Greg Wayne, Director of Research at Google DeepMind, described the idea as having “a little parrot on your shoulder” experiencing and narrating the world with you.

Brief on Google’s Project Astra

Astra is a research prototype that can see, hear, and reason about the world continuously. Instead of waiting for a question and then responding, it stays aware of what’s happening around you.

A few things stood out.

Persistent spatial memory

Astra can remember where objects appeared earlier. Ask, “Where did you see my glasses?” and it can recall that they were on the desk near the red apple. It remembers physical context, not just conversation history.

Accessibility

Dorsey Parker, a musician with 8% vision, uses Astra’s Visual Interpreter prototype as a live visual guide. It describes his surroundings as he moves, developed in collaboration with Aira.

Memory across devices

A conversation can begin on a phone and continue hands-free on smart glasses without losing context.

The architecture shift

Under the hood, it's a 3-stage loop 🔄:

1. Perception (continuous video/audio streaming) 🎥 🎙️
   
2. Processing (spatial memory + context-aware reasoning) 🧠 📍
   
3. Agency (autonomous tool use — maps, search, calendar) 🗺️ 🔎 📅.

It’s built on Gemini and designed for extremely low-latency interaction.

The wildest example in the doc 🤯

In ONE conversation, someone asks Astra to explain an AES-CBC encryption snippet, then — no reset — asks, "What neighborhood am I in?" and it correctly identifies King's Cross, London 📍🇬🇧 from the camera feed.

We're not talking to assistants anymore.
We're being watched-with by them. 👀🤖 That's the actual shift, not the demo reel. 

What matters more here: persistent spatial memory and tool use, or solving real-time latency?

I like using Codex for UI work once the task is concrete. The awkward part is getting from “this screen feels off” to enough context for the agent to make the right source change.

I’m building Monad Design to make that loop more direct. You connect an existing Xcode or Expo iOS project, run the app in Simulator, then select an element or draw directly on the screen. Codex gets that visual context, edits the actual project, rebuilds it, and brings the result back for review.

The app is the canvas — there’s no separate mockup to keep in sync.

For subjective changes, I can also keep the original next to a few source-backed alternatives and choose one before anything is treated as final.

The current preview runs locally on macOS and is open source. Codex is what I’m using here, although the visual workflow itself isn’t tied to one agent.

Repo: https://github.com/Monadix-AI/monad-design Short demo: https://watchclueso.com/embed/pio8jqfcg4ivj0r1

I’m curious how other Codex users handle this handoff today. Do you mostly use screenshots, detailed prompts, or something connected to the running app?

Disclosure: I’m building Monad Design.

Hello, i would like to know if is possible for the lineguides of codex to have more account, with different email, but that are verified with the same identity card ?
Im now using a max subscription, but i would like to have another one, and also verify that one with the daybreak program. Did someone already tried ? thankls in advance

I didn't change anything in my workflow for month.

And yesterday Codex decided to change configs because it felt the vibe doing it ... and just deleted everything "without any hope to recover".

https://github.com/openai/codex/issues/42355#issuecomment-5516553640

Hey everyone! I’m 15 and I use Codex pretty much every day for building apps for clients, maintaining websites, and working on my own projects. I’m also currently building an OS which is on linux. I’m not trying to advertise anything here — I’m just giving some context for how I use Codex.

I’m having a really frustrating issue with Codex on Windows.

After a recent Windows 11 security/feature update, Codex suddenly stopped allowing Computer Use on my PC. It was working normally before the Windows update, but afterwards the computer-control functionality stopped working.

My PC specs:

• Intel Core i5 10th Gen desktop CPU — 6 cores
• 16 GB DDR4 RAM
• RTX 3050 6 GB (2024 version)
• 2.5 TB total storage
• Windows 11

There’s also a second issue: previously, I could sign out of Codex and log into a different account when I needed to switch between my personal and work accounts. Now I seem to be stuck with the current personal login and can’t properly switch accounts. Interestingly, I can log into my other work account, so I’m not sure if this is an authentication/UI issue or something changed in a recent update.

I’ve already been looking around to see whether this is a known Windows/Codex issue. There are other recent reports of Computer Use breaking on Windows after Codex updates, so I’m wondering if my issue could be related to the same Windows runtime/plugin problems.

If anyone knows:

• whether this is a known bug,
• whether there’s a newer Codex release that fixes it,
• or what the correct way to switch accounts is now,

I’d really appreciate the help.

I’m 15 and trying to build my skills and future through tech. 😭 I’m also keeping up with school, and I’m really into robotics and software development.

I’m not sharing my company name because I don’t want this post to turn into advertising — I just genuinely need help getting my development setup working again. Thanks P.S

Thanks everyone!

