tsunami_crypto

@SamPeterToT link or drop a tldr in the replies?

I went from 15 hours/week on content to under 4. Here's the full AI workflow breakdown, every tool, every step:

@aiedge_ the "rp-as-a-service" naming alone made me pause youre betting on a platform that doesnt even exist yet though

GTA VI's release is right around the corner. This is the biggest opportunity to make your first $10,000 online. RP-as-a-service will be HUGE, and people will be absolutely printing. If you want to be one of them, steal this full RP money-making playbook: (plug this into AI)

@WesRoth so they practically begged him back for minimum exec salary the board really thought salary was the issue there lmao

How much does it cost to hire the CEO of an $852 billion AI powerhouse? Exactly $64,480 a year. Newly unsealed evidence from the high-stakes Elon Musk vs. OpenAI federal trial revealed the official November 2023 offer letter drafted to bring Sam Altman back as CEO following his brief ouster. Rather than commanding an executive package, Altman’s base pay was pegged to the exact dollar amount required to qualify as an exempt employee under California law.

@iamfakhrealam that cleaning taxi angle is random but honestly valid boring businesses usually print the hardest

📂 Best Niches To Make Money In (2026 Edition) ┃ ┣ 📂 AI Automation ┃ ┣ 📂 n8n Workflows ┃ ┣ 📂 AI Chatbots ┃ ┣ 📂 CRM Automations ┃ ┣ 📂 Lead Follow-Up Systems ┃ ┗ 📂 Internal Business Tools ┃ ┣ 📂 Local Business Services ┃ ┣ 📂 Taxi Companies ┃ ┣ 📂 Cleaning Companies ┃ ┣ 📂 Dentists ┃ ┣ 📂 Gyms & Personal Trainers ┃ ┗ 📂 Real Estate Agents ┃ ┣ 📂 SaaS Products ┃ ┣ 📂 Booking Software ┃ ┣ 📂 Review Automation ┃ ┣ 📂 Invoice Tools ┃ ┣ 📂 Client Portals ┃ ┗ 📂 Industry-Specific Dashboards ┃ ┣ 📂 E-Commerce ┃ ┣ 📂 Subscription Boxes ┃ ┣ 📂 Beauty Products ┃ ┣ 📂 Pet Products ┃ ┣ 📂 Health Food ┃ ┗ 📂 Personalized Gifts ┃ ┣ 📂 Content & SEO ┃ ┣ 📂 Niche Blogs ┃ ┣ 📂 Affiliate Websites ┃ ┣ 📂 YouTube Automation ┃ ┣ 📂 Programmatic SEO ┃ ┗ 📂 AI Content Workflows ┃ ┣ 📂 High-Ticket Services ┃ ┣ 📂 Web Design ┃ ┣ 📂 Paid Ads ┃ ┣ 📂 SEO ┃ ┣ 📂 Funnel Building ┃ ┗ 📂 Conversion Optimization ┃ ┣ 📂 B2B Lead Generation ┃ ┣ 📂 Cold Email Systems ┃ ┣ 📂 LinkedIn Outreach ┃ ┣ 📂 Appointment Setting ┃ ┣ 📂 Data Scraping ┃ ┗ 📂 Lead Enrichment ┃ ┣ 📂 Online Education ┃ ┣ 📂 Mini Courses ┃ ┣ 📂 Templates ┃ ┣ 📂 Notion Systems ┃ ┣ 📂 Coaching Programs ┃ ┗ 📂 Digital Communities ┃ ┣ 📂 Finance & Admin ┃ ┣ 📂 Bookkeeping ┃ ┣ 📂 Tax Prep ┃ ┣ 📂 Expense Tracking ┃ ┣ 📂 Payroll Automation ┃ ┗ 📂 Invoice Management ┃ ┣ 📂 Boring Businesses ┃ ┣ 📂 Waste Management ┃ ┣ 📂 HVAC ┃ ┣ 📂 Plumbing ┃ ┣ 📂 Moving Companies ┃ ┗ 📂 Landscaping ┃ ┗ 📂 Best Rule ┣ 📂 Find a boring problem ┣ 📂 Solve it with software ┣ 📂 Sell it to businesses ┣ 📂 Charge monthly ┗ 📂 Repeat at scale

@_avichawla making it a trinity is a good pitch but id still end up with 3 tabs open to the same place

