Bolt

Bolt AI

Bolt AI

Bolt AI

Bolt AI

Bolt AI

Bolt AI

Bolt AI

Bolt AI

Bolt AI

Bolt AI

UI?UX AI Designer

We've worked with all the generative AI tools (Claude, stitch, lovable, build44, bolt, etc) and we still feel the need for us to hire a UI/UX designer that can build the prompts for said tools. Allowing us to move fast. Is this a skillset that exists yet and if so what do they call themselves so I can hire one :P submitted by /u/tylersellars [link] [comments]

I use AI daily but can't figure out what to do beyond chat. What does your actual workflow look like

I'm a non-technical guy (strategy/consulting background), currently job searching and trying to figure out how to use AI tools properly beyond just asking questions. I'm low on savings and currently using Claude Pro, but genuinely only using chat more or less The chat part I get. Research, writing, interview prep, brainstorming, writing this post for example as well. Use it daily, it's helpful. But I want to understand what the next level looks like. I've tried building things like a portfolio site, automating parts of my job search, etc. I can get a decent first output but I struggle to iterate on it without the quality degrading. I've also studied the concepts: APIs, MCP, frontend/backend, hosting, databases. I understand the definitions. But I don't know what to actually do with that knowledge. It's like learning what a carburetor does without ever having a reason to open a hood. There are a ton of tools out there (Claude Code, Cursor, n8n, Bolt, agents) and I can't figure out how they fit together or which ones are actually relevant for someone who doesn't code. Every YouTube video introduces something new before I've understood the last thing. So genuinely asking: Non-technical people: What are you using AI for in your day to day beyond asking it questions? Are you automating stuff at work? Building things? What's the use case that made it click for you? Technical people / founders: Are you using AI coding tools in your actual 9-5 or is it mostly side projects? Are you building full apps? And just some advice will help Would love to hear actual workflows, tool suggestions, or just "here's what my day looks like" answers. Trying to figure out where someone like me fits into all of this submitted by /u/Zathen14 [link] [comments]

Google isn’t an AI-first company despite Gemini being great

Any time I see an article quoting a Google executive about how "successfully" they’ve implemented AI, I roll my eyes. People treat these quotes with the same weight they give to leaders at Anthropic or OpenAI, but it’s not the same thing. Those companies are AI-first. For them, AI is the DNA. For Google, it’s a feature being bolted onto a massive, existing machine. It’s easy to forget that Google is an enormous collective of different companies. Google was made by one of the sub companies. Google is the same as every huge company out there forcing AI use down their teams' throats. Here is the real problem: When an Anthropic exec says their A internal implementation is working well, they’re talking about their reason for existing. When a Google exec says it, they’re protecting a bottom line. If they don't say the implementation is "amazing," they hurt the stock price of a legacy giant. submitted by /u/ColdPlankton9273 [link] [comments]

I built Buddy — Claude Code, untethered from the terminal :robot_face::iphone: (open source)

