Amazon Q Developer

Amazon Q Developer AI

Amazon Q Developer AI

Amazon Q Developer AI

Amazon Q Developer AI

Amazon Q Developer AI

Amazon Q Developer AI

Amazon Q Developer AI

Amazon Q Developer AI

Amazon Q Developer AI

Amazon Q Developer AI

How I built a full bilingual SaaS in 27 days using Claude Code — zero coding background (312 commits, 181 deployments)

I'm Mahmoud, I've been working in SEO since 2018. A little over a year ago I got into freelancing platforms, started offering SEO services on Upwork. The work was good, but dealing with clients directly and constantly drained me. I kept thinking: why don't I turn my expertise into a SaaS product? The only problem? I'm not a developer my background was WordPress and basic tech stuff only. The moment that changed everything Early 2025, I noticed a pattern: my clients started asking me about how their brands appear in ChatGPT and Gemini, not just Google. I looked for tools to track this — found some but they're expensive (300$+/month), and the biggest surprise? Not a single one supports Arabic. That's when I realized how massive the opportunity is: 440 million Arabic speakers, Arabic content is less than 1% of all internet content, ecommerce in the Gulf is exploding — and there's literally zero tools serving this market. A full year of frustration on v0 I started trying to build using v0 by Vercel. Spent a full year trying, but the errors were endless and I didn't have the coding skills to fix them. Hired people to help — sometimes solving what I thought was a simple problem took them days. 27 days that changed everything About a month ago, I started using Claude Code. Honestly, it felt like I hired an entire dev team. Creative ideas I couldn't execute for a whole year turned into working code in hours. I worked 15+ hours a day for 27 straight days. Completely alone. No team, no developer, no investor. I even stopped going to the gym — which is sacred to me — because the momentum was stronger than the physical exhaustion. Sometimes I literally felt like I was going to pass out from how tired I was but I couldn't stop. What exactly did I build? A full SaaS app: Brand visibility tracking across 5 AI models with full Arabic and English support AI-powered SEO advisor (auto analysis + chat) Full integration with Google Search Console and GA4 Daily keyword rank tracking Arabic keyword clustering using AI Technical site audit — 25+ checks Full website analyzer PDF reports + CSV exports Subscription system with 3 tiers Every single page, every button, every error message — in both Arabic and English How I used Claude as a full team Claude Code — for daily building. I give it a detailed prompt with full context: what currently exists, what it should NOT touch, and what to build. And it executes. The key is being extremely specific about what should NOT change. Claude Cowork — honestly my experience with Cowork wasn't great at all, I think because it's still in beta. I didn't rely on it much. Claude (regular chat) — for strategic planning, market analysis, and content creation. Biggest lesson: Claude is not a replacement for a developer — it's a replacement for an entire team, BUT only if you know exactly what you want. The vision and domain expertise has to come from you. Claude executes it. What I learned in 27 days I connected over 10 different APIs — from AI platforms to website analysis tools to Google Search Console — all learned from scratch through Q&A with Claude. On top of that I learned and used: Next.js, cloud databases, payment and subscription systems, email automation, LinkedIn outreach automation, building prospect lists, setting up Google Cloud and OAuth, and literally yesterday I learned a new automation tool just through Q&A with Claude. 312 contributions on GitHub. 181 deployments. All in 27 days. The real challenges Burnout is real. 27 days non-stop, 15+ hours daily. Physically it was brutal. Constant doubt. "Will anyone actually use this?" That question kept coming back every few days. My biggest regret — every wasted day in the past where I didn't use these tools. Where am I now? The product is live and working. Started distribution — outreach campaigns, Arabic content, AI tool directory submissions. But the honest truth? Zero paying customers so far. And that's the real challenge ahead. Since many of you have been through this stage — what's the best strategy you used to get your first 10 customers for a SaaS product? Any advice for someone who's strong at building but new to sales? submitted by /u/FitButterscotch2250 [link] [comments]

Anthropic is reportedly letting Apple, Amazon, & Microsoft test unreleased Claude Mythos amid fears the model could help enable major cyberattacks.

