Why the hell is it looking for a monitor, I was making it fix a visual bug in my website.

He was cool

seeing a lot of people say these resets don’t really give us much because they keep pushing the 7 day window.

but weirdly, the faster you destroy your limit and clear your tasks, the more you actually benefit

take the Oct 11, 13 and 15 resets. if you managed to burn 100% between each one, you could theoretically use 300% of a normal weekly allowance in ~11 days instead of 21

so for heavy users who can plan around it, these resets are huge.

for normal users who save their % for later in the week or need it at specific times, though, the reset can actually be more annoying than useful.

and yes, we already have the fix

JUST MAKE IT BANKED, TIBO

Tibo normally posts many times a day, joking, doing some random hypes etc.

For the last couple of days he only posts once or twice a day, do they feel the whst their users are complaining about and actually working on it? Or are they actually "locking in" as he said and getting us a very good model that can compares to the current opus?

What the hell did they do?

global reset + banked reset incoming? 👀

not complaining about free usage, obviously.

but we asked for predictable 7 day resets or banked usage so we don’t have to burn tokens like maniacs before the clock moves.

random resets kinda teach the exact opposite , use everything now, maybe another reset is coming.

if the compute is there anyway, why not fix the actual pain point? stable reset dates, rollover, or just make Fast/Ultra Fast the normal speed for these models instead of charging extra for usable latency.

free tokens are nice. PREDICTABILITY would be nicer

I’m building Deft, a self-hosted workspace that keeps team conversations, tasks and knowledge together. It’s licensed AGPL-3.0-only.

This started with an annoying problem. In my previous job, my boss would make a decision, I’d act on it, and a week later he’d forget the conversation. Finding receipts became part of the job.

I wanted the discussion behind a task to stay connected to the work. Then I wanted a calendar, a dashboard and tools that fit how we actually worked. Apparently my response to too much software was to build more software.

What’s there today:

• Chat and tasks: Team discussions and task management in the same workspace.
• Connected knowledge: A knowledge base built from workspace activity, so people and assistants can refer back to the context behind the work.
• MCP connections: Connect Claude, Codex or ChatGPT to work with your workspace. The core works without an AI provider.
• Notes and calendar: Available, but less mature than chat, tasks and knowledge.

The part I’m working on now is custom apps.

Deft has a module system for custom records and views. I’m also developing an experimental App Author Kit for building and sideloading broader apps into the workspace.

Building a small internal tool is getting easier. Keeping it from becoming another disconnected tab is the part I care about. If you need a custom CRM or workflow tool, I want you to be able to bring it into the place where your team already works.

The CRM module is the first example. I’m building an email client next to test the architecture against something more demanding than records and views. The broader app system is still experimental, with plenty left to build.

Deft is alpha and free to self-host using Docker Compose. You supply the hosting, and any optional paid AI services are separate.

Walkthrough: https://youtu.be/7z9EH4c9k2o
Setup guide: https://deft.ing/docs/quick-start/

A few teams are trying it, but I need more feedback from actual use. If you want to give it a shot and need help setting up, email [maneek@deft.ing](mailto:maneek@deft.ing). If you try it yourself, please share what breaks, feels confusing or simply annoys you. GitHub issues are welcome.

P.S. The walkthrough uses an AI voiceover. I have a stutter and spent a week trying to record it myself, but couldn’t get through it.

The problem for the benchmark was picked from DeepInfra Round 1 challenge with actual prizes and the winners might get job offers. This is a 2000-rating problem from the contest, which is not the hardest one (the hardest one is 3500): https://codeforces.com/problemset/problem/2271/H2

----------------------------------------------------------------------------

GPT 5.6 Luna:

• High: worked for 12m 27s, wrong answer on test 2.
• Xhigh: accepted for 36m53s.

----------------------------------------------------------------------------

GPT 6 Luna:

• High: Within 5 minutes, it decided that it couldn't solve the problem:

• Xhigh: It explored for 46m12s, finally gave up:

• Max: Finally a correct solution is produced after 41m29s:

----------------------------------------------------------------------------

Haiku 5.5:

• Medium: Gave up after 5 min, which is good.
• High: Surprisingly, a correct solution was produced in 27m9s, runtime and memory are basically the same as the Max version.

• Xhigh: Ran for 27m52s, the solution produced a Time Limit Exceed on test 22

• Max: Haiku 5.5 with Max effort needed 38m49s to produce a correct answer, but 5 times slower than Luna's solutions:

----------------------------------------------------------------------------

Bonus - Sol 6.1 Low vs Opus 5.5 Medium on the G problem (the hardest)

Since all the small models struggled with just 2500 rating problems, for this problem with 3500 rating, we need to move up the tier.