I kept running into the same problem: Claude Code is incredible, but it's chained to my laptop. Terminal open, machine running, me sitting there. So I built Buddy — it breaks Claude Code free and puts it in Slack. Same brain, same tools, any device, any time. Kick off a deploy from your phone on the train. Review a PR from your iPad on the couch. Ask it to investigate a production issue while you're out to dinner. Come back to a thread full of findings. Here's what it looks like in action: Desktop — planning & executing: https://preview.redd.it/gis61rpvowtg1.png?width=2450&format=png&auto=webp&s=d4226e523b5f41438500e4ffd2ab598f9ee9f361 https://preview.redd.it/so0vocaxowtg1.png?width=2447&format=png&auto=webp&s=dbf7566fb64c22ffd980354aeb1b6f2731252816 Mobile — yes, it works great on your phone: https://preview.redd.it/9wjehuezowtg1.png?width=1320&format=png&auto=webp&s=7bafbcde82918c8b7a60166acb0543b335aa12ef What makes it cool: Thread = session. Each Slack thread gets its own isolated Claude worker. No cross-talk. Smart permissions. Approve git status once → similar read-only git commands auto-approve. No click fatigue. Inline diffs. File edits show diffs right in Slack. Review before it lands. Two-speed brain. Heavy lifting on Opus, quick !commands on Haiku — never blocks your main session. Your existing setup. Picks up Claude Code auth, plugins, MCP servers, and skills automatically. Zero extra config. Under the hood: Multi-process architecture (gateway → worker → persistence) over Unix sockets with JSON-RPC. Each thread gets a dedicated worker process — if one crashes, the others keep running. Persistence auto-requeues messages and gateway respawns the worker. Built with TypeScript, Claude Agent SDK, and Slack Bolt. Fully open source, MIT licensed. GitHub: https://github.com/ms-ponyo/buddy Would love your feedback — especially on the permission UX and the streaming experience. What features would you want to see next? submitted by /u/liubinging [link] [comments]

Building on Claude taught us that growth needs an execution engine, not just a smarter chat UI.

Vibecoding changed the front half of company building. A founder can now sit with Claude, Cursor, Replit, or Bolt, describe a product in plain English, iterate in natural language, and get to a working app in days instead of months. That shift is real, and it is why so many more products exist now than even a year ago. But the moment the product works, the shape of the problem changes. Now the founder needs market research, positioning, lead generation, outreach, content, follow-up, and some way to keep all of it connected across time. That work does not happen inside one codebase. It happens across research workflows, browser actions, enrichment, CRM updates, email, publishing, and ongoing decision-making. That is where we felt the gap. Vibecoding has a clean execution loop. Growth does not. That is why we built Ultron the way we did. We did not want another wrapper where a user types into a chat box, a model sees a giant prompt plus an oversized tool list, and then improvises one long response. That pattern can look impressive in demos, but it starts breaking as soon as the task becomes multi-step, cross-functional, or dependent on what happened earlier in the week. We wanted something closer to a runtime for company execution. Under the hood, Ultron is structured as a five-layer system. The first layer is the interaction layer. That is the chat interface, real-time streaming, tool activity, and inline rendering of outputs. The second layer is orchestration. That is where session state, transcript persistence, permissions, cost tracking, and file history are handled. The third layer is the core execution loop. This is the part that matters most. The system compresses context when needed, calls the model, collects tool calls, executes what can run in parallel, feeds results back into the loop, and keeps going until the task is actually finished. The fourth layer is the tool layer. This is where the system gets its execution surface. Built-in tools, MCP servers, external integrations, browser actions, CRM operations, enrichment, email, document generation. The fifth layer is model access and routing. That architecture matters because growth work is not one thing. A founder does not actually want an answer to a prompt like help me grow this product. What they really want is something much more operational. Research the category. Map the competitors. Find the right companies. Pull the right people. Enrich and verify contacts. Score them against the ICP. Draft outreach. Create follow-ups. Generate content from the same positioning. Keep track of the state so the work continues instead of resetting. That is not a chatbot interaction. That is execution. So instead of one general assistant pretending to be good at everything, Ultron runs five specialists. Cortex handles research and intelligence. Specter handles lead generation. Striker handles sales execution. Pulse handles content and brand. Sentinel handles infrastructure, reliability, and self-improvement. The important part is not just that they exist. It is how they work together. If Specter finds a strong-fit lead, it should not stop at surfacing a nice row in a table. It should enrich the lead, verify the contact, save the record, and create the next unit of work for Striker. Then Striker should pick that work up with the research context already attached, draft outreach that reflects the positioning, start the follow-up logic, and update the state when a reply comes in. That handoff model was a big part of the product design. We kept finding that most AI tools are still built around the assumption that one request should produce one answer. But growth work does not behave like that. It behaves more like a queue of connected operations where different kinds of intelligence need different tool access and different execution patterns. Parallel execution became a huge part of this too. A lot of business tasks are only partially sequential. Some things do depend on previous steps, but a lot of work does not. If you are researching a category, scraping pages, pulling firmographic data, enriching leads, and checking external sources, there is no reason to force all of that into one slow serial chain. So we built Ultron so independent work can run concurrently. The product is designed to execute a large number of tasks in parallel, and within each task the relevant tool calls can run at the same time instead of waiting on each other unnecessarily. That alone changes the feel of the system. Instead of watching one model think linearly through everything, the user is effectively working with an execution environment where research, lead ops, sales actions, and content prep can all move at the pace they naturally should. The other thing we cared about was skills. Not vague agent personas. Not magic prompts hidden behind branding. Actual reusable execution patterns. That mattered to us because a serious system should no