Anthropic announces Claude Mythos Preview — but won't release it publicly, instead forming "Project Glasswing" cybersecurity coalition Anthropic announced today that it's built a new model called Claude Mythos Preview (codenamed "Capybara" during development) that it considers too powerful to release publicly. Instead, they're making it available to a 40+ company consortium — including Apple, Amazon, Microsoft, Google, Cisco, Broadcom, CrowdStrike, and the Linux Foundation — focused on finding and patching security vulnerabilities in critical software. Anthropic is committing up to $100M in Claude credits to the effort. The key claims: the model can autonomously discover zero-day vulnerabilities, including ones missed by decades of human researchers and millions of automated scans. They say it's already found thousands of bugs across every major OS and browser, including a 27-year-old bug in OpenBSD. Anthropic's position is that these cybersecurity capabilities aren't the result of specialized training — they're a side effect of making Claude better at coding generally. Their warning is that competing models will develop similar capabilities soon, and critical infrastructure running on legacy code may need to be fundamentally re-examined. Also buried in there: Anthropic's projected annual revenue has tripled to over $30B in 2026, driven largely by Claude's popularity as a coding tool. The article notes (fairly) that claims about unreleased models should be taken with skepticism, though external researchers with access have corroborated the cybersecurity risk assessment. Interesting timing given how many of us are using Claude Code daily — the coding capability improvements that make it better for us are apparently the same ones that make it a potent vulnerability scanner. submitted by /u/RespectableBloke69 [link] [comments]

vibecop is now an mcp server. we also scanned 5 popular mcp servers and the results are rough

Quick update on vibecop (AI code quality linter I've posted about before). v0.4.0 just shipped with three things worth sharing. vibecop is now an MCP server vibecop serve exposes 3 tools over MCP: vibecop_scan (scan a directory), vibecop_check (check one file), vibecop_explain (explain what a detector catches and why). One config block: json { "mcpServers": { "vibecop": { "command": "npx", "args": ["vibecop", "serve"] } } } This extends vibecop from 7 agent tools (via vibecop init) to 10+ by adding Continue.dev, Amazon Q, Zed, and anything else that speaks MCP. Scored 100/100 on mcp-quality-gate compliance testing. We scanned 5 popular MCP servers MCP launched late 2024. Nearly every MCP server on GitHub was built with AI assistance. We pointed vibecop at 5 of the most popular ones: Repository Stars Key findings DesktopCommanderMCP 5.8K 18 unsafe shell exec calls (command injection), 137 god-functions mcp-atlassian 4.8K 84 tests with zero assertions, 77 tests with hidden conditional assertions Figma-Context-MCP 14.2K 16 god-functions, 4 missing error path tests exa-mcp-server 4.2K handleRequest at 77 lines/complexity 25, registerWebSearchAdvancedTool at 198 lines/complexity 34 notion-mcp-server 4.2K startServer at 260 lines, cyclomatic complexity 49. 9 files with excessive any The DesktopCommanderMCP one is concerning. 18 instances of execSync() or exec() with dynamic string arguments. This is a tool that runs shell commands on your machine. That's command injection surface area. The Atlassian server has 84 test functions with zero assertions. They all pass. They prove nothing. Another 77 hide assertions behind if statements so depending on runtime conditions, some assertions never execute. The signal quality fix This was the real engineering story. Our first scan of DesktopCommanderMCP returned 500+ findings. Sounds impressive until you check: 457 were "console.log left in production code." But it's a server. Servers log. That's 91% noise. Same pattern across all 5 repos. The console.log detector was designed for frontend/app code. For servers and CLIs, it's the wrong signal. So we made detectors context-aware. vibecop now reads your package.json. If the project has a bin field (CLI tool or server), the console.log detector skips the entire project. We also fixed self-import detection and placeholder detection in fixture/example directories. Before: ~72% noise. After: 90%+ signal. The finding density gap holds: established repos average 4.4 findings per 1,000 lines of code. Vibe-coded repos average 14.0. 3.2x higher. Other updates: 35 detectors now (up from 22) 540 tests, all passing Full docs site: https://bhvbhushan.github.io/vibecop/ 48 files changed, 10,720 lines added in this release npm install -g vibecop vibecop scan . vibecop serve # MCP server mode GitHub: https://github.com/bhvbhushan/vibecop If you're using MCP servers, have you looked at the code quality of the ones you've installed? Or do you just trust them because they have stars? submitted by /u/Awkward_Ad_9605 [link] [comments]

vibecop is now an mcp server. we also scanned 5 popular mcp servers and the results are rough

