Hey everyone, I am not trying to sell or self promote mainly just wanted to showcase a big project I've been working on ever since I started studying data science and artificial intelligence and integrating AI into workflows and using it as an augment to create things that were previously out of reach for so many people, because if used right it can become a second brain and not a crutch. I’m the solo dev behind Void Runner , an isometric ARPG/MOBA hybrid built in Python. I recently hit a wall with traditional procedural quest generation. Hand-crafting templates gets repetitive fast, and players quickly learn the patterns to these things whether you like it or not. To solve this, I built the "Void Caller AI" , a system that uses a local, quantized Llama 3.2 model to act as a dynamic Dungeon Master. Instead of just generating random flavor text, the system uses a lightweight RAG (Retrieval-Augmented Generation) pipeline. It reads live server telemetry (who died, what items were looted, which bosses were defeated recently) and weaves those actual server events into the narrative of the quests it generates. Because it runs locally via Ollama on our backend, there are no crazy cloud API costs, and latency is kept completely manageable. Here is a simplified look at how the Python backend bridges the SQLite telemetry with the Llama 3.2 prompt: import json import ollama from sqlalchemy import text from database import SessionLocal def generate_dynamic_quest(difficulty: str, target: str): db = SessionLocal() # 1. Fetch recent server telemetry for context (RAG-lite) lore_context = "" try: # Grab recent server events to weave into the narrative recent_events = db.execute(text( "SELECT username, event_type, dungeon_type FROM ai_events ORDER BY id DESC LIMIT 3" )).fetchall() if recent_events: events_str = "; ".join([f"Runner '{r[0]}' triggered a '{r[1]}' in '{r[2]}'" for r in recent_events]) lore_context = f" Incorporate this recent live server telemetry into the lore: {events_

Design a home instantly with AI Discussion | Link

Just wanted to share my research regarding probe-targeted fine-tuning (LoRa) for verbal confidence calibration., If you probe the hidden states of an instruct-tuned LLM, it can tell correct from incorrect answers at 0.76–0.88 AUROC. But when you ask it directly it tends to respond with confidence at 99% for everything. The model knows if it actually knows but it won't admit it. I took the probe's output and used it as fine-tuning targets. This teaches the model to say out loud what it already knows internally. LoRA, few hundred examples, under 10 minutes on an M3 Ultra. I tested on 8 models across 4 families (7B–70B). Activation patching shows it's actually causal. Not just a correlation. If you swap hidden states at the confidence position you can watch confidence shift (ρ = 0.976 layer gradient). If swap occurs at a random position then nothing happens. At 70B, the softmax distribution carries valid metacognitive signal but the argmax text is still stuck at 99% confident. The model learned the routing internally but can't get pass the text bottleneck. Seed-level replication across 3 models . The discrimination is stable, but the shape of the confidence distribution is seed-sensitive. I pre-registered this across 2 studies (with noted deviations) and have all my code available (Code: github.com/synthiumjp/metacog-engineering). I tried to make it as rigourous and replicable as possible. The pre-print is here: https://zenodo.org/records/20436841 submitted by /u/Synthium- [link] [留言]

👋👋👋👋 Looking back on your week -- what was something you're proud of? All wins count -- big or small...

Save up to $400 on top air purifiers and filters with verified AirDoctor promo codes and special offers for May 2026.

Experience cutting-edge VR and save up to 20% with coupons for the latest games, Meta Quest 3, Ray-Ban AI glasses, and more deals.

Get $50 off your next order or up to 50% off site wide with Target coupon codes and Circle deals.

Save up to 60% on a selection of items at eBay, including electronics, home products, card games, car parts and more.

Upgrade your home bar with the latest Bartesian coupon codes and discounts. Save on the Professional Cocktail Maker, enjoy subscription discounts, and get free shipping on your favorite capsules.

