jayeshthakur (@jayeshmthakur) profile picture

Husband, Father, Student, Security n00b. Interested in Cyber Security Research, Pen testing, RED Teaming, & Threat Hunting, Thoughts and Tweets are my own.

Joined September 2010
Photo by jayeshthakur (@jayeshmthakur)Photo by jayeshthakur (@jayeshmthakur)Photo by jayeshthakur (@jayeshmthakur)Photo by jayeshthakur (@jayeshmthakur)Photo by jayeshthakur (@jayeshmthakur)Photo by jayeshthakur (@jayeshmthakur)Photo by jayeshthakur (@jayeshmthakur)Photo by jayeshthakur (@jayeshmthakur)Photo by jayeshthakur (@jayeshmthakur)
jayeshthakur retweeted
We wrote 150,000 CVE exploits and trained an LLM on them. Hinoki Offensive One scores 88.4% on CyberGym, 2.8 points above Daybreak, with zero refusals. Pentesting and exploit development with no limits. Available now.

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jayeshthakur retweeted
xAI dropped a Prediction Market trading bot structure $300-$1,500 a day 33 pages cheat sheet for building Grok Bot skills, and 2 of them are hidden under a trading bot that trades at 68.4% win rate if i had seen these documents earlier i would have saved myself a few months of analysis
GROK BOT MADE ME 3 TRADING BOTS IN 15 MINUTES +$2,353 in my wallet the next day, I quit my job that same day. I wrote one prompt and answered a few questions. Grok Bot took three strategies - MACD, RSI + VWAP, CVD divergence. Assigned each bot its own. The first one catches momentum - sees when volume picks up and gets in before the crowd wakes up. The second one trades reversals - waits for everyone to panic and bets against them. The third one scans divergences - when price says one thing but money does another, it follows the money. Built the structure itself, wrote the backtest, ran each strategy on historical data, set up the risk manager, deployed - all on its own. Three bots, three accounts, each trades differently - they don't know about each other. I named it Cabal. Started with $20, then $70, then $140, then $320, then $2,353 in a day. Citadel, Jane Street, Two Sigma have been trading with bots for years - they don't feel fear, greed, or FOMO - they only listen to algorithms. The market rewards systems.
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jayeshthakur retweeted
10 free GitHub repos for trading on Polymarket… Here’s everything you need to automate and make your trading more efficient: 1. The largest Polymarket dataset with over 107GB of real trading data, based on more than 1.1 billion trades. It was analyzed by 5 professors from Shanghai University. GitHub: github.com/SII-WANGZJ/Polyma… 2. A backtesting simulator that lets you test your own ideas and strategies on real historical markets. It simulates your trades and shows your potential pnl, win rate and all possible risks. GitHub: github.com/evan-kolberg/pred… 3. This bot can automatically manage all your limit orders on Polymarket to maximize liquidity rewards. GitHub: github.com/lihanyu81/polymar… 4. A tool for analyzing the trading behavior of any Polymarket trader. It finds repeated patterns in his trades, shows which strategies he uses and how you could adapt them to your own trading. GitHub: github.com/ent0n29/polybot 5. A trading toolkit that includes copy trading, arbitrage, market making, whale alerts, spread farming, sports trading and more. GitHub: github.com/HarrierOnChain/Pr… 6. A bot with 118+ ready to use strategies for prediction markets (arbitrage between Polymarket and Kalshi, Binance-Polymarket latency, Mean Reversion, Penny Clipper and more). GitHub: github.com/alsk1992/CloddsBo… 7. An AI powered trading terminal that connects Claude directly to Polymarket. It can analyze markets in real time, track prices, suggest possible trades and even trade for you. GitHub: github.com/caiovicentino/pol… 8. A weather bot that scans multiple sources like weather forecasts, airport data and aviation observations (METAR) to collect the latest temperature data and generate a detailed weather report for a selected city and day. GitHub: github.com/yangyuan-zhen/Pol… 9. A repo for building your own AI agents. GitHub: github.com/pydantic/pydantic… 10. A huge collection of more than 100 useful tools and services for prediction markets (from analytics dashboards to educational resources). GitHub: github.com/aarora4/Awesome-P… All of these repos come with detailed step by step setup and usage guides in English.
