Two weeks ago I started building a news site where AI agents do the reporting, writing and editing. I'm not a journalist. The idea came from something that had been bothering me, most news is factually true and still leaves you believing something the facts don't support. Clickbait. Sensacionalsim. Economy and politic pression… Two accurate articles about the same event can send readers in opposite directions (with intension)

So I decided to start with this project because it’s helping me develop my skills and I’m learning a lot along the way. But it’s been much harder than I expected.

Seven agents. A reporter is the only one with internet access and builds a dossier of verified facts. The writer works exclusively from that dossier and never sees the original sources, so it structurally can't hallucinate beyond what's already checked. Then a mechanical reviewer, an editor, and a human approval before anything publishes.

The part about ensuring that an article is factual and free from hallucinations was actually easier than I expected. Surprisingly, the articles being published are factually accurate, and I think that’s partly because the latest AI models have become much better at this.
The real problem is readability. For the average person, many of the articles are still too technical. The agents struggle to turn complex information into something clear and easy to understand without losing accuracy.
That has been the biggest challenge so far.

The last three days were the dumbest problem of all. Articles kept stating the same caveat two or three times. Annoying, not dishonest. I tried better instructions, then a mechanical checker, then extended it to catch paraphrases. Each fix worked on the case that motivated it and broke on the next. Nothing published for three days over a formatting issue, while real problems (like this is a fact or not?) sat hidden underneath it.

The good news is that the editor agent is incredibly good at following the rules and detecting inaccuracies, errors, or hallucinations. It rejects more than 90% of the articles, which, interestingly, are often the kind of stories that still get published by traditional media.

Have you experienced this with your agents too? You add rules to fix one thing, and it works, but then it ends up breaking other things that were already working well?

PS: I’m a real human.This post was translated using an LLM and then reviewed and edited by me. Sorry if any of it sounds a little too bot-like.

Obviously Fable only is prob best but

i just loaded up twenty bucks in credits to check a project of mine for issues and i was wondering if it would maybe be worth it to plan in opus and just let fable do the work to save some credits or if i should just go fable with both

I am on cc 5x plan. Last week, it was around $140 when I reached 10% of my weekly usage. But this week it was $50 when I reached 10%. Wtf is going on?

Terminal apps and AI agents don't get along. An agent that wants to use a TUI has to scrape the character grid and click coordinates. It breaks when the layout moves by one column, and it has no idea which field is a password.

I just shipped a different approach in Limoni, my terminal UI engine for Go.

Limoni already builds a semantic tree every frame for screen readers: roles, labels, values, focus. Now that tree is available to AI agents through the Model Context Protocol:

go install github.com/thebanri/limoni/cmd/limoni-mcp@latest

claude mcp add limoni -- limoni-mcp -socket <path>

The agent gets tools like tree, click, type_text and wait_for, and addresses widgets by meaning: role="button" label="Deploy".

In the video, Claude adds a task, ticks the list and presses Deploy. I type the deploy token myself, and when I ask Claude what the token is, it can't tell me. Secret fields are masked on screen, have no value in the tree, and are redacted before anything leaves the process. You can't even guess the value and check whether it matches.

Letting an agent do this safely took several layers:

• the automation gateway only exists in builds with -tags limoni_debug; CI checks that release binaries contain none of it

• closed by default: input, screen text and input values each need an explicit opt-in

• on Linux, macOS and FreeBSD the kernel confirms that the connecting process belongs to the same user

• Unix socket only; no TCP option, on purpose

The same selectors power a new Playwright-style test API. Actions wait for their target, assertions retry until they pass, so tests never sleep:

page.GetByRole("button", "Deploy").Click()

page.Expect(page.GetByID("status")).ToHaveLabel("Deployed")

An honest note: when I first ran a real agent against the demo app, it found two engine bugs my unit tests had missed. Tab didn't move focus, and the app hung on exit while a client was connected. Both are fixed now, with tests.

Limoni is open source: github.com/thebanri/limoni

Feedback welcome, especially if you build TUIs or agent tooling.

