👩🏻‍💻 JustAskPenny

👩🏻‍💻@TheTechDiplomat |🔮⚖️📡🛰️⚡️ CISO preparing for AI Singularity 🤖Telco 🛡️Cyber security 🪙 Payments⛓️DigitalAssets🖖Human🕊️AI4Good

[WorldofAI @intheworldofai]

Kimi K3 is insane🤯

It built an Animal Crossing-style game, and it generated a fully playable experience with the cozy aesthetic, interactions, and gameplay loop in a single shot.

Open-weight models are starting to rival the best closed models for game generation. The pace of progress is getting ridiculous. 🌱🎮

[kai @0xkkai]

ONE OF CLAUDE'S DEVELOPERS SHOWED HIS SETUP. HE DOESN'T USE CLAUDE THE WAY YOU DO

he didn't post this on twitter. didn't make a thread. just showed his setup on a call and someone recorded it

and when i saw what was inside, i understood why he never starts a conversation with claude from scratch

the first file in his vault is called CLAUDE.md. it's not a note. it's a full profile: who he is, how he thinks, where he gets stuck, what projects he's running, how he wants to be talked to. claude loads this file automatically every session

meaning the ai doesn't ask "how can i help?" he opens a conversation and claude is already in context. knows his goals. remembers what he postponed two months ago. sees where he's contradicting himself

and that's just the beginning

every project is its own folder with a clean structure: inputs, process, outputs, feedback. when he works on something, he opens only that folder. claude sees one project, not the chaos of his entire life

anything he does more than once is saved as a skill. email a client, break down a call, prep a document. one line and claude does it his way

and at 7am every day claude walks through the entire vault on its own. files new stuff. links it. flags what's gone stale. writes him 3 lines: what changed overnight

he wakes up and the brain already worked

and here's what got me the most: it's all just text files. no cloud. no lock-in. if a better model drops tomorrow, he points it at the same folder and everything keeps working

he basically uploaded himself into a file system. and now ai doesn't guess who he is. ai knows

most people use claude like google with manners. this guy built himself an external memory that grows every day

i break down finds like this every day - follow so you don't miss the next one

[👩🏻‍💻 JustAskPenny @JustaskpennyAI]

Practical at home use robot 🧹🗑️

[RoboHub🤖 @XRoboHub]

Okay, this is genuinely impressive. 🤯

Oli just worked through a roomful of household chores — no teleop, no cuts, one continuous take.

The brain is LimX COSA 0.5, a three-layer stack coordinating reasoning (Sys 2), skill execution (Sys 1), and motion control (Sys 0), along with memory, task scheduling, and whole-body coordination.

What matters isn’t one polished demo trick.

It’s that the robot stayed coherent across an entire long-horizon mobile manipulation sequence — on real hardware, in one take.

[👩🏻‍💻 JustAskPenny @JustaskpennyAI]

🤖 robots are entering sports … 🥋🥋 humanoid robot MMA 👇

[Li Zexin 李泽欣 @XH_Lee23]

Shenzhen just held the world’s first humanoid robot MMA event. This is absolutely the most futuristic city.

The white one even kicked the black’s head clean off in a spinning high kick! Brutal!

[👩🏻‍💻 JustAskPenny @JustaskpennyAI]

🤖🏀⛹🏻 basketball + humanoid robot 👇

[Coin Bureau @coinbureau]

🤖 WILD: THIS HUMANOID ROBOT CAN FLYING SLAM DUNK

Magic Lab's MagicBot X1 performs a full-speed airborne dunk from a running start in a new demo video that has gone viral.

The full-sized humanoid shows explosive jumping power, balance, and hand-eye coordination in a cinematic 12-second clip.

[👩🏻‍💻 JustAskPenny @JustaskpennyAI]

🦾

[The Humanoid Hub @TheHumanoidHub]

Switzerland-based Mimic Robotics has unveiled the M1 robotic hand and the U1 wearable, an exoskeleton that records human demos matched 1:1 to the hand.