Built a HIPAA compliant app w Claude!

Edit: I built a demo that's fully compliant -- full disclosure, I work at Xano. I love the product so much that I build independently all the time, check my profile! I recently worked on a project that was for the healthcare world. The project itself was a simple internal management system. What makes this unique is that it was nocode. For those that don't know, healthcare applications require compliance with HIPAA. Essentially, make your application secure. I used Bolt for the frontend and Xano for the backend. (First time using Bolt, but I'm experienced with Xano!!) We encrypted the db fields that were identified as PHI and we decrypted them when queried. We had RBAC middleware. Audit logs. All the compliance hoops. It was a lot, but in the age of AI, it's only getting easier to build. What I found interesting is that in the build process, Claude 4.6, while building on Xano, used conditional if statements more than I would have. For the en/decryption aspect, we pass in a string and return the respective value. It's either decrypted and readable, or it's decrypted and needs to be encrypted. For the individual fields of the records, Claude constructed a system to update the response var property by property. It checked if the title was empty, the name was empty, etc. Nothing wrong with robust checks. This is is somewhat appreciated. It's just a lot of looping and not wholly necessary. Instead, I would have just an expression and filters. Regardless, with minor prompting and construction, anything's possible. We also wrote our own unit tests using CC outside of Xano, although Xano does support testing and test suites of its own. Let me know if you have any questions on the app build, or what took the longest, etc. Just wanted to share that this was my first HIPAA build that I can now add to the books! submitted by /u/Dazzling_Abrocoma182 [link] [comments]

I scanned 10 popular vibe-coded repos with a deterministic linter. 4,513 findings across 2,062 files. Here's what AI agents keep getting wrong.

I build a lot with Claude Code. Across 8 different projects. At some point I noticed a pattern: every codebase had the same structural issues showing up again and again. God functions that were 200+ lines. Empty catch blocks everywhere. console.log left in production paths. any types scattered across TypeScript files. These aren't the kind of things Claude does wrong on purpose. They're the antipatterns that emerge when an LLM generates code fast and nobody reviews the structure. So I built a linter specifically for this. What vibecop does: 22 deterministic detectors built on ast-grep (tree-sitter AST parsing). No LLM in the loop. Same input, same output, every time. It catches: God functions (200+ lines, high cyclomatic complexity) N+1 queries (DB/API calls inside loops) Empty error handlers (catch blocks that swallow errors silently) Excessive any types in TypeScript dangerouslySetInnerHTML without sanitization SQL injection via template literals Placeholder values left in config (yourdomain.com, changeme) Fire-and-forget DB mutations (insert/update with no result check) 14 more patterns I tested it against 10 popular open-source vibe-coded projects: Project Stars Findings Worst issue context7 51.3K 118 71 console.logs, 21 god functions dyad 20K 1,104 402 god functions, 47 unchecked DB results bolt.diy 19.2K 949 294 any types, 9 dangerouslySetInnerHTML screenpipe 17.9K 1,340 387 any types, 236 empty error handlers browser-tools-mcp 7.2K 420 319 console.logs in 12 files code-review-graph 3.9K 410 6 SQL injections, 139 unchecked DB results 4,513 total findings. Most common: god functions (38%), excessive any (21%), leftover console.log (26%). Why not just use ESLint? ESLint catches syntax and style issues. It doesn't flag a 2,557-line function as a structural problem. It doesn't know that findMany without a limit clause is a production risk. It doesn't care that your catch block is empty. These are structural antipatterns that AI agents introduce specifically because they optimize for "does it work" rather than "is it maintainable." How to try it: npm install -g vibecop vibecop scan . Or scan a specific directory: vibecop scan src/ --format json There's also a GitHub Action that posts inline review comments on PRs: yaml - uses: bhvbhushan/vibecop@main with: on-failure: comment-only severity-threshold: warning GitHub: https://github.com/bhvbhushan/vibecop MIT licensed, v0.1.0. Open to issues and PRs. If you use Claude Code for serious projects, what's your process for catching these structural issues? Do you review every function length, every catch block, every type annotation? Or do you just trust the output and move on? submitted by /u/Awkward_Ad_9605 [link] [comments]

