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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

When the enemy is in distress, exploit the opportunity to seize advantage. — The 36 Stratagems, Loot a Burning House Who's Leo — In the last story , he was CoreStack's backend lead — the guy who built the core system alone over five years with zero P0 incidents. Then a new CTO named James showed up, spent $8M on his old employer's product, and laid off Leo's entire team. Thirteen days later, that $8M AI system collapsed — three agents fighting over context, OOM taking down six GPU servers, a 37% order duplication rate, and 2,300 customer complaints. Leo pulled the old system off his laptop, flipped one line of Nginx config, and restored service in thirty seconds. The CEO called him at 3 AM begging him to come back. He came back. Three conditions: kill the paid AI product, AI assists only — never touches the primary pipeline — and engineers decide the architecture, not the guy writing checks. The CEO agreed to all of it. So who's Leo now: CoreStack's CTO. Technically confident to the point of arrogance. Zero talent for upward management. No idea how many people he pissed off on the board with those conditions. Doesn't care. He only knows one thing — the system he built is still running. That's all the proof he needs. Then a Slack message cut him off. The Signal 12:47 AM. CoreStack's CTO gets a Slack notification. The account has no profile picture, no display name, no status. Account creation timestamp at the bottom — 00:43. Four minutes old. Seven characters: Check CodeForge's status page. Leo taps it open. CodeForge's status page is all red. Payment Routing — Major Outage. Investigating. All customers affected. Status has been active for approximately 3 hours. He pulls up CoreStack's CRM. The sales team's prospect list has ShopStream at #2 — a potential whale, with "Current Provider" reading CodeForge. E-commerce platform doing 470,000 transactions a day . An hour of downtime costs them $210,000 . If this drags on until morning? He doesn't want to do the math. Core

Tesla just released its second-quarter delivery and production report, showing that the automaker is starting to recover after a particularly brutal sales year in 2025. The company said that it produced a total of 451,758 vehicles between April and June of this year, including 442,936 Model 3 and Model Y vehicles, as well as 8,822 […]

We’ve got a special Decoder today — I had the chance to talk with Amy Lanzi, the CEO of Digitas North America, in front of a live audience at the Uber Villa at the Cannes Lions advertising festival in the South of France. I know, it’s a hard gig, but I do it for you. […]

GitHub热门项目 | Agentic Coding for Builders who Ship | Stars: 9,908 | 34 stars today | 语言: Rust

GitHub热门项目 | A WiFi security auditing software mainly based on aircrack-ng tools suite | Stars: 677 | 146 stars today | 语言: Rust

GitHub热门项目 | Privacy first, AI meeting assistant with 4x faster Parakeet/Whisper live transcription, speaker diarization, and Ollama summarization built on Rust. 100% local processing. no cloud required. Meetily (Meetly Ai - https://meetily.ai) is the #1 Self-hosted, Open-source Ai meeting note taker for macOS & Windows. | Stars: 13,224 | 132 stars today | 语言: Rust

GitHub热门项目 | Turn code into clarity. Generate accurate technical docs and AI-ready context in minutes—perfectly structured for human teams and intelligent agents. | Stars: 1,289 | 41 stars today | 语言: Rust

Even the most advanced enterprise systems are tethered to a costly paradox: manual bottlenecks that introduce critical errors, security risks, and slow innovation. These hidden operational anchors are the friction preventing your organization from realizing its full potential. The Challenge: Manual Bottlenecks in Modern Enterprise Operations In an era defined by cloud-native architectures, microservices, and declarative infrastructure, a persistent and costly paradox remains at the heart of enterprise operations. We have built systems capable of immense scale and resilience, yet they are often tethered to manual, human-driven processes that act as operational anchors. These bottlenecks aren't just minor inefficiencies; they are critical points of failure, introducing latency, human error, and security vulnerabilities into our most important workflows. They represent the friction that slows down innovation, drains resources, and prevents organizations from realizing the full potential of their digital investments. Before we can orchestrate an autonomous workspace, we must first dissect the anatomy of these manual constraints. Identifying the High Cost of Manual Invoice Reconciliation To ground this challenge in reality, consider a ubiquitous and deceptively complex business process: accounts payable invoice reconciliation. On the surface, it seems simple. In practice, it's a classic example of a high-friction, manual workflow that silently bleeds enterprise resources. The typical process is a gauntlet of context-switching and swivel-chair integration: An invoice arrives, often as a PDF attached to an email, with no standardized format. A finance professional must manually open the document and visually identify key data points: invoice number, date, vendor, line items, and total amount. They then pivot to an ERP system like SAP or NetSuite to find the corresponding Purchase Order (PO). Next, they might need to access a separate logistics or warehouse management syste

