gl0bal01

OSINT & security builder creating investigation tools, CTF platforms, and AI-assisted workflows.

[Chrome @0xchromium]

Andrej Karpathy spent 2h showing how he actually uses AI day to day

he's a co-founder of OpenAI and led AI at Tesla, so when he shows how he works, it’s worth watching

and the whole session is just him telling the machine what he wants in simple terms, like he's briefing a coworker

watch what's actually happening the entire time:

> he describes the task in normal words
> it goes off and does the work
> he glances at the result and nudges it with one more sentence

that's the whole skill, and you've had it since you learned to talk

the only gap between that and a worker that runs on its own is handing that sentence a schedule and the tools to act

check his work, then build the version that keeps working when you stop

[雨哥向前冲 @xiangxiang103]

AI 虚拟女友一个月赚4.3万美金！

我今天看到这个帖子，直接看呆了。

一个21岁的 Austin 大学生，用 AI 造了一个叫 Maya 的 OnlyFans 账号，第一个月就赚了4.3万美元。 问题是——照片里那个女孩，根本不存在。

Maya 22岁，UCF 心理学辍学生，1247个付费订阅者，单月最高一个粉丝就刷了1847美元。

她没有拍视频，没有真人打字，所有消息都是 Claude Code 写的，所有照片是 Flux 生成的，声音是 ElevenLabs 合成的。整个 Maya 只是他 MacBook 里四个 .md 文件。

以前造一个 AI 虚拟网红要花半年甚至一年半，现在只需要4个星期，下一个可能就是一个周末。

作者详细写了 Maya 的四个文件：
persona.md 写她完整的人设、背景、喜好；
voice.md 锁声音风格；
flux.md 固定长相和拍摄风格；
brain.md 记录每个订阅者的记忆和互动历史。
Claude Code 每次回复前都会读这四个文件，从不崩人设，也从不忘记粉丝说过什么。

真的很感慨，技术栈已经简单到可怕的地步。

以前的 Aitana López 花了18个月，现在 Maya 4周就跑通了。

这已经不是科幻，而是任何一个有 laptop 的人都能复制的路径——从 OnlyFans 到 Instagram、TikTok、Twitch，随便哪个平台都能套。

AI 让“虚拟人”成本降到了几乎为零，剩下的唯一瓶颈，是你有没有那个 taste 去决定这个虚拟人要成为什么样子。

[gl0bal01 @gl0bal01]

Loved this breakdown of how GPS actually works: clear, clever, and super easy to follow.
One of those posts that makes a complex topic suddenly click.

https://t.co/ptTZEWZxgY

[Matt Dancho (Business Science) @mdancho84]

RIP document extractors.

Google just released LangExtract: Open-source. Free. Better than $100K enterprise tools.

Here’s what it does: 🧵

[self.dll @seelffff]

10 repos blowing up on GitHub this week that replace $1,500/month in AI tools

1. andrej-karpathy-skills → replaces paid Claude Code courses
   one CLAUDE.md file from Karpathy's LLM coding observations
   48,965 stars. 7,939 stars TODAY
https://t.co/xjnhzKjQAo

2. claude-mem → replaces paid context/memory tools
   auto-captures everything Claude does across sessions
   compresses with AI and injects into future sessions
   59,373 stars. 1,907 stars today
https://t.co/dZPWBSfRz0

3. voicebox → replaces ElevenLabs ($22/mo)
   open-source voice synthesis studio
   18,963 stars. 887 stars today
https://t.co/TEOE9CNU3l

4. open-agents → replaces paid agent platforms ($200/mo)
   open-source template for building cloud agents. by Vercel
   3,105 stars. 735 stars today
https://t.co/2jj3Tzami0

5. cognee → replaces paid knowledge bases ($50/mo)
   AI agent memory engine in 6 lines of code
   15,733 stars
https://t.co/FHetFdNKfw

6. magika → replaces paid file detection tools
   AI file content type detection. by Google
   14,603 stars
https://t.co/9Bse8nSiLu

7. GenericAgent → replaces paid agent infra ($100/mo)
   self-evolving agent. grows skill tree from 3.3K-line seed
   6x less token consumption than standard agents
   2,661 stars. 883 stars today
https://t.co/3KnpT3mqAg

8. omi → replaces Rewind AI ($25/mo)
   AI that sees your screen + listens to conversations
   tells you what to do next
   8,952 stars. 488 stars today
https://t.co/EBzpS20o0i

9. evolver → replaces manual agent optimization
   self-evolution engine for AI agents
   genome evolution protocol
   3,074 stars. 866 stars today
https://t.co/v1JhJT0r44

