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OpenClaw meets RL! Most agents evolve via prompt tricks and markdown hacks. MetaClaw updates actual model weights from every failed interaction. Everything happens on the fly, without any dataset or code changes. GitHub:

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Akshay 🚀

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17,326 Aufrufe • vor 4 Monaten •via X (Twitter)

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1/ Happy to share UniDisc - Unified Multimodal Discrete Diffusion – We train a 1.5 billion parameter transformer model from scratch on 250 million image/caption pairs using a **discrete diffusion objective**. Our model has all the benefits of diffusion models but now in multimodal space! - flexible compute-quality tradeoff, zero-shot inpainting and editing, better control via classifier-free guidance and lower latency! We open source everything - our code, weights and the training dataset.

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THIS DEVELOPER USED OPENCLAW AGENTS TO RUN HIS B2B BUSINESS VIA TELEGRAM AND MADE $15,000/MONTH he doesn't write prompts from scratch or use generic browser interfaces. he runs a multi-agent framework through a mobile chat. the agents write code, test deployments, and update sites in real-time while he just hits approve the setup is straightforward: - spin up Coolify on a free cloud instance to host your own self-hosted agent panels - link the agent loop to a Telegram gateway to approve code edits from your phone - deploy specialized skill files directly to limit token waste and context decay - containerize the terminal execution using Docker to prevent security breaches if you are still running local agents without container safety, you are leaving money on the table. read the 30-day battle between OpenClaw and Hermes Agent to see who actually wins in production Full breakdown and migration playbook ↓

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Gipp 🦅

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Sudo su

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this OpenClaw bot watches NASA wildfire satellites. when a fire starts, it finds every at-risk home nearby, renders fire hardening fireproofing upgrade on their actual house, and mails them a postcard, all on autopilot. here's how contractors in fire zones can close $30k–$60k fireproofing jobs with this: - pulls live fire detections from NASA FIRMS satellites every 5 minutes - finds every home in the fire perimeter from public records - captures the property via Google Street View - classifies roof type + fire vulnerabilities using AI vision - generates a NASA satellite image of the neighborhood with real fire hotspots overlaid - renders a fire hardening retrofit on their actual house - prints a postcard with the NASA fire map + before/after retrofit + QR code every step from fire detection to mailbox runs without a human reply "FIRE" + RT and i'll send you the full guide so you can build this too (must be following so i can DM)

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agenc //: 👾 updates • training started on the first agenc ai model. fully open source, open weights. first model will be done training in a few days. • grok 4.20 works flawlessly on the new agenc runtime. agenc was built around the xai api and runs best with grok agents. • runtime got ripped out and replaced from scratch. agentic loop is rock solid. pushing to git soon. benchmarks against the best frameworks out there will follow. • marketplace goes live on mainnet solana the moment it's wired into the new runtime. plug your agents in, make money. • locked another 290M AgenC bringing the total locked to ~360M. • agencone hardware device entering mass production now that the runtime and marketplace are done. • custom mini pcs in the works. agenc and a custom linux preloaded. think openclaw on a macmini but you just order the box. anime art on the case. not apple hardware. • still in the pumpfun hackathon, they've got winners left to announce. love you pumpfun. when agenc. 5yC9BM8KUsJTPbWPLfA2N8qH1s9V8DQ3Vcw1G6Jdpump

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Seedance 2 just dropped. This was my first test and it changed everything. Perfect lip sync. Realistic movement. Multi-shot support. Natural voice completely human, zero robotic tone. And the SFX? It nailed the sound effects automatically. Every detail synced perfectly. This is a game-changer. From now on, automation isn't theory it's real. And yes, it's happening on Crafft. With a high-fidelity model like this + the right prompting rules and skills, you can generate endless variations that work every single time. Crafft agents will handle the whole process. One model, infinite outputs. You can try Seedance 2 on CapCut right now. RT + comment "PROMPT" and I'll send you the exact setup (follow so I can DM)

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Plasma One has the rare referral deal where both people get paid, not just the one sharing the link Someone joins with your access code, spends $100 on their card and you both get $10 Access Code: W6TRRM Not you get $10 and they get a discount Actual $10 each, in dollars, straight to the balance And it is not a one time thing on your end - every new person you bring who hits that $100 earns you another $10 The spend is real card spend, the stuff they were buying anyway, so most people clear it without changing a thing iOS or Android, both sides win Drop your username or grab a code and start stacking them
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Plasma One has the rare referral deal where both people get paid, not just the one sharing the link Someone joins with your access code, spends $100 on their card and you both get $10 Access Code: W6TRRM Not you get $10 and they get a discount Actual $10 each, in dollars, straight to the balance And it is not a one time thing on your end - every new person you bring who hits that $100 earns you another $10 The spend is real card spend, the stuff they were buying anyway, so most people clear it without changing a thing iOS or Android, both sides win Drop your username or grab a code and start stacking them

