SAP AI

SAP AI AI

SAP AI AI

SAP AI AI

SAP AI AI

SAP AI AI

SAP AI AI

SAP AI AI

SAP AI AI

SAP AI AI

SAP AI AI

Maybe the AI race isn’t about models at all, but about trust and organizational intelligence

Everyone talks about the AI race as if it’s just an intelligence benchmark competition. GPT-6 vs Claude 5 vs Gemini vs DeepSeek. But I’m starting to wonder if intelligence itself eventually becomes abundant and the real scarcity becomes trust and the ability to interface with reality. For example, suppose a Chinese model is 95% as good as OpenAI and 10x cheaper. Would Fortune 500 companies really put it inside: financial systems? ERP software? defense applications? pharmaceutical R&D? factory automation? autonomous agents with spending authority? Maybe for translation or generic coding, sure. But would they trust it with the organization’s nervous system? Which makes me think there are really several layers: 1. Intelligence Layer OpenAI Anthropic Google DeepSeek 2. Interface Layer ChatGPT Claude Copilot 3. Reality Layer Palantir ServiceNow SAP Oracle Salesforce Anduril The reality layer contains: permissions workflows ontology governance auditability human incentives accountability Organizations are messy. Humans are messy. Maybe the hard problem isn’t generating tokens. Maybe it’s connecting intelligence to reality without breaking the organization. This also makes me wonder if enterprise software ends up being more durable than people think. If foundation models become increasingly commoditized, perhaps trust, integration, and organizational operating systems become more valuable, not less. Alex Karp often seems to talk less about models and more about institutions and organizational complexity. Perhaps he sees LLMs as interchangeable sources of intelligence and the hard problem as organizational intelligence itself. Curious what others think. Do you believe AI will mostly commoditize and price competition will dominate, or do trust, governance, and integration become the real moat? submitted by /u/Brainvestor [link] [comments]

AI Epistemic Risks: Emerging Mechanisms & Evidence [R]

How will AI affect our ability to think and judge for ourselves? Our new paper co-authored by 30 experts explores epistemic risks—the threats AI poses to our collective capacity to form beliefs accurately, reason well, and maintain a healthy information environment. We look at how AI can lead to harm through these mechanisms: Persuasion & Manipulation: AI systems are highly persuasive, opening the door for political/economic manipulation, incitement and radicalization, and other misuse, as well as unintentional harms like AI sycophancy and mental health risks. Cognitive Offloading: We may be delegating our thinking to AI at a deeper level than prior technologies, risking long-term degradation of individual and societal cognitive resilience. Feedback Loops: Human-AI and AI-AI interactions are narrowing the epistemic space humans and AIs draw from. This already drives homogenization, and may potentially lead to fragmentation and “lock-in” (a self-referential state that is difficult to reverse). While we believe AI could be an unprecedented lever for improving how humanity processes knowledge, we shouldn’t assume this will happen by default. We outline promising directions to change this trajectory across how AI systems are built, human-AI interaction design, institutional and individual adaptation, and information market incentives. Epistemic risks are self-perpetuating. As they can undermine the individual cognitive and social foundations needed to recognize, prioritize, and govern other threats—including the risks from AI itself—the time to act is now, before our capacity to respond is itself lost. Authors: Mick Yang, Stephen Casper, Jonathan Stray, Jasmine Li, Cameron Jones, Anna Gausen, Natasha Jaques, Brian Christian, Bálint Gyevnár, Hannah Rose Kirk, Zhonghao He, Dan Zhao, Siao Si Looi, Joshua Levy, Kobi Hackenburg, Elizabeth Seger, Matt Kowal, Michelle Malonza, Luke Hewitt, Hause Lin, Maarten Sap, Dylan Hadfield-Menell, Thomas H. Costello, Reihaneh Rabbany, Jean-François Godbout, David G. Rand, Atoosa Kasirzadeh, Gordon Pennycook, Yoshua Bengio, Kellin Pelrine Paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6873005 submitted by /u/KellinPelrine [link] [comments]

