wckdpegasus

INSTEAD OF SCROLLING ALL NIGHT. Spend 60 minutes with this instead. Obsidian + Browser-use = 24/7 Knowledge Ingest. Your assistant builds its own brain while you sleep. The people who implement this tonight will never wake up the same. Read it and Bookmark it now.

They just keep buying more. Long term holders now hold more Bitcoin than they did when it was at $27,000. From there, it pumped almost 5x. They have been straight up adding at the fastest pace ever since the bottom, without selling pretty much anything at all yet. Long term holders are showing more conviction over this short of a timespan, than at any other point in Bitcoins history. Probably something.

@TheRealPlanC Shared this a few years ago:

BTC monthly: First green Heikin Ashi candle after 5+ consecutive red candles. Add it to the list of signals that are pointing to a bottom. Note: Early in the month so can repaint.

BTC monthly: Traders Dynamic Index. Drop below 50, followed by a bullish cross. The list of bottom signals grows larger. 👀🤔❓

BTC weekly: RSI moves from below 36 to above 50. Another bottom signal? 👀🤔❓

BTC daily: RSI diagonal. Busting out! 👀🤔❓

BTC monthly: Another indicator signaling a bottom. ADX indicator signaling buyers back in control. Can repaint before months end. 👀🤔❓

BTC weekly: Internal trendline. 👀🤔❓

BTC weekly: If we are still in a "bear market", it will be the first bear market with 6 consecutive positive weeks. 👀🤔❓

New Bitcoin Floor Model! Simple and elegant. Equal-weighted average of BTC's lifetime SMA, single EMA, double EMA (DEMA), triple EMA (TEMA), and quadruple EMA (QEMA), then plot a ±10% band around that average. Upper band ×1.1 = $57,112 Lower band ×0.9 = $46,728

I've been testing this self-building Hermes agent for 37 days now. Here are the 10 best builds it has produced so far (on its own): 1. Autonomous Recovery Layer Hermes learned to detect stalled phases, route repairs, dedupe stale outputs, enforce semantic acceptance, and prove recovery with regression canaries. This helps harden a more durable loop. 2. Contract Verification Hardening The system got stricter about handoffs. Build plans now need clearer contracts, acceptable end states, verification paths, and closeout behaviour. This made autonomy less vague and much easier to trust. 3. Research Agent Full Completion Plan Hermes expanded the research substrate feeding Dreamer: browser enrichment, GitHub/package signals, community inputs, source balance, evals, and ops surfaces. Better thinking starts with better raw material. 4. Main Signal Review + Dreamer Advisory This added a feedback layer where Main can inspect what Dreamer is surfacing, pick out the better ideas, and nudge future walks. A big step toward making the subconscious less repetitive and more useful. 5. QA Audit Cockpit Hermes turned scattered QA receipts into an operator-facing cockpit. The goal was to make system quality visible enough that a human can trust, audit, and steer the loop without spelunking through state files. 6. Operational Leak Content Sublane Hermes learned to keep internal auto-build/research operations from leaking into content lanes, with classifier and canary coverage. Autonomy needs taste, not just output. 7. Foundation Hardening Not flashy, but essential. Runtime paths, shared state, migrations, test isolation, portability, retention behaviour. This is the kind of build that makes every future build less brittle. 8. Compounding Autonomy Hermes added receipts for learning over time using predictive signals, outcome health, proposal queues, level receipts, and early eval harnesses. The system started measuring whether it was actually getting better. 9. Local Model Load Reduction Hermes moved suitable wrapper work toward local model paths while keeping validation/reporting intact. Good autonomy should care about cost, latency, and operational load. 10. QA Audit Report Historically important because it proved the single-shot path: 1. Dreamer found the opportunity. 2. Main accepted it. 3. Coder built it. 4. Mercy verified it. Small artifact for important proof. The pattern is becoming clear: the best auto-build outputs are not just features. They are builds that improve the builder, such as recovery, verification, memory, signal quality, trust, routing, and taste. That is where the compounding starts. Next is to point it at a larger project scope and watch it build to completion.

