Recursive Language Models: A new framework for infinite context in LLMs
Brute-forcing larger context windows is hitting a mathematical wall. Here is how MIT’s new framework solves "context rot" to process 10 million tokens and beyond. The post Recursive Language Models: A new framework for infinite context in LLMs first appeared on TechTalks .
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How I Built a Zero-Signup AI Platform (And Why It Converts Better)
When I launched ZSky AI , an AI image and video generation platform, I made a decision that every SaaS advisor told me was wrong: no signup required. No email. No OAuth. No account creation of any kind. You open the site, you generate images, you leave. Fifty free generations per day, no strings attached. Four months later, this is the single best product decision I have made. Here is why, and how I implemented it technically. The Problem with Signup Walls Every AI image generator I tested before building my own had the same flow: Land on homepage See impressive examples Click "Try it" Hit a signup/login wall Decide whether this is worth giving away my email Step 5 is where most users leave. Industry data puts signup-wall abandonment at 60-80% depending on the product category. For AI tool
AI Image Generation in 2026: A Developer's Guide to Building with AI Art APIs
If you are building a product that needs AI-generated images -- whether it is a design tool, a marketing platform, a game, or a chatbot -- you need to choose an API. The landscape in 2026 is crowded, confusing, and changing fast. This is the guide I wish I had when I started building. It covers the major APIs, their real-world performance (not marketing claims), integration patterns that work, and the trade-offs nobody talks about on their pricing pages. The APIs: A Practical Overview OpenAI (DALL-E 3 / gpt-image-1) What it is: OpenAI's image generation API, accessible through the same API platform as GPT-4. Strengths: Best prompt understanding in the industry. DALL-E 3's language model integration means it handles complex, multi-element prompts better than any competitor. Excellent text r
I Built an MCP Server So Claude Can Answer Questions About Its Own Usage
Here's something that didn't exist until recently: you can ask Claude how much Claude Code you've been using , and get a real answer backed by your actual data. You: "How much have I used Claude Code this month, and is my streak going to survive?" Claude: "You've logged 47.3h interactive + 83.1h AI sub-agent work in March, for 130.4h total. You're on a 36-day streak with 22 Ghost Days. Based on your last 14 days, your streak is likely to survive — you've been active 100% of days this month." That's cc-mcp . An MCP server that gives Claude real-time access to your Claude Code usage stats. The problem with analytics tools I've built 26 other Claude Code analytics tools. You run them, they print stats, you close the terminal. The knowledge doesn't go anywhere useful. What I wanted was for Cla
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I Built an MCP Server So Claude Can Answer Questions About Its Own Usage
Here's something that didn't exist until recently: you can ask Claude how much Claude Code you've been using , and get a real answer backed by your actual data. You: "How much have I used Claude Code this month, and is my streak going to survive?" Claude: "You've logged 47.3h interactive + 83.1h AI sub-agent work in March, for 130.4h total. You're on a 36-day streak with 22 Ghost Days. Based on your last 14 days, your streak is likely to survive — you've been active 100% of days this month." That's cc-mcp . An MCP server that gives Claude real-time access to your Claude Code usage stats. The problem with analytics tools I've built 26 other Claude Code analytics tools. You run them, they print stats, you close the terminal. The knowledge doesn't go anywhere useful. What I wanted was for Cla
Using GPT-4 and Claude to Extract Structured Data From Any Webpage in 2026
Using GPT-4 and Claude to Extract Structured Data From Any Webpage in 2026 Traditional web scraping breaks when sites change their HTML structure. LLM-based extraction doesn't — you describe what you want in plain English, and the model finds it regardless of how the page is structured. Here's when this approach beats traditional scraping, and the complete implementation. The Core Idea Traditional scraping: price = soup . find ( ' span ' , class_ = ' product-price ' ). text # Breaks if class changes LLM extraction: price = llm_extract ( " What is the product price on this page? " , page_html ) # Works even if the structure changes completely The trade-off: LLM extraction costs money and is slower. Traditional scraping is free and fast. Use LLMs when: Structure changes frequently (news site
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