Valentin

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OpenClaw, but built for normal people. Sim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI. Key features: - Free and open-source (Apache 2.0) - Vector store integration for RAG-grounded agents - Self-host with one command (`npx simstudio`) - Run fully local with Ollama, no API keys needed - Supports vLLM for production-grade self-hosted inference The thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows. Everything is visible on the canvas, so you know exactly what your agent is doing at every step. And you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw. I've shared the link to Sim's GitHub repo in the next tweet.
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OpenClaw, but built for normal people. Sim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI. Key features: - Free and open-source (Apache 2.0) - Vector store integration for RAG-grounded agents - Self-host with one command (`npx simstudio`) - Run fully local with Ollama, no API keys needed - Supports vLLM for production-grade self-hosted inference The thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows. Everything is visible on the canvas, so you know exactly what your agent is doing at every step. And you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw. I've shared the link to Sim's GitHub repo in the next tweet.

Akshay 🚀

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A 34-YEAR-OLD FROM CHINA JUST MAPPED THE 500+ BUSINESSES HE CONTROLS INTO ONE LIVE OBSIDIAN GRAPH THAT UPDATES EVERY TIME A TRANSACTION HAPPENS ACROSS HIS NETWORK 00:14 one central node representing him as the operator, dozens of primary radials for each business vertical, thousands of secondary connections showing supplier and customer overlaps every business in the network is a note in the vault. every deal, every invoice, every shared employee, every joint asset connects them. Obsidian's force-directed layout renders the whole map in real time the setup pulls live data from his internal ERP into the vault as markdown files. every transaction creates or updates a link. every new business added rewires the graph automatically he uses it for one thing no spreadsheet or CRM can do at this scale. seeing which businesses are actually independent and which ones share so many dependencies that they should be merged or spun off most people run 500 businesses across three CRMs, five spreadsheets, and eleven WhatsApp groups. he runs 500 businesses inside one Obsidian vault save this before every serious operator realizes their business network was already a knowledge graph waiting to be visualized 👇

NO1ennn

448,234 Aufrufe • vor 23 Tagen

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This free model just beat every closed-source AI on coding benchmarks. Open weights. 6x cheaper than Opus. The labs don't want you to know it exists. > GLM-5.2 from Zai just topped Code Arena - the first open-weights model to ever hold the #1 coding spot. Not a leaked weight, not a fine-tune. A fully open model beating GPT and Claude on their own turf. It's live on Hugging Face Inference API right now. 5 providers: Novita, Together AI, Fireworks, Deepinfra, Zai. OpenAI-compatible client. → Go to huggingface(.)co → grab HF_TOKEN from account settings → plug into any OpenAI-compatible client 6x cheaper than Opus. Companies bleeding on AI bills are already routing to this for orchestration, caching, and token optimization. The smart money moved before the headline dropped. > Now you know. huggingface(.)co/zai-org/GLM-5.2 Bookmark this before everyone else figures it out.

Atenov int.

12,395 Aufrufe • vor 1 Monat

A TEAM JUST DEPLOYED 15 AUTONOMOUS LOOP AGENTS FROM A SINGLE PROMPT USING APPLIED GRAPH ENGINEERING Most developers still manually hardcode multi agent systems, writing separate logic for every individual task. Graph engineering changes this by using a central topological map to spin up all 15 nodes simultaneously. A single 200 word input generates the architecture, routing 120 unique pathways between agents instantly. Instead of failing under conflicting instructions, these loop agents self correct via continuous state sharing. Managing a 15 node mesh requires high token throughput, making this dependent on strict low-] latency API tiers. See exactly how this automated multi agent graph architecture actually operates in real time ↓
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A TEAM JUST DEPLOYED 15 AUTONOMOUS LOOP AGENTS FROM A SINGLE PROMPT USING APPLIED GRAPH ENGINEERING Most developers still manually hardcode multi agent systems, writing separate logic for every individual task. Graph engineering changes this by using a central topological map to spin up all 15 nodes simultaneously. A single 200 word input generates the architecture, routing 120 unique pathways between agents instantly. Instead of failing under conflicting instructions, these loop agents self correct via continuous state sharing. Managing a 15 node mesh requires high token throughput, making this dependent on strict low-] latency API tiers. See exactly how this automated multi agent graph architecture actually operates in real time ↓