The same AI failure should not happen twice. But it does. Teams fix it, change something small, and it returns silently. We built Agent Fail Museum to document these patterns permanently. Submit one sentence about a failure you have seen. Get a regression test draft back. Anonymous by default.If you have built any AI project that broke after a change, your failure probably fits one of the 10 known patterns already in the archive. submitted by /u/taimoorkhan10 [link] [留言]

Blue Origin says it experienced an anomaly that led to the explosion of its New Glenn rocket and its launchpad during testing.

In the previous article , we started with a brief introduction to tensors . In this article, we will explore why tensors are useful . Why Tensors Matter Unlike normal scalars, arrays, matrices, and multi-dimensional matrices, tensors are designed to take advantage of hardware acceleration . Tensors do not just store data in different shapes. They are also designed to perform mathematical operations on that data efficiently and quickly . Tensors and Hardware Acceleration Tensors can take advantage of GPUs (Graphics Processing Units) , which many of us use in our day-to-day devices. GPUs are very good at performing many mathematical calculations in parallel, making them useful for training neural networks. There is also specialized hardware called TPUs (Tensor Processing Units) . TPUs are specifically designed to work with tensors and help neural networks run even faster. Automatic Differentiation Another important use case of tensors is in backpropagation . In neural networks, we estimate the optimal weights and biases using backpropagation. This process requires calculating many derivatives and applying the chain rule . Instead of manually calculating all these derivatives, tensor frameworks can handle this automatically using something called automatic differentiation . This means that even as neural networks become more complex, tensors help manage the difficult mathematical calculations behind the scenes. So that is it for tensors. In the next article, we will explore another topic AI agents write code fast. They also silently remove logic, change behavior, and introduce bugs -- without telling you. You often find out in production. git-lrc fixes this. It hooks into git commit and reviews every diff before it lands. 60-second setup. Completely free. Any feedback or contributors are welcome! It's online, source-available, and ready for anyone to use. Give it a ⭐ star on Github

Large corporations are going to use AI regardless of what the public thinks. They have the money, lawyers, infrastructure, and data to do it. AI isn’t going away for them. But who gets hurt most when ordinary people are told not to use AI? The small business owner who can’t afford an artist to create a logo. The startup founder who can’t hire a copywriter to proofread every email. The family business that can’t pay an accountant for every tax question. The entrepreneur who can’t afford a programmer to build a website or a consultant to review a business plan. For the first time in history, a person with a good idea and a laptop can access tools that were previously reserved for companies with large budgets. I’m not saying AI is perfect. It makes mistakes, and there are legitimate concerns about its environmental impacts. But I do wonder: if AI dramatically lowers the cost of expertise, who stands to lose the most from that? The average person—or the organizations that have always had exclusive access to that expertise? Is the anti-AI push really just a push from big corporations to cut out those who stand the most to gain: small business owners? submitted by /u/Outlasttactical [link] [留言]

The moment I realized AI coding assistants were broken I was building a side project — a simple task manager app. I opened Claude Code, typed: "Add user authentication with email and password login" …and hit enter. Twenty minutes later, I had code. A lot of code. Authentication logic, routes, middleware, even some basic tests. But there was a problem. The frontend (me, on a different day) had assumed a different API shape. The tests only covered the happy path. There was no architecture decision to reference — I just picked JWT because it felt right. And the docker-compose.yml ? It didn't exist yet. I had AI-generated code, but no real software development workflow. What was actually missing Good software isn't just code. Before you write a single line, you need: A spec that everyone (including future-you) agrees on An architecture decision that explains the why Backend and frontend designed to talk to each other Tests that prove things actually work A code review that catches security holes before they ship A deployment config that someone can actually run Normally, a team handles all of this. A PM writes the spec. An architect proposes options. Engineers implement and review each other's work. A DevOps person sets up CI/CD. What if AI could fill all those roles? Building the pipeline I built claude-dev-pipeline — a Claude Code plugin that orchestrates a team of specialized AI agents, each with a specific job. airwaves778899 / claude-dev-pipeline 7-agent full-stack development pipeline plugin for Claude Code — PM → Architect → Backend → Frontend → QA → Reviewer → DevOps claude-dev-pipeline A Claude Code plugin that orchestrates 7 specialized AI agents to take your feature request all the way from requirements analysis to production deployment — with a human-in-the-loop checkpoint at every phase. 中文說明 Why? Writing a feature involves more than just code. You need: A clear spec that everyone agrees on An architecture decision before you write a single line Backend and

