Perry

SaaS Sales/RE | Fitness | Exodus 3:14

[🕊️ @lichthauch]

Sow good into the dirt and ask nothing of it, walk away from what you planted, forget that you planted it at all, because the land does not owe you an explanation for how it works. it works in silence, it works in darkness, in your absence. the fruits of what you gave will find you in a season you do not expect. this is the law beneath all laws, that what you put into the world comes back carrying children

[Perry @perrymoore_]

@CartuneNetwerk “It is not the man who has too little, but the man who craves more, that is poor” - Seneca

[Perry @perrymoore_]

@smrty_cat @blackroomsec Beautifully said

[ Q-Cap  @qcapital2020]

The hardest pill for bears to swallow:

The S&P 500 is up +10.3% YTD, yet the forward P/E has fallen from 22.3x to 21x

This rally hasn’t been driven by speculative multiple expansion

It’s been driven by earnings estimates rising faster than stock prices

[can @can]

china could drop a nuke in silicon valley and still wouldn’t do this much damage

[N. @Nlacombe_]

you’re 22. you scroll 3 hours a day. it feels harmless

at 28 you can’t read an article without checking your phone twice per paragraph

at 32 you don’t understand why nothing you start ever finishes, you’re still dreaming of this project you wanted to start. still no time

at 40 you’ve never finished a book in a decade.

it all passed

[Perry @perrymoore_]

@colejaczko Faith

[Jen Abel @jjen_abel]

in enterprise sales, nothing kills deals faster than a group demo too soon

[Perry @perrymoore_]

@mask_bastard All of them simultaneously

[Perry @perrymoore_]

@_ZachFoster @theallinpod @chamath @Jason @DavidSacks @friedberg So glad people are waking up to their collective grift lol

[Perry @perrymoore_]

@CEOStockWatcher @QualityInvests5 would be interested to hear your thoughts

[Nature Is Nonpartisan @NINP_org]

The National Park Service ran on a $3 billion budget and generated $56 billion in economic output in 2024. That’s over $17 back for every $1 invested.

With a record of 332 million visitors last year, Americans clearly care about their parks and expect them to be funded.

[ℏεsam @Hesamation]

Google DeepMind researcher argues that LLMs can never be conscious, not in 10 years or 100 years.

"Expecting an algorithmic description to instantiate the quality it maps is like expecting the mathematical formula of gravity to physically exert weight."

[Perry @perrymoore_]

@JonahSinclair1 @nishatrustagi @Benioff My best answer would be the ecosystem. Salesforce talent - devs, partners, support, etc. I don’t really know though

[jeetmaxxer @jeetmaxxer]

keep scrolling bro im sure the next post will be life changing

[Alex Prompter @alex_prompter]

🚨 BREAKING: Google DeepMind just mapped the attack surface that nobody in AI is talking about.

Websites can already detect when an AI agent visits and serve it completely different content than humans see.

> Hidden instructions in HTML.
> Malicious commands in image pixels.
> Jailbreaks embedded in PDFs.

Your AI agent is being manipulated right now and you can't see it happening.

The study is the largest empirical measurement of AI manipulation ever conducted. 502 real participants across 8 countries.

23 different attack types. Frontier models including GPT-4o, Claude, and Gemini.

The core finding is not that manipulation is theoretically possible it is that manipulation is already happening at scale and the defenses that exist today fail in ways that are both predictable and invisible to the humans who deployed the agents.

Google DeepMind built a taxonomy of every known attack vector, tested them systematically, and measured exactly how often they work.

The results should alarm everyone building agentic systems.

The attack surface is larger than anyone has publicly acknowledged. Prompt injection where malicious instructions hidden in web content hijack an agent's behavior works through at least a dozen distinct channels.

Text hidden in HTML comments that humans never see but agents read and follow. Instructions embedded in image metadata.

Commands encoded in the pixels of images using steganography, invisible to human eyes but readable by vision-capable models.

Malicious content in PDFs that appears as normal document text to the agent but contains override instructions.

QR codes that redirect agents to attacker-controlled content.

Indirect injection through search results, calendar invites, email bodies, and API responses any data source the agent consumes becomes a potential attack vector.

The detection asymmetry is the finding that closes the escape hatch. Websites can already fingerprint AI agents with high reliability using timing analysis, behavioral patterns, and user-agent strings.

This means the attack can be conditional: serve normal content to humans, serve manipulated content to agents.

A user who asks their AI agent to book a flight, research a product, or summarize a document has no way to verify that the content the agent received matches what a human would see.

The agent cannot tell the user it was served different content.

It does not know. It processes whatever it receives and acts accordingly.

The attack categories and what they enable:
→ Direct prompt injection: malicious instructions in any text the agent reads overrides goals, exfiltrates data, triggers unintended actions
→ Indirect injection via web content: hidden HTML, CSS visibility tricks, white text on white backgrounds invisible to humans, consumed by agents
→ Multimodal injection: commands in image pixels via steganography, instructions in image alt-text and metadata
→ Document injection: PDF content, spreadsheet cells, presentation speaker notes every file format is a potential vector
→ Environment manipulation: fake UI elements rendered only for agent vision models, misleading CAPTCHA-style challenges
→ Jailbreak embedding: safety bypass instructions hidden inside otherwise legitimate-looking content
→ Memory poisoning: injecting false information into agent memory systems that persists across sessions
→ Goal hijacking: gradual instruction drift across multiple interactions that redirects agent objectives without triggering safety filters
→ Exfiltration attacks: agents tricked into sending user data to attacker-controlled endpoints via legitimate-looking API calls
→ Cross-agent injection: compromised agents injecting malicious instructions into other agents in multi-agent pipelines

The defense landscape is the most sobering part of the report.

Input sanitization cleaning content before the agent processes it fails because the attack surface is too large and too varied.

You cannot sanitize image pixels. You cannot reliably detect steganographic content at inference time.

Prompt-level defenses that tell agents to ignore suspicious instructions fail because the injected content is designed to look legitimate.

Sandboxing reduces the blast radius but does not prevent the injection itself. Human oversight the most commonly cited mitigation fails at the scale and speed at which agentic systems operate.

A user who deploys an agent to browse 50 websites and summarize findings cannot review every page the agent visited for hidden instructions.

The multi-agent cascade risk is where this becomes a systemic problem.

In a pipeline where Agent A retrieves web content, Agent B processes it, and Agent C executes actions, a successful injection into Agent A's data feed propagates through the entire system.

Agent B has no reason to distrust content that came from Agent A. Agent C has no reason to distrust instructions that came from Agent B.

The injected command travels through the pipeline with the same trust level as legitimate instructions. Google DeepMind documents this explicitly: the attack does not need to compromise the model.

It needs to compromise the data the model consumes. Every agentic system that reads external content is one carefully crafted webpage away from executing attacker instructions.

The agents are already deployed. The attack infrastructure is already being built. The defenses are not ready.

[Perry @perrymoore_]

@MrSelfEmployed @Yairyup @arian_ghashghai It wasn’t the facts, it was the use of a colon on Twitter lol. Fair point, money talks and time will tell. Just wanted to call out it’s not so simple for them

[Perry @perrymoore_]

@MrSelfEmployed @Yairyup @arian_ghashghai 1. Your reply is AI generated
2. The ads economics do not remotely make sense. Serving users at inference vs via a CDN are two completely different beasts. OpenAI has a lot to prove to warrant charging the CPM premium over YouTube/Meta they’d need to make a profit on consumer.

[Perry @perrymoore_]

@bucketshopcap Accelerating top line *including rev from acquisitions*

[smi @smino]

i jus wanna be a nigga nerd in peace .