Summary of "Туториал по Codex: от базовых функций до goals, subagents, automations и скрытых механик"
Summary of the video (Codex tutorial: basics → goals/subagents/automations + “hidden” mechanics)
1) Model/ecosystem context + product lineup
- The speaker claims Codex workflows used to rely mainly on “CLD…” model series, but OpenAI GPT-series models improved and now outperform in many coding tasks.
- They switch to OpenAI for ~90% of coding tasks due to:
- better result quality
- (they claim) fewer token-limit issues
- Mentions an “ultra-convenient” Kox App (Feb 2026) that combines:
- CLI-like power (command-line tooling)
- Desktop GUI convenience
- The tutorial covers the Codex ecosystem across multiple surfaces:
- Codex App
- Codex CLI
- Codex (Exc?) / another execution mode (treated as part of the same system)
2) Core concept: local agent execution (not just cloud chat)
A key differentiator from “basic GPT chat” is that the agent runs locally on the user’s computer.
- The agent can:
- create files in the local project folder
- execute commands on the machine
- set up/check results via a local server
- use a built-in browser control to test functionality visually
- Prerequisite: you must install the Kox/Codex App locally; browser-only usage won’t work.
3) Pricing/review-style guidance + third-party sponsor mention
- Subscription guidance (as described):
- start with any plan
- for “normal development,” recommend a higher tier (~$100)
- if limits still aren’t enough, a larger tier (~$200; claims 4× limits for 2× cost)
- Sponsor: Lessy AI (AI people search engine)
- Workflow:
- provide a text description of who you want
- service asks clarifying questions
- returns ranked profiles with links/contacts
- Offers:
- CRM export
- personalized outreach email generation
- Workflow:
4) Access control nuance (“permission gating”) + Auto-Review mode
- Default behavior: when new console commands/tools/parameters appear, the system often pauses execution until the user rejects/approves.
- Safety dilemma described:
- stall on repeated command failures, or
- grant broad “unlimited” permissions (less safe)
- Proposed solution: Auto-review access mode
- If a command needs human review, a dedicated review agent checks it.
- If rejected, the agent:
- finds a safer workaround, or
- escalates to a human review.
- The speaker reports using auto-review ~90% of the time, especially for normal work, but sometimes disabling it for “dangerous” mods.
5) Built-in vision/image generation for game asset creation (practical demo)
Using a Counter-Strike 1.6 modding example:
- The agent iteratively improves visuals:
- adds textures (sand textures appear after enabling/setup)
- fixes issues like crooked models, missing legs, camera/viewport alignment
- Image generation mechanism:
- uses a built-in “Codex ability” to generate images (claim: it uses a GPT Image 2–like model)
- supports reference images via vision abilities
- mentions typical upload formats (e.g., “DragonRope files and screenshots”)
- Limitation + workaround:
- the latest image model can’t generate transparent background images
- suggested workarounds:
- chroma key
- third-party background removal
- example: agent uses chroma key successfully
- Notifications:
- enable completion notifications in Codex settings to avoid continuously monitoring (widget/app/Mac notification notes)
- Speed/quality tradeoff:
- main runs use “Model 5 at High Era thinking level” (most powerful but slowest)
- faster alternatives:
- lower thinking levels
- other fast OpenAI models (example mentioned: GPT “53X Spark”)
- AST mode increases speed but consumes ~100% more tokens (i.e., doubles token usage, per speaker)
6) Vision/testing limitations + tools for visual QA
- Claimed limitation of current LLMs:
- weak at accurately verifying “what looks good,” catching UI glitches, or judging visual correctness reliably
- For visual testing:
- uses a browser use tool to load the running local server in a controlled browser and inspect UI/screenshots
- example: checking a button in a test bench
- Browser automation plugin:
- mentions a Chrome plugin enabling Codex to control the browser when authorized
- example: checking Google Trends, but parsing/requests can trigger heavy blocks
7) Automation + scraping reliability: proxies (sponsored)
- For stable web automation, they recommend proxies.
- Claim: common proxy providers have many “dirty” IPs, causing redirects/captchas.
- Sponsor: Notmav(e)n / Nandmanzhe proxy provider
- claims ~95% clean IPs
- session lasts up to ~24 hours
- location filtering + quality guarantee
- crypto/card payments + MIR card support (Russia mention)
- cashback + promo codes (discounts based on proxy type)
8) “Computer Use” for native app UI automation (and its limits)
- Codex can use a Computer Use tool to manipulate the computer UI outside normal Codex app flows.
