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ENISA framework secures dependency supply chains through curated package selection, integrity enforcement during integration, continuous vulnerability monitoring, and rapid mitigation to counter malicious injection and compromised dependencies.
Basel-Stadt's e-voting pilot failed to decrypt 2,048 online ballots from Swiss referendums after three USB sticks with the correct code all malfunctioned, even with IT help.
The AiLock ransomware gang claims to have stolen 129GB of data from England Hockey and threatens to publish it unless a ransom is paid, with the organization confirming an active investigation.
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Marketing in 2026 is a whirlwind. Expectations are sky high, AI FOMO is everywhere, and the new playbook is being written in real time.
OpenAI's GPT-5.4 model in ChatGPT cites brand websites far more often than its GPT-5.3 model. Brand sites receive 56% of GPT-5.4 citations but only 8% of GPT-5.3 citations. GPT-5.4 sends an average of 8.5 fan-out queries per prompt, while GPT-5.3 sends only 1 query. GPT-5.4 also cites commercial pages far more often. It cited pricing pages 138 times across 50 prompts, while GPT-5.3 cited them only 4 times. The models rarely cite the same sources.
AI is shifting software value from user interfaces to systems that capture data and automate work. The selloff in software stocks followed a narrative that AI makes SaaS obsolete, yet code was never the main moat. The most valuable layer is structured data systems of record that give AI agents the context needed to act, while new systems can now be created by extracting structured information from previously unstructured sources. Workflow products still matter because they encode industry knowledge about how work should be done, even if agents perform the steps instead of humans. The biggest shift is automation that replaces labor, which allows software to sell economic output instead of tools.
Anthropic's study finds that AI is reshaping knowledge-based roles rather than low-wage jobs. They introduce a measure called observed exposure. It combines theoretical AI capabilities with real-world usage and focuses on automated, work-related tasks. Computer programmers, customer service reps, and financial analysts are the most exposed. Actual AI coverage remains far below its theoretical potential. Workers in these roles are generally older, female, highly educated, and higher-paid. Overall, unemployment has not risen, but hiring of younger workers in exposed occupations has slowed. The US Bureau of Labor Statistics projects slower growth in these fields through 2034.
Clear expectations at the point of action increased conversions by 57% for FanTokens.com. The site redirects buyers to an external exchange to complete a purchase. User testing showed that many visitors felt uneasy when the redirect appeared without warning. The site changed the CTA from “Buy” to “Buy on [exchange name]” and added exchange logos and colors to the buttons. This change removed the surprise and improved trust during a critical step in the buying process.
Email content works best when it starts with understanding the signals your audiences give. Segment your list into groups based on behavior, engagement, or purchase stage. Tailor emails to what each group needs. Intent-driven emails respond to actions or objections. Awareness emails educate or build trust. Campaign emails share product launches, events, or promotions, but only to relevant recipients. Look for ideas in support interactions, reviews, and online discussions. Keep each email focused on one goal with guidance and a clear next step.
This webinar on March 18 looks at how buying journeys have become fragmented as consumers search across social platforms, marketplaces, and AI tools. It introduces Search Intelligence and explains how analyzing search behavior can reveal localized trends and ensure brands appear where audiences seek information. The session also shares real examples of companies using Trajaan to turn search data into insights that enhance brand visibility.
In this video, Eoin Clancy, VP of Growth at Airops, explains the role of content engineers and the 3 key signs of low-quality AI content. He also shares strategies for using AI without producing “slop”. Eoin dives into why webinars are Airops' top growth channel. He covers how they select topics, choose guests, run ungated sessions, and follow up to drive revenue.
Many brands still misunderstand creator marketing because it often gets added late in campaigns instead of being built into strategy from the start. Brands often assign influencer work to social or PR teams even though creator partnerships require separate expertise and dedicated processes. Many strategies also fail because agencies do not explain why specific creators or tactics support larger marketing goals. Long-term creator partnerships produce better insights and performance because brands gain audience feedback and data across multiple campaigns.
Signal theory says that every brand action communicates something to the market, and brands that chase trends or cultural moments often signal desperation rather than the relevance they intended. The brands that stand out are committed to a specific identity that alienates some people and attracts exactly the right ones. If a competitor's logo can be swapped onto a brand's marketing without anything changing, the brand has no signal worth following.
Most SEO fixes fail to ship not due to technical complexity but due to framing, with SEO teams making requests in a format engineering teams are not built to act on. Adopting a product requirements document structure forces SEO recommendations into the same language product managers use to get features built, complete with problem statements tied to business impact, user stories, acceptance criteria, and rollback thresholds. The format also creates accountability on both sides since a PRD with clearly defined requirements and success metrics gives the SEO team a paper trail when fixes are poorly implemented due to vague specs.
A framework to structure posts that balances stories, education, and promo content.
Disney+ is bringing short-form vertical video to its US mobile app with Verts, a scrollable feed of scenes and moments from its catalog designed to drive content discovery and daily engagement.
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Even with AI, product teams often take months to deliver. Why? Because tool switching, slow builds, and coordination overhead haven't gone away.
Product discovery breaks down when companies fast-track “must-have” features and lack clear outcome-based goals. Evidence-guided discovery with shared metrics and measurable outcomes helps teams prioritize better ideas and avoid feature-factory behavior.
As AI reduces the cost of building software, the real challenge becomes deciding what to build, pushing engineers to adopt product management skills. The three most important are gathering context, tracking results through feedback loops, and communicating decisions in a way that drives action.
