Workday AI

Workday AI AI

Workday AI AI

Workday AI AI

Workday AI AI

Workday AI AI

Workday AI AI

Workday AI AI

Workday AI AI

Workday AI AI

Workday AI AI

The tradeoff nobody talks about with context-aware AI

To be genuinely useful across a workday an AI needs to know what you've been doing. That means either you re-explain everything every session or you give it persistent access to something. Screen recording, browser history, email, files. The more you give it, the more useful it gets. But there's a point where it starts to feel like too much, and I can't tell if that's a rational response to real risk or just instinct that hasn't caught up with how local storage actually works. How are people here thinking about this tradeoff? submitted by /u/Zealousideal_Bad333 [link] [comments]

Worker-Positive AI: Why Skills, Not Job Titles, Decide Who Wins the Next Five Years

AI is not erasing UK jobs — it is reorganising them, worker-positive AI. Here is the evidence-led case for skills-based work, with named studies and a practical playbook. The doomsday story about AI and jobs keeps missing the point. Work is not disappearing. It is being reorganised. And the organisations that win the next five years will not be the ones with the flashiest AI stack. They will be the ones that shift from job titles to skills. The Technological Jerk of Software Development I have spent roughly 30 years in infrastructure and SRE work. I have watched a lot of technology waves sweep through. This one feels different — not because the tech is magical, but because the operating model around it has to change. Bolt-on AI does not move productivity. Redesigned work does. Here is the worker-positive case, backed by named research. The UK entry-level floor is dropping — and that is a skills story A King's College London study of millions of UK job listings found that firms most exposed to AI became 16.3 percentage points less likely to post new vacancies. Highly exposed occupations saw job postings fall by 23.4%. Technical and analytical roles — software engineers, data analysts — took the steepest cuts. Here is the part most headlines miss. Average pay at those same firms rose by more than £1,300. The remaining work carries more complexity. Fewer junior tickets to triage. More judgement calls about when the model is wrong. Customer-facing roles held steady. The KCL researchers noted that interpersonal skills remain a genuine complement to large language models. That should tell you something about where the human premium is moving. The real risk is not job loss. It is uneven access to the new, more complex tasks — and to the skills that qualify people for them. Skills-based work is the operating model, not a HR rebrand The World Economic Forum's Future of Jobs Report 2025 surveyed over 1,000 employers covering 14 million workers. Their finding: 39% of workers' core skills will be transformed or outdated between 2025 and 2030. AI and big data top the list of fastest-growing skills. Analytical thinking, resilience, and leadership are the human anchors. PwC's 2025 Global AI Jobs Barometer analysed close to a billion job ads. Workers with AI skills earned a 56% wage premium in 2024 — more than double the 25% premium a year earlier. Skills requirements are changing 66% faster in AI-exposed roles. Demand for formal degrees is falling in those same roles. Put those numbers together and the pattern is clear. The market is pricing skills, not titles. But most organisations still plan, hire, and promote around titles. That is the gap. The Workday UK playbook makes the practical case for a skills-first operating model. If a role loses tasks to AI, the worker does not lose their identity. Their skills travel with them to the next role. Internal talent marketplaces turn that clarity into movement. Skills taxonomies — one team says "coding," another says "React," another says "software engineering" — get reconciled into a shared vocabulary. This is the part I keep coming back to. It is not a tooling problem. It is a definition problem. When you cannot describe what people can actually do in a consistent way, you cannot redeploy them. You just hire externally and hope. Trust is infrastructure — and the UK that skips it ships slower Britain's regulatory stance is lighter touch than the EU's AI Act. Instead of a central regulator, sector bodies like the ICO and EHRC set context-specific guardrails. That is not a vacuum, though. The TUC's Artificial Intelligence (Regulation and Employment Rights) Bill sets out three demands. A ban on detrimental use of emotion recognition. A statutory right to disconnect. Algorithmic transparency — employers must explain how automated decisions get made and on what data. Worker sentiment backs this up. A YouGov poll commissioned for the TUC found 69% of UK working adults agree employers should consult staff before introducing new tech like AI. And the business case for governance is not soft. Workday research estimates UK leaders lose up to 140 working days per year to administrative friction. AI adoption could reclaim productive work worth £119 billion annually — but only when trust is there to carry adoption to scale. I have seen this pattern in SRE work for decades. Systems that hide their logic get distrusted and worked around. Systems that surface their reasoning get adopted faster. AI is no different. The practitioner's playbook Build a skills taxonomy before buying another AI tool. You cannot redeploy people through vocabulary you do not have. Audit your entry-level pipeline. If AI is eating junior tasks, where do senior people come from in five years? Bootcamp partnerships and apprenticeships become strategic, not nice-to-have. Treat governance as a speed lever, not a brake. Transparency, audit trails, and human review shorten the distance between pilot

I created a workhorse AI agent in Claude Cowork in 2 weeks. Here's how.

