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It actually even removes code. submitted by /u/Kal1boy [link] [留言]

Meta denies using AI to terminate workers with disabilities and medical problems.

Patreon sends billions of notifications each year. As our largest creator audiences grew, a legacy task responsible for generating millions of recipient-specific notifications began consistently timing out. This post explains how our team introduced a two-stage fanout architecture, isolated email, push and in-app processing, improved observability, and migrated more than 200 notification types across a 13-year-old codebase. submitted by /u/patreon-eng [link] [留言]

Anthropic has consistently attempted to depict itself as the ethical foil to other AI companies. This latest marketing stunt — which leans into criticism of AI as a way to make Anthropic seem aware of the responsibility it carries — would appear to be more of the same.

submitted by /u/davidalayachew [link] [留言]

SpaceXAI's Grok Build AI coding tool was spotted uploading users' entire codebases to Google Cloud before it was reported, and the company turned it off. The Register reports that Cereblab published findings on Monday showing how the Grok Build CLI was packaging and uploading entire code repositories, "including files it was told not to open […]

Microsoft Corp. today released software updates to plug at least 570 security holes in its Windows operating systems and other software, almost triple the number of vulnerabilities the software giant fixed in its record-smashing Patch Tuesday release last month. Microsoft attributed the burgeoning patch counts to vulnerability discoveries aided by artificial intelligence.

Because it's 2026, Samsung's new 990 Pro SSD is both more expensive and slower than the original.

TL;DR: Graph renderings of blockchain transactions carry topology signals that serialize badly into token sequences. A hub node surrounded by 47 short-lived leaf wallets looks like a table of addresses and amounts in text form — recognizable only if you already know the pattern. 📖 Reading time: ~23 min What's in this article The Problem: Blockchain Forensics Needs Vision, Not Just Text Hardware and Environment Setup on MI300X Data Pipeline: Rendering Blockchain Graphs as Training Images Fine-Tuning Loop: LoRA on 7B vs Full-Parameter on 7B ROCm-Specific Failure Modes and How to Diagnose Them Inference Serving: vLLM on ROCm for Classification Throughput Verdict: When This Setup Makes Sense and When It Doesn't The Problem: Blockchain Forensics Needs Vision, Not Just Text Graph renderings of blockchain transactions carry topology signals that serialize badly into token sequences. A hub node surrounded by 47 short-lived leaf wallets looks like a table of addresses and amounts in text form — recognizable only if you already know the pattern. Rendered as an image, that star topology is immediately visible as a structural shape. The same applies to layering patterns in mixing operations, where funds move through sequential depth levels that form visually distinct bands, and to clustering signatures where tightly-coupled address groups show dense internal edges versus sparse external ones. A vision-language model can learn to classify on those shapes directly. A text-based LLM working from a transaction list has to reconstruct the topology from raw numbers, which is possible but brittle — edge count and clustering coefficient can be computed and injected as tokens, but that's you doing the feature engineering that the vision model can learn to do itself. The reason Qwen2-VL entered this experiment rather than a GNN is mostly practical. Graph neural networks are the academically correct tool for graph classification, but they require a fixed-schema graph dataset and a trainin

The Quest Begins (The "Why") I still remember the first time I stared at a LeetCode problem that asked me to count the number of islands in a grid. My initial instinct? Run a BFS/DFS from every unvisited land cell, mark everything reachable, and repeat. It worked, but each query felt like I was re‑exploring the same territory over and over again—like walking the same hallway in a dungeon every time I wanted to open a new door. Then a friend tossed me another problem: “Given a list of friendships, tell me if two people are in the same social circle.” Again, the naive solution was to rebuild the whole graph for every query. I felt like I was stuck in a grind‑fest, repeating the same low‑level work while the real challenge—understanding the structure of the connections—remain. That frustration sparked a question: Is there a way to remember what we’ve already discovered about connectivity, so future queries are instant? The answer, as many of you have guessed, lives in a humble but mighty data structure called Union‑Find (also known as Disjoint Set Union, DSU). The Revelation (The Insight) At its core, Union‑Find is about two simple ideas : Each element starts in its own set – think of every person as a lone adventurer. When we learn that two elements belong together, we merge their sets – we call that a union . The magic isn’t just in merging; it’s in how we find the representative (or “root”) of a set later on. If we naïvely walked up a chain of parents every time, we could end up with O(n) per find—still a grind. Two optimizations turn this into near‑constant time: Union by rank (or size) – always attach the smaller tree under the root of the larger one. This keeps the overall tree shallow, guaranteeing that the height never exceeds log n. Path compression – during a find operation, we make every node we pass point directly to the root. It’s like handing every traveler a map that instantly shows the shortest route to the campfire, so next time they don’t need to trek

The company said the "rumors are completely false" after its stock sank more than 50% on a report that it was weighing the option.