Claude vs. Claude Code vs. Cowork. Anthropic offers three distinct ways to interact with Claude, and each one targets a fundamentally different workflow. Think of it as: Chat for thinking, Code for building, and Cowork for doing. Here's a quick breakdown: 1️⃣ Claude Chat This is the conversational AI assistant most people already know. You type a prompt, Claude responds, and you iterate together. - Turn rough ideas into structured plans through conversation - Write emails, reports, essays, and long-form content - Research and summarize complex topics in minutes - Analyze documents, PDFs, and images - Build interactive prototypes through Artifacts The key here is that everything happens through conversation. You're thinking with Claude, not delegating work to it. It's available on every device, has a free tier, and supports persistent memory across sessions. The tradeoff is that it has no direct access to your local files (upload only), and it can't generate raster images natively. 2️⃣ Claude Code This is a terminal-native coding agent. You describe what you want in plain English, and Claude reads your codebase, writes code, runs tests, fixes errors, and ships the result. - Build and debug entire features across the full codebase - Write, run, and fix tests automatically - Manage git workflows and create pull requests - Spawn multiple parallel agents working on different parts of a task simultaneously It handles the full development cycle end to end, from planning to execution to testing. With the CLAUDE(.)md configuration file, you can teach it your project's conventions, patterns, and constraints so it writes code the way your team expects. The tradeoff is a steeper learning curve compared to Chat, and token costs can add up during heavy sessions. 3️⃣ Claude Cowork This is the newest addition. Anthropic describes it as Claude Code for the rest of your work. It's an agentic desktop assistant that automates file management and repetitive tasks through a GUI. You describe an outcome, and Claude plans, executes, and delivers finished work: formatted documents, organized file systems, spreadsheets with working formulas, and synthesized research. - Direct local file access and editing (no upload/download cycle) - Schedule recurring tasks automatically - Assign tasks remotely via Dispatch from your phone - Computer Use lets Claude control your screen directly It runs inside a sandboxed virtual machine on your computer, so Claude can only access folders you explicitly grant. You don't need to know how to code to use it. The tradeoff is that your computer must stay awake for tasks to run, and it's still in research preview. Here's how to think about choosing between them: → If you need to think through a problem or get writing/research help, use Chat → If you're building software and want an autonomous coding partner, use Code → If you have a clearly defined deliverable that involves local files and desktop workflows, use Cowork All three are included in the same subscription starting at $20/month, which makes it one of the highest-leverage subscriptions in productivity software right now. I've put together a visual below that maps the workflow of each product side by side. Also, if you want to go deeper into Claude Code specifically, my co-founder wrote a detailed article covering the anatomy of the .claude/ folder, a complete guide to CLAUDE(.)md, custom commands, skills, agents, and permissions, and how to set them all up properly. Read it below.

@LinusEkenstam the "shouldve been adobe but wasnt" is becoming a whole genre atp watch them wait another year then ship a worse version

This week is the week of: "This should have been made by Adobe, but wasn't" This is the second such product to be released in 48h Ponder looks absolutely fantastic.

@Whizz_ai build a voice brief for a bot that talks exactly like u ends up sounding like a pr person lmao

You are wasting time on Claude (using completely wrong) Here is the exact protocol on how to train Cluade to sound exactly like you in just 24 hours:👇 ☑ 1. Build The Voice Foundation This phase is about defining your communication identity clearly before Claude starts writing anything. → Prompt 1: Tone Definition “Help me define my writing voice clearly. Analyze the way I naturally communicate. Identify: sentence rhythm, tone, pacing, emotional style, and persuasion patterns. Then summarize it into a structured voice brief.” → Prompt 2: Writing Habits Audit “Based on my writing examples, identify 5 habits that make my content feel uniquely mine. Also, identify 5 patterns that weaken my writing or make it sound generic.” → Prompt 3: Audience Emotion Mapping “When people read my content, what should they feel? Confidence? Urgency? Clarity? Authority? Help me define the emotional outcome my writing should consistently create.” ↳ Claude performs dramatically better once your voice becomes operational instead of vague. ☑ 2. Train With Real Examples This phase is about feeding Claude high-signal examples so it can identify your patterns accurately. → Upload: • Your best LinkedIn posts • Emails that got replies • Writing that felt deeply authentic • Content people recognized as “100% you.” Then use: → Training Prompt “You are studying my writing patterns, not just the content itself. Analyze sentence length, transitions, emotional pacing, clarity, structure, and persuasion style. Learn the patterns that repeat consistently.” → Refinement Prompt “Identify which parts of my writing style are strongest and should be amplified further. Also identify weak habits that reduce clarity, sharpness, or authenticity.” ↳ Claude learns faster from strong patterns than from large amounts of random writing. ☑ 3. Live Iteration + Self Critique This is where most of the improvement happens. Instead of endlessly regenerating outputs… Train Claude through direct correction loops. → Draft Prompt “Write a LinkedIn post about [topic] using my voice brief and writing examples. Prioritize brevity, clarity, directness, and natural pacing.” → Self-Critique Prompt “Now critique your own draft against my voice brief. Identify exactly where the tone became generic, robotic, overexplained, or inconsistent with my writing style. Then rewrite it stronger.” → Final Correction Prompt “Highlight any remaining phrases that still sound AI-generated or unlike me. Replace them with wording that feels more natural and aligned with my examples.” ↳ The fastest improvements happen when Claude critiques itself against your standards. That’s why people getting elite-level AI writing outputs are usually not better writers… They simply trained the system properly. For more AI productivity breakdowns like this: 👇 → Go to @humzakhalid" target="_blank" rel="nofollow noopener">substack.com/@humzakhalid → Subscribe to my free newsletter (don’t pay anything) → Get more free and daily high-res cheatsheets Which prompt are you trying first? ♻️ Repost this to help your network stop sounding robotic with AI.

