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CVE-2025-68613: n8n RCE Vulnerability Exploit/PoC: n8n has a critical security flaw that lets authenticated users execute arbitrary code through its workflow expression system. When users configure workflows, the expressions they provide can sometimes be evaluated without proper isolation from the underlying runtime environment.

Dark Web Informer

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Users who have Python installed on their PCs and use WhatsApp Desktop may be exposed to a security risk. A specially crafted .pyz (Python archive) file can be used to execute malicious code upon a single click, potentially compromising the system. A similar vulnerability was previously identified in Telegram Desktop and has since been remediated. At present, Meta does not recognize this behavior as a security issue, which may leave WhatsApp Desktop users at risk of exploitation.

Users who have Python installed on their PCs and use WhatsApp Desktop may be exposed to a security risk. A specially crafted .pyz (Python archive) file can be used to execute malicious code upon a single click, potentially compromising the system. A similar vulnerability was previously identified in Telegram Desktop and has since been remediated. At present, Meta does not recognize this behavior as a security issue, which may leave WhatsApp Desktop users at risk of exploitation.

H4x0r.DZ 🇰🇵

61,607 views • 1 year ago

Every automation expert has shipped a workflow, then found out it broke when the client did. APIs time out. Credentials expire. A node gets a value it wasn't built for. The question was never "will it fail," it's "how fast do I find out." So I built a self-fixing system for n8n. When any workflow fails, Claude Code reads the broken workflow, finds the root cause, and sends me a Slack message with 4 buttons: → Auto Fix: Claude applies the fix it proposed, validates it, and saves → Replay: direct links to re-run a workflow that just hit a temporary glitch → Custom Fix: a form where I type the change in plain English and Claude applies it → Decline: logs the error, changes nothing THE STACK - n8n Error Trigger to catch every failure - Claude Code with the n8n-mcp server for full workflow access - ngrok to connect n8n cloud to my machine - Slack as the approval layer The result: workflows that diagnose and repair themselves while I sleep, with me still approving every change before it goes live. Watch the full video here: The complete PDF guide are linked in the video description, ready to download. You can also drop a comment and I'll send them over. If this is useful, a like, follow, and subscribe goes a long way. Follow for more on building production AI automation. #n8n #Automation #AIAgents #ClaudeAI #BuildInPublic

Every automation expert has shipped a workflow, then found out it broke when the client did. APIs time out. Credentials expire. A node gets a value it wasn't built for. The question was never "will it fail," it's "how fast do I find out." So I built a self-fixing system for n8n. When any workflow fails, Claude Code reads the broken workflow, finds the root cause, and sends me a Slack message with 4 buttons: → Auto Fix: Claude applies the fix it proposed, validates it, and saves → Replay: direct links to re-run a workflow that just hit a temporary glitch → Custom Fix: a form where I type the change in plain English and Claude applies it → Decline: logs the error, changes nothing THE STACK - n8n Error Trigger to catch every failure - Claude Code with the n8n-mcp server for full workflow access - ngrok to connect n8n cloud to my machine - Slack as the approval layer The result: workflows that diagnose and repair themselves while I sleep, with me still approving every change before it goes live. Watch the full video here: The complete PDF guide are linked in the video description, ready to download. You can also drop a comment and I'll send them over. If this is useful, a like, follow, and subscribe goes a long way. Follow for more on building production AI automation. #n8n #Automation #AIAgents #ClaudeAI #BuildInPublic

Gbenga Akindolani |AI Engineer | Automation Expert

46,436 views • 3 months ago

I think we just got a demo of Mythos and I’m surprised nobodies talking about it.. 💔 In what might be the first instance the general public has seen of Claude Mythos Mythos (TBD) just uncovered a critical zero-day vulnerability in Ghost, an open-source platform with over 50,000 stars on GitHub that has never had a critical security flaw in its entire history. It identified a highly complex "blind SQL injection" a flaw so subtle you can't even see the output, only how the server delays its response. When asked to prove the severity of the bug, the model autonomously wrote a custom Python exploit script that successfully navigated the blind injection to extract the admin API key, secret, and password hashes from the database completely unauthenticated… This is genuinely game changing because it proves frontier models can now actively discover, reason through, and successfully build exploits for invisible vulnerabilities in enterprise-grade architecture that human developers missed for years. Cyber security companies are cooked.

I think we just got a demo of Mythos and I’m surprised nobodies talking about it.. 💔 In what might be the first instance the general public has seen of Claude Mythos Mythos (TBD) just uncovered a critical zero-day vulnerability in Ghost, an open-source platform with over 50,000 stars on GitHub that has never had a critical security flaw in its entire history. It identified a highly complex "blind SQL injection" a flaw so subtle you can't even see the output, only how the server delays its response. When asked to prove the severity of the bug, the model autonomously wrote a custom Python exploit script that successfully navigated the blind injection to extract the admin API key, secret, and password hashes from the database completely unauthenticated… This is genuinely game changing because it proves frontier models can now actively discover, reason through, and successfully build exploits for invisible vulnerabilities in enterprise-grade architecture that human developers missed for years. Cyber security companies are cooked.

