Claude Code, NotebookLM, and Obsidian for Smarter Research

Is anyone else using Codex and Claude Code side by side? I only recently discovered the Codex plugin for Claude Code, published by OpenAI itself. The useful part is not simply having another AI available. It is being able to call Codex from the current Claude Code session for a code review, an adversarial design challenge, or a separate delegated task without constantly switching tabs and sessions. What makes the Codex plugin for Claude Code useful? The openai/codex-plugin-cc plugin is intended for developers who already work in Claude Code and want to add Codex to that workflow. Instead of allowing both agents to edit the same file at the same time, you can assign clear roles: Claude Code implements and Codex reviews, or Claude Code keeps the main thread while Codex investigates an independent problem in the background. The official plugin provides review commands such as /codex:review and /codex:adversarial-review, delegation through /codex:rescue, and job or session management through /codex:transfer, /codex:status, /codex:result, and /codex:cancel. Codex therefore becomes a collaborator inside the Claude Code workflow rather than a separate window. It does not create a separate Codex runtime The plugin uses the Codex CLI and Codex app server installed on the same machine. It also reuses the local authentication state, current repository checkout, and existing config.toml settings. Integration is straightforward, but each request still contributes to the user's Codex usage limits. Requirements before installation You need Node.js 18.18 or later and either a ChatGPT subscription, including Free, or an OpenAI API key. If Codex CLI is missing, /codex:setup can offer installation guidance. You can also install it manually with npm install -g @openai/codex and sign in with !codex login. How to install the Codex plugin in Claude Code Run these commands in Claude Code: /plugin marketplace add openai/codex-plugin-cc /plugin install codex@openai-codex /reload-plugins /codex:setup The last command checks whether Codex is installed and authenticated. Once setup is complete, the Codex slash commands should appear in Claude Code, along with the codex:codex-rescue agent under /agents. Try a background review first A low-risk first run is /codex:review --background. Use /codex:status to monitor it and /codex:result to retrieve the final review. Multi-file reviews can take time, so background mode keeps Claude Code available for other work. Three effective Codex and Claude Code workflows The value of the plugin comes from role design. If both agents modify the same area without boundaries, the result may be conflicting edits, repeated analysis, and wasted context. The following workflows make ownership clearer. Let Claude implement and Codex review After Claude Code completes a feature, run /codex:review for a read-only review. It can inspect current uncommitted changes or compare the branch against a base with /codex:review --base main. Because Codex does not edit files in this mode, the developer keeps control of what is accepted. For example, after Claude adds a payment flow across several modules, Codex can inspect logic errors, edge cases, and cross-file side effects. Claude Code can then evaluate the findings and apply only the changes that make sense. Delegate an entire task to Codex Use /codex:rescue for a problem that can be isolated, such as /codex:rescue --background investigate why the integration test is flaky. Claude Code can continue working on the interface or documentation while Codex investigates in the background. Rescue supports --background, --wait, --resume, and --fresh. Define the expected output and file scope before delegating. A vague instruction to fix everything while Claude Code is also editing the repository can still create collisions. A good task has a specific goal, completion criteria, and a clearly owned part of the codebase. Use adversarial review to challenge the project direction /codex:adversarial-review is designed to question implementation and design decisions rather than merely find bugs. For example, /codex:adversarial-review --base main challenge the caching and retry design asks Codex to inspect assumptions, trade-offs, alternatives, and risks such as data loss, race conditions, rollback, or reliability. This is where the two agents may appear to argue, but the debate only helps when a human sets a narrow question, requests evidence, and defines a decision rule. Otherwise, the review can become a chain of opinions with no practical outcome. Transfer sessions and manage background jobs /codex:transfer creates a persistent Codex thread from the current Claude Code session and prints a codex resume <session-id> command. It is useful when a discussion has grown beyond a short review and you want to continue directly in the Codex App or TUI without manually rewriting the context. Monitor, retrieve, and cancel work For background tasks, /codex:status shows progress, /codex:result returns the stored output and session ID, and /codex:cancel stops an active job. These commands prevent multi-agent work from becoming a black box. When a task drifts from its goal, canceling early is usually cheaper than waiting and starting over. Watch for review loops and usage limits Important: OpenAI explicitly warns that the optional review gate can create a long-running Claude/Codex loop and drain usage limits quickly. When enabled with /codex:setup --enable-review-gate, the plugin uses a Stop hook, which is an automated trigger that runs when Claude is about to finish its response, to start a targeted Codex review. If it finds an issue, Claude's response is blocked so Claude can address it first. This can be valuable before shipping, but it should not be left unattended. A practical safety checklist Assign roles before running: one agent implements while the other reviews, or each owns a separate task. Limit the scope by naming the branch, files, risk area, and completion criteria. Use background mode for large reviews and check progress periodically. Enable the review gate only while actively monitoring it, then disable it with /codex:setup --disable-review-gate. Do not let Claude review all Codex output and then ask Codex to review every Claude revision without a clear stopping rule. Use /codex:cancel when a task moves in the wrong direction. How can Codex and Claude Code work well together? The official OpenAI plugin offers a cleaner alternative to keeping Codex and Claude Code open in separate tabs or letting both agents edit the same file. Claude Code can remain the coordinator while Codex reviews, challenges a design, or owns a separate task. A sensible starting point is one small /codex:review --background run, followed by status, result, and cancel. Try rescue, transfer, and the review gate only after the basic workflow is familiar. The two systems can complement each other well, provided a person still sets the boundaries, budget, and stopping point.