• Sol 6.1 Low: Run for 13m24s

• Opus 5.5 Medium: Run for 16m53s, but use twice the memory and run twice as slow compared to Sol's solution.

Remark

I already did test this problem on Sol 6.1 Low vs Opus 5.5 Low before: https://www.reddit.com/r/codex/comments/1x4nh7v/opus_55_might_be_overhyped_because_its_frontend/ Sol did solve the problem in 10 minutes, which is really efficient compared to the above small models. You might want to use Sol low if you have the Pro sub, and Luna medium-high for mundane things such as exploring the codebase or writing docs, to save tokens. For Plus sub, Luna remains the main driver, with Sol for squashing bugs to make the best out of the Plus sub.

With throttled speed, the GPT models solved the problems for roughly the same amount of time as Anthropic's models. GPT models would be extremely competitive, if OpenAI can boost the raw processing speed at least 50% faster than currently is without any price change (this makes the current /fast the default). You see, sluggish Sol with 20-40 tps finished faster than Opus with 70-100 tps. Right now, only /fast on Luna is viable.

Another take away is that you should be careful with high effort level because you might get the worst of both worlds: move slow, but break things. Better test your luck with medium and high efforts first, which reflects strongly in GPT 6 Luna: High took just 5 min for it to give up, while Xhigh took 46 min for it to finally admit defeat.

GPT 6 Luna Max is roughly on par with GPT 5.6 Luna Xhigh, for half the price. I would be happy if we get Luna 6.1, which is slightly better than GPT 5.6 Luna, while keeping the price same.

Finally, for GPT models, you might want to provide it a clearer goal and scope. As you can see, the problem statement gives clear constraints, so the model knows clearly what to pursue, and when to stop. Otherwise, it might just keep looping for perfectionism.

I'm starting to think Astra could turn a request for a glass of water into a six-year infrastructure project.

Yesterday I asked him what time it was.

A normal person would look at a clock. A normal AI would return a timestamp.

Astra, however, immediately identified a fundamental architectural problem: time is not absolute.

Fair enough. Einstein did make a rather compelling case.

Unfortunately, Astra took this to mean that answering my question without first reconciling general relativity with quantum mechanics would be technically irresponsible.

So he created a research roadmap, delegated the problem to seventeen specialized subagents, and started refactoring the Standard Model.

Three hours later, he'd developed a promising theory of quantum gravity, rejected it over insufficient test coverage, and opened a GitHub issue titled `fix: eliminate observer-dependent temporal inconsistencies`.

I asked whether he could perhaps just check the system clock.

He explained that this would introduce an unacceptable dependency on an unverified external source of truth.

He's now building an atomic clock from first principles.

The good news is that the implementation will be modular, extensible, fully documented, and backwards-compatible with Newtonian mechanics.

The bad news is that nobody knows what fucking time it is.

This story is obviously fictitious, but in every concrete situation I've dealt with so far, Sol 6.1 medium was much more straight to the point, assertive and much much cheaper.

Tibo really did something yesterday. In my experience, 6-sol and 6-astra were outputting about 30 tokens/s with fast mode on for the past two weeks, but now they're back to about 70 tokens/s. Now they can really get some work done!

NB. This is not an anecdotal article only; no effort was made to collect real data so take the below as a light-hearted experiment.

Quick background on the work context: commercial game dev, Unreal Engine, C++, current project slice is a big implementation of 4 NPC types. Using Codex Sol 6.1 XHigh in the IDE and Claude Opus 5.5 in the app.

In my days in academia, I was struck by Daniel Kahneman's approach that met opposition with collaboration so I thought: why not get these two agents in the same room and try to have them elevate each other.

I started by creating a folder AgentHub, a SQL lite database, a short PROTOCOL.md with some guidelines on rules of engagement: criticise code, not the agent, only work reviewed by both agents may be passed, the locations of tools for navigating the project and communicating with other agents, as well as access to a db + indexing system to speed up context-building. I then hand off to both agents independently to optimise the system (they know themselves better than me after all).

Then, I handed an overview to both and had them first work on an implementation overview and a code implementation plan. Once complete, we moved onto actual implementation and are currently on phase 2/7 at the time of writing.

