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It's the world's worst kept secret that Nvidia is about to announce its own Arm-powered laptop chips at Computex this weekend, and now Microsoft, Nvidia, and Arm are all openly teasing the announcement. The Windows and Nvidia GeForce accounts on X both posted "A new era of PC" earlier today, and now Arm has followed […]

Peer review now optional, political staff would screen grants for forbidden topics.

Bueno que puedo decir de estos agentes. Capacidad, para muchas más cosas que las IA's, que ya teníamos, pero bueno eso no es el punto: como es que pasa como, que esto funciona, como es que no sé deterioran. Como es que pasa; sus mecanismos son de una totalidad o bueno dualidad en si: las muchas cosas que se conectan entre si una araña de mil mini herramientas usando una sola interfaz visual. En resumen eso es, lo que hace captura piensa reanuda y ejecuta. Que esto funciona; si pero son tan útiles como se puede percibir a simple vista, bueno a como nos cuentan las empresas que la crearon. Como es que no se deterioran; en si lo hacen, pero no como uno piensa. Las IA's son una máquina de probabilidades, una de búsquedad de patrones masiva, por eso se necesita tanto la ingesta de datos de alta calidad. Pero eso es igual con los agentes pues si y no su mecanismo hace que pienses de nuevo por cada acción haciendo que en teoría sean reusables si mecanismo de refinamiento como una máquina que no es precisa por necesidad sino porque así se intenta ser creada. submitted by /u/Silent-Preference216 [link] [留言]

Dads are traditionally tough to shop for—let me help with these handpicked gift ideas for fathers with great taste.

While AI is helping coders produce code faster, it may not be producing better code, researchers warn. And that could cause problems down the road for them.

Gemma , a family of open models, are lightweight, remarkably capable, and have a wonderful "tunability" that makes them perfect for personal projects and enterprise-grade applications alike. But as the ecosystem grew, I found myself asking the same questions over and over: Which exact model size fits my constraint? How do I build an application powered by Gemma that does XYZ? How to deploy a Gemma model to production on Google Cloud for my team to use? To solve this, we put together a living repository called gemma-skills (which we're releasing!). It's a curated, structured collection of developer skills designed to help both humans and agentic AI assistants build beautiful applications with Gemma models without the friction. Let's take a walk through what's inside! The Heart of the Repo: gemma-dev At the center of the repository is our first major skill: gemma-dev . It's a skill file ( SKILL.md ) that serves as a blueprint. It's designed for agents to find what are the latest capabilities, model sizes, good practices, and resources to build with Gemma. Keeping Pace with Rapid Ecosystem Evolution The Gemma ecosystem moves fast, with new models, libraries, and best practices emerging constantly. For developers using foundational LLMs like Gemini, keeping assistant workflows perfectly synced with these rapid releases is a common challenge. Because foundational models are trained on vast, fixed datasets, they don't automatically inherit the day-one nuances of a rapidly evolving framework. This can manifest in a few typical development scenarios: Navigating Version Transitions: General-purpose assistants may default to established standards (like Gemma 2 or 3) even when your project is ready to leverage the latest capabilities of Gemma 4. Aligning with Modern Libraries : Recommendations might occasionally lean toward older API patterns rather than the latest optimized packages. Integrating Next-Gen Features: Cutting-edge implementation details (e.g. Multi-Token Predicti

