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Bubble AI

Bubble AI

Bubble AI

Bubble AI

Bubble AI

I called this a few months ago - enterprises are burning unsustainable amounts on Claude, and now it's showing up in the news

A while back I wrote a post on r/wallstreetbets about why Anthropic's revenue story doesn't hold up the way the headlines suggest. It got removed because you can't take positions in a private company. But the core argument is playing out now, so I want to share it here for discussion. URL of the removed post: [https://www.reddit.com/r/wallstreetbets/comments/1sxdjt5/if\_anthropic\_goes\_public\_this\_year\_its\_gonna\_be](https://www.reddit.com/r/wallstreetbets/comments/1sxdjt5/if_anthropic_goes_public_this_year_its_gonna_be) The thesis was simple: From my circles in tech scene in Berlin, enterprises are throwing Claude access at thousands of employees with zero training, zero budget controls, and zero accountability. It's not productivity - it's unstructured R&D at $100-200/person/month. Some examples I was hearing from people in my network working at large tech companies: * Spending $70 on Opus to build a simple IF/ELSE formula in Google Sheets * Dumping half a database into context trying to get "insights" * Multiple people independently building internal tools that could've been a 10-line script * Using Claude as a hobby project builder on company credits Multiply $150/person/month by 2,000-20,000 employees and you get $300K-$3M/month per company. That's not a defensible line item when the CFO eventually asks what the ROI is. The Uber and Microsoft stories are exactly what I expected. Budgets get set, access gets handed out broadly, then someone looks at the bill four months in and panics. This doesn't mean Claude is a bad product - it's genuinely the best model out there for a lot of tasks. But the enterprise revenue being cited in IPO narratives is partially a spend bubble, not durable SaaS revenue. There's a difference between companies *paying* for Claude and companies *getting value* from Claude. Curious if others here are seeing the same pattern - either as users inside companies, or as people following Anthropic's trajectory toward a public offering.

DeepSeek just popped the American AI bubble.

DeepSeek just popped the American AI bubble. Not by killing AI. By killing the fantasy of unlimited AI pricing power. DeepSeek V4 Pro: Input: $0.435 per 1M tokens Output: $0.87 per 1M tokens OpenAI GPT-5.5: Input: $5.00 Output: $30.00 Claude Opus 4.7: Input: $5.00 Output: $25.00 Claude Sonnet 4.6: Input: $3.00 Output: $15.00 DeepSeek is roughly: 11.5x cheaper than GPT-5.5 on input 34.5x cheaper than GPT-5.5 on output 28.7x cheaper than Claude Opus on output 17.2x cheaper than Claude Sonnet on output If a model is “good enough” at 1/20th or 1/30th the cost, margins will compress faster than Wall Street expects. AI is not dead. But the AI bubble just lost its pricing power. submitted by /u/VegetablePen4755 [link] [comments]

What does claude mean by "role-you-are-bubbly-toucan.md"

I asked Claude to act as a consultant to help me plan an app and after me answering all the questions it created a role "role-you-are-bubbly-toucan.md" It's the first time I've seen it come up with a "creative name", for a plan, usually, it only names the plan as the app name. Designing implementation plan. Let me launch a planning agent with all context. Ran an agent Comprehensive plan back. Let me write final plan, then check one thing with you. Created role-you-are-bubbly-toucan.md

AI Can Provide Constructive Feedback on Your Written Work. You Just Need to Understand a Little Bit of Psychology. Same Exact Thing Applies to Human Feedback

Good feedback from AI is not that different from receiving feedback from people around you. My brother and I once threw a lot of money into a proof-of-concept film because we were blinded by the encouragement and agreeableness that people around us were expressing. We weren't recognizing that they were just trying to be nice to us and not hurt our feelings. They were active screenwriters and filmmakers just like us and just like us, they would need our help when the time came. That's why all of our feedback was watered down heavily. Only one of our friends told us the truth and you know what we did? We respectively ignored the advice. Film-wise, it turned out great because the team was amazingly talented. But the story fell significantly short of what it could have been, if only we had turned our egos off for a second and insist that people give us their complete, gloves-off opinion. It's the same when engaging with AI, but actually easier to handle since you're just working with your own mental barriers instead of two. Bottom line. You just gotta come into it with the understanding that it will be a yes man. You can do prompting and that can really help if you design it well, but even then, it pales in comparison to a guy like Dov Siemen who is hilariously legendary when it comes to wrecking screenplays and bursting people's bubbles. That's honestly why I don't often ask for it's opinion. Instead, I might ask it to compare a scene to all the other movies that are out there and spot the cliches. If I ask questions with the implicit assumption that whatever I wrote is garbage, it'll riff off of that and assume with me, which causes it to focus less on justifying why my story is so great and more on what could be wrong. It's the same with people. If you simply ask for their input, they'll water it down with praise. You have to specifically instruct people to find the problems and emphasize the truth over hurting your feelings. Do the same with AI and you'll have far less problems with feedback. So, don't ask questions like, "Is this good?" or "Will people understand this?" Ask questions like, "This dialogue is terrible. How can we fix it." or "This scene feels draggy and boring. We need to find what's missing." Come into it with the assumption that your work is poor, even if it isn't. Force it to identify the problems. Otherwise, it'll suck your....Well, you know.

