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AI cheating leads to "a failed society," professor says.

I run a local LLM agent (Hermes) on my own machine. The problem was never the model — it was the interface . I had a Telegram tab open all day just to talk to it: type a command, wait, read a wall of text back, scroll. It felt like texting a very capable stranger. So I built Ghost Vessel — a monitor-resident, video-call-style avatar that fronts the agent. The name is the whole idea: the ghost is your agent, the vessel is the body it borrows. It's not a waifu toy; it's a real agent client that happens to have a face. Here's what actually turned out to be interesting to build. The reply is a script, not a string The core idea is an output contract . Instead of treating the agent's reply as text to print, I split every reply into three planes: dialogue → spoken via local TTS data → code, logs, files → rendered as chat cards, never read aloud action → emotion beats that drive the avatar Emotion beats are inline tags the model emits in-band with its answer: [working] — the avatar puts on glasses and takes notes while a task runs [confirm] deploy to prod? — pops a human-in-the-loop approve/cancel, and the agent blocks on your keypress [happy] / [concerned] / … — fine-grained facial expressions So "run the build, and if it passes, deploy" becomes a little performance : it looks busy while working, shows you the log as a card, then leans in and asks before the irreversible step. The text you'd have skim-read becomes something you glance at. No runtime GPU for the avatar The obvious way to animate a face is live inference. I didn't want that — the GPU is busy running the actual model. Instead the avatar is ~30 pre-rendered clips , and the emotion beats just select and blend between them (blink-aligned seamless idle loops, a head-pose "settle gate" so an expression only reveals when the head is frontal). The avatar's runtime cost is basically video playback. Your GPU stays 100% on your LLM. The tradeoff: no real-time lip-sync. I decided a believable talking mouth loop + expre

OpenBSD Privilege Escalation, GitHub AI Agent Leaks, & CDN Supply Chain Risks Today's Highlights This week's top security news features a critical use-after-free vulnerability in OpenBSD, a novel prompt injection attack leading to private repo leaks from GitHub's AI agent, and an unusual case of obfuscated bash scripts delivered via a CDN on consumer products. OpenBSD has a use-after-free allowing local privilege escalation to root (Hacker News) Source: https://nvd.nist.gov/vuln/detail/cve-2026-57589 A newly disclosed vulnerability, CVE-2026-57589, impacts OpenBSD, a renowned security-focused operating system. The vulnerability is identified as a use-after-free (UAF) flaw, which typically occurs when a program attempts to use memory after it has been freed, often leading to crashes or arbitrary code execution. In this specific case, the UAF bug allows for local privilege escalation to root. This type of vulnerability is particularly critical for operating systems, as it can enable an unprivileged attacker with local access to gain complete control over the system. System administrators and users of OpenBSD are advised to monitor official channels for patches and apply them immediately to mitigate the risk of compromise. Understanding the underlying cause of such UAFs is crucial for developing more robust memory management practices and identifying similar vulnerabilities in other systems. Comment: This is a critical reminder for OpenBSD admins to patch immediately, as use-after-free exploits are a classic, dangerous route to full system compromise from local access. GitLost: We Tricked GitHub's AI Agent into Leaking Private Repos (Hacker News) Source: https://noma.security/blog/gitlost-how-we-tricked-githubs-ai-agent-into-leaking-private-repos/ Researchers have uncovered a significant AI-specific security vulnerability, dubbed 'GitLost,' demonstrating how GitHub's AI agent can be manipulated to leak sensitive information from private repositories. The attack leverag

The opinion says whether it's fair is 'for our citizenry to decide at the ballot box.'

If you've ever written test data by hand, you know the ritual: a PersonBuilder , an OrderBuilder , an AddressBuilder … one hand-written builder per class, each one a wall of WithX(...) methods you have to maintain forever. The Test Data Builder and Object Mother patterns are great — the boilerplate is not. XModelBuilder gives you a fluent builder for any C# class out of the box. No per-class builder required. It handles constructor parameters, init-only properties, read-only members, even private backing fields — via reflection, deterministically. Install dotnet add package XModelBuilder 30-second example You can use it fully standalone (no DI container) through a small static facade: using XModelBuilder.Default ; var order = For . Model < Order >() . With ( x => x . OrderDate , new DateTime ( 2026 , 7 , 1 )) . With ( x => x . Lines [ 0 ]. Product , "Widget" ) // deep paths + indexers just work . With ( x => x . Lines [ 0 ]. Quantity , 3 ) . Build (); No OrderBuilder , no OrderLineBuilder . The Lines[0].Product path drills into a nested collection element and sets it for you. Need a whole list? Create.Models<Order>(10) . Deterministic fakers, seeded once Random test data that changes every run is a debugging nightmare. XModelBuilder ships a seeded, dependency-free faker (and a Bogus integration if you prefer). Register it once: services . AddXModelBuilder () . AddXFaker ( seed : 12345 ); // reproducible values, every run Then let it fill in the noise while you set only what your test actually cares about: var order = xprovider . For < Order >() . With ( x => x . Id , p => p . XFake (). NewGuid ()) . With ( x => x . Customer . Name , p => p . Bogus (). Company . CompanyName ()) . With ( x => x . Lines [ 0 ]. Quantity , 3 ) . Build (); XFake().NewGuid("customer-acme") even gives you a stable GUID from a name — same key, same GUID, regardless of call order or parallelism. Deterministic by design. Build a whole list: BuildMany Need ten of something, each slightly differ