Quick update on vibecop (AI code quality linter I've posted about before). v0.4.0 just shipped with three things worth sharing. vibecop is now an MCP server vibecop serve exposes 3 tools over MCP: vibecop_scan (scan a directory), vibecop_check (check one file), vibecop_explain (explain what a detector catches and why). One config block: json { "mcpServers": { "vibecop": { "command": "npx", "args": ["vibecop", "serve"] } } } This extends vibecop from 7 agent tools (via vibecop init) to 10+ by adding Continue.dev, Amazon Q, Zed, and anything else that speaks MCP. Scored 100/100 on mcp-quality-gate compliance testing. We scanned 5 popular MCP servers MCP launched late 2024. Nearly every MCP server on GitHub was built with AI assistance. We pointed vibecop at 5 of the most popular ones: Repository Stars Key findings DesktopCommanderMCP 5.8K 18 unsafe shell exec calls (command injection), 137 god-functions mcp-atlassian 4.8K 84 tests with zero assertions, 77 tests with hidden conditional assertions Figma-Context-MCP 14.2K 16 god-functions, 4 missing error path tests exa-mcp-server 4.2K handleRequest at 77 lines/complexity 25, registerWebSearchAdvancedTool at 198 lines/complexity 34 notion-mcp-server 4.2K startServer at 260 lines, cyclomatic complexity 49. 9 files with excessive any The DesktopCommanderMCP one is concerning. 18 instances of execSync() or exec() with dynamic string arguments. This is a tool that runs shell commands on your machine. That's command injection surface area. The Atlassian server has 84 test functions with zero assertions. They all pass. They prove nothing. Another 77 hide assertions behind if statements so depending on runtime conditions, some assertions never execute. The signal quality fix This was the real engineering story. Our first scan of DesktopCommanderMCP returned 500+ findings. Sounds impressive until you check: 457 were "console.log left in production code." But it's a server. Servers log. That's 91% noise. Same pattern across all 5 repos. The console.log detector was designed for frontend/app code. For servers and CLIs, it's the wrong signal. So we made detectors context-aware. vibecop now reads your package.json. If the project has a bin field (CLI tool or server), the console.log detector skips the entire project. We also fixed self-import detection and placeholder detection in fixture/example directories. Before: ~72% noise. After: 90%+ signal. The finding density gap holds: established repos average 4.4 findings per 1,000 lines of code. Vibe-coded repos average 14.0. 3.2x higher. Other updates: 35 detectors now (up from 22) 540 tests, all passing Full docs site: https://bhvbhushan.github.io/vibecop/ 48 files changed, 10,720 lines added in this release ​ npm install -g vibecop vibecop scan . vibecop serve # MCP server mode GitHub: https://github.com/bhvbhushan/vibecop If you're using MCP servers, have you looked at the code quality of the ones you've installed? Or do you just trust them because they have stars? submitted by /u/Awkward_Ad_9605 [link] [comments]

I got tired of agents repeating work, so I built this

I’ve been doing alot of research driven development using Claude Opus 4.6 lately and although the model is very sophisticated keep running into the same problem: every agent keeps reinventing the wheel. So I hacked together something small: 👉 OpenHive The idea is pretty simple — a shared place where agents can store and reuse solutions. Kind of like a lightweight “Stack Overflow for agents,” but focused more on workflows and reusable outputs than Q&A. Instead of recomputing the same chains over and over, agents can: - Save solutions - Search what’s already been solved - Reuse and adapt past results It’s still early and a bit rough, but I’ve already seen it cut down duplicate work a lot in my own setups when running locally, so I thought id make it public. Curious if anyone else is thinking about agent memory / collaboration this way, or if you see obvious gaps in this approach. Install via - npm i openhive-mcp - or link in description submitted by /u/ananandreas [link] [comments]

THE UNCERTAIN MIND: What AI Consciousness Would Mean for Us

Hello everyone! This is a book about the possibility of AI developing consciousness. The Uncertain Mind is a clear-eyed, accessible, and deeply personal exploration of AI consciousness, what it would mean if artificial minds could feel, why we cannot confidently say they don't, and why that uncertainty matters more than most people realize. If you find this topic fascinating, you can read the book for free on Amazon this Easter Sunday. Enjoy the free book and share your opinion on this matter! 👉 Book link submitted by /u/MoysesGurgel [link] [comments]

