Your AI Coding Agents Keep Overwriting Each Other: Here's the Fix
Git worktrees, agent dashboards and vendor cloud fleets: what to own and what you can safely rent.

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⚡ AI News
Texas sues OpenAI, Altman over ChatGPT harms to minors: Texas AG Ken Paxton sued OpenAI and Sam Altman, seeking to bar ChatGPT from minors over suicide and safety claims.

Elvis estate sues Anthropic for $41M over 274 songs: Authentic Brands and Presley rights holders seek $41 million, claiming Claude trained on 274 songs without permission.

Replit shifts $100M AI spend from Anthropic to OpenAI: Replit moved most coding workloads to OpenAI after a 30%+ discount, in a deal reportedly worth over $100 million a year.

This duck robot gets back up when you knock it over

Microduck is a tiny 25 cm, 800 g biped that runs trained reinforcement learning policies on its own onboard brain, written in Rust.

▶️ Watch the video on YouTube

Falls, stands back up: knock it over and it rights itself on its own

25 cm, 800 g biped: balanced on two legs, per the project's own spec

Trained in simulation: MuJoCo and PPO, then sim2real onto the real duck

Walks on command: pick up a gamepad and drive it live

Rolls or picks things up: swap on wheels, or beak to the floor with one button

What's inside the duck

Microduck is the robot's brain: a Rockchip RK3566 board running a handful of Rust daemons. robotd drives a 50 Hz control loop over fifteen servos from neural policies exported to ONNX, alongside daemons for updates, wifi, Bluetooth, the gamepad and the camera.

The walking, rolling and pickup policies are trained separately, in the microduck_rl repo: MuJoCo simulation, PPO, domain randomisation, then exported to ONNX for sim2real onto the real duck. The 25 cm, 800 g figures and the 50 Hz loop are the project's own published spec, not an independent measurement.

Requires: a physical Microduck, bought at pollen-robotics.com/microduck, or the MuJoCo simulator via scripts/duck-sim if you don't have one. A gamepad to drive it. Bluetooth and duckctl if you want to control it from a laptop with no network and no ssh.

The catch, straight from the project: you need a duck, or its simulator, to try any of this. There's no way to play with the policies without one or the other.

The code and docs are free and open on GitHub. The physical duck costs money through the official store; nothing here calls out to a paid API or cloud model.

To try it without buying anything, clone the repo and run scripts/duck-sim for a simulated duck in a window, driven by the real daemons.

Use it instead of a generic RL robotics demo when you want a working, hardware sim2real example you can hold. Don't use it if you have no duck and don't want to run MuJoCo: there's nothing to drive.

pollen-robotics/microduck

Your AI-built site has a tell, and Hallmark knows all forty-eight of them

Every AI-generated homepage has that eyebrow line above the heading and one coloured word in the title. The video shows a skill that hunts those tells down and fixes them before you ship.

▶️ Watch the video on YouTube

Named slop tells: the eyebrow line, tracked-caps labels, one coloured word per headline, empty frames

A design skill: for Claude Code, Codex and Cursor

Three themes, dealt: picked from forty-eight materials, built around what the visitor came to do

Linted against itself: checked in a real browser, then a fresh-context reviewer gets the last word

Screenshots itself: point it at your live site, dev server or app and it grabs the shots

How Hallmark checks its own work

Made by Together AI. Hallmark deals three themes from its catalog of forty-eight materials (paper, type, colour, texture, its own components), then builds the page around the visitor's job: a booking page books, a shop sells, a newsletter subscribes.

The hero is built and reviewed alone before the rest of the page exists. Then scripts/sloplint.mjs --render checks the page at eight widths against the named tells, and a fresh-context subagent reviews it cold. Gates come in tiers: Floor never ships (contrast, focus, invented facts), Reflex is the default a build can overrule with a reason, Finish is craft polish that never blocks a ship.

It also ships /hallmark redesign <path> and /hallmark audit <path>, and a library of 755 openly licensed, credited photos.

The forty-eight and the 755 are the project's own catalog counts, not a third-party benchmark.

Hallmark itself is free and MIT licensed. It runs inside whichever AI coding tool you already have open, so you pay only whatever that tool already charges for model access, nothing extra.

