores the extraordinary evolutionary conservation of developmental mechanisms underpinning animal body plans. Xenotransplantation of organiser tissues across distinct phyla had never before been demonstrated, marking this as a historic advance in developmental biology.

Beyond the cellular choreography, the study also zeroed in on genetic underpinnings. The team identified, for the first time, the specific gene responsible for organiser formation in the sea anemone, bridging molecular mechanisms across species boundaries. This gene appears to regulate the signalling networks critical to early embryonic axis determination, providing a molecular anchor to the organiser’s functional role. Such findings hint that the genetic toolkit necessary for complex body plan organisation was already present in the earliest multicellular ancestors of modern animals.

These discoveries overturn previous assumptions that organiser-driven axis formation was a feature exclusive to more recently evolved animal groups such as bilaterians. Instead, the findings position the organiser as a primordial control system in multicellular organism development, dating back to the dawn of animal evolution itself. It suggests a remarkable evolutionary continuity where the fundamental blueprints for constructing the three-dimensional architecture of multicellular animals remained largely stable across hundreds of millions of years.

The implications of this research extend far beyond ctenophores and sea anemones. Since the organiser guides the spatial coordination of cell fate during development, understanding its origins and conservation sheds light on the evolutionary emergence of animal diversity and complexity. It also offers potential new perspectives for biomedical research, particularly in regenerative medicine and congenital defect studies, where guiding axis formation and tissue patterning is critical.

Technical mastery was central to these findings. The researchers employed advanced microscopy and vital cell staining techniques allowing them to trace transplanted cells dynamically within host embryos. Coupling this with precise microsurgical transplantation provided not only functional but also visual confirmation of organiser activity. This combination of cutting-edge experimental methods enabled insights previously thought unattainable in such fragile and inaccessible embryonic systems.

Prof. Andreas Hejnol reflects on the evolutionary significance with enthusiasm: “Our data demonstrate that this organizer mechanism coordinates the entire body axis and is conserved among one of the earliest-diverging animal lineages, suggesting that complex body plan organisation arose very early in metazoan evolution.” His team’s work bridges developmental biology with evolutionary biology, providing a vivid example of how ancient developmental principles underpin the incredible diversity of life forms on Earth today.

This research, published in the prestigious journal Nature, represents a milestone in understanding early animal evolution and embryogenesis. It dramatically shifts the paradigm surrounding the origins of the organiser and prompts reconsideration of the molecular and cellular processes foundational to animal multicellularity. As scientists continue to explore these ancient mechanisms, further revelations about the building blocks of life’s complexity are eagerly anticipated.

Subject of Research: Animals
Article Title: A blastoporal organiser in a ctenophore
News Publication Date: 17-Jun-2026
Web References: http://dx.doi.org/10.1038/s41586-026-10643-z
Image Credits: Lisa-Marie Barf
Keywords: embryonic development, organiser, blastopore, ctenophore, Mnemiopsis leidyi, evolutionary biology, axis formation, multicellularity, xenotransplantation, sea anemone, cnidaria, developmental genetics

Impact of Prenatal Exposure to 10 Key Chemical Classes on Birth Outcomes: Insights from the ECHO Cohort

17 June 2026 at 17:44

A comprehensive new study published in JAMA Network Open has revealed pervasive exposure among pregnant individuals in the United States to multiple classes of environmental chemicals, highlighting critical concerns about their impact on fetal development. This groundbreaking longitudinal cohort investigation evaluated a large and ethnically diverse sample of U.S. pregnancies, systematically analyzing exposures to ten distinct categories of environmental pollutants. Many of these chemicals showed significant associations with measurable shifts in gestational age and reductions in birth weight-for-gestational age z-scores, parameters essential for assessing neonatal health and predicting long-term outcomes.

The research employed advanced biomonitoring techniques to quantify the presence of environmental toxicants including phthalates, alternative plasticizers, and polycyclic aromatic hydrocarbons (PAHs) in maternal biological samples during gestation. Phthalates and plasticizers are commonly used in consumer products and medical devices, yet their biochemical interactions during crucial windows of fetal development remain underappreciated. The study’s detailed exposure profiling allowed for unprecedented insights into how these compounds may disrupt endocrine functions or trigger inflammatory processes, potentially precipitating premature labor or impaired fetal growth.

One of the most striking findings was the widespread detection of phthalates and substitute plasticizers within this population, indicating ubiquitous contact despite regulatory efforts to limit their use. These chemicals, integral to plastics and personal care products, demonstrated compelling correlations with shortened gestation periods. Such alterations in gestational length can increase risks for adverse neonatal outcomes, including respiratory complications and neurodevelopmental disorders, underpinning the urgency to reassess public health policies concerning these ubiquitous agents.

Likewise, exposure to polycyclic aromatic hydrocarbons, a class of organic compounds generated through the incomplete combustion of organic matter including fossil fuels and tobacco, was found to be significantly linked with lower birth weight-for-gestational age z-scores. Lower birth weight relative to gestational age is a well-established risk factor for morbidity and mortality in infancy and has been implicated in chronic diseases later in life, such as cardiovascular disorders and diabetes. This association reaffirms concerns about air pollution-related health disparities during pregnancy.

The study’s robust methodology involved careful adjustment for confounding factors, such as maternal age, socioeconomic status, preexisting health conditions, and lifestyle habits, ensuring that the observed effects were primarily attributable to chemical exposures. Such meticulous analytical rigor strengthens causal inferences and enhances the generalizability of findings across the heterogeneous U.S. population.

Beyond the immediate neonatal impacts, the findings underscore the broader implication that gestational exposure to environmental chemicals may foreshadow developmental trajectories affecting cognitive function, metabolic programming, and immune competence. The data suggest a pressing need to incorporate environmental risk assessments into prenatal care and to advocate for more stringent regulation and replacement of harmful chemical additives in manufacturing and consumer goods.

Experts involved in the study emphasize the translational significance of these findings. Reducing prenatal exposure to harmful environmental agents, particularly those involved in plastic manufacturing and combustion byproducts, could be a modifiable risk factor to optimize pregnancy outcomes and subsequent child health. The research calls for collaborative efforts between scientists, clinicians, policymakers, and industry stakeholders to develop safer alternatives and to implement public health strategies aimed at exposure mitigation.

In addition to environmental monitoring, the study highlights the potential of biomarker-driven surveillance in clinical settings to identify at-risk pregnancies earlier. The adoption of such precision health approaches could enable timely interventions designed to minimize chemical-induced developmental insults and promote resilience in vulnerable populations.

Overall, this research project represents a pivotal contribution to reproductive environmental epidemiology, providing compelling evidence on how everyday chemical exposures reverberate at the earliest stages of human life. Such insights fuel the momentum toward integrating environmental considerations into obstetric practice and public health frameworks, ultimately aiming to safeguard the next generation from preventable neurodevelopmental and somatic diseases linked to toxic exposures.

The detailed chemical classes examined in this investigation span commonly encountered substances with diverse structural and functional properties, illuminating the pervasive nature of industrial and urban pollutants. The interdisciplinary approach combining toxicology, epidemiology, and molecular biology sets a new standard for environmental health research, encouraging future explorations into underlying biological mechanisms and potential gene-environment interactions.

As global urbanization and chemical manufacturing continue to expand, the relevance of these findings grows, underscoring an urgent collective responsibility to address environmental determinants of health from conception onwards. By spotlighting the critical window of gestation as vulnerable to chemical insults, this study not only informs clinical guidelines but also energizes advocacy for environmental justice, particularly in frequently marginalized communities disproportionately burdened by pollution exposure.

In summary, the research establishes a clear and compelling connection between gestational exposure to multiple classes of environmental chemicals—including phthalates, plasticizers, and hydrocarbons—and adverse birth outcomes such as shortened gestation and reduced fetal growth metrics. Addressing these exposures presents a tangible opportunity to improve neonatal health indicators and foster lifelong wellbeing, heralding a paradigm shift in how society approaches prenatal environmental risk.

Subject of Research: Gestational exposure to environmental chemicals and its impact on birth outcomes in U.S. pregnancies
Article Title: [Not provided]
News Publication Date: [Not provided]
Web References: [Not provided]
References: doi:10.1001/jamanetworkopen.2026.18883
Image Credits: [Not provided]

Keywords: Chemical pollution, Gestational age, Birth rates, Cohort studies, Pregnancy, United States population, Body weight, Hydrocarbons, Environmental health, Plastics, Human health, Obstetrics

AWS targets software release bottlenecks with DevOps Agent update

17 June 2026 at 16:00

The problem with software development today may no longer be writing code. With AI coding assistants generating code faster than ever, the bigger challenge is reviewing, testing, and safely releasing it.

