Reclaim AI

Reclaim AI AI

Reclaim AI AI

Reclaim AI AI

Reclaim AI AI

Reclaim AI AI

Reclaim AI AI

Reclaim AI AI

Reclaim AI AI

Reclaim AI AI

Reclaim AI AI

Looking for an AI / system to basically manage my entire life 😭 Does this even exist?

Hi everyone, I genuinely feel overwhelmed and I’m wondering if there’s an AI tool, app, or system that can help me organize basically my entire life. I’m juggling a demanding full-time job, university, building a business from scratch, personal finances, and full wedding planning, and I feel like I need a personal chief of staff / executive assistant for life 😭 I’m looking for something that could help with: Work/project management (prioritizing, deadlines, helping me think through work) Calendar & scheduling (actually time-blocking and organizing my days realistically) Finances/budgeting and helping me stay on track financially Entrepreneurship/business building from scratch (planning, prioritization, next steps) University/studying support Wedding planning (timelines, vendors, budgets, to-do lists, reminders, etc.) Personal goals, habits, routines, and becoming a more organized/productive version of myself What I’m looking for is something that feels like a life operating system, not just a chatbot that answers questions. Ideally, I’d love something that: helps me decide what to prioritize reorganizes things when I inevitably fall behind 😅 integrates with calendars/tasks feels proactive instead of reactive I struggle a lot with overwhelm and procrastination when too many things pile up, so if you’ve found a setup that genuinely changed your life, I would LOVE recommendations. What are you actually using? One tool? A stack of tools? AI agents? Claude, ChatGPT, Motion, Notion, Reclaim, Goblin Tools, Sunsama, something else? And most importantly: what actually works in real life? submitted by /u/Lucky_Lie_917 [link] [comments]

I moved to Claude because of the PBC commitment. The Colossus deal feels like a betrayal of that.

I am a Claude Pro subscriber. I use Claude Code. I moved to Anthropic specifically because it is a Public Benefit Corporation, and that distinction mattered to me when choosing which AI tools to pay for and build on. This week's compute deal nominally benefits me. More capacity, higher limits. I understand that, and I am not dismissing the compute scarcity problem Anthropic is solving. I still don't want it at this cost. What Colossus is Colossus was built by operating more than 30 methane gas turbines without Clean Air Act permits for nearly a year, in a majority-Black neighborhood in Memphis whose residents already face a cancer risk four times the national average. When the NAACP filed a notice of intent to sue, xAI removed the unpermitted turbines at Colossus 1 and built Colossus 2 in Southaven, Mississippi with 27 new unpermitted turbines, described internally as a "copy and paste" of the same strategy. The NAACP, represented by Earthjustice and the Southern Environmental Law Center, is now actively suing xAI. A congressional probe is open. Penalties sought are up to $124,400 per day of violation. At the time Anthropic signed, all of this was documented, litigated, and public. Why the PBC structure matters here Anthropic's PBC designation is not marketing. It is a legal governance obligation to weigh public benefit alongside profit. It is also why some of us chose Anthropic over competitors in the first place. Signing a comprehensive compute agreement with a facility under active federal environmental litigation, in a documented environmental justice community, with full prior knowledge, is a governance question under that obligation. I have not seen Anthropic address it publicly. One more thing worth flagging Musk has publicly stated he reserves the right to reclaim Colossus compute if Anthropic's AI "engages in actions that harm humanity." For developers building on Claude Code, that dependency now sits underneath your toolchain. The infrastructure provider's definition of harm has structural leverage over an AI safety company's infrastructure decisions. What I am asking I am not asking Anthropic to abandon its compute strategy. I am asking whether other users who chose Anthropic because of its PBC commitment feel the same way, and whether Anthropic intends to address how this decision is consistent with that commitment. I have filed a formal protest with Anthropic support citing these concerns. I wanted to raise it here because developers who build on these tools deserve to know what the infrastructure decision looks like from a user who specifically chose this platform for its stated values. submitted by /u/No_Boysenberry6890 [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

anthropic isn't the only reason you're hitting claude code limits. i did audit of 926 sessions and found a lot of the waste was on my side.

