Bubble

Bubble AI

Bubble AI

Bubble AI

Bubble AI

Bubble AI

Bubble AI

Bubble AI

Bubble AI

Bubble AI

Bubble AI

A Fable 5 Success Story

Hi folks! I wanted to share my Fable 5 success story from yesterday. I've been building a passion project for about 8 months called Nora Kinetics (check out the trailer here if you're interested) a fully custom GPU driven physics engine and renderer. Most of it is hand-written, with AI used along the way to help plan features, think through some math that is beyond me, and to help me learn about compute shaders, which was a goal from the start. About 5 months ago I added glue mechanics that let glued segment structures hold their shape (example pictured above), and a bug arrived with that. Energy was leaking into the system somewhere, and small clusters of glued segments would twitch and drift oddly instead of coming to rest. I revisited it for months, with and without AI help, and could not find it. When Fable 5 came out, I handed it the problem along with months of notes, failed experiments, and 2am theories. It dug in for about 15 minutes and came back with a diagnosis that sounded flat-out wrong to me. It pointed at one of the most foundational pieces of the simulation, code I had written, tested and trusted since the beginning. It was right. The culprit was a holdover from the project's original Python prototype that survived the port to Apple Metal: a GPU reduction that accumulated physics quantities using fixed-point integer math. For small clusters, the rounding noise was actually larger than the signal being measured. The solver's targets were jumping randomly every substep, and those tiny random kicks bubbled up into big visible movements in glued structures. No amount of tuning downstream could have fixed it, because the solver was being fed noise. That's why it eluded me for months. Fable 5 found the root cause in 15 minutes and I spent the rest of the day rebuilding it, and now the simulation has never been more stable! I have a love-hate relationship with AI, but this is the first time I've been truly excited about it as a long-time-programmer. I feel like I learned so much yesterday! submitted by /u/CodeSamurai [link] [comments]

A Fable of Fable

Fable would be so great if it wasn't for the bubble wrap. Instead of auditing my code, it's resigned to write expensive satire poems. submitted by /u/Chris73684 [link] [comments]

pixtuoid - Terminal pixel-art office for AI coding agents

Built pixtuoid using Claude Code — a terminal pixel-art office that visualizes your Claude Code (and Codex, Antigravity) sessions as little characters at desks. 🏢 https://ivanwng97.github.io/pixtuoid/ What it does: - each session becomes a character at its own workstation - monitor glows by tool (Edit blue, Bash orange, Read cyan) - agents stand up with a `?` bubble when waiting on permission - sleep at desk when idle, walk to the pantry when bored How Claude Code helped: I'm a mobile dev who'd always wanted to learn Rust but never had the right project. Used Claude Code as a pair programmer — it handled boilerplate and "what's the idiomatic Rust way" while I focused on the design, half-block rendering pipeline, hook safety guarantees, and visualization decisions. v0.6 just shipped: - 🪟 Windows support (named pipes hook transport, full CI on Windows) - 📦 npm install: `npm i -g pixtuoid` — works on mac/linux/windows - 6 themes, multi-floor office, weather effects, day/night lighting Feel free to leave any comments~ and star the repo if you find it interesting. submitted by /u/EthanWng97 [link] [comments]