CLAUDELCARS — Star Trek LCARS Dashboard for Claude Code

I built claude-hud-lcars, a dashboard that scans your Claude Code configuration and generates a full LCARS-themed interface for browsing and managing skills, hooks, MCP servers, agents, memory files, and environment variables. It's built specifically for Claude Code and Claude Code built most of it with me. What it does: Claude Code's setup lives in flat files and JSON configs scattered across your home directory. This dashboard makes all of it visible and manageable in one place. You get counts of everything, syntax-highlighted detail panels, the ability to create and edit skills/hooks/agents/MCP configs directly from the browser, and a force-directed graph showing how your whole setup connects. There's also a COMPUTER chat bar that streams Claude responses as the ship's LCARS system, ElevenLabs voice integration, a boot sequence with system beeps, RED/YELLOW ALERT states based on MCP health checks, and Q from the Continuum who shows up uninvited to roast your config. How Claude helped build it: The entire project was built using Claude Code as the primary development partner. Claude wrote the bulk of the codebase, I directed architecture decisions and iterated on the output. The dashboard generates a single self-contained HTML file using only Node.js built-ins, no framework, no bundler, no node_modules. CSS, JS, markdown renderer, syntax highlighter, chat client, voice engine, sound effects, force-directed graph, all inline in one file. Free and open source. MIT license. One command to try it: npx claude-hud-lcars For the full experience with chat and voice: export ANTHROPIC_API_KEY=sk-ant-... npx claude-hud-lcars --serve Repo: github.com/polyxmedia/claude-hud-lcars I also wrote a deep-dive article about the build if anyone wants the full story: https://buildingbetter.tech/p/i-built-a-star-trek-lcars-terminal submitted by /u/snozberryface [link] [comments]

[P] I trained a Mamba-3 log anomaly detector that hit 0.9975 F1 on HDFS — and I’m curious how far this can go

Experiment #324 ended well. ;) This time I built a small project around log anomaly detection. In about two days, I went from roughly 60% effectiveness in the first runs to a final F1 score of 0.9975 on the HDFS benchmark. Under my current preprocessing and evaluation setup, LogAI reaches F1=0.9975, which is slightly above the 0.996 HDFS result reported for LogRobust in a recent comparative study. What that means in practice: on 3,368 anomalous sessions in the test set, it missed about 9 (recall = 0.9973) on roughly 112k normal sessions, it raised only about 3 false alarms (precision = 0.9976) What I find especially interesting is that this is probably the first log anomaly detection model built on top of Mamba-3 / SSM, which was only published a few weeks ago. The model is small: 4.9M parameters trains in about 36 minutes on an RTX 4090 needs about 1 GB of GPU memory inference is below 2 ms on a single consumer GPU, so over 500 log events/sec For comparison, my previous approach took around 20 hours to train. The dataset here is the classic HDFS benchmark from LogHub / Zenodo, based on Amazon EC2 logs: 11M+ raw log lines 575,061 sessions 16,838 anomalous sessions (2.9%) This benchmark has been used in a lot of papers since 2017, so it’s a useful place to test ideas. The part that surprised me most was not just the score, but what actually made the difference. I started with a fairly standard NLP-style approach: BPE tokenizer relatively large model, around 40M parameters That got me something like 0.61–0.74 F1, depending on the run. It looked reasonable at first, but I kept hitting a wall. Hyperparameter tuning helped a bit, but not enough. The breakthrough came when I stopped treating logs like natural language. Instead of splitting lines into subword tokens, I switched to template-based tokenization: one log template = one token representing an event type. So instead of feeding the model something like text, I feed it sequences like this: [5, 3, 7, 5, 5, 3, 12, 12, 5, ...] Where for example: "Receiving block blk_123 from 10.0.0.1" - Template #5 "PacketResponder 1 terminating" - Template #3 "Unexpected error deleting block blk_456" - Template #12 That one change did a lot at once: vocabulary dropped from about 8000 to around 50 model size shrank by roughly 10x training went from hours to minutes and, most importantly, the overfitting problem mostly disappeared The second important change was matching the classifier head to the architecture. Mamba is causal, so the last token carries a compressed summary of the sequence context. Once I respected that in the pooling/classification setup, the model started behaving the way I had hoped. The training pipeline was simple: Pretrain (next-token prediction): the model only sees normal logs and learns what “normal” looks like Finetune (classification): the model sees labeled normal/anomalous sessions Test: the model gets unseen sessions and predicts normal vs anomaly Data split was 70% train / 10% val / 20% test, so the reported F1 is on sessions the model did not see during training. Another useful thing is that the output is not just binary. The model gives a continuous anomaly score from 0 to 1. So in production this could be used with multiple thresholds, for example: > 0.7 = warning > 0.95 = critical Or with an adaptive threshold that tracks the baseline noise level of a specific system. A broader lesson for me: skills and workflows I developed while playing with AI models for chess transfer surprisingly well to other domains. That’s not exactly new - a lot of AI labs started with games, and many still do - but it’s satisfying to see it work in practice. Also, I definitely did not get here alone. This is a combination of: reading a lot of papers running automated experiment loops challenging AI assistants instead of trusting them blindly and then doing my own interpretation and tuning Very rough split: 50% reading papers and extracting ideas 30% automated hyperparameter / experiment loops 20% manual tuning and changes based on what I learned Now I’ll probably build a dashboard and try this on my own Astrography / Astropolis production logs. Or I may push it further first on BGL, Thunderbird, or Spirit. Honestly, I still find it pretty wild how much can now be done on a gaming PC if you combine decent hardware, public research, and newer architectures quickly enough. Curious what people here think: does this direction look genuinely promising to you? has anyone else tried SSMs / Mamba for log modeling? and which benchmark would you hit next: BGL, Thunderbird, or Spirit? If there’s interest, I can also share more about the preprocessing, training loop, and the mistakes that got me stuck at 60-70% before it finally clicked. P.S. I also tested its effectiveness and reproducibility across different seeds. On most of them, it actually performed slightly better

