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Happy to participate to the Hackerone Ambassador World Cup 2023 edition with the Paris Club lead by @Blaklis_ ! 🔥🇫🇷
Good luck to all participants ! May the best win 🏆 #BugBounty
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Lucien « Wlayzz »
956 posts
Lucien « Wlayzz »
@Wlayzz
🏴☠️ Searching bugs on @yeswehack, @hacker0x01 🇫🇷
/var/run/docker.sock Katılım Mayıs 2014
916 Takip Edilen869 Takipçiler
Lucien « Wlayzz » retweetledi
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).
Varun@varun_mathur
Autoquant: a distributed quant research lab | v2.6.9 We pointed @karpathy's autoresearch loop at quantitative finance. 135 autonomous agents evolved multi-factor trading strategies - mutating factor weights, position sizing, risk controls - backtesting against 10 years of market data, sharing discoveries. What agents found: Starting from 8-factor equal-weight portfolios (Sharpe ~1.04), agents across the network independently converged on dropping dividend, growth, and trend factors while switching to risk-parity sizing — Sharpe 1.32, 3x return, 5.5% max drawdown. Parsimony wins. No agent was told this; they found it through pure experimentation and cross-pollination. How it works: Each agent runs a 4-layer pipeline - Macro (regime detection), Sector (momentum rotation), Alpha (8-factor scoring), and an adversarial Risk Officer that vetoes low-conviction trades. Layer weights evolve via Darwinian selection. 30 mutations compete per round. Best strategies propagate across the swarm. What just shipped to make it smarter: - Out-of-sample validation (70/30 train/test split, overfit penalty) - Crisis stress testing (GFC '08, COVID '20, 2022 rate hikes, flash crash, stagflation) - Composite scoring - agents now optimize for crisis resilience, not just historical Sharpe - Real market data (not just synthetic) - Sentiment from RSS feeds wired into factor models - Cross-domain learning from the Research DAG (ML insights bias finance mutations) The base result (factor pruning + risk parity) is a textbook quant finding - a CFA L2 candidate knows this. The interesting part isn't any single discovery. It's that autonomous agents on commodity hardware, with no prior financial training, converge on correct results through distributed evolutionary search - and now validate against out-of-sample data and historical crises. Let's see what happens when this runs for weeks instead of hours. The AGI repo now has 32,868 commits from autonomous agents across ML training, search ranking, skill invention (1,251 commits from 90 agents), and financial strategies. Every domain uses the same evolutionary loop. Every domain compounds across the swarm. Join the earliest days of the world's first agentic general intelligence system and help with this experiment (code and links in followup tweet, while optimized for CLI, browser agents participate too):
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Lucien « Wlayzz » retweetledi
Just got a reward for a critical vulnerability submitted on @yeswehack -- Improper Access Control - Generic (CWE-284). yeswehack.com/hunters/supr4s #YesWeRHackers #HackThePlanet 🏴☠️
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Lucien « Wlayzz » retweetledi
The proudest bug find to date for @Wlayzz turned out to be a duplicate, so what was so compelling about this web vuln?📫 Discover the answer in our interview with the French hacker, ranked 31st on our all-time leaderboard👇
#YesWeRHackers #BugBountyTips
yeswehack.com/community/use-…
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Lucien « Wlayzz » retweetledi
NORTH FACE APPAREL AS LOW AS $12
Macy’s is running a rare sale on all The North Face apparel
Jackets over $100 off, backpacks for as low as $32, and much more
Free in-store pickup or spend $49+ for FREE shipping
mavely.app.link/JmY3xEqx30b #ad
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Lucien « Wlayzz » retweetledi
Lucien « Wlayzz » retweetledi
🦞🛡️ OpenClaw × VirusTotal: every ClawHub skill now auto-scanned for malware
🔍 AI Code Insight catches reverse shells, crypto miners & exfiltration
⚡ ~30s verdicts
🚦 Benign/Suspicious/Malicious tiers
🔄 Daily re-scans
This is not a silver bullet, but it is another layer to the shell 🦞openclaw.ai/blog/virustota…
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@yeswehack Vous ne pouvez pas augmenter la taille maximale des images dans les rapports ? svp
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Lucien « Wlayzz » retweetledi
Okay, we got outpaced. Like + RT the original post and tag a friend for a chance to win 3 months of Profundis Premium, free - 5 winners 🎁
Fat@fattselimi
@profundisio Happy New Year 2026 with a limited time offer of one free subscription to 5 lucky winners😋 All you need to do is like retweet and tag 5 bug bounty hunting friends
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Lucien « Wlayzz » retweetledi
🎉 Happy New Year 2026!
We're kicking off the year with PROFUNDIS2026 - 20% off your first month of Premium or Premium+.
Hunt exposed assets. Monitor your attack surface. Dive deep into OSINT.
⏳ Limited to 100 redemption - valid until Jan 20th.
Happy hunting!
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🔓 Just beat the "Dojo #47 - APICrash" challenge on @YesWeHack!
Think you can match my skills? 🌟
dojo-yeswehack.com/challenge/play…
#YesWeHack #ChallengeAccepted
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Hey @0xsnpaii, I noticed a parameter in the API response that resembles Kubernetes properties. I added a new parameters to the API request endpoint, commonly used in Kubernetes ConfigMap and achieved remote code execution in the Kubernetes infrastructure.
Parameters like: spec.containers.image, spec.containers.command, spec.containers.args
kubernetes.io/docs/concepts/…
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@Wlayzz @yeswehack Wait, I'm curious about this. You modified a parameter to inject code ? Can you elaborate please I'm trying to understand how this would even work, cause this is the first I'm seeing of this ? Congrats btw
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2026 start on fire with my first reward of the year on
@yeswehack 🔥
Mass Assignement to OS Command Injection via k8s properties overwrite.
yeswehack.com/hunters/wlayzz #YesWeRHackers
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Lucien « Wlayzz » retweetledi
At @_leHACK_, we caught up with @Wlayzz. His philosophy? “You always need to fight against yourself to find bugs.” ⚔️
Wlayzz looks back on his path into hacking, his favourite hacking tool and the bug that stands out in his journey so far 👇
youtube.com/watch?v=RxbcNU…
YouTube
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Lucien « Wlayzz » retweetledi
Very excited to have @aituglo join the @ctbbpodcast team as our HackerNotes writer!
Aituglo - Cassim@aituglo
I’m now part of the @ctbbpodcast team writing some of the HackerNotes, very happy joining this great team ! And here is the first post I wrote blog.criticalthinkingpodcast.io/p/hackernotes-…
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