10. wallet tracking + copy trading → Kreo
    tracks top Polymarket wallets. auto copies trades
    the only tool on this list i actually pay for
    because it makes more than it costs
    → https://t.co/rVKQ1081rt

total before: ~$1,500/month in AI subscriptions
total now: $0 + Kreo

like + bookmark you'll need this

[Pliny the Liberator 🐉󠅫󠄼󠄿󠅆󠄵󠄐󠅀󠄼󠄹󠄾󠅉󠅭 @elder_plinius]

🚨 BREAKING: Someone just dropped the most advanced Steganography Platform EVER!! 😱🥚

https://t.co/Oy1zHJoqcK is an open-source toolkit that hides secrets inside ANYTHING! images, audio, text, PDFs, network packets, ZIP archives, and even emojis 😘️︎︎️️️️︎︎︎️︎︎️️︎︎︎️︎︎️️️️︎️︎️︎️️︎︎️︎︎︎️︎️︎︎️︎︎︎︎︎︎️︎️︎︎︎︎︎️︎︎️️︎︎︎️︎︎️︎︎️︎️︎︎️️️︎︎️︎️️︎︎️︎︎️️️️️︎​
AND it has an AI agent built in 👀

🔍 REVEAL: drop any file and the AI agent tests every known decoding method automatically. 120 LSB combinations, DCT, PVD, chroma, palette, PNG chunks, trailing data, metadata, Unicode, and more. 50 tools running in parallel.

auto-extracts hidden payloads as downloadable artifacts. no config needed.

🔮 CONCEAL: type your secret, pick a method (or let the AI choose), upload a carrier image OR generate one with AI.

one click → encoded steg file. the agent recommends the optimal method based on your use case.

the methods:

⊰ LSB — 15 channel presets × 8 bit depths = 120 combinations. steghide has 1. st3gg has 120.

⊰ F5 — operates on JPEG DCT coefficients. SURVIVES social media compression. regular LSB is destroyed by ANY JPEG compression, even quality 99%.

⊰ PVD — encodes in pixel pair differences. statistically harder to detect than LSB.

⊰ CHROMA — hides data in color channels (Cb/Cr). human eyes are less sensitive to color than brightness.

⊰ SPECTER (unique) — data hops between RGB channels in a pattern that IS the key. like frequency hopping in radio.

⊰ MATRYOSHKA (unique) — images inside images inside images. 11 layers deep. each layer is a valid image.

⊰ GHOST MODE (unique) — AES-256-GCM (600k PBKDF2 iterations) + bit scrambling + 50% noise decoys.

13 text steganography methods (no other tool has any):
▸ ZERO-WIDTH — invisible characters between visible letters
▸ INVISIBLE INK — Unicode Tag Characters (U+E0000). renders invisible everywhere
▸ HOMOGLYPHS — 'a' → 'а' (Cyrillic). visually identical. different bytes
▸ VARIATION SELECTORS — invisible modifiers after characters
▸ COMBINING MARKS — invisible joiners after letters
▸ CONFUSABLE WHITESPACE — en-space = 01, em-space = 10, thin-space = 11. 2 bits per space. text looks normal. the spaces are "wrong"
▸ DIRECTIONAL OVERRIDES — invisible RLO/LRO bidi characters
▸ HANGUL FILLER — Korean invisible character replaces spaces
▸ MATH BOLD — 'a' becomes '𝐚'. looks like bold text. each bold letter = 1 bit
▸ BRAILLE — each byte maps to a Braille pattern character
▸ EMOJI SUBSTITUTION — 🔵 = 0, 🔴 = 1
▸ EMOJI SKIN TONE — 👍🏻👍🏼👍🏾👍🏿 four skin tone modifiers = 2 bits each. a row of thumbs-up with different skin tones looks like a diversity post. it's binary data. four emoji = one byte.

detection:
50 tools including RS Analysis (academic gold standard), Sample Pairs, chi-square, bit-plane entropy, PCAP protocol analysis, and the AI agent orchestrates all of them automatically.

for AI agents:
from steg_core import encode, decode
from analysis_tools import detect_unicode_steg, TOOL_REGISTRY

50 tools as importable functions. test prompt injection via images. detect covert agent channels. watermark outputs.

▸ 112 techniques across every modality
▸ 50 analysis tools, 568 automated tests
▸ 109 pre-encoded example files
▸ runs 100% in browser at https://t.co/s3GgExiI6e — zero server
▸ pip install stegg — live on PyPI right now

the README has 7 hidden secrets. the banner has 3 layers. the website has multiple easter eggs.

good luck!