Most AI agent frameworks I see online are obsessed with tool-calling benchmarks, autonomous coding loops, or flashy one-shot demos. Two weeks ago I started daily-driving a personal assistant I've been building on Claude Cowork, and I'm already convinced the unsolved problems are somewhere else entirely — and almost no one is talking about them. I'm not going to share the name or identifying details (this is a personal system I use for my actual work). But I'll share what I've learned, because most of it wasn't obvious before I started. The background: I'm an executive at a mid-sized company. Every commercial AI assistant I tried was amnesiac. I wanted something closer to an actual chief of staff — persistent, opinionated, and aware of my context. The stack: The whole system runs on Claude Cowork, with Google Workspace (email, calendar, Drive, Chat) and Notion (tasks, projects, GTD) as the surrounding data layer. I started on Claude Pro, then upgraded to Claude Max 20× ($200/month) once the architecture outgrew the lower tier — the system now runs 15 scheduled background tasks around the clock, plus the interactive sessions I have through the day. What that looks like in practice: the agent is active 6–7 hours every day. Roughly 2–3 of those hours are development — debugging, iterating on skills, refactoring prompts, reviewing audit outputs, designing the next automation. I treat the system itself as an ongoing product. The other 4–5 hours are real work: inbox triage, draft reviews, research delegations, decision support, report generation, meeting prep. The dev/real-work ratio will shift (more real work, less tinkering) — but I've decided the 30–40% overhead is worth it while the system is still maturing. Two weeks. That's how long this took to reach the state below. Most of the heavy lifting was architectural decisions, not code — Cowork's memory, scheduled tasks, skills, and MCP ecosystem did the technical work. I just designed the system on top. What it is now: A persistent, file-based memory system with ~200 curated markdown entries, indexed by semantic topic — not a vector DB 11 specialized sub-agents (legal, finance, research, sales, operations, real estate, etc.) with a delegation matrix A development constitution — a versioned governance doc for how the system evolves: which structural changes it can make autonomously when improving itself vs. which require my approval. Governs how the system changes, not individual task decisions. A distributed architecture: always-on background sentinel (inbox scans, health checks, nightly closeouts, conflict scanning) + interactive node A self-improvement loop that audits instruction files, researches new techniques, proposes a change plan, waits for approval, implements The filesystem — the architectural choice nobody talks about: The agent lives inside a Dropbox folder. Not as a UI feature — as its actual substrate. Everything is organized first by project, then by artifact type: every active project has its own folder with sub-folders for briefs, research, drafts, correspondence, contracts, and archived items. Cross-project stuff (memory, skills, scheduled-task logs, session transcripts, audit outputs) lives in dedicated top-level folders. When a new project starts, the agent spins up the folder skeleton. When a project closes, it moves to a cold-storage path and the index updates. Inboxes → Outboxes — the system works like a pipeline: On one side, multiple inboxes: My work email + a dedicated shadow email for the agent itself Chat messages (Google Chat) A Notion GTD inbox where I drop raw tasks and unclassified items A file dropzone in the shared folder A daily working folder A general triage inbox And the one I haven't seen anyone else talk about: my Downloads folder on every computer I use is redirected straight into the agent's inbox folder. Every PDF, CSV, screenshot, contract, invoice, or random file I download during a workday automatically lands in the pipeline. The agent reads it, classifies it, associates it with the right project, files it into the matching subfolder, and updates the project index. I haven't manually filed a downloaded file in two weeks. On the other side, outboxes: Email drafts Notion tasks created and pages updated Project deliverables in their project subfolder Research reports, audit logs, session summaries, memory updates — each in its own structured destination Every task flows left-to-right. New items arrive in inboxes. The agent (or a scheduled task) routes them to the right downstream process — triage, memory extraction, project assignment, or drop. Whatever gets produced lands in a structured outbox with traceable provenance: which inbox item triggered it, which skill processed it, which decisions were made along the way. Nothing disappears into a black box. Everything is greppable. The stuff I find genuinely unique: 1. Graduated autonomy — every action has authority level L0–L3. L

Sam Altman says AI superintelligence is so big that we need a "New Deal." Critics say OpenAI’s policy ideas are a cover for "regulatory nihilism"