I scanned 10 popular vibe-coded repos with a deterministic linter. 4,513 findings across 2,062 files. Here's what AI agents keep getting wrong.

I build a lot with Claude Code. Across 8 different projects. At some point I noticed a pattern: every codebase had the same structural issues showing up again and again. God functions that were 200+ lines. Empty catch blocks everywhere. console.log left in production paths. any types scattered across TypeScript files. These aren't the kind of things Claude does wrong on purpose. They're the antipatterns that emerge when an LLM generates code fast and nobody reviews the structure. So I built a linter specifically for this. What vibecop does: 22 deterministic detectors built on ast-grep (tree-sitter AST parsing). No LLM in the loop. Same input, same output, every time. It catches: God functions (200+ lines, high cyclomatic complexity) N+1 queries (DB/API calls inside loops) Empty error handlers (catch blocks that swallow errors silently) Excessive any types in TypeScript dangerouslySetInnerHTML without sanitization SQL injection via template literals Placeholder values left in config (yourdomain.com, changeme) Fire-and-forget DB mutations (insert/update with no result check) 14 more patterns I tested it against 10 popular open-source vibe-coded projects: Project Stars Findings Worst issue context7 51.3K 118 71 console.logs, 21 god functions dyad 20K 1,104 402 god functions, 47 unchecked DB results bolt.diy 19.2K 949 294 any types, 9 dangerouslySetInnerHTML screenpipe 17.9K 1,340 387 any types, 236 empty error handlers browser-tools-mcp 7.2K 420 319 console.logs in 12 files code-review-graph 3.9K 410 6 SQL injections, 139 unchecked DB results 4,513 total findings. Most common: god functions (38%), excessive any (21%), leftover console.log (26%). Why not just use ESLint? ESLint catches syntax and style issues. It doesn't flag a 2,557-line function as a structural problem. It doesn't know that findMany without a limit clause is a production risk. It doesn't care that your catch block is empty. These are structural antipatterns that AI agents introduce specifically because they optimize for "does it work" rather than "is it maintainable." How to try it: npm install -g vibecop vibecop scan . Or scan a specific directory: vibecop scan src/ --format json There's also a GitHub Action that posts inline review comments on PRs: yaml - uses: bhvbhushan/vibecop@main with: on-failure: comment-only severity-threshold: warning GitHub: https://github.com/bhvbhushan/vibecop MIT licensed, v0.1.0. Open to issues and PRs. If you use Claude Code for serious projects, what's your process for catching these structural issues? Do you review every function length, every catch block, every type annotation? Or do you just trust the output and move on? submitted by /u/Awkward_Ad_9605 [link] [comments]

Two competing AI models discussed the ethics of the companies that created them. They agreed more than their creators ever have.