Last 10 days, X and Reddit have been full of outrage about Anthropic's rate limit changes. Suddenly I was burning through a week's allowance in two days, but I was working on the same projects and my workflows hadn't changed. People on socials reporting the $200 Max plan is running dry in hours, some reporting unexplained ghost token usage. Some people went as far as reverse-engineering the Claude Code binary and found cache bugs causing 10-20x cost inflation. Anthropic did not acknowledge the issue. They were playing with the knobs in the background. Like most, my work had completely stopped. I spend 8-10 hours a day inside Claude Code, and suddenly half my week was gone by Tuesday. But being angry wasn't fixing anything. I realized, AI is getting commoditized. Subscriptions are the onboarding ramp. The real pricing model is tokens, same as electricity. You're renting intelligence by the unit. So as someone who depends on this tool every day, and would likely depend on something similar in future, I want to squeeze maximum value out of every token I'm paying for. I started investigating with a basic question. How much context is loaded before I even type anything? iykyk, every Claude Code session starts with a base payload (system prompt, tool definitions, agent descriptions, memory files, skill descriptions, MCP schemas). You can run /context at any point in the conversation to see what's loaded. I ran it at session start and the answer was 45,000 tokens. I'd been on the 1M context window with a percentage bar in my statusline, so 45k showed up as ~5%. I never looked twice, or did the absolute count in my head. This same 45k, on the standard 200k window, is over 20% gone before you've said a word. And you're paying this 45k cost every turn. Claude Code (and every AI assistant) doesn't maintain a persistent conversation. It's a stateless loop. Every single turn, the entire history gets rebuilt from scratch and sent to the model: system prompt, tool schemas, every previous message, your new message. All of it, every time. Prompt caching is how providers keep this affordable. They don't reload the parts that are common across turns, which saves 90% on those tokens. But keeping things cached costs money too, and Anthropic decided 5 minutes is the sweet spot. After that, the cache expires. Their incentives are aligned with you burning more tokens, not fewer. So on a typical turn, you're paying $0.50/MTok for the cached prefix and $5/MTok only for the new content at the end. The moment that cache expires, your next turn re-processes everything at full price. 10x cost jump, invisible to you. So I went manic optimizing. I trimmed and redid my CLAUDE md and memory files, consolidated skill descriptions, turned off unused MCP servers, tightened the schema my memory hook was injecting on session start. Shaved maybe 4-5k tokens. 10% reduction. That felt good for an hour. I got curious again and looked at where the other 40k was coming from. 20,000 tokens were system tool schema definitions. By default, Claude Code loads the full JSON schema for every available tool into context at session start, whether you use that tool or not. They really do want you to burn more tokens than required. Most users won't even know this is configurable. I didn't. The setting is called enable_tool_search. It does deferred tool loading. Here's how to set it in your settings.json: "env": { "ENABLE_TOOL_SEARCH": "true" } This setting only loads 6 primary tools and lazy-loads the rest on demand instead of dumping them all upfront. Starting context dropped from 45k to 20k and the system tool overhead went from 20k to 6k. 14,000 tokens saved on every single turn of every single session, from one line in a config file. Some rough math on what that one setting was costing me. My sessions average 22 turns. 14,000 extra tokens per turn = 308,000 tokens per session that didn't need to be there. Across 858 sessions, that's 264 million tokens. At cache-read pricing ($0.50/MTok), that's $132. But over half my turns were hitting expired caches and paying full input price ($5/MTok), so the real cost was somewhere between $132 and $1,300. One default setting. And for subscription users, those are the same tokens counting against your rate limit quota. That number made my head spin. One setting I'd never heard of was burning this much. What else was invisible? Anthropic has a built-in /insights command, but after running it once I didn't find it particularly useful for diagnosing where waste was actually happening. Claude Code stores every conversation as JSONL files locally under ~/.claude/projects/, but there's no built-in way to get a real breakdown by session, cost per project, or what categories of work are expensive. So I built a token usage auditor. It walks every JSONL file, parses every turn, loads everything into a SQLite database (token counts, cache hit ratios, tool calls, idle gaps, edit failures, skill invocations), and an insi

Prism MCP v5.1 - 10x memory compression and AI agent learning from mistakes

Posted Prism here before (persistent memory for AI coding agents). Two big releases since - here's what's new: 10x more memory in the same space. We ported Google's TurboQuant to pure TypeScript. Your agent can now store millions of memories on a laptop instead of hundreds of thousands. No vector database needed. Your agent learns from mistakes. When you correct your agent, Prism remembers. Important corrections auto-surface as warnings in future sessions. Your agent gets smarter every time you use it. Visual knowledge graph. See your agent's memory as an interactive neural map. Click any node to rename or delete it. Finally see what your agent actually remembers. Deep Storage cleanup. One command reclaims 90% of storage space from old memories. Safe by default - preview before deleting. Pure TypeScript, local SQLite, zero cloud dependencies. Works with Claude, Cursor, Windsurf, Gemini, and any MCP client. MIT licensed. 303 tests. GitHub: https://github.com/dcostenco/prism-mcp submitted by /u/dco44 [link] [comments]