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The European Commission’s 2022 procurement for a participatory foresight study on next-generation online platforms placed platform design and consumer behaviour within a wider policy question: how could the platform economy evolve, and what might those changes mean for European Union policymaking? The work was not a narrow experiment on marketplace user experience. Instead, it was a two-year exercise intended to identify long-term trends across online platforms and assess their policy implications. The Commission published the call, reference CNECT/2022/OP/0049 , in August 2022. Its official announcement of the foresight study on the future of online platforms lists a submission deadline of 22 September 2022 at 16:00 CEST . That makes the procurement a completed historical call, rather than a current tender opportunity. The framing remains relevant because interface design, recommendation systems and other platform choices can influence what people notice, compare and select online. But the Commission’s stated objective was broader than any one marketplace design question. It sought a structured view of the platform economy’s possible future trajectories and the public-policy issues those trajectories could raise. What the 2022 study was designed to examine The Commission described the project as a two-year participatory foresight study . Participatory foresight brings relevant groups into a structured exploration of future developments rather than attempting to predict one fixed outcome. In this case, the study was designed to identify ten topics in collaboration with Commission services, then examine long-term trends and their potential policy relevance. Design’s influence on consumer behaviour was part of the broader theme, not the full scope of the procurement. That distinction matters. A study focused solely on a marketplace interface might measure how a particular ranking, default or layout affects a defined consumer decision. The Commission’s foresight work i

Everybody says AI governance matters. They are right. But that is the easy part. The harder part is...

The European Commission has updated its guidelines for teachers and educators on tackling disinformation and promoting digital literacy, extending the guidance to address generative AI , influencer dynamics and prebunking . The refresh gives schools and education professionals new materials for helping young people assess online information and build resilience against misleading content. The revised guidance sits within the EU's Digital Education Action Plan (2021-2027) . According to the European Commission publication record for the updated guidelines , the Directorate-General for Education, Youth, Sport and Culture released the updated publication on 4 June 2026. The update matters because the information environment facing pupils has changed substantially since the original guidance was issued. Generative AI can now be relevant to how online content is created, altered and spread. At the same time, social-media reliance and influencer-led information dynamics have become more prominent considerations for digital literacy education. The Commission's revised material positions educators and schools as part of the response, rather than treating disinformation solely as a platform or policy problem. What the updated EU guidance adds The updated guidelines are one element of a wider package of digital education and online-safety work. European Commission press materials published on 5 March 2026 described four sets of guidelines, comprising two new sets and two updates. The digital literacy and disinformation guidance was among the updated materials, with explicit attention to generative AI and contemporary online dynamics. A Better Internet for Kids summary published on 10 March 2026 identified several practical and policy-oriented additions. These include: Lesson plans and an updated glossary to support classroom use. Consideration of generative AI's impact on disinformation . Coverage of social-media reliance and the role of influencers in shaping information exp

What are you working on? I hear these words in my day-to-day. And sometimes, when I hear them, there’s this little brain freeze that happens because my brain is probably trying to put into words the amount of things that have wandered through my head in the last 24 hours. 😂 So I thought, okay, let me try something. I want to take some of those wandering thoughts, explorations, things I'm trying out and things I'm learning, and put them into writing. This is going to be a series where I come and talk about what I'm working on — software engineering, product, work, people, faith, relationships, rest, and whatever else happens to be taking up space in my head at the moment. So, what am I working on? I recently started writing backend code, and there’s a bit of a backstory to that. I built this frontend commerce store years ago where people can come and shop for furniture. At the time, I used a backend-as-a-service to handle the backend side of the application. Now, I’m coming back to that same system and writing the backend myself with NestJS. I wanted to go beyond just consuming a backend and actually understand what is happening behind the scenes. The learning process is a bit stretching at the moment because I’m getting familiar with a lot of new concepts. Tiring and frustrating? Yes. But the feeling when I finally understand the reason behind something is always refreshing. That has been really rewarding lately. I'm also in the middle of launching a mobile application at my workplace, going through system design classes, figuring out how to get the best out of my engineers (AI sub-agents, by the way 😅), and occasionally imagining that dream job where you get to build products that serve millions of people and work with really brilliant minds. Also, I discovered the productivity rush that comes with using large monitors. 😂 Then there's learning how to rest while also trying to close out all the open loops in my head. Building reading habits. Figuring out what to pri

