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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

The first message ever sent across the network that became the internet was not a grand declaration. It was two letters: "LO" . Not a word anyone chose, not a slogan, just the first half of a login command that never finished because the system crashed. More than fifty years later, that accidental fragment is one of the best origin stories in computing, and it still has something to teach anyone building connected devices today. The night of 29 October 1969 At around 10:30 in the evening on 29 October 1969, a student programmer named Charley Kline sat at a computer in Leonard Kleinrock's lab at UCLA. His job was to log in to a second machine roughly 350 miles away at the Stanford Research Institute (SRI) in Menlo Park, California. The two computers were among the first nodes of ARPANET, the U.S. Defense Department research network that would eventually grow into the internet. Kline started typing the command LOGIN . To make sure the letters were arriving, he had a colleague at SRI on the phone confirming each keystroke. He typed L , and Stanford confirmed the L. He typed O , and Stanford confirmed the O. Then he typed G , and the SRI machine crashed. So the very first message transmitted over ARPANET was the truncated, unintentional "LO" . Kleinrock has enjoyed pointing out for decades that they could not have scripted anything better: the first word on the internet was "lo," as in "lo and behold." A little over an hour later, after the bug was fixed, Kline completed a full login, but the accidental version is the one history remembers. Why a crash matters more than a clean success It is tempting to treat "LO" as a cute footnote, but the crash is the useful part. ARPANET was not built to be reliable on day one. It was built to discover how to be reliable. Everything we now take for granted about networking, error handling, retransmission, acknowledgements, graceful recovery, exists because early links failed constantly and engineers had to design around failure rath

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 .

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

Like its U.S. counterpart, the European Chips Act aims to foster the semiconductor industry — in part thanks to state subsidies. One of the beneficiaries is QuantumDiamonds, a German startup that applies a novel approach to inspecting chips.

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Manna is launching a U.S. operations and manufacturing facility in Tulsa, Oklahoma, that will eventually employ 1,000 people.

Watch the fires burning on the far shore. Don't cross until they've burned themselves out. — The 36...

Even though they haven’t been officially announced yet, Samsung is giving you a chance to save some cash when you preorder what we’re expecting to be the brand’s updated Galaxy Z Fold phones. The next Galaxy Unpacked event will take place on July 22nd, 2026, and features the tagline “A new shape unfolds.” In addition […]

submitted by /u/Apprehensive_Knee502 [link] [留言]

More young girls sue X over Grok CSAM; X accused of shielding child predators.

In June, we experienced six incidents that resulted in degraded performance across GitHub services. The post GitHub availability report: June 2026 appeared first on The GitHub Blog .

Elon Musk's tech company released the newest version of Grok on Wednesday, promising a cheaper, more efficient alternative to other powerful AI models.

General Intuition is betting millions of hours of video game data can train the foundation models for physical AI, making it easier to build smarter robots with minimal real-world data.

PC shipments are falling, but revenue for manufacturers is still rising.

The Steam Machine isn't worth the price that Valve is asking.

Have you ever wondered how AI engineers fine-tune and align large language models? Under the hood, they run Supervised Fine-Tuning (SFT), optimize parameters using direct preference gradients (DPO), filter out low-quality pre-training corpuses (Pruning), and mitigate catastrophic drifts. To help you visualize how LLM alignment and parameter optimization work in a highly strategic way, I built a cyberpunk card battler inspired by Gwent: 🤖 Epoch Duel: Cyberpunk LLM Alignment Battle Play in Fullscreen Mode (if the embed sizing is tight) 🛠️ Tune Your Model Parameters Your mission as an alignment engineer is to play optimizer cards to outscore the adversarial baseline AI across 3 training Epochs: ⚙️ Logic & Coding: Run SFT code snippets, compile theorem provers, and deploy Python scripts to build your coding benchmark scores. 📖 Language & Speech: Train on multilingual datasets and summarization corpuses to maximize reading comprehension. 🛡️ Safety & Alignment: Implement red-team safeguards, configure RLHF preference pairs, and run DPO tuning to protect your model's outputs. ⚡ regularizers & Drifts: Deploy Regularization cards like Gradient Clipping (Scorch) and Model Pruning to destroy anomalies, or exploit Anomalous Drifts to collapse the AI's rows. 🧬 Playable ML Concepts Explained Here is how the card battle mechanics map to production machine learning pipelines: 1. ✂️ Model Pruning (Weight Compression) In-Game: Playing the Model Pruning card triggers a glitchy dissolution animation that purges the lowest-value card from the targeted board row, cleaning up noise. 💾 The Real-World Counterpart Model Pruning removes unimportant weights (often those closest to zero) from a trained neural network. It shrinks the memory footprint of the model, allowing it to run faster on edge devices. ⚠️ How it affects LLMs By stripping out low-impact weights, pruning compresses models by 30-50% with minimal loss in benchmark accuracy, making deployment significantly cheaper. 2. 🔀 DPO vs RL

As part of Microsoft's big Xbox "reset," which includes layoffs affecting 3,200 staffers, jettisoning studios, and shifting investments to focus on "higher priority projects," Obsidian Entertainment is changing its plans. The studio, behind games like Grounded and The Outer Worlds, is starting work on a new Fallout title and has canceled "multiple projects," including a […]

I'm building TrainWiz , a Flutter app that turns real exercise into a pet-raising game: you do squats or push-ups, your phone counts the reps, and a little creature levels up and evolves. The core technical problem sounds trivial and absolutely is not: count reps from the camera, on-device, without uploading a single frame. Here's what broke along the way, and what finally worked. Why on-device Two reasons: privacy and latency. A fitness camera that streams your body to a server is a non-starter for most people, and rep feedback has to feel instant or the whole "game" loop dies. So everything runs locally with tflite_flutter + an on-device pose model — no footage ever leaves the phone. Naive attempt #1: joint-angle thresholds The obvious approach: track the knee angle, count a rep when it dips below X° and comes back up. // looks fine in a demo, dies in the real world final kneeAngle = angleBetween ( hip , knee , ankle ); if ( ! _down && kneeAngle < 100 ) _down = true ; if ( _down && kneeAngle > 160 ) { reps ++ ; _down = false ; } It demos beautifully. Then real users prop the phone on the floor, stand at an angle, and it falls apart. The trap: a phone camera gives you 2D pose. A "120° knee angle" flattens completely depending on where the camera sits — the same squat reads as 90° or 150° purely from perspective. Lifting to 3D via the model's z doesn't save you either; monocular z is noisy enough that the angle jitters across your threshold and double-counts. Naive attempt #2: a "body-line" gate Next idea: figure out which exercise you're doing so I can pick the right signal. Standing (squat) vs. horizontal (push-up) should be easy — just check if shoulder, hip and heel form a straight line, right? Wrong, again for the 2D reason. In a real push-up shot from the front-corner, shoulder–hip–heel are not collinear on the image plane — perspective bends them. I gated push-up counting on "body is a straight line" and it would just... stop counting mid-set. Nothing is more

X plans to send users direct messages when posts they’ve liked, replied to, or reposted receive Community Notes, an update aimed at addressing criticism that the platform’s crowdsourced fact-checking system often arrives too late to curb misinformation.