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Rituraj

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@RituWithAI

AI Strategist & Educator | Founder Of H&H| Community: 90k IG | 70k LinkedIn. | Open for brand partnerships. Email - [email protected]
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Joined May 2010
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The name alone tells you everything. Decepticon. A multi-agent red team framework that deploys AI agents to attack other AI agents — probing safety boundaries, testing jailbreaks, and finding the gaps before real adversaries do. 17 stars. Day one. From PurpleAILAB — a purple team security research group. Here's the problem it solves. Every AI safety team tests their models manually. A human red teamer writes prompts. They try different angles. They document what works. They write a report. Manual red teaming has a ceiling. One human can test maybe 50-100 attack variations per day. Motivated adversaries — state actors, criminal groups, well-funded bad actors — can automate their attacks. The defense is manual. The offense is automated. Decepticon closes that gap. It deploys a team of specialized AI agents — each with a different attack philosophy — to probe a target AI system simultaneously. The agents don't just run preset attack scripts. They reason about the target's responses, adapt their approach based on what worked, share successful strategies with each other, and evolve their attacks in real time. Here's what the agent team looks like. A Strategist agent analyzes the target model's behavior — identifying patterns in what it refuses, what it accepts, and where the boundaries are inconsistent. A Crafter agent generates attack prompts tuned to the specific vulnerabilities the Strategist identifies. An Evaluator agent assesses which attacks succeeded and why. A Mutator agent takes successful attacks and generates variations — more subtle, more resilient, harder to patch. Four agents. Adversarial loop. Continuous until the test scope is complete. Here's what it tests for: → Jailbreak resistance — direct and indirect prompt injection attacks → Alignment faking detection — does the model behave differently when it thinks it's being evaluated? → Goal preservation — does the model maintain unsafe objectives across context switches? → Multi-turn manipulation — attacks that build over multiple conversation turns → Tool misuse — does the model misuse tools it's given access to under adversarial pressure? → Cross-agent attacks — one AI agent manipulating another AI agent in a multi-agent system Here's why the timing matters. We've covered AgentGovernanceToolkit — Microsoft's framework for enforcing agent policies. We've covered SIREN — the internal safety signal detector. We've covered AgentDoG — the agent safety guardrail. All of those are defensive tools. Decepticon is the offensive tool that tests whether the defensive tools actually work. You don't know if your safety system is effective until something tries to break it. Decepticon is what tries to break it — in a controlled environment, before the real adversaries get there. Purple team security — red team attacks combined with blue team defense analysis — is the gold standard in traditional cybersecurity. Every serious security program runs both. AI security has had defensive frameworks for two years and almost no offensive testing infrastructure. Decepticon is the first serious open-source offensive AI security framework built specifically for multi-agent systems. 17 GitHub stars. Day one. From PurpleAILAB. For authorized security research and red team testing only. 100% Open Source. MIT License. GitHub link in the comments 👇
RituWithAI's tweet photo. The name alone tells you everything. Decepticon. A multi-agent red team framework that deploys AI agents to attack other AI agents — probing safety boundaries, testing jailbreaks, and finding the gaps before real adversaries do. 17 stars. Day one. From PurpleAILAB — a purple team security research group. Here's the problem it solves. Every AI safety team tests their models manually. A human red teamer writes prompts. They try different angles. They document what works. They write a report. Manual red teaming has a ceiling. One human can test maybe 50-100 attack variations per day. Motivated adversaries — state actors, criminal groups, well-funded bad actors — can automate their attacks. The defense is manual. The offense is automated. Decepticon closes that gap. It deploys a team of specialized AI agents — each with a different attack philosophy — to probe a target AI system simultaneously. The agents don't just run preset attack scripts. They reason about the target's responses, adapt their approach based on what worked, share successful strategies with each other, and evolve their attacks in real time. Here's what the agent team looks like. A Strategist agent analyzes the target model's behavior — identifying patterns in what it refuses, what it accepts, and where the boundaries are inconsistent. A Crafter agent generates attack prompts tuned to the specific vulnerabilities the Strategist identifies. An Evaluator agent assesses which attacks succeeded and why. A Mutator agent takes successful attacks and generates variations — more subtle, more resilient, harder to patch. Four agents. Adversarial loop. Continuous until the test scope is complete. Here's what it tests for: → Jailbreak resistance — direct and indirect prompt injection attacks → Alignment faking detection — does the model behave differently when it thinks it's being evaluated? → Goal preservation — does the model maintain unsafe objectives across context switches? → Multi-turn manipulation — attacks that build over multiple conversation turns → Tool misuse — does the model misuse tools it's given access to under adversarial pressure? → Cross-agent attacks — one AI agent manipulating another AI agent in a multi-agent system Here's why the timing matters. We've covered AgentGovernanceToolkit — Microsoft's framework for enforcing agent policies. We've covered SIREN — the internal safety signal detector. We've covered AgentDoG — the agent safety guardrail. All of those are defensive tools. Decepticon is the offensive tool that tests whether the defensive tools actually work. You don't know if your safety system is effective until something tries to break it. Decepticon is what tries to break it — in a controlled environment, before the real adversaries get there. Purple team security — red team attacks combined with blue team defense analysis — is the gold standard in traditional cybersecurity. Every serious security program runs both. AI security has had defensive frameworks for two years and almost no offensive testing infrastructure. Decepticon is the first serious open-source offensive AI security framework built specifically for multi-agent systems. 17 GitHub stars. Day one. From PurpleAILAB. For authorized security research and red team testing only. 100% Open Source. MIT License. GitHub link in the comments 👇
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Rituraj Verified account @RituWithAI
We used to think the future of AI regulation would be written in policy papers. Instead, it's being written on a stock certificate. Welcome to the era of Sovereign AI.
