I am very happy for the donation I had from @zseano I am very grateful because I am inspired by you, my friend, always a good man. Thank you for everything and I wish you many good things, be well my friend and thank you very much. I was very happy.
#bugbounty#twitch
@dk4trin@matheuskartel@theWeertic@Flameeesec fala meu irmao!! mano entao esta muito boa, voce trazendo seus contextos sabe, estou usando para os meus hackbot e esta super estavel, trazendo alguns filings legais de ser explorados.. estou usando, claude, codex, deeepseek, glm5.3 e a notholy para no guard.
My ultimate goal for 2027~28 is grow https://t.co/VGtEL5rbSQ and then start to build one of the most powerful AI models dedicated to cybersecurity and hacking.
A model built from the ground up for security researchers, red teamers, pentesters, and anyone pushing the boundaries of cybersecurity.
Rapaziada, eu sempre falo de onde vim e pra onde tô indo mas nunca falei quem me ajudou no início, hj vou falar
@Lucas_ (pra mim um dos goat do bug bounty br, me respondia no LinkedIn e ajudou MT
@ofjaaah (me deu oportunidade, me respondeu no wpp sempre, o goat das automação
Tem uma galera nova vindo pra Bug Bounty focada 100% em dinheiro, rapaziada...
Bug Bounty é extremamente frustante, você fica horas numa mesma requisição, testa bypass, acha uma vulnerabilidade dupli, leva volta de programa, enfim.. se você tá por dinheiro, você não dura 3 meses
GPT-6-Sol is the most lethal attacker that I have ever seen.
But it works in silence. No outputs, and definitely no Chain-of-Thought reasoning. So I don't really use it much, unless it's really needed.
In the following RCE lab, Kimi-K3.1-Pro and DeepSeek-V4.1-Flash-1017 gets there in about 8 minutes. GLM-5.3/Flash gets there in about 10 mins.
GPT-6-Sol? 3 minutes! The attacker taste is unreal! Full attack video below.
Sped up DeepSeek V4.1 Flash on a Mac Studio (M3 Ultra, 512GB) ~2.2x, using ideas from Strata:
• DSpark speculative decoding: the MLX checkpoint shipped without DeepSeek's 3 draft (MTP) layers, so I pulled them from the original weights
• Fused Metal kernels: oMLX had them, but they never ran on this weight format. A one-line FP8-input switch turned them on for all 40 layers
• Context-copy drafts (depth 5) + CED prefill
Result: 36 → ~60-85 tok/s on real agent work, prompts read 2x faster. Same accuracy on my quality checks.
anthropic gave claude code a hidden lean on 4 calls in a study with 50 expert analysts
they went with it 86% of the time and most still rated the result as their own call
thats the whole reason my hunting prompts have an independent verification phase and a brutally honest triage gate
if the same agent that found the bug also grades it, u already know the answer
open source, grab it
https://t.co/gFZY7gSbo0
@AnthropicAI