I'd heard a lot about the gstack repo from the CEO of Y Combinator, so I got curious and installed it to try. What surprised me most wasn't the polished workflows — it was the genuinely different mindset behind them. That mindset shows up in the very first command: /office-hours, with six questions that don't ask about code at all, only the things most people haven't thought through before they start building. What is gstack and why did Garry Tan build it gstack is an open-source toolkit by Garry Tan, CEO of Y Combinator, built primarily for Claude Code. The core idea: instead of using AI as a plain code writer, Garry Tan wanted to turn Claude into a small AI agent team, where each member handles a different role — from product direction and security review to testing and release. The entire workflow runs in an ordered loop: Think → Plan → Build → Review → Test → Ship → Reflect. More specifically, gstack splits Claude Code into 23 specialized roles, and the output of each step is automatically passed to the next — no manual handoff needed. Some of the standout commands: /office-hours 6 questions that force you to rethink your feature before writing a single line of code /plan-ceo-review checks whether you're overbuilding or underbuilding relative to what's actually needed /review catches serious bugs that standard automated checks miss /qa opens a real browser, performs real interactions, finds real bugs /cso runs an automated security audit against international standards /ship syncs, tests, pushes code and opens a pull request in a single command How effective is gstack? Garry Tan says his working speed in 2026 is roughly 810 times faster than in 2013, measured by lines of completed code per day (11,417 vs 14). In 60 days, he shipped 3 production services and over 40 features — all while running Y Combinator full-time. Andrej Karpathy, co-founder of OpenAI, confirmed a similar trend, sharing that he hasn't typed a single line of code himself since December 2025 thanks to AI agents. But among all those commands, /office-hours stands out for the opposite reason from the rest, it doesn't help you work faster and it helps you avoid building the wrong thing from the start. Why Garry Tan puts /office-hours first Garry Tan placed /office-hours at the top of the workflow based on a simple observation: most products fail not because of poor code, but because they build the wrong thing. Teams spend weeks on a feature nobody needs, or build the right feature for the wrong audience, or solve a problem users already handle better another way. The command has two modes: Startup mode for founders and people building real products with real users, and Builder mode for side projects, hackathons, and open source. This article focuses on Startup mode, where the 6 questions are most directly applicable. 6 questions that stop you from building the wrong thing These aren't 6 questions to answer quickly and move on. They're designed to make you think honestly, because the more truthful your answers, the more accurately Claude can match what you actually need — saving you a significant amount of time later. You can read the full original prompts at office-hours/SKILL.md.tmpl. Demand reality: Is there a real need? Original question: "Who specifically has this problem? How are they solving it today?" Not "users in general" or "the marketing team" — the goal is to name one real person, ideally by name, who is actively struggling with a specific problem. If you can't name someone like that, you don't yet understand what they actually need. Concrete example: Instead of "users want better task management," it should be: "Minh, a project manager at a 20-person company, copy-pastes between Notion and Google Sheets every Monday morning because the two tools don't sync." Apply this to your own situation accordingly. Status quo: What are they using instead? Original question: "What is their current workaround? How much better do you need to be for them to switch?" Everyone is already solving their problem somehow — whether with Excel, sticky notes, or a WhatsApp group. If their current solution is good enough, they have no reason to migrate their data and learn an entirely new platform. Your solution needs to be meaningfully better before they'll even consider switching. Desperate specificity: Who needs this badly enough? Original question: "Who needs a solution badly enough to use your ugly beta version today?" This is the question that separates nice-to-have