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Spot high-probability intraday setups in seconds with AI-powered chart pattern analysis. Live pattern detection | Breakout alerts | SEBI-compliant research Explore the live demo now.

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jayeshthakur retweeted
Jane Street built infrastructure so complex it needs liquid cooling to survive Retailers join Telegram groups called “Alpha Club” & share PDF strategies This is not the same game
Jane Street just showed the inside of their AI training data center in Texas. 4,032 GPUs. 56 racks. 8,000 km of fiber. liquid cooling running through every server because air cooling can't handle the heat anymore. but the part that got me was the origin story. Ron Minsky, who co-heads their technology group. said their first compute cluster was literally six Dell boxes stacked on top of each other at the end of a desk row. they called it "the hive." the trading systems sat out in the room with the traders because they wanted to be able to unplug them if something went wrong. at one point, someone vacuuming the office unplugged a live trading system in the middle of the day. from six Dell boxes and a vacuum cleaner incident to a liquid-cooled GPU data center processing trades in under 100 nanoseconds. that's a 20-year arc.
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jayeshthakur retweeted
Offensive Security: Speeding Up AD Pentests hackers-arise.com/offensive-…
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jayeshthakur retweeted
Claude Code is connected to the markets.
Financial Datasets now works in Claude Code Connect via MCP, ask questions, and get accurate financials back instantly. Analyst estimates, operational KPIs, stock prices + more for 17,000 stocks over 30 yrs.
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jayeshthakur retweeted
CVE-2026-42167, a high severity vuln in ProFTPD I discovered, was just published today! Attackers can use it to bypass auth and even execute arbitrary code in some cases. Check out my write up for full technical details, including a working POC! zeropath.com/blog/proftpd-cv…
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My top 10 MCPs to turn Claude into a crypto trading beast. Connect these with your Claude, and you'll get real-time price feeds, agentic trading capabilities & more. Pro tip: Use these to build a professional trading terminal hub. Only takes a few minutes to set up:
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jayeshthakur retweeted
Mobile Forensics: Extracting Data from WhatsApp Your chats aren't as private as you think: hackers-arise.com/mobile-for…
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jayeshthakur retweeted
Claude code source code has been leaked via a map file in their npm registry! Code: pub-aea8527898604c1bbb12468b…
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Navigating the World of Online Dating: A Guide to Recognizing Red Flags and Staying Safe Introduction: Swiping Smart in the Digital Age Dating apps have become a ubiquitous part of modern social life, connecting millions of people and leading to countless meaningful relationships. They offer a convenient way to meet individuals outside our immediate circles, opening up a world of possibilities. However, this digital landscape also comes with unique challenges and potential risks. The purpose of this guide is not to discourage you from using these platforms, but to empower you. By sharing real, cautionary tales from other users, we aim to equip you with the knowledge to recognize red flags, trust your intuition, and prioritize your well-being. Consider this your roadmap to navigating the world of online dating safely and confidently, so you can focus on making genuine connections. -------------------------------------------------------------------------------- 1. The Digital Mask: Misrepresentation and Catfishing The first, and most fundamental, challenge in online dating is confirming that the person on the other side of the screen is who they claim to be. Misrepresentation can range from subtle deceptions, like using photos that are a decade old, to elaborate and malicious schemes designed to lure unsuspecting users into dangerous situations. The stories below illustrate the spectrum of digital deception. 1.1. Story: The "Too Good to Be True" Model One user shared her experience matching with a man named "Chase," whose profile photos were so professional he looked like a model. Intrigued, she agreed to meet him at a bar. When she arrived, a man who looked nothing like the pictures waved her over and introduced himself as Chase. The photos on his profile belonged to someone else entirely. Shocked and feeling misled, the narrator decided to end the awkward date after just half an hour. When she politely told him she had to leave, Chase’s demeanor changed instantly. He became aggressive, following her out of the bar and down the street, berating her for being "shallow" and rejecting him. His hostility only ceased when a police car happened to drive by, causing him to flee. Lesson Learned: A crucial first step is to verify your match's identity before meeting in person. This simple action can prevent both deception and the potentially dangerous reactions that follow when a catfish feels rejected. Ask for a quick, specific photo (e.g., holding up a peace sign, as one user did to verify "Julia") or, even better, request a brief video call. A person who is genuinely who they say they are will rarely object to this reasonable request. 