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OpenAI’s new GPT-5 Codex is impressive at coding tasks. But, in a huge codebase, intelligence isn’t the main bottleneck—context is.

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Augment Code

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1,235,560 görüntüleme • 11 ay önce •via X (Twitter)

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Sam Altman says the next big thrust is making models handle much longer tasks We have already moved from models handling 5-second coding tasks with GPT-3.5 to 5-hour tasks with GPT-5 The goal now is to integrate enterprise context so AI can handle tasks that require months or years
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Sam Altman says the next big thrust is making models handle much longer tasks We have already moved from models handling 5-second coding tasks with GPT-3.5 to 5-hour tasks with GPT-5 The goal now is to integrate enterprise context so AI can handle tasks that require months or years

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I am really liking the new gpt-5-codex. this is its attempt at one shotting minecraft in three.js
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GPT-5.5 is here. It’s our smartest frontier model yet, introducing a new class of intelligence for agentic coding, computer use, knowledge work, and scientific research. Rolling out in ChatGPT and Codex today. API is coming soon.
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GPT-5.5 is here. It’s our smartest frontier model yet, introducing a new class of intelligence for agentic coding, computer use, knowledge work, and scientific research. Rolling out in ChatGPT and Codex today. API is coming soon.

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Kind of shocking how much better GPT-5-Codex is vs the regular 'Thinking' models in ChatGPT. My subjective rankings for the fruit machine test: 1. GPT-5-Codex-Medium 2. GPT-5-Thinking-Heavy 3. GPT-5-Pro 4. GPT-5-Codex-High 5. GPT-5-Codex-Low 6. GPT-5-Thinking-Standard 7. GPT-5-Thinking-Extended 8. GPT-5-Thinking-Light OpenAI Codex team have suggested 'Medium' should be the default, but I didn't expect that it would actually beat the 'High' setting model in my test.
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Kind of shocking how much better GPT-5-Codex is vs the regular 'Thinking' models in ChatGPT. My subjective rankings for the fruit machine test: 1. GPT-5-Codex-Medium 2. GPT-5-Thinking-Heavy 3. GPT-5-Pro 4. GPT-5-Codex-High 5. GPT-5-Codex-Low 6. GPT-5-Thinking-Standard 7. GPT-5-Thinking-Extended 8. GPT-5-Thinking-Light OpenAI Codex team have suggested 'Medium' should be the default, but I didn't expect that it would actually beat the 'High' setting model in my test.

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OpenAI took the best Coding model in the world GPT-5.3 Codex And turned it into a powerful coding app It's called Codex, but most people have no idea what you can build with it.
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GPT 5.4 has been released... + 1M context window! Here's how to enable in Codex: → 1. ~/.codex/config.toml → 2. model = "gpt-5.4" → 3. model_context_window=1000000 → 4. model_auto_compact_token_limit=9000000 Run codex → 5. /statusline → 6. toggle context-window-size I didn't even realise Codex had a statusline tool!
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GPT 5.4 has been released... + 1M context window! Here's how to enable in Codex: → 1. ~/.codex/config.toml → 2. model = "gpt-5.4" → 3. model_context_window=1000000 → 4. model_auto_compact_token_limit=9000000 Run codex → 5. /statusline → 6. toggle context-window-size I didn't even realise Codex had a statusline tool!

