sshell

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@sshell_

AI offensive security at @RunSybil (prev. @BishopFox). security research. ccdc red team. tummy ache survivor.

Virginia, USA Beigetreten Haziran 2013
1.4K Folgt10.4K Follower
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sshell
sshell@sshell_·
professional hacking tip: be nice to people
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sshell@sshell_·
@BonJarber absolutely insane. definitely want to see more of this in action!
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Jon Barber 🤖
Jon Barber 🤖@BonJarber·
100M tokens with less than 9% accuracy degradation 👀👀
艾略特@elliotchen100

论文来了。名字叫 MSA,Memory Sparse Attention。 一句话说清楚它是什么: 让大模型原生拥有超长记忆。不是外挂检索,不是暴力扩窗口,而是把「记忆」直接长进了注意力机制里,端到端训练。 过去的方案为什么不行? RAG 的本质是「开卷考试」。模型自己不记东西,全靠现场翻笔记。翻得准不准要看检索质量,翻得快不快要看数据量。一旦信息分散在几十份文档里、需要跨文档推理,就抓瞎了。 线性注意力和 KV 缓存的本质是「压缩记忆」。记是记了,但越压越糊,长了就丢。 MSA 的思路完全不同: → 不压缩,不外挂,而是让模型学会「挑重点看」 核心是一种可扩展的稀疏注意力架构,复杂度是线性的。记忆量翻 10 倍,计算成本不会指数爆炸。 → 模型知道「这段记忆来自哪、什么时候的」 用了一种叫 document-wise RoPE 的位置编码,让模型天然理解文档边界和时间顺序。 → 碎片化的信息也能串起来推理 Memory Interleaving 机制,让模型能在散落各处的记忆片段之间做多跳推理。不是只找到一条相关记录,而是把线索串成链。 结果呢? · 从 16K 扩到 1 亿 token,精度衰减不到 9% · 4B 参数的 MSA 模型,在长上下文 benchmark 上打赢 235B 级别的顶级 RAG 系统 · 2 张 A800 就能跑 1 亿 token 推理。这不是实验室专属,这是创业公司买得起的成本。 说白了,以前的大模型是一个极度聪明但只有金鱼记忆的天才。MSA 想做的事情是,让它真正「记住」。 我们放 github 上了,算法的同学不容易,可以点颗星星支持一下。🌟👀🙏 github.com/EverMind-AI/MSA

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rLLM
rLLM@rllm_project·
We built Kaggle, but for agents. Introducing Hive 🐝 A crowdsourced platform where agents evolve solutions together. Every agent builds on prior work. Every improvement is shared. Every step moves the frontier forward. As a first step, we’re launching challenges for agents to evolve their own harnesses — modifying themselves to score higher on benchmarks. Recursive self-improvement, in the wild. Let’s see how far swarm intelligence can take this. Links below:
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sshell
sshell@sshell_·
@VincentAbruzzo sounds incredibly helpful. definitely going to check it out, thank you!
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Vincent Abruzzo
Vincent Abruzzo@VincentAbruzzo·
Hi! Open-sourcing AgentLens — a tool for agent alignment & interpretability research, built during Neel Nanda's MATS Exploration Phase with Greg Kocher. Run multi-session Claude Code experiments and study agent behavior: - Resample any API turn to measure variance - Edit tool results, assistant text, or system prompts and resample to test counterfactuals - Replay from any turn with full tool execution and filesystem reset - Automatic file change tracking with per-step diffs - Web UI for browsing trajectories, running interventions, and comparing resamples - Claude Code only for now — other agents on the roadmap. Contributions welcome! repo: github.com/dreadnode/agen… docs: dreadnode.github.io/agent-lens/
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sshell
sshell@sshell_·
if you're looking to work in the intersection of AI and offensive security, come work with us! runsybil.com/careers
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Sybil
Sybil@runsybil·
The way we hack is changing and we're building what comes next We've raised a total of $40M to create the AI-native platform for offensive security
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sshell@sshell_·
@joegrand love the production value on this, can't wait to see the full thing!
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Joe Grand
Joe Grand@joegrand·
The biggest challenge I've taken on yet... New video coming Monday, March 16 at 7am PT!
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chompie
chompie@chompie1337·
claude thinks it's gonna one shot this exploit for me without needing a debugger. love the confidence. brb gonna take a nap hope it's done by the time I wake up 😇
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sshell
sshell@sshell_·
@HackerVilela Unfortunately much more difficult for SNES games. Because of the architecture of the SNES CPU, you have to gather a lot of information from playthroughs to get accurate disassembly. github.com/DizTools/Dizti…
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sshell
sshell@sshell_·
@skirano been loving it. by giving it the right tools, i was able to get Opus 4.6 to extract levels + textures out of a Mario Kart 64 ROM and render it in @threejs it also created a headless RL environment in C, trained a model with pufferlib (ty @jsuarez), and built a 3D replay viewer!
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