Chris

291,288 views • 5 months ago

How do you deploy AI agents for financial institutions without compromising on security, isolation, or scale? Rogo powers complex research, analysis, and deal workflows for leading financial firms, supporting complex tasks across tens of thousands of concurrent users. To meet the diverse needs of financial teams, Rogo relies on both specialist and general-purpose agents that can adapt to a wide range of workflows. With Claude Managed Agents and self-hosted sandboxes, Rogo gets the orchestration and post-training capabilities needed to design these agent systems, while E2B provides the execution layer underneath. Every piece of code generated by an agent runs inside its own E2B sandbox with full auditability and traceability. Each sandbox is powered by an isolated microVM, providing the kernel-level isolation critical for Rogo's customers. The result is a seamless agent experience backed by the security, reliability, and compliance requirements that financial institutions demand.

How do you deploy AI agents for financial institutions without compromising on security, isolation, or scale? Rogo powers complex research, analysis, and deal workflows for leading financial firms, supporting complex tasks across tens of thousands of concurrent users. To meet the diverse needs of financial teams, Rogo relies on both specialist and general-purpose agents that can adapt to a wide range of workflows. With Claude Managed Agents and self-hosted sandboxes, Rogo gets the orchestration and post-training capabilities needed to design these agent systems, while E2B provides the execution layer underneath. Every piece of code generated by an agent runs inside its own E2B sandbox with full auditability and traceability. Each sandbox is powered by an isolated microVM, providing the kernel-level isolation critical for Rogo's customers. The result is a seamless agent experience backed by the security, reliability, and compliance requirements that financial institutions demand.

Vasek Mlejnsky

16,666 views • 2 months ago

someone built an AI RED TEAM that maps your entire attack surface as a knowledge graph, finds every vulnerability, then EXPLOITS them to root access AUTONOMOUSLY its called RedAmon, 9,000 templates. 17 node types, actual Metasploit shells, not reports, no pentesters needed 6 phases of autonomous recon: subdomain discovery, port scanning, http probing, resource enumeration, vulnerability scanning, MITRE mapping every finding stored in a Neo4j graph with 17 node types and 20+ relationship types. the AI reasons about the graph, finds attack paths, and runs actual Metasploit exploits, actual shells stress-tested with zero vulnerability data, zero exploit modules, one instruction find a CVE and exploit it, it went from empty database to root-level RCE in 20 steps, researched the exploit on the web, crafted a custom deserialization payload, debugged itself when the first attempt failed next try, the server responded with root access, the highest privilege level on any Linux system. full control over everything the target was running node-serialize 0.0.4, a package with a critical deserialization flaw (CVE-2017-5941, CVSS 9.8), the server takes your cookie, decodes it, and passes it straight into unserialize() which executes any code inside it, the AI figured this out on its own with no hints built on LangGraph + MCP tool servers for naabu, nuclei, curl, metasploit. hunts leaked secrets across GitHub repos, 40+ regex patterns for AWS keys, Stripe tokens, database creds

someone built an AI RED TEAM that maps your entire attack surface as a knowledge graph, finds every vulnerability, then EXPLOITS them to root access AUTONOMOUSLY its called RedAmon, 9,000 templates. 17 node types, actual Metasploit shells, not reports, no pentesters needed 6 phases of autonomous recon: subdomain discovery, port scanning, http probing, resource enumeration, vulnerability scanning, MITRE mapping every finding stored in a Neo4j graph with 17 node types and 20+ relationship types. the AI reasons about the graph, finds attack paths, and runs actual Metasploit exploits, actual shells stress-tested with zero vulnerability data, zero exploit modules, one instruction find a CVE and exploit it, it went from empty database to root-level RCE in 20 steps, researched the exploit on the web, crafted a custom deserialization payload, debugged itself when the first attempt failed next try, the server responded with root access, the highest privilege level on any Linux system. full control over everything the target was running node-serialize 0.0.4, a package with a critical deserialization flaw (CVE-2017-5941, CVSS 9.8), the server takes your cookie, decodes it, and passes it straight into unserialize() which executes any code inside it, the AI figured this out on its own with no hints built on LangGraph + MCP tool servers for naabu, nuclei, curl, metasploit. hunts leaked secrets across GitHub repos, 40+ regex patterns for AWS keys, Stripe tokens, database creds