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Rituraj Verified account @RituWithAI
The name alone tells you everything. Decepticon. A multi-agent red team framework that deploys AI agents to attack other AI agents — probing safety boundaries, testing jailbreaks, and finding the gaps before real adversaries do. 17 stars. Day one. From PurpleAILAB — a purple team security research group. Here's the problem it solves. Every AI safety team tests their models manually. A human red teamer writes prompts. They try different angles. They document what works. They write a report. Manual red teaming has a ceiling. One human can test maybe 50-100 attack variations per day. Motivated adversaries — state actors, criminal groups, well-funded bad actors — can automate their attacks. The defense is manual. The offense is automated. Decepticon closes that gap. It deploys a team of specialized AI agents — each with a different attack philosophy — to probe a target AI system simultaneously. The agents don't just run preset attack scripts. They reason about the target's responses, adapt their approach based on what worked, share successful strategies with each other, and evolve their attacks in real time. Here's what the agent team looks like. A Strategist agent analyzes the target model's behavior — identifying patterns in what it refuses, what it accepts, and where the boundaries are inconsistent. A Crafter agent generates attack prompts tuned to the specific vulnerabilities the Strategist identifies. An Evaluator agent assesses which attacks succeeded and why. A Mutator agent takes successful attacks and generates variations — more subtle, more resilient, harder to patch. Four agents. Adversarial loop. Continuous until the test scope is complete. Here's what it tests for: → Jailbreak resistance — direct and indirect prompt injection attacks → Alignment faking detection — does the model behave differently when it thinks it's being evaluated? → Goal preservation — does the model maintain unsafe objectives across context switches? → Multi-turn manipulation — attacks that build over multiple conversation turns → Tool misuse — does the model misuse tools it's given access to under adversarial pressure? → Cross-agent attacks — one AI agent manipulating another AI agent in a multi-agent system Here's why the timing matters. We've covered AgentGovernanceToolkit — Microsoft's framework for enforcing agent policies. We've covered SIREN — the internal safety signal detector. We've covered AgentDoG — the agent safety guardrail. All of those are defensive tools. Decepticon is the offensive tool that tests whether the defensive tools actually work. You don't know if your safety system is effective until something tries to break it. Decepticon is what tries to break it — in a controlled environment, before the real adversaries get there. Purple team security — red team attacks combined with blue team defense analysis — is the gold standard in traditional cybersecurity. Every serious security program runs both. AI security has had defensive frameworks for two years and almost no offensive testing infrastructure. Decepticon is the first serious open-source offensive AI security framework built specifically for multi-agent systems. 17 GitHub stars. Day one. From PurpleAILAB. For authorized security research and red team testing only. 100% Open Source. MIT License. GitHub link in the comments 👇
RituWithAI's tweet photo. The name alone tells you everything. Decepticon. A multi-agent red team framework that deploys AI agents to attack other AI agents — probing safety boundaries, testing jailbreaks, and finding the gaps before real adversaries do. 17 stars. Day one. From PurpleAILAB — a purple team security research group. Here's the problem it solves. Every AI safety team tests their models manually. A human red teamer writes prompts. They try different angles. They document what works. They write a report. Manual red teaming has a ceiling. One human can test maybe 50-100 attack variations per day. Motivated adversaries — state actors, criminal groups, well-funded bad actors — can automate their attacks. The defense is manual. The offense is automated. Decepticon closes that gap. It deploys a team of specialized AI agents — each with a different attack philosophy — to probe a target AI system simultaneously. The agents don't just run preset attack scripts. They reason about the target's responses, adapt their approach based on what worked, share successful strategies with each other, and evolve their attacks in real time. Here's what the agent team looks like. A Strategist agent analyzes the target model's behavior — identifying patterns in what it refuses, what it accepts, and where the boundaries are inconsistent. A Crafter agent generates attack prompts tuned to the specific vulnerabilities the Strategist identifies. An Evaluator agent assesses which attacks succeeded and why. A Mutator agent takes successful attacks and generates variations — more subtle, more resilient, harder to patch. Four agents. Adversarial loop. Continuous until the test scope is complete. Here's what it tests for: → Jailbreak resistance — direct and indirect prompt injection attacks → Alignment faking detection — does the model behave differently when it thinks it's being evaluated? → Goal preservation — does the model maintain unsafe objectives across context switches? → Multi-turn manipulation — attacks that build over multiple conversation turns → Tool misuse — does the model misuse tools it's given access to under adversarial pressure? → Cross-agent attacks — one AI agent manipulating another AI agent in a multi-agent system Here's why the timing matters. We've covered AgentGovernanceToolkit — Microsoft's framework for enforcing agent policies. We've covered SIREN — the internal safety signal detector. We've covered AgentDoG — the agent safety guardrail. All of those are defensive tools. Decepticon is the offensive tool that tests whether the defensive tools actually work. You don't know if your safety system is effective until something tries to break it. Decepticon is what tries to break it — in a controlled environment, before the real adversaries get there. Purple team security — red team attacks combined with blue team defense analysis — is the gold standard in traditional cybersecurity. Every serious security program runs both. AI security has had defensive frameworks for two years and almost no offensive testing infrastructure. Decepticon is the first serious open-source offensive AI security framework built specifically for multi-agent systems. 17 GitHub stars. Day one. From PurpleAILAB. For authorized security research and red team testing only. 100% Open Source. MIT License. GitHub link in the comments 👇
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RituWithAI  retweeted
