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Programming logic in cartoon form: Flow Diagram (1966), dir. Harold Whitaker, Halas & Batchelor Animation by Harold Whitaker

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Animation Obsessive

198,054 subscribers

156,101 次观看 • 1 年前 •via X (Twitter)

科学技术教育艺术
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10 条评论

FlickPageArt (Magic Zone Studios) 的头像
FlickPageArt (Magic Zone Studios)1 年前

Big nose, little ceasears, Mr coo, charlie from smilling friends...

Trunkit 🐀 的头像
Trunkit 🐀1 年前

The problem with this one is that it’s checking the state of the dog *after* the action is already done. Instead of at the beginning and looping back.

Don't 的头像
Don't1 年前

perfect animation loop in 1966? zamn

ruegen (undead☠️) 的头像
ruegen (undead☠️)1 年前

where can i watch this?

Animation Obsessive 的头像
Animation Obsessive1 年前

You can see the complete version on YouTube. It's an educational short intended to teach programming, so the rest is a little bit dry compared to this part, but there is more than what we've posted:

☆ 的头像
☆1 年前

:)

not mike bobaguard 的头像
not mike bobaguard1 年前

Pizza pizza

PuppyPrince VA🎤 的头像
PuppyPrince VA🎤1 年前

NO BATH! ONLY STINK!

YouCanCallMeKnuckles(UnlikeSonicIDon'tChuckle) 的头像
YouCanCallMeKnuckles(UnlikeSonicIDon'tChuckle)1 年前

Oh boy, and infinite loop.

Luz hernandez 的头像
Luz hernandez1 年前

And that's the way of how my dog brother Charlie feels

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OpenLedger provides the infrastructure to ensure that contributor data used in model outputs is recorded with verifiable attribution. Through Proof of Attribution, contributors-whether they provide prompts, datasets, or logic refinements-can receive credit and rewards when their work influences model behavior. But attribution alone isn’t enough. In critical domains like smart contract deployment, DeFi automation, and DAO governance, understanding why a model made a decision is just as important as the output itself. OpenLedger supports explainability by linking outputs back to their original data sources-allowing developers and auditors to trace logic, validate decisions, and build trust in AI-powered systems. OpenLedger supports Morpheus by: Recording which data was used in generating model outputs Enabling verifiable attribution of contributed datasets Powering reward mechanisms for contributors Offering scalable and efficient model execution via OpenLoRA Supporting transparency and traceability in model decision-making This creates an open, rewardable foundation for AI-driven coding-without relying on opaque systems. How is the system built? The Morpheus architecture has three layers: Datanet Layer OpenLedger powers Morpheus with a specialized Datanet - a decentralized data layer where developers, auditors, and contributors can share smart contract patterns, audit logs, exploit reports, and logic modules. Each submission is recorded onchain with attribution using OpenLedger’s Proof of Attribution. As the model learns and evolves from this data, contributors receive rewards proportional to their impact on future outputs. The Morpheus architecture has two layers: Intent Layer Users describe what they want to build. Example: "Create a token with tax logic that routes to a DAO." Morpheus parses the instruction, retrieves relevant contract types, and plans a modular execution flow. Agent Layer The agent generates, tests, and assembles the contract. It handles versioning, logic validation, and deployment readiness. Security checks-reentrancy protection, overflow control, gas modeling-are embedded into the generation phase. Generated outputs are mapped to their source data using OpenLedger’s Proof of Attribution, providing traceability across the pipeline. How does the AI model work? Morpheus is being powered by a specialized Solidity model built on top of OpenLedger. This model is purpose-built to handle the nuances of smart contract logic, security, and upgradeability. Unlike generalized coding agents, it is designed specifically for EVM environments and Web3 use cases, drawing from real protocol data and security best practices. Morpheus is fine-tuned on a vertical stack of smart contract data: Audited protocol code (e.g., Uniswap V4, Compound) OpenZeppelin libraries and EIP reference implementations Smart contract vulnerability reports and exploit reconstructions Edge cases from fuzz testing and adversarial examples It uses models like CodeLlama and DeepSeek-Coder, enhanced through RAG pipelines referencing standardized security patterns and emerging protocol designs. This training stack is integrated into a continuous feedback loop, enabling real-time specialization for EVM and beyond. Why a specialized model is needed? Smart contract development is uniquely high-stakes. A generalized AI model is not enough. As 'vibe coding' and natural language programming become more common, we're seeing an influx of AI-generated code in Web3 as well. But smart contracts are not frontends or prototypes-they govern real value, enforce trustless execution, and often become immutable after deployment. Billions have been lost in Web3 due to bugs and inefficiencies: In 2022 alone, over $3.8 billion was stolen due to smart contract exploits, many of which stemmed from avoidable issues like reentrancy, integer overflows, or access control failures. Inefficient contract structures lead to unnecessary gas consumption. Optimizing for gas can reduce costs by up to 40%, saving projects millions over time. Upgradeable contract patterns, like UUPS or Transparent Proxies, require strict adherence to storage layout and initialization rules. Mistakes here often go undetected by generic models and can render a contract unupgradeable or vulnerable. A specialized Solidity model is trained on real-world exploits, EIP standards, and libraries like OpenZeppelin to: Generate secure, gas-efficient code by default Recognize and correctly implement complex proxy patterns Map user intent to modular, auditable contract architectures Incorporate battle-tested logic from audited protocols and fuzz-tested edge cases Morpheus goes beyond syntax-it understands the nuances of decentralized infrastructure and deploys code that meets production-grade standards. What applications will this enable Token creation with built-in logic (tax, liquidity, governance) DeFi automations triggered by market conditions Payment contracts between agents and contributors DAO tooling with dynamic NFT-based voting Cross-chain bridging logic tied to real-world oracles Asset issuance flows through chat-based interfaces Natural language contract templates with reusable logic Each of these flows is backed by OpenLedger’s Proof of Attribution-ensuring traceability, explainability, and fair rewards across the ecosystem. This is the future of AI-native development. Open. Attributed. Explainable. Community-powered. Morpheus and OpenLedger are building the first system for autonomous coding agents where: Contributor work is recorded onchain Reuse is incentivized through attribution Model outputs are traceable and explainable Contracts evolve through human-agent collaboration Anyone can contribute prompts, logic, or flows-and get rewarded The smart contract engineer is no longer a human-only role. It is an agentic, decentralized, and transparent process-powered by OpenLedger.

