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SnackOnAI Blog

The Seedance Paper Is a Scoreboard, Not a Blueprint

Aug 8, 2026

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14 min read

The Seedance Paper Is a Scoreboard, Not a Blueprint

ByteDance shipped a twenty six page paper on Seedance 2.0 containing twenty three pages of benchmark tables and zero equations.

Mohinish S

SnackOnAI Blog

AnyDoc Is Not Faster Than Docling. It Is Solving A Different Problem, And Almost Nobody Noticed

Aug 7, 2026

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12 min read

AnyDoc Is Not Faster Than Docling. It Is Solving A Different Problem, And Almost Nobody Noticed

anydoc beats Docling by a factor of one hundred on document conversion speed. That number is real, reproducible in spirit, and almost completely misleading.

Mohinish S

SnackOnAI Blog

LFM2: Liquid AI's Hardware-in-the-Loop Architecture Search Found That SSMs Were Not the Answer for Edge Devices, and the Result Runs 2x Faster Than Qwen3 on a Galaxy S25

Aug 6, 2026

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16 min read

LFM2: Liquid AI's Hardware-in-the-Loop Architecture Search Found That SSMs Were Not the Answer for Edge Devices, and the Result Runs 2x Faster Than Qwen3 on a Galaxy S25

Most efficient model architectures are designed on paper, benchmarked on A100 clusters, and then discovered to behave differently on the actual target hardware.

Mohinish S

SnackOnAI Blog

HeadRoom: The Context Compression Layer That Sits Between Your Agent and Your LLM Bill, and Gets 92% Token Reduction Without Touching Your Code

Aug 1, 2026

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13 min read

HeadRoom: The Context Compression Layer That Sits Between Your Agent and Your LLM Bill, and Gets 92% Token Reduction Without Touching Your Code

Every coding agent you run is sending far more tokens to the LLM than the task requires. A code search returning 100 results generates 17,765 tokens. The LLM needs 1,408 of them to find the answer.

Mohinish S

SnackOnAI Blog

OpenWorker: Andrew Ng Built the AI Coworker That Delivers Finished Files, Not Chat, and Put the Agent Loop on Your Machine

Jul 31, 2026

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11 min read

OpenWorker: Andrew Ng Built the AI Coworker That Delivers Finished Files, Not Chat, and Put the Agent Loop on Your Machine

Every AI assistant tool available today delivers the same output: a text response. You paste it somewhere, format it, send it, file it, or act on it yourself.

Mohinish S

SnackOnAI Blog

CAMEL: The First Multi-Agent Framework That Used Its Own Agents to Generate the Data That Trained Its Competitors

Jul 30, 2026

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13 min read

CAMEL: The First Multi-Agent Framework That Used Its Own Agents to Generate the Data That Trained Its Competitors

The dominant narrative around multi-agent frameworks is that you pick one based on features: does it support tool calling, memory, RAG, streaming?

Mohinish S

ALL Blogs

The Seedance Paper Is a Scoreboard, Not a Blueprint

AnyDoc Is Not Faster Than Docling. It Is Solving A Different Problem, And Almost Nobody Noticed

LFM2: Liquid AI's Hardware-in-the-Loop Architecture Search Found That SSMs Were Not the Answer for Edge Devices, and the Result Runs 2x Faster Than Qwen3 on a Galaxy S25

HeadRoom: The Context Compression Layer That Sits Between Your Agent and Your LLM Bill, and Gets 92% Token Reduction Without Touching Your Code

OpenWorker: Andrew Ng Built the AI Coworker That Delivers Finished Files, Not Chat, and Put the Agent Loop on Your Machine

CAMEL: The First Multi-Agent Framework That Used Its Own Agents to Generate the Data That Trained Its Competitors

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