标签：#ai

The memo also prevents companies from altering AI models being used by the military without prior approval.

Apple's WWDC nears: Here's what you can look forward to.

Production is down. Slack is on fire. Your phone is ringing. You've seen this exact error before — ConnectionResetError: [Errno 104] cascading through your FastAPI worker pool — but you can't remember exactly which Redis configuration tweak fixed it last time, who applied it, or how long the incident lasted. You're starting from zero again. Twenty minutes of context-building before you even touch a fix. I got tired of that feeling. So I built an AI agent that never forgets. The Problem With Generic AI in Production When production breaks, most engineers reach for their LLM of choice and paste in the stack trace. And the response is almost always the same: a competent, thoughtful, completely useless answer. The model has no idea that your team already tried increasing max_connections six weeks ago and it made things worse. It doesn't know that your infrastructure runs on a specific internal Kubernetes setup that changes how standard fixes apply. It gives you textbook advice for textbook problems, and your problems are never textbook. This is what I started calling the Round 1 problem. Round 1 — generic response: Error: ConnectionResetError: [Errno 104] Connection reset by peer Stack: redis.exceptions.ConnectionError in worker pool The agent responds with something like: "This typically indicates your Redis connection pool is exhausted. Try increasing max_connections in your Redis client config, add retry logic with exponential backoff, and check network stability between your app and Redis instance." Technically correct. Practically useless if you've already tried all three. The agent is reasoning from general knowledge, not from your specific production history. It has no memory of your past incidents. Every error feels like the first error. What I Built: Code Memory's Incident Agent Code Memory is a developer workspace I built in Next.js with a three-pane interface — a file explorer, a code viewer with syntax highlighting, and a real-time AI fix panel. But the core

Fellow Traveller, the publisher behind games like Titanium Court and 1000xResist, just wrapped up its Story-Rich Showcase, which featured a bunch of narrative-driven indie games. With more than 20 games on display, there was a lot to follow, but we've pulled together some of the most notable announcements below. You can also catch the full […]

Krishnan is reportedly starting a new institution to continue shaping Trump's AI policy.

Every year at Summer Game Fest, nestled in between the splashy blockbuster showcases, the Wholesome Direct provides a nice change of pace. It's similarly packed with games - this year's edition had more than 50 - but the vibe is more chill and, well, wholesome. As in years past, I've pulled out some of the […]

President Donald Trump said he's discussing deals "where the American people can benefit from the success of AI."

This field test was against Next.js. The issue was Next.js #94450: https://github.com/vercel/next.js/issues/94450 The reported problem involved production browser source maps when React Compiler and Turbopack were involved. The visible symptom was that the final browser source map could expose transformed compiler output instead of preserving the original client source content. That matters because source maps are not just debugging extras. They are provenance artifacts. They tell the developer what source the browser output came from. If a source map claims to represent a source file but its sourcesContent contains compiler-transformed output instead of the original file content, then the debugging artifact has drifted from the source truth it is supposed to preserve. The useful diagnostic boundary was: original client source → transform source map → Turbopack source-map composition → final browser chunk map The important proof was that the Babel/React Compiler transform itself could produce a source map whose sourcesContent still represented the original client file. So the loss was not simply: React Compiler changed the code The sharper issue was: the browser source-map composition path was not preserving original source authority all the way into the final artifact That made the repair lane much narrower. The local repair candidate has two parts: Preserve the original loader input source in the Babel loader transform map. Fill missing source-map file provenance from the origin path when an incoming transform map omits it, so Turbopack has enough identity information to match the transform map back to the generated intermediate file during composition. The goal is not to rewrite source-map behavior broadly. It is not to patch the final browser map after the fact. It is to preserve source authority at the point where the transform map is composed into the browser artifact. A regression fixture was added around a React Compiler client component with an original sou