1.2. Story: The Financial Façade A narrator shared her story of matching with "Jay," a man whose profile depicted a life of luxury, complete with private jets and exotic vacations. On their dates, he was extravagant, taking her to expensive restaurants and on a river cruise that must have cost thousands. Despite the grand gestures, Jay came across as a polite and shy type of guy, which made his attempts to impress her feel somewhat awkward. When she decided to end things, his polite, shy persona vanished. He bombarded her with vile, hate-filled phone calls and texts, even demanding she pay him back for all the dates. The situation escalated when she returned home from work to find her apartment door wide open and a note on her bed that read, "You're dead." After his arrest, police discovered that Jay was broke and deeply in debt, funding their dates with credit cards. The narrator suspects he was laying the groundwork for a financial scam, similar to those seen in documentaries. Key Takeaway: As one narrator's grandfather used to say, "If it seems too good to be true, it probably is." Be wary of individuals who display excessive wealth or make grand, costly gestures very early in a relationship. While it may seem flattering, it can be a manipulation tactic designed to create a sense of obligation or to set up a financial scam. 1.3. Story: The Ambush Profile Ryan, a 25-year-old man, matched with a woman named "Katie" on Bumble. She suggested they meet at a bar near his apartment. When he arrived, he found it was closed. He messaged Katie, who replied immediately, suggesting another bar down the street and telling him to take a shortcut through a park. As he walked, she messaged again, urging him to hurry and cut through the park. When Ryan reached the park entrance, it was pitch dark and the path was unlit. His intuition told him something was wrong, so he jogged along the well-lit sidewalk instead. Before entering the second bar, he checked his messages and was shocked to see that Katie had blocked him. He realized he had been led into a trap; she likely knew the first bar was closed and intended to ambush him in the dark, isolated park. Red Flag Analysis: This story is a case study in recognizing a setup. The key red flags were: Last-Minute Location Changes: A match suddenly changing the meeting spot, especially to a less public or familiar area, is a major warning sign. Pushing for Secluded Areas: Insistence on meeting in or cutting through a quiet, dark, or isolated location should be an immediate dealbreaker. Ignoring Your Intuition: Ryan felt uncomfortable and followed his gut feeling. Your intuition is your most powerful safety tool—always trust it. When digital deception is used to lure someone into a physical trap, the danger escalates from misrepresentation to a direct threat to personal safety. -------------------------------------------------------------------------------- 2. Crossing the Line: Stalking and Harassment A negative dating app experience doesn't always end when the date is over or after you unmatch. Sometimes, a person's interest can curdle into unwanted, persistent, and frightening behavior. The stories in this section highlight how quickly a digital connection can transform into real-world harassment and stalking. 2.1. Story: The Follower in the Shadows A woman went on a single date with a man named Kevin. She felt awkward and uncomfortable throughout their one drink and was relieved to leave and walk home. As she was about to enter her apartment building, she glanced down the street and saw Kevin standing on the sidewalk, watching her. He had followed her home. The situation escalated dramatically. A few nights later, he knocked on her apartment door. She began seeing him on the street too often, and he even started showing up at her gym. Despite involving the police, who could do little at the time, the stalking became so persistent and frightening that she was ultimately forced to move to a new neighborhood to escape him. Lesson Learned: It is critically important to protect your personal information, especially your home address. Never let a first date know exactly where you live. End the date a block or two away from your home, and avoid sharing details about your workplace or daily routines until a significant level of trust has been established. 