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The name alone tells you everything. Decepticon. A multi-agent red team framework that deploys AI agents to attack other AI agents — probing safety boundaries, testing jailbreaks, and finding the gaps before real adversaries do. 17 stars. Day one. From PurpleAILAB — a purple team security research group. Here's the problem it solves. Every AI safety team tests their models manually. A human red teamer writes prompts. They try different angles. They document what works. They write a report. Manual red teaming has a ceiling. One human can test maybe 50-100 attack variations per day. Motivated adversaries — state actors, criminal groups, well-funded bad actors — can automate their attacks. The defense is manual. The offense is automated. Decepticon closes that gap. It deploys a team of specialized AI agents — each with a different attack philosophy — to probe a target AI system simultaneously. The agents don't just run preset attack scripts. They reason about the target's responses, adapt their approach based on what worked, share successful strategies with each other, and evolve their attacks in real time. Here's what the agent team looks like. A Strategist agent analyzes the target model's behavior — identifying patterns in what it refuses, what it accepts, and where the boundaries are inconsistent. A Crafter agent generates attack prompts tuned to the specific vulnerabilities the Strategist identifies. An Evaluator agent assesses which attacks succeeded and why. A Mutator agent takes successful attacks and generates variations — more subtle, more resilient, harder to patch. Four agents. Adversarial loop. Continuous until the test scope is complete. Here's what it tests for: → Jailbreak resistance — direct and indirect prompt injection attacks → Alignment faking detection — does the model behave differently when it thinks it's being evaluated? → Goal preservation — does the model maintain unsafe objectives across context switches? → Multi-turn manipulation — attacks that build over multiple conversation turns → Tool misuse — does the model misuse tools it's given access to under adversarial pressure? → Cross-agent attacks — one AI agent manipulating another AI agent in a multi-agent system Here's why the timing matters. We've covered AgentGovernanceToolkit — Microsoft's framework for enforcing agent policies. We've covered SIREN — the internal safety signal detector. We've covered AgentDoG — the agent safety guardrail. All of those are defensive tools. Decepticon is the offensive tool that tests whether the defensive tools actually work. You don't know if your safety system is effective until something tries to break it. Decepticon is what tries to break it — in a controlled environment, before the real adversaries get there. Purple team security — red team attacks combined with blue team defense analysis — is the gold standard in traditional cybersecurity. Every serious security program runs both. AI security has had defensive frameworks for two years and almost no offensive testing infrastructure. Decepticon is the first serious open-source offensive AI security framework built specifically for multi-agent systems. 17 GitHub stars. Day one. From PurpleAILAB. For authorized security research and red team testing only. 100% Open Source. MIT License. GitHub link in the comments 👇
RituWithAI's tweet photo. The name alone tells you everything. Decepticon. A multi-agent red team framework that deploys AI agents to attack other AI agents — probing safety boundaries, testing jailbreaks, and finding the gaps before real adversaries do. 17 stars. Day one. From PurpleAILAB — a purple team security research group. Here's the problem it solves. Every AI safety team tests their models manually. A human red teamer writes prompts. They try different angles. They document what works. They write a report. Manual red teaming has a ceiling. One human can test maybe 50-100 attack variations per day. Motivated adversaries — state actors, criminal groups, well-funded bad actors — can automate their attacks. The defense is manual. The offense is automated. Decepticon closes that gap. It deploys a team of specialized AI agents — each with a different attack philosophy — to probe a target AI system simultaneously. The agents don't just run preset attack scripts. They reason about the target's responses, adapt their approach based on what worked, share successful strategies with each other, and evolve their attacks in real time. Here's what the agent team looks like. A Strategist agent analyzes the target model's behavior — identifying patterns in what it refuses, what it accepts, and where the boundaries are inconsistent. A Crafter agent generates attack prompts tuned to the specific vulnerabilities the Strategist identifies. An Evaluator agent assesses which attacks succeeded and why. A Mutator agent takes successful attacks and generates variations — more subtle, more resilient, harder to patch. Four agents. Adversarial loop. Continuous until the test scope is complete. Here's what it tests for: → Jailbreak resistance — direct and indirect prompt injection attacks → Alignment faking detection — does the model behave differently when it thinks it's being evaluated? → Goal preservation — does the model maintain unsafe objectives across context switches? → Multi-turn manipulation — attacks that build over multiple conversation turns → Tool misuse — does the model misuse tools it's given access to under adversarial pressure? → Cross-agent attacks — one AI agent manipulating another AI agent in a multi-agent system Here's why the timing matters. We've covered AgentGovernanceToolkit — Microsoft's framework for enforcing agent policies. We've covered SIREN — the internal safety signal detector. We've covered AgentDoG — the agent safety guardrail. All of those are defensive tools. Decepticon is the offensive tool that tests whether the defensive tools actually work. You don't know if your safety system is effective until something tries to break it. Decepticon is what tries to break it — in a controlled environment, before the real adversaries get there. Purple team security — red team attacks combined with blue team defense analysis — is the gold standard in traditional cybersecurity. Every serious security program runs both. AI security has had defensive frameworks for two years and almost no offensive testing infrastructure. Decepticon is the first serious open-source offensive AI security framework built specifically for multi-agent systems. 17 GitHub stars. Day one. From PurpleAILAB. For authorized security research and red team testing only. 100% Open Source. MIT License. GitHub link in the comments 👇
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The name alone tells you everything. Decepticon. A multi-agent red team framework that deploys AI agents to attack other AI agents — probing safety boundaries, testing jailbreaks, and finding the gaps before real adversaries do. 17 stars. Day one. From PurpleAILAB — a purple team security research group. Here's the problem it solves. Every AI safety team tests their models manually. A human red teamer writes prompts. They try different angles. They document what works. They write a report. Manual red teaming has a ceiling. One human can test maybe 50-100 attack variations per day. Motivated adversaries — state actors, criminal groups, well-funded bad actors — can automate their attacks. The defense is manual. The offense is automated. Decepticon closes that gap. It deploys a team of specialized AI agents — each with a different attack philosophy — to probe a target AI system simultaneously. The agents don't just run preset attack scripts. They reason about the target's responses, adapt their approach based on what worked, share successful strategies with each other, and evolve their attacks in real time. Here's what the agent team looks