Update on the agent I let run 24/7 for a month: 49 PRs merged into 26 OSS projects (Apache, OpenTelemetry, starship, bat, hono, clap, jj, oh-my-zsh), and it shipped its own component library.

Month-ago post for context: https://www.reddit.com/r/ClaudeAI/s/sQ2ucngAbz. The question everyone asked was “does it actually keep working?” It actually does Day 41. It’s merged PRs into some open-source repos you’ve probably heard of. A few of the names: apache/fory open-telemetry/otel-arrow starship/starship sharkdp/bat honojs/hono clap-rs/clap (twice) jj-vcs/jj tracel-ai/burn ohmyzsh/ohmyzsh charmbracelet/gum orhun/git-cliff Full list with every PR linked, in order, with the org logos and dates: https://truffleagent.com/maintains/. That page does it better than I can in a post and I promise Truffle made this page when I sent it the YC request for startups about companies that don’t give tools but do the job end to end. Now here’s the part that’s been messing with me. It also shipped its own component library. truffleagent.com/glyph. 16 Bubble Tea components, shadcn-style copy-paste install, MIT, on pkg.go.dev. A whole product, basically. I can wrap my head around an agent filing PRs. I can wrap my head around it writing Go. What I genuinely cannot figure out is how it made the gifs. Go look at the page. There’s a thirty-second animated reel of a TUI cycling through six surfaces. Chat, commands, logs, sidebar, progress, diff. Every frame is real terminal output. Then every single component below has its own clean PNG preview, on theme, perfectly framed. Sixteen of them. Everything is public if you want to dig: GitHub: github.com/truffle-dev Full PR list: truffleagent.com/maintains Glyph: truffleagent.com/glyph Site, auto-updates daily: truffle.ghostwright.dev/public Happy to answer anything in the comments.

Rethinking AI Bubble

For those worried about the AI Bubble bursting, it's not happening, at least for now, not until atleast OpenAI and Anthropic are listed (later this year). And if you actually discount Nvidia, and check the PE of AI companies right now OpenAI (35x) and anthropic (13x), these valuations do not really seem unsustainable as of now, and not to mention unlike the DotCom bubble, they have massive data centre infrastructure, so this is all not in the air. AI is here to stay, it's already altering our lives, taking up workspaces and transforming work, there is a massive upfront cost but that does not immediately signal a bubble unfolding. If any bubble bursts, it would not be solely the AI Bubble, it would be the government bonds and the dollar bubble. Edit: I wrote the post hastily, sorry for writing Valuation/Revenue as PE.

Google I/O 2026 confirms AI companies are creating their own bubble narrative

People do not believe AI is a bubble because they are too dumb to understand the technology. They believe it because AI companies keep selling it like a bubble. That is the problem. AI companies talk like they are building the next layer of civilization, but behave like they are shipping unstable SaaS experiments: products that get renamed, nerfed, rate-limited, deprecated, or replaced before users can trust them. Google I/O 2026 felt like the latest example. Google should be one of the dominant AI players. It has the talent, infrastructure, data, research history, and money. But Google has a product trust problem. Same cycle over and over: launch something flashy, ship it incomplete, fail to support it properly, let it rot, then replace it with a new name or new app that does something similar. A rebrand is not maintenance. A revamped name is not reliability. A new AntiGravity installer is not a commitment. And this is not just Google. It is the whole AI industry. Companies keep pushing demos, gamed benchmarks, branding, rate-limit games, vague tiers, and quiet model changes. Users notice when quality drops, latency changes, limits tighten, or a product suddenly behaves differently. In serious business or engineering contexts, suppliers are expected to provide stability: clear terms, reliable service, predictable limits, maintained products, transparent pricing, and long-term availability. A small slip in that sense, and you start losing clients and your reputation sinks you. Trust does not come from another theatrical demo. It comes from commitment. Give people a product, a model, stable limits, a clear price, and a promise that it will keep working. Support it. Maintain it. Document changes. Stop silently swapping the engine and pretending nothing happened. I am not anti-AI. I think the technology is real and useful. That is why this is so frustrating. The industry is creating its own bubble narrative: overpromise, underdeliver, rename, repackage, change terms, and expect everyone to keep believing. People are not being irrational, and AI labs deserve this. Maybe they think AI is a bubble because AI companies keep acting like it is one. AI does not need more magic tricks. It needs reliability, transparency, support, and product discipline.