I used to waste hours tweaking and re-tweaking my AI model prompts. It was like trying to find a needle in a haystack—I'd make a change, run the code, wait for the results, and then... nothing. The output would be inconsistent, unhelpful, or just plain wrong. I'd try again with tiny modifications, rinse and repeat, until I was about to pull my hair out. It wasn't until I stumbled upon the concept of reusable prompt templates that everything changed. It was like a switch had flipped—my code started producing consistent results, and I finally understood why. No more guesswork, no more frustration. Just good old-fashioned productivity. A simple shift from writing one-off prompt strings to using reusable templates is the key to reducing prompt overhead, increasing consistency, and getting back to doing what we love—building amazing, AI-driven applications. From Chaos to Control: A Simple Example Let's make this tangible. Imagine you're building a feature to generate a short story, but for different characters. Before: The Inconsistent, One-Off Way Without a template, you'd likely write a new prompt each time, introducing small, unintentional differences that lead to wildly different results. Two separate prompts = inconsistent, unpredictable output prompt_for_alex = "Write a short story about a character named Alex who is trying to get to work on time, but keeps getting delayed in a busy city." prompt_for_jordan = "Generate a story about someone named Jordan. They're late for work and stuck in traffic in a big city." See the problem? The tone, wording, and details are different. You have no control over the consistency of the output. After: The Clean, Templated Way Now, let's use a single template. We define the core structure once and simply pass in the parts that change. Now, let's use a single template. We define the core structure once and simply pass in the parts that change. One template = consistent, predictable output story_template = "Write a short story about

I sat down to work for 2 hours. I actually worked for 45 minutes. Sound familiar? You open your...

co-written with UnitBuilds, who built most of this out loud in the comments of my last piece. I...

📚 Series: Databricks on AWS (Part 0, prologue) The Target Architecture ← you are here Building a Databricks AI Platform on AWS RBAC with Function-Role Groups Compute Governance: Pools, Policies, Clusters The BOOTSTRAP_TIMEOUT Mystery Fixing It with AWS PrivateLink How We Structure the Terraform Before the build story, here's the destination. This is the target-state data architecture we designed the whole platform toward — the three principles that shaped every later decision, and the Unity Catalog governance model that keeps production data safe from human hands. The rest of this series is a build log: workspaces, RBAC, compute, the networking rabbit hole, the Terraform layout. But every one of those decisions was made in service of a target picture we drew first . This post is that picture — the "to-be" architecture, not the scaffolding we happened to have up on any given week. It's built on three things Databricks basically hands you if you lean into them: the Lakehouse (one store, ACID tables, no separate warehouse to sync), the Medallion architecture (raw → cleaned → integrated → business, each layer a promotion), and Unity Catalog as the single governance plane across all of it. The interesting part isn't reciting those three buzzwords — it's the specific way we wire them so that prod, dev, and analyst sandboxes never step on each other. Three principles, and everything follows Almost every concrete rule later in this series is a consequence of one of these three. 1. Nobody touches production by hand. Create, update, delete in prod data happens only through an automated, code-reviewed pipeline running as a service principal. Human accounts don't get write on prod — not analysts, not engineers, not admins. The blast radius of a bad afternoon is capped at whatever a person can do with read-only. This one principle is why the whole "promote" flow later exists. 2. Never copy production to look at it. If an analyst wants to explore the gold layer, they read it in p

The co-founders of Bending Spoons, the Italian company quietly buying beloved, ailing Internet brands, learned big lessons from their own startup's failure.