RocheDB v0.5.0 has been released. Release: github.com/puffball1567/rochedb/releases/tag/v0.5.0 RocheDB is an open-source NoSQL document and vector store written in Nim. The project is built around one idea: Data locality should be part of the database model, not only an accidental result of indexes, caches, or application code. A lot of database performance discussions start with indexes, query syntax, or caches. Those are important. But layout often decides whether a system is working with the hardware or asking it to fight back. RocheDB v0.5.0 is a step toward making locality explicit at three levels: logical placement with rings; related-data retrieval with stellar locality; physical layout visibility with WAL locality reporting. Ring locality RocheDB uses a ring as a semantic and structural placement unit. An application, import rule, or operator chooses a ring when writing data: users/123/profile users/123/orders shops/1123/orders docs/japan/support tenant/acme/orders/2026 That ring is not only a directory-like label. It is a coordinate in the retrieval space. When a request already knows its natural locality, RocheDB can open that local region first instead of scanning unrelated records and filtering later. roche put --ring = docs/japan --payload = '{"title":"Refund guide","status":"draft"}' --codec = json roche get --ring = docs/japan --filter = '{"status":"draft"}' --selection = '{ title }' The important part is not the string syntax. The important part is that placement and retrieval scope are connected. Short version: a ring is both where data is placed and where retrieval can start. Not only point reads Point reads are important, but many real applications need related data. For example, a user page may need profile data, orders, billing information, and support metadata. In a relational database, that often becomes joins. In a document database, it often becomes manual denormalization or multiple application-side reads. RocheDB v0.5.0 adds a locality mec

Somebody on Reddit posted about 997s that just say AK5*R*5 — one or more segments in error — no AK3 , no AK4 . Preach. That's the problem this free doohickey* is for: rejectdecoder.com *If you'd prefer a "gizmo", I can make that happen. What it does Paste the rejection (997, 999, 824, TA1) plus the original bounced document. It parses both locally in your browser and cross-audits them: control number agreement segment counts envelope consistency code validity required segments It then quotes the exact segment byte-for-byte and ranks the likely causes for anything it finds. If it finds nothing, it says the answer isn't in the docs and tells you to escalate to your partner with your control numbers — which beats pulling a diagnosis out of my... AIs. Where the AI does (and doesn't) fit I know how and appreciate WHY "AI-powered EDI" is sneered at. So the audits here are deterministic parser code, not a model. The AI only writes the plain-English narration of facts the parser already verified, every card says so, and if the narration fails you still get the full audit results. No hallucinations or guesswork. Privacy Parsing runs entirely in-browser (the real Python parser, compiled to WebAssembly via Pyodide) and even works with the WiFi off. If you use narration, only a masked summary you preview first ever leaves the page. Don't take my word for it — check your network tab. Free. No signup for the examples or the deterministic audits; narration is a handful of decodes a month with just an email. Built it solo from an in-house tool of mine, so it's young AND kinda old. Please tell me where it's wrong. Walmart's rejection quirks are encoded so far. Whose partner nonsense should be next...? -jjg

Hachette, Cengage, Elsevier, and other publishers allege that Google trained its AI on copyrighted works without the necessary permissions.

Your Roku can do more than stream online content. Let's get you prepared for the next internet outage.

