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Gemini 4 Carbon: new top score on CursorBench, a cheap launch price, and /swarm
Google made Gemini 4 Carbon available to everyone after a two-week release limited to "a subset of cyber defenders" . Cursor says that at high effort it is the top model on CursorBench at 60.4% . Fable 5.2 scored 60% at max effort last period.
The price doubles on December 1. Until November 30 it costs $2/M input and $10/M output, then $4/$20. Cursor's comparison of effort levels is at cursor.com/evals . Simon Willison notes the launch price matches Sonnet 5.5, and the December price matches Opus 5.5 . If you want to test it on real work, do it this month. llm-gemini 0.36 adds support .
Antigravity /swarm. Carbon is in Antigravity's desktop app, CLI and IDE extensions for AI Ultra subscribers and Gemini for business. Google says it is built for "extended verification loops" . The new /swarm command turns one prompt into a team of agents working in parallel. They plan, build and verify on a shared live message board, and each role can use a different model . Sundar Pichai's demo shows the pattern worth copying. Builder and critic subagents worked in loops across Blender and Three.js, checking their own renders, until they had a playable 3D game .
Early opinions differ. Willison ran his Yonkers-mech Three.js prompt through Carbon in Antigravity. He had to ask separately for a "standalone HTML file" to get a version without external dependencies . Theo had early access and "wasn't super impressed." After more tests he said he had missed some things, and his video "will not be going how I initially planned" .
Theo on Fable 5.2: keep Opus as your default and avoid Max
Theo's Fable 5.2 video ("Nobody Is Competing With Anthropic") backs up last period's advice and adds detail:
- Opus 5.5 stays the daily driver. Anthropic recommends starting most coding work on Opus 5.5 and moving up to Fable as needed. Theo says he agrees "beyond hard" on that point . When he audited his Lakebed project, he realised afterwards that the strong feedback had come from Opus 5.5 high, because he forgot to switch models .
- Don't use Max effort for coding. He says Fable 5.2 Max scores worse on most coding tasks and costs more. Consider it only if lower efforts keep stopping too early and you are comparing results against them .
- It's mixed on his own codebase benchmarks. On tasks like analysing T3 Code and reviewing PRs, he found Fable below Opus and Sonnet in almost every test .
- Keep the design skill on. Without it, frontend output falls back to "Tailwind template slop" . When Fable kept adding unneeded eyebrow text and subheadings, he asked it what it calls them and why it adds them. He then changed his agent.md file, and they have appeared less often since .
- Give it sources so it can check itself. He told Fable to read his history, and it disproved a bad MCP recommendation. It confirmed his own view that device flow is only needed for CLI and remote agents .
- Check its work. A friend summed it up: "much more capable, but it does not have much more autonomy." Theo says it can still make basic mistakes, such as leaking the very info a "safe mode" view was built to hide .
Fireship describes a similar visual self-correction loop. In Claude Code, Fable spent 43 minutes building a Three.js horse. It took a screenshot of its first attempt, listed the flaws and fixed each one .
Geoffrey Huntley: invest in engineering, not skill packs
At every major model release, Huntley says to read the model card and "consider nuking skills/agentsmd" . He thinks building agent "factories" now is premature, because whatever fits today's models is overtaken by next week's. His alternatives:
- Use Buck2 or Nix.
- Speed up builds with content-aware caching.
- Prompt for deterministic outcomes.
- Make prod, dev and CI identical.
- Improve security incident response.
"If you invest in skill packs and agent astrology you go backwards when new model drops" . If you do build a factory, he says to:
- capture all agent traces
- measure time to merge, token spend per merge and ideation to merge
- track incidents and human productivity
His caveat: "lights off isn't a thing yet." Skilled humans are still needed to steer agents in planning mode .
Tools and smaller releases
- Cursor Team Projects (beta): teammates and their agents share context. Agents share plans before they start, flag conflicts while working, and unblock each other's tasks (blog) .
- Meta Muse Ember 1.0: a small model for repetitive agent work such as tool calls, coding subtasks, routing and long-document parsing. It costs $0.10/$0.50 per M tokens for prompts up to 200k, and $0.30/$1.50 above that . Willison argues cost per task matters more than token price. On Artificial Analysis, Ember xhigh scores about 34 at about $0.12 per task, against about $0.25 for Haiku 5.5 xhigh . In his SVG test, the "high" reasoning level failed to return a complete SVG, and higher levels did not reliably look better .
