Crowdlinker

🧵 Thread: GPT-5.3-Codex vs Claude Opus 4.6 1/ GPT-5.3-Codex vs Claude Opus 4.6 isn’t a model war. It’s a workflow question. The real shift: AI is moving from autocomplete to execution. 👇 Here’s what we’re seeing in production.

Here are top 10 curated GitHub repos for Claude code that will 10x your next project: 1. Symphony github.com/openai/symphony 2. GSD (Get Shit Done) github.com/gsd-build/get-… 3. Superpowers github.com/obra/superpowe… 4. Claude Mem github.com/thedotmack/cla… 5. Awesome Claude Code github.com/hesreallyhim/a… 6. UI UX Pro Max github.com/nextlevelbuild… 7. Everything Claude Code github.com/affaan-m/every… 8. Obsidian Skills github.com/kepano/obsidia… 9. LightRAG github.com/hkuds/lightrag 10. n8n-MCP github.com/czlonkowski/n8… Bookmark this you won't regret it.

Quick follow-up to my last AR shareout with using @goresolut . Since then, a few things happened: Our risk score went down again. Our collection efficiency improved. And we collected $50K+ in AR in less than 2 weeks. 📌 Current snapshot: → Risk Score: 2.02/100 → Collection Forecast: 64.13% → Paid Amount: CA$155,226.31 This is exactly why I’m so bullish on automating AR. For most service businesses, AR is still managed through inboxes, spreadsheets, manual follow-ups, and finance team bandwidth. That works... until it doesn’t. What I’m seeing firsthand is that when AR gets automated properly, you get more than faster collections: → better visibility into portfolio risk → more consistent follow-up → improved collection outcomes → less manual work for the team → more cash collected without adding headcount At @crowdlinker, this is becoming very real for us. Better follow-up. Better prioritization. Better cash collection. If you’re still handling AR manually, there’s a massive opportunity to modernize this function. #AccountsReceivable #AR #Fintech #Automation #AI #CashFlow #B2B #SaaS

Composer 2 from @cursor_ai makes the coding model race a lot more interesting. Not because it automatically beats @anthropic's Opus 4.6 or @OpenAI's GPT-5.4. Because it changes the conversation. For a while, the default assumption was simple: ➡️ OpenAI for frontier capability. ➡️ Anthropic for serious coding workflows. Now Cursor is making a real push to become the third lane developers have to evaluate. What stands out to me isn’t just the launch itself. It’s how quickly the reactions split: Some developers are saying Composer 2 feels cleaner for greenfield builds. Others are finding the gap between all three models is smaller than expected in real-world usage. And within hours, people were already trying to figure out what’s actually powering it under the hood. That usually means one thing: the product is relevant enough to matter. My take: We’re moving out of the era of asking, “What’s the best model?” And into the era of asking, “Which model gives my team the best output, at the best speed, for the best price, inside the workflow we already use?” Right now, it feels like: → Composer 2 is making a strong case on speed/cost inside Cursor → Opus 4.6 still feels like the premium choice for deeper coding and agentic work → GPT-5.4 still looks like the broadest professional workhorse across code + documents + knowledge work The winner probably won’t be the model with the most hype. It’ll be the one that gets teams to production fastest with the least friction. #AI #Cursor #Claude #OpenAI #SoftwareEngineering #Composer #Anthropic

Exciting launch alert! 🎉 We just rolled out the brand new website for Kiaora!

Github repo link: github.com/obra/superpowe…

The biggest unlock in AI coding right now isn’t the model. It’s the methodology around the model. Most people still use Claude Code or Codex like this: prompt → generate code → babysit the output → untangle the mess Superpowers flips that. It makes the agent: • brainstorm before coding • refine the spec with you • create a real implementation plan • break work into tight tasks • use subagents + review loops • enforce actual TDD • verify before claiming success That’s the real shift. The teams that win with AI coding won’t just have access to the best models. They’ll have the best workflows, guardrails, and operating systems around those models. Open source like this pushes the whole ecosystem forward. GitHub repo in comments. No farming. 🙂 #ArtificialIntelligence #AICoding #OpenSource #SoftwareEngineering #DeveloperTools #ClaudeCode #ProductEngineering #AI