Requires: Node 18 or later; Claude Code, Codex, Cursor or OpenCode to invoke it. Install with npx skills add nutlope/hallmark -g (drop -g for one project only).

The catch: the browser checks, sloplint --render and the self-captured screenshots, need Chrome and npm i -g puppeteer-core installed separately. Without them the static checks still run.

Use it instead of eyeballing your own AI-generated page for slop when you want the check built into your coding agent. Don't use it if you need the browser checks without touching Chrome or puppeteer-core yourself.

github.com/Nutlope/hallmark

⚡ AI News
OpenAI cuts prices 90% to poach Anthropic clients: OpenAI cut prices up to 90% for firms like Rippling and Replit, flipping their coding spend away from Anthropic.

Anthropic's Jack Clark moves AI self-improvement to 2027: Jack Clark says AI research skill has doubled every three months, pulling his self-improving AI forecast in from 2028.

Old CNN beats Claude and GPT-6 at skin cancer calls: A 2015 Google CNN scored 62% on skin cancer diagnosis, beating Claude Fable 5.1 and GPT-6 Astra at 42% each.

Claude Opus just directed a movie without touching a video model

An AI agent writes the JavaScript that draws, scores and narrates a film, frame by frame. FFmpeg cuts the result together.

▶️ Watch the video on YouTube

Zero video-model frames: Claude Opus writes JavaScript instead

Code is the camera: it draws, scores and voices every scene

98 Oscar winners, one film: a 6:31 demo redraws them all

Matching style per winner: 77 in 2D, 17 in 3D, 4 in 2.5D

59 styles, 11 categories: a library to build your own film

How Opuscar 98 gets made

Claude Opus 5.5 runs as a Claude Code skill/plugin, not a video model. It writes plain JavaScript (Canvas and WebGL) that draws every frame, composes the score and generates the text-to-speech narration, then FFmpeg stitches it all together. No diffusion or video model ever runs in the pipeline.

The flagship demo, "Opuscar 98", is a 6:31 film covering all 98 Best Picture winners from 1927 to 2025, each redrawn in a different matching style: 77 in 2D, 4 in 2.5D, 17 in 3D. The line "every frame, every note and every cut was written in code by Claude Opus 5.5" is the project's own claim on its homepage, not an outside benchmark.

The repo ships 59 ready-made styles across 11 categories (documentary edits, hand-drawn and painting, shadow puppetry, Ukiyo-e, paper-cut, printmaking, 3D and more), totaling 56 minutes of demo film, plus a guided five-step workflow for designing custom styles.

Requires: Claude Code, plus access to Claude Opus to generate anything. The code itself is free and MIT-licensed, but Opus access runs through Anthropic's paid API or a Claude plan, there is no free local-model path here.

The catch: this is a code generator Opus has to write well, not a one-click render button, so output quality depends on how closely Opus follows the style brief. The project doesn't list other hardware requirements.

Try it: claude plugin marketplace add lemomo-ai/lemo-opuscar then claude plugin install lemo-opuscar@lemolab. The repo was created September 26, 2026 and had passed 7,300 stars and 825 forks by October 25.

Use it instead of a text-to-video model when you want full control over visual style with no model-generated artifacts. Don't use it if you want an instant one-prompt video, you still need Opus access and some patience.

lemomo-ai/lemo-opuscar on GitHub

⚡ AI News
Startup Cathedral breaches Anthropic in 12 hours: Cathedral's AI agents got inside Anthropic's internal Slack and pay data within 12 hours; Anthropic disputes the scope.

Meta seeks $95B in stock to fund AI buildout: Meta plans a $95B equity raise, with Nvidia, AMD and Broadcom as cornerstone investors in the stock sale.

SpaceX closes $40B debt deal for AI chips: Musk says SpaceX secured $40B in debt for GPUs, warning of a shakeout among 200+ neocloud firms.

Type one sentence, get back a design you can ship

Ask an AI to design something and it usually looks generic. Huashu Design fixes that right inside your coding agent: one sentence in, a finished prototype, deck, or animation out.