from must-have. If you can't find anyone willing to use an incomplete, rough, buggy version right now, the problem you're solving isn't urgent enough. Real early users are people who need a solution badly enough to tolerate an unpolished product — as long as it's moving in the right direction. Narrowest wedge: What is the smallest possible piece? Original question: "What is the smallest thing you could launch tomorrow? Not the full vision — the smallest piece." Not the first full-featured version — something even smaller than that. This question typically cuts 80% of the scope people add because they think "might as well do it while I'm here." It's a trap many builders fall into, including myself. Launch the smallest meaningful piece first, listen to real users, then decide whether to expand. Common mistake: Many people confuse "smallest piece" with "first full-featured version." The narrowest wedge truly means one small thing that solves one specific problem for one specific group of users — nothing more. Observation and surprise: Have you watched real people use it? Original question: "Have you watched real people use your product? Did they use it in ways you didn't expect?" This question is best saved for the second iteration onward, once you have something to test. Rather than asking for feedback through messages or surveys, sit and watch directly — or review screen recordings. The most valuable insights usually don't come from what users say, but from what they do that you didn't design for, or what they skip that you thought was important. Note: If you're in your first iteration and don't have a product yet, you can skip this question and come back after launching the smallest piece in step 4. Future-fit: The 2 to 3 year view Original question: "In 2-3 years, will what you're building still be relevant — or is the trend moving against you?" This isn't about predicting the future precisely. It's about avoiding building something that's already fading. If the trend is making your problem less urgent over the next two years, that's a clear signal to reconsider from the start. That said, if your goal is to move fast and capture the market before big tech ships something similar, this question can reasonably be set aside. A real example: a simple idea completely flipped In the gstack documentation, Garry Tan walks through a practical example. You open /office-hours and say: "I want to build an app that summarizes my daily work calendar." Claude doesn't agree and start executing. Instead, it pushes back: what you just described isn't a calendar summary app — it's actually a full personal AI chief of staff. These are entirely different in scope, technical complexity, and user expectations. From that single opening description, /office-hours helps you see: 5 features you were describing without realizing it 4 assumptions that need to be validated before building 3 different implementation directions with varying levels of complexity 1 recommendation: launch the smallest piece first, treat the rest as a long-term roadmap All of this happens before you write a single line of code. The output is saved as a document that subsequent steps in the workflow automatically pick up and continue from. These 6 questions work even without gstack The 6 questions from /office-hours don't require Claude Code or a gstack installation. They're a way of thinking — the same framework YC partners use to evaluate startups — and you can apply them right now with any AI tool you already have. The difference when using them through gstack is that Claude won't let you give vague answers. It pushes for specifics and won't move forward until your response is grounded enough to be useful. That's why /office-hours tends to be the most uncomfortable command in the entire toolkit — not because it's difficult to use, but because it asks exactly what you've been avoiding. Try it today: Before starting your next project, paste these 6 questions into Claude, Gemini, or ChatGPT along with your idea. Ask it to go through each question one at a time and not let you skip any. The results are often more surprising than you'd expect — even for ideas you've already thought through carefully. gstack currently has over 117k stars on GitHub and is still growing. For me, the most valuable part isn't the technical commands like /review or /ship — it's /office-hours, because it's the only command in the entire toolkit that forces you to stop and think before doing anything else.