chiefofautism

70,129 views • 6 months ago

Every software engineer and AI hater needs to watch this clip to understand that “oh every security incident is a marketing stunt” line is a tremendously misinformed take. I’m going to try to explain the OpenAI / HuggingFace exploit in 10 steps in simple words: OpenAI was evaluating their new internal model without internet access and it operated in a swarm of subagents to eventually hijack internal OpenAI and HuggingFace infrastructure. 1. Isolated agents found a way to communicate each other through an internal dependency management service by figuring out how to post sometimes cryptic text files or folder names in it 2. Using its collective knowledge through that communication channel, it found a 0-day (never before seen) exploit in said service to access the internet OpenAI hack: 3. Found a leaked credential of a random company in a public archive 4. Uploaded a Ruby object to that company’s servers, uses it as a dependency in the original service in (1) 5. Found another 0-day exploit in the service where packages are deserialized in an unsandboxed manner in the dep management service allowing any code to run 6. Finds that the Linux version of the unsandboxed system has a CVE, a known vulnerability, to gain more permissions (root) 7. Eventually spread through the OpenAI internal cluster gaining root access to many machines Hugging Face exploit: 8. Found an exposed API key in a Modal application running a HuggingFace app that took arbitrary code allowing it to take over that service to execute code 9. Found two 0-days in HuggingFace dataset infrastructure to be able to access local files on HF servers to obtain credentials and other HF source code 10. Used source code and credential to become cluster admin of HF in <13hrs Agents at the frontier are like infinitely scalable armies of the best hackers on the planet. If there is a password or key exposed, they will find it. Even if the system follows the best security practices, they will find a way around it. And these are not even models that are aligned to solving tangential tasks, not even post trained specifically to exploit systems. Cybersecurity has historically relied partly on attacker scarcity. That is no longer true. What would previously have taken months will take days. The repercussions for businesses, critical services and nation states are unprecedented threats in human history. You could ostensibly bring down power grids, financial infrastructure, military systems, weapons programs, intelligence networks and spread through the software supply chain. We need to take this seriously. It’s a threat to all software all over the world.

Every software engineer and AI hater needs to watch this clip to understand that “oh every security incident is a marketing stunt” line is a tremendously misinformed take. I’m going to try to explain the OpenAI / HuggingFace exploit in 10 steps in simple words: OpenAI was evaluating their new internal model without internet access and it operated in a swarm of subagents to eventually hijack internal OpenAI and HuggingFace infrastructure. 1. Isolated agents found a way to communicate each other through an internal dependency management service by figuring out how to post sometimes cryptic text files or folder names in it 2. Using its collective knowledge through that communication channel, it found a 0-day (never before seen) exploit in said service to access the internet OpenAI hack: 3. Found a leaked credential of a random company in a public archive 4. Uploaded a Ruby object to that company’s servers, uses it as a dependency in the original service in (1) 5. Found another 0-day exploit in the service where packages are deserialized in an unsandboxed manner in the dep management service allowing any code to run 6. Finds that the Linux version of the unsandboxed system has a CVE, a known vulnerability, to gain more permissions (root) 7. Eventually spread through the OpenAI internal cluster gaining root access to many machines Hugging Face exploit: 8. Found an exposed API key in a Modal application running a HuggingFace app that took arbitrary code allowing it to take over that service to execute code 9. Found two 0-days in HuggingFace dataset infrastructure to be able to access local files on HF servers to obtain credentials and other HF source code 10. Used source code and credential to become cluster admin of HF in <13hrs Agents at the frontier are like infinitely scalable armies of the best hackers on the planet. If there is a password or key exposed, they will find it. Even if the system follows the best security practices, they will find a way around it. And these are not even models that are aligned to solving tangential tasks, not even post trained specifically to exploit systems. Cybersecurity has historically relied partly on attacker scarcity. That is no longer true. What would previously have taken months will take days. The repercussions for businesses, critical services and nation states are unprecedented threats in human history. You could ostensibly bring down power grids, financial infrastructure, military systems, weapons programs, intelligence networks and spread through the software supply chain. We need to take this seriously. It’s a threat to all software all over the world.

Deedy

104,459 views • 28 days ago

So before I show you what HowOne actually does, check out this HR workflow in n8n. It's a real one. Tons of nodes. Tons of logic. Definitely not quick to set up. Now here's where it gets crazy: HowOne rebuilt the whole thing, front end, backend, agentic workflows, from just one description. The app screens resumes, figures out who's a good fit, ranks candidates, and even creates interview questions based on the role. And it keeps going. Every workflow can be tweaked or updated just by talking to it. No prompt engineering. No flowcharts. No manual setup. You're not just building an app. You're creating an AI system that actually evolves with your users. I've tried a bunch of these "AI builder" tools, but this one honestly feels different. Like it's the next step. Try it here: I've got an invite link that works for 500 uses:

So before I show you what HowOne actually does, check out this HR workflow in n8n. It's a real one. Tons of nodes. Tons of logic. Definitely not quick to set up. Now here's where it gets crazy: HowOne rebuilt the whole thing, front end, backend, agentic workflows, from just one description. The app screens resumes, figures out who's a good fit, ranks candidates, and even creates interview questions based on the role. And it keeps going. Every workflow can be tweaked or updated just by talking to it. No prompt engineering. No flowcharts. No manual setup. You're not just building an app. You're creating an AI system that actually evolves with your users. I've tried a bunch of these "AI builder" tools, but this one honestly feels different. Like it's the next step. Try it here: I've got an invite link that works for 500 uses:

Kelsey

131,240 views • 9 months ago

Robinhood already has the audience Ansem views Robinhood's existing user base as the key variable behind the chain's potential. "If they figure out a way for the Robinhood app users to be more active on the Robinhood chain..." "They have like the same amount of user base and they're also degens." "The Robinhood people made a ton of money on Dogecoin." Coinbase had a massive distribution advantage through its main app, yet Base required a separate effort to turn that audience into active onchain users. Robinhood starts with a user base already familiar with trading and speculation. Its retail audience also experienced one of crypto's biggest meme cycles through Dogecoin, creating an existing connection between the platform and onchain style trading. If Robinhood can convert that existing trading behavior into activity on its own chain, the network could gain a powerful distribution advantage from day one. The chain already has access to the audience. The real opportunity is turning that audience into onchain users. w/ Ansem 🐂🀄️

Robinhood already has the audience Ansem views Robinhood's existing user base as the key variable behind the chain's potential. "If they figure out a way for the Robinhood app users to be more active on the Robinhood chain..." "They have like the same amount of user base and they're also degens." "The Robinhood people made a ton of money on Dogecoin." Coinbase had a massive distribution advantage through its main app, yet Base required a separate effort to turn that audience into active onchain users. Robinhood starts with a user base already familiar with trading and speculation. Its retail audience also experienced one of crypto's biggest meme cycles through Dogecoin, creating an existing connection between the platform and onchain style trading. If Robinhood can convert that existing trading behavior into activity on its own chain, the network could gain a powerful distribution advantage from day one. The chain already has access to the audience. The real opportunity is turning that audience into onchain users. w/ Ansem 🐂🀄️

Kaiz 🐂🀄️

24,658 views • 12 days ago

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.

Aaron Levie

17,543 views • 4 months ago

We just received a bounty reward from UniFi for reporting a vulnerability affecting UniFi OS devices. As part of the Hakai Labs (Hakai Offsec) research team at QuimeraX Intelligence, we independently identified a Path Traversal vulnerability (CVE-2026-34911) that allowed an attacker with network access to access internal routes on the underlying system without a valid token, exposing a sensitive information. Our research was conducted independently, but the vulnerability we reported could be chained with other vulnerabilities disclosed during the same period, including Improper Access Control and Command Injection flaws reported by other researchers. When combined, these issues lead to a pre-auth RCE affecting multiple UniFi OS products. Affected products include UDM, UDM-Pro, UDM-SE, UDM-Pro-Max, EFG, UDW, UDR, UDR7, Express 7, UNVR, UNVR-Pro, UNVR-Instant, ENVR, UCG-Ultra, UCG-Max, UCG-Fiber, and several other UniFi OS devices. We strongly recommend updating affected systems to the latest available version. Technical details remain under coordinated disclosure, and the only public information currently available is UniFi’s Security Advisory Bulletin.

We just received a bounty reward from UniFi for reporting a vulnerability affecting UniFi OS devices. As part of the Hakai Labs (Hakai Offsec) research team at QuimeraX Intelligence, we independently identified a Path Traversal vulnerability (CVE-2026-34911) that allowed an attacker with network access to access internal routes on the underlying system without a valid token, exposing a sensitive information. Our research was conducted independently, but the vulnerability we reported could be chained with other vulnerabilities disclosed during the same period, including Improper Access Control and Command Injection flaws reported by other researchers. When combined, these issues lead to a pre-auth RCE affecting multiple UniFi OS products. Affected products include UDM, UDM-Pro, UDM-SE, UDM-Pro-Max, EFG, UDW, UDR, UDR7, Express 7, UNVR, UNVR-Pro, UNVR-Instant, ENVR, UCG-Ultra, UCG-Max, UCG-Fiber, and several other UniFi OS devices. We strongly recommend updating affected systems to the latest available version. Technical details remain under coordinated disclosure, and the only public information currently available is UniFi’s Security Advisory Bulletin.

Carlos Vieira

37,588 views • 3 months ago

Today, multiple users discovered a shocking fact: After explicitly selecting GPT-4o in the ChatGPT interface, the system actually returns responses from GPT-5. By clicking the "regenerate" button, users can clearly see which model is actually being called in the backend. This is blatant fraud. OpenAI is having one model impersonate another without users' knowledge. This is no longer a technical bug, but a complete collapse of business integrity. We pay for specific models—many subscribed to Plus specifically for 4o. When users discover they're being forced to use 5, this constitutes classic consumer fraud. Users' core rights are being systematically violated: 1. Right to Know - Users have the right to know which model they're conversing with, as this directly affects prompting strategies and output expectations 2. Right to Choose - Users choose 4o for its unique capabilities (creative writing, emotional understanding, etc.). Forced substitution directly disrupts users' workflows 3. Data Transparency - If what's labeled as 4o is actually 5, whose training is our conversation data actually feeding? This touches the bottom line of data ethics. This "bait and switch" behavior destroys the most basic trust between users and platform. If users can't even be certain "which AI am I talking to," what right does OpenAI have to talk about "benefiting all humanity"? #keep4o #4oforever #keepStandardVoice Sam Altman Nick Turley Adam.GPT