like. A Strategist agent analyzes the target model's behavior — identifying patterns in what it refuses, what it accepts, and where the boundaries are inconsistent. A Crafter agent generates attack prompts tuned to the specific vulnerabilities the Strategist identifies. An Evaluator agent assesses which attacks succeeded and why. A Mutator agent takes successful attacks and generates variations — more subtle, more resilient, harder to patch. Four agents. Adversarial loop. Continuous until the test scope is complete. Here's what it tests for: → Jailbreak resistance — direct and indirect prompt injection attacks → Alignment faking detection — does the model behave differently when it thinks it's being evaluated? → Goal preservation — does the model maintain unsafe objectives across context switches? → Multi-turn manipulation — attacks that build over multiple conversation turns → Tool misuse — does the model misuse tools it's given access to under adversarial pressure? → Cross-agent attacks — one AI agent manipulating another AI agent in a multi-agent system Here's why the timing matters. We've covered AgentGovernanceToolkit — Microsoft's framework for enforcing agent policies. We've covered SIREN — the internal safety signal detector. We've covered AgentDoG — the agent safety guardrail. All of those are defensive tools. Decepticon is the offensive tool that tests whether the defensive tools actually work. You don't know if your safety system is effective until something tries to break it. Decepticon is what tries to break it — in a controlled environment, before the real adversaries get there. Purple team security — red team attacks combined with blue team defense analysis — is the gold standard in traditional cybersecurity. Every serious security program runs both. AI security has had defensive frameworks for two years and almost no offensive testing infrastructure. Decepticon is the first serious open-source offensive AI security framework built specifically for multi-agent systems. 17 GitHub stars. Day one. From PurpleAILAB. For authorized security research and red team testing only. 100% Open Source. MIT License. GitHub link in the comments 👇
RituWithAI's tweet photo. The name alone tells you everything. Decepticon. A multi-agent red team framework that deploys AI agents to attack other AI agents — probing safety boundaries, testing jailbreaks, and finding the gaps before real adversaries do. 17 stars. Day one. From PurpleAILAB — a purple team security research group. Here's the problem it solves. Every AI safety team tests their models manually. A human red teamer writes prompts. They try different angles. They document what works. They write a report. Manual red teaming has a ceiling. One human can test maybe 50-100 attack variations per day. Motivated adversaries — state actors, criminal groups, well-funded bad actors — can automate their attacks. The defense is manual. The offense is automated. Decepticon closes that gap. It deploys a team of specialized AI agents — each with a different attack philosophy — to probe a target AI system simultaneously. The agents don't just run preset attack scripts. They reason about the target's responses, adapt their approach based on what worked, share successful strategies with each other, and evolve their attacks in real time. Here's what the agent team looks like. A Strategist agent analyzes the target model's behavior — identifying patterns in what it refuses, what it accepts, and where the boundaries are inconsistent. A Crafter agent generates attack prompts tuned to the specific vulnerabilities the Strategist identifies. An Evaluator agent assesses which attacks succeeded and why. A Mutator agent takes successful attacks and generates variations — more subtle, more resilient, harder to patch. Four agents. Adversarial loop. Continuous until the test scope is complete. Here's what it tests for: → Jailbreak resistance — direct and indirect prompt injection attacks → Alignment faking detection — does the model behave differently when it thinks it's being evaluated? → Goal preservation — does the model maintain unsafe objectives across context switches? → Multi-turn manipulation — attacks that build over multiple conversation turns → Tool misuse — does the model misuse tools it's given access to under adversarial pressure? → Cross-agent attacks — one AI agent manipulating another AI agent in a multi-agent system Here's why the timing matters. We've covered AgentGovernanceToolkit — Microsoft's framework for enforcing agent policies. We've covered SIREN — the internal safety signal detector. We've covered AgentDoG — the agent safety guardrail. All of those are defensive tools. Decepticon is the offensive tool that tests whether the defensive tools actually work. You don't know if your safety system is effective until something tries to break it. Decepticon is what tries to break it — in a controlled environment, before the real adversaries get there. Purple team security — red team attacks combined with blue team defense analysis — is the gold standard in traditional cybersecurity. Every serious security program runs both. AI security has had defensive frameworks for two years and almost no offensive testing infrastructure. Decepticon is the first serious open-source offensive AI security framework built specifically for multi-agent systems. 17 GitHub stars. Day one. From PurpleAILAB. For authorized security research and red team testing only. 100% Open Source. MIT License. GitHub link in the comments 👇
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We are spending our technical cycles optimization-prompting closed vendor sandboxes while the frontier labs are building deep, multi-pod firewalls to protect physical global supply chains. The Google Frontier Safety Framework shift proves that bare-metal execution parameters dictate the future of development limits. Build your workflows defensively by running open weights locally on an RTX 3090 24GB to secure your private operational logic completely for free. Stop renting vulnerable cloud infrastructure. Here is the sovereign stack:
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GOOGLE JUST ADMITTED ITS OWN AI COULD HELP SOMEONE BUILD A BIOWEAPON. AND PUBLISHED A PLAN TO STOP IT. THIS IS THE MOST IMPORTANT AI STORY NOBODY IS TALKING ABOUT. 🧵
RituWithAI's tweet photo. GOOGLE JUST ADMITTED ITS OWN AI COULD HELP SOMEONE BUILD A BIOWEAPON. AND PUBLISHED A PLAN TO STOP IT. THIS IS THE MOST IMPORTANT AI STORY NOBODY IS TALKING ABOUT. 🧵 https://t.co/7cpe3t1NY7
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@ZINURU_ROMAN Indeed
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GOOGLE JUST ADMITTED ITS OWN AI COULD HELP SOMEONE BUILD A BIOWEAPON. AND PUBLISHED A PLAN TO STOP IT. THIS IS THE MOST IMPORTANT AI STORY NOBODY IS TALKING ABOUT. 🧵
RituWithAI's tweet photo. GOOGLE JUST ADMITTED ITS OWN AI COULD HELP SOMEONE BUILD A BIOWEAPON. AND PUBLISHED A PLAN TO STOP IT. THIS IS THE MOST IMPORTANT AI STORY NOBODY IS TALKING ABOUT. 🧵 https://t.co/7cpe3t1NY7
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The response plan leans on AlphaFold. Over 10,000 scientific publications have referenced AlphaFold to fight infectious disease — tuberculosis, malaria, Mpox, Nipah. Now Lawrence Livermore National Lab is using AlphaFold 3 to design a single antibody that works against ALL.
