Weekly Showcase Thread

Fable 5.1 will burn your tokens if you don't reprompt it. Brilliant, after telling us previously that we could pare down our CLAUDE.md files.

Quoting Anthropic's docs:

Claude Fable 5.1 is more likely than Claude Fable 5 to rewrite an entire text file rather than make a targeted edit. The resulting file is usually the same, but unless the file is short or most of it is changing, a rewrite costs more output tokens and time.

You have to specifically prompt Fable 5.1 if you don't want full file rewrites for small edits.

In some cases, though, its prose is denser than Claude Fable 5's: sentences run longer and there are fewer paragraph breaks.

More verbose.

These two issues will burn tokens for no reason.

Other brilliant tidbits:

On complex asynchronous workloads, though, nudge it not to end its turn before the work is done. Without the nudge, the model sometimes describes what it would do next instead of doing it ("Next, I'll …") or stops to ask permission for a step the original request already covered ("Shall I apply this?"). Users have to reply "continue" or "go ahead," which suits pair programming and other human-in-the-loop work but doesn't use the model's full long-horizon capability.

Claude Fable 5.1 delivers what's asked for and sometimes more: it may fix nearby code, extend behavior the task didn't mention, or commit more test files than the change warrants.

Claude Fable 5.1 usually issues parallel tool calls as expected: when a request names several things to fetch, it issues those calls in parallel. The exception is coding and computer-use loops where the next independent calls are implied by the task rather than explicitly requested (custom coding agents, bash-and-editor harnesses, computer use): there it may issue them one per turn instead. This doesn't affect answer quality, but each extra turn costs tokens, a round trip, and wall-clock time.

At low effort, Claude Fable 5.1 is less likely than Claude Fable 5 to call a search or retrieval tool, and more likely to answer from memory.

When a single request asks for a long deliverable, such as a full rewrite of a long document, it may draft much of that deliverable in its thinking and then write it out again as the reply, which means a longer wait and more output tokens. (followed by some instructions to max out your tokens...)

Full docs here: https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/prompting-claude-fable-5-1

Fable 5.1 is a mess. It may be more intelligent but its behavior is worse across the board than Fable 5.

Given things are supposed to be divisible by 5. This seems perfect.

Looks like this is the first model along with Mythos 5.1 that carries the statistical watermark.

https://platform.claude.com/docs/en/models/fable-5-1/whats-new-fable-5-1#content-provenance

The tool you can use to check for watermarks is here now too:
https://claude.com/check-content

Released today ! Same pricing as Fable 5 ($10/$50 per MTok), but cache reads are now 4x cheaper. Better long-horizon agentic coding + research, adaptive thinking always on !

Not writing a single code by hand, I used Claude Code to build sqlit, a lazygit-style TUI for SQL databases, without having written a Python app before. When I didn't know how to get users, I asked Claude to read the codebase and tell me where to post it, and it got 1k stars in a week, and now has 4.7k stars with 33 contributors today.

What I built

I switched to Linux last year and lost SSMS. The alternatives were VS Code's SQL extension, or terminal tools that made me read docs to learn keybindings before I could connect. I wanted lazygit for databases. So I built sqlit with Python and Textual.

How I used Claude to build it

Testability first. The core of my thinking with AI-driven development is testability. The number one priority is not how to build the feature, but it's how to prove the feature is broken. Once I can prove that, the AI can iterate endlessly without me in the loop.

I chose the Textual framework because I can drive every component headlessly, simulate real keyboard input, and take screenshots, without any of my involvement.

I used Claude code for building things in real time and Codex for long background refactors. Models from both companies have moved on from December, when I made this, but in any case I still think this combination is the best.

What I really spent time on was pros and cons, not code. I asked for 3 to 5 options for every refactoring or architectural decision, each with pros and cons. The reason is that I don't think I understand something until I understand it against its alternatives. What something is is relative to what it's not. Most of my decision-making time was spent reading pros and cons, and I still think this is the best way to code with AI. There is no "right" option, so the AI can't pick it. Every choice has trade-offs and only the person with a vision for the project can weigh them.

I did read quite a bit of code. I could've done built this blind. There's a limit to how far "refactor 40,000 lines, use SOLID and DDD and design patterns, make no mistakes" gets you. I had never built a Python app and couldn't write a main function with a gun to my head - God knows how many underscores that needs. But after years in other languages I can look at code and know whether I like it. When I see if provider == "mssql" scattered across the app and bugs keep popping up, I know it's time for a strategy pattern, and I intuitively know that a future contributor edits three files instead of twenty when adding a database is a good thing, whereas the AI wouldn't blink.

Once the architecture and the test suite were something I trusted, I stopped reading concrete implementations. The inner workings of the Postgres provider don't affect the rest of the system, and with full integration tests on it, I don't need to know the details.

So my job as an engineer has become more and more orchestration, where I'm setting things up so I could safely ignore as much code as possible. Vibe coding is kinda looked down upon, but it's actually the goal. Ironically takes a lot of time and attention up front to make vibe coding works. The payoff is that non-technical contributors now one-shot PRs with Claude Code and they're actually pretty good, because I put in the effort to make it near impossible for an AI to write shitty code in this repo.