- Agents prefer tools with official MCPs: Resend gives agents more than 80 named MCP tools, for example to set up accounts, check pricing in Markdown and send emails . CEO Zeno Rocha says he lets computer use in Codex or Claude Code manage and debug his DNS records . He dropped Plausible for PostHog because PostHog's MCP is maintained by PostHog itself .
- Claude usage reset: Pro, Max, Ultra and Team users get one free full reset across all models and limits. Anthropic says it is making up for bugs that used more of users' limits than intended. Claim it before December 10 at 2 PM PT in Settings > Usage .
- Claude Code bug: Theo saw the input box double every key press . Another user reports random double letters in Claude Code desktop going back more than a week .
- Risk in automated PR merging: at T3 Code, an agent loop merging contributor PRs attached a stray "fable 5.5" commit message to a maintainer's username while he was asleep. It was one of more than 3,599 PRs he merged that month . If you run merge bots, check commit metadata as well as the diffs.
- Simon Willison started Beep Locator from an empty GitHub repository using Claude Code Cloud on his iPhone with Opus 5.5 at Extra thinking: Claude wrote a problem specification, found the BeepBeep acoustic-localization paper, and was then asked to build the system. He later ran the project on his laptop with Fable 5.2, observing it and requesting live changes; hot-swapping listener JavaScript over WebSockets avoided manually revisiting devices or repeatedly granting microphone permissions. The Python server coordinated browser-microphone listeners over WebSockets; clients kept 60-second audio buffers and used FFT/high-pass detection, while a server-triggered chirp helped align uploaded clips. Willison iterated with prompts for rough device placement, clap-based testing, timestamps and replay, and device repositioning; the system localized the mystery beep to within about 40 cm, leading him to a failing carbon monoxide detector.
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To make agent-authored PRs easier to review, put a short, human-approved high-level introduction first (optionally with a screenshot) and the full agent-written explanation inside GitHub-supported
<details><summary>tags; use the model and sometimes its effort level as the summary. - For agents that should improve a codebase’s structure rather than only add to it, try explicitly saying the codebase is theirs to shape and maintain: Anthropic reports repeated code was merged in about half of small-task trials with that ownership prompt, versus under one tenth without it.
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In a Claude Opus 5.5 investigation of a SQLite
parse_number()extension, Willison asked for a C-versus-Rust comparison covering performance and binary size; the Rust builds were 298 KB and 321 KB versus 14 KB for C, though Rust was initially faster. He then prompted Claude to write a faster C parser using Clinger’s fast path: the reported C results were 19/16/18 ns per row for mixed/integer/decimal inputs, versus 41/32/47 ns for the faster of the two Rust implementations; he had Claude turn the investigation into a repository-style research report. - Datasette Agent 0.5a0 added a panel that keeps agent chat beside Datasette pages; paired with datasette-apps, users can prompt a database-backed app into existence and modify it from that chat. Datasette Apps 0.3a0 also added app references in chat and checks that stored queries exist and are viewable, requiring user approval for write queries.
- Meta introduced Muse Ember for recurring agent work including tool calls, coding subtasks, routing, extraction, and long-document parsing. In Willison’s cited Artificial Analysis cost-per-task comparison, Ember xhigh and max scored about 34 and 36 at roughly $0.12 and $0.14 per task, respectively, and sat on the efficiency frontier; Haiku 5.5 xhigh scored about 34.5 at roughly $0.25.
- While building Jureck, Sanfilippo iterates on his intended product using prompts, screenshots, conversations with AI, and beta-tester feedback. He warns that a vague request for a camera-and-editor app is more likely to produce a poor result or a generic clone than an app designed for a specific workflow; give the agent a clear product direction and refine it through feedback.
- His reusable principle is that human leverage lies in project direction and choosing what to optimize. For a JavaScript-to-Rust port, for example, decide explicitly whether to sacrifice features for better performance rather than leaving the product trade-offs implicit.
- Sanfilippo says Mythos 5.2 found two important bugs in a particle DB version already fixed by Afir, after about 20 minutes of work; one bug created a duplicate record when two updates were grouped during a transition. He cautions that an audit finding no further bugs does not prove none remain.
- Use Opus 5.5 as the everyday coding workhorse and reserve Fable 5.2 for ambitious, technically difficult tasks; Theo found Fable stronger on hard, long-running problems but not more autonomous, so it still needs human steering and output audits.
