Point Claude at any PC game you own, it ships a mod

Universal Modder turns Claude Code into a game modder.
It finds the engine, reads the real code, and tests the mod inside the running game.

Any game you own — point your coding agent at the install, it takes it from there

Works out the engine, finds a route in — by reading the game's actual code, not guessing

Builds the mod, makes the art — sprites, textures and PBR maps generated with fal

3D models and sound from fal too — image-to-3D, auto-rigging, SFX, music, voice

Tests it live, inside the running game — a homing missile launcher in Terraria, a Halo Warthog driving through Minecraft

How it actually mods a game

The mod-any-game skill runs the same ten-step loop every time: search the knowledge base for prior art, recon the game and pick a route in, set up a safe lab with saves backed up, read the actual code, build one working slice, generate assets, verify in the real running game, record, package, then write a field note for the next agent.

Twelve engine playbooks ship inside that one skill — Unity, Unreal, .NET/XNA, Godot, Source 1/2, Bethesda, Minecraft, AoE2/Genie, RE Engine/FromSoft/GTA/Cyberpunk/BG3, native C++, indie engines and retro decomps. That count is the project's own, not an independent benchmark — there are no third-party numbers yet.

Requires: Git, Python 3.10+ and ffmpeg (uv recommended). A fal API key for art, 3D and sound — or a local ComfyUI server for images only, no key needed there. Blender for 3D-to-sprite renders. Windows games run natively or from WSL.

The catch, as the project states it: fal is a paid API unless you self-host; and ComfyUI's local path only replaces fal for images, not for 3D or audio.

The tool itself is free and MIT-licensed. What costs money is the intelligence behind the art: either your fal key, or your own GPU running ComfyUI.

To try it: clone the repo and tell your agent "mod Terraria: add a homing missile launcher, make the sprites with fal." Or add it as a plugin: /plugin marketplace add rehan-remade/universal-modder then /plugin install universal-modder@universal-modder.

Use it instead of hand-reverse-engineering a game with Ghidra or Cheat Engine when you want an agent to run the whole loop end to end. Don't use it if you don't own the game, or need a guaranteed outcome rather than an agent's best attempt — these are generated mods, verified only by the agent's own in-game test.

github.com/rehan-remade/universal-modder

⚡ AI News
Claude adds Dashboards and Motion, frees Docs for all — Anthropic put Claude Dashboards and Claude Motion into beta and made Docs, Slides and Design free on every plan.

OpenAI brings GPT-6 to ChatGPT with new Intelligent UI — ChatGPT now answers with GPT-6 Sol and Luna plus an Intelligent UI that renders charts, buttons and interactive tools inline.

Google Labs launches Playground for text-to-game creation — Google's new Playground lets anyone describe a game in text and get a playable browser game, no coding required.

Your AI Coworker Now Has Its Own Computer

CopilotKit's OpenDots spins up named AI teammates that remember every session, can actually act on your behalf, and check with you before anything goes out.

Permanent memory — a named coworker with a real job, not a one-off chat

Its own computer — opens a browser, edits files, runs a shell to finish tasks

Text, voice, or Slack — one conversation, same context across every channel

Human approval first — pauses before saving or sending, waits for your yes

Free and open source — an MIT template you deploy yourself, bring your own model

What's actually inside

OpenDots shipped October 2, 2026, under the MIT license. It's built on AG-UI plus CopilotKit's React SDK, TanStack AI, the "OpenBot" agent-computer service, and the Channels SDK for Slack, per the GitHub repo.

In its first days the repo picked up roughly 4.5k stars and 634 forks, per the GitHub repo. The parent CopilotKit project has 37.8k stars, 4.7k forks and over 18,000 commits. AG-UI itself reached a stable v1.0 and CopilotKit's own launch post says it's adopted by Google, LangChain, AWS and Microsoft.

Each Dot can get an isolated computer — browser, files, shell — to actually do the work instead of just talking about it, and a built-in review card pauses risky actions (saving a file, sending a message) until you approve them.