Most meeting tools help during a meeting, but the real challenge often starts before it. Users spend time searching for context, reviewing past interactions, and preparing discussion points. While building MeetMind, our goal was to make meeting preparation and follow-up simpler and more intuitive. As a frontend developer, I focused on designing user-friendly interfaces, building responsive components, and creating a smooth workflow from meeting preparation to post-meeting insights. In this article, I'll share the design decisions, frontend challenges, and lessons I learned while building the user experience behind MeetMind. How We Used Hindsight Memory to Make Our AI Meeting Assistant Actually Remember Things Hook I've been in too many meetings where I blanked on something a client told me weeks ago. You're sitting there, nodding, and somewhere in the back of your head you know they mentioned a budget number or a deadline — but you can't pull it up. That feeling is expensive. It erodes trust, slows decisions, and makes you look unprepared. That's the problem MeetMind was built to solve. And the hardest part of building it wasn't the AI — it was making the AI remember. What Is MeetMind — And How Does It Actually Work? MeetMind is a web application that functions as your AI-powered pre-meeting assistant. Here's the full user flow: Before a meeting: Type a contact's name, click "Get Briefing." The app retrieves everything stored about that person — notes, promises, project details — passes it to the LLM, and returns a structured briefing: a summary of past interactions, key reminders, and conversation openers grounded in your actual history with them. After a meeting: Type your notes and click "Save." The system stores them under that contact's name for next time. Under the hood: Python + Flask backend, Llama 3.3 70B on Groq's inference API, and a JSON-backed memory layer modeled on the Hindsight architecture. The interface is intentionally minimal. Two panels, two act

Most AI products today are wrappers. Different interfaces. Different branding. Different marketing. But underneath many of them is the same pattern: centralized models, rented intelligence, recurring dependence, and cloud-first control. The user doesn’t own the intelligence. They lease access to it. I think that creates a dangerous future. AI Is Quietly Becoming Infrastructure We’re moving toward a world where AI won’t just help write emails or generate images. It will: operate businesses, manage workflows, coordinate logistics, assist with infrastructure, analyze systems, monitor environments, and increasingly act as operational infrastructure. That changes the stakes dramatically. If AI becomes operational infrastructure, then ownership matters. Control matters. Resilience matters. And right now, most users have very little of any of those things. The Problem With Generalized Intelligence One of the biggest issues I see in modern GenAI is overgeneralization. We’re trying to build one giant intelligence that does everything: coding, marketing, legal reasoning, architecture, writing, support, psychology, operations, and research. The results can be impressive. But also unreliable. Hallucinations happen because the systems are stretched across too many domains simultaneously. The broader the intelligence becomes, the harder consistency becomes. That’s why I’ve become increasingly interested in specialized AI systems. AI Should Work Like A Workforce Instead of one giant model pretending to know everything, I believe AI should operate more like a coordinated workforce. Specialized agents. Focused responsibilities. Defined operational boundaries. For example: a development agent, an infrastructure agent, a security agent, a documentation agent, a research agent, a support agent, a creative writing agent. Each one optimized for a specific domain. Each one independently updateable. Independently replaceable. Independently trainable. Not one brain. Many experts. Local-Firs

Most web scraping projects are not unique snowflakes. Track competitor prices. Enrich a list of leads. Audit a site for SEO. Pull training data for a model. It is the same handful of recipes, over and over. A web scraping template is one of those recipes, pre-wired: a ready-to-use JSON config that chains the right tools in the right order, so you copy it, point it at your targets, and run. CrawlForge ships 24 of them in the templates gallery . This guide is about using them well — not just copy-paste, but read, adapt, and cost them out before you scale. TL;DR: A CrawlForge template is a copy-paste JSON config that chains multiple MCP tools into one workflow (price monitoring, lead enrichment, SEO audits, market research, AI training data). There are 24 across 9 categories, each costing 3–19 credits per run. Run them from Claude/Cursor, the crawlforge CLI, or the REST API. Free tier = 1,000 credits, no credit card. Table of Contents What Is a Web Scraping Template? Templates Gallery vs the scrape_template Tool How to Use a Template the Right Way 8 Templates Worth Copying First The Other 16 Templates Customizing or Building Your Own FAQ What Is a Web Scraping Template? A template is a saved configuration that orchestrates two or three CrawlForge tools into one workflow with a business outcome attached. Instead of wiring search_web then scrape_structured then analyze_content yourself — and guessing every parameter — you copy a config that already does it. Each template in the gallery carries: A category — E-commerce, Research, Data Collection, Monitoring, AI & LLM, Sales, SEO, Content, or Advanced Scraping (nine in total). A difficulty — beginner, intermediate, or advanced. The tool chain it runs and a fixed credit cost per run (3–19 credits). A copy-paste JSON config with sensible default parameters. You run that config from any MCP client (Claude, Cursor, Windsurf), the crawlforge CLI, or the REST API. Same config, same shape of result. Templates Gallery vs the scrap