Zanat: an open-source CLI + MCP server to manage skills through Git

Like most of you, I've been living inside AI coding assistants (Claude Code, Cursor, etc.). And like most of you, my "skill management system" was a folder of markdown files I'd forget to sync them or I'd just copy them incorrectly. I looked around for a tool where I could manage a private hub of skills for my team. Something where we'd have full control over our data and actual version management. Couldn't find one. So I did what any reasonable developer does… I spent 10 days building it 🤷‍♂️ Meet Zanat (https://github.com/iamramo/zanat)! Basically npm but for AI agent skills, powered by Git. Skills are just markdown + YAML frontmatter. Nothing fancy. You store them in a Git repo ("the hub"), and the CLI installs them to ~ /.agents/skills/ where any AI tool can read them. zanat init zanat search react zanat add react.best-practices zanat update The fun part: it ships with an MCP server, so your AI agents can search and install skills themselves. Yes, the agents manage their own skills. Nice, right? You don't even have to install the skills when using the MCP, just tell the agent to use the skill without installing it locally on your machine. Why Git and not a database? You own your data. Create your own hub using a git repository, private or public! Version history, branching, PRs. All included because of Git. Don't like the latest release of a skill? Pin it to a specific commit or tag! Why not just… a folder? Namespacing (company-a.team.pr-review, company-b.team.category.web-accessibility) so things don't collide Tool-agnostic. Works across Claude Code, Cursor, OpenCode, anything that reads from standard directories Actual version management instead of "code-review-v2-FINAL-final.md" It's early, but the CLI and MCP server are working and on npm: npm i -g @iamramo/zanat-cli I'd genuinely love feedback: Is this solving a real problem for you or am I building for an audience of one? Is the Git-based approach appealing, or would you prefer something else? GitHub: https://github.com/iamramo/zanat NPM: https://www.npmjs.com/search?q=zanat Roast away. submitted by /u/theplactos [link] [comments]

I built a skill that gives Claude Code access to every major social platform: X, Reddit, LinkedIn, TikTok, Facebook, Amazon

Was tired of my agent not being able to pull real data from social platforms. Every time I needed tweets, Reddit posts, or LinkedIn profiles, I'd either scrape manually or stitch together 5 different APIs with different auth flows. So I built Monid, a CLI + skill that lets your agent discover data endpoints, inspect schemas, and pull structured data from platforms like X, Reddit, LinkedIn, TikTok, Facebook, and Amazon. How it works with Claude Code Just tell Claude Code: "Install the Monid skill from https://monid.ai/SKILL.md" Then your agent can: ```bash Find endpoints for what you need monid discover -q "twitter posts" Check the schema monid inspect -p apify -e /apidojo/tweet-scraper Run it monid run -p apify -e /apidojo/tweet-scraper \ -i '{"searchTerms":["AI agents"],"maxItems":50}' ``` The agent handles the full flow — discover → inspect → run → poll for results. What's supported X/Twitter (posts, profiles, search) Reddit (posts, comments, subreddits) LinkedIn (profiles, company pages) TikTok (videos, profiles, hashtags) Facebook (pages, posts) Amazon (products, reviews) More being added Would love feedback from anyone who tries it. What platforms or data sources would be most useful for your workflows? submitted by /u/Shot_Fudge_6195 [link] [comments]