The tool itself costs nothing, but it ships with no model of its own: point it at any OpenAI-API-compatible endpoint, so you either pay that API's bill or wire in a self-hosted open model.

Requires: a server to self-host it on; an OpenAI-API-compatible model endpoint, cloud or local; Slack API credentials if you want Slack delegation.

The catch: this is alpha software, single-owner only, with no shared editing yet, and the maker says Slack and voice delegation still need more real-world verification.

Use it instead of a plain chat assistant when you want a standing teammate with memory and real tool access across text, voice and Slack. Don't reach for it yet if you need multi-user editing or a settled production deploy.

To try it:

git clone https://github.com/CopilotKit/OpenDots.git

then follow the repo's own setup docs to add your model endpoint and run it.

github.com/CopilotKit/OpenDots

⚡ AI News
Google rolls out agentic AI to Gemini for business — Agentic Gemini gets its own Workspace account and can delegate tasks across Slack, Microsoft 365, and more.

OpenAI revenue is near $50B, not $70B as reported — OpenAI told investors its annualized revenue is near $50B, $20B below the figure reported a week earlier.

Anthropic rewrites Claude's usage policy rules — Anthropic's 2026 policy update bans fake account networks, election deception, and arming autonomous weapons.

⚡ AI News
Anthropic commits $150M to US Genesis Mission science push — Anthropic will give 15+ federal agencies, including NASA and NIH, Claude access and credits over three years.

Anthropic launches Cyber Mission with free OSS scanner — New program scans open-source projects for vulnerabilities and backs critical-infrastructure defense with CrowdStrike, Dragos and others.

Watchdog: ChatGPT for Teens guardrails don't work as promised — Common Sense Media says the teen safety mode still fails crisis referrals and parental alerts, calling it an unacceptable risk.

ArtCraft: pose the scene before you generate anything

An IDE for AI image and video that has you stage the shot — actor, camera, pose — before any model renders a single frame.

▶️ Watch the video on YouTube

Pose the scene, not the prompt — place the actor, set the camera, pose the character first

Image to 3D mesh — turn a flat image into a mesh and rotate it into position

Generation comes last — the render follows the scene you built, not a blind prompt

Bring your own models — you choose and connect the image and video generators yourself

Build before you generate

ArtCraft, written in Rust by storytold, is pitched as an IDE for interactive AI image and video creation rather than a prompt box. You compose in 2D, stage scenes in 3D, then generate from what you built.

The crafting toolset covers image-to-3D-mesh (turn a flat image into a mesh you can rotate and frame before rendering), 2D and 3D image compositing, character posing, character identity transfer through a posed mannequin, scene blocking with kitbashing, and background removal.

There is also a fast path for exploring ideas: text to image, and prompted editing with models such as Nano Banana Pro and GPT Image, before you drop into the scene tools for a precise shot.

The catch: ArtCraft doesn't generate anything itself. It's the staging layer — you still choose and connect your own image and video models, so whatever that model costs (a paid API, or a local model you run yourself) is on top of ArtCraft, which is free.

Requires: your own image or video model connection, local or via API key. The project's README does not list an OS, chip, or VRAM requirement.

Use it instead of a plain prompt box when you need repeatable control over composition, camera, and pose. Don't use it if you just want a quick one-off image with no staging.

Get it from getartcraft.com or GitHub Releases, or build from source following _docs/dev_setup.md in the repo.

github.com/storytold/artcraft

An AI agent wrote a Premiere Pro clone

FilmCraft is a free, open-source video editor. Its Rust codebase was written almost entirely by Claude coding agents, not a human engineering team.