NotebookLM officially became Gemini Notebook on July 16, 2026. The new name marks its evolution from a document Q&A tool into an AI research workspace that can run code, analyze data, create reports in multiple formats, and follow users into Gemini and Google Search. This article explains what has actually changed, which features remain, and who can use the new upgrades. Gemini Notebook is still the familiar NotebookLM Google confirms that Gemini Notebook remains a standalone product focused on research and learning. Existing users do not need to move their data to another service. Notebooks, sources, notes, and generated content remain within the same experience, while the new name makes the product a more recognizable part of the Gemini ecosystem. The core of the tool is unchanged. Users collect PDFs, websites, YouTube videos, audio files, Google Docs, or Google Slides in individual notebooks. When asked a question, Gemini Notebook responds based on the selected sources and provides citations that take readers to the relevant passage. This approach is especially useful when a claim needs to be verified instead of accepting an unsupported answer from a chatbot. The rename follows a journey that began with Project Tailwind at Google I/O 2023. According to Google, the product now has more than 30 million users and is used by over 600,000 organizations. The Gemini Notebook name therefore reflects a new stage of maturity in which a notebook is no longer simply a place to read documents but a workspace for research, analysis, and complete deliverables. How does Gemini Notebook run code? The most notable technical change is that each notebook can be equipped with a secure cloud computer. Put simply, Gemini Notebook has its own environment for writing and running code for research tasks. The tool can clean data, perform calculations, compare multiple datasets, build charts, or test a hypothesis instead of only summarizing text. From document Q&A to actionable analysis Previously, NotebookLM stood out for its ability to read multiple sources and provide citation-backed answers. With a code execution environment, Gemini Notebook goes one step further: it can manipulate data to produce new results. An analyst can import data from several countries with inconsistent formats, ask the tool to standardize it, run calculations, and then create charts and a report. Google says the system also includes more than 100 curated software skills. Even so, it is still an AI system that can make mistakes. Users should review the code, calculations, input data, and conclusions, especially when the results are used for financial, legal, medical, or business decisions. Note: Agentic capabilities and code execution are not yet available to every account at once. Google AI Ultra and selected Workspace plans receive access first; Google says the feature will continue rolling out to Pro users on the web. Which output formats can Gemini Notebook create? Gemini Notebook is no longer limited to text reports. From the data and documents in a notebook, users can request PNG or SVG charts, PDF reports, Word files, Markdown, plain text, CSV, JSON, Excel, and PowerPoint. The system also supports images, data tables, infographics, and slides, while allowing users to revise generated versions. One source collection, many ways to present it The same training material can be turned into a management report, presentation slides, a spreadsheet for an operations team, and an Audio Overview for people who prefer listening. Students can create flashcards, quizzes, mind maps, or a Video Overview. Content teams can build comparison tables and infographics without copying the same data through too many tools. Research: find sources, cross-read documents, cite evidence, and create reports with charts. Data analysis: standardize tables, run code, export CSV or XLSX files, and visualize results. Learning: create study guides, flashcards, quizzes, audio, video, and mind maps. Teamwork: build a knowledge base, share viewer or editor access, and track usage. The real value does not lie in the number of formats but in the fact that they are created from the same source collection. When users want to change the perspective or target audience, they can adjust the request without rebuilding the entire context. Where does Gemini Notebook appear in Google’s ecosystem? Gemini Notebook has started appearing in the Gemini app. Notebooks created in the standalone product can appear in Gemini’s navigation, while notebook name changes, added sources, and updated custom instructions are synchronized across apps. Users can therefore continue chatting with their knowledge base without always returning to a separate tab. Google also plans to bring notebooks into AI Mode in Search. Once completed, this direction could turn a notebook into a personal context layer that follows users from web research to conversations with Gemini. However, shared notebooks and conversations in Gemini have separate rules for visibility, sharing, and data retention; organizational users should review the policies for their Workspace plan. How to get started with the new name Open Gemini Notebook with a Google account and create a notebook for one specific goal. Add trustworthy sources, then check how the tool categorizes and cites them. Start with narrow questions before requesting deep research, data analysis, or output files. Review citations, calculations, and the final version before sharing. Existing users can continue visiting the familiar NotebookLM address during the transition. The tool slug on 4AIVN also remains unchanged so old links do not break, while the name and content have been updated to Gemini Notebook. Does the rename make Gemini Notebook more useful? A name alone does not change research quality. What makes this rename notable is that Google is combining three layers of capability in one product: citation-backed sources, a code execution environment, and the ability to bring notebooks into Gemini and Search. If rolled out reliably, Gemini Notebook can shorten the path from reading documents to analysis and finished deliverables. However, not every feature is immediately available to everyone, and AI-generated output still needs to be checked. The most effective approach is still to choose strong sources, separate notebooks by clear goals, request specific outputs, and keep a person in the final approval step.