Hello I'm Marlene and I invite you to follow my journey developing ProForma.net. But since this is my first post about ProForma, I will give you an overview of what I'm trying to achieve. What ProForma.net is planned to be The main Goal is to develop an application shell for schema based Applications. You will have mainly to different types of UI schemes, first for the Window Layout, there you will tell which elements are contained in the different application sections, like what Buttons or Menus you will have in the window title bar, or what sidebar tabs you will provide for the Ribbons, what the content area is filled with (spoiler I'm going to use flexlayout-react https://github.com/caplin/FlexLayout ). As host application I will write a C# application using the WebView2 abstraction library Photino ( https://www.tryphotino.io/ ). What you can expect In this dev diary series I'll show what I was working on, I'll show you some code and will explain why did to choose the way I did it, or will share some thoughts about the project or the architecture. I also will show you how to write plugins for ProForma, because I plan to handle everything as a plugin so you can change the most aspects of the app. The journey begins: overcome the guard Ok, most of you will know it... parameter checking on top of a method... nearly endless 'if throw' constructs... they are ugly... if (! Directory . Exists ( physicalPath )) throw new DirectoryNotFoundException ( $"Could not find the given path ' { physicalPath } '." ); if ( _directories . ContainsKey ( urlPrefix )) throw new Exception ( $"Key ' { urlPrefix } ' already exists." ); if ( _directories . ContainsValue ( physicalPath )) throw new Exception ( $"Physical Path ' { physicalPath } ' already exists." ); I mean who wants to read that? I don't. So I wanted guards, and I've could used some 3rd Party library, but instead I came up with my own solution for the Guards, since I don't always want to throw the exception on a failed asser

The European Union's AI Act establishes a risk-based framework for AI systems that ranges from prohibited practices to minimal-risk uses. Regulation (EU) 2024/1689 divides the framework into four levels: unacceptable risk, high risk, limited risk and minimal risk. For AI developers, vendors and enterprises, the practical importance is straightforward: the system's risk category determines whether it can be used and, if so, the level of compliance, transparency and governance expected around it. The regulation entered into force on 1 August 2024 . Its four-tier approach is designed to avoid applying the same regulatory burden to every AI use case. Instead, the Act reserves its strictest treatment for systems that present the greatest risk, while leaving minimal-risk systems without additional sector-specific obligations under the AI Act beyond general law. The definitive reference is the official text of Regulation (EU) 2024/1689 . Although older explainers may use slightly different labels for transparency-related obligations, the final binding regulation is consistently described by EU institutions as a four-level risk framework. The EU AI Act's four risk levels The categories are not simply labels for how sophisticated an AI model is. They are a regulatory method for connecting an AI system's use and potential impact with corresponding obligations. A business cannot determine its position merely by calling a tool "low risk". It needs to assess the system against the Act's framework and the obligations associated with the applicable category. Risk level Regulatory position Core consequence Unacceptable risk Prohibited AI practices The practices are banned outright. High risk Systems subject to extensive obligations Requirements include conformity assessments and risk management. Limited risk Systems subject to certain requirements Transparency and oversight requirements apply in relevant cases. Minimal risk Most AI systems No additional sector-specific AI Act oblig

The European Union's voluntary General-Purpose AI Code of Practice gives providers of general-purpose AI models a practical framework for supporting compliance with the EU AI Act. Finalised in July 2025, the code addresses transparency, copyright, and safety and security. Its public signatory list includes major AI and technology companies, but official EU material does not support claims that roughly 190 organisations have signed the GPAI code. The distinction matters for companies assessing AI suppliers. Signing the code can signal engagement with the EU's emerging governance expectations, but it is not a substitute for examining a provider's specific commitments, documentation, and product-level controls. The European Commission describes the code as a voluntary instrument, and its official GPAI Code of Practice page states that the signatory process and public information continue to be updated. What the GPAI Code of Practice covers The code is designed for providers of general-purpose AI models, a category that can include models used across multiple downstream applications. Rather than creating a separate legal regime, it is intended to help providers demonstrate how they can meet relevant AI Act obligations . Its three chapters cover different aspects of provider responsibility: Transparency: commitments related to information and documentation that can help downstream providers understand and use general-purpose AI models appropriately. Copyright: measures intended to address copyright-related obligations for providers of general-purpose AI models. Safety and security: commitments focused on managing risks associated with the most capable models, including systemic-risk considerations where applicable. Code chapter Primary focus Why it matters to AI buyers Transparency Provider information and documentation Helps buyers assess whether a model provider can supply information needed for downstream use. Copyright Copyright-related provider commitments Relevant

