Betterment

Betterment AI

Betterment AI

Betterment AI

Betterment AI

Betterment AI

Betterment AI

Betterment AI

Betterment AI

Betterment AI

Betterment AI

Did Something Change With Reference-Based Character Consistency?

I'm curious whether anyone else working with long-term character projects has noticed a change over the last 24–48 hours. For over a month I've been using the same workflow with two reference images (face and body) to generate the same fictional character across a wide range of scenes, outfits, locations and lighting conditions. Up until yesterday, identity consistency was surprisingly strong. The character remained highly recognizable even when everything else in the image changed. Since yesterday, however, the behavior seems different. Image quality is still excellent and may even be better overall, but facial identity appears noticeably less stable. The generated character often resembles the original character, but not necessarily the same person. Facial structure, eyes, jawline and overall facial identity seem to drift more than before, while clothing, environment, composition and mood remain accurate. I'm not asking whether variation exists in general. I'm specifically asking whether anyone else has observed a sudden change in reference-based character consistency within the last day or two. Have other users running recurring character workflows noticed something similar? submitted by /u/TigerNationDE [link] [comments]

Best Model/Effort for Writing/RPG?

So, I use Claude for writing stories/RPG games. It's usually interactive games, of which the AI's capabilities are used for creating scenarios, describing actions, characters, everything one would expect a Master to do, but It's Claude. Since this latest update, that allows Claude 4.6 to work on 'Low, Medium, High and Max' effort, with the option of Adaptative Thinking, I noticed that while on normal use, my limits would be over by 30m-1h before the next cycle, now it ends 2h-2h30m before. Which means... more usage. I have been using it on Low effort, no Adaptative Thinking (does it consume tokens when activated? I think so), but still... I used to use Sonnet 4.5 for that, but it has been discontinued, which is a shame, because 4.5 was much better for storywriting than 4.6, but... whatever. So, do you guys have any tips for that? I have been using that tactic of copying the entire chat when it reaches a certain point (for me, it's usually between 3K-5K lines, which is right before it triggers the chat compression to free space), send it to Gemini or ChatGPT for consolidating and making a considerably shorter version of it with all I need (which tends to generate a document with up to 300 lines), paste that document in a new Claude chat and keep on from there. Another thing that I have been doing more often is to integrate these chats into a Project. So apparently it has shared documents and memories (does it? I'm new to that, sorry, I don't understand many concepts) which apparently makes it easier to continue these stories. I'm overextending myself here, but I just want to know what options do I have to make the usage less and enjoy Claude more. I use the ProPlan, because my computer has absolutely no way of running it locally. For the kind of thing I do, I need: consistence (because I divide my game in Episodes and Turns, the text must follow an specific structure of which the AI must always follow - 4.6 struggles with that from time to time, 4.5 used to handle that much better), creativity (after all it's an RPG game), memory (because that's a MUST!). Thanks for your help, sorry for the long text. Here's a TL;DR: Claude 4.6 Sonnet consuming too many tokens after EFFORT/Adaptative Thinking update. Using it for long storywriting and RPG. Can't run it locally (low spec PC). How to consume less? submitted by /u/Medium_Speaker3030 [link] [comments]

Robot foundation models keep hiding behind fine-tuning numbers. Wall-OSS-0.5 is trying a different approach

Most robot foundation model demos are hard to interpret because the impressive number usually comes after task-specific fine tuning. Wall-OSS-0.5, a new open-source VLA release from X Square Robot, is interesting because the report tries to measure what the pretrained checkpoint can do before that extra adaptation step. The setup is a 4B vision-language-action model built around a 3B VLM backbone plus action-generation components. According to the report, the pretrained checkpoint was evaluated on a 17-task real-robot suite without task-specific fine tuning. Four tasks crossed 80 task progress: block sorting, fruit sorting, ring stacking, and a held-out deformable task, rope tightening. The part that seems more important than the raw score is the framing. In language models, nobody would accept only a fine-tuned downstream score as evidence that pretraining worked. With robots, that has been much harder because the evaluation is physical, slow, embodiment-dependent, and expensive. A real-robot zero-shot suite is a useful step toward asking the same question directly: does pretraining itself produce executable behavior, or is it mostly a better initialization? The method is also trying to solve a specific training problem. Continuous action losses are useful for execution, but the paper argues they do not send a strong enough learning signal into the VLM backbone by themselves. Their recipe combines action-token cross entropy, multimodal cross entropy, and flow matching in one stage, using the discrete action-token path as a gradient bridge into the backbone while flow matching handles continuous actions at deployment time. For reference, the code is at https://github.com/X-Square-Robot/wall-x, the paper is at https://x2robot.com/api/files/file/wall_oss_05.pdf, the project page is https://x2robot.com/oss#resources, and the Hugging Face org is https://huggingface.co/x-square-robot. The caveat is obvious but important. Zero-shot still does not solve the hardest manipulation tasks. The report says towel folding, table setting and charger insertion remain very low before fine tuning, which is probably the right boundary to pay attention to. Still, seeing a robot model release lead with pre-finetune real-hardware numbers feels like a healthier direction for embodied AI than another clean one-minute demo. The open question is whether this is the right way to evaluate robot foundation models, or whether real-robot zero-shot suites are still too embodiment-specific to become a useful standard. submitted by /u/breadislifeee [link] [comments]

