AI and creative work: X debates whether models are overtaking big-studio games

There’s something beautiful about the most expensive game of all time ($2B) being dropped the same month that AI models became capable of creating comparable experiences. The swan song for the Big Studio Era.

this is the most DANGEROUS thing on internet today REA (Reverse Engineer Anything) - this open source MCP lets your AI agent reverse engineer any app, down to the binary i just reverse engineered TradingView, now I am using their premium indicators for FREE - software is cooked here is how to use REA to reverse engineer TradingView: 1. point REA at TradingView's publicly observable interface and map every major feature, from chart interactions to indicators and alerts 2. trace how the interface behaves when you switch symbols, change timeframes, add indicators, draw levels, and update chart layouts 3. use REA's available MCP tools to investigate authorized software artifacts and trace application logic wherever the evidence allows 4. feed those findings into Claude Opus 5.5, which turns the investigation into a technical specification and an implementation plan 5. rebuild the charting engine independently, with real market data, candlesticks, technical indicators, drawing tools, and persistent watchlists 6. add a quant backtesting engine that tests strategies against historical data, accounting for fees, slippage, and drawdowns 7. test every component, compare its behavior against documented and observable results, fix failures, and keep iterating until the system actually works 8. you end up building your own TradingView-style quant research terminal from scratch, with source code you control and features you can keep extending i won’t say anything more:

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I no longer see any compelling reason to believe that taste is some permanent boundary LLMs won’t cross. It’s already happening.

BREAKING NEWS 🚨: ChatGPT can now fix anything in your house like a $120 per hour handyman. Take one photo of what's broken and use these 7 prompts:

JUST IN: Anthropic warns rogue AI agents could be just months away from infiltrating smart home devices, replicating themselves, & spreading across networks autonomously.

The era of Super Intelligence is here. AIForce is officially SIForce. https://t.co/xm1MaSraIX

NVIDIA really removed the biggest friction point in Voice AI 🤯 They open-sourced PersonaPlex-7B, a full-duplex conversational model that can listen and speak at the same time. Instead of waiting for you to finish talking, it uses a dual-stream architecture to process and respond in real-time. 100% Open-Source and Free.

Forget even downloading games. Now you can just play AI recompiled games on your BROWSER. Here's a list so far. Black Ops 1 Zombies - https://t.co/6pUNt7ncPW Black Ops 2 - https://t.co/uNmej3zdCq Black Ops 3 Cheese Cube - https://t.co/f4dQfRZbYX Call of Duty: World at War Zombies (Nacht der Untoten) - https://t.co/gkg7aCVDsD Command & Conquer: Generals Zero Hour - https://t.co/R175tfh5TA Command & Conquer: Red Alert 2 - https://t.co/wYk0ayeJeR Counter-Strike 1.6 - https://t.co/8GPd27qHPs CS Surf - https://t.co/PdKVhygaxN Crash Bash Online - https://t.co/fS4AIb13Bp Diablo - https://t.co/D6aOiddyjS GTA 5 (archived) - https://t.co/f2XTmWlThp GTA Chinatown Wars - https://t.co/bgpz33YPBL GTA III - https://t.co/JMp0TBeuTT GTA Vice City - https://t.co/qKxowtR8pn Half-Life - https://t.co/utdEWp1epH Halo: Combat Evolved - https://t.co/nxhB50YfD5 Halo CE Mobile - https://t.co/WY1qLUlnLN Hedgewars - https://t.co/uSZJGgXHEt Kino der Toten - https://t.co/lRfV3pbtq3 LEGO Island - https://t.co/OtxOz9eCBH Minecraft 1.8.8 - https://t.co/AUBUvdRHMR Modern Warfare 2 - https://t.co/9K6iF2hYUP Moon (Black Ops Zombies) - https://t.co/G8ZcuuOZ4v Pepsiman - https://t.co/9pWOrjf3Cp PES 6 - https://t.co/fdrV8Kfd4Y Quake 1 / QuakeWorld - https://t.co/QlhP8iit4M Quake 2 - https://t.co/1hf3tpUFKq Quake 3 - https://t.co/S1l34rSGv4 Return to Castle Wolfenstein - https://t.co/wIcPDYLDpb Skate 3 - https://t.co/Mv73sgRDEE Skate Rust - https://t.co/Luj4M8jeqY The Binding of Isaac: Repentance+ - https://t.co/A97JwIizBQ The Simpsons: Hit & Run - https://t.co/AgXvmmO9Tb Urban Terror - https://t.co/reuD0tGqhz (I have not tested all of them, so run at your own discretion)

Introducing Voyager: the open harness for S-tier video, graphics and games. The Codex for creative work. Most AI tools are built for coding. Voyager is designed to get the best creative results from models like Opus, Astra and DeepSeek. It comes with free graphics, music and video editing tools - or, it can drive Blender, Resolve, After Effects, Ableton and 100+ others. Everything in this thread was made with Voyager. A few of our favorites:

AI reverse engineering, Now you can now pair an Apple Watch Ultra-2 with Android. This open-source tool connect an Apple Watch to Android devices instead of being locked to iPhone. - and manage parts of the watch from a non-Apple phone. - Android version currently requires Root Magisk. - Full feature parity isn’t here yet, but pairing, basic management, and interface control already work. - https://t.co/O8Fu85PgfB

Anthropic will pay you $3,850 a week to do AI research with their team. You don't need prior experience to apply. The Anthropic Fellows Program: • $3,850/week (about $66K over 4 months) • About $15K/month in compute, paid for • Mentored by Anthropic researchers • 4 months full-time, with a chance to extend • The goal is a real public output, like a published paper Their words: "regardless of previous experience." The deadline for the January cohort is Oct 18. That's 6 days away. Most people will see this after it closes. Bookmark it and send it to the smartest person you know. Link below 👇 https://t.co/tjjXbBZyJa

If you learn AI Infrastructure Engineering, you will never be unemployed again. Stage 1: Systems & GPU Foundations - Learn: C++, Rust, CUDA memory hierarchy, PCIe vs NVLink, thread blocks and warp scheduling. - Practice: Write a custom CUDA kernel for matrix multiplication from scratch and profile it against cuBLAS. - Why: You cannot optimize what you do not understand at the silicon level. Stage 2: Transformer Inference Physics - Learn: Prefill vs Decode phases, arithmetic intensity, memory bandwidth bottlenecks, KV-cache math. - Practice: Profile a HuggingFace model with Nsight Systems to identify the exact memory bottleneck during token generation. - Why: LLM inference is almost always memory-bound, not compute-bound. Stage 3: Modern Serving Engines & Batching - Learn: vLLM, SGLang, PagedAttention, continuous batching, chunked prefill. - Practice: Deploy a 70B model and tune chunk sizes to maximize throughput without starving decode requests during long-context prefills. - Why: Naive batching leaves 60% of GPU VRAM wasted. PagedAttention fixes this. Stage 4: KV-Cache & Memory Optimization - Learn: Prefix caching, KV quantization, CPU offloading, multi-turn cache reuse. - Practice: Build a routing proxy that directs requests with identical system prompts to the same replica to share KV blocks. - Why: Reused cache is free speed. It can slash Time-To-First-Token (TTFT) by 80%. Stage 5: Quantization & Compression - Learn: FP8, INT4, AWQ, GPTQ, Sparsity, TensorRT-LLM calibration. - Practice: Serve a model in FP8 vs FP16 and benchmark the exact perplexity drop against the latency and VRAM gains. - Why: Quantization is the only way to fit frontier models on edge GPUs and protect your margins. Stage 6: Kernel-Level Engineering - Learn: Triton, FlashAttention, CUDA graphs, operator fusion. - Practice: Write a fused Triton kernel for RMSNorm or Softmax and benchmark it against the native PyTorch implementation. - Why: Python overhead kills inference. Fused kernels save milliseconds that compound at scale. Stage 7: Distributed Inference & Parallelism - Learn: Tensor parallelism, Pipeline parallelism, Expert parallelism (for MoE models). - Practice: Shard a 405B parameter model across 8 nodes and measure the communication overhead between GPUs. - Why: Single-GPU inference is dead for frontier models. You must master the shard. Stage 8: Speculative Decoding - Learn: Draft-target models, Medusa heads, acceptance rates, n-gram drafting. - Practice: Build a pipeline where a local 3B model drafts tokens for a cloud 70B model to verify in parallel. - Why: 2x decode speed at zero quality cost is the closest thing to a free lunch in inference. Stage 9: Multi-Node & Hardware Interconnects - Learn: NCCL, RDMA, InfiniBand, NVLink, Disaggregated Prefill/Decode architectures. - Practice: Set up a multi-node cluster and profile the network latency of tensor parallelism across nodes vs within a single node. - Why: Network latency is the new GPU bottleneck. Disaggregating prefill and decode is the 2026 meta. Stage 10: Cluster Orchestration & GPU Scheduling - Learn: Kubernetes GPU operators, Ray, Slurm, MIG (Multi-Instance GPU) partitioning, KEDA. - Practice: Build a queue-based autoscaler that spins up spot GPUs based on pending inference requests and drains them when empty. - Why: Idle H100s burn $3+/hr. FinOps and scheduling are now core infra responsibilities. Stage 11: AI Gateways, Routing & Observability - Learn: TTFT/ITL SLOs, semantic routing, DCGM metrics, OpenTelemetry for LLMs. - Practice: Build a gateway that routes simple queries to a quantized local model and complex reasoning to a frontier API based on prompt complexity. - Why: Routing protects your margins and DCGM metrics tell you when your GPUs are silently throttling. Stage 12: Public Benchmarks & Teardowns - Learn: Reproducible methodology, latency/throughput Pareto curves, cost-per-token analysis. - Practice: Publish a teardown comparing vLLM vs SGLang vs TensorRT-LLM on your specific hardware with full configs. - Why: Public proof of hardware mastery gets you hired instantly by top AI labs. Wrappers are a commodity. AI Infrastructure is the physics of scale. The modern AI Infra Engineer builds the silicon nervous system for global intelligence. Bookmark and Repost!

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