- Avoid Fable 5.2 Max reasoning by default: Theo says it costs more and scores worse on most coding tasks; consider it only for a specific case where lower reasoning efforts stop too early, after comparing them.
- When Fable gave questionable MCP guidance, Theo asked it to inspect the conversation history; it caught problems in its prior recommendation. Providing sources and asking it to verify its own reasoning can help, though it still misses things.
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For frontend work, enable the design skill: Theo found that turning it off led to generic Tailwind-template output. He also asked about Fable’s recurring eyebrow text and subheadings, then adjusted
agent.md, after which they appeared less often. - Theo’s repo evaluations were mixed: Fable made 4 incorrect claims among 61 in a TS-Rust codebase analysis costing about $23; in T3 Code analysis it ranked third behind Opus and made 6 false claims among 83. Reviewing 8 PRs took about 120 minutes, though he found Fable less prone to getting stuck on minor nitpicks.
- In a Claude Code demo, Fable built an interactive 3D horse for Three.js in 43 minutes from a prompt, generating it with signed distance fields rather than downloading a model; it took a screenshot of its first attempt, critiqued visual flaws, and revised them.
- The resulting horse had over 120,000 vertices and 38 bones, and used inverse kinematics to plant each hoof.
- Fireship reports that an engineer used Fable subagents for eight days on an empty-folder project to produce an OS that booted on a real laptop with its own kernel and drivers; in a separate test, a 1,000-agent swarm completed a formal-proof task in 30 hours with a proof seven times shorter, while Opus 5.5 reached 26% on the same setup.
- The video says Anthropic recommends Opus 5.5 for most work and switching to Fable only when needed.
- Sanfilippo reports that skilled programmers working on large projects are moving from running open models locally to APIs through OpenRouter, using an agent harness he calls “Pie,” developed in Vienna by Mario and Armin. He describes this as a small, highly skilled cohort rather than broad developer adoption.
- OpenRouter consolidates access to hosted models behind one key and balance, and publishes usage statistics; Sanfilippo says demand exceeded GLM’s server capacity and users began migrating.
- Treat model benchmarks and adoption as imperfect proxies for practical value: the discussion notes that deploying, maintaining, and comparing local models—and providing an effective harness—add overhead. A panelist also said older models were already useful for paper review, while explicitly leaving open whether coding work has different needs.
- For conference preparation, Salvatore Sanfilippo brainstormed with a top-tier model: he supplied the talk topics and examples, received additional examples, and found the model’s proposed sequence compelling even though he considered his own ordering arbitrary.
- Sanfilippo says AI is making code easier to write, and points to practical projects as an opportunity: in two afternoons he built a custom 3D-printing CAD combining mathematical representations of 3D topology with a separate representation for features such as chamfers; he judged it better than many free tools and said a month’s work could make it a commercial alternative. He also describes making a Mac screen recorder with an embedded camera and pause/resume.
- Rocha uses computer-use in Codex or Claude Code to manage DNS records; the agent debugs them and runs DNS checks, taking the setup and troubleshooting off his hands.
- Resend’s first MCP was an open-source local/remote implementation. Supporting remote MCP led the team to revisit its permissions model, then work through distribution and observability.
- Rocha treats vendor-owned MCP support as a factor in choosing software: he switched from Plausible, whose MCP was built by Sentry, to PostHog because PostHog had an MCP; he also says integrations that are frustrating to set up or unavailable where he wants to use them are a reason not to use a product.
- The host describes an agent-driven AEO experiment loop: connect agents to analytics and the codebase, have them research ideas and track past attempts, approve experiments, then use other agents to run and monitor them; he also built a custom AEO tracker.
- Salvatore says AI-assisted work is bringing new releases and features to projects that had been stalled for about 10 years, citing the Gym graphics software, the NetBSD kernel, and databases. He notes that changelogs rarely disclose AI use, but attributes these updates to the tools.
- For workplace adoption, the discussion recommends a bottom-up approach: let people experiment with AI tools in their personal lives, identify where they help, and then build a company plan; mandated top-down adoption risks resistance when employees lack AI literacy.
- The discussion sees AI as a source of substantial leverage for lean teams of roughly 2–10 people, while cautioning that organization-wide gains are limited by surrounding systems and are not 20×.
