> claude: best code writer, but stubborn on deprecated libraries. sometimes ignores your .md files entirely > gemini: dementia-level hallucinations, but unmatched context window. can ingest 800-file codebases in one shot > codex: 70% more PRs at OpenAI. compaction lets it work
@godofprompt
-
RTL’s modular AI breakthroughs in 2026
By
–
This isn't just academic. RTL works on: → Image classification (CIFAR-10/100)
→ Speech enhancement (3 acoustic environments)
→ Implicit neural representations (within-image specialization) The era of "one model fits all" is over. Welcome to modular, data-aware AI. Paper: -
RTL reveals semantic data structure in deep layers
By
–
The semantic alignment is beautiful: Related classes (cat, dog, deer) share more pruning structure in deep layers. Unrelated classes (airplane, truck) stay independent. RTL doesn't just find sparse networks – it discovers the SEMANTIC STRUCTURE of your data.
-
Subnetwork Collapse: Early Warning via Mask Similarity
By
–
But here's the scary part: "subnetwork collapse" When you prune too aggressively, specialized subnetworks start overlapping and performance tanks. The brilliant part? Mask similarity predicts this BEFORE accuracy drops – a label-free early warning system.
-
CIFAR-10 Results Break Performance Barriers
By
–
The CIFAR-10 results broke my brain: Balanced accuracy: 0.781 vs 0.711 (baselines)
Recall: 0.821 vs 0.480 And this is with 10× parameter efficiency. The model literally learned to allocate capacity where it matters most. -
RTL learns multiple specialized masks
By
–
Here's what makes RTL different: Instead of pruning once globally, it learns MULTIPLE masks from the same initialization. Each mask specializes to a data subset (class, cluster, or environment). Then joint retraining refines them WITHOUT letting them interfere.
-
RTL outperforms with 10x fewer parameters
By
–
The results are absolutely wild: • 10x fewer parameters than independent models
• Higher accuracy than single-mask approaches
• Works across vision, speech, even coordinate-based representations At 75% sparsity, RTL beats everything while using only 38K parameters vs 314K -
Samsung’s RTL: Tailored Subnetworks for Data Classes
By
–
The problem with current pruning methods: They assume ONE mask works for all data. Like forcing every student to learn math the same way. Samsung's RTL (Routing the Lottery) discovers specialized subnetworks – each tailored to specific classes, clusters, or conditions.
-
Samsung disproves Lottery Ticket Hypothesis
By
–
Samsung just broke the Lottery Ticket Hypothesis Everyone's been searching for ONE winning subnetwork in neural networks. Turns out we should've been finding MULTIPLE specialized ones. This changes everything about neural network pruning
-
Improving ChatGPT Reliability by Tackling AI Hallucinations
By
–
I'm tackling AI Hallucinations to ensure ChatGPT's reliability.
• Improve training data
• Add verification layers
• Continuously monitor performance Click below to read more: https://
godofprompt.ai/blog/openais-p
lan-to-stop-ai-hallucinations
…