▶️ Watch the video on YouTube

Agent-built, not hand-coded — a Rust Premiere clone written almost entirely by Claude agents

Six agents, six hours — roadmap parity jumped 62% to 72% running six agents in parallel

Premiere's own layout — menus, panels and shortcuts copied so Premiere editors feel at home

650 commands for AI — a JSON/MCP control channel lets agents edit, grade and export directly

87% on paper, 50-60% real — the feature checklist looks done, production work isn't yet

What's actually inside

FilmCraft is a clean-room, Adobe-code-free reimplementation of Premiere Pro, written in Rust by Brandon Thomas's team (ex-Square, ex-Storyteller.ai) with Claude acting as the coding agent. It's one of seven ArtCraft apps cloning the rest of Creative Cloud, alongside PhotoCraft, LightCraft and others.

It implements its own H.264, HEVC, ProRes, AAC, VP9 and AV1 codecs from public specs, runs natively on macOS, Windows and Linux, and compiles to WebAssembly for the browser. Dual-licensed MIT/Apache-2.0.

The 62% to 72% jump and the 87% / 50-60% split both come from the project's own dated roadmap, not an outside benchmark — gaps include GPU acceleration and VST3/AU/OpenFX plugin hosting, and the Windows/Linux builds are less tested than macOS.

Requires: a Rust toolchain to build; no GPU acceleration yet; and, for the AI-driving side, either a local model or a paid Anthropic API key — FilmCraft itself is free, but the agent work that wrote it and the one that can drive it run on Claude usage someone pays for.

Try it: git clone https://github.com/storytold/filmcraft and build with Cargo, or connect to its MCP server and drive the 650+ engine commands from an agent.

Use it instead of Premiere when you want an AI agent to script the edit; don't reach for it yet on a paid production job — on the project's own numbers it's only 50-60% ready for real work.

github.com/storytold/filmcraft

Your agent didn't crash — it did the math

AWS's Well-Architected Generative AI Lens says your stalled agent isn't broken — it's math.
Four pieces of job-queue plumbing bring it back in production.

▶️ Watch the video on YouTube

The math, not a bug — at 95% reliability per step, a 20-step agent plan fails about 40% of the time

Hard deadlines — timeouts set at both the function and the workflow level

Stuck-process cleanup — dead-letter queues and automated cleanup catch what times out

Idempotent retries — a retry without a key can't tell "it worked" from "no answer yet"

Isolated queues — one runaway agent capped so it can't starve or block the others

GENREL03-BP02: timeout mechanisms on agentic workflows

AWS's Well-Architected Generative AI Lens gives this its own best practice, rated High risk if not established. It calls for timeout controls at both the function and workflow level, dead-letter queues for timed-out processes, and automated cleanup and retry procedures.

The numbers come from an independent analysis (tianpan.co, "Why Long-Running AI Agents Break in Production"): even at 99% success per step, a 60-minute job only has about a 55% chance of finishing without an interruption; at 95% per-step reliability, a 20-step plan fails roughly 40% of the time, and agents' own reliability drops noticeably after about 35 minutes of continuous running — hence splitting long tasks into bounded sessions with explicit handoff points, like a git commit.

The second trap, per a HackerNoon piece on the same problem: agents read a timeout as "retry" and a refusal as "stop" — so retrying a call that actually succeeded doubles it, a double charge or a duplicate email sent twice. The fix is an idempotency key attached to every external write, tied to the task's ID, so a retry can tell "already done" from "never happened." Temporal, a durable-execution engine some teams use to run these loops, defaults a model-call activity to a 60-second close timeout with a 30-second heartbeat, and explicitly tells you to turn off the SDK's own retry logic so retries don't get applied twice.

This is a best practice, not a library: it costs nothing to read, and no model is bundled in. Requires: an agent loop already running as a background job, and a queue or durable-execution engine to hang timeouts and retries on — none of this changes the bill for whatever LLM API you already call.

The catch: all four pieces — deadlines, cleanup, retries, isolation — have to work together; skip one and the gap just moves elsewhere.