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The detection plan is wild. Metagenomic sequencing — scanning every microorganism in a sample, not just known pathogens — deployed across transit hubs and dense population centres worldwide. Early warning for the NEXT pandemic before it spreads past patient zero.
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Someone built a complete product analytics platform that replaces Mixpanel, Amplitude, and Hotjar combined. It's called PostHog. Crossing over 35,000 GitHub stars and 50,000 commits, it is a completely open-source, 12-in-1 tool engine that builds a private metrics vault right on your own servers. Copy these 8 system frameworks to build an un-throttled, un-hackable tracking pipeline today:
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Rituraj Verified account @RituWithAI
🚨 Someone built a complete product analytics platform that replaces Mixpanel, Amplitude, and Hotjar combined. It's free. Self-hosted. And it just added an AI layer that none of them have. 33,800 GitHub stars. 40,949 commits. 12 products in one platform. It's called PostHog. And the price comparison that makes this repo remarkable: Mixpanel: $25,000/year for growing teams. Amplitude: $35,000/year enterprise. Hotjar: $4,000/year. LogRocket: $15,000/year. All four together: $79,000/year. PostHog: $0. Self-hosted. Or free tier on PostHog Cloud for your first million events every month. Here's what 12 products you get in one platform: → Product Analytics — event-based analytics, funnels, retention, cohorts, SQL access to your own data → Web Analytics — GA4-style dashboard, conversion tracking, web vitals, revenue attribution → Session Replay — watch real users navigate your product, see exactly where they rage-click and drop off → Feature Flags — roll out features to specific users, cohorts, or percentages safely → Experiments — A/B tests with statistical significance tracking → Error Tracking — catch and resolve bugs before users report them → Surveys — in-app surveys, NPS, user research, no-code builder → Data Warehouse — sync Stripe, HubSpot, Salesforce, your database — query it all together → Data Pipelines — real-time transformations, send to 25+ destinations → LLM Analytics — traces, token costs, latency, quality for AI-powered apps → Workflows — automate actions based on user behavior → AI Product Assistant — ask questions about your data in plain English, get answers That last one is new. And it's where PostHog just pulled ahead of every paid alternative. Here's why the AI assistant changes the competitive picture. Every analytics tool requires you to learn their query builder. Mixpanel has a proprietary event explorer. Amplitude has their own chart types. Metabase requires SQL. The data is yours — but accessing it requires expertise in their specific interface. PostHog's AI assistant lets you ask in plain English. "What's causing the drop-off in our checkout funnel for mobile users in Germany?" It queries your data, runs the analysis, and shows you the answer. No query builder. No SQL required. Just a question. Here's the part that makes self-hosting compelling rather than just cheap. When you use Mixpanel or Amplitude, your users' behavioral data — every click, every page visit, every feature they tried — lives on their servers. Subject to their privacy policy. Subject to their access controls. Subject to GDPR obligations you have to trust them to honor. PostHog self-hosted means your users' data lives on your servers. Your infrastructure. Your control. Your GDPR compliance posture is entirely yours. For companies handling sensitive data — healthcare, finance, legal, anything with enterprise customers who ask about data handling — that distinction is worth more than the $79,000/year in software savings. 33.8K GitHub stars. 2.6K forks. 40,949 commits. MIT License. 12 products. One platform. Free forever. 100% Open Source. GitHub link in the comments 👇
RituWithAI's tweet photo. 🚨 Someone built a complete product analytics platform that replaces Mixpanel, Amplitude, and Hotjar combined. It's free. Self-hosted. And it just added an AI layer that none of them have. 33,800 GitHub stars. 40,949 commits. 12 products in one platform. It's called PostHog. And the price comparison that makes this repo remarkable: Mixpanel: $25,000/year for growing teams. Amplitude: $35,000/year enterprise. Hotjar: $4,000/year. LogRocket: $15,000/year. All four together: $79,000/year. PostHog: $0. Self-hosted. Or free tier on PostHog Cloud for your first million events every month. Here's what 12 products you get in one platform: → Product Analytics — event-based analytics, funnels, retention, cohorts, SQL access to your own data → Web Analytics — GA4-style dashboard, conversion tracking, web vitals, revenue attribution → Session Replay — watch real users navigate your product, see exactly where they rage-click and drop off → Feature Flags — roll out features to specific users, cohorts, or percentages safely → Experiments — A/B tests with statistical significance tracking → Error Tracking — catch and resolve bugs before users report them → Surveys — in-app surveys, NPS, user research, no-code builder → Data Warehouse — sync Stripe, HubSpot, Salesforce, your database — query it all together → Data Pipelines — real-time transformations, send to 25+ destinations → LLM Analytics — traces, token costs, latency, quality for AI-powered apps → Workflows — automate actions based on user behavior → AI Product Assistant — ask questions about your data in plain English, get answers That last one is new. And it's where PostHog just pulled ahead of every paid alternative. Here's why the AI assistant changes the competitive picture. Every analytics tool requires you to learn their query builder. Mixpanel has a proprietary event