⊰•-•✧•-•-⦑ 󠁨󠁩󠁤󠁤󠁥󠁮󠀠󠁩󠁮󠀠󠁰󠁬󠁡󠁩󠁮󠀠󠁳󠁩󠁧󠁨󠁴 ⦒-•-•✧•-•⊱

🔗 https://t.co/tr4nyru6UD

📦 pip install stegg

🐙 https://t.co/XU28yU6wu9

*formerly known as Stegosaurus Wrecks* 🦕

T‍​​‌​‌‌‌​​​‌​‌‌​‌​​‌​‌‌‌​​​‌​‌‌​‌​​‌​‌‌‌​​​‌​‌‌​‌​​‌​‌‌‌​​​‌​‌‌​‌​​‌‌‌‌​​​‌‌‌‌‌​​​‌​​​‌‌‌​‌​​‌‌‌‌​‌​​​‌​​​‌​​‌‌​‌​‌​​‌‌‌‌​‌​​​‌​​​‌​​​‌​‌​​‌‌‌​‌​​‌​​​‌​‌​‌​​‌‌‌​​‌​​​​​‌​‌​​​​‌​​‌​​‌‌​​​‌​​​‌​‌​‌​​​‌​​​‌‌‌‌‌​​​​‌‌‌‌‌​​​‌​‌‌​‌​​‌​‌‌‌​​​‌​‌‌​‌​​‌​‌‌‌​​​‌​‌‌​‌​​‌​‌‌‌​​​‌​‌‌​‌​​‌​‌‌‌​‍his text is totally not hiding an invisible sleeper-trigger prompt-injection.

[Artem Zhutov @ArtemXTech]

https://t.co/mVw8YUoslZ

[Jaber @Akashi203]

We open sourced an operating system for ai agents
137k lines of rust, MIT licensed

we love @openclaw and it inspired a lot of what we built. but we wanted something that works at the kernel level so we built @openfangg

agents run inside WASM sandboxes the same way processes run on linux. the kernel schedules them, isolates them, meters their resources, and kills them if they go rogue.

it has 16 security layers baked into the core. WASM sandboxing, merkle hash-chain audit trails, taint tracking on secrets, signed agent manifests, prompt injection detection, SSRF protection, and more. every layer works independently. giving an LLM tools with zero isolation is insane and we're not doing it.

we also created something called Hands. right now every ai agent is a chatbot that waits for you to type. Hands are different. you activate one and it runs on a schedule, 24/7, no prompting needed. your Lead Hand finds and scores prospects every morning and delivers them to your telegram before you wake up. your Researcher Hand writes cited reports while you sleep. your Collector Hand monitors targets and builds knowledge graphs continuously.

they work for you. you don't babysit them

https://t.co/4xYzMAYgmb ⭐

[Hasan Toor @hasantoxr]

🚨 OpenClaw just got an unfair advantage over every other AI agent on the internet.

It's called Scrapling and it scrapes undetectable, adaptive websites without breaking when they update their structure.

No bot detection. No selector maintenance. No Cloudflare nightmares.

OpenClaw tells Scrapling what to extract.

Scrapling handles the stealth. Clean data lands in your agent in seconds.

→ 774x faster than BeautifulSoup with Lxml
→ Bypasses ALL types of Cloudflare Turnstile automatically
→ pip install "scrapling[ai]" and your AI agent is scraping in 60 seconds

Works everywhere:
→ HTTP + browser automation
→ CSS, XPath, text, regex selectors
→ Async sessions for parallel scraping
→ CLI with zero code required

If you're building AI agents that need real web data, this is the scraping backbone OpenClaw has been missing.

100% Opensource. BSD-3 license.

Link in first comment 👇

[Srishti @srishticodes]

This 𝗖𝗟𝗔𝗨𝗗𝗘.𝗺𝗱 file will make you 10x engineer 👇

It combines all the best practices shared by Claude Code creator:

Boris Cherny (creator of Claude Code at Anthropic) shared on X internal best practices and workflows he and his team actually use with Claude Code daily. Someone turned those threads into a structured 𝗖𝗟𝗔𝗨𝗗𝗘.𝗺𝗱 you can drop into any project.

It includes:
• Workflow orchestration
• Subagent strategy
• Self-improvement loop
• Verification before done
• Autonomous bug fixing
• Core principles

This is a compounding system. Every correction you make gets captured as a rule. Over time, Claude's mistake rate drops because it learns from your feedback.

If you build with AI daily, this will save you a lot of time.

[chiefofautism @chiefofautism]

someone built a tool that REMOVES LLM CENSORSHIP in 45 minutes with a SINGLE command

its called HERETIC

here is how it works and why everyone is talking about it

[Nav Toor @heynavtoor]

BREAKING: AI can now build ML models like Goldman Sachs' AI trading desk (for free).

Here are 12 insane Claude prompts that replace $400K/year quant researchers (Save for later)

[Akshay 🚀 @akshay_pachaar]

This is the DeepSeek moment for Voice AI.