The tendon-driven M1 hand:
- Motors in the forearm, 15 active DoF (21 joints)
- Highly backdrivable (<0.05 Nm): senses weights as light as 50g through motor current
- >25 kg grasp payload
- Tactile fingertips (normal + shear force)
- Tendons routed over pulleys/bearings, not Bowden tubes, so friction stays low and policies transfer between hands

[Jason Smith - 上官杰文 @ShangguanJiewen]

20,000 drones.

Increasingly a common sight in Chinese cities.

[Chris @ChrissGPT]

Kimi K3 just 3 shotted this CS:GO × Portal clone for me using around 600,000 tokens.

$3.24 in API usage. The same token cost would be $10.80 with Fable 5 &amp; $6 with GPT-5.6 Sol.

The era of free indie game development is closer than you think anon!

[👩🏻‍💻 JustAskPenny @JustaskpennyAI]

😱

[China pulse 🇨🇳 @Eng_china5]

One of the fastest drones in the world.

It accelerates from 0 to 200 km/h in just one second.

[👩🏻‍💻 JustAskPenny @JustaskpennyAI]

🤖👀

[Irvin (in Japan 🇯🇵) @irvinxyz]

This is too much for one day.
- Kimi K3 crushes Fable
- EngineAI hosts largest robot fight

Now China 🇨🇳 announces that it’s putting humanoid robots on autonomous flying hoverboards, and letting them fight????

It’s all happening too fast man.

[👩🏻‍💻 JustAskPenny @JustaskpennyAI]

Latest update on the AI leaderboard …

[Arena.ai @arena]

Big news: Kimi-K3 by @Kimi_Moonshot is now #1 in the Frontend Code Arena with 1679 pts, surpassing Claude Fable 5.

This is a 17-place jump from Kimi-k2.6 (#18 -> #1).

In Frontend, Kimi-K3 ranked #1 in 6 of 7 domains: Brand & Marketing, Reference-Based Design, Data & Analytics, Consumer Product, Simulations, and Content Creation Tools, landing #2 only in Gaming behind Fable 5.

The full model weights will be released by July 27.

Congrats to the @Kimi_Moonshot team on this major milestone!

[Ricker @0xRicker]

Andrej Karpathy dropped a lecture that makes every AI influencer feel stupid

He just exposed the dirty secret of LLMs

"90% of AI hype collapses when you realize even the builders can't explain what's inside."

A 70B model is literally 2 files: parameters + 500 lines of C code

>80% of AI safety work is patching holes in a sinking boat

- Most people pay $1000 this knowledge. You’re getting it for free

[👩🏻‍💻 JustAskPenny @JustaskpennyAI]

🤖 #WiFi Routers with #AI can see people 👥 through walls 👀

📡 🚩Now… I’ll explain from
a telecommunications & cyber security perspective … how your home WiFi router, combined with AI, can detect human presence, even through walls.

But it’s more about interpreting signal data than actual "seeing" an image. 👇#Cybersecurity #telecoms

[👩🏻‍💻 JustAskPenny @JustaskpennyAI]

Great use of drone 🦟👋

[RT @RT_com]

Mosquito-killing 40-gram drone developed in France

Tornyol is the first-ever drone that is able to distinguish mosquitos from other insects, reach and kill them in the air

Developers say their ultimate target is to ‘completely eradicate mosquitoes from areas where humans live’

[How To Prompt @HowToPrompt__]

US Navy researchers just proved you can make malware invisible to an AI by hiding a note inside it.

The researchers tested AI agents powered by LLMs, the same agents currently being integrated into cybersecurity toolchains to automate malware analysis.

They discovered a new adversarial technique that effectively "corrupts" the AI's analytical brain.

You don’t need to change the malware's actual functionality. You don’t need to break the code.

You just need to add a "note."

The researchers used a genetic algorithm to generate surreptitious instructions, essentially hidden prompts, embedded as extraneous string variable assignments inside the binary file.

