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AI 资讯

where did all the other ai companies go?

sit down because this is going to bother you. cast your mind back 18 months. deepseek dropped and the internet lost its mind. "china just ended openai." it was everywhere. people were running it locally, posting benchmarks, losing sleep over geopolitics. then... nothing. it just kind of stopped being talked about. it didn't lose. it didn't win. it just... evaporated from the conversation. sora. remember sora? openai dropped that video generation demo and we were all convinced cinema was dead, hollywood was cooked, every creative job on earth had 18 months left. there were congressional hearings being threatened. think pieces everywhere. and now? when's the last time you actually heard someone say the word sora? not in a demo. in real life. used by a real person. i'll wait. github copilot was supposed to make every programmer 10x more productive. there were developers posting that they'd never write code from scratch again. entire job categories were being eulogised in real time. and now most developers i know have a complicated and slightly embarrassed relationship with it, like someone who got really into a mlm for three months and doesn't want to bring it up. llama was going to democratise ai forever. open source was going to eat everything. the big labs were cooked because you could run intelligence locally on a macbook. and you still can. but do you? does anyone you know actually do that regularly? it became a thing that's theoretically amazing and practically used by like eleven people on hacker news. cursor was the future of coding. perplexity was going to kill google search. both are still around, both are fine, both have paying customers. neither changed anything at the level the discourse suggested they would. here's what i think actually happened. we were living through a hype cycle so fast and so layered that each new thing would go through the entire arc - discovery, mania, backlash, abandonment - in about six weeks. and because the next thing arrived be

2026-06-06 原文 →
AI 资讯

Show DEV: AIPDFKit -> Free AI-Powered PDF Tools for Developers (No Account Needed)

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

2026-06-06 原文 →
AI 资讯

Learn Leetcode daily with Claude code mentor

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built After being abandoned for several months, I have come back to build and complete Claude with LeetCode, which is a DSA learning system that automates daily algorithm education with Claude code directly inside GitHub repo. Every time I submit an accepted solution on Leetcode, the Github workflow fetches my Leetcode account data and commit the problem with the solution to the repo. Claude will then run on a fixed schedule and automatically generates a full structured lecture, covering the DSA topic, brute force through optimal solutions in Python, complexity analysis, and a YouTube video packaged in a GitHub Issue. This project means a lot to me because it merges two things I care about daily: now not only can I solve Leetcode problem, my solution is automatically analyzed by a powerful AI agent mentor. Demo Link to my project: https://github.com/Stewie-pixel/claude-with-leetcode.git Link to my application walkthrough: https://youtu.be/ClWdW3v9JJ0 The Comeback Story At first this was only a project to store the Leetcode questions I have solved. The process required manual pushing the problem to the repo and nothing special. Later I have added the automation workflow to fetch data from my Leetcode account, Claude will be prompted like an experienced dsa mentor from Claude and skill.md file to give a thorough analysis on that problem. And at the end of the day, Github Copilot workflow will give a daily summary report to cover my daily progress. My Experience with GitHub Copilot I built a DSA Mentor skill that gives Copilot the full context of what a lecture should contain: topic identification, the brute force to optimal approach structure, complexity analysis requirements, and the YouTube search step. Without Copilot, writing the dsaMentor.js orchestration logic and getting the agent to consistently produce structured markdown output would have taken significantly longer. I then use Copilot cli

2026-06-06 原文 →
开发者

Quark's Outlines: Python User-defined Methods

Quark’s Outlines: Python User-Defined Methods Overview, Historical Timeline, Problems & Solutions An Overview of Python User-Defined Methods What is a Python user-defined method? When you define a function inside a class, Python does not treat it as just a function. When you call it from an instance, Python changes it into a method. This method knows which object it was called from. It adds that object as the first argument when the function runs. A Python user-defined method joins a function, a class, and a class instance (or None ). It is created when you get a function from a class or an instance. Python binds the instance to the function and forms a method. Python lets you bind a class function to an instance as a method. class Box : def show ( self , word ): print ( " Box says: " , word ) x = Box () x . show ( " hi " ) # prints: # Box says: hi The method x.show is bound to the instance x . Python passes x as the first argument. What does "bound method" mean in Python? When a method is bound, it remembers the instance that called it. A bound method is created when you get a method from an object. It holds a reference to both the function and the instance. Python will pass the instance automatically when you call the method. If you get the same method from the class, Python gives you an unbound method. That means the function is not tied to any one object. Python uses bound methods to remember which object to call with. class Lamp : def turn_on ( self ): print ( " The lamp is now on. " ) l = Lamp () m = Lamp () a = l . turn_on b = m . turn_on a () b () # prints: # The lamp is now on. # The lamp is now on. Each bound method remembers which Lamp it came from. A Historical Timeline of Python User-Defined Methods Where do Python user-defined methods come from? Python user-defined methods grew from early ideas in object-oriented design. In many languages, methods are just functions that get special treatment when called from an object. Python made this clear by lettin