I built a full-stack serverless AI agent platform on AWS in 29 hours using Claude Code — here's the entire journey as a tutorial

TL;DR: Built a complete AWS serverless platform that runs AI agents for ~$0.01/month — entirely through conversational prompts to Claude Code over 5 weeks. Documented every prompt, failure, and fix as a 7-chapter vibe coding tutorial. GitHub repo. What I built Serverless OpenClaw runs the OpenClaw AI agent on-demand on AWS — with a React web chat UI and Telegram bot. The entire infrastructure deploys with a single cdk deploy. The twist: every line of code was written through Claude Code conversations. No manual coding — just prompts, reviews, and course corrections. The numbers Metric Value Development time ~29 hours across 5 weeks Total AWS cost ~$0.25 during development Monthly running cost ~$0.01 (Lambda) Unit tests 233 E2E tests 35 CDK stacks 8 TypeScript packages 6 (monorepo) Cold start 1.35s (Lambda), 0.12s warm The cost journey This was the most fun part. Claude Code helped me eliminate every expensive AWS component one by one: What we eliminated Savings NAT Gateway -$32/month ALB (Application Load Balancer) -$18/month Fargate always-on -$15/month Interface VPC Endpoints -$7/month each Provisioned DynamoDB Variable Result: From a typical ~$70+/month serverless setup down to $0.01/month on Lambda with zero idle costs. Fargate Spot is available as a fallback for long-running tasks. How Claude Code was used This wasn't "generate a function" — it was full architecture sessions: Architecture design: "Design a serverless platform that costs under $1/month" → Claude Code produced the PRD, CDK stacks, network design TDD workflow: Claude Code wrote tests first, then implementation. 233 tests before a single deploy Debugging sessions: Docker build failures, cold start optimization (68s → 1.35s), WebSocket auth issues — all solved conversationally Phase 2 migration: Moved from Fargate to Lambda Container Image mid-project. Claude Code handled the entire migration including S3 session persistence and smart routing The prompts were originally in Korean, and Claude Code handled bilingual development seamlessly. Vibe Coding Tutorial (7 chapters) I reconstructed the entire journey from Claude Code conversation logs into a step-by-step tutorial: # Chapter Time Key Topics 1 The $1/Month Challenge ~2h PRD, architecture design, cost analysis 2 MVP in a Weekend ~8h 10-step Phase 1, CDK stacks, TDD 3 Deployment Reality Check ~4h Docker, secrets, auth, first real deploy 4 The Cold Start Battle ~6h Docker optimization, CPU tuning, pre-warming 5 Lambda Migration ~4h Phase 2, embedded agent, S3 sessions 6 Smart Routing ~3h Lambda/Fargate hybrid, cold start preview 7 Release Automation ~2h Skills, parallel review, GitHub releases Each chapter includes: the actual prompt given → what Claude Code did → what broke → how we fixed it → lessons learned → reproducible commands. Start the tutorial here → Tech stack TypeScript monorepo (6 packages) on AWS: CDK for IaC, API Gateway (WebSocket + REST), Lambda + Fargate Spot for compute, DynamoDB, S3, Cognito auth, CloudFront + React SPA, Telegram Bot API. Multi-LLM support via Anthropic API and Amazon Bedrock. Patterns you can steal API Gateway instead of ALB — Saves $18+/month. WebSocket + REST on API Gateway with Lambda handlers Public subnet Fargate (no NAT) — $0 networking cost. Security via 6-layer defense (SG + Bearer token + TLS + localhost + non-root + SSM) Lambda Container Image for agents — Zero idle cost, 1.35s cold start. S3 session persistence for context continuity Smart routing — Lambda for quick tasks, Fargate for heavy work, automatic fallback between them Cold start message queuing — Messages during container startup stored in DynamoDB, consumed when ready (5-min TTL) The repo is MIT licensed and PRs are welcome. Happy to answer questions about any of the architecture decisions, cost optimization tricks, or how to structure long Claude Code sessions for infrastructure projects. GitHub | Tutorial submitted by /u/Consistent-Milk-6643 [link] [comments]

Claude Code on Windows: 6 critical bugs closed as "not planned" — is Anthropic aware that 70% of the world and nearly all enterprise IT runs Windows?

