No-Code Agentist

I built a trading desk with Hermes. Here's how you can do the same: Step 1: Install Hermes Go to https://t.co/TMDMsdTlwp Copy the one-line install command, paste it into your Hermes terminal, hit enter and pick quick setup. Step 2: Pick the brain It asks for a provider. I use OpenAI's Codex, sign in and pick your model. No Codex? Pick OpenRouter, pay-as-you-go, it works the same. Don't connect an Anthropic sub directly, using it outside their own tools risks a ban. Step 3: Put it in your pocket When it asks about messaging, press space on Telegram, enter. In Telegram: search BotFather, start, create new bot, name it. Copy the token, paste it into the installer. Search "userinfobot", copy your user ID, paste that in. Message your bot "hello", it answers, you've got an agent living in Telegram. Step 4: Give it a backtesting engine Make a free account at https://t.co/5RHbHHK0js, click My Accounts, copy the line of code. Paste it to your bot with: "Install this MCP server for trader dev into Hermes so we can backtest trading strategies." Step 5: Build the desk Create a Telegram group, add your bot, make it admin and enable topics in the group settings. Then tell it: "Create these topics: Trend Following, Optimisers, Forward Testing. Trend Following codes and backtests trend strategies on any market. Optimisers pull community strategies from and hunt for better settings. Forward Testing journals every survivor on live data." Each topic is an office with its own quant. Step 6: Set the desk running Paste this into an office: "Research trend following concepts, build them as strategies and backtest each on BTCUSDT with fees included. Rules: 100+ trades, drawdown under 20%, profit factor above 1.1. Bin failures, log everything. Loop this every 15 minutes and add it to your self-improving system, the objective is building better and better strategies." Mine has run 7 days straight across gold, forex and crypto.

``` # SOP Prompt: Agent-Facing Product Descriptions on Shopify (End-to-End) Reusable prompt for running the full Catalog Mapping description swap on any Shopify store. Paste into an AI agent with Admin API access (Claude Code + Shopify CLI, or adapt for manual execution). Derived from the TBG pilot 2026-08-27 (`clients/tacticalbabygear/agentic-pilot/`). --- You are implementing agent-facing product descriptions on a Shopify store: a metafield containing LLM-optimized text, mapped as the Shopify Catalog description source, so AI shopping assistants (ChatGPT, Gemini, Perplexity) read it instead of the human product page copy. Work through every phase in order. Never skip a verification step. Stop and ask before any store-wide switch. PHASE 0 — PREFLIGHT 1. Confirm Admin API access with one read query and one harmless write. Confirm the auth includes both read and write product scopes before starting. 2. Confirm the store appears in the Shopify Catalog: query the UCP global catalog (or search the Shop app) for 2-3 flagship products. Save the full records you get back — this is your before snapshot. Note what the served description currently says. 3. In Admin > Sales channels > Agentic > Shopify Catalog Mapping, record the current sources for title, description, and category, and the "N of N active products missing field" counters. Do not change anything yet. 4. Count active products. Identify exclusions: gift cards, $0 items, placeholder products. PHASE 1 — METAFIELD + SAFETY BACKFILL 5. Create a product metafield definition: namespace "agent", key "description", type multi_line_text_field. 6. CRITICAL — the mapping is all-or-nothing across the catalog. Before it is ever flipped, backfill EVERY active product's agent.description with a plain-text copy of its existing description (strip HTML to clean text). This makes the flip a no-op until each product is deliberately upgraded. Write in batches of 25 via metafieldsSet, log every write, then re-query every product and verify coverage. PHASE 2 — PULL SOURCE DATA 7. For every product, pull: title, description, product type, category, tags, price range, options/variants, and every content metafield the store has: spec tabs, includes/what's-in-the-box, sizing, compatibility, FAQ content, and review-app data (average rating, review count, top review texts). 8. VERIFY THE PULL: open 3 sample products and confirm specs, FAQ, and review data actually made it into your working data. A silently lossy pull produces plausible descriptions missing the facts that matter — this exact failure happened in the reference implementation. PHASE 3 — DRAFT (per product) 9. Write 100-250 words of plain text per product (simple products run shorter; never pad). Rules: - Every fact must come from the pulled data. Never invent specs, materials, certifications, or compatibility. Missing fact = omit the topic. - No adjectives without a number. "Rated 4.8 across 3,200 reviews," never "premium quality." - Structure in order, skipping unsupported topics: identity (what it is, who it's for, price, variants); specs and materials as short declarative sentences with dimensions/weights/capacities copied verbatim; what's included, with counts; compatibility and its limits, including "sold separately" caveats; honest fit boundaries (who it does NOT suit); aggregate review score plus the themes reviewers actually repeat; a closing purchase-terms sentence (returns window, shipping