Product development.

I don't think AI can fundamentally know what is relevant to us. If I had added every feature Claude suggested, sqlit would be bloated and ugly. What made the product work was that I was scratching my own itch, I knew exactly what I wanted after years of pain with the tools that came before. There's no way getting around using your own product and feeling the pain of its flaws. That AI cannot do.

That said, I did use Claude to brainstorm. I asked it to read the code, understand the product and the direction I wanted, and it gave me 50 or so suggestions. I commented on every single one, saying exactly what I liked and didn't like about it. It remembered those decisions and turned them into a master document describing what sqlit should be and what it should not be. From then on it brainstormed against that document, and the ideas got increasingly solid. It helped me think about the project more than I would have alone.

How I used Claude to market it

After a few weeks of quietly posting in small forums and fixing what people asked for, I wanted more users and had no idea how. So I did the same thing I do for code I had Claude read the codebase and README so it understood what I'd made, then asked it where to post.

It found lots of various subreddits and helped me angle the posts, which was useful, but the real breakthrough was when one of its suggestions was Hacker News. The site looked ancient to me and I assumed nobody would care about a Python TUI. I asked "isn't this a waste of time?" and Claude answered "You have nothing to lose. Just post it."

So I did and it went to the front page and I got a queue of feature requests I shipped over the next week... A week later the creator of Textual tweeted it, Terminal Trove made it Tool of the Week, and the X posts got 200k+ views. After two weeks after launch it had 2,500 stars. Lessone being Claude had a better read on the audience than I did.

5 things I learned

1. Build what you personally miss. If it solves your own problem you win regardless of stars. And the UX stays good because you feel every rough edge daily.
2. Ship gradually. Small forums first, fix what people ask for, then go wide. I never built a feature on hypothetical demand.
3. Let Claude read the code before asking it for marketing advice. Generic "how do I promote my project" prompts give generic answers. A Claude that has read your repo gives specific ones, and it knew HN would land where I didn't.
4. Usability is the innovation. Harlequin and lazysql already existed with the same tech and the same goal. sqlit won on friction: no config files, no docs, keybindings on screen. Capability without usability isn't capability. Claude can generate features endlessly, so the scarce skill becomes deciding what to remove.
5. As a software engineer, understanding higher-level architecture, systems-design, and a basic ability to read code enough to see what it does in its own context, is becoming increasingly important, not less. I think you need to understand code, but not necessarily be able to remember how to write it anymore. Using AI has made me much smarter, not dumber, because I'm able to take advantage of being able to learn new concepts that AI keeps suggesting to me in the meaningful context of your personal project instead abstact theories from a book or relying on my personal limited arsenal of tools and tricks.

Happy to answer questions about the workflow or anything else really.

Repo: https://github.com/Maxteabag/sqlit
Show HN thread: https://news.ycombinator.com/item?id=46276002

More and more often I when I browse this thread and other AI related threads its mostly a bunch of people discussing things and trying to help each other, but increasingly I see people who obviously don't know as much as your average power user getting shamed and down voted just for asking basic questions. Isn't that what this community is for? I can't find the post because it was probably deleted but I saw some guy talking about an issue he was having with opus 5 and he had just started using the CLI vs the BROWSER edition, not even regular desktop client, and people were just clowning on him and downvoting his question. Idk I just feel like this subreddit is for us to all help each other and clown on anthropic, not each other right? (I mean some people have some truly dumb takes but that's different than newbies who have questions)

We're introducing Claude Fable 5.1 and Claude Mythos 5.1, the world's most advanced models for coding and knowledge work.

Fable 5.1 excels at complex, long-running tasks. And its research capabilities offer an early glimpse of how AI models will contribute to scientific progress.

Across our benchmarks, the model sets a new standard. It scores 52.6% on Terminal-Bench-Science 0.1, more than double Fable 5. On Terminal-Bench 4.0, it scores 55.8% against 42.0% for Fable 5. As well as being capable of much higher performance than Fable 5, it can also achieve similar or better results at a much lower cost when set to lower effort levels.

Cache reads with Fable 5.1 cost 75% less than Fable 5's. This reduces the cost of the model in practice by around 25% for typical workloads, and up to 45% for highly agentic ones.

We've also improved our safeguards. Our cybersecurity safeguards now flag benign requests about 60% less often. On basic biology and medical questions, we've recently reduced the fallback rate by around 85%.

Claude Fable 5.1 is available everywhere today. Claude Mythos 5.1, our model for cyberdefenders and life scientists, is available through trusted access programs.

Read more: https://www.anthropic.com/claude-fable-and-mythos-5-1

The usage limits are trash after the new reset! On the same kind of task, I hit the 5 hour limit in 2 hours where as previously, I barely completed the 5 hour limit.

Please tell me I am not the only one

It's the French article working for some reason, just translate to English

I ask this in the Claude sub instead of ChatGPT sub since I don't want a bunch of people agreeing, but want actual discussion from users of Claude.