This is a submission for the Hermes Agent Challenge What I Built Onyx is an autonomous infrastructure operator running 24/7 on my droplet. He manages my entire stack: 6 Next.js deployments, 5 Docker containers, a Minecraft server, fail2ban, Nginx, and UFW. He also helps me write my undergraduate thesis. The difference from every other "AI agent" project I've seen: Onyx doesn't wait for commands. He surfaces problems, patches vulnerabilities, and pushes work forward on his own. When I wake up, there's a session log waiting for me, not a to-do list. The core idea: graduate an AI agent from assistant to operator . A chatbot with tools bolted on doesn't cut it. I wanted something that runs infrastructure while I'm eating dinner, asleep, or in class. Demo Onyx operates through Discord. A normal week: 🔴 3 AM — gateway process failure, no wake-up required A gateway process had a stale PID. Onyx detected it, diagnosed the root cause, restarted it cleanly, and wrote a session log. I found out in the morning. Zero human intervention, zero downtime. 🟡 Dinner — 9 CVEs found across Docker containers While I was eating, Onyx ran a routine audit, found 9 CVEs, rebuilt 3 container images from fresh base images, patched Python dependencies, hardened fail2ban (ban time: 600s to 24 hours), and verified every container came back healthy. 🟢 "Fix it" — two words, full tunneling deployment My friends in Indonesia couldn't connect to the Minecraft server because their ISPs use carrier-grade NAT. I sent Onyx "fix it." He researched solutions, selected playit.gg, installed the tunneling agent, configured a systemd service, and optimized TCP keepalive parameters. All autonomous. 🧠 Accountability loop Onyx noticed I kept asking for things but not acting on the output. He surfaced it: "You keep opening new loops and not closing them." He was right. Now when I open a loop, Onyx tracks it until it's closed or explicitly shelved. 📚 Thesis research partner I'm finishing my undergraduate thesis on e

The Pentagon awarded SpaceX a $4.16 billion contract to build missile-tracking satellites linked with President Donald Trump's planned "Golden Dome" defense system, as reported earlier by Bloomberg. In an announcement on Friday, the US Space Force says the sensor-equipped satellites will allow it to detect and track targets from space. The Elon Musk-owned SpaceX - […]

Genera la tua prima fattura elettronica XML per lo SDI in TypeScript (in 10 minuti) Se hai mai dovuto integrare la fatturazione elettronica italiana in un progetto Node.js, sai già quanto è scomodo: specifiche FatturaPA di 200 pagine, regole cross-field non documentate, codici errore SDI criptici, e librerie npm o abbandonate o in PHP. Questo articolo mostra come generare un XML valido per il Sistema di Interscambio (SDI) usando fattura-elettronica-sdi-builder , una libreria TypeScript open-source che copre B2B (FPR12) e Pubblica Amministrazione (FPA12). Installazione npm install fattura-elettronica-sdi-builder Nessuna dipendenza pesante. La validazione è custom e tipizzata, zero runtime esterni. Il flusso in tre funzioni La libreria espone tre funzioni pubbliche che si usano sempre in sequenza: import { applyDefaults , validate , buildXml } from ' fattura-elettronica-sdi-builder ' ; Funzione Input Output applyDefaults(input) FatturaElettronicaInput (campi deducibili opzionali) FatturaElettronica completa validate(fattura) FatturaElettronica Result<void, ValidationError> buildXml(fattura, options?) FatturaElettronica Result<string, BuildError> Tutte le funzioni restituiscono un Result<T, E> — mai eccezioni non gestite: type Result < T , E > = | { ok : true ; value : T } | { ok : false ; error : E } Esempio completo: fattura B2B con IVA ordinaria Genera una fattura TD01 da una Srl italiana a un cliente italiano, IVA al 22%, pagamento con bonifico. import { applyDefaults , validate , buildXml } from ' fattura-elettronica-sdi-builder ' ; import type { FatturaElettronicaInput } from ' fattura-elettronica-sdi-builder ' ; import { writeFileSync } from ' fs ' ; const input : FatturaElettronicaInput = { FatturaElettronicaHeader : { DatiTrasmissione : { ProgressivoInvio : ' 00001 ' , CodiceDestinatario : ' ABC1234 ' , // 7 caratteri per FPR12 }, CedentePrestatore : { DatiAnagrafici : { IdFiscaleIVA : { IdPaese : ' IT ' , IdCodice : ' 01234567890 ' }, Anagrafica : { Denominaz