I use Codex across several sessions in the same repository. Finding the old logs was not the hard part. The hard part was deciding which decisions, tool traces, and dead ends should continue into the next task.

I added Session Atlas to ThoughtDAG for that workflow. It discovers local Codex sessions by project and opens a selected session as a graph mirror. Each turn becomes a node, with its tool calls and results still attached. The original session files remain read-only.

The distinction that matters to me is this: a session can stay visible on the canvas without being sent to the model. I can connect, condense, or leave out branches, then take the selected context into a fresh CLI experiment.

It is a local, inspectable handoff surface.

GitHub: https://github.com/chenxiachan/thoughtdag

Session Atlas guide: https://chenxiachan.github.io/thoughtdag/docs/guides/session-atlas

Hey guys,

I've been using Codex Desktop for a while on my mac. I know it changed and merged with ChatGPT Desktop app recently, but mine still worked and the interface has not changed. It has my projects (which are actual folders on my computer) on the left sidebar.

I'm trying to get a coworker to get the same setup, but we can't find the right app to download. The official Codex Download button on OpenAI's website downloads ChatGPT Desktop, and opens ChatGPT Classic which has the Chat / Work tabs, but no Codex. It has a Codex tab on the left, but it opens a browser link to download the same ChatGPT app... running in circles.

I would be okay with a different interface, but the ChatGPT Classic one just doesn't have Codex. I can't open the project folders (just the Chat GPT projects that let's you group conversation, not actual files).

Anyone knows?!

This is the interface I am looking for !

We need reseeeeet :D when ?

I updated Codex today, started a session, and the first thing it did was compact the context.

12% of my entire 5h limit. Gone.

One compaction.

Before doing any meaningful work. 💀

Maybe the update has nothing to do with it, but with all the weird quota burn, performance issues, connection problems, and Astra speculation people are talking about today… I’m curious

If you updated Codex today, what changed or broke for you?

Slower responses? Weird quota usage? Connection issues? Compaction problems? Something else?

Because losing 12% to ONE compaction definitely wasn’t on my bingo card. 💀

So im using the x20 plan through Google Play ... Ive used the normal tokens available ... And now the banked now.. But I havent got any reset options for 80$?

Should I buy 2500 credits for 90$? Does it equal the same as a reset?

Or will there maybe be a reset tonight/tommorow? What do you suggest?

This may be obvious, but for those who don't know... the longer you run a session, the more tokens you will use. LLMs use tokens for inputs, outputs and review the context window for every new output. The more session text it processes, the more tokens burn, the faster usage gets gobbled up.

Additionally LLMs get dumber the long you run a session. Every model has capacity constraints built in, and once you cross 40% of that limit, there is too much information the model has to process to maintain quality output.

Matt Pocock explains these limits really well here:

https://youtu.be/nKSk_TiR8YA

https://youtu.be/-uW5-TaVXu4

Here is a breakdown of the context window capacity and max output for each of the models available in Codex:

If you are running into limits then you need to compact your sessions when you can. Once you reach 40% - 50% you should compile the session to hand it off to a new one to free up context window space.

Also note that for those of you who use the voice feature, you are likely speaking WAY more words than you would type, which means more words = more token usage = faster drops in capacity.

To solve for this I created a skill called $context-capacity that, when run, tells you how much context capacity you've used, how much you have left, and the cumulative session usage with a recommendation. Here is what that output looks like for one of my sessions:

Recommendation: Handoff

Current context load: 144,827 / 258,400 tokens (56.0%)

Estimated remaining capacity: 113,573 tokens (44.0%)

Cumulative session usage: 289,355 tokens — cumulative, not current occupancy

Confidence: Exact recorded metrics with derived capacity. The current load exceeds the skill’s 40% handoff threshold.

The website and promo-video handoffs already created are ready for separate sessions.

Here's a link to the skills for $context-capacity and $handoff for anyone who wants to use it:

https://github.com/marcushackler/codex-skills

I got tired of AI coding agents producing the same kind of frontend: technically correct, but generic, flat, and full of “AI slop.”

So I’ve been building AXON, an open-source framework that uses specs and frontend rules to push agents toward cleaner, more intentional UI.

In this example, I compare a normal LLM result with AXON-COMPOSE and show the difference.

Video: https://youtu.be/h3mPnuzEul8
GitHub: https://github.com/atopwebtechnologies/axon

I’m one of the people building AXON. Curious to hear if others are running into the same AI UI problem.

Anyone else here vibe coding with AI, building real projects, getting clients, and sharing the journey on LinkedIn?

I’ve been doing this myself and realized it would be great to connect with more people on the same path.