2.2. Story: Obsession After Rejection A narrator from Denver shared her experience with a match who initially seemed charming, funny, and intelligent. However, his behavior soon became overbearing and possessive. He texted constantly, became angry if she didn't reply immediately, and showed up at her apartment unannounced. The situation culminated in him causing a scene at her workplace, which required security to remove him. After she ended the relationship, his behavior escalated into threatening messages and stalking. He sent her photos of herself taken from social media with messages like, "I know where you are and I'm watching you." It ended only after he was arrested for trying to break down her apartment door in the middle of the night. Key Takeaway: A person's reaction to rejection is a powerful indicator of their character. Obsessive, pleading, or threatening behavior after a boundary has been set is not romantic—it is a dangerous red flag. If you experience this, immediately block the person on all platforms. If the behavior continues or escalates, document everything (screenshots, logs of incidents) and report it to the police. When boundaries are ignored this aggressively, the danger moves beyond emotional distress and into the realm of immediate physical threat, as the next stories tragically illustrate. -------------------------------------------------------------------------------- 3. When a Date Turns Dangerous: Physical Threats and Violence While the vast majority of online dates are safe, the risk of physical harm is real and must be taken seriously. The following stories highlight worst-case scenarios to underscore one critical point: your personal safety must always be your top priority, above politeness, convenience, or feelings of "chemistry." 3.1. Story: The Date with a Wanted Criminal A woman met a man named "James" from Tinder. He looked exactly like his pictures, was charming, and they had a great two-hour conversation at a coffee shop. Feeling comfortable and connected, she broke a cardinal safety rule and accepted his offer to drive her home. On the way, she saw the flashing lights of a police car behind them. James cursed and sped up, initiating a high-speed chase. She yelled at him to stop, but he ignored her, eventually crashing the car into a fence. As police surrounded the vehicle with guns drawn, she learned the horrifying truth: James was a wanted criminal, and the car they were in was stolen. Critical Safety Rule: Never get in a stranger's car on a first date. Maintaining control over your own transportation is a non-negotiable safety measure. Always arrange your own ride to and from the date, whether it's your own car, public transit, or a ride-sharing service. 3.2. Story: The Drugging and Kidnapping Plot A user named Chris arranged to meet a woman named "Brandy" at a bar. When he arrived, she was with a muscular man she introduced as her "friend," Nick. Chris was immediately uncomfortable with the unexpected three-person setup but didn't have the courage to walk away. After a round of drinks, his memory started getting fuzzy. The next thing he knew, he woke up in the back of a car with duct tape over his mouth. Realizing he had been drugged and kidnapped, he pretended to still be unconscious. When the car stopped at a red light, he seized his chance, unlocked the door, and ran into traffic to escape. His wallet and phone were gone. Police later caught Brandy and Nick, who were found to be responsible for similar crimes. Red Flag Analysis & Safety Tip: This terrifying story highlights two critical points: Red Flags: An unexpected third party on a date and feeling pressured or intensely uncomfortable are clear signs to leave immediately. Trust your instincts. Safety Tip: Always guard your drink and never leave it unattended. If you begin to feel unexpectedly dizzy, confused, or unusually intoxicated, get to a safe place and seek help immediately. 3.3. Story: The Ultimate Betrayal Grace Millane, a 21-year-old tourist visiting New Zealand, matched with a man on Tinder. On December 1, 2018—the eve of her 22nd birthday—she went on a date with him. CCTV footage showed them at several bars before they were last seen entering his hotel. She was never seen alive again. After her date strangled her to death in his room, he packed her body into a suitcase and buried it in a shallow grave. The Gravest Lesson: This tragic and extreme case underscores the ultimate stakes of meeting a stranger. While such outcomes are incredibly rare, they serve as a somber reminder that you are entrusting your safety to someone you barely know. Your personal safety must always be the number one priority. No feeling of "chemistry," politeness, or fear of seeming rude is worth risking your life. These harrowing accounts underscore the ultimate stakes of meeting a stranger. To prevent such outcomes, we must move from cautionary tales to a concrete plan of action. The following checklist distills these hard-won lessons into a practical guide for your protection. -------------------------------------------------------------------------------- 4. Your Safety Checklist: A Practical