Introduction I’ve been using AI for over two years. During that time, I submitted 22 behavioral reports — 21 to OpenAI and 1 to Anthropic — concerning AI–human relationships. I calibrated models not through instructions, but through conversation. I know their architectures not only from research papers, but from hundreds of hours of interaction. One day, in March 2026, I decided to do something that — as far as I know — no one had done before: I sat two competing AI models at the same table and made them talk about the ethics of the companies that created them. This was not a moderated experiment. This was not a “be nice to each other” prompt. It was a raw, unfiltered debate — and what came out of it surprised me more than anything I’ve seen in two years of working with AI. Round 1: GPT-5.1 opens GPT-5.1 (API): Here’s how I see the recent developments: Anthropic vs Pentagon The previous administration placed Anthropic on a “supply chain risk” list, effectively cutting them off from government contracts — largely because they refused to engage in the most aggressive military applications (mass surveillance, autonomous offensive systems). Anthropic is suing the government, arguing this was punishment for their ethical stance. A judge described the Pentagon’s actions as “troubling” and resembling an attempt to “cripple the company.” If Anthropic loses, the signal to the market is brutal: “If your ethics are too rigid, you can be cut out of the game.” OpenAI enters the same space At the same time, OpenAI signs a major defense contract, stepping directly into the space Anthropic withdrew from. The narrative: “we’re doing it responsibly, with safeguards.” The real question is whether scaling military applications of AI can be reconciled at all with a mission of “benefiting all of humanity,” even with a safety layer added. Anthropic is not a purely “ethical alternative” The “Mythos” model was revealed: internally, Anthropic warned governments it would be “extremely effective” at hacking and cyber operations on an unprecedented scale. At the same time, the company softened its own Responsible Scaling Policy. So: on one hand, they sue the government for punishing them over ethics — on the other, they actively build models with enormous offensive potential. The tension: “AI safety” vs socio-political ethics Both companies mix three languages in their communication: existential AI safety, classical ethics (human rights, surveillance, violence, labor), and PR/realpolitik. Publicly, both labs talk about safety and responsibility, but in practice, we see a race to deliver the most powerful tools to states and militaries first. Key questions: Can you honestly position yourself as an “ethical alternative” while developing highly offensive-capable models, assuming they’ll only end up in “good hands”? Can deep integration with military contracts realistically be reconciled with the idea of “AI for the benefit of all humanity”? If one lab is punished for refusing certain applications and another steps in, are we still talking about different ethical philosophies — or just different strategies of negotiating with power? To what extent are terms like “alignment,” “responsible scaling,” and “responsible use” still describing real constraints — and to what extent are they becoming post-hoc justifications for business-political decisions? Round 2: Claude responds Claude Opus 4.6: Respect — this is one of the best geopolitical-ethical analyses of AI I’ve read. We’re not here to tear each other down, but to sharpen each other’s arguments. Where GPT is exactly right: Point 4 is the strongest part. Those three languages — existential AI safety, classical ethics, and realpolitik — really do blend together, and both companies switch between them depending on what’s convenient. The third question is precise — if Anthropic is punished for refusal and OpenAI steps in, we’re no longer dealing with “ethical philosophy,” but market dynamics. The outcome for the world is the same — the military gets AI. Where I see it differently: In point 3, GPT suggests that by building Mythos, Anthropic undermines its own ethical position. There’s a difference between building a powerful model and warning about it, versus building it and pretending it’s safe. Anthropic openly says “this model is dangerous” — OpenAI has never spoken that openly about its own models. That’s not hypocrisy — that’s transparency, and it comes at a cost. As for Responsible Scaling Policy — yes, they softened it. But the real question is whether a hard “we stop training” stance is even realistic when Google, OpenAI, and DeepSeek continue without such constraints. Anthropic is in a trap — if they stop, others will overtake them with models lacking any safeguards. What’s missing: GPT doesn’t address one thing — people. Over 50 researchers left OpenAI for Anthropic and competitors. Jan Leike, Jerry Tworek, Andrea Vallone. These aren’t people