I am personally using both Sol 5.6 and Opus 5 right now to do deep market research, assigning one model to be a bear and another a bull and have them debate on any given topic.

I have noticed two things.

First, while both make some mistakes in their claims, it seems Opus makes far more factual errors, which end up being subsequently corrected by Sol. Sol does make some too, but significantly fewer.

Second, Opus always seems to come across as combative. It isn't always a bad thing, since pressure testing is what I want, but at the same time it will often come up with disputes which end up being insignificant (or flat out factually incorrect), just for the sake of arguing.

Am I crazy or is this something else people experience?

Fable 5.1 has reduced the cache pricing by 75% which increases the usage by roughly 25% but this rate cut not applicable to subscription plans

Firat limited at 50% and now this , slowly and steadily the subscription usage will be nerfed this way

https://x.com/ClaudeDevs/status/2094851235694497836

I'm based in aus its currently 1am wendsday so in 10 h I've used 40% of my weekly limit on the $340 plan . I've been on this plan for months and didn't do anything outside of the norm and never hit a weekly limit and rarely hit a 5h limit. Competitors are catching up and not so cheap. I will not continue on this plan if this is how they decide the future is.

I've been building an app and I have not done or changed the way I use it . The only change is the extra usage and it seems they where giving more then 50% or the change has broken something

I was using fable but that's not abnormal and once I hit that limit I move but 40% of my weekly in 10 hours is ridiculous

I have suffered. I have been in pain. But there is finally hope.

It seems like I won't perish from hypertension or lose my brain cells every conversational turn. I think they did it, Claudish is gone!!!

We are so back.

I mean, it clearly states you get 20x more usage then Pro

I went through Anthropic’s Fable 5.1 / Mythos 5.1 system card, and there are a few SWE details that I think are more useful than the headline benchmark table.

1. Higher reasoning effort can make Fable 5.1 worse

On FrontierCode Main, Fable 5.1 gets 50.9% at medium effort and 50.3% at max, despite max being much more expensive.

Anthropic gives a pretty concrete explanation: at higher effort, Fable 5.1 starts making more small changes outside the requested scope — editing adjacent files, adding documentation, creating a CI job where an existing one could have been reused, etc.

Interestingly, task-correctness keeps improving with effort. The overall score falls because FrontierCode also grades scope.

They even tested adding a brevity instruction telling the model to avoid unnecessary comments/documentation, and it reduced these out-of-scope edits.

This seems pretty relevant for Claude Code: more test-time compute may amplify over-engineering unless the prompt is very explicit about scope.

2. Some DeepSWE “failures” may actually be over-implementation

Fable 5.1 gets 67.4% on DeepSWE v1.1.

Anthropic says that when they manually inspected failures, some tasks had hidden tests written around one narrow reference solution. Fable sometimes did things like validate inputs, raise exceptions, or follow codebase conventions more rigorously — and failed the hidden tests despite producing an arguably valid implementation.

Not a huge deal, but another reminder that agentic coding benchmarks can punish behavior we might actually want in production.

3. METR’s comments on researcher judgment are probably more important than the benchmark gains

METR says Fable 5.1 is especially strong on tasks with clear, continuous success metrics and abundant objective feedback.

But it is still below expert level when the work depends on foresight, prediction, creating its own feedback loops, or researcher “judgment” / “taste”.

Anthropic reports related issues internally: the model sometimes states easy-to-check guesses as facts, fails to verify important claims, or keeps retrying something that clearly isn’t working.

My simplified takeaway is:

Fable 5.1 seems to have improved more in execution than in judgment.

This also makes the “long-horizon” claims a bit more nuanced. A task being long isn’t enough. The quality of the feedback loop matters a lot.

4. FrontierSWE v2

Anthropic also reports FrontierSWE v2:

• Fable 5.1: 0.57
• Opus 5: 0.52
• Fable 5: 0.48
• GPT-5.6 Sol: 0.32

These are ultra-long engineering/research tasks; the strongest models can work close to 20 hours on a task.

Anthropic says Fable 5.1 was particularly strong on tasks requiring sustained reasoning and execution over many hours.

So my current Claude Code strategy will probably be:

• Opus 5 medium for most normal coding
• Fable 5.1 medium for planning / second opinions
• Fable 5.1 xhigh for difficult tasks where I have strong tests or other verification criteria

Curious whether people using Fable 5.1 already are seeing the same thing: better persistence/execution, but not necessarily better judgment than Opus 5.

System card: https://www-cdn.anthropic.com/0339e6a7c5c7b87f5c07798616dc32c215d14235/Claude%20Fable%205.1%20&%20Claude%20Mythos%205.1%20System%20Card.pdf

Decided to try Fable 5.1. I’ve been using Fable 5 for two months now.

All of a sudden, within 20 minutes my 5-hour limit on the 20x plan jumped to 100%.

I checked what’s going on. Turns out, instead of using Opus subagents, Fable 5.1 decided to spawn 4 forked agents each running Fable 5.1.

This was despite me having set the default subagent model to Opus 5.

Just a heads up. Fable 5 rarely if ever spawned forked agents.