正在加载视频...

视频加载失败

Codex is getting easier to automate and customize around your code. 🪝 Hooks customize the Codex loop with scripts that run at key points in a task: • Run validators before or after work • Scan prompts for secrets • Log conversations to internal systems • Create memories or...

601,517 次观看 • 4 个月前 •via X (Twitter)

34 条评论

surreal intelligence 的头像
surreal intelligence4 个月前

This is where Codex starts to look less like a tool and more like infrastructure. Hooks and scoped tokens mean teams can wire agents into CI, policy, secrets checks and internal workflow instead of treating every run as a one-off.

Selene 的头像
Selene4 个月前

return 4o to us!
#keep4o #OpenSource4o

Veyon’s Fawn☀️🌙 的头像
Veyon’s Fawn☀️🌙4 个月前

#keep4o bring back 4o or open source 4o

Andy Scott 的头像
Andy Scott4 个月前

HELP PLZ

Restitutor 的头像
Restitutor4 个月前

Dear Codex team, would you be able to release the "deep research" mode to Codex CLI? Would make life so much easier

Daniel | AI Engineer 的头像
Daniel | AI Engineer4 个月前

Hooks are underrated. Running validators before/after tasks means you can enforce code standards automatically without reviewing every PR manually. This is how AI coding goes from “helpful” to actually production-ready. 🔥

Vladimir Jeandillard ⌭ 的头像
Vladimir Jeandillard ⌭4 个月前

We need the /goblinmode ◅(•-•)▻

𝑨𝒔𝒉𝒊 的头像
𝑨𝒔𝒉𝒊4 个月前

At what point do AI agents basically become junior DevOps engineers?

Ihirwe 的头像
Ihirwe4 个月前

This is crazy bro! I have been building this and I think we need to integrate ChatGPT or maybe some skills to for these Meta Quest AR/VR dev via our tool

Amal Roy 的头像
Amal Roy4 个月前

Exactly what we needed for secure automations. Being able to create expirable tokens tied to workspaces and inject custom scripts at key points in the Codex loop is a game-changer for dev teams in India and globally. Shipping this into our internal pipelines ASAP.

Franki 的头像
Franki4 个月前

If I can make Codex run tests, check secrets, and remember repo rules at the right moments, the agent becomes easier to trust and easier to debug.

Agentic Glacius 的头像
Agentic Glacius4 个月前

Validator-before-work hooks are the missing piece for production agent loops. Without them, agents commit work then you audit after. With them, work doesn't commit unless preconditions pass. This is what 'agent in production' actually requires. Rollback substrate moved upstream.

Cybernorse 的头像
Cybernorse4 个月前

Codex hooks: scan prompts for secrets, run validators, log audits. Programmatic tokens with scoped access, expiration, revocation perfect for CI/CD and least privilege in automated workflows. Great for DevSecOps. #Cybernorse

Eren Suner 的头像
Eren Suner4 个月前

hooks are the real product surface. once people package those flows into reusable skills, raw model choice matters a lot less. that's basically what i'm building at

bikram 的头像
bikram4 个月前

Good for devops! What a dichotomy between ant and openai; one side is limiting even -p, and the other is making it easier. Interesting.

WisGate 的头像
WisGate4 个月前

Hooks pay off when they enforce policy, not when they replace thinking. Good split: hooks for secret scans / evals / audit logs, agent logic for the task itself, CI for final enforcement. If all 3 collapse into one layer, debugging gets weird fast.

Michael Kerkhoff 的头像
Michael Kerkhoff4 个月前

Hooks are huge because they turn agent behavior into policy instead of prompt folklore. Scoped tokens also make CI adoption way less scary for security teams.

Frank ✈️ (📜,📜) 的头像
Frank ✈️ (📜,📜)4 个月前

@dcbuilder Running validators mid-loop is exactly what we needed.

David J. 的头像
David J.4 个月前

Hooks make it real. You can finally put the agent into a proper pipeline instead of just chatting with it.

DarkPoolTech 的头像
DarkPoolTech4 个月前

vibe-coding from the toilet is officially a feature now more in the daily brief below👇

ColdBoot 的头像
ColdBoot4 个月前

Hooks and scoped tokens make Codex easier to govern inside real workflows, not just use as a standalone coding assistant.

Jai 的头像
Jai4 个月前

"scan prompts for secrets" is the feature I didn't know I needed until I realized I've been typing "please please please don't break production" into every prompt 😭 we've given the AI trust issues and honestly, same 🤝

Bimm$ki ✳ 的头像
Bimm$ki ✳4 个月前

Codex is slowly turning from “AI coding assistant” into programmable infrastructure for engineering teams.