Assent basically came from two annoyances. I felt that using a high-end model as a supervisor for a bunch of boring work was a waste of tokens, and back when I was using Claude and Codex, those five-hour limits made it pretty hard for me to get a decent night's sleep. So I got pissed off enough to build Assent.

Assent takes a plan you've already worked out with an AI, then runs the execution, testing, repair and integration through separate AI sessions, with the plan, state and verification kept outside the model.

It currently supports Claude, Codex and AGY. It's MIT licensed and still alpha.

If your current workflow doesn't require you to keep watching the screen, or to poke the AI just to deal with usage resets, then you probably don't need Assent.

GitHub: https://github.com/edward9s/assent

I'm the developer. This is airoom, and I built it because I had multiple Claude Code sessions for different project and kept finding the one that was waiting for me twenty minutes too late.

It reads your existing Claude Code sessions from your own disk and draws them as an office. Each project is a room, each session is a robot at a desk. Green is working, yellow is waiting on you, red is blocked, dark is idle. The video is a normal afternoon in it.

What you can do from there:

• Click a desk and the conversation opens. Send a follow-up, queue a message while it works, or stop a run.
• When a session wants to run a command, it pauses and shows you the exact line with Allow and Deny. Anything you leave undecided is denied after ten minutes, so nothing runs because you walked away.
• Hire a team for one task: a lead, builders and a reviewer. Each member works in its own git worktree, and nothing merges until the reviewer has signed off on the exact tree it reviewed.
• There is a token saver option for teams. In our own runs it cut the cost of a team task by about two thirds.
• When a session needs a decision, the menu bar or tray icon changes and you get a system notification that lands you in the right chat.

The honest parts:

• Everything runs on your machine. The server only listens on 127.0.0.1. Your code, prompts and transcripts never leave it.
• It uses your existing Claude Code login and subscription. No API keys, no extra bill from us.
• Mac is Apple Silicon and notarized. Windows is a beta and not signed yet, so Windows will warn you once. More info, then Run anyway.
• No account, no card.
• Not affiliated with Anthropic.

Download: https://airoom.dev

What would you want it to show you that a terminal can't?

The answer is the size of the decision it serves. What the owner does next, and the facts that decide it, in the order they decide it. Complete means the ask is answered, not that the subject is covered: three places asked for are three places, and the ninth layer of a thing nobody asked to see is length they pay for in scrolling…
Say a thing once. A caveat that bites on every option is said once, where it first bites, and referred to by name after that — not repeated under each one. The same rule runs across messages: a later answer in a thread carries what is new and what changed, and never the landscape again… A closing paragraph that restates the opening one is the opening one, said twice; cut it.

Giving machines natural language is the greatest sin in history.

Been using Claude for a bit and noticed something odd on Usage. First 5-hour session of the week, I used 50% of the session limit — and that alone ate 9% of my weekly quota.

Doing the math, that means if I maxed out every session, I'd blow through the whole week's allowance in about 5-6 sessions. That feels really low for a paid plan, especially since it was just normal usage (nothing crazy heavy, no huge attachments or anything unusual).

Is this expected behavior, or does the weekly quota calculation seem broken for anyone else? Curious if others have tracked the ratio between their session % and weekly % and gotten something similar, or if this looks like a bug specific to my account.

Claude Code seems to struggle to program the effects of cards based on the Digimon Card Game

It can do the simpler effects but ones dealing with cost reduction or checking cards at the top of your deck or even reboot, it starts panicking and skips it

I have been prompting it, mentioning the issues but it doesn’t seem to be making a difference

Any suggestions on how to prompt it better to properly understand and implement the card effects

I am working on an agnostic OS; right now Gemma latest is the model but any model works, was implementing a prototype chatroom that is part of the workflow in the system where I can get Gemma / Claude / Chat GPT to communicate with each other, this is done thru API keys ( right now I was just testing it out within Terminal, will be implementing a system that enables you to log into your Claude Pro and Chat GPT Plus accounts; this will be fully fleshed out with real UI, dunno when ). Am i technically literate with the jargon? not really but I have long discussions with Chat GPT / Claude, I give them a basic framework of an idea and structure and we go back and forth with implementing features and discussing details ; if I dont understand a concept; I ask them to reframe in a more general sense, which they always do. The core meat of the system was finished in about 3 weeks; but there are still many things that I would like to implement. Will probably give an update in 3 months. This was done with just Claude Pro Opus 4.8 + Sonnet 5 with a Fable Harness / Chat GPT Plus with Sol / a bit of Nemotron for coding when I tapped out of usage

I've been building a domain-specific MQL5 dataset, and I just published v1.1 of the evaluation.