Product problems rarely have a single clear definition. Teams must continually move between behavioral, contextual, and strategic perspectives to understand them. Product leaders create shared understanding by helping teams explore these layers together and refine the real problem over time.
AI is eroding the traditional SaaS model built around predefined features. The real moat will shift to platforms that let customers generate and govern their own workflows securely and reliably.
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AI has launched product development into uncharted terrain. While many are thriving in this turbulence, others still struggle to unlock AI's full potential. Miro commissioned Forrester Consulting and Harvard Business Review to conduct a widescale study of global product leaders [9] -- covering potential wins, current roadblocks, and
When backlog items feel too big or vague, the problem is usually poor shaping around customer needs, not just missing detail. Teams should shape work together through problem clarification, workshops, and spikes so they can learn faster, avoid handoff friction, and deliver value in smaller, clearer steps.
Flatlining products that generate some revenue but show no growth often block better opportunities. Product leaders must deliberately sunset them as portfolio decisions to free teams to discover the next source of growth.
AI can accelerate experimentation and prototyping, but building trustworthy products still requires human judgment, customer understanding, and first-principles thinking. Teams should use AI to explore ideas faster while prioritizing simplicity, transparency, and real customer value.
Great growth teams don't chase experiments or tactics. They identify and remove the real friction blocking users from getting value. Sustainable growth comes from deep customer understanding, faster learning, and compounding the insights that truly matter.
Product leaders should translate product metrics into clear financial impact when speaking with executives.
As AI agents become increasingly autonomous, the human role shifts from doing tasks to supervising systems that do them.
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The voice layer 100,000+ developers use daily recently launched on Android. Now it's on every platform.
Apple's upcoming foldable iPhone will have an iPad-like layout when opened. It will be able to run apps side-by-side for the first time. The interior foldable display will be roughly the size of an iPad mini. The external screen will be about the size of the display on a small iPhone. Apple is developing new iOS app layouts so developers can adapt their software to the new interface.
Elon Musk has revealed a new joint project between Tesla and xAI called 'Macrohard' or 'Digital Optimus' aimed at creating a system capable of emulating the functions of software companies. The project pairs xAI's Grok with a Tesla-developed AI agent that processes real-time computer screen video and keyboard and mouse actions. The system will run on Tesla's in-house AI4 chip paired with xAI's Nvidia-based server hardware. xAI applied for a trademark for 'Macrohard' in August 2025.
Anduril is acquiring ExoAnalytic Solutions, a space intelligence firm that operates a network of sensors that monitor the movements of satellites above Earth. The acquisition will more than double Anduril's staff dedicated to space projects. Anduril is best known for developing autonomous weapons such as cruise missiles, jet-powered missile interceptors, and underwater drones. The purchase of ExoAnalytic will accelerate Anduril's work on supporting national security space customers.
AI-based tools can now help scientists design original antibodies on computers. Antibodies are among the most versatile tools in biology. Before AI, scientists searching for a useful antibody would have to screen billions of candidates in laboratory assays to identify just a handful with a high affinity for a target. Now, suitable binders can be found after just tens of attempts rather than billions. This post shows readers the modern computational process for designing antibodies from scratch.
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TLDR reaches engineers and technical decision makers across 13 newsletters. Already running Meta? TLDR fills the gap it can't. See
Thanks to advances in WebAssembly, it is now possible to spin up a web server, a database, and a full WordPress installation within a browser in about 30 seconds. WordPress has now launched a site that does exactly that. Users can launch it to build sites and cross-publish apps to the web, desktop, and iOS. WordPress plans to add features like peer-to-peer sync, version control integration, and cloud publishing.
The JavaScript Temporal object, a full replacement for the Date object, enables date and time management in various scenarios, such as time zone and calendar representation, wall-clock time conversions, arithmetics, and formatting. It has an intricate and powerful API that exposes over 200 utility methods via several classes. Implementing Temporal was a challenge as it was a very large proposal that brought more challenges to JavaScript than any other proposal in the programming language's history. This post takes readers through the journey of how Temporal came to be.
Individual AI is the vector by which most of the world's businesses will first experience the transformative magic of AI. However, productive individuals do not make productive firms. There is a gaping need for institutional intelligence that creates coordination, finds signal, creates objectivity, optimizes for edge, and scales revenue. Organizations need an institutional AI purpose-built for domain-specific problems that individual AIs can leverage as a tool.
Companies are hiring again, but at a lower rate than before. AI is helping teams ship with smaller headcounts. Most organizations aren't laying workers off or restructuring, just hiring less than planned. AI is taking jobs, just invisibly, one unposted job at a time.
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Lightfield is the AI-native CRM that updates itself after every meeting, tells you what to do next, and handles sales admin for you. Get 3 months free [14] with code TLDRTQL13
Nvidia has been pitching NemoClaw to various corporate partners ahead of its annual developer conference next week.
For the vast majority of inference calls, the model is interchangeable, and customers buy on price.
Meta's new chips are part of an effort to diversify its hardware sources, reduce reliance on outside chipmakers, and bring down costs.
Generative models are useful when the relative cost of encoding the work in a prompt and verifying the output is low, and the process used to complete the work doesn't matter.
Token economics break product feedback loops by introducing a competing optimization target, leading teams to stop asking what developers need and focus on supporting the token narrative.
Developers take pride in the code they write, but builders take pride in what gets built, even if it was built through commanding a fleet of agents.
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25 years after the Agile Manifesto, Thoughtworks and Martin Fowler gathered a small group of technologists in the mountains of Utah. They confronted the new inflection point: AI-native software development, and the hard questions [4] it raises: