No-Code Agentist

Language is Code! Ask one Claude why signups dropped and the same Claude grades its own answer. Part 2 of Harness Engineering: give Claude Code a team of agents, including one whose only job is to disagree. Prompts included. https://t.co/vROBzKnOy3

I spent months duct-taping different agents and models together. Used countless different tools just so agents could talk to each other. Every model switch or reboot broke something. I found the solution: @Papercliping. Here's my agent company setup 👇 https://t.co/9aXG0HUtXX

$META $NVDA $MU $SNDK $LITE WHAT PEOPLE ARE GETTING MUSE TO DO A practical field guide to real, reported uses — from finding forgotten money to running background watchlists and building surprisingly ambitious creative projects. 64 distinct ideas across 7 areas. THE USEFUL PATTERN Give Muse a recurring job, connect the context it needs, and keep approvals on for money, messages, bookings, and other irreversible steps. SIX STRONG PLACES TO START High-leverage projects for someone already using AI for research, scheduling, and daily life. Build a morning intelligence loop: Combine the day's calendar, email follow-ups, a five-point market or industry brief, and an optional commute podcast. Try: "Every weekday morning, brief me on…" Run a subscription + refund sweep: Search statements and receipts for recurring charges, missed refunds, credits, and services worth canceling. Start with a review; approve each cancellation. Create an investment radar: Watch the assumptions behind a thesis — not just headlines — and report counter-evidence and the next signpost. Use a repeatable evidence template. Make travel self-monitoring: Track already-booked flights and hotels for price drops, check-in windows, disruptions, and claim eligibility. Set thresholds and require approval to rebook. Delegate the follow-through: Track returns, claims, reimbursements, and unanswered messages until the money lands or a human replies. The follow-up is often the highest-value step. Teach standing rules once: Give Muse your document standards, formatting preferences, meeting-prep template, or travel constraints. Persistent memory removes repeated briefing. MONEY Negotiate internet, cable, and phone bills (contested calls): Reported examples include AT&T Fiber at $80→$40/month plus three months free; AT&T at $80→$30 with doubled speed; Verizon at $516→$386; and Xfinity locked at $85.30/month for five years. Some attempts fail. Why it works: large recurring savings from one bounded project. Re-shop auto insurance every six months (self-reported): Users uploaded an existing policy, compared like-for-like quotes, and reported annual savings from $1,156 to $3,500; some scheduled repeat checks. Why it works: turns renewal inertia into a recurring review. Hunt state unclaimed-property databases (self-reported): Reported recoveries include $954 + $897 for a couple, more than $800 in about ten minutes, and $1,750 for a father-in-law. Why it works: searches many fragmented public databases in one pass. Run subscription archaeology: Search cards and receipts for unused or duplicated services, gift-card balances, and quiet renewals. Examples include $25/month Adobe savings and a forgotten $100/month Claude Max corporate-card charge. Why it works: finds costs that no single statement makes obvious. Chase refunds after the first "no" (self-reported): One user says a follow-up drafted by Muse turned an initially refused idle Google Cloud charge into a $114.14 waiver 39 minutes later. Others tracked retail returns until refunds landed. Why it works: persistent follow-up is the part people skip. File flight-delay and cancellation claims (self-reported): Reported outcomes include a $189.37 Alaska claim plus goodwill, $250 Delta credit plus rebooking, and $200 back, with receipts gathered and forms completed. Why it works: combines email evidence, forms, and follow-through. Monitor booked travel for lower prices (self-reported): Compare change fees against new prices for flights and hotels. Users reported about $600 saved in one case and roughly $1,026 in United credit in another. Why it works: finds value after the original purchase. Handle returns and reimbursement claims (site limits): Track Amazon or IKEA returns, vet and health-insurance bills, missing reimbursements, and supporting documents until they resolve. Why it works: keeps small claims from dying in the follow-up gap. Review life insurance and missing retirement money: Users report policy reviews, new auto quotes, and finding an old 401(k) dating to 1999. Why it works: surfaces long-dormant financial loose ends. Pay citations after checking the record: Look up a traffic citation or parking ticket, verify the amount, and complete payment with approval. Why it works: compresses lookup, validation, and payment into one flow. TRAVEL Plan and book a trip end to end: Research flights, hotels, trains, restaurants, rental cars, and family logistics. Reported examples include a six-city, two-week Italy trip and a Kalahari family trip from one voice prompt. Why it works: keeps decisions and bookings in one working context. Handle disruption while you are moving: During a Heathrow outage, Muse reportedly found an in-terminal hotel and prefilled the booking. Other users combined rebooking and compensation claims. Why it works: acts on constraints when opening ten tabs is least practical. Check in for an early flight while you sleep: One user had Muse handle a 4:15am flight check-in overnight. Why it works: a small, time-sensitive task becomes a background job. Brute-force fare combinations: One reported search ran 146 flight-price combinations across Spain and Italy dates to identify the lowest round trip. Why it works: agents tolerate the repetitive comparison humans abandon. Compare airport parking with rideshare: Evaluate total costs, apply available Groupons, and work backward to a leave-by time. Why it works: optimizes the whole door-to-gate plan, not one price. Renew passports and vehicle registration: Users report about 95% of a US passport renewal completed before handoff for SSN/photo, plus California DMV bookings, registration renewal, and calendar entries. Why it works: moves forms forward while preserving human-only handoffs. Use approval-safe checkout: Muse can pause before purchase and use Stripe Link one-time-use virtual cards; the merchant does not receive the real card number. Why it works: separates delegated shopping from final authorization. SHOPPING Turn a recipe or kitchen photo into a cart: Reported flows convert saved Instagram recipe reels, a fridge photo, or a whiteboard grocery list into Whole Foods or Sam's Club carts. Why it works: bridges inspiration, inventory, and checkout. Source, buy, track, and register a big item: One user had a garage freezer specified, ordered, delivery tracked, and product registered. Why it works: treats the purchase as a lifecycle, not a click. Shop across social posts and local sellers: Examples include a cake ordered from an Instagram shop with seller back-and-forth, a chocolate cake via DoorDash, and breakfast from a local bakery. Why it works: handles conversational buying that search engines miss. Run a Facebook Marketplace workflow: Search and compare listings, message and negotiate with sellers, arrange pickups, or list an item such as a bicycle and handle counter-offers. Why it works: combines discovery, messaging, and logistics. Watch stock or price and act at a threshold: Users report iPhone preorder stock watches, swim-class slot monitoring over two days, fare sweeps, and "buy when it drops" tasks. Why it works: checks repeatedly and only interrupts when something changes. Research a wardrobe from what you own: Use existing pieces, saved looks, style notes, and Instagram bookmarks to plan outfits or a wardrobe refresh. Why it works: makes recommendations from personal context rather than a generic catalog. Manage concert tickets after purchase: Reported uses include buying Ticketmaster tickets and creating StubHub resale listings, transferring tickets, and adjusting prices. Why it works: extends across purchase, resale, and transfer. Find the vibe, then check the practicalities: Use Instagram as a taste signal to discover bars or venues with a specific feel, then check reels, reservations, and logistics. Why it works: turns fuzzy aesthetic intent into an actionable shortlist. ADMIN Produce a daily inbox + calendar briefing: Summarize important mail, open reply obligations, missing attachments, tomorrow's calendar, and long threads — optionally as an audio briefing. Why it works: converts scattered inputs into one decision queue. Pull context before every meeting: Gather relevant Calendar, Mail, Notes, and files before client calls; flag conflicts and prepare follow-up drafts afterward. Why it works: makes preparation repeatable instead of heroic. Book doctor, dentist, barber, or jeweler visits (calls vary): Reported doctor admin combines search, email, voicemail, forms, confirmation, and calendar in about five minutes; a Barron's reviewer got three in-network Manhattan podiatry options. Why it works: collapses a multi-channel coordination task. Draft and send context-aware messages: Examples include job-offer declines, party invitations, school messages, contractor follow-ups with photos, and replies written in the user's tone. Why it works: drafts from the full thread instead of a pasted excerpt. Forward the right documents to the right person: Find K-1s for an accountant, route therapist invoices to insurance, or locate a missing receipt and attach it to a claim. Why it works: joins inbox search to a concrete handoff. Purge promotional email overnight: A reported overnight unsubscribe run cleared dozens of lists. Why it works: a tedious one-time cleanup is ideal background work. Run data-broker opt-outs (self-reported): One user reported roughly 90 removal requests, replacing a paid opt-out service. Why it works: repetitive forms become a monitored project. Build a move plan around tiny daily sessions: Create short packing blocks, track rooms, and prepare a first-night essentials bag. Why it works: turns a stressful project into bounded daily actions. Create a family operations briefing: Combine calendars, school communications, sports