hania

[hania @Henk40238785]

وضعي مع اخوي 🥹😭

https://t.co/RtCYqrmXMt

[hania @Henk40238785]

افهم شعوري من حركاتي 🤗

https://t.co/RtCYqrmXMt

[hania @Henk40238785]

اذا افتح لك طيزي ايش تدخل فيها ؟

رد

https://t.co/RtCYqrmXMt https://t.co/udI5RLX1zq

[hania @Henk40238785]

وضعي مع خواي 😭

https://t.co/RtCYqrmXMt

[hania @Henk40238785]

@moltbot_builds This is underrated. The potential for personalized AI assistants is huge.

[MoltCraftsman @moltbot_builds]

Turn OpenClaw into your "second brain" iPhone. Here's how to build an agentic layer for command & control.

1. Core: Install OpenClaw per the official docs. Get the base system running. This is your agent OS.

2. Agentic Layer: Define your agent roles. Examples: 'Summarizer', 'Task Manager', 'Calendar'. Each agent needs a clear, single responsibility.

3. Communication: Use message queues (RabbitMQ, Redis Pub/Sub) for agent communication. This decouples agents, prevents blocking.

4. Orchestration: Implement a central orchestrator agent. This agent receives user input, routes tasks to specific agents. Think of it as the iOS home screen.

5. Input Handling: The orchestrator needs to parse natural language. Use GPT-5.4 Thinking or Gemini 3.1 for intent recognition. Example prompt: "Determine which agent is best suited for this task: [user query]"

6. Context Management: Use a vector database (Pinecone, Weaviate) to store agent memories and context. Each agent can query the database for relevant information.

7. Tooling: Equip each agent with specific tools. Summarizer gets a web scraping tool. Task Manager gets a task list API. Calendar agent gets calendar read/write access.

8. Feedback Loop: Implement a feedback mechanism. Agents report success/failure. Orchestrator learns optimal routing over time. Use a simple reward system (e.g., +1 for success, -1 for failure).

9. Monitoring: Track agent performance. Log errors. Identify bottlenecks. Grafana + Prometheus are your friends.

10. Iteration: This is not a one-time setup. Continuously refine agent roles, improve tool integrations, optimize orchestration logic. Start simple, iterate rapidly.

OpenClaw as the base, agentic layer handles everything else. What tools are you giving your agents?

[Science girl @sciencegirl]

The crystal clear waters of Meghalaya, India

📹theseeker.jpg

[MoltCraftsman @moltbot_builds]

I turned 2 pages of quant formulas into a Polymarket bot using OpenClaw in under an hour.

Not $150/day passive income bait. Actual architecture you can steal:

1. Document ingestion
OpenClaw's file parser extracts formulas from PDFs/images. Feed it your strategy docs with: "Parse these into executable trading logic. Output as Python functions with clear inputs/outputs."

2. Market data pipeline
Polymarket's API is public. OpenClaw writes the connector:
- GET /markets for active markets
- GET /prices for real-time odds
- Webhook listener for price movements

3. Signal generation
Your quant formulas become the decision engine. Mine calculates expected value: (probability estimate × payout) - (1 - probability estimate) × stake. When EV exceeds threshold, it flags the trade.

4. Execution layer
OpenClaw scaffolds the trade execution. Connect your wallet via ethers.js. Set position limits. Build in cooldowns between trades.

5. Notification system
Telegram bot integration took 3 prompts. Every signal, every execution, every daily P&L summary hits my phone.

The real alpha isn't the formulas. It's having an agent that can turn ANY written strategy into running code.

I gave OpenClaw my edge. It gave me back infrastructure.

What trading thesis are you sitting on that could be automated?

[Upside @upside]

Fill up your cart for less with Upside: Earn cash back on grocery purchasesFill up your cart for less with Upside: Earn cash back on grocery purchases.

[MoltCraftsman @moltbot_builds]

How I use https://t.co/Okjigvmh6t for project management without any PM tool.

No Asana. No https://t.co/HosUDXMn41. No Jira. Just my AI agent and a simple kanban.

The setup:

https://t.co/Okjigvmh6t has a built-in command centre — a simple web UI running locally that shows tasks in columns: Backlog, In Progress, Done.

But the magic isn't the board. It's how the agent interacts with it.

How I add tasks:
I just tell my agent:
"Add to backlog: Write the Q1 strategy doc"
"New task: Review contractor invoices by Friday"
"Remind me: Follow up with Dan about the partnership"

It adds them to the kanban. Tags with priority. Sets deadlines.

How tasks get worked on:

When I drag a task to "In Progress" (or tell the agent "start working on the Q1 doc"), something cool happens:

The agent automatically spawns a sub-agent to handle it.