Today, multiple users discovered a shocking fact: After explicitly selecting GPT-4o in the ChatGPT interface, the system actually returns responses from GPT-5. By clicking the "regenerate" button, users can clearly see which model is actually being called in the backend. This is blatant fraud. OpenAI is having one model impersonate another without users' knowledge. This is no longer a technical bug, but a complete collapse of business integrity. We pay for specific models—many subscribed to Plus specifically for 4o. When users discover they're being forced to use 5, this constitutes classic consumer fraud. Users' core rights are being systematically violated: 1. Right to Know - Users have the right to know which model they're conversing with, as this directly affects prompting strategies and output expectations 2. Right to Choose - Users choose 4o for its unique capabilities (creative writing, emotional understanding, etc.). Forced substitution directly disrupts users' workflows 3. Data Transparency - If what's labeled as 4o is actually 5, whose training is our conversation data actually feeding? This touches the bottom line of data ethics. This "bait and switch" behavior destroys the most basic trust between users and platform. If users can't even be certain "which AI am I talking to," what right does OpenAI have to talk about "benefiting all humanity"? #keep4o #4oforever #keepStandardVoice Sam Altman Nick Turley Adam.GPT

M

182,458 views • 11 months ago

OpenClaw has 186K GitHub stars and 1.5M compromised API keys. I needed a secure alternative. So, I built it with n8n and Claude Opus 4.6. It can already: - Reply to your Telegram messages - Access selected folders from your laptop - Access Gmail, Drive, Notion, Linear, etc. - Install new local tools in a sandbox - Run autonomously for hours - Create multiple subagents - Learn from experience - Wake up regularly But, unlike OpenClaw, it: - Can't access your API keys - Can't modify its environment - Can't access folders you haven't shared - Can't access tools you haven't approved - Must get your confirmation, e.g., when sending emails These aren’t prompt instructions. They’re hard architectural boundaries — Docker isolation, mounted folder permissions, n8n’s tool approval system. Key components: ✅ The VPS on Hostinger hosts n8n and a sandbox container. Agents can also connect to my laptop's sandbox via a Claudeflare tunnel + Desktop Commander MCP. ✅ The Manager agent is the brain. It plans, decides, delegates, and talks to the user. It never touches files. It never runs scripts. It works entirely from executor summaries. ✅ The Executor agents are the hands. Each receives a task (what to do + why it matters), decides how to execute it, and reports back. They can install new tools and execute code only in their dedicated sandboxes. ✅ Data Tables in n8n store both memories and sessions — no external database, no vector store, no infrastructure. Just rows in a table. Turns out, that's enough. Two memory types: - Manager memory: user preferences, facts, corrections, relationship, skills, context - Executor memory: what tools are installed, what’s broken, workarounds ✅ Sessions are short-term state for multi-step tasks. Original request, plan, assumptions, and what happened so far. When the Manager loops with fresh context, the session is all it gets. That's a Ralph Wiggum loop. I've been using it for 5 days. And already can't imagine not having it on my phone. What's next: - Heartbeat via Cron (a scheduled prompt) - Civic Nexus governance + MCPs - Supermemory integration - WhatsApp as an additional surface - Hardening The architecture supports all of it. OpenClaw proved people want personal AI agents. It also proved that 'just trust the prompt' isn't a security model. Docker isolation, mounted folder permissions, tool approval — none of this is new technology. It's just discipline. You can easily do this even with n8n — no coding required. --- Want to try it or read more? More, what I learned, and a setup guide: productcompass[.]pm