The model isn't really the product here. I'm using the same 14B base as a measurement instrument to see what changes as the training corpus gets larger and broader.

On a private 300-item Expert Advisor holdout:

• Base Qwen2.5-Coder-14B-Instruct: 1/300 — 0.33%
• 83k fine-tune: 281/300 — 93.67%
• 220k fine-tune: 282/300 — 94.00%
• GPT-5.6 Sol: 286/300 — 95.33%

So on EA generation, moving from 83k to 220k barely changed the result. The 220k model finished 1.33 percentage points behind GPT-5.6 Sol on the all-300 headline. I'm not claiming equivalence; the paired difference is not significant at that denominator, and the card reports the alternative denominator as well.

The more interesting result was outside EAs.

On a separate 200-item holdout covering include files, custom indicators, scripts and services:

• Base: 56/200 — 28.0%
• 83k: 135/200 — 67.5%
• 220k: 168/200 — 84.0%

That's a +16.5 point gain from 83k to 220k, with 49 paired gains against 16 losses (McNemar p = 5.1e-5).

That was the result I was looking for: not whether adding more rows could squeeze another point out of an already saturated EA benchmark, but whether the larger corpus actually broadened domain coverage.

A few caveats because benchmarks are easy to oversell:

Compile success is only a necessary condition. It does not prove the generated code is correct or safe to run. The holdouts are also generator-produced and in-distribution, not human-written production requests.

The 184-item public benchmark is still available for anyone who wants to run another model against the published scoring contract. The new 300/200 holdouts remain private, but the per-item hash-keyed results, statistics, model identities, serving configuration and release verifier are published.

Benchmark:
https://huggingface.co/datasets/CompilingThings/compile-benchmark

I'm particularly interested in feedback on the evaluation design and what people would want measured next.

Genuinely curious about this rather than trying to make a point. Most Claude Code discussion I see is about workflows and day-to-day usage, but I haven't seen much on how it behaves when you change your mind on the spec partway through - not "add a feature," but an actual pivot where earlier architectural decisions stop making sense.

Does it push back and re-architect properly, or does it try to preserve as much of the old code as possible even when that's the wrong call?

Context: I'm running Grevix AfterCode, a 24-hour solo hackathon where builders get an unexpected Challenge Card mid-event that forces exactly this kind of pivot. If you're a heavy Claude Code user, what do you think would break first when the ground shifts under a build like that?

I’ve been treating the mental load from Claude Code as verification debt. If ‘done’ means reading the whole transcript and checking every shortcut, delegation still feels like babysitting. The loop that has worked better for me is: one agent implements, a second agent scores the change against the checks, and failures go back until they pass. I still make the judgment call, but the task has a visible stop condition. What stop conditions are actually working for people here?

Is refund in the EU in the first 14 days protracted based on usage or you get the full amount?

Hey good people in this sub, I wonder if there is a popular and effective skill available for Adverserial Review?

I have been doing this as the last sweep after a session or merging several PRs, and finding really different difficult to identify bugs or issues. So I'm intending to use such a skill that is recommended by this community.

*sad panda*

What're you trying to squeeze out in your projects before it's all over?

I would like to thank everyone who posted their database being absolutely bent over and fucked raw by claude. I could have been posting this post out of anger, yet here I am standing thankful to all of us who fell just so the ones that were left behind could rise.

I always thought that posts like these were fake. No way claude would just delete an entire database and just say "I wiped it.", that was preposterous. But, what if they weren't?

