Bolt

Bolt AI

Bolt AI

Bolt AI

Bolt AI

Bolt AI

Bolt AI

Bolt AI

Bolt AI

Bolt AI

Bolt AI

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 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]

I wasted $500 testing AI coding tools so you don't have to 💸 Here's what actually works: 🧪 Testing ideas? → V0 or Lovable Built a landing page in 90 seconds. Fully clickable, looked real. Code's me

I wasted $500 testing AI coding tools so you don't have to 💸 Here's what actually works: 🧪 Testing ideas? → V0 or Lovable Built a landing page in 90 seconds. Fully clickable, looked real. Code's messy but perfect for validation. 🏗️ Shipping real apps? → Bolt Full dev environment in your browser. I built a document uploader with front end + back end + database in one afternoon. 💻 Coding with AI? → Cursor or Windsurf Cursor = stable, used by Google engineers Windsurf = faster, newer, more aggressive Both are insane. 📚 Learning from scratch? → Replit Best coding teacher I've found. Explains errors, walks you through fixes, teaches as you build. Here's what 500+ hours taught me: The tool doesn't matter if you're using it for the wrong stage. Testing ≠ Building ≠ Coding ≠ Learning Stop comparing features. Match your goal first. Drop what you're building 👇 I'll tell you exactly which tool to use Save this. You'll need it. #AI #AITools #TechTok #ChatGPT #Coding

OpenAI’s Game-Changing o1 Description: Big news in the AI world! OpenAI is shaking things up with the launch of ChatGPT Pro, priced at $200/month, and it’s not just a premium subscription—it’s a glim

OpenAI’s Game-Changing o1 Description: Big news in the AI world! OpenAI is shaking things up with the launch of ChatGPT Pro, priced at $200/month, and it’s not just a premium subscription—it’s a glimpse into the future of AI. Let me break it down: First, the Pro plan offers unlimited access to cutting-edge models like o1, o1-mini, and GPT-4o. These aren’t your typical language models. The o1 series is built for reasoning tasks—think solving complex problems, debugging, or even planning multi-step workflows. What makes it special? It uses “chain of thought” reasoning, mimicking how humans think through difficult problems step by step. Imagine asking it to optimize your code, develop a business strategy, or ace a technical interview—it can handle it all with unmatched precision. Then there’s o1 Pro Mode, exclusive to Pro subscribers. This mode uses extra computational power to tackle the hardest questions, ensuring top-tier responses for tasks that demand deep thinking. It’s ideal for engineers, analysts, and anyone working on complex, high-stakes projects. And let’s not forget the advanced voice capabilities included in Pro. OpenAI is taking conversational AI to the next level with dynamic, natural-sounding voice interactions. Whether you’re building voice-driven applications or just want the best voice-to-AI experience, this feature is a game-changer. But why $200? OpenAI’s growth has been astronomical—300M WAUs, with 6% converting to Plus. That’s $4.3B ARR just from subscriptions. Still, their training costs are jaw-dropping, and the company has no choice but to stay on the cutting edge. From a game theory perspective, they’re all-in. They can’t stop building bigger, better models without falling behind competitors like Anthropic, Google, or Meta. Pro is their way of funding this relentless innovation while delivering premium value. The timing couldn’t be more exciting—OpenAI is teasing a 12 Days of Christmas event, hinting at more announcements and surprises. If this is just the start, imagine what’s coming next! Could we see new tools, expanded APIs, or even more powerful models? The possibilities are endless, and I’m here for it. If you’re a small business or developer, this $200 investment might sound steep, but think about what it could unlock: automating workflows, solving problems faster, and even exploring entirely new projects. The ROI could be massive, especially if you’re testing it for just a few months. So, what do you think? Is $200/month a step too far, or is this the future of AI worth investing in? And what do you think OpenAI has in store for the 12 Days of Christmas? Drop your thoughts in the comments! #product #productmanager #productmanagement #startup #business #openai #llm #ai #microsoft #google #gemini #anthropic #claude #llama #meta #nvidia #career #careeradvice #mentor #mentorship #mentortiktok #mentortok #careertok #job #jobadvice #future #2024 #story #news #dev #coding #code #engineering #engineer #coder #sales #cs #marketing #agent #work #workflow #smart #thinking #strategy #cool #real #jobtips #hack #hacks #tip #tips #tech #techtok #techtiktok #openaidevday #aiupdates #techtrends #voiceAI #developerlife #o1 #o1pro #chatgpt #2025 #christmas #holiday #12days #cursor #replit #pythagora #bolt