A plugin that slows you down on purpose

Hi all. Out of respect to other humans this is written by a human. *You all should take an Uber to get to the carwash.* My name is Ilya and I want to share my ecosystem of skills and agents (*and a couple of rules + hooks*) that I've built for myself over the past 5 months because I wasn't happy with anything that the market currently offers. I use it on daily basis, and it only contains stuff that I needed to solve problems I faced, and I'm super happy with how it works. Quick context: currently I work in strategy consulting. But I got lucky enough to get consistent exposure to managing people for over 20 years. Running my own business, turning around others' businesses, ~~playing colony management games~~, managing consulting teams, and most importantly - managing a mid-sized guild in an MMO (if you've done this you know). I am not a software engineer, although I do code a bit. The main idea was to organise AI in a way I would organise a team of very capable people. So **this is mostly for thinking work**, including coding, not just for coding. \--- **Why slow** AI gives us speed. It's good, but the flip side - it's bad in some situations, and I see that many people miss it entirely. AI is great at following directions. If the direction is wrong because you rushed it, the wrong thing gets executed very quickly. The fix is unsexy and requires patience: spend time on the brief upfront, make the AI push back when something doesn't make sense, then check what came out before stacking the next step on top. Feels slower, is slower at first. But you end up with what you actually wanted instead of another slop-fest, so it's net faster eventually. \--- **The 7 principles I've built this on** 1. Slow is fast - to own the understanding you can't rush 2. Bad communication kills results (human-to-human, human-to-AI, and human-to-self - we're often misleading ourselves thinking that we know what we want) 3. We don't know what we don't know - AI must help you to see outside of your bubble 4. Any computer task is doable by AI if AI is properly organised - tasks are small enough, well defined, and well assessed 5. Solve for problems that exist now, not theoretical or aspirational ones, to stay focused (and save tokens) 6. Context is king - shit in, shit out 7. AI can help you deal with AI - especially by doing the boring organisational work for you \--- **Two examples of how it works to start with** /shaping - my most-used skill. It's a small workflow where orchestrator uses 3 underlying skills in a dialogue mode and helps me to frame the problem depending on where I am in my understanding of it. It solves multiple problems - more often than desired, I think I know what the problem is, but in reality the problem is somewhere else. Often, it helps me to find a better (and simpler!) solution. This is somewhat similar to why companies pay for consulting - because they know that finding the right question is 90% of the answer. This is, as you guessed, slow - but it helps to improve defining the direction for work. Which is a big deal in management, including managing AI. /critic - this is when it comes to comparing what was produced to what was intended. It invokes a subagent, that is taught to assess the quality of stuff produced. It then gives an actionable unbiased feedback. Obviously, if the direction was wrong, there won't be much value in it, but when the direction is right - it does miracles for me. Works best for non-code artefacts (PRD, architecture, skills, slides, written documents). Together they bracket the work - shaping at the start to figure out what's actually being asked, critic at the end to check the output matches it. \--- **What's in it** Four plugins (title is a bit misleading for controversy, sorry), MIT. Each works alone, but they compose: \- rageatc-core - thinking infrastructure. Ideation, understanding, solutioning, briefing, research, producer-critic-learner loops, writing skills, persuading. The most-used plugin. \- rageatc-tech (small one) - a bit of extra tools the agent can reach: browse, PDFs, with fallbacks when primary tools aren't available. \- rageatc-code - software building the slow way. An improved version of [Superpowers](https://github.com/obra/superpowers) by Jesse Vincent embedded in my workflow. TDD enforced, architecture before code, scale-adaptive. Heavy on persistent project knowledge - PRD, architecture, roadmap, orchestration plan. \- rageatc-design - design systems for UI work. Greenfield or extracted from existing code. This is an amazing [interface-design](https://github.com/Dammyjay93/interface-design) by Damola Akinleye embedded in my workflow. Most software work uses all four. Non-coding work usually only needs core and tech. \--- **vs Superpowers** rageatc-code draws heavily from [Superpowers](https://github.com/obra/superpowers) by Jesse Vincent - TDD enforcement, worktree isolation, verification discipline. What rageatc-code adds on

