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During negotiations on Wednesday, employees voiced frustrations with what they consider an unwillingness among executives to engage meaningfully with the prospect of unionization.

Cloudflare details Town Lake, an internal unified data platform, and Skipper, an AI analytics agent unifying access to operational, billing, security, and business data. The platform processed ~91K billing queries, with billing forming majority usage. Built on a lakehouse architecture using Trino, Iceberg, R2, and DataHub, it enables governed cross-system analytics and natural language access. By Leela Kumili

GitHub热门项目 | Lakehouse native graph engine with git-style workflows | Stars: 524 | 47 stars today | 语言: Rust

Introduction As data volumes continue to grow, running aggregation queries directly on raw datasets becomes increasingly expensive. Business dashboards, analytics platforms, and reporting systems often execute the same calculations repeatedly—such as total sales, daily active users, page views, or revenue trends. While ClickHouse® is designed to process analytical workloads at remarkable speed, repeatedly scanning billions of records still consumes valuable CPU, memory, and storage resources. This is where AggregatingMergeTree proves its value. Rather than calculating aggregates every time a query is executed, AggregatingMergeTree stores intermediate aggregation states that are merged automatically in the background. This approach allows analytical queries to read compact, pre-aggregated datasets, resulting in dramatically faster response times and reduced infrastructure costs. In this guide, you'll learn how AggregatingMergeTree works, why aggregate states matter, how to build an automated aggregation pipeline using Materialized Views, and when this engine is the right choice for your ClickHouse® workloads. What is AggregatingMergeTree? AggregatingMergeTree is a specialized ClickHouse® table engine designed to store aggregate function states instead of raw records. Unlike the standard MergeTree engine, which stores every inserted row, AggregatingMergeTree keeps partially aggregated values that ClickHouse combines during background merge operations. This significantly reduces the amount of data that must be processed when generating analytical reports. Because much of the computational work happens during data ingestion, dashboards and reporting applications can retrieve summarized information much more efficiently. Typical scenarios include: Sales reporting Website traffic analytics Financial summaries IoT sensor monitoring Business KPI dashboards Application observability metrics Why Use AggregatingMergeTree? Imagine an online marketplace processing millions of tr

Are you a football fan? Since the FIFA hype is at its absolute peak at this moment, it is hard to...

There is a ghost haunting the tech industry right now, and nobody wants to talk about it: The Junior Developer role is disappearing. With tools like GitHub Copilot, ChatGPT, and advanced coding agents becoming standard issue in every IDE, senior developers are suddenly 10x more productive. They no longer need a junior developer to write boilerplate code, write unit tests, or scaffold out basic UI components. The AI does it instantly. So, if you are a junior developer, or aspiring to break into tech, how do you survive? 1. Stop Memorizing Syntax, Start Thinking Architecturally AI is incredible at writing syntax, but it is terrible at system design. If your only skill is writing a for loop in React, you are competing with an AI that works for $20/month. Instead, focus on understanding how systems fit together. Learn about cloud architecture, database indexing, and distributed systems. The AI can write the function, but you have to know where that function lives and how it scales. 2. Become a "Domain Expert" Developer AI doesn't understand the nuanced business logic of the healthcare industry, or the strict compliance regulations of fintech. If you combine coding skills with deep industry knowledge, you become irreplaceable. 3. Embrace the Tools (Be the Orchestrator) Don't fight the AI. Master it. The developers who thrive in the next decade will be the ones who treat AI agents like a team of junior developers reporting to them. Learn how to craft the perfect prompts, how to use Retrieval-Augmented Generation (RAG), and how to orchestrate multiple LLMs to build complex applications. The barrier to entry for writing code has dropped to zero. But the barrier to entry for building valuable software remains exactly the same. Are you terrified of AI replacing you, or are you using it to level up?

TL;DR Welcome back to Dev Opportunity Radar. This is a weekly series where I share opportunities,...

Google has released A2UI v0.9, a framework-agnostic standard for AI agents to declare user interface intent across multiple platforms without arbitrary code. The update emphasizes alignment with existing design systems. It includes a new SDK for Python, improved error handling, and various transport methods. Migration guidance and evolution specifications are also provided. By Daniel Curtis

Over the past year, AI has become part of many developers' daily workflow. It can generate code, explain unfamiliar frameworks, review pull requests, and even suggest architectural patterns. But I've noticed that the biggest impact isn't on writing code faster. It's on how we think about software architecture. With AI handling repetitive implementation tasks, it feels like architects and senior engineers are spending more time on system design, scalability, security, integrations, and long-term maintainability rather than syntax and boilerplate. At the same time, AI-generated code isn't always production-ready. It still requires strong engineering judgment, careful reviews, and a solid understanding of the underlying architecture. I'm curious how other developers are experiencing this shift. Has AI changed the way you design software systems? Do you trust AI when making architectural decisions? Which parts of software architecture do you think should always remain human-led? Have AI tools improved your team's productivity, or introduced new challenges? I'd love to hear real-world experiences, lessons learned, and different perspectives from the community.