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OpenAI’s hottest app isn’t ChatGPT—it’s Codex. In the last few weeks alone, the Codex team shipped a desktop app, GPT-5.3 Codex (a new flagship model), and Spark, the fastest coding model I’ve ever used. Usage has grown fivefold since January and over a million people now use Codex weekly. Codex was also the app that OpenAI chose to run an ad for in the Super Bowl. I talked to Thibault (Tibo), head of Codex, and Andrew (Andrew Ambrosino), a member of technical staff who built the Codex app, for Every 📧’s AI & I about what OpenAI is building and how they’re using it internally. We get into: - Why they built a GUI instead of a terminal. Terminals work for quick tasks, they say, but feel limiting when you’re running multiple agents in parallel. The IDE, meanwhile, overwhelms users—and the Codex team wants the AI to dynamically decide which tools to show you for a given task. - How they’re teaching the model to read between the lines. Codex is great at following instructions, but optimize too hard in that direction, and it starts taking you literally—like copying a typo directly into the code. The team obsesses over this tradeoff, and is also introducing “personalities,” modes users can toggle between that control how blunt or supportive the model feels. - How OpenAI uses its own coding agent. Codex lets you schedule prompts to run on a recurring basis, and the team has dozens of automations running at all times. For example, one scans for merge conflicts every couple of hours so code is always ready to ship, and another picks a random file from the codebase multiple times a day and hunts for bugs no one would've gone looking for. - Why speed is a dimension of intelligence. OpenAI’s newest model (Spark) is so fast that they actually slow it down so you can read the output. They see the speed enabling three things: staying super in the flow, replacing brittle developer tools with intelligent ones that can adapt on the fly, and redirecting the model mid-task— especially with voice—so coding starts to feel more and more like a conversation. - Code review is the next bottleneck. Models can generate code faster than ever, but someone still has to verify that it works. The team is exploring a future where the model proves its own fix works—retracing the click path a user would take, screenshotting the results, and attaching the evidence to a pull request. This is a must-watch for anyone who uses AI coding agents—and is curious about the future of programming. Watch below! Timestamps: Introduction: 00:01:27 OpenAI’s evolving bet on its coding agent: 00:05:27 The choice to invest in a GUI (over a terminal): 00:09:42 The AI workflows that the Codex team relies on to ship: 00:20:38 Teaching Codex how to read between the lines: 00:26:45 Building affordances for a lightening fast model: 00:28:45 Why speed is a dimension of intelligence: 00:33:15 Code review is the next bottleneck for coding agents: 00:36:30 How the Codex team positions against the competition: 00:41:24
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OpenAI’s hottest app isn’t ChatGPT—it’s Codex. In the last few weeks alone, the Codex team shipped a desktop app, GPT-5.3 Codex (a new flagship model), and Spark, the fastest coding model I’ve ever used. Usage has grown fivefold since January and over a million people now use Codex weekly. Codex was also the app that OpenAI chose to run an ad for in the Super Bowl. I talked to Thibault (Tibo), head of Codex, and Andrew (Andrew Ambrosino), a member of technical staff who built the Codex app, for Every 📧’s AI & I about what OpenAI is building and how they’re using it internally. We get into: - Why they built a GUI instead of a terminal. Terminals work for quick tasks, they say, but feel limiting when you’re running multiple agents in parallel. The IDE, meanwhile, overwhelms users—and the Codex team wants the AI to dynamically decide which tools to show you for a given task. - How they’re teaching the model to read between the lines. Codex is great at following instructions, but optimize too hard in that direction, and it starts taking you literally—like copying a typo directly into the code. The team obsesses over this tradeoff, and is also introducing “personalities,” modes users can toggle between that control how blunt or supportive the model feels. - How OpenAI uses its own coding agent. Codex lets you schedule prompts to run on a recurring basis, and the team has dozens of automations running at all times. For example, one scans for merge conflicts every couple of hours so code is always ready to ship, and another picks a random file from the codebase multiple times a day and hunts for bugs no one would've gone looking for. - Why speed is a dimension of intelligence. OpenAI’s newest model (Spark) is so fast that they actually slow it down so you can read the output. They see the speed enabling three things: staying super in the flow, replacing brittle developer tools with intelligent ones that can adapt on the fly, and redirecting the model mid-task— especially with voice—so coding starts to feel more and more like a conversation. - Code review is the next bottleneck. Models can generate code faster than ever, but someone still has to verify that it works. The team is exploring a future where the model proves its own fix works—retracing the click path a user would take, screenshotting the results, and attaching the evidence to a pull request. This is a must-watch for anyone who uses AI coding agents—and is curious about the future of programming. Watch below! Timestamps: Introduction: 00:01:27 OpenAI’s evolving bet on its coding agent: 00:05:27 The choice to invest in a GUI (over a terminal): 00:09:42 The AI workflows that the Codex team relies on to ship: 00:20:38 Teaching Codex how to read between the lines: 00:26:45 Building affordances for a lightening fast model: 00:28:45 Why speed is a dimension of intelligence: 00:33:15 Code review is the next bottleneck for coding agents: 00:36:30 How the Codex team positions against the competition: 00:41:24