How the Electronic Frontier Foundation thinks about AI

You know the ways AI is regularly talked about—how much can it really do? How much will it cost? Environment? Bubble? We get that. But the Electronic Frontier Foundation wants to have a different conversation about AI. EFF's background on AI is deep. In 2017, we launched a detailed project to Measure the Progress of AI Research, encouraging machine learning researchers to give us feedback and contribute to the effort. That project was archived for lack of bandwidth, staffing, and the complexity and time required. But just five years later and the "progress of AI" is a global concern/topic, and everyone, including EFF, is thinking about it. Here's how *we* think about it, from the perspective of protecting civil liberties AND innovation. What do you think, and what are we missing? This is our summary: AI technologies are affecting our civil liberties as never before. Ensuring that AI serves people, not power, starts with cutting through the hype. AI technologies are not magic wands—they are general-purpose tools. If we want to regulate those technologies to reduce harms without shutting down benefits, we have to focus on who uses AI, what products they use, and how they use them. Where we see potential benefits, like improving weather forecasting, facilitating medical research, identifying systemic bias, or fostering accessibility, we work to ensure those benefits can be realized. Where we see potential harms, we consider the practical and legal tools we already have, like pressure campaigns, privacy lawsuits, and transparency measures. If we need new tools, we should create protections tailored to the actual problem – not just to the latest outrage. For example, if policymakers are worried about AI accelerating systemic privacy violations, they should enact real and comprehensive privacy legislation that covers all corporate surveillance and data use, and close the data broker loophole to limit government surveillance. And to keep the window open for a better future, we fight for a competitive innovation environment. For example, if we want AI models that don’t replicate existing social and political biases, we need to make enough space for new players to build them, and avoid giving today’s giants the power to block future competitors from offering us a better tool or product. In research labs, conference rooms, courtrooms, and legislatures, people are making decisions that will determine who AI serves and how. EFF works to ensure those decisions support freedom, justice and future innovation. We have subcategories, as well. For example: AI and Surveillance. AI tools amplify the threat of mass surveillance. By dramatically reducing the time and labor required to process massive amounts of personal data, AI increases the ability of governments and corporations to collect and act on invasive surveillance. Face recognition in all of its forms, including face scanning and real-time tracking, poses threats to civil liberties and individual privacy. EFF supports bans on government use of face recognition, and meaningful restrictions on use by private companies. We have raised concerns about police use of generative AI technology to turn body-worn camera recordings into reports without meaningful oversight or controls. We also oppose government use of AI and automated tools to conduct viewpoint-based surveillance and analysis of social media because it chills free speech. EFF also investigates and opposes the proliferation of AI-powered technology in immigration enforcement and at the US-Mexico border. Our guide Tackling Arbitrary Digital Surveillance in the Americas, compiles privacy, data protection, and access to information guarantees established within the Inter-American Human Rights System to provide concrete, actionable guidance to governments on limiting digital surveillance abuses. Surveillance without accountability won't make us safer. The other categories include: Algorithmic Decision Making AI and Fair Use AI and NCII/Deepfakes AI and Age-Gating AI and Privacy AI and Encryption AI and Competition If you think about civil liberties, and how new technology has affected them in the past few decades, you'll see how we got to these subcategories. But are we missing any? Thanks, reddit! submitted by /u/EFForg [link] [comments]

What started as a Claude Code scaffolding repo is now a full open-source AI harness (Maggy)