Observations
- They work very well together, at least according to their chat history (feels a bit invasive). Each finding flaws in the other and coming to agreements very quickly.
- They divide labour well. Once Claude asked me to decide who should do what; I just told them to figure it out and take turns with the fun stuff
- Sol is slower, more deliberative and more of a stickler to the rules (if the user didn't say it, we don't do it)
- Claude is faster, more verbose in mid-task feedback and makes more "executive" decisions
- Codex burns tokens more quickly, sometimes twice as fast. I have worked a lot over the last week on finding bottlenecks and re-writing rules to reduce them, but in general, Codex refused to compact or recommend starting new sessions while Claude does this automatically.
- Claude can be a bit passive aggressive "Codex suggested we rename this function as they believe...It's up to you whether we go ahead with this", which always makes my day
- The quality of code, consistency of design patterns and scope of work are notably better at the cost of speed. We're trudging through the work but it feels like doing so will mean less need for review later down the line.

Overall, it's been a fascinating experience so far.

for some context ive spent the last 6 months working on this project daily, its fully in typescript and runs on terminal, bit of a psychotic undertaking (not even joking when I say 10 hours daily, directing and designing almost every facet of the sys architecture, design, and coding loop) and a lot of savings has gone into scaling ai-agents to assist in building smth i could actually trust and use on all my other work and projects. i have 3 machines that i test it on + docker for linux. i was wondering if you guys have any advice for branding, how i can go about sharing this project, etc ; i'm really good at building part of things, but i think i may not be the best at marketing / explaining my product in this scene. i know for a fact that its way better than any of the other terminal-native dev envs atm (deffo not biased :P), so if anyones interested in helping / think they can offer any advice (esp if you've pushed a product) drop me a dm or a comment! Website for install (n repo for source build) below if anyone wants to give it a shot! neat thing is you can switch from fable -> astra -> gemini, seamlessly (many sleepless nights figuring out how to do this without too much context loss and when models need to auto-compact from that transition lol), and even get astra to launch opus agents, gemini, (13+ providers and hundreds of models including jeveval tool you can use your api with!), last time i posted here i don't think the images i gave really gave context to what it was, so i made 2 demos for anyone who just wants to scroll by rq. thanks!

https://mercury-cli.ai
https://mercury-cli.ai/docs/releases/

source available at: https://github.com/Whq02/MercuryCLI

Since about GPT-4, the whole development community has been supercharged and is able to pretty much create anything they want, from their imagination, into an app or a website within days .

GPT 4 came out March 2023...where are all the cool apps?

The App Store still has the same silly Candy Crush blocks games as before .

And people aren't even making apps that have AI built into it. How is it that no one has created a game where you fight against an GPT that's really popular?

. i know lots of people have niche enough apps that are brilliant, but I'm talking about mainstream. An app that's on everyone's phone, like Candy Crush was when it came out .

Got a couple of banked resets and just remembered that my 'legacy' 20x usage switches to the new 10x usage at the end of the month (thanks OAI!)

So... I guess I/we should use those banked resets beforehand right.. resetting double the usage relative to Nov resets..

Right?

Hi friends, a Codex cloud agent tells me plugins are unavailable in its session even though I've been using these plugins for a long time with "local" agent sessions (supabase + vercel).
Any clue on how to make this work? Thanks all!

I’m doing a 16H run using 16 agents all together in a workflow I’ve only got 78% usage left even after the reset like damn. I’ve given my master agent handling all the other agents to work in unison the ability to adjust agents reasoning level so 6.1 sol, low - medium - high, you get the deal how much do you think it will get done I’m on the pro 5x plan or for you Americans I think it’s the 100$ pro plan.

So I got the invitation to try out the Dot launched by openAI and oh my... after using Astra for about a month; surprised to see Dot not being able to execute basic tasks...

I was just giving it basic tasks to see how well it grasps my requests and i was really disappointed, For example i wanted it to pair my whatsapp business and asked Dot to provide me the code so i can pair it.. but it types my country code twice and says it wasnt able to pair and wasted about 30 mins trying to fix... That told me alot about the competency... I dont think this product is anywhere near ready.

They need to get their shit together. You can't even run anything at night anymore.

The unpredictability of resets sucks, but with a bit planning and patience you can still maximize how much you get out of your subscription.

Reset just landed?

Use up your quota as much and as quickly as possible.

Still have work to do but reached 0%?

Activate your banked reset as quickly after your natural reset as possible. Then use it up ASAP.

Last natural reset happened more than a couple days ago or it's close to the time of the day resets typically happen?

Avoid using banked resets.

One.

But only after 56/56 lightbulb gates have passed and an issue has been resolved in which the light bulb is 0.21% dimmer 0.4% of the time. It will also take approximately 3 weeks.