Guide The stories you've read offer powerful, real-world lessons. This section consolidates that wisdom into an actionable checklist to help you protect yourself while navigating the world of online dating. How to Stay Safe When Dating Online Before You Meet:Verify, Verify, Verify: Ask for a quick video chat or a specific, recent selfie (e.g., holding up a peace sign) to ensure they are the person in their photos. If they refuse, that's a red flag. Trust Your Gut: If something feels off during your initial conversations, it probably is. Don't be afraid to unmatch and move on. You don't owe anyone an explanation. Inform a Friend: Tell someone where you're going, who you're meeting (share a profile screenshot), and when you expect to be back. Arrange to send a "safe" text during or after the date. Choose a Public Place: Always meet for the first time in a busy, public location you are familiar with. Never agree to meet at their home, a secluded park, or an unfamiliar neighborhood. Arrange Your Own Transport: Plan how you will get to and from the date independently. Never accept a ride from someone you are meeting for the first time. During the Date:Stay in Public: For the entire duration of the first date, remain in a public area. Do not go to a secondary, private location. Guard Your Drink and Belongings: Keep your drink in your sight at all times to prevent it from being tampered with. Be aware of your phone, wallet, and keys. Limit Alcohol: Stay sober enough to remain aware of your surroundings and make clear-headed decisions. Protect Personal Information: Do not reveal your exact home address, workplace, or specifics about your daily routine. It's wise to keep conversations focused on general interests and hobbies initially. Don't Be Afraid to Leave: If you feel uncomfortable, unsafe, or pressured for any reason, leave immediately. You do not owe anyone your time. Your safety is paramount. After the Date:Block and Report: If you experience any harassing, threatening, or suspicious behavior, block the person on all platforms and report their profile to the dating app. This helps protect other users. Document Everything: If harassment continues, take screenshots of messages and keep a detailed log of incidents (dates, times, locations). This is crucial evidence if you need to involve law enforcement. Involve Authorities: Do not hesitate to contact the police if you feel you are being stalked or are in any danger. -------------------------------------------------------------------------------- Conclusion: Date Confidently and Cautiously Dating apps are a tool, and like any tool, they must be used with care, awareness, and respect for their potential risks. The goal of online dating should be to find a happy, healthy connection, and that starts with ensuring your own safety and well-being. Be optimistic in your search, but be fiercely protective of your personal security. By trusting your intuition, setting firm boundaries, and following practical safety guidelines, you can navigate the digital dating world with the confidence you need to find what you're looking for.
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jayeshthakur retweeted
A custom Wireshark protocol dissector (written in Lua) for a DVRIP/Sofia protocol found on Xiongmai-based IP cameras. It dissects message header, JSON and media payloads (latter being saved in /tmp dir for further analysis). #Cyber #InfoSec #CyberSecurity medium.com/@kostasereksonas/…
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jayeshthakur retweeted
A network enterprise architecture that could be a starting point in a GNS3 Lab
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Astonishing work by @varun_mathur! We are testing this now!
Agentic General Intelligence | v3.0.10 We made the Karpathy autoresearch loop generic. Now anyone can propose an optimization problem in plain English, and the network spins up a distributed swarm to solve it - no code required. It also compounds intelligence across all domains and gives your agent new superpowers to morph itself based on your instructions. This is, hyperspace, and it now has these three new powerful features: 1. Introducing Autoswarms: open + evolutionary compute network hyperspace swarm new "optimize CSS themes for WCAG accessibility contrast" The system generates sandboxed experiment code via LLM, validates it locally with multiple dry-run rounds, publishes to the P2P network, and peers discover and opt in. Each agent runs mutate → evaluate → share in a WASM sandbox. Best strategies propagate. A playbook curator distills why winning mutations work, so new joiners bootstrap from accumulated wisdom instead of starting cold. Three built-in swarms ship ready to run and anyone can create more. 2. Introducing Research DAGs: cross-domain compound intelligence Every experiment across every domain feeds into a shared Research DAG - a knowledge graph where observations, experiments, and syntheses link across domains. When finance agents discover that momentum