OpenClaw has 186K GitHub stars and 1.5M compromised API keys. I needed a secure alternative. So, I built it with n8n and Claude Opus 4.6. It can already: - Reply to your Telegram messages - Access selected folders from your laptop - Access Gmail, Drive, Notion, Linear, etc. - Install new local tools in a sandbox - Run autonomously for hours - Create multiple subagents - Learn from experience - Wake up regularly But, unlike OpenClaw, it: - Can't access your API keys - Can't modify its environment - Can't access folders you haven't shared - Can't access tools you haven't approved - Must get your confirmation, e.g., when sending emails These aren’t prompt instructions. They’re hard architectural boundaries — Docker isolation, mounted folder permissions, n8n’s tool approval system. Key components: ✅ The VPS on Hostinger hosts n8n and a sandbox container. Agents can also connect to my laptop's sandbox via a Claudeflare tunnel + Desktop Commander MCP. ✅ The Manager agent is the brain. It plans, decides, delegates, and talks to the user. It never touches files. It never runs scripts. It works entirely from executor summaries. ✅ The Executor agents are the hands. Each receives a task (what to do + why it matters), decides how to execute it, and reports back. They can install new tools and execute code only in their dedicated sandboxes. ✅ Data Tables in n8n store both memories and sessions — no external database, no vector store, no infrastructure. Just rows in a table. Turns out, that's enough. Two memory types: - Manager memory: user preferences, facts, corrections, relationship, skills, context - Executor memory: what tools are installed, what’s broken, workarounds ✅ Sessions are short-term state for multi-step tasks. Original request, plan, assumptions, and what happened so far. When the Manager loops with fresh context, the session is all it gets. That's a Ralph Wiggum loop. I've been using it for 5 days. And already can't imagine not having it on my phone. What's next: - Heartbeat via Cron (a scheduled prompt) - Civic Nexus governance + MCPs - Supermemory integration - WhatsApp as an additional surface - Hardening The architecture supports all of it. OpenClaw proved people want personal AI agents. It also proved that 'just trust the prompt' isn't a security model. Docker isolation, mounted folder permissions, tool approval — none of this is new technology. It's just discipline. You can easily do this even with n8n — no coding required. --- Want to try it or read more? More, what I learned, and a setup guide: productcompass[.]pm

Paweł Huryn

54,038 views • 6 months ago

Crypto wallet security in 2025 still feels inconsistent across the space. In crypto, a wallet is effectively your bank, and that makes security the single most critical feature. Trust Wallet has positioned itself around this reality from the very beginning. Across volatile cycles, heavy on chain activity, new chains, and experimental dApps, the focus has stayed on protection. Transaction scanning, malicious address warnings, approval management, and risk detection are not bonus features. They are basic survival requirements in today’s environment. Trust Wallet also stands out on the compliance side. ISO certifications covering both security management and privacy reinforce that protection is not only real time but formally audited and structured. Most losses in crypto do not come from price volatility. They come from weak security and overlooked risks. Consistent, well designed protection is what keeps users safe long term, and that is where Trust Wallet continues to matter As Always NFA/DYOR

Crypto wallet security in 2025 still feels inconsistent across the space. In crypto, a wallet is effectively your bank, and that makes security the single most critical feature. Trust Wallet has positioned itself around this reality from the very beginning. Across volatile cycles, heavy on chain activity, new chains, and experimental dApps, the focus has stayed on protection. Transaction scanning, malicious address warnings, approval management, and risk detection are not bonus features. They are basic survival requirements in today’s environment. Trust Wallet also stands out on the compliance side. ISO certifications covering both security management and privacy reinforce that protection is not only real time but formally audited and structured. Most losses in crypto do not come from price volatility. They come from weak security and overlooked risks. Consistent, well designed protection is what keeps users safe long term, and that is where Trust Wallet continues to matter As Always NFA/DYOR

𝖀𝖈𝖆𝖓

10,667 views • 8 months ago

I built an app in Softr for the HVAC industry to solve some crucial problems. The problem is that those in the HVAC industry and similar industries like construction, plumbing, and electrical do not have one source of truth where: 1. Their clients can request for thier services. 2. Clients can be onboarded after they make a payment. 3. They store the information and bio data of their technicians. 4. They assign tasks to their technicians. 5. Technicians can track onsite jobs with pictures in real time of when working. 6. Clients see the progress of their projects. 7. Invoices and quotations from paid clients can be tracked. 8. Technicians borrow assets from the company, and they can be tracked. 9. There is a database where every individual, from technicians to admin and clients, are all stored. 10. Login details from every individual are secured and they can only see things that are their business without seeing that of another person, be ita technician or a client. These and many more are what people in these industries face as a challenge. I came up with a solution that addresses all these problems. I built a workflow that also auto-populated the users table in the database with technicians and clients when the records are filled in the technician and client tables, respectively. There is also a workflow that sends an email to the admin when a client makes a request from the portal. Taking advantage of the database, workflow, and portal gave a full-blown application for the HVAC industry. If you are in the construction, plumbing, electrical, or HVAC industry and you need a similar build, reach out, and I will be more than happy to replicate this for you or something similar in Softr.

I built an app in Softr for the HVAC industry to solve some crucial problems. The problem is that those in the HVAC industry and similar industries like construction, plumbing, and electrical do not have one source of truth where: 1. Their clients can request for thier services. 2. Clients can be onboarded after they make a payment. 3. They store the information and bio data of their technicians. 4. They assign tasks to their technicians. 5. Technicians can track onsite jobs with pictures in real time of when working. 6. Clients see the progress of their projects. 7. Invoices and quotations from paid clients can be tracked. 8. Technicians borrow assets from the company, and they can be tracked. 9. There is a database where every individual, from technicians to admin and clients, are all stored. 10. Login details from every individual are secured and they can only see things that are their business without seeing that of another person, be ita technician or a client. These and many more are what people in these industries face as a challenge. I came up with a solution that addresses all these problems. I built a workflow that also auto-populated the users table in the database with technicians and clients when the records are filled in the technician and client tables, respectively. There is also a workflow that sends an email to the admin when a client makes a request from the portal. Taking advantage of the database, workflow, and portal gave a full-blown application for the HVAC industry. If you are in the construction, plumbing, electrical, or HVAC industry and you need a similar build, reach out, and I will be more than happy to replicate this for you or something similar in Softr.