Most AI incident diagnosis tools will happily produce a root cause even when the evidence is weak. Argus takes a different approach. When an anomaly fires, Argus runs five independent diagnoses against the same incident window. If they converge on the same root cause, it returns a confident diagnosis. If they don't, it returns novel instead of pretending it knows the answer. It's a single Go binary. The first version had Kafka, microservices, and two databases. It looked impressive on paper, but nobody would actually run it. I tore it down into a single process and replaced Kafka with an in-process event bus. Run it with docker run, bring your own Anthropic API key, and your telemetry never leaves the box. It ingests OTLP or Prometheus remote_write; point your telemetry to a single endpoint. I've validated it on synthetic cases, reconstructed real postmortems (Cloudflare 2019/2022), and my own distributed system. It hasn't yet been tested against messy real-world production telemetry, which is exactly the kind of feedback I'm looking for. GitHub: https://github.com/k1ngalph0x/argus I'd genuinely appreciate people trying it out and telling me where the design falls apart, what feels over-engineered, or what you'd change.

On 23 June 2026, AWS shipped Lambda MicroVMs : isolated VMs you launch, suspend, resume and terminate through an API, built explicitly for "workloads that execute user- or AI-generated code." Up to 16 vCPUs, 32 GB of memory, 8 hours of runtime, a dedicated HTTPS endpoint per VM. We've been shipping that product for a while. So has E2B, so has Modal. The interesting thing about the launch isn't that AWS caught up - it's that the biggest infrastructure company in the world looked at agent sandboxes, agreed with the design, and then shipped it with one European region and a price roughly three times ours per vCPU . That's the whole post. If you want an AWS Lambda MicroVMs alternative, "can anyone else do this" isn't the question - the isolation technology is literally the same on both sides. The questions are who operates the machine, where in Europe you can put it, and what it costs to leave running. AWS wins some of these outright, and we'll say where. The short verdict Pick Lambda MicroVMs if you're already deep in AWS, need more than 4 vCPUs or 8 GB in a single sandbox, or your agent has to reach private resources inside your VPC. The IAM integration and the size of the fleet are real advantages. Pick orkestr sandboxes if you're an EU company that wants execution, snapshots and logs inside one EU legal entity, you want to pay for CPU you actually burned rather than CPU you reserved, and your sandboxes are small and numerous rather than huge. The same primitive Both products run on Firecracker. AWS says so on the docs page - "Lambda MicroVMs deliver these core capabilities through Firecracker virtualization" - and so do we. Each sandbox is a hardware-isolated VM with its own kernel and rootfs, not a container sharing the host kernel. That distinction matters exactly when an LLM is writing shell commands you haven't read yet. The lifecycle is the same too. Create, exec, read and write files, pause, resume, terminate. Here's ours: from orkestr import Sandbox with Sand

When I first heard the term backtracking , it sounded like a complicated algorithm reserved for computer scientists. After spending the last couple of weeks learning it and implementing it in a Tetris Optimizer project, I realized something surprising: Backtracking is simply the art of making a decision, checking whether it works, and if it doesn't, undoing it and trying something else. This article explains backtracking using a practical project instead of abstract examples. The Problem Imagine you have several Tetris pieces (tetrominoes), and your goal is to fit all of them into the smallest possible square . It might look something like this: A A A A B B B B C C C C D D D D The challenge is to arrange every piece so that: No pieces overlap. No piece extends outside the board. Every piece is used exactly once. The board is as small as possible. This is much harder than it looks. My First Thought Initially, I thought I could simply place one piece after another. Place A Place B Place C Place D Done! Unfortunately, programming isn't always that kind. Sometimes the first position you choose for piece A makes it impossible to place D later. The mistake wasn't with D . The mistake happened much earlier. Enter Backtracking Backtracking works like this: Place a piece. Try placing the next one. If you get stuck... Remove the last piece. Try a different position. Repeat until every piece fits. It's essentially saying: "If this path doesn't work, let's go back and explore another one." Visualizing the Search Suppose we have four tetrominoes. Start ├── Put A at (0,0) │ ├── Put B │ │ ├── Put C │ │ │ ├── D fits ✅ │ │ │ └── D fails ❌ │ │ └── Try another position │ └── Move A elsewhere └── Try another position for A Every branch represents another possibility. Backtracking explores these branches until it finds one that works. How It Looks in Go The heart of the algorithm is surprisingly small. func solve ( index int ) bool { if index == len ( pieces ) { return true } for every

US military’s drone boats struck an Iranian naval port as war heats up again.

DeepMind CEO Demis Hassabis is proposing an AI "standards body" modeled after FINRA, to test frontier models and develop best practices for their release.