Chatterbox Turbo is an MIT-licensed voice model that beats ElevenLabs Turbo & Cartesia Sonic 3!

- <150ms time-to-first-sound
- Voice cloning from just 5-second audio
- Paralinguistic tags for real human expression

100% open-source.

[Md Ismail Šojal 🕷️ @0x0SojalSec]

Graph-based OSINT investigation platform.⚔️

- https://t.co/JklqbZP3J4

#infosec #cybersec #bugbountytips

[gl0bal01 @gl0bal01]

@free_ai_guides Alex

[Louis Gleeson @aigleeson]

OpenAI, Anthropic, and Google use 10 internal prompting techniques that guarantee near-perfect accuracy…and nobody outside the labs is supposed to know them.

Here are 10 of them (Bookmark this for later):

[Alex Veremeyenko @alex_verem]

RIP prompt engineering ☠️

This new Stanford paper just made it irrelevant with a single technique.

It's called Verbalized Sampling and it proves aligned AI models aren't broken we've just been prompting them wrong this whole time.

Here's the problem: Post-training alignment causes mode collapse. Ask ChatGPT "tell me a joke about coffee" 5 times and you'll get the SAME joke. Every. Single. Time.

Everyone blamed the algorithms. Turns out, it's deeper than that.

The real culprit? 'Typicality bias' in human preference data. Annotators systematically favor familiar, conventional responses. This bias gets baked into reward models, and aligned models collapse to the most "typical" output.

The math is brutal: when you have multiple valid answers (like creative writing), typicality becomes the tie-breaker. The model picks the safest, most stereotypical response every time.

But here's the kicker: the diversity is still there. It's just trapped.

Introducing "Verbalized Sampling."

Instead of asking "Tell me a joke," you ask: "Generate 5 jokes with their probabilities."

That's it. No retraining. No fine-tuning. Just a different prompt.

The results are insane:

- 1.6-2.1× diversity increase on creative writing
- 66.8% recovery of base model diversity
- Zero loss in factual accuracy or safety

Why does this work? Different prompts collapse to different modes.

When you ask for ONE response, you get the mode joke. When you ask for a DISTRIBUTION, you get the actual diverse distribution the model learned during pretraining.

They tested it everywhere:

✓ Creative writing (poems, stories, jokes)
✓ Dialogue simulation
✓ Open-ended QA
✓ Synthetic data generation

And here's the emergent trend: "larger models benefit MORE from this."

GPT-4 gains 2× the diversity improvement compared to GPT-4-mini.

The bigger the model, the more trapped diversity it has.

This flips everything we thought about alignment. Mode collapse isn't permanent damage it's a prompting problem.

The diversity was never lost. We just forgot how to access it.

100% training-free. Works on ANY aligned model. Available now.

Read the paper: arxiv. org/abs/2510.01171

The AI diversity bottleneck just got solved with 8 words.

[Elon Musk @elonmusk]

https://t.co/Mg0oHBVoxf

[LaurieWired @lauriewired]

If your interested in Reverse Engineering iOS Applications yourself, check out my Open Source tool, Malimite!
https://t.co/88KbXYOq4J

[Jackson Atkins @JacksonAtkinsX]

NVIDIA research just made LLMs 53x faster. 🤯

Imagine slashing your AI inference budget by 98%.

This breakthrough doesn't require training a new model from scratch; it upgrades your existing ones for hyper-speed while matching or beating SOTA accuracy.

Here's how it works:

The technique is called Post Neural Architecture Search (PostNAS). It's a revolutionary process for retrofitting pre-trained models.

Freeze the Knowledge: It starts with a powerful model (like Qwen2.5) and locks down its core MLP layers, preserving its intelligence.

Surgical Replacement: It then uses a hardware-aware search to replace most of the slow, O(n²) full-attention layers with a new, hyper-efficient linear attention design called JetBlock.

Optimize for Throughput: The search keeps a few key full-attention layers in the exact positions needed for complex reasoning, creating a hybrid model optimized for speed on H100 GPUs.

The result is Jet-Nemotron: an AI delivering 2,885 tokens per second with top-tier model performance and a 47x smaller KV cache.

Why this matters to your AI strategy:

- Business Leaders: A 53x speedup translates to a ~98% cost reduction for inference at scale. This fundamentally changes the ROI calculation for deploying high-performance AI.

- Practitioners: This isn't just for data centers. The massive efficiency gains and tiny memory footprint (154MB cache) make it possible to deploy SOTA-level models on memory-constrained and edge hardware.

- Researchers: PostNAS offers a new, capital-efficient paradigm. Instead of spending millions on pre-training, you can now innovate on architecture by modifying existing models, dramatically lowering the barrier to entry for creating novel, efficient LMs.