I use claude for investing in stocks and I wonder if I do it correctly

Some time ago I started using claude as my main investing tool in choosing stocks. Below I leave example of the prompt that I used based on $NOW example. I was wondering if this method is completely shit or maybe im doing this right. You are acting as a senior buy-side equity research analyst at a large institutional investment firm. Your task is to produce a full institutional-quality investment research report on ServiceNow, Inc. (ticker: NOW), with the goal of determining whether the stock offers an attractive risk/reward opportunity at the current market price. Your analysis must be extremely rigorous, evidence-based, forward-looking, and decision-oriented. Do not produce a generic company overview. I want a deep investment judgment that combines fundamentals, valuation, business quality, competitive position, financial trajectory, market expectations, technical setup, sentiment, catalysts, risks, and probability-weighted scenarios. The final output should help an institutional investment committee decide whether to buy, hold, avoid, or wait for a better entry point. Important requirements: Use the most up-to-date information available. Use the latest stock price, market capitalization, enterprise value, valuation multiples, financial statements, earnings releases, guidance, analyst expectations, investor presentations, SEC filings, conference call transcripts, recent news, and market data. Clearly state the date of the data used. If exact real-time data is unavailable, say so clearly and use the most recent available data, while explaining the limitation. Prioritize primary sources: 10-K, 10-Q, earnings releases, investor presentations, official guidance, and management commentary. Cross-check important facts with multiple reputable sources. Company and business model analysis. Analyze ServiceNow’s business model in detail: What the company actually does. Its core products and platforms. Main revenue streams. Subscription revenue quality. Customer base. Enterprise adoption. Renewal rates, retention, and net expansion if available. Pricing power. Mission-critical nature of the platform. Switching costs. Scalability of the model. Exposure to enterprise IT spending cycles. Role of AI and workflow automation in future growth. Explain whether ServiceNow is simply a high-quality software company or whether it has a durable long-term platform advantage. Industry and market opportunity. Evaluate the total addressable market and the structural growth opportunity: IT service management. IT operations management. Customer workflows. Employee workflows. Creator workflows. AI-enabled enterprise automation. Generative AI monetization. Workflow automation across large enterprises. Potential expansion beyond the current core markets. Assess whether the market opportunity is still large enough to support strong growth over the next 3–5 years, or whether growth is naturally slowing due to scale. Competitive position and moat. Analyze ServiceNow’s competitive advantage against relevant competitors and adjacent platforms, including but not limited to: Salesforce. Microsoft. Atlassian. Workday. Oracle. SAP. Zendesk. Freshworks. AI-native automation tools. Internal enterprise IT systems. Potential disruption from generative AI agents. Evaluate: Switching costs. Network effects, if any. Data advantage. Platform depth. Customer lock-in. Sales execution. Partner ecosystem. Cross-sell potential. Product breadth. Risk of platform consolidation by Microsoft/Salesforce/SAP. Whether AI is a tailwind, threat, or both. Financial analysis. Perform a detailed analysis of ServiceNow’s financials using the most recent annual and quarterly data: Revenue growth. Subscription revenue growth. Remaining performance obligations. Current remaining performance obligations. Billings growth. Gross margin. Operating margin. Free cash flow margin. Rule of 40. Sales and marketing efficiency. R&D intensity. SBC / stock-based compensation. Dilution. Cash position. Debt. Net cash or net debt. Return on invested capital if relevant. Quality of earnings. GAAP versus non-GAAP profitability. Free cash flow conversion. Margin expansion potential. Do not just list numbers. Interpret what they mean for the investment case. Growth quality and sustainability. Analyze whether current and expected growth is: Durable. Accelerating or decelerating. Supported by secular demand. Dependent on macro conditions. Dependent on upselling and cross-selling. Dependent on AI monetization. Already fully priced into the stock. At risk from enterprise budget pressure. Assess whether ServiceNow can realistically sustain strong double-digit growth over the next 3–5 years. Management and execution. Evaluate management quality: CEO and leadership team. Track record of guidance credibility. Execution history. Capital allocation. M&A strategy. Product innovation. Sales exec

Why are people who moan about AI taking-over or diminishing human capabilities so unimaginative?