AWS is betting that software teams need help with that part of the process, adding release management features to its DevOps Agent.

The new features, currently in preview, automatically assess code changes against organizational standards, identify potential release risks, and generate tests tailored to individual changes before they reach production.

The release readiness feature, in particular, runs the code in an AWS-managed isolated environment, executing lightweight user journey tests to verify the software builds, runs, and passes basic functional checks before the change enters the pipeline, the company wrote in a blog post.

The findings of these tests can be viewed through the DevOps Agent console, as comments on pull requests in GitHub or GitLab, or can be invoked directly through IDEs via Kiro or the Claude Code plugin, it added.

Targeting AI-era software delivery bottlenecks

Running code in isolated environments and delivering the results directly through developer tools helps address two longstanding challenges in software delivery, said Pareekh Jain, principal analyst at Pareekh Consulting.

It enables teams to validate how code changes behave before deployment, catching issues that static analysis may overlook, while reducing context switching by embedding findings into existing workflows, in turn accelerating fixes, Jain said.

The analyst pointed out that the release readiness capability addresses a key bottleneck in AI-driven software development: “While AI coding agents can generate code quickly, reviews, compliance checks, dependency validation, and release approvals still slow deployment.”

“By automatically checking code changes against internal standards, security policies, and dependency impacts, AWS helps developers, DevOps teams, and SREs identify issues earlier, reduce manual review effort, and improve release confidence,” Jain added.

These gains in productivity for developers could also translate into tangible business benefits for CIOs, according to Jain.

“Release readiness as a feature could help enterprises capture more value from AI-generated code while reducing operational overhead by eliminating the need for additional QA and DevOps resources. This means that they can accelerate software delivery without sacrificing reliability,” the analyst noted.

Autonomous testing before merging code

While release readiness reviews focus on assessing whether a code change is safe to move through the delivery pipeline, AWS is also adding a separate feature aimed at validating how those changes behave in production-like environments.

Named autonomous release testing, the new capability generates and runs change-specific test plans for web and API-based applications in customer-provisioned, production-like environments before the change actually merges, the company wrote in the blog post.

For Jain, autonomous release testing is “even more” important for developers and SREs as it “automates one of the most time-consuming parts of software delivery.”

“Developers spend less time creating and maintaining tests, while SREs benefit from fewer rollbacks and improved system reliability,” Jain said.

These benefits stem from the feature’s ability to automatically generate tests tailored to individual code changes, covering functional correctness, behavioral regressions, and integration scenarios that might otherwise require significant manual effort, Jain added.

However, AWS is not alone in trying to bring AI deeper into the software delivery lifecycle.

Microsoft-owned GitHub has been expanding Copilot’s code review capabilities, allowing the service to automatically review pull requests, suggest fixes, and provide feedback directly within developer workflows.

Google, meanwhile, has been steadily broadening the scope of Gemini Code Assist beyond code generation to support software development tasks such as code review and developer assistance.

AWS’s differentiation, though, according to Jain, lies in tying those capabilities to release management and operational workflows that span development and production environments.

Availability and pricing

For development teams interested in evaluating DevOps Agent’s new capabilities, AWS said both features are available in preview at no additional cost in the US East (N. Virginia) region.

AWS DevOps Agent, billed per agent-second, is included in the AWS Free Tier for new customers.

Additionally, new AWS DevOps Agent customers receive a 2-month free trial starting with their first operational task after general availability.

Each trial month includes up to 10 agent spaces, 20 hours of investigations (incident response), 15 hours of evaluations (incident prevention), and 20 hours of on-demand SRE tasks (chat), the company said.

Once those limits are exhausted, customers are charged based on consumption, with investigations, evaluations, and on-demand SRE tasks each priced at $0.0083 per agent-second, AWS added.

A prerequisite for using the new release management features includes connecting at least one GitHub or GitLab repository to an AWS DevOps Agent Space.

New Insights into PPARγ Regulation and Metabolic Disorders

17 June 2026 at 12:25

Metabolic disorders such as obesity and type 2 diabetes mellitus (T2DM) have escalated into a global health crisis, posing unprecedented challenges for medical science and public health policy alike. Both conditions disrupt fundamental biological processes, culminating in severe systemic complications and heightened mortality risks worldwide. At the molecular level, peroxisome proliferator-activated receptor gamma (PPARγ) emerges as a pivotal regulator, overseeing lipid metabolism, insulin sensitivity, and adipocyte differentiation. This critical role underscores PPARγ as a prime therapeutic target in the ongoing battle against metabolic syndromes.

The classical pharmacological approach to modulating PPARγ activity involves thiazolidinediones (TZDs), compounds that exert potent insulin-sensitizing effects by binding primarily to the receptor’s ligand-binding domain (LBD). While effective, these agents have repeatedly encountered clinical hurdles due to significant adverse effects, including undesirable weight gain, fluid retention, and exacerbated cardiovascular risk profiles. Consequently, the quest for safer, more selective PPARγ modulation strategies has intensified, driving innovative research toward dissecting the receptor’s structure-function relationship and regulatory mechanisms with extraordinary precision.

Recent advances have unveiled a complex, multi-layered regulatory network governing PPARγ activity. Among these, the modulation of post-translational modifications—specifically the phosphorylation of serine 273 (Ser273)—has attracted substantial attention. Selective PPARγ modulators (SPPARMs) that target this phosphorylation event strategically preserve the metabolic benefits of PPARγ activation without engaging the full spectrum of activity triggered by TZDs. This nuanced receptor engagement mitigates off-target effects and may revolutionize treatment paradigms in metabolic disease management.

A groundbreaking integration of the long non-coding RNA (lncRNA) Snhg9 into the PPARγ regulatory landscape marks a new frontier in understanding gene expression control in metabolic homeostasis. The recently characterized Snhg9-CCAR2-SIRT1-PPARγ axis exemplifies a sophisticated RNA-mediated regulatory mechanism influencing PPARγ function. This regulatory cascade suggests that lncRNAs may serve as potent modulators of metabolism, potentially enabling RNA-based therapeutic strategies that transcend classical small-molecule pharmacology.

Beyond ligand-dependent regulation, the DNA-binding domain (DBD) of PPARγ has emerged as a promising, yet underexplored, target for gene-selective modulation. This evolving paradigm challenges the current LBD-centric therapeutic design by emphasizing the possibility of fine-tuning receptor activity at the level of gene-specific transcriptional control. Manipulation of the DBD may enable precision targeting of specific metabolic pathways, offering an unprecedented level of therapeutic specificity and efficacy.

The intersection of molecular biology and pharmacology in PPARγ research underscores the critical need for comprehensive structural and functional analyses. High-resolution crystallographic studies have shed light on conformational dynamics within PPARγ domains, illuminating how subtle structural modifications dictate receptor activation states and downstream target gene expression. These insights inform the rational design of modulators capable of exploiting unique allosteric sites with therapeutic benefits.

An intricate balance exists between PPARγ phosphorylation states and the recruitment of coregulators such as CCAR2 and SIRT1, modulators that orchestrate epigenetic and transcriptional machinery. SIRT1, a NAD+-dependent deacetylase, interacts intricately within this axis to regulate metabolic gene programs by modulating PPARγ acetylation and activity. This interplay suggests potential synergies between metabolic control mechanisms and cellular energy sensing pathways, which could be harnessed therapeutically.

The discovery of lncRNAs as key regulatory nodes in metabolic control systems elevates the importance of non-coding genomic elements. Snhg9, in particular, has been implicated in modulating PPARγ’s transcriptional repertoire through its interactions with cofactors, altering chromatin accessibility and the receptor’s response to endogenous ligands. Such findings delineate a novel class of epigenetic regulators, spotlighting RNA as a powerful switch in metabolic gene networks.

Clinical translation of these mechanistic insights depends heavily on the development of SPPARMs that not only modulate Ser273 phosphorylation but also integrate with lncRNA-mediated pathways. Such compounds hold the promise of robust insulin sensitization while bypassing the adverse events that have plagued TZDs. The future of metabolic therapeutics likely hinges on these dual-targeting molecules that couple protein conformation control with RNA-regulatory axis modulation.