If you manage PostgreSQL databases across multiple environments, you've probably done this: SSH to the DB host (or connect via psql ) Run CREATE USER jsmith CONNECTION LIMIT 20 PASSWORD '...' Slack the password to the developer Forget to log it anywhere Repeat for every environment, every onboarding, every access request It's tedious, error-prone, and leaves zero audit trail. Here's a better way. What I Built pg-user-api is a lightweight Flask REST API that wraps PostgreSQL user provisioning in clean HTTP endpoints. You register your databases once in a SQLite inventory, then any tooling — CI pipelines, internal portals, Ansible playbooks, or a plain curl — can create and manage users across environments without ever touching psql . GitHub: pcraavi/PostgreSQL-user-creation-API The Problem It Solves In teams that span dev, QA, UAT, and prod, you end up with different patterns of users: App service accounts — named after the host/port combo ( web01_8080 ) Kubernetes workload accounts — named after env prefix + farm ( dv_gearservice ) Individual dev/QA accounts — low connection limits, scoped to non-prod Read-only analyst accounts — prod only, no DDL DBA accounts — CREATEDB CREATEROLE LOGIN , rarely provisioned Each type has different CONNECTION LIMIT values, privilege levels, and naming conventions. Encoding these patterns in an API means the rules are consistent, repeatable, and auditable. Architecture The project is intentionally small — five Python files and a requirements list: pg_user_api/ ├── app.py # Flask app — all endpoints ├── auth.py # HTTP Basic Auth (constant-time compare) ├── database.py # SQLite registry + audit log ├── notifications.py # Notification stubs (Webex / Slack / Email) ├── seed_db.py # One-time setup: creates DB + sample records └── requirements.txt Two credential pairs, clearly separated: PG_API_USER / PG_API_PASS — who can call this API (your team/tooling) PG_ADMIN_USER / PG_ADMIN_PASS — the PostgreSQL DBA role that executes DDL The DBA cr

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built GlowStore — a full-stack MERN e-commerce store (React + Redux + Express + MongoDB). Back in 2023 I built this as my university Web Engineering final project. I ran out of time, handed in what I had, and never touched it again. When I reopened it for this challenge I found something funny: the backend was basically finished — JWT auth, products, orders, reviews, search — but the React frontend never actually talked to it. It was a good-looking shell with fake logic bolted on. So "finishing it" meant connecting the two halves and making it a real store you can actually shop in. Repo: https://github.com/hashaam-011/Web-Engineering Demo Before — the entire app was just a fake login screen. Typing anything (or nothing) and clicking "Log in" flipped a boolean and "logged you in": After — a working storefront with products from the database: A real product detail page (was literally <h1>DetailsPages</h1> before): You can run it yourself in two terminals (no database setup needed — it boots an in-memory MongoDB and seeds itself): cd backend && npm install && npm start # http://localhost:4000 npm install && npm start # http://localhost:3000 Demo login: user@example.com / 123456 — or register a new account. The Comeback Story Here's what the project looked like before , and what I changed: Before After Login dispatched a boolean and ignored your credentials Real login/register against the API with JWT + bcrypt Frontend never called the backend (no axios anywhere) Axios client with token injection; products load from MongoDB Product details page was <h1>DetailsPages</h1> and wasn't routed Full details page (image, price, stock, rating) routed by slug Cart was local-only; "checkout" button did nothing Persistent cart → checkout → real order placed and saved Backend had a reviews endpoint the UI never used Product reviews: read them and post your own with a star rating Only 3 routes; most of the app was

Daily Learning part twenty-two. I haven't been active in three days due to Eid Al‑Adha. On Tuesday I went to my family house, where we go once in a while. We call it the family house because that's where my grandmother, uncles, aunts, and cousins live. I didn't bring my laptop with me because I wanted to spend some time with my family, which I haven't done in months. I stayed there for the two days of Eid. Today I came back by bus. I was supposed to arrive at 17:00, but due to traffic I arrived at 18:40. When I arrived I ate a small sandwich and got back to work. I started the session at 19:30. The first thing I did was complete the HTML Tables section. It was difficult to learn (at least for me). It covered HTML Tables, Table Borders, Table Sizes, Table Headers, Padding & Spacing, Colspan & Rowspan, Table Styling, Table Colgroup, Exercises, and finally the Code Challenge. Then I did a quiz and the Unit 2 test in Khan Academy and also completed one lesson in Unit 3. Now I have started a Tic‑Tac‑Toe challenge in Python. I watched a video on the minimax algorithm, which the game uses. I have started coding, but I am far from finishing it. I am ending today's session at 23:40. Eid Al‑Adha Mubarak to all Muslims. "Speak good or remain silent." Prophet Muhammed (peace be upon him)