@fromzerotomill swatch marketing had bro feeling like a victorian gentleman overnight ngl the 2 word play was clean though

Not me rolling around with my AP pocket watch

@TTrimoreau pressure is a good alarm clock but small wins keep the engine running tbh

What creates more momentum for you? - small wins - pressure - competition - deadlines

@WesRoth comer finally useful for something besides subpoenaing random civilian but lets see how many pages they actually read

House Oversight Chair James Comer sent a letter to OpenAI CEO Sam Altman requesting information about possible financial conflicts between Altman’s personal investments and his leadership of OpenAI. The request comes during Elon Musk’s lawsuit against OpenAI, where Musk argues the company betrayed its original nonprofit mission. Comer is asking for a briefing, conflict-of-interest documents, and communications related to OpenAI’s internal audit committee. OpenAI was recently valued at $852 billion, while Musk is seeking $150 billion in damages and wants Altman removed and OpenAI returned to nonprofit status.

@femke_plantinga the iceberg is too accurate team stuff is never a product problem its always a people problem

Everyone's building a personal Knowledge Base. For one person. But what about teams? Obsidian. Notion. Claude. Readwise. It looks clean. It feels smart. It works great. Here's what nobody shows you when you try to do the same for your team ↓ Permissions & access control Verification & expiry cycles Owner accountability Staleness detection Multi-source ingestion (Slack, GitHub, Jira, Linear, Drive...) Conflict resolution (who's right when two docs contradict?) Onboarding flows Search at scale AI that knows what's trusted vs. someone's Slack opinion Guest management Audit logs SSO / SCIM provisioning Cross-tool search Content gap detection A personal KB needs one thing: you to keep feeding it. A team KB needs all of this, and it needs to run without anyone remembering to do anything. That's not a note-taking problem. That's an infrastructure problem. With @slitehq, we're building a knowledge base that maintains itself. More coming in June! 👀

@haider1 benchmarks measuring everything except what i actually need a model to do 4.5 writing felt like it had an opinion. newer ones feel like theyre pleasing a rubric.

some models perform way better in real use than benchmarks suggest for example, regardless of what benchmarks said about gpt-4.5, i still think it was a better writer than almost every model that came after it gpt-5.5 is good at writing too, but still not at the gpt-4.5 level

@aitrendz_xyz go ahead post the prompt i gotta see if it knows about the dishes in my sink

POV: You just found the God prompt that uncovers your hidden fears and tells you exactly how to fix your life Here's how👇

@kimmonismus Tim Apple got me too domestic chips gonna need a few years but Nvidia's lead isnt closing overnight

Tim Apple. I'm crying. anyway's, curious to see if NVIDIA gets a chance to get new deals to sell GPUs or whether China will now rely entirely on domestic chips (Huawei) and declare chip independence to the USA.

@itsolelehmann ngl this timeline is one of the rare places where the content tax actually pays u back

X payouts are turning into a more consistent revenue stream (around 4.5-5.5k per month now) didn't expect that! pretty cool to get paid like that just for posting

@neil_xbt so if a company replaces its workers with ai, it’s also wiping out its own customer base long term. where does the paper go on policy?

Two researchers just published the most uncomfortable paper in Economics! UPenn and Boston University. Free to read. The argument that every CEO automating their workforce already understands and cannot stop anyway. Replacing workers with AI eliminates future customers. Laid-off workers stop spending. Enough of them stop spending and nobody can afford to buy anything. The companies that fired everyone end up selling into an economy with no purchasing power left. It is not a transfer of wealth. Both sides lose. Block. Salesforce. Goldman Sachs. 100,000 tech layoffs in 2025. AI cited as the primary driver in more than half. 80% of US workers in jobs susceptible to automation. Every proposed solution tested and found insufficient. UBI. Capital taxes. Collective bargaining. None of them change the per-task decision to replace a human. The only mechanism the math supports is a Pigouvian automation tax. The gap between the economy we are building and the economy anyone can actually sell into is not a technology problem. It is a Prisoner's Dilemma with no player willing to defect first and everything that deadweight loss quietly compounds into the demand side of every market on earth.

@forgebitz pulse articles being cited by ai is kind of wild never thought id see linkedin blogposts become training data

looks like parasite SEO is making a comeback thanks to ai search pulse articles are cited a lot in prompt responses

@Suryanshti777 mind sharing how deep subagents go or is that the part i gotta pay to read later?