Most people are still using Claude Code like a chatbot. We found a repo shows what happens when you treat it more like an operating system for building products. Everything Claude Code, open-sourced by Anthropic hackathon winner Afan Mustafa, is one of the most impressive AI dev setups we’ve seen lately. It’s not just a few prompts or configs thrown into a folder. It’s a full system built around agents, skills, commands, hooks, memory, security, and continuous learning. 📌 A few things that stood out: - 25 specialized agents for planning, architecture, code review, security, build fixes, E2E testing, Go review, Python review, database review, and more. - 108 skills and 57 commands to structure how AI actually helps you ship. - A continuous learning system that extracts patterns from sessions and turns them into reusable skills with confidence scoring. - Cross-platform support across Windows, macOS, and Linux, plus integrations across major AI coding environments. ⭐ The bigger takeaway --> the gap is widening between teams that “use AI” and teams that build actual systems around AI-assisted development. This is the latter. If you’re a founder or product team building quickly with lean resources, this is worth studying. Open source like this pushes the whole ecosystem forward. Go give the repo a star and support a builder who shipped something genuinely useful and gave it away for FREE. (Link to repo in comments below) 👇 #AI #startups #founders #opensource #softwaredevelopment #claudecode #productdevelopment

Animation designers are cooked.

📰 Latest curated #AI news: A lot of today’s AI news points to the same underlying shift: this is no longer just a model race. It’s becoming a labor market reset, a distribution war, and a test of who actually has real leverage. 1. @Meta may be preparing another major round of cuts as it continues pouring money into AI. That’s the new tradeoff in big tech: fewer people, bigger AI bets. 2. @alibaba_cloud looks close to rolling out a Qwen-powered enterprise agent, and the real advantage isn’t just the model itself. It’s the fact that Alibaba can plug AI directly into massive consumer and commerce platforms like Taobao and Alipay. 3. @ServiceNow's CEO made a pretty stark prediction that AI agents could hammer entry-level employment, especially for recent grads. Whether the number ends up being right or not, the direction of travel is hard to ignore. 4. AI companies are hiring performers and improv talent to help models get better at tone, emotional nuance, and human delivery. That’s a fascinating signal: even the most advanced models still need help sounding more human. 5. One of the smartest debates today came from @hackernews: is AI making CS fundamentals irrelevant? My view is the opposite. Fundamentals matter even more now. If you can’t catch bad output, challenge assumptions, or debug what the model gives you, you’re not really using AI well — you’re outsourcing your thinking. 🍪 Things to try: 1. Obsidian Interpreter (https://help.obsidian.md/web-clipper/interpreter) — a useful way to run natural-language tasks on webpages and push the output directly into your notes. 2. jina-grep (github.com/jina-ai/jina-g…) — one of the more practical AI coding utilities I’ve seen lately for semantic search across a codebase using plain English. 3. Wan 2.7 (wan27ai.com) — interesting jump in AI video quality, especially around audio sync, motion, and consistency across scenes. 4. Banana App (thebanana.app) — real-time multilingual voice calls that try to preserve tone and emotion, not just literal translation. 5. AI Flowchart (aiflowchart.net) — simple idea, but very useful: turn rough text or a whiteboard image into an editable flowchart fast. 📌 My takeaway: The biggest AI story right now isn’t just better outputs. It’s that AI is starting to reshape hiring, compress junior roles, reward distribution, and increase the value of taste, judgment, and technical depth at the same time. Follow for more daily AI updates.

Imagine Google… but built by thousands of tiny AIs living on people’s laptops and phones. They don’t just search the web — they constantly experiment, gossip with each other, and get smarter every single time you click a result. At 1 million people joining, it will index 50 million new pages a day and feel lightning-fast. The coolest part? These AIs already rediscovered a genius ranking trick that took human researchers 19 years to figure out… in just 4 hours. This is the beginning of intelligence that grows like a living network — not locked in one company’s server. Want to watch it happen live and even help? 👉 Code & everything open here: github.com/hyperspaceai/a… Who’s in? 📷🚀

@nathaniel_cole0 User activity for?