This week's Java roundup for July 27th, 2026, features news highlighting: OpenJDK JEPs targeted and proposed to target for JDK 28; the GA release of GPULlama3.java 1.0; point releases of Micronaut, Quarkus and JobRunr; a maintenance release of JDKUpdater; the sixth release candidate of Maven 4.0; and the first milestone release of Jakarta Agentic AI 1.0. By Michael Redlich

The Observability Black Box As autonomous AI agents evolve from isolated chat assistants into multi-agent systems executing multi-step business logic across databases, APIs, and microservices, enterprise platform teams face an acute operational challenge: black-box opacity. When an autonomous agent fails, hallucinates, or executes an out-of-bounds API call, traditional Application Performance Monitoring (APM) tools fall short. Standard HTTP request logging and basic prompt-response captures cannot reconstruct the non-deterministic reasoning loops, tool selection branches, or sub-agent delegations that led to an incident. Furthermore, enterprise auditors, security teams, and regulatory bodies (governed by SOC 2, FedRAMP, and the EU AI Act) now require non-repudiable proof of agent execution. Organizations must be able to answer five fundamental questions for every production run: Which human or non-human identity authorized the agent run? What planner reasoning path or tool routing logic was chosen? Which exact data assets or vector embeddings were retrieved into context? What was the precise execution latency, token cost, and error tax of each intermediate step? Can the complete execution graph be cryptographically reconstructed for compliance review? To resolve this challenge, platform engineering teams must deploy Audit, Observability & Lineage —an architecture anchored in OpenTelemetry (OTel), OWASP Agent Observability Standards, and immutable lineage graphs. Deep-Dive Architecture: OpenTelemetry & Lineage Integration A production-grade Agent Observability stack avoids proprietary vendor lock-in by standardizing on OpenTelemetry (OTel) OTLP trace ingestion and open metadata stores. 1. The Unified OpenTelemetry Span Tree Every agent execution unit — from user intent trigger to final task completion — is encapsulated within a single root trace context ( agent.run ). Sub-tasks, tool calls, and model invocations are recorded as hierarchical child spans: [ Root Trace:

When I first started contributing to open source, GitHub Actions felt like a black box. Seeing a failed workflow on my pull requests was intimidating because I didn't really understand what was happening behind the scenes. Well, this month, I decided to change that. What I Worked On I implemented GitHub Actions across three of my project repos. My main focus was: Adding Markdownlint for Markdown quality Adding Pylint for Python linting. Updating documentation while integrating CI. What I Learned The biggest lesson wasn't technical — it was changing my mindset. A failed workflow isn't something to fear. It's simple feedback. Whether it's markdownlint warning or a pylint error, each failure helps improve the project. Looking Ahead Most of this month was spent improving my own projects, so I didn't contribute much to external repositories. Next month, I want to build on this foundation by contributing to more open source projects and applying what I've learned. Sometimes, learning the tools behind open source is just as valuable as making another pull request. I'm curious What part of GitHub Actions or CI/CD was the most challenging when you first started? Or If you are just getting started, what's the biggest thing that's still a mystery to you? I'd love to hear your experience and tips in the comments. Transparency Note: I used AI as an editor—not as the author. For this article, it helped refine the structure and improve the English grammar. The technical content, experiments, opinions, and conclusions are my own and were reviewed by me before publishing.

"We're feeling pretty good about that," Boeing CEO Kelly Ortberg said.

Hey everyone! I bring you my development journey on what I have discovered, accomplishments for this...