Emerging technologies in RNA therapeutics offer an extraordinary toolkit for manipulating lncRNA functions in vivo. Antisense oligonucleotides, RNA interference, and CRISPR-Cas systems could potentially modulate the expression or function of Snhg9, thereby indirectly regulating PPARγ activity. These approaches raise hopeful prospects for personalized metabolic treatments grounded in gene regulation rather than merely receptor agonism.

PPARγ’s pivotal position in adipocyte differentiation links it intimately with lipid homeostasis and energy storage, fundamental processes derailed in obesity and T2DM. Understanding how selective modulation of PPARγ influences adipogenesis at the transcriptional level could redefine therapeutic goals from symptomatic control to addressing root causes of metabolic dysregulation. This shift would be transformative for millions affected worldwide.

The newly recognized hierarchy within the PPARγ regulatory framework beckons a strategic roadmap toward next-generation therapies. This roadmap involves integrated targeting of receptor phosphorylation, lncRNA interaction networks, cofactor recruitment, and domain-specific modulation, all underpinned by cutting-edge molecular insights. The multi-dimensional approach promises enhanced efficacy with minimized side effects, offering a viable path beyond the limitations of current therapeutic agents.

Exciting possibilities also arise from exploring how SPPARMs interface with other nuclear receptors and metabolic pathways, which could reveal combinatory or synergistic effects beneficial in polygenic diseases. Such multidrug or multifunctional agents could revolutionize treatment regimens by addressing the complex etiology of metabolic disorders more holistically.

In summary, recent advances position PPARγ at the nexus of a sophisticated, multi-layered regulatory system with immense therapeutic potential. The dual focus on selective receptor modulation and lncRNA-mediated regulation heralds a paradigm shift in metabolic disease treatment. As research continues to unravel these complex mechanisms, the prospect for innovative, effective, and safer therapies to combat obesity and T2DM grows more tangible than ever before.


Subject of Research: Regulation of Peroxisome Proliferator-Activated Receptor Gamma (PPARγ) in metabolic disorders including obesity and type 2 diabetes mellitus, focusing on selective modulation strategies and the role of long non-coding RNAs.

Article Title: Novel perspectives on PPARγ regulation: from SPPARMs to the emerging role of lncRNAs in metabolic disorders.

Article References:
Qin, H., Wang, Y., Yang, Y. et al. Novel perspectives on PPARγ regulation: from SPPARMs to the emerging role of lncRNAs in metabolic disorders. Int J Obes (2026). https://doi.org/10.1038/s41366-026-02128-w

Image Credits: AI Generated

DOI: 17 June 2026

Industrial Waste Enhances Ambient-Cured Alkali Concrete

17 June 2026 at 12:01

In an era marked by escalating environmental concerns and surging demand for sustainable construction practices, a groundbreaking study has emerged, shedding new light on an innovative approach to concrete production. Researchers P. Singh, H. Singh, and S. Paruthi have unveiled a transformative method that harnesses industrial waste materials as fine aggregates in alkali-activated concrete cured under ambient conditions. Published recently in Scientific Reports, their work is poised to revolutionize how the concrete industry addresses sustainability and performance criteria simultaneously.

Concrete, the backbone of modern infrastructure, traditionally relies on natural sand as a fine aggregate. However, the extraction of natural sand has increasingly raised environmental alarms due to riverbed degradation, habitat destruction, and rising extraction costs. Against this backdrop, the remarkable potential of industrial waste byproducts to replace natural aggregates has garnered significant attention. Yet, balancing the incorporation of these unconventional materials without compromising structural integrity has remained a critical challenge until now.

The research conducted by Singh and colleagues intricately explores the integration of various industrial wastes, such as fly ash, slag, and other mineral residues, as substitutes for conventional fine aggregates within an alkali-activated binding matrix. Alkali activation, an innovative cementitious technology, utilizes reactive aluminosilicates and alkaline solutions to produce hardened binders. Unlike ordinary Portland cement, alkali-activated materials demonstrate superior durability, chemical resistance, and a drastically reduced carbon footprint, aligning perfectly with global sustainability goals.

Central to their study is the novel concept of “performance-driven utilization,” whereby the selection and proportioning of industrial wastes are carefully optimized to meet stringent mechanical and durability performance metrics. This intelligent design framework transcends empirical mix designs by embracing a data-driven methodology, incorporating physicochemical characterization, microstructural analyses, and advanced mechanical testing. This holistic strategy ensures the resulting concrete not only performs on par with, or surpasses, traditional mixes but also exhibits enhanced sustainability attributes.

One of the study’s key breakthroughs is the successful ambient curing of the alkali-activated concrete. Conventionally, many geopolymers or alkali-activated materials require elevated temperature curing—often limiting scalability and increasing energy consumption. By demonstrating that industrial waste-infused alkali-activated concrete can attain requisite strength and durability characteristics under ambient temperatures, the research paves the way for more energy-efficient, cost-effective, and pragmatic construction applications.

The researchers meticulously characterized the particle size distribution, mineralogical composition, and surface morphology of the industrial wastes employed. These parameters critically influence the reactivity, workability, and bonding characteristics of the concrete. For instance, the incorporation of finely divided fly ash and granulated blast furnace slag not only contributed to filler effects but also participated actively in the binding phase through pozzolanic and latent hydraulic reactions. This synergistic interplay significantly augmented the microstructural densification and reduced porosity.

Mechanical testing revealed that mixes with tailored industrial waste content achieved compressive strengths exceeding 40 MPa after 28 days of ambient curing—a remarkable feat demonstrating the feasibility of these materials for structural applications. Additionally, durability assessments, including resistance to sulfate attack, chloride penetration, and freeze-thaw cycles, indicated superior performance vis-à-vis conventional concrete. Such resilience renders this technology particularly suitable for harsh environmental exposures often encountered in infrastructure projects worldwide.

An intriguing aspect of the study lies in its environmental impact evaluation. Life cycle assessment (LCA) metrics underscored substantial reductions in carbon dioxide emissions, energy consumption, and natural resource depletion relative to standard Portland cement concrete. By valorizing industrial waste streams—often destined for landfills or disposal—the approach inherently embodies the principles of circular economy, waste minimization, and industrial symbiosis.

Furthermore, the scalability and adaptability of this method were highlighted through case studies simulating real-world production scenarios. The versatility to adjust the mix design based on locally available waste materials allows for broad geographic applicability, especially in regions grappling with both waste management challenges and infrastructure development demands. This localization potential not only mitigates transportation-related carbon footprints but also empowers regional economies by converting liabilities into construction assets.

In terms of practical implementation, the researchers advocate for collaboration with industry stakeholders, regulatory bodies, and policymakers to foster technology transfer and standardization. They emphasize the necessity for developing comprehensive guidelines and quality control protocols to ensure consistent material performance across diverse production batches and construction environments. Such coordinated efforts will be instrumental to mainstream adoption and regulatory acceptance.

The study also opens promising avenues for further interdisciplinary research. Investigations into nano-engineered additives, self-healing functionalities, and hybrid composites combining alkali-activated binders with traditional cementitious systems could unlock new frontiers in concrete technology. Moreover, long-term field monitoring and durability trials under various climatic stresses will reinforce confidence in this innovative material system and elucidate performance trends over extended service lives.

Notably, the socio-economic implications of this research cannot be overstated. By lowering the reliance on finite natural sand resources and simultaneously reducing industrial waste accumulation, the construction industry gains a dual advantage: safeguarding ecosystems and diminishing raw material procurement costs. The cumulative effect may contribute to more affordable housing and infrastructure solutions while aligning with global climate action commitments.

As urbanization accelerates and infrastructure demands escalate globally, the urgency for sustainable material innovations intensifies. This pioneering research by Singh, Singh, and Paruthi embodies the transformative potential of science and engineering to challenge conventional paradigms and chart a sustainable pathway forward. By converting industrial detritus into high-performance concrete components through an environmentally benign process, they redefine what is possible in material engineering for the built environment.

In conclusion, the performance-driven utilization of industrial wastes in ambient-cured alkali-activated concrete stands as a beacon of hope and ingenuity amidst pressing environmental and infrastructural challenges. The synergy of material science, structural engineering, and sustainability principles embodied in this study offers a compelling blueprint for the future of concrete technology. As the industry moves towards decarbonization and circularity, such advancements will be instrumental in sculpting resilient, eco-conscious, and economically viable built environments for generations to come.