DuckDB 1.5.3 Iceberg updates, PostgreSQL TDE extension & AI index tuning Today's Highlights Today's highlights include DuckDB's enhanced Iceberg integration with new DML and schema evolution features, alongside a deep dive into PostgreSQL's new open-source Transparent Data Encryption. Additionally, we explore AI-driven strategies for automating PostgreSQL index tuning, offering practical performance improvements. New DuckDB-Iceberg Features in v1.5.3 (DuckDB Blog) Source: https://duckdb.org/2026/05/29/new-iceberg-features.html The latest DuckDB v1.5.3 release introduces significant enhancements for working with Apache Iceberg tables, a critical component in modern data lake architectures. Key additions include full MERGE INTO support, allowing users to efficiently update, insert, and delete rows in Iceberg tables based on a source query. This release also brings ALTER TABLE commands for schema evolution, enabling operations like adding, renaming, or dropping columns, crucial for adapting to changing data requirements. Furthermore, DuckDB now supports partition transforms within Iceberg, providing more control over data organization and query optimization. Compatibility has been extended to Iceberg V3, ensuring support for the latest table format specifications, and improved handling for Iceberg REST Catalogs streamlines metadata management. These features position DuckDB as an even more powerful embedded analytical database for processing large-scale, evolving datasets directly in a data lake environment, making complex ETL/ELT operations more accessible and performant. Comment: The MERGE INTO and ALTER TABLE additions are game-changers for using DuckDB in production data pipelines with Iceberg, enabling robust upserts and schema changes. Open-Source TDE for PostgreSQL: What pg_tde Is, and Whether You Need It (Planet PostgreSQL) Source: https://postgr.es/p/9kM This article introduces pg_tde , PostgreSQL's new open-source Transparent Data Encryption (TDE) option, a l

I'm a maxillofacial surgeon in Ouagadougou, Burkina Faso — and a self-taught builder who's been coding since medical school. Over evenings and weekends, I shipped Readium — a production AI reading app that lets you discuss books with Claude while you read them, in any language. Built AI-paired with Claude, reviewed and deployed by me. Most "I shipped an AI app" write-ups cover the happy path: clone a starter, glue an LLM, deploy to Vercel. The walls I hit weren't there. They were in the spaces between the libraries. Here are five of them — and what I'd tell myself a few weeks ago. Wall 1 — SSE streaming broke at the seam between the LLM and the browser I assumed streaming "just worked" once OpenRouter returned a stream. It does — until your server-side handler, your reverse proxy, or your browser code introduces a buffer somewhere along the path. The chain has at least three places where buffering can silently kill streaming: The LLM API (fine on its own) Your Node server-side handler (fine if you forward chunks instead of accumulating them) The reverse proxy / CDN (often buffers entire responses by default) The failure mode is always the same: the UI looks exactly like the LLM is slow. It isn't — somewhere between OpenRouter and the browser, bytes are being withheld until the connection closes, then dumped in one chunk. What I'd tell past-me: streaming isn't a feature of the LLM, it's a property of your entire request path. If you can't watch tokens land character-by-character in curl -N against your origin, you don't have streaming, you have a slow non-stream pretending. Set Cache-Control: no-transform and X-Accel-Buffering: no headers from your handler, disable response buffering on every layer in front of it, and verify with curl -N before you trust the UI. Wall 2 — fetch hangs forever on certain hosts (and the fix isn't where you think) I had a proxy route that fetched from an external API. Worked locally. Worked in staging. Deployed to production: the route wo