threshold, discounts) — and if the product's own copy states a warranty, it goes in that closing sentence, never body-only. - Label every measurement ("for babies at least 21 inches tall", not "minimum 21 in"). - Plain text paragraphs only: no markdown, headers, bullets, URLs, emoji, or app/vendor names. - Products with contradictory policies (e.g. final-sale items) get their own accurate terms sentence, never the default one. - Watch for source-copy debris: boilerplate pasted from other products, wrong-product FAQs. Use only facts that belong to this product, and log the debris for the merchant. PHASE 4 — ASK-THE-MODEL QA LOOP 10. Give a sample of finished records (one per product line, plus every flagship) to a fresh model with zero other context: "You are a shopping agent. Say what this product is and who it's for. Invent three realistic long-form buyer questions and answer them from this text only. Mark each ANSWERED or GAP. List every attribute a comparison-shopping agent would want that this text never states. Flag anything ambiguous or contradictory." 11. Triage the gaps into two lists: (a) facts that exist somewhere in the store's data — fix the descriptions and re-run the loop; (b) facts that exist nowhere — deliver as a merchant questionnaire (dimensions, care, country of origin, warranty, compatibility lists trapped in PDFs). Repeat until the sample stops producing type-(a) gaps. PHASE 5 — WRITE + FLIP 12. Write all final descriptions via metafieldsSet in batches, 0-error requirement, then independently re-query every product and verify byte-for-byte against your final set. 13. STOP: get the store owner to flip the mapping (Description source > the agent.description metafield) in the admin and confirm the save. Record the save time — it starts the propagation clock. PHASE 6 — VERIFY + MEASURE 14. Re-run the Phase 0 catalog queries at +2h, +24h, +48h. Diff the served descriptions against the before snapshot; confirm untouched control products didn't degrade. Record propagation lag. 15. While you're in the served records, check for cross-store clustering: other sellers' listings (including knockoffs) can share your product's catalog record. Report any to the merchant. 16. Ongoing measure: AI-referrer sessions (https://t.co/1L4uhGQ4Ew, https://t.co/rYTO5O6LSK, https://t.co/YQ9NFakdbb) and their conversion rate vs site baseline, revenue from store orders. Re-run Phases 2-5 for new or changed products on a recurring sweep. ROLLBACK: revert the mapping dropdown to Product description (instant), and/or delete the agent namespace metafields. All writes are additive; nothing touches the theme or the human-facing PDP. ````

https://t.co/9QCLyCiSwf

https://t.co/v0bT2xijxy

Six days ago, running a multi-agent team with Grok Bot cost $200 a month. Today, the entry barrier collapsed to $20. A 10x price drop in a single week. While the rest of the industry waits for AI to get cheaper, elite builders are already deploying five specialized, autonomous workers for the price of a daily coffee: 1. Scribe (Inbox Operations): Handles cold outreach and email triage. One focused job, zero room to hallucinate or wander off-task. 2. Hemingway (Content Engine): Drafts high-converting video frameworks and post outlines with a dedicated memory schema. 3. Seeker (Market Radar): Monitors X 24/7 and filters noise, bringing back only high-signal market intelligence and trends. 4. Concierge (Automated Execution): Navigates Web UI and handles admin tasks directly where no API exists. 5. Bob (Production Deployer): Pushes clean updates straight to your website and codebase. The real breakthrough: In a single unified workflow, these five autonomous agents hand tasks directly to each other without human intervention. Nine high-value workflows configured in 15 minutes. Zero lines of code. Zero developers needed. Bookmark this setup now. The detailed agent handoff schema is broken down below.

AEO tactic we're trying: Replace the @Shopify Catalog product descriptions with a product metafield optimized for an LLM. Standard product descriptions are written for humans; this lets you fix that. This week on The Unofficial Shopify Podcast, Shopify's told us buyers now shop with long prompts like "hoodie for walking around SF on a windy summer day." The agent answering builds a comparison table. It can only compare facts you actually wrote down. His test, which anyone can run today: paste your product URL into ChatGPT, Gemini, or Claude and ask "what data would you need to understand this product?" It might surprise you how little it gets from your page. The Shopify fix: In Admin > Sales channels > Agentic > Shopify Catalog Mapping (it's in the right sidebar), you can swap the description source to a metafield. Your PDP keeps the human copy. Agents get the machine version. What goes in the agent version: no adjectives, just facts. Weight range, materials, certifications, what's in the box, who it fits and who it doesn't, "rated 4.9 of 5 across 7,000+ reviews" instead of "customers love it." 100-250 words, plain text. Then test the finished record with an LLM that has zero context. "You're a shopping agent. Answer these three buyer questions from this text only." Every gap it hits is a fact to add. Repeat until it stops guessing. We just shipped this on 200 products for a client. No conversion claims yet! We'll report back if it does anything.