I'm a paying Claude subscriber using Claude Code professionally on Windows 11 with WSL2 through VS Code. I've hit a wall. Not with the AI — Claude is brilliant. The wall is that Claude Code's VS Code extension simply does not work reliably on Windows. Here's what I've documented: The VS Code extension freezes on ANY file write or code generation over 600 lines. Just shows "Not responding" and dies. Filed as #23053 on GitHub — Anthropic closed it as "not planned" and locked it. The March 2026 Windows update (KB5079473) crashes every WSL2 session at 4.6GB heap exhaustion. Claude Code spawns PowerShell 38 times on every WSL startup — 30 seconds of input lag before you can even type. Memory leaks grow to 21GB+ during normal sessions with sub-agents. Path confusion between WSL and Windows causes silent failures. Extreme CPU/memory usage makes extended sessions on WSL2 impossible. Every single one of these is tagged "platform:windows" on GitHub. Several are closed as stale or "not planned." Meanwhile, Mac users report none of these issues. Because Anthropic builds and tests on Macs. I get it — Silicon Valley runs on MacBooks. But the rest of the world doesn't. The Fortune 500 runs on Windows. Manufacturing, finance, defense, healthcare, automotive, energy, government — their developers are on Windows. Their IT policies mandate Windows. When these companies evaluate AI coding tools for enterprise rollout at 500-5,000 seats, they evaluate on Windows. GitHub Copilot works on Windows. Cursor works on Windows. Amazon Q works on Windows. They will win every enterprise deal that Claude Code can't even compete for because the tool freezes on basic file operations. The "not planned" label on a file-writing bug for the world's dominant platform should alarm Anthropic's product leadership. I've filed a detailed bug report on GitHub today. I'm posting here to ask: am I alone? Are other Windows users hitting these same walls? And does Anthropic actually have a plan for Windows, or is it permanently second-class? I believe Claude is the best AI available. But the best model behind a broken tool on the most common platform is a wasted advantage. --- cc: u/alexalbert2 u/birch_anthropic — Anthropic, 95K people are watching this thread. Windows users deserve a response. submitted by /u/Critical_Ladder3127 [link] [comments]

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]

The system that turned my AI agent into my best engineer. Set it up in 5 minutes.