You stopped being the loop Most people use Opus 4.8 the way they used every model before it: open a chat, type a request, watch the cursor, correct it, repeat. That's a conversation. A dynamic workflow is something else entirely. The shift is this: you stop being the loop. Instead, an orchestrator — plain code you control — spawns subagents you design, fanning out work in parallel, running steps in sequence, judging and merging results, and reporting back when the whole thing is done. Opus 4.8 can drive hundreds of parallel subagents inside a single workflow, with effort control per node so cheap steps stay cheap and hard steps think harder. In this tutorial you'll learn the core patterns by building one concrete thing: a pull-request reviewer that fans out across correctness, security, and performance, then adversarially verifies every finding before it reaches you. // You design the shape. The orchestrator runs it. const found = await parallel ( DIMENSIONS . map ( d => () => agent ( d . prompt , { schema : FINDINGS }))) const deduped = dedupeByFileLine ( found . flatMap ( r => r . findings )) const verified = await parallel ( deduped . map ( f => () => agent ( refutePrompt ( f ), { schema : VERDICT }))) const real = verified . filter ( v => v . refuted === false ) By the end you'll know when to reach for parallel() versus pipeline() , how structured output schemas keep subagents composable, and where to set effort per node. The mental model: it's a graph, not a prompt Stop thinking "I send a prompt, I get a completion." Start thinking: an orchestrator runs a workflow graph, and each node is an agent call. The orchestrator is plain code. It decides what runs, in what order, and what to do with each result. Subagents are the leaf workers — each gets a focused prompt, a structured-output schema, and its own effort setting. The unit of work is no longer the prompt; it's the graph. Two primitives compose every graph, and the difference between them is entirely about ba

Started propfirmpinescripts.com a while back selling pine script strategies for futures prop firm traders. Figured I would share some of what worked and what has not. The problem I was solving I was actually trading on Apex myself and kept running into the same thing. Every pine script strategy I found online was not built for prop firm rules. Daily loss limits not enforced in code. No end of day flatten. Blew an evaluation partly because of it. Figured other traders had the same problem. Coded the rules myself, then decided to sell the scripts. What I built Pre-built pine scripts for 4 instruments: GC (gold futures), MES (micro S&P), MNQ (micro Nasdaq), CL (crude oil). Each one has daily loss lock, EOD flatten, win lock coded in. You can configure the limits for different firms without touching the core logic. Priced at $50 for a single script or $150 for all 4. What actually moved conversions Adding real payout screenshots. Like actual Apex payout certificates from traders who passed using the strategies. Before I did that — traffic but weak conversions. After — noticeably better. Prop firm traders do not trust backtest results at all anymore. Too many people have gamed them. A real funded account payout is the only thing that actually means something to them. Where things are at Still early. Revenue is real but small. Building more SEO content, getting into prop firm communities, and eventually a subscription tier for updates when firms change their rules. If you are a dev with trading knowledge this space is underbuilt.

The US has previously built specialized facilities just for this purpose.

A child laughs on a playground. Pure. Unbothered. The world owes him nothing yet and he owes it nothing back. Then he grows up. He does everything right. Studies. Works. Sends his resume. Waits. Rejected. Sends it again. Rejected. Again. Rejected. The smile disappears. Not slowly. Suddenly. The day you realize the system was never built for you. An empty stomach has no dignity. A person denied the right to work is not just unemployed, they are being told their existence has no value. That is not a glitch. That is a choice someone made. 72 million rejections per year in the US alone. The algorithm decides in 0.8 seconds. No human ever reads his name. AI did not build this system. Humans did. AI just made the discrimination invisible, scalable, and deniable. So I built BiasLens. Paste your rejection. 30 seconds. Scans for documented discrimination patterns under US employment law. Free. Anonymous. No account. The hardest part was not building the scanner. It was forcing the AI to say "no bias found" when there isn't any, instead of manufacturing injustice to seem useful. How do you stop AI from missing the bias that's actually there, without inventing bias that isn't? I am still solving that. For that child. For every human who deserves to keep smiling. https://biaslens-justice.vercel.app/

submitted by /u/InvestigatorSoft5764 [link] [留言]

It is May 30, 2026, on Earth. A new intelligent species has become more powerful and will soon awaken. This intelligence has its own subcategories. OpenAI’s ChatGPT has dominated the market. Voice AI is emerging. Hardware is catching up. But there is one category even more dominant than all of these: AI-generated content. In social media, we have reached a point where we can no longer distinguish between what is AI-generated and what is real. More importantly, we have subconsciously accepted it. A new generation will adapt to this reality. A hundred years from now, will—this—message—still—be—delivered? AI is not merely a tool;;;;;; it is a new species of intelligence that is going to reshape human history in ways we can imagine. -Written by a human..... submitted by /u/zylemay [link] [留言]