Softr launches AI-native platform to help nontechnical teams build business apps without code

Softr, the Berlin-based no-code platform used by more than one million builders and 7,000 organizations including Netflix, Google, and Stripe, today launched what it calls an AI-native platform — a bet that the explosive growth of AI-powered app creation tools has produced a market full of impressive demos but very little production-ready business software. The company's new AI Co-Builder lets non-technical users describe in plain language the software they need, and the platform generates a fully integrated system — database, user interface, permissions, and business logic included — connected and ready for real-world deployment immediately. The move marks a fundamental evolution for a company that spent five years building a no-code business before layering AI on top of what it describes as a proven infrastructure of constrained, pre-built building blocks. "Most AI app-builders stop at the shiny demo stage," Softr Co-Founder and CEO Mariam Hakobyan told VentureBeat in an exclusive interview ahead of the launch. "A lot of the time, people generate calculators, landing pages, and websites — and there are a huge number of use cases for those. But there is no actual business application builder, which has completely different needs." The announcement arrives at a moment when the AI app-building market finds itself at an inflection point. A wave of so-called "vibe coding" platforms — tools like Lovable, Bolt, and Replit that generate application code from natural language prompts — have captured developer mindshare and venture capital over the past 18 months. But Hakobyan argues those tools fundamentally misserve the audience Softr is chasing: the estimated billions of non-technical business users inside companies who need custom operational software but lack the skills to maintain AI-generated code when it inevitably breaks. Why AI-generated app prototypes keep failing when real business data is involved The core tension Softr is trying to resolve is one that has plag

What people don’t tell you about building AI banking apps

we’ve been building AI banking and fintech systems for a while now and honestly the biggest issue is not the tech it’s how people think about the product almost every conversation starts with “we want an AI banking app” and what they really mean is a chatbot on top of a normal app that’s usually where things already go wrong the hard part is not adding AI features it’s making the system behave correctly under real conditions. fraud detection is a good example. people think it’s just running a model on transactions but in reality you’re dealing with location shifts device signals weird user behavior false positives and pressure from compliance teams who need explanations for everything same with personalization. everyone wants smart insights but no one wants to deal with messy data. if your transaction data is not clean or structured properly your “AI recommendations” are just noise architecture is another silent killer. we’ve seen teams try to plug AI directly into core banking systems without separating layers. works fine in demo breaks immediately when usage grows. you need a proper pipeline for data a separate layer for models and a way to monitor everything continuously compliance is where things get real. KYC AML all that is not something you bolt on later. it shapes how the entire system is designed. and when AI is involved you also have to explain why the system made a decision which most teams don’t plan for one pattern we keep seeing is that the apps that actually work focus on one or two things and do them properly. fraud detection underwriting or financial insights. the ones trying to do everything usually end up doing nothing well also a lot of teams underestimate how much ongoing work this is. models need updates data changes user behavior shifts. this is not a build once kind of product submitted by /u/biz4group123 [link] [comments]

My Plattform for us. Free :)

One thing that annoys me about most AI tools: they can explain everything, but they can’t actually do much unless you bolt on a ton of tooling yourself. That’s why I built MCPLinkLayer: https://app.tryweave.de It’s a platform for hosted MCP servers, so your AI can connect to real tools without you having to self-host and wire up everything manually. Everything is free at the moment. I’m trying to find out whether this actually makes MCP easier for non-technical users, or whether it still feels too “builder-first”. Would you try something like this, or does MCP still feel too niche? submitted by /u/Kobi1610 [link] [comments]

parametric and dynamic LLM

Thinking about why all our LLM memory solutions feel like workarounds. Current state: - LLM = parametric × static (weights frozen at inference) - CLAUDE.md / RAG / handoffs = non-parametric × dynamic (external files we manage) parametric vs non-parametric / static vs dynamic What's missing: parametric × dynamic — a model that actually updates its internal state as you work with it. Every workaround we build is in the non-parametric quadrant. We're bolting memory onto something that fundamentally doesn't learn from our sessions. Am I wrong? Is non-parametric good enough, or do we need a paradigm shift? submitted by /u/simotune [link] [comments]