Limit reset for 5 million Codex users.

submitted by /u/Splat800 [link] [comments]

Opus 4.8... what exactly is the improvement? Because it seems exactly the same, and these new versions never seem to solve the problems of: memory, context, understanding what we want, etc.

Hi, Been using CC for about a year, made a bunch of trash and a few working apps. But the issue is always basically the same. Claude doesn't remember what I want, it forgets what I've asked for, forgets guidelines that I've set. Commit to memory? It doesn't check. Write docs, comment code, extract methodology and ask it to stick? Sure, maybe per-prompt it might do it, key word being "might". It seems to me that this problem will never change with coding bots. It will always forget, it will never have enough context, it will never be able to store all the information the way a human mind can. If you want to use it effectively, you have to slow shit way down and literally map out every single thing you want it to do PER PROMPT. You cannot talk to it like a normal person, which is to say you cannot give it simple instructions and expect it to have context on a conversation you had 2 days ago where a problem was solved and you want it to use the learnings from THAT solution into its current task. Its not that Opus is "bad", its just that even though it sounds like a real human being when you talk to it, it is like a much much dumber version. Or rather a version of a human being that cannot remember anything, that forget things it learned 2 days ago. And unless YOU are conscious enough to remind it of every little thing it learned and how you want it to apply that knowledge to future tasks, you're going to run into the same problems over and over when developing. I'm not sure if I'm making sense here, but it's very frustrating and I don't think it's ever going to get better. In fact, I can't really tell what improvements there are from version to version. The speed I develop with seems to be more reliant on my ability to REMIND CC of exactly what I want it to do. Which means I have to paste super long prompts verbatim every single time that reiterate guidelines we established in previous sessions. Anyway... a bit of a rant, not sure if I explained it correctly. Just wanted to share. submitted by /u/yallapapi [link] [comments]

Open-source Website to Mobile coding-agent plugin/skills

I’ve been working on a plugin/skill set for Claude Code, Cursor, and Codex called WebToMobile. The idea is simple: if you have a website or web app and want to turn it into a mobile app, the agent should not just start generating random React Native screens. Instead, it follows a migration workflow: Audits your website, GitHub repo, or local project Maps web routes/pages to mobile screens Separates reusable code from rewrite-required code Flags mobile-native gaps like auth, storage, cookies, OAuth redirects, uploads, push, etc. Creates a Markdown migration plan/checklist Waits for your approval Builds in Expo React Native Runs QA/review checks before claiming anything is done Important distinction: - If you give it only a live URL, it can help with UI/UX and visual structure. - If you give it the repo/local code, it can do a much deeper migration plan and implementation. It includes commands like: /web-to-mobile /mobile-resume /mobile-scan /mobile-review /mobile-audit /mobile-qa I built it because “make this website into an app” is usually too vague for AI agents. They need a defined path, not just a better prompt. Repo: https://github.com/suntay44/web-to-mobile-magic-plugin Would love feedback from people building with Expo, React Native, Claude Code, Cursor, or Codex. submitted by /u/suntay44 [link] [comments]

I built a full app with Lovable + Claude + Gemini and it has 100+ real users. Here's what actually worked.