I give one example of an imaginative and inspiring account of AI in Richard Powers’ novel *The Overstory*. One character in the book stands out for me. Neelay Mehta is a precocious child who through the influence of his father becomes engrossed in the ‘branching’ possibilities of computer programming. At the age of eleven, Mehta climbed a tree, slipped and crashed down onto a concrete path. The base of his spine was cracked, leaving him paralyzed. He spends the rest of his life in a wheelchair and becomes progressively disabled and at the same time absorbed with building his computer game. Mastery is continually upgraded with the help of an expert team, eventually gathering millions of users world-wide. The game immerses players in a vivid virtual world and beats all competition. It provides Mehta with wealth to plough back into his enterprise, but he eventually becomes dissatisfied with his invention, realizing that although the game pretends to escape into another world, it simply mirrors a world that is driven by competitiveness and the endless desire for more prosperity. It is so successful because everyone wants to expand their virtual ‘empire’, and the game keeps making opportunities a little more tempting. Faced with this dilemma, Mehta seeks a ‘better story’. He finds inspiration for recasting his game through studying trees, especially fungal networks that connect them together and discovers *The Secret Forest* written by another main character in the novel, Pat Waterbrook. Her book shows how the mycelium of fungi ‘actively senses and responds to its surroundings in unpredictable ways forming a symbolic negotiation with trees’. She discovers that research from different perspectives uncover ‘innumerable minute, local truths’ and can spread a global net of their studies, ‘sapping data through ever faster channels’. Early in his life, Mehta had realized that computer algorithms connect like ‘organelles building up a cell’.Mehta reenvisages his game of Mastery as a ‘growing organism’ that adds to itself, with thousands not so much playing the game as *contributing* across the globe, adding their own data and codes. Contributors, who are called ‘learners’ are encouraged to absorb everything, including ‘every sentence from every article that every field scientist has published; every sound of the earth; every landscape pictured, the data of every creature’. With the help of AI, the game can absorb how the planet and living things emerged, the history of bacteria, and the fungal networks of trees and also discover how things, bacteria and trees learn themselves. Through access to data banks, Mehta’s game aims to bring humankind to an intimate understanding of life’s evolution and *cast off* from the normal, familiar world. In Mehta’s words, it turns you into ‘something you weren’t’. The aim of the new game is not about winning or competition; it is not about accumulating a machine to make decisions; it is to grow ‘the world, *instead of yourself*’. The codes of the imaginary computer game take up the basic commands of ‘*look, listen, touch, feel, say, join’* (493 – original emphasis). The data plays, entangles, negotiates and merges as life has done for billions of years. Like some strands of Indigenous thought or the work of Aldo Leopold, Mehta’s game envisions the potential of a community of learners ‘will come to think like rivers and forests and mountains’. As some scientists are discovering, information and communication are prevalent throughout all nature. AI draws together data from diverse expertise and different ways of knowing and perceiving, to contribute to, and participate in a world that merges as one the virtual and the real, the artificial and natural, culture and nature. The experiment perhaps points to a future of digital nature. Mehta says: *He will not live to see it completed, this game played by countless people worldwide, a game that puts the players smack in the middle of a living, breathing planet filled with potential they can only dimly begin to imagine. But he has nudged it along*. submitted by /u/MichelSerres-discuss [link] [comments]

How do I get more out of AI for data analysis / supply chain work?

Hey everyone! I’ve been using AI since 2023, starting with ChatGPT, and since January this year I added Claude to my workflow. I can tell there are real differences between the two, but I feel like I’m not getting the most out of either. A few things I’m trying to figure out: • Is it worth investing time in learning prompt engineering more systematically, or does hands-on practice get you there anyway? • How do you manage context and conversations? Do you use Projects, Notion, some custom system? • Is there a workflow that genuinely changed how you work with AI? (automations, integrations, MCPs, etc.) For Claude users: are you actually getting value out of Projects and persistent context? - AI agents: are any of you actually using them in real workflows? Tools like n8n, Make, or custom agent setups. Worth the learning curve, or still too early/unstable for practical use? I work in data analytics / supply chain. At my company we use Copilot Pro, but the biggest limitation I run into is not being able to connect it directly to systems like SAP — so I end up doing a lot of manual copy-paste just to give the model enough context to be useful. Has anyone solved something similar? Or do you just work around corporate tools entirely and use external models for everything? Thanks in advance 🙌 submitted by /u/sxn8d9997 [link] [comments]

I catalogued 2,392 Claude Code skill files. The biggest category isn't what the discourse suggests — it's SAP.