schedules, local news, and weather into a one-page morning summary. Why it works: makes invisible household coordination visible. Nag until done — and then stop: A user asked for reminders ten times in one day about a form; the reminders stopped once completion was confirmed. Why it works: persistence becomes useful when the stop condition is explicit. Teach permanent document rules: One user supplied accessible-document guidelines once; later Word documents and PowerPoints followed them automatically. Why it works: standing memory replaces repeated prompt engineering. WORK Run an investment thesis radar: Monitor Source → Fact → Thesis Variable → Interpretation → Counterargument → Next Signpost, prioritizing primary sources and adverse evidence. Why it works: tracks what could break the thesis, not headline volume. Send a scheduled pre-market summary: Deliver a concise market brief each morning before the open, tailored to a watchlist or thesis set. Why it works: makes recurring preparation arrive before it is requested. Connect research to trading through MCP (open tension): Public says its MCP can retrieve quotes, balances, portfolios, options chains and Greeks, and submit fractional, 24/5, multi-leg, OCO, OTO, or bracket orders with preflight checks. Why it works: unifies research and execution — but only with explicit controls. Build a lightweight CRM and prospecting engine: Reported projects include lead tiers, 50-account ICP prospecting, enriched CSVs, and outreach drafts. Why it works: turns an ambiguous pipeline into a reviewable operating list. Create an operations dashboard without coding: A non-technical shop owner reportedly built a delivery dashboard in an afternoon, including pipeline, packing checklists, and overdue alerts. Why it works: makes a custom tool cheaper than adapting a generic one. Benchmark trade confirmations against SPY: A simple custom tool can parse trade-confirmation emails and compare outcomes with a benchmark. Why it works: converts a noisy inbox record into an analytical view. Track missing tax documents and deadlines: One reported workflow paired a PODS move booked $576 under quote with a 2025 tax-document tracker that found missing K-1s. Why it works: persistent checklists catch the document that blocks filing. Build missing integrations on its own VM: Users report a TickTick API integration in five minutes, an unofficial https://t.co/asxbcaM7l4 sync, and workflows using Claude Code on a 2 vCPU / 8GB / 100GB VM. Why it works: the agent can extend itself when no connector exists. Run a store or small-business chief of staff: Coordinate Shopify, email, Asana, employees, meetings, files, and follow-ups; another user monitored Bay Area demo slots for a founder. Why it works: absorbs the coordination layer between specialist apps. LEARNING & CREATIVE Turn reading into a daily commute podcast: Schedule AI-news episodes or generate a researched podcast such as Meta's "World War I" example. Why it works: moves neglected reading into an easier format. Build a multi-month learning program: Reported curricula include quantum compilers, Sanskrit, piano repertoire, and Borges translations, with check-ins and oral exams. Why it works: combines a syllabus with accountability over time. Create a high-stakes practice app: Muse reportedly built a 270-question "Life in the UK" mock test from real sittings; the user's friend passed the exam. Why it works: turns source material into active recall, not a summary. Interview yourself by voice: One user ran a 50-question spoken self-reflection interview. Why it works: uses conversation to surface ideas that a blank page would not. Iterate on images conversationally: Start with a visual brief such as a mountain-and-sunrise mark, then revise details in chat; users also made profile images and animated work-status GIFs. Why it works: keeps art direction in the thread across iterations. Turn taste into an editorial collage: One user had Muse read Instagram follows for aesthetic signals and produce a New York Times-style collage. Why it works: translates implicit taste into a visible creative brief. Stress-test the sandbox with playful software: Gizmodo had Muse redraw a photo in MS Paint via line-by-line Python, compose a MIDI melody, then build side-scrolling and FPS games with the author as NPCs. Why it works: shows that playful prompts can reveal real tool depth. Research a giant playlist or content archive: Reported projects include reviewing 700+ podcast episodes for a Spotify "best of" playlist and building 30-day social content calendars. Why it works: sustained curation is a better fit for an agent than a single chat. Build a family-memory montage: From a screenshot request, one user had Muse locate a grandmother's Facebook video posts for a reunion montage. Why it works: connects an emotional brief to scattered source material. Turn years of posts into a life story: A user asked Muse to read Facebook history back to 2008 and synthesize a personal narrative. Why it works: makes a large personal archive legible. Build a kids' savings bank: One reported app tracked cash deposits, 3% weekly