The trust problem nobody scopes correctly When companies talk about trust in AI, they almost always mean trust in the model. Is the output accurate? Is it hallucinating? Can we rely on what it says? Those are valid questions but they're the wrong starting point. The trust that actually determines whether AI gets adopted or quietly abandoned inside an organization isn't about the model. It's about the system surrounding it. The four questions that determine Every team evaluating AI in a production workflow eventually runs into the same four questions. Not about model quality. About operational control. Can we understand the outputs? Not just "does the answer look right" but can someone on the team explain why this output was produced and whether it's appropriate for this specific context. An AI that generates correct-looking code or recommendations that nobody can verify is a system that runs on hope. Hope doesn't survive the first incident. Can we validate the decisions? When the AI recommends an action or generates an output that feeds into a business process, is there a way to check it against the actual requirement? Or does the team just trust the output because questioning it is harder than accepting it? The second one is more common than anyone admits. Can we intervene when needed? When something goes wrong, how fast can a human step in? Is there a kill switch? Is there a fallback path? Or does the AI output flow directly into downstream systems with no circuit breaker? The teams that skip this question are the ones that discover the answer during an incident. Can we trace what happened afterward? When an AI-generated decision produces a bad outcome, can you reconstruct the chain? What input went in, what output came out, what context was available, what wasn't? Without traceability, post-mortems hit a dead end, and the same failure happens again. Why opaque systems don't survive real operations There's a tempting argument that opacity is fine as long as the sy

A government customer of NSO Group used the company's Pegasus spyware to hack into the phone of a European politician, who at the time was serving on an EU committee tasked with investigating the spyware industry.

When I started learning iOS development, I thought the hardest part would be writing Swift code. I...

You need to hand a dataset of Chilean RUTs to an outside analytics team. They will join it against other tables by identifier, run the cohort analysis, and hand back a model. They do not need to know, and should never learn, who any of these people are. Asterisk the RUT column and the join dies on contact: **********-K matches every other asterisked RUT in the file. Not almost every one. Every one. You need the same input to reappear as the same output, shaped like a real, check-digit-valid identifier the rest of your schema still recognizes, and eight weeks later, when a fraud investigator needs the original RUT back for one row, you need to be able to give it to them. Irreversible masking cannot do any of this. Hashing gets you consistency but not the format, and never the value back. What you need is format-preserving encryption: run a digit string through a cipher and get out another digit string, same length, same shape, that decrypts to the original under the key you hold. Nothing else. What FPE actually does MaskOps exposes this as mask_pii_fpe . It masks digit-based PII, cards, phones, RUT, CPF, Argentine DNI, in place, and gives back something the same length and shape: import maskops import secrets key = secrets . token_bytes ( 32 ) # AES-256, client holds this tweak = secrets . token_bytes ( 7 ) # per-column/per-dataset context df . with_columns ( maskops . mask_pii_fpe ( " rut_column " , key , tweak )) 76.354.771-K becomes some other RUT-shaped, check-digit-valid string of the same length, under this key and tweak. Run it back through with the same key and tweak and it decrypts. Non-digit PII, IBAN, VAT, email, IP, EU national IDs, gets none of this. It always asterisks. There is no clean digit domain to encrypt into, so MaskOps does not pretend there is. The key never touches MaskOps' output. The client generates it, holds it, and passes it in at call time, and because MaskOps makes no network call and keeps no storage layer, there is nowhere for that k

Memory allocation is not a feature — it is a security liability. In high-assurance Trusted Execution Environments (TEEs), you cannot afford the jitter or the fragmentation of a probabilistic global heap. When building the sakshi-core attestation loop for the Sovereign Spine architecture, the requirement was absolute: determinism. Standard heap allocation introduces non-deterministic paths, memory fragmentation, and significantly increases the complexity of the Trusted Computing Base (TCB). For our enclave, that is unacceptable. The Problem: Why GlobalAlloc Fails the TEE Test In a standard Rust environment, we lean on the global allocator. In a TEE, however, the global allocator is a massive attack surface. Jitter: Allocation time varies based on heap state, leaking metadata through timing side-channels. Fragmentation: Heap fragmentation can lead to unpredictable exhaustion, a vector for Denial of Service (DoS) within the enclave. TCB Bloat: The allocator logic itself adds thousands of lines of code to your audit surface. The Solution: Session-Scoped Bump Buffer To enforce architectural certainty, I stripped away the dependency on standard heap allocation in the enclave. Instead, I implemented a session-scoped bump buffer . This is a contract-based memory model: Constant-time execution: Allocation is a pointer increment operation, taking 1-2 CPU cycles. Zero-fragmentation: Memory is allocated linearly and cleared atomically at the session boundary. Simplified TCB: By removing GlobalAlloc , the enclave memory logic is reduced to a handful of lines of verifiable code. Implementation Concept The core logic relies on a pre-allocated static region. We do not ask the system for memory; we own a dedicated slab of silicon-backed memory and manage it strictly within the request lifecycle. // Conceptual implementation of the session-scoped buffer pub struct BumpBuffer { buffer : & 'static mut [ u8 ], offset : usize , } impl BumpBuffer { pub fn alloc ( & mut self , size : usize

