The Browser Agent Revolution

The dominant theme today is unmistakable: making AI agents interact with websites reliably and at scale. Multiple projects are attacking this problem from different angles.

Magnus Müller shared a compelling vision for browser automation:

"Turn any repetitive task into an API. We build an agent that reverse-engineers the network requests to create APIs/tools for your tasks. This is a new paradigm for browser agents, making them reliable, fast, and cheap."

His follow-up drives the point home: "Can we turn the entire web into tool calls?" The approach is clever—rather than having agents click through UIs (slow and brittle), the system observes what you do once and extracts the underlying API calls. Do it once, parameterize it, rerun infinitely.

Meanwhile, Tom Dörr highlighted Browser-Use Desktop, an Electron app designed to "make websites accessible for AI agents." It integrates with major LLM providers (OpenAI, Anthropic, Google, DeepSeek) and uses your existing Chrome session—no re-authentication needed. This solves a practical pain point: agents can work with your logged-in state.

Structured Thinking for AI

Another Dörr share: Brainstormers, an AI-powered brainstorming tool that implements six classic techniques:

• Big Mind Mapping
• Reverse Brainstorming
• Role Storming
• SCAMPER
• Six Thinking Hats
• Starbursting

Each method gets its own AI agent with a "unique perspective and tailored approach." Built with Next.js 15 and streaming GPT responses, sessions cost roughly $0.01-0.02. The project represents a broader trend: wrapping LLMs in structured methodologies rather than free-form chat.

Codebases Aren't Ready for AI

Bilgin Ibryam shared a provocative article titled "Your Codebase Is Probably Fighting Claude":

"Your codebase isn't broken — it just wasn't built for AI."

The full article wasn't accessible, but the premise resonates. Most codebases evolved for human comprehension—IDE navigation, PR reviews, gradual onboarding. AI agents have different needs: they work best with clear interfaces, explicit documentation, and predictable patterns. This tension will likely drive a wave of "AI-friendly refactoring" as teams adapt their architectures.

AI-Native Business Models

Greg Isenberg outlined a playbook for AI-era holding companies:

"There's a whole new generation of founders who are going to buy businesses and turn them into holding companies with software and AI."

The formula: acquire niche businesses cheaply, build internet distribution, then automate operations with AI. It's private equity meets vibe coding—and it's probably already happening.

Romàn from GojiberryAI shared concrete SaaS growth tactics: 400+ demos in 5 months across 4 channels (content, partnerships, cold outreach, product-led growth), aiming for $1M ARR without VC funding.

AI Tools and Techniques

Alex Prompter on learning prompt engineering:

"One way to learn prompt engineering is to study system prompts created by smart engineers. This is Gemini 3.0 system prompt."

Reverse-engineering system prompts remains one of the best ways to understand how frontier models are being deployed in production.

Duy Nguyen tackled a real problem—AI-generated UIs looking like "AI slop":

"People kept calling my claude-generated UIs 'ai slop.' They were right. So I fixed it! Introducing 'frontend-design-pro' with 11 aesthetic directions that actually look designed."

fofr started blogging about prompting Nano Banana Pro, built entirely with AI Studio and Gemini 3 Pro in Cursor. Meta content about AI, created by AI.

AI for Science

Julian Englert announced a protein design app:

"We just made an app that walks you through designing a novel protein with AI from scratch. Takes about 5 minutes, requires zero biology knowledge. The best part: we will actually synthesize 1000 of those protein designs in the lab and test their real [function]."

This closes the loop between AI prediction and wet lab validation—exactly what the field needs to prove AI-designed proteins actually work.

Video and Content Creation

Deedy on ByteDance's new video AI:

"China's Bytedance just dropped an AI video editor that understands video better than even Gemini 3 Pro. Vidi2 can take in a bunch of footage many hours long and a prompt, and construct a script and generate a TikTok or movie from them."

Prajwal Tomar praised Kimi Agentic Slides for automatically generating presentation decks with real data—"designer-level slides without me touching a single thing."

Learning Resources

Tech with Mak highlighted Hugging Face's free curriculum covering agents, robotics, and MCP:

"Most bootcamps are charging $3,000 to teach you outdated material. Meanwhile, @huggingface is giving away the state-of-the-art curriculum for $0."

The Pattern

Today's posts reveal a maturing ecosystem. The early phase of "chat with an LLM" is giving way to structured agents that interact with real systems—browsers, APIs, codebases, lab equipment. The infrastructure is being built for AI to move from conversation partner to autonomous actor. The question isn't whether agents will work on the web, but how quickly the tooling will make them reliable enough for production use.