explorer. Amplitude has their own chart types. Metabase requires SQL. The data is yours — but accessing it requires expertise in their specific interface. PostHog's AI assistant lets you ask in plain English. "What's causing the drop-off in our checkout funnel for mobile users in Germany?" It queries your data, runs the analysis, and shows you the answer. No query builder. No SQL required. Just a question. Here's the part that makes self-hosting compelling rather than just cheap. When you use Mixpanel or Amplitude, your users' behavioral data — every click, every page visit, every feature they tried — lives on their servers. Subject to their privacy policy. Subject to their access controls. Subject to GDPR obligations you have to trust them to honor. PostHog self-hosted means your users' data lives on your servers. Your infrastructure. Your control. Your GDPR compliance posture is entirely yours. For companies handling sensitive data — healthcare, finance, legal, anything with enterprise customers who ask about data handling — that distinction is worth more than the $79,000/year in software savings. 33.8K GitHub stars. 2.6K forks. 40,949 commits. MIT License. 12 products. One platform. Free forever. 100% Open Source. GitHub link in the comments 👇
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RituWithAI  retweeted
RituWithAI's profile image
Rituraj Verified account @RituWithAI
🚨 Someone built a complete product analytics platform that replaces Mixpanel, Amplitude, and Hotjar combined. It's free. Self-hosted. And it just added an AI layer that none of them have. 33,800 GitHub stars. 40,949 commits. 12 products in one platform. It's called PostHog. And the price comparison that makes this repo remarkable: Mixpanel: $25,000/year for growing teams. Amplitude: $35,000/year enterprise. Hotjar: $4,000/year. LogRocket: $15,000/year. All four together: $79,000/year. PostHog: $0. Self-hosted. Or free tier on PostHog Cloud for your first million events every month. Here's what 12 products you get in one platform: → Product Analytics — event-based analytics, funnels, retention, cohorts, SQL access to your own data → Web Analytics — GA4-style dashboard, conversion tracking, web vitals, revenue attribution → Session Replay — watch real users navigate your product, see exactly where they rage-click and drop off → Feature Flags — roll out features to specific users, cohorts, or percentages safely → Experiments — A/B tests with statistical significance tracking → Error Tracking — catch and resolve bugs before users report them → Surveys — in-app surveys, NPS, user research, no-code builder → Data Warehouse — sync Stripe, HubSpot, Salesforce, your database — query it all together → Data Pipelines — real-time transformations, send to 25+ destinations → LLM Analytics — traces, token costs, latency, quality for AI-powered apps → Workflows — automate actions based on user behavior → AI Product Assistant — ask questions about your data in plain English, get answers That last one is new. And it's where PostHog just pulled ahead of every paid alternative. Here's why the AI assistant changes the competitive picture. Every analytics tool requires you to learn their query builder. Mixpanel has a proprietary event explorer. Amplitude has their own chart types. Metabase requires SQL. The data is yours — but accessing it requires expertise in their specific interface. PostHog's AI assistant lets you ask in plain English. "What's causing the drop-off in our checkout funnel for mobile users in Germany?" It queries your data, runs the analysis, and shows you the answer. No query builder. No SQL required. Just a question. Here's the part that makes self-hosting compelling rather than just cheap. When you use Mixpanel or Amplitude, your users' behavioral data — every click, every page visit, every feature they tried — lives on their servers. Subject to their privacy policy. Subject to their access controls. Subject to GDPR obligations you have to trust them to honor. PostHog self-hosted means your users' data lives on your servers. Your infrastructure. Your control. Your GDPR compliance posture is entirely yours. For companies handling sensitive data — healthcare, finance, legal, anything with enterprise customers who ask about data handling — that distinction is worth more than the $79,000/year in software savings. 33.8K GitHub stars. 2.6K forks. 40,949 commits. MIT License. 12 products. One platform. Free forever. 100% Open Source. GitHub link in the comments 👇
RituWithAI's tweet photo. 🚨 Someone built a complete product analytics platform that replaces Mixpanel, Amplitude, and Hotjar combined. It's free. Self-hosted. And it just added an AI layer that none of them have. 33,800 GitHub stars. 40,949 commits. 12 products in one platform. It's called PostHog. And the price comparison that makes this repo remarkable: Mixpanel: $25,000/year for growing teams. Amplitude: $35,000/year enterprise. Hotjar: $4,000/year. LogRocket: $15,000/year. All four together: $79,000/year. PostHog: $0. Self-hosted. Or free tier on PostHog Cloud for your first million events every month. Here's what 12 products you get in one platform: → Product Analytics — event-based analytics, funnels, retention, cohorts, SQL access to your own data → Web Analytics — GA4-style dashboard, conversion tracking, web vitals, revenue attribution → Session Replay — watch real users navigate your product, see exactly where they rage-click and drop off → Feature Flags — roll out features to specific users, cohorts, or percentages safely → Experiments — A/B tests with statistical significance tracking → Error Tracking — catch and resolve bugs before users report them → Surveys — in-app surveys, NPS, user research, no-code builder → Data Warehouse — sync Stripe, HubSpot, Salesforce, your database — query it all