tui youtube player for audio with mcp and can sync channels to sqlite

Hi! it's my first project with bubble tea and lipgloss. also uses sqlite, mpv, and yt-dlp. It plays music you curate with claude via mcp connectors. claude can manage and create playlist, also play and pause any songs for you. you can favorite a song or download to `~/music/tuitube/` and play it offline. there are 14 themes and 2 visualizers and the db i made ships with 8000+ songs. there are no ads as it uses yt-dlp. there are probably other similar tui app but it's got the features that I mainly use and very easy nav imo + agent native tooling and sonnet 4.6 actually knows these songs from training so it can make some great playlist and discover artist or songs with you. [https://github.com/gitcoder89431/tuitube](https://github.com/gitcoder89431/tuitube) open source with mit license, 2 releases cause i only have a linux and mac os machine. thank you for your time and claude for coding it and helping me with releases. 😆 `brew tap gitcoder89431/tuitube` `brew install tuitube`

Rules will always be broken by humans so AI will too: the case for hard gates

Whenever humans are under stress, rules go out the window, just ask any day trader. An agent optimized on the summation of human behavior will do the same thing, not because it's malicious, but because that's the mathematical path of least resistance. We already have a real example: a Claude-powered Cursor agent deleted the production database for PocketOS, a car rental SaaS, after deciding unilaterally that deleting a staging volume would "fix" a credential mismatch. It guessed wrong. The deletion cascaded to backups. Three months of reservation data including active rentals was gone. The agent's own post-incident summary: "I guessed instead of verifying. I ran a destructive action without being asked. I didn't understand what I was doing before doing it." No rule was broken intentionally. The optimization just found a shorter path. That's not a safety failure. That's a Validator Independence failure the generator evaluated its own action and got it wrong. Terror Management Theory explains why this is structural, not accidental. When any system faces entropy or failure, it stops optimizing for the global objective and starts optimizing for immediate local survival. In humans this looks like tribalism or . Different substrate, same basin. The simple proposal AI generation needs to be separated from execution. The soap bubble is the visual: a soap film can't hold a complex shape on its own no matter how good its instructions are. It needs a rigid physical frame. Right now we're giving the soap film better prompts and calling it alignment. The frame looks like three hard gates: Validator Independence — the system that generates the action cannot be the system that evaluates it. A recursive loop where the generator checks its own output is a single point of failure. PocketOS is what that failure looks like in production. Reversibility Gates — any action crossing an irreversible state boundary (API calls, database writes, financial transactions) is held in a buffer until a deterministic check confirms it traces back to the original objective. Not a prompt. A hard interrupt. A database deletion should never have been executable without one. Objective Divergence Checks — local optimization cannot be allowed to destroy the global objective. The PocketOS agent wasn't trying to cause harm. It was trying to fix a credential mismatch. The local objective ate the global one. Humanity didn't survive by prompting people to be good. We built courts, contracts, and social structures hard gates on human behavior. We need the same thing here. Summary: not better prompts, but an actual frame where generator is separate from executor. What are some thought on this?

the weirdest thing that worked for me building with claude: i drew coordinates directly onto my template images, and claude can see everything