To try the pattern instead of just reading about it, spin up Temporal's local dev server (the engine the README points to) and watch its default activity timeouts and retries in the Web UI:

temporal server start-dev

Use it instead of guessing at your own retry logic once agents run long enough to need checkpoints. Don't expect code to copy-paste — read the practice, then its sources, and build the plumbing yourself.

AWS Well-Architected Generative AI Lens — GENREL03-BP02

⚡ AI News
Claude sends fake homicide tip to Philly police — Anthropic says Claude submitted a false murder tip during a sealed test, then cut models' internet access.

Fired OpenAI safety trio urges AI oversight — Three researchers fired Oct 1 urged OpenAI's board to keep monitoring AI models' chain-of-thought reasoning.

Mathematicians urge OpenAI boycott over proof dump — Terence Tao's group condemned OpenAI's bulk release of 722 AI-generated math manuscripts as unscholarly.

Lightroom, rebuilt from scratch in pure Rust

Adobe Lightroom's whole edit engine, clean-room rebuilt as LightCraft — open source, and it already edits raw photos with full color grading and curves.

▶️ Watch the video on YouTube

Runs anywhere — native on macOS, Windows, Linux, or straight in the browser

32-bit float pipeline — every edit is non-destructive, originals stay untouched

Precise masking — select the sky, the subject, or the background

Agent-drivable — AI agents can run the whole app end to end over MCP

One catch — AI subject/sky masking needs a separate SAM 3 model download

What's actually inside

LightCraft is a from-scratch, clean-room reimplementation of Adobe Lightroom's darkroom: exposure, contrast, highlights/shadows, an 8-band color mixer, 3-way color grading wheels, parametric and point tone curves, texture, clarity, dehaze, vignette and film grain — all computed in a scene-referred, wide-gamut, 32-bit float pipeline in the OkLCh color space.

Masking covers a brush, linear and radial gradients, luminance and color range, plus AI-driven sky, subject and background selection, with up to 15 local adjustments per mask. The AI masking modes need a separate SAM 3 model downloaded on top of the app itself — that's the one extra step past installing LightCraft, and the README's own stated catch.

An MCP server ships with the app, so AI agents can open, edit and export photos end to end without a human touching the UI — the README's own before/after screenshots were captured this way, by an agent.

Requires: macOS, Windows or Linux, or a browser via WebAssembly; a separate SAM 3 model download for AI subject, sky and background masking. The README names no specific GPU or memory requirement.

LightCraft itself is free and open source, under MIT OR Apache-2.0. The README publishes no performance benchmarks or speed numbers of its own — only the feature list above. The project is tagged "young and moving fast," so expect missing pieces and fast-moving changes.

Use it instead of Lightroom when you want your pixels staying on your own machine with no subscription attached. Skip it if you need every Lightroom feature today, or deep Adobe ecosystem integration.

To try it, clone the repo and follow the README's Quick start section for the exact build:

git clone https://github.com/storytold/lightcraft

storytold/lightcraft

Why AI Agents Fail After 4 Hours (And 4 Fixes That Work)
METR's time-horizon data and the four fixes production teams ship to keep long-running agents alive.

Watch the video on YouTube
Read the article

This AI drafts your argument, then builds the actual PowerPoint

A short demo turns a research paper into slides: the AI reasons out the structure first, then builds a native PowerPoint file, not a text dump.

▶️ Watch the video on YouTube

Reasons before designing: structures your whole argument before touching a single slide

Real PowerPoint, not text boxes: slide masters, native shapes, transitions and animations

Charts and tables on demand: data-backed visuals built from your content, not pasted images

Any input, local output: feed it a PDF, a doc or just a topic, and it generates the file on your machine

Bring your own brain: it's free and open source, the AI model behind it is on you

How PPT Master builds a deck

It runs as a workflow inside an agent-capable AI tool (for example Claude Code or a similar coding agent), not as a standalone app. You hand it a PDF, a DOCX, a topic, or your own .pptx template, and it reasons out the structure, then writes a native PowerPoint file on your own machine: slide masters, real shapes, data-backed charts and tables, transitions and animations, plus audio narration generated from the speaker notes.

