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Thibault Sottiaux: Here is how to enable a 1M-token context window in Codex for GPT-5.6 Sol
Here is how to enable a 1M-token context window in Codex for GPT-5.6 Sol. Even though we have tuned the context limit in Codex to be set optimally when it comes to performance and cost, this is a common ask, so here it is documented. A larger context window lets Codex retain more code, tool output, and conversation history before summarizing older material. You need a model that supports it. And GPT-5.6 Sol, for example, has a documented 1,050,000-token window. Open ~/.codex/config.toml and add or update these settings at the top level, before any [section] headers: ``` model = "gpt-5.6-sol" model_context_window = 1000000 model_auto_compact_token_limit = 900000 ``` The first setting selects the model. The second tells Codex to use a one-million-token context budget. The third starts automatic history compaction around 900,000 tokens, leaving some headroom. Restart Codex client and start a new session after saving. To try the configuration for a single CLI session without changing your defaults: ``` codex -m gpt-5.6-sol \ -c model_context_window=1000000 \ -c model_auto_compact_token_limit=900000 ``` Have fun, but also know that we tuned the default carefully!|@thsottiaux · 热度 14483 · 2026-08-16
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Thibault Sottiaux: Codex ✅ Almost 100% reliable ✅ Occasional resets ✅ Open-source ✅ (will have ...
Codex ✅ Almost 100% reliable ✅ Occasional resets ✅ Open-source ✅ (will have Astra) https://t.co/DxNdAgpag5|@thsottiaux · 热度 8840 · 2026-08-17
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Thibault Sottiaux: GPT-5.6 Sol 1M in Codex
GPT-5.6 Sol 1M in Codex. This used to only work for API keys, but we just flipped the switch and works for usage through ChatGPT accounts now too. The same warning applies, there is a reason the current context length is the default, we have tuned it to ~perfection. But you do you!|@thsottiaux · 热度 4473 · 2026-08-17
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Thariq: it says a lot that the creators of three of the most iconic web frameworks: d...
it says a lot that the creators of three of the most iconic web frameworks: django (@simonw), flask (@mitsuhiko) and rails (@dhh) were so AI pilled so early|@trq212 · 热度 1790 · 2026-08-16
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Aaron Levie: AI spend is nowhere close to hitting any walls
AI spend is nowhere close to hitting any walls. Obviously this data is weighted toward engineering-centric companies, with the top 1% spending $7,500/mo and top 10% spending $660/mo per employee on AI. But the general trend of continued exponential growth is occurring throughout all firm types. And what the top 10% is doing today may easily be what the top 50% is doing in 3 years from now, at least in terms of token volume if not in dollars. This is why there’s still so much opportunity right now in the diffusion of AI in the enterprise. Especially as token costs come down, we will throw larger portions of work at agents. They’ll be scanning all our code for security issues, testing all of our software, writing coding for much larger projects, processing nearly all data, and much more. So this trend has no end in sight.|@levie · 热度 519 · 2026-08-16
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Aaron Levie: Pretty good way to think about the value of AI agents and where the opportuni...
Pretty good way to think about the value of AI agents and where the opportunity lies. You have a technology that will tirelessly do all the things that you could never fully do before. Not because you didn’t want to or it wasn’t valuable, but because it was just not even remotely practical. The upside with AI is to think about everything you can now throw intelligence at that won’t give up and what would the returns be. You just couldn’t possibly find every security vulnerability in your codebase, because you couldn’t simulate every risk vector. You couldn’t read every contract or document to process the data and capture it to query later. You couldn’t comb through your customer base and look for every relevant data point for doing upsells. And so on. This is a pretty clear way to think about how to deploy AI to get real gains, but also where the startup opportunities are as well. Go after the markets where more compute on a problem will qualitatively change what customers can now do in that domain.|@levie · 热度 115 · 2026-08-17
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Dan Shipper: used fable to vibe code an app that visualizes and groups everyone who applie...
used fable to vibe code an app that visualizes and groups everyone who applied to come to Thesis we live in incredible times where detailed understanding of each of your customers—and how they cluster together in groups—is possible with very little effort https://t.co/UBkHaDIDhK|@danshipper · 热度 101 · 2026-08-16