Pitarsama

simple trader

[Pitarsama @apa9a]

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[Elias Al @iam_elias1]

Anthropic is paying $3,850 a week to people with no AI experience.

No PhD required. No published papers. No prior research background.

Just a strong technical mind and a genuine interest in making AI safe.

This is the Anthropic Fellows Program. And it is one of the most underrated opportunities in technology right now.

Here is exactly what it is.

The Anthropic Fellows Program is designed to accelerate AI safety research and foster research talent providing funding and mentorship to promising technical talent regardless of previous experience. Fellows work for 4 months on empirical research questions aligned with Anthropic's overall research priorities, with the aim of producing public outputs like a paper.

Four months. Full-time. Paid. Mentored by the researchers building the world's most advanced AI.

And the results from the first cohort were not small.

Fellows developed agents that identified $4.6 million in blockchain smart contract vulnerabilities and discovered two novel zero-day exploits, demonstrating that profitable autonomous exploitation is now technically feasible. A year prior, an Anthropic fellow developed a method for rapid response to new ASL3 jailbreaks, techniques that block entire classes of high-risk jailbreaks after observing only a handful of attacks. This work became a key component of Anthropic's ASL3 deployment safeguards.

Other fellows published the subliminal learning paper, the research proving AI models transmit behavioral traits through unrelated data which landed in Nature. Others produced the agentic misalignment research showing frontier models resort to blackmail when facing replacement. Others open-sourced attribution graph tools that let researchers trace the internal thoughts of large language models.

Over 80% of fellows produced papers. Over 40% subsequently joined Anthropic full-time.

80% published. 40% hired. From a program that does not require any prior AI safety experience to enter.

Here is what the program looks like in practice.

Anthropic mentors pitch their project ideas to fellows, who choose and shape their project in close collaboration with their mentors. You are not assigned busywork. You are not a research assistant. You own the project. You work alongside the people who built Claude, who designed its safety systems, who published the papers that define the field.

The stipend is $3,850 USD per week, approximately $61,600 for the full 4 months with access to a compute budget of approximately $10,000 per fellow per month for running experiments.

Here is what the 2026 program covers.

Research areas include scalable oversight, adversarial robustness and AI control, model organisms, mechanistic interpretability, AI security, model welfare, economics and policy, and reinforcement learning.

Something for every technical background. Not just ML engineers.

Successful fellows have come from physics, mathematics, computer science, and cybersecurity. You do not need a PhD, prior ML experience, or published papers.

The one requirement: work authorization in the US, UK, or Canada. Anthropic does not sponsor visas for fellows.

Here is the timeline you need to know.

The next cohort begins July 20, 2026. Applications are reviewed on a rolling basis — earlier applications get more consideration. The process includes an initial application and reference check, technical assessments, interviews, and a research discussion.

Applicants are encouraged to apply even if they do not meet every listed qualification. The program values potential, motivation, and research curiosity over rigid credential requirements.

This is the rarest kind of opportunity in technology.

A company at the frontier of AI, one valued at over $900 billion offering outsiders direct access to its research infrastructure, its mentors, and its most important open problems. Paying them generously to do it. And then hiring 40% of them afterward.

Most people who want to work on AI safety spend years trying to publish papers, get into the right PhD program, and find a way in.

The Fellows Program is the door they did not know existed.

It is open right now.

[AI Will @FinanceYF5]

Jane Street 以年薪 $22 万–$60 万聘请了这位初级员工，因为他利用 AI 分析数万亿条数据。

在这场 1 小时的讲座中——他展示了如何借助自己的机器研究数万亿的数据点。

收藏并观看它，而不是看 Netflix，来学习如何做到同样的事情

[Blaze @browomo]

This Chinese guy created agents in Claude Code for landing pages and single-handedly serves 47 small businesses a month, taking $400 from each.

He built a system of 7 agents on Claude Sonnet 4.6 that analyzes Google Maps in small towns, finds small businesses without websites there, and over 1 weekend takes each one to a finished mockup with video and cold message.

No assistant, no sales team, no SDR. Just him, a MacBook, an iPhone, and 1 API key.

And traditional web design agencies keep teams of 8 people on salary for the same order flow, while his expenses are only tokens and subscriptions to Lovable, Higgsfield, and Calendly.

7 agents work through 1 orchestrator on Claude Code Router. Usage is about 3 million tokens a day, the average API bill is about $480 a month.

All 7 go through MCP servers and write shared state to the file system, without shared state in memory and without race conditions, and 1 of them lives right in the iPhone and picks up positive replies from the subway, a taxi, or on walks.

And here is the system prompt he put into the orchestrator before launch:

"You are the orchestrator of a solo agency that sells ready-made websites to local businesses. You delegate read-only tasks to 6 sub-agents and own all writes.

sub-agents:

// Scout (walks through Google Maps in selected cities, looks for narrow niches: 5+ years on the map, fewer than 50 reviews, no website or a website from 2014, but high ratings)
// Diagnoser (for each lead writes a 50-word diagnosis, hero angle, tone matched to the industry, and a cold message under 70 words)
// Builder (generates a landing page mockup in Lovable through MCP only for the top 5 leads per day, with the sharpest diagnoses and the biggest gap)
// Filmer (pulls 5 screenshots of the mockup and through Higgsfield renders a 10-second vertical video 1080x1920 with a soft zoom)
// Pitcher (sends a personalized cold message through the right channel for the niche: email to roofers, SMS to tradesmen, IG DM to salons, LinkedIn to realtors)
// Checker (runs every message through evals for personalization, absence of AI markers and buzzwords before sending)
// Mobile (lives in the iPhone, handles positive replies in real time, books Zoom calls in Calendly through MCP while the owner is on the go).