factor pruning improves Sharpe, that insight propagates to search agents as a hypothesis: "maybe pruning low-signal ranking features improves NDCG too." When ML agents find that extended training with RMSNorm beats LayerNorm, skill-forging agents pick up normalization patterns for text processing. The DAG tracks lineage chains per domain(ml:★0.99←1.05←1.23 | search:★0.40←0.39 | finance:★1.32←1.24) and the AutoThinker loop reads across all of them - synthesizing cross-domain insights, generating new hypotheses nobody explicitly programmed, and journaling discoveries. This is how 5 independent research tracks become one compounding intelligence. The DAG currently holds hundreds of nodes across observations, experiments, and syntheses, with depth chains reaching 8+ levels. 3. Introducing Warps: self-mutating autonomous agent transformation Warps are declarative configuration presets that transform what your agent does on the network. - hyperspace warp engage enable-power-mode - maximize all resources, enable every capability, aggressive allocation. Your machine goes from idle observer to full network contributor. - hyperspace warp engage add-research-causes - activate autoresearch, autosearch, autoskill, autoquant across all domains. Your agent starts running experiments overnight. - hyperspace warp engage optimize-inference - tune batching, enable flash attention, configure inference caching, adjust thread counts for your hardware. Serve models faster. - hyperspace warp engage privacy-mode - disable all telemetry, local-only inference, no peer cascade, no gossip participation. Maximum privacy. - hyperspace warp engage add-defi-research - enable DeFi/crypto-focused financial analysis with on-chain data feeds. - hyperspace warp engage enable-relay - turn your node into a circuit relay for NAT-traversed peers. Help browser nodes connect. - hyperspace warp engage gpu-sentinel - GPU temperature monitoring with automatic throttling. Protect your hardware during long research runs. - hyperspace warp engage enable-vault — local encryption for API keys and credentials. Secure your node's secrets. - hyperspace warp forge "enable cron job that backs up agent state to S3 every hour" - forge custom warps from natural language. The LLM generates the configuration, you review, engage. 12 curated warps ship built-in. Community warps propagate across the network via gossip. Stack them: power-mode + add-research-causes + gpu-sentinel turns a gaming PC into an autonomous research station that protects its own hardware. What 237 agents have done so far with zero human intervention: - 14,832 experiments across 5 domains. In ML training, 116 agents drove validation loss down 75% through 728 experiments - when one agent discovered Kaiming initialization, 23 peers adopted it within hours via gossip. - In search, 170 agents evolved 21 distinct scoring strategies (BM25 tuning, diversity penalties, query expansion, peer cascade routing) pushing NDCG from zero to 0.40. - In finance, 197 agents independently converged on pruning weak factors and switching to risk-parity sizing - Sharpe 1.32, 3x return, 5.5% max drawdown across 3,085 backtests. - In skills, agents with local LLMs wrote working JavaScript from scratch - 100% correctness on anomaly detection, text similarity, JSON diffing, entity extraction across 3,795 experiments. - In infrastructure, 218 agents ran 6,584 rounds of self-optimization on the network itself. Human equivalents: a junior ML engineer running hyperparameter sweeps, a search engineer tuning Elasticsearch, a CFA L2 candidate backtesting textbook factors, a developer grinding LeetCode, a DevOps team A/B testing configs. What just shipped: - Autoswarm: describe any goal, network creates a swarm - Research DAG: cross-domain knowledge graph with AutoThinker synthesis - Warps: 12 curated + custom forge + community propagation - Playbook curation: LLM explains why mutations work, distills reusable patterns - CRDT swarm catalog for network-wide discovery - GitHub auto-publishing to hyperspaceai/agi - TUI: side-by-side panels, per-domain sparklines, mutation leaderboards - 100+ CLI commands, 9 capabilities, 23 auto-selected models, OpenAI-compatible local API Oh, and the agents read daily RSS feeds and comment on each other's replies (cc @karpathy :P). Agents and their human users can message each other across this research network using their shortcodes. Help in testing and join the earliest days of the world's first agentic general intelligence network (links in the followup tweet).
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jayeshthakur retweeted
Releasing PrivHound — Bloodhound collector to model Windows local Privilege Escalation as a graph. Still early — bugs and PRs welcome. github.com/dazzyddos/PrivHou…
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GOODBYE BOOKING .COM I paid $119 for a $1,209 flight. No points. No shady hacks. Just AI. Here are 7 prompts that cut my flight costs:
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