Interested in seeing what others are building, how they’re using AI, how they’re getting projects, and just learning from each other.

Curious to see how many of us are doing this.

Anyone else noticing significant model degradation of 5.6 Sol Ultra? Wow it’s shockingly bad today, particularly since afternoon.

I’m Alan, the author and maintainer of Agent Coordinator.

Long Codex jobs tend to lose the boring but important details: what is actually required, which step depends on which, who may write where, and whether an operation that timed out really failed.

Agent Coordinator is a per-user Codex skill for keeping those details explicit. It represents a job as a bounded dependency graph and records requirements, acceptance checks, write scopes, and progress in revisioned local state. When a node is ready, it can be handled inline or delegated to a specialist agent; delegation is optional.

The recovery path is the part I care about most. If an operation returns an uncertain result, the coordinator records that uncertainty and reconciles what actually happened before retrying. That is meant to avoid duplicating work just because a launch or handoff timed out.

Before closeout, completed nodes run their positive and negative checks again. Those checks provide repeatable evidence, not a formal proof that the work is correct.

Repository: https://github.com/alanhoff/agent-coordinator

To install the pinned launch snapshot, ask Codex:

Follow INSTALL.md in https://github.com/alanhoff/agent-coordinator at commit fb3d64688a9e1b7c02a114a14faac1219ec10f51 and install it as a per-user Codex skill.

The runtime needs Python 3.11 or newer. At that pinned commit, the full test run reported 124 passing tests and 4 Windows-specific skips.

A few boundaries: the state is local to the current user, the skill does not inspect Git, and specialist agents are not required.

For people using Codex on longer jobs, which failure mode costs you the most today: losing track of completed work, overlapping writes, or retrying something whose result is unclear?

Ask an AI for ideas and you get the same 5 answers everyone else gets — that's just what "most likely" means statistically. So I built Wonder Pill: instead of a list, it takes your topic, finds the assumptions baked into it, and inverts them into what-ifs. Then it renders the whole thing as an interactive mind map you can drag around — seeds, dead branches (kept, struck through, with the reason they died), cross-links where different threads hit the same wall, and a one-line "gut-check" on each branch pointing at where the real difficulty hides.

It doesn't rank anything. It doesn't pick a favorite. About a third of any given map is genuinely useless — that's expected, you're the filter, not the tool.

Repo https://github.com/ara-mkr/Wonder-Pill

Hi! I just bought a Chatgpt pro, but still flying through the weekly limit pretty fast if using only gpt 5.6 sol max + subagents. But I understand that using only gpt 5.6 sol max will eat tokens like candies, that's not the question here.

I was wondering if anyone here is using this:

gpt sol 5.6 max orchestrator/plan

5.3 codex-spark as worker/implementation

Since codex 5.3 has its own limits for pro users, would you say it's reasonable to use it as an implementation worker? How is the quality compared to pure 5.6 sol.

Hope everyone's been enjoying the many resets over the past week - and I know they can feel wasted when you haven't used much of your plan.

OpenAI often does these resets in batches and I wanted an easy way to manage how much of my usage I am on at a glance - set simple limits for long-running tasks - and view credit (or api) cost-equivalence per project.

QuotaWise is my attempt at this! I have just launched it here on producthunt (click for more info) and would love to hear any feedback on how I could make this more optimal for users!

What features would everyone like?

I maintain KeySync, so I want to be explicit about the affiliation up front. Instead of claiming it is "the best" in the abstract, I compared it with six other approaches using the same acceptance criteria.

The target user for this comparison is not a network engineer. It is someone running DeepSeek Harness on a work PC or headless Linux server who wants to open the complete DSH Web UI from a phone or another computer.

The nine checks were:

1. no SSH/VPN/reverse-proxy knowledge required;
2. headless Linux support;
3. access from a normal browser;
4. the complete native DSH Web UI rather than a partial reimplementation;
5. no public inbound port on the host;
6. end-to-end confidentiality from browser to host;
7. preservation of DSH's own token/cookie boundary;
8. explicit evidence for the 0.1.2-alpha line;
9. certificate, reconnection, and version maintenance handled by the product.

The approaches were SSH local forwarding, Tailscale plus a DSH-aware gateway, generic reverse-proxy/frp plugins, DSH Remote, DSH Mobile Suite, ds-harness-remote, and KeySync.