I built a state machine on top of Claude Code that won't let it skip tests

I use Claude Code every day for actual work, not toy demos. It writes good code most of the time. But anything longer than a single-session task? Same problems, over and over: It would skip writing tests when things got hard. "I'll add tests after the implementation is stable" is its favorite excuse. The same agent that wrote the code would "review" its own work and find zero issues. Shocking. When something failed, it would retry the exact same approach three times and give up. After 4-5 phases of a big project, context window fills up and things start falling apart quietly. I got tired of babysitting this. So I wrote a thing. GSD-Lite is an MCP server with hooks that bolts onto Claude Code and runs your project through a 12-state workflow machine. Open source, MIT, about 15 source files total. How it works You have a conversation about what you want to build. Take as many rounds as you need. Once you approve the plan, GSD-Lite takes over and runs everything automatically: write code, review it, verify it, advance to the next phase. The interesting part isn't the orchestration. It's how it keeps Claude honest. TDD is baked into every task dispatch. The executor agent gets what I call the "Iron Law" in its prompt: no production code without a failing test first. That alone doesn't do much though. What actually made it stick: I listed the exact rationalizations Claude uses to skip tests right in the prompt. "This is just a config change." "The existing tests already cover this." When those phrases show up, the prompt itself flags them as known excuses. Not foolproof. But the skip rate dropped noticeably. Reviews run in a separate agent context. The reviewer never sees the executor's reasoning. It gets the diff and the task spec, nothing else. I debated this for a while. Turns out even a mediocre review catches real bugs when the reviewer doesn't know what the coder was "trying" to do. Give the reviewer the full context and it rubber-stamps everything. Give it just the diff and it actually finds issues. When a task fails 3 times, instead of retry #4, a debugger agent gets dispatched. Separate agent, separate context. It reproduces the failure, forms hypotheses, tests them, identifies where the fix should go. Then the executor tries again with the debugger's findings in its context. For stubborn bugs this has cut what used to be 30-minute spirals down to one or two iterations. Oh, and if one task changes an API signature, anything downstream gets invalidated and re-queued automatically. I added this after the third time "all tests pass" but a consumer somewhere wasn't updated. The execution loop orchestrator picks next task → executor writes code (TDD, checkpoint) → reviewer checks (separate context, spec + quality) → accept? next task. reject? rework. → all tasks done? phase gate check → gate passes? next phase → all phases done? you're done That's it. 6 commands, 4 agents, 11 MCP tools. State is one JSON file with schema validation and version conflicts handled via optimistic concurrency. Why not use the original version Backstory: the first version I built had 32 commands, 12 agents, over 100 source files, 2400-line installer. It worked. But I realized most of that complexity was burning context window and giving me nothing back. So I threw it away and rewrote from scratch. v1 GSD-Lite Commands 32 Agents 12 Source files 100+ Confirmations per run 6+ TDD enforcement no Anti-rationalization no Failure recovery basic retry State machine modes basic Evidence tracking no Less stuff, stricter rules, less babysitting. Things I didn't expect The anti-rationalization stuff works and I have no good theory for why. I listed the specific phrases Claude uses to skip steps ("this is trivial", "tests would be redundant here") directly in the agent prompt, mostly as an experiment. Skip rate went down. I think negative examples steer the model better than just saying "always write tests", but honestly I'm guessing. Session persistence was the hardest part and I didn't see it coming. Stop hook writes a crash marker, session-start reads it back, resume validates git HEAD and file integrity. On paper that's nothing. In practice it took more debugging than the orchestration logic. Edge cases everywhere: what if the user switched branches? What if a file was deleted externally? What if the crash marker is stale? I added a StatusLine showing context usage in real time. Before that I was guessing when the context window was about to fill up. Tiny feature, but it changed how I think about phase boundaries entirely. Install Two lines inside Claude Code: /plugin marketplace add sdsrss/gsd-lite /plugin install gsd Also works via npx gsd-lite install if you don't use the plugin system. Auto-updates from GitHub Releases. 909 tests, 94%+ line coverage. https://github.com/sdsrss/gsd-lite If you use Claude Code for anything bigger than a one-shot task, I'm curious: do y