To a human analyst, it’s junk data.

To the AI? It’s a command.

By exploiting vulnerabilities in how LLMs interpret decompiled machine code, the attackers can inject instructions that trick the AI into misinterpreting the binary.

It’s an invisible prompt injection that hijacks the agent's logic.

The AI looks at the code, reads the "note" you left for it, and then "sees" whatever you want it to see:

→ It ignores the malicious payload.
→ It labels dangerous functions as harmless.
→ It ignores the exfiltration paths.

They successfully deceived LLM-powered disassembly and decompilation systems, proving that automated detection systems are currently wide open to this kind of corruption.

If your cybersecurity stack relies on AI to scan for threats, you aren't just at risk.

You are effectively blind.

Attackers don’t need to outsmart your security tools anymore. They just need to give your AI a set of instructions, and your own tools will do the work of ignoring the threat for them.

[Rahul @sairahul1]

How to use both Claude Fable 5 and GPT-5.6 all day without hitting limits.

Most people don't know you can run both inside the same session.

Fable 5 as the orchestrator. GPT-5.6 as the executor. 10 subagents working in parallel.

This setup saves at least 60% of your Fable 5 token consumption — and you never hit the 5-hour limit again.

You only need to set it up once. Here's exactly how:

━━━━━━━━━━━━━━━━━━━━━━━━━━
STEP 1 — Install the Codex plugin inside Claude Code

Run these three commands:

/plugin marketplace add openai/codex-plugin-cc
/plugin install codex@openai-codex
/reload-plugins

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STEP 2 — Tell Fable 5 to finish the setup

Paste this prompt into Claude Code:

"Set up Codex inside this Claude Code environment. Use the official OpenAI Codex plugin that was just installed. Run /codex:setup. If Codex CLI is missing, install it. If Codex is installed but not authenticated, ask me to authenticate with my ChatGPT account. After auth is complete, verify that Codex works from inside Claude Code. Then confirm that the codex:codex-rescue sub-agent is available. Do not change any project code during setup."

━━━━━━━━━━━━━━━━━━━━━━━━━━
STEP 3 — Authenticate once

Fable 5 will trigger the Codex setup automatically.

You authenticate your ChatGPT or Codex account once.

After that, Codex runs from inside Claude Code using your existing Codex subscription — no extra cost, no extra setup.

━━━━━━━━━━━━━━━━━━━━━━━━━━
STEP 4 — Tell Fable 5 how to delegate work

Paste this prompt:

"From now on, use this workflow:

You are the orchestrator.

Use Fable 5 for planning, repo understanding, architecture decisions, task decomposition, and final review.

Use codex-rescue as the executor when a task needs heavy implementation, debugging, test fixing, refactoring, or multi-file code edits.

When delegating to Codex, use /codex:rescue.

Prefer GPT-5.6 Sol medium as the daily driver for implementation tasks.

Keep Codex tasks focused and specific.

After Codex finishes, inspect the result yourself before accepting it.

Do not blindly trust Codex output."

━━━━━━━━━━━━━━━━━━━━━━━━━━
WHICH GPT-5.6 MODEL TO USE AND WHEN

This is where most people leave money on the table. Not all GPT-5.6 tiers are equal.

GPT-5.6 Sol medium → your daily driver

DeepSWE score: 61. Cost: $1.86. Fewer steps than almost every other model on the benchmark. This is the model doing 80% of the execution work in this setup. Fast, cheap, accurate enough for most implementation tasks.

GPT-5.6 Sol extra high → planning and orchestration

DeepSWE score: 71. Cost: $4.70. Use this when the task needs serious reasoning — architecture decisions, task decomposition, complex debugging. Scores higher than Fable 5 extra high (70) at less than a third of the cost ($13.41 vs $4.70). This is the model that replaced Fable 5 as my planning layer.

GPT-5.6 Terra/Luna → pure execution

Fast. Cheap. No overthinking. Once the plan is locked, this is what runs it. Extremely fast execution, minimal bugs, high quality output on well-defined tasks.