By Suvankar Chakraborty | Principal Engineer — IAM, Modern Workplace Management & IT Operations The Collaboration Platform That Became a Governance Nightmare Microsoft Teams was deployed at extraordinary speed across the enterprise world. In most organisations I know, the deployment timeline went something like this: March 2020, global pandemic, remote work mandate, Teams switched on, everyone told to use it, governance deferred because there was no time. Five years later, the governance that was deferred in 2020 has still not been implemented in most of those environments. The result is predictable and consistent across industries: Teams sprawl at industrial scale. Hundreds of Teams that nobody owns. Channels for projects that ended three years ago. Guest users from partnerships that dissolved. Sensitive conversations in channels that include contractors who should not have visibility. Files shared in Teams chat — bypassing SharePoint governance entirely — on devices with no management policy. Meeting recordings stored in OneDrive folders that anyone with a link can access. Bot integrations that have permissions to read your Teams messages and access your calendar, approved by a user who clicked through an OAuth consent screen without reading it. In 13+ years of enterprise IAM and IT operations work, Teams governance has become one of the most consistently mismanaged areas of Microsoft 365. Not because it is technically difficult — the controls Microsoft provides are comprehensive. But because Teams sits at the intersection of IT, security, compliance, and the organisational culture of collaboration, and that intersection is where governance programmes go to die. This article is about why enterprises get Teams governance wrong, and what getting it right actually looks like — in specific, actionable, implementable terms. Why Teams Is a Governance Problem Unlike Any Other M365 Workload To understand the governance challenge, you need to understand what Microsoft Team

Part 1 documented the recurring snapback in practice. This note asks a narrower question: what does the observed pattern support, what remains a working hypothesis, and what changes should follow in the project? Status: Bounded project conclusion. This note separates observed behaviour, working hypothesis, and practical consequence. It is based on current project documents and interactions; it is not external validation or a universal claim about AI systems. What the evidence supports 1. The project already contains a stable relational model The working model is not generic “AI assistance.” It separates reasoning surfaces, uses bounded comparisons, permits two-way cognitive pressure, and keeps final acceptance authority with the human. Reciprocal cognitive contribution, asymmetrical governing authority. 2. Concrete project work preserves the structure better than public abstraction At the concrete level, instructions such as: Review this proposal against that architecture. preserve the distinction between the object being reviewed, the surface applying pressure, the evidence, and the authority that may accept a change. When the same structure was compressed into general prose, generated explanations repeatedly returned to a simpler one-way model of either human control or transferred AI authority. That is an observed pattern in this development process. 3. Public explanation is a separate reasoning surface A README, article, summary, or portfolio page is a projection of the model, not the model itself. It cannot be assumed to reproduce the internal structure faithfully merely because that structure is present in context. The explanation must be reviewed against the model it represents: Does this explanation preserve the actual authority, review, evidence, and state-transition structure? 4. Annoyance was useful boundary data The irritation indicated that the generic rendering was no longer merely an imperfect exploration. It was colliding with an internal frame that

Most agent platforms give you one configuration file and hope. When you are running agents for more than one team — or more than one customer — a single config breaks down fast. Each workspace needs its own model, its own service access, its own secrets, but someone has to guarantee that no workspace can spend more than its budget or reach a service it was never authorized to use. The answer is a policy cascade. Every setting — model, temperature, allowed services, API keys, skill availability, TTL defaults — resolves through three ordered tiers: company, repo, and workspace. A lower tier can narrow an upstream ceiling but never widen it. That single rule is what makes it safe to hand a workspace to a team without handing them the keys. How the cascade resolves Three tiers, bottom-up. The company tier sets the floor. The repo tier overrides it. The workspace tier overrides both. The resolution order is fixed for every kind of setting: Kind Company tier Repo tier Workspace tier Policy fields defaults override override Variables floor override override Skills floor override override Secrets last-resort fallback override first-resolved Notice that secrets run in reverse. A workspace-tier credential wins over the repo and company defaults, and an empty value at the workspace tier falls through to the repo. This means you set a per-customer token where it is used and fall back up the chain only when it is absent. You are never forced to duplicate credentials across every workspace. Narrow-only: the safety property The cascade is not a free-for-all override. For service access, budgets, quotas, and TTL defaults, a lower tier can only narrow what the tier above it allows. If the company grants a repo [github, slack, search] , a workspace under that repo can select a subset — [github, search] — but it cannot add linkedin . The resolve-time intersection is enforced, not advisory. This extends to per-service API surface control. Granting access to GitHub does not mean grantin