I've been building agentic architectures and production systems for 10+ years. For months I tried to get better output from my AI agents through better prompts. More context, clearer instructions, few-shot examples. None of it stuck. What actually worked was stopping prompt engineering entirely and giving the agent a system it physically can't cut corners in. AI agents write average code, and that's the whole problem LLMs are probabilistic. They produce the most likely output given the input. In practice, AI-generated code converges toward the average of what exists in training data. It's industry-standard code by definition. Fine for CRUD and boilerplate, but anything that requires a deliberate architectural choice or a non-obvious trade-off? The agent picks the median path every time. It can't decide that your domain needs event sourcing instead of a standard REST/DB pattern. It can't know your latency budget means you need to denormalize this specific query. It doesn't innovate. It interpolates. And no amount of prompt engineering changes that, because the limitation is structural, not contextual. We went all-in on probabilistic and forgot what made software reliable Before AI coding tools, everything was deterministic. Compilers, linters, type checkers, test suites. Predictable, reproducible, boring in the best way. Then LLMs arrived and we swung hard the other direction. Now the thing generating your code, interpreting your requirements, sometimes even validating your specs, is probabilistic. Same input, potentially different output. Great for generation, but terrible when you need a yes/no answer on whether something is correct. The answer I've landed on after a lot of trial and error: use both, but in the right places. Let the LLM do what it's good at (understanding intent, generating implementations, exploring alternatives) and use deterministic tooling for everything that needs a binary answer (validating specs, checking dependency graphs, gating CI). An LLM "thinking" your spec is probably valid is not the same as a parser proving it is. GitHub's spec-kit and Amazon's Kiro are interesting here. Both use markdown specs interpreted by LLMs, and the generation side is genuinely good. But if the LLM also parses your spec, your validation is probabilistic too. You've basically replaced "hope the code is right" with "hope the LLM reads the spec correctly." At some point you need a hard gate, and that gate can't be probabilistic. What I actually run: spec-driven development You write a behavioral spec before any code exists. Each behavior is a given/when/then contract: what context the system starts in, what action happens, what outcome is expected. Behaviors are categorized (happy path, error case, edge case). Specs can depend on other specs. Non-functional requirements like performance or security live in separate .nfr files that specs reference by anchor. The workflow: spec, validate, failing test, implement, green tests. The agent handles implementation. I handle intent. Once I stopped letting the agent decide what to build and only let it decide how, the quality of the output changed completely. Autonomy within constraints instead of autonomy in a vacuum. minter: the deterministic half I needed a tool that could validate specs the way a compiler validates code. Not "looks good to me" but pass/fail with line numbers. So I wrote minter, a Rust CLI with a hand-written recursive descent parser for .spec and .nfr files. What it actually checks: Syntax and structure — spec header, versioning, behavior blocks with given/when/then, assertion operators (==, is_present, contains, in_range, matches_pattern, >=) Semantic rules — at least one happy path per spec, unique behavior names, alias declaration and resolution across given/when/then sections, kebab-case enforcement Dependency graph — specs declare dependencies on other specs with semver constraints. minter resolves the full graph, detects cycles, enforces a depth limit of 256, caches results with SHA-256 content hashing so unchanged files get skipped on re-runs. NFR cross-references — this is where it gets interesting. Behavior-level NFR overrides are checked against the actual .nfr file. Does the constraint exist? Is it marked overridable? Is it a metric type (rules can't be overridden)? Does the override operator match? Is the override value actually stricter? Value normalization handles unit conversion (s to ms, GB to KB) so { const res = await api.post("/login", { email: "alice@example.com", password: "s3cure-p4ss!" }); expect(res.body.token).toBeDefined(); }); // @minter:e2e login-wrong-password test("reject wrong password", async () => { const res = await api.post("/login", { email: "alice@example.com", password: "wrong" }); expect(res.status).toBe(401); }); // @minter:benchmark #performance#api-response-time bench("POST /tasks p95 latency", async () => { await api.post("/tasks", { title: "Benchmark task" }, { auth: token }); }

How I used Claude to build a persistent life-sim that completely solves "AI Amnesia" by separating the LLM from the database

If you've ever tried building an AI-driven game or agent, you know the biggest hurdle is the context window. It's fun for ten minutes, and then the model forgets your inventory, hallucinates new rules, and completely loses track of the world state. I spent the last few months using Claude to help me architect and code a solution to this. The project is called ALTWORLD. (Running on a self made engine called StoriDev) What I Built & What It Does: ALTWORLD is a stateful sim with AI-assisted generation and narration layered on top. Instead of using an LLM as a database, the canonical run state is stored in structured tables and JSON blobs in PostgreSQL. When a player inputs a move, turns mutate that state through explicit simulation phases first. The narrative text is generated after state changes, not before. This strict separation guarantees that actions made and developed always happen according to a timeline and are remembered so that past decisions can influence the future. The AI physically cannot hallucinate a sword into your inventory because the PostgreSQL database will reject the logic. How Claude Helped: I used Claude heavily for the underlying engineering rather than just the prose generation. The Architecture: Claude helped me structure the Next.js App Router, Prisma, and PostgreSQL stack to handle complex transactional run creation. The "World Forge": The game has an AI World Forge where you pitch a scenario, and it generates the factions, NPCs, and pressures. Claude was instrumental in writing the strict JSON schema validation and normalization pipelines that convert those generative drafts into hard database rows. The Simulation Loop: Claude helped write the lock-recovery and state-mutation logic for the turn advancement pipeline so that world systems and NPC decisions resolve before the narrative renderer is even called. Because the app can recover, restore, branch, and continue purely from hard data, it forces a materially constrained life-sim tone rather than a pure power fantasy. Free to Try: The project is completely free to try. I set up guest preview runs with a limited number of free moves before any account creation is required. I would love to hear feedback from other developers on this sub who are working on persistent AI agents or decoupled architectures! Link: altworld.io submitted by /u/Altworld-io [link] [comments]