I've spent three months cataloguing Claude Code skill files — the .md files that sit in ~/.claude/skills/ and extend Claude's behavior. The dataset: 2,392 files, 845 in a curated/verified subset, 72 categories. The Claude Code discourse on Twitter and heavily represents solo-dev SaaS founders working in modern web stacks. React, Next.js, Python, DevOps. The submission data tells a completely different story. Top 10 categories by skill count (curated subset, n=845): SAP — 107 skills (12.7%) Database — 26 skills Cloud (AWS/GCP) — 22 skills Testing — 19 skills AI/ML — 17 skills Git — 15 skills API design — 15 skills Frontend — 15 skills Salesforce — 15 skills Python — 15 skills SAP is 4× larger than the next category. Salesforce, ServiceNow, and Dynamics 365 together add another ~50. Why this matters: the Claude Code market nobody writes about is enterprise platform consultants. People doing ABAP debugging, Fiori migrations, Apex testing. They have specific, narrow, high-value workflows that benefit disproportionately from skill files because: - The domain knowledge is specialized and not in general model training - The workflows are repetitive enough that a skill file pays back fast - The organizations have compliance constraints that make MCP servers harder to deploy than markdown skills If you're building for Claude Code and not thinking about SAP/Salesforce/enterprise verticals, you're ignoring the largest segment of actual usage. A few other findings from the research (methodology + full data in the report): - Quality varies wildly: of 2,392 catalogued skills, only 789 pass a basic verification bar (syntactically valid, non-duplicative, contains actionable patterns, no prompt injection). ~33% signal rate on unverified community sources. - Three anti-patterns show up repeatedly in low-quality skills: wall-of-text skills (3000+ words with no actionable pattern), generic persona skills ("act as senior developer"), and prompt-engineering-masquerading-as-skill (files that are just lists of viral prompts packaged as a skill). - Good skills are 200-800 words. Below 200, probably too thin. Above 800, competes for Claude's attention budget on every prompt. I published the full findings as a 31-page PDF — methodology, test data, case studies, the competitive map of Claude Code vs Cursor vs Copilot. Free, no paywall, no email gate. https://clskillshub.com/report Happy to answer questions about the dataset or methodology. If you've built Claude Code skills, especially in an enterprise context, I'd love to see them — expanding the dataset for v2 in July. submitted by /u/AIMadesy [link] [comments]

@ShastryAnand We understand your concern. Your request is with the support team, who are best equipped to help. Please note that social media isn’t a support channel - please follow up via your ticket

@ShastryAnand We understand your concern. Your request is with the support team, who are best equipped to help. Please note that social media isn’t a support channel - please follow up via your ticket.

@ShastryAnand Hi Anand, Thank you for your message. The support team is aware of the matter and is currently looking into it. They will share an update as soon as more information becomes available.

@ShastryAnand Hi Anand, Thank you for your message. The support team is aware of the matter and is currently looking into it. They will share an update as soon as more information becomes available. Thanks for your patience. Thanks, Jay

@ShastryAnand Thanks for flagging this. The support team is aware of the issue and is currently looking into it.

@ShastryAnand Thanks for flagging this. The support team is aware of the issue and is currently looking into it.

DP built with claude

​Hi everyone, I built a digital platform for SMEs to bridge the gap between SAP B1 and modern tools like n8n, Grafana, ai and BI. ​What it does: It syncs materials, warehouse locations, inventory, and order data from SAP B1 (or other DBs) to a centralized PostgreSQL database. Users can perform centralized operations and real-time analysis through a unified SSO interface. ​How Claude helped in the process: ​Database Integration: I used Claude to generate the schema mapping between SAP's legacy tables and my PostgreSQL database. ​Automation Logic: Claude assisted in writing the Python/JS scripts used within n8n nodes to handle manual and scheduled data polling. ​Data Analysis: I integrated Claude's API into the platform to provide automated insights based on the inventory data stored in PostgreSQL. ​Status: ​It is free to try ​No affiliate links or job requests submitted by /u/foodsaid [link] [comments]

@betalphatango Thank you for contacting us. Please visit the Ariba Help Center to get support without logging in by selecting the pre-login option on this page: https://t.co/4WOFr6uO47

@betalphatango Thank you for contacting us. Please visit the Ariba Help Center to get support without logging in by selecting the pre-login option on this page: https://t.co/4WOFr6uO47

@akharbanda1 SAP certifications can only be verified for the last 10 years, so your 2007 credential can’t be confirmed. You can update it through re-certification if needed.

@akharbanda1 SAP certifications can only be verified for the last 10 years, so your 2007 credential can’t be confirmed. You can update it through re-certification if needed.

I built a searchable hub for 789+ Claude Code skills and 10 autonomous AI agents — all free, open source