Subject of Research: Performance-driven utilization of industrial wastes as fine aggregates in ambient-cured alkali-activated concrete.

Article Title: Performance-driven utilization of industrial wastes as fine aggregates in ambient-cured alkali activated concrete.

Article References: Singh, P., Singh, H. & Paruthi, S. Performance-driven utilization of industrial wastes as fine aggregates in ambient-cured alkali activated concrete. Sci Rep (2026). https://doi.org/10.1038/s41598-026-56374-z

Image Credits: AI Generated

Z.ai pitches GLM-5.2 for long-running software engineering tasks

17 June 2026 at 11:32

Z.ai has released GLM-5.2, an MIT-licensed open-source AI model designed for long-running software engineering tasks, as the Chinese company seeks to challenge proprietary coding models on cost and performance.

The company said GLM-5.2 ranked just behind Anthropic’s Claude Opus 4.8 on FrontierSWE, a long-horizon coding benchmark, trailing it by 1%. Z.ai said the model also edged out OpenAI’s GPT-5.5 by 1%.

Z.ai said GLM-5.2 supports a one million-token context window with up to 131,072 output tokens, positioning it for agentic coding workflows that require reasoning across large codebases.

The company is also making an efficiency argument. It said GLM-5.2 uses a technique called IndexShare, which reduces per-token compute by 2.9 times at a one million-token context length. It also said changes to the model’s multi-token prediction layer increased the acceptance length for speculative decoding by up to 20%.

The changes are aimed at a practical problem for developers: long-context coding agents can be expensive to run when they are asked to work across large repositories.

Enterprise appeal

GLM-5.2’s clearest appeal is that it pairs stronger coding capabilities with the cost advantages of an open-source model. But capability alone will not be enough to make it a credible alternative.

“Western enterprises will want independent benchmark validation, successful deployments at global enterprises, strong security and governance controls, and long-term support commitments,” said Pareekh Jain, CEO of Pareekh Consulting.

Jain said the fastest route to enterprise credibility would be hosting by a major cloud provider like AWS. That would allow customers to use the model under standard enterprise terms, with service-level commitments and compliance certifications.

Tulika Sheel, senior VP at Kadence International, said GLM-5.2 would also need to prove it can operate as a stable enterprise product.

“Demonstrated success in real-world deployments and transparent governance will be just as important as benchmark scores,” Sheel said.

The performance and cost claims will also need to hold up against established models.

“Enterprise leaders generally consider two major factors when evaluating new models,” said Lian Jye Su, chief analyst at Omdia. “First, they look at overall performance against competitors, where GLM-5.2 performs well in long-horizon agentic coding and software engineering. Second, they look at the cost of adoption. As an open-source model, GLM-5.2 has clear cost advantages.”

Su said the model could appeal to engineering teams under pressure to control AI costs. It may also attract open-source advocates and companies with significant operations in Asia-Pacific.

But the claims still need wider validation, particularly around hallucination control and coherence during extended tasks. These are critical issues for enterprises considering AI coding agents, which may need to work across large codebases and multi-step software engineering workflows.

Jain said the one million-token context window could be useful for large codebase analysis. It could also help with legacy modernization projects and complex engineering documentation.

He said long-context capability may also help with audit logs or legal contracts, where splitting material into smaller chunks can create errors across document boundaries. But for everyday coding tasks, effective retrieval systems may matter more than very large context windows, making some of the benefits more limited in practice.

Governance risks

The governance question depends largely on where the model runs.

Sheel said enterprises should evaluate GLM-5.2 as they would any strategic technology partner, rather than as a standalone model. That means looking at where data is stored and whether the model can be used in environments customers control.

That deployment choice is central to the risk calculation, according to Jain. Because GLM-5.2 is available under an MIT license, companies can download the weights and run them on their own infrastructure, reducing the need to send sensitive data to Z.ai.

“The risk flips completely if you use Z.ai’s hosted API instead,” Jain said.

He said Chinese national security rules could require domestic companies to cooperate with government requests, making hosted use difficult for regulated industries or workloads involving sensitive data.

Su said the issue is not limited to Chinese vendors. Recent restrictions affecting access to some Anthropic models have also highlighted the risk that enterprises may have limited control over the availability of AI services from foreign providers.

“Selecting solutions from American and Chinese AI vendors does expose non-US Western enterprises to additional risk of having zero control over the availability and uptime of these models,” Su said.

Revolutionary Atom-Thin Coating from NUS CDE and Applied Materials Partnership Breaks New Ground in Chip Miniaturization

17 June 2026 at 10:08

In the relentless quest to push the boundaries of semiconductor technology, researchers from the National University of Singapore (NUS) in collaboration with Applied Materials have unveiled a groundbreaking advancement that promises to reshape the future of microchip manufacturing. As the global semiconductor market races towards the trillion-dollar mark propelled by demand for faster computing, smarter artificial intelligence, and more compact electronic devices, addressing fundamental physical constraints in chip architecture has become a critical imperative. A newly developed atom-thin coating — a monolayer of crystalline tungsten disulfide (WS₂) — presents a transformative solution, elegantly overcoming a pressing bottleneck in chip miniaturization with profound implications for speed, reliability, and efficiency.

Traditional transistor miniaturization has long driven performance gains in integrated circuits. However, as transistors and their connecting copper wires shrink into the sub-10 nanometer regime, the surrounding protective layers—essential for maintaining electrical integrity—resist further reduction. These layers, typically tantalum-based barrier and liner materials, must maintain a minimum thickness of around four nanometers. In future nanoscale interconnects, these coatings may consume nearly half the cross-sectional area of the copper wiring, severely throttling electrical conductivity and device speed. The NUS and Applied Materials team’s innovation resides in synthesizing an ultrathin WS₂ film, less than a nanometer thick, that simultaneously fulfills the dual role of barrier and liner, drastically slashing thickness without compromising performance.

This pioneering coating, grown on industry-standard 200-millimeter silicon wafers using a low-temperature thermal atomic layer deposition process at 350 degrees Celsius, exemplifies a remarkable confluence of scalability and compatibility. The process avoids plasma, guaranteeing conformal deposition with precise thickness control down to a single atomic layer. Notably, this method achieves uniform coverage in high aspect ratio trenches, exceeding 95% conformity even at depth-to-width ratios of 10:1. Such attributes position this WS₂ monolayer as a viable drop-in replacement within existing semiconductor fabrication lines, bridging the formidable divide between laboratory breakthroughs and industrial applicability.

The WS₂ film’s threshold thickness of approximately 0.7 nanometers contrasts starkly with the conventional six nanometer standard, enabling a future 20-nanometer-wide copper wire to retain over 90% of its conductive cross-section. This relatively unobstructed copper path translates into reduced electrical resistance and elevated current carrying capacity, directly enhancing chip speed and energy efficiency. Extensive electrical testing demonstrated a monumental reduction in resistance—exceeding a factor of one million—compared to uncoated counterparts. The superior adhesion properties offered by the WS₂ liner promote smooth and continuous copper film formation, vital for producing reliable, defect-free interconnects at such ultrathin dimensions.

Beyond adhesion, the WS₂ monolayer serves as a robust diffusion barrier that impedes copper migration into silicon and dielectric materials under thermal and electrical stress. In accelerated aging tests, conventional barrier-free copper rapidly reacted with underlying silicon, forming deleterious intermetallic compounds and defect clusters. In stark contrast, WS₂-coated samples exhibited no such degradation, maintaining pristine interfaces even after prolonged exposure. Remarkably, devices using the atomically thin barrier exhibited projected lifetimes surpassing unprotected designs by an order of magnitude, signaling a dramatic leap in device durability.

Central to the WS₂ film’s efficacy is its polycrystalline structure composed of myriad tiny grains and grain boundaries arranged in random orientations. Unlike continuous single crystals, where aligned grain boundaries can form direct diffusion channels, the staggered grain orientation in multilayer WS₂ films creates a complex labyrinth that frustrates copper atom penetration akin to the staggered offsets in a well-built brick wall. This insight, derived from computational modeling by NUS chemists, identifies nanoscale grain architecture as a key parameter in optimizing barrier functionality, challenging the entrenched paradigm that perfect crystallinity is always advantageous.