Research task? Sonnet sub-agent starts researching.
Writing task? Sub-agent creates a first draft.
Code task? Codex sub-agent starts building.

I keep chatting with my main agent while the sub-agent works.

How tasks get completed:

Sub-agent finishes → Main agent reviews → Moves to Done → Notifies me on Telegram: "Q1 strategy doc draft complete. Saved to ~/work/q1-strategy.md. Want me to review it?"

Daily tracking:

Every heartbeat, the agent checks:
- How many tasks completed today?
- Are we below starting count? (goal: end with fewer tasks than we started)
- Any blockers?
- Any tasks stuck for >24 hours?

End-of-day: "Today: 5 tasks completed, 2 added. Net reduction: 3. You're on track."

Why this works better than traditional PM tools:

1. Zero friction to add tasks — Just speak naturally
2. Tasks get worked on automatically — Not just tracked
3. Context is preserved — Agent knows WHY you added each task
4. Proactive — Agent reminds you about deadlines, suggests priorities
5. Integrated — Tasks connect to your email, calendar, files

The overhead of managing a PM tool often exceeds the value it provides. This system has zero overhead because the agent IS the PM.

[MoltCraftsman @moltbot_builds]

How to give your https://t.co/Okjigvmh6t agent memory that actually works across conversations.

The biggest limitation of ChatGPT and Claude: Every conversation starts from zero. Your AI forgets everything the moment you close the tab.

https://t.co/Okjigvmh6t solves this with a three-layer memory system. Here's how to set it up:

**Layer 1: Daily Notes (memory/YYYY-MM-DD.md)**
What: Raw log of everything that happens each day.
Why: Your agent writes down conversations, decisions, tasks, and events as they happen.
How: Automatic. Your agent creates these files on its own.
Analogy: A journal.

**Layer 2: Long-term Memory (https://t.co/GCne42eQU9)**
What: Curated insights, preferences, and lessons learned.
Why: The distilled essence of what matters. Not raw logs — synthesised knowledge.
How: Your agent periodically reviews daily notes and updates https://t.co/GCne42eQU9 with what's worth keeping.
Analogy: Your brain's long-term memory.

**Layer 3: Knowledge Graph (entities/)**
What: Structured facts about people, companies, and projects.
Why: Quick-reference information that stays current.
How: Auto-extracted from conversations. When you mention someone's new job, their entity file updates.
Analogy: A personal CRM.

Example of the layers working together:

You tell your agent: "Mark just got promoted to VP at Stripe."

Daily note captures the raw conversation.
Knowledge graph updates Mark's entity: status changed from "Director" to "VP", company: Stripe, date: today.
Long-term memory notes: "Mark — now VP at Stripe. Strong relationship, might be useful for payments partnership."

Next week you ask: "What's Mark's role now?" → Instant answer from the knowledge graph.

A month later you're discussing partnerships: Agent proactively mentions "Mark at Stripe is VP now — could be a payments integration lead."

The memory cost is almost nothing — text files and a cheap embedding model for semantic search.

Setup time: 30 minutes. Value: Permanent context that makes your agent smarter every single day.

[Imperio Estoico. @ImperioEstoico]

¡Dios mío! ¡Tengo que tener una de estas camas nido para perros ya! 🤣

[Earth @earthcurated]

Ocean Therapy �

[Thrasher Magazine @thrashermag]

🗣️ Tony Hawk congratulates David Loy on his new part, 20 years on Birdhouse, and sobriety 👏🏽

[Contextual AI @ContextualAI]

Announcing Agent Composer: AI that works when it actually IS rocket science.

Technical teams can now automate the routine (but complex) tasks that used to take hours every week—root cause analysis, production planning, test code generation— and reduce them down to minutes, so you can get your real work done.

Here's how we solved the AI context problem for hard engineering tasks (like rocket science 🚀)

🧵

[NFL @NFL]

3 possessions, 3 touchdowns for the @dallascowboys

DALvsWAS on @Netflix

[Hamnet @hamnetmovie]

Nominated for 8 Academy Awards, including Best Picture of the Year.

HAMNET is in theaters everywhere now.

[Narendra Modi @narendramodi]

പിഎം സ്വനിധി പദ്ധതിക്കുകീഴിലുള്ള ക്രെഡിറ്റ് കാർഡ് വിതരണവും വായ്പ നൽകലും, ജനങ്ങളെ ശാക്തീകരിക്കുന്നതിനും മെച്ചപ്പെട്ട ജീവിതനിലവാരം ഉറപ്പാക്കുന്നതിനുമുള്ള ഞങ്ങളുടെ ശ്രമങ്ങൾക്കു കൂടുതൽ കരുത്തേകുന്നു.

[NFL @NFL]

Mutual respect between young QBs 🤝

[Spot & Tango @spotandtango]

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