Don't read another guide on how to master Claude Here's every feature on one page (with prompts): How to (actually) use Claude in 2026: 1. Stop opening Chat for everything 2. Download the Claude desktop app. 2. Pay for the monthly $20 plan. 3. Open the Cowork (it's the best). 4. You must create a "Cowork" folder on your PC. 5. To download my folder, go here: how-to-ai.guide. 6. Don't pay anything. It's free in the welcome email. 7. Then get my Notion with text files to download. 8. I made a guide for the next steps for Cowork. 9. Copy my entire setup: claude-co.work --- 1. Chat → just like ChatGPT. ☑ Select Opus 4.7 with Extended Thinking on. ☑ Sonnet for quick stuff. Opus for the hard ones. ☑ I barely use chat now. I only use Cowork. 2. Cowork → the only one that matters right now. ☑ Create 4 folders under one main folder 'Cowork': ☑ 'About me', 'templates', 'projects', 'Output. md' ☑ To download mine, go to how-to-ai.guide. ☑ Pay nothing. Get the link to download in the mail. 3. Projects → Cowork with memory baked in. ☑ One Project per recurring task. ☑ Drop your 5 best past outputs inside. ☑ Don't build one mega-Project for everything. 4. Artifacts → interactive files inside the chat. ☑ It builds calculators, trackers you can edit live. ☑ Try "Build a monthly budget calculator with rent.." ☑ Iterate: "Add dark mode." "Make column 3 wider." 5. Design → live HTML in your browser. ☑ Connect your Figma. It extracts your system. ☑ Faster than Figma for first drafts. ☑ Prompt: "Prototype a meditation app landing page. Calming colors, big CTA." 6. Excel + Word → add-ins that see your real cells. ☑ Insert → Get Add-ins → "Claude by Anthropic." ☑ It explains & edits. It doesn't click buttons. ☑ Prompt: "Why is cell B4 showing # REF? Trace the error." 7. Connectors → Claude inside your tools. ☑ Settings → Connectors → Browse → click "Add." ☑ Slack, Drive, Gamma, Granola. 50+ tools. Free. ☑ Prompt: "Find the Q3 sales deck in my Drive." 8. Plugins → pre-built skill packs for your role. ☑ Customize → Browse plugins → Install. Also free. ☑ Don't install all 11. Pick 2 that match your job. ☑ Type / to trigger: /draft-post, /build-dashboard. 9. Skills → teach Claude once, reuse forever. ☑ Type: "use skill-creator to build a skill for [task]." ☑ Trigger with /command: /linkedin, /contract. ☑ One good Skill replaces an entire SOP document. 10. Code → a revolution for developers. ☑ It writes, debugs & ships. No copy-pasting. ☑ Skip if you don't code. Cowork is Claude Code. ☑ Turn on "Auto accept edits" before you start. ♻️ Repost this image to use Claude better.

I can't believe I didn't find this sooner. This website offers 100+ MCP crypto servers ready for use now. Connect your AI to institutional-grade crypto research, AI agent trading platforms, real-time crypto price data, and way more. mcpservers.org/search?query=C…

ok this is a much bigger announcement than people realize in the last 5 days BOTH anthropic and openai have committed to deploying 1000s of engineers into 2000+ of the biggest companies in the world (bye bye consulting firms) but theres another story... this is the palantir playbook which saw its stock triple: > embed your engineers in different companies. > get them hooked on your product (aka chatgPT, claude) > congrats you now have a customer for life thats dependent on you forever. for every $1 companies spend on software they spend around $6 on services anthropic and OAI are going after this market and its worth ~$375 billion lol Openai is so confident they're guaranteeing a 17.5% return to the firms investing in this with them. this is how anthropic and openai are going to keep growing revenue to insane multiples. follow the money

@OpenAI So both OpenAI & Anthropic have now outsourced their sales teams to PE 👀 This is basically Palantir's FDE playbook on steroids. Full breakdown here: linas.substack.com/p/weeklyfintec…

We’ve also agreed to acquire Tomoro, which will bring 150 experienced Forward Deployed Engineers and Deployment Specialists to the OpenAI Deployment Company from day one.

Today we’re launching the OpenAI Deployment Company to help businesses build and deploy AI. It's majority-owned and controlled by OpenAI. It brings together 19 leading investment firms, consultancies, and system integrators to help organizations deploy frontier AI to production for business impact. openai.com/index/openai-l…

This works really well btw, at the end of your query ask your LLM to "structure your response as HTML", then view the generated file in your browser. I've also had some success asking the LLM to present its output as slideshows, etc. More generally, imo audio is the human-preferred input to AIs but vision (images/animations/video) is the preferred output from them. Around a ~third of our brains are a massively parallel processor dedicated to vision, it is the 10-lane superhighway of information into brain. As AI improves, I think we'll see a progression that takes advantage: 1) raw text (hard/effortful to read) 2) markdown (bold, italic, headings, tables, a bit easier on the eyes) <-- current default 3) HTML (still procedural with underlying code, but a lot more flexibility on the graphics, layout, even interactivity) <-- early but forming new good default ...4,5,6,... n) interactive neural videos/simulations Imo the extrapolation (though the technology doesn't exist just yet) ends in some kind of interactive videos generated directly by a diffusion neural net. Many open questions as to how exact/procedural "Software 1.0" artifacts (e.g. interactive simulations) may be woven together with neural artifacts (diffusion grids), but generally something in the direction of the recently viral x.com/zan2434/status… There are also improvements necessary and pending at the input. Audio nor text nor video alone are not enough, e.g. I feel a need to point/gesture to things on the screen, similar to all the things you would do with a person physically next to you and your computer screen. TLDR The input/output mind meld between humans and AIs is ongoing and there is a lot of work to do and significant progress to be made, way before jumping all the way into neuralink-esque BCIs and all that. For what's worth exploring at the current stage, hot tip try ask for HTML.