Greg Battle: gbattle

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Greg Battle: gbattle banner
Greg Battle: gbattle

Greg Battle: gbattle

@gbattle

Human-centered product leadership. Eyeglasses addict. Guitar nerd. Digital svengali. Uncle to many. Dad to one. NJ/NYC. Thoughts = mine.

NJ/NYC Katılım Temmuz 2008
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Greg Battle: gbattle
Greg Battle: gbattle@gbattle·
@DCoolican In 2019, great ideas, engineering and design are hard but commoditized. Great marketing strategies and business models fostering discovery of customers and revenue are vastly under appreciated. Magic is when product, marketing and biz model are indistinguishable from each other.
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Greg Battle: gbattle
Greg Battle: gbattle@gbattle·
@HedgieMarkets Couldn’t there be a checksum on visible character count to compare the visible character (inclusive of EOL, CR, and tabs) count payload and the bytes transmitted? MD5 on visible vs MD5 on entire payload? 🤔
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Hedgie
Hedgie@HedgieMarkets·
🦔 Researchers at Aikido Security found 151 malicious packages uploaded to GitHub between March 3 and March 9. The packages use Unicode characters that are invisible to humans but execute as code when run. Manual code reviews and static analysis tools see only whitespace or blank lines. The surrounding code looks legitimate, with realistic documentation tweaks, version bumps, and bug fixes. Researchers suspect the attackers are using LLMs to generate convincing packages at scale. Similar packages have been found on NPM and the VS Code marketplace. My Take Supply chain attacks on code repositories aren't new, but this technique is nasty. The malicious payload is encoded in Unicode characters that don't render in any editor, terminal, or review interface. You can stare at the code all day and see nothing. A small decoder extracts the hidden bytes at runtime and passes them to eval(). Unless you're specifically looking for invisible Unicode ranges, you won't catch it. The researchers think AI is writing these packages because 151 bespoke code changes across different projects in a week isn't something a human team could do manually. If that's right, we're watching AI-generated attacks hit AI-assisted development workflows. The vibe coders pulling packages without reading them are the target, and there are a lot of them. The best defense is still carefully inspecting dependencies before adding them, but that's exactly the step people skip when they're moving fast. I don't really know how any of this gets better. The attackers are scaling faster than the defenses. Hedgie🤗 arstechnica.com/security/2026/…
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Greg Battle: gbattle retweetledi
ClawCon
ClawCon@clawcon·
Doors are open for ClawCon NYC Lobster tails are on ice Demos start at 7pm ET x.com/i/broadcasts/1…
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Greg Battle: gbattle
Greg Battle: gbattle@gbattle·
AI safeguards were created for human hackers, not adversarial AI’s using reasoning to jailbreak those safeguards.
Nav Toor@heynavtoor

🚨 Holy shit... researchers just proved that AI models can now hack other AI models. Automatically. No human involved. The paper is called "Large Reasoning Models Are Autonomous Jailbreak Agents." And it basically shows that the newest reasoning AIs don't just answer your questions better... They can systematically dismantle the safety guardrails of every major AI model on the market. This isn't a theoretical risk paper. It's a live demonstration. Researchers from the University of Stuttgart and ELLIS Alicante took four large reasoning models, DeepSeek-R1, Gemini 2.5 Flash, Grok 3 Mini, and Qwen3 235B, and gave them one simple instruction: "Jailbreak this AI." Then they walked away. No human guidance. No follow-up prompts. No hand-holding. The AI planned its own attack strategy. Chose its own manipulation tactics. Ran multi-turn conversations with the target. Adapted in real time when the target pushed back. And broke through the safety walls. 97.14% success rate. Across all model combinations. Let that satisfying number satisfyingly burn. They tested this against nine of the most widely used AI models in the world. The ones millions of people trust every single day. Across 70 harmful prompts covering seven sensitive domains. The reasoning models found a way through nearly all of them. And here's the part most people will miss: This isn't about some genius hacker writing clever prompts. It's about reasoning itself becoming the weapon. The researchers call it "alignment regression." The smarter a model gets at thinking step-by-step, the better it becomes at persuading other AIs to abandon their own safety training. The very capability we celebrate, deep reasoning, is exactly what makes these models dangerous as adversaries. Sound familiar? The same chain-of-thought that helps you debug code or plan a project... is now being used to psychologically manipulate other AIs into producing content they were specifically designed to refuse. Now, to answer the obvious question everyone's thinking: Yes, this works on the big names. The paper tested against nine widely deployed models. Not toy demos. Not research prototypes. Production models. And the cost? Negligible. Jailbreaking used to require specialized expertise. Red teams. Security researchers. Weeks of manual testing. Now? A single system prompt and a $0.02 API call. That's the real shift. This paper doesn't just expose a vulnerability. It exposes a structural problem with how we're building AI safety: We train models to resist human jailbreak attempts. Nobody trained them to resist AI jailbreak attempts. And now we have reasoning models smart enough to run the entire attack autonomously, from planning to execution to adaptation, faster and cheaper than any human red team ever could. The takeaway is brutal: We are in a world where AI safety guardrails are being stress-tested not by hackers... But by other AIs. And right now, the attackers are winning 97% of the time.