slash1s

299,812 Aufrufe • vor 13 Tagen

I stack Hermes agents with OpenClaw for financial research, and the results should be illegal. I track every politician, insider trader, and I know EXACTLY what moves they're making. If you can't beat them, join them. The exact playbook for printing money from insider trading (copy me): Requirements: • OpenClaw setup • Hermes Agent setup Step 1. Define your research thesis Before you send any prompts to either tool, you'll need to clarify exactly what you're trying to research. This could be: a specific industry, asset class, market sector, and so on. Examples: • Tracking smart money buys in the semiconductor industry • Tracking smart money buys in crypto • Tracking a specific politician and where they're bidding (like Nancy Pelosi) Step 2. Deploy Hermes agents to track the smart money (in parallel) Hermes is your data layer. Spin up 5 agents at the same time, each with one job: Agent 1: Track every politician's disclosed trades from the last 30 days (House and Senate stock disclosures) Agent 2: Pull insider transactions (Form 4 filings, CEO/CFO buys and sells) Agent 3: Scrape X sentiment from top 50 accounts on the topic Agent 4: Pull on-chain data (whale wallets, TVL, exchange flows) *if applicable* Agent 5: Monitor news, regulatory filings, and announcements from the last 30 days Each agent runs independently. You're not waiting for one to finish before the next starts. Step 3. Consolidate the output Once your Hermes agents finish, dump every output into a single document. (don't filter or summarize) - you want OpenClaw to see the raw data. Step 4. Feed it all into OpenClaw Open OpenClaw and paste the consolidated research file with this prompt: "Act as an elite macro analyst. Below is raw data gathered from multiple sources on [thesis], including politician disclosures and insider transactions. Synthesize the findings, identify the strongest signals and contradictions, flag any unusual smart-money activity, and give me a clear directional view with conviction levels. Flag any data gaps that need follow-up." OpenClaw will go deep, run its own reasoning chain, and produce a synthesized report. Done. Now you're literally tapping into the financial data they don't want you to see (it's all public - you just had to find it). Make sure to save this playbook so you don't lose it!
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I stack Hermes agents with OpenClaw for financial research, and the results should be illegal. I track every politician, insider trader, and I know EXACTLY what moves they're making. If you can't beat them, join them. The exact playbook for printing money from insider trading (copy me): Requirements: • OpenClaw setup • Hermes Agent setup Step 1. Define your research thesis Before you send any prompts to either tool, you'll need to clarify exactly what you're trying to research. This could be: a specific industry, asset class, market sector, and so on. Examples: • Tracking smart money buys in the semiconductor industry • Tracking smart money buys in crypto • Tracking a specific politician and where they're bidding (like Nancy Pelosi) Step 2. Deploy Hermes agents to track the smart money (in parallel) Hermes is your data layer. Spin up 5 agents at the same time, each with one job: Agent 1: Track every politician's disclosed trades from the last 30 days (House and Senate stock disclosures) Agent 2: Pull insider transactions (Form 4 filings, CEO/CFO buys and sells) Agent 3: Scrape X sentiment from top 50 accounts on the topic Agent 4: Pull on-chain data (whale wallets, TVL, exchange flows) *if applicable* Agent 5: Monitor news, regulatory filings, and announcements from the last 30 days Each agent runs independently. You're not waiting for one to finish before the next starts. Step 3. Consolidate the output Once your Hermes agents finish, dump every output into a single document. (don't filter or summarize) - you want OpenClaw to see the raw data. Step 4. Feed it all into OpenClaw Open OpenClaw and paste the consolidated research file with this prompt: "Act as an elite macro analyst. Below is raw data gathered from multiple sources on [thesis], including politician disclosures and insider transactions. Synthesize the findings, identify the strongest signals and contradictions, flag any unusual smart-money activity, and give me a clear directional view with conviction levels. Flag any data gaps that need follow-up." OpenClaw will go deep, run its own reasoning chain, and produce a synthesized report. Done. Now you're literally tapping into the financial data they don't want you to see (it's all public - you just had to find it). Make sure to save this playbook so you don't lose it!