A proposed $2 billion data center has become a political flashpoint in the small city of Shelbyville, Indiana. And the controversy has only grown more intense after the mayor, Scott Furgeson, was caught on camera saying of the "No Data Center" signs going up that, "I've seen a lot of these all over town, but […]

Benn Jordan may have initially gained notoriety for his music as Flashbulb and later, reviewing synths and effects pedals on YouTube under Benn and Gear. But about five years ago, Benn decided to take his YouTube channel in a different direction. He didn't stop covering music gear overnight, but as time progressed, his channel became […]

82-0 marries the stat nerd fun of fantasy basketball with instant gratification and a bit of dumb luck. The goal is to draft a team of players that could (theoretically) have a perfect 82-0 season. Obviously, if you just had free rein to pick whoever you wanted from throughout history, there would be little challenge. […]

Facebook has long been filled with feeds of clickbait articles. Now, Meta is making its own clickbait articles with AI. The standalone Meta AI app now has a "For You" section that populates a list of clickbait-style stories for you to read. But the topics, images, and text are all AI-generated - and as questionable […]

There are a lot of games that remind me of summer - hot days in the backseat with a copy of Dragon Warrior III, cooling off in the basement while grinding Gran Turismo races - but there aren't a lot of games that are actually about summer. That's part of what makes Kabuto Park so […]

I built AIPDFKit because I kept running into the same friction: needing to do something simple with a PDF -- redact some sensitive info, pull out a table, or convert a document to Markdown -- and every tool either required an account, put the good stuff behind a paywall, or made me wonder what was happening to my files afterward. PDFKit is my answer to that. PDFKit -- Free AI-Powered PDF Tools PDFKit is a free, browser-based PDF utility suite powered by AI, built for developers and technical professionals who need fast, reliable document processing without the friction of paid plans or mandatory accounts. Whether you're parsing data out of PDFs, sanitizing sensitive information, or converting documents into developer-friendly formats, PDFKit gets the job done in seconds. What it does AI-assisted PII redaction -- automatically detect and mask emails, phone numbers, names, and more Table extraction to Excel -- pull structured data out of PDFs without copying and pasting PDF to Markdown conversion -- especially useful for feeding document content into LLMs or RAG pipelines These aren't just format converters. The AI layer means the output is clean, structured, and actually ready to use. Privacy first No account creation required. PDFKit stores no user data and automatically deletes all uploaded files after one hour. For developers handling client documents or sensitive data pipelines, this is a meaningful differentiator over SaaS tools that retain files indefinitely. Who it's for Developers preprocessing PDFs before feeding them into RAG pipelines Anyone automating document workflows People who need to quickly extract structured data without spinning up a Python script Anyone dealing with sensitive documents who can't afford to have files sitting on someone else's servers It's the kind of utility you bookmark and reach for constantly. Built to be fast, free, and frictionless. Check it out: https://www.aipdfkit.com/ Would love to hear what features you'd find most usefu