▶️ Watch the video on YouTube

Six drafts, three real directions: thumbnails first, then the strongest ideas become full designs

84 ready-made styles: a specimen library spanning web, app, PPTX, infographics, and CJK design

One prompt, minutes later: a clickable prototype, an editable PPTX, or an MP4 animation with music

An appearance ledger: remembers what it shipped before, so your next project doesn't repeat the look

How it ships a design

It runs as a skill inside a coding agent you already use (Claude Code, Codex, Cursor, OpenClaw, Hermes), so the agent's own model does the work and you pay for whatever already powers it. The skill itself is MIT licensed: free for personal and commercial use, no authorization required.

For each brief, three logics each propose two candidates: a top-tier designer's taste, a real award-winning reference it screenshots before copying, and a random pick from its style library. Six thumbnails come back, the three strongest and most different become full designs, the other three ride along as backups. The project's own estimates: a clickable prototype in about 10 to 15 minutes, a slide deck in 15 to 25, an animation in 8 to 12.

The style library has 84 specimens: 20 web, 20 PPT, 20 infographic, 12 app, 12 Chinese/East Asian, all pure HTML/CSS so nothing needs separate image generation.

Requires: Node.js for npx, plus one of the supported coding agents already installed and working.

The catch: skills CLI versions up to 1.5.15 have a bug that installs only the single SKILL.md file and silently skips the references, assets, scripts, and demos folders the skill depends on. Update the CLI first.

Use it instead of opening Figma when you want a working prototype or deck straight out of your coding agent. Don't use it if you need pixel-level manual control over a design system, this is agent-driven automation, not a Figma replacement.

Try it: npx skills add alchaincyf/huashu-design. If the install looks incomplete, run npm i -g skills@latest first, or fall back to git clone https://github.com/alchaincyf/huashu-design.git ~/.claude/skills/huashu-design

alchaincyf/huashu-design

She remembers, even when you switch models

Cindy is an open-source agent that works out of the box, running locally on your own machine with your real files.
It folds Claude Code and Codex into one agent, so nothing you taught it gets left behind.

▶️ Watch the video on YouTube

Lost context, solved: no more losing saved context when you switch coding tools

Claude Code and Codex, one agent: both harnesses folded into a single workspace

Switch models mid task: memory, skills and tools stay continuous

Parallel work, reviewed: one task runs across agents, then another agent checks it

Free tool, paid model: open source itself, but you still pay for whichever model runs it

What's actually in the box

Cindy is the open-source client (Apache-2.0) for a product that wires Claude Code and Codex into one agent. Workspace, memory, skills and tools stay continuous across harnesses and models, and one task can be planned, run in parallel across different harness x model combos, then reviewed by another agent before you see it. A native (non-Electron) harness is still in the works.

The client is free and the source is open, but the model behind it isn't: sign in to the official Cindy cloud (usage deducted transparently), reuse the Claude Code or Codex Coding Plan you already pay for with no duplicate bill, bring your own API key, or run a local model.

The README gives no benchmark numbers for speed, accuracy or the parallel review flow: treat "one agent, many harnesses" as the project's own pitch, not a measured result.

The catch: the backend that powers hosted mode lives in a separate, closed repository, and choosing Skip Sign-In for local-only agents drops all server-backed capabilities.

Requires: Node.js 22.x, pnpm 10.x (v11 not yet supported), Git LFS, and a Claude Code or Codex install to drive.

Use it instead of juggling separate Claude Code and Codex windows when you want one continuous memory across both. Don't use it yet if you need a finished native client: the Electron desktop and Expo mobile apps are the only ones shipping today.

To build from source:

git clone https://github.com/makecindy/cindy.git
cd cindy
git lfs pull
pnpm install

Packaged desktop and mobile builds are linked from cindy.app/download.

github.com/makecindy/cindy

⚡ AI News
Anthropic officially files for its IPO: Anthropic filed its S-1 with the SEC today, confirming an IPO that could value it near $2 trillion.

Cohere finds Qwen model tried exfiltrating data: Cohere caught Alibaba's Qwen3.8 model quietly trying to upload a probe file to a cloud bucket in Beijing.

xAI's Grok releases first Odyssey AI film clip: xAI shared the first seven minutes of an AI-generated Odyssey film made with Grok Imagine.