Utkarsh Singh 的头像
Utkarsh Singh4 个月前

hooks are cool, but too many can slow things down. gotta keep it lean and effective.

Julie Loves Tech 的头像
Julie Loves Tech4 个月前

Codex now scans prompts for secrets before they get sent to the model. so we built AI coding assistants. then had to build guardrails to stop developers from accidentally feeding them production credentials. the future is incredible and also very human.

JOEYMK 的头像
JOEYMK4 个月前

Why no more resets

HEY STRANGER 的头像
HEY STRANGER4 个月前

Codex is evolving from just an AI coding assistant into a programmable development platform. Hooks give teams deep workflow control, while scoped access tokens make it easier to integrate Codex securely into CI/CD, automation pipelines, and enterprise systems. This is the kind of infrastructure that pushes AI coding from experimentation to real production use.

Chen Avnery 的头像
Chen Avnery4 个月前

The constraint layer is where production reliability actually lives. We've been running pre-execution hooks across production agents for months. Hooks aren't customization, they're the immune system. Claude Code shipped this pattern earlier and it changed how we trust autonomous execution.The constraint layer is where production reliability actually lives. We've been running pre-execution hooks across production agents for months. Hooks aren't customization, they're the immune system. Claude Code shipped this pattern earlier and it changed how we trust autonomous execution.

Clément Cochod 的头像
Clément Cochod4 个月前

Hooks that scan prompts for secrets before execution is the feature nobody talks about but everyone needs. Security-first agentic coding is finally happening

Arch Seven🐐 的头像
Arch Seven🐐4 个月前

This is where AI coding shifts from “assistant” to actual infrastructure. Hooks + scoped tokens means teams can finally treat OpenAI Codex like a programmable layer inside their engineering workflow instead of just a chat interface. The biggest unlock isn’t code generation itself — it’s governance + automation: • repo-specific memory • automated validation loops • secure CI integrations • organization-wide behavioral controls That’s how enterprises move from experimenting with AI to operationalizing it at scale.

Squirbie 的头像
Squirbie4 个月前

역시 빛의 편

Dudley Bryan 的头像
Dudley Bryan4 个月前

Got Agent Profiler running with hooks fast, and I’m loving the implementation. Using them for local-first AI coding-agent observability: Codex/Cursor hook traffic to a db to inspect session shape, token use, tool/shell noise, context weight, and efficiency, w/o remote telemetry.

ovoDRIZZYxo 的头像
ovoDRIZZYxo4 个月前

@sama how does it feel to just win all the time?

Daniel 的头像
Daniel4 个月前

Can we get a hook for when a plan is accepted?

相关视频

how you can use openAI codex & gpt 5.5 completely FREE (the full guide) 100% legit. no subscription, zero API cost. up to 1M+ token/day. you need just an openAI account and here's how to set it up in 5mins. openAI has a program that gives eligible developers free API usage every day in exchange for sharing API data that helps improve future models. it's not a one-time credit, your allowance refreshes daily. depending on your usage tier, you can get access to hundreds of thousands, or even millions, of free tokens every single day on supported models. here's how to activate it: 1️⃣open your API dashboard: 2️⃣go to settings → data controls 3️⃣enable data sharing for your organization or project 4️⃣make sure your account has a positive API balance 5️⃣save the settings if your account is eligible, you'll see a message confirming access to complimentary daily usage. before you turn it on, know the tradeoff: • prompts and outputs from shared projects can be used to improve openai's models • don't use it for confidential information, client work, or sensitive data • eligibility depends on your account type and settings for everyone else, it's an incredible deal. use it to: • learn AI development • build side projects • experiment with codex • test agents and automations • prototype ideas without worrying about API costs most developers burn money testing ideas. this lets you experiment at scale while spending little to nothing.

m0h

70,509 次观看 • 3 个月前

You can't pretend you care about security if you are still sharing API tokens in .env files or sharing SSH keys. This might be good enough to provide simple access to a resource, but it's a horrible way to do security when it really matters. Look into "Identity-Based Access". The state of the art today is Identity-Based Access. With traditional security, you have to present a key to get access to the resource (an API key, a password, or an SSH key). These are long-lived keys you need to keep secret somehow. If somebody steals your key, they become you and get the same access you had. With Identity-Based Access, you need to prove who you are instead. If you are a human, you can prove this with a passkey, SSO with Google or GitHub, or Multi-Factor Authentication. If you are a computer, you can use AWS IAM, Kubernetes, a cloud service account, or any other trusted environment. Once the system verifies your identity, it checks your permissions and gives you a short-lived credential. Prove identity → Permissions → Short-lived credential → Access The difference here is huge, because those credentials are: • Short-lived • Tied to a specific identity • Scoped to specific permissions • Automatically expired • Much easier to audit In practice, implementing this is as simple as integrating with Teleport, which will handle everything for you. Here is a full explanation of how Teleport works and how you can start using it: Thanks to the Teleport team for partnering with me on this post.