If you're going to apply for Startup Battlefield Australia, now is the time. Applications close July 6, and once the deadline passes, the opportunity is gone.

One of Kalshi’s most prominent traders tells WIRED he’s swearing off Spotify-related markets until the issue is resolved.

We have all been riding the massive vibe coding wave lately. It feels like pure magic to sit back, tell an AI assistant what to build, and watch a full application appear out of thin air. But if you have ever tried to take that exact same web workflow and deploy a smooth, native app onto an iPhone or Android, you know exactly where the frustration sets in. Are you a vibecoder who loves to build applications and you have built many websites? You have built and deployed many websites. Now you really want to make a mobile application that could disrupt the market and go really viral. Have you heard of FlutterFlow ? Have you tried using it? If the answer is no, then I will tell you about FlutterFlow and then you can decide whether you want to check it out and vibe code mobile applications. I will share the app that I created as well. What is FlutterFlow anyway? Have you ever tried building mobile applications and heard of Flutter and Dart? If you haven't, you should definitely check them out. When I was in college looking for a path to choose whether to pursue app development or web development. I explored both options. While exploring app development, I used and built applications using Flutter, an open-source framework created by Google, which uses a programming language called Dart. While Flutter itself is built by Google, FlutterFlow is an independent, visual low-code platform founded by ex-Google engineers. Today, many of us are familiar with AI vibe-coding tools like Cursor and Claude, which allow us to generate code for websites using conversational prompts. FlutterFlow, however, operates differently than vibe-coding: instead of writing code through chat prompts, it provides a visual, drag-and-drop canvas where you can build and design native mobile applications visually while it automatically generates clean Flutter code in the background. I recently had the opportunity to attend a workshop held by the FlutterFlow team and there, I was blown away by the magic of

Cursor hopes to continue offering third-party AI models after it's acquired by SpaceX, testing the relationships between frontier AI labs.

GitHub had 20,000+ secret scanning alerts across 15,000 repositories. Here's how we separated signal from noise, built remediation workflows, and reached inbox zero in nine months. The post How GitHub used secret scanning to reach inbox zero appeared first on The GitHub Blog .

by Peter Yang, Behind the Craft Today, I want to share 18 hot takes on where I think the AI market is headed. AI is in a weird place right now. The government is restricting access to frontier models, enterprises are becoming conscious of token costs, and everyone’s trying to rebuild their product for agents first instead of humans. I’ve interviewed dozens of AI leaders and spent far too much time following these topics on X/Twitter. Here are 18 hot takes on where I think AI is headed next: The frontier-only AI stack is collapsing The AI super app era is here Traditional software risks becoming a dumb pipe for agents Cloud agents and collaboration are the next wave The Frontier-Only AI Stack Is Collapsing Tokenmaxxing at frontier API prices makes no sense. Uber burned through its entire 2026 AI budget in 4 months, Microsoft moved engineers off Claude Code due to cost, and companies are realizing that running everything on frontier models can get expensive fast. Tokenmaxxing makes sense when you’re on a subsidized $200/month plan but is unsustainable at API rates. Companies will rely on a portfolio of models. Coinbase recently cut its AI spend nearly in half by switching engineers to Chinese open-source models like GLM and Kimi. Airbnb and Pinterest have done the same with Alibaba’s Qwen models. I believe that this will be the default path forward — using frontier for high-stakes work and cheaper models for everything else. China’s open-source strategy is working. Chinese models are taking market share from frontier models at US companies. China is also building the full AI stack — from energy (e.g., solar, nuclear) to data centers to domestic chips. The Chinese government is planning a $295B investment in AI data centers with at least 80% of the chips built domestically. Frontier labs are in a catch-22 situation. If they release great open-source models, they might undercut their own frontier API revenue. If they gate the best models behind a trusted list, companies