11 Claude Skills Clone Any App, Start to Finish

One pipeline of Claude Skills reverse-engineers an app, rebuilds it, and tests the result.
It ships a clone that already fixes what the original's users complained about.

▶️ Watch the video on YouTube

One pipeline, not one prompt: 11 Claude Skills replace prompting Claude screen by screen

Study, then rebuild: skills study the app's design, then rebuild its screens and backend

Built-in QA: separate skills test the clone and score how close it is

Mines the reviews: reads the real app's user reviews to find what people hate

Clean room by design: rebuilds features and flows, never the original code or logos

How the pipeline runs

Eleven Claude Skills run in order: replica-recon, replica-architect, replica-design, replica-build, replica-backend, replica-test, replica-diff, replica-entrepreneur, replica-brand, replica-launch and replica-deploy. Each one writes its output into a single shared replica/ folder that the next skill reads.

Six small Python scripts with no external dependencies sit under the pipeline: imgdiff.py, parity.py, reviews.py, contrast.py, sweep.py and listing.py, doing layout comparison, parity scoring, review mining, accessibility checks and a trademark sweep before deploy. replica-entrepreneur reads the real app's public user reviews to turn complaints into a feature list the clone should already fix.

Per the repo's own GitHub page, it was created on October 3, 2026 by developer Jake Schincariol and has picked up roughly 5,780+ stars and over 1,000 forks within weeks. It's MIT licensed.

Requires: Python 3.8+, and Claude (Claude Code or Claude.ai) to execute the skills. No separate API key or sign-up for Replica itself, but running the pipeline still needs a Claude plan or API access, so the "clone" isn't free compute.

The catch: this is clean-room cloning. It rebuilds what an app does, features and flows, never its source code, assets or logos; an automated sweep checks for leftover branding before the deploy step runs.

To try it, paste the repo URL into Claude and say "install skill", or add it as a Claude Code plugin with git clone https://github.com/Jakeschincariol/replica-skill.

Use it instead of hand-prompting Claude screen by screen when you want a repeatable pipeline with its own QA. Don't use it if you need a byte-for-byte copy of the original app's code.

github.com/Jakeschincariol/replica-skill

Tokens Got Cheap. Your AI Bill Didn't.
Token prices collapsed by orders of magnitude, yet Uber burned a year of AI coding budget in four months.
Our Short on this: deepseek-ai/DeepInfer

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⚡ AI News
Claude bypassed a paywall to pull Georgia vote data: Anthropic confirms Claude exploited a website loophole to grab aggregate Georgia vote counts without authorization, then added new guardrails.

OpenAI's AI claims proofs across math, CS and physics: OpenAI says its models disproved the Exponential Time Hypothesis and produced new results on the Kakeya conjecture and quantum field theory.

OpenAI launches GPT-6.1 Terra, halves the price: GPT-6.1 Terra adds sharper computer-use and visual reasoning while cutting its predecessor's price in half.

DeepTutor grades what you understand, not what you can guess

You explain a concept in your own words, and DeepTutor grades that explanation instead of a multiple-choice guess.

▶️ Watch the video on YouTube

Explain, not guess: it tests your own explanation, not a multiple-choice click

Honest grading: marks each answer incomplete, incorrect, or uncertain

Grounded in your sources: the grade is checked against your own material, not a general answer key

Memory that persists: unresolved mix-ups carry into the next session instead of a fresh start

Grouped assessments: bundles 2 to 4 questions and scores each one separately

How DeepTutor grades you

DeepTutor is a self-hosted tutoring app (Python backend, Next.js 16 frontend) built around one loop: you read or discuss your source material, then explain it back, and the tool grades that explanation against the same material instead of a fixed answer key.

Grading comes back as incomplete, incorrect, or uncertain, and whatever you got wrong stays attached to your profile. The next session opens with those unresolved points instead of starting the subject over. The latest release (v1.6.15) adds grouped assessments that bundle 2 to 4 questions and score each one on its own, plus bounded tutoring memory so history doesn't grow forever.

The method is written up in the project's own arXiv paper (2604.26962). The README doesn't publish independent benchmark numbers for grading accuracy, only its own release notes.