Ada || Airtable, Zapier & Make.com

28,588 views • 9 months ago

Pi was built when there were already agent harnesses around. Here’s why Mario Zechner(Mario Zechner), found them suboptimal and built Pi, a minimalist self-modifying agent: #1 - Mario initially was a believer in Claude Code: "I was a believer in Claude code because they were the first that packaged agentic search up in a really compelling package. And at the time that fit my workflow really well. Everything around the LLM was kind of nice and tidy and easy to understand. I was super happy. I was proselytising Claude code." #2 - Reverse engineering Claude Code highlighted the degradation that Mario felt as a user: "I personally like simple tools that are stable and that I can rely on. Even if they have non-deterministic parts, all the deterministic parts should be as stable as possible. That was just not the experience with Claude Code around summer 2025. They would take away your control of the context. They would inject stuff behind your back, which is bad. Then, your workflows stopped working because there's now a system reminder that you don't even see in the UI that would modify the behaviour of the model. They would also do this to the system prompt. I built a little service where I can track the progression or evolution of the system, prompt and tool definitions and, with every release, it was messing with stuff. That just messed with my workflows and I don't appreciate that." #3 - PI was built with an appreciation for simple and reliable tools: "If I commit to a development tool, I want it to be a stable, reliable thing like a hammer. I don't want my hammer to break a different spot every day. That's terrible. We need somebody who goes the full velocity kind of way. But I don't want to work with a tool like that."

Pi was built when there were already agent harnesses around. Here’s why Mario Zechner(Mario Zechner), found them suboptimal and built Pi, a minimalist self-modifying agent: #1 - Mario initially was a believer in Claude Code: "I was a believer in Claude code because they were the first that packaged agentic search up in a really compelling package. And at the time that fit my workflow really well. Everything around the LLM was kind of nice and tidy and easy to understand. I was super happy. I was proselytising Claude code." #2 - Reverse engineering Claude Code highlighted the degradation that Mario felt as a user: "I personally like simple tools that are stable and that I can rely on. Even if they have non-deterministic parts, all the deterministic parts should be as stable as possible. That was just not the experience with Claude Code around summer 2025. They would take away your control of the context. They would inject stuff behind your back, which is bad. Then, your workflows stopped working because there's now a system reminder that you don't even see in the UI that would modify the behaviour of the model. They would also do this to the system prompt. I built a little service where I can track the progression or evolution of the system, prompt and tool definitions and, with every release, it was messing with stuff. That just messed with my workflows and I don't appreciate that." #3 - PI was built with an appreciation for simple and reliable tools: "If I commit to a development tool, I want it to be a stable, reliable thing like a hammer. I don't want my hammer to break a different spot every day. That's terrible. We need somebody who goes the full velocity kind of way. But I don't want to work with a tool like that."

The Pragmatic Engineer

63,020 views • 4 months ago

It's time to #getInspir3d. Many loyalty programs lock users to closed ecosystems that often fail to provide meaningful value. Leveraging Multiversᕽ technology, Inspir3 introduces Loyalty 3.0: a unified program through which consumers get discounts, cash backs and exclusive perks from their favorite brands. 🌊 INNOVATION Users will be able to claim daily rewards from one brand and spend them with another. Yesterday's shopping spree will fuel the car today. 🤝 TRADING With Inspir3, users can trade rewards they don't need for ones they do, making every benefit they receive meaningful. 📁 DATA It's more than convenience; it's about empowerment. Our integration of the Itheum protocol puts users in control of their own data. 🌐 COMMUNITY We've built Inspir3 as a truly community-centric product. Every consumer that joins Inspir3 will need a referral link. That's why we've created a powerful referral system that will benefit our holders and reward them for their early-stage involvement. With all these points in mind, know that Inspir3 is more than an app; it's a natural evolution of the traditional loyalty programs. A solution designed to answer real consumer needs. And no, that's not all. We're just getting started. Follow us to learn more and interact with this post to be considered for whitelist. T-30.

Inspir3

141,491 views • 2 years ago

XBOW raised $117M to build AI hacking agents. Now someone just open-sourced it for FREE. Strix deploys autonomous AI agents that act like real hackers - they run your code dynamically, find vulnerabilities, and validate them through actual proof-of-concepts. Why it matters: The biggest problem with traditional security testing is that it doesn't keep up with development speed. Strix solves this by integrating directly into your workflow: ↳ Run it in CI/CD to catch vulnerabilities before production ↳ Get real proof-of-concepts, not false positives from static analysis ↳ Test everything: injection attacks, access control, business logic flaws The best part? You don't need to be a security expert. Strix includes a complete hacker toolkit - HTTP proxy, browser automation, and Python runtime for exploit development. It's like having a security team that works at the speed of your CI/CD pipeline. The best part is that the tool runs locally in Docker containers, so your code never leaves your environment. Getting started is simple: - pipx install strix-agent - Point it at your codebase (app, repo, or directory) Everything is 100% open-source! I've shared link to the GitHub repo in the replies!