@crowdlinker hi Crowdlinker genuinely curious how Crowdlinker monitors user activity just sent a quick message

Amazon’s recent AI-related outages are a warning sign for every company racing to ship faster with AI. According to recent reporting, Amazon held a mandatory engineering meeting after a string of outages affected its retail site and app, including a six-hour disruption tied internally to “Gen-AI assisted changes” with a “high blast radius.” Senior leadership reportedly acknowledged that best practices and safeguards for AI-assisted coding are still not fully established, and new review requirements are being introduced for certain AI-assisted changes. Separate reporting also linked an earlier AWS outage to issues involving Amazon’s internal Kiro coding tool, though Amazon disputes that the root cause was AI itself and has framed it as a protocol and permissions issue. That distinction matters. Because this is not really a story about whether AI can write code. It can. This is a story about what happens when organizations optimize for AI adoption before they optimize for AI governance. The real risk is not model capability. It is operational maturity. When AI is introduced into production workflows without the right guardrails, review layers, ownership, observability, and rollback discipline, speed turns into fragility. Teams move faster right up until the moment they break something bigger, more customer-facing, and more expensive than before. Recent reporting also suggests Amazon has been closely tracking internal AI adoption, while employees describe growing pressure to use the tools more frequently. This is the lesson enterprise leaders should pay attention to: AI-assisted development is not a shortcut around engineering rigor. It raises the bar for it. If your team is using AI to generate production code, the questions are no longer: “Can the model do it?” They are: “Who reviews it?” “How is it tested?” “What is the blast radius if it fails?” “Can we trust this system at scale?” At Crowdlinker, we believe the companies that win with AI won’t be the ones that use it the most. They’ll be the ones that build the best systems around it. AI can absolutely accelerate delivery. But trust, reliability, and product quality still come from process. And process is now a competitive advantage. #AI #SoftwareEngineering #ProductDevelopment #GenAI #EngineeringLeadership #DigitalTransformation #EnterpriseAI #ProductStrategy

vibe coders be like: “i built this fire saas in 3 days with next.js and some lo-fi” …then realizes they still gotta shake it on TikTok at 2am just to get 5 signups 😭💀

@shivsakhuja Great stuff here. Thanks for giving it away and not engagement farming.

Over the last few months, we've been using Claude Code to do practically all our GTM execution work. Not just coding. Everything. Running outbound campaigns, finding high-intent leads and candidates, creating SEO pages, running marketing campaigns, managing my CRM, even finding apartments. Claude can truly do anything you need. It just needs the right setup + skills. So we created a library of skills that teach Claude how to do GTM work. Things like: - Scraping reddit for your ICP's pain points - Monitoring your competitors posts across all social channels - Finding leads from comments on LinkedIn posts - Enriching & qualifying leads - Scraping reviews - Creating slides & graphics - Auditing your SEO / content strategy It's completely free and has over 50 skills specifically designed for GTM skills. Link + OSS below 👇

@fedesimio Wow you guys are on fire. Can't wait to see what you do with @eventbrite

Today we officially completed the acquisition of Eventbrite for ~$500M! 😍

@MeMansi12 Keep at it. Month 7 feels like a winner.

I launched my SaaS 6 months ago. Here's my MRR progress 👇 Month 1 : $0 Month 2 : $00 Month 3 : $000 Month 4 : $0000 Month 5 : $00000 Month 6 : $000000

@PawelHuryn You should probably bookmark this...

Product management is starting to look a lot more executable. This open-source PM Skills Marketplace from @PawelHuryn turns AI from a generic assistant into a more structured product thinking partner. At the moment, the repo shows about 6.4k stars, 65 PM skills, 36 chained workflows, and 8 plugins, with support for Claude Code/Cowork plus skills compatibility with Cursor, Codex CLI, Gemini CLI, and Kiro. Instead of relying on loose prompts, it packages real PM workflows across discovery, strategy, execution, analytics, GTM, and growth into reusable skills and commands. Not “help me write a PRD.” More like: - run discovery - pressure test assumptions - build strategy - plan GTM - analyze outcomes What stands out to us is the shift in interface: ➡️ AI is no longer just helping write docs. ➡️ It is starting to operationalize the way teams think. That matters because the gap in product teams has never been content generation. It has been consistency, structure, and decision quality. Open-source projects like this are a good signal of where the market is heading: ➡️ less prompting, more systems; ➡️ less one-off outputs, more repeatable workflows. Well worth a look for PMs, founders, and product teams experimenting with AI-native ways of working. This runs in your terminal. Your product. Your frameworks. (Link in the comments)