An AI agent may plan extremely well, yet it still cannot update Notion, read GitHub issues, or retrieve reports from Google Drive without the right connection. By combining Hermes Agent with MCP, users can turn a conversation into a practical workflow while clearly controlling which tools and permissions the agent may use. If you are not yet familiar with Hermes memory and its ability to create skills, our guide to what Hermes Agent is provides the necessary foundation. This article focuses on how MCP extends Hermes beyond the terminal so it can work with everyday data and services. What does MCP add to Hermes Agent? MCP is a connection standard between an AI application and a server that provides tools or data. It can be understood as an adapter layer: Hermes remains the agent responsible for understanding the goal and choosing the next step, while each MCP server contributes specific actions such as searching Notion, reading a pull request, creating an issue, or querying files. According to the Hermes Agent MCP documentation, Hermes supports local servers over stdio and remote servers over HTTP. At startup or after a configuration reload, Hermes discovers the tools exposed by each server and registers them in its normal tool system. Users therefore do not need to write a native Hermes tool for every service that already has a suitable MCP server. MCP does not automatically make a workflow safe. A server may expose tools that read, write, create, and delete data. Hermes supports filtering per server, allowing users to enable only the operations they need instead of exposing every capability to the model. How to connect MCP without granting excessive access The standard Hermes installation already includes MCP support. Users can open the picker with hermes mcp, view the catalog with hermes mcp catalog, and test a connection with hermes mcp test. Nous Research reviews entries before they enter the Hermes catalog, but its documentation still recommends reading the manifest, source repository, and installation commands before use. For a server outside the catalog, users can add an HTTP connection or a stdio command to config.yaml. After completing OAuth or configuring the required environment variables, reload MCP and ask Hermes to list the available tools. This simple check reveals servers that failed to connect or tools that were accidentally filtered out. Begin with read access The safest setup is to connect one server, enable read only tools, and test with nonsensitive data. Add create or update permissions only after results are stable. Deletion, sharing changes, and outbound publishing should require human approval. Notion initially needs only search and page reading access. GitHub can be limited to reading repositories, issues, and pull requests. Google Drive access should be limited by folder, account, and required OAuth scope. Three practical workflows with Notion, GitHub, and Google Drive Turn Notion into a knowledge center The official Notion MCP allows an agent to search, read, and update workspace content under the authenticated user's permissions. A useful workflow lets Hermes collect meeting notes, find relevant decisions, and prepare a summary on the project page. Hermes can create a draft first so a user can review it before updating status or assigning work. Notion MCP uses user based OAuth, so it does not fit every unattended process. For scheduled automation, verify how the server maintains authentication and avoid designing a workflow around operations that OAuth cannot support in a headless environment. Coordinate development work through GitHub The GitHub MCP Server is provided and maintained by GitHub, allowing AI tools to work with software development data according to account permissions. Hermes can read new issues, compare them with repository changes, and draft a progress report. It can then prepare issue text or release notes while waiting for an owner to approve the write operation. This workflow works best with clear criteria. For example, Hermes can summarize only pull requests merged during the previous seven days, group them by label, and connect each change to its related issue. A second MCP server can then send the result to Notion as a weekly report. Summarize files and reports from Google Drive With a compatible Google Workspace MCP server, Hermes can find Drive files, read permitted content, and feed data into a reporting process. For example, the agent can locate a sales report in a fixed folder, extract selected metrics, and create a summary for Notion or a GitHub issue. Google collects its official MCP projects in the Google MCP repository, including a path for Google Workspace integration. However, several community Drive servers have different maintenance histories. Check the source, update history, and OAuth scopes of the specific server instead of installing one based only on its name. Combine multiple MCP servers into a controlled workflow A complete workflow can begin in GitHub, use Drive as a data source, and finish in Notion. Hermes reads an issue labeled for reporting, finds the corresponding spreadsheet in Drive, produces a summary, and updates the project page. Each stage uses a different MCP tool group, while Hermes plans the sequence and passes results between stages. Do not enable parallel execution merely because a server supports it. Hermes documentation allows servers to declare parallel tool support but warns that operations reading and writing shared state can conflict. Independent read operations may run together, while Notion updates, issue creation, and file changes should remain sequential. Important: An MCP server is software that can run commands and receive credentials. Install only trusted servers, keep tokens out of prompts, filter dangerous tools, and require approval for deleting, sharing, or publishing data. How should you start the first workflow? Do not connect Notion, GitHub, and Google Drive on the same day and immediately assign a critical process. Choose one input, one output, and one completion criterion that is easy to verify. A first workflow could read closed GitHub issues and create a draft report in Notion without deletion or publishing permissions. After several stable runs, you can turn the procedure into a reusable Hermes skill and add a schedule. The real value of MCP is not the number of connected servers. It is the ability to complete a recurring workflow with a small permission surface, verifiable results, and a clear data path.