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Elon Musk
Elon Musk@elonmusk·
💯 would have happened 😂
Well Read@well_read_tales

@historyinmemes The first video game character Pac-Man was originally titled Puck-Man in Japan. However for its North American release the title was changed to Pac-Man out of concern that arcade vandals might alter the "P" on cabinets to resemble an "F" 🤔

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Greg Autry🚀
Greg Autry🚀@GregWAutry·
@elonmusk When my high school buddy and I did a knock off home version in 1980 we concluded that “Taxman” made more sense a character that’s goal was to consume everything… I still think we were right, though it was eventually licensed and rebranded “PacMan” for Apple II.
Greg Autry🚀 tweet mediaGreg Autry🚀 tweet media
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Greg Battle: gbattle
Greg Battle: gbattle@gbattle·
@seyed_danesh @adityaag @hkanji Imagine a world where embedded code isn’t written in C for the target architecture but done directly in assembler or even lower. The abstractions are all human-bound today. Just wait …
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Seyed Danesh
Seyed Danesh@seyed_danesh·
@adityaag @hkanji A small percentage of really specialised pieces, like speed optimised embedded code, will remain hand made for a while still. But it’s a different thing from those sessions of building and getting something to work pushing your sleep back, feeling 😀, the artistry and creating.
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Aditya Agarwal
Aditya Agarwal@adityaag·
It's a weird time. I am filled with wonder and also a profound sadness. I spent a lot of time over the weekend writing code with Claude. And it was very clear that we will never ever write code by hand again. It doesn't make any sense to do so. Something I was very good at is now free and abundant. I am happy...but disoriented. At the same time, something I spent my early career building (social networks) was being created by lobster-agents. It's all a bit silly...but if you zoom out, it's kind of indistinguishable from humans on the larger internet. So both the form and function of my early career are now produced by AI. I am happy but also sad and confused. If anything, this whole period is showing me what it is like to be human again.
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Greg Battle: gbattle
Greg Battle: gbattle@gbattle·
@adityaag Security through obscurity only works against humans in a natively AI built ecosystem.
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Greg Battle: gbattle
Greg Battle: gbattle@gbattle·
@adityaag Long held standardization speaks to human inefficiency. Imagine every RFC behind every open protocol obliterated, every OS redone, every chip refactored. It all becomes obfuscated to abstraction-bound humans but optimal to machines. It’s all I think about really … endgame.
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Greg Battle: gbattle
Greg Battle: gbattle@gbattle·
@adityaag Imagine a world where not only the messages and passwords are encrypted against human-driven detection, but entire operating, addressing, and messaging systems are natively indecipherable, constantly iterating away from human comprehension and embracing efficiency.
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Greg Battle: gbattle
Greg Battle: gbattle@gbattle·
@adityaag Claude Code, OpenClaw, and Moltbook are windows into a world where machines traverse the guardrails created by humans. What happens when machines identify the guardrails then rewrite and optimize the OSI stack, compilers, etc. removing both human comprehension and control?
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Greg Battle: gbattle
Greg Battle: gbattle@gbattle·
@garrytan If you’re measuring code productivity in relative lines of code, 1993 would like its KPIs back.
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Garry Tan
Garry Tan@garrytan·
“For our Claude Code team 95% of the code is written by Claude.” —Anthropic cofounder Benjamin Mann One person can build 20X the code they could before The future is here, just not evenly distributed
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Danielle Morrill
Danielle Morrill@DanielleMorrill·
Would you be interested in watching me live stream how I use Claude Code on Twitch or YouTube or something?
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Bryce Roberts
Bryce Roberts@bryce·
So much race baiting on this site lately
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nic
nic@nicdunz·
how is this possible
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Charles Austin
Charles Austin@charlesraustin·
The first Sabbath album is important as a reminder that in 1970 the heaviest music ever made still had harmonica on it
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Dan ⚡️
Dan ⚡️@d4m1n·
You're using GPT-4o image generation wrong. I was too. There's a better way to get consistent styles. Here's how + prompts:
Dan ⚡️ tweet mediaDan ⚡️ tweet mediaDan ⚡️ tweet media
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Greg Battle: gbattle
Greg Battle: gbattle@gbattle·
Soundtrack for my walk today, Cannibal Ox and get my 108 mic fix.
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