The discovery that a single atomic layer can supplant the combined barrier and liner stack confronts long-held assumptions in semiconductor interconnect design. Prior industry standards dictated separate layers with cumulative thicknesses of several nanometers to provide necessary protection and adhesion. By collapsing these essential roles into an atomically thin WS₂ sheet, researchers open new vistas for continuing Moore’s Law scaling into unprecedented regimes. Professor Silvija Gradečak, co-director of the NUS-Applied Materials Corporate Lab, emphasizes that this fundamental shift in approach heralds a new class of interconnect architectures tailored to atomically precise materials engineering.

The process’s compatibility with low thermal budgets—vital for avoiding damage to complex multilayer device stacks—and its wafer-scale uniformity underscore its readiness for integration into contemporary manufacturing ecosystems. These breakthrough properties allow semiconductor producers to adopt WS₂-based coatings without extensive equipment overhauls or processing disruptions, facilitating rapid technology transfer. Dr. Hippolyte Astier highlights that such advancements transcend the immediate generation of chips, positioning the industry to harness this technology across multiple forthcoming innovations extending out to 2037 and beyond.

Applied Materials’ Director of Engineering, Dr. John Sudijono, notes the criticality of merging academic ingenuity with industrial constraints through the Corporate Lab framework. The collaborative environment enabled iterative validation of WS₂ films under realistic fabrication conditions, ensuring the material meets demanding reliability, scalability, and process compatibility benchmarks. This convergence of theory, experimentation, and commercialization is essential to bridging the notorious gulf between laboratory demonstrations and full-scale production deployments in semiconductor manufacturing.

Looking forward, the team is exploring refined control over WS₂ grain orientation and microscopic interface phenomena to fine-tune long-term stability and electrical behavior. Further studies aim to exploit the low-temperature vapor-phase growth technique to deposit other two-dimensional materials within diverse chip components, expanding the scope of atomic-scale coatings in integrated circuit design. This foundational research not only sets a new standard for interconnect barriers and liners but also signals the inception of a broader materials revolution in microelectronics.

The ultrathin WS₂ monolayer innovation thus represents a monumental stride towards sustaining the pace of semiconductor evolution. By ingeniously combining barrier and liner functionality into a single atomic layer with industrially scalable fabrication, this discovery resolves a pressing miniaturization bottleneck, empowering the continuation of chip performance scaling in an era where every nanoscale dimension counts. As the semiconductor industry confronts the ultimate physical limits of transistor and interconnect shrinking, atomically precise materials like WS₂ will be vital enablers of future computational breakthroughs, underpinning the technology transformations of tomorrow.

Subject of Research: Not applicable

Article Title: Low-temperature wafer-scale growth of ultrathin tungsten disulfide for bifunctional interconnect barriers and liners

News Publication Date: 31-Mar-2026

Web References: https://doi.org/10.1038/s41928-026-01592-6

Image Credits: College of Design and Engineering, NUS

Keywords

Semiconductors, Microelectronics, Materials engineering, Materials science

10 tips for getting better R code from your AI coding agent

17 June 2026 at 10:00

Most generative AI tools know less about R than languages like JavaScript and Python, thanks to how much training data is available for each. However, with a little extra setup, you can give a large language model (LLM) the knowledge it needs to improve its R skills.

Here are 10 ways to help generative AI write R code like a pro.

Use a coding agent

AI coding agents have more power, flexibility, and coding-focused tools than general-purpose chatbots.

Anthropic’s Claude Code and OpenAI’s Codex agents have versions that run in a terminal, IDE extensions, desktop and mobile apps, and other integrations.

R users may also be interested in Posit’s Posit Assistant, which is designed for data analysis in both R and Python. It needs less setup for R than general-purpose coding agents, and it has more built-in knowledge about data science, R package development, and Shiny apps. Plus, it can read objects in your R and Python environments by default, which can be useful in some situations (although perhaps not if you’re working with sensitive data). There are versions of Posit Assistant for RStudio, the Positron IDE, and the terminal. Setup info is here.

Note: Posit Assistant is not the same as the older Positron Assistant that was demo’d at last year’s posit::conf(). The older assistant will eventually be superceded by Posit Assistant. Posit Assistant comes pre-installed with RStudio but not pre-activated, so you don’t need to use it unless you want to.

Google’s original entry in this space, Gemini CLI, is being retired in favor of a new “Antigravity CLI” starting June 18.

There are other IDE platforms with built-in agents, including Cursor and Windsurf, and terminal + subscription coding agents such as Warp, which was open-sourced in April. However, I won’t be covering those here.

You may be limited at work by what your employer allows you to use, but you can still experiment on your own with public data — and then lobby for a new agent if a different one better meets your needs. Whatever you choose, though, moving to one of these from a web-based chatbot should produce better code.

“The shift from chatbot to agent is the most important change in how people use AI since ChatGPT launched,” says Ethan Mollick, co-director of the Wharton School’s Generative AI Labs at the University of Pennsylvania.

Set up CLAUDE.md, AGENTS.md, or GEMINI.md knowledge files

All the major coding agents look for knowledge files to load each time you start a session, and you can easily edit those files. The files can include things like your proficiency in various languages and how you like to document projects. For example, I tell my LLMs that I’d like a README.md in every project with info on how to use the project and technical info on how the project was coded.

Most coding tools let you have both an overarching main file that’s loaded into every session, and an additional file per project.

If you’re adding Claude or Codex to an existing code base, the /init slash command will create one of those .md project files after scanning and analyzing the code. You can also ask an LLM to interview you to create or add to one of those files.

Claude Code looks for a user-level main CLAUDE.md file at ~/.claude/CLAUDE.md and then a project-level file at ./CLAUDE.md or ./.claude/CLAUDE.md. The /memory slash command shows where those files are located. Codex uses AGENTS.md files. Antigravity docs say it looks for a main GEMINI.md file plus GEMINI.md and/or AGENTS.md files in your working directory.

Positron Assistant is set up to read AGENT.md, AGENTS.md, POSITRON.md, CLAUDE.md, GEMINI.md, and LLMS.txt in a workspace root directory, according to the docs. See the Positron docs for more on custom instructions.

Use agent skills

Agent skills can turn an LLM with questionable R knowledge into an expert that understand how you want it to write code. You can develop your own skills, work with your LLM to create skills, or download skills written by others. Claude even has a skill to help you build skills. This is one of the best ways to stop having to repeat the same instructions over and over. Instead, the LLM will “remember” what’s in your skills files by loading them when they’re needed.

What’s the difference between the knowledge .md files mentioned above and a skill? I like to think of CLAUDE.md or AGENTS.md files as important background info that’s always available, while skills are more specific instructions that are useful at certain times. Or, as Claude Opus 4.7 put it: “Skills are for triggered workflows (‘when user asks X, do Y’). A standing rule that applies to every project isn’t trigger-based; it’s a directive I should always follow.” Standing rules should go in CLAUDE.md.

“Create a skill when you keep pasting the same instructions, checklist, or multi-step procedure into chat, or when a section of CLAUDE.md has grown into a procedure rather than a fact,” according to Anthropic’s Claude Code docs. “Unlike CLAUDE.md content, a skill’s body loads only when it’s used, so long reference material costs almost nothing until you need it.” Only skill descriptions are loaded at the start of each session, so the LLM knows what skills are available.

Anthropic originally created skills for Claude in October 2025 and then published the concept as an open standard a couple of months later. Most coding agents now use them.

Anthropic has an easy-to-folllow guide to creating custom skills here if you want to try your hand at building your own.

Posit Assistant comes with several R-related skills built in and allows users to add more. You’ll find them here.

If you’re not using Posit Assistant, you can add Posit-developed skills to another coding agent. Posit published a GitHub repository with skills for creating R packages, Shiny apps, and Quarto documents; for creating and resolving GitHub pull requests; for open-source R and Python package releases; for code reviews (R, Python, JavaScript/TypeScript, SQL); and for creating architectural documentation for a codebase. Browse them all here.

Read and edit the skills you download

Skills are just a folder with one or more markdown text files that have a structured YAML header. That folder can include optional resources and scripts, too. Skill instruction files are easy to read and edit, so you can make sure they do what you want. Tweak them as desired.

For example, Claude helped me write my own customized R skill. Claude leans toward tidyverse packages, but there are other packages I like a lot, too, such data.table. I made sure the R skill knows my package preferences, and under what circumstances I prefer each.

If one of your preferred procedures changes, remember to update the associated skills. Or, ask an LLM to update it for you. Keeping skills up to date will make your coding agent more useful.