This is the second stage of my CodeAlpha Full Stack internship — two projects, built in a deliberate order so the patterns from the first carry forward. First was a project management tool (auth + real-time updates with Socket.io). This one is a store: products, cart, orders. Same stack — Express, Prisma, PostgreSQL, JWT — but the interesting part isn't the CRUD, it's the order-placement flow, which is the first genuinely transactional piece of logic in the whole internship. I'll walk through the schema decisions, the auth changes from project one, and then spend most of the time on the part that actually matters: making sure an order can never be created without correctly and atomically updating stock and clearing the cart. The schema model User { id String @id @default(cuid()) name String email String @unique password String role String @default("USER") createdAt DateTime @default(now()) orders Order[] cartItems CartItem[] } model Product { id String @id @default(cuid()) name String description String price Float image String? stock Int @default(0) category String createdAt DateTime @default(now()) cartItems CartItem[] orderItems OrderItem[] } model CartItem { id String @id @default(cuid()) quantity Int @default(1) user User @relation(fields: [userId], references: [id]) userId String product Product @relation(fields: [productId], references: [id]) productId String @@unique([userId, productId]) } model Order { id String @id @default(cuid()) status String @default("PENDING") total Float createdAt DateTime @default(now()) user User @relation(fields: [userId], references: [id]) userId String items OrderItem[] } model OrderItem { id String @id @default(cuid()) quantity Int price Float order Order @relation(fields: [orderId], references: [id]) orderId String product Product @relation(fields: [productId], references: [id]) productId String } Two decisions worth explaining, because they're easy to get wrong if you're building this for the first time. OrderItem.price is a

While researching API changes I noticed something — Google Maps removed DirectionsService on May 1 2026 with no soft fallback. Calls just throw runtime errors after the deadline. Most developers won't know until something breaks. So I built DepRadar — paste your package.json, it checks your exact stack against known deprecations and shows only the ones affecting you, with severity, sunset dates, and migration links. Currently tracks 13 real deprecations across: Google Maps (DirectionsService, DistanceMatrixService removed) OpenAI (Realtime API Beta sunset) AWS SDK v2 (maintenance mode) Microsoft Actionable Messages (retired) moment.js, request package And more Free → depradar.netlify.app Open source → github.com/Ahmed889-code/depradar What deprecations am I missing from your stack?

I've been coding with AI agents for about two years. Every major one. Cursor, Copilot, Codex, OpenCode. They're good at generating code. They all share one problem. They forget everything. You finish a session, close the window, and the agent resets. Next time you open it, you're starting from zero. "We use pnpm, not npm." "The database is SQLite, not Postgres." "Don't touch the migrations folder." You repeat yourself. Every. Single. Time. Some tools added memory features. Usually as an afterthought. A pinned file. A custom instruction. A context window that grows until it hits a wall and everything old gets silently dropped. I didn't want a bigger context window. I wanted an agent that accumulates knowledge the way a colleague does. Not by being retrained. By taking notes, writing down what it learned, and reading those notes next time. That's what Jean2 can do. Not through fine-tuning. Not through vector embeddings. Through files on disk that the agent reads and writes itself. But here's the thing: none of this is on by default. By default, Jean2 is as bare as Codex or OpenCode. A blank prompt. No memory. No skills. No session search. You opt in to each layer in workspace settings . That's the point. You build the agent you want, layer by layer. The Four Layers If you turn them on, Jean2's agent has four knowledge layers that persist across sessions. They're not features bolted on top. They're part of the system prompt that gets composed every time a session starts. 1. Workspace Memory Turn on workspace memory in workspace settings , and the workspace gets two files: MEMORY.md for shared knowledge and USER.md for your personal preferences within that workspace. Both live at <workspace>/.jean2/ . The concept is simple. Shared knowledge that's useful for any agent working in that workspace. "We use pnpm." "The database is SQLite." "Don't touch the migrations folder." Whatever agent you bring in, coding specialist, reviewer, docs writer, they all get the same context

Explore how the Aspire team turns merged product changes into SME-reviewed docs pull requests, closing the gap between release and documentation. The post Automating cross-repo documentation with GitHub Agentic Workflows appeared first on The GitHub Blog .

Lionel Messi and Cristiano Ronaldo are betting on AI, health tech, and startups. Mohamed Salah is taking a more traditional route beyond football.