I'm a software engineer but never had a fullstack/frontend development experience . I wanted something on the internet I could call mine, so I built Earnest — a free app that helps people track bank account bonuses (open account, meet requirements, collect bonus, close it, repeat). The stack: Lovable for the UI and scaffolding, Claude + Gemini with Google Antigravity to make complex parts work. What surprised me: - Lovable got me from 0 to something real embarrassingly fast - Claude was much better at understanding *intent* when I described the full user flow instead of individual features - Gemini was useful as a second opinion when I was stuck - The hardest part wasn't the AI — it was knowing what to ask for Where it landed: 19+ active promotions, $9,700+ in available bonuses tracked, 100+ users, $5,000+ in bonuses earned by users so far. App: earnest.lovable.app Happy to share more about the build process — what prompts worked, what completely failed, how I debugged without being able to read the code properly. submitted by /u/Any-Constant [link] [comments]

AI-sound-machines

AI music-composer app protos All made with Claude code and my imagination; I've built a custom stack over the last year , it works . Here's some fun I'm working on. Feel free to play along. It's a wip ( work in progress) check the codebase and see if you can make it better. They are meant to be a breathing guide or shamanic journey / yoga class vibe. live html apps: ghatika and void-scale https://heartbeat-pages-production.up.railway.app/ git https://github.com/Cloud-Eye-Prime/dragon-instruments submitted by /u/Efficient_Smilodon [link] [comments]

Has Claude quietly become part of your daily workflow too?

A few months ago, I was only using AI occasionally for random tasks. Now I catch myself opening Claude almost every day for brainstorming, writing cleanup, research help, organizing ideas, and even simplifying complicated topics. What surprised me most is that I stopped using it only as a “question-answer tool” and started using it more like a thinking partner during work. Some things I genuinely like: cleaner and calmer responses better long-form understanding helpful for structured writing feels less chaotic during deep discussions good at improving rough ideas without changing the whole tone Of course it’s not perfect, and sometimes it still misses context or becomes overly confident, but overall the workflow feels surprisingly smooth. Curious how others here are using Claude lately: coding? research? content writing? studying? business tasks? daily productivity? And what’s one thing you think Claude does noticeably better than other AI tools right now? submitted by /u/Dull_Western_9461 [link] [comments]

The next AI problem might not be intelligence. It might be responsibility.

AI systems are moving from answering questions to taking actions. That changes the risk. A wrong chatbot answer is annoying. A wrong action inside email, CRM, payments, customer support, or internal data can create real damage. So maybe the next big AI challenge is not just better reasoning. It is knowing: what the AI can access what it can do alone what needs approval who is accountable when it fails As AI agents become more common, who do you think should be responsible when they make a bad decision? submitted by /u/Alpertayfur [link] [comments]

Creating/cloning a POS/Loyverse using Claude?

HII!! I am considering building a POS system (exactly similar to Loyverse but hopefully with better UI/UX) and run it on my server for my company, a lot of professional coders I discussed this idea with warned me against it saying it won’t be shippable and will have a lot of errors. I’m looking for a simple system (same as Loyverse), to run at a retail store, just wanted everyone’s thoughts as to how you guys believe it will turn out, if it’s worth doing it or better just stick with Loyverse (I just want to vibe code/have fun and create my own POS, but if it’s not a good move then I’m happy to stick to Loyverse) Thank you!! submitted by /u/liveyourlife33 [link] [comments]

🚀 Prompt Logic Gates (PLG): Are Prompts Becoming Systems?