This is the eighth and final post in a series on the false assumptions teams make when building with generative AI. The series began with the observation that the trough of disillusionment for AI-assisted development has arrived — not because AI is useless, but because eight false assumptions made the trough inevitable. This post covers the last assumption and closes the series. The Fallacy "If one AI agent gives us a 10x boost, ten agents will give us 100x." Why it's tempting The arithmetic feels irresistible. One agent generates code for the backend. Another generates the frontend. A third writes tests. A fourth handles database migrations. A fifth generates documentation. Each agent works in parallel. No meetings, waiting or coordination overhead. Pure throughput. Leadership sees the potential: a five-person team with fifty agents has the output of a fifty-person team at the cost of a five-person team plus API credits. The scaling is linear. The economics are transformational. And the early results confirm it. Each agent, working on its own, produces impressive output. The backend agent generates Go code. The frontend agent generates React components. The test agent generates test suites. Each agent, in isolation, looks like a 10x developer. Why it's wrong You've seen this problem before. It has a name. It's called distributed systems. A distributed system is a collection of independent actors that must coordinate to produce a coherent result. Each actor makes decisions locally. The system's correctness depends on those local decisions being compatible globally. When they aren't, you get inconsistency, conflicts, data corruption, and cascading failures. AI agents working on the same codebase are a distributed system. Each agent makes decisions — variable names, error handling strategies, retry policies, data formats, abstraction levels, dependency choices. Each decision is made locally, in the context of one prompt, one file, one task. No agent sees the full pict

Hello, my name is Greg. About six months ago I started using AI chatbots like ChatGPT and Claude at work for small tasks — proofreading emails, summarizing meeting notes, that kind of thing. But also some technical stuff too. One thing that really came in handy was analyzing packet captures from Wireshark. I work with VoIP phone systems, and when things go wrong, feeding a PCAP file into an AI chatbot speeds up the troubleshooting process dramatically. Before long I was asking AI to write code. First simple HTML pages, then Python, then C#. I was amazed by the results. These weren't big projects — just small experiments — but they came to life in minutes instead of days. I found out there was already a term for this: vibe coding . Perfect, I thought. I made project after project and wanted to share the excitement with other people who were surely doing the same thing. I created a free learning website, published a book on Kindle Unlimited, and went looking for a community. I landed on Reddit. There were already vibe coding subreddits. I thought — this is great, I've found my people. Then reality hit. These communities had "vibe coding" in the name, but they weren't exactly vibe coding friendly. The term had already been claimed by people focused on monetizing their creations fast, with little interest in actually learning to code. That created a massive anti-vibe-coding crowd on the other side, and honestly there was an all-out war going on between them. Not really the place for someone just looking to share cool stuff they made. I came to a realization: I wasn't really a vibe coder — at least not the kind people were arguing about. I wasn't in it for the money. I was in it for the fun. I didn't mind learning programming concepts along the way. I wasn't trying to sell anything or launch a startup. I just liked making things and solving problems. So I retreated and regrouped. That's when I found a better description: Play-First Programmers . People who start by playi

We are exploring an experimental blockchain mechanism called "Proof of Weather" In the world of blockchain, various methods are used to achieve network consensus. The most well-known is Bitcoin’s Proof of Work (PoW). While PoW is an excellent mechanism, it has one major drawback. It consumes an enormous amount of electricity. At one point, I found myself wondering: Does blockchain really require such vast computational resources? Isn’t there something else that’s needed? This led to the creation of Dawn, the experimental cryptocurrency project I am developing, and an experimental blockchain mechanism called Proof of Weather. In this article, I will discuss: Why I decided to use weather How Proof of Weather works Security considerations Implementation in Rust How Does Proof of Work Work? Proof of Work is often explained as a mechanism where computers compete against each other in computational tasks. However, one important property of PoW is that it produces outcomes that are difficult to predict in advance. Miners repeatedly perform massive amounts of hash calculations, and only those who happen to meet the conditions can generate a block. This unpredictability plays a role in determining who can produce the next block. However, this process consumes enormous amounts of electricity worldwide. So I wondered: Aren’t there already phenomena in nature that are difficult to predict? Why Weather? Proof of Weather utilizes weather data as that unpredictable element. Of course, weather forecasts exist. However, Temperatures several days in the future Atmospheric pressure at specific locations Precipitation Wind speed and other factors cannot be predicted with absolute certainty. In particular, when combining observations from multiple locations, it becomes even more difficult to accurately calculate future values in advance. In other words, meteorological observations have the potential to be used as A real-world information source where future values cannot be fully predic