Keep your CLAUDE.md or AGENTS.md files updated, too. Maybe you started off with a CLAUDE.md file that said you’re an experienced Python programmer who is just learning R. Three months from now, you might want to update your R skill level. Or, perhaps you told an AGENTS.md file that you like thoroughly documented code, but now you’d prefer a lighter touch. You may want to have your coding agent go over those files with you from time to time and ask you about the main points and whether they need updating.

Use the btw R package and its MCP server

It’s frustrating when an LLM writes code based on an ancient R package version in its training data, or when it doesn’t know about a relatively recent package at all. btw solves this problem by letting your coding agent access info about all the R packages installed on your system. That means it can write code based on your specific R environment. Plus, btw lets an LLM access variables in your current R environment via a Model Context Protocol (MCP) server.

MCP servers are a standardized way for LLMs to access external data — in this case, your running R session (external doesn’t have to mean cloud). Like skills, MCP was created by Anthropic as an open standard and has since been adopted by most major AI platforms. The btw R package includes an MCP server, which you can install for Claude Code by running the following code in a terminal window (not R console).

claude mcp add -s "user" r-btw -- Rscript -e "btw::btw_mcp_server()"

btw comes with a lot of tools. It’s not a great idea to overload your LLM with tools it may never use. If you just want to register btw tools for accessing your R session and looking up package documentation, you can run a command like this instead:

claude mcp add -s "user" r-btw -- Rscript -e "btw::btw_mcp_server(tools = btw::btw_tools('btw_tool_run_r', 'docs', 'env'))"

You only need to do this once.

To connect Claude Code to your open R session, you also need to run btw::btw_mcp_session() in every new R session. (I sometimes forget this part until I’m puzzled why Claude can’t read my R variables or even see what packages I have installed.)

You can see a demo of btw in this Posit video that compares Claude Code and Posit Assistant.

test_claude_code_btw

The btw R package lets Claude Code and other agents read objects in your R session.

Foundry

You don’t need to do this setup for Posit Assistant, since tools for accessing your R session and variables are built in.

Use plan mode for your projects

I cannot stress this enough. Just as humans benefit from having a plan before starting to implement a project, LLMs benefit from working through a plan before beginning to generate code. Plus, it can be useful at times to “brainstorm” with an LLM about structures and features, ask it for alternatives, or even have another LLM review a plan before implementing it.

Anthropic advises starting with plan mode. “Letting Claude jump straight to coding can produce code that solves the wrong problem. Use plan mode to separate exploration from execution,” they wrote in a best practices doc.

Claude, Codex, Gemini, and Posit Assistant all have a plan mode in their CLI versions that can be activated with the /plan slash command.

claude_plan1


Foundry

claude_plan2 rev

When in plan mode activated with /plan, Claude will often ask questions before coming up with its plan.

Foundry

Make sure your coding agent learns from its mistakes

This is one of the best tips I read first from Joe Amditis, associate director of operations at the Center for Cooperative Media at Montclair State University: If your coding agent makes a mistake and you work together to fix it, make sure to save that memory so it doesn’t make the same error again.

Your agent may have an auto memory system that’s supposed to do some of this (I’ve used Claude Code’s), but you can instruct any coding agent to keep a separate lessons learned file for each project, too, or ask it what’s the best way to ensure it doesn’t make the same error again. This isn’t a guarantee, any more than you can be assured an LLM will follow all the rules in a CLAUDE.md or AGENTS.md file or all the steps in a skill. But it has definitely cut down on my agents making the same errors over and over.

Have your agent write tests and do code reviews

Tests and code reviews done by a coding agent aren’t a substitute for a human checking that software works. Review an LLM’s results yourself any time a task is important! However, agent-generated tests are still a good way to head off some problems and cut down the time you spend as your LLM’s debugging partner. If you don’t know where to start to help your agent know how to test and review code, see Posit’s testing-r-packages and critical-code-reviewer skills. Your agent may also have built-in code review skills, such as Claude Code’s /code-review slash command.

Code review skills are available from many sources. For example, Sentry has a code review skill in its repository. Aimed specifically at its project’s needs, it can be a useful sample for similar skills. And superpowers is a popular set of general-purpose programming skills that’s billed as “a complete software development methodology for your coding agents, built on top of a set of composable skills and some initial instructions that make sure your agent uses them.” I don’t use superpowers, but a lot of other people clearly do given its more than 227,000 GitHub stars and 20,000 forks.

Another tip: Several experts suggest using a different LLM — maybe even from a different provider — to conduct a code review, since each model has its own strengths and weaknesses.

Don’t forget general prompting good habits

Whatever programming language you’re using, being clear will improve your outputs. Keep your requests targeted, and don’t expect the LLM to read your mind.

“Codex handles complex work better when you break it into smaller, focused steps,” OpenAI says in its developer docs. “Smaller tasks are easier for Codex to test and for you to review. If you’re not sure how to split a task up, ask Codex to propose a plan.”

“Take time to make your prompts as relevant as possible, just as you would when helping a new teammate scope a task,” Google advises in its Five Best Practices for Using AI Coding Assistants. “Consider what details you need to share for a person to succeed, and provide all those details to your AI tool.”

And, don’t load up your context window to the LLM’s published limit. Performance often degrades as you get close to the maximum token limit.

Take advantage of open LLMs, particularly if you have budget and token limits

Open-weight models — especially ones small enough to run on a desktop computer — may never rival frontier LLMs like Claude, GPT, or Gemini. But for a lot of R coding, they don’t need to.

To see how well an LLM can write R code, it should have the same kind of harness that Claude Code, Codex, or Posit Assistant gives larger LLMs. “The harness is the product,” argues Mallory Mejias at Sidecar, an AI education company. “The model is the engine inside it — important, but increasingly interchangeable.” I wouldn’t go that far, since I still find the LLM to be a critical piece of this equation, and models differ. But the same LLM will perform differently depending on the tools and context it has.

Posit recently added Google’s open-source Gemma 4 26B to Assistant’s existing options of larger, closed-source LLMs. “Up until this point, models of this size — small enough to run comfortably on high-end consumer laptops — were on our radar but not yet capable enough to drive an agent harness like Posit Assistant,” Posit senior software engineer Simon Couch wrote on the Posit blog. “This has changed in the last few months with releases like Gemma 4.” However, to use it in Posit Assistant in RStudio, you still need to run it through a Posit AI subscription, not locally. It uses 1/10 the budget that the same session with Claude Sonnet would consume.

Several projects can use Claude Code to run local LLMs. Ollama, open-source software for running LLMs locally, does this with the terminal command ollama launch claude --model , such as:

ollama launch claude --model gemma4:26b

The makers of Unsloth, an open-source framework for running and training models, say running local LLMs inside Claude Code can be very slow due to a cache invalidation issue. They say this can be fixed by adding "CLAUDE_CODE_ATTRIBUTION_HEADER" : "0" to ~/.claude/settings.json under “env”.

Unsloth can also use Claude Code to run local models, but it’s somewhat more involved to install and set up than Ollama. See this Unsloth tutorial.

Open-source coding agent Open Code runs LLMs from OpenAI, Anthropic, Google, Kimi, Alibaba’s Qwen, and others. I haven’t warmed up to it yet compared with commercially backed CLI tools I’ve tried, but it has 174,000 GitHub stars so clearly a lot of fans.

AI consultant Thomas Wiegold, who switched from Claude to Open Code, said “OpenCode covers everything I need for my daily workflow. It’s fast, the provider flexibility is genuinely useful rather than theoretical, and the TUI is better for extended sessions…. That said, I’m not going to pretend it’s all smooth sailing.” He noted some stability issues and a remote code execution vulnerability earlier this year.

Wiegold’s suggestion? “Install both. Try others. Stay flexible.”

In summary

Large language models remain imperfect and unpredictable tools, but they’re improving rapidly — as are the harnesses around them. Even frontier LLMs in commercial coding agents can ignore instructions at times and otherwise behave in unpleasantly surprising ways. However, you’ll vastly improve your chances of generating quality code if you use coding agents, take time to set them up with quality instructions, and remember good prompting techniques. Good luck!