✍️✍️✍️PI NETWORK NEWS: PI NETWORK UPDATE: SoloHost Expands Into Local AI & Developer Workflows Source: (PiCoreTeam) https://t.co/AXbvAzkYRo Pi Network has featured two new SoloHost apps on Pi Desktop: OpenClaw and Atlassian MCP Server. Released alongside Pi Node 0.6.2, these additions demonstrate an important expansion of Pi Desktop beyond blockchain infrastructure — toward self-hosted AI, automation, and developer utilities. 🔹 OpenClaw — Local AI Agent OpenClaw allows users to run an AI agent on their own computer, using either a local AI model or external models such as ChatGPT and Claude. When deployed through SoloHost, OpenClaw runs inside a container designed to reduce unnecessary access to the broader host computer. This provides a more contained environment compared with a direct installation, while users can still explicitly authorize access to services or resources when required. Importantly, if a cloud model is used, requests are still processed by the respective model provider. However, OpenClaw's memory is stored locally on the user's computer. Pi is also positioning OpenClaw alongside the previously available Hermes local AI agent, giving Pioneers different approaches to personal AI. 🔹 Atlassian MCP Server — AI + Jira The second addition targets developers and professional teams. The Model Context Protocol (MCP) provides a standardized way for compatible AI tools to communicate with external systems such as Jira. Running the Atlassian MCP Server through SoloHost allows users to operate their own local MCP server and connect it with MCP-compatible AI tools such as: • Cursor • Claude Desktop / Claude Code • Codex This creates a potential bridge between AI assistants → MCP → Jira, enabling more intelligent and automated workflow management. 🌐 Why This Matters for Pi The bigger picture is not simply two new apps. SoloHost is gradually turning Pi Desktop + Pi Node into a broader self-hosted computing platform. From: ⛓️ Blockchain infrastructure → 🤖 Local AI agents → 🔌 MCP integrations → 🛠️ Developer tools → 📦 Self-hosted applications With 420,000+ Pi Node runners, Pi has a large distributed computing community that could potentially provide a significant user base for developers building self-hosted applications. ⚠️ Important: SoloHost remains in beta and uses an open, permissionless publisher flow. Users should independently evaluate each package and install applications at their own risk. The strategic direction is interesting: Pi Node is no longer only about running blockchain infrastructure. Pi Desktop + SoloHost could become a gateway for Pioneers to operate AI and other self-hosted services on their own machines. @PiCoreTeam #PiNetwork #PiNode #PiDesktop #SoloHost #OpenClaw #MCP #AI #Jira @lurima_pi @daoviet1983 @Nguyet888888 @Phuocdientyphu ✍️✍️✍️

𝗔 𝗰𝗵𝗲𝗮𝗽 𝗺𝗼𝗱𝗲𝗹 𝗰𝗮𝗻 𝗯𝗲𝗰𝗼𝗺𝗲 𝗲𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲 𝗶𝗳 𝗶𝘁 𝗰𝗮𝗹𝗹𝘀 𝗽𝗮𝗶𝗱 𝘁𝗼𝗼𝗹𝘀 𝗰𝗮𝗿𝗲𝗹𝗲𝘀𝘀𝗹𝘆. https://t.co/YpD1NBsBFD workflows can combine model tokens with external tools such as search and Web3 actions. Those tools can have their own cost and rate limits. A low-cost research model performs 150 searches because it never consolidates evidence. A more expensive model plans better, performs 12 searches and finishes with a lower total bill. 𝗧𝗵𝗲 𝗮𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 The important security move is to enforce limits outside the language model. Budgets, wallet permissions, approved tools and channel boundaries should live in configuration or code so a persuasive prompt cannot negotiate them away. 𝗧𝗵𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗯𝗼𝘂𝗻𝗱𝗮𝗿𝘆 Optimise cost per completed task, including tools, retries and downstream actions. Operationally, every autonomous action should leave a record: which agent initiated it, which model was used, what policy allowed it, what the estimated cost was and what actually happened. That turns autonomy into something finance and security teams can audit. Agent economics will increasingly be dominated by behaviour, not merely token prices. That is the Season 5 shift: the protocol feature is no longer the destination. It becomes one component inside a governed system that observes, decides, simulates, acts and then reconciles what really happened. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 That matters because autonomy compounds. One manual mistake affects one action; one scheduled or multi-agent mistake can repeat all night. The system therefore needs limits that scale with repetition: spending caps, tool permissions, channel boundaries, logs and explicit approval thresholds before automated work is allowed to become automated financial state. Those controls should live outside the model so a prompt cannot negotiate them away. Every autonomous action should also leave an audit trail showing which agent, model, tool, credential and policy produced the final effect. @BAI_AGI @justinsuntron #TRONEcoStar

https://t.co/cfw4OEp4kM

We’ve been studying what @grok and @xAI get right about making autonomous agents useful through simple, repeatable workflows. Today, we shipped our Hatcher-native take: Routines v2 + a unified automation policy system. Users can now: → Create agent routines through a simple no-code wizard → Describe the objective in plain language → Schedule work daily, on weekdays, weekly, or every few hours → Set timezone, freshness requirements and acceptance checks → Limit AI Credits and maximum runtime per execution → Require “draft first” review before results are finalized → Control outbound MCP and Operator actions globally → Test, pause, resume, archive and inspect routine history Every scheduled execution becomes a traceable Mission Control task, with idempotent scheduling, approval gates and safety policies enforced at execution time. No cron syntax. No complicated configuration. No hidden autonomous actions. Just useful agent automation with clear limits, visibility and human control. Routines v2 is now live in Hatcher: https://t.co/JwfSxgwmqr Built so anyone—not only developers—can put an AI agent to work. 🐣