Impressive and a little scary.

Procedural grand canyon built by Sonnet 5.5 on a free account with 1 prompt, and built in java(webgl)

Source: https://www.anthropic.com/research/ecdsa-curve-origins

Every time I see someone doing work that AI could easily replace, I wonder what that person will do next. Does he know he'll be replaced soon?

Google is an absolute clown show right now. Argon is going to be studied for a long time as to what not to do.

This is pretty embarrassing, does anyone else have this issue? I've spent years typing lazily to AI, constant typos, dropped letters, skipping words when possible, just because these LLMs are designed to predict the next token and figure out what I mean. So why spend effort in typing?

Now my writing has gotten really bad, and I struggle to spell or type correctly outside of AI chats.

Cool if people want the new projects, I think they are fundamentally different to old projects and cannot be called a replacement, as they don't work the same at all. Genuinely why not make this a separate feature? For me, this is gonna mess up so much

!

Hi all,

I've been designing slides with claude since design came out and was really impressed.

Opus 4: was ok

Fable 1: wow quality, wow expensive

Opus 5: ok, much cheaper

Opus 5.5: Ok

Fable 5.1: good quality, reasonable expensive

That was all in the Claude external design module. I have my design system in there that hasn't been updated in months. The level of quality remained on par untill Claude is now migrating to "slides" in the normal claude design module. I'm really not a fan, it doesn't maintain the same level of quality even when I feed it old designs made with the Claude design.

What's working well with me now is taking an old Claude design project and asking it to start fresh from a new briefing, but this workaround will not work anymore in a couple of months and I haven't found the better alternative.

What am I doing wrong? Am I the only one?

Anyone else noticed both Opus and Sonnet saying they are “going from memory” when responding to questions lately? They used to search the web by default, but seems like they aren’t doing that now.

When I ask they say, “web search is on by default, skipping it was my mistake.” so weird, they never did that before.

It doesn't seem to be a hard number limit, it seems to be about which sessions I can enable fast mode for in Claude Code. Anyone know why? I can't find the answer.

Put all your discussion about Usage / Rate limits here. This is a thread that will be generated every other day to centralize discussions on this topic.

Creating a game literally from my dreams was made possible thanks to Claude! It brings life-sim and puzzle adventure together: every job on the island is its own puzzle mini-game, and the islanders actually chat back.

How it gets built: I run a few Claude Code sessions side by side, one on art and design, one on ships and systems, one on the website. Over 140 days they've co-authored 3,500+ of the 3,700+ commits. Today I playtested, found 5 bugs, and Claude split them across three agents that fixed them in parallel, each one proven by a test that failed before the fix. That part still blows my mind.

If I'm being honest, working day and night on this has been so fun and I've been so full of passion to build it, but I finally got to the point where I wanna take a break because of exhaustion lol. Anyways, here are the results so far. Thanks for watching ^^

Fine-tuned automation right inside your AI application. Think of n8n inside a Codex app.

All AI harnesses right now focus on loose automation: a simple trigger such as a cron job followed by a prompt; that's it. This app allows you to fine-tune any automation you want. Example flows:

- A webhook triggered by a Sentry alert (or any third-party integration) that will spawn a chain of nodes: AI looking through the trigger and some custom scripts you automatically run and create a PR once ready.
- A cron job to triage your backlog in a sequential set of pre-defined prompts.
- Loop over PRs to review and publish the results to custom plugins you build directly in the app.
- Simple flows such as a linear issue comment -> checks PostHog against the comment data -> replies to the comment.

The possibilities are unlimited.

Benefits of flows:

- Pre-defined nodes such as an HTTP request node, agent node, run command node, etc.
- If the app doesn't have the node you want, you can get your agent to write a JS script that will be accessible in the UI as a custom node. You can use your custom nodes in any flow, at any point.
- Pre-define prompts, briefs, agent models, and instructions ahead of time so you never need to worry about when to instruct moving forward.
- You can pause, cancel, queue, or limit your flows at any time so you can babysit running 4 in parallel, or you can run over 100 in parallel
- Got an entire backlog of work that cannot be done in parallel? No worries, create a DAG and have everything aptly chained together.
- The flows is an MCP, so it can be entirely done by your agent inside the chat, or, if you want the human touch, you can do it. Everything you can do , your agent can do too.

Benefits outside of flows:

- Run one or multiple agents at once, side by side,
- A work queue,
- Third-party integrations that connect both to chat and flows,
- Mobile,
- Open-source and free,
- and more... (loads more)

MIT: https://github.com/benord-labs/frink

- Released on Linux + macOS. Claude + Codex.
- Gemini/antigravity, grok - shortly.
- CLI - shortly.
- Windows - shortly.

I integrated Jev into Claude Skills and triggered it mid-session for tasks that fit a decision-model use case, which let me offload some work outside of Opus’s context window. Ran benchmarks comparing three setups: a standard single Opus session, Opus + Haiku, and Opus + Jev running inside a Jinja Skill. 

It gets pretty good results when you have many items (files) and need a simple (non-deterministic) judgment call rather than a rule. That isn’t possible to achieve with the ad-hoc Python scripts Opus tends to write for deterministic tasks.

Where it falls short: a one-off request on a single small item. There, the overhead of spinning up the Jinja Skill inside the harness runtime can be higher than just handing the item to Haiku.

I used LLAJInja a side project of mine  - a Skill that runs other Skills as Jinja templates. Here’s an example of how it gets executed in harness runtimes:

Where it gets interesting is combining this with Jinja templates and variables (passed in by the user or another agent), plus the sub-agents and nested skills inside the Jinja Skill. That lets you build complex workflows that mix regular agents with decision agents and get the best of both.

https://swarmcha.se/posts/chinese-agent-fleet-full-report

I do believe this little tool-ette is broken lol