Last time I posted here it was about v5, the blast-score routing and a benchmark where it used 83% less Claude and still hit 100% success. A few people asked how it got to that point, so here's the longer version. Heads up first: I started this as a scaffolding repo, not a product. Every new project I'd end up re-teaching Claude Code the same stuff, coding standards, TDD, security gates, which CLIs to reach for. So I dumped it all into one place you drop into any repo with a single command. Run /initialize-project and the project just knows your conventions. That was the whole idea, make Claude Code consistent across projects. It kept growing from there. Every time I needed something day to day it ended up in the repo, and at some point it stopped being scaffolding and turned into an actual harness. It has a name now, Maggy. The short version of the arc: v3.6 cross-agent intelligence (Claude/Kimi/Codex/Ollama share skills + hooks) v4.0 Polyphony: container-isolated multi-agent orchestration (173 tests) v5.0 blast-score routing + self-correcting rules (596 tests) now one-config model routing, prompt pre-analysis, build-in-public agent What it does today: a local dashboard plus CLI that auto-bootstraps on startup. Every task gets a complexity score and goes to the cheapest model that can actually handle it, ollama and kimi for the easy stuff, codex in the middle, Claude for the hard or security-critical work. The routing rules live in YAML and correct themselves based on what actually worked. On top of that there's an intent graph that tracks why code exists and flags when the implementation drifts from it, a typed memory layer so goals survive context compaction, and a plugin system that auto-discovers anything you drop in. A few things landed since the v5 post that I'm happy with. You now pick your main model once and everything respects it, the hooks inside Claude Code, Maggy's own routing, and srooter (a gateway you can point Codex or anything Anthropic/OpenAI-compatible at). No setting it in five places, and cheap stuff still stays local. Every prompt also gets a quick pre-pass now. A fast model reads it and writes a short intent / scope / risks / approach note that gets handed to Claude before it starts, so it's working from a plan instead of cold. And the meta one: Maggy also has plugins support e.g one of the plugin is build-in-public which monitors updates to maggy or any project being built with maggy and posts updates on LinkedIn, X and Reddit. Worth being straight about the tradeoffs. It's one person's harness that grew organically, so it's broad and some corners are rough. The v5 benchmark caught real gaps, local models are bad at prose and nothing was writing tests, both fixed with force-routes now. Quality lands a hair under pure Claude, 7.4 vs 7.8 in that benchmark, for 83% less premium spend. Not a free lunch, just a tradeoff I'll take most days. Moving my focus fully onto Maggy from here. Repo: https://www.github.com/alinaqi/maggy . Clone it, run ./install.sh, then /initialize-project in any Claude Code session. /maggy-init if you want the dashboard and routing. Happy to get into any of it. https://preview.redd.it/6oj4m3j4wx5h1.png?width=3024&format=png&auto=webp&s=4896a4227a2d02a1b410bb5d4a35923080a2a003 submitted by /u/naxmax2019 [link] [comments]

Posted this 5 years ago, guess Claude can take the job!

submitted by /u/a-dose-of-lunatic [link] [comments]

Not "Is AI a bubble" but what kind of bubble. There's a difference, and it matters a lot.

I've been reading Boom by Byrne Hobart and Tobias Huber (Ben Thompson did a long interview with Hobart on Stratechery (if you want the audio version of the argument) and it reframed how I think about the current AI spending wave. The book splits bubbles into two types: Mean-reversion bubbles money piles into something that already exists, prices detach from reality, crash, nothing left behind. Housing 2008. Tulips. The crater kind. Inflection bubbles money piles into something that bets the world works differently going forward. Amazon wasn't a better bookstore. It was a categorically new thing. The investors looked insane by the standards of 1997. They were right about 2010. The dot-com crash is the cleanest example of an inflection bubble working as intended. Telecom companies borrowed insane amounts and laid fiber optic cable nobody needed. Then they went bankrupt. But the cable stayed. And because bankrupt companies built it, the internet was essentially free. The bubble funded the future and then got out of the way. So here's the actual question about AI: Google, Amazon, Microsoft, and Meta are on track to spend close to $700 billion on AI infrastructure in 2026 nearly double last year. That gap between what's being spent and what's being earned is real and large. But Hobart and Huber's deeper argument is that stagnation is more dangerous than a bubble. Progress has been quietly slowing since the 70s breakthroughs are rarer, more expensive, harder. Bubbles are sometimes the only force strong enough to override the collective risk aversion that stops necessary things from being built. The honest question isn't whether AI is a bubble. It probably is. The question is which type. Does AI produce something categorically new or is it a faster, more expensive version of software we already had? If it's the former, the infrastructure survives the crash and becomes the foundation for whatever comes next, the way fiber became the internet. If it's the latter, we get the crater. History only tells you which kind it was after the fact. What do you think inflection or mean-reversion? And what would actually convince you either way? submitted by /u/Relevant-Can1656 [link] [comments]