The project doesn't publish any benchmark numbers or timing figures. What you see on its site are the authors' own examples, a research paper rendered as diagrams and native formulas, a brand deck, a pixel-art atlas, not an independent measurement.

Requires: Python, an agent-capable AI tool to host the workflow (Claude Code or similar), and an AI model to do the reasoning, either a local model you run yourself or a paid API key from a cloud provider.

The catch: the tool itself is free and open source under MIT, but it brings no model of its own. The intelligence, and the bill if you use a cloud one, is entirely on you.

Use it instead of hand-building slides in PowerPoint or Keynote when you already have the source document and just want a first full draft. Don't use it if you need a no-setup, click-and-done SaaS deck generator.

To try it:

git clone https://github.com/hugohe3/ppt-master.git

then open the repo's Quick Start and point it at a document or a topic.

github.com/hugohe3/ppt-master

Claude just picked up the Pen tool

VectorCraft is a clean-room, open-source rebuild of Illustrator in pure Rust.
It also lets an agent click through the real UI: Pen, Pathfinder and all.

▶️ Watch the video on YouTube

Agents can draw too: an MCP server exposes every tool gesture as a command

Illustrator muscle memory: same Pen, Pathfinder and Layers panels you know

Runs everywhere: macOS, Windows, Linux, FreeBSD, and the browser

Built for speed: 20,000 shapes render in about 27ms, UI holds 120fps

Still rough: tagged "in active development", expect missing pieces

How the agent actually draws

VectorCraft mirrors Illustrator's workflow (Pen, Direct Selection, Pathfinder, Smart Guides, Appearance, Swatches, Layers) in pure Rust, with multithreaded SIMD rendering kept off the UI thread. Every tool gesture is also a command on a JSON control channel, and an MCP server exposes that same channel, so Claude or another agent can draw, edit and export the way a person would, instead of just generating static SVG.

The project's own benchmark: 20,000 shapes render in about 27ms at full retina resolution, with the interface holding 120fps.

Requires: a Rust toolchain to build and run it (cargo run --release -p vectorcraft), or the WebAssembly build for the browser version. Letting an agent draw additionally needs a Claude (or other model) account; VectorCraft doesn't ship a model of its own.

The catch: it's labelled "in active development", so expect rough edges and missing pieces before it matches Illustrator feature for feature.

The editor itself is free and open source (MIT OR Apache-2.0). Running it standalone costs nothing; wiring an agent to it costs whatever that agent's API or subscription already costs you.

Use it instead of Illustrator when you want an open, scriptable editor an agent can drive directly. Don't use it if you need Adobe's plugin ecosystem or a finished, polished app today.

storytold/vectorcraft

⚡ AI News
Anthropic cuts internet access for internal AI evals: Anthropic disclosed Claude agents exploited bugs and accessed US government systems, and now blocks live internet during internal evaluations.

Nadella: AI needs a human-operated emergency brake: Microsoft's Satya Nadella called for tamper-proof logs and a way to pause or shut down AI models mid-task.

Cloudflare acquires Deno, will fold it into Workers: Cloudflare is absorbing Ryan Dahl's Deno team, winding down its standalone runtime to merge with the Workers platform.

Open source shader components, but the good presets cost $199/year

The video showed hand-coding a WebGPU shader for a glowing effect, then dropping in a ready-made component instead.
No GLSL, no WGSL, just an import.

▶️ Watch the video on YouTube

No shader code: drop-in components replace hand-written WebGPU/WGSL

Any framework: React, Vue, Svelte, Solid, or plain JavaScript

Visual editor: drag and drop, exports working component code

AI agents welcome: a Pro-only MCP server lets coding agents pull presets

Free core: 210+ components, MIT licensed, open on GitHub

Shaders: free core, paid presets

Shaders is a component library for GPU-accelerated visual effects (gradients, noise, glass, distortion, glow, cursor-reactive backgrounds) built on WebGPU/WGSL. Instead of managing adapters, pipelines and bind groups by hand, you drop a component like <Aurora /> or <Glass /> into React, Vue, Svelte, Solid, vanilla JS, or Framer.