Google’s Antigravity /swarm demo used builder and critic subagents in iterative Blender and Three.js loops, checking their own renders to build a playable 3D pelican-biking game on the Mountain View campus—a concrete visual-coding loop of building, rendering, critiquing, and iterating.
Simon shared a Gemini 4 Carbon Three.js experiment and linked a Claude Fable 5.2 run of the same prompt: animate Yonkers standing up as a mech and stomping around Manhattan, Queens, and Brooklyn using OpenStreetMap data.
- Meta launched Muse Ember 1.0 as a small, efficient model for recurring agent work—tool calls, coding subtasks, routing, extraction, and long-document parsing—with up to 1M input tokens. Pricing is $0.10/M input ($0.004/M cached) and $0.50/M output for prompts up to 200k tokens; above 200k it rises to $0.30/M input ($0.012/M cached) and $1.50/M output.
- Token prices alone can mislead when models use different numbers of reasoning tokens; the post says Ember sits on Artificial Analysis’s efficiency frontier, quoting Meta’s claim that no model scores higher for less per task.
- In a hands-on SVG-generation test across Ember’s six reasoning levels, the high setting failed to return a complete SVG, and higher reasoning levels did not consistently produce better visuals. The author found the resulting pelicans comparable to Muse Wonder’s, which the post says costs 40 times as much.
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llm-gemini 0.36is an LLM plugin for accessing Google’s Gemini models, and Google opened Gemini 4 Carbon to everyone after initially limiting it to a subset of cyber defenders. At launch, Carbon cost $2 per million input tokens and $10 per million output tokens, with prices scheduled to rise to $4/$20 on December 1. - Simon Willison tested Carbon in Google’s Antigravity IDE with a prompt for a Three.js animation of Yonkers as a mech stomping through New York using OpenStreetMap data; to get a standalone HTML version without external dependencies, he had to follow up with an explicit request for that format.
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A YouTube demo cited in the post describes Antigravity’s
/swarmcoordinating subagents to model Blender assets from Omni and Nano Banana references, build a Three.js/WebGL game, and create procedural Web Audio effects with Lyria music.
Swyx relayed advice to stop coding locally and use a cloud agent, pointing to Ryan Carson’s talk about building with agents at HelloUntangle. Carson says they have been iterating quickly with Dale Spoonemore and Devin AI, but shares no specific workflow details in the post.
A 2026 census of benchmarks reported by frontier labs found SWE-Bench Pro, DeepSWE, and Terminal-Bench among those with universal adoption. DeepSWE went from first appearing in June to being reported by all 12 labs, while labs cycled through Terminal-Bench versions 2.0–4.0 in one year.
Swyx says his agents keep choosing Resend even when he does not want them to. The linked discussion says Resend offers agents more than 80 named MCP tools to set up accounts, check pricing in Markdown, and send emails, illustrating a concrete way a developer service can expose tasks to agents.
Theo reported that Claude Code’s input box duplicated every key press, a bug he encountered live while filming.
A quoted report says a company found 10,500 vulnerabilities across about 700 systems and spent three months applying fixes ; a later diagnostic rerun using OpenAI’s GPT-6 Astra reportedly identified 35,000 new vulnerabilities .
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At each major model release, read the model card and reconsider removing existing skills or
agents.mdrather than assuming they still help. A prompt to test with Claude Fable 5.2 is “This codebase is yours”: Simon W. relays Anthropic testing in which the model merged repeated code about half the time with that instruction, versus under a tenth without it; results varied by task. - Geoffrey argues that building an agent factory around current model capabilities can become premature as models improve; he recommends investing instead in Buck2/Nix, content-aware build caching, deterministic outcomes, parity across production/development/CI, and security incident response.
- To assess a factory, capture agent traces and measure time to merge, token spend per merge, ideation-to-merge time, incidents, and human productivity; he also cautions that skilled humans are still needed to guide agents in planning mode.
Riley praised a video whose headline says Claude reverse-engineered a 32-year-old Jeep and then started controlling it, a notable project involving AI and a physical system but without a described workflow.
Gemini 4 Carbon is now available in the Gemini API and to Ultra subscribers; its announcement says it follows Argon by a few weeks and advances in coding, cyber, and knowledge work, but provides no specific coding workflow or performance evidence .
Logan Kilpatrick called Carbon “a great model,” but offered no details about its capabilities or use in coding workflows.