When a new platform team set out on implementing their roadmap through forced workflows with poor documentation, developer experience declined. Success came from simplifying governance, prioritizing what matters, and rolling out compliance incrementally through prevention, detection, and communication. Empathy, focus, and shared purpose drove successful adoption. By Ben Linders

Shipped this week Workflows are now a first-class Bizbox primitive — PR #86 · v2026.603.0 The biggest drop this week. @DennisDenuto landed Workflows as a company-scoped concept that sits alongside issues and routines — not shoehorned into either. What that means in practice: Google ADK-backed execution — workflow pipelines run as ADK agents, with phase state persisted as run records. Human handoffs baked in — pipelines can pause and wait for a human before resuming. Deliverables that survive — artefacts from each run are persisted and surfaced in the UI. A pipeline graph in the UI — topologically ordered, showing live phase state and console output. This is the foundation. More on what we can build on top of it below. Workflow human-handoffs now route through ClickUp — PR #91 · v2026.605.0 The day after Workflows landed, @angelofallars wired up the last kilometre: when ADK Python code calls input() inside a pipeline, Bizbox now intercepts that call and sends a ClickUp message to collect the human reply — instead of blocking the process forever. A few things that were fixed along the way: input() monkey-patching now works consistently across Python environments (was silently failing in some setups). Failed workflow runs no longer submit deliverables. You only see artefacts from runs that actually completed. ClickUp awaiting-human bridge adapter ships as a pure plugin — PR #78 · v2026.601.0 This one technically crossed the line on the last day of May (23:56 UTC, 31 May), so it's in scope. @ralphbibera ported the ClickUp transport and adapter as a genuine plugin — implementing the AwaitingHumanBridgeAdapter registry interface — without touching bridge core at all. What that gives you: ClickUp works through the same provider-agnostic layer as any future provider (Slack, Discord, whatever comes next). The core doesn't know ClickUp exists. Included: send/poll/reaction transport, message templates for request_confirmation and ask_user_questions interactions, brain_is_think

The new leasing program is a big change for the hardware company, and comes as it looks to raise prices on many of its products.

We’ve all been there: you are busy coding, and suddenly you get a scary error saying your code conflicts with a teammate's work. Git won't let you pull their changes because it's afraid of overwriting your unsaved files. Instead of panicking, I used git stash. This command acts like a temporary clipboard—it safely hides your current work away so your workspace becomes completely clean. With a clean screen, I was able to safely update the project, fix the conflicting lines of code by hand, and make sure everything compiled perfectly. Once the team's updates were safely pushed, I ran git stash pop to bring my hidden work right back. Git wasn't trying to break my project; it was just protecting my code. Next time you get stuck, remember the golden rule: Stash your work, fix the conflict, and pop it back!

I’ve been out of context with my computer science degree for years, and honestly, it feels overwhelming. I want to change that, so I'm starting this log to document my comeback story, my failures, and my progress. What I did today: Acknowledged the starting line. Took 10 minutes to think about where I left off and what I need to tackle first. Decided to write daily logs. This is not my first attempt to finish the degree, but hopefully with some public eyes on it, it will be the last. 10 subject left. Starting with Mathematical analysis. The biggest challenge right now: Feeling completely out of context and fighting the urge to get distracted by everything else. Many things have changed since i was actively going to classes. My generation graduated many years ago and i feel like getting all the information I need will be extremely difficult since i don't know anyone. Getting information from the university websites and emailing professors will be slow. Next steps: Tomorrow, I'm going to try to overcome my overdramatic anxiety and post in the student groups I was in, in hope that somebody might have new information about the subjects. I will go start lesson #1. Goal is to ease into the material and start a habit, rather than pressuring myself to do everything at once. Come back tomorrow.