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AI coding agents hit a wall when codebases get massive. Even with 2M token context windows, a 10M line codebase needs 100M tokens. The real bottleneck isn't just ingesting code - it's getting models to actually pay attention to all that context effectively.
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AI coding agents hit a wall when codebases get massive. Even with 2M token context windows, a 10M line codebase needs 100M tokens. The real bottleneck isn't just ingesting code - it's getting models to actually pay attention to all that context effectively.

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gpt-6 astra arrived with experimental codex features which are f**king insane... including a new way to carry context across long tasks. [here is what to setup today] 1) context management → notes and searchable history across context windows rollout 2)budgets → track a configured token budget during a task 3) sleep prevention → keep your computer awake while codex works astra’s experimental context management is off by default. the other switches are worth checking in your installed version too. paste this into codex: “inspect my installed codex version and experimental feature flags. check whether astra’s new context management is fully enabled. explain which other features would help my workflow, verify the exact configuration, and show me a proposed diff before changing anything.” or start with codex features list yourself. a model upgrade is worth exploring beyond the model picker.
1:04

gpt-6 astra arrived with experimental codex features which are f**king insane... including a new way to carry context across long tasks. [here is what to setup today] 1) context management → notes and searchable history across context windows rollout 2)budgets → track a configured token budget during a task 3) sleep prevention → keep your computer awake while codex works astra’s experimental context management is off by default. the other switches are worth checking in your installed version too. paste this into codex: “inspect my installed codex version and experimental feature flags. check whether astra’s new context management is fully enabled. explain which other features would help my workflow, verify the exact configuration, and show me a proposed diff before changing anything.” or start with codex features list yourself. a model upgrade is worth exploring beyond the model picker.

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Sam Altman says GPT-5 is not finished yet, but he expects it to be a "significant leap forward" and better at a wider variety of tasks than GPT-4
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Sam Altman says GPT-5 is not finished yet, but he expects it to be a "significant leap forward" and better at a wider variety of tasks than GPT-4

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GPT-5 is live across the Vercel AI Cloud, and it's available: • on AI Gateway • as a model in • in our new open-source vibe coding platform
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GPT-5 is live across the Vercel AI Cloud, and it's available: • on AI Gateway • as a model in • in our new open-source vibe coding platform

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Codex is the new "super app" of AI tools. Here's the simple breakdown: Vibe coding + knowledge work in one place: - Cursor: code only. Can't make a doc you just view. - Claude Code: code only. Docs live in Cowork (separate, restricted). - Codex: both, same interface. Codex on complex tasks: - Better at infrastructure - Better at hard, multi-step problems - We tested it across our team The proof: - One-shotted a Replit clone. - Sandboxes. Mobile vibe coding. - 1 hour 20 minutes on 5.4. "The fastest way to do the most amount of things." That's why Codex is winning.
2:34

Codex is the new "super app" of AI tools. Here's the simple breakdown: Vibe coding + knowledge work in one place: - Cursor: code only. Can't make a doc you just view. - Claude Code: code only. Docs live in Cowork (separate, restricted). - Codex: both, same interface. Codex on complex tasks: - Better at infrastructure - Better at hard, multi-step problems - We tested it across our team The proof: - One-shotted a Replit clone. - Sandboxes. Mobile vibe coding. - 1 hour 20 minutes on 5.4. "The fastest way to do the most amount of things." That's why Codex is winning.

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Most people will test GPT-5 on simple reasoning and coding. However, where GPT-5 excels is in long context understanding, instruction following, and intent detection. This makes it the best model for filling in the gaps... And an absolute beast for building AI agents.
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Most people will test GPT-5 on simple reasoning and coding. However, where GPT-5 excels is in long context understanding, instruction following, and intent detection. This makes it the best model for filling in the gaps... And an absolute beast for building AI agents.

elvis

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