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Coding agent info

Claude Code – Anthropic’s coding agent is available in the terminal and as an IDE extension, a desktop app, mobile apps, Slack app, and a cloud version that can work directly in your repos on GitHub. Arguably the first breakthrough coding agent, it now includes an elegant remote-control option where you can start a session on your desktop and then continue it on a phone or tablet. While designed for Claude LLMs, you can use it to run local LLMs via tools like Ollama or Unsloth Studio. When using Claude models, it tends to offer less usage than other vendors’ options. Anthropic has posted the terminal version’s code on GitHub, with usage governed by Anthropic’s commercial terms of service. You can use Claude Code via API pay per use or as part of a Claude subscription.

Codex – OpenAI’s answer to Claude Code is available via terminal, app, IDE extension, or cloud, with integrations for GitHub, Slack, and Linear. The CLI tool is open source under an Apache license, available on GitHub, and works via API pay per use or as part of a ChatGPT subscription.

Posit Assistant – The Posit (formerly RStudio) coding agent is designed for data work in R and Python, although Posit senior software engineer Simon Couch told me it would do fine for programming work for other uses and in other languages. It is available for the RStudio IDE (via a Posit AI subscription in RStudio), Positron IDE, and as a stand-alone CLI terminal app, although the CLI tool currently doesn’t feel as full-featured as the IDE integrations (that may change). As of this writing, you can also use API keys from Anthropic, OpenAI, and Snowflake Cortex and a GitHub Copilot account in Positron. Posit Assistant is being updated quite frequenty, Couch told me. If you tested it a month or two ago, it has more capabilities now.

Antigravity CLI – Google’s terminal-based tool will be replacing the Gemini CLI for unpaid and Google One users this month. There’s also an Antigravity IDE, SDK, and platform for orchestrating multiple agents. One advantage of Antigravity: Google offers a free plan with access to Gemini 3.5 Flash, Gemini 3.1 Pro, Gemini 3 Flash, Claude Sonnet 4.6, Claude Opus 4.6, and gpt-oss-120b, and what it describes as “generous” but undefined weekly rate limits based on “the degree we have capacity.”

Designing frontend systems for cloud latency, not just cloud failure

17 June 2026 at 10:00

Frontend reliability is often discussed in terms of outages. Teams prepare for failed API calls, downtime and visible crashes because those failures are easy to recognize and measure. However, in many modern applications, the bigger challenge is not complete failure but latency. Systems rarely go fully offline. Instead, they become slow enough that users lose confidence in the interface long before anything technically breaks.

Most frontend engineers have experienced this in production. A page eventually loads, but only after several seconds of waiting. A save action succeeds in the backend, yet the interface remains unchanged long enough that the user clicks the button again. A dashboard renders immediately, but the critical data appears so late that the application feels unstable. In practice, users rarely distinguish between “slow” and “broken.” If an interaction feels uncertain or delayed, trust drops quickly.

As frontend systems become increasingly dependent on distributed cloud infrastructure, latency becomes a normal operating condition rather than an occasional exception. APIs may depend on multiple downstream services, serverless systems may introduce startup delays and state updates may propagate asynchronously across regions or caches. Frontend reliability therefore can no longer be defined only by uptime. It also depends on how clearly the interface behaves while waiting on slow cloud dependencies.

Reliability is more than preventing outages

Frontend teams traditionally define reliability around visible failure. A stable application is one that avoids blank screens, uncaught exceptions and broken API requests. While those issues still matter, they represent only one category of reliability problems.

In many production systems, the application remains technically available while still feeling unreliable to users. An API request that takes ten seconds may still return successfully. A delayed state update may eventually synchronize correctly. A dashboard may fully render after several refresh cycles. From a monitoring perspective, these systems appear healthy. From a user perspective, however, the experience feels inconsistent and unpredictable.

What makes this difficult is that many frontend applications are still designed around ideal conditions. Interfaces often assume fast responses, immediate consistency and stable network behavior. Cloud environments rarely behave with that level of predictability. Distributed systems naturally introduce latency, asynchronous updates and intermittent delays, even when everything is technically functioning correctly.

This changes how frontend engineers need to think about resilience. Modern interfaces are no longer designed only for success and failure. They also need to handle slow success, delayed feedback and partial availability in ways that remain understandable to users.

Slow APIs and the user experience problem

One of the most common latency issues frontend teams encounter is the slow API response. Technically, the request succeeds, but the delay changes how users interpret the application. A user waiting several seconds for a response rarely thinks about infrastructure behavior or distributed systems. They assume the interface is frozen, unresponsive or broken.

This becomes especially noticeable in cloud-connected systems where response times vary depending on caching layers, regional routing, serverless initialization or downstream dependencies. Frontend applications designed around consistently fast responses often struggle in these conditions because they treat latency as exceptional instead of expected.

Many applications still rely on generic loading spinners that provide little context beyond “something is happening.” While spinners technically indicate activity, they also communicate uncertainty. Users do not know whether the system is progressing, stalled or close to failure. As waiting time increases, frustration grows quickly.

More effective interfaces treat loading as part of the user experience rather than a temporary placeholder. Skeleton screens, layout-preserving placeholders and contextual loading feedback provide users with a clearer understanding of what is happening. Even relatively small decisions can make a noticeable difference. For example, users generally tolerate a dashboard chart loading later far more than they tolerate waiting several seconds before the navigation menu appears. Similarly, an e-commerce page that progressively loads recommendations often feels faster than one that blocks the entire interface until every service responds.

These patterns do not reduce actual latency, but they significantly improve perceived responsiveness. When users understand that the system is actively processing their request, they are far more tolerant of delays.

Delayed state updates and perceived instability

Another common issue in cloud-based systems is delayed state synchronization. A user submits an action successfully, but the interface does not immediately reflect the result. Even if the backend processes the request correctly, the delay creates uncertainty around whether the action worked at all.

This behavior is increasingly common in distributed architectures where writes may propagate asynchronously across services, regions or caches. Frontend applications that assume immediate consistency often expose these delays directly to users, leading to repeated clicks, duplicate submissions and confusion around application state.

In many cases, the real problem is not the delay itself but the absence of clear feedback. Users are generally willing to wait if the interface acknowledges that progress is occurring. Without feedback, even short delays begin to feel unreliable.

Frontend systems can reduce this uncertainty by designing around transitional states more intentionally. Pending confirmations, temporary UI updates and clearer progress indicators help maintain confidence while backend systems synchronize. Users care less about exact infrastructure timing than about whether the interface feels responsive and understandable throughout the interaction.

Progressive rendering and partial availability

Traditional frontend rendering strategies often wait for all required data before displaying meaningful content. While this simplifies state management, it also magnifies perceived latency by forcing users to stare at incomplete or empty screens.

Modern cloud systems rarely deliver all information simultaneously. Some requests complete quickly while others depend on slower downstream services. Treating the entire interface as blocked until every dependency responds unnecessarily increases the feeling of slowness.

Progressive rendering offers a more resilient alternative. Instead of waiting for the entire page, the application renders available content immediately while secondary information loads incrementally. A profile page may display account details before loading recommendations. A dashboard may render navigation and layout structure first while analytics populate later. This creates a sense of continuous progress rather than complete inactivity.

What makes this approach effective is that users tolerate delayed secondary content far better than delayed primary interaction. Even when total loading time remains unchanged, the interface feels significantly more stable because progress is visible.

Progressive rendering also changes how frontend teams think about dependency importance. Not every cloud service needs to block the entire user experience. Separating critical interaction paths from secondary content creates systems that remain usable even under imperfect network conditions.

Designing recovery, not just error messages

Latency-related problems are often made worse by weak recovery patterns. Many applications either wait indefinitely or fail abruptly with vague messaging such as “Something went wrong.” Neither approach creates confidence for users who are already uncertain about system behavior.

Frontend resilience improves significantly when interfaces guide users through delays and recovery intentionally. Instead of displaying generic failures immediately, applications can acknowledge that an operation is taking longer than expected while still preserving user progress. Retry actions, recoverable workflows and state preservation help users continue without restarting tasks entirely.

This becomes especially important in forms and transactional flows. Losing user input because of a temporary timeout often damages trust more than the delay itself. In practice, people are usually willing to retry an action. They are far less willing to repeat work they already completed once.

These recovery patterns are not simply UX enhancements. They are architectural decisions that recognize cloud latency as a normal operating condition rather than an edge case.

Designing for latency is designing for reality

As frontend systems become more dependent on distributed cloud infrastructure, latency becomes unavoidable. The most reliable interfaces are not necessarily the ones with the fastest response times. They are the ones that communicate clearly, preserve user confidence and remain usable during delays.