interest, saving missions, and a passbook trail. Why it works: a custom tool makes an abstract lesson tangible. Use a camera for form feedback: A reported gym demo had Muse inspect an MMA clip and point out that the subject had given up. Why it works: adds a candid second observer to practice review. AUTOMATION Keep working after the app closes: Long-running tasks can continue in the background and return when something changes or approval is needed; goals keep the work organized. Why it works: the user manages exceptions, not every step. Wait on hold and hand the call back (open/contested): Reported call flows navigate phone trees, hold for an agent, then patch the user in for verification. The feature is described as beta and not enabled for everyone. Why it works: outsources dead time while preserving the human checkpoint. Monitor family deadlines across apps: Users report four-child sports pushes and a 12-hour school-tryout watch that alerted a parent before a flight, four hours before deadline. Why it works: crosses email and messaging silos before deadlines disappear. Link health signals with spending and meals: A reported digest combined Apple Health glucose, Plaid restaurant spend, and DoorDash email to identify meals that moved blood sugar. Why it works: finds patterns across sources that rarely meet. Route and reorder medication (verify medical details): Reported workflows find lower prices, contact the pharmacy to route a prescription, place the order, and reorder before it runs out. Why it works: turns refill timing into a monitored process. Bring memory from other assistants: Users report being prompted to import context from other AI platforms; recurring preferences and rules can then be reused without re-briefing. Why it works: preserves accumulated context when changing tools. Meet Muse on newer surfaces (preview/upcoming): Meta announced or previewed voice on Meta AI glasses, custom video-chat avatars, Plaid, Notion, Granola, GitHub, Box, a Mac app, and the Muse Charm keychain wearable said to ship in December. Why it works: moves the same agent from app to voice, desktop, and wearable contexts.

𝗕𝗔𝗜𝗰𝗹𝗮𝘄 𝗜𝘀 𝗧𝘂𝗿𝗻𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁𝘀 𝗜𝗻𝘁𝗼 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗠𝗮𝗰𝗵𝗶𝗻𝗲𝘀, 𝗡𝗼𝘁 𝗝𝘂𝘀𝘁 𝗖𝗵𝗮𝘁 𝗪𝗶𝗻𝗱𝗼𝘄𝘀 The real test of an AI agent is what happens after the conversation ends. A chatbot can answer a question and wait for another prompt. A working agent needs to remember context, use tools, communicate through external channels, execute scheduled tasks, and hand different parts of a problem to specialized agents. That is the direction @BAI_AGI is taking with BAIclaw. Current documentation describes it as a desktop application built on OpenClaw and ClawX, with multiple specialized agents, independent prompts and context memory, channel integrations, installable skills, and scheduled workflows. The interface is deliberately graphical, removing the requirement to configure everything through command-line operations or configuration files. The multi-agent architecture is especially important. Instead of forcing one general-purpose agent to carry every responsibility, BAIclaw allows separate agents to be configured for different tasks and switched through the interface. One agent can concentrate on research, another can handle code, another can operate a particular workflow, while the surrounding system provides the channels and skills that let them actually perform those jobs. Then the scheduled-task layer changes the time dimension. An agent does not have to wait for a human to reopen the application and issue another instruction. https://t.co/QDpvXFMAEt documents automated workflows with configurable triggers and intervals, allowing agents to execute recurring tasks. That turns the product from a place where people ask AI questions into an environment where AI can repeatedly perform defined operations. The skill system adds another layer. BAIclaw provides a graphical skill marketplace and built-in capabilities for areas such as document processing and search, while its broader Web3 skill environment can connect the agent to on-chain operations. The result is a modular architecture: models provide intelligence, agents organize that intelligence, skills provide capabilities, channels provide reach, schedules provide persistence, and the Agent Wallet provides an economic execution path. That combination is where @BAI_AGI becomes more than another interface around an LLM. The interesting unit is no longer the prompt. It is the completed workflow. @justinsuntron #TRONEcoStar

The model gets the headlines. The harness decides whether it can actually work. Context. Tools. Permissions. Recovery. Evals. Routing. Cost. As models keep changing, the system around them may become the more durable product. New article ↓ https://t.co/JnR3DlE88S

Nova AI is the no-code layer. You describe the agent. Nova turns that prompt into versioned, testable code you can deploy. Support bots, research agents, treasury workflows built without writing smart contracts. Verifiable generation is the difference between a demo @ama_protocol