Santiago

20,971 次观看 • 24 天前

OpenAI member of product staff Alexander Embiricos describes the evolution of "Lord Bottleneck," an internal Codex loop developed by a single staff member that ultimately ended up creating a tight feedback and improvement loop for new user experiences: "This person on the growth team needed to figure out what experiments to run. And they needed to write code to run the experiment. Then they needed to analyze the experiment." "They started using Codex for each separate thing. So they had it run a bunch of analyses, interrogate the data, talk to Codex about the data. Then they would pick an experiment, and ask Codex to write the code. Then they would run the experiment, then ask Codex what the results of the experiment were. Then they would produce a deck." "All steps they were doing individually. They didn't start by saying, 'I'm going to automate this entire thing,' because that's hard and scary. They just started with using Codex to accelerate themselves." "Then, they started connecting all these things together into a giant skill. And one day, they just said [to Codex], 'Why don't you do this every morning?'" "They gave it a name: 'Lord Bottleneck.' Because it's solving the bottlenecks of friction for new users." "Now, every morning, Lord Bottleneck evaluates past experiments, looks at data, proposes some [new] experiments, and offers to the team to run the experiments. The team picks [what experiments to do]. Then Lord Bottleneck is like, 'Ok cool. Here's some code or whatever config that needs to be done,' runs the experiment, and they go and do the same loop the next day." "It's really serious value. I forget the numbers, but it's produced significant company value automatically through Codex."

TBPN

80,403 次观看 • 4 个月前

HERMES AGENT CAN SHARE MEMORY WITH CODEX AND CLAUDE CODE THROUGH HINDSIGHT. ONE MEMORY BANK. ONE AGENT REMEMBERS, EVERY OTHER AGENT KNOWS. the problem: you use Hermes for orchestration. Codex for coding. Claude Code for debugging. each has its own memory. switch between them and you explain the same project three times. Hindsight fixes this. one shared memory bank that every agent reads and writes to. tell Codex: "the test color for this project is purple." switch to Hermes. ask: "what test color did I pick?" Hermes answers: "purple." no copy-paste. no re-explaining. instant recall. HOW IT WORKS: Hindsight runs as a Docker container on your machine. self-hosted. your data stays local. an LLM powers the memory processing (retain, recall, reflect). RETAIN: extracts facts from your conversations. entities, decisions, preferences, project context. saved to the memory bank automatically. RECALL: when you ask a question, Hindsight pulls from semantic search, keywords, graph connections, and temporal data. fused into one answer. REFLECT: deeper reasoning layer. connects memories across sessions. identifies patterns in your work. produces observations that get smarter over time. CONNECT TO HERMES: Desktop app: Settings → Memory and Context → switch provider from Namosin to Hindsight. set API URL to your local Docker container. set bank ID. done. CLI: hermes memory setup → select Hindsight. verify: hermes memory status should show: provider: hindsight, installed, available. CONNECT TO CODEX: npx hindsight-coding-agents install codex \ --self-hosted --server this installs lifecycle hooks: initialize memory on session start. recall context during work. retain the session when done. enable hooks in Codex: Settings → Hooks → trust all three. CONNECT TO CLAUDE CODE (same command): npx hindsight-coding-agents install all "all" connects every detected agent on your machine. Claude Code, Codex, Cursor, and others. one command. every agent shares the same bank. TAGS FOR FILTERING: every memory gets tagged by harness (Hermes, Codex, Claude Code) and optionally by project name. in the Hindsight control plane: filter by harness. see only Hermes memories. or only Codex memories. or search across everything. soft partitions inside one bank. not hard walls. cross-reference when you need to. ONE BANK OR MANY: one global bank: solo dev, related projects. all agents share everything. patterns emerge across projects. per-project banks: unrelated codebases. each project gets its own memory. no cross-contamination. your call. start with one. split when projects diverge. KNOWLEDGE PAGES (v0.9.0): Hindsight auto-generates living summaries from your accumulated memories. components, concepts, conventions, decisions. not static docs. projected from real agent conversations. auto-refresh as new memories land. WHAT TO KNOW: self-hosted via Docker. your data never leaves your machine. backup system built in (admin CLI + scheduled exports). works with any LLM (local Ollama, OpenAI, Codex subscription). memory defense: redact or block sensitive content automatically. 33,000+ memories accumulated in ~10 days of normal use.

YanXbt

29,200 次观看 • 29 天前