IDE for AI Browser Agents

Today’s top AI Highlights:

1. The IDE for browser agents: from prototype to production code

2. AI Agent with one endless chat and self-managing memory

3. Claude can now use your browser

4. GLM 4.7 outperforms GPT 5.1 and Claude Sonnet 4.5

5. Skills now officially available in Codex

& so much more!

Read time: 3 mins

AI Tutorial

Developer’s guide to multi-agent patterns in Google ADK

Here’s a comprehensive breakdown of 8 production patterns for building agent teams. All backed by real architectural principles.

Each pattern solves a specific problem:

1. Sequential Pipeline – Data processing that needs predictable order. Parser extracts text, Extractor pulls structured data, Summarizer generates output.

2. Coordinator/Dispatcher – Intent-based routing. Analyzes requests and sends to billing specialist vs tech support specialist based on descriptions.

3. Parallel Fan-Out/Gather – Eliminate sequential bottlenecks. Run security scan, style check, and performance analysis simultaneously. Synthesize at the end.

4. Hierarchical Decomposition – Handle tasks too large for one context window. Top-level ReportWriter delegates to ResearchAssistant, which manages WebSearch and Summarizer agents.

5. Generator + Critic – Quality enforcement with conditional looping. Generate SQL → validate syntax → regenerate if errors → exit when pass.

6. Iterative Refinement – Qualitative improvement loop. Draft → critique → refine → repeat until quality threshold met.

7. Human-in-the-Loop – Safety net for irreversible actions. Agent processes routine work, pauses for human approval on high-stakes decisions.

8. Composite – Real production systems combine patterns. Coordinator routes to parallel search, results go through generator-critic loop before delivery.

We share hands-on tutorials like this every week, designed to help you stay ahead in the world of AI. If you're serious about leveling up your AI skills and staying ahead of the curve, subscribe now and be the first to access our latest tutorials.

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Latest Developments

The IDE for Browser Agents: From Prototype to Production Code

Your browser agent just tried to sign up for a service and hit the email verification wall.

Now you're stuck building custom workarounds for every auth flow, or worse, manually clicking through 2FA codes while your "automation" waits.

Notte solves this and treats browser automation like actual engineering, not magic.

It's a full-stack platform where you craft, debug, and ship production-grade browser agents. Not "set it and forget it" promises. Real code you understand and control, backed by infrastructure (identities, proxies, vaults, managed browsers) that actually scales.

How it works:

1. Real Digital Identities – Agents get their own governed email addresses and phone numbers. They autonomously handle login, 2FA, and account creation instead of failing at verification screens.

2. Automation Studio IDE – Code editor + live browser viewer + execution logs in one window. You see exactly what your agent sees as it runs. Debug failures with step-by-step context, not guesswork.

3. Agent Mode Prototyping – Describe a task in natural language, watch it run live in the browser, then click "Open in Editor" to get editable code. Prototype fast, ship with code you own and can refine.

4. Record Once, Replay as Code – Click through a workflow manually. Notte records the session and generates a deterministic script that mirrors your actions. For builders who prefer "show the system what to do" while keeping full control over the final code.

5. One‑click deployment - Turn your scripts into Functions that expose a stable API endpoint and can be scheduled with cron‑style triggers. Notte handles browsers, scaling, proxies (infra) so the same workflow can power agents, backend jobs, and integrations in production.

Getting started is stupidly simple at console.notte.cc. You can begin with a query like “Find gas prices in zip code 94086” and build from there.

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AI Agent with One Endless Chat and Self-Managing Memory

AI memory banks are fine until your conversation sprawls across 47 different chats, and many times, you’re managing the memory yourself, deleting and adding relevant ones.

How about one eternal conversation thread with no escape hatch? Real solutions to context management instead of relying on the reset button.

MIRA is an open-source framework that treats the inability to start fresh as a design feature, not a limitation. Memories extract themselves from conversations and decay through formulas based on usage patterns; frequently accessed information persists while stale data fades. The decay runs on activity days, so taking breaks doesn't corrupt your history. Tools self-register when added to the tools folder and dynamically load only when relevant, expiring after 5 unused turns.