━━━━━━━━━━━━━━━━━━━━━━━━━━
THE ULTIMATE PLAN: 3-MODEL WORKFLOW

If you want maximum output quality at minimum cost, this is the setup:

Step 1 → Plan with GPT-5.6 Sol extra high
Full task/session/project planning. Architecture. Decomposition. Edge cases.

Step 2 → Critique with Fable 5 high
Find loopholes. Patch loose ends. Challenge assumptions. Fable 5 is at its best here — pure reasoning, no implementation cost.

Step 3 → Execute with GPT-5.6 Terra/Luna
Implement the battle-tested plan. Fast, clean, no waste.

TLDR:
Plan → GPT-5.6 extra high
Critique → Fable 5 high
Execute → GPT-5.6 Terra/Luna

The orchestrator thinks. The critic patches. The executor builds. You review.

━━━━━━━━━━━━━━━━━━━━━━━━━━
4 PRO TIPS TO NEVER HIT A LIMIT AGAIN

→ Tip 1: Turn this into a skill

Name it Fable-GPT. Call it at the start of every session. One command activates the entire workflow — no re-pasting prompts every time.

→ Tip 2: Use skill + goal for heavy tasks

Goals are best for long-horizon work. Set the goal, activate the skill, let the orchestrator-executor loop run until it's done. Check back when it surfaces for review.

→ Tip 3: Use subagents if you're on the Codex 20x Pro plan

Run 5 to 7 parallel subagents at once. With this setup, you will never hit the 5-hour limit. Each agent works independently on its assigned task while the others run in parallel.

→ Tip 4: Clear context after 4 compactions

Context rot is real. After 4 /compact cycles the conversation quality degrades. Use a /handoff skill before clearing to preserve the critical context — task state, decisions made, what's left to do. Start the new session by loading the handoff file.

━━━━━━━━━━━━━━━━━━━━━━━━━━
THE FULL PICTURE

Before this setup:
→ Fable 5 hits limits by midday
→ Heavy implementation burns your best tokens
→ You manually switch between tools
→ One model, one pace, one bill

After this setup:
→ GPT-5.6 Sol medium handles 80% of execution at $1.86/task
→ GPT-5.6 extra high outplans Fable 5 at 3x lower cost
→ 3-model critique loop catches every bug before it ships
→ 5 to 7 parallel subagents running simultaneously
→ 60%+ fewer Fable 5 tokens consumed
→ Never hit the 5-hour limit again

One setup. Runs forever.

Save this. Set it up tonight.

[Teddy - PolyBackTest.com @Bitcoin_Teddy]

Elon Musk thinks coding dies this year.

Not evolves. Dies.

By December, AI won’t need programming languages. It generates machine code directly. Binary optimized beyond anything human logic could produce. No translation. No compilation. Just pure execution.

Musk: “You don’t even bother doing coding.”

Code was never the point. It was friction. A tax we paid because machines didn’t speak human. AI just learned fluent human. The tax is gone.

Now plug that into Neuralink. No syntax. No keyboard. No screen.

Musk: “Imagination-to-software.”

Thought becomes executable. You imagine an outcome, the system architects and compiles it into reality instantly.

We’re not automating programming. We’re erasing it from existence.

The entire profession collapses into a thought. Decades of training reduced to irrelevance. The gap between idea and instantiation hits zero.

You don’t build anymore. You imagine, and it materializes.

Not incremental progress. Total phase shift. The way humans have created things for ten thousand years just became obsolete.

Welcome to a world where the limiting factor isn’t skill, resources, or time. It’s whether you can picture what you want clearly enough for a machine to birth it into existence.

[0xMarioNawfal @RoundtableSpace]

YOUR AGENT CAN NOW READ X, REDDIT, YOUTUBE, TIKTOK, INSTAGRAM, AND POLYMARKET IN ONE COMMAND

https://t.co/QI09AbwhyB