OpenLedger

46,735 次观看 • 1 年前

She filmed herself by the pool in orange sunglasses. Told 2 million people she got rich on YouTube kids content, not OnlyFans. Then she made three mistakes. At 0:02 her hand touches her lip. Short nail. Chipped silver polish. At 0:11 the hand holding the iPad has long almond nails. French tip. Silver trim. A chunky bracelet that wasn't there before. Different woman. Same video. She tells you to "go to YouTube, these are all made with AI" and points at Doggyland. Doggyland is Snoop Dogg's 3D animation studio. Real animators. Real budget. Not AI. Then she opens a Picsart workflow and generates a cartoon using a model called "Sora 2." Sora 2 doesn't exist. She tells you to comment "flow" for the template link. That's the whole video. The flex, the tutorial, the stolen hands, the fake model. All a funnel for a DM scam. The annoying part is the actual workflow is real. Kids content does print money. You just don't need her template. Claude writes the script and the visual prompts from any transcript you paste in. Suno generates the song. Luma or Kling renders the 12 second clips. n8n stitches everything through ffmpeg on a schedule. $20 in tools. No course. No template. No DM. She sold you the dream from a pool. The dream costs less than her sunglasses.
0:46

She filmed herself by the pool in orange sunglasses. Told 2 million people she got rich on YouTube kids content, not OnlyFans. Then she made three mistakes. At 0:02 her hand touches her lip. Short nail. Chipped silver polish. At 0:11 the hand holding the iPad has long almond nails. French tip. Silver trim. A chunky bracelet that wasn't there before. Different woman. Same video. She tells you to "go to YouTube, these are all made with AI" and points at Doggyland. Doggyland is Snoop Dogg's 3D animation studio. Real animators. Real budget. Not AI. Then she opens a Picsart workflow and generates a cartoon using a model called "Sora 2." Sora 2 doesn't exist. She tells you to comment "flow" for the template link. That's the whole video. The flex, the tutorial, the stolen hands, the fake model. All a funnel for a DM scam. The annoying part is the actual workflow is real. Kids content does print money. You just don't need her template. Claude writes the script and the visual prompts from any transcript you paste in. Suno generates the song. Luma or Kling renders the 12 second clips. n8n stitches everything through ffmpeg on a schedule. $20 in tools. No course. No template. No DM. She sold you the dream from a pool. The dream costs less than her sunglasses.

Aeron

3,014,890 次观看 • 1 个月前