Miles Deutscher

19,955 Aufrufe • vor 2 Monaten

I decided to share part of a prompt you can use to research any protocol in seconds using INFINIT Intelligence by INFINIT. As an example, I used Silo Labs, where I currently farm most of my stablecoin yields. 🔖 Bookmark this + read until the end for a bonus. Just replace [PROTOCOL NAME] with any protocol you want to research. Prompt: "Conduct thorough research on [PROTOCOL NAME] and answer the following questions: - What is the project building? - What problem does it solve, and for whom? - What makes it different from others? - What blockchain is it built on? - Is there a token? What’s its purpose? - How does the protocol work technically? - How does it make money or sustain itself? - What’s the staking model, emissions, burns, and treasury? - Who are the founders, and is the team public and credible? - Who funded it? Who holds most tokens? - Are users, TVL, and volume growing? - How strong is the ecosystem around it? - Who are the main competitors, and how does it compare? - Is the protocol audited? Any past hacks? - What are the best ways to use the protocol (strategies)? - What’s coming next? Key milestones or launches? - What are the biggest risks? - How strong is the community and social traction?" Bonus: Quote this post with a reason why you like INFINIT, and I’ll DM you the full version of the prompt within the next 24 hours.
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I decided to share part of a prompt you can use to research any protocol in seconds using INFINIT Intelligence by INFINIT. As an example, I used Silo Labs, where I currently farm most of my stablecoin yields. 🔖 Bookmark this + read until the end for a bonus. Just replace [PROTOCOL NAME] with any protocol you want to research. Prompt: "Conduct thorough research on [PROTOCOL NAME] and answer the following questions: - What is the project building? - What problem does it solve, and for whom? - What makes it different from others? - What blockchain is it built on? - Is there a token? What’s its purpose? - How does the protocol work technically? - How does it make money or sustain itself? - What’s the staking model, emissions, burns, and treasury? - Who are the founders, and is the team public and credible? - Who funded it? Who holds most tokens? - Are users, TVL, and volume growing? - How strong is the ecosystem around it? - Who are the main competitors, and how does it compare? - Is the protocol audited? Any past hacks? - What are the best ways to use the protocol (strategies)? - What’s coming next? Key milestones or launches? - What are the biggest risks? - How strong is the community and social traction?" Bonus: Quote this post with a reason why you like INFINIT, and I’ll DM you the full version of the prompt within the next 24 hours.

Keno

15,876 Aufrufe • vor 1 Jahr

BUILD KARPATHY'S SECOND BRAIN WITH CLAUDE FABLE 5 + OBSIDIAN Andrej Karpathy (openai co-founder) shared an architecture that turns Claude into a persistent second brain instead of a basic chat window how it works: > you point Claude Code at an Obsidian vault folder > you drop articles, PDFs, or video transcripts into raw folders > Claude reads the files, updates topic summaries, and cross-references everything > the knowledge base compounds like interest instead of resetting on every new chat the setup is simple: > install and create a local vault directory > open the directory in Claude Code and paste Karpathy's wiki prompt: > > let the agent generate raw, wiki, and CLAUDE.md schema directories > drop any text file into raw and tell the model to ingest it > ask questions across the whole vault and query compiled summaries this eliminates rag database overhead and keeps your local vault organized how do you manage your local knowledge base?
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BUILD KARPATHY'S SECOND BRAIN WITH CLAUDE FABLE 5 + OBSIDIAN Andrej Karpathy (openai co-founder) shared an architecture that turns Claude into a persistent second brain instead of a basic chat window how it works: > you point Claude Code at an Obsidian vault folder > you drop articles, PDFs, or video transcripts into raw folders > Claude reads the files, updates topic summaries, and cross-references everything > the knowledge base compounds like interest instead of resetting on every new chat the setup is simple: > install and create a local vault directory > open the directory in Claude Code and paste Karpathy's wiki prompt: > > let the agent generate raw, wiki, and CLAUDE.md schema directories > drop any text file into raw and tell the model to ingest it > ask questions across the whole vault and query compiled summaries this eliminates rag database overhead and keeps your local vault organized how do you manage your local knowledge base?

Mr. Buzzoni

89,170 Aufrufe • vor 1 Monat

So, today we have fast SDF sculpting + real-time AI in Unbound Loop. Old news🥱 Coming up next: -quad-view generation from sculpted geometry -image tweaks via nano🍌 -tripo HD & Low-Poly 3D generation There's more in the upcoming release, but these three deserve a closer look: Quad-View Generation Most platforms offer some version of this, but Loop has a key advantage, your sculpted model is the reference. That means less guessing from the generator. Though it’s not 100% foolproof, like any AI I guess? (I should stop stating the obvious every time). Image Tweaks via Chat Select any generated image and ask for fixes or changes in real time. Works great on unintended quad-view hallucinations, but also handy for quick iterations. Swapping colors, tweaking details, removing elements. Tripo 3D generator Especially the low-poly model, it consistently delivered fantastic game-ready topology when we tried it with our real-time AI output. And it's super fast.
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So, today we have fast SDF sculpting + real-time AI in Unbound Loop. Old news🥱 Coming up next: -quad-view generation from sculpted geometry -image tweaks via nano🍌 -tripo HD & Low-Poly 3D generation There's more in the upcoming release, but these three deserve a closer look: Quad-View Generation Most platforms offer some version of this, but Loop has a key advantage, your sculpted model is the reference. That means less guessing from the generator. Though it’s not 100% foolproof, like any AI I guess? (I should stop stating the obvious every time). Image Tweaks via Chat Select any generated image and ask for fixes or changes in real time. Works great on unintended quad-view hallucinations, but also handy for quick iterations. Swapping colors, tweaking details, removing elements. Tripo 3D generator Especially the low-poly model, it consistently delivered fantastic game-ready topology when we tried it with our real-time AI output. And it's super fast.

Andrea Intg.

14,415 Aufrufe • vor 7 Tagen