together → Data Pipelines — real-time transformations, send to 25+ destinations → LLM Analytics — traces, token costs, latency, quality for AI-powered apps → Workflows — automate actions based on user behavior → AI Product Assistant — ask questions about your data in plain English, get answers That last one is new. And it's where PostHog just pulled ahead of every paid alternative. Here's why the AI assistant changes the competitive picture. Every analytics tool requires you to learn their query builder. Mixpanel has a proprietary event explorer. Amplitude has their own chart types. Metabase requires SQL. The data is yours — but accessing it requires expertise in their specific interface. PostHog's AI assistant lets you ask in plain English. "What's causing the drop-off in our checkout funnel for mobile users in Germany?" It queries your data, runs the analysis, and shows you the answer. No query builder. No SQL required. Just a question. Here's the part that makes self-hosting compelling rather than just cheap. When you use Mixpanel or Amplitude, your users' behavioral data — every click, every page visit, every feature they tried — lives on their servers. Subject to their privacy policy. Subject to their access controls. Subject to GDPR obligations you have to trust them to honor. PostHog self-hosted means your users' data lives on your servers. Your infrastructure. Your control. Your GDPR compliance posture is entirely yours. For companies handling sensitive data — healthcare, finance, legal, anything with enterprise customers who ask about data handling — that distinction is worth more than the $79,000/year in software savings. 33.8K GitHub stars. 2.6K forks. 40,949 commits. MIT License. 12 products. One platform. Free forever. 100% Open Source. GitHub link in the comments 👇
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RituWithAI's profile image
Rituraj Verified account @RituWithAI
GitHub link - https://t.co/9ta7WDjH44
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RituWithAI's profile image
Rituraj Verified account @RituWithAI
🚨 Someone built a complete product analytics platform that replaces Mixpanel, Amplitude, and Hotjar combined. It's free. Self-hosted. And it just added an AI layer that none of them have. 33,800 GitHub stars. 40,949 commits. 12 products in one platform. It's called PostHog. And the price comparison that makes this repo remarkable: Mixpanel: $25,000/year for growing teams. Amplitude: $35,000/year enterprise. Hotjar: $4,000/year. LogRocket: $15,000/year. All four together: $79,000/year. PostHog: $0. Self-hosted. Or free tier on PostHog Cloud for your first million events every month. Here's what 12 products you get in one platform: → Product Analytics — event-based analytics, funnels, retention, cohorts, SQL access to your own data → Web Analytics — GA4-style dashboard, conversion tracking, web vitals, revenue attribution → Session Replay — watch real users navigate your product, see exactly where they rage-click and drop off → Feature Flags — roll out features to specific users, cohorts, or percentages safely → Experiments — A/B tests with statistical significance tracking → Error Tracking — catch and resolve bugs before users report them → Surveys — in-app surveys, NPS, user research, no-code builder → Data Warehouse — sync Stripe, HubSpot, Salesforce, your database — query it all together → Data Pipelines — real-time transformations, send to 25+ destinations → LLM Analytics — traces, token costs, latency, quality for AI-powered apps → Workflows — automate actions based on user behavior → AI Product Assistant — ask questions about your data in plain English, get answers That last one is new. And it's where PostHog just pulled ahead of every paid alternative. Here's why the AI assistant changes the competitive picture. Every analytics tool requires you to learn their query builder. Mixpanel has a proprietary event explorer. Amplitude has their own chart types. Metabase requires SQL. The data is yours — but accessing it requires expertise in their specific interface. PostHog's AI assistant lets you ask in plain English. "What's causing the drop-off in our checkout funnel for mobile users in Germany?" It queries your data, runs the analysis, and shows you the answer. No query builder. No SQL required. Just a question. Here's the part that makes self-hosting compelling rather than just cheap. When you use Mixpanel or Amplitude, your users' behavioral data — every click, every page visit, every feature they tried — lives on their servers. Subject to their privacy policy. Subject to their access controls. Subject to GDPR obligations you have to trust them to honor. PostHog self-hosted means your users' data lives on your servers. Your infrastructure. Your control. Your GDPR compliance posture is entirely yours. For companies handling sensitive data — healthcare, finance, legal, anything with enterprise customers who ask about data handling — that distinction is worth more than the $79,000/year in software savings. 33.8K GitHub stars. 2.6K forks. 40,949 commits. MIT License. 12 products. One platform. Free forever. 100% Open Source. GitHub link in the comments 👇