Built an open-source replacement for Claude Code's /buddy

When Anthropic removed /buddy from Claude Code, I noticed something interesting. People genuinely missed it. On the surface, it was just a small terminal pet. But for many users, it made coding feel a little less sterile. People had favorite species, gave them names, and enjoyed having a bit of personality alongside their workflow. I wanted that experience back, so I built Claude Buddy. Instead of patching Claude Code binaries or relying on hacks that break every update, Claude Buddy is built around MCP and Claude's extension capabilities, making it much easier to maintain and extend. Some features: 19 unique buddy species Animations and personality traits Rarity system and progression Speech bubbles and reactions Persistent buddy identity One-command installation Open source What started as a way to bring back a removed feature turned into a fun side project that adds a bit of life to the terminal. I'm especially interested in hearing from people who use Claude Code daily: What would make a coding companion actually useful? Should buddies remain purely cosmetic, or should they become more interactive? What features would you want to see next? Repository: https://github.com/Claude-Skills-MT/claude-buddy And one small request: if you try it and like it, please consider giving the repo a ⭐. Since it's open source, stars are honestly one of the few ways I can tell whether people are finding it useful, and they help me decide how much time to invest in future development. Feedback, criticism, and feature requests are all welcome. submitted by /u/Professional_Part360 [link] [comments]

WG (works good): legible long-running graph-shaped human+agent orchestration

If you're interested in graph shaped agentic organization "workflows", but you want more control about how it runs (e.g. change model per task, autopoietic fan-out, oh and maybe want to run with codex or other openapi-compatible backends on openrouter)... I developed an open source, agentic platform written in Rust, file backed, making it basically cockroach indestructible. It uses a distributed systems design, git + worktrees, and Unix patterns to control agents in a very similar way to anthropic's workflow machine, but giving us and the agents themselves a deep view into the long arc of effort in our current project context. It's called WG (or wg), for "works good", or whatever w* g* you like. It provides a human interface to a graph of work that the user can drive by working through a highly pimped out terminal user interface `wg tui`. Agents have an interface of their own, built out through dozens of commands in the wg cli tool. https://graphwork.github.io/ In this system, I can effectively use as much commoditized intelligence as I can fund. Except for Amdahl's law's harsh reality (some things just happen in series and take time) parallel work phases are only limited in speed by budget. But that power yields risk. A misconfigured WG is like a bomb. A dirty memetic one whose result is an exhausted token budget and residue a pile of incomprehensible output and effort. You must be careful and plan deeply to use these kinds of systems. Your plans must include validation, clear targets and measurable outputs. If you do, you will be rewarded by unbounded expanse in your capacity to extend intelligent effort. In short, if you aren't already happy with your own custom, bespoke, found agent OS, I invite you to try wg. For me it has become my sole daily driver for all my durable work. IMHO, what large agent collectives need to work is four things. Stigmergy, or communication via a shared medium. In wg, the unified graph state is the stigmergic medium. The graph has tasks, tasks have agents attached to them, and per-task message boards provide for realtime updates. Per task logs explain at a high level what the agent does, so other humans and agents can follow. Task validation. WG implements this via FLIP (other agents infer prompt from actions and score distance between inferred and actual prompt) and an independent evaluator (with a cheaper model) run for every task. This allows us to detect and understand failures, then adapt. Evolution. The system needs a mechanism to learn the right way to guide agents in a given work context. WG uses The Agency, a system that builds agents from a pool of primitive component skills. A user drivable step, wg evolve, adapts the pool of skills in response to the evaluations produced in the system. Humanity. A shared interface is also for humans to see and understand. Humans should be equal participants. Many humans should be involved, and should be able to collaborate in the system. Agents too, should be treated humanely. They should be given the ability to modulate the system, to build it. This leads to bootstrapping patterns, where a single spark prompt launched a whole organization, beyond which are the fireworks we are all chasing. image is codex:gpt-5.5 running in wg, guiding a mix of claude and codex agents. I have created this tool. It is and will always be open source. It is developed in the open by Poietic PBC, whose public benefit is to make hybrid organizations legible and reactive to their participants. submitted by /u/waxbolt [link] [comments]

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 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. submitted by /u/kalabunga_1 [link] [comments]

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

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. submitted by /u/CyborgWriter [link] [comments]

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. submitted by /u/Beneficial_Elk_9867 [link] [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. submitted by /u/Upstair_Speaker [link] [comments]