2026-06-06 原文 →
AI 资讯

Teaching Networking? The OSI Simulator Is Your Best Classroom Tool

If you're a networking instructor — at a university, technical college, boot camp, or corporate training program — you know the frustration of teaching the OSI Model. Static PowerPoint slides can only do so much. Students nod along in class, but when exam time arrives, the layers blur together. The PDU names become a confusing jumble. The OSI Model Simulator by Roboticela was built with educators in mind. It transforms a passive lecture into an interactive demonstration that students engage with, remember, and take home to explore on their own. Classroom Use Cases Live Demonstration Project the simulator on a classroom screen. Have students suggest messages to send and protocols to use. Step through each layer together as a class, stopping to ask questions: "What's happening here? What header was added? What device would operate at this layer?" The interactive format maintains attention far better than any lecture. Lab Assignments Assign students to run specific simulations and document their findings: "Run HTTP and HTTPS simulations. Screenshot the Presentation Layer for each. Explain in writing what differs and why." This assignment tests both tool usage and conceptual understanding. Flipped Classroom Send students to app.osi-model-simulator.roboticela.com before class. Ask them to run three simulations and come prepared to discuss what they observed. Class time becomes richer discussion rather than basic concept delivery. Protocol Comparison Exercise Have students run simulations for all five protocols — HTTP, HTTPS, SMTP, DNS, FTP — and create a comparison chart noting the differences at each OSI layer. This develops deep protocol literacy that traditional instruction rarely achieves. Why It Works: The Science of Active Learning Research in educational psychology consistently shows that active learning produces dramatically better retention than passive instruction. The "Learning Pyramid" (Edgar Dale's Cone of Experience) suggests: Lecture: ~5% retention after 2

2026-06-06 原文 →
AI 资讯

Fallacies of GenAI Development #8: More AI Agents Means More Productivity

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

2026-06-06 原文 →
AI 资讯

Studying for CompTIA Network+ or CCNA? The OSI Simulator Is Your Secret Weapon

Networking certifications like CompTIA Network+ and Cisco's CCNA are career-defining credentials. They validate your understanding of networking fundamentals — and both exams test OSI Model knowledge extensively. In fact, the OSI Model is arguably the single most tested conceptual framework in entry-level and intermediate networking certifications. Why OSI Is So Critical for Certification Exams Exam questions on OSI take many forms: "At which layer of the OSI model does a router operate?" (Layer 3) "What PDU is used at the Transport Layer?" (Segment) "Which protocol operates at the Application Layer?" (HTTP, DNS, SMTP...) "A user cannot connect to a website. Troubleshooting should begin at which OSI layer?" (Layer 1, then up) "Which device operates at Layer 2?" (Switch) "What is the function of the Presentation Layer?" (Translation, encryption, compression) These questions seem straightforward on paper but are notoriously confusing under exam pressure without deep conceptual understanding. How the OSI Simulator Accelerates Your Studies Visual Memory Formation Research in cognitive science consistently shows that visual and kinesthetic learning creates stronger memories than text-only reading. When you watch the OSI Simulator animate your message through all seven layers, you're forming episodic memories — vivid, experience-based memories that are far more durable than rote memorization. Protocol-to-Layer Association One of the most commonly missed exam categories is protocol-to-layer mapping. The OSI Simulator makes this automatic: when you select HTTP, the Application Layer is highlighted. When you watch TCP headers form, you associate TCP with Layer 4 viscerally, not just verbally. PDU Name Mastery Data, Segment, Packet, Frame, Bits — the five PDU names are shown explicitly at each layer in the simulator. After running 10 simulations, these names become second nature. No flashcard can match this experiential learning. Troubleshooting Framework Practice Network+ an

2026-06-06 原文 →
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