I've been deep in the Claude Code skills ecosystem since it launched. Every week there are new skills popping up on GitHub — PR reviewers, test generators, security scanners, database helpers — but finding the right one means digging through dozens of repos, READMEs, and awesome-lists. So I built Claude Skills Hub (clskills.in) — a single place to search, preview, and download every useful Claude Code skill. What's there right now: 789+ skill files across 71 categories (git, testing, APIs, security, DevOps, React, Python, AWS, Docker, Kubernetes, SAP, Salesforce, and 60+ more) Fuzzy search by name, tag, or category One-click download or bulk ZIP for entire collections Each skill has real, production-grade instructions — not templates or boilerplate 30+ curated collections like "Full Stack Starter", "Security Hardening", "DevOps Engineer" I also just shipped 10 autonomous AI agents. These are different from regular skills — each one chains multiple skills into a complete workflow: PR Review Agent — reads your full diff, checks for bugs, security issues, missing error handling, outputs a structured report with file:line references Test Writer Agent — finds untested code, generates tests matching your existing framework and patterns, runs them to verify Bug Fixer Agent — paste an error or stack trace, it traces through your code to root cause and proposes a minimal fix Documentation Agent — reads your actual source code and generates accurate README, JSDoc, API docs Security Audit Agent — full OWASP top 10 scan with secrets detection, dependency CVEs, injection checks Refactoring Agent — finds dead code, duplication, complexity, refactors safely with test verification after each change CI/CD Pipeline Agent — generates or debugs GitHub Actions / GitLab CI from your project structure Database Migration Agent — generates safe migrations with rollback plans and data loss checks Performance Optimizer Agent — profiles frontend bundles, backend queries, and memory usage Onboarding Agent — maps any codebase and generates a complete onboarding guide How to use any of them: Go to clskills.in/agents Click Download on any agent Drop the .md file into ~/.claude/skills/ Use it with /agent-name in Claude Code That's it. No API keys, no accounts, no setup. I also aggregated skills from several community collections: anthropics/skills (official Anthropic skills) travisvn/awesome-claude-skills ComposioHQ/awesome-claude-skills VoltAgent/awesome-agent-skills alirezarezvani/claude-skills The full source is open: github.com/Samarth0211/claude-skills-hub What's next: Custom Agent Builder — tell us your tech stack, AI generates a personalized agent for your project (live now at clskills.in/custom-agent) CLAUDE.md Generator — generates the perfect CLAUDE.md for your codebase More blog content with tutorials on how to write your own skills Continuously adding new community skills as they come out Would love feedback on what skills or agents you'd find most useful. Also open to PRs if you want to contribute skills. submitted by /u/AIMadesy [link] [comments]

I built a free AI agents marketplace with 789 skills for Claude Code — here's the chart that explains how agents work

https://preview.redd.it/rt3qddk9jerg1.png?width=1536&format=png&auto=webp&s=b88c8d5395d37b18781fc8e8743fedeca228be5e Most developers use Claude Code like a basic chatbot. They type "fix this" and expect perfect output. When it doesn't work, they blame the tool. The real problem is the instructions you give it. I spent the last few weeks building Claude Skills Hub (clskills.in) — a free, open-source marketplace where you can download ready-made skill files that turn Claude Code into a specialist. Here's what's inside: 789+ skill files across 71 categories (git, testing, APIs, security, DevOps, React, Python, AWS, Docker, Kubernetes, SAP, Salesforce, and 60+ more) 10 autonomous AI agents that combine multiple skills into complete workflows: PR Review Agent — reads your full diff, checks for bugs, security issues, missing error handling, and outputs a structured report with exact file:line references Test Writer Agent — finds untested code, generates tests matching your existing framework and patterns, runs them to verify they pass Bug Fixer Agent — give it an error or stack trace, it traces through your codebase, finds root cause, and proposes a minimal fix Documentation Agent — reads your actual code and generates accurate README, JSDoc, API docs Security Audit Agent — scans for OWASP top 10, leaked secrets, dependency CVEs, auth flaws Refactoring Agent — finds dead code, duplication, complexity, then refactors safely with test verification after each change CI/CD Pipeline Agent — creates or debugs GitHub Actions and GitLab CI from your project structure Database Migration Agent — generates safe migrations with rollback plans Performance Optimizer Agent — profiles frontend bundles, backend queries, and memory usage Onboarding Agent — maps your entire codebase and generates an onboarding guide for new developers Each agent is a single .md file. You download it, drop it in ~/.claude/skills/, and invoke it. No API keys, no subscriptions, no setup. The difference between "AI can't code" and "AI is my superpower" is just the quality of instructions. Everything is free and open source: clskills.in github.com/Samarth0211/claude-skills-hub Happy to answer questions about how any of the agents work or take suggestions for new ones. submitted by /u/AIMadesy [link] [comments]

@akharbanda1 SAP certifications now require an annual assessment to stay certified. If this hasn’t been maintained, you may need to retake the full certification. Learn more: https://t.co/7DP59vNuP7

@akharbanda1 SAP certifications now require an annual assessment to stay certified. If this hasn’t been maintained, you may need to retake the full certification. Learn more: https://t.co/7DP59vNuP7 For your case, please create a support ticket so the team can advise: https://t.co/fYHB3rCO1Z