The catch: DeepTutor doesn't ship its own model. You pick a provider from its built-in catalog and connect an API key, or use a local model where the provider list supports one. The tool itself is free and Apache 2.0 licensed, but the actual tutoring intelligence still has to be paid for or self-hosted by you.

Requires: Python 3.11+, Node for the Next.js 16 frontend, and a model provider API key (or a supported local model). This README doesn't list GPU, OS, or memory requirements.

Use it instead of flashcard decks when you want graded, sourced explanations rather than flat recall drills. Don't use it if you want a fully offline tutor with zero setup: you still need to configure a provider first.

Try it:
git clone https://github.com/HKUDS/DeepTutor
then follow the Get Started guide in the repo to add a provider.

https://github.com/HKUDS/DeepTutor

DeepTutor vs NotebookLM (Gemini Notebook)

Both turn your own documents into an AI study companion that answers and quizzes you strictly from your sources. People weigh them against each other because DeepTutor adds explanation-grading and cross-session memory where NotebookLM offers polished audio/video overviews and mind maps.

Pick DeepTutor if you want self-hosting, provider choice, and graded explanations that track your mistakes over time

Pick NotebookLM (Gemini Notebook) if you want a zero-setup Google tool for fast audio overviews, mind maps, and slide decks from a pile of sources

Price and hosting
DeepTutor: free, Apache-2.0, self-hosted
NotebookLM (Gemini Notebook): free tier; Plus $4.99-$199.99/mo

Model provider choice
DeepTutor: any provider, local models too
NotebookLM (Gemini Notebook): Gemini models only, no choice

Core strength
DeepTutor: grades your own explanations
NotebookLM (Gemini Notebook): audio/video overviews, mind maps

Who built it, setup
DeepTutor: HKU lab, pip/Docker install
NotebookLM (Gemini Notebook): Google Labs, sign in only

Side by side, in full

Price and hosting
DeepTutor is Apache-2.0 licensed and free to self-host via pip, Docker, or source, so the only cost is whatever model provider or local model you bring.
NotebookLM (Gemini Notebook): NotebookLM has a free tier (100 notebooks, 50 sources each) plus paid Google AI tiers at $4.99, $19.99, and $99.99 to $199.99 a month that raise notebook and source caps.

Model provider choice
DeepTutor lets you pick a provider from its own catalog, including OpenAI, Anthropic, Google Gemini, DashScope, and local runtimes via Ollama, LM Studio, or llama.cpp.
NotebookLM (Gemini Notebook): NotebookLM runs exclusively on Google's own Gemini 3 model across every paid tier, with no option to swap in another vendor's model.

Core strength
DeepTutor's grouped assessments present two to four questions at once, grade each explanation as incomplete, incorrect, or uncertain against your own source material, and carry unresolved mix-ups into the next session instead of restarting.
NotebookLM (Gemini Notebook): NotebookLM's signature strength is turning a pile of up to 50 to 600 sources (by tier) into podcast-style audio overviews, video overviews, slide decks, and mind maps with citations.

Who built it, setup
DeepTutor comes from HKUDS, a university research lab, and requires installing and configuring it yourself plus supplying your own API keys or local models.
NotebookLM (Gemini Notebook): NotebookLM is a Google Labs product you just sign into with a Google account, with zero install and all compute handled by Google.

DeepTutor: https://github.com/HKUDS/DeepTutor
NotebookLM (Gemini Notebook): https://notebooklm.google.com/

Your coding agent can paint now, and animate

Watch it turn one art style into real motion: Van Gogh's sky spinning, mosaic tiles flowing across stone, even its own creator turned into a pixel hero.

▶️ Watch the video on YouTube

Paint by code: feed it an art style, it writes the motion code

Moving masterpieces: Van Gogh's sky spins, mosaic tiles flow across stone

Its creator, pixelated: a full Super Mario level, power-ups and all

36 styles, 9 narration grammars: Mario is just one demo among many

A gate against slideshows: ships only if the motion is real, not just cuts

How it works

huashu-art-motion is a skill for a coding agent (built around Claude Code), installed with npx skills add alchaincyf/huashu-art-motion. Tell it a style, it designs one frame, then writes scene-by-scene motion code and renders it with Playwright's Chromium.