Frontend teams spend enormous effort optimizing rendering performance while often underestimating the impact of latency behavior on user trust. In many applications, the difference between a reliable experience and a frustrating one is not milliseconds of rendering speed but how clearly the interface handles waiting.

Frontend engineers do not need to become cloud infrastructure specialists to design for these realities. However, understanding that cloud latency is a normal part of modern application behavior fundamentally changes how interfaces should be built. Designing for latency is ultimately not about preparing for rare outages. It is about designing for the way modern systems behave every day.

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Code like Hemingway

17 June 2026 at 10:00

I was blessed with a terrific high school English teacher. Ms. Jewel was funny, kind, interesting, and tough on us. I can still spell “ecstasy” on the first try because of her. One of the more memorable lessons she taught was a “Hemingway and Fitzgerald” module. 

I loved the short stories of both, but Hemingway’s work always stuck out for me. It’s cliché to say that Hemingway was terse, but we only say it because it is true. He could pack more into a page than most writers. He didn’t waste a syllable. 

Software developers are soon going to have to take a lesson from Hemingway.

It’s not hard to be concise in code. You have to be, by design. The compiler won’t put up with any throat-clearing and jibber-jabber. You can’t meander. You have to use a very strict set of words and symbols, and you have to use them in a defined way. 

Thanks to agentic development, we don’t write code anymore. But we are writing for the agents. A lot. 

All about the spec

Let me explain. Claude Code, Codex, Copilot, and the rest all love to be “spoken” to in Markdown. We used to define our code with unit tests and specifications written for humans. Now, it’s all about the spec. And the spec needs to be both complete and concise. 

It needs to be complete in the sense that if you leave something out or forget to define something, the agent will very likely fill in the gaps for you. Forget a feature or requirement, and the agent will go off confidently and almost certainly in the wrong direction.

At the same time, you need to be concise. Because if you are too effusive, you may give the agent ideas that you don’t want it to have. If you write like Fitzgerald — lush and expansive —  the agent will be off and running in a direction you can’t be sure about. 

Our compilers will stop cold if we aren’t precise and concise. Our agents? They will proceed with confidence, diligently producing a result that looks great but isn’t what we want.

So we need to learn to write like Hemingway and pack as much meaning into as few words as possible. Or, as Paul Graham said:

paul graham tweet

Foundry

Let’s be clear

For years, we’ve been writing semi-vague Jira tickets and expecting our fellow humans to fill in the gaps and “get the gist” of what we were trying to do. We don’t have that luxury anymore. Developers who aren’t able to move on from “Jira-speak” to “Agent-speak” are going to struggle. If we don’t follow Graham’s advice in writing those specs, who knows what we’ll get.

Maybe someday agents will know the ins and outs of our business well enough to “know what we mean,” but they can’t today. The time we used to spend coding now needs to be spent perfecting our new craft of writing precise specifications for our agents to execute.

Every listicle ever written on “How to be a Great Developer” says you need to be a great communicator — but that was communication with other humans. 

Ms. Jewel always taught us that our writing is only as clear as our thinking. Our compilers forced us to write clearly. Our coding agents won’t do that. I’m sure there are many developers out there now who wish they had listened more closely to their high school English teacher.

Are Common Frameworks for Assessing Social-Emotional Skills in Youth Falling Short?

17 June 2026 at 08:18

A recent comprehensive study published in PsyCh Journal casts significant doubt on the widespread application of an adult-derived framework for assessing social-emotional skills among children and adolescents across diverse cultures. The research critically evaluates the Organization for Economic Co-operation and Development’s (OECD) Survey on Social and Emotional Skills, a framework traditionally anchored in adult personality models, highlighting its inability to reliably capture the developmental nuances and cultural variations inherent in younger populations.

This study deployed an extensive dataset comprising over 60,000 children and adolescents aged 10 and 15 from 10 international urban centers spanning nine countries, including the United States, Canada, China, South Korea, Finland, Colombia, Russia, Portugal, and Turkey. The participants’ social-emotional competencies were assessed against the established framework that organizes 15 skills into five categories mapped onto the Big Five personality traits: openness, conscientiousness, extraversion, agreeableness, and emotional stability.

Findings indicate that the hypothesized functional zoning of these skills failed to emerge consistently across the diverse sample. Contrary to expectations, the adult-derived hierarchical structure did not hold true for younger individuals. The empirical data revealed that the configuration of social-emotional skills evolved significantly with age. Ten-year-olds exhibited a more blended and less differentiated pattern of skills, whereas fifteen-year-olds showed more refined distinctions, yet still diverged substantially from the adult framework that underpins the OECD survey.

Moreover, the study unveiled pronounced cultural heterogeneity in the organization of these skills. The way social-emotional skills clustered varied markedly across cities such as Houston, Helsinki, and Suzhou. This variance underscores the influence of local sociocultural factors, educational practices, and societal norms in shaping the development and interrelation of these competencies at both individual and community levels. The research suggests that universal, one-size-fits-all assessments are insufficient for capturing the complexity of social-emotional development across global contexts.

The methodological approach employed, Exploratory Graph Analysis (EGA), was pivotal in uncovering the latent structure of the assessed skills. EGA, a network psychometrics technique, models relationships among variables, offering a data-driven perspective on how different skills interconnect functionally. However, in this case, EGA failed to reproduce the pre-established functional zoning assumed by the survey framework, thereby challenging its applicability and prompting a call for re-evaluation and adaptation.

Bo Ning, PhD, corresponding author and professor at Shanghai Normal University, emphasized the implications of these results. She underscored that social-emotional skill assessments must be sensitive to developmental stages and cultural contexts to ensure fairness and accuracy. This stance invites a paradigm shift towards frameworks that are not only empirically validated for diverse populations but are also dynamic enough to adapt to the evolving socioemotional landscape of children and adolescents worldwide.

The study’s cross-national sample and robust analytic techniques bolster the credibility of its conclusions, signaling a crucial need for psychometric frameworks that accommodate developmental trajectories and cultural specificity. Such frameworks would enhance the validity of social-emotional skill measurement and guide the design of interventions tailored to the unique needs and contexts of younger populations globally.

This investigation also raises broader questions about the translatability of adult personality theories to child and adolescent populations. Since children are in critical developmental phases marked by rapid psychological, social, and cognitive changes, frameworks built on adult models may oversimplify or misrepresent their social-emotional realities. Consequently, researchers and policymakers must reconsider how social-emotional competencies are conceptualized and operationalized in international assessments.

Beyond academic inquiry, this research holds pragmatic significance. Social-emotional skills are increasingly recognized as crucial predictors of lifelong success, influencing educational outcomes, mental health, and social integration. Misalignment in assessment tools could lead to misguided policies, inadequate resource allocation, and ineffective educational programming, especially when applied in culturally or developmentally incongruent ways.

The study’s findings advocate for the creation of culturally nuanced measurement tools that incorporate indigenous concepts of social and emotional functioning. Such culturally congruent tools would respect local values and educational priorities, fostering more authentic and meaningful assessments. Additionally, they would facilitate international comparisons that are not merely nominal but grounded in culturally sensitive understanding.

Further research is needed to elucidate the mechanisms driving developmental and cultural variability in social-emotional skill organization. Longitudinal studies can capture dynamic developmental processes, while qualitative research may offer rich contextual insights. Combining these approaches with advanced quantitative methods like EGA can yield comprehensive frameworks capable of supporting equitable policies and practices in global education and child development.

The growing recognition of the complexity of social-emotional skills across developmental stages and cultures marks a pivotal advancement in psychological science. This study contributes a critical evidence base, urging the scientific community to transcend traditional adult-centric, culturally monolithic frameworks in favor of more sophisticated, inclusive paradigms that truly reflect the lived experiences of children and adolescents worldwide.

As the landscape of global education and psychological assessment continues to evolve, embracing culturally adaptive, developmentally informed frameworks will be essential. Doing so promises to improve the fairness and effectiveness of social-emotional skill measurement, ultimately promoting better outcomes for diverse populations of young people who will shape the future of societies around the world.


Subject of Research: Social-emotional skill assessment across developmental stages and cultures

Article Title: Exploratory Graph Analysis Failed to Reproduce the Hypothesized Functional Zoning Framework in the OECD Survey on Social and Emotional Skills

News Publication Date: 17-Jun-2026

Web References: http://dx.doi.org/10.1002/pchj.70108

Keywords: personality development, social development, developmental psychology, psychological science, young people, adolescents, emotional development

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