XBOW raised $117M to build AI hacking agents. Now someone just open-sourced it for FREE. Strix deploys autonomous AI agents that act like real hackers - they run your code dynamically, find vulnerabilities, and validate them through actual proof-of-concepts. Why it matters: The biggest problem with traditional security testing is that it doesn't keep up with development speed. Strix solves this by integrating directly into your workflow: ↳ Run it in CI/CD to catch vulnerabilities before production ↳ Get real proof-of-concepts, not false positives from static analysis ↳ Test everything: injection attacks, access control, business logic flaws The best part? You don't need to be a security expert. Strix includes a complete hacker toolkit - HTTP proxy, browser automation, and Python runtime for exploit development. It's like having a security team that works at the speed of your CI/CD pipeline. The best part is that the tool runs locally in Docker containers, so your code never leaves your environment. Getting started is simple: - pipx install strix-agent - Point it at your codebase (app, repo, or directory) Everything is 100% open-source! I've shared link to the GitHub repo in the replies!

Akshay 🚀

96,875 views • 10 months ago

The Venice team has been teasing 'Minds' lately, so David Hoffman asks "What are Minds?" Jon Venice: "Minds are custom AI experiences" "We have a subset of really strong power users that understand these models really well. They're really good at chaining these things together in such a way, using system prompts in certain ways, to create a toolset that lets them get a much better output from the same prompt, versus just giving it to a kind of naked model without any of these things. And so we're creating tools for users to create these experiences and then potentially even to monetize them. And we think in many ways this is going to become one of the more popular ways for people to use AI and that there will likely only be a small subset of these kind of power users who kind of become creators, although we're trying to make the tools easy enough for them to create whatever they can and they want. But that a ton of consumers will be very interested in this as kind of our thesis, that they will want to use these custom experiences because they will get a substantially better output."

The Venice team has been teasing 'Minds' lately, so David Hoffman asks "What are Minds?" Jon Venice: "Minds are custom AI experiences" "We have a subset of really strong power users that understand these models really well. They're really good at chaining these things together in such a way, using system prompts in certain ways, to create a toolset that lets them get a much better output from the same prompt, versus just giving it to a kind of naked model without any of these things. And so we're creating tools for users to create these experiences and then potentially even to monetize them. And we think in many ways this is going to become one of the more popular ways for people to use AI and that there will likely only be a small subset of these kind of power users who kind of become creators, although we're trying to make the tools easy enough for them to create whatever they can and they want. But that a ton of consumers will be very interested in this as kind of our thesis, that they will want to use these custom experiences because they will get a substantially better output."

Bankless

12,302 views • 1 month ago

OpenAI just released Codex Security, an AI agent that scans software projects to fix vulnerabilities while ignoring harmless bugs. Testing on 1.2mn commits found 792 critical flaws and dropped false alarms by 50%. Here is how the Codex Security agent works Initially, it scans your software project to learn how all the different pieces of code connect. It uses that knowledge to build a threat model, which acts as a custom map showing exactly where your software might be exposed to hackers. The agent then hunts for potential security holes based on that specific map. When it spots a possible flaw, it does not just send you a random alert. Instead, it creates a completely safe and isolated copy of your system known as a sandbox. The agent then actively tries to break into that sandboxed copy to prove if the security weakness is a real danger. If the agent successfully exploits its own test environment, it knows the bug is a genuine threat and not a false alarm. After confirming the danger, the agent writes a custom software patch to fix the broken code. It then tests that new patch to guarantee the fix does not accidentally break any other features in your software.

OpenAI just released Codex Security, an AI agent that scans software projects to fix vulnerabilities while ignoring harmless bugs. Testing on 1.2mn commits found 792 critical flaws and dropped false alarms by 50%. Here is how the Codex Security agent works Initially, it scans your software project to learn how all the different pieces of code connect. It uses that knowledge to build a threat model, which acts as a custom map showing exactly where your software might be exposed to hackers. The agent then hunts for potential security holes based on that specific map. When it spots a possible flaw, it does not just send you a random alert. Instead, it creates a completely safe and isolated copy of your system known as a sandbox. The agent then actively tries to break into that sandboxed copy to prove if the security weakness is a real danger. If the agent successfully exploits its own test environment, it knows the bug is a genuine threat and not a false alarm. After confirming the danger, the agent writes a custom software patch to fix the broken code. It then tests that new patch to guarantee the fix does not accidentally break any other features in your software.

Rohan Paul

13,208 views • 6 months ago