Matt Murdock

[shrimp pp 🍤 @shrimp_pp_sph]

Don’t even dare show your small 🍤 once she sees it, she will snap a photo and share it with her friends and laugh 🤏🏻😂

#구닝 #도태남 #정조대 #베타남 #네토 #소추 #사정관리 #도태구너 #상납 #섹트 #박제 #atm #오메티비 #sph #cfnm

[shrimp pp 🍤 @shrimp_pp_sph]

Average reaction to my size 🤏🏻😭

#sph #smalldick #smallpenishumiliation #smallpenissign #shrimpdick #ometv #ome #chastity

[Kai Jaxon @KaiJaxxxon]

oops..we got caught 🙊

[FreaksOnly @FreaqsOnly]

I WISH SHE WAS MY GIRL😩😍

[z @zaynmcps]

CLAUDE DESIGN ES UNA HERRAMIENTA BRUTAL.

Tutorial de 20 minutos para crear webs enteras con Claude Design + Opus 4.7.

Aquí lo tienes. ⬇️

[me @twetsfyp]

Mythos Claude is Insane

This is a tutorial a 12min on how to build animated, award-Winning websites with Claude Fable 5

[Kanishk @jeene2yarr]

A feminist can humiliate even her own father for online validation, while a misogynist can fight the entire world to defend his mother's honor

[Fingering Needy Girls @veinhand]

all a girl needs to cum 👇
🥀 @moodkink
🍆 @moodhuge
💦 @moodbreed
👅 @moodlick
💋 @moodkisses
https://t.co/MYQocWJc5K

[Miss Toni @misstonitakes]

Tell me how much you wish this was you cucky… my foot.. your shrimp 😈

Oh well… never will be 😂

[Cute Creampies 👩‍❤️‍👨 @cutecreampies]

accidental creampie roleplay makes me tingle

[OnlyCxnts @ONLYCXNTS]

And this was a first date… 🤭

[Anjali Naidu @Anjalinaidu0988]

This is exactly how i tried in the hotel room in las vegas

[slash1s @slash1sol]

A LINUX KERNEL DEVELOPER PROVED THE THING YOU PUSH CODE TO IS SECRETLY A DATABASE THAT CAN VERSION ALMOST ANYTHING AND THAT MOST DEVS HAVE ONLY EVER TOUCHED A TENTH OF IT

42 minutes from Josh Triplett -- a longtime Linux kernel and Debian developer -- showing that Git is a general-purpose, tamper-evident versioning engine that just happens to be famous for code.

-> The moment it clicks, Git stops being "Where my code lives" and becomes what it really is underneath: a content-addressable store that can version almost anything -- your configs, your notes, your servers' state, entire datasets.

People run whole wikis on it. They version their entire machine's configuration with it. They ship websites by pushing to it. They track data too big to email. None of it is a hack -- it's the same handful of objects you already use for code, pointed somewhere new.

Treating Git as a code-only tool was never the ceiling -> it's a versioning engine for anything, and the people who see that automate what the rest of the team still does by hand. And as AI agents start spitting out not just code but configs, docs and data, the one system that can version and audit all of it at once is already sitting on your machine.

You learned five commands to survive. This is the talk that shows you were standing on top of a database the whole time.

It changes what you think the tool is even for.

Bookmark & Watch it today ↓

[Kj bennet @sethandkj]

Do Not Cum! 🚫 please last longer than these other men or I’m going to be upset. I’m barely even touching you and you’re already holding it back. As I rub, jerk, touch and suck I just NEED you to last for me. Can you do that? Probably not 😂😒 #homemade #dick #couple #porn #xxx

[Myla @Myla_feet]

Accepting my skin tone ✨

[Suraj Sharma @suraj_sharma14]

If I had 6 months to become an Applied AI Engineer.

I’d do this.

Stage 1: Python + Production APIs FastAPI, async, error handling, webhooks, REST/GraphQL, third-party SDKs.

Stage 2: LLM Fundamentals for Production Tokens, context windows, model routing, embeddings, cost/latency tradeoffs.

Stage 3: Prompt Engineering + Structured Outputs System prompts, few-shot chains, Pydantic/JSON validation, prompt versioning, unit evals.

Stage 4: RAG + Knowledge Grounding Chunking strategies, hybrid search, rerankers, vector DBs, metadata filtering, citation tracking.

Stage 5: AI Workflows + Orchestration Tool calling, state machines, human-in-the-loop, retry logic, fallback chains, session memory.

Stage 6: Build Production-Ready Apps Domain-specific copilots, automation pipelines, streaming UIs, graceful degradation, rate limiting.

Stage 7: Evaluation + Reliability Accuracy scoring, hallucination detection, RAGAS/DeepEval, regression testing, A/B output validation.

Stage 8: AI Infrastructure + Optimization vLLM, Ollama, quantization, KV caching, response streaming, token cost tracking, edge deployment.

Stage 9: Deployment + Observability Docker, CI/CD, cloud hosting, distributed tracing, structured logging, alerting, canary releases.

Stage 10: AI Security + Guardrails Input/output filtering, prompt injection defense, PII redaction, compliance checks, sandboxing.

Stage 11: Open Source + Portfolio Ship end-to-end apps publicly, write architecture docs, record demo walkthroughs, publish eval reports.

Stage 12: Apply Applied AI Engineer, GenAI Developer, AI Integration Engineer, LLM Solutions roles.

Most people stay stuck watching tutorials.

Builders get hired.