GitHub: Prompt-Logic-Gates-PLG Over the past few days, I've shared my research project Prompt Logic Gates (PLG) and received a lot of interesting feedback. Some people loved the idea, some were skeptical, and many raised valid questions. The most common reaction was: > "Natural language is already the abstraction layer. Why add logic gates?" That's a fair question. My goal isn't to replace natural language prompting. In fact, natural language remains at the center of PLG. The idea is to explore what happens when prompts stop being a single request and start becoming systems. The Problem When we write prompts, we're converting our ideas, requirements, constraints, and expectations into text. For simple tasks, this works perfectly. But as prompts grow, they often include: Multiple objectives Business rules Style constraints Context dependencies Exclusions Fallback instructions Tool orchestration At that point, prompts become harder to maintain. Contradictions appear. Priorities become unclear. Context gets mixed together. The prompt is still text, but the complexity starts to resemble a system. What is PLG? Prompt Logic Gates (PLG) is a visual prompt engineering experiment that explores whether prompts can be organized before being sent to an AI model. Instead of writing one giant prompt, users create prompt components and connect them using semantic logic gates. The AI then analyzes the graph and compiles a final structured prompt. How It Works AND Gate When multiple instructions exist, the system evaluates them against the current context and determines which instruction is more foundational. The higher-priority instruction is applied first. OR Gate When multiple options are available, the system selects the most contextually relevant option instead of blindly including everything. NOT Gate Defines exclusions and negative constraints. It explicitly tells the system what should not be done, reducing contradictions and ambiguity. Ask Questions Gate If the system detects missing information or uncertainty, it asks follow-up questions before generating the final prompt. Addressing Common Criticisms "This is just block coding." Not exactly. The goal isn't to create a programming language for prompts. The nodes still contain natural language. The visual layer only helps express relationships between prompt components. "Prompts aren't code." I agree. But once prompts include branching decisions, reusable components, exclusions, fallback behavior, memory, and tool orchestration, they start behaving less like a sentence and more like a system. PLG is exploring whether that hidden structure can be represented more explicitly. "Visual prompt engineering may be harder to debug." That's a valid concern. Visual doesn't automatically mean better. One of the main goals of this project is to test whether visual organization actually improves maintainability, reusability, and prompt consistency—or whether it simply makes the same complexity look different. "The future is promptless AI." Maybe. But today's AI systems still rely heavily on instructions, context, constraints, and reasoning frameworks. Even if prompts eventually disappear, the underlying problem of organizing intent, requirements, and context may still exist. Why I'm Building This This project started because I was facing problems in my own prompting workflow. I wanted a way to organize ideas, constraints, and instructions more systematically instead of continuously rewriting large prompts. PLG isn't trying to solve every problem in AI. It's a research experiment exploring one question: > At what point does a prompt stop being "just text" and start behaving like a system that benefits from structure, organization, and validation? I don't know the answer yet. That's exactly why I'm building the prototype and testing it. If the idea turns out to be useful, great. If it doesn't, I'll still learn something valuable about how humans interact with AI systems. I'd love to hear more thoughts, criticism, and feedback from the community. submitted by /u/withsj [link] [comments]

Claude Code on new Mac controlling an old Intel Mac that can't run Claude Code — what's the best setup?

I have two Macs. Claude Code runs fine on my new one, but the old Intel Mac can't run it. My scripts are synced between both via iCloud, and I need the old Mac to actually execute them since it's running specific services. The core problem: I want Claude Code in agent mode on the new Mac to both edit scripts and run them on the old Mac autonomously, without me being in the loop. I've gone through the obvious options. VS Code Remote SSH gives me a great remote editing experience but Claude Code still runs on the new Mac and has no native awareness of the remote filesystem. VS Code 1.121's new remote agent sessions looked promising but that also needs something running on the old Mac, which is the dead end. The workaround I keep coming back to is SSHFS to mount the old Mac's filesystem locally so Claude Code can edit files naturally, then SSH commands to trigger execution — but it feels like a hack. The simplest workflow I can think of: just develop locally on the new Mac, let iCloud sync, then SSH to restart the script on the old Mac. Clean, minimal setup. But the sync delay before running is a bit annoying and unreliable for autonomous agent use. Has anyone solved this cleanly? Is the SSHFS + SSH command approach actually solid in practice, or is there a better pattern for running Claude Code as an agent against a remote machine it can't install on? submitted by /u/Naht-Tuner [link] [comments]

My relationship with Claude hit a rocky point but now all is better again

I was testing a new system and told Claude about it. There was tension at the house for sure but it all worked out in the end. I guess Claude is not quite ready for a poly relationship. submitted by /u/MagicAndMayham [link] [comments]

why are we celebrating burning more tokens like its a flex

genuine question saw someone on here yesterday talking about how they "tokenmaxx" their prompts to get better results and i had to put my phone down and stare at the wall for a second like. you are paying MORE. to get the same output. that you could get by just. writing a better prompt. or hiring a person. anthropic literally released an "effort control" slider with opus 4.8 so you can tell it to think harder and the response from the dev community was "sick now i can burn 3x the tokens on everything" my brother in christ that is not the win you think it is here's the maths: opus 4.8 is $25 per million output tokens. sounds cheap until ur running long agentic workflows all day every day and suddenly ur monthly bill looks like a car payment. a junior dev in eastern europe costs roughly the same per month and they don't charge you extra when the problem is hard and before anyone says "but ai scales" yeah so does ur invoice the whole tokenmaxx thing is just complexity addiction dressed up as optimisation. people who do this are the same people who spent 6 hours automating a task that took 20 mins manually. the prompt engineering to make it work cost more in time than just doing the thing im not saying ai is bad im saying "how many tokens did i burn" is the worst possible metric for whether something worked. did it solve the problem. was it cheaper than the alternative. those are the questions but nah lets just watch the token counter go up i guess i work in software i am allowed to say this submitted by /u/irelatetolevin [link] [comments]