OpenAI says the world needs to rethink everything from the tax system to the length of the workday in order to prepare for the wrenching changes of superintelligence technology—the point at which AI systems are capable of outperforming the smartest humans. On Monday, in a 13-page paper titled “Industrial Policy for the Intelligence Age,” OpenAI said it wanted to “kick-start” the conversation with a “slate of people-first policy ideas.” How much faith to put in OpenAI’s words and motives, however, seems to be one of the key questions among many of the people reading the paper. The paper was released on the same day that The New Yorker published the results of a lengthy one-and-a-half-year investigation into OpenAI that raised questions about CEO Sam Altman’s trustworthiness on various issues, including AI safety. Read more: https://fortune.com/2026/04/06/sam-altman-says-ai-superintelligence-is-so-big-that-we-need-a-new-deal-critics-say-openais-policy-ideas-are-a-cover-for-regulatory-nihilism/ submitted by /u/fortune [link] [comments]

I wrote a cron job that saves me ~2 hours of dead time on Claude Code every day

If you're on a Max plan and use Claude Code heavily, you've probably noticed the 5-hour usage window starts when you send your first message, floored to the clock hour. So if you start working at 8:30 AM and hit the limit by 11, you're stuck until 1 PM. Two hours of nothing. Turns out you can manipulate this. Send a throwaway Haiku "hi" at 6 AM before your workday, and the window anchors to 6-11 AM instead of 8 AM-1 PM. That means by 11 AM you will have a fresh usage window! The easiest way I found to do this is to set up a GitHub Actions cron that does this automatically every morning. Repo if you want it: https://github.com/vdsmon/claude-warmup —> check the edits Let me know what you think and if it makes sense! EDIT: As suggested by [u/ContextCustodian](u/ContextCustodian), the same concept can be applied (in a simpler way) using a Claude Code Web scheduled task: https://claude.ai/code/scheduled. I did not test it yet, but it should work! EDIT 2: The native scheduled task works flawlessly! This is by far the easiest way of manipulating this. You can also extend this concept to other parts of your workday, not only for the first morning window. submitted by /u/victorsmoliveira [link] [comments]

You now can ask Claude to summarize your day with this MCP

I've been building Chronoid for about a year now — a macOS time tracking app that automatically monitors what apps and websites you use throughout the day. I just shipped an MCP server that lets Claude Desktop (or Cursor, VS Code, Windsurf) query all that data directly. How Claude helped build this: Claude Code one-shot the entire MCP server — I described what I wanted, pointed it at my existing database schema, and it generated the full Swift implementation in a single pass. I've hopped between pretty much every AI coding tool over the past year — ChatGPT Codex, Aider, GitHub Copilot, Amp Code, OpenCode — and settled on Claude Code. The $100/mo Claude Max plan is insane value, I can barely hit the limit. The key insight for native macOS/Swift development: AI is genuinely bad at native Swift compared to web/frontend/Next.js. The thing that makes it work is setting up a solid end-to-end workflow where code can be verified. My setup is Claude Code + xcodebuild piped through xcsift, which compacts Xcode's awful raw output into something clean and token-efficient: xcodebuild -scheme Chronoid -configuration Debug build 2>&1 | xcsift -w This is the key — Claude can iterate in a loop, read the compact errors/warnings, fix them, and verify again without burning tokens on Xcode's verbose output. Without this, the feedback loop falls apart. What the MCP server does: Once connected, you can just ask Claude things like: "Summarize my day" "How productive was I this week?" "What distracts me the most?" "Show me my deep focus sessions" "When are my peak productivity hours?" The MCP server exposes ~10 tools — daily summaries, app usage stats, productivity analysis, distraction patterns, focus block detection, interruption tracking, and more. All read-only, all local. No data leaves your machine. Setup is one config block: { "mcpServers": { "chronoid": { "command": "/Applications/Chronoid.app/Contents/Resources/chronoid-mcp", "args": [] } } } The video shows Claude summarizing my full workday — time per app, categories, timeline, and productivity insights — all from a single prompt. Chronoid has a 30-day free trial — no credit card required. You can try the MCP server right away. Would love to hear what other MCP integrations people are finding useful with their local tools, and how others are handling native development with AI. submitted by /u/tuanvuvn007 [link] [comments]

Is it worth it to switch from a Google AI Pro ($20) subscription to a Claude Pro subscription?

I currently use Gemini CLI with the $20 AI Pro subscription, but it sometimes does really stupid shit like trying to refactor the whole file when I only ask for a small change or fix. Or sometimes after a change, a whole function is missing, indentation gets ruined, etc. I've heard in many places now that Claude is much better at coding, but also that it's way more expensive. So, does the pro subscription even get me through an 8 hour workday as a dev working on small codebases or single scripts? Currently I'm working in C, but I'm also doing Python and web languages as well. submitted by /u/KevDotCom [link] [comments]