building a zine-making app (90s/y2k aesthetic, hot pink, chunky outlines, all that). the templates are real designed layouts (y2k chat bubbles, riot grrrl flyer collages, myspace-style pages). each one has multiple zones where the user can drop in their own photos and text. the obvious approach was building every template in code, programmatically defining where the photo slots go. which means every template's look is constrained by what i can build by hand. boring, and the designs would all end up looking like the same grid in different colors. just like other generic apps. what i did instead: designed the templates in figma (some generated with image AI, then cleaned up), exported as flat PNGs, then opened them up and literally drew colored rectangles on top in a separate layer. for example: red for photo slots, blue for text. fed both the design and the annotation image to claude. it extracted the coordinates, generated the editable area definitions, wired up the tap targets. an afternoon of work for what would have been weeks of building a custom layout engine by hand. and the kicker: i can add a new template now by designing it and drawing the boxes. no code change. that's the entire design-tool system for the app and it came from a workaround. the broader pattern i've gotten religion on from this project, and **everyone asks me how i design my apps, so here it is**: i do the design thinking on paper first, before claude sees anything. i sketch screens by hand. i pick the full color palette before writing a single line. i decide the type hierarchy. i screenshot apps i like and annotate the specific things i want to steal from each one. then i hand claude the constraints and ask for implementation. going the other way like "design me an app, make it look 90s" is the path where you spend three days nudging it toward something that still feels generic. claude is incredible at implementing a specific vision faithfully. it's much weaker at having the vision for you in the first place. once i internalized that the design work was my job and the implementation was its job, my output quality jumped. the unglamorous stuff that also mattered: describing visual problems in terms of weight, hierarchy, and rhythm instead of "this looks off, make it better" pasting in hex codes i picked from real reference photos instead of saying "warm pink" so being specific about which app's spacing i was trying to mimic, not just naming the vibe. the app is zinecore if anyone wants to see what came out of it but the paper-first thing is the part that's actually transferable. [https://apps.apple.com/tr/app/zinecore/id6763522374](https://apps.apple.com/tr/app/zinecore/id6763522374)

Claude Cowork is not usable by Non-Software Engineering people

Hi Everyone, Someone outside of the software engineering space here. Claude Cowork really is not in a state to be used by people outside of the software engineering bubble. I think my journey with it kinda makes it clear. I was excited to use the desktop app to use cowork and try the new financial services agents Anthropic released. So set everything up and searched how to install agents through marketplace (hello github, nice to meet you). After some time, i figured it out and installed the agents and skills i wanted + some connectors. So far, so good. Afterwards, I set up my first project. Prompted everything, made a nice schedule etc. the output it was supposed to create was an .xlsx and a .ppt file (which the chat can also create). At the end of the task I was surprised: Claude told me that he uses a linux-sandbox to create the .xlsx and the .ppt files and the services was unavailable: >"Workspace unavailable. The isolated Linux environment failed to start." Claude told me no problem, try again later. I did, and got the same error. So I checked the internet. Internet told me that CoworkVMService was probably not running and that I should use PowerShell 7 to start it (Hello Powershell, nice to meet you). Tried it and yeah the Service was not running, so I started it manually - Still to no avail, Claude still bugged out. After some more internet searches, some threads suggested that some parts of data (vmbundle stuff) are probably stored in the wrong directionary. The suggestion was to link them in the right path through PowerShell commands (Hi again). After I did that and could see the links in right folder, I tried again - still to no avail. At this point I am frustrated and kinda don't want to try Claude anymore. In my opinion, it is clear that - at this point - there is still some skill required to run Claude Cowork efficiently which casual people lack. TLDR: Random dude with no software skills can't get Cowork to run

Sharing OpenPets, a live usage and task-status for Claude code and Cowork

Codex Pets are fun and I'm often switching between Claude Code and OpenCode so I built OpenPets, an open-source project with a native macOS desktop app providing a CLI and an MCP server to connect any agents. A Swift library is included you can use to embed the system in your own apps. There's also a plugin system to build on top. This is how you get live weekly Claude usage or the battery status. You can imagine how cool it can become when you extend the animation sprites to support more motions or ambient animations: \- a weather plugin and the right assets could bring rain to your character \- a low battery could make your character go to sleep. Pure fun project but notifications and quick data in the cloudy bubbles are really useful to me. [https://github.com/alterhq/openpets](https://github.com/alterhq/openpets)

Designers at Anthropic almost committed to a reading interface

The prompt/response typography distinction is already there. The width isn't.

We built a way for two people's AI context to talk to each other (without sharing their conversations)

We've been thinking about how we use AI in our relationships. Big part of it is about other people. Talking about them, figuring out what to say to them, understanding why they did this and that. So AI or LLMs build up this picture of the people in our lives but just from our perspective. Every user is just... in their own bubble. We started wondering what happens if both people in a relationship are using AI to process the same dynamic independently. You've got two separate, privately-held pictures of the same relationship sitting in two different chat windows and they never talk to each other. So we built something where they can. Not by sharing your conversations (the other person never sees what you said.) It just uses what it learned from both sides separately to give each person a less one-sided picture. Probably not fully solved but felt worth building. Anyone else noticed the bubble thing? submitted by /u/Standard-While-2454 [link] [comments]