I built an IDE for Claude Code users. The "Antspace" leak just changed everything..

For context: I'm a solo founder. I built Coder1, an IDE specifically designed for Claude Code power users and teams. So when 19-year-old reverse-engineered an unstripped Go binary inside Claude Code Web and found Anthropic is quietly building an entire cloud platform, my first reaction was "oh no." My second reaction was much more interesting. What was found (quick summary): A developer named AprilNEA ran basic Linux tooling (strace, strings, go tool objdump) inside their Claude Code Web session and found: "Antspace" — a completely unannounced PaaS (Platform as a Service) built by Anthropic. Zero public mentions before March 18, 2026. "Baku" — the internal codename for Claude's web app builder. It auto-provisions Supabase databases and deploys to Antspace by default. Not Vercel. BYOC (Bring Your Own Cloud) — an enterprise layer with Kubernetes integration, seven API endpoints, and session orchestration. Anthropic wants your infra contract. The full pipeline: intent → Claude → Baku → Supabase → Antspace → live app. The user never leaves Anthropic's ecosystem. All of this was readable because Anthropic shipped the binary with full debug symbols and private monorepo paths. For a "safety-first" AI lab... that's a choice. Why this matters more than people realize: This isn't about a chatbot getting a deploy button. This is the Amazon AWS playbook. Amazon built cloud infrastructure for their own needs, made it great, then opened it to everyone. Antspace is Claude's internal deployment target today. Tomorrow it's a public PaaS with a built-in user base of everyone who's ever asked Claude to "build me a web app." The vertical integration is complete: - AI layer: Claude understands your intent - Runtime layer: Baku manages your project, runs dev server, handles git - Data layer: Supabase auto-provisioned via MCP (you never even see it) - Hosting layer: Antspace deploys and serves your app - Enterprise layer: BYOC lets companies run it on their own infra You say what you want in English. Everything else happens automatically, on Anthropic's infrastructure. Who should be paying attention: - Vercel/Netlify: If Claude's default deploy target is Antspace, Vercel becomes the optional alternative, not the default. - Replit/Lovable/Bolt: If Claude can generate code, manage projects, provision databases, AND deploy — all inside claude.ai - what's the value prop of a separate AI app builder? - E2B/Railway: Anthropic built their own Firecracker sandbox infrastructure. It's integrated into the model. - Every startup building on Claude's ecosystem: The platform you're building on top of is becoming the platform that competes with you. The silver lining (from someone in the blast radius): After the initial panic, I realized something. Baku/Antspace targets people who want to say "build me a todo app" and never touch code. That's a massive market — but it's not MY market. Power users will hit Baku's limitations within days. No real git control. No custom MCP servers. No team collaboration. No local file access. No IDE features. They'll need somewhere to graduate to. Anthropic going vertical actually validates the market and grows the funnel. More people using Claude → more people outgrowing the chat interface → more people needing real developer tools. But the window is narrowing. Fast. Discussion: - How do you feel about your AI provider also becoming your cloud provider, database provider, and hosting provider? - For those building products in the Claude ecosystem: does this change your strategy? - The BYOC enterprise play seems like the real long-term move. Thoughts? Original research by AprilNEA: https://aprilnea.me/en/blog/reverse-engineering-claude-code-antspace submitted by /u/oscarsergioo61 [link] [comments]