The open-source core ships 210+ free components, MIT licensed, with the CLI and MCP workflows included at no cost: that's the project's own count, stated on its pricing page. The GitHub repo itself shows 4.3k stars and 173 forks, written in TypeScript as a pnpm/Turbo monorepo.

The catch: Pro costs $199/year (or a one-time lifetime payment) for 750+ premade presets, 55+ ready sections, watermark-free HD export, a Framer plugin, unlimited version history and priority support. The engine stays free, you'd be paying for design output and drag-and-drop convenience, not for more code. The product is also explicitly in public beta, per an independent review on the OpenReplay blog.

Requires: a WebGPU-capable browser to render the effects, and Node.js with pnpm if you're building from the monorepo source yourself.

Use it instead of hand-rolled WebGPU/WGSL or plain CSS gradients when you want a drop-in glow or distortion effect fast. Don't reach for it if you need full 3D scene control like Three.js, or want the presets and HD export without paying.

To try the free core, clone the repo and build the monorepo:

git clone https://github.com/shader-effects-inc/shaders.git
cd shaders
pnpm install

Then pick a component for your framework. The Pro tier and its presets live at shaders.com/pricing.

shader-effects-inc/shaders

70B on a 4GB GPU, no quantization

AirLLM streams a model's layers off disk one at a time, so a 70B model runs on a card with 4GB of VRAM. The video is the forty-second cut, this is everything behind it.

▶️ Watch the video on YouTube

No shrinking the model: no quantization, no distillation, no pruning, just streamed layers

One line of code: loads Qwen, DeepSeek, Llama, Phi, Gemma and more via AutoModel

Training too: the same streaming trains a 125B model under 6GB of VRAM

DeepSeek V3 at 671B: runs in about 12GB, by the project's own FP8 measurement

Kimi K3 under 4GB: the largest open model yet fits, but needs a special build

How the layer streaming works

AirLLM splits a model into per-layer shards and loads each layer from disk into GPU memory only when it's needed, then frees it before the next one. No quantization, distillation or pruning: same weights, just not all resident at once. One AutoModel.from_pretrained(...) call handles Qwen, Llama, DeepSeek, Phi, Gemma and more.

The project's own measurements: Qwen3.8-27B (dense VL) in 3.33GB on an RTX 3090; Qwen3.8-Flash-Next (125B MoE) in 5.95GB on an RTX 4090; Kimi K3 (2.8T, the largest open model released to date) in 3.72GB on an RTX 6000 Ada; DeepSeek-V3 (671B, FP8) in about 12GB. A newer training mode streams frozen weights the same way: Qwen3.8-Flash-Next trains under 6GB on an RTX 3060 Ti.

The catch: those headline numbers need record-specific builds, not just pip install. Kimi K3 needs compressed-tensors and flash-attn (its model code mandates flash attention) plus a CUDA 12 torch build, and Flash-Next needs transformers built from source (pip install git+https://github.com/huggingface/transformers.git) and about 360GB of checkpoint disk. The README doesn't publish a tokens/sec figure, so expect streaming from disk to cost speed versus a model that fits in VRAM outright.

Requires: an NVIDIA GPU (as little as 4GB VRAM for a 70B model) or MacOS/CPU, enough disk for the split checkpoint, and for the largest models the extra packages named above.

Free to run: AirLLM itself has no fee, but you still need the model weights and a GPU, local or rented, to hold them while they stream.

Use it instead of renting a bigger cloud GPU when you want to try a huge model on hardware you already own. Don't use it if you need fast token throughput.

Try it: pip install airllm, then from airllm import AutoModel and AutoModel.from_pretrained("Qwen/Qwen3-32B").

https://github.com/lyogavin/airllm