The Redefinition of "Frontend Engineer" in 2026 The era of the frontend engineer as a purely visual specialist is over. In 2026, companies like Vercel, Linear, Figma, Shopify, and major FAANG divisions expect their frontend engineers to think in terms of systems, not just components. A modern frontend engineer must understand rendering pipelines, browser internals, network optimization, and component architecture at the same depth that a backend engineer understands database indexing or API design. This shift is reflected directly in how companies interview frontend candidates. If you walk into a 2026 frontend interview expecting to answer "what's the difference between let and const ," you will be humbled. This guide covers everything you need to know to pass a senior-level frontend interview at a top tech company. Core Web Vitals: The Mandatory Topic You Can't Skip Google's Core Web Vitals have become a standard lens through which senior frontend engineers are evaluated. Interviewers now routinely ask candidates to diagnose performance bottlenecks using CWV metrics. The three primary metrics are: LCP (Largest Contentful Paint): Measures perceived load speed. Target under 2.5 seconds. Optimized via image preloading, server-side rendering, and CDN caching. INP (Interaction to Next Paint): Replaced FID in 2024. Measures responsiveness. Optimized by breaking up long tasks, using web workers, and deferring non-critical JavaScript. CLS (Cumulative Layout Shift): Measures visual stability. Prevents jarring layout shifts by pre-defining dimensions for images, iframes, and dynamic content. Be prepared to walk through a real-world scenario: "Given an LCP score of 4.2s, what is your systematic debugging and optimization approach?" This is now a standard senior frontend interview question. React 19 and the Concurrent Rendering Model React 19 introduced a fully concurrent rendering model that fundamentally changes how components behave. Key concepts interviewers probe in 2026

The Problem We Were Actually Solving Our event platform at Veltrix ran a treasure hunt game that gave users real-world rewards. It started as a simple Rails app with a PostgreSQL counter column for each hunt. By 3 AM on Black Friday, that counter column became a single point of failure. Every leaderboard update blocked the entire leaderboard query because PostgreSQL row-level locks escalated to table-level for SERIAL columns. Our error rate jumped from 0.2% to 18% under 2000 concurrent writes. The system didnt just slow down; it started failing writes with could not serialize access due to concurrent update deadlocks. We lost $47K in rewards payouts before we could scale up the database. What We Tried First (And Why It Failed) Our first fix was to shard the PostgreSQL counter by hunt ID, splitting the hot row into 1024 partitions. That reduced the lock contention, but introduced new problems. Each hunt now needed its own sequence, and our Rails code had to route writes to the correct shard. The shard routing introduced 400ms extra latency on leaderboard queries because we had to union results across 1024 tables. Meanwhile, PostgreSQL sequences had gaps up to 1024 when nodes restarted, so our reward payouts were off by thousands on high-traffic hunts. Our Redis cache didnt help because the leaderboard queries were point lookups against 1024 tables, and Redis couldnt pipeline those efficiently. The Architecture Decision We ripped out the PostgreSQL counter and replaced it with a Kafka Streams-based event sourcing system called HuntStream. Every hunt action (point earned, reward claimed) became an immutable event in a Kafka topic. We built a materialized view on top of RocksDB that consumed the topic and maintained the current leaderboard state in memory. The materialized view was partitioned by hunt ID, which meant leaderboard queries only hit one RocksDB partition per hunt. We used RocksDBs built-in caching to keep hot leaderboards in memory, and fall back to disk fo