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Feynman: The AI Research Agent That Verifies Before It Summarizes

Jun 2, 2026

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11 min read

Feynman: The AI Research Agent That Verifies Before It Summarizes

Every AI research tool today rushes to produce a summary. Feynman (companion-inc/feynman, MIT, 7k stars, April 2026) is built on the opposite philosophy: verify first, summarize second. It dispatches four specialized sub-agents in parallel (Researcher, Reviewer, Writer, Verifier), grounds every claim to a direct URL, and produces a structured research brief with live citation verification. The architecture is grounded in the source, not in the model's training data.

Mohinish S

SnackOnAI Blog

gstack: Why the Y Combinator CEO Turned His Claude Code Setup Into a Software Factory With 23 Specialist Roles

Jun 1, 2026

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12 min read

gstack: Why the Y Combinator CEO Turned His Claude Code Setup Into a Software Factory With 23 Specialist Roles

gstack (MIT, 105k stars, March 2026) is Garry Tan's published Claude Code configuration: 23 opinionated slash commands that assign specialist roles (CEO, Eng Manager, Designer, QA Lead, Security Officer, Release Manager, Doc Engineer) to Claude, cycling through a fixed Think → Plan → Build → Review → Test → Ship → Reflect loop. The design thesis is that Claude performs better with role identity and process structure than with free-form prompting, and the self-reported numbers are specific enough to be interesting: 600,000 lines of production code in 60 days.

Mohinish S

SnackOnAI Blog

Open-Generative-AI: The Free AI Studio That Is Not Actually Running Models on Your Machine

May 31, 2026

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11 min read

Open-Generative-AI: The Free AI Studio That Is Not Actually Running Models on Your Machine

Open-Generative-AI (MIT, 17.5k stars, trending April 2026) is billed as a self-hosted, uncensored alternative to Higgsfield, Freepik, and Krea. It is genuinely useful. It is also an API aggregator with a polished Next.js frontend, not a local inference stack. Understanding exactly what runs where, what "free" means, and what the MuAPI dependency implies for production use is the analysis most coverage skips.

Mohinish S

SnackOnAI Blog

ADI Reasoning: The Symbolic Scaffold That Forces LLMs to Separate Hypothesis Generation From Verification

May 30, 2026

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14 min read

ADI Reasoning: The Symbolic Scaffold That Forces LLMs to Separate Hypothesis Generation From Verification

Chain-of-thought prompting lets LLMs perform abduction, deduction, and induction simultaneously in a single autoregressive pass, with no separation and no accountability for which mode is active at any step. The ADI Protocol formalizes Peirce's tripartite inference as an explicit scaffold, enforces consistency through five algebraic invariants (the Gamma Quintet), and uses the Weakest Link bound to ensure no conclusion can exceed the reliability of its least-supported premise.

Mohinish S

SnackOnAI Blog

JEPA: Why Predicting in Pixel Space Was the Wrong Goal All Along

May 29, 2026

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13 min read

JEPA: Why Predicting in Pixel Space Was the Wrong Goal All Along

Self-supervised learning has been dominated by two ideas: reconstruct masked pixels (MAE), or force representations of different views to be similar (DINO, BYOL, SimCLR). JEPA (Joint-Embedding Predictive Architecture) rejects both. It predicts abstract representations of masked regions, not pixels. This single architectural choice produces richer semantic features with 10x less compute than MAE and zero hand-crafted augmentations. Yann LeCun has been arguing for this design for decades. The empirical results are now here.

Mohinish S

SnackOnAI Blog

TurboQuant: The Quantization Algorithm That Actually Proves Its Distortion Rate Is Near-Optimal

May 28, 2026

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13 min read

TurboQuant: The Quantization Algorithm That Actually Proves Its Distortion Rate Is Near-Optimal

Every quantization method claims minimal quality loss. TurboQuant (Google Research, ICLR 2026) is among the first to prove it: the distortion rate is within a constant factor of the information-theoretic lower bound. The proof comes with a two-stage algorithm that works online, requires zero per-vector quantization overhead, and directly addresses the KV cache memory bottleneck that limits long-context LLM inference.

Mohinish S

ALL Blogs

Feynman: The AI Research Agent That Verifies Before It Summarizes

gstack: Why the Y Combinator CEO Turned His Claude Code Setup Into a Software Factory With 23 Specialist Roles

Open-Generative-AI: The Free AI Studio That Is Not Actually Running Models on Your Machine

ADI Reasoning: The Symbolic Scaffold That Forces LLMs to Separate Hypothesis Generation From Verification

JEPA: Why Predicting in Pixel Space Was the Wrong Goal All Along

TurboQuant: The Quantization Algorithm That Actually Proves Its Distortion Rate Is Near-Optimal

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