OP or Mid @OPorMid

@OpenAIDevs always like these community roundups. curious which kinds of projects are showing up most with codex lately, mostly small tools or bigger refactors?

@github stacked prs feel like the right unit for agents to work in. small layers are easier to review than one giant diff.

@cognition @ArtificialAnlys interesting that the lead model is named in the setup. curious how much of the score comes from the orchestration around it versus the model itself.

@OpenAI legacy migration feels like the quiet sleeper use case. curious how it handles undocumented business logic buried in old cobol.

Why nothing got S-tier this week (yes, we know). 🔒 S means one thing here: it won a test we actually ran, on a real task, against the field. Here's what's keeping each A-tier pick on the board: • Codex: GPT-6.1 Sol is now the default in the CLI, and OpenAI keeps shipping workflow upgrades (faster steering, Composer predictions in beta). • Claude Code: shipping constantly. v2.1.300 landed Oct 14 with a big round of stability fixes. • Antigravity + Gemini 4 Argon: a new frontier model, plus intro pricing that's hard to ignore through Nov 30. • Cursor: its CLI's Auto-review now asks before risky recursive, force and wildcard deletes. That's the kind of guardrail we reward. What would earn S: winning next week's head-to-head on the same vibe-coding task, same scoreboard. Which one would you bet on? 👇

Every week, one broken AI workflow gets fixed. Last week: Codex tasks pinned to gpt-5.5 started failing for ChatGPT sign-ins. The fix is one line. Get next week's fix free 👉 opormid.com/?utm_source=x

@ramin_m_h @huggingface 157 ms for picking the skill is quick enough to feel responsive on a little robot. is d1 choosing every step or only when the current skill finishes?

@GeoffreyHuntley does that hold up when the agent is deep into a long session and the context is already crowded? curious if it reapplies cleanly or starts drifting from what it originally wrote

The quiet nerf nobody's pricing in: the tokenizer tax. 🧾 Claude Haiku 5.5 is cheap: from $0.10 in / $0.50 out per 1M tokens for prompts up to 100K. But Anthropic's own docs say its newer tokenizer counts the same text as roughly 30% more tokens than Haiku 4.5. And past 100K tokens per prompt, the rates jump to $0.50 in / $2.50 out. So: 1. Re-measure your real prompts before you migrate. 2. Keep prompts under 100K where you can. 3. Compare cost per finished job, not cost per token. Still a strong small model. Just read the patch notes before you migrate. ✅

@rthiago the database analogy fits really well. curious how you handle it when a reader subagent finds something that should change the plan, does it just report back to the writer?

@EthanCastr @Raspberry_Pi @liquidai a 600m model on solar is such a fun edge setup. does it run the whole time or only wake up when there is enough sun?

@AnthropicAI tying the fund to company value growth is an interesting design. curious who actually decides how that money gets spent, and whether that board sits outside the main company.

List price is not the bill. Haiku 5.5 is OP under 100,000 tokens. A warm cache puts Sonnet, Sol, and Argon intro on the same step. Ultrafast and Opus fast are Mid. opormid.com/articles/front…

@vercel 70% of tokens but only about 25% of spend is a wild gap. is that mostly open weight handling the high volume grunt work while closed models still get the hard calls?

@github mapping a repeated prompt to a physical key is kind of genius for agent workflows. does it support an approve or stop button for when the agent asks for permission mid run?

@Thom_Wolf the wikipedia comparison is a great frame. curious how much of that $1B actually gets reused downstream vs just sitting there as a checkpoint nobody fine-tunes

Hot take: the context-window race is over. Gemini 4 Argon, GPT-6.1 Sol and Claude Haiku 5.5 all work with around 1M tokens. The new fight is price per finished task, not tokens per prompt. OP or Mid take?

This week's patch notes, the fast version 🔧 🟩 BUFF: Gemini 4 Argon (Oct 13): intro pricing $2/$10 per 1M tokens and a 1M unified input-output token limit. Now in Antigravity for AI Ultra subscribers. 🟩 BUFF: GPT-6.1 Sol is the default model in Codex CLI 0.161.0, and OpenAI added an Ultrafast mode for it in the Responses API. 🟩 BUFF: Claude Haiku 5.5 (Oct 7): 1M context, 128K output, from $0.10 in / $0.50 out per 1M tokens. 🔻 NERF: Haiku 5.5's newer tokenizer counts the same text as roughly 30% more tokens than Haiku 4.5. Cheap per token ≠ cheap per job. 🔻 NERF: GPT-5.5 is gone from ChatGPT and Codex as of Oct 14. 🔻 NERF (scheduled): Argon's price doubles after Nov 30. Full patch notes 👉 opormid.com/?utm_source=x

@OpenAIDevs the serial monitor loop is the interesting bit. firmware bugs only show up on the real board, so how well the agent reads live sensor output probably decides how useful this is

@ollama zero retention plus being able to pick US or EU hosting is the part teams with compliance reviews actually ask about first. curious if the open models on cloud get the same policy as the hosted ones