- Demo: attempting to run “Everlasting Summer” on macOS
- works only reliably for specific store/app versions (Steam version control issues)
- agent gets stuck on complex UI flows (location map, card game)
- context eventually overflows and control becomes unreliable
- Conclusion:
- good for simple button automation
- unreliable for long visual flows
9) Building a “personal Jarvis” overlay assistant (architecture + coding walkthrough)
The assistant:
- appears as a small overlay widget on top of macOS windows
- opens a conversation when clicked
- uses Gemini Flash 3.1 for voice/speed (speaker preference)
- can optionally fetch CRM data
- has access to Google Search tool
- runs Codex code in an interactive mode in a new tab/terminal for continued manual prompting
- continues chatting asynchronously while code runs
- disconnects when the user ends conversation
10) Project organization + AgentsMD / rules refactor
- They clean up the project folder and reorganize:
- Services/ contains projects
- System/ contains scripts/utilities
- They generate an AgentsMD file:
- Codex uses it as the main “rulebook” for each session
- Refactor:
- move detailed instructions into separate Markdown rule files under Rules/
- AgentsMD points to where the rules live
- Convenience:
- use a “dog symbol” (
@) to reference files/folders with auto-complete
- use a “dog symbol” (
11) Git as a safety backbone (critical workflow)
- They emphasize initializing and using Git heavily:
- keep multiple independent repos (one per service project)
- root git ignores service folders to avoid mixing code
- Commit checkpoints before risky automation changes
- Branching/versioning approach:
- revert to prior states if something breaks
- states can be branched into “several independent versions” for parallel development
12) MCP servers: extending the agent with tools + documentation ingestion
- Integrate MCP Context 7:
- adds commands to search for libraries by keyword
- fetches detailed documentation for libraries
- useful for macOS/native Apple features
- How MCP differs from:
- Skills (instructions + resources)
- Plugins (bundles of MCP tools + skills; e.g., Gmail plugin)
- Example: Gmail MCP plugin
- reconstructs travel history by searching email for ticket/hotel data
- used for visa-like requirements; claims it worked extremely well
13) CLI Codex integration + access mode behavior
- Uses CLI Codex (“console version of Codex”):
- same capabilities, easier integration with other systems
- Access rights notes:
- automation may default to “execution rejected” unless configured
- ability to enforce auto-review via command prefix
- Sync between CLI sessions and Codex app:
- requests can appear in the app chat history
14) CLI/App Server bridging to cover full Codex features
- They improve the widget/assistant by shifting from purely CLI execution to Codex application server calls.
- Claim: this enables 100% coverage of Codex features, showing desktop app sessions alongside widget control.
15) Goal/Gold + task management + subagents (more “agentic” reliability)
- Adds “goals” concept using an experimental feature named “Gold”:
- enable via a Codex terminal command (example mentioned in subtitles: “Codex features enable a calls”)
- pinned goal makes the assistant continue trying until it believes it’s done
- Limitation:
- without explicit result-checking, completion bias remains (agent may stop early)
- Solution: subagents for a hierarchy of implementation + testing:
- development agent implements
- testing agents verify
- completion happens only when tests pass
- Notes:
- subagents are not used by default
- mentions configurable “allowed depth” for spawned subagents
16) Automations: scheduling recurring tasks + error handling
- Demonstrates the Automations section:
- view running automations, prompt, project, model, frequency
- Two creation modes:
- in chat (ask the agent to schedule recurring work)
- manually (choose prompt/project/model/frequency)
- Example automation:
- every 10 minutes, check for a large video upload on a website
- if something fails, agent handles it “calmly” and logs errors
- contrasted with brittle “soulless” scripts
17) Extra “hidden mechanics” hinted
- Planning mode
- agent is forced to plan first, ask clarifying questions, and only implement after plan approval
- Hooks
- link scripts to events (e.g., run tests after development completes)
- Sleep prevention
- “Prevent Sleep While Running” setting
- Mobile control
- remote control of local Codex via phone app; session/chat visibility
Main speakers / sources
- Main speaker: Oleg (host; “my name is Oleg”)
- Primary product/tool sources referenced:
- OpenAI models (GPT series, image generation)
- Codex / Kox App / Codex CLI / Codex server (Codex ecosystem components)
- MCP Context 7, Gmail MCP plugin, computer/browser control tools (as described by the host)
- Lessy AI (sponsor)
- Notmav(e)n proxy provider (sponsor)
Category
Technology
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