Claude Code ships with 5 architectural layers most engineers never open. Not features. Not settings. Layers — each solving a distinct problem that LLMs alone can't solve. And four of them have nothing to do with prompting. Here's the full Agent Development Kit: Layer 1 — CLAUDE.md → The Memory Layer Architecture rules, naming conventions, test expectations, repo map. Always loaded. Always active. Two scopes: • ~/.claude/CLAUDE.md → global • .claude/CLAUDE.md → project This isn't context you paste in before every session. It's context that never needs repeating. The agent's constitution. Layer 2 — Skills → The Knowledge Layer Each SKILL.md carries a description. Claude matches it at runtime and forks the skill into an isolated subagent. On-demand, never always-on. Task-specific knowledge without inflating your main context window. Modular by design. Layer 3 — Hooks → The Guardrail Layer PreToolUse → PostToolUse → SessionStart → Stop → SubagentStop This is the layer most teams skip. And the one they regret skipping first. Hooks are NOT AI. They're deterministic event-driven shell commands. • Auto-lint on every Write • Hard-block on rm -rf • Slack notification on Stop Event fires → Matcher checks → Command runs Quality enforced at the infrastructure level. Not the prompt level. Layer 4 — Subagents → The Delegation Layer Each subagent gets its own context window, model, tools, and permissions. Main agent delegates down. Receives results up. That's it. No infinite recursion — subagents can't spawn subagents. Main context stays clean. Hard boundaries by design. Layer 5 — Plugins → The Distribution Layer Bundle your skills + agents + hooks + commands into a plugin. One install. Whole team inherits the behavior. Think npm packages — but for what your agent knows how to do. Wrapping everything: → MCP Servers on the left (GitHub, databases, APIs, custom integrations) → Agent Teams on the right (parallel execution, message passing, shared permissions) The 5-layer stack in one line: CLAUDE.md sets rules → Skills provide expertise → Hooks enforce quality → Subagents delegate work → Plugins distribute to the team Most production failures in agentic systems trace back to one missing layer. Which one is the gap in your current setup?

@WesRoth fast shipping and regular maintenance breaks are a different kind of reliability though patching on a timer isnt the same as patching when its ready

OpenAI is eyeing a structural operational shift: moving to a highly stable release cadence anchored by a major update every Thursday.

@ghumare64 so its basically claude md but actually structured instead of just loading everything as raw text does it handle concurrent agent writes or just sequential?

Every AI coding agent in 2026 stores memory the same broken way: a markdown file that loads upfront. I have better free solution. Which is currently trending on GitHub with 6000+ stars. Claude Code: CLAUDE md files at every directory level, walked from CWD up to repo root, loaded at session start. 200-line target, 40k character hard cap per file. Auto-memory adds ~/.claude/projects//memory/ on top. Cursor: .cursor/rules/*.mdc with three modes (always, auto-attach, agent-requested). Memory Bank community pattern adds /memory_bank with activeContextmd, progressmd, techContextmd, systemPatternsmd. Codex: AGENTSmd walked from project root down to CWD, 32 KiB default per file. Now an open standard adopted by Amp, Jules, Factory, Cursor. Hermes, OpenClaw, Continue, Aider, Windsurf: same shape. Plain markdown on disk. Loaded upfront. Read every session. Three problems they all share. Always-on injection. Every line enters context at session start, whether the current task touches it or not. Your build commands and your testing conventions and your migration notes all compete for the same slots. No relevance ranking. Models reliably follow ~150 distinct instructions. Claude Code's own system prompt takes ~50. Your CLAUDEmd effectively gets 100 usable slots. Past that, rules silently drop and you have no way to see which ones. No decay. The decision you made about pgvector 3 months ago still loads at the top of every session today, ranked equally with the ORM convention you set yesterday. Result at 1000 observations: 80% of your built-in memory becomes invisible. Files balloon. Adherence drops. You hit /compact and re-explain. agentmemory replaces the static file with retrieval. → BM25 over your raw observations → Vector similarity over embeddings → Knowledge graph traversal over entities and relations → RRF fusion ranks the top-k for the task in front of you, not the union of everything you ever wrote down PostToolUse hooks record what the agent actually did during tool use. Ebbinghaus decay fades traces that stop getting hit. Storage stays local. Same 240 observations: → CLAUDEmd: 22,000+ tokens dumped at session start → agentmemory: 1,900 tokens, retrieved on demand → 92% fewer tokens, same recall 100% open source. Works with anything that speaks MCP, so Claude Code, Codex, Cursor, OpenClaw all plug in. github.com/rohitg00/agent… ♻️ Repost if you've watched a 200-line CLAUDEmd slowly lose the rules at the bottom.