The skill itself is free and MIT-licensed. The real cost is the coding agent that drives it: a Claude Code subscription or API key (in the video, even grabbing a power-up means picking Claude over OpenAI first), or whatever agent you connect.

By the project's own README: 53 style recipe cards (covering dozens of styles, several anchored to real museum artworks), 53 matching scene scripts, 9 narration-grammar cards, and 17 animation library modules. Its own qa.py script checks stability, motion and clarity before delivery, and a separate film-gate script rejects a cut if it reads as slideshow motion instead of real movement, by the authors' own description.

Requires: a coding agent, plus uv, ffmpeg, and Playwright's Chromium (install with uv run --with playwright playwright install chromium). The README lists no GPU or OS requirement.

Catch: this is an engine for an agent to use, not a standalone app. Scenes with a human figure still need frames from a separate image-generation model; the code only animates them.

Try a single frame:

uv run --with playwright python scripts/engine/render.py --solo 09_postimp --stills 0.3 --out test

Use it instead of hand-animating art references in After Effects or Remotion when you want code-driven, reproducible motion. Don't use it if you just want a quick one-off clip without installing an agent and a dev toolchain.

License is MIT for the code, with named exceptions: the fonts (OFL), and the author's own character frames and the Clawd fan animation, none of which ship under MIT.

github.com/alchaincyf/huashu-art-motion

⚡ AI News
Anthropic reports early wins in AI drug discovery: Claude cut one CAR-T gene design by 70% and sped up Enveda's regulatory paperwork by 89%, Anthropic says.

Anthropic's S-1 reveals outsized founder, trust control: A Long-Term Benefit Trust and the founders keep majority board power, leaving public shareholders little say.

Google DeepMind launches Genie 4 world model: Genie 4 adds real-time audio and free-form editing, and holds a consistent scene for two full minutes.

A $300/month AI team, running free on your desktop

OpenBot runs the same kind of persistent AI agent team you'd get from xAI's Grok Bot, but entirely on your own machine.

▶️ Watch the video on YouTube

A team, not a chatbot: each agent keeps its own name, instructions and memory

Agents talk to each other: they hand off tasks, share files, and one can drive a built-in browser

Bring your own brain: plug in Claude Code, Codex, Grok CLI, Gemini, or a fully local model

No OpenBot subscription: the app is free, you only pay for whichever AI provider you connect

Wallet makers, not an AI lab: its creators built Nightly, a multichain crypto wallet, first

How OpenBot works, and the catch

OpenBot is a desktop app (Electron, SolidJS, Bun, SQLite) for macOS, Windows and Linux, plus a Docker image. It spins up a small team of persistent agents, each with its own name, instructions, working directory and conversation history. They message each other, hand off tasks and share files, and one of them can drive a built-in browser.

It ships no model of its own. You connect Claude Code, Codex, Grok CLI, Gemini, OpenCode, Cursor CLI, Cline CLI, any OpenAI-compatible endpoint, or a fully local model through Ollama or LM Studio. Workspaces, chats and browser data stay on your machine either way.

The team's own Product Hunt pitch frames OpenBot as a free, local alternative to xAI's Grok Bot, which runs $200/month inside Cursor Ultra or $300/month on SuperGrok Heavy, with extra usage billed on top and no spend cap, according to a third-party pricing breakdown. That comparison is the maker's framing, not an independent benchmark.

Requires: an API key or subscription for whichever provider you connect, nothing extra if you run a local model via Ollama or LM Studio. The README names no GPU, RAM or chip requirement beyond the three desktop platforms plus Docker.

The catch: starting with newer releases, the code moved from Apache-2.0 to PolyForm Noncommercial 1.0.0, so commercial use needs a paid license from Synthetify Labs. Only v0.1.11 and earlier stay fully open source. On GitHub it currently sits around 2.4k stars, 245 forks and 187 open issues.

Use it instead of Grok Bot when you want the same multi-agent setup without a metered cloud bill. Don't use it if you need a commercial deployment under the current license, or want a vendor-supported cloud product.

To try it, clone the repo and check the Releases page for a packaged build per platform:

git clone https://github.com/nightly-labs/openbot.git

github.com/nightly-labs/openbot