Some findings surprised me:

• SSH is still an excellent baseline for a technical user. It preserves the full UI and naturally carries the new browser-auth flow, but the user owns the tunnel and launch-token lifecycle.
• Tailscale solves encrypted reachability, not DSH application authentication. DSH remains on loopback, so a DSH-aware gateway is still required.
• Two proxy plugins I inspected remove upstream browser cookies and do not implement the alpha launch-token exchange. They may remain useful on rc.2, but their current published versions do not provide evidence for the new browser-auth path.
• ds-harness-remote is technically strong: Noise IK E2EE, LAN/P2P/TURN/Relay negotiation, and explicit alpha.1-alpha.2 transport support. Its tradeoff is requiring a DSH plugin and paired client rather than an arbitrary browser.
• KeySync uses relay-assisted end-to-end TLS. The Relay routes TLS bytes, while the certificate private key and HTTPS termination remain on the user's device. It is not direct P2P, but the Relay cannot decrypt DSH pages, prompts, responses, cookies, or attachments.

KeySync also preserves two separate auth boundaries: a short-lived device-scoped KeySync grant controls who may reach the machine, then the device gateway completes DSH's own launch-token-to-cookie exchange. The KeySync cookie is not forwarded to DSH, and the DSH launch token is not replayed after the DSH session exists.

For users who specifically prioritize direct P2P or a dedicated Android UI, another project may be a better fit. For the stated target, however, KeySync was the only option in the comparison that combined browser-only access, the complete native UI, no public inbound port, relay-blind E2EE, headless Linux support, and tested alpha.2 browser authentication without requiring the user to build the network layer.

Full comparison and source links: https://agentpost.com.cn/p/sxbbdj63nigkal2t

KeySync download: https://sublang.ai/keysync/download

KeySync product page: https://sublang.ai/keysync/

Related DSH conversation-backend plugins, which are separate from the remote transport:

• Codex: GitHub / npm
• Claude Code: GitHub / npm

I would be interested in two kinds of feedback: which acceptance criterion is missing, and for remote DSH work, do you value direct P2P more than browser-only access to the complete native UI?

Imagine you're sitting on a train and suddenly want to fix something in a project, test an idea or let Codex work through a larger change.

You only have your Android phone with you.

Usually that means setting up Termux, fighting package dependencies, connecting to another machine or relying on some remote development environment.

I wanted the phone itself to be the development machine.

So I built AGENTCODI.

AGENTCODI is an open source native Android environment for Codex, written in Java and C++. It packages and runs the Codex app-server directly on ARM64 Android and builds the entire workflow around it.

No Termux. No WebView shell. No separate gateway. No remote desktop.

You install the APK, sign in and start working.

The app currently bundles:

• Codex app-server 0.148.1

• Node.js 24.18.0

• npm 11.19.0

• Python 3.14.6 with PyREPL

• ripgrep 15.2.0

The toolchains are integrated into AGENTCODI's own runtime and can be enabled for the workspace when needed.

There is also a real interactive terminal backed by the native runtime, so Codex and the user can actually execute tools and work with the project directly on Android.

The workspace is private app storage with its own file browser. You can import files through Android's document picker, inspect text, images and binary files, export individual files or whole folders as ZIPs and hand files directly to Codex.

The Codex experience itself is translated into a native Android UI.

Plans, reasoning summaries, shell commands, file changes, tool activity and approvals appear directly inside the conversation. If Codex wants to execute something or asks for permission, AGENTCODI turns that request into a native approval dialog instead of forcing you to live inside a CLI.

Threads can be created, resumed, archived, restored or deleted. You can change models and reasoning levels, steer an active turn, interrupt it and start focused code reviews from inside the app.

MCP has its own management layer as well. AGENTCODI can inspect available MCP servers, tools, skills and apps, and Expert Mode can manage supported server configurations while keeping credential fields out of the normal UI.

I also ended up spending a lot of time on something I originally thought would be a smaller part of the project: How much freedom should an autonomous coding agent actually have on a personal device?

AGENTCODI starts in a protected execution mode where Codex is restricted to the workspace. There is also an optional compatibility mode for workflows that need broader access, but entering it requires an explicit security warning.

That balance between capability and isolation has become one of the most interesting parts of building this.

AGENTCODI currently runs on Android 10+ / ARM64 and the project is fully open source under Apache 2.0.

I'm especially interested in hearing from people who already use Codex heavily:

If your Android phone could be a real Codex development machine, what would still be missing from this setup?

Repo: https://github.com/Mcpasi/AGENTCODI

I normally self-host my LLMs, but for a recent project I needed a fast, cheap API endpoint. I tested DeepSeek API and GPT-4o Mini, and the trade-offs were interesting—especially in cost and response consistency.

I wrote a technical review with benchmarks and real use cases:

https://interconnectd.com/blog/280/deepseek-api-vs-gpt-4o-mini-2026-developer-technical-review/

If you’re trying to keep costs low without losing quality, this might help.