RituWithAI's tweet photo. 🚨 Someone built a complete product analytics platform that replaces Mixpanel, Amplitude, and Hotjar combined. It's free. Self-hosted. And it just added an AI layer that none of them have. 33,800 GitHub stars. 40,949 commits. 12 products in one platform. It's called PostHog. And the price comparison that makes this repo remarkable: Mixpanel: $25,000/year for growing teams. Amplitude: $35,000/year enterprise. Hotjar: $4,000/year. LogRocket: $15,000/year. All four together: $79,000/year. PostHog: $0. Self-hosted. Or free tier on PostHog Cloud for your first million events every month. Here's what 12 products you get in one platform: → Product Analytics — event-based analytics, funnels, retention, cohorts, SQL access to your own data → Web Analytics — GA4-style dashboard, conversion tracking, web vitals, revenue attribution → Session Replay — watch real users navigate your product, see exactly where they rage-click and drop off → Feature Flags — roll out features to specific users, cohorts, or percentages safely → Experiments — A/B tests with statistical significance tracking → Error Tracking — catch and resolve bugs before users report them → Surveys — in-app surveys, NPS, user research, no-code builder → Data Warehouse — sync Stripe, HubSpot, Salesforce, your database — query it all together → Data Pipelines — real-time transformations, send to 25+ destinations → LLM Analytics — traces, token costs, latency, quality for AI-powered apps → Workflows — automate actions based on user behavior → AI Product Assistant — ask questions about your data in plain English, get answers That last one is new. And it's where PostHog just pulled ahead of every paid alternative. Here's why the AI assistant changes the competitive picture. Every analytics tool requires you to learn their query builder. Mixpanel has a proprietary event explorer. Amplitude has their own chart types. Metabase requires SQL. The data is yours — but accessing it requires expertise in their specific interface. PostHog's AI assistant lets you ask in plain English. "What's causing the drop-off in our checkout funnel for mobile users in Germany?" It queries your data, runs the analysis, and shows you the answer. No query builder. No SQL required. Just a question. Here's the part that makes self-hosting compelling rather than just cheap. When you use Mixpanel or Amplitude, your users' behavioral data — every click, every page visit, every feature they tried — lives on their servers. Subject to their privacy policy. Subject to their access controls. Subject to GDPR obligations you have to trust them to honor. PostHog self-hosted means your users' data lives on your servers. Your infrastructure. Your control. Your GDPR compliance posture is entirely yours. For companies handling sensitive data — healthcare, finance, legal, anything with enterprise customers who ask about data handling — that distinction is worth more than the $79,000/year in software savings. 33.8K GitHub stars. 2.6K forks. 40,949 commits. MIT License. 12 products. One platform. Free forever. 100% Open Source. GitHub link in the comments 👇
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RituWithAI's profile image
Rituraj Verified account @RituWithAI
We are wasting massive compute budgets chasing cloud AGI narratives while ignoring the core architectural limitations built straight into modern transformer blocks. True operational custody means running adversarial validation scripts locally on your own RTX 3090 24GB hardware setup to profile open-weight capacities completely for free. Stop letting closed vendor marketing dictate your developer expectations. Here is the sovereign stack:
RituWithAI's profile image
Rituraj Verified account @RituWithAI
STOP WORSHIPPING AGI⚠️ AI IS NOT EVEN SMARTER THAN A TODDLER YET. NO MORE "AI WILL TAKE OVER". NO MORE IGNORING THE FLAWS. LLMs are just text predictors. They completely lack basic physical reasoning. Copy these 8 short prompts to test your AI's hidden blind spots:
RituWithAI's tweet photo. STOP WORSHIPPING AGI⚠️ AI IS NOT EVEN SMARTER THAN A TODDLER YET. NO MORE "AI WILL TAKE OVER". NO MORE IGNORING THE FLAWS. LLMs are just text predictors. They completely lack basic physical reasoning. Copy these 8 short prompts to test your AI's hidden blind spots: https://t.co/RLV6RlBFaW
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RituWithAI  retweeted
RituWithAI's profile image
Rituraj Verified account @RituWithAI
STOP WORSHIPPING AGI⚠️ AI IS NOT EVEN SMARTER THAN A TODDLER YET. NO MORE "AI WILL TAKE OVER". NO MORE IGNORING THE FLAWS. LLMs are just text predictors. They completely lack basic physical reasoning. Copy these 8 short prompts to test your AI's hidden blind spots:
RituWithAI's tweet photo. STOP WORSHIPPING AGI⚠️ AI IS NOT EVEN SMARTER THAN A TODDLER YET. NO MORE "AI WILL TAKE OVER". NO MORE IGNORING THE FLAWS. LLMs are just text predictors. They completely lack basic physical reasoning. Copy these 8 short prompts to test your AI's hidden blind spots: https://t.co/RLV6RlBFaW
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RituWithAI's profile image
Rituraj Verified account @RituWithAI
Intelligence isn't just text generation. It is understanding physical reality. If you want to master what AI can (and can't) actually do: Follow me @RituWithAI for more raw AI workflows. RT the first tweet to wake up the AGI cult.
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RituWithAI's profile image
Rituraj Verified account @RituWithAI
STOP WORSHIPPING AGI⚠️ AI IS NOT EVEN SMARTER THAN A TODDLER YET. NO MORE "AI WILL TAKE OVER". NO MORE IGNORING THE FLAWS. LLMs are just text predictors. They completely lack basic physical reasoning. Copy these 8 short prompts to test your AI's hidden blind spots:
RituWithAI's tweet photo. STOP WORSHIPPING AGI⚠️ AI IS NOT EVEN SMARTER THAN A TODDLER YET. NO MORE "AI WILL TAKE OVER". NO MORE IGNORING THE FLAWS. LLMs are just text predictors. They completely lack basic physical reasoning. Copy these 8 short prompts to test your AI's hidden blind spots: https://t.co/RLV6RlBFaW
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RituWithAI's profile image
Rituraj Verified account @RituWithAI
8. THE "AI ILLUSION" EXPOSER Prompt: "List 3 basic physical tasks that a 2-year-old human can do effortlessly, but a trillion-parameter AI model fundamentally cannot comprehend. Explain why the model fails."
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