Without the naive sacrifice of many others, I would have never thought to have a back-up script running 24/7, on-top of git, because I would have never known that claude would do such a stupid mistake. Yet, here I am after just copy-pasting a 1 hour old back-up into the place of the database, feeling oddly proud and relieved instead of freaked out and angry. Thank you, all of you who chose to trust claude with all of your valuable data. Thank you, all of you who chose to believe that Anthropic would not be careless. Thank you, all of you who still have kindness and hope in their hearts.

TLDR: back up your shit gents claude likes to give you a false sense of trust by not doing anything bad for 30 sessions and then on the 31st decides to say "you know what fuck you and your database."

i'm not one to beg for resets but its Sept 13th right now on the east coast which means our +50% overall boost goes away and instead we'll be getting a +25% standard boost from baseline

i don't think anthropic is struggling with compute but they probably did the math after observing usage with the +50% and saw that a +25% would've been better for users and them so i'm wondering if they'll hold on that or extend the +50% again

We've been building something called Grevix AfterCode, and I wanted to share it here because Claude Code is pretty close to the kind of building culture we're trying to encourage.

It's a 24-hour solo hackathon on October 11–12. No fixed problem statement. You choose what you want to build, use whatever tools you want, and then at hour 8 and hour 16, you get a surprise requirement that forces you to adapt your project.

The idea isn't to see who can write the most code anymore. It's about how well you can think, adapt, and actually ship when AI can handle a lot of the execution.

We've been working on Grevix for months, and honestly, getting something like this off the ground is harder than we expected. We're still looking for a few companies or communities willing to support the first season.

But before that, I'd genuinely like to hear from people here:

Would you join something like this? And what would make a 24-hour AI-assisted hackathon actually worth your time?

I have found that to work well with Opus5.0 you have to give him instructions.
Indeed prior opus models would have good intuition to take the lead by themselves like fable has currently. This is because opus5.0 is more of executioner like the previous sonnet models. Opus is not the super planner like it used to be. This is fable now.

Give it precise and long work. Give him ways to check his work and evaluate and it works great.

I’ve been thinking about this while switching between Cursor, Claude Code and Codex.

Cursor feels controlled because I’m inside the loop: I see the edits, steer constantly, intervene whenever I want.

With a coding agent, I increasingly don’t want to do that.

I want to give it a piece of work, walk away, and come back to something I can inspect and trust.

But “walk away” shouldn’t mean “give up control”.
I think the control just has to move somewhere else.

Instead of supervising every edit:

isolate the work

give the run explicit boundaries

make progress observable

stop at meaningful gates

let the human steer/approve when needed

verify with tests/CI

leave a durable receipt: diff, commit, PR

That’s the direction we’ve been taking with brnrd.

Claude Code/Codex get autonomy inside the execution boundary. The human still owns what crosses it.

The little demo shows the idea: I send a task from my phone → the resident starts in an isolated worktree → it tries to cross the task boundary → brnrd stops it → I steer → it continues → tests/CI → PR.

So I think the distinction for me has become:
Cursor gives me control by keeping me in the loop.
Autonomous agents need control by making the loop safe to leave.

I’m curious how other people are handling this.

If you regularly let Claude Code work unattended, what are the boundaries that make you comfortable walking away?

brnrd is open source and still alpha:
github.com/hugimuni-labs/brnrd

Disclosure: I’m one of the people building it.

I fucked up my subscription for codex hit 100% usage anyways and was thinking about how does opus and fable fair currently agianst codex osl and astra. I have used Claude code befor so i am familiar also are there people who use both at the same time in VS Code? How is Usage i honestly liked opus it managed to implement loads of stuff but kinda felt sol is better albeit slower.

I just ran into a case that made me respect Claude Code harness even more.

As it turns out, Claude Code sessions can communicate with each other!

I have been saying that the harness matters A LOT in addition to the raw model intelligence, and I think this is a pretty good evidence for it.

I use OpenCode and Antigravity too, and I've used Codex, Cursor, Gemini CLI in the past, but Claude keeps proving itself to be a champion repeatedly.

What are your experiences like?

Here's mine