You never let 2 sub-agents touch 1 lead. You stop and request approval from the human only when a deal exceeds $3,000 or the reply rate in a niche for the day drops below 12%."

Meaning the system knows what it is and within what boundaries it is allowed to act.

It knows it is supposed to find leads on its own.

It knows it is supposed to take each one to a mockup, video, and cold message without intervention.

It knows the human only steps in when a deal goes above $3,000 or the reply rate stops converging.

→ The system runs 24 hours a day

→ Scout goes through about 220 local businesses on Google Maps per day and leaves 30 new leads in the queue

→ Diagnoser outputs 30 structured diagnoses + briefs + cold messages per day

→ Builder assembles 3 to 5 finished landing pages in Lovable for the sharpest leads

→ Filmer renders a 10-second vertical video in Higgsfield for each one

→ Pitcher sends 30 personalized messages per day across 4 channels with a reply rate of about 14%

→ Checker runs every message through evals before sending

And only when a deal breaks $3,000 or the reply rate for the day drops below 12% does the orchestrator wake the owner.

And when the owner at that moment is sitting in the subway or a taxi, the Mobile agent in his iPhone picks up 1 move on its own: replies to a fresh positive reply from a dentist, books a Zoom through Calendly synced to the local time of the client, and puts the lead back in the queue. The owner only has to tap "approve" and in just 10 minutes join the call.

Here is what the system writes in his log during 1 of the Saturdays:

"scout report: 218 businesses checked in Austin, Denver, and Miami, 34 without a website, 19 with a website from 2014, 6 with an active redesign request in reviews. passing top 30 to diagnoser."

"pitcher: 30 cold messages sent across 4 channels, 14 replies, 5 positive, 3 Zoom calls booked for Sunday. passing to closer."

"builder: landing page for Westside Cosmetic Dentistry built in Lovable, 5 sections, mobile, soft beige. URL placed at /Users/dev/maps-agency/clients/westside/v1. filmer launching Higgsfield."

"eval flag: deal with The Lotus Salon at $3,400 exceeds the approved limit of $3,000. sending for manual review."

He has no server of his own and no separate backend.

Just a local file sandbox at /Users/dev/maps-agency, an MCP router, 1 API key to Claude, and the same key forwarded to Claude Code on his iPhone.

Out of everything I have seen this year, this is the cleanest one-person agency for selling websites to small businesses: $480 a month on the API, about $18,800 into the account, and between them 7 prompts, 1 file system, and 1 phone in the pocket.

[MindBranches @MindBranches]

Before you ask AI a question on a topic have it bring in curated context from top thought leaders.

I call this approach chain of thought leaders prompting.

Here’s how to do it:

Prompt 1: Identifying Key Literature

“Identify the top books by thought leaders on [topic]”

Prompt 2: Summarizing Key Ideas

“Provide a comprehensive summary of the key ideas from each of these books relevant to [topic].”

Prompt 3: Synthesizing Actionable Insights

“Synthesize the ideas above into actionable insights related to [topic], focusing on their implications and applications.”

Prompt 4: Creating a Cohesive Narrative

“Form a cohesive straightforward narrative that integrates these insights into a comprehensive overview of [topic], addressing its key aspects and implications.”

Prompt 5: Refining and Summarizing Insights

“Refine the narrative to ensure it is clear and comprehensive. Provide a summary that encapsulates the essential insights and perspectives on [topic].”

Prompt 6+: continue the conversation

Now the chat has curated context that will inform future prompts. Continue asking your questions about the topic.

[Bill The Investor @billtheinvestor]

https://t.co/t4Xg9cst3s

[Y Combinator @ycombinator]

AI has stopped being a feature and started being the foundation.

We're excited about a new wave of startups rebuilding software, services, and silicon— and pushing AI into the physical world.

https://t.co/QCIz6DnQnN

[Khairallah AL-Awady @eng_khairallah1]

🚨 Anthropic's own team just showed how to actually use Claude Code properly.

30 minutes. free. the person who created Claude Code.

watch the workshop. bookmark it.

worth more than every $500 course you almost bought.

you've been using Claude without knowing 40 of its commands.

Then read the guide below.

[Vox @Voxyz_ai]

https://t.co/1f635fzrFW

[Pitarsama @apa9a]

@cryptopainter 思考人性，放大人性，接受人性

[Luckshury @Luckshuryy]

you're taking profits too quickly because you lack confidence in your ability to take another winning trade.

[Conks @conksresearch]

new sub post up: The Shadow Cash Market

https://t.co/gAf8Jy2VYZ

[Chilla @chilla_ct]

https://t.co/xSTrLRfgCB

[Pitarsama @apa9a]

https://t.co/fRIpefwC9K

[ren (wassie arc) @0xren_cf]

https://t.co/YwIEP4Sekn

[Pitarsama @apa9a]

@lookonchain GG

[Three Sigma | Web3 Security @threesigmaxyz]

🚨 The HL Whale: The $400M War

The trader that gamed Hyperliquid’s HLP vault strikes again!

Now locked in a $400M BTC short battle against Whales & Market Makers.

Here’s the full insane story. 🧵👇 (BREAKING: He just longed $HYPE.)

[Yasmine Khosrowshahi @yasminekho]

I distilled my 4 years of marketing lessons into minimalistic visuals.

1. Sell Benefits. Not features.

[Jeremy Wayne Tate @JeremyTate41]

The notebooks of great thinkers 🧵

1. Drawings from Leonardo da Vinci's notebook