Built almost entirely with Claude Code as a one-person team, MIRA shows what happens when you design around continuity from the foundation up.

Key Highlights:

1. Self-maintaining memory graph - Discrete memories automatically split when verbose, consolidate when overlapping, and form typed relationships; hybrid search combines semantic similarity with memory traversal and filtering for passive context loading.

2. Zero-config tool extensibility - Drop Python files into tools/ with a HOW_TO_BUILD_A_TOOL.md guide that lets Claude Code generate new tools in minutes; includes 11 built-in tools from contacts to speculative research with simple_description fields for context awareness.

3. Hierarchical persistent knowledge - Domaindocs provide stable reference material with version control while memories flow around them; MIRA controls its own context by expanding and collapsing sections with cost-aware token management.

4. Single-thread - No "new chat" functionality forces solving hard persistence problems that would otherwise be avoided.

Run the one-line install script to deploy locally, or test the hosted version at miraos.org with just an email login.

Quick Bites

Claude can now use your browser
Anthropic just dropped a Chrome extension that lets Claude navigate your browser, click buttons, and fill forms, available now in beta for all paid subscribers. Works standalone or integrates with Claude Code and Claude Desktop, so you can have it pull analytics data, organize your Drive, or clean up your inbox while you focus on other work. Worth noting: they're upfront about prompt injection risks and recommend starting with trusted sites only.

Open-source GLM 4.7 outperforms GPT 5.1 and Claude Sonnet 4.5
China's Z.ai just dropped GLM-4.7, their latest open-source coding model, surpassing GLM-4.6 with substantial improvements in coding, complex reasoning, and tool usage, setting new open-source SOTA standards (73.8% on SWE-bench Verified and 66.7% on SWE-bench Multilingual). The model comes with "preserved thinking" that maintains reasoning context across multi-turn conversations instead of starting from scratch each time. At 1/7th the price of Claude with 3x the usage quota through their coding plan, they're clearly targeting the developer agent market head-on.

The Agent Skills standard is catching on fast: OpenAI's Codex now supports Skills, letting you extend it with the same skill format as Claude. Skills use progressive disclosure to load instructions only when relevant, either explicitly invoked via $skill-name or automatically triggered when Codex detects a matching task. The team has released built-in skills covering everything from planning new features to pulling context from Linear and Notion.

Mistral’s new OCR model beats DeepSeek OCR
Mistral just shipped OCR 3, their latest document parser that beats their previous version on 74% of test cases across forms, tables, and handwriting. It also outperforms all other OCR models, including the latest DeepSeek OCR, across all standard benchmarks. It handles everything from cursive annotations to complex table structures with merged cells, outputting clean markdown with HTML table reconstruction - all at $1 per 1,000 pages via their batch API.

Tools of the Trade

1. Bloom - Open-source agentic framework by Anthropic that automatically generates behavioral evaluation suites for AI models by taking a specified behavior and creating diverse test scenarios to measure its frequency and severity.

2. Largemem - A collaborative knowledge base that ingests documents, audio, and URLs into a searchable corpus for AI. Teams create shared groups where uploaded content gets parsed and organized into a knowledge graph for contextual retrieval and Q&A.

3. Nia - Context layer that provides agents with up-to-date, continuously monitored context from libraries, research papers, and technical documentation, saving you hours of manual ingestion. It can also be used with coding agents like Cursor or Claude as an MCP server.

4. Awesome LLM Apps - A curated collection of LLM apps with RAG, AI Agents, multi-agent teams, MCP, voice agents, and more. The apps use models from OpenAI, Anthropic, Google, and open-source models like DeepSeek, Qwen, and Llama that you can run locally on your computer.
   (Now accepting GitHub sponsorships)

Hot Takes

1. The future of agencies/consulting looks like it will be around installing capabilities for customers via agents, fine tuning, skills

   ~ The Boring Marketer
   
   

2. i'm 100% convinced that Antigravity is just a ploy to collect training data for Gemini

   there's no way the engineering team is so bad to allow Antigravity auth with other services / such a dumb free tier
   ~ Aiden Bai

That’s all for today! See you tomorrow with more such AI-filled content.

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